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These data support a paired USGS publication and document the use of retention ponds on commercial poultry farms by wild waterfowl.
Categories: Data;
Tags: Chesapeake Bay,
USGS Science Data Catalog (SDC),
Wildlife Disease,
avian influenza,
biota,
The Murderer’s Creek mule deer herd winters south of U.S. Route 26 in river valleys near Canyon Creek, Murderer’s Creek, and the South Fork John Day River. The herd’s winter ranges are characterized by western juniper, big sagebrush, and Columbia Basin grassland communities, with medusahead and other non-native grasses invading lower elevations. In the spring, mule deer mainly migrate southeast to summer ranges distributed throughout Gilbert Ridge and the Aldrich Mountains, some traveling as far south as Devon Ridge and east to Ironside Mountain. Summer ranges in these areas contain mixed-conifer forests, ponderosa pine, and low sagebrush communities. A smaller portion of this herd migrates northeast in the spring,...
Categories: Data;
Types: Downloadable,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
Shapefile;
Tags: John Day,
Oregon,
United States,
animal behavior,
biota,
The Trout Creek mule deer herd is composed of residents and migrants that make short-range elevational migrations. Mule deer mainly winter at lower elevations surrounding Blue Mountain and the slopes of the Oregon Canyon Mountains. In spring, some of these mule deer migrate to higher elevations in the Oregon Canyon Mountains. Other members of the herd winter in the southwestern portion of the herd’s range, inhabiting areas near Hawks Mountain, the Pueblo Mountains, and the foothills of the Trout Creek Mountains. These mule deer migrate to summer ranges on the crests of Holloway Mountain and the Trout Creek Mountains. Notably, one mule deer formerly wintering on the Trout Creek Mountains migrated south from a summer...
Categories: Data;
Types: Downloadable,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
Shapefile;
Tags: Fields,
Oregon,
United States,
animal behavior,
biota,
The Trout Creek mule deer herd is composed of residents and migrants that make short-range elevational migrations. Mule deer mainly winter at lower elevations surrounding Blue Mountain and the slopes of the Oregon Canyon Mountains. In spring, some of these mule deer migrate to higher elevations in the Oregon Canyon Mountains. Other members of the herd winter in the southwestern portion of the herd’s range, inhabiting areas near Hawks Mountain, the Pueblo Mountains, and the foothills of the Trout Creek Mountains. These mule deer migrate to summer ranges on the crests of Holloway Mountain and the Trout Creek Mountains. Notably, one mule deer formerly wintering on the Trout Creek Mountains migrated south from a summer...
Categories: Data;
Types: Downloadable,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
Shapefile;
Tags: Fields,
Oregon,
United States,
animal behavior,
biota,
South of Interstate 40 elk reside primarily in Arizona’s Game Management Unit (GMU) 8. Upon completing population surveys in 2021, approximately 4,000 elk were estimated to inhabit GMU 8. Their summer range is primarily characterized by high-elevation ponderosa pine forests and grasslands. The elk radiate out from various origin points within their summer range to their winter range, comprised of rims of canyons in the area, including Sycamore Canyon, Tule Canyon, and Government Canyon. This series of canyons creates an impermeable southern boundary for this herd. Their winter range along the rim country is primarily characterized by pinyon-juniper, manzanita, and scrub oak. Interstate 40 is the primary threat to...
Categories: Data;
Types: Downloadable,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
Shapefile;
Tags: Arizona,
Flagstaff,
United States,
animal behavior,
biota,
These data represent total vegetation and surface water along approximately 12 kilometers of the Paria River upstream from the confluence of the Colorado River at Lees Ferry, Arizona. They are derived from airborne, multispectral imagery obtained in late May 2009, 2013, and 2021, collected with a push-broom sensor with 4 spectral bands depicting Blue, Green, Red and Near-Infrared wavelengths at a spatial resolution of 20 centimeters. The vegetation classification data were created using a supervised classification algorithm provided by Harris Geospatial in ENVI version 5.6.3 (Exelis Visual Information Solutions, Boulder, Colorado). The water data were created using a Green Normalized Difference Vegetation Index...
This data release includes physical and chemical characteristics of field collected sediment and soil samples in Missouri representing potential sediment/soil that may enter the water column during construction related activities. Three samples were collected, including Spring River sediment, Osage River bank soil and Columbia crushed limestone. The impacts of increased suspended solid level due to the three samples on early-stage freshwater mussels were examined using three freshwater mussel species, including Fatmucket (Lampsilis siliquoidea), Arkansas Brokenray (Lampsilis reeveiana), and Washboard (Megalonaias nervosa). Specifically, toxicity endpoints including survival, biomass, and growth of juveniles were...
