Search

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. [ABSTRACT FROM AUTHOR]

Bio‐logging data obtained by tagging animals are key to addressing global conservation challenges. However, the many thousands of existing bio‐logging datasets are not easily discoverable, universally comparable, nor readily accessible through existing repositories and across platforms, slowing down ecological research and effective management. A set of universal standards is needed to ensure discoverability, interoperability and effective translation of bio‐logging data into research and management recommendations.We propose a standardisation framework adhering to existing data principles (FAIR: Findable, Accessible, Interoperable and Reusable; and TRUST: Transparency, Responsibility, User focus, Sustainability and Technology) and involving the use of simple templates to create a data flow from manufacturers and researchers to compliant repositories, where automated procedures should be in place to prepare data availability into four standardised levels: (a) decoded raw data, (b) curated data, (c) interpolated data and (d) gridded data. Our framework allows for integration of simple tabular arrays (e.g. csv files) and creation of sharable and interoperable network Common Data Form (netCDF) files containing all the needed information for accuracy‐of‐use, rightful attribution (ensuring data providers keep ownership through the entire process) and data preservation security.We show the standardisation benefits for all stakeholders involved, and illustrate the application of our framework by focusing on marine animals and by providing examples of the workflow across all data levels, including filled templates and code to process data between levels, as well as templates to prepare netCDF files ready for sharing.Adoption of our framework will facilitate collection of Essential Ocean Variables (EOVs) in support of the Global Ocean Observing System (GOOS) and inter‐governmental assessments (e.g. the World Ocean Assessment), and will provide a starting point for broader efforts to establish interoperable bio‐logging data formats across all fields in animal ecology. [ABSTRACT FROM AUTHOR]

Shipping routes in the ocean are analogous to terrestrial roads, in that they are regularly used thoroughfares that concentrate the movement of vessels between multiple locations. We applied a terrestrial road ecology framework to examine the ecological impacts of increased global shipping on "marine giants" (ie great whales, basking sharks [Cetorhinus maximus], and whale sharks [Rhincodon typus]). This framework aided in identifying where such "marine roads" and marine giants are likely to interact and the consequences of those interactions. We also reviewed known impacts of shipping routes on these species, and then applied the road ecology framework to detect unknown and potentially threatening processes. In the marine environment, such a framework can be used to incorporate knowledge of existing shipping impacts into management practices, thereby reducing the detrimental effects of future expansion of shipping routes on marine giants. [ABSTRACT FROM AUTHOR]

The behavior of colony-based marine predators is the focus of much research globally. Large telemetry and tracking data sets have been collected for this group of animals, and are accompanied by many empirical studies that seek to segment tracks in some useful way, as well as theoretical studies of optimal foraging strategies. However, relatively few studies have detailed statistical methods for inferring behaviors in central place foraging trips. In this paper we describe an approach based on hidden Markov models, which splits foraging trips into segments labeled as 'outbound', 'search', 'forage', and 'inbound'. By structuring the hidden Markov model transition matrix appropriately, the model naturally handles the sequence of behaviors within a foraging trip. Additionally, by structuring the model in this way, we are able to develop realistic simulations from the fitted model. We demonstrate our approach on data from southern elephant seals ( Mirounga leonina) tagged on Kerguelen Island in the Southern Ocean. We discuss the differences between our 4-state model and the widely used 2-state model, and the advantages and disadvantages of employing a more complex model. [ABSTRACT FROM AUTHOR]

In animal ecology, a question of key interest for aquatic species is how changes in movement behavior are related in the horizontal and vertical dimensions when individuals forage. Alternative theoretical models and inconsistent empirical findings mean that this question remains unresolved. Here we tested expectations by incorporating the vertical dimension (dive information) when predicting switching between movement states (''resident'' or ''directed'') within a state-space model. We integrated telemetry-based tracking and diving data available for four seal species (southern elephant, Weddell, antarctic fur, and crabeater) in East Antarctica. Where possible, we included dive variables derived from the relationships between (1) dive duration and depth (as a measure of effort), and (2) dive duration and the postdive surface interval (as a physiological measure of cost). Our results varied within and across species, but there was a general tendency for the probability of switching into ''resident'' state to be positively associated with shorter dive durations (for a given depth) and longer postdive surface intervals (for a given dive duration). Our results add to a growing body of literature suggesting that simplistic interpretations of optimal foraging theory based only on horizontal movements do not directly translate into the vertical dimension in dynamic marine environments. Analyses that incorporate at least two dimensions can test more sophisticated models of foraging behavior. [ABSTRACT FROM AUTHOR]

