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

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Presentation on theme: "How well do environment- based models predict species abundances at a coarse scale? Volker Bahn and Brian McGill McGill University CSEE, Toronto, May,"— Presentation transcript:

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

2 Distribution Map Rose-breasted Grosbeak

3 Distribution Map Rose-breasted Grosbeak

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

5 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 Distribution Modelling

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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 locations species Environmental data Regression trees/ Random forests

10 Contagion

11 Results DependentIndependentR 2 * Bird abundanceEnvironment0.32 Bird abundanceContagion0.43 Sim. RangesEnvironment0.24 *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

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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),

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

23 Mueller-Dombois and Ellenberg (1974)

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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

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

29 Temp max ≥ 28.6 Yearly var precip ≥ 0.2 Seasonal var precip ≥ 0.3 Precip ≥ 66.1 Temp min ≥ n = n = n = n = n = n = 54

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