Joint estimation over multiple individuals improves behavioural state inference from animal movement data

Go Multivariate: Recommendations on Bayesian Multilevel Hidden Markov Models with Categorical Data

The multilevel hidden Markov model (MHMM) is a promising method to investigate intense longitudinal data obtained within the social and behavioral sciences. The MHMM quantifies information on the latent dynamics of behavior over time. In addition, heterogeneity between individuals is accommodated with the inclusion of individual-specific random effects, facilitating the study of individual differences in dynamics. However, the performance of the MHMM has not been sufficiently explored. We performed an extensive simulation to assess the effect of the number of dependent variables (1-8), number of individuals (5-90), and number of observations per individual (100-1600) on the estimation performance of a Bayesian MHMM with categorical data including various levels of state distinctiveness and separation. We found that using multivariate data generally alleviates the sample size needed and improves the stability of the results. Moreover, including variables only consisting of random noise was generally not detrimental to model performance. Regarding the estimation of group-level parameters, the number of individuals and observations largely compensate for each other. However, only the former drives the estimation of between-individual variability. We conclude with guidelines on the sample size necessary based on the level of state distinctiveness and separation and study objectives of the researcher.

Spatial ecology and conservation of leatherback turtles (Dermochelys coriacea) nesting in Bioko, Equatorial Guinea

Bioko Island (Equatorial Guinea) hosts important nesting habitat for leatherback sea turtles, with the main nesting beaches found on the island's southern end. Nest monitoring and protection have been ongoing for more than two decades, although distribution and habitat range at sea remains to be determined. This study uses satellite telemetry to describe the movements of female leatherback turtles (n = 10) during and following the breeding season, tracking them to presumed offshore foraging habitats in the south Atlantic Ocean. Leatherback turtles spent 100% of their time during the breeding period within the Exclusive Economic Zone (EEZ) of Equatorial Guinea, with a core distribution focused on the south of Bioko Island extending up to 10 km from the coast. During this period, turtles spent less than 10% of time within the existing protected area. Extending the border of this area by 3 km offshore would lead to a greater than threefold increase in coverage of turtle distribution (29.8 ± 19.0% of time), while an expansion to 15 km offshore would provide spatial coverage for more than 50% of tracking time. Post-nesting movements traversed the territorial waters of Sao Tome and Principe (6.4%of tracking time), Brazil (0.85%), Ascension (1.8%), and Saint Helena (0.75%). The majority (70%) of tracking time was spent in areas beyond national jurisdiction (i.e. the High Seas). This study reveals that conservation benefits could be achieved by expanding existing protected areas stretching from the Bioko coastal zone, and suggests shared migratory routes and foraging space between the Bioko population and other leatherback turtle rookeries in this region.

Crabs ride the tide: incoming tides promote foraging of Giant Mud Crab (Scylla serrata)

BACKGROUND

Effective fisheries management of mobile species relies on robust knowledge of animal behaviour and habitat-use. Indices of behaviour can be useful for interpreting catch-per-unit-effort data which acts as a proxy for relative abundance. Information about habitat-use can inform stocking release strategies or the design of marine protected areas. The Giant Mud Crab (Scylla serrata; Family: Portunidae) is a swimming estuarine crab that supports significant fisheries harvest throughout the Indo-West Pacific, but little is known about the fine-scale movement and behaviour of this species.

METHODS

We tagged 18 adult Giant Mud Crab with accelerometer-equipped acoustic tags to track their fine-scale movement using a hyperbolic positioning system, alongside high temporal resolution environmental data (e.g., water temperature), in a temperate south-east Australian estuary. A hidden Markov model was used to classify movement (i.e., step length, turning angle) and acceleration data into discrete behaviours, while also considering the possibility of individual variation in behavioural dynamics. We then investigated the influence of environmental covariates on these behaviours based on previously published observations.

RESULTS

We fitted a model with two well-distinguished behavioural states describing periods of inactivity and foraging, and found no evidence of individual variation in behavioural dynamics. Inactive periods were most common (79% of time), and foraging was most likely during low, incoming tides; while inactivity was more likely as the high tide receded. Model selection removed time (hour) of day and water temperature (°C) as covariates, suggesting that they do not influence Giant Mud Crab behavioural dynamics at the temporal scale investigated.

CONCLUSIONS

Our study is the first to quantitatively link fine-scale movement and behaviour of Giant Mud Crab to environmental variation. Our results suggest Giant Mud Crab are a predominantly sessile species, and support their status as an opportunistic scavenger. We demonstrate a relationship between the tidal cycle and foraging that is likely to minimize predation risk while maximizing energetic efficiency. These results may explain why tidal covariates influence catch rates in swimming crabs, and provide a foundation for standardisation and interpretation of catch-per-unit-effort data-a commonly used metric in fisheries science.

Decadal migration phenology of a long-lived Arctic icon keeps pace with climate change

Animals migrate in response to seasonal environments, to reproduce, to benefit from resource pulses, or to avoid fluctuating hazards. Although climate change is predicted to modify migration, only a few studies to date have demonstrated phenological shifts in marine mammals. In the Arctic, marine mammals are considered among the most sensitive to ongoing climate change due to their narrow habitat preferences and long life spans. Longevity may prove an obstacle for species to evolutionarily respond. For species that exhibit high site fidelity and strong associations with migration routes, adjusting the timing of migration is one of the few recourses available to respond to a changing climate. Here, we demonstrate evidence of significant delays in the timing of narwhal autumn migrations with satellite tracking data spanning 21 y from the Canadian Arctic. Measures of migration phenology varied annually and were explained by sex and climate drivers associated with ice conditions, suggesting that narwhals are adopting strategic migration tactics. Male narwhals were found to lead the migration out of the summering areas, while females, potentially with dependent young, departed later. Narwhals are remaining longer in their summer areas at a rate of 10 d per decade, a similar rate to that observed for climate-driven sea ice loss across the region. The consequences of altered space use and timing have yet to be evaluated but will expose individuals to increasing natural changes and anthropogenic activities on the summering areas.

