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Overview

If you have purchased a subscription to PRIMER 8 with PERMANOVA+, then you will have access to the PERMANOVA+ main menu item that allows you to perform a broad range of additional analyses using a suite of routines that are not available in the basic PRIMER 8 Lite package. Check out our website to see a Detailed list of all features.

Note also that the PERMANOVA routine has been expanded substantially in the leap from PRIMER 7 to PRIMER 8. For a more complete guide to all of these important improvements, please see the essential resource: 'What's new in PRIMER 8'. You can also consult the historical PERMANOVA+ user manual for fundamental information and references regarding some of these routines and their underlying statistical details.

Here, we will run through a quick example of how to set up and run a multi-factor PERMANOVA analysis in PRIMER 8. Permutational multivariate analysis of variance (PERMANOVA) partitions variation in the space of a chosen dissimilarity measure in response to one or more factors in a specified sampling protocol or experimental design ( Anderson (2001a) , Anderson (2017) ). Tests of individual terms in a PERMANOVA model are achieved by constructing correct (pseudo-)F ratios on the basis of expectations of mean squares (EMS), and p-values are obtained using correct permutation algorithms given the full study design. We know of no other software package that accomplishes this.

Importantly, the PERMANOVA routine in PRIMER allows the user:

  • to specify whether factors are fixed, random, finite, or of a type called 'whole-plot/subject' that caters to designs lacking replication at different scales,
  • to account for heterogeneity in multivariate dispersions when testing for differences in centroids,
  • to specify whether a factor is nested in one or more other factors,
  • to test interaction terms,
  • to include one or more quantitative covariates in the analysis,
  • to group covariates (accommodating cyclical/seasonal models),
  • to re-order, pool or remove individual terms from a model,
  • to handle correctly:
    • mixed models
    • user-specified contrasts
    • BACI designs (before-after/control-impact),
    • asymmetrical designs (e.g., in environmental impact studies),
    • randomised blocks,
    • split plots and splt-split-plots,
    • hierarchical designs,
    • repeated measures,
    • unbalanced designs (Type I, II or III sums of squares),
    • ... and more.
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