Can we manage the complexity of forest ecosystems?

Transcription

1 Can we manage the complexity of forest ecosystems? Can we do this using traditional, simple decision support tools, or do we need ecosystem-level models and visualization? Hamish Kimmins Forest Ecologist Canada Research Chair in Modeling the Sustainability of Forest EcosystemsE UBC Faculty of Forestry Research Seminar, April 2007

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

3 Outline Why do we need to deal with complexity? Is science able to deal with complexity? How much complexity do we need in decision support tools? How do we communicate complexity?

4 Why do we need to deal with complexity in forest management? Ecosystem attributes: Structure Function Interconnectedness Complexity Change over time temporal complexity

5 Forestry is about people - values, needs, desires Decision support tools should address a diversity of values Wood Non-wood botanical products Water Aesthetics Recreation Wildlife/fish Complex Employment Biodiversity Spiritual values Environmental protection Ecosystem processes Economics - wealth creation Bioenergy - fuel

6 Is science able to deal with complexity? Yes, if it Knowing incorporates all three of the Understanding components of science Predicting

7 Is science able to deal with complexity? Repeated testing Scientific principles Scientific laws Knowing Theory or Postulate Deduction Reductionism Hypotheses Hypothesis testing Inductive soft science Induction Observations, Descriptions, Classifications, Knowledge Foundations of science Deduction Problem, Issue, Desired Future Computer models Synthesis into complex hypotheses Validation Induction, Experience Belief systems Induction Hypothesis testing Predictions Experimental observations Scenario analysis supported by visualization Policy, Practice Un-synthesized experimental data Understanding Deductive hard science Predicting Synthesis soft science Unsuitable Kimmins et al Science in forestry: why does it sometimes disappoint or even fail us? Forestry Chronicle 81:

8 Complexity in forest modeling how much is enough? Occam: Einstein: As simple as possible, but as complex as necessary As simple as possible, but not simpler It depends on the application

9 Evolution of Forestry Sustainable Exploitation Passive Management Active Management Replaced by T I M E 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

10 Evolution of Decision-support Tools T I M E Exploitation generally no DSTs; ; perhaps inventory control Administrative timber management simple, historical bioassay G and Y models Ecologically-based timber management generally, simple light-driven population models; but should be ecosystem-level, production ecology models Ecosystem management for multiple values ecosystem-level, multi-scale, multi-value, scenario and value tradeoff analysis models

11 Early Warning from Germany Late 1700 s development of yield tables By early 1800 s failure of yield tables to predict yield decline in Scots pine Study by Ebermeyer 1876 the start of modern forest science Concluded that historical bioassay (experiencebased) DST s are only valid for unchanging conditions. For changing conditions, should be based on understanding of ecosystem processes

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

13 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

14 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

15 Decision Support Systems in Support of Sustainable Forestry Should they be ecosystem-level management simulators? YES Should they represent the key processes explicitly? trees grow by photosynthesis, not dbh; - DSTs should be driven by the fundamentals of production ecology and key population and community processes

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

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

18 Stand Dynamics Seed banks, seed rain Seedbeds Non-crop vegetation Nutrients Light Moisture Herbivory Below-ground competition Light Density dependant mortality Snow, wind Carbon allocation Pathogens Light Nutrients Moisture Pathogens Density independent mortality Below-ground competition Light Nutrients Moisture Pathogens Density independent mortality Below-ground competition Minor vegetation OG Regeneration of same species = Stand dynamics Regeneration (invasion) of different species = Succession

19 Succession -Concept of Ecological Theatre Biodiversity Temporal diversity OG OG OG OG T I M E Climate, geology, topography, soil. Physical disturbances - fire, wind, erosion, flood. Ecological actors - species Ecological play - succession Ecological stage - site Ecological diversity

20 Decision Support Systems in Support of Sustainable Forestry Should they be ecosystem-level management simulators? YES Should they represent the key processes explicitly? YES Should they be able to model stand dynamics and complex successional pathways? YES

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

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

23 Simple light-driven model of succession No disturbance WRONG!

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

25 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

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

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 ) Stand Age (y) FORECAST light only 0.6 TIPSY 0.4 Ratio 0.2 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 volume, 2000 sph, FORECAST at various levels of complexity 2 FORECAST/TIPSY Ratio L+N L L+N+U L+U Year

35 Comparison for Douglas-fir volume at various levels of planting density: light, nutrients, understory FORECAST/TIPSY ratio ,000 3,000 1, Year Fd Planting density

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 Mean Difference σ 2 mean difference R 2 observed vs. predicted Top height Phase (m) Phas e Merchantable Volume (m 3 ha -1 ) Phase Phase Phase Density (stem ha -1 ) Phase Janus quotient Stem biomass Phase (Mg ha -1 ) Phase Aboveground biomass (Mg ha -1 ) Phase Phase Theil s coefficient Modelling eficiency 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 A test of FORECAST simulations of boreal mixedwoods (in press) yielded a Modelling Efficiency rating for seven stand variables for four species: 0.71 to 0.98

44 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

45 The FORECAST family of hybrid simulation, multi value, ecosystem management decision support tools

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

47 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?

48 Stand level ecosystem management models: FORECAST Non-spatial ecosystem management stand model NAVIGATOR : FORECAST User Interface Effect of Douglas-fir over-story on shrub biomass Timber management table:

49 Stand level ecosystem management models: FORCEE A spatially-explicit individual tree, complex stand model

50 FORCEE: complex stand, multi-value simulator e.g. Boreal and temperate mixedwoods; agroforestry Light and litter footprints Soil and canopy gaps

51 Landscape-level ecosystem management models: LLEMS Local landscape ecosystem management model for complex cut block design under development: NSERC- INTERFOR 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 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?

53 Landscape-level ecosystem management models: POSSIBLE FOREST FUTURES: Multiple value, landscape management scenario analysis tool for education, extension and management gaming

54 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

55 How do we communicate complexity in space, time, structure and function? The importance of visualization we are an emotional species that depends strongly for decisions on our eyes and our heart

56 The Power of Visualization Analytical vs fuzzy logic Visual images as an almost universal communication medium The power of movies vs jigsaw puzzle science Visualization snapshots and animation (movies)

57 Landscape Visualization F orest P ractices C ode S cenario Y ear 25 World Construction Set output Z oning S cenario Y ear 25 Arrow Lakes TSA IFPA: Lemon Creek

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

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

60 CALP FORESTER visualization output showing 20% dispersed retention

61 Defining grouped retention with a mouse

62 A closer view of the LLEMS landscape visualization

63 Conclusions Einstein and the two edges of Occam s s Razor: simple as possible, complex as necessary Forestry is getting more complex: decision support tools should reflect this Ecosystem management is our goal: EM dst s should be the focus of forest science Communication/education is pivotal: tools should respect the cognitive abilities of the audience