Adaptation strategies to manage risk in Australia s plantations

Transcription

1 RESOURCES PROJECT NUMBER: PNC JULY 2014 Adaptation strategies to manage risk in Australia s plantations This report can also be viewed on the FWPA website FWPA Level 4, Queen Street, Melbourne VIC 3000, Australia T +61 (0) F +61 (0) E info@fwpa.com.au W

2 Adaptation strategies to manage risk in Australia s plantations Prepared for Forest & Wood Products Australia by Libby Pinkard, Jody Bruce, Michael Battaglia, Stuart Matthews, David Drew, Geoff Downes, Deborah Crawford, Maria Ottenschlaeger

3 Publication: Adaptation strategies to manage risk in Australia s plantations Project No: PNC This work is supported by funding provided to FWPA by the Department of Agriculture, Fisheries and Forestry (DAFF) Forest & Wood Products Australia Limited. All rights reserved. Whilst all care has been taken to ensure the accuracy of the information contained in this publication, Forest and Wood Products Australia Limited and all persons associated with them (FWPA) as well as any other contributors make no representations or give any warranty regarding the use, suitability, validity, accuracy, completeness, currency or reliability of the information, including any opinion or advice, contained in this publication. To the maximum extent permitted by law, FWPA disclaims all warranties of any kind, whether express or implied, including but not limited to any warranty that the information is up-to-date, complete, true, legally compliant, accurate, non-misleading or suitable. To the maximum extent permitted by law, FWPA excludes all liability in contract, tort (including negligence), or otherwise for any injury, loss or damage whatsoever (whether direct, indirect, special or consequential) arising out of or in connection with use or reliance on this publication (and any information, opinions or advice therein) and whether caused by any errors, defects, omissions or misrepresentations in this publication. Individual requirements may vary from those discussed in this publication and you are advised to check with State authorities to ensure building compliance as well as make your own professional assessment of the relevant applicable laws and Standards. The work is copyright and protected under the terms of the Copyright Act 1968 (Cwth). All material may be reproduced in whole or in part, provided that it is not sold or used for commercial benefit and its source (Forest & Wood Products Australia Limited) is acknowledged and the above disclaimer is included. Reproduction or copying for other purposes, which is strictly reserved only for the owner or licensee of copyright under the Copyright Act, is prohibited without the prior written consent of FWPA. ISBN: Forest & Wood Products Australia Limited Level 4, Queen St, Melbourne, Victoria, 3000 T F E info@fwpa.com.au W

4 Researcher/s: Libby Pinkard Jody Bruce Michael Battaglia Stuart Matthews David Drew CSIRO Ecosystem Sciences Final report received by FWPA in July 2014 Forest & Wood Products Australia Limited Level 4, Queen St, Melbourne, Victoria, 3000 T F E info@fwpa.com.au W

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6 Executive Summary Recent decades have seen an increase in the incidence and severity of droughts, catastrophic fires, heatwaves and intense storms, as well as a trend towards warmer mean temperatures and reduced precipitation over much of the temperate plantation estate. These changes may have dramatic effects on the productivity of the plantation estate in both the short and longer term. Such changes in climate also affect pest distribution and abundance, and fire hazard. The consequence of these hazards is that the plantation estate has been classed as vulnerable in the face of future climate projections (AGO, 2005). The objective of this project was to provide plantation managers with tools for assessing the risks and impacts of climatic variability, on productivity and wood properties, and for making decisions about cost-effective management options to moderate the effects of climatic variability and change. The key elements to the project were: updating national assessments of plantation productivity for 2030 and 2050 examining the consequences of climate change for wood properties quantifying threats to productivity, carbon stocks and wood products from climatic variability and associated changes in drought, pests and fire defining uncertainty associated with estimates determining the adaptive capacity of current management practices, and where new production systems (e.g. new species, new products) may instead be required; identifying adaptation strategies for the current plantation estate and the effects of these on productivity and wood properties; developing tools to assist industry decision-making around hazard and productivity management in Australia s plantations. As a consequence of this project, the Australian plantation industry is better placed to quantify and understand and manage risks from climate variability and change at a range of scales from region to site. The nine regional reports produced in the project provide a snapshot of key changes anticipated by 2030 in each plantation growing region, for either E. globulus or P. radiata. These can be used to identify areas of concern that may warrant further investigation or action. The maps provided highlight regions that will be more vulnerable to damage or production loss in the future. The spatial database developed in the project and made available to partners provides the industry with the capacity to examine at a fine scale the likely outcomes of climate change for their estate, and to integrate the spatial layers into their broader decision-making processes. The process-based model of forest growth, CABALA, was updated in the project can be used to examine site-specific responses to climate and the effectiveness of adaptive management strategies, as well as to perform sensitivity analyses to identify situations and conditions that might increase vulnerability with changing climate. All project outputs are derived from models. In order to gain confidence in the project outputs, industry should test these outputs, by monitoring plantation productivity, recording successes and failures, and establishing sites to test suggested adaptation strategies. As with all climate change studies there is considerable uncertainty in the estimates presented, i

7 and revision of the analyses is desirable as more information becomes available about direction and magnitude of climate change. The benefits of this project will be maximised if project partners can integrate the spatial layers into their operational planning systems to provide additional, climate change-specific, information for decision-making and risk analysis. Increased within company literacy in the use of tools such as CABALA and CAMBIUM, or in-house adoption capacity will maximise options for applying precision forestry at a site scale that incorporates the knowledge gained in this project. ii

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9 Table of Contents Executive Summary... i Chapter 1 General introduction... 7 Chapter 2 Recent past and projected future climatic trends Direct effects of climate change on future E. globulus and P. radiata plantation productivity Chapter 3 Projected impacts of climate change including drought on plantation productivity Chapter 4 Climate impacts on wood properties: an overview Chapter 5 Case study: modelling the effects of varied temperature and rainfall on wood density and stand volume of Pinus radiata Chapter 6 Productivity losses due to defoliation Indirect effects of climate change on future E. globulus and P. radiata plantation productivity Chapter 7 Fire hazard Chapter 8 Pest hazard Adaptation to climate change Chapter 9 A history of management responses to climatic variability in the Australian forest industry Chapter 10 The role of decision support tools in climate change adaptation Chapter 11 Using DSS to explore adaptation strategies for the forest industry Appendix 1: review of climatic triggers for key pests of Australia s plantations ii

10 List of tables Table 1.. Assessment of the vulnerability criteria for plantation forestry in Australia, and capacity to adapt. From AGO (2005) Table 2. List of data contained in the project spatial database Table 3. Mean change in maximum and minimum temperature ( C) and rainfall (mm climate (average from ) and 2030 or 2050, projected using two GCM s (CSIRO 3.5 and MIROC-M) ( Annual and seasonal changes are given at a state level Table 4. Description of standard soil used in each region. OM% is percent organic matter, and C:N ratio is the carbon: nitrogen ratio. SA GT is South Australia/Green Triangle, NSW is New South Walers, and SWWA is south west Western Australia. For low fertility a net nitrogen mineralisation rate of ~40 kg N/ha/yr was assumed; for high fertility net nitrogen mineralisation >100kg/N/yr. Numbers in brackets refer to the C:N ratio Table 5 Climate summary for the sites used in the sensistivity to eco Table 6: Site and regimes summary, including the original silviculture (i.e. that actually applied at each site) and the standard silviculture (i.e. same silviculture applied across all sites) Site and regimes summary, including the original silviculture (i.e. that actually applied at each site) and the standard silviculture (i.e. same silviculture applied across all sites) Table 7: Average ranges in predicted wood density from simulations under original (i.e. that actually applied at each site) and standard silviculture (i.e. same silviculture applied across all sites), and overall mean wood densities, from the six study sites Table 8. Mean final stand volume of E. globulus for 8 plantation sites in southern Australia, in the absence of defoliation, as predicted using the stand productivity model CABALA. Stands were grown on a 15 year rotation, planted at 1111 stems ha -1. Values are the average of 20 model runs, each with a different planting date between 1975 and The reduction in volume equivalent to 5, 10 or 15% reductions in final stand volume are also presented. High, moderate and low fertility is defined as per Table Table 9. Mean final stand volume (including thinning) of P. radiata for 8 plantation sites in southern Tasmania, in the absence of defoliation, as predicted using the stand productivity model CABALA. Stands were grown on a 35 year rotation, planted at 1111 stems ha -1, and thinned at 10, 18 and 25 years of age. Values are the average of 20 model runs, each with a different planting date between 1975 and The reduction in volume equivalent to 5, 10 or 15% reductions in final stand volume are also presented. High, moderate and low fertility is defined as per Table Table 10. Defoliation thresholds associated with early and late age defoliation of E. globulus, and high, moderate or low fertility (see Table 4), for eight sites in southern Australia. Thresholds were defined as the percentage reduction in needle area that reduced final volume by 5% or greater Table 11. Defoliation thresholds associated with early and late age defoliation of P. radiata, and high, moderate or low fertility (see Table 4), for eight sites in southern Australia. Thresholds were defined as the percentage reduction in needle area that reduced final volume Table 12. The major pests of Australian softwood and hardwood plantations, the states in which they occur and their significance as damaging agents, as identified in a project steering committee workshop and via consultation with forest health experts Table 13. For the major pest species of Australia s plantations, the drivers of susceptibility, age at which the host is most susceptible, the main seasons of damage, the organs affected and (for defoliators) the pattern of damage

11 Table 14. Possible responses of key pests of Australian plantations to climate changes including warmer mean temperatures and increasing frequency and intensity of droughts, heatwaves and storm events. Indications of host attributes required for pests to respond to climate triggers are provided Table 15. The Ecoclimatic Index ranges for marginal, suitable and optimal climatic suitability for four eucalypt and three pine pests in Australia Table 16. The area of the eucalypt and pine estate currently falling into four climatic suitability classes for eucalypt and pine pests, and the percentage change in area in each class projected for 2030 and CLIMEX data were used for the analysis generated using two climate models (CSIRO 3.0 and Miroc H). Red denotes >15% increases, and blue denotes >15% decreases compared to current climate. 126 Table 17. Relationship between site suitability for a given pest, the anticipated severity of damage associated with each severity rating, and the anticipated proportion of years in which outbreaks would occur. Proportion of years with outbreaks was determined using the population model Dymex (E. californica) or CLIMEX (Tertosphaeria); the anticipated severity of damage was determined using Dymex (E. californica) or through consultation with forest health experts (Teratosphaeria) Table 18. Summary of alternative species either deployed in the field in, or identified as having suitable traits for, drought-prone sites Table 19. Summary of historical responses of the Australian forest industry to water availability Table 20. Summary of historical responses of the Australian forest industry to frost Table 21. Summary of historical responses of the Australian forest industry to strong winds Table 22. Summary of historical responses of the Australian forest industry to increasing mean annual temperatures Table 23 Summary of 2030 climate for each of the reference sites. This includes the 5 GCMs used for the Analysis. *Evaporation is the average of the current climate List of figures Figure 1 Current distribution of hardwood and softwood plantations in Australia (National Forest Inventory 2010)... 8 Figure 2. Trends in mean annual increment of Radiata pine plantations in South-Eastern Australia over a 35 year period. MAI was measured at age 11 across many sites, soil types and planting years. From Leishout et al. (1996) Figure 3. Factors influencing vulnerability (or resilience) of plantations to climate variability and climate change. From AGO (2005) Figure 4. Relationship between hazard assessment and risk management. This project focused on hazard assessment Figure 5. Historical trends in mean maximum and minimum temperature, the number of extreme hot days, frost nights, heavy rainfall events, and warm spell duration in Australia. From the Bureau of Meteorology. ( 17 Figure 6. Output from the Climate Futures Framework Selection of the best, worst and most likely models for the NRM regions that fall within the range of the temperate plantation estate Figure 7. Ratio of maximum to mean wind speed for daily observations from automatic weather stations. Line is power function fit to mean values. Boxes are 25% to 75% quartiles, whiskers are 1.5 times the inter-quartile range Figure 8. Projected changes in relative humidity in 2030 and 2050 across Australia s temperate plantation estate

12 Figure 9. Projected changes in wind speed in 2030 and 2050 across Australia s temperate plantation estate Figure 10. Some examples of the predicted volume response of E. globulus to increasing atmospheric CO 2 concentrations: (A) cold wet site with either shallow nutrient poor soil or deep nutrient rich soil; (B) cold wet or hot wet site with deep nutrient rich soils; (C) hot wet site with deep nutrient rich soil or hot dry site with shallow nutrient rich soil. Soil characteristics defining low or high nutrition or shallow or deep soils are given in Table Figure 11a. End of rotation volume plus thinning volume for The difference between the total volume achieved at 2030 (with and without eco 2 ) and the 1990 volume is shown in b.and c. The survival of Pinus radiata plantations under 1990 and 2030 climate change conditions with and without eco 2. Survival refers to the number of rotations that survived out of 20. The climate model is the CSIRO MK3.5 GCM and the soils are medium fertility, deep soils. The definition of medium and deep varies across the regions and more information can be found in Table Figure 12a. End of rotation volume plus thinning volume for The difference between the total volume achieved at 2050 (with and without eco 2 ) and the 1990 volume is shown in b.and c. The survival of Pinus radiata plantations under 1990 and 2050 climate change conditions with and without eco 2. Survival refers to the number of rotations that survived out of 20. The climate model is the CSIRO MK3.5 GCM and the soils are medium fertility, deep soils. The definition of medium and deep varies across the regions and more information can be found in Table Figure 13 Percentage change in total volume of P. radiata in 2030 and 2050 compared with 1990 total volumes under assumption of eco 2 and no eco 2 response. The climate model is the CSIRO MK3.5 GCM and the soils are medium fertility, deep soils. The definition of medium and deep varies across the regions and more information can be found in Table Figure 14. Impact of fertility on responses of E. globulus productivity to changing climates, total volumes are shown for 1990 and the percentage change for The climate model is the CSIRO MK3.5 GCM, no eco 2 response and the soils are medium fertility soils (a. and c.) and low fertility soils (b. and d.). The depth of the soil remains the same. The definition of medium and low fertility varies across the regions and more information can be found in Table Figure 15 Percent change from current production of E. globulus under a range of modelling assumptions in 2030, a. percentage change from 1990 values with no eco 2 response and mortality is not modelled, b. percentage change from 1990 values with an eco 2 response and mortality is not modelled, c. percentage change from 1990 values with no eco 2 response and mortality is modelled and d. percentage change from 1990 values with an eco 2 response and mortality is modelled, The climate model is the CSIRO MK3.5 GCM and the soils are medium fertility, deep soils. The definition of medium and deep varies across the regions and more information can be found in Table Figure 16 Percent change from current production of E. globulus under a range of modelling assumptions in 2050, a. percentage change from 1990 values with no eco 2 response and mortality is not modelled, b. percentage change from 1990 values with an eco 2 response and mortality is not modelled, c. percentage change from 1990 values with no eco 2 response and mortality is modelled and d. percentage change from 1990 values with an eco 2 response and mortality is modelled, The climate model is the CSIRO MK3.5 GCM and the soils are medium fertility, deep soils. The definition of medium and deep varies across the regions and more information can be found in Table Figure 17. Changes in the variability of E. globulus productivity at 1990 and 2030 with the assumption of eco 2 and no eco 2 response. The climate model is the CSIRO MK3.5 GCM and the soils are low fertility, deep soils and mortality is modelled. The definition of low and deep varies across the regions and more information can be found in Table Figure 18. Probability of an E. globulus plantation surviving to harvest ages shown as the number of rotations out of 20 simulations in which stands survived to age 10 years. Where no cells are shown (white space) there is no change in the number of rotations that survived values are shown in a. and d. The difference at 2030 compared to 1990 is shown in b. with no eco 2 response and mortality is 3

13 not modelled, c. with an eco 2 response and mortality is not modelled, e. with no eco 2 response and mortality is modelled and f. with an eco 2 response and mortality is modelled, The climate model is the CSIRO MK3.5 GCM and the soils are medium fertility, deep soils. The definition of medium and deep varies across the regions and more information can be found in Table Figure 19. End of rotation survival of E. globulus as stems per hectare after initial planting density of 1000 sph in 2030 under ambient and eco 2 The climate model is the CSIRO MK3.5 GCM and the soils are medium fertility, deep soils. The definition of medium and deep varies across the regions and more information can be found in Table Figure 20. Effect of sampling on the detection of variation trends in wood properties (based on Downes et al. 1997) Figure 21. (a) The pattern of growth over a rotation, as influenced by environment and silviculture will change the resultant wood properties produced. (b) each annual increment is in turn the net effect of growth rate variation over seasons Figure 22. Drought index, and wood density and radial cell diameter measured by SilviScan (Evans, 1994), for the period August 1986 to August 1988 at (a) breast height and (b) 15 metres. Changes in density and cell diameter are attributable to changes in drought Figure 23: Interaction of original silviculture and hotter conditions ( C change from present temperature shown on x-axis) and drier/wetter conditions (% change from present conditions shown on y-axis) at six sites on predicted wood density (kg m -3 ). X indicates present conditions Figure 24: Interaction between standard silviculture and hotter conditions ( C change from present temperature shown on x-axis) and drier/wetter conditions (% change from present conditions shown on y-axis) at six sites on predicted wood density (kg m -3 ). X indicates present conditions Figure 25: Predicted total stand volume with original silviculture (final volume + thinning) (m 3 Ha -1 ), under hotter conditions ( C change from present temperature shown on x-axis) and drier/wetter conditions (% change from present conditions shown on y-axis) at six sites. X indicates present conditions Figure 26: Predicted total stand volume with standard silviculture (final volume + thinning) (m 3 Ha -1 ), under hotter conditions ( C change from present temperature shown on x-axis) and drier/wetter conditions (% change from present conditions shown on y-axis) at six sites. X indicates present conditions Figure 27: Predicted changes in board outputs and their stiffness properties from the low wood density (upper figure in each case) and high wood density (lower figure in each case) scenarios from each site, grown under the standard silviculture regime (see Error! Reference source not found.). The size of each log (shown at breast height for these simulations) is scaled relative to the size of the Rennick low density log, which had a diameter underbark of of 35.3 cm. The largest tree, at Miles (low density) had an underbark diameter of 45.6 cm. In each case the within-tree variation in modulus of elasticity (MOE) is shown graded from yellow (low MOE) to red (high MOE), and is visible as annual rings, with low MOE in the earlywood and high MOE in the latewood Figure 28: Comparison of predicted wood density (kg m -3 ) in 10-year-old E. globulus and 35-year-old P. radiata at Tas site Figure 29: Comparison of predicted relative stem size (at 1.3 m height) and board outputs from P. radiata (grown for 35 years) and E. globulus (grown for 10 years) at Tas site Figure 30. Location of the eight sites used for the defoliation impact analyses. Sites were selected to cover a range of stand productivity, where dark green indicates highest productivity, and dark red indicates lowest productivity Figure 31. Examples of predicted changes in leaf area index (LAI) over time in two undefoliated E. globulus stands, for a planting date of 1981 and a moderate site fertility rating. The red arrow indicates peak LAI. The dashed green lines indicate the timing of early and later-age defoliation events

14 Figure 32. Difference in final volume between undefoliated and defoliated E. globulus stands at eight sites in southern Australia. Five defoliation levels were applied (0, 20, 40, 60 or 80% reduction in leaf area), and analyses were done for three site fertility levels (low, moderate and high, see Table 4). Absolute volume reductions can be calculated from Table Figure 33. Time course of stand volume of E. globulus predicted for two sites at a moderate site fertility, where defoliation levels ranging between 0 and 80% were applied at 3 or 9 years of age. VNSW is a Victorian-NSW site; SA is a South Australian site (Figure 30) Figure 34. Difference in final volume between undefoliated and defoliated P. radiata stands at eight sites in southern Australia. Five defoliation levels were applied (0, 20, 40, 60 or 80% reduction in leaf area), and analyses were done for three site fertility levels (low, moderate and high see Table 4). Rotation length was 35 years Figure 35. Time course of stand volume of P. radiata predicted for two sites at a moderate site fertility (see Table 4), where defoliation levels ranging between 0 and 80% were applied at 3 or 9 years of age. VNSW is a Victorian-NSW site; SA is a South Australian site Figure 36. Examples of predicted changes in leaf area index (LAI) over time in two undefoliated P. radiata stands, for a planting date of 1981 and a moderate site fertility rating (see Table 4). The red arrows indicate the timing of early and later-age defoliation events Figure 37. The predicted effect of top down (TD) versus bottom-up (BU) defoliation on E. globulus. Values are the difference between undefoliated and defoliated final stand volume, calculated as the mean of 20 model runs with sequential planting dates. Data are presented for early and later age defoliation and for three site fertilities (low, moderate and high see Table 4)) Figure 38. The predicted effect of top down (TD) versus bottom-up (BU) defoliation on P. radiata. Values are the difference between undefoliated and defoliated final stand volume, calculated as the mean of 20 model runs with sequential planting dates. Data are presented for early and later age defoliation and for three site fertilities (low, moderate and high see Table 4) Figure 39. The effect of one versus three defoliation events on E. globulus stand volume. Values are the difference between undefoliated and defoliated final stand volume, calculated as the mean of 20 model runs with sequential planting dates. Data are presented for early and later age defoliation and for three site fertilities (low, moderate and high see Table 4) Figure 40. The effect of one versus three defoliation events on P. radiata stand volume. Values are the difference between undefoliated and defoliated final stand volume, calculated as the mean of 20 model runs with sequential planting dates. Data are presented for early and later age defoliation and for three site fertilities (low, moderate and high see Table 4) Figure 41. Litter fuel load for the baseline period (a) and predicted changes for 2030 (b) and 2050 (c) for the CSIRO model and (d,e) MIROC model Figure 42. Comparison of FFDI calculated by Lucas et al. (2007) and this study. Left) Lucas et al. (2007) study sites. Right) Comparison of annual sum of FFDI for the points shown at left. Dashed lines are ±20% 101 Figure 43. Annual sum of FFDI for the baseline period (a) and predicted changes for 2030 (b) and 2050 (c) for the CSIRO model and (d,e) MIROC model Figure 44. Number of fire weather days (FFDI > 25) for the baseline period (a) and predicted changes for 2030 (b) and 2050 (c) for the CSIRO model and (d,e) MIROC model Figure 45. Number of fire damage days (Intensity > 4000 kw m-1 for the baseline period (a) and predicted changes for 2030 (b) and 2050 (c) for the CSIRO model and (d,e) MIROC model Figure 46. Changes in fire danger for south-west Western Australia. Top) Median and 99% FFDI. Bottom) Number of days when at least 10% of sites have FFDI above Figure 47. Changes in fire danger for the green triangle. Left) Median and 99% FFDI. Right) Number of days when at least 10% of sites have FFDI above

15 Figure 48. Changes in fire danger for Tasmania. Left) Median and 99% FFDI. Right) Number of days when at least 10% of sites have FFDI above Figure 49. Changes in fire danger for Victoria and southern New South Wales. Left) Median and 99% FFDI. Right) Number of days when at least 10% of sites have FFDI above Figure 50. Changes in fire danger for Northern New South Wales. Left) Median and 99% FFDI. Right) Number of days when at least 10% of sites have FFDI above Figure 51. Relationship between climate change impacts, adaptation responses and the potential benefits from adaptation (after Howden et al., 2010) Figure 52. Classification of important issues in agriculture and putative research methodology relevant to issue exploration (from Carberry et al. 2005) Figure 53 Locations of the a., E. globulus reference sites and b., the P. radiata sites. Where appropriate, the locations of the reference sites are the same Figure 54 a. Site A End of rotation volume for E. globulus over 100 rotations (5 GCM s with 20 planting dates for each GCM). The y axis represents the number of rotations reaching that volume b. End of rotation percentage of trees that survived the rotation Figure 55 a. Site B End of rotation volume for E. globulus over 100 rotations (5 GCM s with 20 planting dates for each GCM). The y axis represents the number of rotations reaching that volume b. End of rotation percentage of trees that survived the rotation Figure 56 a. Site C E. globulus over 100 rotations (5 GCM s with 20 planting dates for each GCM). The y axis represents the number of rotations reaching that volume b. End of rotation percentage of trees that survived the rotation; the peak below 20% represents catastrophic failure with little or no trees surviving Figure 57 a. Site D End of rotation volume for E. globulus over 100 rotations (5 GCM s with 20 planting dates for each GCM). The y axis represents the number of rotations reaching that volume b. End of rotation percentage of trees that survived the rotation Figure 58a. Site E End of rotation volume for E. globulus over 100 rotations (5 GCM s with 20 planting dates for each GCM). The y axis represents the number of rotations reaching that volume b. End of rotation percentage of trees that survived the rotation; the peak below 20% represents plantation failure with little or no trees surviving Figure 59a. Site F End of rotation volume for E. globulus over 100 rotations (5 GCM s with 20 planting dates for each GCM). The y axis represents the number of rotations reaching that volume b. End of rotation percentage of trees that survived the rotation; the peak below 20% represents plantation failure with little or no trees surviving Figure 60 End of rotation volume for P. radiata over 100 rotations (5 GCM s with 20 planting dates for each GCM). The y axis represents the number of rotations reaching that volume Figure 61 Site F.End of rotation volume for P. radiata over 100 rotations (5 GCM s with 20 planting dates for each GCM). The peak below 100 m 3 ha -1 represents plantation failure with little or no trees surviving. a. represents the results from comparing the silvicultural options explored for the sites in Figure 60. b. examines multiple short rotations

16 Chapter 1 General introduction Background Project overview and scope Climate changes: recent past and projected Generation of climate data for the project 7

17 Introduction There are over 2 M ha of timber plantations in Australia, and plantation forestry directly employs 66,000 people and contributes around 0.6% to national GDP (ABARES, 2012). Softwood plantations have a long history in Australia and until the 1980 s were the predominant source of domestic plantation-grown products. More recently, there has been a rapid expansion of the hardwood plantation estate, from 29% of all plantations in 1999 to 49% in 2009 (DAFF, 2010). The bulk of these plantations are located in the south of Australia (Figure 1), and the most common species are Eucalyptus globulus and Pinus radiata. Plantations have been managed for wood production in Australia for over 100 years (Jacobs, 1955), and silvicultural practices for establishment and growth are well-developed, particularly for the more established parts of the industry such as the softwood sector (Snowdon and James 2007). Over this period the increased productivity of plantations in southern Australia is attributed to continual improvements in silviculture and genetic selection (Leishout et al., 1996; O'Hehir and Nambiar, 2010a) (Figure 2). This has occurred despite a general trend from around 1950 of increasing mean temperatures and decreasing annual rainfall in this region (CSIRO and Bureau of Meteorology, 2014). Figure 1 Current distribution of hardwood and softwood plantations in Australia (National Forest Inventory 2010) Australia s climate is changing Over the past 40 years much of Australia s plantation-growing regions have experienced an increase in mean annual temperature when compared with the preceding 50 years. Additionally there has been an increase in the number of days on which the maximum temperatures exceeded 35 C; and a decline in mean annual rainfall (Nicholls, 2008; Steffen, 2009). Seven of the 10 warmest years on record have occurred since 1998, with a five-fold increase in the frequency of very warm months over the same period (CSIRO and Bureau of Meteorology, 2013). Mean annual rainfall has declined across the country by between 0 and 50 mm/10 years with greatest decreases 8

18 observed in eastern regions. An abrupt decline of around 17% in mean annual rainfall in south west Western Australia has also occurred since the mid-1970s (CSIRO and Bureau of Meteorology, 2013). In the southern and eastern Australian areas where plantations are grown, mean maximum temperatures have increased by between 0.15 and 0.4 C/10 years ( and mean minimum temperatures have increased by between 0 and 0.3 C/10 years over the past 40 years. Seven of the 10 warmest years on record have occurred since 1998, with a five-fold increase in the frequency of very warm months over the same period (CSIRO and Bureau of Meteorology, 2013). Mean annual rainfall has declined across the country by between 0 and 50 mm/10 years over the past 40 years. An abrupt decline of around 17% in mean annual rainfall in south west Western Australia has also occurred since the mid-1970s (CSIRO and Bureau of Meteorology, 2013). Climate projections suggest that these trends are likely to continue into the future (CSIRO and Bureau of Meteorology 2014) MAI (m 3 ha -1 yr -1 ) Planting year Figure 2. Trends in mean annual increment of Radiata pine plantations in South-Eastern Australia over a 35 year period. MAI was measured at age 11 across many sites, soil types and planting years. From Leishout et al. (1996). Vulnerability to changing climate Climate strongly influences plantation productivity and survival. Consequently, a result of the drying and warming climate is that Australian plantation forests are classified as moderately vulnerable to climate change (Hennessy et al. 2007) (Table 1), where vulnerability is defined as the degree to which a system is susceptible to climate change (IPCC 2007). This vulnerability is a function of exposure to unfavourable climatic conditions, and the sensitivity of plantations to those unfavourable conditions (Figure 3). Unfavourable climatic conditions can have direct impacts on plantation productivity and survival, through effects on tree physiology, as well as indirect impacts through their effect on hazards such as fire and pests. However some of the indirect impacts interact with the direct ones, for example climatic conditions can influence the capacity of a stand to recover from defoliation, and defoliation can in turn affect the capacity of a stand to recover from unfavourable climatic events. In this report we examined the direct impacts of climate change on plantation productivity and wood properties, as well as the indirect effects through changes in pest and fire hazard. 9

19 Table 1.. Assessment of the vulnerability criteria for plantation forestry in Australia, and capacity to adapt. From AGO (2005). Criteria Exposure Sensitivity Adaptive capacity Potential to benefit from adaptation Assessment Moderate Moderate High High Adaptive capacity Adaptation is defined as adjustment in natural or human systems in response to actual or expected climatic stimuli or their effects, which moderates harm or exploits beneficial opportunities (Innes et al., 2009). Research on forest adaptation to climate variability or change is relatively recent, and only a few studies have documented evidence of successful adaptation strategies (Innes et al., 2009). Hence few tools exist with which to examine adaptation. Australia s forest industry is considered to have a high capacity to adapt and high benefit associated with adaptation (AGO, 2005; Hennessy et al., 2007). Recent studies support this. Battaglia et al. (2009) concluded that forecast climate change will not necessarily be beneficial to plantation productivity, and that considerable between-site variation (with both positive and negative effects) can be expected. It is clear from work undertaken by Pinkard et al. (2008) that the probability of increased pest activity is high under future climates, and that the impact of pest outbreaks on productivity may well increase. This means that productivity predictions under future climates will be overestimates unless pests are explicitly considered. There has been no assessment of the impacts of climate change on drought-induced mortality in plantations in Australia, but there is good anecdotal evidence of increases in drought deaths over recent years, and again productivity will be overestimated if this is not considered. Coupled with changes in fire hazard associated with increased rates of fuel drying (Matthews et al., 2009) and predicted increases in extreme fire weather conditions (Hennessy et al., 2005), it is clear that the vulnerability of plantations is likely to increase in Australia. Interactions between climate and site are complex meaning that outcomes for productivity, and for wood properties, are not easily anticipated. Forest sector responses to climate variability have been mostly reactive, responding to specific climatic events that have affected productivity, survival or capacity to perform maintenance and harvesting operations. Anticipatory, or planned, adaptation allows coordinated planning for the future between industry, government and the community, while addressing uncertainty in projections of changing climate and its impacts (Innes et al., 2009). Anticipatory adaptation is considered to be important when dealing with the consequences of significant climatic change. 10

20 Exposure Changes in background climate conditions Sensitivity Responsiveness of system to climatic influences Potential impact Adaptive capacity Capacity to modify exposure and sensitivity Vulnerability/resilience Figure 3. Factors influencing vulnerability (or resilience) of plantations to climate variability and climate change. From AGO (2005). In Australia, as in many parts of the world, a major hurdle to anticipatory adaptation is the lack of tools for assessing impact and exploring adaptation options (DAFF, 2009). Hazard assessment is currently ad hoc, and impact assessment does not account for the role of pests, drought and fire. This severely limits our capacity to understand adaptive strategies for reducing the impacts of climate change. Additionally, while plantation management decisions are made in an economic framework, there are few tools available to integrate impact assessment and adaptation planning with other enterprise management decisions. There may be many reasons, apart from lack of appropriate tools, why adaptation to climate variability or change is not integrated into a business enterprise. Lack of understanding of consequences of climate change on plantation productivity and wood products is one factor. Purpose of the study In 2009 FWPA released a report, Climate change and Australia's plantation estate: Analysis of vulnerability and preliminary investigation of adaptation options (Battaglia et al., 2009a), that provided the first detailed assessment of the vulnerability of Australia s temperate plantations to climate change. Improvements in future climate projections, and a capacity to downscale climate data to a resolution more closely matching the needs of the forest industry (Whetton et al., 2012), as well as recent improvements to the way some forest productivity models can capture the effects of elevated atmospheric CO 2 concentrations (eco 2 ), drought and temperature effects (White et al., 2011a), highlights the need for regular reassessment of climate change impacts to ensure forest managers have the most up-to-date information to help with their decision-making. This study addresses this issue by reassessing the direct and indirect effects of climatic variability and change on plantation productivity, using improved climate surfaces and productivity models. Unlike the earlier study we examined both production impacts of climate change and the effect of drought mortality, pests and fire. 11

21 Objectives of the study were to: inform forest managers of the effects of climatic variability on stand productivity, wood properties such as basic density, and hazards associated with the indirect effects of climate change, by updating and extending national predictions and discussing the outcomes with industry develop, in consultation with the forest industry, tools for understanding the direct and indirect consequences of climatic variability and change, and provide industry with the knowledge to use those tools use the tools to examine adaptation strategies to manage the consequences of climate variability and change for plantation forests, and to promote understanding and uptake of adaptation within the industry Project scope This project specifically focused on the biophysical hazards associated with climate change and variability for Australia s temperate plantations (Figure 4), and the probability of a change of state (in terms of volume, wood properties) resulting in response to those hazards. It examined the role of changes in management in reducing productivity losses associated with the hazards of climate change for Australia s temperate plantations. Our results can be integrated into a broader analysis of risk that includes, for example, financial and social elements. The tools we have developed can provide site and region-specific indications of impacts on stand volume, and can be used to explore a range of what if scenarios to inform management decision-making. This is primarily a modelling project. The models used have all been extensively tested against observed data and predict well under current climatic conditions. However there are no right answers when projecting into the future as we do here. There are large uncertainties associated with all climate change analyses, and rather than presenting averaged results, we have highlighted where uncertainty exists and how large that uncertainty might be, in recognition that this is important information for making decisions about how to respond to climate change impacts and hazards. Our results provide information about trends for a range of possible futures. The predictions should be updated regularly as more information becomes available on climate trends and tree responses to climate and rising atmospheric CO 2 concentrations. Project outputs This report provides an overview of the main findings of the analyses undertaken in the project. A very large spatial database was generated in the project (Table 2), and it is not possible to summarise all of the results in this report. The report provides a summary of possible outcomes of climate change for temperate plantation productivity and survival, hazards that may affect plantations, and the role of adaptation in managing the impacts of climate change. More detail of regional-scale changes is presented in 9 regional reports (five regions (south west Western Australia, Green Triangle, Tasmania, Victoria, southern NSW and northern NSW) x two species (E. globulus, P. radiata; only P. radiata in northern NSW). For assessment of local consequences of climate change and adaptation strategies that might be effective in reducing negative impacts, the spatial database can be utilised by forest managers as an additional decision-making tool. Additionally, substantial changes were made to the process-based model CABALA so that it could better represent the effects of higher atmospheric CO 2 concentrations on tree function and stand 12

22 mortality associated with drought. This tool is available for site-level assessments of climate change consequences for survival and productivity. Hazard assessment Risk management Weather Species traits Site characteristics Outcomes: volume, quality, heterogeneity, water Business model and systems Targets, processes, discount rates Loss Management Other stakeholders Licence to operate Figure 4. Relationship between hazard assessment and risk management. This project focused on hazard assessment. Table 2. List of data contained in the project spatial database Variable Data Years centred around 2030 and 2050 Climate Annual rainfall Annual maximum and minimum temperature Regions SW Western Australia South Australia/Green Triangle Victoria/southern NSW Northern NSW Tasmania Stand growth Volume Stems per hectare Stress indicators Minimum predawn water potential 13

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24 Chapter 2 Recent past and projected future climatic trends Summary Over the past 40 years many parts of Australia have experienced an increase in mean annual temperature compared with the preceding 50 years. There has been an increase in the number of days with maximum temperatures more than 35 C; and a decline in mean annual rainfall. These warming and drying trends have been observed throughout the main plantation-growing regions, located primarily in southern, south western and eastern Australia. Unprecedented increases in intensity of extreme events such as heatwaves and droughts also have been observed. We used the Climate Futures Framework to select climate models most representative of the main plantation-growing regions. Models were selected based on best case, most likely and worst case outcomes. Annual rainfall is projected to decrease across most of south eastern Australia with smaller changes in south west Western Australia, although projections vary with the climate model used. A general warming of the climate is projected, although as with rainfall the intensity and pattern of change varies with the climate model used. Only small changes in relative humidity are forecast by models, with the reduction strongest in inland areas and Victoria, south-east of Melbourne. There may be a small increase in relative humidity in northern NSW, and a decrease in Tasmania. A small decrease in wind speed is predicted by models, particularly in south west Western Australia but also across Victoria and NSW. There may be a small increase in wind speed in Tasmania. We did not explicitly account for changes in frequency and duration of extreme climatic events in our analyses because of limitations in our understanding of how these are likely to change in the future, but it is likely that these will play an important role in defining future plantation productivity and survival. 15

25 Introduction Over the past 40 years many parts of Australia have experienced an increase in mean annual temperature and a decline in mean annual rainfall compared with the preceding 50 years (Nicholls, 2008; Steffen, 2009). These warming and drying trends have been observed throughout the main plantation-growing regions, located primarily in southern and eastern Australia (Figure 5), where over the past 40 years mean maximum temperatures have increased by between 0.15 and 0.4 C/10 years ( Mean minimum temperatures have increased by between 0 and 0.3 C/10 years. Seven of the 10 warmest years on record have occurred since 1998, with a five-fold increase in the frequency of very warm months over the same period (CSIRO and Bureau of Meteorology, 2013). Concurrently, there has been a trend towards a reduction in the number of nights with frost in the main plantation-growing regions of Australia. Mean annual rainfall has declined across the country over the past 40 years, by between 0 and 50 mm/10 years with greatest decreases observed in eastern regions ( An abrupt decline of around 17% in mean annual rainfall in south west Western Australia has also occurred since the mid-1970s (CSIRO and Bureau of Meteorology, 2013). Declines in winter and spring rainfall have occurred particularly in southern Australia (CSIRO and Bureau of Meteorology, 2013). Additionally, there has been an increase in extreme weather events. The number of days per year on which the maximum temperatures exceeded 35 C has increased. The 2013/14 summer was unprecedented in its high temperatures and the duration of the high temperature events, and the 2012/13 drought in south-eastern Australia was the driest since records began (Climate Commission, 2013). The drought (the millennium drought) in southern Australia was also unprecedented in historical records in terms of its protracted severity (compared with the short but intense 2012/13 event).. There has also been an increase in climatic conditions resulting in extreme fire weather since the 1970s (CSIRO and Bureau of Meteorology, 2013). If greenhouse gas emissions continue to rise at current or predicted levels, it is predicted that radiative forcing will cause mean temperatures to rise by between 2.2 and 5.0 C above averages by 2050 (CSIRO and Bureau of Meteorology, 2013). Rainfall across southern Australia is projected to decrease by as much as 30% by 2070, with largest reductions during winter and spring (CSIRO and Bureau of Meteorology, 2013). These higher temperatures are expected to increase evaporation and reduce relative humidity (Hennessy et al. 2007), resulting in more frequent droughts and a longer fire seasons and coupled with more extremely hot days lead to increased fire danger (CSIRO and Bureau of Meteorology, 2013). While frost frequency may decrease (Nicholls 2008), frost severity may increase in some regions (Nitschke and Hickey 2007). An increase in the intensity of extreme rainfall is projected for most regions, and cyclones are expected to be fewer but more severe (CSIRO and Bureau of Meteorology, 2013). 16

