Data from: A new analytical approach to landscape genetic modeling: least-cost transect analysis and linear mixed models

Landscape genetics aims to assess the effect of the landscape on intraspecific genetic structure. To quantify interdeme landscape structure, landscape genetics mostly uses landscape resistance surfaces and least-cost paths or straight-line transects. However, both approaches have drawbacks. Parameterization of resistance surfaces is a subjective process, and least-cost paths represent a single migration route. A transect-based approach might oversimplify migration patterns by assuming rectilinear migration. To overcome these limitations, we combined these two methods in a new landscape genetic approach: least-cost transect analysis (LCTA). Habitat-matrix resistance surfaces were used to create least-cost paths, which were subsequently buffered to form transects in which the abundance of several landscape elements was quantified. To maintain objectivity, this analysis was repeated so that each landscape element was in turn regarded as migration habitat. The relationship between landscape predictor variables and genetic distances was then assessed following a mixed modeling approach to account for the non-independence of values in distance matrices. Subsequently, predictor variables were selected making use of the R_β^2 statistic. We applied LCTA and the mixed model approach to an empirical genetic dataset on the endangered damselfly, Coenagrion mercuriale. We compared the results to those obtained from traditional least-cost, effective and resistance distance analysis and showed that LCTA not only outperforms existing methods in a statistical way, but also provides more information about the migration ecology of the focal species. Although we believe the statistical approach to be an improvement for the analysis of distance matrices in landscape genetics, more stringent testing is needed.

景观遗传学（Landscape genetics）旨在评估景观对种内遗传结构的影响。为量化种群间景观结构，景观遗传学通常采用景观阻力表面（landscape resistance surfaces）、最小成本路径（least-cost paths）或直线样带（straight-line transects）方法。然而这两种方法均存在局限性：阻力表面的参数化过程带有主观性，且最小成本路径仅能表征单一迁移路径；基于样带的方法则会因假设直线迁移而过度简化迁移模式。为克服上述局限，本研究将两种方法结合，提出一种全新的景观遗传学研究方法：最小成本样带分析（least-cost transect analysis, LCTA）。本研究首先利用生境基质阻力表面生成最小成本路径，随后对其进行缓冲区分析以生成样带，并量化多条景观要素在样带内的丰度。为保证分析的客观性，本研究重复执行该流程，将每一种景观要素依次视为迁移生境。随后，本研究采用混合模型方法评估景观预测变量与遗传距离间的关联，以解决距离矩阵中数值非独立性的问题。后续，研究借助R_β²统计量完成预测变量的筛选。本研究将LCTA与混合模型方法应用于濒危蜻蛉（Coenagrion mercuriale）的实证遗传数据集，将该方法的结果与传统最小成本、有效距离及阻力距离分析的结果进行对比，结果显示，LCTA不仅在统计学性能上优于现有方法，还能为研究对象物种的迁移生态学提供更丰富的信息。尽管本研究认为该统计方法可优化景观遗传学中的距离矩阵分析，但仍需开展更严格的验证测试。