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This dataset depicts Marten (Martes americana) habitat in the Northern Appalachians predicted using the spatially explicit population model PATCH under the current trapping rates plus timber harvest plus climate change scenario (FL2; Carrol 2007). This dataset represents one of several scenarios testing the interacting effects of trapping, timber harvest, habitat restoration, and climate change on marten populations. Static habitat suitability models for marten were fed through PATCH to predict source and sink habitat areas across the landscape. The static models for marten were created based on annual snowfall and percentage of older conifer and mixed forest. Demographic parameters were obtained from the literature...
Predicted probability of marten occurrence on the Lassen National Forest during summer (May – November). Weighted average of 6 best logistic regression models (Rustigian-Romsos and Spencer, 2010).
Agreement in predicted marten year-round distribution derived from future (2076-2095) climate projections and vegetation simulations using 3 GCMs (Hadley CM3 (Johns et al. 2003), MIROC (Hasumi and Emori 2004), and CSIRO Mk3 (Gordon 2002)) under the A2 emissions scenario (Naki?enovi? et al. 2000). Projected marten distribution was created with Maxent (Phillips et al. 2006) using marten detections (N = 302, spanning 1990 – 2011) and eight predictor variables: mean potential evapotranspiration, mean annual precipitation, mean fraction of vegetation carbon burned, mean forest carbon (g C m2), mean fraction of vegetation carbon in forest, understory index (fraction of grass vegetation carbon in forest), average maximum...
This dataset depicts Marten (Martes americana) habitat in the Northern Appalachians predicted using the spatially explicit population model PATCH under the current trapping rates plus timber harvest scenario (L2; Carrol 2007). This dataset represents one of several scenarios testing the interacting effects of trapping, timber harvest, habitat restoration, and climate change on marten populations. Static habitat suitability models for marten were fed through PATCH to predict source and sink habitat areas across the landscape. The static models for marten were created based on annual snowfall and percentage of older conifer and mixed forest. Demographic parameters were obtained from the literature and from calibration...
Future (2046-2065) predicted probability of marten year-round occurrence projected under the A1fi emissions scenario with the Hadley CM3 GCM model (Gordon et al. 2000, Pope et al. 2000). Predicted probability of marten year-round occurrence created with Maxent (Phillips et al. 2006) using marten detections (N = 302, spanning 1990 – 2011) and eight predictor variables: mean annual precipitation, mean summer (July – September) precipitation, mean summer temperature amplitude, mean annual temperature maximum, mean fraction of vegetation carbon burned, mean understory index, mean vegetation carbon (g C m-2), and modal vegetation class. Predictor variables had a grid cell size of 10 km, vegetation variables were simulated...
Predicted probability of marten occurrence on the Lassen National Forest during winter (December – April). Weighted average of 10 best logistic regression models (Rustigian-Romsos and Spencer, 2010).
Resistance surface used for marten least cost corridor modeling. Total cost was based the sum of costs assigned to land cover types, wildfires, elevations, and slopes. Areas with urban and open water cover types were assigned the maximum cost (175), and potential habitat (defined as predicted probability of marten occurrence > 0.4) with an area > 500 ha (approximately one female home range) was assigned the minimum cost of 1.
Polygons with predicted probability of occurrence > 0.4 (from predicted probability of marten occurrence in summer) and area > 2500 ha (approximately 5 female home ranges). Areas with urban or open water cover types and slopes > 80% were removed. Areas within 1260 m of one another were considered as part of the same core. Some polygons meeting the criteria for potential core habitat were removed based on expert opinion on their occupancy and suitability.
This dataset depicts Marten (Martes americana) habitat in the Northern Appalachians predicted using the spatially explicit population model PATCH under the increased survival in parks plus forest restoration scenario (R1; Carrol 2007). This dataset represents one of several scenarios testing the interacting effects of trapping, timber harvest, habitat restoration, and climate change on marten populations. Static habitat suitability models for marten were fed through PATCH to predict source and sink habitat areas across the landscape. The static models for marten were created based on annual snowfall and percentage of older conifer and mixed forest. Demographic parameters were obtained from the literature and from...
Predicted probability of marten year-round occurrence derived from future (2076-2095) climate projections and vegetation simulations. Projected marten distribution was created with Maxent (Phillips et al. 2006) using marten detections (N = 302, spanning 1990 – 2011) and nine predictor variables: mean winter (January – March) precipitation, mean amount of snow on the ground in March, mean understory index (fraction of grass vegetation carbon in forest), mean fraction of total forest carbon in coarse wood carbon, average maximum tree LAI, mean fraction of vegetation carbon burned, mean forest carbon (g C m2), mean fraction of vegetation carbon in forest, and modal vegetation class. Future climate drivers were...
Agreement in predicted marten year-round distribution derived from future (2076-2095) climate projections and vegetation simulations using 2 GCMs (Hadley CM3 (Johns et al. 2003) and MIROC (Hasumi and Emori 2004)) under the A2 emissions scenario (Naki?enovi? et al. 2000). Projected marten distribution was created with Maxent (Phillips et al. 2006) using marten detections (N = 302, spanning 1990 – 2011) and nine predictor variables: mean winter (January – March) precipitation, mean amount of snow on the ground in March, mean understory index (fraction of grass vegetation carbon in forest), mean fraction of total forest carbon in coarse wood carbon, average maximum tree LAI, mean fraction of vegetation carbon burned,...
