Foraging habits of Northwest Atlantic hooded seals over the past 30 years: Future habitat suitability under global warming.

The Arctic is a global warming 'hot-spot' that is experiencing rapid increases in air and ocean temperatures and concomitant decreases in sea ice cover. These environmental changes are having major consequences on Arctic ecosystems. All Arctic endemic marine mammals are highly dependent on ice-associated ecosystems for at least part of their life cycle and thus are sensitive to the changes occurring in their habitats. Understanding the biological consequences of changes in these environments is essential for ecosystem management and conservation. However, our ability to study climate change impacts on Arctic marine mammals is generally limited by the lack of sufficiently long data time series. In this study, we took advantage of a unique dataset on hooded seal (Cystophora cristata) movements (and serum samples) that spans more than 30 years in the Northwest Atlantic to (i) investigate foraging (distribution and habitat use) and dietary (trophic level of prey and location) habits over the last three decades and (ii) predict future locations of suitable habitat given a projected global warming scenario. We found that, despite a change in isotopic signatures that might suggest prey changes over the 30-year period, hooded seals from the Northwest Atlantic appeared to target similar oceanographic characteristics throughout the study period. However, over decades, they have moved northward to find food. Somewhat surprisingly, foraging habits differed between seals breeding in the Gulf of St Lawrence vs those breeding at the "Front" (off Newfoundland). Seals from the Gulf favoured colder waters while Front seals favoured warmer waters. We predict that foraging habitats for hooded seals will continue to shift northwards and that Front seals are likely to have the greatest resilience. This study shows how hooded seals are responding to rapid environmental change and provides an indication of future trends for the species-information essential for effective ecosystem management and conservation.

Accounting for behaviour in fine-scale habitat selection: A case study highlighting methodological intricacies.

Animal habitat selection-central in both theoretical and applied ecology-may depend on behavioural motivations such as foraging, predator avoidance, and thermoregulation. Step-selection functions (SSFs) enable assessment of fine-scale habitat selection as a function of an animal's movement capacities and spatiotemporal variation in extrinsic conditions. If animal location data can be associated with behaviour, SSFs are an intuitive approach to quantify behaviour-specific habitat selection. Fitting SSFs separately for distinct behavioural states helped to uncover state-specific selection patterns. However, while the definition of the availability domain has been highlighted as the most critical aspect of SSFs, the influence of accounting for behaviour in the use-availability design has not been quantified yet. Using a predator-free population of high-arctic muskoxen Ovibos moschatus as a case study, we aimed to evaluate how (1) defining behaviour-specific availability domains, and/or (2) fitting separate behaviour-specific models impacts (a) model structure, (b) estimated selection coefficients and (c) model predictive performance as opposed to behaviour-unspecific approaches. To do so, we first applied hidden Markov models to infer different behavioural modes (resting, foraging, relocating) from hourly GPS positions (19 individuals, 153-1062 observation days/animal). Using SSFs, we then compared behaviour-specific versus behaviour-unspecific habitat selection in relation to terrain features, vegetation and snow conditions. Our results show that incorporating behaviour into the definition of the availability domain primarily impacts model structure (i.e. variable selection), whereas fitting separate behaviour-specific models mainly influences selection strength. Behaviour-specific availability domains improved predictive performance for foraging and relocating models (i.e. behaviours with medium to large spatial displacement), but decreased performance for resting models. Thus, even for a predator-free population subject to only negligible interspecific competition and human disturbance we found that accounting for behaviour in SSFs impacted model structure, selection coefficients and predictive performance. Our results indicate that for robust inference, both a behaviour-specific availability domain and behaviour-specific model fitting should be explored, especially for populations where strong spatiotemporal selection trade-offs are expected. This is particularly critical if wildlife habitat preferences are estimated to inform management and conservation initiatives.

Individual variability in diving behavior of the Black-vented Shearwater in an ever-changing habitat.

Oceanic mesoscale systems are characterized by inherent variability. Climatic change adds entropy to this system, making it a highly variable environment in which marine species live. Being at the higher levels of the food chain, predators maximize their performance through plastic foraging strategies. Individual variability within a population and the possible repeatability across time and space may provide stability in a population facing environmental changes. Therefore, variability and repeatability of behaviors, particularly diving behavior, could play an important role in understanding the adaptation pathway of a species. This study focuses on characterizing the frequency and timing of different dives (termed simple and complex) and how these are influenced by individual and environmental characteristics (sea surface temperature, chlorophyll a concentration, bathymetry, salinity, and Ekman transport). This study is based on GPS and accelerometer-recorded information from a breeding group of 59 Black-vented Shearwater and examine consistency in diving behavior at both individual and sex levels across four different breeding seasons. The species was found to be the best performing free diver in the Puffinus genus with a maximum dive duration of 88 s. Among the environmental variables assessed, a relationship was found with active upwelling conditions enhancing low energetic cost diving, on the contrary, reduced upwelling and warmer superficial waters induce more energetically demanding diving affecting diving performance and ultimately body conditions. The body conditions of Black-vented Shearwaters in 2016 were worse than in subsequent years, in 2016, deepest and longest complex dives were recorded, while simple dives were longer in 2017-2019. Nevertheless, the species' plasticity allows at least part of the population to breed and feed during warmer events. While carry-over effects have already been reported, the effect of more frequent warm events is still unknown.