Interactions between upper trophic-level predators and their prey remain poorly understood due to their inaccessibility during foraging at sea. This uncertainty has fuelled debate on the impact of predation by species such as the grey seal ( Halichoerus grypus) on fish stocks., The Vemco Mobile Transceiver (VMT) has provided us with new knowledge on interactions between pinnipeds and fish species. However, the necessity to recover the VMT for data retrieval has limited deployments to locations where confidence in instrument recovery is high, and has thus restricted both species and geographical sampling., To overcome these limitations, a Bluetooth link was integrated into the VMT and GPS satellite-linked transmitter. The two-unit design allows data collected by the VMT to be transmitted via Bluetooth to the satellite transmitter, which relays the interaction data to the ARGOS satellite system for retrieval., To evaluate in-situ performance, units were deployed on two adult female grey seals on Sable Island, NS in October 2012 and recovered during the subsequent breeding season. Data archived by the VMT were compared with data uploaded via ARGOS., The deployment periods were 76-84 days. The total number of valid detections archived was 179. All detections archived by the first unit ( n = 66) were transmitted via ARGOS, while all but two of the 113 archived detections from the second unit were transmitted. Detections recovered from both units were from other VMT-tagged grey seals ( n = 173) and moored V13 transmitters on Middle Bank, Eastern Scotian Shelf ( n = 6)., These preliminary results are proof-of-concept that integrated Bluetooth VMTs can be used on a broader variety of marine predators to collect data on species interactions in otherwise inaccessible environments and without the need to recover instruments. [ABSTRACT FROM AUTHOR]

Knowledge of foraging movements during the breeding season is key to understanding energetic stresses faced by seabirds. Using archival light loggers (geolocators), a Bayesian state-space model, and stable isotope analysis, we compared foraging movements of Leach's storm-petrels Oceanodroma leucorhoa during their incubation periods in 2012 and 2013. Data were collected from two colonies, Bon Portage Island and Country Island, which are 380 km apart along the coast of Nova Scotia, Canada. Based on allometry for procellariiform mass, predicted foraging ranges for Leach's storm-petrels are 200 km; however, observed maximum distances from the colony were 3 to 5 times that. Storm-petrels from Country Island travelled 1015 ± 238 km southeast to the Laurentian fan and south of the Grand Banks whereas storm-petrels from Bon Portage Island travelled 613 ± 167 km southeast, beyond the continental slope, east of Georges Bank. The average distance travelled in a return trip was 2287 ± 603 km and 1303 ± 351 km for Country Island and Bon Portage Island, respectively. There were no differences between years in cumulative distances travelled within islands, but foraging trips did not last as long in 2013 (4.7 ± 1.5 d) as they did in 2012 (6.2 ± 2.1 d). Stable isotope analyses indicated that, during the incubation period, prey items from Country Island were from higher trophic levels and possibly had higher energy content than those from Bon Portage Island, perhaps explaining the more distant and longer foraging trips for Country Island birds. [ABSTRACT FROM AUTHOR]

Summary 1. Double-tagging experiments are invaluable for determining the accuracy and precision of location data provided by different telemetry technologies used with free-ranging animals. 2. We developed a state-space modelling framework for estimating the precision of telemetry location data based on double-tagging experiments. The model is simultaneously fitted to multiple data types with different temporal resolutions while including errors in all data. 3. We used the model to estimate the precision of a specific geolocation method based on light and sea surface temperature applied to a large marine telemetry dataset. Data were available from double-tagging experiments on 111 animals representing seven marine species including 4 sharks, 2 birds and 1 pinniped. Study animals carried electronic tags that provided geolocation estimates as well as more precise satellite-based location data (Argos and Global Positioning System). 4. Estimates of the precision of geolocations were similar to previous findings. The overall estimated SD of geolocation errors for each species ranged from 0·5 to 3·9° for longitude and 0·8 to 3·6° for latitude. 5. While these results are specific to this particular type of location estimation method, the state-space framework presented here is a robust approach to estimating the precision of various types of telemetry location data from double-tagging experiments. The model simultaneously allows for appropriate inferences about true animal locations and movement. [ABSTRACT FROM AUTHOR]

In many large pelagic animals, observing behavior is limited to observation by radio or satellite telemetry. In many cases, discriminating different behaviors from telemetry data has been a key, but often elusive, goal. Here we use state-space models (SSMs) to fit a correlated random walk (CRW) model that switches between two unobserved behavioral states (nominally foraging and traveling) to 41 male and 43 female adult grey seal (Halichoerus grypus) satellite telemetry tracks. The SSM results reveal markedly different spatial behavior between the sexes, fitting well with sexual size dimorphism and known dietary differences, suggesting that the sexes deal with seasonal prey availability and reproductive costs differently. From these results we were also able to produce behaviorally informed habitat use maps, showing a complex and dynamic network of small, intensely used foraging areas. Our flexible SSM approach clearly demonstrates sex-related behavioral differences, fine scale spatial and temporal foraging patterns, and a clearer picture of grey seal ecology and role in the Scotian Shelf ecosystem. [ABSTRACT FROM AUTHOR]

The article presents a study which describes a modeling approach which can be used to time series of length composition data in order to estimate vital rates. It cites that the approach simultaneously allows the process and observation uncertainty which is the Bayesian state-space model. It also facilitates the incorporation of prior information on population dynamics of animals and on the monitoring process. Moreover, it indicates that the study utilized these models to the endangered or threatened populations of winter skate.

