Abstract

Humpback whales (Megaptera novaeangliae) perform seasonal migrations from high latitude feeding grounds to low latitude breeding and calving grounds. Feeding grounds at polar regions are currently experiencing major ecosystem modifications, therefore, quantitatively assessing species responses to habitat characteristics is crucial for understanding how whales might respond to such modifications. We analyzed satellite telemetry data from 22 individual humpback whales in the Southwest Atlantic Ocean (SWA). Tagging effort was divided in two periods, 2003-2012 and 2016-2019. Correlations between whale's movement parameters and environmental variables were used as proxy for inferring behavioral responses to environmental variation. Two versions of a covariate-driven continuous-time correlated random-walk state-space model, were fitted to the data: i) Population-level models (P-models), which assess correlation parameters pooling data across all individuals or groups, and ii) individual-level models (I-models), fitted independently for each tagged whale. Area of Restricted Search behavior (slower and less directionally persistent movement, ARS) was concentrated at cold waters south of the Polar Front (similar to 50 degrees S). The best model showed that ARS was expected to occur in coastal areas and over ridges and seamounts. Ice coverage during August of each year was a consistent predictor of ARS across models. Wind stress curl and sea surface temperature anomalies were also correlated with movement parameters but elicited larger inter-individual variation. I-models were consistent with P-models' predictions for the case of females accompanied by calves (mothers), while males and those of undetermined sex (males +) presented more variability as a group. Spatial predictions of humpback whale behavioral responses showed that feeding grounds for this population are concentrated in the complex system of islands, ridges, and rises of the Scotia Sea and the northern Weddell Ridge. More southernly incursions were observed in recent years, suggesting a potential response to increased temperature and large ice coverage reduction observed in the late 2010s. Although, small sample size and differences in tracking duration precluded appropriately testing predictions for such a distributional shift, our modelling framework showed the efficiency of borrowing statistical strength during data pooling, while pinpointing where more complexity should be added in the future as additional data become available.