Applications of Accelerometers and Other Bio-Logging Devices in Captive and Wild Animals.

Bio-logging devices have been widely used in ecology across a range of species to acquire information on the secret lives of animals in the wild, which would otherwise be challenging to obtain via direct observations [...].

The role of individual variability on the predictive performance of machine learning applied to large bio-logging datasets.

Animal-borne tagging (bio-logging) generates large and complex datasets. In particular, accelerometer tags, which provide information on behaviour and energy expenditure of wild animals, produce high-resolution multi-dimensional data, and can be challenging to analyse. We tested the performance of commonly used artificial intelligence tools on datasets of increasing volume and dimensionality. By collecting bio-logging data across several sampling seasons, datasets are inherently characterized by inter-individual variability. Such information should be considered when predicting behaviour. We integrated both unsupervised and supervised machine learning approaches to predict behaviours in two penguin species. The classified behaviours obtained from the unsupervised approach Expectation Maximisation were used to train the supervised approach Random Forest. We assessed agreement between the approaches, the performance of Random Forest on unknown data and the implications for the calculation of energy expenditure. Consideration of behavioural variability resulted in high agreement (> 80%) in behavioural classifications and minimal differences in energy expenditure estimates. However, some outliers with < 70% of agreement, highlighted how behaviours characterized by signal similarity are confused. We advise the broad bio-logging community, approaching these large datasets, to be cautious when upscaling predictions, as this might lead to less accurate estimates of behaviour and energy expenditure.

Training in the Dark: Using Target Training for Non-Invasive Application and Validation of Accelerometer Devices for an Endangered Primate (Nycticebus bengalensis).

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).

Energy-mediated responses to changing prey size and distribution in marine top predator movements and population dynamics.

Climate change is modifying the structure of marine ecosystems, including that of fish communities. Alterations in abiotic and biotic conditions can decrease fish size and change community spatial arrangement, ultimately impacting predator species which rely on these communities. To conserve predators and understand the drivers of observed changes in their population dynamics, we must advance our understanding of how shifting environmental conditions can impact populations by limiting food available to individuals. To investigate the impacts of changing fish size and spatial aggregation on a top predator population, we applied an existing agent-based model parameterized for harbour porpoises Phocoena phocoena which represents animal energetics and movements in high detail. We used this framework to quantify the impacts of shifting prey size and spatial aggregation on porpoise movement, space use, energetics and population dynamics. Simulated individuals were more likely to switch from area-restricted search to transit behaviour with increasing prey size, particularly when starving, due to elevated resource competition. In simulations with highly aggregated prey, higher prey encounter rates counteracted resource competition, resulting in no impacts of prey spatial aggregation on movement behaviour. Reduced energy intake with decreasing prey size and aggregation level caused population decline, with a 15% decrease in fish length resulting in total population collapse Increasing prey consumption rates by 42.8 ± 4.5% could offset population declines; however, this increase was 21.3 ± 12.7% higher than needed to account for changes in total energy availability alone. This suggests that animals in realistic seascapes require additional energy to locate smaller prey which should be considered when assessing the impacts of decreased energy availability. Changes in prey size and aggregation influenced movements and population dynamics of simulated harbour porpoises, revealing that climate-induced changes in prey structure, not only prey abundance, may threaten predator populations. We demonstrate how a population model with realistic animal movements and process-based energetics can be used to investigate population consequences of shifting food availability, such as those mediated by climate change, and provide a mechanistic explanation for how changes in prey structure can impact energetics, behaviour and ultimately viability of predator populations.

Quantifying energetic and fitness consequences of seasonal heterothermy in an Arctic ungulate.

