Movement Ecology: Stepping into the Mainstream

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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  1. Åkesson, Susanne, Odin, Catharina, Hegedüs, Ramón, Ilieva, Mihaela, Sjöholm, Christoffer, Farkas, Alexandra and Horváth, Gábor (2015) Testing avian compass calibration: comparative experiments with diurnal and nocturnal passerine migrants in South Sweden. Biology Open, 4(1), 35–47.
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  1. Auger-Méthé, Marie, Albertsen, Christoffer M., Jonsen, Ian D., Derocher, Andrew E., Lidgard, Damian C., Studholme, Katharine R., Bowen, W. Don, Crossin, Glenn T. and Flemming, Joanna Mills (2017) Spatiotemporal modelling of marine movement data using Template Model Builder (TMB). Marine Ecology Progress Series, 565, 237–249.
  1. Auger-Méthé, Marie, Lewis, Mark A. and Derocher, Andrew E. (2016) Home ranges in moving habitats: polar bears and sea ice. Ecography, 39(1), 1600-0587.
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  1. Bestley, Sophie, Patterson, Toby A., Hindell, Mark A. and Gunn, John S. (2010) Predicting feeding success in a migratory predator: integrating telemetry, environment, and modeling techniques. Ecology, 91(8), 2373–2384.
  1. Bestley, Sophie, Jonsen, Ian D., Hindell, Mark A., Guinet, Christophe and Charrassin, Jean-Benoît (2012) Integrative modelling of animal movement: incorporating in situ habitat and behavioural information for a migratory marine predator. Proceedings of the Royal Society of London B: Biological Sciences, 280, 20122262.
  1. Bestley, Sophie, Jonsen, Ian D., Hindell, Mark A., Harcourt, Robert G. and Gales, Nicholas J. (2015) Taking animal tracking to new depths: synthesizing horizontal-vertical movement relationships for four marine predators. Ecology, 96(2), 417–427.
  1. Bestley, Sophie, Jonsen, Ian, Harcourt, Robert G., Hindell, Mark A. and Gales, Nicholas J. (2016) Putting the behavior into animal movement modeling: Improved activity budgets from use of ancillary tag information. Ecology and Evolution, 6(22), 8243–8255.
  1. Biermann, Lauren, Guinet, Christophe, Berster, Marthan, Brierley, Andrew and Boehme, Lars (2015) An alternative method for correcting fluorescence quenching. Ocean Science, 11, 83-91.
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  1. Capaldi, Elizabeth A., Smith, Alan D., Osborne, Juliet L., Fahrbach, Susan E., Farris, Sarah M., Reynolds, Donald R., Edwards, Ann S., Martin, Andrew. Robinson, Gene E., Poppy, Guy M. and Riley, Jospeh R. (2000) Ontogeny of orientation flight in the honeybee revealed by harmonic radar. Nature, 403(6769), 537–540.
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  1. DeRuiter, Stacy L., Langrock, Roland, Skirbutas, Tomas, Goldbogen, Jeremy A., Calambokidis, John, Friedlaender, Ari S., and Southall, Brandon L. (2017) A multivariate mixed hidden Markov model to analyze blue whale diving behaviour during controlled sound exposures. Annals of Applied Statistics, (In press).
  1. Dragon, Anne-Cécile, Monestiez, Pascal, Bar-Hen, Avner and Guinet, Christophe (2010) Linking foraging behaviour to physical oceanographic structures: Southern elephant seals and mesoscale eddies east of Kerguelen Islands. Progress in Oceanography, 87(1), 61–71.
  1. Dragon, Anne-Cécile and Bar-Hen, Avner and Monestiez, Pascal and Guinet, Christophe (2012) Horizontal and vertical movements as predictors of foraging success in a marine predator. Marine Ecology Progress Series, 447, 243–257.
  1. Gordine, Samantha Alex, Fedak, Michael and Boehme, Lars (2015) Fishing for drifts: detecting buoyancy changes of a top marine predator using a step-wise filtering method. Journal of Experimental Biology, 218(23), 3816–3824.
  1. Hooker, Sascha K., Biuw, Martin, McConnell, Bernie J., Miller, Patrick J. O. and Sparling, Carol E. (2007) Bio-logging science: Logging and relaying physical and biological data using animal-attached tags. Deep Sea Research Part II: Topical Studies in Oceanography, 54(3), 177–182.
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  1. Leos-Barajas, Vianey, Photopoulou, Theoni, Langrock, Roland, Patterson, Toby A., Watanabe, Yuuki Y., Murgatroyd, Megan and Papastamatiou, Yannis P. (2016) Analysis of animal accelerometer data using hidden Markov models. Methods in Ecology and Evolution, 8, 161–173.
