The development of migratory strategies that enable juveniles to survive to recruitment is critical for species that exploit seasonal niches. For animals that forage via breath-hold diving this requires a combination of both physiological and foraging skill development. Here, we assess how migratory and dive behaviour develop over the first months of life for a migratory Arctic top predator, the harp seal, tracked using animal-borne satellite relay data loggers. We reveal similarities in migratory movements and differences in diving behaviour between juveniles from breeding populations in the Northwest Atlantic and Greenland Sea. In both regions, periods of resident and transient behaviour during migration were associated with proxies for food availability; sea ice concentration and water depth. However, while ontogenetic development of dive behaviour was similar for both groups of juveniles over the first 25 days, after this time Greenland Sea animals performed shorter and shallower dives and were more closely associated with sea ice than Northwest Atlantic animals. Together, these results highlight the role of both intrinsic and extrinsic factors in shaping early-life behaviour. Differences in the environmental conditions experienced during early-life may shape how populations respond to the rapid changes occurring in the Arctic ocean ecosystem.

This datafile contains the movement and dive data used by Grecian et al. to examine the drivers of variation in the migratory and diving ontogeny of juvenile harp seals.

• The 'dive_summaries.csv' file contains 6 hour summary data transmitted by SMRU satellite relay data loggers.
• The 'individual_dives.csv' file contains the individual dive metrics transmitted by SMRU satellite relay data loggers.
• The 'move_persistence.csv' file contains the movement paths of 22 animals equiped with either SMRU satellite relay data loggers or Wildlife Computer SPOT or SPLASH tags. These paths have been regularised to 12 hour time steps using a continous-time state space model.

For details of the regularisation procedure see: Jonsen, I.D., Patterson, T.A., Costa, D.P. et al. A continuous-time state-space model for rapid quality control of argos locations from animal-borne tags. Mov Ecol 8, 31 (2020). https://doi.org/10.1186/s40462-020-00217-7

For details of the SMRU dive data see: http://www.smru.st-andrews.ac.uk/protected/specs/DatabaseFieldDescriptions.pdf

R code to analyse these data are supplied on GitHub here: