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.

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.

In this data set, records (rows) represent GPS locations of ducks marked with telemetry in California and whether locations were overlapping (within 300 m of) locations of marked ducks in other consecutive years (2015-16, 2016-17, and 2017-18) during October - March. Years 2015-16, 2016-17, and 2017-18 represented drought, non-drought, and non-drought, respectively. Matchett and company (2020; see Larger Work section for citation) summarized this data set in tables E3 and E4 to compare overlap of duck locations between consecutive years to investigate interannual habitat stability in relationship with drought, habitat management (daytime roosts and night feeding sites), and in two regions (Suisun Marsh and California except Suisun Marsh). Coincident use of space by ducks across years suggests that the landscape is relatively stable, in terms of where and when flooding occurs, or that birds are actively selecting those portions of the landscape that are consistently flooded even in drought years. We additionally thought that areas used in daytime relative to night would be more consistent across years because of reliable water management for sanctuaries on wildlife areas and national refuges used as daytime roosts. We also hypothesized that areas used in Suisun Marsh would be more consistent across years because water availability is less limited by drought in Suisun and most habitats are flooded each year. Data set columns refer to temporal and spatial attributes of locations in relationship with overlapping duck locations. Column 1 is Region (Suisun Marsh or California excluding Suisun Marsh) where locations were recorded, column 2 is Time of day (day or night) that locations were recorded, and column 3 is Year of use (2015-16, 2016-17, 2017-18) referencing a year's locations being compared with all locations recorded in the other two years. Columns 4-6 (Year 2015-16, Year 2016-17, Year 2017-18) reference the years being compared with Year of use to determine interannual overlap in space use.

In this data set, records (rows) represent GPS locations of ducks marked with telemetry in California and whether locations were overlapping (within 300 m of) locations of marked ducks in other consecutive years (2015-16, 2016-17, and 2017-18) during October - March. Years 2015-16, 2016-17, and 2017-18 represented drought, non-drought, and non-drought, respectively. Matchett and company (2020; see Larger Work section for citation) summarized this data set in tables E3 and E4 to compare overlap of duck locations between consecutive years to investigate interannual habitat stability in relationship with drought, habitat management (daytime roosts and night feeding sites), and in two regions (Suisun Marsh and California except Suisun Marsh). Coincident use of space by ducks across years suggests that the landscape is relatively stable, in terms of where and when flooding occurs, or that birds are actively selecting those portions of the landscape that are consistently flooded even in drought years. We additionally thought that areas used in daytime relative to night would be more consistent across years because of reliable water management for sanctuaries on wildlife areas and national refuges used as daytime roosts. We also hypothesized that areas used in Suisun Marsh would be more consistent across years because water availability is less limited by drought in Suisun and most habitats are flooded each year. Data set columns refer to temporal and spatial attributes of locations in relationship with overlapping duck locations. Column 1 is Region (Suisun Marsh or California excluding Suisun Marsh) where locations were recorded, column 2 is Time of day (day or night) that locations were recorded, and column 3 is Year of use (2015-16, 2016-17, 2017-18) referencing a year's locations being compared with all locations recorded in the other two years. Columns 4-6 (Year 2015-16, Year 2016-17, Year 2017-18) reference the years being compared with Year of use to determine interannual overlap in space use.

This dataset describes mesopredator locations (raccoons and striped skunks), dabbling duck nest locations (mallard, gadwall, and cinnamon teal), northern harrier nest locations, and random locations in relation to a set of habitat features in Suisun Marsh, California during 2016 through 2019. We designed a series of questions to link fine-scale predator movements and vulnerability of dabbling duck nests to predator encounters and egg predation. We conducted our study over a 4-year period using 41 GPS-collared raccoons and striped skunks, two of the most widespread mammalian predators of waterfowl eggs in North America, and more than 2000 monitored duck nests. Each night of movement for mesopredators contains the distance between successive locations (each unique night of foraging is identified using the julian date on which the night started), which can be added up to obtain an estimate for the distance traveled per night in between day resting sites. The dataset also includes a set of dabbling duck nests and distances to habitat features that were used to examine the probability of a nest being discovered and depredated by a predator. This dataset also includes a set of nests found within the home ranges of the GPS-collared raccoons and skunks that encountered the most duck nests in the core upland nesting area and whether each nest was encountered or not on a given night by a collared animal. These nest encounters were used to examine the probability of encounter by a collared predator in relation to a set of habitat features.