26 a b c d e f g h Figure 5. Historical trends in mean maximum and minimum temperature, the number of extreme hot days, frost nights, heavy rainfall events, and warm spell duration in Australia. From the Bureau of Meteorology. ( 17

27 Global climate models and SRES scenarios Because of the many complex factors determining the climate response to elevated CO 2, global climate models (GCMs) are used to forecast future climate. While many of these models predict similar outcomes, differences and divergence exist. Acknowledging and working with this uncertainty is a fundamental part of assessing the impacts of future climates on risks to production forests. Currently there are 24 global circulation models (23 from CMIP3 plus the CSIRO-Mk3.5 model) that are well tested for Australia. It is often not possible for end-users with limited resources to run all 24 models to cover the range in potential futures. While it may be tempting to use a single mid-range model, this overlooks other out-lying and potentially important future climates (Clarke et al. 2011). The Climate Futures Framework (CFF) (Whetton et al. 2012), overcomes these limitations by classifying the projected changes from the full suite of climate models into classes defined by two climate variables, the most common being annual mean temperature and rainfall. Relative likelihoods are assigned to each class or climate future based on the number of climate models that fall within that category. A subset of models can be selected to represent the range in climate futures, ideally the most likely, best and worst case future climate. Assumptions underpinning emission scenarios can have a major influence on GCM projections. They are presented as Special Report on Emissions scenarios (SRES) (IPCC 2013). SRES present storylines based on alternative demographic, economic and technological futures (Whetton 2007). For example, an A1 storyline describes a high emission future with rapid population growth and rapid introduction of new technologies. An A2 storyline describes a more moderate emissions scenario, with slower population and technological growth. An A1FI storyline is one of the higher IPCC emissions scenarios (Nakicenovic et al. 2000), consistent with observations that emissions are tracking at the higher end of the range of SRES scenarios. Climate data are an integral part of the analyses described in this report. In the following, we describe the data used in the productivity and risk modelling and how it was generated. We also provide a summary of projected climate changes for 2030 and 2050 for two of the global climate models used in the project. We chose to use a stationary approach for future climates (modification of historical climate using monthly averages at two time frames) rather than continuous change directly from GCM s. This allowed us to select a wide range of GCM s to capture the uncertainty between the models and the decadal variation seen historically at a fine resolution. This approach captures the overall trends but does not explicitly account for changes in frequency and duration of extreme events such as storms and drought. To some extent these events are captured within the intra-annual variation in the climate data. This is a conservative approach compared to using dynamic change data, however dynamic data from GCM s is generally spatially coarse and limited to one or two GCM s. Methods used to generate climate data for this project Historical data Historical climate data were obtained from the Bureau of Meteorology's Data Drill for each of the regions (Jeffrey et al., 2001). The data in the Data Drill is synthetic; consisting of interpolated grids splined using data from meteorological station records but has the benefit of being available for all locations in Australia on a scale of 0.05 degrees. Blocks of 30 years of historical data were used for the base data, covering the years as defined by the IPCC as the base historical climate. 18

28 Data for 2030 and 2050 Using the CCF we selected 5 models per region that spanned the worst, most likely and best case climate outcomes. Figure 6 shows the model selection for best, worst and most likely outcomes using the CFF. We used the A2 emissions scenario for all models. Future climate was calculated by perturbing baseline climate using patterns of change from the selected climate models (Ricketts and Page 2007). A relatively simple stationary approach was used to modify the historical weather. The temperature and rainfall was modified using monthly averages from the potential future climates. Radiation was not adjusted as it is expected there will be only small changes of between -1 to + 2% (CSIRO 2007). The monthly changes in temperature for the 2030 and 2050 time period were added to the historical data. Rainfall was modified using proportional change (a simple additive approach is not appropriate given the variation in absolute rainfall across a single cell in the GCM grids (~25km 0.25 degree), particularly in areas of high terrain). A regular grid of 0.1 degree (~10km) was applied across all regions and was used to sample climate. One representative location in the centre of each grid was selected. There is some risk that the topography at this point may not be indicative of the grid cell as a whole. There are some limitations in using the CFF. Currently the CFF is based on Natural Resource Management Group (NRM) boundaries, which do not always follow climatic gradients, and as a result there will be occasions where the worst and most likely future climates are reversed (or in some instances, the best). This is an issue particularly when close to the edge of the NRM boundaries (see Figure 6).This presents difficulties when using the CFF for large regional studies such as this, where a particular climate model may represent the most likely and the worst case scenario (e.g. MIROC3_medres in WA). To over this problem we ran 5 models for all regions and used the output (i.e. the projected impact) to define the best, worst and average potential futures. This overcomes the problem created by the artificial NRM boundaries. Data generated using the full set of climates is available through the accompanying project spatial database. 19

29 Figure 6. Output from the Climate Futures Framework Selection of the best, worst and most likely models for the NRM regions that fall within the range of the temperate plantation estate. Wind speed SILO does not provide wind speed data, but wind speed is a critical variable in fire danger modelling. Development of wind speed data sets has proved challenging because the observation network is less dense that those for temperature or rainfall and because homogeneity is a significant problem (Lucas et al. 2007; McVicar et al. 2008). Approaches to dealing with this have included: making only point calculations at locations with high quality observations (Lucas et al. 2007), use of forecast wind (Dowdy et al. 2009), or development of gridded data from the more extensive wind run observation network (McVicar et al. 2008). We used the McVicar et al. (2008) mean wind data set as the basis for analysis. Fire danger calculations require afternoon wind speed and so direct use of daily mean wind speed would significantly under-estimate fire danger. To scale the mean wind data we compared station observations with the corresponding grid points in the McVicar et al. (2008) data set. Station observations were taken from the 374 mainland Bureau of Meteorology sites held in the US National Climate Data Centre s Global Summary Of the Day (GSOD) data set. Maximum wind speed was estimated by modelling the ratio of maximum to mean wind speed as a function of mean wind speed (Davis and Newstein 1968; Graybeal 2006). Figure 2 shows box plots of wind ratio in 5 km h 1 increments along with modelled wind ratio. The best fit function was: 20

30 R = (Um + 10) where R is wind ratio and Um is mean wind speed. Modelling mean wind ratio inevitably results in underestimation of extreme high ratios. This effect is most prominent at low wind speed and less of a problem at the higher wind speed associated with high fire danger. Future work could mitigate this by modelling a distribution of ratios. Mean climate data for the baseline ( ) period are shown in Figure 3 Figure 7. Ratio of maximum to mean wind speed for daily observations from automatic weather stations. Line is power function fit to mean values. Boxes are 25% to 75% quartiles, whiskers are 1.5 times the inter-quartile range. Climate data for pest hazard modelling Climate data developed specifically for use with the species niche model CLIMEX were downloaded from the CliMond website (Kriticos et al., 2012). The underlying historical data available at this website are sourced from the WorldClim and Climate Research Unit datasets, reformatted and adjusted for use in CLIMEX. The historical data centre on 1975 ( ). The CliMond site provides CLIMEX data for two global climate models, CSIRO Mk 3.0 and MIROC- H. These models do not exactly match the models selected using the CCF for the productivity and fire modelling. The CSIRO 3.0 model projects annual average decreases in rainfall across all of Australia in all seasons, except for increases across the east coast in summer, but to a lesser degree than the CSIRO 3.5 model used in the other modelling. However temperature is the major climatic variable affecting the pests included in the pest distribution modelling, and there is more agreement between the two models in this term.. Summary of projected changes in weather variables in 2030 and 2050 Air temperature changes for two climate models (CSIRO 3.5 and MIROC-H) in 2030 and 2050 are shown in Table 3, as an example of the range of climate outcomes that are projected for the plantation-growing regions. Warming is projected to be more intense for the CSIRO model with a median increase of 1.7 C in 2050 compared to 1.4 C for MIROC. There are also some differences in the pattern of warming. Using the CSIRO model warming over inland Victoria and New South 21

31 Wales is more intense than in south-west Western Australia and Tasmania. Using the MIROC model, warming is projected to be uniform over most of the mainland sites but lower in Tasmania and some coastal areas of South Australia. Annual rainfall is projected to decrease with both models but the decrease is much larger for the CSIRO model particularly by 2050 (Table 3). For this model the greatest decreases occur across south east Australia and Tasmania with much smaller change in south west Western Australia. The MIROC model projects small decreases in annual rainfall across southern Australia but small increases in most parts of NSW north of Canberra. Figure 8. Projected changes in relative humidity in 2030 and 2050 across Australia s temperate plantation estate Changes in rainfall are not evenly distributed through the year (Table 3). The CSIRO model projects the strongest decreases in all locations during spring. During the summer months there are large decreases projected for Victoria and Tasmania but smaller decreases in south west Western Australia and increased rainfall in NSW. During autumn and winter, rainfall is projected to decrease across the temperate plantation estate, with the strongest changes in inland areas of Victoria and NSW. For areas where decreased rainfall is predicted by the MIROC model these decreases occur mainly in spring and summer with lesser changes in autumn and winter. For parts 22

32 of NSW with projected increased annual rainfall, this increase occurs in autumn and winter with only small changes in spring and summer. Both models project only small changes in relative humidity (Figure 8). This reduction is strongest in inland areas and south east regions. The MIROC model projects a small increase in relative humidity for northern NSW, while the CSIRO model projects a decrease in Tasmania. A small decrease in wind speed is projected by the CSIRO and MIROC models (Figure 9). Both models project the majority of this decrease to occur in south west Western Australia, but the MIROC model also projects a reduction in wind speed across Victoria and NSW as well. The MIROC model projects a small increase in wind speed in Tasmania. Figure 9. Projected changes in wind speed in 2030 and 2050 across Australia s temperate plantation estate 23

33 Table 3. Mean change in maximum and minimum temperature ( C) 2004) and and rainfall 2030 (mm) or 2050, between projected base climate (average fro using two GCM s (CSIRO 3.5 and MIROC-M) ( Annual and seasonal changes are given at a state level State Annual change Spring Winter Autumn Summer Annual change Spring Winter Autumn Summer CSIRO 3 MIROC CSIRO 3 MIROC CSIRO 3 MIROC CSIRO 3 MIROC CSIRO 3 MIROC CSIRO 3 MIROC CSIRO 3 MIROC CSIRO 3 MIROC CSIRO 3 MIROC CSIRO 3 MIROC Maximum temperature QLD NT WA SA NSW VIC ACT TAS Minimum temperature QLD NT WA SA NSW VIC ACT TAS Rainfall (mm) QLD NT WA SA NSW VIC ACT TAS

34 Conclusions There is widespread agreement that Australia s climate is changing (CSIRO and Bureau of Meteorology 2014). In the above analyses, we did not account for changes in frequency and severity of extreme climatic events, but it is likely that these will play an important role in defining future plantation productivity and survival (Mitchell et al., 2014). The climate data generated in these analyses were used in productivity and hazard assessments, as described in subsequent chapters. 25

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36 Direct effects of climate change on future E. globulus and P. radiata plantation productivity Updated assessments of productivity including drought impacts Climate impacts on wood properties Role of defoliation in influencing productivity 27

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38 Chapter 3 Projected impacts of climate change including drought on plantation productivity Summary Some regions of the Radiata pine and blue gum estates may show decreased productivity in 2030 compared to now. Other regions may show increased productivity however the response will be strongly determined by local conditions of soil depth and fertility. Model predictions are highly sensitive to the responsiveness of plantation species to eco 2. If forests are not responsive to eco 2 and sustained photosynthetic rate increases are not observed, 5-15% or higher decreases in productivity may be seen in the Green Triangle, Gippsland and south-west Western Australia may be seen for both bluegum and radiata pine. These are currently some of the most productive plantation areas. If plantations respond favourably to eco 2, then productivity is predicted to increase in most regions except at the drier margins of the plantation estate where increased mortality will reduce expected production. Cold wet sites (for example plantations in the highlands of Victoria) where nutrients are limited may see an additional growth response due to increased nitrogen mineralisation under warmer temperatures. This benefit is not predicted for drier environments where water is the main resource limiting to growth. We predict a general decrease in survival in warm dry regions if the response to eco 2 is limited. In cold environments, survival generally improves in response to warmer temperatures. Those sites currently in the well performing core of the plantation estate may be slightly affected in production (up or down) by climate change, but our modelling shows little change by 2030 or even However, areas at the dry margins of the estate are vulnerable and in the worst instances look highly likely to fail. 29

39 Introduction In 2009 FWPA released a report, Climate change and Australia's plantation estate: Analysis of vulnerability and preliminary investigation of adaptation options (Battaglia et al. 2009), that provided the first detailed assessment of the vulnerability of Australia s temperate plantations to climate change. This report found that there may be positive or negative effects of climate change on plantation productivity, with substantial between-site variation in responses likely. It also concluded that productivity was more likely to decrease if the species planted could not physiologically take advantage rising atmospheric CO 2 concentrations (eco 2 ), particularly if hot dry climatic conditions become more frequent and severe. However, if plantation species are able to respond to eco 2 by increasing photosynthetic rates and sustain this over the long term, production may increase in many regions and particularly in cool wet locations and in drier areas may compensate for decreased water through increased water use efficiency and sustain or even slightly increase production. Since then, other reports have reinforced some of the uncertainties around predicting future plantation productivity (Medlyn et al 2010; ABARES 2011); including uncertainty around the effects of high CO 2 on tree growth, and the impacts of warmer, drier conditions and more frequent and severe droughts and heatwaves. Improved climate modelling, the recently acquired capacity to downscale climate data to a resolution more closely matching the needs of the forest industry (Whetton et al 2012), as well as recent improvements to the way some forest productivity models can capture eco 2 effects, drought and temperature effects (White et al. 2011), are an opportunity to provide forest managers with revised estimates. We reanalysed rotation-length productivity and survival of E. globulus and P. radiata plantations in southern Australia under current and projected 2030 and 2050 climate to identify those parts of the plantation estate where productivity might be changed (upwards or downwards), identify those parts of the plantation estate where variability in production might increase; and calculate the probability of plantations failing as a result of drought death. We aimed to: Show spatially the effects of climate change on future forest production using downscaled global climate models that represent differing, but plausible, climate futures; Using these data examine the frequency distribution of production outcomes and compare future and current variability in expected plantation production from one rotation to the next; Examine spatially the likelihood of drought death in plantations, providing information on the number of rotations in 20 that are likely to totally fail, and show the impact on production of stands in which some drought death occurs; Spatially show how site factors of fertility and soil depth will influence these future predictions; While 5 climate models were used in each region for the analyses and the data is available in the spatial database that accompanies the report, in this chapter we focus on predictions from one model only, to improve interpretation of results. Methods The model The forest productivity model CABALA (Battaglia et al. 2004; White et al. 2011) was used in this analysis. As recommended for climate change impact studies such as the one undertaken here (Medlyn et al. 2011), the model is a fully coupled carbon, water and biogeochemistry model. As shown in the predecessor report 30

40 (Battaglia et al. 2009) and other studies (Kirschbaum et al. 1998, Medlyn et al. 2000, Norby et al. 2010) the interactions and feedbacks are critical to representing the site specific nature of climate change on plantation growth where the ability of plants to utilise addition carbon dioxide is modulated by temperature, nutrient and water effects. The formulation of the model used here is that described in White et al. (2011), that utilises the Farquhar photosynthesis model (Farquhar et al. 1980) and the SPA hydraulic model (Williams and Rastetter 1999). While nitrogen limits to increases in growth and photosynthesis are capture through the use of the linked carbon-biogeochemical model, the limiting effects of other nutrients, especially phosphorus is not. To deal with situations where other factors prevent photosynthetic upregulation in response to eco 2, we also consider a worst case scenario in all site by condition combinations in which we assume no photosynthetic (and hence growth) response to eco 2. We believe the analysis that includes upregulation in response to eco2 is the best possible representation of the short term response of short-term response, however there is not sufficient information currently to determine if these responses will be sustained. While there is increasing evidence from some Free to Air CO 2 Experiments (FACE) that they may be sustained in many cases (e.g. Leakey et al. 2010) there is nevertheless some experimental evidence, including the species of study in this report, that suggests the contrary (e.g. Griffin et al. 2000; Greenep et al 2003). A notable limitation of this model is its inability to simulate the limiting-effects of nutrients other than nitrogen. In many Australian soils phosphorus can limit productivity, an effect that may limit the capacity of forests take advantage of eco 2 (Kirschbaum 2003). In such cases the no eco2 simulation simulates a lower bound. Climate data The climate data used in the analyses are described in the Climate chapter. Scenarios There is large variation in soil type and depth across Australia s temperate plantation estate. Lack of reliable spatial coverage of variables that determine productivity (such as soil depth below 2m) meant that we could not capture this spatial variation in our analyses. We note that considerable progress is currently being made in the development of spatial soils data relevant at least to agriculture is being made and some of this information may be relevant to forestry. We simplified the analyses by identifying standard soil types and depths that would be indicative of plantation response to a range of soil characteristics typical of each plantation region. Six standard soil types were used as site inputs (low, medium and high fertility for each of a shallow and a deep soil defined relative to the range observed in regions) and an even grid used to simulate climate spatial variation across each plantation region. The fertility and soil depths used in the simulations account for the range of regional variation of soils that occur in the region (refer to Table 4). Similar to soils, silvicultural regimes vary between regions and forest management enterprises. We selected standard regimes that will approximate most regimes applied in the field but may vary in some elements. The silvicultural regime used for E. globulus was a 10 year rotation planted at 1000 stems per hectare (sph). For P. radiata, the regime used was more elaborate: planting was at 1333 sph, with a rotation length of 35 years, and three commercial thinning events. The first thinning was at age 11 with the reduction to 750 sph, the second at age 19 years to 450 sph and the final thinning at age 26 years to 250 sph. For each combination of species by climate scenario by time period, 20 separate rotations were simulated by running the model with 20 different planting dates over a 30 year block of weather data. Where 31

41 rotation lengths were longer than 10 years, the 30 year block of weather data was looped. From this the coefficient of variation (the ratio of the standard deviation and the mean) was used to describe variability at specific locations. This provides a normalized measure of dispersion of a probability distribution, and is independent of the unit of measurement thereby allowing easy comparison of data with widely different means. For each region the model was run to include the factorial combinations of 6 soils, 2 species (pine only in northern NSW), 5 climate models, 3 time frames, (current, 2030, 2050) and 20 planting dates for each time period. For E. globulus, these scenarios were run with +- eco 2 and +- mortality. At present the mortality model is only suitable for E. globulus. Hence for radiata pine, these scenarios were run only with +- eco 2. By running the model with no eco 2 we are not assuming that CO 2 has not and is not rising, but we are taking a worst case assumption of forest response that assumes no photosynthetic or stomatal response. Finally, we predicted outcomes assuming stand mortality did or did not occur (E. globulus only), to provide a best and worst-case outcome. Database A wide range of outputs were generated from the modelling, including volume, average tree dimensions (by age), stocking, leaf area, All data were compiled into a spatially referenced database. For display in this report a subset of variables relating to volume, final stocking, survival and the coefficient of variation for current and 2030 projections are displayed. Table 4. Description of standard soil used in each region. OM% is percent organic matter, and C:N ratio is the carbon: nitrogen ratio. SA GT is South Australia/Green Triangle, NSW is New South Walers, and SWWA is south west Western Australia. For low fertility a net nitrogen mineralisation rate of ~40 kg N/ha/yr was assumed; for high fertility net nitrogen mineralisation >100kg/N/yr. Numbers in brackets refer to the C:N ratio. High Fertility OM% and C:N ratio () top 10cm Medium fertility OM% and C:N ratio () top 10cm Low fertility OM% and C:N ratio () top 10cm Shallow soil Region depth (m) Northern NSW 4.2 (15) 2.5 (22) 1.3 (30) SA/GT 4 (18) 2 (30) 1.2 (38) SW WA 4 (18) 3 (22) 1.5 (28) 5 9 Tasmania 7 (15) 2.5 (20) 1.3 (28) Vic and Southern NSW 5 (15) 2.5 (22) 1.2 (28) Deep soil depth (m) Statistical analysis Mean volume was predicted as the average of at-harvest entire stem volume from the 20 planting dates. Where the stand did not survive the full rotation a zero was used as the final volume for the calculation. In practice death late in the rotation would have resulted in harvestable wood, with the result that the predictions in this report will be worst-case outcomes. Survival was calculated as the number of rotations out of 20 that survived to harvest age. The final stocking was calculated as the average of the number of 32

42 stems/ha at the end of the surviving rotations for most sites the full 20 rotations survived, in the worst cases it was as low as 3. The coefficient of variation was calculated for the 20 planting dates, assuming all rotations survived. Results This chapter presents broad high level summaries of national plantation responses to climate change. Detailed regional reports were developed in addition to this report, that provide more detailed analyses in 5 regions (south west Western Australia, Green Triangle, Tasmania, Victoria/southern NSW, northern NSW). Uncertainty From a validation of 28 P. radiata plots in Victoria, Tasmania and South Australia spanning final standing volumes of 127 to 623 m 3 /ha with up to three thinning and a range of silvicultural treatments the root mean square error (RMSE) was 46 m 3 /ha. RMSE is used in place of standard deviation where there may be bias from systematic error. For 110 E. globulus plots from Victoria, South Australia, Western Australia and Tasmania ranging in final standing volume of 30 to 450 m 3 /ha the RMSE was 19 m 3 /ha. These results give us confidence that the model was predicting well under current climatic conditions. Validation of predicted responses to changed climate or eco 2 are not possible; longer-term monitoring will be required to improve our capacity to model these responses into the future. Some confidence regarding predictions into future climates can be gain from the overlap between the plots in the validation set and the future climate conditions (see Battaglia et al. 2009). Predictions of mortality are similarly uncertain observational data with which to validate models are sparse. Very few records are available for plantations in which more than 20% of trees die. Responses to elevated CO 2 Our predictions of eco 2 response are consistent with the few data available (changes in photosynthesis with eco 2 and temperature interactions, water use efficiency and leaf area) (Tissue et al., 2001; Crous et al., 2013; Duan et al., 2013), however longer term effects in larger and older trees are not known. Cold E. globulus sites were predicted to be less responsive to eco 2 than warm sites (Figure 10B) (refer to Table 5 for the descriptions of climate). When other factors are non-limiting we predicted around a 50% increase in volume as CO 2 was increased from 300 to 900 ppm at the cold site, compared with an almost doubling at the hot site. Where nitrogen was limiting to production (Figure 10A) we predicted a limited response (refer to Table 4 soils information, SWWA sites were used). Where water was limiting however (Figure 10C) we predicted a continual and proportional increase with eco 2 that was greater than for wet sites, suggesting water use efficiency gains from eco 2. Where responsiveness to eco 2 was predicted, this responsiveness was greatest at atmospheric CO 2 concentrations below 600 ppm. Table 5 Climate summary for the sites used in the sensitivity to eco 2 Sites Av annual Rainfall (mm) Av max Temperature ( o C) Av min Temperature ( o C) Cold dry Cold wet Hot dry Hot wet

43 A B Stand volume, m3/ha C Atmospheric CO2 partial pressure, ppm Atmospheric CO2 partial pressure, ppm Figure 10. Some examples of the predicted volume response of E. globulus to increasing atmospheric CO 2 concentrations: (A) cold wet site with either shallow nutrient poor soil or deep nutrient rich soil; (B) cold wet or hot wet site with deep nutrient rich soils; (C) hot wet site with deep nutrient rich soil or hot dry site with shallow nutrient rich soil. Soil characteristics defining low or high nutrition or shallow or deep soils are given in Table 4. Pinus radiata productivity and survival Under current climatic conditions the highest productivity of P. radiata stands was predicted for coastal regions in northern Tasmania, the Green Triangle, Gippsland and south west Western Australia (Figure 11), with predictions ranging up to 2000 m 3 /ha entire stem volume at harvest including thinning volumes. The spatial pattern in the predictions show a strong mean annual rainfall effect, with temperature exerting limitation in colder locations in Tasmania and the higher regions of Victoria. Predictions of volume in 2030 and 2050 were strongly influenced by the assumptions of responsiveness to eco 2. If we assumed no response to eco 2 (and this would be similar to situations where the response is short lived or confined only to very young trees), in 2030, large parts of the most productive pine estate were projected to either remain at similar levels of productivity, or to reduce final volume by up to 200 m 3 /ha. For example, in large areas of the Green Triangle, south west Western Australia and Gippsland a 5 15% decline in productivity was predicted (Figure 11), and there were a few areas in inland Victoria, where trees are already temperature and water stressed where a decline of >15% was predicted. By 2050 substantial areas of the pine estate are forecast to show decreased productivity, with large areas declining by more than 15%. If the assumption is made that stands are responsive to eco 2, then in 2030 increases in productivity of up to 300 m 3 /ha were predicted across much of the estate. The predicted range of increased productivity was 34

44 between 5 and >15% compared with current levels. However, even allowing for eco 2 gains we expect parts of the estate to show production decline in 2050, including parts of East Gippsland and areas in far northern Victoria. While many areas are forecast to show productivity gains by 2050 if forests respond positively and in a sustained manner to eco 2, only modest gains are forecast in central western Victoria and the northern parts of the Green Triangle regions areas where increases in other risks such as pests and fire (see later chapters) may erode these modest gains. The greatest gains were predicted in cold areas and in dry areas the former due the removal of temperature limitations to growth and the latter through increased water use efficiency. There will be considerable local response variation to climate change driven by local soil and weather conditions that are averaged out in the 10km pixel shown in our maps. Consequently, the figures here can only provide general and spatially averaged indications of anticipated changes. More detailed, site-specific information can be extracted from the spatial database that accompanies the report, or can be generated using the CABALA model using specific site conditions. Under current conditions, the bulk of the pine estate was predicted to have 100% survival, the exception being some stands in north eastern NSW where it is predicted that up to two rotations out of 20 may fail due to drought. If the plantations are unable to benefit in terms of water use efficiency from eco 2, we predict small to substantial increase in the drought risk for a few inland locations (Figure 11). If plantations do increase in water use efficiency (as is likely) then drought risk will not be exacerbated and consequently the risk to the pine estate will be similar to that of the present day. Eucalyptus globulus productivity and survival Under current conditions, we predicted mean entire stand volumes of E. globulus at age 10 years at between 50 m 3 /ha (MAI 5m 3 /ha/yr) in the dry Tasmanian Midlands, up to 250 m 3 /ha in some coastal regions of south west Western Australia, the Green Triangle, Gippsland and northern and south eastern Tasmania (Figure 14). If no response to eco 2 is assumed, we predicted that in 2030 there will be either a decrease in productivity or, at best, no change in the higher productivity regions in south west Western Australia, South Australia, Victoria and Tasmania (Figure 15). Productivity is only forecast to increase in inland regions of Victoria (up to >15%) where decreases in frosts and cold night time temperatures were simulated as sufficient to offset the impact of drying on production, or in temperature limited areas at higher elevations such as North-East Victoria inland in Tasmania Inclusion of mortality obviates the potential gains in some of the drier areas (north and east of Albany through to Esperance in SWWA, south of Bendigo, Ararat, and Benalla in Victoria) (Figure 16). Impacts are predicted to be greatest at lower fertility sites (Figure 14) If responsiveness to eco 2 is assumed, increased productivity was predicted across the estate, irrespective of assumptions about whether mortality occurred or not. Between-planting date variability was predicted to decrease at some of the cold limited regions (around central Victoria, most inland regions of Tasmania) (Figure 17). In some of the hot dry regions in south west Western Australia (east of Albany through to Esperance in south west Western Australia) variability between rotations was modelled to increase if a positive photosynthetic response to eco 2 was not simulated. Where there a positive response to eco 2 was simulated, the modelled variation was similar to that simulated in the current climate. 35

45 Most of the current E. globulus estate is predicted to have a high probability of survival (Figure 18), close to 100%. Lower rates of survival were predicted for central Victoria, the south coast of Western Australia, and the Midlands of Tasmania. Survival was predicted to decrease in inland areas by 2030, with little difference in other areas, irrespective of assumptions about eco 2 responsiveness. The modelled consequences of this for stand stocking at the end of rotation are shown in Figure 19. Under current climate we predicted close to full stocking for most of the estate, with lower levels in inland south west Western Australia, north central Victoria and Tasmania where drought induced self thinning is modelled to occur. By 2030 only small changes to this were predicted. This is unlikely to change with assumptions of CO 2 responsiveness. Discussion The potential response to eco 2 is a critical unknown of modelled future productivity, and how it manifests will determine if the bulk of the existing plantation estate will benefit from or be adversely affected by climate change. If the full beneficial effects of eco 2 are realised then we predict that plantation productivity will improve across most areas of Australia s plantation estate as a result of increased photosynthetic rate and improved water use efficiency in the drier areas and improved growth in the colder areas. The trends and levels of response to eco 2 are similar for both species in this analysis. If those eco 2 gains are not, or only slightly realised, then significant (5-15%) production decreases may be seen in the Green Triangle, Gippsland, south-west Western Australia and northern NSW( Pinus radiata only). These are some of the most productive areas. Evidence emerging from long term Free Air CO 2 Enrichment (FACE) experiments is currently leaning to sustained photosynthetic upregulation under eco 2 (Leakey et al. 2009), and it is almost certain that even if photosynthetic rates do not remain elevated plantations will reap some benefits from increased water-use efficiency (Barton et al ). This is generally support by global observation of the increase of the global vegetation carbon sink (Cais et al 2013). However, deep uncertainty exists, and the earlier experiments in open topped chambers with one of the species studied here suggest that some level of acclimation may occur (Greendep et al. 2003). This question will not be resolved by modelling studies such as this but will require experimental analysis, supplemented with a process understanding of plant photosynthetic regulation. Some long term, multi-rotation, forestry experiments where identical germplasm and silviculture have been used through 3 or more rotations may provide some indication when combined with growth modelling. An instance of this exists in New Zealand and should be pursued. As with the 2009 study, despite the inherent uncertainty that will determine future response uncertainty in climate change, plant response and the extent to which the local environment will modulate the outcome some definite conclusions can be reached. High quality (fertile sites with deep soil) are likely to be relatively unaffected in the core areas of the existing plantation estates (usually worse case is a prediction of -5 to +5% change which if it was manifest is likely to be detectible on the ground as a change). These regions in coastal south-west WA, coastal areas of the Green Triangle and the Otway peninsula and northern Tasmania show little variation in final outcome across all scenarios (Fig. 15) Under any scenario construction south-west Western Australia, and the areas well inland of the Great Dividing Range in Victoria, respond less favourably (in that they either respond less positively in the case of combinations of site and response to eco 2 or more negatively in other constructs) than other regions of the estate. In many of the scenarios the east regions of Gippsland are similarly disadvantaged. Areas in central Victoria, drier and higher areas in Tasmania and Victoria and the eastern plantation estate in Western Australia show considerable variation in predicted change with scenario construction. 36

46 In addition to variability in simulations due to the soil and eco 2 scenario used, we have also attempted to quantify the variation from rotation to rotation due to the weather sequence used within a particular future climate scenario (remembering that 30 simulated weather sequences have been used for each combination of location by climate change model by eco 2 response by soil setting). The inter rotation variability has been assessed using two measures, standard deviation (SDev) and coefficient of variation (CV). The SDev varies across the estate under future scenarios but is generally in proportion to the average volume i.e. as volume increases, the SDev goes up. Using the CV removes this effect and highlights those areas where there has been a shift in the inherent variability at a site. The regions currently limited by temperature generally show a predicted decrease in variability as survival improves because climate models predict rising low temperatures with reduced frost intensity. These regions include areas that are bioclimatically close to the failure point for plantations, and plantations are simulated to fail or be damaged in some rotations, and climate change shifts them to a less risky bioclimate. The predicted variability increases in the drier regions in south-western WA and northern Victoria, when a positive response to eco 2 is not simulated, largely due to drought mortality. Where a positive response to eco 2 is simulated, the change in stomatal conductance and consequent increased water use efficiency (and hence slower rate of water use into drier seasons) results in simulated variability similar to that simulated under the current climate. We predicted that on dry pine sites there would be an increased survival at the establishment phase (0-5 years) under simulated eco 2 conditions. The simulated increase in WUE of the pine seedlings drove this response. After establishment, simulated growth and survival of pines was similar to bluegums, with modelling under eco 2 increasing WUE and resulting in greater production on water limited sites. For E. globulus in colder regions where low temperatures limit growth, (parts of Tasmania and Victoria) survival was predicted generally to improve with warmer future temperatures regardless of the whether or not photosynthetic upregulation to eco 2 was sustained. On drier, warmer sites, survival was predicted to be similar or slightly superior where there a positive response to eco 2 was simulated. Where the response to eco 2 was not simulated, survival was predicted to decrease (parts of south-western WA and northern Victoria). The impact of mortality on productivity in these regions was simulated to be significant in some case, particular where mortality was forecast to be high (greater than 20%). Where tree mortality was forecast as low the impact on production was slight, and merely reflected an accelerated self thinning effect, and sufficiently high tree stocking was maintained to retain full site occupancy. However what is clear from these simultaneous simulations of mortality and productivity is that both need to be predicted to gain a full picture of future impacts. Simulations that ignore threshold changes in conditions that result in tree mortality may be in danger of under-predicting climate change impacts on future wood yields in some cases. In the cold regions in Victoria and Tasmania, plantations on low fertility soils show a greater predicted percentage increase in productivity than the medium fertility soils. This is in part a result of predicted increased nitrogen mineralisation under higher temperatures. In warmer, drier regions plantings on the lower fertility sites struggle to become established and the increase in productivity is lower than compared with the medium fertility sites. But absolute productivity will still be substantially lower than the higher fertility sites even when the percentage increase is higher i.e. a 10% increase in productivity of site with the potential to grow of 50 m 3 ha -1 is the same as a 5% increase at a site with the potential for 100 m 3 ha -1. These predictions highlight the importance of simulating future climate change impacts with linked carbonwater-nutrient models as highlighted by Medlyn et al (2011). Ultimately the response of change in temperature and rainfall will manifest in plantations through the way they affect the most limiting growth factor. Climate change will not just manifest through gas and water exchange attributes of vegetation but will also drive ecosystem responses through changes in soil attributes such as decomposition and rates of nutrient mineralisation. 37

47 Figure 11a. End of rotation volume plus thinning volume for The difference between the total volume achieved at 2030 (with and without eco 2 ) and the 1990 volume is shown in b. and c. The survival of Pinus radiata plantations under 1990 and 2030 climate change conditions with and without eco 2. Survival refers to the number of rotations that survived out of 20. The climate model is the CSIRO MK3.5 GCM and the soils are medium fertility, deep soils. The definition of medium and deep varies across the regions and more information can be found in Table 4. 38

48 Figure 12a. End of rotation volume plus thinning volume for The difference between the total volume achieved at 2050 (with and without eco 2 ) and the 1990 volume is shown in b. and c. The survival of Pinus radiata plantations under 1990 and 2050 climate change conditions with and without eco 2. Survival refers to the number of rotations that survived out of 20. The climate model is the CSIRO MK3.5 GCM and the soils are medium fertility, deep soils. The definition of medium and deep varies across the regions and more information can be found in Table 4. 39

49 Figure 13 Percentage change in total volume of P. radiata in 2030 and 2050 compared with 1990 total volumes under assumption of eco 2 and no eco 2 response. The climate model is the CSIRO MK3.5 GCM and the soils are medium fertility, deep soils. The definition of medium and deep varies across the regions and more information can be found in Table 4. 40

50 Figure 14. Impact of fertility on responses of E. globulus productivity to changing climates, total volumes are shown for 1990 and the percentage change for The climate model is the CSIRO MK3.5 GCM, no eco 2 response and the soils are medium fertility soils (a. and c.) and low fertility soils (b. and d.). The depth of the soil remains the same. The definition of medium and low fertility varies across the regions and more information can be found in Table 4. 41

51 Figure 15 Percent change from current production of E. globulus under a range of modelling assumptions in 2030, a. percentage change from 1990 values with no eco 2 response and mortality is not modelled, b. percentage change from 1990 values with an eco 2 response and mortality is not modelled, c. percentage change from 1990 values with no eco 2 response and mortality is modelled and d. percentage change from 1990 values with an eco 2 response and mortality is modelled, The climate model is the CSIRO MK3.5 GCM and the soils are medium fertility, deep soils. The definition of medium and deep varies across the regions and more information can be found in Table 4. 42

52 Figure 16 Percent change from current production of E. globulus under a range of modelling assumptions in 2050, a. percentage change from 1990 values with no eco 2 response and mortality is not modelled, b. percentage change from 1990 values with an eco 2 response and mortality is not modelled, c. percentage change from 1990 values with no eco 2 response and mortality is modelled and d. percentage change from 1990 values with an eco 2 response and mortality is modelled, The climate model is the CSIRO MK3.5 GCM and the soils are medium fertility, deep soils. The definition of medium and deep varies across the regions and more information can be found in Table 4. 43

53 Figure 17. Changes in the variability of E. globulus productivity at 1990 and 2030 with the assumption of eco 2 and no eco 2 response. The climate model is the CSIRO MK3.5 GCM and the soils are low fertility, deep soils and mortality is modelled. The definition of low and deep varies across the regions and more information can be found in Table 4. 44

54 Figure 18. Probability of an E. globulus plantation surviving to harvest ages shown as the number of rotations out of 20 simulations in which stands survived to age 10 years. Where no cells are shown (white space) there is no change in the number of rotations that survived values are shown in a. and d. The difference at 2030 compared to 1990 is shown in b. with no eco 2 response and mortality is not modelled, c. with an eco 2 response and mortality is not modelled, e. with no eco 2 response and mortality is modelled and f. with an eco 2 response and mortality is modelled, The climate model is the CSIRO MK3.5 GCM and the soils are medium fertility, deep soils. The definition of medium and deep varies across the regions and more information can be found in Table 4. 45

55 Figure 19. End of rotation survival of E. globulus as stems per hectare after initial planting density of 1000 sph in 2030 under ambient and eco 2 The climate model is the CSIRO MK3.5 GCM and the soils are medium fertility, deep soils. The definition of medium and deep varies across the regions and more information can be found in Table 4. 46

56 Chapter 4 Climate impacts on wood properties: an overview Summary Within a tree wood properties vary radially and longitudinally. Sampling protocols can determine the variation patterns reported. Predicting variability is best achieved through process-based models that capture the logic and nature of interactions between environment and physiology Environmental changes over seasons and rotations affect patterns and rates of growth. Patterns of growth affect average wood properties as wood properties vary seasonally Temperature generally tends to increase wood density, but opposite trends have been observed. The effects of water availability on wood properties are largely mediated through changes in growth phenology. In general increased water availability reduces wood density, via increased growth rates and the production of larger diameter cells with thinner walls. Direct effects of elevated CO 2 on wood density are small but tend to increase cell wall thickness and hence density increases. Effects of elevated CO 2 are small relative to effects of temperature and water availability 47