Modeling abundance, distribution, movement and space use with camera and telemetry data

Studies of animal abundance and distribution are often conducted independently of research on movement, despite the important links between processes. Movement can cause rapid changes in spatial variation in density, and movement influences detection probability and therefore estimates of abundance from inferential methods such as spatial capture-recapture (SCR). Technological developments including camera traps and GPS telemetry have opened new opportunities for studying animal demography and movement, yet statistical models for these two data types have largely developed along parallel tracks. We present a hierarchical model in which both datasets are conditioned on a movement process for a clearly defined population. We fitted the model to data from 60 camera traps and 23,572 GPS telemetry locations collected on 17 male white-tailed deer in the Big Cypress National Preserve, Florida, USA during July 2015. Telemetry data were collected on a 3-4 h acquisition schedule, and we modeled the movement paths of all individuals in the region with a Ornstein-Uhlenbeck process that included individual-specific random effects. Two of the 17 deer with GPS collars were detected on cameras. An additional 20 male deer without collars were detected on cameras and individually identified based on their unique antler characteristics. Abundance was 126 (95% CI: 88-177) in the 228 km² region, only slightly higher than estimated using a standard SCR model: 119 (84-168). The standard SCR model, however, was unable to describe individual heterogeneity in movement rates and space use as revealed by the joint model. Joint modeling allowed the telemetry data to inform the movement model and the SCR encounter model, while leveraging information in the camera data to inform abundance, distribution and movement. Unlike most existing methods for population-level inference on movement, the joint SCR-movement model can yield unbiased inferences even if non-uniform sampling is used to deploy transmitters. Potential extensions of the model include the addition of resource selection parameters, and relaxation of the closure assumption when interest lies in survival and recruitment. These developments would contribute to the emerging holistic framework for the study of animal ecology, one that uses modern technology and spatio-temporal statistics to learn about interactions between behavior and demography.

Individual and joint estimation of humpback whale migratory patterns and their environmental drivers in the Southwest Atlantic Ocean

Humpback whales (Megaptera novaeangliae) perform seasonal migrations from high latitude feeding grounds to low latitude breeding and calving grounds. Feeding grounds at polar regions are currently experiencing major ecosystem modifications, therefore, quantitatively assessing species responses to habitat characteristics is crucial for understanding how whales might respond to such modifications. We analyzed satellite telemetry data from 22 individual humpback whales in the Southwest Atlantic Ocean (SWA). Tagging effort was divided in two periods, 2003-2012 and 2016-2019. Correlations between whale's movement parameters and environmental variables were used as proxy for inferring behavioral responses to environmental variation. Two versions of a covariate-driven continuous-time correlated random-walk state-space model, were fitted to the data: i) Population-level models (P-models), which assess correlation parameters pooling data across all individuals or groups, and ii) individual-level models (I-models), fitted independently for each tagged whale. Area of Restricted Search behavior (slower and less directionally persistent movement, ARS) was concentrated at cold waters south of the Polar Front (~ 50°S). The best model showed that ARS was expected to occur in coastal areas and over ridges and seamounts. Ice coverage during August of each year was a consistent predictor of ARS across models. Wind stress curl and sea surface temperature anomalies were also correlated with movement parameters but elicited larger inter-individual variation. I-models were consistent with P-models' predictions for the case of females accompanied by calves (mothers), while males and those of undetermined sex (males +) presented more variability as a group. Spatial predictions of humpback whale behavioral responses showed that feeding grounds for this population are concentrated in the complex system of islands, ridges, and rises of the Scotia Sea and the northern Weddell Ridge. More southernly incursions were observed in recent years, suggesting a potential response to increased temperature and large ice coverage reduction observed in the late 2010s. Although, small sample size and differences in tracking duration precluded appropriately testing predictions for such a distributional shift, our modelling framework showed the efficiency of borrowing statistical strength during data pooling, while pinpointing where more complexity should be added in the future as additional data become available.

Habitat models and assessment of habitat partitioning for Kemp’s ridley and loggerhead marine turtles foraging in Chesapeake Bay (USA)

Understanding the spatial distribution of a species is required to enact effective conservation. Complications to effective conservation can arise when the distributions of multiple target species are non-overlapping. Conservation efforts meant to protect one species may shift threats into the distribution of another species. Two species of marine turtle, loggerhead Caretta caretta and Kemp’s ridley Lepidochelys kempii, are common seasonally in Chesapeake Bay, a large estuary on the US east coast. Both species are protected under the US Endangered Species Act and face spatially complex threats in the region. We created habitat suitability models for these 2 species to inform conservation efforts in the region and explore the extent of overlap between their distributions. Argos satellite tags were deployed on 24 Kemp’s ridley and 10 loggerhead turtles to record animal locations within the Bay. Boosted regression tree models were created for each species using presence-only animal locations, predicting suitable habitat within the Bay. Habitat for Kemp’s ridley turtles was predicted in shallow, coastal areas of the southern Bay as well as in brackish areas of rivers. Loggerhead turtle habitat was predicted to extend farther north than Kemp’s ridley habitat and was generally found in deeper areas of the middle Bay. There is some evidence that these 2 species are partitioning habitat. Any conservation measures adopted to conserve marine turtles in the Chesapeake Bay should consider the habitat of both species holistically to avoid shifting impacts from one species to another.

Elephant seal foraging success is enhanced in Antarctic coastal polynyas

Antarctic polynyas are persistent open water areas which enable early and large seasonal phytoplankton blooms. This high primary productivity, boosted by iron supply from coastal glaciers, attracts organisms from all trophic levels to form a rich and diverse community. How the ecological benefit of polynya productivity is translated to the highest trophic levels remains poorly resolved. We studied 119 southern elephant seals feeding over the Antarctic shelf and demonstrated that: (i) 96% of seals foraging here used polynyas, with individuals spending on average 62% of their time there; (ii) the seals exhibited more area-restricted search behaviour when in polynyas; and (iii) these seals gained more energy (indicated by increased buoyancy from greater fat stores) when inside polynyas. This higher-quality foraging existed even when ice was not present in the study area, indicating that these are important and predictable foraging grounds year-round. Despite these energetic advantages from using polynyas, not all the seals used them extensively. Factors other than food supply may influence an individual's choice in their use of feeding grounds, such as exposure to predation or the probability of being able to return to distant sub-Antarctic breeding sites.

