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Extrapolating cetacean densities to quantitatively assess human impacts on populations in the high seas

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  • Journal Title:
    Conservation Biology
  • Personal Author:
  • NOAA Program & Office:
    NMFS (National Marine Fisheries Service)
  • Description:
    As human activities expand beyond national jurisdictions to the high seas, there is an increasing need to consider anthropogenic impacts to species inhabiting these waters. The current scarcity of scientific observations of cetaceans in the high seas impedes the assessment of population‐level impacts of these activities. We developed plausible density estimates to facilitate a quantitative assessment of anthropogenic impacts on cetacean populations in these waters. Our study region extended from a well‐surveyed region within the U.S. Exclusive Economic Zone into a large region of the western North Atlantic sparsely surveyed for cetaceans. We modeled densities of 15 cetacean taxa with available line transect survey data and habitat covariates and extrapolated predictions to sparsely surveyed regions. We formulated models to reduce the extent of extrapolation beyond covariate ranges, and constrained them to model simple and generalizable relationships. To evaluate confidence in the predictions, we mapped where predictions were made outside sampled covariate ranges, examined alternate models, and compared predicted densities with maps of sightings from sources that could not be integrated into our models. Confidence levels in model results depended on the taxon and geographic area and highlighted the need for additional surveying in environmentally distinct areas. With application of necessary caution, our density estimates can inform management needs in the high seas, such as the quantification of potential cetacean interactions with military training exercises, shipping, fisheries, and deep‐sea mining and be used to delineate areas of special biological significance in international waters. Our approach is generally applicable to other marine taxa and geographic regions for which management will be implemented but data are sparse.
  • Keywords:
  • Source:
    Conserv Biol. 2017 Jun; 31(3): 601–614.
  • DOI:
  • Pubmed ID:
    27775847
  • Pubmed Central ID:
    PMC5435923
  • Document Type:
    Journal Article
  • Rights Information:
    CC BY-NC-ND
  • Compliance:
    PMC
  • Main Document Checksum:
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