Towards a better characterisation of deep-diving whales’ distributions by using prey distribution model outputs?
Authors
Virgili, A. (Auriane); Authier, M. (Matthieu); Boisseau, O. (Oliver); Cañadas, A. (Ana); Claridge, D. (Diane); Cole, T. (Tim); Corkeron, P. (Peter); Dorémus, G. (Ghislain); David, L. (Léa); Di-Méglio, Nathalie; Dunn, C.(Charlotte); Dunn, T.E. (Tim E.); García-Barón, I. (Isabel); Laran, S. (Sophie); Lewis, M. (Mark); Louzao-Arsuaga, M. (Maite); Mannocci, L. (Laura); Martínez-Cedeira, J.A. (José Antonio); Palka, D. (Debra); Panigada, S. (Simone); Pettex, E.; Roberts, J.; Ruiz-Sancho, L.; Santos, M.B. (María Begoña); Saavedra, C. (Camilo); Van-Canneyt, O. (Olivier); Vázquez-Bonales, J.A. (José Antonio); Monastiez, P.; Ridoux, V. (Vincent)Date
2021-08-04Type
research articleKeywords
cetaceansdeep-diving whales
prey
distribution
Abstract
In habitat modelling, environmental variables are assumed to be proxies of lower trophic levels
distribution and by extension, of marine top predator distributions. More proximal variables,
such as potential prey fields, could refine relationships between top predator
distributions and their environment. In situ data on prey distributions are not available over
large spatial scales but, a numerical model, the Spatial Ecosystem And POpulation DYnamics
Model (SEAPODYM), provides simulations of the biomass and production of zooplankton
and six functional groups of micronekton at the global scale. Here, we explored whether
generalised additive models fitted to simulated prey distribution data better predicted deepdiver
densities (here beaked whales Ziphiidae and sperm whales Physeter macrocephalus)
than models fitted to environmental variables. We assessed whether the combination of
environmental and prey distribution data would further improve model fit by comparing their
expla ...
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