Use of tri-axial accelerometers to assess terrestrial mammal behaviour in the wild

Tri-axial accelerometer tags provide quantitative data on body movement that can be used to characterize behaviour and understand species ecology in ways that would otherwise be impossible. Using tags on wild terrestrial mammals, especially smaller species, in natural settings has been limited. Poor battery power also reduced the amount of data collected, which limits what can be derived about animal behaviour. Another challenge using wild animals, is acquiring observations of actual behaviours with which to compare tag data and create an adequate training set to reliably identify behavioural states. Brown hares were fitted with accelerometers for 5 weeks to evaluate their use in collecting detailed behaviour data and activity levels. Collared hares were filmed to associate actual behaviours with tag data. Observed behaviours were classified using Random Forests (ensemble learning method) to create a supervised model and then used to classify hare behaviour from the tags. Increased tag longevity allowed acquisition of large quantities of data from each individual and direct observation of tagged hare's behaviour. Random Forests accurately classified observed behaviours from tag data with an 11% error rate. Individual accuracy of behaviours varied with running (100% accuracy), feeding (94.7%) and vigilance (98.3%) having the highest classification accuracy. Hares spent 46% of their time being vigilant and 25% feeding when active. The combination of our tags and Random Forests facilitated large amounts of behavioural data to be collected on animals where observational studies could be limited, or impossible. The same method could be used on a range of terrestrial mammals to create models to investigate behaviour from tag data, to learn more about their behaviour and be used to answer many ecological questions. However, further development of methods for analysing tag data is needed to make the process quicker, simpler and more accurate

Wild animals' biologging through machine learning models

In recent decades the biodiversity crisis has been characterised by a decline and extinction of many animal species worldwide. To aid in understanding the threats and causes of this demise, conservation scientists rely on remote assessments. Innovation in technology in the form of microelectromechanical systems (MEMs) has brought about great leaps forward in understanding of animal life. The MEMs are now readily available to ecologists for remotely monitoring the activities of wild animals. Since the advent of electronic tags, methods such as biologging are being increasingly applied to the study of animal ecology, providing information unattainable through other techniques. In this paper, we discuss a few relevant instances of biologging studies. We present an overview on biologging research area, describing the evolution of acquisition of behavioural information and the improvement provided by tags. In second part we will review some common data analysis techniques used to identify daily activity of animals

Monitoring canid scent marking in space and time using a biologging and machine learning approach

For canid species, scent marking plays a critical role in territoriality, social dynamics, and reproduction. However, due in part to human dependence on vision as our primary sensory modality, research on olfactory communication is hampered by a lack of tractable methods. In this study, we leverage a powerful biologging approach, using accelerometers in concert with GPS loggers to monitor and describe scent-marking events in time and space. We performed a validation experiment with domestic dogs, monitoring them by video concurrently with the novel biologging approach. We attached an accelerometer to the pelvis of 31 dogs (19 males and 12 females), detecting raised-leg and squat posture urinations by monitoring the change in device orientation. We then deployed this technique to describe the scent marking activity of 3 guardian dogs as they defend livestock from coyote depredation in California, providing an example use-case for the technique. During validation, the algorithm correctly classifed 92% of accelerometer readings. High performance was partly due to the conspicuous signatures of archetypal raised-leg postures in the accelerometer data. Accuracy did not vary with the weight, age, and sex of the dogs, resulting in a method that is broadly applicable across canid species’ morphologies. We also used models trained on each individual to detect scent marking of others to emulate the use of captive surrogates for model training. We observed no relationship between the similarity in body weight between the dog pairs and the overall accuracy of predictions, although models performed best when trained and tested on the same individual. We discuss how existing methods in the feld of movement ecology can be extended to use this exciting new data type. This paper represents an important frst step in opening new avenues of research by leveraging the power of modern-technologies and machine-learning to this feldFil: Bidder, Owen. University of California at Berkeley; Estados UnidosFil: Di Virgilio, Agustina Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; ArgentinaFil: Hunter, Jennifer. University of California at Berkeley; Estados UnidosFil: McInturff, Alex. University of California at Berkeley; Estados UnidosFil: Gaynor, Kaitlyn. University of California at Berkeley; Estados UnidosFil: Smith, Alison. University of California at Berkeley; Estados UnidosFil: Dorcy, Janelle. University of California at Berkeley; Estados UnidosFil: Rosell, Frank. University of South-Eastern Norway; Norueg

Establishing best practice for the classification of shark behaviour from bio-logging data

