Corkeron, Peter J,Collins, Gianna Minton Tim,Findlay, Ken,Willson, Andrew,Baldwin, Robert
Spatial models of sparse data to inform cetacean conservation planning: an example from Oman Journal Article
In: Endangered Species Research, vol. 15, no. 353, pp. 39-52, 2011, ISBN: 1863-5407.
Abstract | Links | BibTeX | Tags: Arabian Sea, cetaceans, Distribution, General linear model, habitat modelling, habitat use, Humpback Whale, megaptera novaeangliae, Oman, Spatial autocorrelation
@article{,
title = {Spatial models of sparse data to inform cetacean conservation planning: an example from Oman},
author = {Corkeron, Peter J,Collins, Gianna Minton Tim,Findlay, Ken,Willson, Andrew,Baldwin, Robert},
url = {https://www.int-res.com/articles/esr_oa/n015p039.pdf},
issn = {1863-5407},
year = {2011},
date = {2011-01-01},
journal = {Endangered Species Research},
volume = {15},
number = {353},
pages = {39-52},
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.},
keywords = {Arabian Sea, cetaceans, Distribution, General linear model, habitat modelling, habitat use, Humpback Whale, megaptera novaeangliae, Oman, Spatial autocorrelation},
pubstate = {published},
tppubtype = {article}
}
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.
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.