Utility of computer simulations in landscape genetics

Bryan K. Epperson, 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

Research output: Contribution to journalArticle

127 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)3549-3564
Number of pages16
JournalMolecular Ecology
Volume19
Issue number17
DOIs
StatePublished - Sep 2010

Fingerprint

computer simulation
Computer Simulation
population genetics
Population Genetics
Genetic Phenomena
simulation
demographic statistics
genetic trend
stochasticity
Demography
environmental effect
genetic structure
gene flow
genetic variation
population size
life history
mathematical models
statistical analysis
Gene Flow
Genetic Structures

Keywords

  • individual-based models
  • landscape ecology
  • population genetics
  • simulations
  • spatial statistics

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Genetics

Cite this

Epperson, B. K., McRae, B. H., Scribner, K., Cushman, S. A., Rosenberg, M. S., Fortin, M. J., ... Dale, M. R. T. (2010). Utility of computer simulations in landscape genetics. Molecular Ecology, 19(17), 3549-3564. https://doi.org/10.1111/j.1365-294X.2010.04678.x

Utility of computer simulations in landscape genetics. / Epperson, Bryan K.; McRae, Brad H.; Scribner, Kim; Cushman, Samuel A.; Rosenberg, Michael S.; Fortin, Marie Josée; James, Patrick M A; Murphy, Melanie; Manel, Stéphanie; Legendre, Pierre; Dale, Mark R T.

In: Molecular Ecology, Vol. 19, No. 17, 09.2010, p. 3549-3564.

Research output: Contribution to journalArticle

Epperson, BK, McRae, BH, Scribner, K, Cushman, SA, Rosenberg, MS, Fortin, MJ, James, PMA, Murphy, M, Manel, S, Legendre, P & Dale, MRT 2010, 'Utility of computer simulations in landscape genetics', Molecular Ecology, vol. 19, no. 17, pp. 3549-3564. https://doi.org/10.1111/j.1365-294X.2010.04678.x
Epperson BK, McRae BH, Scribner K, Cushman SA, Rosenberg MS, Fortin MJ et al. Utility of computer simulations in landscape genetics. Molecular Ecology. 2010 Sep;19(17):3549-3564. https://doi.org/10.1111/j.1365-294X.2010.04678.x
Epperson, Bryan K. ; McRae, Brad H. ; Scribner, Kim ; Cushman, Samuel A. ; Rosenberg, Michael S. ; Fortin, Marie Josée ; James, Patrick M A ; Murphy, Melanie ; Manel, Stéphanie ; Legendre, Pierre ; Dale, Mark R T. / Utility of computer simulations in landscape genetics. In: Molecular Ecology. 2010 ; Vol. 19, No. 17. pp. 3549-3564.
@article{067cbc33319e40d88d457d01497565b0,
title = "Utility of computer simulations in landscape genetics",
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.",
keywords = "individual-based models, landscape ecology, population genetics, simulations, spatial statistics",
author = "Epperson, {Bryan K.} and McRae, {Brad H.} and Kim Scribner and Cushman, {Samuel A.} and Rosenberg, {Michael S.} and Fortin, {Marie Jos{\'e}e} and James, {Patrick M A} and Melanie Murphy and St{\'e}phanie Manel and Pierre Legendre and Dale, {Mark R T}",
year = "2010",
month = "9",
doi = "10.1111/j.1365-294X.2010.04678.x",
language = "English (US)",
volume = "19",
pages = "3549--3564",
journal = "Molecular Ecology",
issn = "0962-1083",
publisher = "Wiley-Blackwell",
number = "17",

}

TY - JOUR

T1 - Utility of computer simulations in landscape genetics

AU - Epperson, Bryan K.

AU - McRae, Brad H.

AU - Scribner, Kim

AU - Cushman, Samuel A.

AU - Rosenberg, Michael S.

AU - Fortin, Marie Josée

AU - James, Patrick M A

AU - Murphy, Melanie

AU - Manel, Stéphanie

AU - Legendre, Pierre

AU - Dale, Mark R T

PY - 2010/9

Y1 - 2010/9

N2 - 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.

AB - 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.

KW - individual-based models

KW - landscape ecology

KW - population genetics

KW - simulations

KW - spatial statistics

UR - http://www.scopus.com/inward/record.url?scp=77952095479&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77952095479&partnerID=8YFLogxK

U2 - 10.1111/j.1365-294X.2010.04678.x

DO - 10.1111/j.1365-294X.2010.04678.x

M3 - Article

C2 - 20618894

AN - SCOPUS:77952095479

VL - 19

SP - 3549

EP - 3564

JO - Molecular Ecology

JF - Molecular Ecology

SN - 0962-1083

IS - 17

ER -