ALBERT

All Library Books, journals and Electronic Records Telegrafenberg

feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    Publication Date: 2022-05-25
    Description: © The Author(s), 2014. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Ecological Modelling 276 (2014): 38–50, doi:10.1016/j.ecolmodel.2014.01.005.
    Description: Sea-level rise and human development pose significant threats to shorebirds, particularly for species that utilize barrier island habitat. The piping plover (Charadrius melodus) is a federally-listed shorebird that nests on barrier islands and rapidly responds to changes in its physical environment, making it an excellent species with which to model how shorebird species may respond to habitat change related to sea-level rise and human development. The uncertainty and complexity in predicting sea-level rise, the responses of barrier island habitats to sea-level rise, and the responses of species to sea-level rise and human development necessitate a modeling approach that can link species to the physical habitat features that will be altered by changes in sea level and human development. We used a Bayesian network framework to develop a model that links piping plover nest presence to the physical features of their nesting habitat on a barrier island that is impacted by sea-level rise and human development, using three years of data (1999, 2002, and 2008) from Assateague Island National Seashore in Maryland. Our model performance results showed that we were able to successfully predict nest presence given a wide range of physical conditions within the model's dataset. We found that model predictions were more successful when the ranges of physical conditions included in model development were varied rather than when those physical conditions were narrow. We also found that all model predictions had fewer false negatives (nests predicted to be absent when they were actually present in the dataset) than false positives (nests predicted to be present when they were actually absent in the dataset), indicating that our model correctly predicted nest presence better than nest absence. These results indicated that our approach of using a Bayesian network to link specific physical features to nest presence will be useful for modeling impacts of sea-level rise or human-related habitat change on barrier islands. We recommend that potential users of this method utilize multiple years of data that represent a wide range of physical conditions in model development, because the model performed less well when constructed using a narrow range of physical conditions. Further, given that there will always be some uncertainty in predictions of future physical habitat conditions related to sea-level rise and/or human development, predictive models will perform best when developed using multiple, varied years of data input.
    Description: Funding for the research presented in this paper was provided by the North Atlantic Landscape Conservation Cooperative and the U.S. Geological Survey.
    Keywords: Bayesian network ; Development ; Habitat ; Piping plover ; Sea-level rise ; Shorebird
    Repository Name: Woods Hole Open Access Server
    Type: Article
    Format: application/pdf
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 2
    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
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 3
    Publication Date: 2022-05-26
    Description: © The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Journal of Geophysical Research: Earth Surface 120 (2015): 2452–2475, doi:10.1002/2015JF003671.
    Description: Quantifying geomorphic variability of coastal environments is important for understanding and describing the vulnerability of coastal topography, infrastructure, and ecosystems to future storms and sea level rise. Here we use a Bayesian network (BN) to test the importance of multiple interactions between barrier island geomorphic variables. This approach models complex interactions and handles uncertainty, which is intrinsic to future sea level rise, storminess, or anthropogenic processes (e.g., beach nourishment and other forms of coastal management). The BN was developed and tested at Assateague Island, Maryland/Virginia, USA, a barrier island with sufficient geomorphic and temporal variability to evaluate our approach. We tested the ability to predict dune height, beach width, and beach height variables using inputs that included longer-term, larger-scale, or external variables (historical shoreline change rates, distances to inlets, barrier width, mean barrier elevation, and anthropogenic modification). Data sets from three different years spanning nearly a decade sampled substantial temporal variability and serve as a proxy for analysis of future conditions. We show that distinct geomorphic conditions are associated with different long-term shoreline change rates and that the most skillful predictions of dune height, beach width, and beach height depend on including multiple input variables simultaneously. The predictive relationships are robust to variations in the amount of input data and to variations in model complexity. The resulting model can be used to evaluate scenarios related to coastal management plans and/or future scenarios where shoreline change rates may differ from those observed historically.
    Description: U.S. Geological Survey (USGS) Coastal and Marine Geology Program; U.S. Fish and Wildlife Service
    Keywords: Barrier island ; Coastal evolution ; Bayesian network ; Assateague Island ; Skill ; Validation
    Repository Name: Woods Hole Open Access Server
    Type: Article
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 4
    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
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...