Energy expenditure of adult green turtles ( Chelonia mydas ) at their foraging grounds and during simulated oceanic migration

Multivariate analysis of biologging data reveals the environmental determinants of diving behaviour in a marine reptile

Diving behaviour of 'surfacers' such as sea snakes, cetaceans and turtles is complex and multi-dimensional, thus may be better captured by multi-sensor biologging data. However, analysing these large multi-faceted datasets remains challenging, though a high priority. We used high-resolution multi-sensor biologging data to provide the first detailed description of the environmental influences on flatback turtle (Natator depressus) diving behaviour, during its foraging life-history stage. We developed an analytical method to investigate seasonal, diel and tidal effects on diving behaviour for 24 adult flatback turtles tagged with biologgers. We extracted 16 dive variables associated with three-dimensional and kinematic characteristics for 4128 dives. K-means and hierarchical cluster analyses failed to identify distinct dive types. Instead, principal component analysis objectively condensed the dive variables, removing collinearity and highlighting the main features of diving behaviour. Generalized additive mixed models of the main principal components identified significant seasonal, diel and tidal effects on flatback turtle diving behaviour. Flatback turtles altered their diving behaviour in response to extreme tidal and water temperature ranges, displaying thermoregulation and predator avoidance strategies while likely optimizing foraging in this challenging environment. This study demonstrates an alternative statistical technique for objectively interpreting diving behaviour from multivariate collinear data derived from biologgers.

Fine scale behaviour and time-budget in the cryptic ectotherm European pond turtle Emys orbicularis

For ectotherms, behaviour and associated energetic costs are directly related to thermal conditions. In the present context of global change, estimating time-budget for these species is relevant to assess and predict their capacity to adapt to near future. We tested the hypothesis that in ectotherms where reproduction is highly energy consuming, energy expenditure should vary throughout the breeding season with a maximum around nesting events. To test this hypothesis, we assessed the fine-scale behaviour, time-budget and estimated energetic costs in eight adult female European pond turtles Emys orbicularis equipped with data-loggers recording ambient temperature, pressure, light and the animals’ 3-axis acceleration. Deployments occurred over four months throughout the nesting season 2017 in semi-natural captive conditions in Alsace, France. All study turtles showed a clear daily pattern over the 24h cycle, with four distinct phases (referred to as Night, Morning, Midday and Evening), associated with different behaviours and activity levels. Before oviposition, turtles were mostly active during Morning, and activity was positively driven by ambient temperature. Activity levels doubled during the nesting period, mostly due to the increased activity in the Evening, when nesting events occurred. Throughout the active season, basking occurrence at Midday was related to air temperature but cloud coverage was an even more important factor. Our results are a first step in predicting the seasonal time and energy budgets of the European pond turtle, and demonstrate the usefulness of animal-borne accelerometers to study free living freshwater turtles over extended periods of time.

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

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.

Fidelity to foraging sites after long migrations

Patterns of animal movement associated with foraging lie at the heart of many ecological studies and often animals face decisions of staying in an environment they know versus relocating to new sites. The lack of knowledge of new foraging sites means there is risk associated with a decision to relocate (e.g. poor foraging) as well as a potential benefit (e.g. improved foraging). Using a unique long-term satellite tracking dataset for several sea turtle species, combined with capture-mark-recapture data extending over 50 years, we show how, across species, individuals generally maintain tight fidelity to specific foraging sites after extended (up to almost 10,000 km) migration to and from distant breeding sites as well as across many decades. Migrating individuals often travelled through suitable foraging areas en route to their 'home' site and so extended their journeys to maintain foraging site fidelity. We explore the likely mechanistic underpinnings of this trait, which is also seen in some migrating birds, and suggest that individuals will forgo areas of suitable forage encountered en route during migration when they have poor knowledge of the long-term suitability of those sites, making relocation to those sites risky.

Modeling the emergence of migratory corridors and foraging hot spots of the green sea turtle

Environmental factors shape the spatial distribution and dynamics of populations. Understanding how these factors interact with movement behavior is critical for efficient conservation, in particular for migratory species. Adult female green sea turtles, Chelonia mydas, migrate between foraging and nesting sites that are generally separated by thousands of kilometers. As an emblematic endangered species, green turtles have been intensively studied, with a focus on nesting, migration, and foraging. Nevertheless, few attempts integrated these behaviors and their trade-offs by considering the spatial configurations of foraging and nesting grounds as well as environmental heterogeneity like oceanic currents and food distribution. We developed an individual-based model to investigate the impact of local environmental conditions on emerging migratory corridors and reproductive output and to thereby identify conservation priority sites. The model integrates movement, nesting, and foraging behavior. Despite being largely conceptual, the model captured realistic movement patterns which confirm field studies. The spatial distribution of migratory corridors and foraging hot spots was mostly constrained by features of the regional landscape, such as nesting site locations, distribution of feeding patches, and oceanic currents. These constraints also explained the mixing patterns in regional forager communities. By implementing alternative decision strategies of the turtles, we found that foraging site fidelity and nesting investment, two characteristics of green turtles' biology, are favorable strategies under unpredictable environmental conditions affecting their habitats. Based on our results, we propose specific guidelines for the regional conservation of green turtles as well as future research suggestions advancing spatial ecology of sea turtles. Being implemented in an easy to learn open-source software, our model can coevolve with the collection and analysis of new data on energy budget and movement into a generic tool for sea turtle research and conservation. Our modeling approach could also be useful for supporting the conservation of other migratory marine animals.

