Animal Tracking Data Research Articles

From spatial prioritization to conservation management in the Southern Ocean using the marine IBAs approach

Implementing effective conservation action requires spatial prioritization exercises to be functionally integrated with a process for developing an implementation strategy. There is great potential for animal tracking data to inform marine management in the Southern Ocean. Using information on penguin distribution, a set of marine Important Bird Areas (mIBAs) has recently been identified around Antarctica. Large-scale spatial analyses like this are key to guide resources and the attention of decision-makers towards areas of significant value. Yet, protecting marine resources requires translating prioritization exercises into legally-binding conservation measures. Here we use one of the largest gentoo colonies in Antarctica as a case study to explore pathways for the utilization of the mIBAs approach in the design and implementation of conservation measures in the Southern Ocean. For scientists and organizations willing to have a policy impact, there are two main routes to contribute to Antarctic Treaty System (ATS) decision-making: through Parties' National Delegations, or through Experts and Observers. We provide three main recommendations for incorporating the results of spatial prioritization analyses into the agenda of ATS governance bodies using the mIBAs approach: 1. Differentiate the potential contribution of mIBAs to spatial prioritization from the potential contribution to conservation planning, two different stages in the conservation process; 2. Use methods, criteria and data for delineating boundaries of potential conservation areas according to the stage of the conservation process that the outputs are expected to contribute to; 3. Understand how Antarctic mIBAs might fit into the ATS conservation measures framework and ongoing deliberations.

Simultaneous estimation of the temporal and spatial extent of animal migration using step lengths and turning angles

BackgroundAnimals of many different species, trophic levels, and life history strategies migrate, and the improvement of animal tracking technology allows ecologists to collect increasing amounts of detailed data on these movements. Understanding when animals migrate is important for managing their populations, but is still difficult despite modelling advancements.MethodsWe designed a model that parametrically estimates the timing of migration from animal tracking data. Our model identifies the beginning and end of migratory movements as signaled by change-points in step length and turning angle distributions. To this end, we can also use the model to estimate how an animal’s movement changes when it begins migrating. In addition to a thorough simulation analysis, we tested our model on three datasets: migratory ferruginous hawks (Buteo regalis) in the Great Plains, barren-ground caribou (Rangifer tarandus groenlandicus) in northern Canada, and non-migratory brown bears (Ursus arctos) from the Canadian Arctic.ResultsOur simulation analysis suggests that our model is most useful for datasets where an increase in movement speed or directional autocorrelation is clearly detectable. We estimated the beginning and end of migration in caribou and hawks to the nearest day, while confirming a lack of migratory behaviour in the brown bears. In addition to estimating when caribou and ferruginous hawks migrated, our model also identified differences in how they migrated; ferruginous hawks achieved efficient migrations by drastically increasing their movement rates while caribou migration was achieved through significant increases in directional persistence.ConclusionsOur approach is applicable to many animal movement studies and includes parameters that can facilitate comparison between different species or datasets. We hope that rigorous assessment of migration metrics will aid understanding of both how and why animals move.

Wildlife watch

The escalating biodiversity loss demands a paradigm shift in wildlife conservation. This paper proposes an innovative AI system for holistic wildlife management. The deep learning algorithms identify individual animals through biometrics in camera traps, drone footage, and bioacoustics. This surpasses traditional methods, enabling tracking across vast landscapes. The real-time animal tracking data, analyzed by machine learning, allows for early detection of poaching, habitat disturbances, and animal distress. Furthermore, the system integrates environmental sensors to provide a holistic understanding of ecological conditions. The correlating animal movement with environmental data helps identify crucial habitats and predict climate threats. This unified platform empowers proactive wildlife management, transitioning conservation from reactive to evidence-based practices for long-term biodiversity preservation.

Fission-fusion dynamics in sheep: the influence of resource distribution and temporal activity patterns.

