Predicting krill swarm characteristics important for marine predators foraging off East Antarctica

• Published in: Ecography
• Main Authors: Bestley, S, Raymond, B, Gales, NJ, Harcourt, RG, Hindell, MA, Jonsen, ID, Nicol, S, Peron, C, Sumner, MD, Weimerskirch, H, Wotherspoon, SJ, Cox, MJ
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: Blackwell Munksgaard 2017
• Subjects: acoustic survey; krill swarms; marine predators; foraging migrations; East Antarctic ecosystem; ecological modelling; spatial predictions; Antarctic; Austral; East Antarctica; Southern Ocean; The Antarctic; Antarc*; Antarctic Krill; Antarctica; Euphausia superba
• Online Access: https://eprints.utas.edu.au/25599/; https://doi.org/10.1111/ecog.03080

Open ocean predator-prey interactions are often difficult to interpret because of a lack of information on prey fields at scales relevant to predator behaviour. Hence, there is strong interest in identifying the biological and physical factors influencing the distribution and abundance of prey species, which may be of broad predictive use for conservation planning and evaluating effects of environmental change. This study focuses on a key Southern Ocean prey species, Antarctic krill Euphausia superba, using acoustic observations of individual swarms (aggregations) from a large-scale survey off East Antarctica. We developed two sets of statistical models describing swarm characteristics, one set using underway survey data for the explanatory variables, and the other using their satellite remotely sensed analogues. While survey data are in situ and contemporaneous with the swarm data, remotely sensed data are all that is available for prediction and inference about prey distribution in other areas or at other times. The fitted models showed that the primary biophysical influences on krill swarm characteristics included daylight (solar elevation/radiation) and proximity to the Antarctic continental slope, but there were also complex relationships with current velocities and gradients. Overall model performance was similar regardless of whether underway or remotely sensed predictors were used. We applied the latter models to generate regional-scale spatial predictions using a 10-yr remotely-sensed time series. This retrospective modelling identified areas off east Antarctica where relatively dense krill swarms were consistently predicted during austral mid-summers, which may underpin key foraging areas for marine predators. Spatiotemporal predictions along Antarctic predator satellite tracks, from independent studies, illustrate the potential for uptake into further quantitative modelling of predator movements and foraging. The approach is widely applicable to other krill-dependent ecosystems, and our findings are relevant to similar efforts examining biophysical linkages elsewhere in the Southern Ocean and beyond.