A flexible Bayesian framework for individualized inference via adaptive borrowing

The explosion in high-resolution data capture technologies in health has increased interest in making inferences about individual-level parameters. While technology may provide substantial data on a single individual, how best to use multisource population data to improve individualized inference remains an open research question. One possible approach, the multisource exchangeability model (MEM), is a Bayesian method for integrating data from supplementary sources into the analysis of a primary source. MEM was originally developed to improve inference for a single study by asymmetrically borrowing information from a set of similar previous studies and was further developed to apply a more computationally intensive symmetric borrowing in the context of basket trial; however, even for asymmetric borrowing, its computational burden grows exponentially with the number of supplementary sources, making it unsuitable for applications where hundreds or thousands of supplementary sources (i.e., individuals) could contribute to inference on a given individual. In this article, we propose the data-driven MEM (dMEM), a two-stage approach that includes both source selection and clustering to enable the inclusion of an arbitrary number of sources to contribute to individualized inference in a computationally tractable and data-efficient way. We illustrate the application of dMEM to individual-level human behavior and mental well-being data collected via smartphones, where our approach increases individual-level estimation precision by 84% compared with a standard no-borrowing method and outperforms recently proposed competing methods in 80% of individuals.

Flexibility in migratory strategy contrasts with reliance on restricted staging and overwintering grounds for Sabine’s gulls from the Canadian High Arctic

Sabine’s gulls (Xema sabini) undertake the longest migration of any gull, a trans-equatorial journey between Arctic breeding and southern hemisphere wintering areas. For such long-distance migrants, quantifying within- and between-individual variation in migratory strategy is key towards understanding resilience to environmental variability encountered over migration. We tracked 22 birds on 32 migrations from the Canadian Arctic to evaluate strategies and quantify flexibility among individuals and years. All birds undertook extended stopovers in a geographically-restricted staging area halfway through migration in the California Current System in both directions. Individuals were otherwise flexible in most aspects of migration but were repeatable in arrival date and duration of the southbound staging phase. Routes taken during southbound migration and overlap in overwintering areas were significantly larger within the same year than among years. Overall, birds showed high individual flexibility in migratory strategies but made similar decisions to one another in the same years. Every year, all birds showed repeatable, consistent reliance on the staging grounds as a key stopover site in both directions. This suggests Sabine’s gulls adjust to environmental change in many aspects of their migration but may be vulnerable to climate change and other anthropogenic influences during critical stages of the journey.

The Diversity of Eyes and Vision

Every aspect of vision, from the opsin proteins to the eyes and the ways that they serve animal behavior, is incredibly diverse. It is only with an evolutionary perspective that this diversity can be understood and fully appreciated. In this review, I describe and explain the diversity at each level and try to convey an understanding of how the origin of the first opsin some 800 million years ago could initiate the avalanche that produced the astonishing diversity of eyes and vision that we see today. Despite the diversity, many types of photoreceptors, eyes, and visual roles have evolved multiple times independently in different animals, revealing a pattern of eye evolution strictly guided by functional constraints and driven by the evolution of gradually more demanding behaviors. I conclude the review by introducing a novel distinction between active and passive vision that points to uncharted territories in vision research. Expected final online publication date for the Annual Review of Vision Science, Volume 7 is September 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Hidden Markov models identify major movement modes in accelerometer and magnetometer data from four albatross species

BACKGROUND

Inertial measurement units (IMUs) with high-resolution sensors such as accelerometers are now used extensively to study fine-scale behavior in a wide range of marine and terrestrial animals. Robust and practical methods are required for the computationally-demanding analysis of the resulting large datasets, particularly for automating classification routines that construct behavioral time series and time-activity budgets. Magnetometers are used increasingly to study behavior, but it is not clear how these sensors contribute to the accuracy of behavioral classification methods. Development of effective  classification methodology is key to understanding energetic and life-history implications of foraging and other behaviors.

METHODS

We deployed accelerometers and magnetometers on four species of free-ranging albatrosses and evaluated the ability of unsupervised hidden Markov models (HMMs) to identify three major modalities in their behavior: 'flapping flight', 'soaring flight', and 'on-water'. The relative contribution of each sensor to classification accuracy was measured by comparing HMM-inferred states with expert classifications identified from stereotypic patterns observed in sensor data.

RESULTS

HMMs provided a flexible and easily interpretable means of classifying behavior from sensor data. Model accuracy was high overall (92%), but varied across behavioral states (87.6, 93.1 and 91.7% for 'flapping flight', 'soaring flight' and 'on-water', respectively). Models built on accelerometer data alone were as accurate as those that also included magnetometer data; however, the latter were useful for investigating slow and periodic behaviors such as dynamic soaring at a fine scale.

CONCLUSIONS

The use of IMUs in behavioral studies produces large data sets, necessitating the development of computationally-efficient methods to automate behavioral classification in order to synthesize and interpret underlying patterns. HMMs provide an accessible and robust framework for analyzing complex IMU datasets and comparing behavioral variation among taxa across habitats, time and space.