  1. Leos-Barajas, Vianey, Gangloff, Eric, Adam, Timo, Langrock, Roland, van Beest, Floris M, Nabe-Nielsen, Jacob and Morales, Juan M. (2017) Multi-scale modeling of animal movement and general behavior data using hidden Markov models with hierarchical structures. ArXiv, arXiv:1702.03597.
  1. McKellar, Ann E., Langrock, Roland, Walters, Jeffrey R. and Kesler, Dylan C. (2015) Using mixed hidden Markov models to examine behavioral states in a cooperatively breeding bird. Behavioral Ecology, 26(1), 148-157.
  1. Mitani, Yoko, Sato, Katsufumi, Ito, Shinichiro, Cameron, Michael F., Siniff, Donald B., and Naito, Yasuhiko (2003) A method for reconstructing three-dimensional dive profiles of marine mammals using geomagnetic intensity data: results from two lactating Weddell seals. Polar Ecology, 26(5), 311-317.
  1. Murgatroyd, Megan, Underhill, Les G., Bouten, Willem and Amar, Arjun (2016) Ranging behaviour of Verreaux’s eagles during the pre-breeding period determined through the use of high temporal resolution tracking. PLoS One, 11(10), e0163378.
  1. Parton, Alison, Blackwell, Paul G, and Skarin, Anna (2016) Bayesian inference for continuous time animal movement based on steps and turns. ArXiv, arXiv:1608.05583v2.
  1. Patrick, Samantha C and Weimerskirch, Henri (2014) Personality, foraging and fitness consequences in a long lived seabird. PLoS One, 9(2), e87269.
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  1. Photopoulou, Theoni, Fedak, Michael A., Thomas, Len and Matthiopoulos, Jason (2014) Spatial variation in maximum dive depth in gray seals in relation to foraging. Marine Mammal Science, 30(3), 923–938.
  1. Photopoulou, Theoni, Lovell, Phil, Fedak, Michael A., Thomas, Len and Matthiopoulos, Jason (2015a) Efficient abstracting of dive profiles using a broken-stick model. Methods in Ecology and Evolution, 6(3), 278-288.
  1. Photopoulou, Theoni, Fedak, Michael A, Matthiopoulos, Jason, McConnell, Bernie and Lovell, Phil (2015b) The generalized data management and collection protocol for conductivity-temperature- depth satellite relay data loggers. Animal Biotelemetry, 3(1), 21.
  1. Pohle, Jennifer, Langrock, Roland, van Beest, Floris and Schmidt, Niels M. (2017) Selecting the number of states in hidden Markov models – pitfalls, practical challenges and pragmatic solutions. ArXiv, arXiv:1701.08673v1.
  1. Riotte-Lambert, Louise, Benhamou, Simon and Chamaillé-Jammes, Simon (2013) Periodicity analysis of movement recursions. Journal of Theoretical Biology, 317, 238–243.
  1. Riotte-Lambert, Louise, Benhamou, Simon and Chamaillé-Jammes, Simon (2015) How memory-based movement leads to nonterritorial spatial segregation. The American Naturalist, 185(4), E103–E116.
  1. Riotte-Lambert, Louise, Benhamou, Simon and Chamaillßé-Jammes, Simon (2016) From randomness to traplining: a framework for the study of routine movement behavior. Behavioral Ecology, 28(1), 280-287.
  1. Russell, Deborah JF, McConnell, Bernie, Thompson, David, Duck, Callan, Morris, Chris, Harwood, John and Matthiopoulos, Jason (2013) Uncovering the links between foraging and breeding regions in a highly mobile mammal. Journal of Applied Ecology, 50(2), 499–509.
  1. Russell, Deborah J.F., Brasseur, Sophie M.J.M., Thompson, Dave, Hastie, Gordon D., Janik, Vincent M., Aarts, Geert. McClintock, Brett T., Matthiopoulos, Jason, Moss, Simon E.W. and McConnell, Bernie (2014) Marine mammals trace anthropogenic structures at sea. Current Biology, 24(14), R638–R639.
  1. Russell, Deborah J.F., McClintock, Brett T., Matthiopoulos, Jason, Thompson, Paul M., Thompson, Dave, Hammond, Phil S., Jones, Esther L., MacKenzie, Monique L., Moss, Simon and McConnell, Bernie J. (2015) Intrinsic and extrinsic drivers of activity budgets in sympatric grey and harbour seals. Oikos, 124(11), 1462–1472.
