Complexity in Forestry Decision Support Tools: How much do we need?

Transcription

1 Complexity in Forestry Decision Support Tools: How much do we need? J. P. (Hamish) Kimmins Professor of Forest Ecology Canada Research Chair in Forest Ecosystem Modeling Dept. of Forest Sciences, Faculty of Forestry University of British Columbia, Vancouver, B.C.

2 Outline The argument for ecosystem-level hybrid simulation decision support tools Complexity in modeling how much is enough? The FORECAST family of hybrid simulation models The importance of visualization

3 Take-home Message It is time for process-based, ecosystem-level, hybrid decision support tools to go mainstream in forestry Issues in forestry are complex, multi-value, cross multiple spatial scales, and involve anticipated climate change and time scales of several cycles of disturbance

4 1. Why do we need ecosystem-level decision support tools in forestry?

5 Evolution of Forestry Sustainable Exploitation Passive Management Active Management Replaced by Non-sustainable exploitation Leads to Administrative forestry Evolves into Ecologically-based forestry (EBM), initially timber biased Resource depletion Variable results; often single value Sustained timber production +++? Application of social and biophysical sciences that respect the ecology and sociology of desired values Integrated management of stands and landscapes for multiple values. FOREST ECOSYSTEM MANAGEMENT

6 Evolution of Decision-support Tools Exploitation no DSTs Administrative timber management simple, historical bioassay, G and Y models Ecologically-based timber management simple, generally light-driven, population models Ecosystem management for multiple values ecosystem-level, multi-scale, multi value scenario and value tradeoff models

7 Paradigms for a New Forestry Ecosystem-based management Ecosystem management Adaptive management Zonation Variable retention Natural range of variation Results-based vs regulations Monitoring/certification All need multi-value forecasting tools and decision-support support systems at a variety of spatial scales

8 Forestry is about people - values, needs, desires Decision support tools should address the following values Wood Non-wood products Water Wildlife Biodiversity Aesthetics Recreation Employment Spiritual values Environmental protection Ecosystem processes Economics - wealth creation Bioenergy - fuel

9 Three Main types of DSTs in Forestry Traditional, experience-based, single value forecasting tools - historical bioassay models - cannot address futures that are significantly different from those of the past

10 Experience-based prediction tools based on induction Single value (e.g. yield table) historical bioassay models Observations, Descriptions, Classifications, Knowledge Simple; implicit complexity, but inflexible Foundations of science Induction, experience based models Problem, Issue, Desired Future Policy, Practice

11 Three Main types of DSTs in Forestry Traditional, experience-based forecasting tools - historical bioassay models - cannot address futures that are significantly different from those of the past Predictive planning tools based on understanding of ecosystems - process models - are generally too complex or too simple if they are not

12 Simple process simulation models based on our understanding of ecosystem subunits, based on traditional hard science Reductionism Theory or Postulate Deduction Hypotheses Hypothesis testing Induction Experimental observations Observations, Descriptions, Classifications, Knowledge Foundations of science Explicit complexity, but generally single or few values and incomplete Application of disciplinary knowledge in subecosystem models Problem, Issue, Desired Future Policy, Practice

13 Three Main types of DSTs in Forestry Traditional, experience-based forecasting tools - historical bioassay models - cannot address futures that are significantly different from those of the past Predictive planning tools based on understanding of ecosystems - process models - are generally too complex or too simple if they are not Models based on both experience and understanding hybrid models - have flexibility without all of the problems of purely process models

14 Complex decision support tools based on the synthesis of our understanding of the key Scientific principles Scientific laws Repeated testing components and processes of ecosystems Theory or Postulate Deduction Reductionism Hypotheses Hypothesis testing Deduction Induction Observations, Descriptions, Classifications, Knowledge Foundations of science Problem, Issue, Desired Future Validation Computer models Synthesis into complex hypotheses Induction, Experience Induction Hypothesis testing Predictions Experimental observations Scenario analysis supported by visualization Policy, Practice Explicit, ecosystem-level complexity addressing multiple values

15 Decision Support Systems in Support of Sustainable Forestry Should they be ecosystem-level simulators? the issues in forestry are mostly ecosystem level, not population level or community level, and they require management.

