Data from: Including community composition in biodiversity-productivity models

1. Studies on biodiversity and ecosystem functioning (BEF) have elicited
debate over the interpretation of the positive relationship between
species richness and plant productivity. Manipulating richness cannot be
achieved without affecting composition; it is thus essential to consider
the latter in statistical models. 2. We firstly review existing approaches
that use species richness as an explanatory variable and propose
modifications to improve their performance. We use an original dataset to
illustrate the analyses. The classical method where composition is coded
as a factor with a level for each different species mixture can be
improved by defining the levels using clustering. Methods based on
ordinations reduce the dimensionality of plant composition and use the new
coordinates as fixed effects; they provide a much better fit to our
observations. 3. Secondly, we develop a new method where composition is
included as a similarity matrix affecting the residual
variance-covariance. Similarity in composition between plots is treated in
the same way as shared evolutionary history between species in
phylogenetic regression. We find that it outperforms the other models. 4.
We discuss the different approaches and suggest that our method is
particularly suited for observational studies or for manipulative studies
where plant diversity is not kept constant by weeding. By treating species
composition in an intuitive and sensible way, it offers a valuable and
powerful complement to existing models.