Spatially structured statistical network models for landscape genetics

Erin E. Peterson, Ephraim Mont Hanks, Mevin B. Hooten, Jay M. Ver Hoef, Marie Josée Fortin

Research output: Contribution to journalArticle

Abstract

A basic understanding of how the landscape impedes, or creates resistance to, the dispersal of organisms and hence gene flow 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 affect 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 flow. 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.

Original languageEnglish (US)
Article numbere01355
JournalEcological Monographs
Volume89
Issue number2
DOIs
StatePublished - May 1 2019

Fingerprint

genetic structure
gene flow
matrix
information networks
analytical framework
ecologists
statistical models
hillslope
genetic relationships
roads
statistical analysis
statistics
road
ecology
rivers
synthesis
organisms
river
modeling
method

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics

Cite this

Peterson, E. E., Hanks, E. M., Hooten, M. B., Ver Hoef, J. M., & Fortin, M. J. (2019). Spatially structured statistical network models for landscape genetics. Ecological Monographs, 89(2), [e01355]. https://doi.org/10.1002/ecm.1355
Peterson, Erin E. ; Hanks, Ephraim Mont ; Hooten, Mevin B. ; Ver Hoef, Jay M. ; Fortin, Marie Josée. / Spatially structured statistical network models for landscape genetics. In: Ecological Monographs. 2019 ; Vol. 89, No. 2.
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Spatially structured statistical network models for landscape genetics. / Peterson, Erin E.; Hanks, Ephraim Mont; Hooten, Mevin B.; Ver Hoef, Jay M.; Fortin, Marie Josée.

In: Ecological Monographs, Vol. 89, No. 2, e01355, 01.05.2019.

Research output: Contribution to journalArticle

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