Data from: Performance of partial statistics in individual-based landscape genetics

Individual-based landscape genetic methods have become increasingly popular for quantifying fine-scale landscape influences on gene flow. One complication for individual-based methods is that gene flow and landscape variables are often correlated with geography. Partial statistics, particularly Mantel tests, are often employed to control for these inherent correlations by removing the effects of geography while simultaneously correlating measures of genetic differentiation and landscape variables of interest. Concerns about the reliability of Mantel tests prompted this study, in which we use simulated landscapes to evaluate the performance of partial Mantel tests and two ordination methods, distance-based redundancy analysis (dbRDA) and redundancy analysis (RDA), for detecting isolation by distance (IBD) and isolation by landscape resistance (IBR). Specifically, we described the effects of suitable habitat amount, fragmentation and resistance strength on metrics of accuracy (frequency of correct results, type I/II errors and strength of IBR according to underlying landscape and resistance strength) for each test using realistic individual-based gene flow simulations. Mantel tests were very effective for detecting IBD, but exhibited higher error rates when detecting IBR. Ordination methods were overall more accurate in detecting IBR, but had high type I errors compared to partial Mantel tests. Thus, no one test outperformed another completely. A combination of statistical tests, for example partial Mantel tests to detect IBD paired with appropriate ordination techniques for IBR detection, provides the best characterization of fine-scale landscape genetic structure. Realistic simulations of empirical data sets will further increase power to distinguish among putative mechanisms of differentiation.MER_14_0246_RcodeR code for spatial principal components analysis, partial Mantel test, distance-based redundancy analysis, and redundancy analysisSimulationResults_MER_14_0246Test statistics and p-values from simulated data. IBR corresponds to landscapes that had a pixel resistance of 5 or higher and all pixels had a resistance of 1 in the IBD landscapes. Results are separated by statistical test and type of landscape.SampleInputFile_CDPOPInput file for CDPOP. All categories are described in the CDPOP manual.SampleResistanceFileSample Resistance File that is an input for Mantel tests and CDPOP.CoordinatesSample Coordinate File that is an input for dbRDA and RDA.ExampleBufferFileExample of a the percent of habitat found in a buffer around each individual. This file is an input for RDA and dbRDA.ExampleEuclideanDistanceExample of a Euclidean distance file that is used as an input for both CDPOP and Mantel tests.ExampleGenepopFileExample of a genepop file that is the input to both Spagedi and adegenet to calculate Rousset's a and sPCA scores respectively.ExampleRoussetsafileExample of a matrix of Rousset's a between all individuals calculated in Spagedi. These serve as the response variable in both partial Mantel tests and dbRDA.ExamplesPCAScoresFileExample of scores from the first two sPCA axes calculated in adegenet. This data serves as the response variable in RDA.SampleResistanceFileExample of pair-wise resistances values calculated in ArcGIS. These files serve as the input for CDPOP and partial Mantel tests