TY - JOUR
T1 - landgenreport
T2 - A new R function to simplify landscape genetic analysis using resistance surface layers
AU - GRUBER, Bernd
AU - ADAMACK, Aaron
PY - 2015
Y1 - 2015
N2 - We describe functions recently added to the r package popgenreport that can be used to perform a landscape genetic analysis (LGA) based on landscape resistance surfaces, which aims to detect the effect of landscape features on gene flow. These functions for the first time implement a LGA in a single framework. Although the approach has been shown to be a valuable tool to study gene flow in landscapes, it has not been widely used to date, despite the type of data being widely available. In part, this is likely due to the necessity to use several software packages to perform landscape genetic analyses. To apply LGA functions, two types of data sets are required: a data set with spatially referenced and genotyped individuals, and a resistance layer representing the effect of the landscape. The function outputs three pairwise distance matrices from these data: a genetic distance matrix, a cost distance matrix and a Euclidean distance matrix. Statistical tests are performed to test whether the cost matrix contributes to the understanding of the observed population structure. A full report on the analysis and outputs in the form of plots and tables of all intermediate steps of the LGA is produced. It is possible to customize the LGA to allow for different cost path approaches and measures of genetic distances. The package is written in the r language and is available through the Comprehensive r Archive. Comprehensive tutorials and information on how to install and use the package are provided at the authors' website (www.popgenreport.org).
AB - We describe functions recently added to the r package popgenreport that can be used to perform a landscape genetic analysis (LGA) based on landscape resistance surfaces, which aims to detect the effect of landscape features on gene flow. These functions for the first time implement a LGA in a single framework. Although the approach has been shown to be a valuable tool to study gene flow in landscapes, it has not been widely used to date, despite the type of data being widely available. In part, this is likely due to the necessity to use several software packages to perform landscape genetic analyses. To apply LGA functions, two types of data sets are required: a data set with spatially referenced and genotyped individuals, and a resistance layer representing the effect of the landscape. The function outputs three pairwise distance matrices from these data: a genetic distance matrix, a cost distance matrix and a Euclidean distance matrix. Statistical tests are performed to test whether the cost matrix contributes to the understanding of the observed population structure. A full report on the analysis and outputs in the form of plots and tables of all intermediate steps of the LGA is produced. It is possible to customize the LGA to allow for different cost path approaches and measures of genetic distances. The package is written in the r language and is available through the Comprehensive r Archive. Comprehensive tutorials and information on how to install and use the package are provided at the authors' website (www.popgenreport.org).
KW - Circuit theory
KW - Landscape genetics
KW - Least-cost path
KW - Population genetics-empirical
KW - Resistance surface
UR - http://www.scopus.com/inward/record.url?scp=84938986479&partnerID=8YFLogxK
UR - http://www.mendeley.com/research/landgenreport-new-r-function-simplify-landscape-genetic-analysis-using-resistance-surface-layers
U2 - 10.1111/1755-0998.12381
DO - 10.1111/1755-0998.12381
M3 - Article
SN - 1755-098X
VL - 15
SP - 1172
EP - 1178
JO - Molecular Ecology Resources
JF - Molecular Ecology Resources
IS - 5
ER -