16 Levels of biological organization Levels of biological integration C O M P L E X I T Y Ecosystem Community Population Individual Organ systems Organs, tissues Cell Sub-cellular - Understanding - Understanding - Understanding - Understanding - Understanding - Prediction - Prediction - Prediction Ecosystem Individual Cell The need for the ecosystem level : PREDICTION

17 Decision Support Systems in Support of Sustainable Forestry Should they be ecosystem-level management simulators? YES Should they represent the key processes explicitly? the fundamentals of production ecology, and key population and community processes

18 Productive Capacity of Ecosystems Leaf area and photosynthetic efficiency Light Water Nutrients Net photosynthesis Solar energy Respiration Carbon allocation Net primary production Litterfall,, plant death, root death, herbivory Net biomass accumulation Climate Unharvested biomass/energy Harvestable biomass/energy

19 Decision Support Systems in Support of Sustainable Forestry Should they be ecosystem-level management simulators? YES Should they explicitly represent the key processes? YES Should they be able to model complex successional pathways? - the multiple values desired by society are related to succession as well as stand dynamics

20 Examples of Over-simplification in DST s succession on northern Vancouver Island - light, shade tolerance, nutrition, nutrient cycling, mycorrhizae,, wind, diseases, allelopathy,, minor vegetation

21 CH Two stand types on northern Vancouver Island HA Succession?? How???

22 Simple light-driven model of succession WRONG!

23 Ecosystem model of the role of disturbance on northern Vancouver Island: The PhD work of Adrian Weber

24 Examples of Over-simplification in DST s Succession on northern Vancouver Island Chinese fir yield decline nutrients, light, competition from herbs and shrubs, seed bank vs bud bank

25 Conceptual Model of Chinese fir Yield Decline supported by simulation Data

26 2. Complexity in forest modeling how much is enough? Occam: Einstein: As simple as possible, but as complex as necessary As simple as possible, but no simpler It depends on the application

27 Model Performance at Different Levels of Complexity Comparison of FORECAST and the growth and yield model TIPSY: 1. Boreal white spruce in BC 2. Douglas-fir on the BC coast

28 FORECAST-TIPSY Comparison: Light Only Top Height Ref SI: 16 Species: Sw Regen: 1600 sph Top Height (m) Stand Age (y) FORECAST light only TIPSY Ratio FORECAST/TIPSY Ratio

29 FORECAST-TIPSY Comparison: Light Only Stand Density Ref SI: 16 Species: Sw Regen: 1600 sph Stems per hectare FORECAST light only TIPSY Ratio Stand Age (y) FORECAST/TIPSY Ratio

30 FORECAST-TIPSY Comparison: Light Only Gross Volume Ref SI: 16 Species: Sw Regen: 1600 sph Gross Vol. (m 3 ha -1 ) Stand Age (y) FORECAST light only TIPSY Ratio FORECAST / TIPSY Ratio

31 FORECAST-TIPSY Comparison: Light Only Merchantable Volume (12.5cm top) Ref SI: 16 Species: Sw Regen: 1600 sph Merch Vol. (m 3 ha -1 ) FORECAST light only 0.6 TIPSY 0.4 Ratio Stand Age (y) FORECAST / TIPSY Ratio

32 FORECAST-TIPSY Comparison: Adding Ecology FORECAST/TIPSY Ratio Ratio of FORECAST to TIPSY Merchantable Volume Stand Age (y) Ref SI: 16 Species: Sw Regen: 1600 sph US: Calamagrostis 1:1 line Light Light + Nut Light + US All

33 FORECAST-TIPSY Comparison: Understory Competition FORECAST/TIPSY Ratio Ratio of FORECAST to TIPSY Merchantable Volume Stand Age (y) Ref SI: 16 Species: Sw Regen: 1600 sph US: Calamagrostis 1:1 line No US Moderate US Heavy US

