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This raster represents a continuous surface of sage-grouse habitat suitability index (HSI) values for northeastern California. HSIs were calculated for spring (mid-March to June), summer (July to mid-October), and winter (November to March) sage-grouse seasons, and then multiplied together to create this composite dataset.
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Five principal components are used to represent the climate variation in an original set of 12 composite climate variables reflecting complex precipitation and temperature gradients. The dataset provides coverage for future climate (defined as the 2040-2070 normal period) under the RCP8.5 emission scenarios. Climate variables were chosen based on their known influence on local adaptation in plants, and include: mean annual temperature, summer maximum temperature, winter minimum temperature, annual temperature range, temperature seasonality (coefficient of variation in monthly average temperatures), mean annual precipitation, winter precipitation, summer precipitation, proportion of summer precipitation, precipitation...
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Data presented are results of surveys in 2021 for San Diego Cactus Wren (Campylorhynchus brunneicapillus) in southern San Diego County. Surveys were conducted at 378 plots. Plots were surveyed twice in 2021 and the number, age (adult or juvenile), banding status (color banded or not), and breeding status (paired, unpaired, or unknown) of all wrens recorded. Habitat covariate data were collected including amount of dead and stressed cactus in the plot, percent cover of bare ground, and the dominant and percent cover of invasive species.
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These data represent trapping effort and captures of deer mice at Point Reyes National Seashore, Marin County, California. Deer mice were captured and marked with ear tags to allow identification of individuals. The location of captures can be used in a spatially explicit capture recapture model to estimate density of mice and how mouse density varies by site and habitat type.
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Preserving native species diversity is fundamental to ecosystem conservation. Selecting appropriate native species for use in restoration is a critical component of project design and may emphasize species attributes such as life history, functional type, pollinator services, and nutritional value for wildlife. Determining which species are likely to establish and persist in a particular environment is a key consideration. Species distribution models (SDMs) characterize relationships between species occurrences and the physical environment (e.g., climate, soil, topographic relief) and provide a mechanism for assessing which species may successfully propagate at a restoration site. In conjunction with information...
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Preserving native species diversity is fundamental to ecosystem conservation. Selecting appropriate native species for use in restoration is a critical component of project design and may emphasize species attributes such as life history, functional type, pollinator services, and nutritional value for wildlife. Determining which species are likely to establish and persist in a particular environment is a key consideration. Species distribution models (SDMs) characterize relationships between species occurrences and the physical environment (e.g., climate, soil, topographic relief) and provide a mechanism for assessing which species may successfully propagate at a restoration site. In conjunction with information...
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Preserving native species diversity is fundamental to ecosystem conservation. Selecting appropriate native species for use in restoration is a critical component of project design and may emphasize species attributes such as life history, functional type, pollinator services, and nutritional value for wildlife. Determining which species are likely to establish and persist in a particular environment is a key consideration. Species distribution models (SDMs) characterize relationships between species occurrences and the physical environment (e.g., climate, soil, topographic relief) and provide a mechanism for assessing which species may successfully propagate at a restoration site. In conjunction with information...
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The GIS shapefile Extra limit counts of southern sea otters 2019 is a point layer representing the locations of sea otter sightings that fall outside the officially recognized range of the southern sea otter (Enhydra lutris nereis) in mainland California. These data were collected during the spring 2019 range-wide census. The USGS range-wide sea otter census has been undertaken each year since 1982, using consistent methodology involving both ground-based and aerial-based counts. The spring census provides the primary basis for gauging population trends by State and Federal management agencies. Sea otter distribution in California (the mainland range) is considered to comprise a band of potential habitat stretching...
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Lidar-derived digital elevation models often contain a vertical bias due to vegetation. In areas with tidal influence the amount of bias can be ecologically significant, for example, by decreasing the expected inundation frequency. We generated a corrected digital elevation mode (DEM) for Suisun marsh using a modification of the Lidar Elevation Adjustment with NDVI (LEAN) technique (Buffington et al. 2016). GPS survey data (6912 points, collected across public and private land in 2018), Normalized Difference Vegetation Index (NDVI) derived from an airborne multispectral image (June 2018), a 1 m lidar DEM from September 2018, and a 1 m canopy surface model were used to generate models of predicted bias across the...