57 Introduction The Australian forest industry is largely non-vertically integrated. The growers of trees are generally different organisations from those that process logs. The returns to the grower are based primarily on volume, reducing the incentive to improve or consider wood quality. The log processors gain a return for both the volume and quality of their product, and wood quality impacts significantly on the dollar return. The predominant wood property affecting softwood sawn timber production is stiffness (MOE) which is affected equally by wood density and microfibril angle (MFA). Wood density and wood fibre properties also impact on chemical and mechanical pulp properties, engineered wood and fibre board properties. Consequently the interactive effects of genotype, climate and forest management on volume and wood quality have significant ramifications for the overall profitability of the forest industry. The establishment of plantations on marginal sites, the frequency of drought and flood events, and the seasonal spread of rainfall interacting with soil type all have the potential to affect not only average wood properties, but wood variability across a forest resource. Many studies have measured wood properties to evaluate variability at some level; variation between species, stands, sites, trees, within trees or within rings (Zobel and van Buijtenen, 1989; 2001; Cutter et al., 2004). The most commonly studied property is wood density (Roper et al., 2004a; Jordan et al., 2008) as it is relatively easy to measure gravimetrically and is related to many product performance properties such as timber stiffness, strength, pulp and paper productivity (Wimmer et al., 2002c). Many wood properties vary co-linearly; their variance arising from common causes. Variation in cell size and wall thickness defines much of the variation in wood density, most commonly exacerbating, but sometimes masking individual effects on density (Evans et al., 1997; Molteberg and Høibø, 2007). The impact of growth rate on wood density has been a major focus, with many studies describing often conflicting correlations in reported effects (Downes and Raymond, 1997). The objective of this chapter was to summarise the current state of knowledge of climate effects on wood properties. This is a large area of research with the potential to highlight the multiplicity of interacting factors and the deep physiological complexity rather than generate clear messages for industry. Because the overwhelming bulk of the literature focuses on wood density, the review will be biased towards this property, and touch on other properties as appropriate. In order to develop a clear message the review will follow the following basic structure provide a framework of thinking about the causes of wood property variation briefly review the relevant published and (where accessible) unpublished literature synthesise the observations within the framework of thinking provide an overview of current work to build a process-based modelling of wood variation, which is essentially a form of literature review encoded in software Causes of wood property variation Sampling effects The variability of wood density relationships reported in the literature arises in part from the way this property is measured. Typically, while determining the average wood density of the whole tree is the objective, it is an impractical measure to make. Wood density varies greatly, both radially and longitudinally, in the tree stem and it is typically estimated as the average of multiple 48

58 samples taken at different points within the tree (Downes et al., 1997; Raymond et al., 1998; Evans et al., 2001). Alternatively, wood density at a specific point in the stem (e.g. breast height (BH)) can be measured and used as a predictor of whole-tree value. In such cases it is often only the outer wood at BH that is used (Roper et al., 2004a; Cown et al., 2005; Cown et al., 2007) to compare sites and genotypes. Understanding the effect of these different approaches to sampling wood variability is important (Downes and Drew, 2008) when considering trends or relationships (Greaves et al., 1997). For example, different studies report that wood density exhibits contrasting patterns of variation within trees both longitudinally and radially. The absence of annual ring structure in many eucalypts (particularly tropical species where there is no strong seasonal climate variation) makes it difficult to resolve data to annual increments and discern patterns of variability. Different studies address this problem in a variety of ways. For example Figure 20 illustrates the effect of sampling strategy on identifying the true radial and longitudinal pattern of density variation. If the true pattern is illustrated by the solid line, the trend detected (dotted line) by various intensities of sampling will differ. Many of the different patterns described in the literature can be attributed to varying sampling strategies Basic density X X X X X X X X X X X X X X X 70% 0% Basic density X 0 X X X X X X X X 0 70 Distance from pith Tree height (%) Figure 20. Effect of sampling on the detection of variation trends in wood properties (based on Downes et al. 1997) Growth effects The increasing availability of measurement technologies (Evans, 1994), and the drive to understand wood variation within plantation resources, has prompted studies that explore withintree patterns of variation at high spatial/temporal resolution. Studies that have investigated within-tree variation at high spatial resolution (Evans et al., 2001; Kibblewhite et al., 2004) allow a better understanding of true variability within trees, identification of general patterns of variation (Roper et al., 2004a), and how this variation in trees relates to harvested or engineered product variability (Roper et al., 2004b). The large volumes of data generated in such studies allow deeper and more considered analysis, and can provide insight into cause and effect relationships. If annually-resolved data is present then variation in wood can be expressed as juvenile / mature wood ratios (Rowland et al., 2004), or as annual trajectories, allowing the effect of annual or seasonal climate or silvicultural treatment to be inferred (Nyakuengama et al., 2003). Sub annual data, where earlywood (EW) is defined typically as a density threshold value, or theoretically by Mork s ratio (Zahner et al., 1964), allows EW / 49

59 latewood (LW) proportions to be quantified (Wimmer and Downes, 2003). If annual rings structures are not clear (e.g. plantation-grown E. globulus) then the radial patterns can be defined in radial increments or as a rate of change of wood property (Downes et al., 2012). Growth phenology and growth rate Commercial forestry practice typically assesses growth rate in terms of the volume achieved over a rotation. Mean annual increment (MAI) is one measure of this, but it is self evident that a given MAI can be obtained by a variety of patterns of annual increment (phenology). In general wood density, expressed as annual averages or radial increments, increases from pith to bark (Zobel and van Buijtenen, 1989). Similarly MFA decreases from pith to bark (Evans et al., 2001; Cown et al., 2007). Consequently wood stiffness, as determined by both the properties, also increases from pith to bark, and for many structural products it is stiffness that determines commercial value. The concept of juvenile wood has become a common way to summarise this variation (Larson et al., 2001). In most commercial softwoods in Australia, the inner 10 rings, closest to the pith represent the time over which density (and other properties) are most rapidly changing. The rapid advances in radiata pine silviculture and breeding has resulted in tree growth that allows rotation length to be reduced as merchantable stem diameters are attained at a younger age (O Hehir and Nambiar, 2010), resulting in logs of a given diameter with greater proportions of less valuable juvenile wood (Kennedy, 1995; Larson et al., 2001). Consequently understanding and improving juvenile wood properties (Li and Wu, 2005; Gapare et al., 2006; Baltunis et al., 2007), as well as the effect of silvicultural treatments to accelerate growth during the stage when mature wood is produced (Nyakuengama et al., 2002), has been a recent research focus. Diameter growth (cm) a Tree 1 Tree 2 Diameter growth (mm) b False ring due to slow summer growth, Uniform growth More earlywood More latewood Fast spring growth, Moderate earlywood, Less latewood Ring width Time (years) Spring Summer Autumn Winter Time Figure 21. (a) The pattern of growth over a rotation, as influenced by environment and silviculture will change the resultant wood properties produced. (b) each annual increment is in turn the net effect of growth rate variation over seasons. 50

60 Figure 21a illustrates the hypothetical annual growth of two trees over a rotation cycle, demonstrating the effect that thinning and fertilisation can have on juvenile wood proportion. Given the known variation in wood properties between juvenile (JW) and mature (MW) wood (Rowland et al., 2004), the growth release in tree 1, while locally affecting wood properties like density (Nyakuengama et al., 2002), would have resulted in more MW than JW. Consequently tree 2 would have a higher proportion of JW and potentially exhibit lower density and higher MFA than tree 1, despite being slower growing. These effects interact with site climate driven variation, and looking at tree averages without considering growth phenology may lead to wrong conclusions being drawn. The effects of averaging also apply at the annual level. Wood property variation is greatest within annual rings in trees growing in seasonal climates (Labosky and Ifju, 1972). In softwoods, density, MFA, and tracheid diameter all vary markedly between wood formed in spring (EW) and wood formed in summer and autumn (LW). Larson (1969) and others (Fritts, 1976; Nicholls and Wright, 1976; Fritts et al., 1999) argue that EW is formed when needles are expanding and auxin production is high. LW is formed when needle elongation has ceased and auxin production is low. Figure 2b compares differing sub-annual patterns of growth in three, hypothetical trees. If growth rate is defined in terms of the width of the annual increment, then two trees have the same growth rate while the third is relatively slow growing. However, it is evident that the growth rate of the latter is faster in spring than the tree with a more uniform growth phenology when the tree is producing EW, which characteristically has low density and high MFA, relative to LW (Moschler et al., 1989; Wimmer and Grabner, 2000). Comparing the trees with the same ring width, it is evident that the pattern of growth differs, which should result in different average annual wood properties. (Downes et al., 2006). Correlating ring width and wood density in trees like these could produce potentially confusing results. This was evident in a study in spruce (Wimmer and Downes, 2003) where the correlation between ring width and wood density was compared for each annual ring over a period of 40 years. The changing magnitude and sign of the correlation was mostly explained by variation in LW proportion. Similarly, Downes et al investigated the effect of growth rate on wood density and kraft pulp yield, explaining the lack of effect in terms of subannual growth patterns, as measured by automated point dendrometers. Comparing irrigated and drought treatments on the same site, significant effects were evident between treatments but not within diameter classes within treatments. Growth patterns within treatments were remarkably similar (Downes et al., 1999), despite differences in diameter ranging from 100mm to 250mm. While Figure 2 represents hypothetical growth measurements, the patterns shown are consistent with real data collected in Pinus radiata and E. globulus (Drew et al., 2008; Drew et al., 2009b; Drew et al., 2010a). These observations support the view that wood property variation is more affected by the phenology of stem growth (over seasons and years) rather than the averaged rate of growth expressed as MAI (Nicholls and Wright, 1976). In assessing sub-annual growth patterns, we are trying to match seasonal variation in growth, measured temporally, with variation in wood properties, measured spatially. For example in Figure 22, the effect of rainfall on wood properties is shown for a Pinus radiata tree near Tallaganda, Australia, where over two years ( ) the daily drought index was also recorded. In January 1987 a period of severe drought was alleviated by a 70mm rainfall event within a 24 hour period. This resulted in a resumption of cambial activity and a flush of growth recorded in the x-ray densitometry data as a false ring, caused by a marked drop in density. The net effect of these events affecting growth, combine to define the overall effect on average wood properties within annual rings, or over a rotation. 51

61 Drought Index a. b. Air-dry Density (kg/m 3 ) Radial Cell Diameter (um) Aug 86 Aug 87 Aug 88 Aug 86 Aug 87 Aug Distance from pith Figure 22. Drought index, and wood density and radial cell diameter measured by SilviScan (Evans, 1994), for the period August 1986 to August 1988 at (a) breast height and (b) 15 metres. Changes in density and cell diameter are attributable to changes in drought Climate effects temperature Most reviews of climate effects on growth and wood properties have considered temperature and/or water availability. The latter is typically considered in terms of rainfall patterns and amounts, but rainfall interacts with soil type and temperature in its effect on trees. Cremer (1975) studied the effects of temperature on height and diameter growth in E. regnans growing in Tasmania, finding, consistent with previous work, that temperature was a strong driver of height growth, explaining over 80% of the weekly pattern. In contrast the effects of both temperature and rainfall on stem diameter growth were much weaker (33 and 11% respectively). The phenology contrasted between the height and diameter measurements, with diameter increasing in early to mid-spring and declining over summer months. Height growth was greatest over summer. This contrasts with Specht and Brouwer (1975) who reported, for eucalypts growing near Brisbane, a bimodal pattern of shoot growth peaking in spring and autumn. The literature on broad scale climatic effects on eucalypt wood properties is sparse. Overwhelmingly the studies have addressed the relationship between growth rate and density (Wilkes, 1984; Downes and Raymond, 1997), largely concluding the independence of density variation from growth rate (Bamber et al., 1982; Downes et al., 2006). In contrast to softwoods, however, there is little literature describing relationships between temperature and wood density between sites. The modelling analysis of Roderick et al (2001) conclude that in contrast to gymnosperms, for angiosperms such as eucalypts, changes in temperature should have little direct effect on wood density. This is based on their modelling of hydraulic conductivity, and the effect of wood anatomy on water flow in stems. Thomas et al (2007) report a generally positive relationship between temperature and wood density in eucalypts, and in their study of E. grandis seedlings found higher temperatures did associate with density increases from 10 o C up to 30 o C but found a density decrease at 35 o C. 52

62 In softwoods Cregg et al. (1988) studying 10 year old loblolly pine over 2 years following thinning, found average summer temperature changed its effect on growth from positive in early summer to negative in late summer. Increasingly climate proxies are being derived from wood properties of annual rings rather than the ring widths themselves. A growing body of research indicates that wood property variation within and between annual rings can in many cases be utilised for climate reconstruction (Fonti et al., ; Panyushkina et al., 2003; Poussart and Schrag, 2005; Gagen et al., 2006; Fonti and García- González, 2008; Grudd, 2008). The bulk of past research in this area has used variation in wood density and stable isotopes, including carbon and oxygen (Briffa et al., 2002; McCarroll and Loader, 2004; Vaganov et al., 2006). Very recent work has also shown that wood properties such as cell diameter and MFA may be good recorders of summer temperature and stream flow (potentially a surrogate for available soil water) (Allen et al., 2012; Drew et al., 2012; Xu et al., 2012). Studies over the past decade in Australia and New Zealand have been conducted to describe regional patterns of variation in basic density and stiffness in radiata pine, much of which has been unpublished. In order to assess environmental / site effects on these properties across the geographical range, Cown et al (2005) reported on the national benchmarking study for the New Zealand Wood Quality Initiative. This data set was subsequently analysed by Mason and Dzierzon (2007) confirming the findings of a previous study (Cown et al., 1991) which showed that, in general, wood density decreased with increasing latitude and elevation (temperature). The 2005 study also assessed other wood properties and found cell wall thickness increased with mean temperature, and MFA decreased. The reduction in MFA, they suggested, may be related to a decrease in LW proportion on cooler sites. Similarly in Tasmania (Cown et al., 2007), wood density decreased with altitude and increased with mean annual temperature across a wide range of sites. Rainfall was not correlated with wood density or standing tree or log acoustic velocity (stem stiffness). However site index, together with mean temperature and tree age was found to predict 74% of the variance in outerwood density, and typically site index reflects (in part) rainfall interacting with other site characteristics. Broader studies have shown wood density to be linked to elevation (negatively) and latitude (positively) (Swenson and Enquist, 2007). Similarly mean annual temperature was a stronger predictor of average density than mean annual rainfall across a northern hemisphere gradient from the 29 o to 52 o N (Wiemann and Williamson, 2002), although both were important predictors. Within that gradient precipitation was a better predictor in the warmer tropical regions. Climate effects water availability Cregg et al. (1988) showed that, in concert with warmer temperature, late summer rainfall increased the LW proportion and annual ring density compared to years with low summer rainfall. This is consistent with the earlier finding of Nicholls and Wright (1976) in radiata pine grown at 5 sites around Victoria, Australia. The number of growing days in autumn correlated with LW formation, and LW proportion was affected by the length of the autumn growing season. They looked for relationships between minimum EW and maximum LW density and temperature and rainfall variation, and found none. Other earlier studies support these general observations between summer / autumn water availability and late wood proportion (Gilmore et al., 1966). Wilkes (1989) found summer rainfall to be positively related to density across 22 sites in New 53

63 South Wales, while winter rainfall was negatively associated. He found no relation to average temperature. Eucalyptus globulus trees growing across three contrasting sites (low, moderate, high rainfall) in northern Tasmania (Wimmer et al., 2008) showed that the low rainfall, low productivity site had higher density, lower pulp yield and shorter fibres than the high rainfall site. This affected the hand sheet properties made from the pulp. The phenology of eucalypt stem growth was analysed in E. globulus and E. nitens grown at Lewisham, Tasmania under irrigated and droughted conditions using automated stem growth measurements (Downes et al., 1999). When water availability was not limiting growth and evaporative demand was low, stem growth rates commenced in mid-august, peaking in September - October. In both irrigated and droughted treatments, stem growth declined during summer months, albeit the irrigated trees maintained good growth rates in summer and autumn. This study was extended (Wimmer et al., 2002a) to show that, in irrigated E. nitens trees where soil water was not limiting, wood density was lower early in the growing season and increased over summer. In trees that were periodically droughted then released from drought, wood density increased during periods of increasing water stress, similar to that reported by Drew et al (2009a; 2011) for E. globulus. Similar but inverse patterns were observed for MFA (Wimmer et al., 2002b) in E. nitens, with MFA in continuously irrigated trees increasing during the period of faster spring growth and declining into the summer. In droughted trees MFA changes were linked to changes in diameter growth mediated by soil water availability, exhibiting large, rapid increases when released from drought. Wind was the only external factor that had a direct effect on MFA independent of growth rate. Subsequent work (Downes et al., 2006) suggested that variation in whole-tree basic density and pulp yield were more related to growth phenology than annual growth increment. Raymond and Muneri (2000) noted an interaction between fertiliser treatment and rainfall (site). Applying fertiliser decreased basic density and pulp yield on drier but not wetter sites, suggestive of an increase in EW proportion. The drier sites had higher density. In contrast, in Peru (Montes, 2008) wood density of Guazuma crinita, a fast growing hardwood, increased as rainfall and humidity decreased. Macfarlane and Adams (1998) found droughted trees to have lower basic density than trees from a less drought-prone site, arguing that drought stopped cambial growth when more dense LW was forming. The latter study used stable isotopes to infer drought as the variation in isotopic ratios has been linked to variation in plant available water (Bert et al., 1997; Warren et al., 2001; Drew et al., 2009c). The effects of temperature, rainfall and seasonality were summarised in a meta-analysis approach (Chave et al., 2009) using multiple regression to predict average wood density as a function of mean annual temperature, annual precipitation, precipitation seasonality and temperature seasonality. They explained nearly 50% of the variance in average wood density across the globe using these predictors. Climate effects elevated CO 2 The concentration of CO 2 in the atmosphere is increasing, at an annual average rate of 2ppm or more. This increase in concentration has been demonstrated to increase productivity in some (Norby et al., 1999; Ainsworth and Long, 2005; Cuntz, 2011) but not all cases particularly in older trees or where production is limited by other factors such as nutrients (Oren et al., 2001; Norby et al., 2002). In line with the earlier discussion, eco 2 has been found to affect the growth phenology of Scots pine (Jach and Ceulemans, 1999) with bud burst being advanced under elevated 54

64 conditions. Effects on growth rate were stronger in the first of the two years studied suggesting a declining effect as trees age. Elevated CO 2 effects in combination with elevated temperature were studied in 20 year old Scots pine (Kilpeläinen et al., 2007). Doubling the atmospheric CO 2 concentration above ambient tended to increase tracheid wall thickness compared with trees grown at ambient CO 2, but interacted with increasing temperature of between 2 and 6 C to produce thinner walls compared to ambient conditions. Elevated CO 2 increased ring width most years. Overall increasing temperature had a greater effect than eco 2 on wood anatomy, predominantly in the EW. Similarly Conroy et al. (1990) showed wood density in radiata pine increased with eco 2, due to a thickening of tracheid walls, in contrast to Donaldson et al. (1987) who reported no effect on wood density. Ceulemans et al. (2002), studying young Scots pine also reported no significant effect of eco 2 on wood density, but did observe increases in stem biomass and volume. Increases in volume were primarily due to increased EW width, containing larger tracheid diameters leading to significantly lower wood strength. Pinus taeda grown in open top chambers with CO 2 enrichment had wider rings and increased wood density when measured by displacement than trees at ambient CO 2. The authors concluded that the dominant effect of eco 2 was on growth. The effect was most pronounced in the first (of four) years and decreased with age. The declining influence with age was also evident in New Zealand grown radiata pine (Atwell et al., 2003) with sapwood density increasing slightly with age, resulting from a thickening of cell walls. Hattenschwiler et al (1996) also report increased wood density under eco 2 conditions but this decreased with nitrogen addition. The effects were predominantly observed in EW, with LW density unaffected. In summary, the general effect of eco 2 is to slightly increase wood density through thickened cell walls, but this effect is variable and minor compared to changes resulting from silviculture, (Kostiainen et al., 2004) rainfall and temperature affects on growth phenology. Limiting factors This review shows that the potential to identify any simple, universally consistent effect of climate on wood properties is limited. The majority of effects of climate on wood variation are mediated by the effects on the rate and duration of growth. Fritts (1976) defined the effects of environment (site factors interacting with climate) in terms of limiting factors. He summarises this complexity as follows:..a climatic factor can affect structural characteristics such as ring width only as it influences the operational environment of the plant and limits the rate of physiological processes that in turn influence growth. The actual relationships that are involved may vary, depending upon the condition of the growing tissues, the relative activities of the controlling processes, and the operational history of the plant s environment that has preconditioned it. Also, the most limiting conditions to plant processes can change markedly throughout the year, so that one particular climatic factor may be directly correlated with ring width at one time, inversely correlated with it at another time, and totally uncorrelated at still other times. For example, during the spring, growth may begin earlier in a warm year than in a cool year, because low temperature is most limiting to the growth-initiating process. In such a case, temperature would be directly correlated with ring width. Later in the growing season, when temperatures are higher, the hottest weather may be most limiting because it can limit the production of enzymes and hormones which are necessary for certain processes to occur and because it accentuates the loss of water from tissues 55

65 in which water is already deficient. Thus, temperature at this time of year becomes inversely correlated with ring width. An application of this to wood properties was illustrated earlier in the changing nature of the relationship between wood density and ring width (Wimmer and Downes, 2003). The correlation between annual growth and density varied from negative to positive over a 40 year period, with the change to positive associated with increased late-season rainfall. This supports the view that the effects of rainfall and temperature on wood properties are related more to the effect on the phenology of growth over a year, and the relative proportions of wood with different properties being formed at any given time. Modelling variation The complicated nature of the relationship between climate and wood production (ring width and wood properties) led Fritts et al (1999) to attempt to predict wood property variation by modelling the physiology of tree growth and cambial activity. By combining current knowledge into a mathematical / logical formulation, they predicted radial profiles of variation in tracheid diameter and wall thickness, and consequently wood density, on a daily time step. Previous efforts had focussed on more empirical approaches which proved unsatisfactory in offering broadly applicable models (Fritts et al., 1991; Fritts and Shashkin, 1995). Various other attempts at similar models have been reported (Deleuze and Houllier, 1998; Vaganov et al., 2006; Holtta et al., 2010) reflecting the need to understand the complexity of environmental effects on wood formation that go beyond simple statistical relationships. A recent model of wood formation in plantation eucalypts (ecambium) (Drew et al., 2010b) represents the first such model application to hardwoods. The complexity of the hardwood xylem structure requires this be done in a two dimensional framework. The effects of the differentiation of different cell types (e.g. vessels vs. fibres), and the consequent different physiological processes these cell types require, is allowed for. The current formulation requires, as input, the output of the process-based growth model CABALA (Battaglia et al., 2004a) or 3-PG (Landsberg and Waring, 1997a). This provides the whole-tree context for cambial function in terms of daily patterns of water stress, canopy production, growth regulator fluxes and amounts of carbohydrate available for wood production. Future work will apply these models to examine the effects of changing rainfall and temperature regimes on wood property variation across a range of site types and management regimes. 56

66 Chapter 5 Case study: modelling the effects of varied temperature and rainfall on wood density and stand volume of Pinus radiata Summary This case study provides some initial insights into how wood properties might be affected by climate change, using the process based model ecambium. Under varied temperature and rainfall conditions, in some stands of P. radiata there may be gains in both wood density and stand volumes. In general, however, gains in stand volume will probably be accompanied by a reduction in wood density. On the other hand, the reductions in stand volume more likely in hotter, drier conditions will probably be accompanied by increases in wood density. But whether or not any potential improvements in wood quality would be sufficient to compensate for overall stand volume losses, will require further, detailed economic analysis. Trees grown under much lower rainfall conditions predicted to result in higher density wood, were not predicted to have the reject quality boards in the juvenile core that were predicted under the higher rainfall (and higher temperature) conditions that produced much lower density wood. The larger trees in the latter cases, however, were predicted to produce 6 more boards per tree, all of high stiffness, which may make up for any product losses in the juvenile core. Exploratory comparison of blue gum and radiata pine found similar patterns of change in wood density with changing temperature/rainfall, and overall wood density was predicted to decrease with increasing rainfall in both species. 57

67 Introduction Knowledge of how wood properties vary in response to climatic variation is limited. Direct effects on wood may be small, with most variation being mediated through effects on growth and phenology (see previous chapter). Consequently quantifying net effects can be complicated and reported findings in the literature sometimes contradictory. This complexity is best addressed through process-based models. These models provide a means to simplify the study of climate effects on tree growth and wood variability, by holding factors other than climate variability constant. A case study approach was taken to explore the conclusions of the previous chapter about the possible impacts of climate change on wood properties. The aims of the study were to: identify the effects of varying temperature and rainfall on wood properties of P. radiata, including the impact of site undertake a preliminary assessment of the possible consequences of this for board outputs and stiffness properties undertake an exploratory comparison of P. radiata and E. globulus wood property responses to climate. Brief background to the ecambium model A new model of wood formation in radiata pine (ecambium)) has recently been developed as part of a separate FWPA project. The model provides detailed predictions of variation in the following wood properties: Wood density Tracheid diameter and wall thickness Microfibril angle (MFA) Modulus of elasticity (MOE) and wood stiffness Virtually sawn boards of user-prescribed dimensions in terms of number and grade The ecambium model is process-based. It predicts radial stem growth and wood property variation at high resolution, effectively simulating the kind of data that the SilviScan system measures, by mathematically describing known and hypothesized processes of wood formation, linked to estimated whole tree physiological variables (e.g. daily leaf water potential and net primary productivity) simulated by models such as CABALA and 3-PG. The development of the model thus far has used a synthesis of the scientific literature as well as detailed measurements of tree growth and wood properties from 16 contrasting sites. The model is unique in that it contains the first known process-based attempt to predict MFA, a major determinant of wood stiffness, in a complete modelling framework. Methods Varied climatic conditions, particularly temperature and rainfall, can be expected to lead to changes in growth and mortality of plantation forests. These conditions will also lead to changes in the properties of wood formed by the growing trees. For the study described here, a set of six sites were used as the basis for a set of simulations around the potential effects of hotter, as well as either drier or wetter conditions, on average wood density in Pinus radiata, using the newly developed ecambium model. The model utilized pre-existing simulations from CABALA (Battaglia 58

68 et al., 2004), parameterized for P. radiata, as inputs. These simulations were not representative of responses in the future, per sé, as they did not take into account changes in atmospheric CO 2 concentrations because of lack of information about how eco 2 might affect wood properties. We focused on potential changes in average wood density at six sites (three in Tasmania, three in the Green Triangle) (Table 6). The silviculture applied at the sites (based on records from the relevant growers) was very different. In order to isolate potential differences due to the effects of silviculture, two separate sets of simulations were conducted: the first using the original regimes ( original silviculture ), and the second in which all sites were treated in exactly the same way ( standard silviculture ) (Table 6). The standard silviculture applied the same planting, thinning and harvesting schedules to all sites. Results and discussion Overall, the site that had the highest predicted average wood density was a southern Tasmanian site, Tas site 2 (Table 7). By contrast, the high altitude site (Tas site 3, also in Tasmania) had the lowest average predicted wood density. The simulations at the Miles site (Green Triangle) exhibited a very small overall range in wood density under differing temperature or rainfall scenarios (a range of 13 kg m -3 compared to Tas site 2, for example, where the change in wood density from the lowest density to the highest density scenario was predicted to be as much as 66 kg m -3 ). Table 6: Site and regimes summary, including the original silviculture (i.e. that actually applied at each site) and the standard silviculture (i.e. same silviculture applied across all sites) Site and regimes summary, including the original silviculture (i.e. that actually applied at each site) and the standard silviculture (i.e. same silviculture applied across all sites) Site name Lat and Long (deg S and E) Tas site 1 Lat: Long: Tas site 2 Lat: Long: Tas site 3 Lat: Long: Miles Lat: Long: Rennick Lat: Long: Harvey s Lat: Long: Soil summary 1.6 m sandy loam 0.8 m loamy sand 2.2 m clay loam 2.4 m loamy sand 1.8 m sand over sandy clay loam 1.5 m fine sand MAT / MAP 10.8 C / 811 mm 11.4 C / 704 mm 9.8 C / 1192 mm 13.7 C / 754 mm 13.7 C / 778 mm 13.8 C / 689 mm Original silviculture Planted 1983 at 1389 sph Thinned age 16 to 613 sph Thinned age 20 to 324 sph Clearfelled at age 23 Planted 1978 at 2000 sph Thinned age 19 to 673 sph Clearfelled at age 27 Planted 1979 at 1420 sph Thinned age 16.5 to 392 sph Thinned age 21.8 to 247 sph Clearfelled at age 27 Planted 1975 at 1905 sph Thinned age 11 to 1067 Thinned age 17 to 417 sph Thinned age 24 to 273 sph Clearfelled at age 28 Planted 1982 at 1600 sph Thinned age 13 to 496 sph Thinned age 19 to 278 sph Clearfelled at age 25 Planted 1974 at 1600 sph Thinned age 15 to 750 Thinned age 22 to 435 sph Thinned age 28 to 250 sph Clearfelled at age 32 Standard silviculture Planted 1975 at 1333 sph Thinned age 11 to 700 sph Thinned age 19 to 450 sph Thinned age 26 to 250 sph Clearfelled at age 35 59

69 Table 7: Average ranges in predicted wood density from simulations under original (i.e. that actually applied at each site) and standard silviculture (i.e. same silviculture applied across all sites), and overall mean wood densities, from the six study sites. Predicted site average wood density (kg m -3 ) Site name Original silviculture Standard Silviculture Overall mean Tas site 3 (Tas) (range = 44) (range = 29) Miles (GT) (range = 13) (range = 13) Tas site 1 (Tas) (range = 51) (range = 51) Rennick (GT) (range = 39) (range = 45) Harvey s (GT) (range = 44) (range = 51) Tas site 2 (Tas) (range = 66) (range = 48) The application of differing silvicultural regimes had a large effect on predicted site average wood density (Table 6). At all sites, there was a marked increase in predicted average wood density when using the standard silviculture as compared to the prediction using original silviculture (Table 7; and see Table 6) (least so at Harvey s). These changes in regime did not, however, cause much change in the predicted patterns of wood density variation under variable temperatures or rainfall conditions (Figure 23, Figure 24). It was generally evident (regardless of the type of silviculture applied) that under wetter conditions, average wood density decreased, while the opposite was true under drier conditions. The extent of the change varied between sites (Figure 23, Figure 24). The largest total difference between the driest and wettest conditions was at Tas site 2. At the Miles site, the pattern was not as clear as at the other sites, but this was mainly due to the very small change in mean wood density that was predicted overall (only about 13 kg m -3 ). The small range in wood density at this site was partly ascribable to relatively small changes in the average stem diameter and annual ring widths of trees across temperature/rainfall combinations compared to other sites. Sites from the two regions (Tasmania and the Green Triangle) appeared to exhibit different predicted wood density responses at the highest and lowest temperatures in the simulated range. At the Tasmanian sites, wood density tended to remain similar, or increase, with higher temperatures. This was most obvious at site Tas site 3, at which wood density was predicted to be highest under the hottest, driest conditions. At Rennick and Harvey s, by contrast, the model predicted that, under current rainfall conditions, wood density would decrease as temperature increased. This can be partly explained by the fact that the Tasmanian sites, and Tas site 3 in particular, are likely to be more temperature limited under current conditions than the Green Triangle sites (See MAT & MAP in The standard silviculture applied the same planting, thinning and harvesting schedules to all sites. Results and discussion Overall, the site that had the highest predicted average wood density was a southern Tasmanian site, Tas site 2 (Table 7). By contrast, the high altitude site (Tas site 3, also in Tasmania) had the lowest average predicted wood density. The simulations at the Miles site (Green Triangle) exhibited a very small overall range in wood density under differing temperature or rainfall scenarios (a range of 13 kg m-3 compared to Tas site 2, for example, where the change in wood 60

70 density from the lowest density to the highest density scenario was predicted to be as much as 66 kg m-3). As a general pattern, where the models predicted lower wood density, they predicted higher stand volumes (Figure 25 and 26). That is, at the Tasmanian sites, predicted stand volumes were highest at moderate, to much higher temperatures (2-4 C above current temperatures) and with higher rainfall. At Green Triangle sites, stand volumes were highest at current temperatures to slightly raised temperatures (0-2 C above current temperatures) and higher rainfall. This pattern was not altered appreciably by a standard silviculture applied across the sites. Overall stand volume was, however, higher under standard compared to original silvicultural regimes at all sites. The range in stand volume across temperature/rainfall combinations was lowest at Miles. Figure 23: Interaction of original silviculture and hotter conditions ( C change from present temperature shown on x-axis) and drier/wetter conditions (% change from present conditions shown on y-axis) at six sites on predicted wood density (kg m -3 ). X indicates present conditions. 61

71 Figure 24: Interaction between standard silviculture and hotter conditions ( C change from present temperature shown on x-axis) and drier/wetter conditions (% change from present conditions shown on y-axis) at six sites on predicted wood density (kg m -3 ). X indicates present conditions. 62

72 Figure 25: Predicted total stand volume with original silviculture (final volume + thinning) (m 3 Ha -1 ), under hotter conditions ( C change from present temperature shown on x-axis) and drier/wetter conditions (% change from present conditions shown on y-axis) at six sites. X indicates present conditions. 63

73 Figure 26: Predicted total stand volume with standard silviculture (final volume + thinning) (m 3 Ha -1 ), under hotter conditions ( C change from present temperature shown on x-axis) and drier/wetter conditions (% change from present conditions shown on y-axis) at six sites. X indicates present conditions. In summary, relative to current conditions (see the X marked in Figure 23to Figure 26 ), and the most significant, effects were as follows: In Tasmania: At Tas site3, which is very likely temperature limited, much hotter (3 4 C higher than present) and somewhat drier conditions (80 90 % of present rainfall = mm/annum) led to a moderate increase in stand volume and a large increase in wood density At other sites, moderately hotter conditions (1 2 C increase), with higher rainfall (10 20 % increase) led to large gains in stand volume but with only small to moderate reductions in wood density and 64

74 Reduction in rainfall (10 20 % decrease) at present or slightly hotter conditions (0 2 C increase) led to moderate to large increases in wood density, with little loss in stand volume. In the Green Triangle: The Miles site predictions suggested some characteristics of being temperature limited, with no gain in stand volume or wood density with increased or decreased rainfall, under current temperatures. At the other sites, moderately hotter conditions (1 2 C increase), with higher rainfall (10 20 % increase), led to moderate gains in predicted stand volume, but with a substantial decrease in wood density and reduction in rainfall (10 20 % decrease) at present or slightly hotter conditions (1 2 C increase) led to marked increases in wood density, but with a substantial loss of stand volume. Our analysis suggests that under varied temperature and rainfall conditions, in some stands of P. radiata there may be gains in both wood density and stand volumes. In general, however, gains in stand volume will probably be accompanied by a reduction in wood density. On the other hand, the reductions in stand volume more likely in hotter, drier conditions will probably be accompanied by increases in wood density. But whether or not any potential improvements in wood quality would be sufficient to compensate for overall stand volume losses, is difficult to assess without a more detailed economic analysis, and probably unlikely. To understand the relative benefits of increased wood density (and other determinants of wood quality), it is necessary to understand the effects on the kind of product that might be expected. Substantial differences in wood density, in combination with variation in other properties like microfibril angle (data not shown), may lead to significant improvements in, for example, wood modulus of elasticity (MOE) and the consequent stiffness of sawn boards or other products. To provide an indication of this potential, the ecambium model produces a simplified representation of potential MGP (stiffness) classes of sawn boards that might be expected from a log of a predicted diameter (Figure 27). These representations are not meant to provide an absolute prediction of expected board classes, but rather to give an indication of broad-scale differences that might be expected in potential product quality and out-turn from a site. Importantly, the modeled board grade classes do not take any visual or other features into account, which means these estimates will inevitably be over-estimates of actual board quality. In addition, in the present simulation, model runs were done at breast height (1.3 m height), whereas this board assessment should realistically be done at the small diameter end of a butt log (e.g. 6 m). Nevertheless, for this report, it was considered a useful demonstration of the possible implications of different temperature and rainfall conditions on product quality. Figure 27 provides a summary of predicted changes in board outputs and their stiffness properties based on density. At four of the six sites, the predicted stem diameter in the scenario under which the highest wood density was formed (upper diagram in each case) was smaller than diameter under which lower density was formed (lower diagram in each case) (Figure 27) (exceptions were Tas site 3 and Miles). 65

75 Figure 27: Predicted changes in board outputs and their stiffness properties from the low wood density (upper figure in each case) and high wood density (lower figure in each case) scenarios from each site, grown under the standard silviculture regime (see Error! Reference source not found.). The size of each log (shown at breast height for these simulations) is scaled relative to the size of the Rennick low density log, which had a diameter under bark of 35.3 cm. The largest tree, at Miles (low density) had an under bark diameter of 45.6 cm. In each case the within-tree variation in modulus of elasticity (MOE) is shown graded from yellow (low MOE) to red (high MOE), and is visible as annual rings, with low MOE in the earlywood and high MOE in the latewood. 66

76 Note also the very large differences in final diameter, and between the sizes of the low density juvenile cores, of the different sites despite, in this output, all trees being grown on an identical regime. It is also notable how much larger the trees at Miles were compared to the other sites, which may partly explain why density variation as a function of temperature and rainfall was not as marked. Differences in predicted board stiffness grades between the lowest density and highest density scenarios were most notable in the juvenile core (Figure 27 At Rennick and Tas site 1, for example, the model predicted that trees grown under much lower rainfall conditions, which led to higher density wood, did not have the reject quality boards in the juvenile core that were predicted under the higher rainfall (and higher temperature) conditions that produced much lower density wood. The larger trees in the latter cases, however, were predicted to produce 6 more boards per tree, all of high stiffness, which would very likely make up for any product losses in the juvenile core. An exploration of eucalypts compared with pine The ecambium model was developed for radiata pine. However initial tests (using a modified parameter set) against Eucalyptus nitens and E. globulus SilviScan data, from a Tasmanian irrigation experiment, have shown promising results (Figure 28). It is possible that in its present form, or with small modifications, ecambium can be adapted for use with eucalypts. Here we present an initial comparison of the possible wood property differences at Tas site 1 (see Table 6) under a 35-year P. radiata standard silviculture regime (see Table 6), compared to a 10 year E. globulus rotation. The simulations could only be successfully run for hotter scenarios (temperature > 1.5 C current temperatures) for E. globulus, which is less cold tolerant than P. radiata. The models predicted reasonably similar patterns of change in wood density with changing temperature/rainfall, overall: wood density was predicted to decrease with increasing rainfall in both species (Figure 28, Figure 29). Note, however, that the trees were very different, and the comparison is difficult to make under these circumstances). Further work to simulate wood property variation from different species would, however, be very useful to pursue in the future. Conclusions This case study provides some initial insights into how wood properties might be affected by climate change, using the process based model ecambium. In general, gains in stand volume will probably be accompanied by a reduction in wood density. On the other hand, the reductions in stand volume more likely in hotter, drier conditions will probably be accompanied by increases in wood density. Trees grown under much lower rainfall conditions predicted to result in higher density wood, were not predicted to have the reject quality boards in the juvenile core that were predicted under the higher rainfall (and higher temperature) conditions that produced much lower density wood. The larger trees in the latter cases, however, were predicted to produce 6 more boards per tree, all of high stiffness, which may make up for any product losses in the juvenile core. This suggests careful analysis will be required to determine the implications of any climaterelated changes in wood density on stand productivity. There is an overall lack of wood density and other wood properties data across a range of site conditions, against which to validate these model predictions. Data would most usefully be collected across both temperature and rainfall gradients, and there may be scope to utilise existing taxa trials for this purpose. 67

