How well do environment-based models predict species abundances at a coarse scale?

1. How well do environment-based models predict species abundances at a coarse scale? Volker Bahn and Brian McGill McGill University CSEE, Toronto, May, 2007 www.volkerbahn.com

2. Distribution MapRose-breasted Grosbeak http://www.natureserve.org/

3. Distribution MapRose-breasted Grosbeak http://www.mbr-pwrc.usgs.gov/bbs/bbs.html

4. Species Distributions • Central to ecology • Krebs, C. J. 1972. Ecology: The experimental analysis of distribution and abundance. • Andrewartha, H. G., and L. C. Birch. 1984. The ecological web: More on the distribution and abundance of animals. • Conservation of species

5. Distribution Modelling • How does distribution modelling work? • Occurrence or abundance data at some locations • Record environmental conditions • Build statistical model relating sample data to environmental predictors • Predict occurrence for non-surveyed areas

8. Research Questions • How well does niche-based distribution modelling work? • How can one assess the predictive ability of distribution models? • Which influence does the evaluation scheme have on the assessment of the models?

9. Methods • Breeding Bird Survey • 1996-2000 • 1293 locations • 79 - 190 species • Environmental data • Regression trees/ Random forests

10. Contagion

11. Results Dependent Independent R2* Bird abundance Environment 0.32 Bird abundance Contagion 0.43 Sim. Ranges Environment 0.24 *Averaged over 190 species Bahn, V and McGill, B.J. (2007) Can niche-based distribution models outperform spatial interpolation? Global Ecology and Biogeography: online early.

12. Resubstitution – no split

13. Random split

14. Strips

15. Halves

17. Conclusion • When training and test data are interspersed, interpolation does the job just as well as niche-based models • Niche-based models predict poorly into new areas • Evaluations are dependent on information content and testing scheme

18. Outlook • If environmental conditions are not a good predictor then what are we missing? • We don’t get the right information from remotely sensed data • Processes are not stationary • Spatial processes: dispersal and population dynamics

19. Acknowledgements • Thousands of volunteers, CWS & USGS for BBS data • Grad students, friends and collaborators in the lab and beyond • Family • Funding from NSERC

20. Discussion?

21. Species Peak at Optimum? • Typically not • Mueller-Dombois, D. & Ellenberg, H. (1974) Aims and methods of vegetation ecology. Wiley, New York. • Rehfeldt, G.E., Ying, C.C., Spittlehouse, D.L. & Hamilton, D.A., Jr. (1999) Genetic responses to climate in Pinus contorta: Niche breadth, climate change, and reforestation. Ecological Monographs, 69(3), 375-407.

22. Species Peak at Optimum? • Wang et al. 2006. Use of response functions in selecting lodgepole pine populations for future climates. J Global Change Biology 12(12):2404-2416 • Frazier, M., R.B. Huey, and D. Berrigan. 2005. Thermodynamics constrains the evolution of insect population growth rates: "warmer is better." American Naturalist 168:512-520.

23. Mueller-Dombois and Ellenberg (1974)

25. Dispersal Bahn, V., W.B. Krohn, and R.J. O'Connor. Under review. Dispersal leads to autocorrelation in animal distributions: a simulation model. Submitted to Journal of Applied Ecology.

26. Before/after Dispersal

28. Conditional Autoregressive Y = Xβ + ρC(Y – Xβ) + ε

29. Temp max ≥ 28.6 Temp min ≥ 3.2 Yearly var precip ≥ 0.2 Seasonal var precip ≥ 0.3 0.7n = 115 1.2n = 145 1.0n = 66 Precip ≥ 66.1 1.7n = 273 1.6n = 24 2.8n = 54