Riverscape genetics, which applies concepts in landscape genetics to riverine ecosystems, lack appropriate quantitative methods that address the spatial autocorrelation structure of linear stream networks and account for bidirectional geneflow. To address these challenges, we present a general framework for the design and analysis of riverscape genetic studies. Our framework starts with the estimation of pairwise genetic distance at sample sites and the development of a spatially structured ecological network (SSEN) on which riverscape covariates are measured. We then introduce the novel bidirectional geneflow in riverscapes (BGR) model that uses principles of isolation‐by‐resistance to quantify the effects of environmental covariates on genetic connectivity, with spatial covariance defined using simultaneous autoregressive models on the SSEN and the generalized Wishart distribution to model pairwise distance matrices arising through a random walk model of geneflow. We highlight the utility of this framework in an analysis of riverscape genetics for brook trout (Salvelinus fontinalis) in north central Pennsylvania, USA. Using the fixation index (F_ST) as the measure of genetic distance, we estimated the effects of 12 riverscape covariates on geneflow by evaluating the relative support of eight competing BGR models. We then compared the performance of the top‐ranked BGR model to results obtained from comparable analyses using multiple regression on distance matrices (MRM) and the program STRUCTURE. We found that the BGR model had more power to detect covariate effects, particularly for variables that were only partial barriers to geneflow and/or uncommon in the riverscape, making it more informative for assessing patterns of population connectivity and identifying threats to species conservation. This case study highlights the utility of our modeling framework over other quantitative methods in riverscape genetics, particularly the ability to rigorously test hypotheses about factors that influence geneflow and probabilistically estimate the effect of riverscape covariates, including stream flow direction. This framework is flexible across taxa and riverine networks, is easily executable, and provides intuitive results that can be used to investigate the likely outcomes of current and future management scenarios.

中文翻译：

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一种新颖的河景遗传学定量框架。

Riverscape遗传学将景观遗传学中的概念应用到河流生态系统中，但缺乏适当的定量方法来解决线性流网络的空间自相关结构并解释双向基因流。为了解决这些挑战，我们提出了设计和分析河景遗传研究的通用框架。我们的框架从估算样本点的成对遗传距离开始，并发展出可测量河流景观协变量的空间结构化生态网络（SSEN）。然后，我们介绍了新颖的双向河水双向基因流（BGR）模型，该模型使用抗性隔离原理来量化环境协变量对遗传连通性的影响，使用SSEN上的同时自回归模型和广义Wishart分布定义的空间协方差，以建模通过基因流的随机游走模型产生的成对距离矩阵。我们在分析河鳟的河景遗传学时着重介绍了该框架的实用性（Salvelinus fontinalis）在美国宾夕法尼亚州中北部。使用注视指数（F _ST）作为遗传距离的量度，我们通过评估八个竞争性BGR模型的相对支持，估算了12个河景协变量对基因流的影响。然后，我们将排名最高的BGR模型的性能与使用距离矩阵（MRM）的多元回归和程序STRUCTURE进行的可比分析获得的结果进行了比较。我们发现，BGR模型具有检测协变量效应的能力，尤其是对于仅部分阻碍基因流和/或在河流景观中不常见的变量而言，这使其在评估种群连通性模式和识别对物种保护的威胁方面更具参考价值。本案例研究突显了我们的建模框架在河流景观遗传学中优于其他定量方法的实用性，尤其是能够严格检验有关影响基因流量的因素的假设并能概率性地估算河景协变量（包括水流方向）的影响的能力。该框架在分类单元和河流网络之间具有灵活性，易于执行，并提供直观的结果，可用于调查当前和将来的管理方案的可能结果。