Predicted probability of marten year-round occurrence derived from future (2076-2095) climate projections and vegetation simulations. Projected marten distribution was created with Maxent (Phillips et al. 2006) using marten detections (N = 102, spanning 1993 – 2011) and eight predictor variables: mean potential evapotranspiration, mean annual precipitation, mean fraction of vegetation carbon burned, mean forest carbon (g C m2), mean fraction of vegetation carbon in forest, understory index (fraction of grass vegetation carbon in forest), average maximum tree LAI, and modal vegetation class. Future climate drivers were generated using statistical downscaling (simple delta method) of general circulation model projections,...
This dataset depicts Marten (Martes americana) habitat in the Northern Appalachians predicted using the spatially explicit population model PATCH under the current trapping rate scenario (B2; Carrol 2007). This dataset represents one of several scenarios testing the interacting effects of trapping, timber harvest, habitat restoration, and climate change on marten populations. Static habitat suitability models for marten were fed through PATCH to predict source and sink habitat areas across the landscape. The static models for marten were created based on annual snowfall and percentage of older conifer and mixed forest. Demographic parameters were obtained from the literature and from calibration of the model. Several...
Least cost corridors are only meaningful when viewed in context with the cores they are connecting. Normalized least cost corridors were calculated between nearest neighbor cores. The analysis extent was defined as the study area. Normalized least cost corridors are calculated by adding cost-weighted distances from each of the 2 cores being connected and subtracting the minimum least-cost path distance (Washington Wildlife Habitat Connectivity Working Group, 2010. Washington Connected Landscapes Project: Statewide Analysis. Washington Departments of Fish and Wildlife, and Transportation, Olympia, WA. Downloaded Jan. 31, 2011 from http://www.waconnected.org).
Predicted probability of marten year-round occurrence derived from future (2076-2095) climate projections and vegetation simulations. Projected marten distribution was created with Maxent (Phillips et al. 2006) using marten detections (N = 102, spanning 1993 – 2011) and eight predictor variables: mean potential evapotranspiration, mean annual precipitation, mean fraction of vegetation carbon burned, mean forest carbon (g C m2), mean fraction of vegetation carbon in forest, understory index (fraction of grass vegetation carbon in forest), average maximum tree LAI, and modal vegetation class. Future climate drivers were generated using statistical downscaling (simple delta method) of general circulation model projections,...
Predicted probability of marten year-round occurrence derived from future (2046-2065) climate projections and vegetation simulations. Projected marten distribution was created with Maxent (Phillips et al. 2006) using marten detections (N = 102, spanning 1993 – 2011) and eight predictor variables: mean potential evapotranspiration, mean annual precipitation, mean fraction of vegetation carbon burned, mean forest carbon (g C m2), mean fraction of vegetation carbon in forest, understory index (fraction of grass vegetation carbon in forest), average maximum tree LAI, and modal vegetation class. Future climate drivers were generated using statistical downscaling (simple delta method) of general circulation model projections,...
Predicted probability of marten year-round occurrence created with Maxent (Phillips et al. 2006) using marten detections (N = 102, spanning 1993 – 2011) and eight predictor variables: mean potential evapotranspiration, mean annual precipitation, mean fraction of vegetation carbon burned, mean forest carbon (g C m2), mean fraction of vegetation carbon in forest, understory index (fraction of grass vegetation carbon in forest), average maximum tree LAI, and modal vegetation class. Predictor variables had a grid cell size of 800 m by 800 m, vegetation variables were simulated with MC1 dynamic global vegetation model (Bachelet et al. 2001) and historical climate variables were provided by the PRISM GROUP (Daly et al....
Predicted probability of marten year-round occurrence derived from future (2046-2065) climate projections and vegetation simulations. Projected marten distribution was created with Maxent (Phillips et al. 2006) using marten detections (N = 102, spanning 1993 – 2011) and eight predictor variables: mean potential evapotranspiration, mean annual precipitation, mean fraction of vegetation carbon burned, mean forest carbon (g C m2), mean fraction of vegetation carbon in forest, understory index (fraction of grass vegetation carbon in forest), average maximum tree LAI, and modal vegetation class. Future climate drivers were generated using statistical downscaling (simple delta method) of general circulation model projections,...
Predicted probability of marten year-round occurrence derived from future (2046-2065) climate projections and vegetation simulations. Projected marten distribution was created with Maxent (Phillips et al. 2006) using marten detections (N = 102, spanning 1993 – 2011) and eight predictor variables: mean potential evapotranspiration, mean annual precipitation, mean fraction of vegetation carbon burned, mean forest carbon (g C m2), mean fraction of vegetation carbon in forest, understory index (fraction of grass vegetation carbon in forest), average maximum tree LAI, and modal vegetation class. Future climate drivers were generated using statistical downscaling (simple delta method) of general circulation model projections,...
Predicted probability of marten year-round occurrence created with Maxent (Phillips et al. 2006) using marten detections (N = 302, spanning 1990 – 2011) and nine predictor variables: mean annual precipitation, mean summer (July – September) precipitation, mean summer temperature amplitude, mean annual temperature maximum, mean fraction of vegetation carbon burned, mean understory index, mean vegetation carbon (g C m2), modal vegetation class, and average maximum tree LAI. Predictor variables had a grid cell size of 10 km, vegetation variables were simulated with MC1 (Lenihan et al. 2008) and climate variables were provided by the PRISM GROUP (Daly et al. 1994). This marten distribution model has a 10-fold cross-validated...
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