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Endangered Species Research

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ESR 15:39-52 (2011)  -  DOI:

Spatial models of sparse data to inform cetacean conservation planning: an example from Oman

Peter J. Corkeron1,2,3,8,*, Gianna Minton,4,5,Tim Collins4,6, Ken Findlay7, Andrew Willson4, Robert Baldwin

1Integrated Statistics, Woods Hole, Massachusetts 02543, USA
2Bioacoustics Research Program, Cornell Lab of Ornithology, Ithaca, New York 14850, USA
3The New England Aquarium, Central Wharf, Boston, Massachusetts 02110-3399, USA
4Environment Society of Oman, Ruwi, Sultanate of Oman
5Sarawak Dolphin Project, Institute of Biodiversity and Environmental Conservation, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia
6Ocean Giants Program, Wildlife Conservation Society, Bronx, New York 10460-1099, USA
7MaRe, Oceanography Department, University of Cape Town, Rondebosch 7701, South Africa
8Present address: NOAA Northeast Fisheries Science Center, Woods Hole, Massachusetts 02543, USA

ABSTRACT: Habitat models are tools for understanding the relationship between cetaceans and their environment, from which patterns of the animals’ space use can be inferred and management strategies developed. Can working with space use alone be sufficient for management, when habitat cannot be modeled? Here, we analyzed cetacean sightings data collected from small boat surveys off the coast of Oman between 2000 and 2003. The waters off Oman are used by the Endangered Arabian Sea population of humpback whales. Our data were collected primarily for photo-identification, using a haphazard sampling regime, either in areas where humpback whales were thought to be relatively abundant, or in areas that were logistically easy to survey. This leads to spatially autocorrelated data that are not amenable to analysis using standard approaches. We used quasi-Poisson generalized linear models and semi-parametric spatial filtering to assess the distribution of humpback and Bryde’s whales in 3 areas off Oman relative to 3 simple physiographic variables in a survey grid. Our analysis focused on the spatial eigenvector filtering of models, coupled with the spatial distribution of model residuals, rather than just on model predictions. Spatial eigenvector filtering accounts for spatial autocorrelation in models, allowing inference to be made regarding the relative importance of particular areas. As an exemplar of this approach, we demonstrate that the Dhofar coast of southern Oman is important habitat for the Arabian Sea population of humpback whales. We also suggest how conservation planning for mitigating impacts on humpback whales off the Dhofar coast could start.

KEY WORDS: Spatial eigenvector models · Spatial planning · Marine Protected Area · ­Generalized linear models · Oman · Whales

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Cite this article as: Corkeron PJ, Minton G, Collins T, Findlay K, Willson A, Baldwin R (2011) Spatial models of sparse data to inform cetacean conservation planning: an example from Oman. Endang Species Res 15:39-52.

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