Extending isolation by resistance to predict genetic connectivity
Extending isolation by resistance to predict genetic connectivity
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DOI：
10.1111/2041-210x.13975
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发表时间：
2022-09-03
影响因子：
6.6
通讯作者：
Austin, James D.
中科院分区：
文献类型：
--
作者：
Fletcher, Robert J., Jr.;Sefair, Jorge A.;Austin, James D.
遗传连通性是进化理论的核心，景观遗传学已经迅速发展，以了解基因流如何受到环境的影响。通常通过使用电路理论来推断的景观阻力的隔离越来越被认为对于预测复杂景观中的遗传连接性至关重要。然而，迁徙的景观障碍可能源于根本不同的过程，例如导致定向迁徙和迁徙过程中死亡的景观梯度，这可能很难解决。空间吸收马尔可夫链（SAMC）已被引入来理解和预测影响生态环境连通性的这些（和其他）过程，但该框架与景观遗传学的关系仍不清楚。在这里，我们将 SAMC 与群体遗传学理论联系起来，提供模拟来解释 SAMC 预测遗传指标的程度，并以濒危物种巴拿马城小龙虾 Procambarus econfinae 为例演示如何将 SAMC 应用于基因组数据，假设该物种会发生定向迁移。 SAMC 在景观遗传学中的使用可以基于与使用电路理论类似的理由来证明，因为我们展示了电路理论如何成为该框架的一个特例。 SAMC 可以通过量化迁移的方向阻力并承认迁移死亡率和迁移阻力之间的差异来扩展电路理论连接模型。我们的实证例子强调，通过承认该物种的不对称环境梯度（即斜率）和迁移死亡率，SAMC 比电路理论和最低成本分析更好地预测种群结构。这些结果为将SAMC应用于景观遗传学奠定了基础。该框架扩展了抗性隔离模型，以解释一些可能影响基因流的常见过程，这可以改善对复杂景观中遗传连接性的预测。
Genetic connectivity lies at the heart of evolutionary theory, and landscape genetics has rapidly advanced to understand how gene flow can be impacted by the environment. Isolation by landscape resistance, often inferred through the use of circuit theory, is increasingly identified as being critical for predicting genetic connectivity across complex landscapes. Yet landscape impediments to migration can arise from fundamentally different processes, such as landscape gradients causing directional migration and mortality during migration, which can be challenging to address. Spatial absorbing Markov chains (SAMC) have been introduced to understand and predict these (and other) processes affecting connectivity in ecological settings, but the relationship of this framework to landscape genetics remains unclear. Here, we relate the SAMC to population genetics theory, provide simulations to interpret the extent to which the SAMC can predict genetic metrics and demonstrate how the SAMC can be applied to genomic data using an example with an endangered species, the Panama City crayfish Procambarus econfinae, where directional migration is hypothesized to occur. The use of the SAMC for landscape genetics can be justified based on similar grounds to using circuit theory, as we show how circuit theory is a special case of this framework. The SAMC can extend circuit-theoretic connectivity modelling by quantifying both directional resistance to migration and acknowledging the difference between migration mortality and resistance to migration. Our empirical example highlights that the SAMC better predicts population structure than circuit theory and least-cost analysis by acknowledging asymmetric environmental gradients (i.e. slope) and migration mortality in this species. These results provide a foundation for applying the SAMC to landscape genetics. This framework extends isolation-by-resistance modelling to account for some common processes that can impact gene flow, which can improve predicting genetic connectivity across complex landscapes.