Fission-fusion events, i.e. changes to the size and composition of animal social groups, are a mechanism to adjust the social environment in response to short-term changes in the cost-benefit ratio of group living. Furthermore, the time and location of fission-fusion events provide insight into the underlying drivers of these dynamics. Here, we describe a method for identifying group membership over time and for extracting fission-fusion events from animal tracking data. We applied this method to high-resolution GPS data of free-ranging sheep (Ovis aries). Group size was highest during times when sheep typically rest (midday and at night), and when anti-predator benefits of grouping are high while costs of competition are low. Consistent with this, fission and fusion frequencies were highest during early morning and late evening, suggesting that social restructuring occurs during periods of high activity. However, fission and fusion events were not more frequent near food patches and water resources when adjusted for overall space use. This suggests a limited role of resource competition. Our results elucidate the dynamics of grouping in response to social and ecological drivers, and we provide a tool for investigating these dynamics in other species.

AniMotum, an R package for animal movement data: Rapid quality control, behavioural estimation and simulation

Abstract Animal tracking data are indispensable for understanding the ecology, behaviour and physiology of mobile or cryptic species. Meaningful signals in these data can be obscured by noise due to imperfect measurement technologies, requiring rigorous quality control as part of any comprehensive analysis. State–space models are powerful tools that separate signal from noise. These tools are ideal for quality control of error‐prone location data and for inferring where animals are and what they are doing when they record or transmit other information. However, these statistical models can be challenging and time‐consuming to fit to diverse animal tracking data sets. The R package aniMotum eases the tasks of conducting quality control on and inference of changes in movement from animal tracking data. This is achieved via: (1) a simple but extensible workflow that accommodates both novice and experienced users; (2) automated processes that alleviate complexity from data processing and model specification/fitting steps; (3) simple movement models coupled with a powerful numerical optimization approach for rapid and reliable model fitting. We highlight aniMotum's capabilities through three applications to real animal tracking data. Full R code for these and additional applications is included as Supporting Information, so users can gain a deeper understanding of how to use aniMotum for their own analyses.

Mitigating pseudoreplication and bias in resource selection functions with autocorrelation‐informed weighting

Abstract Resource selection functions (RSFs) are among the most commonly used statistical tools in both basic and applied animal ecology. They are typically parameterized using animal tracking data, and advances in animal tracking technology have led to increasing levels of autocorrelation between locations in such data sets. Because RSFs assume that data are independent and identically distributed, such autocorrelation can cause misleadingly narrow confidence intervals and biased parameter estimates. Data thinning, generalized estimating equations and step selection functions (SSFs) have been suggested as techniques for mitigating the statistical problems posed by autocorrelation, but these approaches have notable limitations that include statistical inefficiency, unclear or arbitrary targets for adequate levels of statistical independence, constraints in input data and (in the case of SSFs) scale‐dependent inference. To remedy these problems, we introduce a method for likelihood weighting of animal locations to mitigate the negative consequences of autocorrelation on RSFs. In this study, we demonstrate that this method weights each observed location in an animal's movement track according to its level of non‐independence, expanding confidence intervals and reducing bias that can arise when there are missing data in the movement track. Ecologists and conservation biologists can use this method to improve the quality of inferences derived from RSFs. We also provide a complete, annotated analytical workflow to help new users apply our method to their own animal tracking data using the ctmm R package.

Spatial-temporal interpolation of satellite geomagnetic data to study long-distance animal migration

IntroductionIncreased access to remote sensing datasets presents opportunities to model an animal's in-situ experience of the landscape to study behavior and test hypotheses such as geomagnetic map navigation. MagGeo is an open-source tool that combines high spatiotemporal resolution geomagnetic data with animal tracking data. Unlike gridded remote sensing data, satellite geomagnetic data are point-based measurements of the magnetic field at the location of each satellite. MagGeo converts these measurements into geomagnetic values at an animal's location and time. The objective of this paper is to evaluate different interpolation methods and data frameworks within the MagGeo software and quantify how accurately MagGeo can model geomagnetic values and patterns as experienced by animals. MethodWe tested MagGeo outputs against data from 109 terrestrial geomagnetic observatories across 7 years. Unlike satellite data, ground-based data are more likely to represent how animals near the Earth's surface experience geomagnetic field dynamics. Within the MagGeo framework, we compared an inverse-distance weighting interpolation with three different nearest-neighbour interpolation methods. We also compared model geomagnetic data with combined model and satellite data in their ability to capture geomagnetic fluctuations. Finally, we fit a linear mixed-effect model to understand how error is influenced by factors like geomagnetic activity and distance in space and time between satellite and point of interest. Results and conclusionsThe overall absolute difference between MagGeo outputs and observatory values was <1% of the total possible range of values for geomagnetic components. Satellite data measurements closest in time to the point of interest consistently had lowest error which likely reflects the ability of the nearest neighbour in time interpolation method to capture small continuous daily fluctuations and larger discrete events like geomagnetic storms. Combined model and satellite data also capture geomagnetic fluctuations better than model data alone across most geomagnetic activity levels. Our linear mixed-effect models suggest that most of the variation in error can be explained by location-specific effects originating largely from local crustal biases, and that high geomagnetic activity usually predicts higher error though ultimately remaining within the 1% error range. Our results indicate that MagGeo can help researchers explore how animals may use the geomagnetic field to navigate long distances by providing access to data and methods that accurately model how animals moving near the Earth's surface experience the geomagnetic field.

