The movements of wild animals have always fascinated humans. Animal migrations have been important milestones for human society, from the arrival or departure of migratory birds signaling changes in seasons, to hunter-gatherers following the movement of herds across steppes and savannahs and fishermen following salmon runs and the progress of fish stocks between feeding and breeding grounds. Animal migrations have also been an integral part of the development of human culture, as evidenced by pictures drawn thousands of years ago on cave walls. Today, the study of the ecology of non-human animal (hereafter, animal) movement is a well-established field of science (Nathan, 2008) encompassing a coherent research community with dedicated publication outlets (e.g., Movement Ecology, http://link.springer.com/journal/40462 and Animal Biotelemetry https://link.springer.com/journal/40317) and symposia largely dedicated to animal movement (https://www.bio-logging.net/Symposium/).

Despite our long-standing interest, description of the movement patterns of some animals, particularly birds, and aquatic species such as marine mammals and fishes have presented many challenges, largely because these animals live in environments where humans cannot easily follow their path. Today, these issues are being overcome through the development of sophisticated telemetry technologies that allow researchers to remotely locate and track animals. Over the last 30 years, such telemetry studies have generated insights into the otherwise invisible lives of the animals that occupy the skies, the forests and the open oceans well-beyond our sight.

Given the effort that has been expended on describing the movement patterns of animals for nearly two centuries (Figure 1), it is somewhat ironic that humans have become the subject of tracking studies only very recently (Brockmann et al., 2006; Eagle and Pentland, 2006; Gonzalez et al., 2008) (Figure 1). Human tracking studies have been enabled by the growth of the internet coupled with technological innovations such as smartphones and wearables (e.g., smart watches and fitness trackers) that have generated immense and readily accessible geo-referenced data on human mobility and data on human activities such as heart rate monitoring, sports, and sleep tracking (de Arriba-Pérez et al., 2016). These large datasets, amounting in volume to “big data,” are now being analyzed to describe patterns of human movement (Simini et al., 2012, 2013), features (e.g., sleep, stress, and activities) (de Arriba-Pérez et al., 2016) and interactions (Simini et al., 2012, 2013; Meekan et al., 2017) with a degree of detail, immediacy and precision that was never before possible for any animal species (Meekan et al., 2017). Moreover, such studies have characterized the movement patterns of humans at global scales for the first time (e.g., Brockmann et al., 2006; Gonzalez et al., 2008).

The rapid uptake of telemetry for the study of wild animals means that big data approaches to understand movement can now be extended beyond human subjects. Collaborative research initiatives such as the Tagging of Pacific Pelagics (TOPP) program (http://www.gtopp.org/), and online repositories such as the Ocean Tracking Network (OTN) (http://oceantrackingnetwork.org/), Movebank (Wikelski and Kays, 2010), ZoaTrack (Dwyer et al., 2015) and Birdlife International (http://www.birdlife.org/) (see Campbell et al., 2016 for a full list of repositories), collectively document the movements of tens to hundreds of thousands of wild animals across diverse taxa spanning all continents and biomes (Hussey et al., 2015; Kays et al., 2015). Such datasets now offer the opportunity to transfer big data analytical approaches developed in the field of human mobility to animal movement ecology. As has been pointed out for the field of ecology in general (Hampton et al., 2013), big data analyses have the ability to promote significant advances in our understanding of animal movement ecology, including insights challenging the limits of current theoretical frameworks, by searching for and describing universal patterns, collective behaviors and emergent properties in both terrestrial and marine ecosystems.

Here, we show how developments in human mobility research underpinned by big data analysis (Blondel et al., 2015) can be used to catalyze progress and derive new insights into the global movement patterns of animals. A key requirement for this task will be the creation of global, open access databases of animal tracking. These will not only improve our understanding of the movement ecology of animals, but also provide the opportunity to engage researchers from the broader scientific community, including physicists, mathematicians, computer, and visualization scientists and those interested in complex systems.

