pH, temperature, soil type), than univariate summary statistics, such as taxon richness, diversity, or biomass ( Micheli et al., 1999 Chao et al., 2014), and hence more informative about causal processes ( Collins et al., 2008). Multi-taxon, multivariate datasets can be more sensitive to changes in explanatory or predictor variables (e.g. Temporal changes in taxon richness and composition can also provide both simple summaries of ecosystem change and information that can be translated rapidly into action, either to protect and restore declining taxa, or to staunch unsustainable growth of unwanted organisms ( Alday et al., 2013). Understanding the temporal dynamics of community structure can illuminate fundamental ecological processes, including effects of individual life histories on rates of ecosystem change, the relative importance of biotic and abiotic factors in determining community structure, or how taxa and the networks in which they are embedded respond to environmental change ( Tilman, 1999 Hobbs, Yates & Mooney, 2007 Kampichler et al., 2012). These temporal dynamics are governed by both intrinsic factors, including intraspecific and interspecific interactions, colonisations and extinctions, and extrinsic factors, which are processes that relate to periodic disturbances and changing environmental conditions, for example shifts in temperature or drought ( Connell & Slatyer, 1977 Brown & Lawson, 2010). The structure of communities-the identities (‘composition’), number (‘richness’) and relative abundances (‘evenness’) of taxa-changes through time because of fluctuations of individual populations and local colonisations and extinctions ( Micheli et al., 1999 Legendre & Gauthier, 2014).
We illustrate these methods with four case-studies analysed using the R data analysis environment.Ī key task for ecologists is to monitor, quantify, analyse, and predict temporal changes in the structure of ecological communities as a function of ongoing changes in climate, land use, and other environmental drivers.
We discuss appropriate methods and important considerations for using them such as sample size, assumptions, and statistical power. Here, we present a roadmap for the analysis of temporal change in short-time-series, multivariate, ecological datasets.
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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. Structural changes reflect population dynamics, changes in biomass and relative abundances of taxa, and colonisation and extinction events observed in samples collected through time. PeerJ 9: e11096 Įffective 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. Measuring change in biological communities: multivariate analysis approaches for temporal datasets with low sample size. Cite this article Buckley HL, Day NJ, Case BS, Lear G. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. Licence This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed.
3 School of Biological Sciences, University of Auckland, Auckland, New Zealand DOI 10.7717/peerj.11096 Published Accepted Received Academic Editor Leslie Ries Subject Areas Biodiversity, Ecology, Microbiology, Statistics Keywords Beta diversity, Community variation, Biodiversity, Compositional change, Multivariate analysis, Species turnover, Temporal change, Zeta diversity, Temporal variability, Time series Copyright © 2021 Buckley et al.
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