The Role of Climate, Oceanography, and Prey in Driving Decadal Spatio-Temporal Patterns of a Highly Mobile Top Predator

Marine mammals have been proposed as ecosystem sentinels due to their conspicuous nature, wide ranging distribution, and capacity to respond to changes in ecosystem structure and functioning. In southern European Atlantic waters, their response to climate variability has been little explored, partly because of the inherent difficulty of investigating higher trophic levels and long lifespan animals. Here, we analyzed spatio-temporal patterns from 1994 to 2018 of one of the most abundant cetaceans in the area, the common dolphin (Delphinus delphis), in order to (1) explore changes in its abundance and distribution, and (2) identify the underlying drivers. For that, we estimated the density of the species and the center of gravity of its distribution in the Bay of Biscay (BoB) and tested the effect of three sets of potential drivers (climate indices, oceanographic conditions, and prey biomasses) with a Vector Autoregressive Spatio Temporal (VAST) model that accounts for changes in sampling effort resulting from the combination of multiple datasets. Our results showed that the common dolphin significantly increased in abundance in the BoB during the study period. These changes were best explained by climate indices such as the North Atlantic Oscillation (NAO) and by prey species biomass. Oceanographic variables such as chlorophyll a concentration and temperature were less useful or not related. In addition, we found high variability in the geographic center of gravity of the species within the study region, with shifts between the inner (southeast) and the outer (northwest) part of the BoB, although the majority of this variability could not be attributed to the drivers considered in the study. Overall, these findings indicate that considering temperature alone for projecting spatio-temporal patterns of highly mobile predators is insufficient in this region and suggest important influences from prey and climate indices that integrate multiple ecological influences. Further integration of existing observational datasets to understand the causes of past shifts will be important for making accurate projections into the future.