Résumé
1. Quantifying the effects of species interactions is key to understanding
the relationships between biodiversity and ecosystem functioning but
remains elusive due to combinatorics issues. Functional groups have been
commonly used to capture the diversity of forms and functions and thus
simplify the reality. However, the explicit incorporation of species
interactions is still lacking in functional group-based approaches. Here
we propose a new approach based on an a posteriori clustering of species
to quantify the effects of species interactions on ecosystem functioning.
2. We first decompose the observed ecosystem function using null models,
in which species diversity does not affect ecosystem function, to separate
the effects of species interactions and species composition. This allows
the identification of a posteriori functional groups that have contrasting
diversity effects on ecosystem functioning. We then develop a formal
combinatorial model of species interactions in which an ecosystem is
described as a combination of co-occurring functional groups, which we
call an assembly motif. Each assembly motif corresponds to a particular
biotic environment. We demonstrate the relevance of our approach using
datasets from a microbial experiment and the long-term Cedar Creek
Biodiversity II experiment. 3. We show that our a posteriori approach is
more accurate, more efficient and more parsimonious than a priori
approaches. The discrepancy between a priori and a posteriori approaches
results from the way each clustering is set up: a priori approaches are
based on ecosystem or species properties, such as ecosystem size (number
of species or functional groups) or species’ functional traits, whereas
our a posteriori approach is based only on the observed interaction and
composition effects on ecosystem functioning. 4. Our findings demonstrate
that an a posteriori approach is highly explanatory: it identifies who
interacts with whom, and quantifies the effects of species interactions on
ecosystem functioning. They also highlight that a combinatorial modelling
of ecosystem functioning can predict the functioning of an ecosystem
without any hypothesis about the biotic or environmental determinants or
any information on species functional traits. It only requires the species
composition of the ecosystem and the observed functioning of others that
share the same assembly motif.