DOI: 10.5061/dryad.b2rbnzsbr
title: R code and example data for using genogeographic clustering approach

description: While in recent years there have been considerable advances in discerning
 spatial genetic patterns within species, the task of identifying common
 patterns across species is still challenging. Approaches using new data
 from co-sampled species permit rigorous statistical analysis but are often
 limited to a small number of species; meta-analyses of published data can
 encompass a much broader range of species, but are usually restricted by
 uneven data properties. There is a need for new approaches that bring
 greater statistical rigour to meta-analyses, and are also able to discern
 more than a single spatial pattern among species. We propose a new
 approach for comparative multi-species meta-analyses of published
 population genetic data that addresses many existing limitations. This
 analysis takes a three-stage approach: (i) use common genetic metrics to
 measure location-specific diversity across the sampled range of each
 species, (ii) use an innovative graphing technique to describe spatial
 patterns within each species, and (iii) quantitatively cluster species by
 their similarity in pattern. We apply this technique to 21 species of
 intertidal invertebrate from the New Zealand coastline, to resolve common
 spatial patterns from disparate profiles of genetic diversity. The
 genogeographic curves are shown to successfully capture the known spatial
 patterns within each intertidal species, and readily permit statistical
 comparison of those patterns, regardless of sampling and marker
 inconsistencies. The species clustering technique is shown to discern
 groups of species that clearly share spatial patterns within groups but
 differ significantly among groups. The species groups defined were not
 identifiable a priori from their taxonomy or life history, but their
 spatial genetic patterns appear biologically relevant. Genogeographic
 species clustering provides a novel approach to discerning multiple common
 spatial patterns of diversity among a large number of species. It will
 permit more rigorous comparative studies from diverse published data, and
 can be easily extended to a wide variety of alternative measures of
 genetic diversity or divergence. We see the approach best used as an
 exploratory method, to uncover the patterns often hidden in multi-species
 communities, likely to be followed by more targeted model-testing
 analyses.
description: This R code and example data file is provided to use
 "Genogeographic clustering approach" to discerning multiple
 common spatial patterns of diversity among a large number of species. It
 will permit more rigorous comparative studies from diverse published data,
 and can be easily extended to a wide variety of alternative measures of
 genetic diversity or divergence. We see the approach best used as an
 exploratory method, to uncover the patterns often hidden in multi-species
 communities, likely to be followed by more targeted model-testing
 analyses. The dataset was built from previously
 published data of single-species population genetics studies. For each
 species, a genetic diversity measure was computed within each geographic
 location. The measure of genetic diversity used depended on the genetic
 marker available. Haplotype diversity “H” was used for mitochondrial DNA,
 and the analogous allelic expected heterozygosity
 “H<sub>e</sub>” for nuclear DNA microsatellites. Collectively,
 these values are henceforth referred to simply as allelic diversity, “H”.
 Values were either reported directly in the publications used, or
 alternatively calculated using ARLEQUIN v 3.5 (Excoffier & Lischer
 2010) and GeneAlEx
 (Peakall & Smouse 2012) from the genetic data associated with the
 publication. Adjustments for small sample sizes were included in the
 calculation of all parameters, using unbiased estimators (Nei
 1972).
description: All the R code and instructions to perform the analyses described
 in the following paper: Arranz, Vanessa, Rachel M. Fewster, and Shane D.
 Lavery. "Genogeographic clustering to identify cross‐species
 concordance of spatial genetic patterns." Diversity and Distributions
 (2022) https://doi.org/10.1111/ddi.13474 . Information and instructions can be found in the README.txt file.