77 Figure 28: Comparison of predicted wood density (kg m -3 ) in 10-year-old E. globulus and 35-year-old P. radiata at Tas site 1. 68

78 Figure 29: Comparison of predicted relative stem size (at 1.3 m height) and board outputs from P. radiata (grown for 35 years) and E. globulus (grown for 10 years) at Tas site 1. 69

79 70

80 Chapter 6 Productivity losses due to defoliation Summary Considerable between-site variation can be expected in responses to defoliation, making it difficult to generalise about defoliation outcomes and suggesting sitelevel assessments are most appropriate, particularly when looking at the interaction of climate and defoliation In general terms, defoliation was not predicted to have large impacts on final volume of either E. globulus or P. radiata. Maximum reductions of 25% were predicted to occur infrequently for E. globulus, and maximum volume reductions predicted for P. radiata were in the order of 15%, with most reductions in the order of 5% or less. Threshold defoliation levels likely to reduce final stand volume by more than 5% were predicted to be between 40 and 70% for eucalypts. For pines, the threshold varied between 60 and 80% defoliation, with site fertility and timing of defoliation playing an important role. Later age defoliation is of more concern than early-age defoliation, having a much greater impact on final stand volume of eucalypts and pines. Defoliation of the upper crown is likely to have a more serious impact on final volume than defoliation of the lower crown, particularly when defoliation occurs later in the rotation. Chronic defoliation is likely to have a much greater impact on final volume than is a single defoliation event, particularly if defoliation occurs later in the rotation Our analyses do not explicitly address the role of climatic variables such as rainfall and temperature on defoliation impacts, including the implications of climate change. However it highlights conditions under which defoliation may amplify the effects of climate change on stand productivity, and the degree to which this might occur. 71

81 Introduction Defoliating insects and fungi comprise upwards of 90% of Australia s key plantation pests (see pest hazard chapter). While rarely causing tree mortality, defoliation can reduce stand productivity (Candy et al., 1992; Elek, 1997; Carnegie and Ades, 2002; Collett and Neumann, 2002; Smith, 2005; Pinkard et al., 2006c), and can amplify the impact of primary stressors such as drought and heatwaves (Mitchell et al., 2013). There has been little quantification of rotation-length impacts of defoliation in Australia s plantations (Candy et al., 1992; Rapley et al., 2009), and perhaps because of this, defoliation impacts on productivity have commonly been ignored. Defoliation is not routinely monitored in many plantation estates, so there is a poor understanding of the levels of damage being sustained within plantations and how this varies spatially and temporally. The lack of quantitative data leaves us with the option of using models to examine defoliation scenarios to identify threshold levels of damage and to explore questions around the timing, frequency and pattern of damage. The process-based model CABALA (Battaglia et al., 2004b; Battaglia et al., 2011a) is one of the few forest productivity models with the capacity to capture defoliation impacts across host species (Pinkard et al., 2011). Currently there are no modelling tools available to examine the impacts of other types of pest damage on plantation productivity. In the following we describe an analysis of rotation-length impacts of defoliation on E. globulus and P. radiata plantations growing in southern Australia. Climate change is not explicitly included in this analysis. Previous analyses have used the outputs of pest species niche models to define damage levels as inputs to productivity models (Pinkard et al. 2008). However in this study, we did not have the capacity, for the range of pest species under consideration, to link pest climatic suitability to anticipated damage levels. The results of the study therefore provide a guide to conditions where defoliation may amplify the impacts of climate change. The objectives of the analysis were to (1) identify defoliation thresholds above which plantation productivity will be affected; (2) determine the effects of timing, frequency and pattern of defoliation on these thresholds; and (3) examine how defoliation impacts are affected by site productivity and fertility. Methods The model The forest productivity model CABALA (Battaglia et al., 2004b; Battaglia et al., 2011a) was used to examine the consequences of defoliation on harvest volume. Defoliation events are specified within CABALA for three horizontal and two vertical crown zones. The model has been validated using sites with a range of defoliation histories and severities (Pinkard et al., 2010b), and for E. globulus and P. radiata defoliation. It is best validated for E. globulus, reflecting the greater range and number of validation sites available for this species. Sites and regime files Sites were selected from a range of locations throughout southern Australia (Figure 30). They represent higher and lower productivity sites in each region (Table 8, Table 9). Site productivity 72

82 was manipulated in the site files through manipulation of soil fertility, to represent high, moderate and low fertility as discussed in the productivity chapter. High fertility sites were assumed to have a nitrogen mineralisation rate of >100 kg N/ha/year, and a carbon:nitrogen ratio of between 4 and 7. Low fertility sites were assumed to have a nitrogen mineralisation rate of around 40 kg N/ha/year, and a carbon:nitrogen ratio of A standard silvicultural regime was developed for each. For E. globulus this was: Year 0: plant at 1111 stems/hectare Year 0.25: fertilise with urea at 50 kg/ha Year 15: clearfell For P. radiata the silvicultural regime applied was: Year 0: plant at 1333 stems/ha Year 0.25: fertilise with urea at 50 kg/ha Year 10.9: commercial thin, 5 th row out row, final stocking 700 stems/ha Year 18.9: commercial thin to 450 stems/ha Year 25.9: commercial thin to 250 stems/ha Year 35: clearfell For each site, the model was run 20 times with sequential planting dates ( ), to allow us to examine variation in final volumes based on year-to-year climatic variation. Climate data For each site, daily climate data were obtained using a SILO data drill ( ) for years Future climate scenarios were not included in this study. Figure 30. Location of the eight sites used for the defoliation impact analyses. Sites were selected to cover a range of stand productivity, where dark green indicates highest productivity, and dark red indicates lowest productivity. 73

83 Defoliation regimes In the pest hazard chapter later in the document, the following guide was developed to capture key features of defoliation damage caused by the 15 main defoliating pests: Early age defoliation only (to capture the effects of species such as Teratosphaeria and M. privata that target juvenile foliage) Later-age defoliation only (to capture the effects of species such as E. californica that target older plantations A combination of early and later-age defoliation (i.e. chronic defoliation) that allows for the potential of different species defoliating at different ages Spring versus autumn defoliation because some pests cause significant damage in autumn as well as spring (e.g. eucalypt beetles) Bottom-up versus top-down defoliation, recognising that different pest species target different foliage age classes This was used to develop defoliation regimes. For eucalypts, the following defoliation events were defined: 1. Single defoliation event at either 3 or 9 years of age, to remove 0, 20, 40, 60 or 80% of foliage from throughout the crown. This analysis was used to examine defoliation thresholds 2. Three defoliation events in consecutive years, starting at either age 3 or 9, to compare against single defoliation events 3. For ages 3 and 9, either removal of 60% of leaf area via top down or bottom up defoliation, to compare pattern of defoliation 4. For ages 3 and 9, either spring or autumn defoliation, to determine whether a given season of defoliation had more of an impact on outcomes. For pines, the defoliation events were the same, except that the timing was changed to start at ages 11 and 19. Analysis The percent difference in final volume between undefoliated and defoliated treatments was calculated. Analysis of variance was used to estimate differences between means for both final volume and percent difference in final volume, for the site x fertility x defoliation treatment interaction. For the P. radiata analyses, final volume included the volume removed with thinning. GENSTAT 15 was used for all analyses. Standard errors were calculated. Results General In the absence of defoliation, the year of planting had only a small effect on predicted final volume in either species. The variation was between 0.8 and 1.5% of final volume. Using a different 74

84 weather sequence may influence between-year variation in final volume estimates that would influence uncertainty of outcomes. There was considerable variation between sites in defoliation responses, for both eucalypts and pines. This is consistent with the findings of other studies (Pinkard et al., 2008), and highlights the desirability of undertaking site-level, or at least region-specific, assessments of defoliation impacts. In eucalypts, rotation-length impacts of defoliation varied from minor (<5% reduction in final volume) up to around a 20% reduction. Across all treatments, sites and fertility levels the mean reduction in final volume was 4.1%, equivalent to an absolute volume reduction of 13 m 3 /ha. In pines, the rotation-length impacts of defoliation varied from slightly positive (+0.3%) to around a 14% reduction in final volume. Across all treatments, sites and fertility levels the mean reduction in final volume was 2.9%, equivalent to an absolute volume reduction of 44 m 3 /ha. Table 8 and Table 9 provide an indication of the reduction in absolute volume that might be expected from a 5, 10 or 15% reduction, and summarises mean stand volume for the eight sites. The analysis provided guides as to threshold levels of damage, and the implications of timing and season of defoliation, and pattern and frequency of defoliation, on final volume. This is discussed below. Defoliation thresholds For the purposes of this analysis we defined a <5% reduction in final volume as the acceptable level of loss. This can be altered to fit individual enterprise models. We identified the defoliation level at which the final volume reduction was >5% as the defoliation threshold above which defoliation damage was of concern. The thresholds are presented in Table 10 (eucalypts) and Table 11 (pines). Eucalypt thresholds Thresholds varied from 40% defoliation up to 70% defoliation, across the three fertility levels, and there was no consistent pattern between sites or site fertility. Experimental studies in Tasmania suggested that the defoliation threshold for E. globulus is around 50% loss of foliage (Pinkard et al., 2007), which may decline to 40% at lower productivity sites (Pinkard and Beadle, 1998). This was supported to some degree in the thresholds defined here. It is important to note that the experiments presented by Pinkard et al and Pinkard and Beadle were short term only, whereas here we are presenting at-harvest volumes. There were four sites where a single early defoliation (age 3 years) had little impact on final volume, and the 5% threshold was not reached. The South Australian and Western Australian sites also did not reach the 5% threshold at later age defoliation when fertility was high. The results in general suggest that the level of defoliation at which the 5% volume reduction threshold is reached is lower for early-age than later-age defoliation. It is also likely to be related to rates of leaf area production and crown recovery associated with the two times of defoliation. Figure 31 illustrates changes in leaf area index (LAI) over time. The arrows indicate peak LAI, and the two hatched lines indicate the timing of the two defoliation events. At the time of the first 75

85 defoliation, LAI is rapidly increasing, and hence crowns can recover faster than at the time of the second defoliation when LAI is past its peak and declining towards a steady state. Once the 5% volume reduction threshold was exceeded, there were clear trends in the magnitude of the defoliation response (Figure 32). At the warmer and drier sites (South Australia, Western Australia), the magnitude of the volume reduction was much greater following later age defoliation. This was not the case in the cooler, wetter sites (Vic-NSW and Tasmania), where early and later-age defoliation resulted in a similar magnitude of response. This may reflect a larger timeframe for crown recovery at the warmer and drier sites than at the cooler sites (White et al., 2009). The effect of site fertility on the magnitude of the response varied considerably between sites (Figure 32). At the two South Australian sites, low fertility resulted in the greatest volume loss from early age defoliation, whereas moderate fertility resulted in the greatest volume loss following later age defoliation. At the Western Australian sites, high fertility resulted in the greatest volume loss from early age defoliation, whereas low fertility resulted in the greatest volume loss following later age defoliation. At the Tasmanian sites, the magnitude of volume loss following early age defoliation was similar irrespective of site fertility, but the volume loss following later-age defoliation was greatest with either high or moderate fertility. For the Vic- NSW sites, high fertility led to the greatest magnitude of response to early age defoliation, but moderate fertility had the greatest effect following later age defoliation. Figure 31 helps to explain some of the between-site variation. The South Australian site has a lower intrinsic LAI than the Vic-NSW site. The capacity for crown recovery in the CABALA model is dependent on production and net of respiration in the presence of sufficient nitrogen for refoliation, and hence the lower the initial LAI the more defoliation impacts on production and hence the longer the recovery time for a given level of defoliation. Consequently higher soil fertility will also increase rates of crown recovery and hence reduce the impact of defoliation, as was observed at SA GT200. In contrast, at the Vic-NSW site, with a higher intrinsic LAI, similar magnitudes of response were estimated irrespective of timing of defoliation or site fertility. VNSW 79 SA GT Leaf area index Leaf area index Stand age Stand age Figure 31. Examples of predicted changes in leaf area index (LAI) over time in two undefoliated E. globulus stands, for a planting date of 1981 and a moderate site fertility rating. The red arrow indicates peak LAI. The dashed green lines indicate the timing of early and later-age defoliation events. 76

86 Table 8. Mean final stand volume of E. globulus for 8 plantation sites in southern Australia, in the absence of defoliation, as predicted using the stand productivity model CABALA. Stands were grown on a 15 year rotation, planted at 1111 stems ha -1. Values are the average of 20 model runs, each with a different planting date between 1975 and The reduction in volume equivalent to 5, 10 or 15% reductions in final stand volume are also presented. High, moderate and low fertility is defined as per Table 4. Site Fertility Mean volume (m 3 ha -1 ) Reduction in volume equivalent to percentage reduction 5% 10% 15% SA GT200 High Moderate Low SA GT47 High Moderate Low SWWA 125 High Moderate Low SWWA 36 High Moderate Low TAS 186 High Moderate Low TAS326 High Moderate Low VNSW 373 High Moderate Low VNSW 79 High Moderate Low As well as looking at final volume, it is useful to examine how defoliation affects stand volume over time. Figure 33 shows an example of predicted time course of stand volume in response to early and later-age defoliation under moderate site fertility for a single planting date (1981). At both sites later-age defoliation had the greatest impact, but this was particularly extreme at the South Australian site where 80% defoliation late in the rotation virtually stopped volume production. This response is similar to observed responses to later-age defoliation in northeastern Tasmania (Forestry Tasmania unpublished data). P. radiata thresholds Site, site fertility and age of defoliation played a strong role in determining the level of defoliation at which the 5% volume reduction threshold was exceeded. Following early-age defoliation, the model predicted that the 5% threshold would only be exceeded at low site fertility, with a defoliation level of 60 70% (Table 11). Following later-age defoliation the 5% volume reduction threshold was exceeded at high fertility sites when defoliation was between 70 80%; at moderate fertility sites when defoliation was between 60 75%; and at low fertility sites when 77

87 defoliation was between 60 65%. The threshold defoliation levels were generally similar between early and later-age defoliation. Table 9. Mean final stand volume (including thinning) of P. radiata for 8 plantation sites in southern Tasmania, in the absence of defoliation, as predicted using the stand productivity model CABALA. Stands were grown on a 35 year rotation, planted at 1111 stems ha -1, and thinned at 10, 18 and 25 years of age. Values are the average of 20 model runs, each with a different planting date between 1975 and The reduction in volume equivalent to 5, 10 or 15% reductions in final stand volume are also presented. High, moderate and low fertility is defined as per Table 4. Site Fertility Mean volume (m 3 ha -1 ) Reduction in volume equivalent to percentage reduction 5% 10% 15% SA GT200 High Moderate Low SA GT47 High Moderate Low SWWA 125 High Moderate Low SWWA 36 High Moderate Low TAS 186 High Moderate Low TAS326 High Moderate Low VNSW 373 High Moderate Low VNSW 79 High Moderate Low Table 10. Defoliation thresholds associated with early and late age defoliation of E. globulus, and high, moderate or low fertility (see Table 4), for eight sites in southern Australia. Thresholds were defined as the percentage reduction in needle area that reduced final volume by 5% or greater. Defoliation threshold Site Early Later High Moderate Low High Moderate Low SA GT % - 60% 60% SA GT % - 60% - SWWA % % 70% SWWA % 70% TAS % 40% 40% 70% 70% - TAS % 60% 60% 60% 40% 60% VNSW % 60% 60% 70% 70% 70% VNSW 79 50% 60% 60% 60% 50% 60% 78

88 Once the 5% threshold was exceeded, there was a clear trend in the predicted magnitude of the response to defoliation. Later-age defoliation was always predicted to result in an increased magnitude of response, irrespective of site fertility level. This contrasts with predicted responses of E. globulus (Figure 34). The time course of volume in P. radiata responses to defoliation is not as dramatic as that observed for E. globulus (Figure 35). At both sites, the effects of early-age defoliation were observed until the time of second thinning (10 years), after which they were predicted to be close to non-existent. Following later-age defoliation, differences were still observable at the time of harvest, 15 years after defoliation). At SAGT200, the initial effects of later-age defoliation were more pronounced than at VNSW79. As with E. globulus, LAI was predicted to decline over time (Figure 36), and while the peak LAI was higher at VNSW79 than at SAGT200, there was little difference between sites in final LAI. This decline in LAI at least partly contributed to the increasing impact of defoliation at later than earlier ages. Table 11. Defoliation thresholds associated with early and late age defoliation of P. radiata, and high, moderate or low fertility (see Table 4), for eight sites in southern Australia. Thresholds were defined as the percentage reduction in needle area that reduced final volume Site Defoliation threshold Early Later High Moderate Low High Moderate Low SA GT % 70% 60% 60% SA GT % 80% 75% 65% SWWA % 80% 75% 60% SWWA % - 75% 65% TAS % - 70% 65% TAS % % VNSW % 80% 65% 60% VNSW % 80% 75% 65% 79

89 Early-age defoliation High Med Low SA GT Later-age defoliation Early-age defoliation High Med Low TAS 186 Later-age defoliation % reduction in final volume High Med Low SA GT47 High Med Low SWWA % reduction in final volume High Med Low TAS High -5 Med -10 Low VNSW High 0 Med -5 Low SWWA % leaf loss % leaf loss % leaf loss High Med Low VNSW % leaf loss Figure 32. Difference in final volume between undefoliated and defoliated E. globulus stands at eight sites in southern Australia. Five defoliation levels were applied (0, 20, 40, 60 or 80% reduction in leaf area), and analyses were done for three site fertility levels (low, moderate and high, see Table 4). Absolute volume reductions can be calculated from Table 1. 80

90 VNSW70-mod fertility Final volume Nodef D20E D40E D60E D80E Final volume Nodef D20L D40L D60L D80L Stand age Stand age SA GT200-mod fertility Final volume Nodef D20E D40E D60E D80E Final volume Nodef D20L D40L D60L D80L Stand age Stand age Figure 33. Time course of stand volume of E. globulus predicted for two sites at a moderate site fertility, where defoliation levels ranging between 0 and 80% were applied at 3 or 9 years of age. VNSW is a Victorian-NSW site; SA is a South Australian site (Figure 30). Pattern of defoliation Eucalypts There were clear trends in modelled responses to pattern of defoliation (Figure 37). Following early age defoliation, the bottom-up pattern was predicted to have the greatest impact on final volume. Following later-age defoliation it was top-down defoliation that had the greatest impact. This pattern was consistent across all sites. It is at odds with the only experimental data, from a Tasmanian one-year old stand where measurements only continued for 2 years after defoliation(pinkard et al., 2006b). There is very little other evidence on which to assess the validity of the predictions, highlighting the need for longer-term experiments with which to validate model predictions. The model predicts that crowns extend to close to the ground at each site until around 4 years of age, and that branch length is such that at the 3 x 3m spacing canopy closure would not occur until after the age of early defoliation. Hence it is probable that the lower crown was illuminated, and under such circumstances removal of the lower crown would be expected to have a large impact. The reduced effect of top down defoliation at this time might be explained by the fact that this part of the crown would be rapidly replaced, and the lower crown would experience higher illumination levels while crown replacement was occurring. 81

91 Because canopy closure had occurred before the time of the later age defoliation, it would be expected that removal of the upper crown would have the greater impact on stand volume at this time. Pines Unlike eucalypts, the model predicted that top down defoliation would in general have the greatest impact on pine final volumes, and particularly at low site fertility (Figure 38). This was irrespective of whether defoliation was early or later age, although later-age top-down defoliation had a greater impact than early-age top-down defoliation. Defoliation frequency Eucalypts Defoliating eucalypt stands for three consecutive years resulted in a greater predicted impact on final volume than did defoliation in a single year. This was particularly true for later-age defoliation (Figure 39). The degree of impact was not associated with site fertility. The effect of a single versus three defoliation events at an early age had similar effects on final volume at the lower productivity sites (Figure 39: S GT200; SWWA 36; TAS 186; VNSW373). Pines As with the eucalypts, the model predicted that increasing frequency of defoliation would increase impact on final volume, particularly at low site fertility, and particularly when defoliation occurred later in the rotation (Figure 40). Defoliation in three consecutive years later in the rotation resulted in the greatest predicted reductions in final volume, of between 5 and 15%. Earlier in the rotation, these sorts of volume reductions were only predicted to occur with low site fertility. Season of defoliation Eucalypts There was a lot of between-site variation in the effects of spring versus autumn defoliation (data not presented). It has been demonstrated experimentally in short term studies (2 years duration starting at age 6 months) that autumn or late season defoliation has a longer-term impact than spring or early-season defoliation (Pinkard et al., 2006b). In this analysis, there was little consistency in response to spring versus autumn defoliation. Pines There was also a lot of between-site variation in the effects of spring versus autumn defoliation in P. radiata stands (data not presented). There was no consistent pattern of response between sites or between early and later-age defoliation at the same site. In general the differences between spring and autumn defoliation within a site were smaller than the differences between early and later-age defoliation, or with site fertility class, suggesting that season of defoliation is perhaps of lesser importance in determining final stand volume than pattern, frequency and severity of defoliation. 82

92 Early-age defoliation Later-age defoliation Early-age defoliation Later-age defoliation High Med Low SA GT High Med Low TAS % reduction in final volume High Med Low SA GT47 High Med Low SWWA % reduction in final volume High Med Low TAS High Med Low VNSW High Med Low SWWA % leaf loss % leaf loss High Med Low VNSW % leaf loss % leaf loss Figure 34. Difference in final volume between undefoliated and defoliated P. radiata stands at eight sites in southern Australia. Five defoliation levels were applied (0, 20, 40, 60 or 80% reduction in leaf area), and analyses were done for three site fertility levels (low, moderate and high see Table 4). Rotation length was 35 years. 83

93 Final volume VNSW79 - mod fertility Stand age Final volume Stand age Final volume SAGT200 - mod fertility Stand age Final volume Stand age Figure 35. Time course of stand volume of P. radiata predicted for two sites at a moderate site fertility (see Table 4), where defoliation levels ranging between 0 and 80% were applied at 3 or 9 years of age. VNSW is a Victorian-NSW site; SA is a South Australian site Figure 36. Examples of predicted changes in leaf area index (LAI) over time in two undefoliated P. radiata stands, for a planting date of 1981 and a moderate site fertility rating (see Table 4). The red arrows indicate the timing of early and later-age defoliation events. 84

94 Early-age defoliation Later-age defoliation Early-age defoliation Later-age defoliation % reduction in final volume % reduction in final volume Figure 37. The predicted effect of top down (TD) versus bottom-up (BU) defoliation on E. globulus. Values are the difference between undefoliated and defoliated final stand volume, calculated as the mean of 20 model runs with sequential planting dates. Data are presented for early and later age defoliation and for three site fertilities (low, moderate and high see Table 4)). 85

95 Early-age defoliation Later-age defoliation Early-age defoliation Later-age defoliation BU TD SA GT200 High Med Low High Med Low High Med Low BU TD TAS High Med Low % reduction in final volume BU TD SA GT47 High Med Low BU TD SWWA 125 High Med Low High Med Low High Med Low BU TD TAS 326 High Med Low BU TD VNSW 373 High Med Low High Med Low High Med Low High Med Low BU TD SWWA High Med Low High Med Low BU TD VNSW High Med Low Figure 38. The predicted effect of top down (TD) versus bottom-up (BU) defoliation on P. radiata. Values are the difference between undefoliated and defoliated final stand volume, calculated as the mean of 20 model runs with sequential planting dates. Data are presented for early and later age defoliation and for three site fertilities (low, moderate and high see Table 4). 86

96 0 Early-age defoliation One -15 Three -20 SA GT High Med Low Later-age defoliation High Med Low Early-age defoliation One -15 Three -20 TAS High Med Low Later-age defoliation High Med Low % reduction in final volume One Three SA GT47 High Med Low One Three SWWA 125 High Med Low High Med Low High Med Low One Three TAS 326 High Med Low One Three VNSW 373 High Med Low High Med Low High Med Low High Med Low One Three SWWA High Med Low High Med Low 87 One Three VNSW High Med Low Figure 39. The effect of one versus three defoliation events on E. globulus stand volume. Values are the difference between undefoliated and defoliated final stand volume, calculated as the mean of 20 model runs with sequential planting dates. Data are presented for early and later age defoliation and for three site fertilities (low, moderate and high see Table 4).

97 0 Early-age defoliation 0 Later-age defoliation 0 Early-age defoliation 0 Later-age defoliation One Three SA GT200 High Med Low High Med Low High Med Low One Three TAS High Med Low % reduction in final volume High Med Low One Three SA GT47 One Three High Med Low High Med Low One Three TAS 326 One Three High Med Low SWWA 125 High Med Low High Med Low VNSW 373 High Med Low High Med Low High Med Low One Three SWWA High Med Low High Med Low One Three VNSW High Med Low Figure 40. The effect of one versus three defoliation events on P. radiata stand volume. Values are the difference between undefoliated and defoliated final stand volume, calculated as the mean of 20 model runs with sequential planting dates. Data are presented for early and later age defoliation and for three site fertilities (low, moderate and high see Table 4). 88

98 Discussion This analysis focused on defining defoliation thresholds and identifying the role that season, pattern, timing and frequency of defoliation have on final stand volume, using a modelling approach. A key conclusion is that considerable between-site variation can be expected in responses to defoliation, making it difficult to generalise about impact. Our analyses were of necessity simplifications, in that they used standardised soil, fertility and silvicultural regimes. Estimates of impact could be improved by undertaking site and regime-specific analyses, and the CABALA tool is available to partners to undertake individual case-studies. On average, defoliation was not predicted to have large impacts on final volume of either E. globulus or P. radiata. However some defoliation events were predicted to have large impacts. Maximum reductions of 25% were predicted to occur infrequently for E. globulus, although in many cases reductions in final volume were predicted to be no more than 10%. Maximum volume reductions predicted for P. radiata were in the order of 15%, with most reductions in the order of 5% or less. Percentage volume reductions need to be considered in terms of what this might mean for absolute volume reductions. For a eucalypt stand with a mean final volume of 300 m 3 /ha, a reduction of 25% is equivalent to 75 m 3 /ha. For a pine stand with a mean final volume (including thinnings) of 1500 m 3 /ha, a 15% reduction is equivalent to 225 m 3 /ha. While we applied a blanket threshold of 5% volume reduction, this may not be appropriate for all sites or business models. However the results of our analyses can be reassessed for any volume threshold. Based on this 5% threshold, we were able to identify the levels of defoliation that might be of concern. This information needs to be used in combination with on-site monitoring. It could also be used, in conjunction with further site or regional modelling, to identify high risk sites/regions where monitoring and remedial actions might be warranted. Climate, and particularly temperature and rainfall, can significantly influence the abundance and distribution of pests. Any analysis of how climate change might affect plantation productivity should consider both changes in pest risk and impact. Although it might be ideal to link changes in pest risk and impact in the one analysis, it is methodologically complex (Pinkard et al 2010). In this project we were unable to undertake an integrated risk and impact analysis because we lacked information linking climatic suitability (for key pests) to damage levels that could be used as inputs for CABALA. The thresholds identified here can be used in conjunction with the pest risk analysis presented later to identify when pest damage levels might be expected to increase in the future. The prediction that later-age defoliation will generally have the greatest effect may be an important one. It suggests that growers of solid wood over longer rotations should be concerned about later-age defoliation and seek to understand the risk and management options available to them. Chronic defoliation is also likely to be of concern. In general early age defoliation is likely to be of much less consequence over the course of a rotation. This analysis was a modelling exercise, and was useful as a way of identifying defoliation scenarios that might be of concern to growers. These kinds of analyses will never be a substitute for quantitative data, which can be used to validate the model predictions and 89

99 improve our understanding of defoliation impacts on plantation forests. There is very little such quantitative data available in Australia, and until this situation improves, the results of studies such as this can be used as guides and a way of exploring defoliation scenarios most likely to cause significant reductions in final stand volume. 90

100 Indirect effects of climate change on future E. globulus and P. radiata plantation productivity Fire hazard Pest hazard 91

101 92

102 Chapter 7 Fire hazard Summary Fire weather was predicted to worsen over the entire study area when measured as both annual sum of fire danger index and number of days with fire weather likely to be associated with damaging and difficult to control fires. The largest changes were predicted for inland areas with smaller changes for Tasmania, coastal areas of SA, WA, and VIC and northern NSW. Fuel loads increased in almost all locations in response to changes in photosynthesis and water use efficiency predicted by the forest growth model. Increases were smaller in the drier climate scenario and regional changes in litter load were correlated with local changes in rainfall. The number of fire damage days, calculated as days with fire intensity above 4000 kw/m was higher than the number fire weather days because for most of the country fuel loads were quite high. For a fuel load of 22.8 t/ha, the national average, fire damage days occur for FFDI above 12, much lower than the cut-off of 25 used to calculate fire weather days. This means that for stands with high litter loads, damaging fires may occur under even moderate conditions. There was significant regional variation in fire danger and damage estimates which has implications for management of fire in plantations. Inland areas which currently have the greatest exposure to fire risk are also likely to experience the greatest increases in fire risk on all three measures examined here. The areas experiencing the largest changes are inland areas of Victoria and the northern end of the plantation area in Western Australia. For these areas management of fuels (or silvicultural treatments to reduce fire intensity may be suitable management options to reduce risk of damage. 93

103 Introduction Fire poses a significant hazard to forest values, including plantation forests. Fire behaviour is governed by weather, topography and fuel. Of these, weather and fuels are both susceptible to the effects of climate change (Bradstock 2010). The potential for climate change to affect the occurrence of landscape fire has long been recognized (Beer et al. 1988; Flannigan et al. 2009). The majority of studies investigating climate and fire have used fire danger indices to predict changes in fire potential, as recently reviewed by Flannigan et al. (2008, 2009). These indices combine temperature, relative humidity, wind speed and a measure of long-term moisture deficit (through soil dryness or grass curing) to provide an assessment of the potential for fires to start and spread (McArthur 1967; van Wagner 1987; Burgan 1988). Interactions between climate, fire and vegetation distribution have also been investigated using dynamic vegetation models in the USA (Malanson and Westman 1991; Bachelet et al. 2003; Lenihan et al. 2008), Europe (Mouillot et al. 2002; Schumacher and Bugmann 2006) and tropical savannahs (Hoffmann et al. 2002). Studies to date in Australian forests have focused on changes in fire weather through fire danger indices, using the forest fire danger index, FFDI (McArthur 1967) and the grassland fire danger index, GFDI (McArthur 1966). Early studies (Beer and Williams 1995; Cary 2001; Williams et al. 2001) used climate model outputs to investigate a number of measures of fire danger, including distributions of daily values, monthly mean value and seasonal sum. More recent studies have used a variety of statistical methods to better understand the nature and uncertainty of predicted changes in fire danger (Hennessy et al. 2005; Lucas et al. 2007; Pitman et al. 2007; Clarke et al. 2011) or behaviour (Sullivan 2010) and have also investigated changes in synoptic weather patterns (Hasson et al. 2009). Matthews (2013) used an empirical model to examine the effects of changing climate on fuel load and fire behaviour in a native Eucalytpus forest but did not consider the effects of CO 2 fertilisation on litter production. The fire behaviour potential of Eucalyptus and Radiata pine plantations differs from native fuels and in many cases fire behaviour in plantations is less severe than in surrounding forests as a result of lower amounts of often discontinuous fuels combined with a dense canopy (McCaw and Smith 2009; Geddes 2006). For plantations up to 3 years old, vulnerability depends largely on management practice, with little potential for fires to spread except under the most extreme conditions if there are few inter-row weeds or green grass is present (de Mar 2011). If cured grasses or logging debris are present then fires may behave as grass fires. Once the canopy closes but only a very small amount of possibly discontinuous litter has been deposited there is limited potential for fire spread. Potential for fire spread then increases as the litter layer deepens, and with this there is also a corresponding increase in likely flame height and potential for crown and stem damage. The majority of recorded losses of Eucalyptus plantations have occurred in plantations over 6 years of age, the time at which a continuous litter layer develops (McCaw and Smith 2009; 94

104 Geddes 2006). At High or greater FFDI (McArthur 1967) crown scorch and defoliation have been recorded in 6 year old plantations (McCaw and Smith 2009). On the other hand, in Extreme conditions 5 year old plantations, particularly in spring conditions with some green grass present, have not been substantially damaged (McCaw and Smith 2009). Overall, 75% of historical plantation losses have occurred on Very High or Extreme fire danger days (Geddes 2006). In this chapter we investigated the effects of climate change on fire risk to Eucalyptus and Radiata pine plantations. Two climate models were used to consider best and worst case scenarios for changes to rainfall in a warming climate for the years 2030 and 2050 under a high-emissions scenario. In addition, changes in forest growth and litter layer development of Eucalyptus are modelled using the CABALA model described earlier (Battaglia et al. 2004; White et al. 2011), providing an estimate of fuel loads. Fire weather and fire behaviour predictions were made using the McArthur fire danger meter (McArthur 1967), the most suitable model for forests where litter is the primary fuel. Fire risk was assessed using measures of the frequency of occurrence of high fire danger in all forest types and damaging fire intensity in plantations. Background Fire behaviour in plantations While fire behaviour in Eucalyptus and pine plantations has much in common with that in native forests; plantation fuels have distinct characteristics that mean that direct application of existing fire danger indices is not straightforward. In particular, fire danger and behaviour models are applied to a forest that is in an equilibrium state, and because only a small percentage of land in south eastern and south west Australia burns every year (Price and Bradstock 2011) this is usually a long unburned state. In contrast, plantation forests undergo dramatic changes in fuel structure during a year rotation. In Eucalyptus and pine plantations there are three distinct fuel structure trajectories depending on how the plantation is managed: 1. First rotation without woody weeds. In this structure, the site is cleared and some form of weed control is undertaken. This structure is most commonly found on grazing or crop land that has been converted to plantations. For the first 3 years of the rotation the only fuel present is grass in the inter-row spaces as juvenile trees do not shed significant amounts of leaves. Once the canopy begins to close, grasses gradually die out due to lack of sunlight and the fuel bed changes to a thin, usually discontinuous leaf litter layer in the inter-row spaces. In plantations older than 7 years, the fuel bed is usually continuous and as the plantation continues to grow the litter layer depends to reach an equilibrium load. In this later period a near surface fuel layer may also develop, consisting of twigs and small branches shed as the trees self prune, and leaf material suspended in the shed branches. 2. Plantation with woody understory. In areas which were previously forested and in which woody weeds are not controlled, woody weeds may contribute significantly to 95

105 plantation fuels. In the first few years after establishment, the plantation may appear to be a shrub landscape with weeds of similar height to the trees. While the shrub layer may thin as the tree canopy increases in height and competes for light, a significant shrub layer persists and older plantations may resemble native forests in most respects. 3. Second rotation with harvest debris. Fuels in subsequent rotations depend on how the site was harvested and prepared. If logging debris is removed or burned, then the site will behave similarly to a first rotation site. If debris is left on site, then there will be a large fine fuel bed consisting of discarded limbs and fine fuels from the date of establishment. The fire behaviour potential and vulnerability of each structural type varies as the plantation ages. For fuels up to 3 years old, vulnerability depends largely on management practice, with little potential for fires to spread except under the most extreme conditions if there are few inter-row weeds or green grass is present. If cured grasses or logging debris are present then fires may behave as grass fires. Once the canopy closes but only a very small amount of possibly discontinuous litter has been deposited, there is limited potential for fire spread. Potential for fire spread then increases as the litter layer deepens, and with this there is also a corresponding increase in likely flame height and potential for crown and stem damage. Second rotation plantations with harvest debris are likely to maintain flammability during early canopy closure (4-6 years), without the drop in flammability associated with loss of grasses in first rotation plantations. Because there is greater vertical continuity of fuels in plantations with a shrub layer, these plantations have greater potential for higher flame heights to develop than the other plantation types. However, the dense understory also shelters surface fuels from the sun and wind. This means that all other things being equal, these fuels will remain moister and fire may be excluded by the fuels being too wet to burn under conditions where plantations without an understory would be dry enough to burn. Prescribed burning guides for eucalypt forests recommend maintaining fire intensity below an absolute maximum of 2000 kw m -1, flame heights below 4 m, and fire danger index below 10 (Moderate) (Marsden-Smedley 2009; Cheney 1982; Lacey 2008). Fires spreading in plantations with this intensity and flame height are likely to cause significant canopy scorch and result in reduction in wood quality and possible mortality. Significant damage may also be caused by fires with intensities as low as 500 kw m -1, depending on canopy height and species vulnerability. Direct calculation of fire intensity and flame height requires quantification of the relationship between fuel structure and fire behaviour as well as suitable fire behaviour models. de Mar and Adshead (2011) developed a fire hazard rating guide for eucalypt plantations without woody weeds but suggest that existing fire behaviour models are likely to be unreliable if used in plantations with the possible exception of fuels greater than 6 years old. This caution is corroborated by experimental burning in plantations with a grass understory for which fire behaviour model predictions had large errors in many cases (Lacey 2008). 96

106 Given the difficulties of predicting fire behaviour in plantations outlined here, we take the following conservative approach to modelling the vulnerability of eucalypt plantations to fire: Plantations are vulnerable to fire on days with fire danger index of 25 (Very High) or greater (some studies have used 24 as a cut-off value. Here we follow Hennessy et al. (2005) and Lucas et al. (2007) in using 25). Vulnerability is calculated for plantations with sufficient fuel to sustain fire: first rotation fuels aged 6 years or older, second rotation fuels of any age (de Mar and Adshead 2011), and fuels with a shrub layer of any age. The forest growth modelling used in this project grows multiple rotations on each site to avoid confounding stand age and weather effects in predictions. Similarly, for the fire risk assessment we do not track the age of specific stands but instead make predictions assuming mature (7+ year old) fuels. The effects of climate change on the rate at which plantations develop to a vulnerable fuel load will be considered separately. Methods Study area The study is the same as that used for the growth modelling component of the project. All calculations were made on a 0.1 grid, approximately 10 x 10 km. Baseline data Calculation of drought and fire danger indices requires daily values of maximum air temperature, relative humidity, wind speed, and rainfall. All data except wind speed were taken from the SILO data base (Jeffrey et al. 2001) from January 1, 1975 to December 31, This is the same data set that was used in the growth modelling study (see Chapter 3). Wind speed was calculated as described in the Chapter 2. Climate modelling Models and scenarios This section uses the same climate models and emissions scenarios as the growth modelling and pest studies, as described in the climate chapter. Climate change effects on fire hazard to plantations Models and application Fire behaviour models take a number of forms, from purely physical in which all processes are explicitly modelled, through purely empirical, in which no processes are incorporated but their interactions parameterised through statistical regressions of input variables (Sullivan, 2009a). In all parts of the world, fire behaviour models used for operational purposes (for the purpose of fighting, controlling or warning of wildfires) are of empirical or quasi-empirical construction (Sullivan 2009). Here the Forest Fire Danger Meter model (FFDM, McArthur, 1967), a model in widespread use in eastern Australian forests, is used. 97

