THE DIMENSIONALITY AND STRUCTURE OF SPECIES TRAIT SPACES.
Mouillot D, Loiseau N, Grenie M, Algar AC, Allegra M, Cadotte MW, Casajus N, Denelle P, Gueguen N, Maire A, Maitner M, McGill BJ, McLean M, Mouquet N, Munoz F, Thuiller T, Villeger S, Violle C and, Auber A (2021).
Ecology Letters, doi:10.1111/ele.13778
Key message : 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.
Influence of the number of dimensions (number of retained PCoA axes) used to build the 30 species trait spaces on the space quality assessed by the area under the curve (AUC) criteria. The black dots and dotted lines correspond to the elbow-based optimal dimensionality for each dataset. The values indicate the elbow-based dimensionality, the total species richness (#S) and the total number of traits (#T) in each dataset. Datasets are ranked (top left to bottom right and from dark green to dark red) following the number of species.
OTHER TOPICS: Aesthetics of Biodiversity, Biodiversity & Ecosystem Functioning, Biogeography, Macroecology & Ecophylogenetics, Experimental Evolution,
Functional Biogeography, Functional Rarity, Metacommunities, Metaecosystems, Reviews and Synthesis, Trophic Biogeography & Metaweb