Energyscapes pinpoint marine megafauna feeding hotspots in the Mediterranean.

Ocean giants shape the structure and functioning of marine food webs via trophic top-down controls, landscapes of fear, vertical and horizontal redistribution of nutrients, energy, and matter. Yet, they face threats from overfishing, pollution, habitat degradation, and climate change, and one-third of marine megafauna species are at risk of extinction, ultimately endangering the resilience of entire ecosystems. In such a context, knowing when and where megafauna find resources to balance their substantial energy requirements is critical for their management.

Deep ocean drivers better explain habitat preferences of sperm whales Physeter macrocephalus than beaked whales in the Bay of Biscay.

Species Distribution Models are commonly used with surface dynamic environmental variables as proxies for prey distribution to characterise marine top predator habitats. For oceanic species that spend lot of time at depth, surface variables might not be relevant to predict deep-dwelling prey distributions. We hypothesised that descriptors of deep-water layers would better predict the deep-diving cetacean distributions than surface variables.

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

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.

How many sightings to model rare marine species distributions.

Despite large efforts, datasets with few sightings are often available for rare species of marine megafauna that typically live at low densities. This paucity of data makes modelling the habitat of these taxa particularly challenging. We tested the predictive performance of different types of species distribution models fitted to decreasing numbers of sightings.