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Incorporating species traits into a predictive science

Published On: 6/10/2013

FWS Featured Article: Incorporating species traits into a predictive science

 
Featured articles are open access for three months so you can see what’s hot in the current issue of FWS.

Verberk, W.C.E.P., C.G.E. van Noordwijk, and A.G. Hildrew. 2013. Delivering on a promise: integrating species traits to transform descriptive community ecology into a predictive science. Freshwater Science 32:531–547

Highlight: A hierarchical framework for species traits is presented in which interactions among biological traits are utilized to develop a more mechanistic understanding of species occurrences.

Abstract: The use of species traits in basic and applied ecology is expanding rapidly because trait-based approaches hold the promise to increase our mechanistic understanding of biological responses. Such understanding could transform descriptive field studies in community ecology into predictive studies. Currently, however, trait-based approaches often fail to reflect species–environment relationships adequately. The difficulties have been perceived mainly as methodological, but we suggest that the problem is more profound and touches on the fundamentals of ecology and evolution. Selection pressures do not act independently on single traits, but rather, on species whose success in a particular environment is controlled by many interacting traits. Therefore, the adaptive value of a particular trait may differ across species, depending on the other traits possessed by the species and the constraints of its body plan. Because of this context-dependence, trait-based approaches should take into account the way combinations of traits interact and are constrained within a species. We present a new framework in which trade-offs and other interactions between biological traits are taken as a starting point from which to develop a better mechanistic understanding of species occurrences. The framework consists of 4 levels: traits, trait interactions, trait combinations, and life-history strategies, in a hierarchy in which each level provides the building blocks for the next. Researchers can contribute knowledge and insights at each level and their contributions can be verified or falsified using logic, theory, and empirical data. Such an integrated and transparent framework can help fulfill the promise of traits to transform community ecology into a predictive science.





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