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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 Type:	Refereed Article
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
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