Movements and behaviour of blue whales satellite tagged in an Australian upwelling system

Knowledge about the movement ecology of endangered species is needed to identify biologically important areas and the spatio-temporal scale of potential human impacts on species. Blue whales (Balaenoptera musculus) are endangered due to twentieth century whaling and currently threatened by human activities. In Australia, they feed in the Great Southern Australian Coastal Upwelling System (GSACUS) during the austral summer. We investigate their movements, occupancy, behaviour, and environmental drivers to inform conservation management. Thirteen whales were satellite tagged, biopsy sampled and photo-identified in 2015. All were genetically confirmed to be of the pygmy subspecies (B. m. brevicauda). In the GSACUS, whales spent most of their time over the continental shelf and likely foraging in association with several seascape variables (sea surface temperature variability, depth, wind speed, sea surface height anomaly, and chlorophyll a). When whales left the region, they migrated west and then north along the Australian coast until they reached West Timor and Indonesia, where their movements indicated breeding or foraging behaviour. These results highlight the importance of the GSACUS as a foraging ground for pygmy blue whales inhabiting the eastern Indian Ocean and indicate the whales’ migratory route to proposed breeding grounds off Indonesia. Information about the spatio-temporal scale of potential human impacts can now be used to protect this little-known subspecies of blue whale.

Mobile robotic platforms for the acoustic tracking of deep-sea demersal fishery resources

Knowing the displacement capacity and mobility patterns of industrially exploited (i.e., fished) marine resources is key to establishing effective conservation management strategies in human-impacted marine ecosystems. Acquiring accurate behavioral information of deep-sea fished ecosystems is necessary to establish the sizes of marine protected areas within the framework of large international societal programs (e.g., European Community H2020, as part of the Blue Growth economic strategy). However, such information is currently scarce, and high-frequency and prolonged data collection is rarely available. Here, we report the implementation of autonomous underwater vehicles and remotely operated vehicles as an aid for acoustic long-baseline localization systems for autonomous tracking of Norway lobster (Nephrops norvegicus), one of the key living resources exploited in European waters. In combination with seafloor moored acoustic receivers, we detected and tracked the movements of 33 tagged lobsters at 400-m depth for more than 3 months. We also identified the best procedures to localize both the acoustic receivers and the tagged lobsters, based on algorithms designed for off-the-shelf acoustic tags identification. Autonomous mobile platforms that deliver data on animal behavior beyond traditional fixed platform capabilities represent an advance for prolonged, in situ monitoring of deep-sea benthic animal behavior at meter spatial scales.

Foraging in a dynamic environment: Response of four sympatric sub-Antarctic albatross species to interannual environmental variability

Seasonal and annual climate variations are linked to fluctuations in the abundance and distribution of resources, posing a significant challenge to animals that need to adjust their foraging behavior accordingly. Particularly during adverse conditions, and while energetically constrained when breeding, animals ideally need to be flexible in their foraging behavior. Such behavioral plasticity may separate "winners" from "losers" in light of rapid environmental changes due to climate change. Here, the foraging behavior of four sub-Antarctic albatross species was investigated from 2015/16 to 2017/18, a period characterized by pronounced environmental variability. Over three breeding seasons on Marion Island, Prince Edward Archipelago, incubating wandering (WA, Diomedea exulans; n = 45), grey-headed (GHA, Thalassarche chrysostoma; n = 26), sooty (SA, Phoebetria fusca; n = 23), and light-mantled (LMSA, P. palpebrata; n = 22) albatrosses were tracked with GPS loggers. The response of birds to environmental variability was investigated by quantifying interannual changes in their foraging behavior along two axes: spatial distribution, using kernel density analysis, and foraging habitat preference, using generalized additive mixed models and Bayesian mixed models. All four species were shown to respond behaviorally to environmental variability, but with substantial differences in their foraging strategies. WA was most general in its habitat use defined by sea surface height, eddy kinetic energy, wind speed, ocean floor slope, and sea-level anomaly, with individuals foraging in a range of habitats. In contrast, the three smaller albatrosses exploited two main foraging habitats, with habitat use varying between years. Generalist habitat use by WA and interannually variable use of habitats by GHA, SA, and LMSA would likely offer these species some resilience to predicted changes in climate such as warming seas and strengthening of westerly winds. However, future investigations need to consider other life-history stages coupled with demographic studies, to better understand the link between behavioral plasticity and population responses.

Satellite derived offshore migratory movements of southern right whales (Eubalaena australis) from Australian and New Zealand wintering grounds

Southern right whales (Eubalaena australis) migrate between Austral-winter calving and socialising grounds to offshore mid- to high latitude Austral-summer feeding grounds. In Australasia, winter calving grounds used by southern right whales extend from Western Australia across southern Australia to the New Zealand sub-Antarctic Islands. During the Austral-summer these whales are thought to migrate away from coastal waters to feed, but the location of these feeding grounds is only inferred from historical whaling data. We present new information on the satellite derived offshore migratory movements of six southern right whales from Australasian wintering grounds. Two whales were tagged at the Auckland Islands, New Zealand, and the remaining four at Australian wintering grounds, one at Pirates Bay, Tasmania, and three at Head of Bight, South Australia. The six whales were tracked for an average of 78.5 days (range: 29 to 150) with average individual distance of 38 km per day (range: 20 to 61 km). The length of individually derived tracks ranged from 645–6,381 km. Three likely foraging grounds were identified: south-west Western Australia, the Subtropical Front, and Antarctic waters, with the Subtropical Front appearing to be a feeding ground for both New Zealand and Australian southern right whales. In contrast, the individual tagged in Tasmania, from a sub-population that is not showing evidence of post-whaling recovery, displayed a distinct movement pattern to much higher latitude waters, potentially reflecting a different foraging strategy. Variable population growth rates between wintering grounds in Australasia could reflect fidelity to different quality feeding grounds. Unlike some species of baleen whale populations that show movement along migratory corridors, the new satellite tracking data presented here indicate variability in the migratory pathways taken by southern right whales from Australia and New Zealand, as well as differences in potential Austral summer foraging grounds.

