Disentangling the impacts of diversity on ecosystem functioning in combinatorial experiments

Biodiversity includes both taxonomic and functional aspects, each of which can play significant roles in ecosystem functioning. The number of functional groups, specifically intratrophic group (e.g., plant or herbivore) functional richness (F), serves as a simple index of ecological diversity, while species richness (S) serves as a simple index of taxonomic diversity. F and S are, however, roughly correlated measures of biodiversity, and disentangling the relative influence of one over the other on ecosystem functioning (H) requires a multivariate index. Appropriate multivariate biodiversity indices can be derived by applying principal component analyses to the set of possible combinations of S and F in an experimental design. The first principal component (PCAI) represents covariation between F and S, while the second principal component (PCAII) provides information on functioning that is associated with the independent effects of F and S. Thus, one can replace the conventional model H = f(F, S) with H = f(PCAI, PCAII). This approach obviates a number of statistical problems encountered when following the traditional approach. Furthermore, if the question being addressed concerns the relationship between biodiversity and ecosystem functioning and not the relative contributions of F and S, PCAI may be used to develop more tractable, yet effective experimental designs than the conventional, exhaustive F × S experimental studies currently in favor. I explore the theoretical foundation for this multivariate approach and provide an example using the results from experimental prairie grassland plant assemblages at Cedar Creek Natural History Area, Minnesota, USA. This study highlights the need to adapt traditional, taxonomic approaches to biodiversity research to include functional diversity.