Methods to detect spatial biases in tracking studies caused by differential representativeness of individuals, populations and time

AbstractAimOver the last decades, the study of movement through tracking data has grown exceeding the expectations of movement ecologists. This has posed new challenges, specifically when using individual tracking data to infer higher‐level distributions (e.g. population and species). Sources of variability such as individual site fidelity (ISF), environmental stochasticity over time, and space‐use variability across species ranges must be considered, and their effects identified and corrected, to produce accurate estimates of spatial distribution using tracking data.InnovationWe developed R functions to detect the effect of these sources of variability in the distribution of animal groups when inferred from individual tracking data. These procedures can be adapted for their use in most tracking datasets and tracking techniques. We demonstrated our procedures with simulated datasets and showed their applicability on a real‐world dataset containing 1346 year‐round migratory trips from 805 individuals of three closely related seabird species breeding in 34 colonies in the Mediterranean Sea and the Atlantic Ocean, spanning 10 years. We detected an effect of ISF in one of the colonies, but no effect of the environmental stochasticity on the distribution of birds for any of the species. We also identified among‐colony variability in nonbreeding space use for one species, with significant effects of population size and longitude.Main conclusionsThis work provides a useful, much‐needed tool for researchers using animal tracking data to model species distributions or establish conservation measures. This methodology may be applied in studies using individual tracking data to accurately infer the distribution of a population or species and support the delineation of important areas for conservation based on tracking data. This step, designed to precede any analysis, has become increasingly relevant with the proliferation of studies using large tracking datasets that has accompanied the globalization process in science driving collaborations and tracking data sharing initiatives.

MoveApps: a serverless no-code analysis platform for animal tracking data

BackgroundBio-logging and animal tracking datasets continuously grow in volume and complexity, documenting animal behaviour and ecology in unprecedented extent and detail, but greatly increasing the challenge of extracting knowledge from the data obtained. A large variety of analysis methods are being developed, many of which in effect are inaccessible to potential users, because they remain unpublished, depend on proprietary software or require significant coding skills.ResultsWe developed MoveApps, an open analysis platform for animal tracking data, to make sophisticated analytical tools accessible to a global community of movement ecologists and wildlife managers. As part of the Movebank ecosystem, MoveApps allows users to design and share workflows composed of analysis modules (Apps) that access and analyse tracking data. Users browse Apps, build workflows, customise parameters, execute analyses and access results through an intuitive web-based interface.Apps, coded in R or other programming languages, have been developed by the MoveApps team and can be contributed by anyone developing analysis code. They become available to all user of the platform. To allow long-term and cross-system reproducibility, Apps have public source code and are compiled and run in Docker containers that form the basis of a serverless cloud computing system. To support reproducible science and help contributors document and benefit from their efforts, workflows of Apps can be shared, published and archived with DOIs in the Movebank Data Repository.The platform was beta launched in spring 2021 and currently contains 49 Apps that are used by 316 registered users. We illustrate its use through two workflows that (1) provide a daily report on active tag deployments and (2) segment and map migratory movements.ConclusionsThe MoveApps platform is meant to empower the community to supply, exchange and use analysis code in an intuitive environment that allows fast and traceable results and feedback. By bringing together analytical experts developing movement analysis methods and code with those in need of tools to explore, answer questions and inform decisions based on data they collect, we intend to increase the pace of knowledge generation and integration to match the huge growth rate in bio-logging data acquisition.