34 Comparison for Douglas-fir at various levels of planting density FORECAST/TIPSY ratio ,000 3,000 1, Year Fd Planting density

35 Comparison for Douglas-fir, 2000 sph, FORECAST at various levels of complexity 2 FORECAST/TIPSY Ratio L+N L L+N+U L+U Year

36 So, which is nearer to reality, if we know what that is? FORECAST or TIPSY? Test of FORECAST against CFS Shawnigan Lake data

37 SHAWNIGAN LAKE: Experimental design Douglas-fir stand 60 years old Thinning and fertilization trials established in 1970 Treatments Thinning: T0: control T1: thinning 30% stems T2: thinning 60% stems Fertilization: F0: control F1: 224 kg N ha -1 F2: 448 kg N ha -1 Pacific Ocean Vancouver Island Pacific Ocean Victoria

38 CONTROL PLOTS

39 THINNING 60% STEMS

40 FERTILIZATION 448 kg N ha-1

41 THINNING 60% STEM + FERTILIZATION 448 kg N ha -1

42 Global evaluation Statistic Top height Phase 1 (m) Phas e 2 Merchantable Volume (m 3 ha -1 ) Phase 1 Phase 2 Density (stem ha -1 ) Phase 1 Phase 2 Stem biomass (Mg ha -1 ) Mean Difference σ 2 mean difference R 2 observed vs. predicted Aboveground biomass (Mg ha -1 ) Janus quotient Phase Phase Phase Phase Theil s coefficient Modelling eficiency 3 1 Janus quotient: Model accuracy (0 ) J = 0 Accurate model (Gadd and Wold 1964) 2 Theil s equality coefficient: Model behaviour (Theil 1967) (0 ) T = 0 Effective model T > 1 Model less effective than no-change hypothesis 3 Modelling efficiency: Model efficiency (Vanclay y Skovgaard 1997) (0 1) E = 1 Efficient model E = 0 Model as efficient as the average of the mean of the observations

43 Other ecosystem-level processes STAND AGE LITTERFALL PRODUCTION Litterfall (kg ha -1 a -1 ) N in Litterfall (kg ha -1 a -1 ) N concentration (mg g -1 ) Trofymow et al. (1991) Mitchell et al. (1996) FORECAST FOLIAR EFFICIENCY kg aboveground biomass / kg foliage biomass Brix (1982) Barclay et al. (1986) Mitchell et al. (1996) FORECAST kg stemwood / kg foliage biomass NUTRIENT UPTAKE Net uptake (kg ha -1 a -1 ) Mitchell et al. (1996) FORECAST MORTALITY Biomass (kg ha -1 a -1 ) Mitchell et al. (1996) FORECAST N content (kg ha -1 a -1 ) N concentration (mg g -1 )

44 A test of FORECAST simulations of boreal mixedwoods (in press) yielded a Modelling Efficiency rating for 7 stand variables for four species from

45 Conclusion FORECAST appears to perform acceptably well, raising questions about the efficacy of simple G&Y models like TASS and relatively simple light models like SORTIE for the challenges of ecosystem management

46 3. The FORECAST family of hybrid simulation, multi value, ecosystem management decision support tools

47 Complex cutblock ( VR ) - level Small watershed level Timber supply level Stand-level FORECAST Visualization Individual tree, complex stand level

48 FORECAST Non-spatial ecosystem management stand model Visualization software stand and landscape POSSIBLE FOREST FUTURES: watershed landscape management model LLEMS: complex cutblock simulator LLEMS Local Landscape Ecosystem Management Simulator FORCEE: Individual tree, complex stand model Trees Ecotone Open * Is this a clearcut? * What will the future forest species composition be? * How will Douglas-fir compete with western hemlock? * Will shade tolerant hardwoods be able to grow?

