1. Big questions in global change science What controls biodiversity? How will it be affected by climate change? Includes students, postdocs, other faculty on campus: Pankaj Agarwal, Dave Bell, Mike Dietze, Alan Gelfand, Michelle Hersh, Ines Ibanez, Shannon LaDeau, Scott Loarie, Sean McMahon, Jessica Metcalf, Jackie Mohan, Emily Moran, Carl Salk, Rob Schick, Mike Wolosin, Hai Yu

2. Nature supports huge diversity

3. It is threatened with extinction Nature, 2004: 15-37% 'committed to extinction.' IPCC: 20-30% risk extinction if temperatures rise 2°C. Ara ú jo: from 92% range reduction to 322% expansion. Predicted bird losses 10% 60%

4. Conservation and Policy A Framework for Debate of Assisted Migration in an Era of Climate Change JASON S. M C LACHLAN, ＊ †‡ JESSICA J. HELLMANN,† AND MARK W. SCHWARTZ ＊ Conservation Biology 21, No. 2, 297–302 Hannah, L., Midgley, G. F., Lovejoy, T., Bond, W. J., Bush, M., Lovett, J. C., Scott, D. & Woodward, F. I. Conservation of Biodiversity in a Changing Climate. Conservation Biology 16, 264-268. ?

5. Guidance from science: We can’t get coexistence in models Diversity in nature, but not in models of it Stochasticity can help, but not much What’s missing?

6. Brief history of ecological theory 1920’s to today: Systems of non- linear differential equations -Experiments to mimic these models 1970’s to today: Forward simulation -Large models produce a mish- mash of output -Parameterization by guesswork -Simple models with careful designs extend analytical results 2000’s:Inferential modeling -Assimilate information -Understand more of the processes Insights: –Need N limiting factors to explain N species Insights: –Variation can increase diversity, but not by much –Still cannot predict diverse ecosystems Hypothesis: –Many processes required to maintain diversity –Species-specific

7. A role for modeling/computation Simple deterministic models cannot predict diverse ecosystems Adding stochastic elements to an otherwise simple model is not enough Need to better understand complexity

8. Challenges Many indirect and sparse sources of information Complex interactions, poorly understood

9. Many types of data Experimental hurricanes

10. CO 2 fumigation of forests Effects of high CO 2 on demography

11. Remote sensing Inference on light capture by canopies

12. Telemetry of animal movement Inferring pronghorn responses

13. Wireless sensor networks at Duke Forest Where could a model stand in for data? Slow variables Predictable variables Events Less predictable

14. Molecular evidence for infection Pathogen detection site j another site Host survival environment at j Dispersal among sites Transmission within sites Host species Pathogen taxa

15. An application Hypothesis: tradeoffs among traits needed for coexistence Challenge: cannot estimate the traits –They interact in unknown ways –Many types of data, all indirect Approach: – hierarchical Bayes inference on all traits simultaneously

16. Acer trees and seeds Experimental gaps Demographic monitoring Pretreatment /intervention Spatio temporal covariates Spatio-temporal demographic data

17. Individual responses with interactions Seed bank Seedling Immature tree Mature tree maturationgermination growth mortality Fecundity/dispersal dormancy Demography of an individual tree

18. Individual responses with interactions Resources, envir Resources, environ A forest

19. Information Seed bank Seedling Immature tree Mature tree maturationgerminationgrowth mortality Fecundity/dispersal dormancy Seed Traps Remote sensing: Canopy light Covariate data: Temp, soil moisture, elevation, CO2, N Covariates Demographic census: Size, survival, maturation status, canopy status Priors

20. Example data model Seed rain conditionally depends on all trees f ij,t - fecundity of tree i t - year j - plot k - seed trap sample s jk,t - seed count A jk - seed trap area g jk,t - dispersal from trees on j f ij,t K(r) g jk,t

21. Latent states vs predictive intervals Mortality Fecundity Growth Green dots are posterior means

22. Joint life history prediction Parameters for process and observation errors Fixed year effects Random effects (growth and fecundity) Latent states (canopy area, diameter, fecundity, maturation status, mortality risk)

23. Evaluation -200 yr ahead prediction has good coverage of tree-ring data - note: no age data enter model Dashed line: 95% predictive interval Green lines: tree ring data

24. Tradeoffs among species? Not classical tradeoffs Within species variance large-- consistent with multiple limitations Predictive means Individual variation

25. What’s ahead Seed bank Seedling Immature tree Mature tree A shift to inferential modeling (including prediction) –Getting the data in –Determining how things work –Finding what’s important Revisit analysis with new insight on how to simplify and where to retain complexity