The understanding of the interplay of movement, behaviour and physiology that biologging offers has applied relevance for a range of fields, including evolutionary ecology, wildlife conservation and behavioural ecology. In recognition of this, the Journal of Animal Ecology has an upcoming Special Feature on Biologging  (submissions due 20th September).

An advantage of biologging is that it can be applied to a wide range of animal species. To demonstrate this we hear from Dr Theoni Photopoulou, a quantitative ecologist and Newton International Fellow at the University of St Andrews, who is interested in the movement ecology of swimming, diving and flying animals. Here, she gives us an example of each and explains what can be done with the squiggly lines they create!

I was introduced to animal tracking data as a master’s student by a fabulous, natural historian of a supervisor, whose enthusiasm was contagious. I had seen him give a talk at the department’s weekly seminar series and plucked up the courage to approach him about supervising my research project. I was completely unaware of where that single act of courage by a shy student would take me – it defined my career path so far.

I knew I was interested in animal behaviour from early on in my studies, but it was the first time I saw satellite tracks from southern elephant seals that my interest really took root. There is nothing like a squiggly line cutting across a seemingly blank stretch of ocean to awaken one’s curiosity about what that animal is looking for, or following, or experiencing. My curiosity latched onto the world of biologging and biotelemetry, like a barnacle larva finding the right rock to settle on. It was also those squiggly lines on a map of the South Atlantic ocean that kindled my interest in statistics. It became clear that the ANOVAs and t-tests that I learned during my undergraduate years were as useful for making inferences from telemetry data, as a spoon is for cutting through stringy celery. Frustrating and fruitless. And so, my interest in finding out what animals are doing out there, has aligned with finding the best tools for the job. Most of the time that means using new tools other people develop, but sometimes I end up to creating a new tool myself.

Since those early days, I have moved on from only working on marine animals and​ my interests have expanded (Best and Photopoulou 2016, Boehme et al 2016, Michelot et al 2017, Photopoulou et al 2017). I like using statistical methods to learn about pattern and process in the ecology and biology of animals, but my main interest remains the movement ecology of animals – mainly flying, swimming and diving animals.

After my master’s I carried on and worked on diving behaviour in phocid seals for a PhD. I looked at the relationship between diving behaviour and physical environmental variables (Photopoulou et al 2014) as well as the mechanisms behind diving, and the way we collect diving information (Photopoulou et al 2015a, Photopoulou et al 2015b). I didn’t realise it at the time, but biologging is an enormous field full of exciting potential and many different avenues. Because telemetry depends on using complex instruments which produce complex data from animals living in a complex environment, this forces one to develop lots of different skills. Skills ranging from handling animals in the field, to handling people in the field, and from statistical modelling to understanding the engineering that goes into device hard- and software.

When I finished my PhD I moved to a new university and a new study medium – the air. I became involved in a project about Verreaux’s eagles and learned about the use of accelerometers for understanding animal movement (Leos Barajas et al 2017), taking a different perspective to what I was used to. Looking at movement in the air gave me a fresh view of the resources that animals exploit. I had spent 5 years thinking of diving behaviour, which manifests due to the separation of two very tangible resources, food at depth and oxygen at the surface. For this and other large eagles, the aerial environment does not offer a resource that can be physically consumed, since they hunt prey on the ground, but one that can be harnessed. Verreaux’s eagles exploit constantly changing ephemeral updrafts to stay aloft with minimal energy expenditure. We have no idea how they identify “profitable” areas, whether they remember them or defend them, but we know a little bit about how they vary across the landscape (Murgatroyd et al 2018) and we are only just starting to understand how this might affect their population dynamics.

More recently I have gravitated back to marine creatures, and was introduced to a slightly different kind of telemetry data. As fish, great white sharks don’t surface to breathe, in fact they spend very little time at the surface. When we want to study them long-term, this means that we need to use a different approach to what we would for air-breathing animals. Passive acoustic monitoring is being used widely to collect data on where fish go. Most fish do not make sounds themselves and in these cases sound-emitting tags can be used and can produce long-term datasets. This involves a two-part system: 1) an array of underwater receivers that detect and record sounds, and 2) tagged fish that swim around emitting sound that is detected by the receivers. The resulting data tell you primarily when and where the tagged fish was detected and this is already very useful for understanding seasonal patterns (Kock et al 2018). However, if you think of detections as a time series of locations where a fish was seen, you can learn something about where it might have been during the periods when you didn’t see it. I’m really excited about this work, which is almost written up, and I hope that the method we are developing is going to be useful for a lot of people who have similar data.

For my current postdoc I am back where I started – studying the diving behaviour of seals. It is very good to be back, but this is certainly not where I plan to end. One of my favourite things about this field is how diverse it is, and how many opportunities there are. Biologging presents opportunities both for studying individual systems and exciting animals that we still need to know basic things about, but also for unifying what we have learned over the last few decades, and growing what was a very practical and applied field by bringing in more ecological theory. For myself, I look forward to working on both of these things and continuing to feel excited by squiggly lines, new methods and big ideas, alike.

More Info

Best PB and Photopoulou T. 2016. Identifying the “demon whale-biter:” Patterns of scarring on large whales attributed to a cookie-cutter shark. PLoS ONE 11(4): e0152643

Boehme L, Baker A, Fedak M, Årthun M, Nichols K, Robinson P, Costa D, Biuw M and Photopoulou T. 2016. Bimodal winter haul-out patterns of adult Weddell seals (Leptonychotes weddellii) in the southern Weddell Sea. PLoS ONE 11(5): e0155817

Photopoulou T, Ferreira I, Kasuya T, Best PB and Marsh H. 2017. Evidence for a postreproductive phase in female false killer whales Pseudorca crassidens. Frontiers and Zoology 14:30

​Michelot T, Langrock R, Bestley S, Jonsen ID, Photopoulou T and Patterson TA. 2017. Estimation and simulation of foraging trips in land-based marine predators. Ecology 98(7): 1932-1944

Photopoulou T, Fedak MA, Thomas L, Matthiopoulos J. 2014. Spatial variation in maximum dive depth in gray seals in relation to foraging. Marine Mammal Science 30(3): 923-938

Photopoulou T, Lovell P, Fedak MA, Thomas L, Matthiopoulos J. 2015. Efficient abstracting of dive profiles using a broken-stick model. Methods in Ecology and Evolution 6(3): 278-288

Photopoulou T, Fedak M., Matthiopoulos J, McConnell BM, and Lovell P. 2015. The generalized data management and collection protocol for Conductivity-Temperature-Depth Satellite Relay Data Loggers. Animal Biotelemetry 3:21

Leos-Barajas V, Photopoulou T, Langrock R, Patterson TA, Watanabe Y, Murgatroyd M, Papastamatiou Y. 2016. Analysis of animal accelerometer data using hidden Markov models. Methods in Ecology and Evolution 8(2): 161-173

Murgatroyd M, Photopoulou T, Underhill L, Bouten W and Amar A. 2018. Where eagles soar: fine resolution tracking reveals the spatiotemporal use of differential soaring modes in a large raptor. Ecology and Evolution (in press)

Kock AA, Photopoulou T, Durbach I, Mauff K, Meyer M, Kotze D, Griffiths C and O’Riain J. Summer at the beach: Spatio-temporal patterns of white shark occurrence along the inshore areas of False Bay, South Africa. Movement Ecology 6:7