Résumé
Understanding the relationship between biodiversity and ecosystem
functioning has so far resulted from two main approaches: the analysis of
species' functional traits, and the analysis of species interaction
networks. Here we propose a third approach, based on the association
between combinations of species or of functional groups, which we term
assembly motifs, and observed ecosystem functioning. Each assembly motif
describes a biotic environment in which species interactions have
particular effects on a given ecosystem function. Clustering species in
functional groups generates a classification of ecosystems based on their
assembly motif. We evaluate the quality of each species clustering, that
is its ability to predict an ecosystem function, by the coefficient of
determination of the ecosystem classification. An iterative process then
enables identifying the species clustering in functional groups that best
accounts for the functioning of the observed ecosystems. We test this
approach using experimental and simulated datasets. We show that our
combinatorial analysis makes it possible to identify the combinations of
functional groups of species whose interactions govern ecosystem
functioning without any a priori knowledge of the species themselves or
their interactions. Our combinatorial approach reproduces the associative
learning of empirical ecologists, and proves to be powerful and
parsimonious.