The emergence of the modern study of animal movement can be traced to the development of ring banding in the 1900's (Bairlein, 2001) and radio-transmitter telemetry in the 1950's (LeMunyan et al., 1959) (Figure 1). At the first iteration of the latter technique, radio signals emitted by transmitters deployed on animals were detected by receivers carried by researchers or mounted on platforms so that tagged animals could be located by triangulation of the signals from multiple receivers. Tracking programs were thus limited by the range of the receivers (25–35 km; line of sight). The launch of the ARGOS (Advanced Research and Global observation satellite) satellite network in the late 1970s overcame this problem, as receivers were placed in earth-orbiting satellites and by the 1980's, animals were tracked with satellite transmitters for the first time (Schweinsburg and Lee, 1982) (Figure 1). In subsequent years, satellite telemetry has progressed rapidly with miniaturization of electronics, improved battery capacity and the integration of the Global Positioning System (GPS), allowing position estimates with much lower error (Dujon et al., 2014) and faster acquisition of satellite data. Today these satellite tags have been attached to a range of terrestrial and marine animals and have catalyzed discovery (Hussey et al., 2015; Kays et al., 2015). Although understanding where animals go has been the main focus of telemetry studies, advanced tags now incorporate sensors that report information on behavior, physiological status (See Brown et al., 2013) and environmental conditions experienced by an animal during its movements (See Biuw et al., 2007). At the same time, telemetry techniques have also been developed to track aquatic organisms such as fish that do not return to the surface to breathe. Because radio signals are rapidly attenuated by water, part of this technology has focused on the use of sonar-emitting tags that are deployed either externally or internally on animals. Signals from the tags are detected and recorded at receiver stations that are now spread in networks through parts of the world's oceans (see http://oceantrackingnetwork.org/; Cooke et al., 2011; Hoenner et al., 2018).

Telemetry was first used in the marine environment, due to the fact that the ocean is a boundary to human observation of marine animals (Boyd et al., 2004). For example the first time-depth recorders were attached to Weddell seals (Kooyman, 1965) and innovations continue to come from the marine biology field such as the multi-sensor “daily diary” tag (Wilson et al., 2008), and other technological developments such as the CTD-SRDL tag which samples oceanographic variables experienced by tagged animals at the same time as monitoring their movements (Fedak, 2004).

In contrast to the long history of animal movement, one of the first studies of individual human mobility occurred as late as 2006, and tracked the movement patterns of 100 MIT students based on the locations of the cell towers from which their mobile phone calls were made (Eagle and Pentland, 2006). The first continent-wide study of human mobility was published in the same year (Brockmann et al., 2006) and used a crowd-sourcing approach, with individuals voluntarily reporting the location of marked $1 bills across the United States (see wheresgeorge.com). However, the appearance of smartphones (portable technologies with geolocation capability) is the milestone that allowed researchers to study human mobility at truly global scales and in unprecedented detail. Integrated GPS tracking in smartphones together with data provided by geolocated internet posts via text or photographs via twitter and the Flickr photo sharing platform, public transportation cards and credit cards are now providing direct and high-resolution data on human locations, trajectories, opinions, and interactions, allowing researchers to develop and validate models of human mobility across different spatial scales (e.g., city to country, Simini et al., 2012). The importance of social interactions to human individuals has created our willingness to carry our own tags (e.g., smartphones), pay for the associated costs, and document our activity through social networks, underpinning the rapid data expansion on human mobility. The numbers of internet-connected electronic devices currently in use, such as smartphones and tablets, are estimated to be in the range of 8–10 billion, i.e., more than the entire human population of the planet (Cisco, 2015). Thus, despite the relatively short history of research on human mobility compared to animal movement, studies of human mobility have recently begun to match or outstrip the number of publications in the field of animal movement today (Figure 2). They also allow, for the first time, tracking the majority of the individuals of a single very abundant species—humans.

