Towards a better characterisation of deep-diving whales’ distributions by using prey distribution model outputs?

• Published in: PLOS ONE
• Main Authors: Virgili, Auriane, Hedon, Laura, Authier, Matthieu, Calmettes, Beatriz, Claridge, Diane, Cole, Tim, Corkeron, Peter, Dorémus, Ghislain, Dunn, Charlotte, Dunn, Tim E., Laran, Sophie, Lehodey, Patrick, Lewis, Mark, Louzao, Maite, Mannocci, Laura, Martínez-Cedeira, José, Monestiez, Pascal, Palka, Debra, Pettex, Emeline, Roberts, Jason J., Ruiz, Leire, Saavedra, Camilo, Santos, M. Begoña, Van Canneyt, Olivier, Bonales, José Antonio Vázquez, Ridoux, Vincent
• Other Authors: Paiva, Vitor Hugo Rodrigues, Direction Générale de l'Armement, ramón y cajal postdoctoral contract of the spanish ministry of economy, challenges project of the spanish ministry of economy, industry and competitiveness
• Format: Article in Journal/Newspaper
• Language: English
• Published: Public Library of Science (PLoS) 2021
• Subjects: Physeter macrocephalus
• Online Access: http://dx.doi.org/10.1371/journal.pone.0255667; https://dx.plos.org/10.1371/journal.pone.0255667

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 deep-diver 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 explanatory power. For both taxa, results were suggestive of a preference for habitats associated with topographic features and thermal fronts but also for habitats with an extended euphotic zone and with large prey of the lower mesopelagic layer. For beaked whales, no SEAPODYM variable was selected in the best model that combined the two types of variables, possibly because SEAPODYM does not accurately simulate the organisms on which beaked whales feed on. For sperm whales, the increase model performance was only marginal. SEAPODYM outputs were at best weakly correlated with sightings of deep-diving cetaceans, suggesting SEAPODYM may not accurately predict the prey fields of these taxa. This study was a first investigation and mostly highlighted the importance of the physiographic variables to understand mechanisms that influence the distribution of deep-diving cetaceans. A more systematic use of SEAPODYM could allow to better define the limits of its use and a development of the model that would simulate larger prey beyond 1,000 m would ...