Print PageIt has been well established that increasing biodiversity has a positive effect on many ecosystem functions. One method for modelling Biodiversity-Ecosystem Function (BEF) is to use Diversity-Interactions (DI) models where the ecosystem function is modelled on the proportion of each species present in the plot (identity effects; βi Pi), overall density and interactions between species proportions (pairwise interactions; δijPiPj ). However a community containing S species leads to S(S-1)/2 pairwise interactions estimates so as the number of species increases it may not be possible to estimate all of the pairwise interactions. The aim of this work was to develop a more parsimonious DI modelling approach using random effects. We assume a fixed average interaction effect δav for all pairwise interactions and that deviations from this, δav-δij, are randomly distributed. This approach was applied to three datasets, two grassland datasets, containing up to 60 and 9 species per community respectively, and a bacterial community dataset containing up to 72 species per community.
Results/Conclusions
Using goodness of fit tests we found that the random effects models did provide a good fit to the data. We concluded that the random effects model could be a very useful method of analysis for explaining interactions among species in communities with many species.