A review of Canadian Arctic killer whale (Orcinus orca) ecology

The killer whale (Orcinus orca (Linnaeus, 1758)) is a widely distributed marine predator with a broad ecological niche at the species level with evidence of specialization and narrow ecological niches among populations. Their occurrence in Canadian Arctic waters is limited by sea ice and it has been suggested that climate warming, which has caused increases in the area of ice-free water and duration of the ice-free season, has led to an increased killer whale presence during the open-water period. In this review, we summarize our knowledge of Canadian Arctic killer whale demographics and ecology, synthesizing published and previously unpublished information in a single document. More specifically, we summarize our knowledge of killer whale population size and trends, distribution and seasonality (including results from recent satellite-tracking studies), feeding ecology, and threats, and identify research priorities in the Canadian Arctic. Despite increased research efforts during the past decade, our demographic and ecological knowledge remains incomplete. An improved ecological understanding is necessary for effective management of killer whales and their prey, species of ecological, economic, and cultural importance to Canadian Inuit and the marine ecosystem. This knowledge will allow us to better understand the ecological consequences of a changing Arctic climate.

Killer whale presence drives bowhead whale selection for sea ice in Arctic seascapes of fear

The effects of predator intimidation on habitat use and behavior of prey species are rarely quantified for large marine vertebrates over ecologically relevant scales. Using state space movement models followed by a series of step selection functions, we analyzed movement data of concurrently tracked prey, bowhead whales (Balaena mysticetus; n = 7), and predator, killer whales (Orcinus orca; n = 3), in a large (63,000 km²), partially ice-covered gulf in the Canadian Arctic. Our analysis revealed pronounced predator-mediated shifts in prey habitat use and behavior over much larger spatiotemporal scales than previously documented in any marine or terrestrial ecosystem. The striking shift from use of open water (predator-free) to dense sea ice and shorelines (predators present) was exhibited gulf-wide by all tracked bowheads during the entire 3-wk period killer whales were present, constituting a nonconsumptive effect (NCE) with unknown energetic or fitness costs. Sea ice is considered quintessential habitat for bowhead whales, and ice-covered areas have frequently been interpreted as preferred bowhead foraging habitat in analyses that have not assessed predator effects. Given the NCEs of apex predators demonstrated here, however, unbiased assessment of habitat use and distribution of bowhead whales and many marine species may not be possible without explicitly incorporating spatiotemporal distribution of predation risk. The apparent use of sea ice as a predator refuge also has implications for how bowhead whales, and likely other ice-associated Arctic marine mammals, will cope with changes in Arctic sea ice dynamics as historically ice-covered areas become increasingly ice-free during summer.

Navigating through the r packages for movement

The advent of miniaturized biologging devices has provided ecologists with unprecedented opportunities to record animal movement across scales, and led to the collection of ever-increasing quantities of tracking data. In parallel, sophisticated tools have been developed to process, visualize and analyse tracking data; however, many of these tools have proliferated in isolation, making it challenging for users to select the most appropriate method for the question in hand. Indeed, within the r software alone, we listed 58 packages created to deal with tracking data or 'tracking packages'. Here, we reviewed and described each tracking package based on a workflow centred around tracking data (i.e. spatio-temporal locations (x, y, t)), broken down into three stages: pre-processing, post-processing and analysis, the latter consisting of data visualization, track description, path reconstruction, behavioural pattern identification, space use characterization, trajectory simulation and others. Supporting documentation is key to render a package accessible for users. Based on a user survey, we reviewed the quality of packages' documentation and identified 11 packages with good or excellent documentation. Links between packages were assessed through a network graph analysis. Although a large group of packages showed some degree of connectivity (either depending on functions or suggesting the use of another tracking package), one third of the packages worked in isolation, reflecting a fragmentation in the r movement-ecology programming community. Finally, we provide recommendations for users when choosing packages, and for developers to maximize the usefulness of their contribution and strengthen the links within the programming community.

A matter of taste: Spatial and ontogenetic variations on the trophic ecology of the tiger shark at the Galapagos Marine Reserve

Sharks are top predators across ocean food webs and have a major ecological role in marine ecosystems. Investigating the trophic ecology of this group of species is thus essential to understand ecosystem functioning and inform specific management actions aimed at shark conservation. The Galapagos Islands represent one of the last ocean wildernesses, where populations of sharks and other top marine predators come close to a pristine status. Here we provide the first study on the trophic ecology of the tiger shark (Galeocerdo cuvier) within the Galapagos Marine Reserve (GMR), using a combination of stable isotope analysis, satellite tracking, and passive acoustic telemetry to investigate ontogenetic and spatial variations at two regions. The mean estimated δ¹³C and δ¹⁵N at Isabela island (western region) were -13.9 ± 0.5‰ and 13.7 ± 0.7‰; and for Santa Cruz island (central region) were -13.8 ± 0.3‰ and 13.4 ± 0.7‰, respectively. Green sea turtles (Chelonia mydas) were the main prey item for large tiger sharks (>280 cm TL), while smaller sharks mainly fed on squid and pelagic fish. Tiger sharks exhibited a high degree of philopatry around green sea-turtle nesting areas, with the majority of sharks detected around green sea-turtle nesting areas for at least 10 months after their capture date, and some individuals were even present during the entire three-year study period. Although we did not report statistically significant differences between the two regions, isotopic and electronic tagging data suggest that tiger sharks in the Galapagos could be segregated into specific populations separated by geographical scales of <100 km. The high productivity of the archipelago, along with the protection from industrial fishing granted by the GMR, result in abundant and predictable sources of prey. This high food abundance, combined with the presence of suitable habitats throughout the tiger shark life cycle, might result in a reduction of migratory behaviours when compared to movement patterns of tiger sharks in other ocean basins. Additional studies using genetic tools could provide further evidence on the presence of separate management units, as it has been recently revealed for other shark species inhabiting the GMR.

Preferred, small-scale foraging areas of two Southern Ocean fur seal species are not determined by habitat characteristics

Background

To understand and predict the distribution of foragers, it is crucial to identify the factors that affect individual movement decisions at different scales. Individuals are expected to adjust their foraging movements to the hierarchical spatial distribution of resources. At a small local scale, spatial segregation in foraging habitat happens among individuals of closely situated colonies. If foraging segregation is due to differences in distribution of resources, we would expect segregated foraging areas to have divergent habitat characteristics.

