Detection, Analysis and Prediction of Change in Ecology – Problems of Living Systems

1. Detection, Analysis and Prediction of Change in Ecology – Problems of Living Systems Travis E. Huxman Ecology and Evolutionary Biology University of Arizona UNESCO - Tucson, Arizona - March 26-28, 2007

2. Outline • Living things & earth system processes • Ecological theory • Water as a nutrient • Time in biological systems • Understanding change • Directions

3. Outline • Living things & earth system processes • Ecological theory • Water as a nutrient • Time in biological systems • Understanding change • Directions

4. C-sedimentation rates, [CO2] in the atmosphere, plant evolution, and ecohydrology Berner 1997, Science; Berner et al., 1998

5. Coming to the ideas in this presentation • Summarize the challenges you are facing detecting and analyzing change and feedbacks and assigning causality to them • In ecology we have three problems that limit our ability to think about change (1) homeostasis, (2) density-dependence, and (3) life-environment interactions • How do you define change (e.g. time scale, magnitude, direction, significance of change, what signal to noise ratios are you used to working with)? • Defining change is most difficult in ecosystem science – pick your process (ANPP and Tilman; Community composition and Collins; Energetics and Enquist; PCA’s and Potts) • Placing change in context is additionally difficult (multiple stable states – ball-and-cup models; alternative stable states, ecosystem decline (desertification example)) • Time-scales are attempted to be understood within the context of the demography of the dominant organisms – creating difficulty in making measurements and detecting significant change or direction of change • What are the most important changes being investigated in your field and why – what is the motivation? • Does your science deal with changes in extremes or changes in average behavior? • Both – tolerances affect species presence / absence, changes in average behavior are likely important for structuring ecological interactions. • What are the most important positive and negative feedback loops and what methods do you use to identify feedbacks? • When does the delta change approach (or incremental change approach) fail? • In ecology this approach consistently fails to predict significant system wide shifts of interest to Earth Systems • Amazon example from PIRE • How do you treat non-linearities? • We are forced to use models to interpret data relating to non-linearities. • In approaching the problem – do you typically collect more data, pool data, use simulation approaches, or some combination thereof? • Data-model interactions are typically the only way forward, from simplistic interpretations of demography, to complex ecosystem models. • Classical vs non-classical statistical methods – are there advantages to one or the other in your field? • Bayesian forms are expanding in their use. • When you get a final estimate of change, how do you describe it? With what confidence? • Ecology is still grappling with becoming a predictive science • How do you establish design criteria (e.g. high-tides with a 100 year return period, wind loads on structures, etc.) in the midst of change, if applicable? • Active area of research • How do you deal with model outputs from other fields, if they are used (i.e. GCMs)? • We assume hydrologist get it right! (Soil moisture example)

6. Where and what kind of biology might matter (maybe) Plot-scale, vegetation-soil coupling Disturbance, extreme events

7. Approaches for prediction / understanding change Conceptual approaches / challenges Statistical approaches to understanding Physicochemical Systems Ecology Mechanistic process models as tools for understanding

8. Bridgham et al., 1995 Resource Theory / Stoichiometry • Changes in mean and variance of resource density can predict ecosystem behavior (or be used to understand change) • Known characteristics of biology can be incorporated (high efficiency species, low efficiency species) Pastor and Bridgham 1999

9. Terrestrial Water Limitation Number of Months where Precipitation < Potential Evapotranspiration Blue = Never Darker Orange = Increasing number of months (1-12) Data from Ahn and Tateishi 1994; Cramer on going

10. Rain Use Efficiency and PPT Precipitation acts like any limiting resource? Predictable use-efficiency across gradients of availability Global data set highlights the increase in RUE with decreasing PPT How does variation in biome specific RUE functions behave across PPT gradients?

11. Across biomes – compensatory behavior in the sensitivity of ANPP & runoff to D PPT Water doesn’t work well as a nutrient because its function in environment-life couplings is so scale dependent Lack of predictable stoichiometry

12. Outline • Living things & earth system processes • Ecological theory • Water as a nutrient • Time in biological systems • Understanding change • Directions

13. Characteristics (problems) of Life Characteristics of Life • Homeostasis • Organization • Metabolism • Growth • Reproduce • Adapt • Interacts with, & modifies its environment Interactions are emergent properties of trade-offs in 1-7

14. Approaches for prediction / Understanding change Conceptual approaches / challenges Physicochemical Systems Ecology Statistical approaches to understanding Mechanistic process models as tools for understanding Biological “Interactions” Dominated Systems

15. Sensitivity of leaf area is temporal sequence of soil moisture and temp LA RWC SWC Tmin PPT Gutschick and Bassirad 1999

16. Sensitivity of leaf area to drought by functional type Gutschick and Bassirad 1999 The importance of buffering temporal variation – bet hedging

17. Productivity and diversity relationships in terrestrial ecosystems Tilman et al., (2001)

18. Relatively simplistic view of vegetation-soil coupling Scheffer et al., (2005) Schemes that do not consider the complexity of species behavior over-emphasizes the importance of ‘optimization’ in biological systems as they are coupled to physical processes Does that mean that all schemes should include species dynamics? (no-but they should not always imply optimization)

