Data from: Assessing the permeability of landscape features to animal movement: using genetic structure to infer functional connectivity

Bootstrap31Example of Bootstrap Iteration for 3 m segment width. ID = pair-wise comparison, cat = dependent variable for logistic regression, pmem = dependent variable for multiple regression, forest = proportion of forest in 3 m segment, ntc = proportion of non-treed corridors in 3 m segment, road = proportion of roads in 3 m segment, grass = proportion of grassland in 3 m segment, shrub = proportion of shrubland in 3 m segment, treedcorr = proportion of treed corridors in 3 m segment, urban = proportion of urban land in 3 m segment, water = proportion of water/wetland in 3 m segmentBootstrap101Example of Bootstrap Iteration for 10 m segment width. ID = pair-wise comparison, cat = dependent variable for logistic regression, pmem = dependent variable for multiple regression, forest = proportion of forest in 10 m segment, ntc = proportion of non-treed corridors in 10 m segment, road = proportion of roads in 10 m segment, grass = proportion of grassland in 10 m segment, shrub = proportion of shrubland in 10 m segment, treedcorr = proportion of treed corridors in 10 m segment, urban = proportion of urban land in 10 m segment, water = proportion of water/wetland in 10 m segmentBootstrap251Example of Bootstrap Iteration for 25 m segment width. ID = pair-wise comparison, cat = dependent variable for logistic regression, pmem = dependent variable for multiple regression, forest = proportion of forest in 25 m segment, ntc = proportion of non-treed corridors in 25 m segment, road = proportion of roads in 25 m segment, grass = proportion of grassland in 25 m segment, shrub = proportion of shrubland in 25 m segment, treedcorr = proportion of treed corridors in 25 m segment, urban = proportion of urban land in 25 m segment, water = proportion of water/wetland in 25 m segmentBootstrap501Example of Bootstrap Iteration for 50 m segment width. ID = pair-wise comparison, cat = dependent variable for logistic regression, pmem = dependent variable for multiple regression, forest = proportion of forest in 50 m segment, ntc = proportion of non-treed corridors in 50 m segment, road = proportion of roads in 50 m segment, grass = proportion of grassland in 50 m segment, shrub = proportion of shrubland in 50 m segment, treedcorr = proportion of treed corridors in 50 m segment, urban = proportion of urban land in 50 m segment, water = proportion of water/wetland in 50 m segmentBootstrap1001Example of Bootstrap Iteration for 100 m segment width. ID = pair-wise comparison, cat = dependent variable for logistic regression, pmem = dependent variable for multiple regression, forest = proportion of forest in 100 m segment, ntc = proportion of non-treed corridors in 100 m segment, road = proportion of roads in 100 m segment, grass = proportion of grassland in 100 m segment, shrub = proportion of shrubland in 100 m segment, treedcorr = proportion of treed corridors in 100 m segment, urban = proportion of urban land in 100 m segment, water = proportion of water/wetland in 100 m segmentBootstrap2001Example of Bootstrap Iteration for 200 m segment width. ID = pair-wise comparison, cat = dependent variable for logistic regression, pmem = dependent variable for multiple regression, forest = proportion of forest in 200 m segment, ntc = proportion of non-treed corridors in 200 m segment, road = proportion of roads in 200 m segment, grass = proportion of grassland in 200 m segment, shrub = proportion of shrubland in 200 m segment, treedcorr = proportion of treed corridors in 200 m segment, urban = proportion of urban land in 200 m segment, water = proportion of water/wetland in 200 m segmentBootstrap4001Example of Bootstrap Iteration for 400 m segment width. ID = pair-wise comparison, cat = dependent variable for logistic regression, pmem = dependent variable for multiple regression, forest = proportion of forest in 400 m segment, ntc = proportion of non-treed corridors in 400 m segment, road = proportion of roads in 400 m segment, grass = proportion of grassland in 400 m segment, shrub = proportion of shrubland in 400 m segment, treedcorr = proportion of treed corridors in 400 m segment, urban = proportion of urban land in 400 m segment, water = proportion of water/wetland in 400 m segmentBootstrap10001Example of Bootstrap Iteration for 1000 m segment width. ID = pair-wise comparison, cat = dependent variable for logistic regression, pmem = dependent variable for multiple regression, forest = proportion of forest in 1000 m segment, ntc = proportion of non-treed corridors in 1000 m segment, road = proportion of roads in 1000 m segment, grass = proportion of grassland in 1000 m segment, shrub = proportion of shrubland in 1000 m segment, treedcorr = proportion of treed corridors in 1000 m segment, urban = proportion of urban land in 1000 m segment, water = proportion of water/wetland in 1000 m segmentChip_LogisticRegressionResultsResults of logistic regression models across 1000 bootstrap iterations. This file includes estimates, z values and p-values for parameter estimates and full models.Chip_MultipleRegressionResultsResults of multiple regression models across 1000 bootstrap iterations. This file includes estimates, t values and p-values for parameter estimates and full models.ChipmunkFull3Full dataset for 3 m segment. ID = pair-wise comparison, cat = dependent variable for logistic regression, pmem = dependent variable for multiple regression, forest = proportion of forest in 3 m segment, ntc = proportion of non-treed corridors in 3 m segment, road = proportion of roads in 3 m