Atlantic coast piping plover (Charadrius melodus) nest sites are typically found on low-lying beach and dune systems, which respond rapidly to coastal processes like sediment overwash, inlet formation, and island migration that are sensitive to climate-related changes in storminess and the rate of sea-level rise. Data were obtained to understand piping plover habitat distribution and use along their Atlantic Coast breeding range. A smartphone application called iPlover was developed to collect standardized data on habitat characteristics at piping plover nest locations. The application capitalized on a network of trained monitors that observe piping plovers throughout their U.S. Atlantic coast breeding range as part of the species’ recovery plan. Monitors used iPlover to document nest locations as well as randomly distributed points at beaches and barrier islands over ~1500 km of coast between Maine and North Carolina, USA. This work is one component of a larger research and management program that seeks to understand and sustain ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise. Tabular digital data generated by field data collection with iPlover with accompanying site photographs in JPEG format are presented in this data release.

Atlantic coast piping plover (Charadrius melodus) nest sites are typically found on low-lying beach and dune systems, which respond rapidly to coastal processes like sediment overwash, inlet formation, and island migration that are sensitive to climate-related changes in storminess and the rate of sea-level rise. Data were obtained to understand piping plover habitat distribution and use along their Atlantic Coast breeding range. A smartphone application called iPlover was developed to collect standardized data on habitat characteristics at piping plover nest locations. The application capitalized on a network of trained monitors that observe piping plovers throughout their U.S. Atlantic coast breeding range as part of the species’ recovery plan. Monitors used iPlover to document nest locations as well as randomly distributed points at beaches and barrier islands over ~1500 km of coast between Maine and North Carolina, USA. This work is one component of a larger research and management program that seeks to understand and sustain ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise. Tabular digital data generated by field data collection with iPlover with accompanying site photographs in JPEG format are presented in this data release.

This dataset includes XY coordinates and species and behavioral observations of birds detected in aerial imagery captured over coastal islands along Maine's coast during 30 May. - 3 June 2019. The Partenavia P.68 Observer plane flew over 268 islands within the Gulf of Maine (GOM). The plane flew at an altitude of 310 m above ground level and was equipped with a PhaseOne iXU-RS1000 (100 megapixel) multispectral camera and 70 mm lens to capture 4-band (red, green, blue, (RGB) and near-infrared (NIR) light) imagery with a ground sampling distance of 2 cm/px. Timing for the aerial image collection was selected to reflect peak nesting periods for the focal species (Herring Gull, HERG; Great Black-backed Gull, GBBG; Double-crested Cormorant, DCCO; Common Eider, COEI; and terns, Tern spp.), during which one parent is typically present on the nest. Imagery was captured during daylight in clear weather (i.e., no rain or high winds) for ten separate surveys beginning at different times and with various flight path extents. Although the target was colonial nesting seabirds, any birds that could be identified were noted in the dataset. All images were orthorectified, georeferenced, and mosaicked into images spanning one or more islands. Three observers reviewed the images, identified detected birds to species and behavior, and reconciled any differences. The dataset includes the detections recorded by each observer and the reconciled species and behavior identification. Summaries of the methods and final counts are presented in two theses: Kline, Logan R. 2022. Characteristics contributing to uncertainty in image-based artificial intelligence classifications of colonial nesting birds. M.S. Thesis, Ecology and Environmental Sciences, University of Maine, Orono. 118 pp. Lewis, Meredith A. 2022. A bird's eye view: observer uncertainty in aerial image counts of colonial seabirds and an assessment of the status of coastal island gull and cormorant populations. M.S. Thesis, Ecology and Environmental Sciences, University of Maine, Orono. 113 pp.

Several bureaus within the Department of Interior compiled available information from seabird observation datasets from the Atlantic Outer Continental Shelf into a single database, with the goal of conducting research and informing coastal and offshore planning activities. The cooperators were the Bureau of Ocean Energy Management's (BOEM) Environmental Studies Program (www.boem.gov/Environmental-Stewardship/Environmental-Studies/Environmental-Studies.aspx), the U.S. Fish and Wildlife Service's (USFWS) Division of Migratory Bird Management (www.fws.gov/migratorybirds/) and the U.S. Geological Survey's (USGS) Patuxent Wildlife Research Center (www.pwrc.usgs.gov). The resulting product is the Atlantic Offshore Seabird Dataset Catalog, which characterizes the survey effort and bird observations that have been collected across space and time. As of December 2013, the database contains over 70 datasets from 1906-2013 with about 300,000 records of seabird observations. The data is comprised of roughly 50 datasets from 1938-2013 with about 260,000 observation records. This archive is a subset of the main database, excluding datasets from surveys where the scientific design was not specifically designed to sample marine birds (e.g., coastal portions of National Audubon Society's Christmas Bird Counts). The full archive of scientific data contains information on individual observations as well as survey effort. Each observation record has a unique point location, date and time, species and observation count. There may also be biological information related to the sighting, such as animal age or behavior. The survey effort information (i.e., weather variables) may have been recorded for each individual observation but was more often recorded at the transect (line along which the plane or boat traveled) level. The dataset contains data primarily for seabirds, but some other observations accompanied bird data submissions and were not discarded: marine mammals, turtles, fish, and non-biological sightings such as other boats, fishing gear and trash. The data is in CSV format, with an associated file detailing the data structure in CSV format. A detailed metadata record in Federal Geographic Data Committee (FGDC) format and a final report in .pdf format is included with these data. Data use must take into account use constraints (data limitations) listed within the included metadata record, and cite the Atlantic Offshore Seabird Dataset Catalog, USGS, 2013.

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.

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.