A digital polygon layer showing waterfowl concentration and staging areas along Maryland's tidal Chesapeake Bay watershed.

Complete dataset that comprised the manuscript, "Shallow lake management enhanced habitat and attracted water birds during fall migration" by Danelle M. Larson and others. The data collection and processing was funded by the Minnesota Department of Natural Resources.

Technological advancements in Global Positioning System (GPS) telemetry markers allow almost real-time observation of waterfowl movements and habitat selection. Telemetry data on ducks marked with GPS transmitters can be used to evaluate performance of remote sensing data (for example, dynamic open-water maps produced by Point Blue Conservation Science) for classifying habitats that are flooded and available for waterfowl. Translating dynamic open-water maps to waterfowl-relevant habitat maps provides a major improvement for wildlife researchers and managers to assist in their assessments of the areas and habitats used by waterfowl as hydrologic conditions change, both temporally and spatially. Suitable habitat maps developed using dynamic water data should accurately and consistently characterize those flooded habitats used by ducks. Because ducks prefer flooded habitats like wetlands and rice fields, duck locations recorded with telemetry technology can be used to validate and enhance maps developed to characterize waterfowl habitats that change temporally with drought or water management. Additionally, high-resolution telemetry data recorded in near real-time can provide information on waterfowl responsiveness to water-management decisions intended to provide adequate habitat for waterfowl. For example, telemetry data can be analyzed to infer duck response to drought in terms of distance traveled to feed and overlap in use of space or habitats by ducks, which have implications for the population dynamics of ducks.

This dataset is a collection of species identification values that pair with cropped images of avian targets.

Data set consists of polygons of impoundments sampled for winter waterfowl use following the Integrated Waterbird Monitoring Protocol on Overflow NWR.

This data package presents legacy data collected during 1985-1987 at Izembek Lagoon on the western end of the Alaska Peninsula. The data were collected in support of a multi-year study focused on aircraft disturbance of waterfowl, particularly Black Brant, Emperor Geese, and Cackling Geese. The study was conducted at Izembek Lagoon and included several study sites within the lagoon: Halfway Point, Grant Point, Outer Marker, Quarter Point, Applegate Cove, Norma Bay, Round Island, and Banding Island. The primary objectives were to 1) define the time periods that are most important for geese, 2) determine if certain portions of Izembek Lagoon are more important than other areas, 3) evaluate factors that influence distribution of geese within Izembek Lagoon, and 4) compare use by geese of Izembek Lagoon with use of other adjacent lagoons to determine relative importance. The raw data are provided in a .CSV format, however, PDF digital scans of original field data sheets, tables, figures, and maps are also available upon request.

There is a growing movement within natural resource management to view wildlife health as a cumulative outcome of many different factors, rather than simply the absence of disease. This inclusive understanding of health opens the door to management options that are more creative than traditional techniques to prevent or mitigate pathogens. The public health field uses a determinants of health framework to understand the physical, social, and cultural systems that impact health at the individual and community levels (National Academies of Sciences, Engineering, and Medicine 2016). Applying a similar framework to wildlife can help managers focus on tangible actions to positively impact wildlife health in the absence of disease (Wittrock et al. 2019). In the south-central United States, changes in water availability and quality resulting from changing temperature, precipitation, and land-use patterns can have significant impacts on the health of migratory birds that depend on wildlife refuges as seasonal habitat. These data were collected in partnership with several U.S. Fish and Wildlife Service National Wildlife Refuges across Oklahoma (n=2), New Mexico (n=3), and Louisiana (n=1, 8 individual refuges administratively grouped within the Southeast Louisiana Refuge Complex) to examine how managers define determinants of health for migratory birds and assess how those determinants may be impacted by local changes in water regimes. This data release contains nineteen (19) related datafiles and their associated metadata. For each participating refuge there are three files: an image file of the final conceptual diagram, a .csv file containing information about the elements in the diagram, and a .csv file containing information about the connections in the diagram. The conceptual diagram of migratory bird health was constructed using information collected through interviews with refuge personnel and supplemented (to gain additional context, when necessary) with refuge management documents and information on their public-facing websites. The diagrams are provided as .jpg files exported from the free system mapping and visualization program Kumu, in which they were created. Each diagram consists of nodes (referred to as elements) and relationships (referred to as connections). The elements and connections represent elements of the refuge system that were highlighted by personnel as playing an important role in migratory bird health or water availability. Detailed, narrative descriptions for each system component are provided in their respective .csv files. There is also a .csv file containing data from a pre-interview survey sent to each refuge to gather basic information about the high-priority migratory bird guilds on their refuge, their management objectives in relation to migratory birds, and the water-related threats they consider highest concern.

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 into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.