Animals have adapted behavioral and physiological strategies to conserve energy during periods of adverse conditions. Heterothermy is one such adaptation used by endotherms. While heterothermy-fluctuations in body temperature and metabolic rate-has been shown in large vertebrates, little is known of the costs and benefits of this strategy, both in terms of energy and in terms of fitness. Hence, our objective was to model the energetics of seasonal heterothermy in the largest Arctic ungulate, the muskox (Ovibos moschatus), using an individual-based energy budget model of metabolic physiology. We found that the empirically based drop in body temperature (winter max ~-0.8°C) overwinter in adult females resulted in substantial fitness benefits in terms of reduced daily energy expenditure and body mass loss. Body mass and energy reserves were 8.98% and 14.46% greater in modeled heterotherms compared to normotherms by end of winter. Based on environmental simulations, we show that seasonal heterothermy can, to some extent, buffer the negative consequences of poor prewinter body condition or reduced winter food accessibility, leading to greater winter survival (+20%-30%) and spring energy reserves (+10%-30%), and thus increased probability of future reproductive success. These results indicate substantial adaptive short-term benefits of seasonal heterothermy at the individual level, with potential implications for long-term population dynamics in highly seasonal environments.

Environment and physiology shape Arctic ungulate population dynamics.

Species conservation in a rapidly changing world requires an improved understanding of how individuals and populations respond to changes in their environment across temporal scales. Increased warming in the Arctic puts this region at particular risk for rapid environmental change, with potentially devastating impacts on resident populations. Here, we make use of a parameterized full life cycle, individual-based energy budget model for wild muskoxen, coupling year-round environmental data with detailed ontogenic metabolic physiology. We show how winter food accessibility, summer food availability, and density dependence drive seasonal dynamics of energy storage and thus life history and population dynamics. Winter forage accessibility defined by snow depth, more than summer forage availability, was the primary determinant of muskox population dynamics through impacts on calf recruitment and longer term carryover effects of maternal investment. Simulations of various seasonal snow depth and plant biomass and quality profiles revealed that timing of and improved/limited winter forage accessibility had marked influence on calf recruitment (±10-80%). Impacts on recruitment were the cumulative result of condition-driven reproductive performance at multiple time points across the reproductive period (ovulation to calf weaning) as a trade-off between survival and reproduction. Seasonal and generational condition effects of snow-rich winters interacted with age structure and density to cause pronounced long-term consequences on population growth and structure, with predicted population recovery times from even moderate disturbances of 10 years or more. Our results show how alteration in winter forage accessibility, mediated by snow depth, impacts the dynamics of northern herbivore populations. Further, we present here a mechanistic and state-based model framework to assess future scenarios of environmental change, such as increased or decreased snowfall or plant biomass and quality to impact winter and summer forage availability across the Arctic.

Environmental conditions alter behavioural organization and rhythmicity of a large Arctic ruminant across the annual cycle.

The existence and persistence of rhythmicity in animal activity during phases of environmental change is of interest in ecology, evolution and chronobiology. A wide diversity of biological rhythms in response to exogenous conditions and internal stimuli have been uncovered, especially for polar vertebrates. However, empirical data supporting circadian organization in behaviour of large ruminating herbivores remains inconclusive. Using year-round tracking data of the largest Arctic ruminant, the muskox (Ovibos moschatus), we modelled rhythmicity as a function of behaviour and environmental conditions. Behavioural states were classified based on patterns in hourly movements, and incorporated within a periodicity analyses framework. Although circadian rhythmicity in muskox behaviour was detected throughout the year, ultradian rhythmicity was most prevalent, especially when muskoxen were foraging and resting in mid-winter (continuous darkness). However, when combining circadian and ultradian rhythmicity together, the probability of behavioural rhythmicity declined with increasing photoperiod until largely disrupted in mid-summer (continuous light). Individuals that remained behaviourally rhythmic during mid-summer foraged in areas with lower plant productivity (NDVI) than individuals with arrhythmic behaviour. Based on our study, we conclude that muskoxen may use an interval timer to schedule their behavioural cycles when forage resources are low, but that the importance and duration of this timer are reduced once environmental conditions allow energetic reserves to be replenished ad libitum. We argue that alimentary function and metabolic requirements are critical determinants of biological rhythmicity in muskoxen, which probably applies to ruminating herbivores in general.

Movement patterns of large juvenile loggerhead turtles in the Mediterranean Sea: Ontogenetic space use in a small ocean basin.