Since human dispersal was able to be tracked at the level of individuals (Brockmann et al., 2006; Eagle and Pentland, 2006) it has been found that our trajectories are similar to those of animals such as albatrosses (Viswanathan et al., 1996), monkeys (Ramos-Fernández et al., 2004), and a range of marine predators (Sims et al., 2008). All of these studies showed that trajectories are approximated by a Levy flight—a random walk for which step size follows a power-law distribution (y = x^−k) with the exponent k < 2. While the existence of Levy flights in animal and human movement data remains controversial (Reynolds, 2008; Edwards, 2011; Petrovskii et al., 2011; Gautestad and Mysterud, 2013; Pyke, 2015) our main point is simply that, as dispersing agents, both humans or animals, travel only short distances most of the time, but occasionally travel very long distances. Other similarities include the collective movement of pedestrians showing synchronization (Helbing and Molnár, 1995; Vicsek et al., 1995) similar to flocks of birds (Cavagna et al., 2015), herds of ungulates and schools of fish (Toner and Tu, 1998; Vicsek and Zafeiris, 2012). Given that we are now attaining situations where descriptions of both human and animal movements are data-rich and we have recognized the potential similarities between their form and underlying motivations (Meekan et al., 2017), it is an appropriate time to examine, test and apply techniques used to analyse large-scale (e.g., regional to global scale) data on human mobility to animal movement.

In the last decade alone, more than 700,000 ARGOS-linked satellite transmitters have been deployed on animals (Figure 3). These devices are the main platform currently in use by animal movement ecologists (at least in marine systems for mammals, birds, and reptiles; Hussey et al., 2015) and also transmits the data to calculate GPS fixes for tags that are equipped with GPS technology. Although this represents only a tiny fraction of the number of internet-connected electronic devices that are used to track humans (i.e., currently greater than the number of humans on the planet), the outputs of satellite tag deployments on animals are now also approaching big data status (Figure 3). Given that routine satellite tracking has a 30-year history and that these totals do not include non-Argos linked devices [e.g., radio and acoustic telemetry (the main technology for teleost fishes), and biologging devices], we conservatively estimate that there have been more than one million deployments of satellite-linked tags on animals since the beginning of this field of research and these deployments have global coverage (Kays et al., 2015). However, only a small fraction of these data reside in online databases and even less reside in databases that are publically accessible. For example in a review of the literature from 2000 to 2012 in Australasia alone, animal telemetry devices (including all types) were deployed on 12,656 animals and only 9% of these had their data stored in a discoverable and accessible manner (Campbell et al., 2007).

A major challenge for animal movement ecology is the cost of tracking devices. Depending on the number of sensors fitted to the satellite-linked tag, most cost somewhere in the range of USD $1,000–$10,000, with satellite communication time an additional impost. High instrument costs generally result in low numbers of tagged animals, with sample sizes further reduced by tag loss and the failure of some tags to report position data (Hays et al., 2007). Combined with the logistics involved in capturing target organisms, it is perhaps not surprising that many researchers are reluctant to share data. However, one of the consequences of low sample sizes is that tracking datasets generally feature a large amount of variation in movements among species, individuals, sexes, sites, and seasons, which further complicates the identification of general patterns. Pooling of datasets among studies through the formation of synthesis groups and open access repositories could provide a means for researchers to escape the constraint of small sample sizes and move analyses from local to larger spatial and temporal scales and from the specific to the generic in their application to the field. Progress in the study of human mobility shows that this would generate four key opportunities for utilizing big data approaches which we outline below; (1) identification of emergent properties in animal movement, (2) analysis of networks of animal movement and behavior, (3) development of machine learning algorithms to understand and characterize patterns from “big” animal movement data and (4) advanced visualization techniques for complex datasets of movement. We do not attempt to review all the methodological developments in each of the human mobility and animal movement fields, but instead focus on these four specific areas that we believe can provide valuable new insight into animal movement.

