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

PLoS One

James F. Cahill¹  Pamela Twerdy²  Lindsey R. Leighton²  Frank L. Forcino³ 

[1]University of Alberta, Department of Biological Sciences, CW 405, Biological Sciences Building, Edmonton, AB, Canada, T6G 2E9

[2]University of Alberta, Earth & Atmospheric Sciences, 1–26 Earth Sciences Building, Edmonton, AB, Canada, T6G 2E3

[3]Western Carolina University, Geosciences and Natural Resources Department, 331 Stillwell Building, Cullowhee, NC, United States of America, 28723, (828) 227–7367, fax: [phone]

关键词: Community ecology;    Grasslands;    Normal distribution;    Bivalves;    Ecological metrics;    Ecology;    Malacology;    Taxonomy;   

DOI  :  10.1371/journal.pone.0128379

学科分类：医学（综合）

来源: Public Library of Science

【 摘 要 】

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.

【 授权许可】

CC BY