A basic understanding of how the landscape impedes, or creates resistance to, the dispersal of organisms and hence gene flow is paramount for successful conservation science and management. Spatially structured ecological networks are often used to represent spatial landscape‐genetic relationships, where nodes represent individuals or populations and resistance to movement is represented using non‐binary edge weights. Weights are typically assigned or estimated by the user, rather than observed, and validating such weights is challenging. We provide a synthesis of current methods used to estimate edge weights and an overview of common model types, stressing the advantages and disadvantages of each approach and their ability to model landscape‐genetic data. We further explore a set of spatial‐statistical methods that provide ecologists with alternative approaches for modeling spatially explicit processes that may affect genetic structure. This includes an overview of spatial autoregressive models, with a particular focus on how correlation and partial correlation are used to represent neighborhood structure with the inverse of the covariance matrix (i.e., precision matrix). We then demonstrate how to model resistance by specifying an appropriate statistical model on the nodes, conditioned on the edge weights, through the precision matrix. This integration of network ecology and spatial statistics provides a practical analytical framework for landscape‐genetic studies. The results can be used to make statistical inferences about the relative importance of individual landscape characteristics, such as the vegetative cover, hillslope, or the presence of roads or rivers, on gene flow. In addition, the R code we include allows readers to explore landscape‐genetic structure in their own datasets, which will potentially provide new insights into the evolutionary processes that generated ecological networks, as well as valuable information about the optimal characteristics of conservation corridors.

中文翻译：

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景观遗传学的空间结构化统计网络模型

对景观如何阻止或抵抗生物扩散以及因此产生基因流的基本了解，对于成功的保护科学和管理至关重要。空间结构化的生态网络通常用于表示空间景观与遗传的关系，其中节点表示个人或种群，而运动阻力则使用非二进制边缘权重表示。权重通常是由用户分配或估计的，而不是被观察到的，并且验证这种权重是具有挑战性的。我们提供了用于估计边缘权重的当前方法的综合，并概述了常见的模型类型，强调了每种方法的优缺点以及它们对景观遗传数据进行建模的能力。我们进一步探索了一套空间统计方法，为生态学家提供了替代方法，以对可能影响遗传结构的空间显式过程进行建模。这包括对空间自回归模型的概述，特别关注如何使用相关性和部分相关性来表示具有协方差矩阵（即精度矩阵）的逆函数的邻域结构。然后，我们演示如何通过精度矩阵在节点上指定适当的统计模型（以边缘权重为条件）来对阻力进行建模。网络生态学和空间统计的这种整合为景观遗传研究提供了一个实用的分析框架。结果可用于对各个景观特征的相对重要性进行统计推断，例如植物生长，山坡，道路或河流的存在等。此外，我们包含的R代码使读者可以在自己的数据集中探索景观遗传结构，这将潜在地提供有关生成生态网络的演化过程的新见解，以及有关保护走廊最佳特征的有价值的信息。