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Recent developments in computer modeling add ecological realism to landscape genetics
Citation:
White, J., N. H. SCHUMAKER, A. F. BROOKES, AND B. McRae. Recent developments in computer modeling add ecological realism to landscape genetics. Presented at Ecological Society of America, Portland, OR, August 05 - 10, 2012.
Impact/Purpose:
A factor limiting the rate of progress in landscape genetics has been the shortage of spatial models capable of linking life history attributes such as dispersal behavior to complex dynamic landscape features.
Description:
Background / Question / Methods A factor limiting the rate of progress in landscape genetics has been the shortage of spatial models capable of linking life history attributes such as dispersal behavior to complex dynamic landscape features. The recent development of new models such as Circuitscape (McRae et. Al. 2008, Ecology 10:2712-2724), CDPOP (Landguth and Cushman, 2010, Mol. Ecol. Resour. 10:156-161), and UNICOR (Landguth et. al. 2012, Ecography 35:9-14), is ameliorating this problem, but the discipline’s long term growth will require tools that capture the additional ecological realism necessary to perform management-relevant forecasting, and work quantitatively with adaptive genetic traits. Here, we look at the short and long term computational needs of the discipline, and ask to what extent current modeling tools will be able to meet these demands. Results / Conclusions Our background research suggests that the principal focus of work in landscape genetics today involves neutral markers and hindcasting, with a goal of illustrating how current allele distributions might be explained by landscape features. The need to develop management strategies that ensure species’ viability in the midst of climate change, invasive species, habitat loss, and other anthropogenic disturbances will force landscape geneticists to do more forecasting, and to incorporate interacting stressors into their models. We illustrate an approach for doing just this using the recently developed HexSim model, which includes a landscape genetics toolkit. Our HexSim example involves a simulated predator-prey system in which the benefits of prey capture efficiency impart a selective pressure upon the predator genome. We use this example, and preliminary results from our own research, to illustrate how advances in simulation model development might assist landscape genetics in meeting the practical challenges the future is sure to hold.