49 DECISION SUPPORT SYSTEM: Modelling Framework Projection Forest-level Timber Supply Model (ATLAS) Interpretation Wildlife Habitat Supply Model (SimFor) Polygon- Based Raster- Based Stand-level Model (FORECAST) Merchantable Volume Snags (>25cm dbh) Ecosystem C Storage Early Seral Shrub Cover (%) Visualization Software

50 FORCEE: complex stand simulator Litter footprint Light map

51 Variable Retention Complex Cutblocks LLEMS Local Landscape Ecosystem Management Simulator Trees Ecotone Open Questions * Is this a clearcut? * What will the future forest species composition be? * How will Douglas-fir compete with western hemlock? * Will shade intolerant hardwoods be able to grow? * Wind, diseases?

52 POSSIBLE FOREST FUTURES Multiple value, landscape management scenario analysis tool for education, extension and management gaming Watershed management scenario analysis model Based on FORECAST and FORWADY

53 4. The importance of visualization we are an emotional species that depends strongly for decisions on our eyes and our heart

54 Visualization snapshots and animation (movies)

55 Landscape Visualization Forest Practices Code Scenario Year 25 Zoning Scenario Year 25

56 Wildlife Habitat Interpretation of Landscape Visualization Year 165 SIMFOR - Swainson s thrush Habitat FPC Scenario SIMFOR - Swainson s thrush Habitat Zoning Scenario

57 CALP FORESTER - interactive visualization tool for LLEMS: using a mouse to select a cutblock boundary for dispersed retention

58 CALP FORESTER visualization output showing 20% dispersed retention

59 Defining grouped retention with a mouse

60 A closer view of the LLEMS landscape visualization

61 Conclusions Ecosystem management requires ecosystem- level management tools Hybrid models incorporate both experience and understanding Should be as simple as possible but as complex as necessary it depends on the application Cross scale models are needed, linked to visualization

62 Postscript The predictions of an ecosystem model depend heavily on how much of the structure and key processes you include. This suggests that structurally and functionally incomplete models may give very inaccurate forecasts. Sufficient complexity should be represented explicitly to do the required task Take-home message: as simple as possible, but as complex as necessary

63 Stand-level concepts of sustainability Biodiversity Temporal diversity Ecological actors - species Ecological play - succession Ecological diversity T I M E Climate, geology, topography, soil. Physical disturbances - fire, wind, erosion, flood. Ecological stage - site The Concept of Ecological Theatre

64

65 What Is The Hybrid Simulation Approach? Combines experience with understanding: empirical historical bioassay + process simulation Strong empirical foundation: linked to process simulation of ecosystem function Process rates: calculated from empirical measures of the products of key processes

66 FORECAST Stand-level, multi-value (timber and non-timber) ecosystem management A management oriented, ecosystem-level, multi-value modeling framework

67 M anagement and other events which can be simulated: forestry and agroforestry Site preparation Planting / Regeneration Weed control Stocking control Pruning Intermediate harvests Final harvests Utilization level Fertilization Nurse crops Alternating or mixed species Rotation length Seedling size and quality Wildfire / broadcast burn Insect defoliation Wildlife browsing Organic waste recycling Clearcutting / patch cut Uniform partial harvesting - e.g. shelterwood, seedtree

68 FORECAST Stand-level, multi-value (timber and non-timber) ecosystem management A management oriented, ecosystem-level, multi-value modeling framework Uses the hybrid simulation approach: experience + understanding

69 Core ecosystem processes represented in FORECAST ESM Maximum potential foliage biomass set by moisture FOLIAGE NITROGEN CONTENT AVAILABLE LIGHT 1. Plant growth and carbon allocation PHOTOSYNTHETIC EFFICIENCY 2. Light limitation 3. Nutrient limitation AVAILABLE SOIL MOISTURE NET PRIMARY PRODUCTION AVAILABLE SOIL NUTRIENTS 4. Moisture limitation ALLOCATION 5. Competition for resources ROOTS STEMS FOLIAGE

