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A structured and dynamic framework to advance traits‐based theory and prediction in ecology
Author(s) -
Webb Colleen T.,
Hoeting Jennifer A.,
Ames Gregory M.,
Pyne Matthew I.,
LeRoy Poff N.
Publication year - 2010
Publication title -
ecology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.852
H-Index - 265
eISSN - 1461-0248
pISSN - 1461-023X
DOI - 10.1111/j.1461-0248.2010.01444.x
Subject(s) - ecology , trait , ecosystem ecology , functional ecology , function (biology) , biology , computer science , ecosystem , evolutionary biology , programming language
Ecology Letters (2010) 13: 267–283 Abstract Predicting changes in community composition and ecosystem function in a rapidly changing world is a major research challenge in ecology. Traits‐based approaches have elicited much recent interest, yet individual studies are not advancing a more general, predictive ecology. Significant progress will be facilitated by adopting a coherent theoretical framework comprised of three elements: an underlying trait distribution, a performance filter defining the fitness of traits in different environments, and a dynamic projection of the performance filter along some environmental gradient. This framework allows changes in the trait distribution and associated modifications to community composition or ecosystem function to be predicted across time or space. The structure and dynamics of the performance filter specify two key criteria by which we judge appropriate quantitative methods for testing traits‐based hypotheses. Bayesian multilevel models, dynamical systems models and hybrid approaches meet both these criteria and have the potential to meaningfully advance traits‐based ecology.