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Nest selection by snow petrels Pagodroma nivea in East Antarctica Validating predictive habitat selection models at the continental scale
Citation
Olivier, F and Wotherspoon, SJ, Nest selection by snow petrels Pagodroma nivea in East Antarctica Validating predictive habitat selection models at the continental scale, Ecological Modelling, 210, (4) pp. 414-430. ISSN 0304-3800 (2008) [Refereed Article]
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Copyright Statement
The definitive version is available at http://www.sciencedirect.com
Official URL: http://www.sciencedirect.com
DOI: doi:10.1016/j.ecolmodel.2007.08.006
Abstract
Little is known on the factors controlling distribution and abundance of snow petrels in
Antarctica. Studying habitat selection through modeling may provide useful information on
the relationships between this species and its environment, especially relevant in a climate
change context, where habitat availabilitymay change.Validating the predictive capability of
habitat selection models with independent data is a vital step in assessing the performance
of such models and their potential for predicting species’ distribution in poorly documented
areas.
Fromthe results of ground surveys conducted in the Casey region (2002–2003,Wilkes Land,
East Antarctica), habitat selection models based on a dataset of 4000 nests were created to
predict the nesting distribution of snowpetrels as a function of topography and substrate. In
this study, the Casey modelswere tested at Mawson, 3800km away from Casey. The location
and characteristics of approximately 7700 snow petrel nests were collected during ground
surveys (Summer 2004–2005). Using GIS, predictive maps of nest distribution were produced
for the Mawson region with the models derived from the Casey datasets and predictions
were compared to the observed data. Models performance was assessed using classification
matrixes and Receiver operating characteristic (ROC) curves. Overall correct classification
rates for the Casey models varied from 57% to 90%. However, two geomorphologically different
sub-regions (coastal islands and inland mountains) were clearly distinguished in terms
of habitat selection by Casey model predictions but also by the specific variations in coefficients
of terms in new models, derived from the Mawson data sets. Observed variations in
the snow petrel aggregations were found to be related to local habitat availability.
We discuss the applicability of various types of models (GLM, CT) and investigate the effect
of scale on the prediction of snow petrel habitats. While the Caseymodels created with data
collected at the nest scale did not perform well at Mawson due to regional variations in nest
micro-characteristics, the predictive performance of models created with data compiled at
a coarser scale (habitat units) was satisfactory. Substrate type was the most robust predictor
of nest presence between Casey and Mawson. This study demonstrate that it is possible
to predict at the large scale the presence of snow petrel nests based on simple predictors
such as topography and substrate, which can be obtained from aerial photography. Such
methodologies have valuable applications in the management and conservation of this top
predator and associated resources and may be applied to other Antarctic, Sub-Antarctic and
lower latitudes species and in a variety of habitats.
Item Details
Item Type: | Refereed Article |
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Keywords: | Habitat selection, Nest distribution, Model validation, Generalized linear model (GLM), Classification tree (CT) |
Research Division: | Biological Sciences |
Research Group: | Zoology |
Research Field: | Animal behaviour |
Objective Division: | Environmental Management |
Objective Group: | Coastal and estuarine systems and management |
Objective Field: | Assessment and management of coastal and estuarine ecosystems |
UTAS Author: | Olivier, F (Dr Frederique Olivier) |
UTAS Author: | Wotherspoon, SJ (Dr Simon Wotherspoon) |
ID Code: | 54219 |
Year Published: | 2008 |
Web of Science® Times Cited: | 2 |
Deposited By: | IASOS |
Deposited On: | 2009-02-12 |
Last Modified: | 2009-06-10 |
Downloads: | 0 |
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