A comprehensive approach to analyzing community dynamics using rank abundance curves

TitleA comprehensive approach to analyzing community dynamics using rank abundance curves
Publication TypeJournal Article
Year of Publication2019
AuthorsAvolio, ML, Carroll, I, Collins, SL, Houseman, GR, Hallett, LM, Isbell, FL, Koerner, SE, Komatsu, KJ, Smith, MD, Wilcox, KR
Accession NumberKNZ001953

Univariate and multivariate methods are commonly used to explore the spatial and temporaldynamics of ecological communities, but each has limitations, including oversimplification or abstractionof communities. Rank abundance curves (RACs) potentially integrate these existing methodologies bydetailing species-level community changes. Here, we had three goals:first, to simplify analysis of commu-nity dynamics by developing a coordinated set of R functions, and second, to demystify the relationshipsamong univariate, multivariate, and RACs measures, and examine how each is influenced by the commu-nity parameters as well as data collection methods. We developed new functions for studying temporalchanges and spatial differences in RACs in an update to the R package library(“codyn”), alongside othernew functions to calculate univariate and multivariate measures of community dynamics. We also devel-oped a new approach to studying changes in the shape of RAC curves. The R package update presentedhere increases the accessibility of univariate and multivariate measures of community change over timeand difference over space. Next, we use simulated and real data to assess the RAC and multivariate mea-sures that are output from our new functions, studying (1) if they are influenced by species richness andevenness, temporal turnover, and spatial variability and (2) how the measures are related to each other.Lastly, we explore the use of the measures with an example from a long-term nutrient addition experiment.Wefind that the RAC and multivariate measures are not sensitive to species richness and evenness andthat all the measures detail unique aspects of temporal change or spatial differences. We alsofind that spe-cies reordering is the strongest correlate of a multivariate measure of compositional change and explainsmost community change observed in long-term nutrient addition experiment. Overall, we show that spe-cies reordering is potentially an understudied determinant of community changes over time or differencesbetween treatments. The functions developed here should enhance the use of RACs to further explore thedynamics of ecological communities.