Results

We investigated how environmental characteristics of preferred foraging areas differ between two closely situated Subantarctic fur seal (Arctocephalus tropicalis) colonies and a single Antarctic fur seal (A. gazella) colony that forage in different pelagic areas even though they are located well within each other’s foraging range. We further investigated the influence of the seasonal cycle on those environmental factors. This study used tracking data from 121 adult female Subantarctic and Antarctic fur seals, collected during summer and winter (2009–2015), from three different colonies. Boosted Regression Tree species distribution models were used to determine key environmental variables associated with areas of fur seal restricted search behaviour. There were no differences in the relative influence of key environmental variables between colonies and seasons. The variables with the most influence for each colony and season were latitude, longitude and magnitude of sea-currents. The influence of latitude and longitude is a by-product of the species’ distinct foraging areas, despite the close proximity (< 25 km) of the colonies. The predicted potential foraging areas for each colony changed from summer to winter, reflecting the seasonal cycle of the Southern Ocean. The model predicted that the potential foraging areas of females from the three colonies should overlap, and the fact they do not in reality indicates that factors other than environmental are influencing the location of each colony’s foraging area.

Conclusions

The results indicated that small scale spatial segregation of foraging habitats is not driven by bottom-up processes. It is therefore important to also consider other potential drivers, e.g. competition, information transfer, and memory, to understand animal foraging decisions and movements.

Mesoscale eddies release pelagic sharks from thermal constraints to foraging in the ocean twilight zone

New dynamic approaches to managing marine fisheries promise more effective management in a changing climate. However, they require detailed knowledge of the links between oceanographic features and marine megafauna. Here, we demonstrate that satellite tracking of animal movements, combined with ocean remote sensing and numerical models, can provide this critical information for the most exploited pelagic shark in the Atlantic Ocean. We find that this predator dives deep in warm, swirling water masses called eddies that have traditionally been considered ocean “deserts.” Sharks use these warm features as a conduit to forage in the ocean twilight zone, a region of the deep ocean that contains the largest fish biomass on Earth, highlighting the importance of these deep ocean prey resources.

Mesoscale eddies are critical components of the ocean’s “internal weather” system. Mixing and stirring by eddies exerts significant control on biogeochemical fluxes in the open ocean, and eddies may trap distinctive plankton communities that remain coherent for months and can be transported hundreds to thousands of kilometers. Debate regarding how and why predators use fronts and eddies, for example as a migratory cue, enhanced forage opportunities, or preferred thermal habitat, has been ongoing since the 1950s. The influence of eddies on the behavior of large pelagic fishes, however, remains largely unexplored. Here, we reconstruct movements of a pelagic predator, the blue shark (Prionace glauca), in the Gulf Stream region using electronic tags, earth-observing satellites, and data-assimilating ocean forecasting models. Based on >2,000 tracking days and nearly 500,000 high-resolution time series measurements collected by 15 instrumented individuals, we show that blue sharks seek out the interiors of anticyclonic eddies where they dive deep while foraging. Our observations counter the existing paradigm that anticyclonic eddies are unproductive ocean “deserts” and suggest anomalously warm temperatures in these features connect surface-oriented predators to the most abundant fish community on the planet in the mesopelagic. These results also shed light on the ecosystem services provided by mesopelagic prey. Careful consideration will be needed before biomass extraction from the ocean twilight zone to avoid interrupting a key link between planktonic production and top predators. Moreover, robust associations between targeted fish species and oceanographic features increase the prospects for effective dynamic ocean management.

Spatially explicit network analysis reveals multi-species annual cycle movement patterns of sea ducks

Conservation of long-distance migratory species poses unique challenges. Migratory connectivity, that is, the extent to which groupings of individuals at breeding sites are maintained in wintering areas, is frequently used to evaluate population structure and assess use of key habitat areas. However, for species with complex or variable annual cycle movements, this traditional bimodal framework of migratory connectivity may be overly simplistic. Like many other waterfowl, sea ducks often travel to specific pre- and post-breeding sites outside their nesting and wintering areas to prepare for migration by feeding extensively and, in some cases, molting their flight feathers. These additional migrations may play a key role in population structure, but are not included in traditional models of migratory connectivity. Network analysis, which applies graph theory to assess linkages between discrete locations or entities, offers a powerful tool for quantitatively assessing the contributions of different sites used throughout the annual cycle to complex spatial networks. We collected satellite telemetry data on annual cycle movements of 672 individual sea ducks of five species from throughout eastern North America and the Great Lakes. From these data, we constructed a multi-species network model of migratory patterns and site use over the course of breeding, molting, wintering, and migratory staging. Our results highlight inter- and intra-specific differences in the patterns and complexity of annual cycle movement patterns, including the central importance of staging and molting sites in James Bay, the St. Lawrence River, and southern New England to multi-species annual cycle habitat linkages, and highlight the value of Long-tailed Ducks (Calengula haemalis) as an umbrella species to represent the movement patterns of multiple sea duck species. We also discuss potential applications of network migration models to conservation prioritization, identification of population units, and integrating different data streams.

Classifying grey seal behaviour in relation to environmental variability and commercial fishing activity - a multivariate hidden Markov model

Classifying movement behaviour of marine predators in relation to anthropogenic activity and environmental conditions is important to guide marine conservation. We studied the relationship between grey seal (Halichoerus grypus) behaviour and environmental variability in the southwestern Baltic Sea where seal-fishery conflicts are increasing. We used multiple environmental covariates and proximity to active fishing nets within a multivariate hidden Markov model (HMM) to quantify changes in movement behaviour of grey seals while at sea. Dive depth, dive duration, surface duration, horizontal displacement, and turning angle were used to identify travelling, resting and foraging states. The likelihood of seals foraging increased in deeper, colder, more saline waters, which are sites with increased primary productivity and possibly prey densities. Proximity to active fishing net also had a pronounced effect on state occupancy. The probability of seals foraging was highest <5 km from active fishing nets (51%) and decreased as distance to nets increased. However, seals used sites <5 km from active fishing nets only 3% of their time at sea highlighting an important temporal dimension in seal-fishery interactions. By coupling high-resolution oceanographic, fisheries, and grey seal movement data, our study provides a scientific basis for designing management strategies that satisfy ecological and socioeconomic demands on marine ecosystems.

