Introduction

PRIMER 8 with PERMANOVA+ is a substantial upgrade on its predecessor, offering a host of marvelous new tools and statistical methods. These range from simple utilities to make your life easier (such as a tool to easily rename the levels of a factor), through to sophisticated novel analytical methods that are found no-where else (such as dissimilarity-based multivariate control charts, and a valid PERMANOVA test for differences in centroids when dispersions differ).

This book has been written for those who have some familiarity with prior versions of PRIMER/PERMANOVA+ software. To get started quickly using PRIMER 8 software, first consult the following resource:

Anderson, M.J. (2026). "Get Started with PRIMER 8." PRIMER-e Learning Hub. PRIMER-e, Auckland, New Zealand.

For additional details regarding the methods and tools available in the software produced by PRIMER-e, please consult the following historical books and manuals, available in the PRIMER-e Learning Hub:

Clarke KR, Gorley RN, Somerfield PJ & Warwick RM. (2014). "Change in Marine Communities, 3rd edition." PRIMER-E Ltd: Plymouth, UK.
Clarke KR & Gorley RN. (2015). "PRIMER v7: User Manual / Tutorial." PRIMER-E Ltd: Plymouth, UK.
Anderson MJ, Gorley RN & Clarke KR. (2008). "PERMANOVA+ for PRIMER: Guide to Software and Statistical Methods." PRIMER-E Ltd: Plymouth, UK.

In this text, we will refer to the new version (including PERMANOVA+) as 'P8', and to the previous version as 'P7'. What follows is a short list of the most important new features in P8. These items have been classified into two major groups: New Statistical Methods in P8 and New Tools & Utilities in P8. Just click on any link from within either of these lists to explore what is new!

Introduction

New Statistical Methods in P8

New Tools & Utilities in P8

1.1 Expansion from P7 to P8

1.2 Definitions of statistics

1.3 Biotic data: summary stats

1.4 Split summary stats results by groups

1.5 Environmental data: summary stats

2.1 What is an empirical distribution?

2.2 Example: Empirical distributions of oyster sizes

3.1 Plots of empirical densities

3.2 Example: Dotplot of oyster sizes

3.3 Example: Violin plot of kelp holdfast volumes

4.1 Wilcoxon signed-rank test

4.2 Example: Plankton hauls

4.3 Mann-Whitney U test

4.4 Example: Snapper in marine reserves

4.5 Kruskal-Wallis test

4.6 Example: A bivalve species from Ekofisk

4.7 Kolmogorov-Smirnov test

4.8 Example: Sizes of oysters

4.9 Test of Association

4.10 Example: Ekofisk diversity

4.11 Example: Associations between species

Overview of new 'Design' options and tools

6.1 Overview - Allow heterogeneity

6.2 ANOVA in a nutshell

6.3 The Behrens-Fisher problem (BFP)

6.4 Multivariate Behrens-Fisher problem

6.5 Solution to the multivariate BFP

6.6 Example: one-way PERMANOVA allowing heterogeneity

6.7 Heterogeneity in more complex designs

6.8 Example: two-way crossed PERMANOVA allowing heterogeneity

7.1 Overview - Finite factors

7.2 Dichotomy: fixed vs random factors

7.3 Not a dichotomy: a progression from fixed to random

7.4 Example: environmental impact on molluscs

7.5 Broader implications for detecting impact

8.1 Designs lacking replication

8.2 Example: Split-plot - Woodstock vegetation

8.3 Example: Repeated measures - Victorian avifauna

9.1 Why group covariables together?

9.2 Periodic and cyclical models

9.3 Example: Annual monthly cycles - B.C. macroalgae

10.1 Ordinations for multi-factor designs

10.2 Main effects plot

10.3 Interaction plot

10.4 Example: NZ fish assemblages

11.1 What are 'residual' distances?

11.2 Example: Plankton (revisited)

12.1 Overview - Control charts

12.2 Classical univariate control chart

12.3 Classical multivariate control chart

12.4 Bivariate normal example: NZ fish

12.5 Dissimilarity-based multivariate control chart

12.6 Additional notes on implementing control charts

12.7 Example: Birds from Grand Forks

13.1 Overview

13.2 Analysing cumulative standardised data

13.3 Example: Mussel sizes in the Gulf of Alaska

13.4 Example: Gulf of Maine invertebrates - functional resemblance

14.1 Overview

14.2 Example: NE Pacific groundfish vs depth

15.1 New default colour palette

15.2 New selection options

15.3 Re-name levels of a factor (or indicator)

15.4 Add customised values/labels to graphical axes

15.5 Split data sheet by factor/indicator

15.6 Line plots for samples

15.7 Output group-level stats from dispersion (or variability) weighting

15.8 Output diagnostic plots from CAP

15.9 New diagnostics for PCA/PCO plots

Introduction