Categories: Data;
Tags: Acute toxicity,
Aquatic Biology,
Boone County,
Chronic toxicity,
Osage River,
LANDFIRE's (LF) 2022 Forest Canopy Cover (CC) describes the percent cover of the tree canopy in a stand. CC is a vertical projection of the tree canopy cover onto an imaginary horizontal plane. CC supplies information for fire behavior models to determine the probability of crown fire initiation, provide input in the spotting model, calculate wind reductions, and to calculate fuel moisture conditioning. To create this product, plot level CC values are calculated using the canopy fuel estimation software, Forest Vegetation Simulator (FVS). Pre-disturbance CC and Canopy Height (CH) are used as predictors of disturbed CC using a linear regression equation per Fuel Vegetation Type (FVT), disturbance type/severity, and...
LANDFIRE (LF) 2022 Fuel Vegetation Cover (FVC) represents the LF Existing Vegetation Cover (EVC) product, modified to represent pre-disturbance EVC in areas where disturbances have occurred over the past 10 years. EVC is mapped as continuous estimates of canopy cover for tree, shrub, and herbaceous lifeforms with a potential range from 10% to 100%. Continuous EVC values are binned to align with fuel model assignments when creating FVC. FVC is an input for fuel transitions related to disturbance. Fuel products in LF 2022 were created with LF 2016 Remap vegetation in non-disturbed areas. To designate disturbed areas where FVC is modified, the aggregated Annual Disturbance products from 2013 to 2022 in the Fuel Disturbance...
These data were compiled for/to modeling efforts for U.S. Bureau of Reclamation National Environmental Policy Act (NEPA) analyses for the Colorado River in Grand Canyon, Arizona. Objective(s) of our study were to create revised monthly Lake Powell elevations and outflows from Bureau of Reclamation Colorado River Mid-term Modeling System (CRMMS) traces that incorporate the alternatives in the sEIS documents and indicate when potential actions may occur and how that changes water movement and storage. These data represent monthly hydrologies for Lake Powell: inflow, outflow, and elevation forecasts for 2024-2027, as well as volumes of water in outflows for different water mangement strategies in NEPA supplemental...
The LANDFIRE (LF) Canadian Forest Fire Danger Rating System (CFFDRS) product depicts fuel types as an identifiable association of fuel elements of distinctive species, form, size, arrangement, and continuity. CFFDRS exhibits characteristic fire behavior under the specified burn conditions. In LF 2022 Canadian fuel models are derived from the Fuel Model Guide to Alaska Vegetation (Alaska Fuel Model Guide Task Group, 2018) and subsequent updates. The LF CFFDRS product contains the fuel models used for the Fire Behavior Prediction (FBP) system fuel type inputs. Default values assigned to the Canadian Fuel Models required to run the Prometheus fire behavior software (Prometheus, 2021) are added as attributes to the...
LANDFIRE's (LF) 2022 Forest Canopy Height (CH) describes the average height of the top of the canopy for a stand. CH is used in the calculation of Canopy Bulk Density (CBD) and Canopy Base Height (CBH). CH supplies information for fire behavior models, such as FARSITE (Finney 1998), that can determine the starting point of embers in the spotting model, wind reductions, and the volume of crown fuels. To create this product, plot level CH values are calculated using the canopy fuel estimation software, Forest Vegetation Simulator (FVS). Pre-disturbance Canopy Cover and CH are used as predictors of disturbed CH using a linear regression equation per Fuel Vegetation Type (FVT), disturbance type/severity, and time since...
LANDFIRE (LF) disturbance products are developed to provide temporal and spatial information related to landscape change. LF 2022 Fuel Disturbance (FDist) uses the latest Annual Disturbance products from the effective disturbance years of 2013 to 2022. FDist is created from LF 2022 Historical Disturbance (HDist) which in turn aggregates the Annual Disturbance products. FDist groups similar disturbance types, severities and time since disturbance categories which represent disturbance scenarios within the fuel environment. FDist is used in conjunction with Fuel Vegetation Type (FVT), Cover (FVC), and Height (FVH) to calculate Canopy Cover (CC), Canopy Height (CH), Canopy Bulk Density (CBD), Canopy Base Height (CBH),...