This dataset describes mesopredator locations (raccoons and striped skunks), dabbling duck nest locations (mallard, gadwall, and cinnamon teal), northern harrier nest locations, and random locations in relation to a set of habitat features in Suisun Marsh, California during 2016 through 2019. We designed a series of questions to link fine-scale predator movements and vulnerability of dabbling duck nests to predator encounters and egg predation. We conducted our study over a 4-year period using 41 GPS-collared raccoons and striped skunks, two of the most widespread mammalian predators of waterfowl eggs in North America, and more than 2000 monitored duck nests. Each night of movement for mesopredators contains the distance between successive locations (each unique night of foraging is identified using the julian date on which the night started), which can be added up to obtain an estimate for the distance traveled per night in between day resting sites. The dataset also includes a set of dabbling duck nests and distances to habitat features that were used to examine the probability of a nest being discovered and depredated by a predator. This dataset also includes a set of nests found within the home ranges of the GPS-collared raccoons and skunks that encountered the most duck nests in the core upland nesting area and whether each nest was encountered or not on a given night by a collared animal. These nest encounters were used to examine the probability of encounter by a collared predator in relation to a set of habitat features.

This dataset includes wetland habitat measurements, surrounding land cover metrics, and waterbird abundance and diversity during 240 surveys of 117 individual wetland units enrolled in the California Waterfowl Habitat Program. This dataset was generated from wetland surveys conducted in the Sacramento Valley, San Joaquin Valley, and Suisun Marsh in 2022 and 2023.

These data describe bird locations obtained from backpack-mounted GPS loggers on several species of waterfowl and of Northern Harrier captured and marked in California and Oregon. Both cursory and opportunistic quality control have been performed on these data during the course of data collection. These methods include identification of mortalities and translational errors in digital transmission of data resulting in gross positional or temporal errors such as transposed digits.

We used Point Blue Conservation Science's dynamic open-water dataset of water distribution and our telemetry data for duck locations to develop frequently updated habitat maps for the Central Valley and Suisun Marsh in California during October-March of 2014-15 through 2017-18. Telemetry data additionally was used to compare performance of each of three series of habitat maps produced. To create this tabular dataset, we intersected telemetry locations for ducks (vector point data) with habitat maps (raster mosaics) in a Geographic Information System (GIS) and attributed duck locations with map pixel values representing habitat, non-habitat, or unclassified (if data were missing). To develop maps of waterfowl habitat, we used open water data (version without cloud-filling) publicly available on Point Blue Conservation's California Water Tracker web site and which Point Blue Conservation Science derived at 16-18 day intervals from mosaics of Landsat 8 imagery for the region including the Central Valley and Suisun Marsh. Each record in the data set represents a duck location by species (column 1) bound to the spatial extent of the Central Valley and Suisun Marsh for October-March of 2014-15 through 2017-18. Columns 2 - 4 represent attributes assigned to duck locations by each of the three maps that were developed and assessed for accuracy. We used the data set to summarize and compare the proportion of duck locations in habitat for the three maps. We further summarized proportions in habitat by duck species (Mallard versus Northern Pintail) and biological activity (for example, feeding and roosting versus molting).

We used Point Blue Conservation Science's dynamic open-water dataset of water distribution and our telemetry data for duck locations to develop frequently updated habitat maps for the Central Valley and Suisun Marsh in California during October-March of 2014-15 through 2017-18. Telemetry data additionally was used to compare performance of each of three series of habitat maps produced. To create this tabular dataset, we intersected telemetry locations for ducks (vector point data) with habitat maps (raster mosaics) in a Geographic Information System (GIS) and attributed duck locations with map pixel values representing habitat, non-habitat, or unclassified (if data were missing). To develop maps of waterfowl habitat, we used open water data (version without cloud-filling) publicly available on Point Blue Conservation's California Water Tracker web site and which Point Blue Conservation Science derived at 16-18 day intervals from mosaics of Landsat 8 imagery for the region including the Central Valley and Suisun Marsh. Each record in the data set represents a duck location by species (column 1) bound to the spatial extent of the Central Valley and Suisun Marsh for October-March of 2014-15 through 2017-18. Columns 2 - 4 represent attributes assigned to duck locations by each of the three maps that were developed and assessed for accuracy. We used the data set to summarize and compare the proportion of duck locations in habitat for the three maps. We further summarized proportions in habitat by duck species (Mallard versus Northern Pintail) and biological activity (for example, feeding and roosting versus molting).