Reexamining Sample Size Requirements for Multivariate, Abundance-Based Community Research: When Resources are Limited, the Research Does Not Have to Be.

Frank L Forcino,Pamela Twerdy,James F Cahill,Lindsey R Leighton,Covadonga Orejas

doi:10.1371/journal.pone.0128379

Frank L Forcino, Pamela Twerdy + Show 3 more

Open Access

https://doi.org/10.1371/journal.pone.0128379

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Journal: PloS one	Publication Date: Jun 9, 2015
Citations: 91	License type: CC BY 4.0

Affiliation: Western Carolina University, University of Alberta

Abstract

Community ecologists commonly perform multivariate techniques (e.g., ordination, cluster analysis) to assess patterns and gradients of taxonomic variation. A critical requirement for a meaningful statistical analysis is accurate information on the taxa found within an ecological sample. However, oversampling (too many individuals counted per sample) also comes at a cost, particularly for ecological systems in which identification and quantification is substantially more resource consuming than the field expedition itself. In such systems, an increasingly larger sample size will eventually result in diminishing returns in improving any pattern or gradient revealed by the data, but will also lead to continually increasing costs. Here, we examine 396 datasets: 44 previously published and 352 created datasets. Using meta-analytic and simulation-based approaches, the research within the present paper seeks (1) to determine minimal sample sizes required to produce robust multivariate statistical results when conducting abundance-based, community ecology research. Furthermore, we seek (2) to determine the dataset parameters (i.e., evenness, number of taxa, number of samples) that require larger sample sizes, regardless of resource availability. We found that in the 44 previously published and the 220 created datasets with randomly chosen abundances, a conservative estimate of a sample size of 58 produced the same multivariate results as all larger sample sizes. However, this minimal number varies as a function of evenness, where increased evenness resulted in increased minimal sample sizes. Sample sizes as small as 58 individuals are sufficient for a broad range of multivariate abundance-based research. In cases when resource availability is the limiting factor for conducting a project (e.g., small university, time to conduct the research project), statistically viable results can still be obtained with less of an investment.

Highlights

Community ecologists commonly perform multivariate techniques to assess patterns and gradients of taxonomic variation [1,2,3,4,5,6]
With the exception of one dataset, the Mantel Test R-statistics were greater than R = 0.88 for all sample sizes greater than 28 individuals (Fig 1a)
Examining 44 previously published and 220 created datasets with simulated abundance structures, we found evidence that smaller sample sizes (i.e., 58 individuals) produce the same community results as larger sample sizes. This finding is most important for ecologists with limited resources

Summary

Introduction

Community ecologists commonly perform multivariate techniques (e.g., ordination, cluster analysis) to assess patterns and gradients of taxonomic variation [1,2,3,4,5,6]. Due to the enormous number of individuals in most ecological communities, ecologists typically rely on a collected sample that is representative of the complete natural system as opposed to collecting everything within a natural system [7,8,9,10,11,12,13,14,15]. This fundamental unit of sampling must contain a sufficient number of individuals; otherwise it may misrepresent the natural system leading to erroneous conclusions. We determine the smallest required sample size at which a statistically robust result can be achieved using multivariate statistical techniques

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