Deployment areas of Scots pine and Norway spruce seeds revisited and revised. NordGen Forest Conference 2014 Son, Norway

Transcription

1 Deployment areas of Scots pine and Norway spruce seeds revisited and revised. NordGen Forest Conference 2014 Son, Norway

2 Presentation overview Climate change & general background The Scots pine project for Finland and Sweden. - Data and modeling - Results (prel.) and implementation The Norway spruce project - Background & comparison to Pine project - Data and modelling approaches

3 Climate change and breeding Two time frames. Now and next generation of breeding material (20-30 years in the future). Breeding programs (e.g. multiple populations) and breeding goals (including robustness in selection) Develop deployment recommendations for contemporary seed sources taking both current and future climate into account.

4 Scots pine project outline Project involving Skogforsk, METLA, SMHI, FMI. Financed by e.g. Noveltree, Swedish Forest Tree Breeding Association Develop new Scots pine transfer functions for growth and survival valid for both n. Sweden and Finland The new transfer functions should be based state-of-theart, high-resolution climate data and compatible to established and new (radiative forcing) scenarios. Implement these functions in a new web-based desicion support tool ( Planter s guide 2 ).

5 Field data Sweden and Finland Provenance- and progeny trials Entries with known origin (Lat/Long) Recorded height and survival Large geographic range (trials/origins) Large dataset: - 48 provenance trials (30 F, 18 S) - 71 provenances progeny trials (259 F, 71 S) check-lots (62 F, 41 S)

6 Climate models GCM RCM GCM: km resolution RCM: 5-25 km resolution RCM compatible to climate scenarios Many climate indices available In total grids 4x4 km grid size in Sweden 10x10 km grid size in Finland Illustrations: SMHI

7 Selection of climate data Site/location: Latitude, Longitude, Altitude Climate: Temperature sum, Mean temperature, Vegetation period, precipitation, drought. Climate index Unit Resolution Variable name Total precipitation mm year, season Precip_sum Daily mean temperature 1 C year, JAN, FEB, JUL T2mean_mean Start of vegetation period 2 Day nr year T2mean_dayVegStart5 End of vegetation period 3 Day nr year T2mean_dayVegEnd5 Length of vegetation period No. days year T2mean_lenVegPeriod5 Day degrees during vegetation period 4 Day degrees year T2mean_GDD5 Longest continuous period with precipitation <1 mm/dag days/year maxdryspell1

8 Modeling structure Climate data Selected climate indices Climate and Field data Field data Provenance & progeny trials [Growth, Survival] ~ f(climate/site indices)

9 Preliminary results Robust models valid for both Sweden and Finland. The models are biologically reasonable and consistent with established knowledge. Transfer functions are driven by photoperiod and its interaction with temperature sum and altitude. Site characteristics are explained by temperature sum and yearly mean temperature.

10 Preliminary results - height Mild sites: Northward transfer = increased growth Harsh sites: Zero transfer seems optimal for growth Northward transfer Southward transfer

11 Preliminary results - survival Harsh sites: Large increase in survival by southward transfer Mild sites: Slight increase in survival by southward transfer Northward transfer Southward transfer

12 Project overview Climate data Selected climate indices Climate and Field data Field data Provenance & progeny trials [Growth, Survival] ~ f(climate/site indices) Climate data Current climate Deployment recommendations - Current climate - Future climate Seed orchard data Scenario data Future climate

13 Dissemination of deployment recommendations Devlopment of a publicly available webtool New and improved production functions Covering Sweden and Finland User-defined climate scenarios available

14 The Norway spruce project Collaborative effort involving Sweden, Finland, Norway and the Baltic States. Develop new Norway spruce transfer functions for growth, survival (damages) applicable to Sweden and the collaborating countries. The new functions will be based on state-of-the-art, high resolution climate data and compatible to established and new (radiative forcing) scenarios. Implement these functions in a new web-based desicion support tool ( Planter s guide 2 ).

15 Spruce vs Pine Similar basic structure in both species/projects but there are some differences: Covers a much larger and more climatically variable geographic area. Norway spruce is not a pioneer plant and growth rhythm is very important for its performace (and in addition highly heritable). Particularly damages from late spring frosts have been considered a problem e.g. in southern Sweden.

16 Field data Vast amounts of field data. Several hundred trials identified as suitable for this study. Data primarily from Sweden, Finland and Norway (also Baltic States). Provenances also from Central and Eastern Europe. Growth and vitality records used.

17 New climate data EURO4M Global atmospheric state: ERA Interim reanalysis data High-resolution (5x5 km) Bias-corrected Available during 2014 Covering entire Europe Regional atmospheric state: HIRLAM/3DVar data assimilation - ERA Interim on the borders and as large scale constraint - 60 vertical levels - 22 km horizontal resolution Surface meteorological data: 2D: MESAN - HIRLAM/3DVar as first guess - Surface parameters - 5 km grid Tomas Landelius/Lars Bärring, SMHI, oktober 2013

18 Selection of climate data Site/location: Latitude, Longitude, Altitude Climate: Temperature sum, Mean temperature, Vegetation period, precipitation, drought. Indices relating growth rhythm to frost risk and damages/reduced growth. Collaboration initiated with Lund university and SMHI Rossby centre to develop such indices.

19 Development of new indices A need to develop model variables relating geographic origin with growth rhythm. Example: Critical temperature-sum requirement for bud-burst TSUM ack (Ref: Hannerz, 1999)

20 Approach for frost index TSUM ACK =120 TSUM ACK =180 TSUM ACK =150 TSUM ACK =195

21 Spruce project - status 2013 Collaborations initiated Select, pre-analyze and compile field data from collaboration countries in a common database Develop relevant climate indices and calculate these for the new high-resolution climate models. - Connect field data with climate data in a database - Analysis of data. Development of new Norway spruce transfer functions. - Implement new models in a web-tool for deployment recommendations ( Planter s guide 2 )

22 Benefits from collaboration Transfer functions based on a very large dataset. Possibilites to find more complex patterns. Common deployment recommendations possible. A web-tool platform is available. Large database with climate and field data available for separate studies in each country.

23 Project researchers/partners and funding agencies Scots pine project Skogforsk: Mats Berlin, Bengt Andersson Gull, Torgny Persson, Gunnar Jansson SMHI/Rossby Centre: Lars Bärring, Anna Lilja Metla: Matti Haapanen, Seppo Ruotsalainen, Egbert Beuker FMI Norway spruce project Skogforsk: Mats Berlin, Bengt Andersson Gull, Johan Westin, Bo Karlsson Bergvik skog AB: Jenny Lundströmer (PhD-student) SMHI/Rossby Centre: Lars Bärring, Karin Nyström, Researchers from Finland (Metla, ), Norway (Skog og landskap, Skogfrøverket, ), Estonia, Latvia, Lithuania. Climate researchers in collaborating countries