Ecologists typically track community change by simplifying community structure using diversity indexes or abstract multivariate measures of the whole community. Thus, analyses of compositional changes are highly species specific, limiting generality across ecosystems. At SESYNC, I propose to develop new metrics for identifying compositional changes in generalizable ways using rank abundance curves (RACs). RACs, which display the abundance of each species in the community, can change in curve shape as well as by species reordering, and gain/loss of species. Other than methods comparing shapes of RACs, there are no rigorous statistical tests available to compare RACs, limiting the usefulness of RACs and our understanding of community changes. For example, studying the degree to which global change drivers result in species re-ordering gives essential information that richness cannot detect.
I, along with colleagues from a previously funded LTER Network Office working group, conceptualized a framework to study community changes by integrating multivariate patterns of community similarity with changes in RACs. We also hypothesized how changes in RACs might be reflected in multivariate community patterns, which I also propose to directly test at SESYNC.
To accomplish my objectives, I will run simulations and analyze data from 82 global change experiments, including 24 LTER experiments, which are already compiled as a product of the working group. Working with Drs. Scott Collins and Greg Houseman, my goal is to ultimately produce an R package to enable ecologists to rigorously test changes in RACs and detect changes in the entire plant community.