ArticleInternational journal of molecular sciences2011
Comparison of Bayesian clustering and edge detection methods for inferring boundaries in landscape genetics.
Article in International journal of molecular sciences, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Population genetic considerations for using biobanks as international resources in the pandemic era and beyond.BMC genomics · 2021Pooled it
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- Genetic signature of the natural gene pool ofEcology and evolution · 2021Article
- Identifying Priority Giant Anteater (Ecology and evolution · 2021Article
- Biome stability predicts population structure of a southern African aridland bird species.Ecology and evolution · 2020Article
- Assessment of spatial genetic structure to identify populations at risk for infection of an emerging epizootic disease.Ecology and evolution · 2020Article
- High genetic diversity and stable Pleistocene distributional ranges in the widespread Mexican red oakEcology and evolution · 2020Article
- Population genetics of the Mediterranean corn borer (Sesamia nonagrioides) differs between wild and cultivated plants.PloS one · 2020Article
- Landscape effects on the contemporary genetic structure of Ruffed Grouse (Ecology and evolution · 2019Article
- Genetic diversity and structure ofHereditas · 2019Article
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- Can secondary contact following range expansion be distinguished from barriers to gene flow?PeerJ · 2018Article
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- A longitudinal genetic survey identifies temporal shifts in the population structure of Dutch house sparrows.Heredity · 2016Article
- Evaluating the influence of life-history characteristics on genetic structure: a comparison of small mammals inhabiting complex agricultural landscapes.Ecology and evolution · 2016Article
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5 authors.
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Abstract
Recently, techniques available for identifying clusters of individuals or boundaries between clusters using genetic data from natural populations have expanded rapidly. Consequently, there is a need to evaluate these different techniques. We used spatially-explicit simulation models to compare three spatial Bayesian clustering programs and two edge detection methods. Spatially-structured populations were simulated where a continuous population was subdivided by barriers. We evaluated the ability of each method to correctly identify boundary locations while varying: (i) time after divergence, (ii) strength of isolation by distance, (iii) level of genetic diversity, and (iv) amount of gene flow across barriers. To further evaluate the methods' effectiveness to detect genetic clusters in natural populations, we used previously published data on North American pumas and a European shrub. Our results show that with simulated and empirical data, the Bayesian spatial clustering algorithms outperformed direct edge detection methods. All methods incorrectly detected boundaries in the presence of strong patterns of isolation by distance. Based on this finding, we support the application of Bayesian spatial clustering algorithms for boundary detection in empirical datasets, with necessary tests for the influence of isolation by distance.
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