segment, grass = proportion of grassland in 3 m segment, shrub = proportion of shrubland in 3 m segment, treedcorr = proportion of treed corridors in 3 m segment, urban = proportion of urban land in 3 m segment, water = proportion of water/wetland in 3 m segmentChipmunkFull10Full dataset for 10 m segment. ID = pair-wise comparison, cat = dependent variable for logistic regression, pmem = dependent variable for multiple regression, forest = proportion of forest in 10 m segment, ntc = proportion of non-treed corridors in 10 m segment, road = proportion of roads in 10 m segment, grass = proportion of grassland in 10 m segment, shrub = proportion of shrubland in 10 m segment, treedcorr = proportion of treed corridors in 10 m segment, urban = proportion of urban land in 10 m segment, water = proportion of water/wetland in 10 m segmentChipmunkFull25Full dataset for 25 m segment width. ID = pair-wise comparison, cat = dependent variable for logistic regression, pmem = dependent variable for multiple regression, forest = proportion of forest in 25 m segment, ntc = proportion of non-treed corridors in 25 m segment, road = proportion of roads in 25 m segment, grass = proportion of grassland in 25 m segment, shrub = proportion of shrubland in 25 m segment, treedcorr = proportion of treed corridors in 25 m segment, urban = proportion of urban land in 25 m segment, water = proportion of water/wetland in 25 m segmentChipmunkFull50Full Dataset for 50 m segment. ID = pair-wise comparison, cat = dependent variable for logistic regression, pmem = dependent variable for multiple regression, forest = proportion of forest in 50 m segment, ntc = proportion of non-treed corridors in 50 m segment, road = proportion of roads in 50 m segment, grass = proportion of grassland in 50 m segment, shrub = proportion of shrubland in 50 m segment, treedcorr = proportion of treed corridors in 50 m segment, urban = proportion of urban land in 50 m segment, water = proportion of water/wetland in 50 m segmentChipmunkFull100Full dataset for 100 m segment. ID = pair-wise comparison, cat = dependent variable for logistic regression, pmem = dependent variable for multiple regression, forest = proportion of forest in 100 m segment, ntc = proportion of non-treed corridors in 100 m segment, road = proportion of roads in 100 m segment, grass = proportion of grassland in 100 m segment, shrub = proportion of shrubland in 100 m segment, treedcorr = proportion of treed corridors in 100 m segment, urban = proportion of urban land in 100 m segment, water = proportion of water/wetland in 100 m segmentChipmunkFull200Full dataset for 200 m segment. ID = pair-wise comparison, cat = dependent variable for logistic regression, pmem = dependent variable for multiple regression, forest = proportion of forest in 200 m segment, ntc = proportion of non-treed corridors in 200 m segment, road = proportion of roads in 200 m segment, grass = proportion of grassland in 3 m segment, shrub = proportion of shrubland in 200 m segment, treedcorr = proportion of treed corridors in 200 m segment, urban = proportion of urban land in 200 m segment, water = proportion of water/wetland in 200 m segmentChipmunkFull400Full dataset for 400 m segments. ID = pair-wise comparison, cat = dependent variable for logistic regression, pmem = dependent variable for multiple regression, forest = proportion of forest in 400 m segment, ntc = proportion of non-treed corridors in 400 m segment, road = proportion of roads in 400 m segment, grass = proportion of grassland in 400 m segment, shrub = proportion of shrubland in 400 m segment, treedcorr = proportion of treed corridors in 400 m segment, urban = proportion of urban land in 400 m segment, water = proportion of water/wetland in 400 m segmentChipmunkFull1000Full dataset for 1000 m segments. ID = pair-wise comparison, cat = dependent variable for logistic regression, pmem = dependent variable for multiple regression, forest = proportion of forest in 1000 m segment, ntc = proportion of non-treed corridors in 1000 m segment, road = proportion of roads in 1000 m segment, grass = proportion of grassland in 1000 m segment, shrub = proportion of shrubland in 1000 m segment, treedcorr = proportion of treed corridors in 1000 m segment, urban = proportion of urban land in 1000 m segment, water = proportion of water/wetland in 1000 m segmentChipmunkValidation_ResultsValidation results for each regression. LR = Logistic regression, MR = Multiple regression. Results are given as count data in 50 bootstrap increments and proportions of incorrect and correct data. Each bootstrap iteration was resampled 1000 times where 14 individuals were randomly selected.Sub1Example of subsample used for validation. ID = pair-wise comparison, cat = dependent variable for logistic regression, pmem = dependent variable for multiple regression, forest = proportion of forest in segment, ntc = proportion of non-treed corridors in segment, road = proportion of roads in segment, grass = proportion of grassland in segment, shrub = proportion of shrubland in segment, treedcorr = proportion of treed corridors in segment, urban = proportion of urban land in segment, water = proportion of water/wetland in segmentTs_295gGeneland genotype input for cell 295.Ts_295xyGeneland coordinate input for cell 295.Ts_295LGeneland ID file input for cell 295.Ts_allfst1-fstFst/1-Fst between 33 study cells.Ts_allgeoEuclidean distances between 33 study cells in meters.Ts_allgenotypesandcoord_useGenalex genotype file and spatial coordinates for entire Tamias striatus dataset. Genotypes are separated by study cell.Anderson_PloSOneCode copyR code for regression and validation analyses.