70 Nutrient Cycling in FORECAST Input A. Based on a mass balance approach Precipitation Inputs Fertilizer Inputs Input B. Nutrients exist in 3 main ecosystem pools C. Transfers between pools 1. Geochemical cycle Input Biological N Fixation 2 Plant Biomass trees, herbs, shrubs Nutrient Uptake Internal Cycling Foliar Leaching Available Soil Nutrients Upslope Seepage Mineral Weathering Soil Leaching Input Input 2. Biological cycle 3. Management activities Harvest Natural Mortality Litterfall Herbivory Decomposition Fire Loss Loss Loss Litter and Soil Organic Matter Site Prep Loss

71 Flow of information through the model Data files Contains data describing how trees / plants have grown in the past (age series or chronosequence) for a range of site qualities, litter & humus decomposition, etc. Verification output Setup Programs Simulation Rules Starting Management condition data Ecosystem Simulation Module.... These derive information about the rates of key ecosystem processes from field measures of the end products of these processes (input data).... Summarized information from setup programs defining each specie s s growth attributes and ecosystem processes used in the ecosystem simulation module.... Projects future ecosystem conditions based on simulation rules, starting conditions and simulated management Output

72 File structure of FORECAST Input files TREEDATA PLANTDATA BRYODATA SOILDATA SETUP Programs TREEGROW PLANTGROW BRYOGROW SOILS Output files TREEPLOT PLANTPLOT BRYOPLOT SOILPLOT ECOSYSTEM SIMULATION TREETRND ECODATA PLANTTRND ECOSYSTM BYROTRND ECOSTATE ENDSTATE SOILTRND INITSTATE OUTPUT ASSESSMENT GRAPHICAL OUTPUT TABULAR OUTPUT MGMT ECOSYSECONOM ECONOM ENERGYCARBON

73 FORECAST Stand-level, multi-value (timber and non-timber) ecosystem management A management oriented, ecosystem-level, multi-value modeling framework Uses the hybrid simulation approach: experience + understanding Major focus: sustainability of a variety of values under alternative management strategies for changing/uncertain futures

74 FORECAST: Applications & Output Potential Applications Output - Exploring alternative stand-level silvicultural systems - Support for analysis of SFM and Certification Analysis of multiple rotations Examine management impacts on indicators of sustainability Biophysical Indicators Species composition Site productivity Stand structure Soil organic matter Snags & CWD Nutrient status Growth & Yield Total Volume Merch.. Volume Height growth Individual stem size distributions etc. Economic Indicators Value of timber Management costs Employment Carbon Budgets Energy Budgets

75 FORECAST: Evaluation Methodology Model output vs. 35 years of data from permanent plots Two calibration levels: Phase 1: general calibration with generalized regional data (management level). Phase 2: site-specific calibration for the study site with local field data (research level). Phase 1 General calibration FORECAST calibrated with data from TASS / TIPSY TIPSY: statistical model based on a regionally extensive data base from permanent sample plots Phase 2 Site-specific calibration Phase 1 setup files modified with field data from control plots at Shawnigan Lake (B.C., Canada) Site-specific data on biomass, litter decomposition rates, plant tissue nutrient concentrations, etc.

76 WEAKNESS AND FUTURE IMPROVEMENTS Hydrology and moisture limitation implicit representation Climate change annual time steps Disturbances deterministic model Wildlife Model improvements under development: - New moisture limitation module explicit representation - Representation of seasonality daily time step - Habitat suitability and fire susceptibility index

77 Do we need this level of complexity? It depends!

78

79 Quo Vadis? in Forestry Local people with experience-based wisdom Non-locals without local knowledge Pressure from ecologically inappropriate belief systems and incomplete knowledge about nature Sustainable Exploitation Passive Management Active Management Replaced by Non-sustainable exploitation Leads to Administrative forestry Evolves into Ecologically-based forestry (EBM), initially timber biased Resource depletion Variable results; often single value Sustained timber production +++? Application of social and biophysical sciences that respect the ecology and sociology of desired values Integrated management of stands and landscapes for multiple values. FOREST ECOSYSTEM MANAGEMENT