Movements of southern elephant seals (Mirounga leonina) from Davis Base, Antarctica: combining population genetics and tracking data

Marine animals such as the southern elephant seal (Mirounga leonina) rely on a productive marine environment and are vulnerable to oceanic changes that can affect their reproduction and survival rates. Davis Base, Antarctica, acts as a moulting site for southern elephant seals that forage in Prydz Bay, but the mitochondrial haplotype diversity and natal source populations of these seals have not been characterized. In this study, we combined genetic and animal tracking data on these moulting seals to identify levels of mitochondrial haplotype diversity, natal source population, and movement behaviours during foraging and haul-out periods. Using partial sequences of the mitochondrial control region, we identified two major breeding mitochondrial lineages of seals at Davis Base. We found that the majority of the seals originated from breeding stocks within the South Atlantic Ocean and South Indian Ocean. One seal was grouped with the Macquarie Island breeding stock (South Pacific Ocean). The Macquarie Island population, unlike the other two stocks, is decreasing in size. Tracking data revealed long-distance foraging activity of the Macquarie Island seal around Crozet Islands. We speculate that changes to the Antarctic marine environment can result in a shift in foraging and movement strategies, which subsequently affects seal population growth rates.

Population‐level inference for home‐range areas

Abstract Home‐range estimates are a common product of animal tracking data, as each range represents the area needed by a given individual. Population‐level inference of home‐range areas—where multiple individual home ranges are considered to be sampled from a population—is also important to evaluate changes over time, space or covariates such as habitat quality or fragmentation, and for comparative analyses of species averages. Population‐level home‐range parameters have traditionally been estimated by first assuming that the input tracking data were sampled independently when calculating home ranges via conventional kernel density estimation (KDE) or minimal convex polygon (MCP) methods, and then assuming that those individual home ranges were measured exactly when calculating the population‐level estimates. This conventional approach does not account for the temporal autocorrelation that is inherent in modern tracking data, nor for the uncertainties of each individual home‐range estimate, which are often large and heterogeneous. Here, we introduce a statistically and computationally efficient framework for the population‐level analysis of home‐range areas, based on autocorrelated kernel density estimation (AKDE), that can account for variable temporal autocorrelation and estimation uncertainty. We apply our method to empirical examples on lowland tapir Tapirus terrestris, kinkajou Potos flavus, white‐nosed coati Nasua narica, white‐faced capuchin monkey Cebus capucinus and spider monkey Ateles geoffroyi, and quantify differences between species, environments and sexes. Our approach allows researchers to more accurately compare different populations with different movement behaviours or sampling schedules while retaining statistical precision and power when individual home‐range uncertainties vary. Finally, we emphasize the estimation of effect sizes when comparing populations, rather than mere significance tests.

A Quantitative Framework for Identifying Patterns of Route-Use in Animal Movement Data

Animal movement along repeatedly used, “habitual” routes could emerge from a variety of cognitive mechanisms, as well as in response to a diverse set of environmental features. Because of the high conservation value of identifying wildlife movement corridors, there has been extensive work focusing on environmental factors that contribute to the emergence of habitual routes between protected habitats. In parallel, significant work has focused on disentangling the cognitive mechanisms underlying animal route use, as such movement patterns are of fundamental interest to the study of decision making and navigation. We reviewed the types of processes that can generate routine patterns of animal movement, suggested a new methodological workflow for classifying one of these patterns—high fidelity path reuse—in animal tracking data, and compared the prevalence of this pattern across four sympatric species of frugivorous mammals in Panama. We found the highest prevalence of route-use in kinkajous, the only nocturnal species in our study, and propose that further development of this method could help to distinguish the processes underlying the presence of specific routes in animal movement data.

