OOS 44-4 - Predicting the ranges of species across time spans relevant to conservation: Species distribution modeling of vertebrates observed by Joseph Grinnell and contemporary resurveys

Thursday, August 5, 2010: 2:30 PM
301-302, David L Lawrence Convention Center
Adam B. Smith1, Rauri Bowie2, Carla Cicero2, Chris Conroy2, Peter N. Epanchin3, James Patton3, Michelle Koo3, Toni Lyn Morelli4, Karen Rowe3, Kevin Rowe3, Emily Rubidge3, Morgan W. Tingley5, Steven R. Beissinger6 and Craig Moritz7, (1)Center for Conservation and Sustainable Development, Missouri Botanical Garden, Saint Louis, MO, (2)Museum of Vertebrate Zoology, University of California, Berkeley, Berkeley, CA, (3)Museum of Vertebrate Zoology, University of California, Berkeley, CA, (4)University of Massachusetts, Northeast Climate Science Center, Amherst, MA, (5)Woodrow Wilson School, Princeton University, Princeton, NJ, (6)Environmental Science, Policy & Management, University of California, Berkeley, Berkeley, CA, (7)Research School of Biology, Australia National University, Canberra, Australia
Background/Question/Methods

Species distribution models are commonly used to project the ranges of species of concern into the future, but unlike within-era projections to the same region, their accuracy cannot be assessed because of lack of data from the future.  One method for testing projections across time is to project ranges based on data collected from the past to the present and assess model performance using contemporary records.  Here, we assess the ability of SDMs to project ranges accurately by comparing projections trained on historical museum records of vertebrates collected by Joseph Grinnell and his colleagues between 1900 and 1940 and resurveys of these same sites since 2003.

Results/Conclusions

We compare several common modeling techniques that have performed well when used for modeling the same era and region from which data are drawn, and we assess whether high within-era performance predicts cross-era performance and under what circumstances models perform poorly.  Although within-era model performance can be high and in general is a good predictor of cross-era performance, good performance can be due to overfitting in some cases.  Thus, projections to different eras can suffer vis-à-vis models that perform poorly within-era but generate smoother response functions.

Copyright © . All rights reserved.
Banner photo by Flickr user greg westfall.