Fellow Marmamers
I'm pleased to announce the publication of a new paper, "Spatial models
of sparse data to inform cetacean conservation planning: an example from
Oman ", by myself, Gianna Minton, Tim Collins, Ken Findlay, Andy Willson
and Robert Baldwin.
The paper is out in the latest issue of Endangered Species Research, and
is part of the ESR Special on "Beyond Marine Mammal Habitat Modeling:
Applications For Ecology And Conservation". It's Open Access and can be
downloaded from the Endangered Species Research website at:
http://www.int-res.com/abstracts/esr/v15/n1/p39-52/
Note that the modeling technique outlined in the paper can reduce
spatial autocorrelation due to haphazard sampling, making it likely
useful for other photo-identification studies, or those conducted from
platforms of opportunity (e.g. whale-watching vessels).
The Abstract is:
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.
Peter Corkeron
--
Leader, Large Whale Team
Protected Species Branch
NOAA Northeast Fisheries Science Center
166 Water St
Woods Hole MA 02543
phone: 508-495-2191
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