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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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The Eastern Brown Pelican (Pelecanus occidentalis carolinensis) is a large-bodied seabird that nests in colonies of 10 to upwards of 5,000 pairs, on nearshore islands in subtropical and tropical North American waters. It breeds between March and August, laying 2–3 eggs and raising 1–2 chicks per year. The species is facultatively migratory during nonbreeding, with some individuals remaining resident and others leaving breeding areas. Pelicans forage in near- and offshore waters and capture schooling fish by plunge-diving. This dataset summarizes diet composition and mass of meals delivered to chicks throughout the chick-rearing period, from hatch (late April) through fledging (early August) in 10 breeding colonies...
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The Eastern Brown Pelican (Pelecanus occidentalis carolinensis) is a large-bodied seabird that nests in colonies of 10 to upwards of 5,000 pairs, on nearshore islands in subtropical and tropical North American waters. It breeds between March and August, laying 2–3 eggs and raising 1–2 chicks per year. The species is facultatively migratory during nonbreeding, with some individuals remaining resident and others leaving breeding areas. Pelicans forage in near- and offshore waters and capture schooling fish by plunge-diving. This dataset summarizes provisioning rates of Brown Pelicans throughout the chick-rearing period, from hatch (late April) through fledging (early August), in 8 colonies in Texas (2014), Alabama,...
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The Eastern Brown Pelican (Pelecanus occidentalis carolinensis) is a large-bodied seabird that nests in colonies of 10 to upwards of 5,000 pairs, on nearshore islands in subtropical and tropical North American waters. It breeds between March and August, laying 2–3 eggs and raising 1–2 chicks per year. The species is facultatively migratory during nonbreeding, with some individuals remaining resident and others leaving breeding areas. Pelicans forage in near- and offshore waters and capture schooling fish by plunge-diving. This dataset summarizes taxonomical information for all prey species identified in the diet of Brown Pelican collected in 2013-2015 at 10 breeding colonies in the northern Gulf of Mexico from...
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Funding for this study was provided by the Bureau of Ocean Energy Management and U.S. Geological Survey (Interagency Agreement no. M12PG00014). This study focuses on obtaining information about populations of Brown Pelican (Pelecanus occidentalis carolinensis) across the northern Gulf of Mexico. Study objectives are to (1) document dispersal, seasonal and annual movements, seasonal home range, and site fidelity of marked adult Brown Pelicans among nesting colonies from the Gulf coast, (2) compare contaminant exposure risk, contaminant levels, and health parameters in adult and nestling Brown Pelicans from various colony sites, and (3) document the relationship of local environmental, nest site characteristics and...


map background search result map search result map Spatial and physiological ecology of Brown Pelican in the Gulf of Mexico Composition of diet of juvenile Brown Pelican in the northern Gulf of Mexico (2013-2015) Provisioning rate of Brown Pelican in the northern Gulf of Mexico (2014-2015) Reference_Taxonomical identification of Brown Pelican prey in the northern Gulf of Mexico (2013-2015) DisMOSH, Cost, MOSHShoreline: Distance to foraging areas for piping plovers (foraging shoreline, cost mask, and least-cost path distance): Cedar Island, VA, 2010–2011 DisMOSH, Cost, MOSHShoreline: Distance to foraging areas for piping plovers (foraging shoreline, cost mask, and least-cost path distance): Cedar Island, VA, 2012–2013 DisMOSH, Cost, MOSHShoreline: Distance to foraging areas for piping plovers (foraging shoreline, cost mask, and least-cost path distance): Edwin B. Forsythe NWR, NJ, 2012 DisMOSH, Cost, MOSHShoreline: Distance to foraging areas for piping plovers (foraging shoreline, cost mask, and least-cost path distance): Edwin B. Forsythe NWR, NJ, 2013–2014 DisMOSH, Cost, MOSHShoreline: Distance to foraging areas for piping plovers (foraging