19. Outline • Living things & earth system processes • Ecological theory • Water as a nutrient • Time in biological systems • Understanding change • Benchmarks • Tools for understanding • Directions

20. Benchmarks to consider Plot-scale, vegetation-soil coupling, management dynamics Disturbance, extreme events

21. Shrub encroachment – San Pedro River Scott et al., 2006

22. Benchmarks to consider Plot-scale, vegetation-soil coupling, management dynamics Disturbance, extreme events

23. Benchmark change to consider • Increase in the frequency of large fires • Increase in the length of the fire season A. L. Westerling et al., 2006 Science

24. Can be spatially expressed to provide assessments of risk for vegetation transformation A. L. Westerling et al., 2006 Science

25. Benchmarks to consider Plot-scale, vegetation-soil coupling, management dynamics Disturbance, extreme events

26. The benchmark - > 3 million acres of forest affected by tree mortality http://www.fs.fed.us/r3/resources/health/beetle/index.shtml

27. 2000s drought 1950s drought 1900s drought Predicting plant response to drought DRY DRY DRY WET WET Breshears et al. 2005 PNAS, 102:15144-15148; graphic from Neil Cobb

28. Could we have predicted this? Breshears et al. 2005 PNAS, 102:15144-15148; graphic from Neil Cobb

29. Complex responses that derive from vegetation-soil coupling Scheffer et al., (2005) If we haven’t seen it before, we have a difficult time relating specific mechanisms to phenomena (assigning causality) direct effects of drought? indirect effects of drought? (e.g., pathogens)

30. Benchmarks to consider Plot-scale, vegetation-soil coupling, management dynamics Disturbance, extreme events

31. Climate induced mortality vs climate related disturbance High-frequency, small scale events - vs - wide-spread, synchronous events at low temporal frequencies We’re data limited, despite a greater fundamental understanding of the problem (e.g., plant growth vs community assembly) http://www.fs.fed.us/r3/resources/health/beetle/index.shtml A. L. Westerling et al., 2006 Science

32. Where and what kind of biology might matter (maybe) Plot-scale, vegetation-soil coupling Equilibrium, systems dynamics approaches Disturbance, extreme events Highly non-linear, poorly scaled processes

33. Change in the context of these contrasting processes • Predictable perturbation relating to equilibrium biology (may or may not be operating over large scales) • Problems of biology as they often relate to time: (1) homeostasis, (2) density-dependence, (3) life-environment couplings

34. Outline • Living things and earth system processes • Ecological theory • Water as a nutrient • Time in biological systems • Understanding change • Benchmarks • Tools for understanding • Directions

35. Precipitation and production Contrasts, constrained within a domain (time or space) compared across domains (time or space) Obvious drawbacks are related to resolving feedbacks and populating the analysis with sufficient data Benefits relate to ability to falsify ecological theory Huxman et al., (2004) Nature Huxman et al., 2004

36. Process Modeling • Statistical approaches incorporating both “systems ecology” and “traditional ecology” • e.g., Ecological Demography (Moorcroft et al., 2001) Utilizes our understanding of systems organized around disturbance and body size (e.g., gap systems) Clark (2007) Biotropica

37. We use our understanding of an ecological principle to coordinate disparate data sets (e.g., ecosystem stocks, species behavior, environmental variability)

38. Outline • Living things and earth system processes • Ecological theory • Water as a nutrient • Time in biological systems • Understanding change • Benchmarks • Tools for understanding • Directions • Tools • Theory

39. Process Modeling • Hierarchical Bayesian Modeling • Evaluate feedback structure for well constrained processes operating at different scales • Example, A leaf photosynthesis – stomatal conductance model embedded in a water uptake model (Ogle et al., 2004)

40. Outline • Living things and earth system processes • Ecological theory • Water as a nutrient • Time in biological systems • Understanding change • Benchmarks • Tools for understanding • Directions • Tools • Theory

42. Ecosystem response to rain • Points – • Rapid change in state-space • Slower recovery following a alternative steady-state? • Strong resilience? Potts et al., 2006

43. Approaches for prediction Interception of approaches / challenges Statistical approaches to understanding Physicochemical Systems Ecology ? Biological “Interactions” Dominated Systems Mechanistic process models as tools for understanding

44. Ecology – density, body size and metabolism Average mass (kg) Maximum density (# m-2)

45. Metabolic scaling theory Enquist, et al., (2003) Nature

46. Ecosystem metabolic response to temperature Many ecosystem have similar ‘functional responses’ – (slopes) Magnitude of ecosystem flux differs across biomes (including NA and Euro) Standardized ecosystem metabolic rates increase with latitude (cooler mean annual temperature – homeostatic adjustments at large-scales) Enquist et al., (2003) Nature

47. Terrestrial Water Limitation Number of Months where Precipitation < Potential Evapotranspiration Blue = Never Darker Orange = Increasing number of months (1-12) Data from Ahn and Tateishi 1994; Cramer on going

48. Outline • Living things and earth system processes • Ecological theory applied to water • Water as a nutrient • Time in biological systems • Understanding change • Benchmarks • Tools for understanding • Directions • Tools • Theory • Statement…

49. Jackson et al., 2006