107 The FFDM was developed from a series of experimental fires conducted over a year period in various types of native forest around the country (McArthur, 1967). The majority of these experimental fires were conducted under low- to moderate- fire weather conditions and were generally of small scale (<0.5 ha), lasting less than 1 hour from ignition to completion, augmented by ad hoc wildfire observations. The FFDM has been used in all studies that have examined fire danger and climate change in Australia. For the FFDM, F is the forest fire danger index (FFDI), a meteorologically based index: F = 2 exp ( log (D) H T U) where, D is the drought factor (0-10), H is the relative humidity (%), T is the air temperature (C) and U is the wind speed at 10 m in the open (km h -1 ). Drought index and FFDI were calculated daily for the baseline and future climates using the weather variables described above. Drought factor is a measure of fuel dryness calculated as a function of rain amount and time since rain. Fire danger is a useful indicator of the potential for fires to occur but the behaviour and effects of a fire depend on the fuels consumed. Fire intensity, I (kw m -1 ) is used to estimate difficulty of suppression and damage to trees (Byram 1959): I=HwR where H is the heat content of the fuel, 18, 600 kj kg -1 (Byram 1959), w is fuel load (kg m -2 ), and R is rate of spread (m s -1 ). Rate of spread can be calculated from FFDI (Noble et al. 1980): R=0.0033Fw Data Analysis The quantity most commonly used to characterise fire danger in climate change studies is the seasonal or annual sum of FFDI. This provides a general assessment of fire risk across large areas of the landscape. We consider two other measures here. Firstly, the number of days with FFDI>25 (Hennessy et al. 2005; Lucas et al. 2007), termed fire weather days. This quantity is used as an indicator of the frequency of risk of fires starting, possibly outside plantations in native forests or grassland, and impacting on plantations. Secondly, the number of days with fire intensity above 4,000 kw m -1, a threshold at which fire suppression is extremely difficult and extensive damage is likely (Gill et al. 1987, Alexander and Cole 1995), termed fire damage days. Damage to wood quality may also occur at lower fire intensities (Lacey 2008) but the extent of damage is likely to be lower if fires are suppressible. To remove the influence of climate variability on the development of individual plantations and litter loads, 20 rotations were simulated for present and future climate. Rotations were planted in each of the first 20 years of the climate periods and litter load was calculated as the average of the litter load in the 10 th year of all rotations. Fire indices were averaged over 30 years for current and future climate. 98

108 This approach considers only changes in the exposure of plantations to risk of damage or destruction by fire. We do not consider vulnerability of plantations to damage as changes in plant physiology which would alter vulnerability have not been examined. Therefore, changes in consequence of exposure to fire is not considered either since this will not change, i.e. exposure to sufficiently intense fire will result in the same damage or destruction for current and future trees. Results Fuel loads Predicted litter loads for present climate ranged from 12 to 53 t/ha and were strongly correlated with rainfall. Values were in agreement with observations from similar dry sclerophyll forests (Paul and Polglase 2004). In spite of reduced rainfall over most of the country predicted litter amounts increased for both climate models. Values are presented for 2 climate models, CSIRO 3.5 and Miroc-M (Figure 41). Median increases in 2050 were 3.3% for the CSIRO model and 7.8% for MIROC (Figure 41). Both models showed similar patterns of regional variation, with the strongest increases in inland Victoria and drier parts of South Australia and Western Australia. Decreases in litter load were seen in some locations in Tasmania for both models and in parts of Victoria for the CSIRO model only. Fire hazard As noted above, there have been several previous studies of fire danger climatology using slightly different data sets. On average, our method estimates the sum of FFDI to be 5% lower (Figure 40) than that predicted by Lucas et al. (2007), most likely as a result of use of gridded wind and rain data (Finkele et al. 2006). Patterns of 90% FFDI were similar to those produced by Dowdy et al. (2009) using a mix of observed and forecast weather. FFDI calculations are very sensitive to choice of calculation method with up to 30% variation observed by Lucas et al. (2007) depending on the use of 15:00 or daily extrema. Given this sensitivity, we consider the small differences between data sets to be acceptable. Baseline FFDI climatology was correlated with large-scale patterns of rainfall and temperature (Figure 43). Annual sum of FFDI ranged from below 500 in the wettest parts of north-west Tasmania to above 3500 in drier parts of WA. The combination of high annual rainfall and low air temperatures resulted in the lowest FFDI occurring in north-west Tasmania, but the rainfall gradient across the state produced higher FFDI in the east. Overall FFDI for Tasmania was lower than the mainland (i.e. all areas except Tasmania). On the mainland, patterns of sum of FFDI were largely shaped by decrease in rainfall with distance from the coast. Number of fire weather days followed a similar pattern (Figure 44). Most sites experienced fewer than 10 days per year but higher occurrence was recorded for drier parts of south-west WA and inland parts of Victoria and South Australia. 99

109 Figure 41. Litter fuel load for the baseline period (a) and predicted changes for 2030 (b) and 2050 (c) for the CSIRO model and (d,e) MIROC model Average numbers of fire damage days were higher than fire danger days for all regions (Figure 45). This occurred because of the influence of litter load on fire intensity, e.g. for the mean litter load of 2.3 kg m -2 damage days occur for FFDI above 12. Spatial patterns for fire damage days were more complex than for fire weather because of this influence of litter load on fire intensity. Low litter loads meant that inland areas of Western Australia and South Australia had low numbers of damage days while coastal areas had higher risk, the opposite of the pattern seen for FFDI and fire weather days. Victoria had more than 50 fire damage days per year for all areas except for a very wet location in the south-east of the state. Tasmania had very few fire damage days except in the north-west of the state where litter loads were above 3.7 kg m -2. Similar patterns of change were predicted for FFDI for both models (Figure 43) but with more intense change for the CSIRO model due to the larger decrease in rainfall. All areas were predicted to have increases in annual sum of FFDI with no grid cells having a decrease. For both models, the most intense changes were seen in inland areas of Victoria and Western Australia. Fire weather days occurred almost exclusively in summer and on days which were rain free, so that changes in number of days were driven largely by incremental changes in temperature, humidity, and wind speed. Both models predicted increases in fire weather days across all mainland states (Figure 44). Increases were largest for inland 100

110 Victoria and South Australia and drier parts of south west WA. Only slight increases were seen for Tasmania because fire weather days were rare in the base climatology. Patterns of change in fire damage days were broadly similar to changes in fire weather days with the largest increases in Victoria and parts of Western Australia (Figure 45). Decreases in litter load in some areas of Victoria had a small moderating effect. Changes in South Australia were smaller because litter loads remained low for future conditions in both models. Tasmania was the only area with predicted decreases in damage days due to the combination of decreased litter loads and moderate fire weather. Figure 42. Comparison of FFDI calculated by Lucas et al. (2007) and this study. Left) Lucas et al. (2007) study sites. Right) Comparison of annual sum of FFDI for the points shown at left. Dashed lines are ±20% 101

111 Figure 43. Annual sum of FFDI for the baseline period (a) and predicted changes for 2030 (b) and 2050 (c) for the CSIRO model and (d,e) MIROC model 102

112 Figure 44. Number of fire weather days (FFDI > 25) for the baseline period (a) and predicted changes for 2030 (b) and 2050 (c) for the CSIRO model and (d,e) MIROC model 103

113 Figure 45. Number of fire damage days (Intensity > 4000 kw m-1 for the baseline period (a) and predicted changes for 2030 (b) and 2050 (c) for the CSIRO model and (d,e) MIROC model Regional fire weather summaries This section presents time series of fire danger indices to examine seasonal changes more closely. Baseline and change statistics were calculated for each of the five study regions using the same daily data as the change surfaces presented above. Three measures are presented: Median monthly FFDI. This measure is similar to the sum of FFDI used for the change surfaces. Medians are calculated by pooling FFDI for all years for all sites in each region in a particular month. 99 percentile FFDI. This is an indicator of change in the most extreme fire danger days. Number of days where a significant portion, chosen as 10%, of the region has FFDI above 25. This is an indicator of the frequency of days on which there is potential for significant loss over at least some of the region. As with the change surfaces, there may be biases in the baseline statistics and so more emphasis is placed on changes in values. 104

114 South-west Western Australia Figure 46. Changes in fire danger for south-west Western Australia. Top) Median and 99% FFDI. Bottom) Number of days when at least 10% of sites have FFDI above 25. FFDI is predicted to remain largely unchanged during winter and early spring (Figure 46) but median FFDI is likely to increase throughout the period from December to April. Both climate models predicted an increase in 99% FFDI but with some differences in timing and amount. Interestingly, the MIROC model produced the highest 99% FFDI in south west Western Australia during the peak of summer but lower increases than CSIRO during autumn. The count of days with FFDI above 25 suggests a lengthening of the fire season at both ends with both models predicting increases in November and May as well as increases of up to 4 days per month during the summer months. Green triangle Figure 47. Changes in fire danger for the green triangle. Left) Median and 99% FFDI. Right) Number of days when at least 10% of sites have FFDI above 25. Both climate models predicted an earlier start to the fire season for the green triangle region with an increase in days with FFDI > 25 in November (Figure 47). Predictions for autumn differ, with a later finish to the season predicted by CSIRO while MIROC predicted little change. Median and 99% FFDI both increase during November to March but as with the length of the fire season MIROC predicted relatively minor changes in autumn. 105

115 Tasmania Figure 48. Changes in fire danger for Tasmania. Left) Median and 99% FFDI. Right) Number of days when at least 10% of sites have FFDI above 25. Tasmania has the lowest fire danger of the five regions and absolute changes for all quantities are small relative to mainland sites (Figure 48). There were small increases predicted in median FFDI for both models from October to March and a possible earlier start to the fire season but values are so small that it is difficult to make a robust assessment. There were small changes predicted to 99% FFDI throughout the year with the largest changes in summer. Victoria and southern New South Wales Figure 49. Changes in fire danger for Victoria and southern New South Wales. Left) Median and 99% FFDI. Right) Number of days when at least 10% of sites have FFDI above 25. The two models produced slightly different predictions for Victoria and southern NSW (Figure 49). The CSIRO model predicted an increase in median FFDI for an extended period from July to April, while changes predicted by MIROC are concentrated in October to March. Both models predicted an earlier start to the fire season. The CSIRO model also predicted a later finish while MIROC suggests little change. Both models predict an increase in the number of days with FFDI above 25 during the summer months. 106

116 Northern New South Wales Figure 50. Changes in fire danger for Northern New South Wales. Left) Median and 99% FFDI. Right) Number of days when at least 10% of sites have FFDI above 25. The two models provided slightly different predictions for Northern NSW (Figure 50). The CSIRO model predicted an increase in median FFDI for all months, while changes predicted by MIROC are concentrated in October to February. The CSIRO model predicted both an earlier start and later finish to the fire season while results using the MIROC model suggest little change in fire season length. Both models predicted an increase in the number of days with FFDI above 25 during the summer months but CSIRO also predicted an increase in this number in spring. Discussion By combining climate change scenarios with models of fire danger, fuel load, and fire behaviour we have examined potential changes to fire risk from a number of perspectives. Fire weather was predicted to worsen over the entire study area when measured as both annual sum of fire danger index and number of days with fire weather likely to be associated with damaging and difficult to control fires. This result was seen for both climate scenarios presented in spite of differences in rainfall changes, and increases in fire weather were larger in the drier scenario. These results are consistent with other studies using similar approaches but different models (Lucas et al. 2007; Clarke et al. 2011) in Australia and other parts of the world (Stocks 1998; Flannigan et al. 2009; Bedia et al. 2014). Fuel loads increased in almost all locations in response to changes in photosynthesis and water use efficiency predicted by the forest growth model. Increases were smaller in the drier climate scenario and regional changes in litter load were correlated with local changes in rainfall. A previous study examining climate change and litter load effects on fire behaviour (Matthews et al. 2012) found decreasing litter load due to decreasing rainfall. This is in contrast to our prediction because Matthews (2013) used an empirical litter load model which did not include the effects of eco

117 The number of fire damage days, calculated as days with fire intensity above 4000 kw/m was higher than the number fire weather days because for most of the country fuel loads were quite high. For a fuel load of 22.8 t/ha, the national average, fire damage days occur for FFDI above 12, much lower than the cut-off of 25 used to calculate fire weather days. This means that for stands with high litter loads, damaging fires may occur under even moderate conditions. It is possible that for sites with very high fuel loads, e.g. areas of Tasmania with loads above 40 t/ha, this approach to estimating damage is unrealistic because predictions of high fire intensity may occur on days where the fire danger is so low that fires are easily contained. There were some feedbacks in the response of fuels, weather, and fire to differences between the two climate models. The CSIRO model, which predicted a larger increase in rainfall than MIROC, predicted smaller relative increases in litter load and larger increases in FFDI. In contrast the MIROC model predicted larger increases in litter load and smaller increases in FFDI. When both quantities were combined in the calculation of fire damage days these differences were smaller but MIROC tended to predict a larger increase in number of damage days. These results suggest that despite uncertainties in rainfall projections for the future there may not be as much uncertainty in outcomes for fire behaviour. Rainfall for future climate was calculated by perturbing the amount of rain falling on each rainy day but did not alter the frequency or length of rainfall events. Also, our results do not take into account shifts in climate patterns such as El Nino Southern Oscillation (ENSO) or the larger atmospheric circulation. If changes in weather systems result in more (Suppiah and Hennessy 1998) or fewer rain days (Smith and Timbal 2012) then the number of days on which fuels are dry enough to burn would be expected to change, potentially changing the number of fire weather and fire damage days beyond our predictions, although changes in ENSO are still uncertain (Collins et al. 2010). There was significant regional variation in fire danger and damage estimates which has implications for management of fire in plantations. Inland areas which currently have the greatest exposure to fire risk are also likely to experience the greatest increases in fire risk on all three measures examined here. The areas experiencing the largest changes are inland areas of Victoria and the northern end of the plantation area in Western Australia. For these areas management of fuels (Lacey 2008) or silvicultural treatments to reduce fire intensity may be suitable management options to reduce risk of damage. In contrast areas such as South Australia, with low fuel loads, and Tasmania, with milder climate, are expected to experience smaller increased in fire risk measured as high fire danger or intensity in spite of relative increases in mean fire danger that are similar to the rest of the country. 108

118 Chapter 8 Pest hazard Summary Of 20 key pest species of eucalypt and pine plantations in Australia identified in the study, 15 were defoliators. Leaf chewers, sap suckers and foliar pathogens were all represented. They have a range of modes of action, targeting plantations of different ages, and imparting different patterns of damage in different seasons. Current distribution, drivers of host susceptibility and anticipated responses to climate change are summarised in detail for each of the 20 identified pests. Warmer temperatures may have a positive effect on the distribution and abundance of leaf chewers and sapsuckers, through increased overwintering survival, increased developmental rates and an extended host growing season. Declining rainfall may have a negative effect on species requiring high relative humidity, such as foliar pathogens Stressed host trees may become more susceptible to stem borers and some sap-sucking insects. Drought stress in particular may result in increased damage from stem borers. Modeling analysis of climatic suitability for seven pest species suggested that only small changes are likely in the distribution of key plantation species between now and 2030 or Abundance of some pests may increase, suggesting more frequent or intense pest outbreaks in some areas. 109

119 Introduction Insects and fungi are a natural component of Australia s forests, which impart a baseline level of damage to plant species within those forests and periodically cause significant damage when conditions conducive to outbreaks occur. A number of insects and fungi found in native forests have become pests of plantations (Loch and Floyd, 2001; Carnegie and Ades, 2002; Steinbauer and Matsuki, 2004; Carnegie and Angel, 2005); this has been augmented by invasions from overseas. For example, Ips grandicollis was first observed in pine plantations in Australia in 1943 (Bungey, 1966), followed closely by Sirex noctilio in 1952 (Carnegie et al., 2006). More recent incursions include Essigella californica in pines (Kimber et al., 2010) and Puccinia psidii, a potential pest of eucalypts (Glen et al., 2007). A range of studies have demonstrated the impacts of pest damage on plantation productivity (Candy et al., 1992; Carnegie and Ades, 2002; Collett and Neumann, 2002; Pinkard et al., 2006b; Loch and Matsuki, 2010), although this has not necessarily translated into pest management programs (Loch and Matsuki, 2010; Wardlaw et al., 2011). In order to understand the impact of pests and identify damage thresholds and appropriate management strategies, it is necessary to understand factors that affect the distribution and abundance of these species. A range of factors affect pests and how they interact with their hosts, including climate, host characteristics and edaphic/site characteristics. These are discussed in more detail below. Climatic change has the potential to alter insect/fungi damage to hosts by (1) direct effects on development, survival and distribution of the insect and fungal species and the resilience of a species to change; (2) physiological changes in the host species that affect tree vigour and defence; and (3) indirect effects from changes in abundance of natural enemies, competitors and beneficial organisms (Whittaker, 2001). The response of a forest system to insect and fungal pests depends on its resilience, which is in turn related to the degree of stress it experiences (e.g. from fire, drought or storm damage), and the recovery time between outbreaks and stress events (Jeger and Pautasso, 2008). There is little quantitative understanding within Australia of the possible impacts of climate change on the distribution and abundance of plantation pest species. Old and Stone (Old and Stone, 2005) undertook an initial analysis in 2005, based on literature review and expert knowledge. The objective of this study was to build on their review and address the following questions: What are the key plantation pest species in 2013? How are key plantation pest species likely to respond to the changes in temperature, rainfall, heatwaves, drought and storm events projected for Australia in 2030 and 2050? How is climatic suitability likely to change for a subset of those species? What are the implications of climate change for frequency and severity of pest outbreaks? How do key pest species damage their hosts and how can this be captured into impact models? We addressed these questions through a combination of literature review, expert elicitation and species niche modelling. 110

120 Key pest species of Australian plantations Key pest species of eucalypt and pine plantations in Australia were identified via consultation with project partners at a workshop in March 2012 and subsequent consultation with forest health experts in New South Wales (C. Stone), Queensland (S. Lawson), Victoria (D. Smith), the Green Triangle (D. Smith, F. Tovar), Western Australia (F. Tovar), and Tasmania (T. Wardlaw, C. Mohammed). This was complemented with a literature review. The results are listed in Table 1. Only those pests considered to be of high significance in terms of the damage they impart were included in the table. These are pests that have been observed to periodically affect large area of plantations and to result in periodic severe damage and mortality in seedlings and/or trees. The pests listed in Table 12 are predominantly (80%) foliage pests including chewers, suckers and pathogens. Many are endemic to Australia and have adapted to the plantation environment (e.g. eucalypt beetles, Teratosphaera (Mycosphaerella) leaf disease). Others have been introduced to Australia (E.g. Sirex wood wasp, Monterey pine beetle). Eucalypt rust (Puccinia psidii) a recent introduction to Australia, is included in the list although there is no evidence to date of damage to plantations (C. Mohammed per s comm.). There is considerable interest within the forest industry in this species and its potential as a future pest of plantation eucalypts. Current distribution of key pests in Australia Current distributions for the pests in Table 12 were compiled using the Australian Plant Pest Database ( supplemented by additional data where possible. Data for E. californica were supplemented from Wharton and Kriticos (Wharton and Kriticos, 2004); for S. noctilio were supplemented from(ireland et al., under review-a); for U. lugens were supplemented from Kriticos et al. ((Kriticos et al., 2007) and for C. minus were supplemented by Podger and Wardlaw (Podger and Wardlaw, 1990). The P. psidii distribution data were from Kriticos et al. (Kriticos et al., 2013). There are gaps in pest distributions for some species, notably plantation pests that have recently become more of a problem such as Andoplognathus sp. and Lipatetrus sp., and species with a more localised distribution (e.g. Cyclaneusma minus). The records, particularly those from the Australian Plant Pest Database, have not necessarily come from plantations; hence the distributions are indicative only. Pest distribution maps, together with an overview of possible responses to changing climate, are presented in Appendix 1. Defining how pests damage their hosts Drivers of host susceptibility A range of pest species of Australian plantations target young, flushing foliage (e.g. Gonipterus sp.; Mnesampela privata; paropsis beetles; Uraba lugens; Kirramyces eucalypti; Teratosphaeria sp.) (Table 13). This foliage commonly has a higher ratio of leaf area:dry mass and lower leaf toughness than older foliage (Pinkard and Beadle, 1999; Steinbauer, 2001), and may have a higher nitrogen content making it more nutritious (Pinkard et al., 1998; O'Reilly-Wapstra et al., 2005). Trees growing on high productivity sites are likely to have a greater proportion of this type of foliage during the growing season. Hence high productivity sites may be more susceptible to these pests than lower productivity sites. 111

121 Similarly, trees may be more susceptible during times of rapid crown expansion such as during the peak growing period and following stand coppicing. Trees growing on high productivity sites may however recover more rapidly following damage due to a greater capacity for crown recovery than at lower productivity sites (Pinkard et al., 2006a; Pinkard et al., 2007). For example, fertilised E. globulus experienced more severe Teratosphaeria damage in north western Tasmania, but crown recovery was more rapid than that of unfertilised trees (Wardlaw et al., 2005). Table 12. The major pests of Australian softwood and hardwood plantations, the states in which they occur and their significance as damaging agents, as identified in a project steering committee workshop and via consultation with forest health experts. Pest Common name Regions where Host species Significance significant damage has been reported Leaf pests Chewers Anoplognathus spp Christmas beetles Vic, SA Eucalypts H, localised Gonipterus spp Eucalypt weevil All states Eucalypts H Heteronyx spp spring beetles, scarab beetles WA, Vic, SA Eucalypts H, regional Liparetrus spp spring beetles, scarab beetles WA, Vic, SA Eucalypts H, regional Mnesampela privata Autumn gum moth Vic, Tas, SA, WA Eucalypts H, localised Paropsis, Eucalypt beetles All states Eucalypts H Paropsisterna, Chrysomelid spp Uraba lugens Gum leaf skeletoniser WA, TAS, VIC, SA Eucalypts H, localised Suckers Creiis lituratus Psyllid NSW, Vic E. dunnii H, localised Essigella californica Monterey pine aphid NSW, Vic, SA,TAS Pines H Pathogens Cyclaneusma spp Spring needle cast Tas Pines H, localised Dothistrima Dothistroma needle blight Tas, Vic, SA Pines H, localised septosporum Kirramyces eucalypti Septoria leaf blight Tas, Vic, NSW Eucalypts H, localised Teratosphaeria spp Teratosphaeria/Mycosphaerella Tas, NSW, Vic, SA, Eucalypts H, localised (Mycosphaerella) leaf disease WA Puccinia psidii Eucalypt (myrtle) rust Qld, NSW, Vic Eucalypts Unknown Quambalaria spp Quambalaria shoot blight Qld, NSW Eucalypts H, localised Stem pests Endoxyla cinereus Giant wood moth NSW, Qld Eucalypts H, localised Ips grandicollis Five spined bark beetle NSW, Qld Pines H, localised Phorocantha spp, Eucalypt stem borer All states Eucalypts H, localised Sirex noctillio Sirex wood wasp NSW, Vic, Tas, SA, Qld Pines H Root pests Phytophthora cinnamomi Phytophthora root rot Tas, NSW, WA Eucalypts, pines Some pests, such as Teratosphaeria sp. and M. privata, target juvenile foliage and cause little damage to adult foliage. Hence the host is most susceptible prior to phase change (Jordan et al., 1999). 112 H

122 Host stress can increase susceptibility to damage from a range of pests. Trees stressed by waterlogging are more susceptible to Creiis lituratus attack, for example ((Carnegie and Angel, 2005; Stone et al., 2010), and take longer to recover following damage (Carnegie and Angel, 2005). In contrast, drought-stressed trees are less susceptible to this pest (Stone et al., 2010). Stressed hosts are also more susceptible to a range of stem pests. Drought-stressed trees may be susceptible to stem borers such as Phorocantha sp. (eucalypts) or Sirex noctilio and Ips grandicollis (pines). For example, drought predisposes E. globulus to Phorocantha mastersi damage (Pook and Forrester, 1984; Wardlaw and Bashford, 2007), although there is evidence that low water availability is less favourable for species such as P. solida in Queensland (Lawson, 2002) Climatic/edaphic and management drivers of susceptibility Climatic and edaphic drivers interact with host susceptibility. These drivers include relative humidity, soil wetness and the presence of overwintering sites for pests. Additionally, management activities may increase or reduce susceptibility. May and Carlyle (May and Carlyle, 2003) demonstrated that Essigella californica is favoured by low relative humidity conditions, with a strong negative relationship between relative humidity or rainfall and aphid numbers. Sites with dry autumns are particularly susceptible to E. californica. Recently thinned stands also are more susceptible, possibly because relative humidity declines in the short term in the canopy of thinned stands (May, 2004). In contrast, foliar pathogens are generally favoured by high relative humidity and associated increased leaf wetness duration (Margarey et al., 2005). Higher relative humidity can be associated with climatic characteristics such as high rainfall, warm temperatures, fogs and dew, or to stand characteristics such as high tree density or the presence of dense weed cover. Stand treatments such as coppicing may increase relative humidity and leaf wetness duration by increasing crown density, as well as promoting a flush of new foliage, thereby increasing plantation susceptibility to pathogens such as Teratosphaeria sp. and K. eucalypti. The presence of overwintering sites increases susceptibility to some pests. For example, the presence of slash can contribute to damage from I. grandicollis and S. noctilio (Bungey, 1966; Morgan, 1989). Proximity to Poa grasslands is an important driver of susceptibility of eucalypts to Paropsisterna bimaculata in Tasmania (Wardlaw et al., 2011). Biological control agents are used to manage populations of E. californica, S. noctilio and I. grandicollis. The pine aphid parasitoid Diaeretus essigellae was released in 2009 to control E. californica (Kimber et al., 2010). The nematode Beddingia siricidicola and the parasitic wasp Ibalia leucospoides are used for biological control of S. noctilio and I. grandicollis (Morgan, 1989; Collett and Elms, 2009). The effectiveness of these species as biological control agents is likely to be influenced by climatic and edaphic factors. 113

123 Effects of climate change on pest distribution and abundance Climate is an important element influencing the presence or absence of pests, but it does not by itself indicate where a pest will be found. Proximity to host plants (presence/absence), the host vigour, host defence mechanisms, proximity to overwintering sites, stand management practices (including pest control measures) and site productivity all will influence the distribution and abundance of pests. However it is undisputed that temperature, precipitation and relative humidity all influence pest species abundance and distribution (Sutherst et al., 2007a), and there are indications that higher atmospheric CO 2 concentrations also may influence abundance (Chakraborty and Datta, 2003; McElrone et al., 2005). Hence understanding climatic impacts on distribution and abundance is an important step in understanding future changes in pest hazard. There have been many reviews of the likely consequences of climate change for forest pests (e.g. (Ayres and Lombardero, 2000; Harrington et al., 2001; Whittaker, 2001; Chakraboty, 2005; Chakraborty et al., 2008), and here we provide a summary only of the key responses that might be expected. We focus on temperature, rainfall and storm events, and do not consider atmospheric CO 2 because the effects of elevated CO2 are largely unknown for the species in Table 1. Key climatic triggers affecting pest distribution and abundance Temperature is the major abiotic variable affecting the distribution and abundance of insects (Ayres and Lombardero, 2000). Species operate within an optimal temperature range, and temperatures higher or lower than this range influence a species persistence and vigour (Sutherst, 2003). The increasing mean temperatures projected for much of the plantation-growing region in Australia are likely to result in shifts in species distributions towards higher altitudes and latitudes (Pinkard et al., 2008). Warmer winter temperatures may increase overwintering survival of some insect pests (Ayres and Lombardero, 2000), thereby improving the capacity of species to survive at sites where their distribution is currently limited by low temperatures. However, reduced overwintering survival also may occur due to greater activity of predators. Warmer summer temperatures are likely to accelerate insect development and reproductive rates (Whittaker, 2001), thereby reducing the risk of larval predation (Bale et al., 2002). In multivoltine species warmer temperatures may result in an increased number of generations per year (Tobin et al., 2008), even when photoperiodic cues may be the primary drive of diapauses (Bale et al., 2002). Where rising temperatures improve growing conditions for both host and insect, the potential for increased pest development and reproduction is great. A predicted 2 C increase in temperature has the potential to result in between 1 5 additional generations per year, depending on the species (Harrington et al., 2001). Rainfall patterns also influence the distribution and abundance of some insect pests. For example, E. californica is favoured by dry autumns (May, 2004). Heavy rainfall events can wash these insects from their host, and high humidity associated with such events increases the risk of fungal attack (May, 2004). 114

124 Some insect species are attracted to stressed hosts. Psyllids, for example Cardiaspina sp, are attracted to drought stressed trees (Carnegie and Angel, 2005; Kirschbaum et al., 2007). Some stem borers are also favoured by drought conditions, where drought stress in the host increases susceptibility to damage (Wermelinger et al., 2008). An increase in the duration and frequency of droughts is likely to be beneficial to these species. In contrast, Creiis lituratus is attracted to waterlogged trees (Stone et al., 2010). Changes in rainfall patterns that result in periodic waterlogging of the soil may favour this species. The distribution and abundance of fungal pathogens is influenced by both temperature and precipitation patterns. In general, foliar pathogens are favoured by warm, moist conditions where high relative humidity is maintained during sporulation and spore germination (Margarey et al., 2005). A higher duration of favourable conditions for growth and reproduction increases the potential number of epidemic cycles per season. Warmer winters may increase the survival of fungal pathogens that experience population bottlenecks during overwintering, which may result in significant changes in the severity of disease (Burdon et al., 2006). On current trajectories, atmospheric CO 2 concentrations are projected to rise from the current 400 ppm to 500 ppm by 2050 (CSIRO 2010). Rising atmospheric CO 2 concentration has been shown to influence the abundance of some insects and foliar pathogens. While there is some evidence of direct leaf chewer responses to eco 2 (Stiling and Forkner, 2010), it is mainly the effects on the host that are transmitted to the insect. Herbivores that feed on phloem do however show increased development and reproduction in response to eco 2 (Whittaker, 2001). Elevated CO 2 is known to stimulate fungal pathogen growth rates, aggressiveness and fecundity (Jeger and Pautasso, 2008; McElrone et al., 2010), as well as sporulation in some species (Kobayashi et al., 2006). The effects of climate change on pest species are complex, and can differ between seasons and bioclimatic regions, making generalisations difficult (Robinet and Roques, 2010). Pest distribution and abundance will be influenced by the effects of climate change on the host, for example any host response that reduces the food quality of host tissues may slow insect development rates and reduce egg and egg batch size (Veteli et al., 2002). Changing temperature and rainfall patterns will influence both pests and their competitors and predators (Roy et al., 2004; Leech and Crick, 2007) meaning that changes in pest abundance may not be as anticipated. Climatic triggers for pests in Table 12 Current understanding of the effects of climate on the pests listed in Table 12 is summarised in Appendix 1 and in Table 14. Warmer temperatures may increase the number of generations per year of some of the species listed in Table 14, due to faster thermal accumulation rates and earlier diapause termination. However the success of additional generations will be influenced by the state of the host, for example whether it has appropriate foliage for the pest, the quality of the foliage present, and the activity and abundance of predators. If combined with high relative humidity, foliar pathogens may increase spore production, infection and growth. As well as additional generations per year, 115

125 increasing temperatures may reduce overwintering mortality for a range of plantation pest species. The effect of heatwaves on the pests listed in Table 14 is less clear. Increased mortality can be anticipated, but the upper temperature limits have not been documented for many species. High temperatures increase mortality of Gonipterus (Loch and Matsuki, 2010), and have been observed to result in later diapauses initiation and earlier termination in paropsis beetles (Nahrung, 2004; Nahrung and Allen, 2004). Soil temperatures above 18 C result in autumn rather than summer emergence of M. privata adults (Lukacs, 1999), and soil temperatures greater than 30 C can result in mortality of Anoplognathus sp. Drought reduces larval survival of Anoplognathus sp (Hassan 1975). It reduces the abundance of C. lituratus (Stone et al., 2010), but increases the abundance of E. californica if relative humidity is low (May, 2004). Drought is generally not favourable to pathogens that require high relative humidity for spore production and infection. It is likely to result in increased abundance of stem pests such as I. grandicollis and P. mastersi. An increasing frequency of storm events may favour leaf pathogens if leaf wetness duration is increased, resulting in increased rates of spore production and infection. However they may reduce the abundance of stem borers. Root pathogens such as P. cinnamomi may be favoured by increased storminess, if storm events result in increased soil wetting and drying cycles (Tregonning and Fagg, 1984). Despite its potential importance in determining both host condition and pest abundance, we did not examine effects of rising atmospheric CO 2 concentrations on the species in Table 12. There is insufficient understanding of the effects of eco 2 on these species. Future changes in climatic suitability for Australian plantation pests We examined how climatic suitability might change by 2030 and 2050 for four eucalypt pests and three pests of P. radiata, using a modelling approach. We had anticipated using these pests as examples of how similar groups of pests, or functional guilds, might respond to climate change. However, Ireland et al. (Ireland et al., 2011) cautions against this approach, arguing that the generic climatic requirements for functional groups may be difficult to define without examining individuals within these functional groups. Instead, our approach provides insights into the potential for range shifts of the seven species, the proportion of the plantation estate that is likely to fall into suitable or optimal climatic categories, and what these categories might mean in terms of frequency of epidemics. The model CLIMEX takes information about a species climatic requirements for growth and development, and using climate data calculates a weekly growth index (GI) which is analogous to instantaneous intrinsic population growth rate (Sutherst et al., 2007b). The weekly GI is integrated to an annual value and rescaled to a percentile scale. Persistence at a site is limited by a series of stress indices relating to the species tolerance to wet, dry, hot 116

126 Table 13. For the major pest species of Australia s plantations, the drivers of susceptibility, age at which the host is most susceptible, the main seasons of damage, the organs affected and (for defoliators) the pattern of damage. Pest species Drivers of susceptibility Susceptible Season of Affected organs Foliage targeted Defoliation References Host Climate/edaphic stage damage pattern Leaf pests Chewers Anoplognathus sp Moist soil All SPR, SU, AUT Leaves, shoots Juvenile, adult Entire crown (CSIRO, 2007) Gonipterus spp Young soft foliage; rapid host growth High site productivity All SPR, SU Leaves, shoots Juvenile, adult Top-down (Loch and Matsuki, 2010; Matsuki and Tovar, 2010a) Heteronyx spp New foliage growth Moist soil All SPR, SU, AUT* Leaves, shoots Juvenile, adult Top-down (Matsuki and Tovar, 2012; Walker and Allen, 2013) Liparetrus spp Mnesampela privata Paropsis, Paropsisterna, Chrysomelid spp Uraba lugens New foliage growth Young, soft foliage; rapid host growth Young, soft foliage; rapid host growth Young, soft foliage; rapid host growth Moist soil High site productivity High site productivity, proximity to Poa grasslands High site productivity Sap suckers Creiis lituratus Stressed host Intermittent waterlogging Essigella Older trees Low relative californica# humidity Pathogens Cyclaneusma minus Stressed host High relative humidity, slash Seedlings and trees <2 year old Seedlings and young trees SPR, SU, AUT Leaves, buds, shoots Juvenile Entire crown (Matsuki and Tovar, 2010b) SU, AUT Leaves Juvenile Bottom up (Loch and Floyd, 2001; Steinbauer and Matsuki, 2004) All SPR, SU, AUT Leaves, buds Juvenile, adult Top down Candy et al 1992; Nahrung 2004; Wardlaw et al 2011 All SPR, SU, AUT, WIN Leaves Juvenile, adult Bottom up (Phillips, 1992; Farr, 2002; Farr et al., 2004) All SPR, SU, AUT Leaves Juvenile Top-down (Carnegie and Angel, 2005; Stone et al., 2010) Post-canopy AUT, WIN, Needles 1 YO needles Bottom-up (May and Carlyle, 2003) closure SPR Post-canopy closure SPR, AUT Needles 1 YO needles; not current needles Entire crown SCION 2013 Dothistroma High relative All SPR, SU Needles Any age Bottom-up (Bradshaw, 2004) 117

127 Pest species Drivers of susceptibility Susceptible Season of Affected organs Foliage targeted Defoliation References Host Climate/edaphic stage damage pattern septosporum humidity, illuminated crown Kirramyces eucalypti Young, soft foliage; rapid host growth High relative humidity All SPR, SU, AUT Leaves Juvenile Top-down (Carnegie, 2007) Teratosphaeria spp (Mycosphaerella) Puccinia psidii^ Quambalaria spp Young, soft foliage; rapid host growth Young, soft foliage Young, soft foliage; rapid host growth High relative humidity High relative humidity High relative humidity Stem pests Endoxyla cinereus Stressed host Environmental stress; stem damage Ips grandicollis** Stressed host Environmental stress; stem damage; slash Phorocantha spp, Stressed host Environmental wood moths stress; stem damage Sirex noctillio** Stressed host Dense stands; stem damage; slash Root pests Phytophthora cinnamomi Stressed host Temperatures > 15 C; rainfall > 600 mm; soils with poor drainage Before phase change SPR, SU, AUT Leaves Juvenile Top-down (Park, 1988) Young SPR, SU, AUT Leaves, tips Juvenile Top-down (Glen et al., 2007; Kriticos et al., 2013) Pre-canopy SPR, SU Leaves, tips Juvenile Top-down (Pegg et al., 2009) closure Post-canopy closure Post-canopy closure Post-canopy closure Post-canopy closure SU, AUT Stem, roots - - Lawson 2002 SPR, SU, AUT Stem - - (Morgan, 1989) SU, AUT Stem - - (Pook and Forrester, 1984) SU, AUT, WIN Stem - - (Morgan, 1989; Carnegie et al., 2006) <2 year old SPR, SU, AUT Root - - (Shearer et al., 2007; Cahill et al., 2008) ^ P. psidii was first observed in Australia in 2010 and little is known of its pathogenicity towards plantation eucalypts (Glen et al., 2007) # Populations are controlled by an introduced biological control agent, Diaeretus essigellae (Kimber et al., 2010) *The season of activity is difficult to predict for Heteronyx and can vary within and between plantations, regions and year (Matsuki and Tovar, 2012) ** Populations are controlled by two introduced, biological control agents, the parasitoid wasps Roptrocerus xylophagorum and Dendrostoter sulcatus (Morgan, 1989) 118

128 or cold conditions and their interactions. The parameters for the stress functions are usually derived primarily from species presence data using inverse modelling techniques, though information from other knowledge domains can also be used to inform parameter selection. The relative suitability of a location for the species, the Ecoclimatic Index (EI), is then calculated as the product of GI and stress indices (Sutherst et al., 2007). Theoretically EI is scaled between 1 and 100, and the larger the EI, the more suitable the climate for the species, although in practice near perfect conditions are rarely encountered, and then only in highly stable climates. Long-term monthly averages of minimum and maximum temperature, rainfall and relative humidity are model inputs. Climate data The climate data used in the CLIMEX modelling are described in the Climate chapter. Modelling climatic suitability Climatic suitability was examined using CLIMEX. We used the Compare Locations function to calculate an annual index of climatic suitability, the EI. This reflects the combined potential for population growth during favourable periods and survival during stressful periods (Kriticos et al., 2013). We utilised previously published models for three pests of P. radiata: E. californica (Wharton and Kriticos, 2004), D. septosporum (Watt et al., 2009) and S. noctilio (Ireland et al., under review-a); and three common pests of plantation eucalypts: M. privata (Pinkard et al., 2008), Teratosphaeria spp (Pinkard et al., 2010a), U. lugens (Kriticos et al., 2007). We also included the recently-introduced eucalypt pest species P. psidii (Kriticos et al., 2013). While a parameter set exists for Phytophthora cinnamomi (Desprez-Loustau et al., 2007), the modelled distribution does not reflect the observed distribution within Australia, and further parameter development was beyond the scope of the project. It was beyond the scope of this project to develop CLIMEX parameter sets for other species. However it is likely that there is sufficient lifecycle and distribution data for the future development of parameter sets for Paropsis atomaria, Paropsisterna bimaculata, Gonipterus scutellatus, and P. cinnamomi. Climatic suitability for each pest was categorised as marginal, suitable or optimal based on published EIs (Table 15). For Teratosphaeria these categories were developed from the relationship between field-based damage and EI (Pinkard et al., 2010a), and for E. californica the categories were developed using population dynamics modelling (Pinkard et al., 2008). The levels of damage associated with the EI categories for these species are given in Table 5. The EI classifications for the remaining species were more arbitrary, based on general understanding of species persistence as defined by the developers of the parameter sets. While basing the categories on field observations of damage is preferable, it is difficult to develop clear relationships, often because the quality of health surveillance data is not sufficient, or because management interventions have masked damage levels (Ireland et al 20143). 119