shoreline, cost mask, and least-cost path distance): Fire Island, NY, 2012 DisMOSH, Cost, MOSHShoreline: Distance to foraging areas for piping plovers (foraging shoreline, cost mask, and least-cost path distance): Fire Island, NY, 2014–2015 DisMOSH, Cost, MOSHShoreline: Distance to foraging areas for piping plovers (foraging shoreline, cost mask, and least-cost path distance): Rockaway Peninsula, NY, 2010–2011 DisMOSH, Cost, MOSH_Shoreline: Distance to foraging areas for piping plovers including foraging shoreline, cost mask, and least-cost path distance: Coast Guard Beach, MA, 2013-2014 DisMOSH, Cost, MOSH_Shoreline: Distance to foraging areas for piping plovers including foraging shoreline, cost mask, and least-cost path distance: Monomoy Island, MA, 2013-2014 DisMOSH, Cost, MOSH_Shoreline: Distance to foraging areas for piping plovers including foraging shoreline, cost mask, and least-cost path distance: Cape Lookout, NC, 2014 DisMOSH, Cost, MOSH_Shoreline: Distance to foraging areas for piping plovers including foraging shoreline, cost mask, and least-cost path distance: Cobb Island, VA, 2014 DisMOSH, Cost, MOSH_Shoreline: Distance to foraging areas for piping plovers including foraging shoreline, cost mask, and least-cost path distance: Metompkin Island, VA, 2014 DisMOSH, Cost, MOSH_Shoreline: Distance to foraging areas for piping plovers including foraging shoreline, cost mask, and least-cost path distance: Myrtle Island, VA, 2014 DisMOSH, Cost, MOSH_Shoreline: Distance to foraging areas for piping plovers including foraging shoreline, cost mask, and least-cost path distance: Ship Shoal Island, VA, 2014 DisMOSH, Cost, MOSH_Shoreline: Distance to foraging areas for piping plovers including foraging shoreline, cost mask, and least-cost path distance: Smith Island, VA, 2014 DisMOSH, Cost, MOSH_Shoreline: Distance to foraging areas for piping plovers including foraging shoreline, cost mask, and least-cost path distance: Wreck Island, VA, 2014 DisMOSH, Cost, MOSH_Shoreline: Distance to foraging areas for piping plovers including foraging shoreline, cost mask, and least-cost path distance: Wreck Island, VA, 2014 DisMOSH, Cost, MOSH_Shoreline: Distance to foraging areas for piping plovers including foraging shoreline, cost mask, and least-cost path distance: Ship Shoal Island, VA, 2014 DisMOSH, Cost, MOSH_Shoreline: Distance to foraging areas for piping plovers including foraging shoreline, cost mask, and least-cost path distance: Myrtle Island, VA, 2014 DisMOSH, Cost, MOSH_Shoreline: Distance to foraging areas for piping plovers including foraging shoreline, cost mask, and least-cost path distance: Cobb Island, VA, 2014 DisMOSH, Cost, MOSH_Shoreline: Distance to foraging areas for piping plovers including foraging shoreline, cost mask, and least-cost path distance: Smith Island, VA, 2014 DisMOSH, Cost, MOSH_Shoreline: Distance to foraging areas for piping plovers including foraging shoreline, cost mask, and least-cost path distance: Monomoy Island, MA, 2013-2014 DisMOSH, Cost, MOSHShoreline: Distance to foraging areas for piping plovers (foraging shoreline, cost mask, and least-cost path distance): Rockaway Peninsula, NY, 2010–2011 DisMOSH, Cost, MOSHShoreline: Distance to foraging areas for piping plovers (foraging shoreline, cost mask, and least-cost path distance): Edwin B. Forsythe NWR, NJ, 2012 DisMOSH, Cost, MOSHShoreline: Distance to foraging areas for piping plovers (foraging shoreline, cost mask, and least-cost path distance): Edwin B. Forsythe NWR, NJ, 2013–2014 DisMOSH, Cost, MOSHShoreline: Distance to foraging areas for piping plovers (foraging shoreline, cost mask, and least-cost path distance): Fire Island, NY, 2012 DisMOSH, Cost, MOSHShoreline: Distance to foraging areas for piping plovers (foraging shoreline, cost mask, and least-cost path distance): Fire Island, NY, 2014–2015 DisMOSH, Cost, MOSH_Shoreline: Distance to foraging areas for piping plovers including foraging shoreline, cost mask, and least-cost path distance: Cape Lookout, NC, 2014 Composition of diet of juvenile Brown Pelican in the northern Gulf of Mexico (2013-2015) Provisioning rate of Brown Pelican in the northern Gulf of Mexico (2014-2015) Reference_Taxonomical identification of Brown Pelican prey in the northern Gulf of Mexico (2013-2015) Spatial and physiological ecology of Brown Pelican in the Gulf of Mexico