[Show abstract][Hide abstract] ABSTRACT: Cetaceans are mobile and spend long periods underwater. Because of this, modelling their habitat could be subject to a serious problem of false absence. Furthermore, extensive surveys at sea are time and money consuming, and presence–absence data are difficult to apply. This study compares the ability of two presence–absence and two presence-only habitat modelling methods and uses the example of the sperm whale (Physeter macrocephalus) in the northwestern Mediterranean Sea. The data consist of summer visual and acoustical detections of sperm whales, compiled between 1998 and 2005. Habitat maps were computed using topographical and hydrological eco-geographical variables. Four methods were compared: principal component analysis (PCA), ecological niche factor analysis (ENFA), generalized linear model (GLM) and multivariate adaptive regression splines (MARS). The evaluation of the models was achieved by calculating the receiver operating characteristic (ROC) of the models and their respective area under the curve (AUC). Presence–absence methods (GLM, AUC=0.70, and MARS, AUC=0.79) presented better AUC than presence-only methods (PCA, AUC=0.58, and ENFA, AUC=0.66), but this difference was not statistically significant, except between the MARS and the PCA models. The four models showed an influence of both topographical and hydrological factors, but the resulting habitat suitability maps differed. The core habitat on the continental slope was well highlighted by the four models, while GLM and MARS maps also showed a suitable habitat in the offshore waters. Presence–absence methods are therefore recommended for modelling the habitat suitability of cetaceans, as they seem more accurate to highlight complex habitat. However, the use of presence-only techniques, in particular ENFA, could be very useful for a first model of the habitat range or when important surveys at sea are not possible.
Deep Sea Research Part I Oceanographic Research Papers 04/2009; 56(4-56):648-657. DOI:10.1016/j.dsr.2008.11.001 · 2.57 Impact Factor
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