Literature DB >> 33012124

The effect of gene flow from unsampled demes in landscape genetic analysis.

Andrew J Shirk1, Erin L Landguth2, Samuel A Cushman3.   

Abstract

An assumption of correlative landscape genetic methods is that genetic differentiation at neutral markers arises solely from the degree to which the intervening landscape between individuals or populations resists gene flow. However, this assumption is violated when gene flow occurs into the sampled population from an unsampled, differentiated deme. This may happen when sampling within only a portion of a population's extent or when closely related species hybridize with the sampled population. In both cases, violation of the modelling assumptions has the potential to reduce landscape genetic model selection accuracy and result in poor inferences. We used individual-based population genetic simulations in complex landscapes within a model selection framework to explore the potential confounding effect of gene flow from unsampled demes. We hypothesized that as gene flow from outside the sampling extent increased, model selection accuracy would decrease due to the formation of a hybrid zone where allele frequencies were perturbed in a way that was not correlated with effective distances between sampled individuals. Surprisingly, we found this expectation was unfounded, because the reduced accuracy due to admixture was counteracted by an increase in allelic diversity as alleles spread from the unsampled deme into the sampled population. These new alleles increased the power to detect landscape genetic relationships and even slightly improving model selection accuracy overall. This is a reassuring result, suggesting that sampling the full extent of a population or related species that may hybridize may be unnecessary, as long as other well-established sampling requirements are met.
© 2020 John Wiley & Sons Ltd.

Keywords:  accuracy; deme; gene flow; landscape genetics; model selection; sampling

Year:  2020        PMID: 33012124     DOI: 10.1111/1755-0998.13267

Source DB:  PubMed          Journal:  Mol Ecol Resour        ISSN: 1755-098X            Impact factor:   7.090


  3 in total

1.  Evidence of spatial genetic structure in a snow leopard population from Gansu, China.

Authors:  Luciano Atzeni; Samuel A Cushman; Jun Wang; Philip Riordan; Kun Shi; David Bauman
Journal:  Heredity (Edinb)       Date:  2021-11-06       Impact factor: 3.821

Review 2.  Microbial Pathogenicity in Space.

Authors:  Marta Filipa Simões; André Antunes
Journal:  Pathogens       Date:  2021-04-09

3.  The effect of sampling density and study area size on landscape genetics inferences for the Mississippi slimy salamander (Plethodon mississippi).

Authors:  Stephanie M Burgess; Ryan C Garrick
Journal:  Ecol Evol       Date:  2021-05-01       Impact factor: 2.912

  3 in total

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