A comprehensive approach to analyzing community dynamics using rank abundance curves

Meghan L. Avolio, Ian T. Carroll, Scott L. Collins, Gregory R. Houseman, Lauren M. Hallett, Forest Isbell, Sally E. Koerner, Kimberly J. Komatsu, Melinda D. Smith, Kevin R. Wilcox

Research output: Contribution to journalArticlepeer-review

100 Scopus citations

Abstract

Univariate and multivariate methods are commonly used to explore the spatial and temporal dynamics of ecological communities, but each has limitations, including oversimplification or abstraction of communities. Rank abundance curves (RACs) potentially integrate these existing methodologies by detailing species-level community changes. Here, we had three goals: first, to simplify analysis of community dynamics by developing a coordinated set of R functions, and second, to demystify the relationships among univariate, multivariate, and RACs measures, and examine how each is influenced by the community parameters as well as data collection methods. We developed new functions for studying temporal changes and spatial differences in RACs in an update to the R package library(“codyn”), alongside other new functions to calculate univariate and multivariate measures of community dynamics. We also developed a new approach to studying changes in the shape of RAC curves. The R package update presented here increases the accessibility of univariate and multivariate measures of community change over time and difference over space. Next, we use simulated and real data to assess the RAC and multivariate measures that are output from our new functions, studying (1) if they are influenced by species richness and evenness, 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. We find that the RAC and multivariate measures are not sensitive to species richness and evenness and that all the measures detail unique aspects of temporal change or spatial differences. We also find that species reordering is the strongest correlate of a multivariate measure of compositional change and explains most community change observed in long-term nutrient addition experiment. Overall, we show that species reordering is potentially an understudied determinant of community changes over time or differences between treatments. The functions developed here should enhance the use of RACs to further explore the dynamics of ecological communities.

Original languageEnglish (US)
Article numbere02881
JournalEcosphere
Volume10
Issue number10
DOIs
StatePublished - Oct 1 2019

Bibliographical note

Publisher Copyright:
© 2019 The Authors.

Keywords

  • R package
  • codyn
  • community composition
  • long-term data
  • multivariate analysis
  • richness
  • spatial variability
  • temporal variability

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