Generalizing the first-difference correlated random walk for marine animal movement data

Animal telemetry data are often analysed with discrete time movement models. These models are defined with regular time steps. However, telemetry data from marine animals are observed irregularly. To account for irregular data, a time-irregularised first-difference correlated random walk model with drift is introduced. The model generalizes the commonly used first-difference correlated random walk with regular time steps by allowing irregular time steps, including a drift term, and by allowing different autocorrelation in the two coordinates. The model is applied to data from a ringed seal collected through the Argos satellite system, and is compared to related movement models through simulations. Accounting for irregular data in the movement model results in accurate parameter estimates and reconstruction of movement paths. Further, the introduced model can provide more accurate movement paths than the regular time counterpart. Extracting accurate movement paths from uncertain telemetry data is important for evaluating space use patterns for marine animals, which in turn is crucial for management. Further, handling irregular data directly in the movement model allows efficient simultaneous analyses of several animals.

Humpback whale migrations to Antarctic summer foraging grounds through the southwest Pacific Ocean

Humpback whale (Megaptera novaeangliae) populations typically undertake seasonal migrations, spending winters in low latitude breeding grounds and summers foraging in high latitude feeding grounds. Until recently, a broad scale understanding of whale movement has been derived from whaling records, Discovery marks, photo identification and genetic analyses. However, with advances in satellite tagging technology and concurrent development of analytical methodologies we can now detail finer scale humpback whale movement, infer behavioural context and examine how these animals interact with their physical environment. Here we describe the temporal and spatial characteristics of migration along the east Australian seaboard and into the Southern Ocean by 30 humpback whales satellite tagged over three consecutive austral summers. We characterise the putative Antarctic feeding grounds and identify supplemental foraging within temperate, migratory corridors. We demonstrate that Antarctic foraging habitat is associated with the marginal ice zone, with key predictors of inferred foraging behaviour including distance from the ice edge, ice melt rate and variability in ice concentration two months prior to arrival. We discuss the highly variable ice season within the putative foraging habitat and the implications that this and other environmental factors may have on the continued strong recovery of this humpback whale population.

Scaling marine fish movement behavior from individuals to populations

Understanding how, where, and when animals move is a central problem in marine ecology and conservation. Key to improving our knowledge about what drives animal movement is the rising deployment of telemetry devices on a range of free-roaming species. An increasingly popular way of gaining meaningful inference from an animal's recorded movements is the application of hidden Markov models (HMMs), which allow for the identification of latent behavioral states in the movement paths of individuals. However, the use of HMMs to explore the population-level consequences of movement is often limited by model complexity and insufficient sample sizes. Here, we introduce an alternative approach to current practices and provide evidence of how the inclusion of prior information in model structure can simplify the application of HMMs to multiple animal movement paths with two clear benefits: (a) consistent state allocation and (b) increases in effective sample size. To demonstrate the utility of our approach, we apply HMMs and adapted HMMs to over 100 multivariate movement paths consisting of conditionally dependent daily horizontal and vertical movements in two species of demersal fish: Atlantic cod (Gadus morhua; n = 46) and European plaice (Pleuronectes platessa; n = 61). We identify latent states corresponding to two main underlying behaviors: resident and migrating. As our analysis considers a relatively large sample size and states are allocated consistently, we use collective model output to investigate state-dependent spatiotemporal trends at the individual and population levels. In particular, we show how both species shift their movement behaviors on a seasonal basis and demonstrate population space use patterns that are consistent with previous individual-level studies. Tagging studies are increasingly being used to inform stock assessment models, spatial management strategies, and monitoring of marine fish populations. Our approach provides a promising way of adding value to tagging studies because inferences about movement behavior can be gained from a larger proportion of datasets, making tagging studies more relevant to management and more cost-effective.

From Chilean Patagonia to Galapagos, Ecuador: novel insights on blue whale migratory pathways along the Eastern South Pacific

Background

The most traditional scheme for migration among baleen whales comprises yearly migrations between productive waters at high latitude summer feeding grounds and warmer waters at lower latitudes where whales calve and mate, but rarely feed. Evidence indicates, however, that large departures from this scheme exist among populations and individuals. Furthermore, for some populations there is virtually no information on migratory pathways and destinations. Such is the case of Chilean blue whales throughout the Eastern South Pacific; hence, the goal of this study was to assess its migratory behavior.

Methods

Dedicated marine surveys and satellite tagging efforts were undertaken during the austral summer and early autumn on blue whale feeding grounds off Chilean Northern Patagonia (CNP) during 2013, 2015 and 2016. Positional data derived from satellite tags regarding movement patterns and behavior were analyzed using Bayesian switching first-difference correlated random walk models.

Results

We instrumented 10 CNP blue whales with satellite transmitters and documented individual variation in departure time, northbound migratory routes and potential wintering grounds. The onset of migration occurred from mid/late austral autumn to well into the austral winter. Blue whales moved in various directions, but ultimately converged toward a general NW movement direction along a wide corridor exceeding 2,000 km. Area-Restricted Search behavior was exhibited within fjords and channels of CNP and also South of Galapagos Archipelago (GA) and northern Peru, but never during migration. Interestingly, dive profiles for one whale that reached GA showed a sharp and consistent increase in depth north of 5°S and extreme deep dives of up to 330 m.

