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Let the concept of trait be functional!

Authors

  • Cyrille Violle,

  • Marie-Laure Navas,

  • Denis Vile,

  • Elena Kazakou,

  • Claire Fortunel,

  • Irène Hummel,

  • Eric Garnier


Cyrille Violle (cyrille.violle@cefe.cnrs.fr), Marie-Laure Navas, Denis Vile, Elena Kazakou, Claire Fortunel, Irène Hummel and Eric Garnier, CNRS, Centre d'Ecologie Fonctionnelle et Evolutive, UMR 5175, 1919, Route de Mende, FR-34293 Montpellier Cedex 5, France. – MLN also at: Montpellier Supagro, 2 Place Viala, FR-34060 Montpellier Cedex 1, France. DV also at: Dépt de Biologie, Univ. de Sherbrooke, Sherbrooke (QC), Canada, J1K2R1.

Abstract

In its simplest definition, a trait is a surrogate of organismal performance, and this meaning of the term has been used by evolutionists for a long time. Over the last three decades, developments in community and ecosystem ecology have forced the concept of trait beyond these original boundaries, and trait-based approaches are now widely used in studies ranging from the level of organisms to that of ecosystems. Despite some attempts to fix the terminology, especially in plant ecology, there is currently a high degree of confusion in the use, not only of the term “trait” itself, but also in the underlying concepts it refers to. We therefore give an unambiguous definition of plant trait, with a particular emphasis on functional trait. A hierarchical perspective is proposed, extending the “performance paradigm” to plant ecology. “Functional traits” are defined as morpho-physio-phenological traits which impact fitness indirectly via their effects on growth, reproduction and survival, the three components of individual performance. We finally present an integrative framework explaining how changes in trait values due to environmental variations are translated into organismal performance, and how these changes may influence processes at higher organizational levels. We argue that this can be achieved by developing “integration functions” which can be grouped into functional response (community level) and effect (ecosystem level) algorithms.

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