Loggerhead sea turtle (Caretta caretta) diving changes with productivity, behavioral mode, and sea surface temperature

The relationship between dive behavior and oceanographic conditions is not well understood for marine predators, especially sea turtles. We tagged loggerhead turtles (Caretta caretta) with satellite-linked depth loggers in the Gulf of Mexico, where there is a minimal amount of dive data for this species. We tested for associations between four measurements of dive behavior (total daily dive frequency, frequency of dives to the bottom, frequency of long dives and time-at-depth) and both oceanographic conditions (sea surface temperature [SST], net primary productivity [NPP]) and behavioral mode (inter-nesting, migration, or foraging). From 2011-2013 we obtained 26 tracks from 25 adult female loggerheads tagged after nesting in the Gulf of Mexico. All turtles remained in the Gulf of Mexico and spent about 10% of their time at the surface (10% during inter-nesting, 14% during migration, 9% during foraging). Mean total dive frequency was 41.9 times per day. Most dives were ≤ 25 m and between 30-40 min. During inter-nesting and foraging, turtles dived to the bottom 95% of days. SST was an important explanatory variable for all dive patterns; higher SST was associated with more dives per day, more long dives and more dives to the seafloor. Increases in NPP were associated with more long dives and more dives to the bottom, while lower NPP resulted in an increased frequency of overall diving. Longer dives occurred more frequently during migration and a higher proportion of dives reached the seafloor during foraging when SST and NPP were higher. Our study stresses the importance of the interplay between SST and foraging resources for influencing dive behavior.

Estimating the energy expenditure of free-ranging polar bears using tri-axial accelerometers: A validation with doubly labeled water

Measures of energy expenditure can be used to inform animal conservation and management, but methods for measuring the energy expenditure of free-ranging animals have a variety of limitations. Advancements in biologging technologies have enabled the use of dynamic body acceleration derived from accelerometers as a proxy for energy expenditure. Although dynamic body acceleration has been shown to strongly correlate with oxygen consumption in captive animals, it has been validated in only a few studies on free-ranging animals. Here, we use relationships between oxygen consumption and overall dynamic body acceleration in resting and walking polar bears Ursus maritimus and published values for the costs of swimming in polar bears to estimate the total energy expenditure of 6 free-ranging polar bears that were primarily using the sea ice of the Beaufort Sea. Energetic models based on accelerometry were compared to models of energy expenditure on the same individuals derived from doubly labeled water methods. Accelerometer-based estimates of energy expenditure on average predicted total energy expenditure to be 30% less than estimates derived from doubly labeled water. Nevertheless, accelerometer-based measures of energy expenditure strongly correlated (r 2 = 0.70) with measures derived from doubly labeled water. Our findings highlight the strengths and limitations in dynamic body acceleration as a measure of total energy expenditure while also further supporting its use as a proxy for instantaneous, detailed energy expenditure in free-ranging animals.

Why implantation of bio-loggers may improve our understanding of how animals cope within their natural environment

Bio-loggers are miniaturized autonomous devices that record quantitative data on the state of free-ranging animals (e.g. behavior, position and physiology) and their natural environment. This is especially relevant for species where direct visual observation is difficult or impossible. Today, ongoing technical development allows the monitoring of numerous parameters in an increasing range of species over extended periods. However, the external attachment of devices might affect various aspects of animal performance (energetics, thermoregulation, foraging as well as social and reproductive behavior), which ultimately affect fitness. External attachment might also increase entanglement risk and the conspicuousness of animals, leaving them more vulnerable to predation. By contrast, implantation of devices can mitigate many of these undesirable effects and might be preferable, especially for long-term studies, provided that the many challenges associated with surgical procedures can be mastered. Implantation may then allow us to gather data that would be impossible to obtain otherwise and thereby may provide new and ecologically relevant insights into the life of wild animals. Here, we: (i) discuss the pros and cons of attachment methods; (ii) highlight recent field studies that used implanted bio-loggers to address eco-physiological questions in a wide range of species; and (iii) discuss logger implantation in light of ethical considerations.