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2 results for “GenoGeographer”
R code and example data for using genogeographic clustering approach
<p>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.</p> <p>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.</p> <p>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 <em>priori</em> from their taxonomy or life history, but their spatial genetic patterns appear biologically relevant.</p> <p>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.</p>
R code and example data for using genogeographic clustering approach
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