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Habitat selection studies can make important contributions to habitat prioritization efforts for species of conservation concern. We present a large-scale collaborative effort to develop habitat selection models for Greater Sage-grouse (Centrocercus urophasianus) across large landscapes (Wyoming, USA) and multiple seasons. Greater Sage-grouse are limited to western semi-arid landscapes in North America, range-wide population declines have been documented, and the species is currently listed a “warranted but precluded” from listing under the U.S. Endangered Species Act. Wyoming is predicted to remain a stronghold for Sage-grouse populations and contains approximately 37% of the remaining birds. We developed Resource...
Habitat selection studies can make important contributions to habitat prioritization efforts for species of conservation concern. We present a large-scale collaborative effort to develop habitat selection models for Greater Sage-grouse (Centrocercus urophasianus) across large landscapes (Wyoming, USA) and multiple seasons. Greater Sage-grouse are limited to western semi-arid landscapes in North America, range-wide population declines have been documented, and the species is currently listed a “warranted but precluded” from listing under the U.S. Endangered Species Act. Wyoming is predicted to remain a stronghold for Sage-grouse populations and contains approximately 37% of the remaining birds. We developed Resource...
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The Southeast Conservation Blueprint is a map of important areas for conservation and restoration across the Southeast and Caribbean. The Blueprint is the primary product of the Southeast Conservation Adaptation Strategy (SECAS). Through SECAS, diverse partners are working together to design and achieve a connected network of lands and waters that supports thriving fish and wildlife populations and improved quality of life for people.Southeast Blueprint 2020 SECAS BoundaryThis SECAS boundary layer depicts the extent of the SECAS geography. Extent of SECAS BoundaryThe SECAS geography encompasses the states and territories that are part of the Southeast Association of Fish and Wildlife Agencies, which includes 15...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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This indicator measures the protected status or potential stress (i.e., shipping traffic, dredge disposal) of solid substrate and rocky outcroppings.Reason for SelectionHardbottom extent and condition is particularly important for a variety of marine species. Hardbottom provides an anchor for important seafloor habitat such as deepwater corals, plants, and sponges, supporting associated invertebrate and fish species. It is impacted by landscape scale stressors (e.g., water quality degradation, mining, dredging, and beach renourishment), can be monitored and modeled with existing information, and is widely used and understood by diverse partners.Input Data– The Nature Conservancy’s (TNC) South Atlantic Bight Marine...
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This indicator is a continuous index of water quality, sediment quality, and benthic community condition that reflects the overall abiotic status of open water estuaries.Reason for SelectionThis index measures the overall abiotic condition of the estuarine system and captures human impacts on the environment like nonpoint source pollution. It is well-monitored and is synthesized by the Environmental Protection Agency (EPA) every five years. It integrates measures of water quality, sediment quality, and benthic habitat quality.Input Data– The EPA Coastal Condition Index (CCI) is a ranking derived from a water quality index, sediment quality index, benthic index, coastal habitat index, and fish tissue contaminants...
Mule deer in the Sheep Creek sub herd are part of the larger Area 6 herd that occupies portions of Elko, Lander, and Eureka counties. The primary winter range of this population is located along the eastern flank of the Sheep Creek Range and the west side of Boulder Valley. Most deer migrate approximately 30 miles from winter ranges in upper Boulder Creek and Antelope Creek drainages to summer ranges on the west side of the Tuscarora Mountains. However, some deer in this population migrate much farther – approximately 80 miles – and connect with mule deer that summer east of the Humboldt River. This deer herd faces several challenges, including migration routes that pass through increased mineral extraction activities...
This data set includes the relative production scenarios for Indiangrass [0.17(Precip) + 0.02(Sand) - 7.4]; this is the model from Epstein, et al. (1998). Soil texture (percent by weight) came from the Earth Systems Science Center (2008) which provided processed soils data from NRCS (gSSURGO), mean annual temperature (Celsius) and/or mean annual precipitation (millimeters) came from contemporary (1981 - 2010) estimates (Maurer et al. 2002) or a GCM. Global Climate Models (GCM) providing scenarios included: warmer-wetter scenario (CESM1-BGC, RCP4.5, Neale et al., 2010), warmer drier scenario (GISS-E2-R, RCP4.5, Schmidt, 2014), hotter-wetter scenario (Miroc-ESM, RCP8.5, Watanabe et al., 2011), and hotter-drier scenario...
This data set includes the relative production scenarios for tobosagrass [0.08(Temp) - 0.58]; this is the model from Epstein, et al. (1998). Soil texture (percent by weight) came from the Earth Systems Science Center (2008) which provided processed soils data from NRCS (gSSURGO), mean annual temperature (Celsius) and/or mean annual precipitation (millimeters) came from contemporary (1981 - 2010) estimates (Maurer et al. 2002) or a GCM. Global Climate Models (GCM) providing scenarios included: warmer-wetter scenario (CESM1-BGC, RCP4.5, Neale et al., 2010), warmer drier scenario (GISS-E2-R, RCP4.5, Schmidt, 2014), hotter-wetter scenario (Miroc-ESM, RCP8.5, Watanabe et al., 2011), and hotter-drier scenario (ACCESS...
