Dataset Information

Behavioural inference from signal processing using animal-borne multi-sensor loggers: a novel solution to extend the knowledge of sea turtle ecology.

ABSTRACT: The identification of sea turtle behaviours is a prerequisite to predicting the activities and time-budget of these animals in their natural habitat over the long term. However, this is hampered by a lack of reliable methods that enable the detection and monitoring of certain key behaviours such as feeding. This study proposes a combined approach that automatically identifies the different behaviours of free-ranging sea turtles through the use of animal-borne multi-sensor recorders (accelerometer, gyroscope and time-depth recorder), validated by animal-borne video-recorder data. We show here that the combination of supervised learning algorithms and multi-signal analysis tools can provide accurate inferences of the behaviours expressed, including feeding and scratching behaviours that are of crucial ecological interest for sea turtles. Our procedure uses multi-sensor miniaturized loggers that can be deployed on free-ranging animals with minimal disturbance. It provides an easily adaptable and replicable approach for the long-term automatic identification of the different activities and determination of time-budgets in sea turtles. This approach should also be applicable to a broad range of other species and could significantly contribute to the conservation of endangered species by providing detailed knowledge of key animal activities such as feeding, travelling and resting.

SUBMITTER: Jeantet L

PROVIDER: S-EPMC7277266 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Behavioural inference from signal processing using animal-borne multi-sensor loggers: a novel solution to extend the knowledge of sea turtle ecology.

Jeantet Lorène L Planas-Bielsa Víctor V Benhamou Simon S Geiger Sebastien S Martin Jordan J Siegwalt Flora F Lelong Pierre P Gresser Julie J Etienne Denis D Hiélard Gaëlle G Arque Alexandre A Regis Sidney S Lecerf Nicolas N Frouin Cédric C Benhalilou Abdelwahab A Murgale Céline C Maillet Thomas T Andreani Lucas L Campistron Guilhem G Delvaux Hélène H Guyon Christelle C Richard Sandrine S Lefebvre Fabien F Aubert Nathalie N Habold Caroline C le Maho Yvon Y Chevallier Damien D

Royal Society open science 20200513 5

The identification of sea turtle behaviours is a prerequisite to predicting the activities and time-budget of these animals in their natural habitat over the long term. However, this is hampered by a lack of reliable methods that enable the detection and monitoring of certain key behaviours such as feeding. This study proposes a combined approach that automatically identifies the different behaviours of free-ranging sea turtles through the use of animal-borne multi-sensor recorders (acceleromete ...[more]

PMID: 32537218

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Project description:Growing interest in the structure and dynamics of animal social networks has stimulated efforts to develop automated tracking technologies that can reliably record encounters in free-ranging subjects. A particularly promising approach is the use of animal-attached 'proximity loggers', which collect data on the incidence, duration and proximity of spatial associations through inter-logger radio communication. While proximity logging is based on a straightforward physical principle - the attenuation of propagating radio waves with distance - calibrating systems for field deployment is challenging, since most study species roam across complex, heterogeneous environments.In this study, we calibrated a recently developed digital proximity-logging system ('Encounternet') for deployment on a wild population of New Caledonian crows Corvus moneduloides. Our principal objective was to establish a quantitative model that enables robust post hoc estimation of logger-to-logger (and, hence, crow-to-crow) distances from logger-recorded signal-strength values. To achieve an accurate description of the radio communication between crow-borne loggers, we conducted a calibration exercise that combines theoretical analyses, field experiments, statistical modelling, behavioural observations, and computer simulations.We show that, using signal-strength information only, it is possible to assign crow encounters reliably to predefined distance classes, enabling powerful analyses of social dynamics. For example, raw data sets from field-deployed loggers can be filtered at the analysis stage to include predominantly encounters where crows would have come to within a few metres of each other, and could therefore have socially learned new behaviours through direct observation. One of the main challenges for improving data classification further is the fact that crows - like most other study species - associate across a wide variety of habitats and behavioural contexts, with different signal-attenuation properties.Our study demonstrates that well-calibrated proximity-logging systems can be used to chart social associations of free-ranging animals over a range of biologically meaningful distances. At the same time, however, it highlights that considerable efforts are required to conduct study-specific system calibrations that adequately account for the biological and technological complexities of field deployments. Although we report results from a particular case study, the basic rationale of our multi-step calibration exercise applies to many other tracking systems and study species.

Project description:Effective conservation management strategies require accurate information on the movement patterns and behaviour of wild animals. To collect these data, researchers are increasingly turning to remote sensing technology such as radio-frequency identification (RFID). RFID technology is a powerful tool that has been widely implemented in ecological research to identify and monitor unique individuals, but it bears a substantial price tag, restricting this technology to generously-funded disciplines and projects. To overcome this price hurdle, we provide detailed step-by-step instructions to source the components for, and construct portable RFID loggers in house, at a fraction of the cost (~5%) of commercial RFID units. Here, we assess the performance of these RFID loggers in the field and describe their application in two studies of Australian mammal species; monitoring nest-box use in the Northern quolls (Dasyurus hallucatus) and observing the foraging habits of quenda (Isoodon fusciventer) at feeding stations. The RFID loggers performed well, identifying quenda in >80% of visits, and facilitating the collection of individual-level behavioural data including common metrics such as emergence time, latency to approach, and foraging effort. While the technology itself is not novel, by lowering the cost per unit, our loggers enabled greater sample sizes, increasing statistical power from 0.09 to 0.75 in the quoll study. Further, we outline and provide solutions to the limitations of this design. Our RFID loggers proved an innovative method for collecting accurate behavioural and movement data. With their ability to successfully identify individuals, the RFID loggers described here can act as an alternative or complementary tool to camera traps. These RFID loggers can also be applied in a wide variety of projects which range from monitoring animal welfare or demographic traits to studies of anti-predator responses and animal personality, making them a valuable addition to the modern ecologists' toolkit.

