public read Forest Ecosystem Monitoring Cooperative

705 Spear StreetSouth BurlingtonVermont05403United States of America

(802) 656-0683femc@uvm.eduwww.uvm.edu/femcNortheastern States Research Cooperative funderUniversity of Vermont Rubenstein School of Environment and Natural ResourcesleadUnited States Department of Agriculture (USDA), Forest Service Green Mountain National ForestVermont Fish and Wildlife Department Maine Department of Inland Fisheries and Wildlife New Hampshire Fish and Game Department Mammalian Ecology and Conservation Unit University of California - DavispartnerThis dataset contains species-specific landscape resistance surfaces across Vermont, New Hampshire, and Maine: American black bear, American marten, bobcat, coyote, fisher, moose, raccoon, red fox, striped skunk, and white-tailed deer. Lower values represent lower resistance, and higher values represent higher resistance (scales vary by species). We collected and analyzed 1,028 samples across the 10 species, and optimized species-specific landscape resistance surfaces using the ResistanceGA package in R (Peterman 2018) to determine how various ecological variables influence genetic distance. We used model selection to rank candidate resistance surfaces, and the top models for each species are included in this dataset.Forest Ecosystem Monitoring CooperativeJamesMurdochprincipalInvestigatorCaitlinDrashercontentProviderStephanieMcKayprincipalInvestigatorThis project integrated ecological and genetic data using a new circuit theory approach to measure and map connectivity for 10 terrestrial mammal species with high ecological, economic, and cultural importance: American black bear, American marten, bobcat, coyote, fisher, moose, raccoon, red fox, striped skunk, and white-tailed deer. Within the study region encompassing Vermont, New Hampshire, and Maine, we collected genetic data from each focal species and optimized landscape resistance surfaces to identify landscape features that best explain observed patterns of genetic differentiation for each species. We then combined the landscape resistance data with species occurrence data to model and map species-specific landscape connectivity across the region. This work resulted in models of wildlife movement and genetic connectivity that can be applied across a range of land, infrastructure, and management contexts, providing tools and insights for connectivity planning in the northeastern United States. This project was funded by the Northeastern States Research Cooperative and was a partnership between UVM's Rubenstein School of Environment and Natural Resources, the US Forest Service, the Vermont Fish and Wildlife Department, New Hampshire Fish and Game, and the Maine Department of Inland Fisheries and Wildlife. Genetically optimized landscape resistance surfacesThis dataset contains species-specific landscape resistance surfaces across Vermont, New Hampshire, and Maine: American black bear, American marten, bobcat, coyote, fisher, moose, raccoon, red fox, striped skunk, and white-tailed deer. Lower values represent lower resistance, and higher values represent higher resistance (scales vary by species). We collected and analyzed 1,028 samples across the 10 species, and optimized species-specific landscape resistance surfaces using the ResistanceGA package in R (Peterman 2018) to determine how various ecological variables influence genetic distance. We used model selection to rank candidate resistance surfaces, and the top models for each species are included in this dataset.VMC.1884.4153mySQLhttps://www.uvm.edu/femc/data/archive/project/integrating-genetic-and-ecological-data-to-measure-and-map-terrestrial-wildlife-connectivity-across-the-northeastern-united-states/dataset/genetically-optimized-landscape-resistance-surfaces2021-01-012026-05-31No AttributesNo Definitionno data