Literature DB >> 20618894

Utility of computer simulations in landscape genetics.

Bryan K Epperson1, Brad H McRae, Kim Scribner, Samuel A Cushman, Michael S Rosenberg, Marie-Josée Fortin, Patrick M A James, Melanie Murphy, Stéphanie Manel, Pierre Legendre, Mark R T Dale.   

Abstract

Population genetics theory is primarily based on mathematical models in which spatial complexity and temporal variability are largely ignored. In contrast, the field of landscape genetics expressly focuses on how population genetic processes are affected by complex spatial and temporal environmental heterogeneity. It is spatially explicit and relates patterns to processes by combining complex and realistic life histories, behaviours, landscape features and genetic data. Central to landscape genetics is the connection of spatial patterns of genetic variation to the usually highly stochastic space-time processes that create them over both historical and contemporary time periods. The field should benefit from a shift to computer simulation approaches, which enable incorporation of demographic and environmental stochasticity. A key role of simulations is to show how demographic processes such as dispersal or reproduction interact with landscape features to affect probability of site occupancy, population size, and gene flow, which in turn determine spatial genetic structure. Simulations could also be used to compare various statistical methods and determine which have correct type I error or the highest statistical power to correctly identify spatio-temporal and environmental effects. Simulations may also help in evaluating how specific spatial metrics may be used to project future genetic trends. This article summarizes some of the fundamental aspects of spatial-temporal population genetic processes. It discusses the potential use of simulations to determine how various spatial metrics can be rigorously employed to identify features of interest, including contrasting locus-specific spatial patterns due to micro-scale environmental selection.

Mesh:

Year:  2010        PMID: 20618894     DOI: 10.1111/j.1365-294X.2010.04678.x

Source DB:  PubMed          Journal:  Mol Ecol        ISSN: 0962-1083            Impact factor:   6.185


  30 in total

Review 1.  Computer simulations: tools for population and evolutionary genetics.

Authors:  Sean Hoban; Giorgio Bertorelle; Oscar E Gaggiotti
Journal:  Nat Rev Genet       Date:  2012-01-10       Impact factor: 53.242

2.  A new eigenfunction spatial analysis describing population genetic structure.

Authors:  José Alexandre Felizola Diniz-Filho; João Vitor Barnez P L Diniz; Thiago Fernando Rangel; Thannya Nascimento Soares; Mariana Pires de Campos Telles; Rosane Garcia Collevatti; Luis Mauricio Bini
Journal:  Genetica       Date:  2013-10-27       Impact factor: 1.082

3.  skelesim: an extensible, general framework for population genetic simulation in R.

Authors:  Christian M Parobek; Frederick I Archer; Michelle E DePrenger-Levin; Sean M Hoban; Libby Liggins; Allan E Strand
Journal:  Mol Ecol Resour       Date:  2016-11-16       Impact factor: 7.090

4.  Isolation-by-distance in landscapes: considerations for landscape genetics.

Authors:  M J van Strien; R Holderegger; H J Van Heck
Journal:  Heredity (Edinb)       Date:  2014-07-23       Impact factor: 3.821

5.  Genetic data simulators and their applications: an overview.

Authors:  Bo Peng; Huann-Sheng Chen; Leah E Mechanic; Ben Racine; John Clarke; Elizabeth Gillanders; Eric J Feuer
Journal:  Genet Epidemiol       Date:  2014-12-13       Impact factor: 2.135

6.  Fine-scale landscape genetics of the American badger (Taxidea taxus): disentangling landscape effects and sampling artifacts in a poorly understood species.

Authors:  E M Kierepka; E K Latch
Journal:  Heredity (Edinb)       Date:  2015-08-05       Impact factor: 3.821

7.  Simulation of molecular data under diverse evolutionary scenarios.

Authors:  Miguel Arenas
Journal:  PLoS Comput Biol       Date:  2012-05-31       Impact factor: 4.475

Review 8.  Integrating the landscape epidemiology and genetics of RNA viruses: rabies in domestic dogs as a model.

Authors:  K Brunker; K Hampson; D L Horton; R Biek
Journal:  Parasitology       Date:  2012-07-20       Impact factor: 3.234

9.  A complex speciation-richness relationship in a simple neutral model.

Authors:  Philippe Desjardins-Proulx; Dominique Gravel
Journal:  Ecol Evol       Date:  2012-06-27       Impact factor: 2.912

10.  Computer programs and methodologies for the simulation of DNA sequence data with recombination.

Authors:  Miguel Arenas
Journal:  Front Genet       Date:  2013-02-01       Impact factor: 4.599

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