In a study conducted by the U.S. Geological Survey (USGS) and the New Hampshire Department of Environmental Services, detectable concentrations of per- and polyfluoroalkyl substances (PFAS) were found in the soil at every site despite targeting locations with no known PFAS sources (Santangelo and others, 2022). The widespread distribution of PFAS concentrations in New Hampshire has since sparked critical interest into understanding whether recharge to groundwater contains significant concentrations of PFAS after infiltration through soils. To address this concern, the USGS implemented a pilot study designed to evaluate whether PFAS infiltrate through shallow soil into shallow groundwater. Five sites were selected...
Categories: Data;
Tags: Eastern United States,
Environmental Health,
Hydrology,
New Hampshire,
Northeast,
The Maumee River transports huge loads of nitrogen (N) and phosphorus (P) to Lake Erie. The increased concentrations of N and P are causing eutrophication of the lake, creating hypoxic zones, and contributing to phytoplankton blooms. It is hypothesized that the P loads are a major contributor to harmful algal blooms that occur in the western basin of Lake Erie, particularly in summer. The Maumee River has been identified by the United States Environmental Protection Agency as a priority watershed where action needs to be taken to reduce nutrient loads. This study quantified rates of biogeochemical processes affecting downstream flux of N and P by 1) measuring indices of potential sediment P retention and 2) measuring...
These data were collected to support the development of detection and classification algorithms to support Bureau of Ocean Energy Management (BOEM) studies and assessments associated with offshore wind energy production. There are 3 child zip files included in this data release. 01_Codebase.zip contains a codebase for using deep learning to filter images based on the probability of any bird occurrence. It includes instructions and files necessary for training, validating, and testing a machine learning detection algorithm. 02_Imagery.zip contains imagery that were collected using a Partenavia P68 fixed-wing airplane using a PhaseOne iXU-R 180 forward motion compensating 80-megapixel digital frame camera with...
The document describes the field protocol used during the 2019 field season of the distance sampling pilot project on the Yukon Delta.
Categories: Data;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: ANIMALS/VERTEBRATES,
ANIMALS/VERTEBRATES,
ANIMALS/VERTEBRATES,
BIOLOGICAL CLASSIFICATION,
BIOLOGICAL CLASSIFICATION,
This R code (Nest_Cards_QC.R) is used to process the raw legacy nest card data (1985 - 2019) and produce the quality controlled legacy nest card data for analysis and sharing. This code does not quality control the double searched plots (R2 plots in 1995-99), which is done in the file ‘PrepData.R’. Basic operations include transforming missing value to a common code, transforming other variable to match the data dictionary for nest cards, and resolving non-unique observer initials. The code requires the raw data (“1985-2019_Nest Cards_10-24-19_cleaned.xlsx”) and the table of observer names (“Observer Names_1985-2019_10-24-19.xlsx”). The code writes the QC nest card file “Nest_Cards_1985_2019_QC.csv”. Mostly obseration...
Categories: Data,
Software;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: ANIMALS/VERTEBRATES,
ANIMALS/VERTEBRATES,
ANIMALS/VERTEBRATES,
ANIMALS/VERTEBRATES,
ANIMALS/VERTEBRATES,
The data herein are geochemical (from X-Ray fluorescence spectrometry), grain size (percent clay, silt, sand), lithological (loss on ignition data), bathymetric, reconstructed IVT, and radioactive isotopes (14-C, 210-Pb, 226-Ra, and 137-Cs). These data were collected from sediments from Leonard Lake, Mendocino County, California, USA starting in 2014. Together, these data provide evidence for a record of extreme precipitation going back three millennia, showing regional pluvial and drought cycles.
Categories: Data;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: California,
Climatology,
Geography,
Hydrology,
Limnology,
These tables serve as input data for hierarchical models investigating interactions between raven density and Greater Sage-grouse nest success. Observations were recorded over an 11 year time period, spanning from 2009 through 2019. The model is run in JAGS via R, the code is publicly available via the U.S. Geological Survey's GitLab (O'Neil et al. 2023). We recommend not making any changes or edits to the tables unless the user is experienced with hierarchical modeling. References: O'Neil, S.T., Coates, P.S., Webster, S.C., Brussee, B.E., Dettenmaier, S.J., Tull, J.C., Jackson, P.J., Casazza, M.L., and Espinosa, S.P., 2023, Code for a hierarchical model of raven densities linked with sage-grouse nest survival...
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