Human-altered environments often challenge native species with a complex spatial distribution of resources. Hostile landscape features can inhibit animal movement (i.e., genetic exchange), while other landscape attributes facilitate gene flow. The genetic attributes of organisms inhabiting such complex environments can reveal the legacy of their movements through the landscape. Thus, by evaluating landscape attributes within the context of genetic connectivity of organisms within the landscape, we can elucidate how a species has coped with the enhanced complexity of human altered environments. In this research, we utilized genetic data from eastern chipmunks (Tamias striatus) in conjunction with spatially explicit habitat attribute data to evaluate the realized permeability of various landscape elements in a fragmented agricultural ecosystem. To accomplish this we 1) used logistic regression to evaluate whether land cover attributes were most often associated with the matrix between or habitat within genetically identified populations across the landscape, and 2) utilized spatially explicit habitat attribute data to predict genetically-derived Bayesian probabilities of population membership of individual chipmunks in an agricultural ecosystem. Consistency between the results of the two approaches with regard to facilitators and inhibitors of gene flow in the landscape indicate that this is a promising new way to utilize both landscape and genetic data to gain a deeper understanding of human-altered ecosystems.

#### Bootstrap31

Example 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 segment

#### Bootstrap101

Example 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 segment

#### Bootstrap251

Example 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 segment

#### Bootstrap501

Example 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 segment

#### Bootstrap1001

Example 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 segment

#### Bootstrap2001

Example 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 segment

#### Bootstrap4001

Example 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 segment

#### Bootstrap10001

Example 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 segment

#### Chip_LogisticRegressionResults

Results of logistic regression models across 1000 bootstrap iterations. This file includes estimates, z values and p-values for parameter estimates and full models.

#### Chip_MultipleRegressionResults

Results of multiple regression models across 1000 bootstrap iterations. This file includes estimates, t values and p-values for parameter estimates and full models.

#### ChipmunkFull3

Full 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 segment

#### ChipmunkFull10

Full 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 segment

#### ChipmunkFull25

Full 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 segment

#### ChipmunkFull50

Full 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 segment

#### ChipmunkFull100

Full 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 segment

#### ChipmunkFull200

Full 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 segment

#### ChipmunkFull400

Full 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 segment

#### ChipmunkFull1000

Full 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 segment

#### ChipmunkValidation_Results

Validation 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.

#### Sub1

Example 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 segment

#### Ts_295g

Geneland genotype input for cell 295.

#### Ts_295xy

Geneland coordinate input for cell 295.

#### Ts_295L

Geneland ID file input for cell 295.

#### Ts_allfst1-fst

Fst/1-Fst between 33 study cells.

#### Ts_allgeo

Euclidean distances between 33 study cells in meters.

#### Ts_allgenotypesandcoord_use

Genalex genotype file and spatial coordinates for entire Tamias striatus dataset. Genotypes are separated by study cell.

#### Anderson_PloSOneCode copy

R code for regression and validation analyses.