Mechanisms that determine how, where, and when ontogenetic habitat shifts occur are mostly unknown in wild populations. Differences in size and environmental characteristics of ontogenetic habitats can lead to differences in movement patterns, behavior, habitat use, and spatial distributions across individuals of the same species. Knowledge of juvenile loggerhead turtles' dispersal, movements, and habitat use is largely unknown, especially in the Mediterranean Sea. Satellite relay data loggers were used to monitor movements, diving behavior, and water temperature of eleven large juvenile loggerhead turtles (Caretta caretta) deliberately caught in an oceanic habitat in the Mediterranean Sea. Hidden Markov models were used over 4,430 spatial locations to quantify the different activities performed by each individual: transit, low-, and high-intensity diving. Model results were then analyzed in relation to water temperature, bathymetry, and distance to the coast. The hidden Markov model differentiated between bouts of area-restricted search as low- and high-intensity diving, and transit movements. The turtles foraged in deep oceanic waters within 60 km from the coast as well as above 140 km from the coast. They used an average area of 194,802 km2, where most individuals used the deepest part of the Southern Tyrrhenian Sea with the highest seamounts, while only two switched to neritic foraging showing plasticity in foraging strategies among turtles of similar age classes. The foraging distribution of large juvenile loggerhead turtles, including some which were of the minimum size of adults, in the Tyrrhenian Sea is mainly concentrated in a relatively small oceanic area with predictable mesoscale oceanographic features, despite the proximity of suitable neritic foraging habitats. Our study highlights the importance of collecting high-resolution data about species distribution and behavior across different spatio-temporal scales and life stages for implementing conservation and dynamic ocean management actions.

An application of upscaled optimal foraging theory using hidden Markov modelling: year-round behavioural variation in a large arctic herbivore.

BACKGROUND: In highly seasonal environments, animals face critical decisions regarding time allocation, diet optimisation, and habitat use. In the Arctic, the short summers are crucial for replenishing body reserves, while low food availability and increased energetic demands characterise the long winters (9-10 months). Under such extreme seasonal variability, even small deviations from optimal time allocation can markedly impact individuals' condition, reproductive success and survival. We investigated which environmental conditions influenced daily, seasonal, and interannual variation in time allocation in high-arctic muskoxen (Ovibos moschatus) and evaluated whether results support qualitative predictions derived from upscaled optimal foraging theory. METHODS: Using hidden Markov models (HMMs), we inferred behavioural states (foraging, resting, relocating) from hourly positions of GPS-collared females tracked in northeast Greenland (28 muskox-years). To relate behavioural variation to environmental conditions, we considered a wide range of spatially and/or temporally explicit covariates in the HMMs. RESULTS: While we found little interannual variation, daily and seasonal time allocation varied markedly. Scheduling of daily activities was distinct throughout the year except for the period of continuous daylight. During summer, muskoxen spent about 69% of time foraging and 19% resting, without environmental constraints on foraging activity. During winter, time spent foraging decreased to 45%, whereas about 43% of time was spent resting, mediated by longer resting bouts than during summer. CONCLUSIONS: Our results clearly indicate that female muskoxen follow an energy intake maximisation strategy during the arctic summer. During winter, our results were not easily reconcilable with just one dominant foraging strategy. The overall reduction in activity likely reflects higher time requirements for rumination in response to the reduction of forage quality (supporting an energy intake maximisation strategy). However, deep snow and low temperatures were apparent constraints to winter foraging, hence also suggesting attempts to conserve energy (net energy maximisation strategy). Our approach provides new insights into the year-round behavioural strategies of the largest Arctic herbivore and outlines a practical example of how to approximate qualitative predictions of upscaled optimal foraging theory using multi-year GPS tracking data.

On the interplay between hypothermia and reproduction in a high arctic ungulate.

For free-ranging animals living in seasonal environments, hypometabolism (lowered metabolic rate) and hypothermia (lowered body temperature) can be effective physiological strategies to conserve energy when forage resources are low. To what extent such strategies are adopted by large mammals living under extreme conditions, as those encountered in the high Arctic, is largely unknown, especially for species where the gestation period overlaps with the period of lowest resource availability (i.e. winter). Here we investigated for the first time the level to which high arctic muskoxen (Ovibos moschatus) adopt hypothermia and tested the hypothesis that individual plasticity in the use of hypothermia depends on reproductive status. We measured core body temperature over most of the gestation period in both free-ranging muskox females in Greenland and captive female muskoxen in Alaska. We found divergent overwintering strategies according to reproductive status, where pregnant females maintained stable body temperatures during winter, while non-pregnant females exhibited a temporary decrease in their winter body temperature. These results show that muskox females use hypothermia during periods of resource scarcity, but also that the use of this strategy may be limited to non-reproducing females. Our findings suggest a trade-off between metabolically-driven energy conservation during winter and sustaining foetal growth, which may also apply to other large herbivores living in highly seasonal environments elsewhere.

Taking movement data to new depths: Inferring prey availability and patch profitability from seabird foraging behavior.

