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This data set includes geospatial data and tables providing location, environmental, and vegetation data collected in 2017 and 2018 at the Little Saint Francis River chat pile restoration site, Fredericktown, Madison County, Missouri. Restoration actions are being implemeneted as part of the settlement for the Madison County Mines Superfund site to compensate the public for losses of natural resources and the services they provide as part of the Natural Resource Damage Assessment and Restoration Southeast Missouri Lead Mining District case. Data were collected prior to and during the early stages of restoration actions to restore bottomland forest habitat, reduce invasive plant species abundance, and improve upland...
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wy_lvl7_coarsescale: Wyoming hierarchical cluster level 7 (coarse-scale) for Greater sage-grouse We developed a hierarchical clustering approach that identifies biologically relevant landscape units that can 1) be used as a long-term population monitoring framework, 2) be repeated across the Greater sage-grouse range, 3) be used to track the outcomes of local and regional populations by comparing population changes across scales, and 4) be used to inform where to best spatially target studies that identify the processes and mechanisms causing population trends to change among spatial scales. The spatial variability in the amount and quality of habitat resources can affect local population success and result in different...
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wy_lvl2_finescale: Wyoming hierarchical cluster level 2 (fine-scale) for Greater sage-grouse We developed a hierarchical clustering approach that identifies biologically relevant landscape units that can 1) be used as a long-term population monitoring framework, 2) be repeated across the Greater sage-grouse range, 3) be used to track the outcomes of local and regional populations by comparing population changes across scales, and 4) be used to inform where to best spatially target studies that identify the processes and mechanisms causing population trends to change among spatial scales. The spatial variability in the amount and quality of habitat resources can affect local population success and result in different...
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Active channel as defined by remote sensing before (2010 and after (2011) a 40 year return period flood (December 2010) within the lower Virgin River, Nevada.
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Data on 17 metrics of shale gas development in the Pennsylvania portion of the Upper Susquehanna River basin that was collated from a variety of sources and summarized at the upstream catchment scale. Data were also standardized by upstream area and transformed into rank scores based on metric distribution and then summarized into a Disturbance Intensity Index (DII). See Maloney et al. 2018 for detailed descriptions of each data sets and limitations of data. (Maloney, K. O., J. A. Young, S. P. Faulkner, A. Hailegiorgis, E. T. Slonecker, and L. E. Milheim. 2018. A detailed risk assessment of shale gas development on headwater streams in the Pennsylvania portion of the Upper Susquehanna River Basin, U.S.A. Science...
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nv_lvl6_coarsescale: Nevada hierarchical cluster level 6 (coarse-scale) for Greater sage-grouse We developed a hierarchical clustering approach that identifies biologically relevant landscape units that can 1) be used as a long-term population monitoring framework, 2) be repeated across the Greater sage-grouse range, 3) be used to track the outcomes of local and regional populations by comparing population changes across scales, and 4) be used to inform where to best spatially target studies that identify the processes and mechanisms causing population trends to change among spatial scales. The spatial variability in the amount and quality of habitat resources can affect local population success and result in different...
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wy_lvl8_coarsescale: Wyoming hierarchical cluster level 8 (coarse-scale) for Greater sage-grouse We developed a hierarchical clustering approach that identifies biologically relevant landscape units that can 1) be used as a long-term population monitoring framework, 2) be repeated across the Greater sage-grouse range, 3) be used to track the outcomes of local and regional populations by comparing population changes across scales, and 4) be used to inform where to best spatially target studies that identify the processes and mechanisms causing population trends to change among spatial scales. The spatial variability in the amount and quality of habitat resources can affect local population success and result in different...
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This data set defines boundaries of oil and gas project areas, greater sage-grouse (Centrocercus urophasianus) core areas, and non-core and non-project areas within the Wyoming Landscape Conservation Initiative (WLCI; southwestern Wyoming). Specifically, the data represents results from the manuscript “Combined influences of future oil and gas development and climate on potential Sage-grouse declines and redistribution” for high oil and gas development, low population size, and no climate component. The oil and gas development scenario were based on an energy footprint model that simulates well, pad, and road patterns for oil and gas recovery options that vary in well types (vertical and directional) and number...
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This shapefile compares the approximate location of mangroves within the boundary of Ten Thousand Islands NWR in 2005 to their location in 2014.