  1. Russell, Debbie J.F., Hastie, Gordon D., Thompson, David, Janik, Vincent M., Hammond, Philip S., Scott-Hayward, Lindesay A.S., Matthiopoulos, Jason, Jones, Esther L., McConnell, Bernie J. (2016) Avoidance of wind farms by harbour seals is limited to pile driving activities. Journal of Applied Ecology, 53(6), 1642–1652.
  1. Scales, Kylie L., Miller, Peter I., Embling, Clare B., Ingram, Simon N., Pirotta, Enrico and Votier, Stephen C. (2014) Mesoscale fronts as foraging habitats: composite front mapping reveals oceanographic drivers of habitat use for a pelagic seabird. Journal of the Royal Society Interface, 11(100), 20140679.
  1. Scales, Kylie L, Hazen, Elliott L., Jacox, Michael G., Edwards, Christopher A., Boustany, Andre M., Oliver, Matthew J. and Bograd, Steven J. (2016) Scale of inference: on the sensitivity of habitat models for wide-ranging marine predators to the resolution of environmental data. Ecography, 40(1), 210–220.
  1. Schlägel, Ulrike E and Lewis, Mark A. (2014) Detecting effects of spatial memory and dynamic information on animal movement decisions. Methods in Ecology and Evolution, 5(11), 1236–1246.
  1. Shepard, Emily L.C., Lambertucci, Sergio A., Vallmitjana, Diego and Wilson, Rory P. (2011) Energy beyond food: foraging theory informs time spent in thermals by a large soaring bird. PLoS One, 6(11), e27375.
  1. Shepard, Emily L.C., Wilson, Rory P., Rees, W. Gareth, Grundy, Edward, Lambertucci, Sergio A. and Vosper, Simon B. (2013) Energy landscapes shape animal movement ecology. The American Naturalist, 182(3), 298–312.
  1. Shepard, Emily L.C., Ross, Andrew N. and Portugal, Steven J. (2016) Moving in a moving medium: new perspectives on flight. Philosophical Transactions of the Royal Society B: Biological Sciences, 371, 20150382.
  1. Shiomi, Kozue, Sato, Katsufumi, Mitamura, Hiromichi, Arai, Nobuaki, Naito, Yasuhiko and Ponganis, Paul J. (2008) Effect of ocean current on the dead-reckoning estimation of 3-D dive paths of emperor penguins. Aquatic Biology, 3(3), 265–270.
  1. Shiomi, Kozue, Narazaki, Tomoko, Sato, Katsufumi, Shimatani, Kenichiro, Arai, Nobuaki, Ponganis, Paul J. and Miyazaki, Nobuyuki (2010) Data-processing artefacts in three-dimensional dive path re-construction from geomagnetic and acceleration data. Aquatic Biology, 8(3), 299–304.
  1. Strandburg-Peshkin, Ariana, Farine, Damien R., Couzin, Iain D. and Crofoot, Margaret C. (2015) Shared decision-making drives collective movement in wild baboons. Science, 348(6241), 1358–1361.
  1. Strandburg-Peshkin, Ariana, Farine, Damien R., Crofoot, Margaret C. and Couzin, Iain D. (2017) Habitat and social factors shape individual decisions and emergent group structure during baboon collective movement. eLife, 6, e19505.
  1. Struve, Juliane, Lorenzen, Kai, Blanchard, Julia, Börger, Luca, Bunnefeld, Nils, Edwards, Charles, Hortal, Joaquín, MacCall, Alec, Matthiopoulos, Jason, Van Moorter, Bram, Ozgul, Arpat, Royer, François, Singh, Navinder, Yesson, Chris and Bernard, Rodolphe (2016) Lost in space? Searching for directions in the spatial modelling of individuals, populations and species ranges. Biology Letters, 6(5), 575–578.
  1. Thums, Michele, Bradshaw, Corey J.A. and Hindell, Mark A. (2008) Tracking changes in relative body composition of southern elephant seals using swim speed data. Marine Ecology Progress Series, 370, 249-261.
  1. Thums, Michele, Bradshaw, Corey J. A. and Hindell, Mark A. (2011) In situ measures of foraging success and prey encounter reveal marine habitat-dependent search strategies. Ecology, 92, 1258-1279.
  1. Vacquié-Garcia, Jade, Guinet, Christophe, Dragon, Anne-Cécile, Viviant, Morgane, El Ksabi, Nory and Bailleul, Frédéric (2015) Predicting prey capture rates of southern elephant seals from track and dive parameters. Marine Ecology Progress Series, 541, 265–277.

 

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