Big data analyses reveal patterns and drivers of the movements of southern elephant seals

The growing number of large databases of animal tracking provides an opportunity for analyses of movement patterns at the scales of populations and even species. We used analytical approaches, developed to cope with big data, that require no a priori assumptions about the behaviour of the target agents, to analyse a pooled tracking dataset of 272 elephant seals (Mirounga leonina) in the Southern Ocean, that was comprised of >500,000 location estimates collected over more than a decade. Our analyses showed that the displacements of these seals were described by a truncated power law distribution across several spatial and temporal scales, with a clear signature of directed movement. This pattern was evident when analysing the aggregated tracks despite a wide diversity of individual trajectories. We also identified marine provinces that described the migratory and foraging habitats of these seals. Our analysis provides evidence for the presence of intrinsic drivers of movement, such as memory, that cannot be detected using common models of movement behaviour. These results highlight the potential for big data techniques to provide new insights into movement behaviour when applied to large datasets of animal tracking.

💡 Research Summary

This study leverages a massive satellite‑tracking dataset of southern elephant seals (Mirounga leonina) to uncover population‑level movement patterns and their underlying drivers. Between 2004 and 2013, 272 individuals were equipped with Argos‑linked tags at seven locations across the Southern Ocean, yielding 550,537 location fixes. Rather than relying on traditional step‑selection or turning‑point methods that impose a priori behavioural states, the authors applied big‑data analytical tools originally developed for human mobility studies, allowing them to treat the data without predefined assumptions.

Displacements were calculated for multiple temporal windows (T) and normalized by the mean displacement ⟨d⟩, producing a dimensionless step length D = d/⟨d⟩. The probability density function p(D; T) collapsed onto a universal curve across all T, exhibiting a truncated power‑law form: for D < 1, p(D) ∝ D⁻⁰·⁶⁰, and for D > 1 the distribution decays sharply. This scale‑free signature indicates that seals employ the same search strategy over a wide range of spatial and temporal scales. Both the mean displacement and the mean‑square displacement scale with time as power laws with exponents a = 0.83 and b = 2a = 1.66, respectively—values higher than those of simple Brownian diffusion (a = 0.5) and characteristic of directed movement.

Spatial occupancy analysis, performed on a regular grid, revealed a highly heterogeneous use of the ocean. Approximately 20 % of grid cells are high‑occupancy zones visited repeatedly with short, low‑speed steps, whereas the remaining 80 % are low‑occupancy zones traversed by long, high‑speed displacements. The high‑occupancy cells retain the power‑law displacement distribution (exponent ≈ 1.17), indicating that they dominate the overall scale‑free pattern.

Individual trajectory analysis showed a broad range of gyration radii (10 km to 2000 km), underscoring substantial inter‑individual variation. By reshuffling the order of steps to destroy temporal correlations, the authors demonstrated that real trajectories explore more grid cells and have higher entropy (average S > 0.6) than randomized ones. Entropy‑based limits of predictability (Π_max) for most seals lie between 0.2 and 0.4, with a few short tracks approaching Π_max ≈ 1. This suggests that seal movement is not purely random but exhibits non‑Markovian memory effects that constrain future steps.

Community‑detection applied to the transition probability matrix identified a hierarchical set of “marine provinces.” At a one‑day transition interval, two large provinces (level 0) and six sub‑provinces (level 1) emerged. Notably, 80 % of seals spent over 80 % of their time within a single province, indicating strong spatial fidelity and consistent use of specific foraging corridors across sub‑populations.

In the discussion, the authors contrast their findings with the Lévy‑foraging hypothesis, which predicts power‑law exponents near 1 under sparse resource conditions. The observed exponent γ = 0.60 falls below this range and lies outside the stable regime of the central limit theorem, supporting the interpretation that memory, rather than stochastic resource searching, drives the observed movement. The dichotomy between high‑occupancy (short, foraging‑like steps) and low‑occupancy (long, transit‑like steps) zones aligns with a mixed deterministic–probabilistic strategy: deterministic, large‑scale migrations combined with probabilistic, area‑restricted searches at finer scales.

Overall, the paper demonstrates that big‑data techniques can reveal intrinsic behavioural drivers—particularly memory and spatial fidelity—in marine megafauna. The identification of scale‑free movement, heterogeneous occupancy, entropy‑based predictability, and coherent marine provinces provides a comprehensive framework for understanding elephant seal ecology and offers valuable insights for conservation planning in the face of climate‑driven ocean changes.