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The NABat sampling frame is a grid-based finite-area frame spanning Canada, the United States, and Mexico consisting of N total number of 10- by 10-km (100-km2) grid cell sample units. This grain size is biologically appropriate given the scale of movement of most bat species, which routinely travel many kilometers each night between roosts and foraging areas and along foraging routes. A draw of sample units from a finite sampling frame using the GRTS design produces an ordered list of units such that any ordered subset of that list is also randomized and spatially balanced. This vector dataset is the individual grid-based sampling grid for Continental United States at a 10x10km resolution.
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wy_lvl5_coarsescale: Wyoming hierarchical cluster level 5 (coarse-scale) for Greater sage-grouse We developed a hierarchical clustering approach that identifies biologically relevant landscape units that can 1) be used as a long-term population monitoring framework, 2) be repeated across the Greater sage-grouse range, 3) be used to track the outcomes of local and regional populations by comparing population changes across scales, and 4) be used to inform where to best spatially target studies that identify the processes and mechanisms causing population trends to change among spatial scales. The spatial variability in the amount and quality of habitat resources can affect local population success and result in different...
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wy_lvl4_moderatescale: Wyoming hierarchical cluster level 4 (moderate-scale) for Greater sage-grouse We developed a hierarchical clustering approach that identifies biologically relevant landscape units that can 1) be used as a long-term population monitoring framework, 2) be repeated across the Greater sage-grouse range, 3) be used to track the outcomes of local and regional populations by comparing population changes across scales, and 4) be used to inform where to best spatially target studies that identify the processes and mechanisms causing population trends to change among spatial scales. The spatial variability in the amount and quality of habitat resources can affect local population success and result...
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wy_lvl1_finescale: Wyoming hierarchical cluster level 1 (fine-scale) for Greater sage-grouse We developed a hierarchical clustering approach that identifies biologically relevant landscape units that can 1) be used as a long-term population monitoring framework, 2) be repeated across the Greater sage-grouse range, 3) be used to track the outcomes of local and regional populations by comparing population changes across scales, and 4) be used to inform where to best spatially target studies that identify the processes and mechanisms causing population trends to change among spatial scales. The spatial variability in the amount and quality of habitat resources can affect local population success and result in different...
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This data set defines boundaries of oil and gas project areas, greater sage-grouse (Centrocercus urophasianus) core areas, and non-core and non-project areas within the Wyoming Landscape Conservation Initiative (WLCI; southwestern Wyoming). Specifically, the data represents results from the manuscript “Combined influences of future oil and gas development and climate on potential Sage-grouse declines and redistribution” for low oil and gas development, low population size, and with effects of climate change under an RCP 8.5 scenario (2050). The oil and gas development scenario were based on an energy footprint model that simulates well, pad, and road patterns for oil and gas recovery options that vary in well types...
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The data provide location and data quality information for ground control points (GCP) deployed at Palmyra Atoll for acquisition of imagery using small unoccupied aerial systems (sUAS) in October 2016. Thales ProMark 3 handheld geographic positioning systems (GPS) were used as both a local base station and to record locations of individual GCPs, with occupancy times of approximately 30 minutes per GCP. Location data for GCPs were post-processed against base station data using Mobile Mapper Office software to yield local position accuracy of approximately 0.1 m.
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nv_lvl7_coarsescale: Nevada hierarchical cluster level 7 (coarse-scale) for Greater sage-grouse We developed a hierarchical clustering approach that identifies biologically relevant landscape units that can 1) be used as a long-term population monitoring framework, 2) be repeated across the Greater sage-grouse range, 3) be used to track the outcomes of local and regional populations by comparing population changes across scales, and 4) be used to inform where to best spatially target studies that identify the processes and mechanisms causing population trends to change among spatial scales. The spatial variability in the amount and quality of habitat resources can affect local population success and result in different...
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nv_lvl2_finescale: Nevada hierarchical cluster level 2 (fine-scale) for Greater sage-grouse We developed a hierarchical clustering approach that identifies biologically relevant landscape units that can 1) be used as a long-term population monitoring framework, 2) be repeated across the Greater sage-grouse range, 3) be used to track the outcomes of local and regional populations by comparing population changes across scales, and 4) be used to inform where to best spatially target studies that identify the processes and mechanisms causing population trends to change among spatial scales. The spatial variability in the amount and quality of habitat resources can affect local population success and result in different...
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wy_lvl6_coarsescale: Wyoming hierarchical cluster level 6 (coarse-scale) for Greater sage-grouse We developed a hierarchical clustering approach that identifies biologically relevant landscape units that can 1) be used as a long-term population monitoring framework, 2) be repeated across the Greater sage-grouse range, 3) be used to track the outcomes of local and regional populations by comparing population changes across scales, and 4) be used to inform where to best spatially target studies that identify the processes and mechanisms causing population trends to change among spatial scales. The spatial variability in the amount and quality of habitat resources can affect local population success and result in different...
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Active channel as defined by remote sensing before (2010) a 40 year return period flood (December 2010) within the lower Virgin River, Nevada.
This sampling frame is a set of grid-based finite-area frames spanning Canada, the United States, and Mexico. The grid for the United States is broken into individual grids for the continental United States, Hawaii, and Puerto Rico. Alaska is combined with Canada into a single grid. Each country/state/territory extent consists of four nested sampling grids at 50x50km, 10x10km, 5x5km, and 1x1km resolutions. The original 10x10km continental United States grid was developed by the Forest Service, U.S. Department of Agriculture for use in the interagency “Bat Grid” monitoring program in the Pacific Northwest and was expanded across Canada, the United States, and Mexico for the North American Bat Monitoring Program (NABat)....


map background search result map search result map Shale gas data used in development of the Disturbance Intensity Index for the Pennsylvania portion of the Upper Susquehanna River basin in Maloney et al. 2018 North America Bat Monitoring Program (NABat) Grid-Based Sampling Frame: Continental United States at a 10x10km resolution North American Grid-Based Sampling Frame: Alaska and Canada at a 5x5km resolution Comparison of mangrove extent from 2005 and 2014 of Ten Thousand Islands NWR, Florida, USA Greater sage-grouse population change (percent change) in a high oil and gas development, low population estimate scenario, and with no effects of climate change (2006-2062) Greater sage-grouse population change (percent change) over 50-years in a low oil and gas development, low population estimate scenario, and with effects of climate change under an RCP 8.5 scenario (2050) Pre-restoration vegetation data, Little Saint Francis River chat pile site, Missouri, USA, 2017 and 2018 Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 2 (Nevada), Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 6 (Nevada), Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 7 (Nevada), Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 1 (Wyoming), Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 2 (Wyoming), Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 4 (Wyoming), Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 5 (Wyoming), Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 6 (Wyoming), Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 7 (Wyoming), Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 8 (Wyoming), Interim Active channel in the Lower Virgin River before and after a 40 yr flood (December 2010) Active channel in the Lower Virgin River before a 40 yr flood (December 2010) Orthoimagery and elevation data derived from UAS imagery for Palmyra Atoll, USA 2016-GCPs 2016 Pre-restoration vegetation data, Little Saint Francis River chat pile site, Missouri, USA, 2017 and 2018 Orthoimagery and elevation data derived from UAS imagery for Palmyra Atoll, USA 2016-GCPs 2016 Comparison of mangrove extent from 2005 and 2014 of Ten Thousand Islands NWR, Florida, USA Active channel in the Lower Virgin River before a 40 yr flood (December 2010) Active channel in the Lower Virgin River before and after a 40 yr flood (December 2010) Shale gas data used in development of the Disturbance Intensity Index for the Pennsylvania portion of the Upper Susquehanna River basin in Maloney et al. 2018 Greater sage-grouse population change (percent change) over 50-years in a low oil and gas development, low population estimate scenario, and with effects of climate change under an RCP 8.5 scenario (2050) Greater sage-grouse population change (percent change) in a high oil and gas development, low population estimate scenario, and with no effects of climate change (2006-2062) Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 1 (Wyoming), Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 2 (Wyoming), Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 4 (Wyoming), Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 5 (Wyoming), Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 6 (Wyoming), Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 7 (Wyoming), Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 8 (Wyoming), Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 2 (Nevada), Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 6 (Nevada), Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 7 (Nevada), Interim North America Bat Monitoring Program (NABat) Grid-Based Sampling Frame: Continental United States at a 10x10km resolution North American Grid-Based Sampling Frame: Alaska and Canada at a 5x5km resolution