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A structured and dynamic framework to advance traits-based theory and prediction in ecology
Colleen T Webb1, Jennifer A Hoeting, Gregory M Ames
1Department of Biology, Colorado State University, Fort Collins, CO 80524, USA. ctwebb@lamar.colostate.edu
Ecology Letters
|May 12, 2010
Summary
Predicting ecological changes requires a new framework. This approach uses trait distributions and performance filters to forecast community shifts and ecosystem functions, advancing predictive ecology.
Area of Science:
- Ecology
- Ecological modeling
Background:
- Predicting ecological changes is a significant challenge.
- Traits-based approaches are of interest but lack generalizability.
Purpose of the Study:
- To propose a theoretical framework for advancing predictive ecology using traits.
- To identify quantitative methods suitable for testing traits-based hypotheses.
Main Methods:
- Proposed a framework with three elements: trait distribution, performance filter, and dynamic projection.
- Defined criteria for quantitative methods based on performance filter dynamics.
- Identified Bayesian multilevel models, dynamical systems models, and hybrid approaches as suitable.
Main Results:
- The proposed framework enables prediction of trait distribution changes and their impact on community composition and ecosystem function.
- The framework's criteria guide the selection of appropriate quantitative methods.
Conclusions:
- A coherent theoretical framework is essential for advancing traits-based ecology.
- Specific quantitative methods can effectively test traits-based hypotheses within this framework.
- This approach offers a path toward more general and predictive ecological insights.
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