Aim Deep‐diving cetaceans are oceanic species exposed to multiple anthropogenic pressures including high intensity underwater noise, and knowledge of their distribution is crucial to manage their conservation. Due to intrinsic low densities, wide distribution ranges and limited presence at the sea surface, these species are rarely sighted. Pooling data from multiple visual surveys sharing a common line‐transect methodology can increase sightings but requires accounting for heterogeneity in protocols and platforms. Location North Atlantic Ocean and Mediterranean Sea. Time period 1998 to 2015. Major taxa Ziphiidae; Physeteriidae; Kogiidae. Methods About 1,240,000 km of pooled effort provided 630 sightings of ziphiids, 836 of physeteriids and 106 of kogiids. For each taxon, we built a hierarchical model to estimate the effective strip width depending on observation conditions and survey types. We then modelled relative densities in a generalized additive modelling framework. Geographical predictions were limited to interpolations identified with a gap analysis of environmental space coverage. Results Deeper areas of the North Atlantic gyre were mostly environmental extrapolation in the predictions, thereby highlighting gaps in sampling across the different surveys. For the three species groups, the highest relative densities were predicted along continental slopes, particularly in the western North Atlantic Ocean where the Gulf Stream creates dynamic frontal zones and eddies. Main conclusions Pooling a large number of surveys provided the first basin‐wide models of distribution for deep‐diving cetaceans, including several data‐deficient taxa, across the North Atlantic Ocean and the Mediterranean Sea. These models can help the conservation of elusive and poorly known marine megafauna.

No abstract

No 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 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.

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. We combined static variables and dynamic variables integrated over different depth classes of the water column into Generalised Additive Models to predict the distribution of sperm whales Physeter macrocephalus and beaked whales Ziphiidae in the Bay of Biscay, eastern North Atlantic. We identified which variables best predicted their distribution. Although the highest densities of both taxa were predicted near the continental slope and canyons, the most important variables for beaked whales appeared to be static variables and surface to subsurface dynamic variables, while for sperm whales only surface and deep-water variables were selected. This could suggest differences in foraging strategies and in the prey targeted between the two taxa. Increasing the use of variables describing the deep-water layers would provide a better understanding of the oceanic species distribution and better assist in the planning of human activities in these habitats.