Skip to main content
USGS - science for a changing world
Advanced Search

Filters: Tags: marten (X)

45 results (68ms)   

View Results as: JSON ATOM CSV
thumbnail
Agreement in predicted marten year-round distribution derived from future (2046-2065) 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...
thumbnail
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,...
thumbnail
This dataset depicts Marten (Martes americana) habitat in the Northern Appalachians predicted using the spatially explicit population model PATCH under the increased trapping intensity scenario (B4; 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....
thumbnail
Future (2076-2095) predicted probability of marten year-round occurrence projected under the A2 emissions scenario with the PCM1 GCM (Washington et al. 2000; Meehl et al. 2003). The projected marten distribution was 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...
thumbnail
Least cost paths are only meaningful when viewed in context with the cores they are connecting. Least cost paths were calculated between nearest neighbor cores. This data set represents a single path (pixel-wide) of least cumulative resistance between target cores.
thumbnail
This dataset depicts Marten (Martes americana) habitat in the Northern Appalachians predicted using the spatially explicit population model PATCH under the increased trapping Area scenario (B3; 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....
thumbnail
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 = 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...
thumbnail
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...
thumbnail
Agreement in predicted marten year-round distribution derived from future (2046-2065) 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,...
thumbnail
This dataset depicts Marten (Martes americana) habitat in the Northern Appalachians predicted using the spatially explicit population model PATCH under the increased trapping area plus timber harvest scenario (L3; 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...
thumbnail
Marten potential summer habitat was defined as any cell with a predicted probability of marten occurrence > 0.4 (from predicted probability of marten occurrence in summer).
thumbnail
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 = 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...
thumbnail
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 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. Predictor variables had a grid cell size of 4 km by 4 km, vegetation variables were simulated with MC1 dynamic global vegetation model (Bachelet...
thumbnail
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 with MC1 (Hayhoe et al. 2004), historical climate variables were provided by the PRISM GROUP (Daly et al. 1994), and future climate projections were obtained from the Hadley Center...
thumbnail
This dataset depicts Marten (Martes americana) habitat in the Northern Appalachians predicted using the spatially explicit population model PATCH under the increased trapping intensity plus timber harvest scenario (L4; 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...
thumbnail
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 scenario (B1; 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....
thumbnail
This dataset depicts Marten (Martes americana) habitat in the Northern Appalachians predicted using the spatially explicit population model PATCH under the increased trapping intensity plus climate change scenario (FB4; 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...
thumbnail
Future (2046-2065) predicted probability of marten year-round occurrence projected under the A2 emissions scenario with the PCM1 GCM (Washington et al. 2000; Meehl et al. 2003). The projected marten distribution was 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...
thumbnail
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).
thumbnail
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 timber harvest scenario (L1; 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...


map background search result map search result map Overlay of projected marten distributions, 2046-2065, 800 m resolution Overlay of projected marten distributions, 2046-2065, 4 km resolution Predicted probability of marten year-round occurrence, 2076-2095, Hadley CM3 A2, 800 m resolution Predicted probability of marten year-round occurrence, 2076-2095, MIROC A2, 4 km resolution Predicted probability of marten year-round occurrence, 2046-2065, MIROC A2, 4 km resolution Predicted probability of marten year-round occurrence, 2046-2065, Hadley CM3 A2, 4 km resolution Predicted probability of marten year-round occurrence, 1986-2005, 4 km resolution Predicted probability of marten year-round occurrence, 2076-2095, PCM1 A2, 10 km resolution Predicted probability of marten year-round occurrence, 2046-2065, PCM1 A2, 10 km resolution Predicted probability of marten year-round occurrence, 1986-2005, Hadley CM3 A1fi, 10 km resolution Predicted probability of marten occurrence on the Lassen National Forest during summer (May – November) Marten least cost paths Marten potential summer habitat Predicted Marten Habitat in the Northern Appalachians: Increased Trapping Intensity Scenario Predicted Marten Habitat in the Northern Appalachians: Increased Trapping Intensity + Climate Change Scenario Predicted Marten Habitat in the Northern Appalachians: Increased Trapping Area + Timber Harvest Scenario Predicted Marten Habitat in the Northern Appalachians: Increased Trapping Intensity + Timber Harvest Scenario Predicted Marten Habitat in the Northern Appalachians: Increased Survival in Parks + Timber Harvest Scenario Predicted Marten Habitat in the Northern Appalachians: Increased Trapping Area Scenario Predicted Marten Habitat in the Northern Appalachians: Increased Survival in Parks Scenario Predicted probability of marten occurrence on the Lassen National Forest during summer (May – November) Marten potential summer habitat Marten least cost paths Overlay of projected marten distributions, 2046-2065, 800 m resolution Predicted probability of marten year-round occurrence, 2076-2095, Hadley CM3 A2, 800 m resolution Predicted Marten Habitat in the Northern Appalachians: Increased Trapping Area + Timber Harvest Scenario Predicted Marten Habitat in the Northern Appalachians: Increased Trapping Intensity + Timber Harvest Scenario Predicted Marten Habitat in the Northern Appalachians: Increased Survival in Parks + Timber Harvest Scenario Predicted Marten Habitat in the Northern Appalachians: Increased Trapping Intensity + Climate Change Scenario Predicted Marten Habitat in the Northern Appalachians: Increased Trapping Intensity Scenario Predicted Marten Habitat in the Northern Appalachians: Increased Trapping Area Scenario Predicted Marten Habitat in the Northern Appalachians: Increased Survival in Parks Scenario Overlay of projected marten distributions, 2046-2065, 4 km resolution Predicted probability of marten year-round occurrence, 2076-2095, MIROC A2, 4 km resolution Predicted probability of marten year-round occurrence, 2046-2065, MIROC A2, 4 km resolution Predicted probability of marten year-round occurrence, 2046-2065, Hadley CM3 A2, 4 km resolution Predicted probability of marten year-round occurrence, 1986-2005, 4 km resolution Predicted probability of marten year-round occurrence, 2076-2095, PCM1 A2, 10 km resolution Predicted probability of marten year-round occurrence, 2046-2065, PCM1 A2, 10 km resolution Predicted probability of marten year-round occurrence, 1986-2005, Hadley CM3 A1fi, 10 km resolution