Making sense of multivariate community responses in global change experiments

Ecological communities are being impacted by global change worldwide. Experiments are apowerful tool to understand how global change will impact communities by comparing controland treatment replicates. Communities consist of multiple species, and their associated abundancesmake multivariate methods an effective approach to study community compositional differencesbetween control and treated replicates. Dissimilarity metrics are a commonly employedmultivariate measure of compositional differences; however, while highly informative,dissimilarity metrics do not elucidate the specific ways in which communities differ. Integratingtwo multivariate methods, dissimilarity metrics and rank abundance curves (RACs), have thepotential to detect complex differences based on dissimilarity metrics and detail the how thesedifferences came about through differences in richness, evenness, species ranks, or species identity.Here we use a database of 106 global change experiments located in herbaceous ecosystems andexplore how patterns of ordinations based on dissimilarity metrics relate to RAC-baseddifferences. We find that combining dissimilarity metrics alongside RAC-based measures clarifieshow global change treatments are altering communities. We find that when there is no differencein community composition (no distance between centroids of control and treated replicates), thereare rarely differences in species ranks or species identities and more often differences in richnessor evenness alone. In contrast, when there are differences between centroids of control and treatedreplicates, this is most often associated with differences in ranks either alone or co-occurring withdifferences in richness, evenness, or species identities. We suggest that integrating these twomultivariate measures of community composition results in a deeper understanding of how globalchange impacts communities