Detailed information acquired using tracking technology has the potential to provide accurate pictures of the types of movements and behaviors performed by animals. To date, such data have not been widely exploited to provide inferred information about the foraging habitat. We collected data using multiple sensors (GPS, time depth recorders, and accelerometers) from two species of diving seabirds, razorbills (Alca torda, N = 5, from Fair Isle, UK) and common guillemots (Uria aalge, N = 2 from Fair Isle and N = 2 from Colonsay, UK). We used a clustering algorithm to identify pursuit and catching events and the time spent pursuing and catching underwater, which we then used as indicators for inferring prey encounters throughout the water column and responses to changes in prey availability of the areas visited at two levels: individual dives and groups of dives. For each individual dive (N = 661 for guillemots, 6214 for razorbills), we modeled the number of pursuit and catching events, in relation to dive depth, duration, and type of dive performed (benthic vs. pelagic). For groups of dives (N = 58 for guillemots, 156 for razorbills), we modeled the total time spent pursuing and catching in relation to time spent underwater. Razorbills performed only pelagic dives, most likely exploiting prey available at shallow depths as indicated by the vertical distribution of pursuit and catching events. In contrast, guillemots were more flexible in their behavior, switching between benthic and pelagic dives. Capture attempt rates indicated that they were exploiting deep prey aggregations. The study highlights how novel analysis of movement data can give new insights into how animals exploit food patches, offering a unique opportunity to comprehend the behavioral ecology behind different movement patterns and understand how animals might respond to changes in prey distributions.

The use of an unsupervised learning approach for characterizing latent behaviors in accelerometer data.

The recent increase in data accuracy from high resolution accelerometers offers substantial potential for improved understanding and prediction of animal movements. However, current approaches used for analysing these multivariable datasets typically require existing knowledge of the behaviors of the animals to inform the behavioral classification process. These methods are thus not well-suited for the many cases where limited knowledge of the different behaviors performed exist. Here, we introduce the use of an unsupervised learning algorithm. To illustrate the method's capability we analyse data collected using a combination of GPS and Accelerometers on two seabird species: razorbills (Alca torda) and common guillemots (Uria aalge). We applied the unsupervised learning algorithm Expectation Maximization to characterize latent behavioral states both above and below water at both individual and group level. The application of this flexible approach yielded significant new insights into the foraging strategies of the two study species, both above and below the surface of the water. In addition to general behavioral modes such as flying, floating, as well as descending and ascending phases within the water column, this approach allowed an exploration of previously unstudied and important behaviors such as searching and prey chasing/capture events. We propose that this unsupervised learning approach provides an ideal tool for the systematic analysis of such complex multivariable movement data that are increasingly being obtained with accelerometer tags across species. In particular, we recommend its application in cases where we have limited current knowledge of the behaviors performed and existing supervised learning approaches may have limited utility.

Modelling foraging movements of diving predators: a theoretical study exploring the effect of heterogeneous landscapes on foraging efficiency.

Foraging in the marine environment presents particular challenges for air-breathing predators. Information about prey capture rates, the strategies that diving predators use to maximise prey encounter rates and foraging success are still largely unknown and difficult to observe. As well, with the growing awareness of potential climate change impacts and the increasing interest in the development of renewable sources it is unknown how the foraging activity of diving predators such as seabirds will respond to both the presence of underwater structures and the potential corresponding changes in prey distributions. Motivated by this issue we developed a theoretical model to gain general understanding of how the foraging efficiency of diving predators may vary according to landscape structure and foraging strategy. Our theoretical model highlights that animal movements, intervals between prey capture and foraging efficiency are likely to critically depend on the distribution of the prey resource and the size and distribution of introduced underwater structures. For multiple prey loaders, changes in prey distribution affected the searching time necessary to catch a set amount of prey which in turn affected the foraging efficiency. The spatial aggregation of prey around small devices (∼ 9 × 9 m) created a valuable habitat for a successful foraging activity resulting in shorter intervals between prey captures and higher foraging efficiency. The presence of large devices (∼ 24 × 24 m) however represented an obstacle for predator movement, thus increasing the intervals between prey captures. In contrast, for single prey loaders the introduction of spatial aggregation of the resources did not represent an advantage suggesting that their foraging efficiency is more strongly affected by other factors such as the timing to find the first prey item which was found to occur faster in the presence of large devices. The development of this theoretical model represents a useful starting point to understand the energetic reasons for a range of potential predator responses to spatial heterogeneity and environmental uncertainties in terms of search behaviour and predator-prey interactions. We highlight future directions that integrated empirical and modelling studies should take to improve our ability to predict how diving predators will be impacted by the deployment of manmade structures in the marine environment.