Autocorrelation‐informed home range estimation: A review and practical guide

AbstractModern tracking devices allow for the collection of high‐volume animal tracking data at improved sampling rates over very‐high‐frequency radiotelemetry. Home range estimation is a key output from these tracking datasets, but the inherent properties of animal movement can lead traditional statistical methods to under‐ or overestimate home range areas.The autocorrelated kernel density estimation (AKDE) family of estimators was designed to be statistically efficient while explicitly dealing with the complexities of modern movement data: autocorrelation, small sample sizes and missing or irregularly sampled data. Although each of these estimators has been described in separate technical papers, here we review how these estimators work and provide a user‐friendly guide on how they may be combined to reduce multiple biases simultaneously.We describe the magnitude of the improvements offered by these estimators and their impact on home range area estimates, using both empirical case studies and simulations, contrasting their computational costs.Finally, we provide guidelines for researchers to choose among alternative estimators and anRscript to facilitate the application and interpretation of AKDE home range estimates.

A guide to pre‐processing high‐throughput animal tracking data

Modern, high‐throughput animal tracking increasingly yields ‘big data’ at very fine temporal scales. At these scales, location error can exceed the animal's step size, leading to mis‐estimation of behaviours inferred from movement. ‘Cleaning’ the data to reduce location errors is one of the main ways to deal with position uncertainty. Although data cleaning is widely recommended, inclusive, uniform guidance on this crucial step, and on how to organise the cleaning of massive datasets, is relatively scarce.A pipeline for cleaning massive high‐throughput datasets must balance ease of use and computationally efficiency, in which location errors are rejected while preserving valid animal movements. Another useful feature of a pre‐processing pipeline is efficiently segmenting and clustering location data for statistical methods while also being scalable to large datasets and robust to imperfect sampling. Manual methods being prohibitively time‐consuming, and to boost reproducibility, pre‐processing pipelines must be automated.We provide guidance on building pipelines for pre‐processing high‐throughput animal tracking data to prepare it for subsequent analyses. We apply our proposed pipeline to simulated movement data with location errors, and also show how large volumes of cleaned data can be transformed into biologically meaningful ‘residence patches’, for exploratory inference on animal space use. We use tracking data from the Wadden Sea ATLAS system (WATLAS) to show how pre‐processing improves its quality, and to verify the usefulness of the residence patch method. Finally, with tracks from Egyptian fruit bats Rousettus aegyptiacus, we demonstrate the pre‐processing pipeline and residence patch method in a fully worked out example.To help with fast implementation of standardised methods, we developed the R package atlastools, which we also introduce here. Our pre‐processing pipeline and atlastools can be used with any high‐throughput animal movement data in which the high data‐volume combined with knowledge of the tracked individuals' movement capacity can be used to reduce location errors. atlastools is easy to use for beginners while providing a template for further development. The common use of simple yet robust pre‐processing steps promotes standardised methods in the field of movement ecology and leads to better inferences from data.

Mobilizing Animal Movement Data: API use and the Movebank platform

Movebank, a global platform for animal tracking and other animal-borne sensor data, is used by over 3,000 researchers globally to harmonize, archive and share nearly 3 billion animal occurrence records and more than 3 billion other animal-borne sensor measurements that document the movements and behavior of over 1,000 species. Movebank’s publicly described data model (Kranstauber et al. 2011), vocabulary and application programming interfaces (APIs) provide services for users to automate data import and retrieval. Near-live data feeds are maintained in cooperation with over 20 manufacturers of animal-borne sensors, who provide data in agreed-upon formats for accurate data import. Data acquisition by API complies with public or controlled-access sharing settings, defined within the database by data owners. The Environmental Data Automated Track Annotation System (EnvDATA, Dodge et al. 2013) allows users to link animal tracking data with hundreds of environmental parameters from remote sensing and weather reanalysis products through the Movebank website, and offers an API for advanced users to automate the submission of annotation requests. Movebank's mobile apps, the Animal Tracker and Animal Tagger, use APIs to support reporting and monitoring while in the field, as well as communication with citizen scientists. The recently-launched MoveApps platform connects with Movebank data using an API to allow users to build, execute and share repeatable workflows for data exploration and analysis through a user-friendly interface. A new API, currently under development, will allow calls to retrieve data from Movebank reduced according to criteria defined by "reduction profiles", which can greatly reduce the volume of data transferred for many use cases. In addition to making this core set of Movebank services possible, Movebank's APIs enable the development of external applications, including the widely used R programming packages 'move' (Kranstauber et al. 2012) and 'ctmm' (Calabrese et al. 2016), and user-specific workflows to efficiently execute collaborative analyses and automate tasks such as syncing with local organizational and governmental websites and archives. The APIs also support large-scale data acquisition, including for projects under development to visualize, map and analyze bird migrations led by the British Trust for Ornithology, the coordinating organisation for European bird ringing (banding) schemes (EURING), Georgetown University, National Audubon Society, Smithsonian Institution and United Nations Convention on Migratory Species. Our API development is constrained by a lack of standardization in data reporting across animal-borne sensors and a need to ensure adequate communication with data users (e.g., how to properly interpret data; expectations for use and attribution) and data owners (e.g., who is using publicly-available data and how) when allowing automated data access. As interest in data linking, harvesting, mirroring and integration grows, we recognize needs to coordinate API development across animal tracking and biodiversity databases, and to develop a shared system for unique organism identifiers. Such a system would allow linking of information about individual animals within and across repositories and publications in order to recognize data for the same individuals across platforms, retain provenance and attribution information, and ensure beneficial and biologically meaningful data use.

