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  • 1
    Publication Date: 2022-10-27
    Description: © The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Zeigler, S. L., Gutierrez, B. T., Hecht, A., Plant, N. G., & Sturdivant, E. J. Piping plovers demonstrate regional differences in nesting habitat selection patterns along the U. S. Atlantic coast. Ecosphere, 12(3), (2021): e03418, https://doi.org/10.1002/ecs2.3418.
    Description: Habitat studies that encompass a large portion of a species’ geographic distribution can explain characteristics that are either consistent or variable, further informing inference from more localized studies and improving management successes throughout the range. We identified landscape characteristics at Piping Plover nests at 21 sites distributed from Massachusetts to North Carolina and compared habitat selection patterns among the three designated U.S. recovery units (New England, New York–New Jersey, and Southern). Geomorphic setting, substrate type, and vegetation type and density were determined in situ at 928 Piping Plover nests (hereafter, used resource units) and 641 random points (available resource units). Elevation, beach width, Euclidean distance to ocean shoreline, and least-cost path distance to low-energy shorelines with moist substrates (commonly used as foraging habitat) were associated with used and available resource units using remotely sensed spatial data. We evaluated multivariate differences in habitat selection patterns by comparing recovery unit-specific Bayesian networks. We then further explored individual variables that drove disparities among Bayesian networks using resource selection ratios for categorical variables and Welch’s unequal variances t-tests for continuous variables. We found that relationships among variables and their connections to habitat selection were similar among recovery units, as seen in commonalities in Bayesian network structures. Furthermore, nesting Piping Plovers consistently selected mixed sand and shell, gravel, or cobble substrates as well as areas with sparse or no vegetation, irrespective of recovery unit. However, we observed significant differences among recovery units in the elevations, distances to ocean, and distances to low-energy shorelines of used resource units. Birds also exhibited increased selectivity for overwash habitats and for areas with access to low-energy shorelines along a latitudinal gradient from north to south. These results have important implications for conservation and management, including assessment of shoreline stabilization and habitat restoration planning as well as forecasting effects of climate change.
    Description: Funding for this work was provided by the North Atlantic Landscape Conservation Cooperative and U.S. Fish and Wildlife Service through a U.S. Geological Survey Mendenhall Fellowship to Zeigler. All other funding was through the U.S. Geological Survey (Zeigler, Gutierrez, Plant, and Sturdivant) and the U.S. Fish and Wildlife Service (Hecht). Zeigler, Plant, and Hecht conceived and designed the study and secured funding.
    Keywords: Barrier islands ; Bayesian network ; Charadrius melodus ; Coastal ecosystems ; Early successional habitat ; Habitat selection ; Piping Plovers
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 2
    Publication Date: 2022-05-26
    Description: © The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Wildlife Society Bulletin 41 (2017): 666–677, doi:10.1002/wsb.820.
    Description: Understanding patterns of habitat selection across a species’ geographic distribution can be critical for adequately managing populations and planning for habitat loss and related threats. However, studies of habitat selection can be time consuming and expensive over broad spatial scales, and a lack of standardized monitoring targets or methods can impede the generalization of site-based studies. Our objective was to collaborate with natural resource managers to define available nesting habitat for piping plovers (Charadrius melodus) throughout their U.S. Atlantic coast distribution from Maine to North Carolina, with a goal of providing science that could inform habitat management in response to sea-level rise. We characterized a data collection and analysis approach as being effective if it provided low-cost collection of standardized habitat-selection data across the species’ breeding range within 1–2 nesting seasons and accurate nesting location predictions. In the method developed, 〉30 managers and conservation practitioners from government agencies and private organizations used a smartphone application, “iPlover,” to collect data on landcover characteristics at piping plover nest locations and random points on 83 beaches and barrier islands in 2014 and 2015. We analyzed these data with a Bayesian network that predicted the probability a specific combination of landcover variables would be associated with a nesting site. Although we focused on a shorebird, our approach can be modified for other taxa. Results showed that the Bayesian network performed well in predicting habitat availability and confirmed predicted habitat preferences across the Atlantic coast breeding range of the piping plover. We used the Bayesian network to map areas with a high probability of containing nesting habitat on the Rockaway Peninsula in New York, USA, as an example application. Our approach facilitated the collation of evidence-based information on habitat selection from many locations and sources, which can be used in management and decision-making applications. © 2017 The Authors. Wildlife Society Bulletin published by Wiley Periodicals, Inc. on behalf of The Wildlife Society.
    Description: U.S. Fish and Wildlife Service; U.S. Department of Interior Hurricane Sandy Recovery Program; North Atlantic Landscape Conservation Cooperative; U.S. Geological Survey Coastal and Marine Geology Program
    Keywords: Atlantic coast ; Barrier islands ; Bayesian network ; Charadrius melodus ; Coastal geomorphology ; Habitat availability ; iPlover ; Nesting
    Repository Name: Woods Hole Open Access Server
    Type: Article
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