Understanding the behaviours of free-ranging animals over biologically meaningful time scales (e.g. diel, tidal, lunar, seasonal, annual) gives important insights into their ecology. Bio-logging tools allow the remote study of elusive or inaccessible animals by recording high resolution multi-channel movement data, however archival device recording duration is limited to relatively short temporal-scales by memory and battery capacity. Machine learning (ML) is becoming common for automatic classification of behaviours from large data sets. This thesis develops a framework for the programming of bio-loggers for the classification of shark behaviour through the optimisation of sampling frequency (Chapter 2) and the choice of movement sensor (Chapter 3). The effects of sampling frequency on behavioural classification were assessed using data published in a previous study collected from accelerometer equipped juvenile lemon sharks (Negaprion brevirostris) during captive trials in Bimini, Bahamas. The impacts of different combinations of movement sensors (accelerometer, magnetometer and gyroscope) were assessed using data collected from sub adult sicklefin lemon sharks (Negaprion acutidens). Sharks were equipped with multi-sensor devices recording acceleration, angular rotation and angular velocity during captive trials at St Joseph Atoll, Seychelles. Catalogues of discrete classes of behaviours (ethograms) were developed by observing sharks during captive trials. Behaviours (swim, rest, burst, chafe, headshake) were classified using a random forest ML algorithm with predictor variables extracted from the ground-truthed data. A range of sampling frequencies (30, 15, 10, 5, 3 and 1 Hz) and combinations of movement sensors were tested. For each dataset, a confusion matrix was determined from model predictions for calculation and comparison of evaluation metrics. Classifier performance was best described by the class or macro F- score, a measure of model performance, one indicating perfect classification and zero indicating no classification. As sampling frequency decreased, classifier performance decreased. Best overall classification was achieved at 30 Hz (F- score >0.790), although 5 Hz was appropriate for classification of swim and rest (>0.964). Behaviours characterised by complex movements (headshake, burst, chafe) were best classified at 30 Hz (0.535- 0.846). Classification of behaviours was best with a tri-sensor combination (0.597), although incorporating an additional sensor (magnetometer or gyroscope) resulted in little increase in classifier performance compared to using an accelerometer alone (0.590 compared to 0.535 respectively). These results demonstrate the ideal sampling frequencies and movement sensors for best-practice programming of bio-logging devices for classifying shark behaviour over extended durations. This thesis will inform future studies incorporating behaviour classification, enabling improved classifier performance and extending recording duration of bio-logging devices

Prying into the intimate secrets of animal lives; software beyond hardware for comprehensive annotation in ‘Daily Diary’ tags

Smart tags attached to freely-roaming animals recording multiple parameters at infra-second rates are becoming commonplace, and are transforming our understanding of the way wild animals operate. However, interpretation of such data is complex and currently limits the ability of biologists to realise the value of their recorded information. This work presents a single program, FRAMEWORK 4, that uses a particular sensor constellation described in the?Daily Diary? tag (recording tri-axial acceleration, tri-axial magnetic field intensity, pressure and e.g. temperature and light intensity) to determine the 4 key elements considered pivotal within the conception of the tag. These are; animal trajectory, behaviour, energy expenditure and quantification of the environment in which the animal operates. The program takes the original data recorded by the Daily Dairy and transforms it into dead-reckoned movements,template-matched behaviours, dynamic body acceleration-derived energetics and positionlinked environmental data before outputting it all into a single file. Biologists are thus left with a single data set where animal actions and environmental conditions can be linked across time and space.Fil: Walker, James S.. Swansea University. College Of Sciences; Reino UnidoFil: Jones, Mark W.. Swansea University. College Of Sciences; Reino UnidoFil: Laramee, Robert S.. Swansea University. College Of Sciences; Reino UnidoFil: Holton, Mark D.. Swansea University; Reino UnidoFil: Shepard, Emily L. C.. Swansea University. College Of Sciences; Reino UnidoFil: Williams, Hannah J.. Swansea University. College Of Sciences; Reino UnidoFil: Scantlebury, D. Michael. The Queens University Of Belfast; IrlandaFil: Marks, Nikki, J.. The Queens University Of Belfast; IrlandaFil: Magowan, Elizabeth A.. The Queens University Of Belfast; IrlandaFil: Maguire, Iain E.. The Queens University Of Belfast; IrlandaFil: Grundy, Ed. Swansea University. College Of Sciences; Reino UnidoFil: Di Virgilio, Agustina Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Patagonia Norte. Instituto de Investigación En Biodiversidad y Medioambiente; Argentina. Universidad Nacional del Comahue; ArgentinaFil: Wilson, Rory P.. Swansea University. College Of Sciences; Reino Unid

Deep Time-Series Clustering: A Review

We present a comprehensive, detailed review of time-series data analysis, with emphasis on deep time-series clustering (DTSC), and a case study in the context of movement behavior clustering utilizing the deep clustering method. Specifically, we modified the DCAE architectures to suit time-series data at the time of our prior deep clustering work. Lately, several works have been carried out on deep clustering of time-series data. We also review these works and identify state-of-the-art, as well as present an outlook on this important field of DTSC from five important perspectives