Another paper published in Ecological Monographs – this time describing spatial statistical network models for landscape genetics!

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Peterson E.E., Hanks E.M., Hooten M.B., Ver Hoef J.M., and Fortin M.-J. (In Press) Spatially structured statistical network models for landscape genetics.  Ecological Monographs.

A basic understanding of how the landscape impedes, or creates resistance
to, the dispersal of organisms and hence gene ow 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 aect
genetic structure. This includes an overview of spatial autoregressive models,
with a particular focus on how correlation and partial correlation are used to
represent neighborhood structure with the inverse of the covariance matrix (i.e.,
precision matrix). We then demonstrate how to model resistance by specifying
an appropriate statistical model on the nodes, conditioned on the edge weights,
through the precision matrix. This integration of network ecology and spatial
statistics provides a practical analytical framework for landscape-genetic
studies. The results can be used to make statistical inferences about the
relative importance of individual landscape characteristics, such as the
vegetative cover, hillslope, or the presence of roads or rivers, on gene ow. In
addition, the R code we include allows readers to explore landscape-genetic structure in their own datasets, which will potentially provide new insights into
the evolutionary processes that generated ecological networks, as well as
valuable information about the optimal characteristics of conservation corridors.

 

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