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A statistical test and sample size recommendations for comparing community composition following PCA.

John R Skalski1, Shelby M Richins1, Richard L Townsend1

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This study introduces Analysis of Distance (ANODIS), a new statistical method to formally test for differences in ecological community composition. ANODIS enhances ecological impact assessments by providing robust statistical analysis for complex ecological data.

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Area of Science:

  • Ecology
  • Environmental Science
  • Statistical Ecology

Background:

  • Ecological studies often assess impacts by detecting changes in variables or community composition.
  • Ordination techniques like Principal Components Analysis (PCA) and Canonical Correspondence Analysis (CCA) simplify complex ecological datasets.
  • Visual inspection of PCA/CCA scatterplots is common for comparing community composition between groups.

Purpose of the Study:

  • To present a multidimensional extension of analysis of variance called Analysis of Distance (ANODIS).
  • To provide a formal statistical test for differences in ecological community composition using ordination dimensions.
  • To enable power and sample size calculations for ecological study designs.

Main Methods:

  • Developed a multidimensional Analysis of Variance based on Analysis of Distance (ANODIS).
  • Adapted F-statistics for significance testing in multidimensional ecological data.
  • Applied ANODIS to bivariate PCA scatterplots from three published ecological studies.

Main Results:

  • ANODIS provides a formal statistical framework to test for differences in community composition across multiple dimensions.
  • The method is parametric, allowing for straightforward power and sample size calculations.
  • Demonstrated ANODIS utility with examples from existing ecological research.

Conclusions:

  • ANODIS offers a statistically rigorous approach to analyze community composition differences derived from ordination.
  • This method improves the design and interpretation of ecological studies investigating anthropogenic and natural impacts.
  • Facilitates robust statistical inference in complex ecological community analyses.