Modelling short‐term energetic costs of sonar disturbance to cetaceans using high‐resolution foraging data

Abstract

Abstract Anthropogenic noise is a pervasive and increasing source of disturbance to wildlife. Marine mammals exhibit behavioural and physiological responses to naval sonar and other sound sources. The lost foraging opportunities and elevated locomotor effort associated with sonar disturbance likely carry energetic costs, which may lead to population‐level consequences. We modelled the energetic costs associated with behavioural responses using (a) empirical datasets of cetacean feeding rates and prey characteristics and (b) allometry of swimming performance and metabolic rates. We applied our model to compare the short‐term (i.e. the scale of the disturbance response; hours to days) energetic costs of a variety of observed behavioural responses. Efficient foragers (e.g. baleen whales) incur a greater relative energetic cost for mild behavioural responses as compared to the most extreme observed response for larger odontocetes (e.g. beaked whales). Energetic costs are more sensitive to lost feeding opportunities than increased energy expenditure from elevated locomotor effort. To scale up from short‐term costs to long‐term effects (months to years), future research should address individuals’ capacity to compensate for energetic losses as well as energetic thresholds for demographic rates (survival, fecundity). We discuss how relative energetic costs correlate with species’ pace of life and the implications for conservation planning. Synthesis and applications. Current approaches towards understanding the Population Consequences of Disturbance (PCoD) often must rely on expert opinion due to data deficiency. Our model provides an empirical method for linking behaviour to energetics, which is critical for managers to make informed decisions on actions that may affect marine mammal species. Furthermore, our model is applicable to other forms of disturbance, such as vessel traffic or seismic exploration, and our scaling approach enables risk projections for understudied species. ​