Home range variation in leopards living across the human density gradient

AbstractHome range size is a fundamental measure of animal space use, providing insight into habitat quality, animal density, and social organization. Human impacts increasingly are affecting wildlife, especially among wide-ranging species that encounter anthropogenic disturbance. Leopards (Panthera pardus) provide a useful model for studying this relationship because leopards coexist with people at high and low human densities and are sensitive to human disturbance. To compare leopard home range size across a range of human densities and other environmental conditions, we combined animal tracking data from 74 leopards in multiple studies with new analytical techniques that accommodate different sampling regimes. We predicted that home ranges would be smaller in more productive habitats and areas of higher human population density due to possible linkage with leopard prey subsidies from domestic species. We also predicted that male leopards would have larger home ranges than those of females. Home ranges varied in size from 14.5 km2 in India to 885.6 km2 in Namibia, representing a 60-fold magnitude of variation. Home range stability was evident for 95.2% of nontranslocated individuals and 38.5% of translocated individuals. Leopard home range sizes were negatively correlated with landscape productivity, and males used larger areas than females. Leopards in open habitats had a predicted negative correlation in home range size with human population density, but leopards in closed habitats used larger home ranges in areas with more people.

Fusion of wildlife tracking and satellite geomagnetic data for the study of animal migration

BackgroundMigratory animals use information from the Earth’s magnetic field on their journeys. Geomagnetic navigation has been observed across many taxa, but how animals use geomagnetic information to find their way is still relatively unknown. Most migration studies use a static representation of geomagnetic field and do not consider its temporal variation. However, short-term temporal perturbations may affect how animals respond - to understand this phenomenon, we need to obtain fine resolution accurate geomagnetic measurements at the location and time of the animal. Satellite geomagnetic measurements provide a potential to create such accurate measurements, yet have not been used yet for exploration of animal migration.MethodsWe develop a new tool for data fusion of satellite geomagnetic data (from the European Space Agency’s Swarm constellation) with animal tracking data using a spatio-temporal interpolation approach. We assess accuracy of the fusion through a comparison with calibrated terrestrial measurements from the International Real-time Magnetic Observatory Network (INTERMAGNET). We fit a generalized linear model (GLM) to assess how the absolute error of annotated geomagnetic intensity varies with interpolation parameters and with the local geomagnetic disturbance.ResultsWe find that the average absolute error of intensity is − 21.6 nT (95% CI [− 22.26555, − 20.96664]), which is at the lower range of the intensity that animals can sense. The main predictor of error is the level of geomagnetic disturbance, given by the Kp index (indicating the presence of a geomagnetic storm). Since storm level disturbances are rare, this means that our tool is suitable for studies of animal geomagnetic navigation. Caution should be taken with data obtained during geomagnetically disturbed days due to rapid and localised changes of the field which may not be adequately captured.ConclusionsBy using our new tool, ecologists will be able to, for the first time, access accurate real-time satellite geomagnetic data at the location and time of each tracked animal, without having to start new tracking studies with specialised magnetic sensors. This opens a new and exciting possibility for large multi-species studies that will search for general migratory responses to geomagnetic cues. The tool therefore has a potential to uncover new knowledge about geomagnetic navigation and help resolve long-standing debates.