Observations (reduced to detected/not detected) of 45 vertebrate species (seven mammals, seven amphibians, and 31 reptiles) across Southern California pitfall sampling projects conducted between 1995 through 2015. Habitat patch locations of every pitfall sampling project presented in a shapefile. Habitat patches were measured based on the size when pitfall sampling began within each. Sampling projects within the same geographic area may have different sized patches based on date of project sampling and if patch erosion occurred. A matrix of whether each species was expected within each habitat patch's species pool based on range maps and published records is also included. These data support the following publication:...
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This dataset contains data pertaining to ground surface cover in 30 meter plots around a random selection of points within chaparral from Santa Barbara county south to San Diego County in southern California, USA. These data were obtained from historical aerial imagery from 1943 to 1959 and current imagery from 2016 to 2018 and they were compared to quantify changes in cover type over time. These data support the following publication: Syphard, A.D., Brennan, T.J., Rustigian‐Romsos, H. and Keeley, J.E., 2022. Fire‐driven vegetation type conversion in southern California. Ecological Applications, p.e2626. https://doi.org/10.1002/eap.2626.
These data include potential Yuma Ridgway’s rail (Rallus obsoletus yumanensis) habitat classified from concurrently occurring Landsat images taken during telemetry field studies of and call count surveys for Yuma Ridgway's rail in 2016.
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Lidar-derived digital elevation models often contain a vertical bias due to vegetation. In areas with tidal influence the amount of bias can be ecologically significant, for example, by decreasing the expected inundation frequency. We generated a corrected digital elevation mode (DEM) for tidal marsh areas around San Francisco Bay using the Lidar Elevation Adjustment with NDVI (LEAN) technique (Buffington et al. 2016). Survey-grade GPS survey data (6614 points), NAIP-derived Normalized Difference Vegetation Index, and original 1 m lidar DEM from 2010 were used to generate a model of predicted bias across tidal marsh areas. The predicted bias was then subtracted from the original lidar DEM and merged with the NOAA...
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We evaluated the expected success of habitat recovery in priority areas under 3 different restoration scenarios: passive, planting, and seeding. Passive means no human intervention following a fire disturbance. Under a planting scenario, field technicians methodically plant young sagebrush saplings at the burned site. The seeding scenario involves distributing large amounts of sagebrush seeds throughout the affected area.
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Preserving native species diversity is fundamental to ecosystem conservation. Selecting appropriate native species for use in restoration is a critical component of project design and may emphasize species attributes such as life history, functional type, pollinator services, and nutritional value for wildlife. Determining which species are likely to establish and persist in a particular environment is a key consideration. Species distribution models (SDMs) characterize relationships between species occurrences and the physical environment (e.g., climate, soil, topographic relief) and provide a mechanism for assessing which species may successfully propagate at a restoration site. In conjunction with information...
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Preserving native species diversity is fundamental to ecosystem conservation. Selecting appropriate native species for use in restoration is a critical component of project design and may emphasize species attributes such as life history, functional type, pollinator services, and nutritional value for wildlife. Determining which species are likely to establish and persist in a particular environment is a key consideration. Species distribution models (SDMs) characterize relationships between species occurrences and the physical environment (e.g., climate, soil, topographic relief) and provide a mechanism for assessing which species may successfully propagate at a restoration site. In conjunction with information...
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This shapefile represents habitat suitability categories (High, Moderate, Low, and Non-Habitat) derived from a composite, continuous surface of sage-grouse habitat suitability index (HSI) values for northeastern California during the winter season (November to March), and is a surrogate for habitat conditions during periods of cold and snow.
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Map of cumulative 38-day nest survival predicted from a Bayesian hierarchical shared frailty model of sage-grouse nest fates. The midpoint of coefficient conditional posterior distributions of 38-day nest survival were used for prediction at each 30 meter pixel across the landscape.
