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

Auriane Virgili (1) , Laura Hedon (1) , Matthieu Authier (1, 2) , Beatriz Calmettes (3) , Diane Claridge (4) , Tim Cole (5) , Peter Corkeron (5) , Ghislain Dorémus (1) , Charlotte Dunn (4) , Tim Dunn (6) , Sophie Laran (1) , Patrick Lehodey (3) , Mark Lewis (5) , Maite Louzao (7) , Laura Mannocci (8) , José Martínez-Cedeira (9) , Pascal Monestiez (10, 11) , Debra Palka (5) , Emeline Pettex (2, 12) , Jason Roberts (13) , Leire Ruiz (14) , Camilo Saavedra (15) , M. Begoña Santos (15) , Olivier van Canneyt (1) , José Antonio Vázquez Bonales (16) , Vincent Ridoux (1, 11)

1 PELAGIS - Observatoire pour la Conservation de la Mégafaune Marine
2 LEB Aquitaine Transfert-ADERA
3 CLS Space Oceanography Division
4 Bahamas Marine Mammal Research Organisation
5 NOAA Northeast Fisheries Science Center
6 Joint Nature Conservation Committee
7 BRTA - Basque Research and Technology Alliance
8 UMR MARBEC - MARine Biodiversity Exploitation and Conservation
9 CEMMA
10 BioSP - Biostatistique et Processus Spatiaux
11 CEBC - Centre d'Études Biologiques de Chizé - UMR 7372
12 Cohabys - ADERA, Cellule Cohabys
13 Duke University [Durham]
14 AMBAR Elkartea Organisation [Bizkaia, Spain]
15 IEO - Instituto Español de Oceanografía - Spanish Institute of Oceanography
16 Alnilam Research and Conservation [Madrid, Spain]

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 probably better characterise the prey of deep-diving cetaceans.

Domaines: Sciences de l'environnement

Auriane Virgili, Laura Hedon, Matthieu Authier, Beatriz Calmettes, Diane Claridge, et al.. Towards a better characterisation of deep-diving whales’ distributions by using prey distribution model outputs?. PLoS ONE, 2021, 16 (8), pp.e0255667. ⟨10.1371/journal.pone.0255667⟩. ⟨hal-03335810⟩