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

Multiclass semi-supervised learning for animal behavior recognition from accelerometer data

In this paper we present a new Multiclass semi-supervised learning algorithm that uses a base classifier in combination with a similarity function applied to all data to find a classifier that maximizes the margin and consistency over all data. A novel multiclass loss function is presented and used to derive the algorithm. We apply the algorithm to animal behavior recognition from accelerometer data. Animal-borne accelerometer data are collected from free-ranging animals and then labeled by a human expert. The resulting data are used to train a classifier. However, labeling is not easy from accelerometer data only and it is often not feasible to observe animals fitted with an accelerometer. All current approaches to this behavior recognition task use supervised or unsupervised learning. Since unlabeled data are easy to acquire and collect, a semi-supervised approach seems appropriate and reduces the human efforts for labeling. Experiments with accelerometer data collected from free-ranging gulls and benchmark UCI datasets show that the algorithm is effective and compares favorably with existing algorithms for multiclass semi-supervised learning