129 Projected changes in pest distribution Table 16 shows the area (ha) of eucalypt and pine estates falling into the four climatic suitability classes for each pest, and how this is projected to change in the future. Currently, a large proportion of the eucalypt estate has suitable or optimal climatic conditions for M. privata (85%). Only 34% of the estate currently has suitable or optimal climatic conditions for Teratosphaeria sp; and the bulk of the estate is currently classed as unsuitable for P. psidii and U. lugens. Much of the pine estate currently has suitable or optimal climatic conditions for D. septosporum and S. noctilio, whereas most of the estate does not have suitable or optimal climate for E. californica. The models projected an increase in the proportion of the estate unsuitable for M. privata, and a decrease in the proportion of the estate in the optimal class (CSIRO model only). A shift from unsuitable to marginal and suitable classes is projected for Teratosphaeria. An increase in the proportion of the estate in the suitable class is projected for P. psidii (from 15 20%). It was projected that for U. lugens climatic suitability would decline, with large decreases in the proportion of the estate in the suitable and optimal classes. Climatic conditions for D. septopsorum are projected to become less favourable in the future, with a decline in the proportion of the estate falling into the optimal class, and an increase in the proportion in the suitable class (CSIRO model only). For E. californica, a shift from unsuitable to marginal classes is projected, particularly in A decrease in the proportion of the estate in suitable, and an increase in the proportion of the estate in optimal climate classes in projected for S. noctilio (Miroc model only). These projected changes in climatic suitability are in general small, and suggest that we do not need to plan for major changes in climatic suitability for the species examined. It would be useful to expand the analysis to include more of the pest species in Table 1, although this was beyond the scope of this project. Frequency of outbreaks and severity of damage The potential frequency of outbreaks of two pest species are summarised in Climate is only one element of pest hazard, but it can play an important role in defining species distribution and abundance. We have demonstrated there is potential for pest hazard to increase due to climate change, with pest range changes, and an increase in areas with suitable climate and hence an increase in potential frequency of outbreaks. We know little however of the implications of climate change for predators or beneficial organisms of pest species. We also have limited understanding of the effects of climate change on host susceptibility. The future analysis of impacts of climate change on host productivity will help to address this question. Our results provide some indication of how pests might benefit from changing climate, areas where pest monitoring may be most advantageous now and in the future, and how pest ranges might change as a result of climate change. Table 17 (Pinkard et al., 2008). For Teratosphaeria the Compare Years function in CLIMEX was used to examine inter-year variation in EI over a 30 year period ( ), and the 120

130 proportion of years falling into marginal, suitable or optimal categories was determined using the approach of Pinkard et al. (2010a). Likely severity of damage associated with the different climate suitability classes was identified through expert elicitation. For E. californica, the population dynamics model DYMEX (Maywald et al., 2007) was used to examine pest abundance over a 10-year period centred on 1975, and the proportion of years with outbreak numbers was calculated (Pinkard et al., 2008). Potential damage associated with the climate suitability classes also was calculated using Dymex. 121

131 Table 14. Possible responses of key pests of Australian plantations to climate changes including warmer mean temperatures and increasing frequency and intensity of droughts, heatwaves and storm events. Indications of host attributes required for pests to respond to climate triggers are provided. Pest Common name Potential response to climate change Host attributes influencing climate change Warmer temp Heatwaves Drought Storm events Other responses Leaf pests Chewers Anoplognathus sp Christmas beetles activity mortality if soil temp > 30 C Gonipterus spp, Eucalypt weevil gens/yr mortality Heteronyx sp spring beetles, activity scarab beetles Liparetrus spp spring beetles, activity scarab beetles Mnesampela privata Autumn gum moth gens/yr mortality Paropsis, Paropsisterna, Chrysomelid spp Eucalypt beetles gens/yr mortality Uraba lugens Gum leaf skeletoniser gens/yr mortality Suckers Creiis lituratus Psyllid gens/yr mortality Essigella californica Pine aphid gens/yr mortality mortality autumn adult emergence* mortality -Earlier diapause termination -Later diapause initiation mortality larval survival and emergence 122 Wet soil a trigger for adult emergence mortality abundance abundance if host is waterlogged abundance if low RH abundance due to heavy Windiness reduces activity -requires newly-flushing foliage (Loch and Matsuki, 2010) -requires active growing hosts (de Little, 1983) -insect attracted to decreased host vigour? (Farr et al., 2004) -attracted to stressed host plants (Stone et al., 2010) -older stand age; recent thinning may increase abundance due to lower RH (May,

132 Pest Common name Potential response to climate change Host attributes influencing climate change Warmer temp Heatwaves Drought Storm events Other responses rain 2004) Pathogens Cyclaneusma spp Spring needle cast Dothistrima septosporum Dothistroma needle blight spores and growth if also higher RH spores and growth spores and growth if higher RH Kirramyces eucalypti Septoria leaf blight spores and growth if also higher RH Teratosphaeria spp (Mycosphaerella) Teratosphaeria/Myco sphaerella leaf disease spores and growth if also higher RH Puccinia psidii Eucalypt (myrtle) rust spores and growth if also higher RH for >8 h Quambalaria spp Quambalaria shoot blight spores and growth if also higher RH for >8 h spores and growth spores and growth spores and growth spores and growth 123 spores and growth if higher RH spores and growth if higher RH spores and growth if higher RH for > 8h spores and growth if higher RH for > 8h e.g. increased summer rainfall Low light for at least 6 h -generally found on juvenile foliage (Carnegie and Ades, 2002) Stem pests Endoxyla cinereus Giant wood moth abundance abundance Ips grandicollis Bark beetle gens/yr mortality abundance abundance Phorocantha spp, wood moths Phorocantha stem borer abundance abundance -May be attracted to drought-stressed hosts (Wardlaw and Bashford, 2007), or not (Lawson, 2002) Sirex noctillio Sirex wood wasp gens/yr mortality abundance abundance Biological control agent has similar requirements -attracted to drought-stressed hosts (Madden, 1981)

133 Pest Common name Potential response to climate change Host attributes influencing climate change Warmer temp Heatwaves Drought Storm events Other responses Root pests Phytophthora cinnamomi Phytophthora root rot lifecycles per season sporulation and growth lifecycles per season -increased temperatures and water stress may disrupt genetic controllers of resistance in the host (Desprez-Loustau et al., 2006) *timing of adult emergence linked to soil temperatures of 18 C (Lukacs, 1999) 124

134 Table 15. The Ecoclimatic Index ranges for marginal, suitable and optimal climatic suitability for four eucalypt and three pine pests in Australia. Species Climatic suitability based on Ecoclimatic Index (EI) Reference Marginal Suitable Optimal Eucalypts Mnesampela privata >25 (Pinkard et al., 2008) Puccinia psidii >45 (Kriticos et al., 2013) Uraba lugens >20 (Kriticos et al., 2007) Mycosphaerella spp Pines Dothistroma septosporum Essigella califormica >38 (Pinkard et al., 2010a) >25 (Watt et al., 2009) >69 (Wharton and Kriticos, 2004) Sirex noctilio >20 (Ireland et al., Under review-b) It was predicted that for sites with marginal climatic suitability, outbreaks would occur in 20% of years for Teratosphaeria, and 30% of years for E. californica. For sites with suitable climate, outbreaks would occur in 60% of years for Teratosphaeria and E. californica. For sites with optimal climate suitability, outbreaks were predicted to occur in 80% of years for Teratosphaeria and 100% of years for E. californica. The severity of damage in these outbreaks would be low (i.e. below 30%) for sites with marginal climatic suitability. At sites with suitable climate, E. californica damage severity would range between 10 and 50% of the crown affected, and Teratosphaeria damage would vary between 30 and 60% of the crown affected. Where climate was optimal it was predicted that >50% of the crown would be damaged by E. californica and >60% of the crown would be damaged by Teratosphaeria. These levels of damage are known to reduce stand productivity (May and Carlyle, 2003; Pinkard et al., 2006b). Discussion The pests that are most active in Australia s plantations have a range of modes of action. Some target young plantations while others target older plantations and yet others may be active throughout a rotation. They vary in the time of year they are active and in the pattern of damage they impart. We have highlighted the damage traits for key pest species, and the climatic, edaphic and host triggers of susceptibility to these pests. 125

135 Table 16. The area of the eucalypt and pine estate currently falling into four climatic suitability classes for eucalypt and pine pests, and the percentage change in area in each class projected for 2030 and CLIMEX data were used for the analysis generated using two climate models (CSIRO 3.0 and Miroc H). Red denotes >15% increases, and blue denotes >15% decreases compared to current climate. Pest species Climatic Area of % of estate in each class suitability estate in Current class each class (ha) CSIRO 3.0 Miroc-H CSIRO 3.0 Miroc-H Eucalypt pests M. privata Unsuitable Marginal Suitable Optimal Teratosphaeria Unsuitable Marginal Suitable Optimal P. psidii Unsuitable Marginal Suitable Optimal U. lugens Unsuitable Marginal Suitable Optimal Total eucalypt estate Pine pests D. septosporum Unsuitable Marginal Suitable Optimal E. californica Unsuitable Marginal Suitable Optimal S. noctilio Unsuitable Marginal Suitable Optimal Total pine estate Climate is only one element of pest hazard, but it can play an important role in defining species distribution and abundance. We have demonstrated there is potential for pest hazard to increase due to climate change, with pest range changes, and an increase in areas with suitable climate and hence an increase in potential frequency of outbreaks. We know little however of the implications of climate change for predators or beneficial organisms of pest species. We also have limited understanding of the effects of climate change on host susceptibility. The future analysis of impacts of climate change on host productivity will help to address this question. 126

136 Our results provide some indication of how pests might benefit from changing climate, areas where pest monitoring may be most advantageous now and in the future, and how pest ranges might change as a result of climate change. Table 17. Relationship between site suitability for a given pest, the anticipated severity of damage associated with each severity rating, and the anticipated proportion of years in which outbreaks would occur. Proportion of years with outbreaks was determined using the population model Dymex (E. californica) or CLIMEX (Tertosphaeria); the anticipated severity of damage was determined using Dymex (E. californica) or through consultation with forest health experts (Teratosphaeria). Pest species Severity rating EI Anticipated severity of damage Frequency (% of years in rotation) Essigella californica Marginal % 30 Suitable % 60 Optimal >69 >50% 100 Teratosphaeria sp Marginal % 20 Suitable % 60 Optimal >38 >60% 80 The results of this study are indicative only. There is uncertainty in the outcomes of the modelling work based on uncertainty embedded in the models, uncertainty about how the climate will change, and uncertainty about how pests and their natural and introduced enemies will respond to progressive climate change. It is impossible to avoid large uncertainties in studies such as these. Some of the uncertainties can be reduced, for example by further research into climatic responses of pest species, by avoiding climate projections too far into the future so that we have more certainty of outcome, and by implementing and maintaining forest health surveillance to help validate the results presented above. Ongoing monitoring is a critical part of both understanding and managing pest hazard into the future. Australia has seen a contraction of forest health expertise in recent years. The results of this study suggest that retaining this expertise will become increasingly important in the future, as pest ranges change and the proportion of the plantation estate providing suitable or optimal climatic conditions for pest increases. Implications for modelling pest damage impacts While it was not possible to perform the CLIMEX climatic suitability analyses based on functional guilds, we concluded that it is possible to determine damage regimes based on damage characteristics described in Table 12. This model has the capacity to examine pest impacts dynamically (Pinkard et al., 2011). However it can only deal with defoliation and not with stem or root damage. The model inputs for examining defoliation impacts are simple, and revolve around the year/s of defoliation and the percentage of crown lost in six crown positions (lower, middle or upper third; outer or inner crown). Based on the above discussion, key elements to capture in defoliation regimes include: 127

137 Early age defoliation only (to capture the effects of species such as Teratosphaeria and M. privata that target juvenile foliage) Later-age defoliation only (to capture the effects of species such as E. californica that target older plantations A combination of early and later-age defoliation (i.e. chronic defoliation) that allows for the potential of different species defoliating at different ages Spring versus autumn defoliation because some pests cause significant damage in autumn as well as spring (e.g. eucalypt beetles) Bottom-up versus top-down defoliation, recognising that different pest species target different foliage age classes Outer versus inner-crown defoliation, again recognising that some pest species favour different foliage age classes 128

138 Adaptation to climate change Historical responses to climatic variability Role of decision support systems in climate change adaptation Using DSS to explore adaptation strategies 129

139 130

140 Chapter 9 A history of management responses to climatic variability in the Australian forest industry Summary Climate change presents both a threat and an opportunity for the forest industry, and forward planning to minimise the threats and take advantage of opportunities will help maintain the viability of the industry and the communities it supports, into the future. Capacity to respond to climatic variability in Australian plantations currently rests on a strong legacy of past forest management research and substantial investment in understanding how forests respond to environmental conditions. The techniques available to plantation managers for maximising production and minimising risk in a changing climate may have been developed already in response to previous climatic variability, or as a consequence of plantation expansion into new areas with different sets of climatic challenges. While climatic changes remain within the bounds of climate experienced historically, the past will provide an indication of management that will work in the future. If climatic conditions move outside that range, more extreme measures may be required for which there is less or no historical precedent. The strategies used historically by the forest industry revolve around local, on-the-ground activities. Responding to more extreme climatic changes will require policy and infrastructure support and greater planning at regional and national levels. 131

141 Introduction The plantation species in Australia can grow under a range of environmental conditions (Booth et al., 2008) For example, the Atlas of Living Australia indicates the annual rainfall range for Pinus radiata (P. radiata) sites in Australia is between about 350 and 3200 mm rainfall with 90% of the major commercial plantings are between mm rainfall (Booth and McMurtrie, 1988), and a range of mean annual temperatures from around 8 to 20 C (Battaglia et al., 2009a). Projected changes in mean annual temperature and rainfall for the first half of this century are not considered to pose threats to the productivity of species such as P. radiata and Eucalyptus globulus within the majority of their present plantation extent (Booth et al., 2008; Battaglia et al., 2009a). Trees have a great capacity to adjust to gradual changes in climatic conditions, as illustrated by seasonal temperature and water stress acclimation responses (Battaglia et al., 1996; Chaves et al., 2003). Incremental changes in climate may have few negative effects on tree growth and survival until thresholds are reached above which further acclimation is limited, and in some locations are projected to increase productivity (see Chapter 3). Forests, however, are likely to be sensitive to extreme climatic events that disrupt plant function. In particular, the increasing frequency of drought and heatwave events projected for many regions (IPCC, 2011) may reduce productivity and increase mortality (Chapter 3), an impact that is already being observed in parts of the world s forests (Allen et al., 2009). Drought, storms and heatwaves have affected Australia s plantations in the past, and changes in management practices have accommodated these events (e.g. (Butcher, 1977). These past management responses to damaging events may provide insights into the strategies for the future. Recent, large-scale Eucalyptus globulus plantation development into new regions closer to the ecological limits of those species, for instance, in dry inland regions of Western Australia, provides an opportunity to examine a range of strategies that have been used to manage damage from frost, drought and high temperatures. While not developed as a direct response to climate change, these strategies can provide good insights into management options to deal with an increasingly variable climate. In this review we examine some historical responses to unfavourable climatic conditions in Australian plantation forestry, and their role as possible future adaptation strategies for dealing with the consequences of a changing and more variable climate. Recognising the role of experience held within agencies as well as researcher insight, published and grey literature has been accessed, as well as expert elicitation (as personal communications). Forest industry responses to recent climatic trends Industry responses to past climatic variability have in many instances been successful in maintaining a productive and viable plantation estate in Australia. As we show below, the responses have generally been incremental, involving small changes in existing management practices, but in some cases more extreme responses, such as changing species or location, have been applied. Here, we examine the range of strategies implemented. 132

142 Drought and water availability As early as the 1960s in Western Australia, tree deaths due to drought were documented in Pinus radiata and P. pinaster plantations (Butcher, 1976). These occurred primarily at sites with shallow soils, and when stands were close to canopy closure, and on all tree size classes. Mortality has also occurred in P. radiata plantations in other Australian states, although drought effects are confounded with other factors such as pest damage. For example, in South Australia mortality was observed in response to dry conditions, on shallow soils, but it was difficult to separate direct drought effects from the secondary effects of damage from insects (Morgan, 1989). Similarly in P. radiata plantations in southern New South Wales, the interaction of drought and insect damage resulted in mortality of older P. radiata stands (Stone et al., 2012). Eucalypt plantations have a shorter history in Australia, with the bulk of expansion occurring over the last 30 years (ABARES, 2012). Large-scale drought mortality events in eucalypt stands have occurred from the early 1990s (e.g. (Dutkowski, 1995; Mendham et al., 2007; White et al., 2009), reflecting both changes in rainfall patterns and expansion of the estate into regions closer to the margins of climatic suitability for the species. However mortality events are not confined to dry environments, but also occur in regions with high annual rainfall (Mitchell et al., in press, Tim Wardlaw, pers. comm.). At the other extreme is the issue of waterlogging, which reduces stem and root growth in E. globulus and P. radiata (Hollingsworth et al., 1994; Marcar et al., 2002), and in extreme cases kills trees. In E. globulus and E. nitens plantations, for example, waterlogging reduced stand growth by between 30 and 50% compared with well drained stands (Mummery and Battaglia, 2002) Plant responses to waterlogging may predispose them to damage from insects and fungi. For example, waterlogged Eucalyptus dunnii in eastern Australia showed increased susceptibility to the lerp Creiis lituratus (Stone et al., 2010). The following summarises the range of historical responses to variable water availability in the plantations Site preparation Fallowing before planting improves soil water stores and promotes seedling survival and early growth. Longer-term benefits are noted, particularly for successive crops were incomplete soil water recharge between rotations leads to declining productivity (Mendham et al., 2011a). This approach is new and is being considered in E. globulus plantations in Western Australia. A range of other factors will be involved in the decision-making. Depending on the method and timing of site preparation, the delay in re-establishment can variously affect weed competition and fire risk from slash from earlier rotations. Any delay in planting over a year also represents an opportunity cost in foregone production. While investment in establishment is deferred and these costs are not compounded, site productivity is effectively decreased by the ratio of fallow period to rotation length and return on investment of any land investment is decreased 133

143 (Mendham et al, 2011). Notwithstanding any impact of wood quality, stand heterogeneity or increased risk from pests, simple analysis would suggest that the additional production from the rotation resulting from the fallow needs to offset the foregone production of the fallow year. It is common practice in many regions of Australia to mound sites prone to waterlogging (Neilsen, 1990a). The practice is believed to improve establishment and early growth, however during dry seasons this practice can leave seedlings exposed to drier soil conditions resulting in reduced survival and early growth (Sandra Hetherington, pers. comm.). Planting seedlings on large mounds can also have negative impacts on windy sites. To reduce this, planting has been directed to the lee side of the mound (Sandra Hetherington, pers. comm.). An alternative approach is to avoid establishing plantations on sites prone to waterlogging (Stone et al., 2010). In far north eastern Australia engineering solutions to divert water have been used to avoid inundation deaths (Andrew Callister, pers. comm.). Nursery management and early establishment Although it has been demonstrated experimentally that drought-hardening in the nursery can enhance survival of eucalypt seedlings (Guarnaschelli et al., 2003), it is not a common operational practice for eucalypts. The method generally involves removing a portion of the stem and leaves to reduce evapotranspiration and improve the root:shoot ratio, but there is the potential for negative effects on tree form and survival. This method of drought hardening is however practised with P. radiata in some regions, where the shoot is reduced in size by around one third. Root wrenching to improve hardening via changes in the shoot:root ratio has been perfected for radiata pine and subtropical pines over decades (Sadanandan Nambiar, pers. comm.). Nutritional hardening in the nursery (Close and Beadle, 2003), sometimes practised for frost and browsing resistance, has proven very successful in southern Australia in imparting drought resistance to E. globulus and Eucalyptus nitens seedlings planted during dry spring months (Ian Ravenwood, pers. comm.). This approach has been applied in the field for over 40 years and has extended the planting window into drier months. Evapotranspiration can be reduced through the use of anti-transpirants (Hodges et al., 2006). In the early 1990s anti-transpirants were tested in Tasmania with open-rooted eucalypt seedlings (Sandra Hetherington, pers. comm.). While initial results looked promising, after a six week period considerable mortality was observed in plants treated with anti-transpirant spray. It was hypothesised that the anti-transpirant blocked stomata, resulting in plant death (Sandra Hetherington, pers. comm.). Water retentive gels have been used extensively in some plantation estates in north eastern and Western Australia over a 10 year period to extend planting into times with less favourable conditions (Andy Wright, Andrew Callister, pers. comm.). This is particularly important in north eastern Australia where the summer wet season poses major site access issues and planting just prior to the onset of the wet season is preferable. 134

144 In South Australia and south eastern Queensland, seedling type was found to influence early survival rates, particularly if planting occurred outside optimal planting times (ForestrySA., 2010). Containerised seedlings or cuttings had much higher survival rates than open-rooted seedlings. In regions such as south eastern Queensland, sporadic rainfall means that planting can occur in any month, increasing the importance of using containerised stock (Ian Last, pers. comm.). Weed control is necessary in many regions to promote early establishment and survival. In drier regions in southern Australia weed control continues for longer than in higher rainfall zones, in recognition that weeds are strong competitors for site resources including water (Baker et al., 1988). Weed control for up to three years is recommended for drier sites in South Australia (Pinkard and Bruce, 2011). In contrast, the intensity of early age weed control in Queensland has reduced in recent years in response to (a) lack of long-term growth responses to intensive early age treatments and (b) to better meet forest certification objectives for reduced chemical use (Ian Last, pers. comm.). Fallowing is used for weed control, which can also improve soil water stores. A fallow period of 12 months is used to encourage weed germination, especially pine wilding germination, prior to second rotation plantation establishment. Subsequent management through herbicide application is targeted at specific emerging weed species prior to planting (Sandra Hetherington, pers. comm.). In recent years controlled release fertilisers rather than broad scale fertiliser application have been more commonly used during establishment in Australian plantations (Ian Ravenwood, pers. comm.). These are prilled (i.e. pelletized) soluble fertilisers encapsulated in a semi-permeable resin coating, that allows the prill of fertiliser to absorb water vapour slowly through the membrane and release the fertiliser over time into the soil solution. The main benefit of these fertilisers is that prolonged contact between the roots and fertiliser is facilitated, resulting in rapid early growth rates with prolific root development. This in turn increases the volume of soil that can be explored for soil moisture, resulting in improved survival. However, rapid early growth rates and concomitant rapid increases in leaf area index may increase drought risk, meaning that careful management of fertiliser applications is required (White et al., 2009). If rainfall during the planting season becomes more variable or decreases, these types of methods, that reduce evapotranspiration, increase root:shoot ratios, reduce disturbance to roots, or reduce competition for water and nutrients, may provide a means of enhancing survival and early growth. Post-establishment stand management The pine plantation industry in Western Australia responded to drought mortality in the 1970s by introducing pre-commercial thinning as a way of managing drought risk (Butcher, 1976, 1977). This involved reduction of an initial stocking of stems/ha, to 740 stems/ha at age 5 6 years, with a further reduction to 200 stems/ha at age 11 years for P. radiata and 247 stems/ha at age 14 years for the slower-growing P. pinaster. 135

145 The introduction of a pulp market for P. radiata thinnings in southern Australia in the 1990s (Neilsen and Pinkard, 2003) changed the economics of pre-commercial thinning and it is now a less common practice. In P. radiata plantations growing in the Hume region of New South Wales, management of recent drought mortality instead revolves around commercial thinning before a critical age of 18 on high quality sites and 16 on low quality sites, and confining new plantations to elevations higher than 600 m ASL (Stone et al., 2012). An empirical drought hazard analysis of data from the Hume region (Stone et al., 2012) suggested that these management strategies would reduce risk of drought mortality without the need for drastic changes to stand management practices. Sites assessed with high risk of drought mortality where thinning age and elevation strategies cannot be met are unlikely to be established as plantations in the future in this region. P. radiata grown on dry sites in parts of Tasmania are thinned 1 2 years earlier than wet sites. This reduces the effects of moisture stress and also reduces the incidence of damage from pest species such as Essigella californica and Sirex noctilio (Sandra Hetherington, pers. comm.). White et al. (2009) demonstrated for a range of E. globulus sites in Western Australia that pre-commercial thinning from 1200 to 600 stems per hectare when stands were around 6 m in height provided protection against drought mortality with little loss of production over a 10 year rotation. Thinning is not routinely carried out in short rotation eucalypt plantations. However, in response to the results of White et al. (2009) parts of the Western Australian and Queensland industry adopted a lower initial stocking rate at the drier end of the estate, from 1200 stems/ha to 1000 or 800 stems/ha, based on a guide that 800 mm of annual rainfall can support approximately 800 stems/ha (Don White, pers. comm.). In long-rotation sites in south eastern Queensland, early age thinning is practised to manage drought risk (Ian Last, pers. comm.). There are clear indications from the industry s past experience that drought impacts in established pine and eucalypt plantations, both in terms of productivity and survival, can be managed through thinning, spacing and judicious use of fertilisers. These are all treatments that manipulate leaf area index. There are now modelling tools available to assist plantation mangers to identify sites at high risk of drought mortality (Mendham et al., 2011a; White et al., 2011a; Stone et al., 2012), where altered spacing, thinning and fertilising may improve hazard profiles. Species selection In order to grow plantations in drier environments in Western Australia, the industry has expanded its suite of species from P. radiata and E. globulus, to include P. pinaster and more recently E. smithii. While P. pinaster is more resistant to drought than P. radiata (Warren and Adams, 2000), there is little evidence thus far that E. smithii is more drought resistant than E. globulus (Mitchell et al., 2012), highlighting the need for detailed study before species changes are made. 136

146 Table 18. Summary of alternative species either deployed in the field in, or identified as having suitable traits for, drought-prone sites Climatic zone Plantation type Alternative species Reference Temperate Softwood P. pinaster Hardwood E. smithii E. cladocalyx (Bush et al., 2009) E. occidentalis C. maculata Subtropical Softwood P. brutia (Dieters, 2008) P. halapensis P. pinaster P. ayacahuite P. canariensis P. eldarica P. greggii P. leiophylla P. maximinoi P. pinea P. taeda Hardwood C. citriodora subsp. variegata E. longirostrata E. dunnii E. grandis E. argophloia C. citriodora subsp. citriodora (Lee et al., 2011) Within-species variation in drought tolerance has also been exploited. At least two E. globulus breeding programs have selected for drought tolerance, resulting in development and deployment of drought tolerant provenances (Dutkowski and Potts, 2012); Andrew Callister, pers. comm.). There have been a number of studies aimed at identifying tree species and products suitable for plantations in drier parts of Australia (Table 18). The Australian Low Rainfall Tree Improvement Group (ALRTIG) trials (Bush et al., 2009) summarised results from 30 breeding trials established across the mm rainfall zone of southern Australia. The study concluded that Eucalyptus cladocalyx (sugar gum), E. occidentalis (swamp yate) and Corymbia maculata (spotted gum) had potential for commercial wood products, although wood properties and growth rates differed from those of the current suite of commercial plantation species. This study has contributed to the commercialisation of E. cladocalyx and C. maculata in southern Australia, particularly in 137

147 areas of Western Australia and Victoria, although planting areas are not large (Andrew Callister, pers. comm.). (Lee et al., 2011) similarly recommended hardwood species that perform well across a range of climatic conditions in north-eastern Australia, including Corymbia citriodora ssp variegata, E. longirostrata, E. dunnii, E. grandis. E. argophloia and C. citriodora ssp citriodora. In Queensland, C. citriodora var variegata and E. argophloia now form the basis of the hardwood estate, because of their tolerance to drought and frost (Ian Last, per s comm.). (Dieters, 2008) assessed pine species suitable for low rainfall zones in Australia, concluding that there were at least eight Pinus species in addition to Pinus brutia, P. halepensis and P. pinaster that could be developed for low rainfall environments (Table 18). While there are some exceptions, the outcomes of these studies have resulted in few species changes in response to climate change in commercial plantations, and further analysis of climatic and edaphic drivers of growth for these species is necessary to determine how they might respond to changing rainfall patterns (Dieters, 2008; Lee et al., 2011). Changing species is not a straightforward adaptation strategy and may require the development of new markets, different processing methods, new silvicultural management techniques, and tree improvement programs. Such changes will result in a time lag for deployment, and considerable expense compared with utilising the existing species mix. Continued reliance on the present suite of plantation species for existing markets means that managing drought risk or rainfall variability will continue to rely on silvicultural and genetic approaches, as is the case in agriculture. Pest management Water availability is important in pest management because pests such as Sirex noctillio (Sirex wood wasp), Ips grandicollis (Ips bark beetle), Hylastes ater (Pine bark beetle) and Phoracantha spp (Eucalypt borer) are attracted to trees experiencing stress including drought-stress (Bungey, 1966; Morgan, 1989; Wardlaw and Bashford, 2007). Sirex and Ips have been a pest problem in P. radiata plantations for several decades in Australia (Bungey, 1966) and management strategies including biological control are well developed, although its impact on coastal sub-tropical plantations (where both climate and species mix differ) is currently unknown. Phoracantha has emerged as a pest in eucalypt plantations grown on longer rotations, with incursions coinciding with host water stress at around the time of thinning (Wardlaw and Bashford, 2007). Invasion of this species occurs via bark injury (e.g. cracks due to drought) or existing stem damage (Pook and Forrester, 1984). There is some evidence that the pine aphid, Essigella californica, is also attracted to drought-stressed hosts (May, 2004). This species has a widespread distribution in eastern Australian P. radiata plantations (Carver and Kent, 2000). Historically, Sirex has been managed through strategic thinning of stands to reduce drought stress (Morgan 1989); improved stand hygiene through slash management; and biological control (Morgan 1989). The nematode Beddingia siricidicola and the parasitic wasp Ibalia leucospoides have been the most effective of a range of species trialled for 138

148 biological control of Sirex (Collett and Elms, 2009). In some P. radiata regions the effectiveness of the Sirex biological control program was observed to be in decline due to the presence of a defective strain of B. siricidicola. This has been addressed by replacement with a more virulent strain called the Kamona strain. The stand management strategies developed for Sirex are also effective for Ips (Morgan 1989). Hylastes damage in second rotation P. radiata plantations in Tasmania has been reduced by slash management and monitoring (Griggs, 1998). A biological control agent, Diaeretus essigellae, was introduced into Australia in 2009 to reduce the population of Essigella. It is too soon to determine the effectiveness of this program. The effects of a changing and more variable climate on the efficacy of pest biological control agents are largely unknown. Given the strong reliance on these agents for control of some serious plantation pests, ongoing monitoring and potentially further research are warranted. The recent incursion into Australia by eucalypt rust (Puccinia psidii) highlights the vulnerability of Australia s vegetation to new pest species (Booth and Jovanovic 2012), and makes ongoing monitoring and quarantine barriers even more important. Summary The historical industry responses to drought and waterlogging described above provide an indication of the sorts of management strategies that may reduce the impact of water availability on seedling establishment, stand productivity and survival (Table 19). These historical approaches have largely focused on ways to manage leaf area index and therefore water stress as well as physically avoiding areas with excessive amounts of water. These strategies have been apparently successful, but there is little documentation of where the strategies may have been applied unsuccessfully. Documenting where and when these strategies have been successful (or not) will advance our understanding of appropriate approaches for managing water availability, and provide a basis for cost benefit analyses. If, as is anticipated, drought frequency, duration and intensity increase in the future, the industry may benefit from drought hazard assessment at a regional or local scale. Examples of assessment approaches are given by Stone et al. ((Stone et al., 2012) and White et al. (White et al., 2012). Site hazard assessment will help to identify sites where it may be strategic to establish alternative species, and this assessment also could be used to explore the capacity of stand silviculture, as opposed to species change, to manage drought risk. Further consideration of alternative species may be necessary for sites that currently are more marginal in terms of the rainfall and temperature requirements of the current suite of plantation species. It is likely that there is already a sufficient suite of trials in place to inform alternative species choices, although ongoing monitoring and management and periodic data analysis is required to make best use of these trials. 139

149 Ongoing monitoring of pest populations and the effectiveness of management is warranted to identify the effects of climate. In addition, the effects of changing and more variable climate on the efficacy of biological control agents are largely unknown. Table 19. Summary of historical responses of the Australian forest industry to water availability Motivation for action Improved establishment survival Avoid stand productivity decline, mortality Reduce pest damage to stressed trees Historical response Species References Harden seedlings in nursery by increasing root:shoot Plant containerised rather than openrooted seedlings Weed control up to 3 years post establishment in drier environments, including fallowing Plant on mounds in waterlogged sites Pre-commercial thinning Reduced initial stocking Manage soil fertility to manipulate leaf area index Species/genotype selection for drought resistance Monitoring programs P. radiata (ForestrySA, 2006) P. radiata (ForestrySA., 2010) P. radiata, E. globulus (Pinkard and Bruce, 2011) P. radiata, E. globulus P. radiata, P. pinaster (Butcher, 1976) E. globulus (White et al., 2009) E. globulus (White et al., 2009) P. pinaster, E. smithii (Butcher, 1977) Sirex noctillio, Ips grandicollis, Phorocantha mastersi, Essigella californica, (Bungey, 1966; Morgan, 1989; Carnegie et al., 2006) Biological control Sirex, Essigella (Collett and Elms, 2009; Glatz et al., 2010) Given the strong reliance on these agents for control of some serious pests, ongoing monitoring and potentially further research are warranted. The recent incursion into Australia by eucalypt rust (Puccinia psidii), its rapid spread from NSW to Queensland and Victoria with an exponential host expansion, highlights the possible vulnerability of Australia s vegetation to new pest species (Booth and Jovanovic 2012). Frosts While there is an historical and predicted future trend towards a reduced number of frost days per year, the incidence of early or late season frosts may increase in the future (Steffen, 2009). Frost inhibits rates of photosynthesis through damage to photosynthetic enzymes and photoinhibition, it can damage tissue and may cause mortality (Davidson et al., 2004). Species have the capacity to acclimate to low 140

150 temperatures, and damage can be limited by acclimation (Booth, 2012b). However early or late season frost events, where limited or no acclimation has occurred, can be particularly damaging. While frost periodically affects established trees, frost damage in plantations is generally limited to young plants during the establishment phase. Plant tissue damage from frost may increase as atmospheric CO2 concentrations increase related to physiological responses to higher atmospheric CO2 concentrations. Barker et al. (Barker et al., 2005) reported ten times as much frost damage on E. pauciflora (snow gum) when seedlings were raised at double ambient CO2 concentrations. Conifers are amongst the most cold-tolerant of species (Roden et al., 2009). Pinus radiata grown in Australia is generally not affected by the magnitude of frosts experienced within the currently planted range. However, in Queensland Hoop pine (Araucaria cunninghamii), Caribbean pine (Pinus caribaea var. hondurensis) and its hybrid with slash pine (Pinus elliotii var. elliottii) are all sensitive to frost (Ian Last, pers. comm.), and in Tasmania frost mortality has been experienced in newly-planted P. radiata seedlings if plants are not hardened in the nursery before planting (Sandra Hetherington, pers. comm.). Eucalypt species vary in susceptibility to frost damage (Davidson et al., 2004). Species and site selection E. globulus is more frost susceptible than E. nitens (Close et al., 2000), and similar low temperatures can result in leaf senescence and seedling mortality in E. globulus while E. nitens remains largely unaffected in terms of growth and survival. In Tasmania, where both species are grown, frost damage is managed by matching site and species. An elevation of 250 m ASL is considered the threshold below which E. globulus is planted, and above which E. nitens is planted (Neilsen, 1990a). Matching species to site like this may not alleviate the effects of early or late season frosts and it must be recognised that a forced species change from E. globulus to E. nitens represents a significant alteration of product quality and values. Nevertheless, if such frosts become more common, the Tasmanian experience does demonstrate that changing to a more frost tolerant species is an option to improve early growth and survival (Hamilton et al., 2008). In southeast Queensland, on the other hand, selection of species for frost tolerance (e.g. E. argophloia) may also result in reduced volume production compared to higher-risk alternatives (e.g. Corymbia hybrids) (Ian Last, pers. comm.). Hybridisation of E. globulus and E. nitens has been explored to increase frost resistance (Tibbits et al., 1991; Volker et al., 1994). Interspecific hybrids have not been commercialised in Australia, although they have been utilised in Chile (Fernandez et al., 2012). In Queensland, frost-prone sites within the Hoop pine estate have been established with frost tolerant species such as P. radiata, P. patula or Bunya pine (Araucaria bidwilii) since the 1970s. To enable these sites to be re-planted with the more desirable yet more frost sensitive Hoop pine (A. cunninghamii), frost prevention prescriptions were developed for these sites, which involved maintaining weed-free ground for the first few 141

151 years to encourage the build-up and release of radiant heat from the soil at night. This proved effective at increasing minimum overnight temperatures above a critical threshold (Ian Last, pers comm.). Nursery practices and establishment Newly-planted eucalypt seedlings are extremely susceptible to cold-induced photoinhibition (Close et al., 2000). Close et al. (2000) found that nutrient starvation acted as a preconditioning treatment for E. nitens, and while photoinhibition was not reduced, anthocyanin concentrations in the leaves increased which reduced tissue damage. Nutrient preconditioning in the nursery is practiced in some states, for both frost and browsing control (Neilsen, 1990a). In frost prone areas in northern Tasmania, mounding is an effective site establishment practice that helps place seedling higher in the cold air layer that forms during a frost event (Ian Ravenwood, pers comm.). While lower leaves may be damaged during a frost event, the mounding ensures much of the seedling crown remains undamaged. Summary Although frost occurrence is projected to decline in future decades in Australia, out-ofseason frosts may result in tissue damage and mortality of young plantations. Limited management strategies have been applied historically to manage frost damage. In addition to the strategies listed above and in Table 20, documenting where and when early or late season frosts damage plantations may help to identify changes in the timing, frequency and severity of frost events and inform future species choices and nursery management practices. Table 20. Summary of historical responses of the Australian forest industry to frost Motivation for action Less tissue damage and mortality Historical response Species References Choose more frost resistant species Nutrient preconditioning in the nursery to reduce tissue damage E. globulus (Neilsen, 1990b; Davidson et al., 1995; Close et al., 2000) E. nitens (Close and Beadle, 2003) Strong wind events Strong wind events can cause extensive damage in plantations, including stem breakage, uprooting, undesirable tree form and stem damage from falling trees (Wood et al., 2008). Strong winds can range from relatively localised events based on terrain characteristics, up to the extreme winds associated with cyclones. Plantation sawlog regimes involve commercial thinning to both maximise solid wood production and provide a financial return during the rotation (Neilsen, 1990a). This later-age thinning commonly involves reducing stand density from the initial stocking 142

