Marine predators are integral to the functioning of marine ecosystems, and their consumption requirements should be integrated into ecosystem-based management policies. However, estimating prey consumption in diving marine predators requires innovative methods as predator–prey interactions are rarely observable. We developed a novel method, validated by animal-borne video, that uses tri-axial acceleration and depth data to quantify prey capture rates in chinstrap penguins ( Pygoscelis antarctica ). These penguins are important consumers of Antarctic krill ( Euphausia superba ), a commercially harvested crustacean central to the Southern Ocean food web. We collected a large data set ( n = 41 individuals) comprising overlapping video, accelerometer and depth data from foraging penguins. Prey captures were manually identified in videos, and those observations were used in supervised training of two deep learning neural networks (convolutional neural network (CNN) and V-Net). Although the CNN and V-Net architectures and input data pipelines differed, both trained models were able to predict prey captures from new acceleration and depth data (linear regression slope of predictions against video-observed prey captures = 1.13; R ² ≈ 0.86). Our results illustrate that deep learning algorithms offer a means to process the large quantities of data generated by contemporary bio-logging sensors to robustly estimate prey capture events in diving marine predators.

Accelerometers offer unique opportunities to study the behaviour of cryptic animals but require validation to show their accuracy in identifying behaviours. This validation is often undertaken in captivity before use in the wild. While zoos provide important opportunities for trial field techniques, they must consider the welfare and health of the individuals in their care and researchers must opt for the least invasive techniques. We used positive reinforcement training to attach and detach a collar with an accelerometer to an individual Bengal slow loris (Nycticebus bengalensis) at the Shaldon Wildlife Trust, U.K. This allowed us to collect accelerometer data at different periods between January–June 2020 and January–February 2021, totalling 42 h of data with corresponding video for validation. Of these data, we selected 54 min where ten behaviours were present and ran a random forest model. We needed 39 15-min sessions to train the animal to wear/remove the collar. The accelerometer data had an accuracy of 80.7 ± SD 9.9% in predicting the behaviours, with 99.8% accuracy in predicting resting, and a lower accuracy (but still >75% for all of them apart from suspensory walk) for the different types of locomotion and feeding behaviours. This training and validation technique can be used in similar species and shows the importance of working with zoos for in situ conservation (e.g., validation of field techniques).

Accelerometers are powerful tools for behavioral ecologists studying wild animals, particularly species that are difficult to observe due to their cryptic nature or dense or difficult to access habitats. Using a supervised approach, e.g., by observing in detail with a detailed ethogram the behavior of an individual wearing an accelerometer, to train a machine learning algorithm and the accelerometer data of one individual from a wild population of Javan slow lorises (Nycticebus javanicus), we applied a Random Forest model (RFM) to classify specific behaviors and posture or movement modifiers automatically. We predicted RFM would identify simple behaviors such as resting with the greatest accuracy while more complex behaviors such as feeding and locomotion would be identified with lower accuracy. Indeed, resting behaviors were identified with a mean accuracy of 99.16% while feeding behaviors were identified with a mean accuracy of 94.88% and locomotor behaviors with 85.54%. The model identified a total of 21 distinct combinations of six behaviors and 18 postural or movement modifiers in this dataset showing that RFMs are effective as a supervised approach to classifying accelerometer data. The methods used in this study can serve as guidelines for future research for slow lorises and other ecologically similar wild mammals. These results are encouraging and have important implications for understanding wildlife responses and resistance to global climate change, anthropogenic environmental modification and destruction, and other pressures.

Protected areas are a widely adopted resource management strategy for mitigating the consequences of global change and preserve functioning ecosystems. Long-term species monitoring programmes, aided by bio-logging technology, provide insights into the extent and spatial variation of areas occupied by wild animals and inform conservation and management. High-resolution GPS-acceleration data offer a more accurate understanding of animal behavior and area use, compared to location-based inference, emphasizing the significance of specific sites amid long-term climate change. We based our case-study on the largest colony of little penguins ( Eudyptula minor ) located at Phillip Island. Based on a ten-year bio-logging dataset (247 individual tracks), we combine high-resolution bio-logging data from GPS-accelerometer loggers with proxies for resource availability (e.g. Sea Surface Tenperature, thermocline, water turbidity). Using machine learning techniques and Generalized Additive Mixed Models, we quantify the environmental factors determining spatio-temporal variability in foraging effort (defined as hunting time) across different breeding seasons and stages. Little penguins increased their hunting time by reducing spatial displacement (shorter step length) and diving deeper, with a slower increase in hunting effort below 10 m depth. In relation to environmental conditions, penguins increased hunting effort in coastal areas with high turbid and productive waters and decreased effort with increasing Sea Surface Temperature. This gives insights into how these animals allocate effort differently according to shifting environmental conditions. Our analysis offers crucial long-term insights into little penguin area usage in the Bass Strait at sufficient spatial and temporal resolution for management and conservation planning. The Bass Strait is facing intense climatic and anthripogenic pressures, and the findings here on intensity of area usage and strategy shifting according to environmental conditions, are of great relevance for the marine spatial planning currently under development along the coast. Policy implications: High-resolution behavioral information obtained from bio-logging data using GPS-accelerometer tags provides understanding of how species shift strategies in response to environmental variability. This is vital to implement climate-adaptive conservation and management strategies. Given the growing availability of long-term accelerometer datasets within the ecological community, we recommend integrating such high-resolution information into conservation programs.