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

## Dryad Repository

- Dryad Digital Repository
- →
- Main
- →
- Dryad Data Packages
- →
- View Item

JavaScript is disabled for your browser. Some features of this site may not work without it.

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

Date Published: March 3, 2015

DOI: http://dx.doi.org/10.5061/dryad.p5hd0

Content in the Dryad Digital Repository is offered "as is." By downloading files, you agree to the Dryad Terms of Service. To the extent possible under law, the authors have waived all copyright and related or neighboring rights to this data.

Title | Bootstrap31 |
---|---|

Downloaded | 11 times |

Description | 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 |

Download | Bootstrap31.txt (2.827 Kb) |

Details | View File Details |

Title | Bootstrap101 |
---|---|

Downloaded | 6 times |

Description | 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 |

Download | Bootstrap101.txt (2.841 Kb) |

Details | View File Details |

Title | Bootstrap251 |
---|---|

Downloaded | 3 times |

Description | 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 |

Download | Bootstrap251.txt (2.851 Kb) |

Details | View File Details |

Title | Bootstrap501 |
---|---|

Downloaded | 3 times |

Description | 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 |

Download | Bootstrap501.txt (2.884 Kb) |

Details | View File Details |

Title | Bootstrap1001 |
---|---|

Downloaded | 5 times |

Description | 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 |

Download | Bootstrap1001.txt (2.991 Kb) |

Details | View File Details |

Title | Bootstrap2001 |
---|---|

Downloaded | 7 times |

Description | 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 |

Download | Bootstrap2001.txt (3.091 Kb) |

Details | View File Details |

Title | Bootstrap4001 |
---|---|

Downloaded | 8 times |

Description | 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 |

Download | Bootstrap4001.txt (3.255 Kb) |

Details | View File Details |

Title | Bootstrap10001 |
---|---|

Downloaded | 5 times |

Description | 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 |

Download | Bootstrap10001.txt (3.401 Kb) |

Details | View File Details |

Title | Chip_LogisticRegressionResults |
---|---|

Downloaded | 5 times |

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

Download | Chip_LogisticRegressionResults.xlsx (2.672 Mb) |

Details | View File Details |

Title | Chip_MultipleRegressionResults |
---|---|

Downloaded | 41 times |

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

Download | Chip_MultipleRegressionResults.xlsx (2.058 Mb) |

Details | View File Details |

Title | ChipmunkFull3 |
---|---|

Downloaded | 6 times |

Description | 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 |

Download | ChipmunkFull3.txt (2.872 Kb) |

Details | View File Details |

Title | ChipmunkFull10 |
---|---|

Downloaded | 3 times |

Description | 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 |

Download | ChipmunkFull10.txt (2.901 Kb) |

Details | View File Details |

Title | ChipmunkFull25 |
---|---|

Downloaded | 6 times |

Description | 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 |

Download | ChipmunkFull25.txt (2.895 Kb) |

Details | View File Details |

Title | ChipmunkFull50 |
---|---|

Downloaded | 4 times |

Description | 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 |

Download | ChipmunkFull50.txt (2.935 Kb) |

Details | View File Details |

Title | ChipmunkFull100 |
---|---|

Downloaded | 5 times |

Description | 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 |

Download | ChipmunkFull100.txt (3.033 Kb) |

Details | View File Details |

Title | ChipmunkFull200 |
---|---|

Downloaded | 3 times |

Description | 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 |

Download | ChipmunkFull200.txt (3.136 Kb) |

Details | View File Details |

Title | ChipmunkFull400 |
---|---|

Downloaded | 5 times |

Description | 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 |

Download | ChipmunkFull400.txt (3.311 Kb) |

Details | View File Details |

Title | ChipmunkFull1000 |
---|---|

Downloaded | 8 times |

Description | 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 |

Download | ChipmunkFull1000.txt (3.464 Kb) |

Details | View File Details |

Title | ChipmunkValidation_Results |
---|---|

Downloaded | 4 times |

Description | 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. |

Download | ChipmunkValidation_Results.txt (2.325 Kb) |

Details | View File Details |

Title | Sub1 |
---|---|

Downloaded | 8 times |

Description | 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 |

Download | Sub1.txt (730 bytes) |

Details | View File Details |

Title | Ts_295g |
---|---|

Downloaded | 6 times |

Description | Geneland genotype input for cell 295. |

Download | Ts_295g.txt (13.86 Kb) |

Details | View File Details |

Title | Ts_295xy |
---|---|

Downloaded | 11 times |

Description | Geneland coordinate input for cell 295. |

Download | Ts_295xy.txt (2.288 Kb) |

Details | View File Details |

Title | Ts_295L |
---|---|

Downloaded | 7 times |

Description | Geneland ID file input for cell 295. |

Download | Ts_295L.txt (1.287 Kb) |

Details | View File Details |

Title | Ts_allfst1-fst |
---|---|

Downloaded | 5 times |

Description | Fst/1-Fst between 33 study cells. |

Download | Ts_allfst1-fst.txt (3.907 Kb) |

Details | View File Details |

Title | Ts_allgeo |
---|---|

Downloaded | 5 times |

Description | Euclidean distances between 33 study cells in meters. |

Download | Ts_allgeo.txt (4.251 Kb) |

Details | View File Details |

Title | Ts_allgenotypesandcoord_use |
---|---|

Downloaded | 9 times |

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

Download | Ts_allgenotypesandcoord_use.xlsx (194.1 Kb) |

Details | View File Details |

Title | Anderson_PloSOneCode copy |
---|---|

Downloaded | 10 times |

Description | R code for regression and validation analyses. |

Download | Anderson_PloSOneCode copy.txt (5.433 Kb) |

Details | View File Details |

When using this data, please cite the original publication:

Anderson SJ, Kierepka EM, Swihart RK, Latch EK, Rhodes Jr OE (2015) Assessing the permeability of landscape features to animal movement: using genetic structure to infer functional connectivity. PLOS ONE 10(2): e0117500. http://dx.doi.org/10.1371/journal.pone.0117500

Additionally, please cite the Dryad data package:

Anderson SJ, Kierepka EM, Swihart RK, Latch EK, Rhodes Jr. OE (2015) Data from: Assessing the permeability of landscape features to animal movement: using genetic structure to infer functional connectivity. Dryad Digital Repository.
http://dx.doi.org/10.5061/dryad.p5hd0

We encourage organizations to:

- Become a member
- Sponsor data publishing fees
- Integrate your journal(s), or
- All of the above