Post provided by Theoni Photopoulou

“Movement is the glue that ties ecological processes together”
from Francesca Cagnacci et al. 2010

CTD-SRDL telemetry tags being primed for deployment. ©Theoni Photopoulou
CTD-SRDL telemetry tags being primed for deployment. ©Theoni Photopoulou

Movement ecology is a cross-disciplinary field. Its main aim is to quantitatively describe and understand how movement relates to individual and population-level processes for resource acquisition and, ultimately, survival. Today the study of movement ecology hinges on two 21st century advances:

  1. Animal-borne devices/tags (biologging science, Hooker et al., 2007) and/or remote sensing technology to quantify movement and collect data from remote or otherwise challenging environments
  2. Computational power sufficient to manipulate, process and analyse substantial volumes of data

Although datasets often involve small numbers of individuals, each individual can have thousands – sometimes even millions – of data points associated with it. Study species have tended to be large birds and mammals, due to the ease of tag attachment. However, the trend for miniaturisation of tags and the development of remote detection technologies (such as radar, e.g. Capaldi et al., 2000), have allowed researchers to track and study ever smaller animals.

The Many Faces of Movement Ecology

The field of movement ecology is still quite new. It’s diverse and attracts researchers from a wide range of research areas, from population ecologists to physiologists, mathematicians and engineers. One of the reasons for this is that there are many factors contributing to the patterns of animal relocation we eventually observe.

©Theoni Photopoulou
The ability to navigate is vital to reaching resources. ©Theoni Photopoulou

At the most basic level, in order to move around, an animal must have the capacity for movement. This relates to its internal state, which in turn is influenced by hunger levels, body condition and health as well as physiological adaptations. These are some of the hardest things to reliably measure in the field in longitudinal studies, but important advances have been made in certain systems (e.g. Beck et al., 2000; Thums et al., 2008; Aoki et al., 2011; Gordine et al., 2015, Russell et al. 2015).

The ability to navigate is vital to reaching or detecting locations where resources can be found. Substantial progress has been made towards understanding animal navigation in aerial systems (e.g., Åkesson et al., 2007, 2015, 2016). At the level of ecosystems, movement is modulated by predation risk, reproduction and resource distribution. The latter is the focus of a large body of work and involves some of the most pressing questions in all of ecology (e.g., Struve et al. 2010, Dragon et al. 2010, Thums et al. 2011, Dragon et al. 2012, Russell et al. 2013, Scales et al. 2014, Vacquié-Garcia et al. 2015, Cox et al. 2016).

Locations Then and Now

One question that most movement ecologists concern themselves with at some point in their careers is: how do animals use the resources available in their environment to survive and successfully reproduce? In the early days of movement ecology, the main goal was to find out where, in geographical space, animals were going, with the help of technologies like VHF radio.

The CLS-ARGOS satellite system helps estimate locations in polar regions. ©Theoni Photopoulou
The CLS-ARGOS satellite system helps estimate locations in polar regions. ©Theoni Photopoulou

More recently, locations have been estimated with the help of satellites. Despite the ability of GPS to provide high-resolution locations, this is still not an option for obtaining locations in many systems where satellite connectivity is not possible (e.g. underwater, underground or in dense forest cover). In the polar regions, for example, GPS satellites pass overhead so rarely that it’s not practical as a data transmission system. Instead, the polar-orbiting CLS-ARGOS satellite system (which can give very large errors) is used. Here, even something as seemingly basic as estimating location accurately involves substantial computation. However, this has recently been made much faster thanks to new software for overcoming measurement error in locations (Auger-Méthé et al., 2017).

When satellite connectivity is sparse, because of the geographical area or the study system for example, data loggers and tracking devices need to have efficient software in order to collect as much information as possible, as cleverly as possible (e.g. Photopoulou et al., 2015a, 2015b). In extreme cases, when no satellite connectivity is possible, movement paths can be estimated via dead-reckoning (Shiomi et al. 2008, 2010) or accelerometer and magnetormeter data (Mitani et al. 2003).