152 (e.g sph) down to the final stocking (e.g sph) in one operation, and this dramatic reduction in stand volume leaves stands susceptible to wind damage. The conditions resulting in windthrow associated with strong winds have been examined in P. radiata and eucalypt plantations. An extensive windthrow event in P. radiata plantations near Canberra in 1974 (Cremer et al., 1974) highlighted conditions conducive to wind damage: recent upwind clearfelling; thinning when mean dominant height was more than 30 m; saturated or waterlogged soils; the occurrence of winds gusting over 50 kph; and long wind runs. More recent windthrow in solid wood eucalypt plantations in Tasmania found similar causal factors, as well as a height:diameter ratio of >1. An analysis of damage following recent cyclones in northern Queensland also highlighted that height:diameter ratio of > 1, high stand density and poorly-drained sites made trees more susceptible to damage (Select.Carbon, 2012). Other contributing factors included lower wood density and large tree crowns. In some instances young plantations were more susceptible to cyclone damage than older plantations. However, young pine plantations were less affected than older stands because they readily stood up again after the event, and it was concluded that they are likely to suffer only minimal long-term impacts on volume and stem form (Ian Last, pers. comm.) Site selection and establishment There are well-developed guidelines and tools available for identifying windthrow risk and selecting sites and establishment practices that reduce this risk. Forestry Tasmania, for example, recommends avoiding the establishment of plantations on sites with shallow or waterlogged soils (Wood et al., 2008), as does Select Carbon in cyclone-prone areas of Queensland (Select.Carbon, 2012). Forestry Tasmania developed a windthrow hazard assessment tool, WindRISK (Wood et al., 2008), which assesses site hazard based on soil, topography and climate, and calculates windthrow risk based on silvicultural management and stand attributes such as mean dominant height and height:diameter ratio. The tool was used to map windthrow exposure for the Forestry Tasmania plantation estate, and categorise it into high, intermediate or low exposure, based on a range of topographic features (Wood et al., 2008). It is used to inform planning decisions about plantation location and management. An initial stocking of less than 1200 sph is utilised in Tasmania for sites at risk of windthrow and where thinning is planned (Neilsen, 1990a), to improve the height:diameter at the time of thinning. Species selection Following recent cyclones in northern Queensland, a range of species were assessed for their resistance to extreme wind damage (Select.Carbon, 2012). Species with higher wood density and smaller crown size suffered less damage. Select Carbon (Select.Carbon, 2012) provided a detailed table of type and severity of damage experienced by 26 species during cyclones that can assist plantation managers in cyclone-prone areas to select more cyclone resilient species. Within species, provenances and varieties from more cyclone-prone environments were more cyclone 143

153 resistant than those from locations with low incidence of cyclones. For example, P. caribaea var caribaea, native to the hurricane-prone Caribbean Islands such as Cuba, was much more wind-firm compared to P. caribaea var hondurensis plantations derived from the (inland) Mountain Pine Ridge provenance in Belize (Ian Last pers. comm.). Similarly, provenance and clonal variation was observed in trials of Eucalyptus pellita, Khaya senegaensis and Tectona grandis (Lindsay and Dickinson, 2012). Provenance selection of Eucalyptus pellita has been undertaken in Queensland to improve wind-firmness (Andrew Callister pers. comm.). Wind damage has also been included as a selection criterion for commercial Tectona grandis (teak) clones in Queensland (Andrew Callister, pers. comm.). Stand management Solid wood growers in Australia have developed management regimes to reduce the risk of windthrow associated with thinning. Progressive non-commercial thinning would help to manage risk, however there is an economic imperative for commercial thinning for most solid wood growers. In P. radiata stands, thinning before a maximum mean dominant height of 30 and a height:diameter of 1 is the target to reduce the risk of windthrow in south-eastern Australia (Cremer et al., 1974). In E. globulus and E. nitens stands, a mean dominant height of 20 and a height:diameter of <1 at the time of thinning were recommended (Neilsen, 1990a). Two-stage thinning has also been used at high risk sites (Neilsen, 1990a), allowing the progressive acclimation of stands to reduced stocking and improved stand resistance to strong wind damage. In Queensland, cyclone damage was found to be reduced in stands with lower stocking rates (around 650 sph), and in stands where crowns were relatively uniform (Select.Carbon, 2012). As with other parts of Australia, recently-thinned stands (< 2 years post thinning) were considered to be particularly susceptible, suggesting that thinning operations should occur after the cyclone season (Lindsay and Dickinson, 2012). Summary The main site and stand features contributing to enhanced windthrow hazard are well understood (Table 21). Analysis of damage from previous cyclones in Queensland and strong wind events in Tasmania has resulted in identification of species traits that might result in greater wind-firmness. Rising mean annual and extreme temperatures Rising mean annual temperatures and extreme high temperature events can affect plantation survival and growth. Photosynthetic processes are strongly affected by temperature (Kirschbaum, 2004), and species exhibit temperature optima and the capacity to acclimate to different temperatures seasonally. For example, E. globulus has a broad temperature optima for photosynthesis of between 15 to 30 C in summer and 10 to 25 C in winter (Battaglia et al., 1996). P. radiata, in contrast, exhibits limited thermal acclimation between seasons, and in an experiment in New Zealand showed a 144

154 gradual increase in photosynthetic capacity as daytime temperatures increased from 9 to 26 C suggesting its temperature optima for photosynthesis is higher than 26 C (Ow et al., 2010). Table 21. Summary of historical responses of the Australian forest industry to strong winds. Motivation for action Reduced windthrow, stem damage Historical response Species References Thin when MDH* < 20 Eucalypts (Wood et al., 2008; Select.Carbon, 2012) Thin when MDH <30 Pines (Cremer et al., 1974; Cremer et al., 1982) Thin when height:diameter >1 All (Cremer et al., 1974; Neilsen, 1990a) Map wind hazard and use for site management planning Select more windresistant species E. globulus, E. nitens, P. radiata (Wood et al., 2008) Range (Select.Carbon, 2012) *MDH indicates mean dominant height, defined here as the average height of a stand determined at the rate of the tallest 50 or 100 evenly distributed stems per hectare (definitions vary between organisations). Eucalypts have an indeterminate growth pattern, and if soil resources are sufficient growth can occur whenever environmental conditions are favourable. Hence warmer winter temperatures may increase growth during winter, and may advance the start and end of the spring-summer growth flush (Pinkard et al., 2010c). In contrast, P. radiata and P. pinaster have determinate growth, and while they cannot capitalise on warmer winter temperatures in the same way as eucalypts, they may be favoured by a longer growing season provided growth is not limited by water. Under water-limited conditions, extreme high temperatures can amplify drought via higher soil evaporation and evapotranspiration. This combination of conditions has been implicated in the mortality of both eucalypts and pines in Western Australia (Butcher, 1976; Ogren and Evans, 1992). As well as affecting evaporation, high temperatures can damage or kill trees directly. Short term exposure to temperatures of between 40 to 50 C, or longer-term exposure to temperatures as low as 35 to 40 C, have been linked to tissue damage and mortality in E. globulus (Macfarlane, 1998). Trees rely on evaporation through stomata to cool leaves. High vapour-pressure deficits that occur during heatwaves result in stomatal closure and rapid increases in leaf temperature (Macfarlane, 1998). Nursery practices and site establishment Most plantations in southern Australia are established in late autumn, winter or early spring, avoiding times of peak temperatures to reduce temperature stress and desiccation. When planting outside the optimal timeframe, the establishment strategies described in the Drought and water availability section (above) could also reduce 145

155 temperature stress, particularly methods that increase the root:shoot ratio. There are no examples of establishment methods that may reduce damage in plantations during extreme high temperatures. However, slightly warmer winter temperatures can dramatically improve root regeneration in radiata pine after transplanting ( Nambiar et al. 1979). The germination rate of E. globulus in the nursery is sensitive to high temperature (Rix et al., 2011). Rix et al (2011) demonstrated that at 25 C, 96% of E. globulus seed produced normal seedlings, while temperatures above 30 C delayed germination, reduced germination percentage, caused a high proportion of seed death and suppressed seedling development. Similar observations have been made for E. nitens seed exposed to temperatures higher than 30 C (Ian Ravenwood, pers. comm.) Operationally, the main response of forest nurseries in Australia to high temperature stress is to schedule sowing times to avoid high temperature events (Andrew Callister, pers comm.). There are also examples of warmer temperatures affecting eucalypt flowering and hence seed production, for example E. nitens in South Africa (Booth, 2012a). Pest management Temperature is the major abiotic variable affecting the development and abundance of forest insects (Ayres and Lombardero, 2000; Harrington et al., 2001), and there are numerous international examples of changes in climate triggering major pest outbreaks (e.g. mountain pine beetle in northern America, (Kurz et al., 2008)) or eliciting range shifts and expansions (e.g. pine processionary moth in Europe, (Moore and Allard, 2008)). The incidence of fungal pathogens is influenced by both temperature and precipitation. In general, leaf fungi are favoured by warm moist conditions where high relative humidity is maintained during sporulation and spore germination, thereby facilitating infection of the host (Margarey et al., 2005). Root and stem fungi also benefit from warmer temperatures, but may have varying moisture requirements for sporulation and germination. Increases in the duration of favourable conditions for growth and reproduction of pest species increases the potential number or length of epidemic cycles per year, and may reduce overwintering mortality (Ayres and Lombardero, 2000); (Kurz et al., 2008). In response to increasing insect damage in its plantations Forestry Tasmania developed a tool for identifying sites at high risk of damage from eucalypt leaf beetle (Wardlaw et al., 2011). Sites assessed as high risk are monitored for beetle damage, and when damage reaches a threshold, spraying programs are invoked. While this approach was not initiated in response to climatic triggers alone, it provides an example of how high levels of insect damage associated with more favourable climate can be managed costeffectively. In northwest Tasmania, several consecutive warm and moist years resulted in serious damage to E. globulus plantations from Mycosphaerella leaf disease (MLD) (Wardlaw, 2001). While genetics trials illustrated that there is genetic variation in resistance to MLD (Carnegie et al 1994), there has not yet been deployment of more resistant genotypes in the field. Instead, a change in species has occurred in regions identified as 146

156 at high risk of MLD, from the highly susceptible E. globulus to the more resistant E. nitens (Wardlaw, 2001). Pest surveillance is an adaptation tool used by some parts of the industry for tracking changes in pest species composition, distribution and abundance. For example, the Industry Pest Management Group in Western Australia ( established a series of field plots for monitoring pest species abundance and the damage caused, as well as pest exclusion plots to identify productivity lost from pest damage (Loch and Matsuki, 2010). This group shares data between industry partners, thereby increasing knowledge of emerging and established pest issues and potential management strategies. The value of long-term monitoring is well demonstrated in Tasmania where it has provided a means for modelling pest risk based on site and climatic conditions (Pinkard et al., 2010a; Wardlaw et al., 2011), resulting in more targeted application of control measures (Wardlaw et al., 2011). Fire management Risk of fire is high with high temperatures and heatwave events, especially when coincided with strong winds, low humidity and accumulation of growth and dry matter. Australia has a long history of managing fire risk in plantations, and many organisations allocate considerable resources to managing fire risk. Considerable effort is put into developing plantation fire management plans that cover prevention, preparedness, response, and recovery (CFA, 2011b, a). Increases in the frequency of high fire risk days and length of fire season in recent decades (Clarke et al., 2013) have placed increased demands on fire fighting resources. Fire risk is monitored using meteorological indices, the Forests Fire Danger Index (McArthur, 1967) in most of Australia and the Grass Fire Danger Index (McArthur, 1966) in Western Australia. During summer (the period of greatest fire risk due to the combination of high temperatures and low humidity), forestry operations monitor onsite weather conditions regularly, and hazardous activities are shut down when weather conditions reach moderate to high danger levels. For example, hazardous forestry operations are suspended when the Forest Fire Danger Index reaches High 15 or humidity falls to 30% or less (Sandra Hetherington, pers. comm.) in Tasmania and fire management plans in other states may require suspension of operations on days of extreme fire danger (DEPI, 2009). Prediction models for fire spread in plantations have been developed for prescribed burning (Lacey, 2008) and bushfire conditions (de Mar and Adshead 2011) but other forest fire models are also commonly used. Fire risk to plantations is managed using a combination of plantation design, fire suppression, and fuel modification (Cheney, 1985, 1988; DEPI, 2009). Selection of fire resistant species has been suggested (Cheney, 1988), but is not usually a consideration given other factors have higher priority when choosing species (see above). Plantation design to reduce fire risk encompasses layout of roads and signage to allow access for suppression, inclusion of fire breaks to limit fire spread, and positioning of water supply points for fire fighting (CFA, 2011a). 147

157 Throughout Australia, plantation managers combine with other land management agencies for integrated fire suppression and control because bushfires occurs at large landscape or regional level and move across tenure boundaries land use including farms and native forests.. Plantation managers may be required to maintain their own suppression resources, depending on the size and location of operations (CFA, 2011b). Forest managers have learnt the importance of responding to fires with large amounts of resources to reduce the development of large (and expensive) fires and since the 2000s have invested considerable resources in early fire response and suppression as a way of managing risk (Ian Ravenwood pers. comm.). While fire risk may increase in the future, better response and suppression tactics may keep the number of significant fires in check and at historic levels. Fuel modification to reduce risk is achieved by a combination of: mechanical modification by slashing grasses in the inter-row spaces or pruning trees (CFA, 2011a); chemical modification by spraying weeds; removal of fuel by grazing; or prescribed fire (Cheney and Richmond, 1980; Lacey, 2008). These practices are most common in plantations with an understory dominated by grass. Prescribed burning to remove forest litter is much rarer, but burning to remove slash prior to establishment occurs in some areas. Recent experiences with extreme fire events in Victoria (Booth et al., 2009) suggest that the forest fire danger index may require revision, particularly the assumptions around rates of fuel drying (Booth et al., 2009), to better reflect changes in rates of fuel drying now being experienced during hot and dry conditions. Summary While rising mean annual temperatures may benefit Australia s plantations via enhanced growth rates, this will only occur if there is adequate water and pest species multiply under the changed conditions. There are few explicit examples of adaptation to rising mean annual temperatures or heatwaves. In addition to the responses outlined above and in Table 22, alternative species with different temperature optima could be identified from existing taxa trials (Bush et al., 2009; Lee et al., 2011). The main industry responses to changes in mean annual temperature revolve around pest management and health surveillance. Central to these responses is the development of hazard assessment tools that can be used to identify sites with high hazard, monitoring of pest populations on these sites to identify when numbers exceed thresholds for damage, and the strategic use of control agents. This integrated pest management approach is likely to remain important into the future. For some pest species (e.g. Sirex) biological control programs have played a major role. Changes in the efficacy of the Sirex biological control agents (Collett and Elms, 2009) highlights the need for ongoing research and monitoring to ensure biological control programs remain effective. Recent experiences with extreme fire danger conditions in Victoria (Booth et al., 2009) suggest that the forest fire danger index may require revision, particularly the 148

158 assumptions around rates of fuel drying (Booth et al., 2009), to better reflect changes in rates of fuel drying now being experienced during hot and dry conditions. Can past experience inform solutions for the future? It is reasonable to assume that while climatic conditions remain within the bounds of those experienced historically for a particular species across its commercial distribution, the past will guide development of management in the future. If climatic conditions at particular forest locations become more extreme, new measures may be required outside our current experience. The gradual increases in average temperatures, or the gradual declines in rainfall projected for the main plantation-growing regions, may not be of significance for some time, however shifts in temperatures may result in more frequent and extreme droughts or heatwaves that may cause substantial impacts on forest health or production. Unprecedented climatic conditions, such as the high mean temperatures and longer duration of high temperature events recorded in south-eastern Australia in the summer of 2013/14 (CSIRO and Bureau of Meteorology, 2013), and the unprecedented severity of the drought in southern Australia (CSIRO and Bureau of Meteorology 2010) are already occurring in areas that contain plantations. The extent to which the past can inform climate change adaptation rests on the extent to which production can be shaped by past management shifts. If it can be, much of future adaptation needs will be found within the performance record of the current plantation species range. A major concern occurs when plantations fall outside the current known range of suitable climatic conditions for that species (Booth et al., 2014), and particularly when the frequency and severity of extreme climate events drives changes in production and survival. Table 22. Summary of historical responses of the Australian forest industry to increasing mean annual temperatures Motivation for action Reduced tissue damage, improved productivity, less mortality Improved management of pest damage Historical response Species References Establish new sites when temperatures are cooler Use nursery practices that increase the root:shoot Select more resistant genotypes Risk assessment tools to identify high risk sites Monitoring and integrated pest management for high risk sites Range P. radiata (ForestrySA., 2010) E. globulus (Wardlaw, 2001) E. globulus, E. nitens Battaglia et al 2009 E. globulus, E. nitens, P. radiata 149

159 Incremental versus transformational change Management responses to climate can range from incremental changes in sequential management, through to transformational, where current landuse practices are no longer viable (Figure 51) and major changes are made to adapt. The nature of responses to climate change that will be appropriate or necessary will depend on the degree of change that has occurred or is anticipated. The more the climate changes, the more complex and potentially costly, risky and disruptive, changing management becomes (Stafford Smith et al. 2011). This is illustrated in Figure 51. When climate changes are relatively small, incremental changes to existing management practices will in many cases be sufficient response, as has been the case in the past. For example incremental changes in management (albeit not in response to climate) of P. radiata plantations in southern Australia resulted in increased plantation production between 1950 and 1985 (Figure 1) (Leishout et al., 1996) despite a drying trend (CSIRO and Bureau of Meteorology 2014). Most of the past responses documented above fall into this category. Complexity, risk and cost Landuse change; new products Shift to new species or genotypes; new management practices Incremental change using current practices Climate change Zone 3 Zone 2 Zone 1 Figure 51. Relationship between climate change impacts, adaptation responses and the potential benefits from adaptation (after Howden et al., 2010) As the climate continues to change, we suggest that there will be a threshold at which there is no further benefit from these types of responses, and it will be necessary to move to a new level of response involving more significant changes (Figure 3). Selecting more resilient species, or diversifying the business, are examples of such changes. These types of response may be more expensive and complex than incremental changes to existing management practices, particularly if development of new genetic material or management approaches is required before the methods can be deployed in the field. For example, while there are a range of taxa trials across environmental gradients in 150

160 Australia (Bush et al., 2009; Lee et al., 2011) that provide indications of species and genotypes that might be more resilient to drier, warmer conditions, there is little understanding of the trade-offs between productivity and wood products for these species. The silvicultural systems to manage these alternative species, wood processing methodologies and markets for their wood products are largely undeveloped. It is not possible to respond reactively at this level of response to climate change; a planned response is required that accounts for impacts of management changes across the value chain. As climate change impacts becomes more severe, benefit from this second level of response also plateaus, and a new response may be required (Figure 51). This phase involves transformational change, for example to new forest products such as biofuels or biosequestration, or away from forestry to other land uses. In common with the previous level of response, responses at this level are potentially more costly and disruptive to the forest industry and community more generally than are incremental management changes. While most of the Australian plantation estate currently falls into Zone 1 of Figure 3, some parts of the estate fall into Zones 2 and 3 (Battaglia et al., 2009a; White et al., 2011a), and there are some examples of significant management changes that have occurred to accommodate this (e.g. changes in species from P. radiata to P. pinaster, and from E. globulus to E. smithii, in Western Australia, and opting to not re-establish sites after harvest if they have not performed to expectation (Sara Mathieson, pers. comm.)). The management changes required to shift from Zone 1 to Zones 2 and 3 are often outside historical experience, and will require major investment in research and development to maintain forest productivity. Identifying which parts of the estate are in each zone, and appropriate management for each, will be critical for maintaining productive plantations into the future. Work that seeks to benchmark plantation performance and determine the causes of yield gap, and analyses how this might change as climate changes, would be constructive. Clearly having a well founded understanding of the drivers of system performance and how systems respond to management interventions is critical in decision-making (Mendham et al., 2011). This work is greatly enhanced with the rapid rise of spatial soils information (e.g. Australian Soil Resource Information System, ASRIS, and improvements in climate downscaling (e.g. Grose et al., 2010). Short versus longer timeframes The timeframe over which management decisions are made influences which management responses will be most appropriate. This is related to the degree of uncertainty about how the climate is likely to change (Stafford Smith et al., 2011). Long timeframes between decisions and crop harvesting in forestry differentiate it from many other crop species (Hochman and Carberry 2011). Global climate models that are used to project future climatic conditions are reasonably consistent in their projections of climate change in the short term. For example, projections by the Intergovernmental Panel on Climate Change (IPCC, 2007) for a range 151

161 of emissions scenarios suggest consensus between models in global warming projections up to about Beyond this, there is more uncertainty around how much warming will occur, related to uncertainty about emissions scenarios (CSIRO and Bureau of Meteorology, 2013). When the timeframe for decision is short and there is reasonable certainty about how the climate might change, identifying appropriate management strategies is more feasible than it is when the decisions impinge on longer term. Over longer time scales, management needs to allow for a range of possible futures. In terms of forestry, from now to 2030 gives one rotation of hardwood production in Australia, although in some cases wood supply commitments are made that extend beyond one rotation. The same period reaches to about the first thinning age at half rotation length for a radiata pine plantation. In summary, forestry decisions being made today extend into the period of future climate change uncertainty. Hallegatte (2009) suggests five possible strategies that may be beneficial when dealing with longer time-frames. They aim to improve the robustness of decision-making when faced with uncertainty. The first is to implement no regret strategies, which yield benefits even in the absence of climate change. Examples of this might be the development of genotypes more resistant to warmer and drier conditions, or diversifying products such that there is the flexibility to change the rotation length depending on climatic conditions, such as plantations for biosequestration. Another example is the shift to lower stocking in Western Australian hardwood plantations described earlier which has little or no impact on yield in average years, decreases harvesting cost (less stems) and greatly reduces drought mortality risk. The second is to implement reversible strategies that aim to limit the cost of making the wrong management decision. For example, a decision could be made not to establish plantations in an area considered to be at high risk of severe drought. If climate change is less extreme than projected, then the decision can be reversed. The third is to add safety margins into management strategies, which reduce vulnerability, and can help deal with uncertainty. For example, in areas where drought is projected to be an increasing problem, making the decision to establish all plantations at a low stocking, or to routinely practice pre-commercial thinning, could be implemented. Another example might be the extension of rotation lengths, which can potentially decrease the proportion of the rotation when plantations are most vulnerable and drought most impacts on yield, and has little negative effect on production (Battaglia et al 2009). The fourth is to reduce decision time-frames, so that there is more certainty around climatic conditions during that timeframe. For instance, species or products needing a shorter rotation length could be selected for high risk areas. So instead of looking to produce sawlogs from longer rotation hardwood plantations with big logs for example, a decision might be made to supply small logs for engineered wood products. 152

162 The final approach advocated by Hallegatte (2009) is labelled soft strategies, which recognise that in some cases institutional or financial tools can contribute to climate change adaptation. Developing a legislative framework for groundwater allocation in dry environments with appropriate community engagement, so that there is certainty around future water allocation to the forestry sector, is an example of such a strategy. Adaptive management Many of the approaches above are instances or elements of what can be viewed as adaptive management, defined in general terms, as flexible decision-making that can be adjusted in the face of uncertainties as outcomes from management actions and other events become better understood. Careful monitoring of these outcomes both advances scientific understanding and helps adjust policies or operations as part of an iterative learning process (National Research Council 2004), or more colloquially as learning by doing. Some elements of learning and practice refinement in forest management as outlined above provide an example of adaptive management by some practitioners (O'Hehir and Nambiar, 2010b). This learning from successful and failed management has led to constant refinement of technical practice that has underpinned the sustained productivity growth highlighted earlier (Leishout et al., 1996). Foresters have had a long history of adapting to changing social, environmental and economic conditions, and have responded to these changes despite uncertainty of the consequences. Long time lags between action and consequence in forest management, and the extensive and highly heterogeneous nature of forests, have meant that silviculture changes have often been best guess changes with monitoring of outcomes guiding subsequent iterations of practice and application to other areas. Increasingly, models, that can be both processbased or more traditional inventory tools applied for multi-objective optimisation, have been used to explore what-if scenarios of environment or practice change in the deliberative or goal setting (c.f. iterative or learning and evaluation) phase of the adaptive management process (e.g. (Battaglia and Sands, 1998b; Almeida et al., 2004b). Resource monitoring through inventory or pest assessments have provided feedback on management practice success. Future no-analog systems that may occur under climate-change where the past can offer little insight into the future may be assisted by deepened technical knowledge, underpinning adaptive management with ecophysiological knowledge (Bolte et al., 2009; Chmura et al., 2011). While analysis (Booth et al., 2008; Battaglia et al., 2009a) suggests over much of the current geographic range of the major plantation species past climatic variability will be representative of conditions experienced until 2030 or beyond, there will be cases where this is not so, and less considered factors such as heat-waves of unprecedented severity and new pest problems might require evaluation. Monitoring may provide important clues as to how new conditions are challenging forests, and indicate additional factors that have not been adequately assessed in vulnerability assessments. Equally important will be participatory research that seeks stakeholder engagement both in terms of objective setting and in co-production of science to inform decision making to enrich technical information with contextual, 153

163 business and other types of information (Klenk et al., 2011; Williams and Brown, 2014). Embracing adaptive management more fully and addressing existing weaknesses in its application in forestry may be important under a changing future. Conclusions Capacity to respond to climatic variability in Australian plantations currently rests on a strong legacy of past forest management research and substantial investment in understanding how forests respond to environmental conditions. Much of this work, and many of its greatest successes (for example, addressing second rotation decline in Australia s pine industry and various pest outbreaks), have been grounded in transdisciplinary activities that have brought together industry practitioners with multidisciplinary research teams. Plantation research capability has reduced dramatically over the past two decades, and is now around one fifth of its 1985 resources (AFPA, 2013). There have been similar declines in forest health management capability. The decline has both been in research providers but also, and equally importantly, in those in the practice interface who have typically resided in industry and helped in problem definition and in directing research to practical and implementable outcomes. This decline affects the capacity of the industry to respond to future climatic events, particularly those outside current experience, and has major implications for the longterm viability of the industry in Australia. In collating this review, comprehensive or consistent documentation were not apparent for (1) climatic events/conditions that have affected plantation management; (2) the management responses that were utilised; and (3) how effective (or otherwise) were these responses. These are missing links in the adaptive management response previously described. Improved documentation, both within and between companies, will assist in future analyses of appropriate management responses to changing and more variable climate, and adaptive learning. Establishment of a centralised database may be appropriate to capture where (and when) extreme climatic events occur, their consequences for plantations, and management responses (what and how effective). This would be a valuable resource for hazard assessments at regional and local scales. Climate change is one of a number of challenges facing Australia s forest industry at present, as the industry struggles to remain viable in a highly competitive market (Semple 2013). Vulnerability assessments indicate that climate change may have large consequences for sections of the industry in the future (Battaglia et al. 2009). Nevertheless, climate change presents both a threat and an opportunity for the forest industry, and forward planning to minimise the threats and take advantage of opportunities will help maintain the viability of the industry and the communities it supports, into the future. While there are threats to production in the current heartland of Australia s plantation estate, overall climate change response by society may increase demand for sustainable forest products and may spawn new product opportunities in forest carbon, biofuels and bioenergy. 154

164 Chapter 10 The role of decision support tools in climate change adaptation Summary The role of decision support systems (DSS) in highly structured technical problems uncomplicated by context lies in resolving uncertainty about the relationship between action and outcome, though the situated learning of managers rapidly removes the need for elaborate decision support. Only where environmental uncertainty remains high (such as planning for climate change) or where there are disruptions through new technology, markets or regulatory requirements is there an on-going role for DSS. In order to be successful, DSS need to be embedded in a support network consisting of forest growers, consultants and researchers. They should aim to inform managers intuition rather than replace it with optimised recommendations. DSS should enable users to experiment with options and explore scenarios. At the stand scale there has been considerable research investment in process-based models in Australia. Interest in process-based DSS (along with hybrid and empirical DSS) has peaked at times of practitioner uncertainty: where there was rapid expansion of plantation estates into new regions and with new species, at times when new product mixes were demanded, or where the industry is confronted with the potential of climate change impacts. For the forest industry, climate change is a case of making long term decisions for an unknown future. Given the level of uncertainty on future climates and impacts, it is probably unwise to reduce the diversity of future options, and to use observation in parallel with DSS to learn, revise and refine practices. 155

165 Introduction The use and adoption of decision support has been widely and deeply discussed in agriculture (e.g. McCown, 2002; Hochman et al., 2009; McCown et al., 2009). The conclusion is that the promise contrasts with evidence of limited adoption, referred to as the implementation problem. A number of conclusions were reached that are of relevance to the development of decision support tools for forestry. After reviewing the case histories of 14 agricultural DSS models, McCown 2002 concluded that the role of decision support systems (DSS) in highly structured technical problems uncomplicated by context was clear, though the situated learning of managers rapidly removed the need for elaborate decision support. DSS served in these situations to resolve uncertainty about the relationship between action and outcome. Once this was established the marginal value of DSS in practice was low since the residual uncertainty, or the potential operation performance improvement was small compared with the many other elements influencing decision making and not worth the cost in resources or management time. In this phase farmers in complex and dynamic situations probably pursue a strategy of satisfying (after Simon, 1956) that attempts to meet multiple criteria for adequacy, in contrast to a strategy of optimum management strategy, where the objective is not getting it right but of pragmatically constructing a plausible and defensible course of action (Weick et al., 2005). In this process the DSS was seen as a tool in decision-making, not as a substitute or proxy for the decisionmaking itself which involved a range of practical/social considerations in addition to the technical optimisation. A key requirement of DSS was to support and educate farmer intuition and not replace it with optimised recommendations (Hochman and Carberry, 2011). Only where environmental uncertainty remained high (such as planning for climate change) or where there where disruptions through new technology, markets or regulatory requirements was there an on-going role for DSS, or in the latter case a renewed interest in DSS. Without persistent uncertainty or change, farmers soon internalised DSS learning into enriched world views, and DSS offered little additional support for decisions. Where uncertainty remained high or changes in the regulatory or technological innovation occurred, DSS was seen as having value in facilitating what-if analyses and discussions (WIFADS), and supporting thought experiments by providing probability distributions that were useful for shaping expectations of outcomes (McCown et al., 2009). McCown et al suggest that for all but the simplest technical problems (Figure 52), and where the solution to these problems is relatively uncontested socially, successful application of DSS requires embedding in a broader program of action research. WIFADS are one means of this, but a broader program encompassing simulation-aided design and evaluation of best-practice demonstrations in commercial environments, collaborative evaluation of decision support, and negotiation of joint research programs and public discussion of results are other elements that can facilitate collective (researcher and farmer) learning, and simulator-aided farm consulting that can assist 156

166 practice recommendation and adoption. In these cases the DSS acts as a boundary object to help communication between researchers and (and among) stakeholder groups with differing knowledge and expertise (Thorburn et al., 2011). This broader engagement comes at a time-cost to researchers and farmers, and a learning investment cost to consultants. It may also diminish research rigour, impacting on funding and publication opportunities for the researchers (Matthews et al., 2008). Farmer surveys indicate that best practice DSS development involved the following elements (Hochman et al., 2009): 1) It is essential to have a plan for delivery of the DSS beyond the initial funding period. 2) DSS need to be embedded in a support network consisting of farmers, consultants and researchers. 3) DSS development requires the commitment of a critical mass of appropriately skilled people. 4) A DSS should aim to educate farmers intuition rather than replace it with optimised recommendations. 5) A DSS should enable users to experiment with options that satisfy their needs rather than attempt to present optimised solutions. 6) DSS tools stand on the quality and authority of their underlying science and require ongoing improvement, testing and validation. 7) DSS development should not commence unless it is backed by marketing information and a plan for delivery of the DSS beyond the initial funding period. Figure 52. Classification of important issues in agriculture and putative research methodology relevant to issue exploration (from Carberry et al. 2005) 157

167 History of DSS in forestry By contrast to agriculture, DSS have featured prominently in forestry with adoption principally by corporate or government forestry managers. Many of the early applications were linear programming systems for timber scheduling, initial for nonspatial harvest scheduling [e.g. FORPLAN (Alston and Iverson, 1987), FOLPI (Manly 1996)] and later for spatially explicit harvest scheduling with multiple values such as carbon, timber, age structure requirements for biodiversity consideration [e.g. MELA (Nuutinen et al., 2006), WOODSTOCK (Hennigar et al., 2008)]. These applications are increasingly incorporating uncertainty in evaluations, though long time frames, poorly defined risks and large spatial scales create complex and ill-defined problems (Pasalodos-Tato et al., 2013). Widespread application of DSS for estate level optimisation in forestry contrasts with agricultural. This difference may result from the inherent uncertainty in decisions resulting from long time scales in forestry, the larger and more heterogeneous scale of application, and the greater oversight and requirement for demonstrate stewardship of common-goods and environmental values driven by certification or environmental reporting requirements. At the coupe or planting and harvest unit level the utilisation of decision support is less evident beyond the application of empirical growth and yield functions, which are ubiquitous to forestry (Vanclay, 1994). In this later application decision-support is part of the inventory and accounting systems (consistent with DSS being an aid to the land manager in taking control of an activity that may once have been delegated to another (McCown, 2002; Matthews et al., 2008)). While at times they may serve to provide exploration and what-if analysis for estate level analysis, their application is rarely to support coupe level decision making. It has been said of farmers that nothing characterises the family farmer more than values concerning discretionary freedom, agency and opportunity to display good judgement (McCown, 2002). The same in many ways is true of forest managers, where scepticism is often shown to the diversity of scientific view (reflected in the alternative DSS and their application) and inappropriately framed questions. In response, study suggests that most forest managers prefer making up their own mind (von Detten and Faber, 2013). These authors suggest that management actions directed at climate change adaptation may be rationalized with decision tools, however in general forest managers adopt more pragmatic approaches that maintain their decision autonomy. At this scale of application there has been considerable research investment in processbased models (Battaglia and Sands, 1998a; Makela et al., 2000; Sands et al., 2000; Landsberg, 2003; Fontes et al., 2010) with similar implementation problem concerns as expressed in agriculture (Groenhout and Beck, 2011). Consistent with observation in the agricultural literature interest in process-based DSS (along with hybrid and empirical DSS) has peaked at times of practitioner uncertainty: where rapid expansion of plantation estates into new geographies and with new species occurred (White et al., 2003; Miehle et al., 2010), at times when new product mixes were demanded (Pinkard et al., 1999) or as we see now where the industry is confronted with the potential of climate change impacts (Kirschbaum, 2000; Medlyn et al., 2011). 158

168 Experience with the implementation of process-based DSS in forestry has revealed an uneasy tension between complexity and generality (Battaglia and Sands, 1998a). Two models, CABALA (Battaglia et al., 2004a) and 3PG (Landsberg and Waring, 1997b) can both be observed to become parametrically and site input variable more demanding over their use lives as use demand expands to new areas of interaction or function. The model 3PG for example developed from a simplified case at publication, to subsequent versions with representation of temperature effects on photosynthesis (Sands and Landsberg, 2002; Almeida et al., 2004a), allowed for partial canopy cover (Sands and Landsberg, 2002), complex representation of soils (Almeida et al., 2007; Feikema et al., 2010), methods for coupling with models of soil fertility to provide required site variables (Vega-Nieva et al., 2013) and finally inclusion of elevated atmospheric CO2 effects (Almeida et al., 2009). CABALA although starting as a more complex model than 3PG has similarly been enhanced over time to deal with specific questions including defoliation and pest damage (Battaglia et al., 2011b), handling elevated CO2 (Battaglia et al., 2009b), predicting drought mortality vulnerability (White et al., 2011b) and linked to sub-models of wood property development (Drew et al., 2010b) and individual tree size class distribution (in prep). Perceived as too complex for routine operational use (though this is belied by specific instances where both of these models have been used as such where good researcher-industry user collaborations have been established) these models have been used to develop derivative tools for industry (such as FPOS, and various industry site selection guidelines (Mummery and Battaglia, 2001)) and to explore the basis of production, establish silvicultural principals and explore risk [for example understanding soil water use through multiple rotations (Mendham et al., 2011b), sustaining plantation productivity (White et al., 2013), refining silvicultural prescriptions (Pinkard et al., 1999; Battaglia and Pinkard, 2000), explore climate change impacts and helping design and guide discussions on adaptation options (Battaglia et al., 2009b; White et al., 2011b)]. As suggested by experience with agricultural DSS (Hochman and Carberry, 2011), the use of process-based models is best facilitated into the forest industry when close partnership is established between model practitioner/developer and the management branch of the forest management agency (e.g. Almeida et al., 2003; Almeida et al., 2004b). The complexity and unknowness of the model in such cases is shielded from management and comes through as a broader package of technical information supplemented within company practical knowledge. With the forestry industry in Australia in such flux characterised by both high staff turnover and high rates of ownership turnover (URS_Forestry, 2007) there is an emerging role for forestry DSS as part of corporate memory and rapid in-silico learning and training for new staff. Within the forest industry in Australia there are many commonalities with the findings from Europe (von Detten and Faber, 2013). Some relevant findings are: 1. Uncertainty has two poles: a problem due to missing knowledge and a problem of paralysis because of too many interpretations. Summary information in the form of climate risk maps and identification of risk thresholds can resolve the ambiguity and uncertainty for decision makers. 159

169 2. It is suggested that while scientists (as published and reported, and the literature is too abundant to review here) often frame climate change as a problem of gradual change of forest growth conditions, practitioners focus more on the increased likelihood of extreme events like fire, drought, storms and pests, leading to a divergence in development of decision support tools and products to identify changed performance, compared with on the ground requirements to monitor and handle extreme events. Conclusions In the context of the current project, the above analysis suggests that DSS have an important role to play in assisting in decision making around managing forests under a changing climate. For forestry, climate change is a case of making long term decisions (what and where to plant, for what return period events should infrastructure be built, where should processing facilities be situated) for an unknown future. Given the level of uncertainty on future climates and impacts, it is probably unwise to reduce the diversity of future options, and to use observation in parallel with DSS to learn, revise and refine practices. The predictions, maps and outputs from DSS and climate models should be seen as starting points rather than conclusions (von Detten and Faber, 2013, page 65), and used as a means for engaging in conversations and researcher/practitioner interaction about future goals and alternatives shaping the future through changed practice. The focus in this report on the implication of increases in extreme events and indirect effects (pests, fire) and the adaptation options to reduce the risk are likely to be of equal or more interest than recommendations for optimising production. 160

170 Chapter 11 Using DSS to explore adaptation strategies for the forest industry Summary For 2030, in many parts of the plantation estate good silvicultural management has the potential to mitigate the negative impacts of climate change. For E. globulus, modelling suggests reducing the initial stocking to 800 sph in water limited environments can substantially reduce the risk of mortality in most instances without impacting on productivity. Fertiliser application can increase productivity to current levels but in some cases this will be at increased risk of mortality. For P. radiata, modelling suggests that in most cases, increasing the initial stocking to 1600 sph or reducing the number of thinnings to two and delaying the first thinning will increase productivity, though it is uncertain how the risk of mortality will change under this management. For P. radiata in very marginal sites, where extreme droughts are possible, shortening the rotation may improve overall productivity by reducing the exposure to extreme events. In locations where no adaptation options could be identified for E. globulus, P. radiata may be a suitable alternative species to plant. 161