Although there has been significant progress on the synthesis of some of the larger animal tracking data sets (e.g., TOPP, Block et al., 2011; Sequeira et al., in press), for the most part, these data do not reside in repositories that are open to the general scientific community, even after publication. A lack of easy access complicates and hinders any attempt to search for general patterns in animal movement and document large-scale movement patterns. This major impediment to research is well-recognized (Campbell et al., 2007; Rutz and Hays, 2009; Hays, 2014; Hussey et al., 2015; Kays et al., 2015) and some attempts have been made to rectify it, notably through initiatives such as Movebank (Wikelski and Kays, 2010) (see Campbell et al., 2016 for a full list of repositories), however they do not provide open access to data. In addition, these data facilities are almost invariably focused on one group of animals (in the case of Movebank, birds) and thus represent only a small fraction of existing data. The opportunity to seek and explore general patterns and underlying principles in animal movement is consequently limited to the taxa contained in the database. Clearly, open access to tracking data represents a major bottleneck to the advance of animal movement ecology and to the realization of the full value of the investment in time and money that many thousands of individual researchers and institutions have made in collecting these data over the last decades. Development of a culture to deposit the data in open-access data repositories as a resource for the research community, as is now the case in many fields of biology such as genetics (Mount, 2004), must be a critical priority for all grant agencies, government institutions and commercial entities that invest in this type of research. Some progress is now being made on this issue with journals that are the primary outlets of this type of research insisting that data sets on which results are based also be published online (e.g., Nature), mostly driven by efforts to address the current crisis of reproducibility of results. Although initiatives such as Movebank may shift the field toward an open access model, such a global, one-size-fits-all storage facility may be difficult to manage in the long term and perhaps regional data repositories such as ZoaTrack (Dwyer et al., 2015) may be less challenging to implement (Campbell et al., 2007). In the latter, data become open access after an initial moratorium period. Such an approach has also been successful for the Integrated Marine Observing System (IMOS) Animal Tracking Database (http://imos.org.au/facilities/animaltracking/). Open access datasets need to be coupled with appropriate, standardized metadata providing the important contextual information on the tracking data (Campbell et al., 2016). For example, details of the season, sex and maturity status of the animal tracked and details of the type of tag and its program (e.g., duty cycles, etc.) should be included. Also important is the need to include details on the data processing and provide access to the raw datasets. Such constraints are not trivial and databases will need careful consideration of how to standardize the collection of such data in order to provide the context needed to facilitate big data analyses, without this being onerous to data owners. Campbell et al. (2016) provides details of the data reporting standards that should be implemented for data collected by animal borne telemetry devices. However, such challenges are not unique to ecology and standardized, machine readable meta-data software (e.g., Morpho) might assist here (Hampton et al., 2013). Hampton et al. (2013) provide an informative list of action items for ecologists to ensure their data endure for future big data analyses and Raffaelli et al. (2014) provide lessons to be learnt from previous attempts to analyse and organize big data for ecology. Open access databases combined with the availability of supercomputing resources through cloud computing services such as Amazon Web services and others will make big data analyses more accessible to a larger community of researchers.

The amount of accessible digital data and the ease by which it can be collected on nearly any aspect of human activity has shifted research from a theory and hypothesis-led basis to one that is data-driven. These approaches are not necessarily incompatible (Smalheiser, 2002) and need not represent a threat to the study of animal movement ecology. Big data can be used to formulate new hypotheses that could not be tested in the past due to lack of data or conceptual constraints imposed by dominant paradigms. Importantly, big data analyses could uncover patterns and relationships in the data leading to discoveries of behaviors previously unknown or perhaps even contemplated, a situation likely where many of the animal subjects that we study have sensory systems so divergent from our own and are thus likely to perceive the environment in a manner very different to our experience (e.g., echolocating animals).

