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Data

Northern Prairie produces objective, high-quality data that is relevant to decision-makers, resource managers, the scientific community, and the public. Information on all USGS datasets can be found at the USGS Science Data Catalog.

Filter Total Items: 108

Whooping crane migration habitat selection disturbance data and maps

These data and maps were developed to support an effort to understand how whooping cranes (Grus americana) select stopover habitat in the presence of human infrastructure. Location and associated data came from whooping cranes from the Aransas-Wood Buffalo Population, 2010–2016. We marked a sample of 57 whooping cranes with leg-mounted transmitters that acquired locations via the global positionin

10-year (2003-2012) bird and vegetation data collected at wind facilities in North Dakota and South Dakota

This data release contains eight datasets that represent the entirety of the data collected for a study that examined breeding-bird densities in native mixed-grass prairie from 2003 to 2012 at and adjacent to wind facilities in North Dakota and South Dakota, USA. Data were collected to determine breeding-bird density per 100 hectares (ha) by distance bands from turbines and by excluding habitat th

Wolf noninvasive methods trial from 2019-2021 in the Superior National Forest metadata

This dataset contains gray wolf (Canis lupus) study area section counts of pack wolves by method (observing radiocollared wolves and their packmates via aerial telemetry and also noninvasive methods including ground snow tracking, aerial snow tracking, camera trapping, community scientist reports) from a three winter noninvasive methods trial during 2019-2021 in the USGS Wolf Project study area (2

Water and ice characteristics from Hobart Lake National Wildlife Refuge, Barnes County, North Dakota, USA, 2021

This data release presents data that were collected as part of a larger effort to refine knowledge pertaining to the origin, composition, and seasonality of dissolved organic matter in lakes. This work was part of an international collaborative effort with the Global Lake Ecological Observatory Network (GLEON). Water samples were collected monthly during 2021 and shipped to GLEON for determination

Cottonwood Lake Study Area - Water Surface Elevations (ver. 2.0)

This dataset contains water surface elevations for the wetlands in the Cottonwood Lake Study Area, Stutsman County, North Dakota.

Cottonwood Lake Study Area - Specific Conductance (ver. 2.0)

This dataset contains the specific conductance values of water for all wetlands in the Cottonwood Lake Study Area, Stutsman County, North Dakota.

Cottonwood Lake Study Area - Groundwater Elevations (ver. 2.0)

This dataset contains discrete groundwater elevation measurements for wells in the Cottonwood Lake Study Area, Stutsman County, North Dakota.

Cottonwood Lake Study Area - Breeding Birds (ver. 2.0)

This dataset contains the number of breeding pairs of bird species surveyed in all Cottonwood Lake Study Area wetlands.

Cottonwood Lake Study Area - Amphibians (ver. 2.0)

This dataset contains the number of captured individuals recorded for each amphibian species surveyed in all wetlands in the Cottonwood Lake Study Area, Stutsman County, North Dakota. While amphibians were the target of the surveys, reptiles (i.e., turtles, snakes) were also captured over the course of the surveys and are included in the data file.

Cottonwood Lake Study Area - Invertebrate Counts (ver. 2.0)

This dataset contains macroinvertebrate sampling data for the wetlands in the Cottonwood Lake Study Area, Stutsman County, North Dakota.

Cottonwood Lake Study Area - Invertebrate Weights (ver. 2.0)

This dataset contains macroinvertebrate sampling data (weights) for wetlands in the Cottonwood Lake Study Area, Stutsman County, North Dakota.

Methane flux model for wetlands of the Prairie Pothole Region of North America: Model input data and programming code

This data release presents input data for plot- and landscape-scale models of Prairie Pothole Region wetland methane emissions as a function of explanatory variables and remotely sensed predictors. Field data for the plot- and landscape-scale models span the years 2003-2016 and 2005-2016, respectively. The data release also includes R programming code to run the generalized additive model (GAM; pl