Merging computational fluid dynamics and machine learning to reveal animal migration strategies

Abstract Understanding how migratory animals interact with dynamic physical environments remains a major challenge in migration biology. Interactions between migrants and wind and water currents are often poorly resolved in migration models due to both the lack of high‐resolution environmental data, and a lack of understanding of how migrants respond to fine‐scale structure in the physical environment. Here we develop a generalizable, data‐driven methodology to study the migration of animals through complex physical environments. Our approach combines validated computational fluid dynamic (CFD) modelling with animal tracking data to decompose migratory movements into two components, namely movement caused by physical forcing and movement due to active locomotion. We then use a flexible recurrent neural network model to relate local environmental conditions to locomotion behaviour of the migrating animal, allowing us to predict a migrant's force production, velocity and trajectory over time. We apply this framework to a large dataset containing measured trajectories of migrating Chinook salmon through a section of river in California's Sacramento‐San Joaquin Delta. We show that the model is capable of describing fish migratory movements as a function of local flow variables, and that it is possible to accurately forecast migratory movements on which the model was not trained. After validating our model, we show how our framework can be used to understand how migrants respond to local‐flow conditions, how migratory behaviour changes as overall conditions in the system change and how the energetic cost of migratory movements depends on environmental conditions in space and time. Our framework is flexible and can readily be applied to other species and systems.

A Novel Framework to Predict Relative Habitat Selection in Aquatic Systems: Applying Machine Learning and Resource Selection Functions to Acoustic Telemetry Data From Multiple Shark Species

Resource selection functions (RSFs) have been widely applied to animal tracking data to examine relative habitat selection and to help guide management and conservation strategies. While readily used in terrestrial ecology, RSFs have yet to be extensively used within marine systems. As acoustic telemetry continues to be a pervasive approach within marine environments, incorporation of RSFs can provide new insights to help prioritize habitat protection and restoration to meet conservation goals. To overcome statistical hurdles and achieve high prediction accuracy, machine learning algorithms could be paired with RSFs to predict relative habitat selection for a species within and even outside the monitoring range of acoustic receiver arrays, making this a valuable tool for marine ecologists and resource managers. Here, we apply RSFs using machine learning to an acoustic telemetry dataset of four shark species to explore and predict species-specific habitat selection within a marine protected area. In addition, we also apply this RSF-machine learning approach to investigate predator-prey relationships by comparing and averaging tiger shark relative selection values with the relative selection values derived for eight potential prey-species. We provide methodological considerations along with a framework and flexible approach to apply RSFs with machine learning algorithms to acoustic telemetry data and suggest marine ecologists and resource managers consider adopting such tools to help guide both conservation and management strategies.

Analysis of local habitat selection and large-scale attraction/avoidance based on animal tracking data: is there a single best method?

BackgroundNew wildlife telemetry and tracking technologies have become available in the last decade, leading to a large increase in the volume and resolution of animal tracking data. These technical developments have been accompanied by various statistical tools aimed at analysing the data obtained by these methods.MethodsWe used simulated habitat and tracking data to compare some of the different statistical methods frequently used to infer local resource selection and large-scale attraction/avoidance from tracking data. Notably, we compared spatial logistic regression models (SLRMs), spatio-temporal point process models (ST-PPMs), step selection models (SSMs), and integrated step selection models (iSSMs) and their interplay with habitat and animal movement properties in terms of statistical hypothesis testing.ResultsWe demonstrated that only iSSMs and ST-PPMs showed nominal type I error rates in all studied cases, whereas SSMs may slightly and SLRMs may frequently and strongly exceed these levels. iSSMs appeared to have on average a more robust and higher statistical power than ST-PPMs.ConclusionsBased on our results, we recommend the use of iSSMs to infer habitat selection or large-scale attraction/avoidance from animal tracking data. Further advantages over other approaches include short computation times, predictive capacity, and the possibility of deriving mechanistic movement models.