Project description:The idea that interspecific variation in trophic morphology among closely related species effectively permits resource partitioning has driven research on ecological radiation since Darwin first described variation in beak morphology among Geospiza. Marine turtles comprise an ecological radiation in which interspecific differences in trophic morphology have similarly been implicated as a pathway to ecopartition the marine realm, in both extant and extinct species. Because marine turtles are charismatic flagship species of conservation concern, their trophic ecology has been studied intensively using stable isotope analyses to gain insights into habitat use and diet, principally to inform conservation management. This legion of studies provides an unparalleled opportunity to examine ecological partitioning across numerous hierarchical levels that heretofore has not been applied to any other ecological radiation. Our contribution aims to provide a quantitative analysis of interspecific variation and a comprehensive review of intraspecific variation in trophic ecology across different hierarchical levels marshalling insights about realised trophic ecology derived from stable isotopes. We reviewed 113 stable isotope studies, mostly involving single species, and conducted a meta-analysis of data from adults to elucidate differences in trophic ecology among species. Our study reveals a more intricate hierarchy of ecopartitioning by marine turtles than previously recognised based on trophic morphology and dietary analyses. We found strong statistical support for interspecific partitioning, as well as a continuum of intraspecific trophic sub-specialisation in most species across several hierarchical levels. This ubiquity of trophic specialisation across many hierarchical levels exposes a far more complex view of trophic ecology and resource-axis exploitation than suggested by species diversity alone. Not only do species segregate along many widely understood axes such as body size, macrohabitat, and trophic morphology but the general pattern revealed by isotopic studies is one of microhabitat segregation and variation in foraging behaviour within species, within populations, and among individuals. These findings are highly relevant to conservation management because they imply ecological non-exchangeability, which introduces a new dimension beyond that of genetic stocks which drives current conservation planning. Perhaps the most remarkable finding from our data synthesis is that four of six marine turtle species forage across several trophic levels. This pattern is unlike that seen in other large marine predators, which forage at a single trophic level according to stable isotopes. This finding affirms suggestions that marine turtles are robust sentinels of ocean health and likely stabilise marine food webs. This insight has broader significance for studies of marine food webs and trophic ecology of large marine predators. Beyond insights concerning marine turtle ecology and conservation, our findings also have broader implications for the study of ecological radiations. Particularly, the unrecognised complexity of ecopartitioning beyond that predicted by trophic morphology suggests that this dominant approach in adaptive radiation research likely underestimates the degree of resource overlap and that interspecific disparities in trophic morphology may often over-predict the degree of realised ecopartitioning. Hence, our findings suggest that stable isotopes can profitably be applied to study other ecological radiations and may reveal trophic variation beyond that reflected by trophic morphology.

Project description:BackgroundAnimal-borne data loggers today often house several sensors recording simultaneously at high frequency. This offers opportunities to gain fine-scale insights into behaviour from individual-sensor as well as integrated multi-sensor data. In the context of behaviour recognition, even though accelerometers have been used extensively, magnetometers have recently been shown to detect specific behaviours that accelerometers miss. The prevalent constraint of limited training data necessitates the importance of identifying behaviours with high robustness to data from new individuals, and may require fusing data from both these sensors. However, no study yet has developed an end-to-end approach to recognise common animal behaviours such as foraging, locomotion, and resting from magnetometer data in a common classification framework capable of accommodating and comparing data from both sensors.MethodsWe address this by first leveraging magnetometers' similarity to accelerometers to develop biomechanical descriptors of movement: we use the static component given by sensor tilt with respect to Earth's local magnetic field to estimate posture, and the dynamic component given by change in sensor tilt with time to characterise movement intensity and periodicity. We use these descriptors within an existing hybrid scheme that combines biomechanics and machine learning to recognise behaviour. We showcase the utility of our method on triaxial magnetometer data collected on ten wild Kalahari meerkats (Suricata suricatta), with annotated video recordings of each individual serving as groundtruth. Finally, we compare our results with accelerometer-based behaviour recognition.ResultsThe overall recognition accuracy of > 94% obtained with magnetometer data was found to be comparable to that achieved using accelerometer data. Interestingly, higher robustness to inter-individual variability in dynamic behaviour was achieved with the magnetometer, while the accelerometer was better at estimating posture.ConclusionsMagnetometers were found to accurately identify common behaviours, and were particularly robust to dynamic behaviour recognition. The use of biomechanical considerations to summarise magnetometer data makes the hybrid scheme capable of accommodating data from either or both sensors within the same framework according to each sensor's strengths. This provides future studies with a method to assess the added benefit of using magnetometers for behaviour recognition.

Dataset Information

Behavioural inference from signal processing using animal-borne multi-sensor loggers: a novel solution to extend the knowledge of sea turtle ecology.

Publications

Behavioural inference from signal processing using animal-borne multi-sensor loggers: a novel solution to extend the knowledge of sea turtle ecology.

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