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

Abstract

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.

How Big Data Fast Tracked Human Mobility Research and the Lessons for Animal Movement Ecology

“…While more advanced methods exist in animal movement ecology to deal with these limitations such as Brownian (Horne et al, 2007) and biased bridges (Benhamou, 2011) these methods are undertaken on individual trajectories and scaling up to population level inference requires a representative sample of individual tracks and secondary analyses (e.g., overlaying home ranges in GIS software, use of random effects for parameters). Community detection algorithms used to represent patterns of human space use may be ideal for this task, as they are not subject to the same limitation and importantly, they can also determine how sub-populations might be connected at larger spatial scales (Rodríguez et al, 2017). Again, the power of these algorithms relies on the use of massive data that examines the movements of hundreds of individuals across ecosystems (e.g., Rodríguez et al, 2017), an approach that is still relatively uncommon in animal studies.…”

“…Community detection algorithms used to represent patterns of human space use may be ideal for this task, as they are not subject to the same limitation and importantly, they can also determine how sub-populations might be connected at larger spatial scales (Rodríguez et al, 2017). Again, the power of these algorithms relies on the use of massive data that examines the movements of hundreds of individuals across ecosystems (e.g., Rodríguez et al, 2017), an approach that is still relatively uncommon in animal studies. At a wider scale, such analyses would aid the development of effective conservation and management across socio-political borders.…”

Embracing Complexity and Complexity-Awareness in Marine Megafauna Conservation and Research

“…Finally, a number of recent advances have been made in understanding marine megafauna behavior by addressing the dynamic state space of behavior and by developing big-data approaches that require no "a priori" assumptions about the behaviors of study animals (Beyer et al, 2013). Other examples include the use of Stochastic Dynamic Programming (SDP) and state-dependent behavioral theory to investigate how disturbance affects pinniped pup recruitment (McHuron et al, 2017), a dynamic state model of blue whale migratory behavior and physiology to explore the effects of perturbations on reproductive success (Balaenoptera musculus) (Pirotta et al, 2018), and a study of tagged southern elephant seals (Mirounga leonina) that identifies intrinsic drivers of movement, to describe the migratory and foraging habitats (Rodríguez et al, 2017). State space models have also been used to characterize dynamic movement of sea turtles (Jonsen et al, 2007;Bailey et al, 2008), seabirds (Dean et al, 2013), other marine mammal species (Moore and Barlow, 2011), and sharks (Block et al, 2011).…”

Sonification of Animal Tracks as an Alternative Representation of Multi-Dimensional Data: A Northern Elephant Seal Example

“…Satellite tags are particularly appropriate for large marine mammals because they move across considerable distances, often spanning entire ocean basins, surfacing regularly to breath (Hussey et al, 2015). Whereas, most animal tracking papers focus on the detailed analyses of movement of one or a few animals, projects tracking dozens to hundreds of animals are emerging allowing a better understanding of animal use of the ocean ecosystem and how populations of organisms use them (e.g., Block et al, 2011;Costa et al, 2012;Hays et al, 2016;Rodríguez et al, 2017). Indeed, big-data approaches to animal tracking are emerging (e.g., Rodríguez et al, 2017), following developments in human mobility studies enabled by massive data delivered by smart phones .…”

“…Whereas, most animal tracking papers focus on the detailed analyses of movement of one or a few animals, projects tracking dozens to hundreds of animals are emerging allowing a better understanding of animal use of the ocean ecosystem and how populations of organisms use them (e.g., Block et al, 2011;Costa et al, 2012;Hays et al, 2016;Rodríguez et al, 2017). Indeed, big-data approaches to animal tracking are emerging (e.g., Rodríguez et al, 2017), following developments in human mobility studies enabled by massive data delivered by smart phones . However, most studies to date are based on the analysis of trajectories one animal at a time, as the analyses of concerted movements of multiple tagged animals remains challenging, due to the high dimensionality of the data (4-D movement by N animals).…”