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Lidar-derived digital elevation models often contain a vertical bias due to vegetation. In areas with tidal influence the amount of bias can be ecologically significant, for example, by decreasing the expected inundation frequency. We generated a corrected digital elevation model (DEM) for wetlands throughout Collier county using a modification of the Lidar Elevation Adjustment with NDVI (LEAN) technique (Buffington et al. 2016). GPS survey data (15,223 points), NAIP-derived Normalized Difference Vegetation Index (2010), a 10 m lidar DEM from 2007, and a 10 m canopy surface model were used to generate a model of predicted bias across marsh, mangrove, and cypress habitats. The predicted bias was then subtracted from...
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The data are .csv files of tagged sea otter re-sighting locations (henceforth, resights) collected in the field using a combination of VHF radio telemetry and direct observation using high powered (80x) telescopes. Sea otters were tracked by shore based observers from the date of tagging until the time of radio battery failure or the animal’s death, whichever comes first. The frequency of re-sighting was opportunistic, depending on logistical factors such as coastal access, but generally ranged from daily to weekly. Location coordinates are reported as X and Y coordinates in the projection/datum California Teale-Albers NAD 1927. Each file contains resight data for one individual sea otter collected over a period...


map background search result map search result map Geospatial Data Collected from Tagged Sea Otters in Central California, 1998-2012 LEAN-corrected San Francisco Bay Digital Elevation Model, 2018 Composite Habitat Suitability Index Raster Dataset Winter Season Habitat Categories Shapefile LEAN-Corrected DEM for Suisun Marsh Principal components of climate variation in the Desert Southwest for the future time period 2040-2070 (RCP 8.5) Annual California Sea Otter Census: 2019 Extra Limit Observations Shapefile LEAN-Corrected Collier County DEM for wetlands Greater Sage-grouse Nest Survival, Nevada and California 2019 Species Observations from Pitfall Trap Arrays, Species Pool Matrices, and Patch Locations in Southern California from 1995-2015 Species Distribution Model (SDM) for Ambrosia dumosa in the Mojave Desert Species Distribution Model (SDM) for Hilaria rigida in the Mojave Desert Species Distribution Model (SDM) for Muhlenbergia porteri in the Mojave Desert Species Distribution Model (SDM) for Oenothera deltoides in the Mojave Desert Species Distribution Model (SDM) for Stephanomeria parryi in the Mojave Desert Rail Potential Habitat, Salton Sea, California, 2016 Vegetation Type Conversion in Southern California Between 1943 and 2018 Sagebrush Restoration Under Passive, Planting, and Seeding Scenarios Following Fire Disturbance in the Virginia Mountains, Nevada (2018) Surveys and Monitoring of Coastal Cactus Wren in Southern San Diego County, 2021 Captures and Trapping Effort for Deer Mice (Peromyscus sonoriensis) at Point Reyes National Seashore, California, USA from 2021 to 2022 LEAN-Corrected DEM for Suisun Marsh Surveys and Monitoring of Coastal Cactus Wren in Southern San Diego County, 2021 Captures and Trapping Effort for Deer Mice (Peromyscus sonoriensis) at Point Reyes National Seashore, California, USA from 2021 to 2022 Rail Potential Habitat, Salton Sea, California, 2016 Sagebrush Restoration Under Passive, Planting, and Seeding Scenarios Following Fire Disturbance in the Virginia Mountains, Nevada (2018) LEAN-corrected San Francisco Bay Digital Elevation Model, 2018 LEAN-Corrected Collier County DEM for wetlands Composite Habitat Suitability Index Raster Dataset Winter Season Habitat Categories Shapefile Species Observations from Pitfall Trap Arrays, Species Pool Matrices, and Patch Locations in Southern California from 1995-2015 Geospatial Data Collected from Tagged Sea Otters in Central California, 1998-2012 Vegetation Type Conversion in Southern California Between 1943 and 2018 Species Distribution Model (SDM) for Ambrosia dumosa in the Mojave Desert Species Distribution Model (SDM) for Hilaria rigida in the Mojave Desert Species Distribution Model (SDM) for Muhlenbergia porteri in the Mojave Desert Species Distribution Model (SDM) for Oenothera deltoides in the Mojave Desert Species Distribution Model (SDM) for Stephanomeria parryi in the Mojave Desert Greater Sage-grouse Nest Survival, Nevada and California 2019 Principal components of climate variation in the Desert Southwest for the future time period 2040-2070 (RCP 8.5)