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David K. Skelly
Yale University
School of Forestry & Environmental Studies
370 Prospect Street
New Haven, CT 06511 USA

Yale FES

 

 


Prediction in Ecology
OVERVIEW

Ecology is evolving from an exploration of what can happen to a science focused on what does, or will happen.  As it has changed there has been a rising premium for developing the role of prediction in ecology.

Distributional patterns are at the core of ecological science and are central to my research.  Despite a tradition of study extending over 100 years, we still have relatively rudimentary capabilities in predicting the pattern of a species' distribution in space and how that distribution will change over time.  Improved predictive ability is important on at least two fronts.  First, as critics of ecology have pointed out, prediction is the acid test of our science.  If we cannot predict distributional patterns then ecologists have little right to claim that they understand how ecological systems work.  Second, prediction is a critical task for ecologists involved in conservation.  Many biologists contributing to conservation efforts are pressed to predict what will happen to species or system X if perturbation Y is allowed to happen. 

Our efforts are focused on developing more effective predictions that are based on the types of data that are most likely to be available.  Consequently, for some of this work, we have focused on modeling patterns of presence and absence from spatially distributed surveys.  Highlights from our results include the development of rule based models to evaluate competing hypotheses underlying distributional change, and the discovery that low cost, widely available climate data can be equal in predictive ability compared with much more expensive GIS based vegetation models (e.g., GAP Analysis).


Predicted distribution of spring peepers (Pseudacris crucifer).

 

Pickerel frog (Rana palustris)

REPRESENTATIVE PUBLICATIONS

Skelly, D. K. and E. Meir. 1997. Rule-based models for evaluating mechanisms of distributional change. Conservation Biology 11:531-538.  Link

Beard, K. H., N. Hengartner, and D. K. Skelly. 1999. Effectiveness of predicting breeding bird distributions using probabilistic models. Conservation Biology 13:1108-1116.  Link

Skelly, D. K., K. H. Beard, and N. Hengartner. 2000. Animal-distribution modeling in gap analysis: an evolving science: Reply. Conservation Biology 14:1224-1225.  Link