From Locations to Behaviours, Home Ranges to Hidden Process Models

Before locations could be obtained at high frequencies, movement data were mostly used to estimate space or habitat use, and home ranges. While these analyses are still common, and indeed useful (Photopoulou et al., 2014; Russell et al., 2014; Riotte-Lambert et al., 2015; Jones et al., 2015; Auger-Méthé et al., 2016), the real novelty of obtaining frequent locations for extended periods of time, is the ability to fit individual-based models to time series data. Models such as state-space models (SSMs) and hidden Markov models (HMMs, a special case of a SSM) have revolutionised our ability to infer behaviour based on movement and properly account for the serial autocorrelation in the data (McKellar et al., 2015; Leos-Barajas et al., 2016; DeRuiter et al., 2017). The usefulness of these models has only really come into its own thanks to the availability of high resolution movement data, such as GPS locations (Cagnacci et al. 2010).

The newer generation of tags don’t only collect information on animal locations more frequently than ever before, they also collect crucial ancillary movement data, such as depth and acceleration, at the same time (Bestley et al., 2010, 2015; Leos-Barajas et al., 2016; DeRuiter et al., 2017). This information has been incredibly useful for inferring behaviour. Some tags can also collect environmental data, which can contribute to physical environmental research (e.g. Biermann et al., 2015, for oceanography). For some time the analytical methods for movement data were lagging behind our ability to collect it. This period is not completely behind us, but I would cautiously say that we are gaining ground.

The Future of Movement Ecology

©Theoni Photopoulou
Animal-borne devices have changed the questions we ask and the answers we get. ©Theoni Photopoulou

The availability of animal-borne devices has changed the questions we ask, the way we analyse movement data and, undoubtedly, the kinds of answers we can get. We are now able to ask questions about what animals are doing at specific locations, how that relates to the environment they are experiencing (Bestley et al., 2010, 2015), and how individual responses vary (Patrick et al., 2014a, 2014b). We are also frequently pairing animal locations with underlying environmental variables, though this should be done with caution (Scales et al. 2016). Many of the most exciting methodological innovations are emerging in the area of mechanistic models (e.g., Parton et al., 2016; Leos-Barajas et al., 2017; DeRuiter et al., 2017).

Although movement ecology originally emerged from the bridging of field data and ecological theory, most studies (including some of my own) use empirical models or descriptive methods. We are perhaps guilty of a certain lack of ‘experimental design’ in our tag deployments other than wanting to know ‘where animals go’. This is often enough (especially in novel systems that have not been adequately explored), but with three decades of biologging behind us, perhaps it’s time for us to underpin our tag deployments with specific ecological questions, drawing on what we have learned about where animals go and, perhaps, what they are doing there. The future of movement ecology is an exciting one, where we have the opportunity to draw on theory in truly innovative ways to generate new ways of thinking of about movement processes and new ways of learning about them.

Some recent studies have been breaking new ground in this direction and pushing back the boundaries of movement ecology. Riotte-Lambert et al. (2015) and Schlägel and Lewis (2014) have worked on questions about the role of memory in movement, while Riotte-Lambert et al. (2013, 2016) have developed a framework for studying routine or recursive movement. Bestley et al. (2010, 2015) have integrated vertical (depth) and horizontal (longitude, latitude) movement in the marine environment to learn about behaviour. Studies of movement ecology in the air have challenged traditional views of what we think of as ‘resources’ (Shepard et al., 2011, 2013) and provided some valuable insights about moving in such a dynamic medium (Shepard et al., 2016). Lastly, the effect of human activities on movement ecology is one we cannot afford to ignore while investing in understanding more basic interactions. We are altering the environment sufficiently to bring about measurable changes in movement and behaviour (e.g., Russell et al. 2014, 2016), which present both opportunities to learn about, and responsibilities to manage those changes.

Movement Ecology Resources

The movement ecology community is active and growing. There was a movement ecology special issue in the January 2016 issue of the Journal of Animal Ecology and there is now a dedicated movement ecology journal. The International Biologging Symposium has long had movement sessions, while the International Statistical Ecology Conference featured two movement ecology sessions in Seattle in July 2016 – the most so far. As of December 2016, there’s also a British Ecological Society Movement Ecology Special Interest Group, which promises to facilitate regular exchanges for the community of ecologists with an interest in movement. There is no doubt that it’s an exciting time to be studying movement ecology!

This article references 55 publications led by first authors with female-identifying first names.

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