Spatially structured statistical network models for landscape genetics

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 autoregressi...清晰认知景观如何阻碍乃至产生阻力，进而影响生物扩散与基因流，是成功开展保护科学研究与管理工作的关键前提。空间结构化生态网络（spatially structured ecological networks）常被用于表征空间景观-遗传关系，其中节点代表个体或种群，移动阻力通过非二元边权重（non-binary edge weights）进行表征。此类权重通常由研究者手动赋值或估算，而非实地观测所得，因此对其进行验证颇具挑战。本研究系统梳理了当前用于估算边权重的各类方法，并概述了常见模型类型，着重分析了每种方法的优劣及其对景观-遗传数据的建模能力。此外，我们还探讨了一系列空间统计方法，为生态学家提供了用于建模可能影响遗传结构的空间显性过程的替代方案，其中包括对空间自回归（spatial autoregressi...）的概述。