1. Mark-recapture models are valuable for assessing diverse demographic and behavioural parameters, yet the precision of traditional estimates is often constrained by sparse empirical data. Bayesian inference explicitly recognizes estimation uncertainty, and hierarchical Bayes has proven particularly useful for dealing with sparseness by combining information across data sets. 2. We developed a general hierarchical Bayesian multi-state mark-recapture model, tested its performance on simulated data sets and applied it to real ecological data on stopovers by migratory birds. 3. Our hierarchical model performed well in terms of both precision and accuracy of parameters when tested with simulated data of varying quality (sample size, capture and survivorship probabilities). It also provided more precise and accurate parameter estimates than a non-hierarchical model when data were sparse. 4. A specific version of the model, designed for estimation of daily transience and departure of migratory birds at a mid-route stopover, was applied to 11 years of autumn migration data from Atlantic Canada. Hierarchical estimates of departure and transience were more precise than those derived from parallel non-hierarchical and frequentist methods, and indicated that inter-annual variability in parameters suggested by these other methods was largely due to sampling error. 5. Synthesis and applications. Estimates of demographic parameters, often derived from mark-recapture studies, provide the basis for evaluating the status of species at risk, for developing conservation and management strategies and for evaluating the results of current protocols. The hierarchical Bayesian multi-state mark-recapture model presented here permits partitioning of complex parameter variation across space or time, and the simultaneous analysis of multiple data sets results in a marked increase in the precision of estimates derived from sparse capture data. Its structural flexibility should make it a valuable tool for conservation ecologists and wildlife managers. [ABSTRACT FROM AUTHOR]

1. Biological and statistical complexity are features common to most ecological data that hinder our ability to extract meaningful patterns using conventional tools. Recent work on implementing modern statistical methods for analysis of such ecological data has focused primarily on population dynamics but other types of data, such as animal movement pathways obtained from satellite telemetry, can also benefit from the application of modern statistical tools. 2. We develop a robust hierarchical state–space approach for analysis of multiple satellite telemetry pathways obtained via the Argos system. State–space models are time-series methods that allow unobserved states and biological parameters to be estimated from data observed with error. We show that the approach can reveal important patterns in complex, noisy data where conventional methods cannot. 3. Using the largest Atlantic satellite telemetry data set for critically endangered leatherback turtles, we show that the diel pattern in travel rates of these turtles changes over different phases of their migratory cycle. While foraging in northern waters the turtles show similar travel rates during day and night, but on their southward migration to tropical waters travel rates are markedly faster during the day. These patterns are generally consistent with diving data, and may be related to changes in foraging behaviour. Interestingly, individuals that migrate southward to breed generally show higher daytime travel rates than individuals that migrate southward in a non-breeding year. 4. Our approach is extremely flexible and can be applied to many ecological analyses that use complex, sequential data. [ABSTRACT FROM AUTHOR]

Remotely sensed tracking data collected on animal movement is vastly underutilized due to a lack of statistical tools for appropriate analysis. Features of such data that make analysis particularly challenging include the presence of estimation errors that are non-Gaussian and vary in time, observations that occur irregularly in time, and complexity in the underlying behavioral processes. We develop a state-space framework that simultaneously deals with these features and demonstrate our method by analyzing three seal pathway data sets. We show how known information regarding error distributions can be used to improve inference of the underlying process(es) and demonstrate that our frame work provides a powerful and flexible method for fitting different behavioral models to tracking data. [ABSTRACT FROM AUTHOR]

The study of animal movement and behavior is being revolutionized by technology, such as satellite tags and harmonic radar, that allows us to track the movements of individual animals. However, our ability to analyze and model such data has lagged behind the sophisticated collection methods. We review problems with current methods and suggest a more powerful and flexible approach, state-space modeling, and we illustrate how these models can be posed in a meta-analytic framework so that information from individual trajectories may be combined optimally. State-space models enable us to deal with the complexity of modeling animals interacting with their environment but, unlike other methods, they allow simultaneous estimation of measurement error and process noise that are inherent in animal-trajectory data. A Bayesian framework allows us to incorporate important prior information when available and also allows meta-analytic techniques to be incorporated in a straightforward fashion. Meta-analysis enables both individual and broader-level inference from observations of multiple individual pathways. Our approach is powerful because it allows researchers to test hypotheses regarding animal movement, to connect theoretical models to data, and to use modern likelihood-based estimation techniques, all under a single statistical framework. [ABSTRACT FROM AUTHOR]