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Landscape genetics combines population genetics, landscape ecology, and spatial analysis to identify landscape and genetic factors that influence genetic and genomic variation. Progress in the field depends on a strong conceptual foundation and the means of identifying mechanistic connnections between environmental factors, landscape features, and genetic or genomic variation. Many existing approaches and much of the software commonly in use was developed for population genetics or statistics and is not entirely appropriate for landscape genetics. Probabilistic graph models provide a statistically rigorous and flexible means of constructing models directly applicable to landscape genetics. Probabilistic graph models also allow construction of mechanistic models, which are crucial elements in testing hypotheses. Sophisticated software exists for the analysis of graph models; however, much of it does not handle the types of data used for landscape genetics, model structures involving autoregressive spatial interaction between variables, or the scale of landscape genetics problems. Thus, an important priority for the field is to develop suitably flexible software tools for graph models that overcome these problems and allow landscape geneticists to explore meaningfully mechanistic and flexible models. We are developing such a library and applying it to examples in landscape genetics.
This paper has been extended from the previous version by including explicit examples of coding and by including library source code as supplemental material.
Compressed tar file of source code for GraphModel library v0.1.2