A conceptual framework for the spatial analysis of landscape genetic data

• Published in: Conservation genetics Vol. 14; no. 2; pp. 253 - 261
• Main Authors: Wagner, Helene H., Fortin, Marie-Josée
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.04.2013; Springer Nature B.V
• Subjects: alleles; analytical methods; Animal Genetics and Genomics; autocorrelation; Biodiversity; Biogeography; Biomedical and Life Sciences; Conservation biology; Conservation Biology/Ecology; Ecology; Evolutionary Biology; Gene flow; Genetic diversity; Genetic linkage; genetic variation; Genetics; global change; Heterogeneity; Landscape ecology; landscapes; landscaping; Life Sciences; Metapopulations; Plant Genetics and Genomics; Population genetics; probability; Research Article; Spatial analysis; Spatial distribution; Link analyses; Neighborhood analyses; Neutral genetic variation; Adaptive genetic variation; Node analyses; Boundary analyses; Spatial statistics
• ISSN: 1566-0621; 1572-9737
• DOI: 10.1007/s10592-012-0391-5

Understanding how landscape heterogeneity constrains gene flow and the spread of adaptive genetic variation is important for biological conservation given current global change. However, the integration of population genetics, landscape ecology and spatial statistics remains an interdisciplinary challenge at the levels of concepts and methods. We present a conceptual framework to relate the spatial distribution of genetic variation to the processes of gene flow and adaptation as regulated by spatial heterogeneity of the environment, while explicitly considering the spatial and temporal dynamics of landscapes, organisms and their genes. When selecting the appropriate analytical methods, it is necessary to consider the effects of multiple processes and the nature of population genetic data. Our framework relates key landscape genetics questions to four levels of analysis: (i) node-based methods, which model the spatial distribution of alleles at sampling locations (nodes) from local site characteristics; these methods are suitable for modeling adaptive genetic variation while accounting for the presence of spatial autocorrelation. (ii) Link-based methods, which model the probability of gene flow between two patches (link) and relate neutral molecular marker data to landscape heterogeneity; these methods are suitable for modeling neutral genetic variation but are subject to inferential problems, which may be alleviated by reducing links based on a network model of the population. (iii) Neighborhood-based methods, which model the connectivity of a focal patch with all other patches in its local neighborhood; these methods provide a link to metapopulation theory and landscape connectivity modeling and may allow the integration of node- and link-based information, but applications in landscape genetics are still limited. (iv) Boundary-based methods, which delineate genetically homogeneous populations and infer the location of genetic boundaries; these methods are suitable for testing for barrier effects of landscape features in a hypothesis-testing framework. We conclude that the power to detect the effect of landscape heterogeneity on the spatial distribution of genetic variation can be increased by explicit consideration of underlying assumptions and choice of an appropriate analytical approach depending on the research question.