Climate Impacts Group, College of the Environment, University of Washington, Seattle, WA, USA.
Computational Ecology Laboratory, School of Public and Community Health Sciences, University of Montana, Missoula, MO, USA.
Mol Ecol Resour. 2021 Feb;21(2):394-403. doi: 10.1111/1755-0998.13267. Epub 2020 Oct 23.
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.
关联景观遗传方法的一个假设是,中性标记的遗传分化仅源于个体或种群之间的景观在多大程度上阻碍基因流动。然而,当基因从未被采样的、分化的居群流入被采样的居群时,这种假设就被违反了。当仅对居群范围的一部分进行采样时,或者当密切相关的物种与被采样的居群杂交时,就会发生这种情况。在这两种情况下,违反建模假设都有可能降低景观遗传模型选择的准确性,并导致较差的推断。我们使用基于个体的群体遗传模拟,在模型选择框架内,在复杂的景观中,探讨了来自未采样居群的基因流动的潜在混杂效应。我们假设,随着来自采样范围之外的基因流动的增加,由于形成了杂种区,等位基因频率受到干扰,与采样个体之间的有效距离不相关,因此模型选择的准确性会降低。令人惊讶的是,我们发现这种预期是没有根据的,因为由于混合导致的准确性降低被来自未采样居群的等位基因扩散到被采样居群中而导致的等位基因多样性的增加所抵消。这些新的等位基因增加了检测景观遗传关系的能力,甚至略微提高了整体模型选择的准确性。这是一个令人放心的结果,表明只要满足其他既定的采样要求,就不需要对居群或可能杂交的相关物种进行全面采样。