171 Introduction Recent reports, and the preceding chapters, have highlighted the vulnerability of Australia s plantations to climate change (Battaglia et al 2009; Medlyn et al 2010). Vulnerability can be modified through adaptation, where adaptation to climate change is defined as adjustment in natural or human systems in response to actual or anticipated climatic stimuli or their effects, which moderates harm or exploits beneficial opportunities (Innes et al., 2009). The historical analysis in Chapter 9 demonstrated that past adaptation to climate change in Australia was largely reactive to specific climate triggers as they arose, rather than planned or anticipatory of future climate risks. Australia is already experiencing unprecedented droughts and heatwaves, and future climate projections suggest an increase in intensity, duration and frequency of such events (CSIRO and Bureau of Meteorology 2014). As climatic conditions move outside of our historical experience, prior responses may no longer be effective. Planned adaptation may avoid future loss if the probability of future outcomes and amelioration through practice change can be demonstrated. A critical element of facilitating planned adaptation is provision of tools to aid in decision-making (Innes et al., 2009). These tools need to be able to quantify possible losses associated with climate change while accounting for uncertainty of those future outcomes (Spittlehouse, 2005). Properly constructed they enable cost-benefit analyses so that forest managers can make informed decisions about the cost-effectiveness of management strategies (European Environment Agency, 2007; Adger and Barnett, 2009). Because climate information is rapidly changing, and because we are constantly learning about system responses as new conditions with no historical precedence occur it is important that such systems have the capacity to integrate new information as it becomes available (Spittlehouse and Stewart, 2003). The tools developed in this project address these issues. A probabilistic approach has been taken that helps define uncertainty of future outcomes. By quantifying the probability of loss of productivity, the outputs can be used as part of cost-benefit analyses. The model used for analyses can integrate new information about species responses to climate change: it represents a codified embodiment of our current system understanding that can pass beyond the tenure of particular researchers and managers (and relationships between them), or indeed in the fast moving and high turnover forestry sector, the life of particular companies and institutions. Adaptation to climate change involves a range of responses, from incremental changes in management through to transformational change to other land uses (Seppala et al., 2009). The tools developed in this project can be used to aid decision-making across this spectrum. The analyses in Chapter 3 have helped to define locations where growing plantations may not be economical in the future without some intervention. In the following, we examine the conditions under which management interventions may be effective in building stand resilience to climate change. 162

172 To learn from experience we draw on industry advice through workshops and steering committee to select relevant adaptation strategies. A range of adaptation strategies were recommended: for E. globulus, initial spacing to manage water stress, and fertilising to promote crown development following pest attack: for P. radiata, initial spacing to manage water stress, reducing the number of thinnings and thinning later to fully utilise the site and in some cases reducing the rotation length to minimise exposure to extreme events, to examine the impact of these strategies on survival and productivity of locations identified in Chapter 3 as being at risk under climate change. Methods The model The forest productivity model CABALA (Battaglia et al. 2004; White et al. 2011) described in chapter 3 was used in these analyses. Climate data The approach to generating future climates is described in chapter 2. Sites It is beyond the scope of this project to model a range of adaptation options across the entire plantation estate so a broad range of reference sites were selected for more detailed analysis than had been done in Chapter 3 (refer to Figure 53). The focus for site selection was limited to locations where a drop in production of between 15 and 5% or greater than 15% by 2030 was predicted (refer to chapter 3). This generally restricted selection to sites where modelling suggests water is likely to become limiting to growth. Where appropriate, E. globulus and P. radiata reference sites were co-located to allow comparison between species, in particular where there may be limited options for E. globulus. Additional sites are available in the regional scorecards. Avg Maximum Avg Minimum Avg Annual Avg no of Minimum Maximum Maximum no Temperature Temperature Avg Annual Evaporation Frost (below Annual Annual of Frost Site (deg C) (deg C) Rainfall (mm) (mm) * 1deg C) Rainfall (mm) Rainfall (mm) (below 1deg C) Site A Site B Site C Site D Site E Site F Site G Site H Site I Table 23 Summary of 2030 climate for each of the reference sites. This includes the 5 GCMs used for the Analysis. *Evaporation is the average of the current climate. 163

173 Site B Site A Site C Site E Site F Site D a. E. globulus reference sites Site G Site F Site C Site H Site E Site D b. P. radiata reference sites Site I Figure 53 Locations of the a., E. globulus reference sites and b., the P. radiata sites. Where appropriate, the locations of the reference sites are the same. Elevated CO 2 levels As described in Chapter 3, model predictions are highly sensitive to the responsiveness of plantation species to eco 2. If plantations are not responsive to eco 2 and sustained photosynthetic rate increases are not observed, 5-15% or higher decreases in productivity may be seen in the Green Triangle, Gippsland and south-west Western Australia for both bluegum and radiata pine (refer to Chapter 3 for more details). If plantations respond favourably to eco 2, then productivity is predicted to increase in most regions except at the drier margins of the plantation estate. In this analysis, adaptation options were only explored where there was a need for adaptation, where modelling suggests there will be a negative impact on production under future climates. Only potential scenarios where there was no response to eco 2 were used in this analysis. The results describe adaptation to the worst case scenario. Scenarios We used the standard regimes from the productivity modelling in Chapter 3 as the base for comparison with adaptation options. 164

174 In constructing the scenarios and adaptation response explored in this chapter we have focussed on drought induced mortality and following from the experience in the Western Australian situation have investigated means of controlling leaf area through thinning and fertilisation. Leaf area has a direct effect on tree transpiration and work has shown that its control can reduce the intensity and duration of tree water stress during dry periods. The standard silvicultural regime used for E. globulus was a 10 year rotation planted at 1000 stems per hectare (sph). For E. globulus, a range of adaptation options were assessed. Stocking was varied from 600sph to 1200 in 100 sph increments. Fertiliser applications were modelled at 200kg of elemental N for ages 2, 4 and 6 and low levels of pruning to stimulate up regulation of photosynthesis. Planting at an initial stocking rate of 800 sph minimised the impacts on productivity while reducing the risk of mortality for the range of sites tested. Reducing the stocking to 600sph did little to improve the risk of mortality on the marginal sites and reduced productivity on the majority of sites. Fertiliser application was generally most beneficial at age 2 (though not substantially different from age 4). Application of fertiliser at age 2 was selected as most generally similar to industry practice. Pruning did not demonstrate any significant increases in production or reduce the risk of mortality. For P. radiata, the standard regime used was more complex: planting was at 1333 sph, with a rotation length of 35 years, and three commercial thinning events. The first thinning is at age 11 with the reduction to 750 sph, the second at age 19 to 450 sph and the final thinning at age 26 to 250 sph. A range of adaptation options was considered from changes in initial stocking from 1000sph to 1600sph at 100sph increments, reducing the number of thinnings and changing the age of thinning and the number of stems removed. Fertiliser application was also explored with fertiliser applied just after each thinning at 200kg of elemental N. Two regimes that improved levels of productivity at most sites were selected: reducing the number of thinning s to two with the first thinning at age 15 to 450 sph and the second at 26 to 250 sph, and increasing the stocking levels at planting to 1600sph. For P. radiata sites where there were limited adaptation options, more extreme alternatives were explored such as changing to a short, small log regime: planting was at 1333 sph, one thinning at age 13 to 600 sph with a clearfall age of 23. Where the short rotations were explored the outcomes of multiple rotations were compared with the standard regime. Two standard regimes with a one year fallow (71 years in total) were compared with three short rotations with two years fallow (71 years in total) to allow comparison across the same weather sequence. Volumes were then adjusted to allow comparison across 70 growing years. To capture the variation seen in historical weather sequences as well as the impact of changes in climate, 100 scenarios were generated for each combination of species and adaptation option for the references sites. Twenty separate rotations were simulated by running the model with 20 different planting dates over the 30 year block of base weather data for each of the 5 modified future climates. Where rotation lengths were longer than 10 years, the 30 year block of weather data was looped. 165

175 Data analysis Volume was predicted as the average of at-harvest entire stem volume for the stand. For E. globulus, where drought induced self thinning is modelled to occur, survival was calculated as the percentage of stems that survived the modelled rotation. Where plantations failed (i.e. the modelled rotation did not survive through to harvest age) percentage survival is counted as zero. For P. radiata we do not model mortality within the stand with any certainty (due to lack of validation data for modelling), and consequently only plantation failure was determined i.e. the plantation remains fully stocked until water resources are depleted and the stand dies. For both species, plantation failure resulted from severe water stress in the modelled scenario. Results The results are shown here as distributions of potential outcomes from the combination of planting date and future climates (100 scenarios for each adaptation option). Given they the many uncertainties in climate, inputs and models they are not real probability distributions but they are the modelled likelihood given it is assumed that the model and future climate inputs are correct. Data analysis for E. globulus in SW WA Modelling, consistent with field observation, suggests reducing the stems per ha from 1000 to 800 resulted in little change in production (refer to Figure 54a). Survival improved (to 100%) when stands were thinned to 800 sph compared survival when stocking was left at 1000sph where it ranged between 89% and 99% (refer to Figure 54b). Applying fertiliser to the 800sph treatment increased production by ~8% on average without increasing mortality (<1% mortality). At this site the risk of mortality was reduced to insignificant levels and production increased without changing the risk profile, suggesting that reducing initial stocking could be an effective way of managing risk with minimal impact on production. Fertilising may provide an additional benefit in terms of final volume with no additional risk. 166

176 a. b. Figure 54 a. Site A End of rotation volume for E. globulus over 100 rotations (5 GCM s with 20 planting dates for each GCM). The y axis represents the number of rotations reaching that volume b. End of rotation percentage of trees that survived the rotation. a. b. Site B Figure 55 a. Site B End of rotation volume for E. globulus over 100 rotations (5 GCM s with 20 planting dates for each GCM). The y axis represents the number of rotations reaching that volume b. End of rotation percentage of trees that survived the rotation. At site B, modelling suggests reducing the stems per ha from 1000 to 800 would result in little change in production for the 800sph option compared to the 1000sph option (refer to Figure 55a). Mortality was reduced substantially with the reduction in sph with 80% of rotations experiencing less than 5% mortality compared to less than 25% for the 1000sph option (refer to Figure 55b). Applying fertiliser to the 800 sph option increased production by ~10% on average but this was at an increased risk of tree death with 30% of rotations experiencing up to 10% mortality and 5% of rotations experiencing mortality up to 55%. At this site, where water is limiting growth, (average rainfall is around 750mm), reducing the initial stocking to 800 sph may minimise the risk of mortality and while production can be increased with the application of fertiliser, the risk of mortality will also increase. 167

177 a. a. Site C b. Figure 56 a. Site C E. globulus over 100 rotations (5 GCM s with 20 planting dates for each GCM). The y axis represents the number of rotations reaching that volume b. End of rotation percentage of trees that survived the rotation; the peak below 20% represents catastrophic failure with little or no trees surviving At site C, where rainfall can be as low as ~380mm and evaporation high, (refer to Table 23), the modelling predicted a high proportion of failure irrespective of silvicultural treatment, giving a bimodal distribution of yields, with those rotations that fail during critically dry years at one end of the scale (two consecutive years under 500mm at this site) and those that survive yielding modest production at the other end of the scale (refer to Figure 56a). Reducing the initial stocking may improve production slightly for those rotations that survive (refer to Figure 56a). For drought prone areas such as site C, fertiliser application increases the risk of failure with an additional 20% of sites failing, and reduces overall production through exacerbated drought effects resulting in mortality (refer to Figure 56b). There are limited silvicultural options available to reduce the risk of plantation failure at this site. Data analysis for E. globulus in SE Australia For south east Australia the model predicts a large proportion of plantation failure across many scenarios. This is primarily a result of a severe drought in the historical weather sequence used to create future climates ( is the historical weather sequence defined by the IPCC). During 1982/83 SE Australia experienced extreme drought, with annual rainfalls up to 40% less than average. Combined with the predicted reductions in rainfall under future climates these events often result in plantation failure in the modelling. 168

178 a. b. Site D SPH 1000 (Original) SPH 1000 fertiliser (Option 1) SPH 800 (Option 2) SPH 800 fertiliser (Option3) Figure 57 a. Site D End of rotation volume for E. globulus over 100 rotations (5 GCM s with 20 planting dates for each GCM). The y axis represents the number of rotations reaching that volume b. End of rotation percentage of trees that survived the rotation. Modelling suggests that planting at 1000sph in this region will produce marginally greater volumes ~3% on average than planting at 800sph (refer to Figure 57a). The model predicted a small risk of mortality, less than 10% for the majority of stands where initial stocking was 1000sph, and 1 incidence of plantation failure (refer to Figure 57b). At an initial stocking of 800sph modelling suggests mortality is unlikely. The addition of fertiliser improves productivity at both levels of stocking (~7.7% for 1000sph and ~9.6% for 800sph) to bring production levels almost equal. The risk of mortality is relatively unchanged when fertiliser is added to the original 1000sph silvicultural regime. Adding fertiliser to the 800sph adaptation option increases the risk of mortality slightly with 2 incidents of plantation failure predicted. At this site, where low rainfalls result in water stress (average rainfall ~600mm, potential evaporation ~1200mm), reducing the number of sph to 800 can substantially reduce potential mortality with only a small reduction in production. a. b. Site E SPH 1000 (Original) SPH 1000 fertiliser (Option 1) SPH 800 (Option 2) SPH 800 fertiliser (Option3) Figure 58a. Site E End of rotation volume for E. globulus over 100 rotations (5 GCM s with 20 planting dates for each GCM). The y axis represents the number of rotations reaching that volume b. End of rotation percentage of trees that survived the rotation; the peak below 20% represents plantation failure with little or no trees surviving 169

179 Growth rates can be increased by up to ~10 with the addition of fertiliser, with little change in the risk of mortality. At site E modelling suggests there is little difference in growth rates between the original 1000sph silvicultural regime and 800sph adaptation option (refer to Figure 58a). At initial planting rates of 1000sph there was only a marginal risk of mortality (~2% on average across the rotations) compared to no mortality predicted for the 800sph regime (refer to Figure 58b). The application of fertiliser is predicted to increase productivity by up to 12% at the 1000sph regime and up to 16% for the 800sph silvicultural regime. However, at this site where rainfall can drop to ~300mm (refer to Table 23) modelling suggests there will be a significant increase in the risk of plantation failure for both the 1000 and 800 sph silvicultural regimes (~20 out of 100 rotations). At this site reducing the initial stocking to 800sph had little impact on plantation productivity or the risk of mortality. Fertilisation can increase the growth rates of the stand, up to 16%, but the risk of mortality was substantially higher for both levels of initial stocking. At site F the model predicted there will be a high proportion of failures irrespective of silviculture giving a bimodal distribution of yields, with those rotations that fail during critically dry years at one end of the scale (two consecutive years with rainfall between 300 and 350mm) and those that survive yielding modest production (refer to Figure 59a). Reducing the initial stocking to 800 sph marginally improves production for those rotations that survive (refer to Figure 59a) but overall volumes are still low. Fertiliser application at this site results in an increase in the production of around 7% for those rotations that survive but the risk of failure increases with an additional 10% of sites failing. While there is potential to improve the productivity of surviving stands, there are limited silvicultural options available to reduce the risk of plantation failure at this site where average rainfall is very low (~410mm) and evaporation is high (~1580mm). a. b. Site F SPH 1000 (Original) SPH 1000 fertiliser (Option 1) SPH 800 (Option 2) SPH 800 fertiliser (Option3) Figure 59a. Site F End of rotation volume for E. globulus over 100 rotations (5 GCM s with 20 planting dates for each GCM). The y axis represents the number of rotations reaching that volume b. End of rotation percentage of trees that survived the rotation; the peak below 20% represents plantation failure with little or no trees surviving 170

180 Data analysis for P. radiata SPH1300 (Original) SPH1600 (Option 1) SPH Thin (Option 2) Site C Site D Site E Site G Site H Site I Figure 60 End of rotation volume for P. radiata over 100 rotations (5 GCM s with 20 planting dates for each GCM). The y axis represents the number of rotations reaching that volume. For P. radiata it is not currently possible to model mortality within the stand with any certainty, so only the changes in production are shown here. At all the sites in Figure 60, modelling suggests increasing the level of initial stocking or reducing the number of thinnings to two will improve end of rotation volumes for P. radiata. For most sites the 171

181 gains are relatively small, around 5% on average for the 1600 sph treatment and 8-10% on average where the number of thinnings is reduced. Increasing the level of initial stocking means the site is fully utilised earlier than planting at 1333sph. P. radiata is a species which takes a wait it out approach to drought and is less prone to drought death in the short term than E. globulus (Mitchell et al., 2013). It is possible this strategy can lead to greater volumes at higher planting rates as the stand remains relatively well stocked compared to species such as E. globulus and is able to recover once the site is released from drought. However, CABALA does not fully capture the physiological effects of carbohydrate loss than occurs during drought periods in P radiata (Mitchell et al., 2013) and the subsequent delay in crown recovery that this involves, and the modelled recovery from drought may be faster than realised in the field. The reduction in thinning strategy also increased the utilisation of the site by leaving trees in the ground for longer and harvesting larger trees at thinning and clearfall Site F SPH1300 (Original) SPH1600 (Option 1) SPH Thin (Option 2) a. b Two 35 Year Rotations (Original) Three 23 Year Rotations (Option 1) Site F Frequency Frequency Volume m 3 ha Volume m 3 ha -1 Figure 61 Site F. End of rotation volume for P. radiata over 100 rotations (5 GCM s with 20 planting dates for each GCM). The peak below 100 m 3 ha -1 represents plantation failure with little or no trees surviving. a. represents the results from comparing the silvicultural options explored for the sites in Figure 60. b. examines multiple short rotations. As was predicted with E globulus, at site F the model predicted a high proportion of failures irrespective of silviculture, giving a bimodal distribution of yields, with those rotations that fail during critically dry years at one end of the scale (two consecutive years with rainfall between 300 and 350mm) as seen in Figure 61a. Most silvicultural regimes at this location resulted in high levels of plantation failure. Short rotations have the potential to improve overall productivity by limiting the exposure to critically dry years. Two standard regimes (35 year rotation with a one year fallow) were compared with three short rotations with two years fallow (23 year rotation with two 1 year fallow) to allow comparison across the same weather sequence. Volumes were then adjusted to allow comparison across 70 growing years. The number of plantation failures was reduced for the short rotation with up to 77% survival compared to 55% for the longer rotations did when viewed over a 70 year period. The increase in volume seen for the short rotations was predominately a result of increased survival. Where both long rotations survived, cumulative end of rotation volumes were similar for the 3 short rotations. 172

182 Discussion This analysis focused on analysing plantation response to a number of established adaptation options known to control leaf area index and ultimately forest water use. In thinking about adaptation it is worth remembering, as highlighted in the previous chapter, that there are a number of strategies for dealing with adaption to climate change over long time frames and at the estate level. Hallegatte (2009) suggests strategies such as no regrets management (which yield benefits even in the absence of climate change) and building safety margins into management strategies, that reduce vulnerability and can help deal with uncertainty. Here we have tried to explore this for a range of reference sites through approaches that may improve productivity and/or minimise risk. The results suggest there are three broad situations to be considered for E. globulus: 1) sites where small changes in silviculture will minimise risk and allow options to improve productivity without increasing risk, 2) sites where risk can be minimised without impacting on productivity but any increases in production are likely to come at a higher risk, and, 3) sites where standard silvicultural practices are unlikely to reduce risk or improve productivity and transformational change is required For P. radiata, in most instances maximising the utilisation of the sites through increased initial planting rates or delaying and reducing the number of thinnings improved productivity, though it was not possible to determine the risk associated with these strategies, except in the cases where the model predicted total plantation failure due to canopy collapse. In these situations, a shorter rotation improved relative productivity through the reduction of exposure to critically dry years within any given rotation. Some further analysis is required here and a clear understanding of critical times in the rotation for plantation failure is required. For instance, probability of plantation failure is higher during establishment (or at any particular growth phase that is a short part of the total rotation length) or if salvaging of dead trees has less value for young stands than old stands then longer rotations may in fact yield higher production or expected value. For E. globulus two adaptation options were examined in detail: reducing the number of stems per hectare to reduce water stress and the application of fertiliser to improve productivity. For all the reference sites in this analysis, water is limiting production. Reducing the number of stems per hectare from 1000 to 800 reduced mortality for sites A, B, D and E without significantly impacting on production. This represents a no regrets management option for water limited regions, where a small change can reduce risk without cost. The application of fertiliser has the potential to improve productivity but for many sites in water limited environments there will be an increase in the risk of tree death. As shown in White et al., 2009, rapid increases in leaf area index following fertilisation may increase drought risk. For sites A and D, reducing the number of sph to 800 can significantly reduce the risk associated with fertiliser application but for sites B and E risk will increase and the trade off between risk and productivity must be carefully considered. For sites C and F, no adaption options for E. globulus were identified that 173

183 could significantly reduce the risk of plantation failure. Transformational change, in the form of species change or land-use-change, is required for these sites. For P. radiata two main slivicultural options were examined in detail for all sites: increasing the initial stocking to 1600 sph, reducing the number of thinnings to two and postponing the time of first thinning to age 15. These strategies are designed to fully utilise the site for longer periods but we are unable to capture the risk of mortality that is sometimes seen in the field with higher stocking or the delayed thinning of stands, as mortality in P. radiata is often a combination of drought and pest damage (Stone et al., 2012, Jody Bruce pers. comm.). At site F where catastrophic mortality was simulated to occur in some years and it was found that reducing the overall rotation length by 12 years substantially reduced the risk of drought death. The high level of mortality predicted was inpart due a severe drought in the historical weather sequence. It may be that as climates dry it is the impact of these infrequent but intense events rather than any average climate shift (or more specifically the effect of declining average on the magnitude of extreme events) that needs to be considered in designing adaptation. In parts of the national plantation estate, in particular the E. globulus plantings, transformational change may be required in some situations at the margins of the estate. For sites C and F, E. globulus had high rates of mortality from drought. P. radiata may be a suitable alternative species as modelling suggests reasonable growth rates can be achieved, in particular for site C. At site F, where there is potential for extreme drought, short rotations can significantly reduce the risk of failure by reducing the exposure to extreme drought. The ecophysiological differences observed between our two main plantation species offers some management options: we can utilise the differences by matching attributes to sites. P. radiata utilises a wait it out strategy with strong regulation of water status that enables the species to survive long periods of drought but results in depletion of carbohydrates within the whole tree which can inhibit recovery with repeated droughts (Mitchell et al., 2013). E. globulus typifies a resilience type strategy, where rapid loss of hydraulic function means the trees succumb to drought quickly but without substantial carbohydrate depletion (Mitchell et al., 2013). This means that, where mortality is minimal (a self thinning type mortality event), stored carbohydrates are available for recovery of hydraulic pathways and ultimately production. Where mortality is very high or the drought is prolonged (e.g. over two years), there is high probability of plantation failure because of complete failure of hydraulic pathways. Conclusion Come 2030, good silvicultural management has the potential to mitigate the negative impacts of climate change for both P. radiata and E. globulus over much of their planted range. Small changes in management can reduce the level of risk of mortality markedly, in particular for E. globulus, with little impact on production. However, there are some parts of the edge of the current estate that will require transformational change, such as a change in plantation species or land use. These areas are highlighted in the maps presented in Chapter 3. The inherent differences in the physiological response of our two main plantation species to drought can be exploited by matching attribute to site in some cases. 174

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213 Acknowledgements We are indebted to the members of our Project Steering Committee (Christine Stone, Sandra Hetherington, Jan Rombouts, Ian Ravenwood, Ian Last, Sara Mathieson, Andrew Callister, David Ellsworth, Tim Wardlaw, Rob Musk, and Caroline Mohammed) for their guidance and inputs into the project, and particularly to the Chair of that committee, Chris Mitchell. We thank the following organisations for their in-kind contributions to the project: Norske Skog, University of Western Sydney, University of Tasmania, WAPRES, Forestry Tasmania, Forestry SA, State Forests NSW, Treehouse Consulting, Private Forests Tasmania, CO2 Australia, Forest Products Queensland. Steve Read provided valuable input into decisions around the tools the project should produce. We held useful discussions with Phil Lacey, Andrew Moore, Phil Mason and Andrew Lyon about adaptation strategies and decision tools. 204

214 Appendix 1: review of climatic triggers for key pests of Australia s plantations 205

215 Introduction The following summarises the current understanding of distribution, and climatic factors influencing distribution, of the species listed in Table 11. Information is quite detailed for some species, but other species have not been studied extensively. Hence the lack of discussion of specific climatic triggers for a species does not necessarily indicate that those triggers are unimportant; rather, it suggests a lack of information. Further study of climatic triggers for the following species would be beneficial in understanding the implications of climate change for pest hazard. A1.1 Leaf chewers- eucalypts Species: Paropsis atomaria Common name: Eucalyptus tortoise beetle Significance: H Defoliation pattern: Top-down Current distribution: This species has a wide range, from temperate regions through to the wet tropics (Figure A1, Source: The Australian Plant Pest Database ( Outbreaks are largely confined to the wet tropics. General: Paropsis atomaria is a key defoliating pest of eucalypt plantations. Host species include at least 20 eucalypts. It is a documented pest of plantation-grown Eucalytpus grandis, E. cloeziana and E. pilularis plantations in Queensland and New South Wales, E. camaldulensis and E. dunnii in New South Wales (Nahrung et al. 2008), and E. globulus in Victoria and South Australia (Collett 2001). P. atomaria has four larval instars and longlived adults, and all life stages feed on new growth targeting apical leaves. The species is considered to be one of the major pest risks to subtropical eucalypt plantations. It is bivoltine in NSW but may undergo up to four generations in southern Queensland (Nahrung et al. 2008). Anticipated responses to climate change: This species responds strongly to temperature. It requires 769 degree days above 6.4 C to complete one lifecycle, and this is consistent across the species range (Nahrung et al. 2008). Its capacity for greater numbers of generations in the warmer than the cooler parts of its range highlights the potential for an increase in the number of generations per season as temperatures warm. However mortality rates also increase with temperature, with temperatures above 24 C causing significant mortality in laboratory trials (Nahrung et al. 2008). Increased voltinism will also be dependent on host leaf quality; if the young flush foliage is not present then this will affect the insect s development rates and survival. The start of diapause is likely to be controlled primarily by photoperiod, but diapause may be subverted by high temperatures (Nahrung et al. 2004). Outbreaks are mostly confined to the subtropics, suggesting that a warming climate may result in a southwards shift in outbreaks. Lawson (in (Ireland et al. 2011)) examined the response of P. atomaria to climate change using the population dynamics model ParopSys (Nahrung et al. 2008). He predicted low likelihood of significant range change for the species in 2030 or 2070, except for expansion of the potential range into Tasmania. By 206

216 2070, beetle performance within its range was predicted to improve in the wet tropics and in inland NSW and Victoria. Species: Paropsisterna agricola Common name: Eucalypt leaf beetle Significance: H Defoliation pattern: Top-down Current distribution: P. agricola s distribution encompasses Tasmania, Victoria and New South Wales (Figure A1, Source: The Australian Plant Pest Database). General: P. agricola is a serious pest of eudcalypt plantations in south eastern Australia, with host species icluding E. globulus, E. nitens and E. camaldulensis. (Nahrung 2004). It occurs in NSW, Victoria and Tasmania. The adults and larvae feed on young foliage, with adults preferentially browsing on adult foliage and laying eggs on juvenile foliage (Nahrung and Allen 2003). The adults feed before overwintering in shelter found onsite or adjacent to plantations (Nahrung and Reid 2002). The most vigorous feeding by adults occurs prior to overwintering, and this late season derfoliaton can have a greater impact than early-season defoliation because crown recovery mayb e delayed until the following growing season (Pinkard et al. 2006b). Overwintering adults emerge in october-november, and lay eggs between November and March (Ramsden and Elek 1998). The species is usually univoltine in Tasmania and bivoltine in Victoria and NSW (Collett 2001). Anticipated responses to climate change: The lifecycle of P. agricola is closely linked to temperature. It requires 400 degree days above 8 C to complete one lifecycle (Nahrung et al. 2004). Termination of diapause requires 615 degree days above 6.7 C. The start of diapause is largely controlled by photoperiod, but diapause may be subverted by high temperatures (Nahrung et al. 2004). Warmer winter temperatures may therefore result in an earlier termination of diapause, and heatwaves in autumn may affect the initiation of diapause. Earlier diapause initiation may result in a high mortality rate if the host is not actively growing, but it may also provide opportunity for an additional generation per season. Even though this additional generation may not mate before the onset of winter, the larvae could inflict severe damage in autumn. Species: Paropsisterna bimaculata Common name: Eucalypt leaf beetle Significance: H Defoliation pattern: Top-down Current distribution: This species is confined to Tasmania (Figure A1, Source: The Australian Plant Pest Database), where it occurs in all plantation-growing regions. General: P. bimaculata is the major defoliating insect in plantations and native forests in Tasmania (Candy et al. 1992a; Clarke 1998b; Delittle 1983; Elliott et al. 1993), and recent surveys have highlighted a steady increase in the area of plantations experiencing moderate or severe defoliation from this species(wardlaw et al. 2011). Both adult and larval stages feed on juvenile and mature eucalypt foliage meaning that damage can 207

217 occur throughout a rotation, and some of the most significant damage occurs in older stands; volume increment is close to zero when later age defoliation is above around 60% (Wardlaw et al. 2011). The species feeds in colonies on new season s growth, resulting in a top-down defoliation pattern (Delittle 1983). It has a wide host range, including E. globulus and E. nitens. P. bimaculata is generally univoltine (Delittle 1983). Anticipated responses to climate change: As with the other paropsine beetles, P. bimaculata responds strongly to temperature cues. It needs 496 degree days above a threshold temperature of between 3.7 and 5.4 C to complete its lifecycle from egg to adult (Clarke 1998a). Diapause has not been as well studied for this species as for P. agricola, but there is evidence of similar termination requirements for the two species (615 degree days above 6.7 C) (Nahrung 2004). Warmer winter temperatures may therefore result in an earlier termination of diapause, which may provide opportunity for an additional generation per year. Even though this additional generation may not mate before the onset of winter, the larvae could inflict severe damage in autumn. The amount of newly-generated foliage present plays an important role in stimulating oviposition in P. bimaculata (Steinbauer et al. 1998); hence oviposition success may be reduced if the host is not actively growing. Figure A1. Current distribution of 3 species of eucalypt beetle. From Australian Plant Pest database Species: Gonipterus scutellatus Common name: Eucalypt weevil Significance: H Defoliation pattern: top down; includes damage to young stems Number of generations per year: 1 in sw Australia, 1+ elsewhere Current distribution: Gonipterus scutellatus and its sub-species are found in plantationgrowing regions in Queensland, NSW, Victoria, Tasmania and WA. The distribution of G. scutellatus is shown in Figure A2 (from the Australian Plant Pest database) General: G. scutellatus is a major pest of eucalypt plantations across southern Australia. It is considered to be the most damaging pest in south west Western Australian eucalypt plantations (Loch and Matsuki 2010), and causes significant damage in other parts of Australia and countries such as South Africa and Chile (Miles et al. 2011; Ojeda and Alarcon 2011). Both larvae and adults of G. scutellatus feed on new season s growth, resulting in a top-down defoliation pattern (Loch and Floyd 2001) and tip dieback (Elliott et al. 1998; Matsuki and Tovar 2010b). Larvae will feed on both juvenile and adult foliage, and is the most damaging life stage (Matsuki and Tovar 2010b). Adults prefer 208

218 adult foliage, and hence plantations younger than two years are rarely damaged by adults (Matsuki and Tovar 2010b). The adults can be active for 8 months of the year. In SW Western Australia there is a single generation per year (Loch and Matsuki 2010), whereas in South Australia there are two generations per year (Phillips 1992a), Anticipated responses to climate change: As with the paropsine beetles, G. scutellatus responds strongly to temperature. To complete a full life cycle from egg to adult, the weevil requires a mean of ± degree days above a base temperature of 6.1 C (Santolamazza-Carbone et al. 2006). This developmental time is strongly influenced by temperature; at 10 C the required degree days is 1125, but at 26 C the degree-day requirement is It was predicted that in Spain there was scope for up to 10 generations per year at warmer sites, which suggests that warming climate will be beneficial to this species (Santolamazza-Carbone et al. 2006). However the success of further generations will depend on the presence of the new foliage that the species prefers; in Western Australia for example it is hypothesised that the species is univoltine partly because of the lack of young flushing foliage in summer and autumn (Loch and Matsuki 2010). The eggs of G. scutellatus do not develop at temperatures below 6.5 C (Santolamazza-Carbone et al. 2006). Figure A2. G. scutellatus current distribution. From Australian Plant Pest Database Species: Mnesampela privata Common name: Autumn gum moth Significance: H Defoliation pattern: bottom up Number of generations per year: 1-2 in SE Australia, 1 in SW Australia Current distribution: This species occurs in New South Wales, Victoria, Tasmania, South Australia and Western Australia (Figure A3, from (Pinkard et al. 2008) General: Autumn gum moth is a native Australian species that has a diverse host range including the plantation species E. globulus, E. grandis, E. nitens, E. camaldulensis, Corymbia maculata and E. dunnii (Ostrand et al. 2008; Paine et al. 2011; Steinbauer and Matsuki 2004). It causes significant damage to plantations in Tasmania, Victoria, South Australia, Western Australia and New South Wales, although its activity tends to be localised. Adults emerge in autumn and commence oviposition immediately. Populations of M. privata can outbreak and cause whole-tree defoliation of entire plantations (Steinbauer et al. 2001). The larvae feed through autumn and into winter before entering diapause. The females show a preference for waxy foliage meaning that juvenile foliage is primarily targeted (Ostrand et al. 2008). Hence damage is generally confined to young plantations (< 3 years of age). Defoliation is generally restricted to the lower crown, although in severe cases whole trees may be affected (Matsuki and Tovar 2010a), including buds. 209

219 Anticipated responses to climate change: M. privata requires 1268 degree days above 5 C in order to complete its lifecycle (Lukacs 1999). Temperature also influences the time taken for pupae to develop from 50 days at 11 C to 10 days at C (Lukacs 1999). Soil temperatures affect adult development rates, with soil temperatures less than 18 C resulting in summer emergence of adults, and soil temperatures greater than 18 C resulting in autumn emergence. Diapause is triggered by daylength, and adults emerging before the autumn equinox do not enter diapauses whereas adults emerging after the equinox enter diapauses (Steinbauer et al. 2004). Hence, warmer air temperatures will reduce the number of days taken to complete a generation, and increase the likelihood of multiple generations per season., although warmer soil temperatures may delay adult emergence to a time when shorter daylength triggers diapauses (Lukacs 1999). There is inter-genetic variation in foliar waxes and chemical constitutents of eucalypt foliage that are known to affect M. privata survival and larval performance (Rapley et al. 2004; Steinbauer and Matsuki 2004). Reduced larval feeding has also been linked to lower specific leaf area (area: weight ratio) (Steinbauer 2001). Exposure to elevated atmospheric CO 2 concentrations has been shown to increase cuticular waxes in trembling aspen (Populus tremuloides) (Percy et al. 2002), and can affect the composition of defence chemicals (Hunter 2001) and decrease specific leaf area (Ainsworth and Long 2005). The consequences of climate change to the host will clearly influence M. privata in the future. Figure A3. M. privata current distribution, from Australian Plant Pest Database Species: Uraba lugens Common name: gum leaf skeletonizer Significance: H, localised Defoliation pattern: Bottom up Current distribution: U. lugens occurs throughout southern Australia and into Queensland (Figure A4, from the Australian Plant Pest database) General: Uraba lugens is native to Australia. It has a broad geographical distribution (Figure A4) and a wide host range that encompasses the plantation species E. globulus, E. nitens, E. dunnii, E. grandis and E. camaldulensis (Berndt and Allen 2010; Farr 2002). It is responsible for significant but localised damage in eucalypt plantations. Young larvae feed on the upper and lower epidermis, palisade tissue and spongy mesophyll resulting in leaves being skeletonised, while older larvae consume all leaf tissue (Phillips 1992b). Most damage occurs in the late caterpillar stages. Inland and coastal forms of U. lugens can have two generations per year, with damage occurring between December 210

220 and March and from April to May. The highland form has a single generation per year, with damage occurring from June to July (Phillips 1992b). Anticipated responses to climate change: The change in voltinism between high and low altitude sites for this species has been suggested to be related to temperature (Berndt and Allen 2010; Farr et al. 2004), and highlights the potential for the species to respond favourably to warmer temperatures. Allen and Keller (Allen and Keller 1991) report that larval development has a lower temperature threshold of 12.2 C and requires 460 degree days; pupal development requires 223 degree days and a lower temperature threshold of 9.1 C. Outbreaks have been associated with drought and flood cycles, although it is unclear whether the insect responds directly to these cues or to decreases in host vigour (Farr et al. 2004). Figure A4. U. lugens current distribution, from Australian Plant Pest Database Species: Heteronyx spp Common name: Spring or scarab beetles Significance: H, regional Defoliation pattern: Top-down Current distribution: These species occur throughout Australia. The species responsible for damage in eucalypt plantations are found in Western Australia, South Australia, Tasmania and New South Wales (Figure A5, from Australian Plant Pest Database). General: A number of Heteronyx species have been observed to cause damage in E. globulus and E. nitens plantations in WA, Tasmania and the Green Triangle, including H. dimidiate, H. elongatus, H. excius and H. imitator (Matsuki and Tovar 2012). In E. nitens plantations oviposition occurs outside of plantations meaning few larvae are present (Walker and Allen 2013). Overwintering adult beetles are present in plantations in spring, and newly eclosed beetles invade plantations during late summer and autumn (Walker and Allen 2013). The adults feed on new adult leaves, and when populations are high shoots can also be damaged resulting in branch and tip death. Some species also feed on juvenile leaves. The larvae and/or adults of some species also feed on seedling roots, causing significant mortality (eg H. elongatus). In southern Australia the larvae are generally found from autumn to early summer. At maturity, adult beetles of most species emerge at night to feed, returning to the soil before dawn. Mass emergence (swarming) can occur, and is thought to be controlled by environmental conditions such as temperature, soil moisture, and relative humidity (Matsuki and Tovar 2012). These species can take more than one year to complete their lifecycle (Steinbauer and Weir 2007), which increases the unpredictability of mass swarming events. 211

221 Anticipated responses to climate change: Little is understood of the effects of climate on the Heteronyx species found in Australian plantations. There is some evidence from studies with other Melolonthinae that temperature influences distribution (Garcia-Lopez et al. 2012). Walker and Allen (Walker and Allen 2013) found that soil temperatures above 12 C and solar radiation above 6 MJ m -2 increased H. dimidiata activity on trees, while windy days reduced activity. Figure A5. Current distribution of two Heteronyx species. From Australian Plant Pest Database Species: Liparetrus spp Common name: Spring or scarab beetles Significance: H, regional Defoliation pattern: Entire crown Current distribution: The distribution of Lipatertus spp are poorly documented in Australia. Figure A6 shows the distribution of two species found in eucalypt plantations, L. jenkensi and L. discipennis (from the Australian Plant pest database), but it is likely that these distributions do not cover all plantation locations where the species cause problems. General: A number of Liparetrus species are known to cause defoliation of E. globulus seedlings and young trees in southern Australia, including L. jenkensi (WA) and L. discipennis (GT) (Matsuki and Tovar 2010c). The larvae live in the soil and feed on fine roots. Adults emerge from pupation when rain softens the soil, resulting in mass emergence of adults. They emerge from the soil to feed when temperatures are greater than 21 C. These pests feed on leaves, branches and young stems, causing moderate to severe damage. Anticipated responses to climate change: We could find no explicit reference to the climatic triggers for Liparetrus spp, and assume they are similar to Heteronyx. 212