Measuring change in biological communities: multivariate analysis approaches for temporal datasets with low sample size

PeerJ. 2021 Apr 8:9:e11096. doi: 10.7717/peerj.11096. eCollection 2021.


Effective and robust ways to describe, quantify, analyse, and test for change in the structure of biological communities over time are essential if ecological research is to contribute substantively towards understanding and managing responses to ongoing environmental changes. Structural changes reflect population dynamics, changes in biomass and relative abundances of taxa, and colonisation and extinction events observed in samples collected through time. Most previous studies of temporal changes in the multivariate datasets that characterise biological communities are based on short time series that are not amenable to data-hungry methods such as multivariate generalised linear models. Here, we present a roadmap for the analysis of temporal change in short-time-series, multivariate, ecological datasets. We discuss appropriate methods and important considerations for using them such as sample size, assumptions, and statistical power. We illustrate these methods with four case-studies analysed using the R data analysis environment.

Keywords: Beta diversity; Biodiversity; Community variation; Compositional change; Multivariate analysis; Species turnover; Temporal change; Temporal variability; Time series; Zeta diversity.

Associated data

  • Dryad/10.5061/dryad.3m8p1

Grants and funding

Hannah Buckley and Bradley Case were supported by a Charles Bullard Fellowship at Harvard Forest. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.