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The dimensionality and structure of species trait spaces
Author(s) -
Mouillot David,
Loiseau Nicolas,
Grenié Matthias,
Algar Adam C.,
Allegra Michele,
Cadotte Marc W.,
Casajus Nicolas,
Denelle Pierre,
Guéguen Maya,
Maire Anthony,
Maitner Brian,
McGill Brian J.,
McLean Matthew,
Mouquet Nicolas,
Munoz François,
Thuiller Wilfried,
Villéger Sébastien,
Violle Cyrille,
Auber Arnaud
Publication year - 2021
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/ele.13778
Subject(s) - trait , species richness , robustness (evolution) , biology , ecology , taxon , biodiversity , evolutionary biology , computer science , biochemistry , gene , programming language
Trait‐based ecology aims to understand the processes that generate the overarching diversity of organismal traits and their influence on ecosystem functioning. Achieving this goal requires simplifying this complexity in synthetic axes defining a trait space and to cluster species based on their traits while identifying those with unique combinations of traits. However, so far, we know little about the dimensionality, the robustness to trait omission and the structure of these trait spaces. Here, we propose a unified framework and a synthesis across 30 trait datasets representing a broad variety of taxa, ecosystems and spatial scales to show that a common trade‐off between trait space quality and operationality appears between three and six dimensions. The robustness to trait omission is generally low but highly variable among datasets. We also highlight invariant scaling relationships, whatever organismal complexity, between the number of clusters, the number of species in the dominant cluster and the number of unique species with total species richness. When species richness increases, the number of unique species saturates, whereas species tend to disproportionately pack in the richest cluster. Based on these results, we propose some rules of thumb to build species trait spaces and estimate subsequent functional diversity indices.