We have identified a number of approaches from studies of human mobility that could increase the progress of research on animal movement. In order to attain the same rates of advance achieved by studies of human mobility, it is imperative that data that already exists is gathered in central repositories and made freely available to researchers. Data sharing can be achieved through collaborations of researchers across locations and subject areas, but also by funders (governments, NGOs etc.) and scientific journals insisting that the data supporting publications are uploaded to public-access repositories accompanied by appropriate meta-data. There has been a ground-swell of support for this initiative across the research community interested in animal movement. This will also attract the communities that have catalyzed the development of human mobility studies, notably computer and data scientists and scientists working within the complex-system paradigm, to help propel the field of animal movement forward.

Beyond the insights into patterns of movement of animals that might be gained from a transfer of analytical techniques between the research communities involved with human mobility and animal movement, an integration of human and animal movement will help deliver more effective conservation and management options, as these are critically dependent on interactions between humans and wildlife. This is necessary because patterns of human mobility have a direct relationship to many of the anthropogenic threats faced by animal populations worldwide (Meekan et al., 2017). For example, road kills (Clevenger et al., 2003) and ship-strike (Elvin and Taggart, 2008; Silber et al., 2015) pose major risks to the conservation of many animals throughout the world, and the infrastructure deployed to support human mobility, including train and road lines and cities and harbors, also fragment animal habitats (van Bohemen, 1998). Indeed, to identify possible human-animal interactions and to better inform conservation strategies, data on human presence in the environment also needs to be integrated with animal tracking data. Just as data on human mobility were a fundamental underpinning of developments such as “smart-cities” where electronic developments were used to improve the decision-making process by engaging citizens with democratic activities (Paskaleva, 2009), data on animal movement can potentially aid the construction of “smart-environments” where a better understanding of movement patterns of both humans and wildlife would assist conservation and management planning both interactively and in the long-term (Meekan et al., 2017).

The opportunity to accelerate progress in animal movement ecology based on approaches used in human mobility studies will require the development of supporting infrastructure and communities. Once quality-controlled data are available through the use of open repositories, analysis code developed on open platforms such as R (R Core Team, 2017) and web-based visualization and analytical resources can be written for the estimation of new metrics that describe movement patterns. These developments will catalyze progress by enabling the search for common or analogous mechanisms that describe the ways in which animals move and use their environments, as well as identifying impacts and disruptions to patterns of animal movement by both natural and anthropogenic threats and teleconnections in animal movement at global scales. Most importantly, because animals perceive their environments through sensory systems that are often very different from those of humans, the analysis of big data offers the opportunity to search for and identify patterns within animal movement data sets for which we are effectively “blind,” yet may be critical in organizing the ecology and behavior of these species.

Research on human mobility initially borrowed analytical techniques developed for the study of animal mobility (Viswanathan et al., 1996). This process has now begun to move in a full circle, through the application of new techniques developed for or applied to the analysis and visualization of large data sets of human mobility to animal models. In many ways, the data now available from the latest generations of animal tags are very similar to that recorded by smart devices now being worn by humans. New smartwatches include an accelerometer, thermometer, heart rate monitor, altimeter, barometer, compass, chronograph, cell phone and GPS navigation, an almost identical range of sensors to those deployed on the “daily diary” tags used to reveal animal behaviors such as foraging, resting and migrating. The significance of being able to passively obtain data on the habits of millions of users from these new technologies has already been recognized (Kwapisz et al., 2011). Perhaps the major difference between tags that track animals and the smart devices that now track humans is the fact that animals must be caught and restrained to have a tag fitted, whereas humans wear such devices voluntarily and typically pay for the privilege to do so. The convergence between these technologies emphasizes the possibilities for cross-fertilization and collaboration between the research fields of animal movement and human mobility. Given the concerns about the conservation future of large wild animals, the cross-fertilization and collaboration advocated here are not only necessary to catalyze scientific advances, but ultimately an imperative for effective conservation and survival of many animals, since the principal threats to their existence now are largely anthropogenic.

CD conceived of the idea. MT, AMMS, CD, and VE obtained the seed funding to bring the group together to draft the manuscript. MT led the writing with contributions from all authors.