2012
DOI: 10.1093/sysbio/sys038
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A Unifying Model for the Analysis of Phenotypic, Genetic, and Geographic Data

Abstract: Recognition of evolutionary units (species, populations) requires integrating several kinds of data, such as genetic or phenotypic markers or spatial information in order to get a comprehensive view concerning the differentiation of the units. We propose a statistical model with a double original advantage: (i) it incorporates information about the spatial distribution of the samples, with the aim to increase inference power and to relate more explicitly observed patterns to geography and (ii) it allows one to… Show more

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Cited by 139 publications
(126 citation statements)
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References 68 publications
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“…Unlike other methods described here, the unified model of Guillot et al (2012) can analyse nongenetic data (phenotypical, geographical, behavioural) in addition to genetic data. Their approach implemented a Bayesian clustering algorithm that assumes that each cluster in a geographical domain can be approximated by polygons that are centred around points generated by a Poisson process.…”
Section: Box 1 Systems In Which Species Delimitation Methods Are Applmentioning
confidence: 99%
“…Unlike other methods described here, the unified model of Guillot et al (2012) can analyse nongenetic data (phenotypical, geographical, behavioural) in addition to genetic data. Their approach implemented a Bayesian clustering algorithm that assumes that each cluster in a geographical domain can be approximated by polygons that are centred around points generated by a Poisson process.…”
Section: Box 1 Systems In Which Species Delimitation Methods Are Applmentioning
confidence: 99%
“…A thorough examination of the statistical methods to be applied in species delimitation based on phenotypic data alone or in combination with genotypic caracters can be found elsewhere (Wiens & Servedio 2000;Guillot et al 2012;Solís-Lemus et al 2015) and it is beyond the scope of the present compilation. As a general rule, a choice on a given statistical test will usually require some assumptions to be met by the data and deviations from these could produce spurious results.…”
Section: Usage Of Statistics In Call Comparisonsmentioning
confidence: 99%
“…The clustering approach can combine data on genotypic polymorphism with geographic coordinates projected on a rasterized map. The optimal number of clusters k and the posterior probabilities of cluster membership for any unit of the sampling map were identified by a Markov chain Monte Carlo procedure under the assumption that there is no admixture between populations and that populations are at Hardy-Weinberg equilibrium with linkage equilibrium between loci (Guillot et al, 2012). Here, we assumed that allele frequencies of the cts gene were independent between samples.…”
Section: Inference Of Population Structures By Landscape Clusteringmentioning
confidence: 99%