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The challenge of habitat modelling for threatened low density species using heterogeneous data: the case of Cuvier’s beaked whales in the Mediterranean Cañadas, A.; Aguilar de Soto, N.; Aissi, M.; Arcangeli, A.; Azzolin, M.; B-Nagy, A.; Bearzi, G.; Campana, I.; Chicote, C.; Cotte, C.; Crosti, R.; David, L.; Di Natale, A.; Fortuna, C.; Frantzis, A.; Garcia, P.; Gazo, M.; Gutierrez-Xarxa, R.; Holcer, D.; Laran, S.; Lauriano, G.; Lewis, T.; Moulins, A.; Mussi, B.; Notarbartolo di Sciara, G.; Panigada, S.; Pastor, X.; Politi, E.; Pulcini, M.; Raga, J.A.; Rendell, L.; Rosso, M.; Tepsich, P.; Tomás, J.; Tringali, M.; Roger, Th. (2018). The challenge of habitat modelling for threatened low density species using heterogeneous data: the case of Cuvier’s beaked whales in the Mediterranean. Ecol. Indic. 85: 128-136. https://dx.doi.org/10.1016/j.ecolind.2017.10.021 In: Ecological Indicators. Elsevier: Shannon. ISSN 1470-160X; e-ISSN 1872-7034

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Beschikbaar in
VLIZ: Non-open access 349544 [ aanvragen ]

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Trefwoorden

Ziphius cavirostris Cuvier, 1823 [WoRMS] Marien/Kust

Author keywords

Cuvier’s beaked whales; Abundance; Distribution; Conservation; Density surface modelling; Correction factor; Mediterranean sea

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Abstract

The Mediterranean population of Cuvieŕs beaked whale (Ziphius cavirostris), a deep-diving cetacean, is genetically distinct from the Atlantic, and subject to a number of conservation threats, in particular underwater noise. It is also cryptic at the surface and relatively rare, so obtain robust knowledge on distribution and abundance presents unique challenges. Here we use multiplatform and multiyear survey data to analyse the distribution and abundance of this species across the Mediterranean Sea. We use a novel approach combining heterogeneous data gathered with different methods to obtain a single density index for the region. A total of 594,996 km of survey effort and 507 sightings of Cuvier’s beaked whales, from 1990 to 2016, were pooled together from 24 different sources. Data were divided into twelve major groups according to platform height, speed and sea state. Both availability bias and effective strip width were calculated from the sightings with available perpendicular distance data. This was extrapolated to the rest of the sightings for each of the twelve groups. Habitat preference models were fitted into a GAM framework using counts of groups as a response variable with the effective searched area as an offset. Depth, coefficient of variation of depth, longitude and marine regions (as defined by the International Hydrographic Organization) were identified as important predictors. Predicted abundance of groups per grid cell were multiplied by mean group size to obtain a prediction of the abundance of animals. A total abundance of 5799 (CV = 24.0%) animals was estimated for the whole Mediterranean basin. The Alborán Sea, Ligurian Sea, Hellenic Trench, southern Adriatic Sea and eastern Ionian Sea were identified as being the main hot spots in the region. It is important to urge that the relevant stakeholders incorporate this information in the planning and execution of high risk activities in these high-risk areas.

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