Using Accelerometers To Monitor Behavioral Changes In Seals
Marine predators are crucial components of marine food webs. To conserve these species it is necessary to understand how they respond to environmental challenges. This study utilizes machine learning to automatically detect behaviors from accelerometers. This data can be used to monitor behavioral changes over time and identify major drivers behind population changes.
Dr. Monique A. Ladds
School of Mathematics and Statistics, Victoria University of Wellington, Wellington, New Zealand
Prof Robert Harcourt
Marine Predator Research Group, Macquarie University, Sydney, Australia
The Conservation Challenge
Marine predators play a large role in the functioning of the marine ecosystems. Increasing sea-surface temperatures and increased fishing pressures have led to ecosystem changes that are altering prey density and distribution. Understanding the working interaction between the ecosystems and human populations is difficult, and finding ways of protecting these animals is complicated. It is essential to the survivorship of these species that we predict their responses to these challenges. Activity budgets developed for marine predators are a useful tool for monitoring their responses to these challenges. Developing activity budgets allow us to monitor marine predators foraging effort, in turn informing about prey availability.
To understand where conservation strategies may be implemented we first need to understand how marine predators respond to challenges. Foraging for marine predators requires traveling great distances and diving to depth in order to find food. The effort used to forage successfully over time will change in response to changing environmental factors. Accelerometers and machine learning can help to monitor these responses. Accelerometers attached to seals record behaviour, and we can use artificial intelligence to automatically classify those behaviours. This allows us to monitor marine predators over time, discovering where and how they are spending their time.
Juvenile fur seals in South-East Australia were equipped with accelerometers so that we could learn about what kind of behaviors they use, and for how long, while out-of-sight.
Description of Technology Used
To ensure that we gained the greatest recognition from the accelerometers, we tested a suite of machine learning algorithms (logistic regression, support-vector machines, random forests and gradient boosting machines). For each algorithm, we used a training and testing set which were built from captive experiments, where each behaviour was manually coded from video of the animal wearing the accelerometer. Using the training set each algorithm was taught the pattern of each behavior. Then the algorithm was used to predict behaviours from the test set. This resulted in accuracies of over 80%.
This shows foraging bout, transition and haul-out of a female 40kg juvenile Australian fur seal and the different outputs from the accelerometer, we can see when the seal was grooming, resting, foraging and travelling.
Our major limitation was to define foraging. In the wild, marine predators would actively pursue live prey. We were unable to recreate these scenarios in captivity, therefore it was difficult to predict this category with great accuracy.
Opportunities/Call To Action
The results from this study have been shared through peer-reviewed journals and are already being taken up to identify the behaviours of other species, such as sharks. The use of accelerometers to identify behaviours can be used to make activity budgets to monitor animals over time and identify how they adapt to changes in their environment.
Now it is time to apply this technology in the wild after validating with captive animals. It is hoped that we will be able to learn more about the behaviours of animals that are difficult or impossible to study in the wild.
In the Marine Predator Research Group we have broad interests in the behaviour, ecology and conservation of animals which occupy higher trophic levels in marine food webs. Since formation at Macquarie in 1997 we have been conducting research on subjects as diverse as animal personality, animal communication, mating preferences, individual differences, sociality and population genetics through to more applied research on foraging ecology, animal movements, population dynamics, conservation physiology and anthropogenic interactions. We are a large lab with collaborators around Australia and across the world and always happy to talk with new enthusiasts.