Discussion

Information derived from satellite tagged blue whales in this study is the first of its kind off the Eastern Southern Pacific. Our results provide valuable information on their migratory timing, routes and behavior on their northbound migration, particularly regarding the varied migratory plasticity for this particular population. Our results also highlight the first record of two complete migratory paths between CNP and GA and strengthen the hypothesis that GA waters correspond to a potential wintering destination for CNP blue whales. We further hypothesize that this area might be selected because of its biological productivity, which could provide feeding opportunities during the breeding season. Our results suggest that special efforts should be put forward to identify blue whale critical areas and understand key behavioral aspects in order to provide the basis for their conservation on a regional context (i.e., reducing potential ship strike and promote Marine Protected Area (MPA) implementation in Chile, Ecuador and Peru). Indeed, we suggest joint blue whale conservation efforts at the regional level in order to identify and determine potential threats and impacts and, most importantly, implement prospective management actions.

Dynamic foraging of a top predator in a seasonal polar marine environment

The seasonal movement of animals at broad spatial scales provides insight into life-history, ecology and conservation. By combining high-resolution satellite-tagged data with hierarchical Bayesian movement models, we can associate spatial patterns of movement with marine animal behavior. We used a multi-state mixture model to describe humpback whale traveling and area-restricted search states as they forage along the West Antarctic Peninsula. We estimated the change in the geography, composition and characteristics of these behavioral states through time. We show that whales later in the austral fall spent more time in movements associated with foraging, traveled at lower speeds between foraging areas, and shifted their distribution northward and inshore. Seasonal changes in movement are likely due to a combination of sea ice advance and regional shifts in the primary prey source. Our study is a step towards dynamic movement models in the marine environment at broad scales.

Sustained disruption of narwhal habitat use and behavior in the presence of Arctic killer whales

Although predators influence behavior of prey, analyses of electronic tracking data in marine environments rarely consider how predators affect the behavior of tracked animals. We collected an unprecedented dataset by synchronously tracking predator (killer whales, [Formula: see text] = 1; representing a family group) and prey (narwhal, [Formula: see text] = 7) via satellite telemetry in Admiralty Inlet, a large fjord in the Eastern Canadian Arctic. Analyzing the movement data with a switching-state space model and a series of mixed effects models, we show that the presence of killer whales strongly alters the behavior and distribution of narwhal. When killer whales were present (within about 100 km), narwhal moved closer to shore, where they were presumably less vulnerable. Under predation threat, narwhal movement patterns were more likely to be transiting, whereas in the absence of threat, more likely resident. Effects extended beyond discrete predatory events and persisted steadily for 10 d, the duration that killer whales remained in Admiralty Inlet. Our findings have two key consequences. First, given current reductions in sea ice and increases in Arctic killer whale sightings, killer whales have the potential to reshape Arctic marine mammal distributions and behavior. Second and of more general importance, predators have the potential to strongly affect movement behavior of tracked marine animals. Understanding predator effects may be as or more important than relating movement behavior to resource distribution or bottom-up drivers traditionally included in analyses of marine animal tracking data.

A hidden Markov movement model for rapidly identifying behavioral states from animal tracks

Electronic telemetry is frequently used to document animal movement through time. Methods that can identify underlying behaviors driving specific movement patterns can help us understand how and why animals use available space, thereby aiding conservation and management efforts. For aquatic animal tracking data with significant measurement error, a Bayesian state-space model called the first-Difference Correlated Random Walk with Switching (DCRWS) has often been used for this purpose. However, for aquatic animals, highly accurate tracking data are now becoming more common. We developed a new hidden Markov model (HMM) for identifying behavioral states from animal tracks with negligible error, called the hidden Markov movement model (HMMM). We implemented as the basis for the HMMM the process equation of the DCRWS, but we used the method of maximum likelihood and the R package TMB for rapid model fitting. The HMMM was compared to a modified version of the DCRWS for highly accurate tracks, the DCRWSNOME, and to a common HMM for animal tracks fitted with the R package moveHMM. We show that the HMMM is both accurate and suitable for multiple species by fitting it to real tracks from a grey seal, lake trout, and blue shark, as well as to simulated data. The HMMM is a fast and reliable tool for making meaningful inference from animal movement data that is ideally suited for ecologists who want to use the popular DCRWS implementation and have highly accurate tracking data. It additionally provides a groundwork for development of more complex modeling of animal movement with TMB. To facilitate its uptake, we make it available through the R package swim.

Classification of Animal Movement Behavior through Residence in Space and Time

Identification and classification of behavior states in animal movement data can be complex, temporally biased, time-intensive, scale-dependent, and unstandardized across studies and taxa. Large movement datasets are increasingly common and there is a need for efficient methods of data exploration that adjust to the individual variability of each track. We present the Residence in Space and Time (RST) method to classify behavior patterns in movement data based on the concept that behavior states can be partitioned by the amount of space and time occupied in an area of constant scale. Using normalized values of Residence Time and Residence Distance within a constant search radius, RST is able to differentiate behavior patterns that are time-intensive (e.g., rest), time & distance-intensive (e.g., area restricted search), and transit (short time and distance). We use grey-headed albatross (Thalassarche chrysostoma) GPS tracks to demonstrate RST’s ability to classify behavior patterns and adjust to the inherent scale and individuality of each track. Next, we evaluate RST’s ability to discriminate between behavior states relative to other classical movement metrics. We then temporally sub-sample albatross track data to illustrate RST’s response to less resolved data. Finally, we evaluate RST’s performance using datasets from four taxa with diverse ecology, functional scales, ecosystems, and data-types. We conclude that RST is a robust, rapid, and flexible method for detailed exploratory analysis and meta-analyses of behavioral states in animal movement data based on its ability to integrate distance and time measurements into one descriptive metric of behavior groupings. Given the increasing amount of animal movement data collected, it is timely and useful to implement a consistent metric of behavior classification to enable efficient and comparative analyses. Overall, the application of RST to objectively explore and compare behavior patterns in movement data can enhance our fine- and broad- scale understanding of animal movement ecology.