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The Mojave Plots Points data are 1,219 plot locations in the Central Mojave Desert where field data were recorded and repeat photographs were taken from 1997-1999 to provide context for the classification of the Central Mojave Desert into various vegetation classes. The 1,219 plot locations in the plots points shapefile (plots_points.shp) are each assigned a unique identifier called the FinalPlotCode. The FinalPlotCode can be used to match the shapefile points to the Mojave Field Data. The attached CSV file (Mojave_Images_Crosswalk.csv) can be used to relate the FinalPlotCodes to the associated repeat photographs that are attached as zipped TIFF images to the ScienceBase page. There are 1,695 total repeat photographs...


map background search result map search result map Proportion of All Big Sagebrush Land Cover (1-km scale) in the Wyoming Basins Ecoregional Assessment area Proportion of All Big Sagebrush Land Cover (18-km scale) in the Wyoming Basins Ecoregional Assessment area Conifer Forest Land Cover in the Wyoming Basins Ecoregional Assessment area Proportion of Juniper Land Cover (270-m scale) in the Wyoming Basins Ecoregional Assessment area Indicator: Potential Hardbottom Condition Central Mojave Desert Vegetation Mapping Project, California, 1997-1999: Plots Points and Repeat Photographs DisMOSH, Cost, MOSHShoreline: Distance to foraging areas for piping plovers (foraging shoreline, cost mask, and least-cost path distance): Cedar Island, VA, 2013–2014 DisMOSH, Cost, MOSHShoreline: Distance to foraging areas for piping plovers (foraging shoreline, cost mask, and least-cost path distance): Fire Island, NY, 2010–2011 DisMOSH, Cost, MOSHShoreline: Distance to foraging areas for piping plovers (foraging shoreline, cost mask, and least-cost path distance): Rockaway Peninsula, NY, 2013–2014 DisMOSH, Cost, MOSH_Shoreline: Distance to foraging areas for piping plovers including foraging shoreline, cost mask, and least-cost path distance: Parker River, MA, 2014 DisMOSH, Cost, MOSH_Shoreline: Distance to foraging areas for piping plovers including foraging shoreline, cost mask, and least-cost path distance: Cape Hatteras, NC, 2014 DisMOSH, Cost, MOSH_Shoreline: Distance to foraging areas for piping plovers including foraging shoreline, cost mask, and least-cost path distance: Rhode Island National Wildlife Refuge, RI, 2014 DisMOSH, Cost, MOSH_Shoreline: Distance to foraging areas for piping plovers including foraging shoreline, cost mask, and least-cost path distance: Fisherman Island, VA, 2014 Indicator: Estuarine Coastal Condition Southeast Blueprint 2020 SECAS Boundary Migration Corridors of Mule Deer in the Sheep Creek Range in Nevada DisMOSH, Cost, MOSH_Shoreline: Distance to foraging areas for piping plovers including foraging shoreline, cost mask, and least-cost path distance: Fisherman Island, VA, 2014 DisMOSH, Cost, MOSHShoreline: Distance to foraging areas for piping plovers (foraging shoreline, cost mask, and least-cost path distance): Cedar Island, VA, 2013–2014 DisMOSH, Cost, MOSHShoreline: Distance to foraging areas for piping plovers (foraging shoreline, cost mask, and least-cost path distance): Fire Island, NY, 2010–2011 DisMOSH, Cost, MOSH_Shoreline: Distance to foraging areas for piping plovers including foraging shoreline, cost mask, and least-cost path distance: Rhode Island National Wildlife Refuge, RI, 2014 DisMOSH, Cost, MOSH_Shoreline: Distance to foraging areas for piping plovers including foraging shoreline, cost mask, and least-cost path distance: Cape Hatteras, NC, 2014 Migration Corridors of Mule Deer in the Sheep Creek Range in Nevada Central Mojave Desert Vegetation Mapping Project, California, 1997-1999: Plots Points and Repeat Photographs Indicator: Estuarine Coastal Condition Indicator: Potential Hardbottom Condition Proportion of All Big Sagebrush Land Cover (1-km scale) in the Wyoming Basins Ecoregional Assessment area Proportion of All Big Sagebrush Land Cover (18-km scale) in the Wyoming Basins Ecoregional Assessment area Conifer Forest Land Cover in the Wyoming Basins Ecoregional Assessment area Proportion of Juniper Land Cover (270-m scale) in the Wyoming Basins Ecoregional Assessment area Southeast Blueprint 2020 SECAS Boundary