Telemetry is an increasingly common tool for studying the ecology of wild fish, with great potential to provide valuable information for management and conservation. For researchers to conduct a ...robust telemetry study, many essential considerations exist related to selecting the appropriate tag type, fish capture and tagging methods, tracking protocol, data processing and analyses, and interpretation of findings. For telemetry-derived knowledge to be relevant to managers and policy makers, the research approach must consider management information needs for decision-making, while end users require an understanding of telemetry technology (capabilities and limitations), its application to fisheries research and monitoring (study design), and proper interpretation of results and conclusions (considering the potential for biases and proper recognition of associated uncertainties). To help bridge this gap, we provide a set of considerations and a checklist for researchers to guide them in conducting reliable and management-relevant telemetry studies, and for managers to evaluate the reliability and relevance of telemetry studies so as to better integrate findings into management plans. These considerations include implicit assumptions, technical limitations, ethical and biological realities, analytical merits, and the relevance of study findings to decision-making processes.
Acoustic telemetry is a popular approach used to track many different aquatic animal taxa in marine and freshwater systems. However, information derived from focal studies is typically resource‐ and ...geography‐limited by the extent and placement of acoustic receivers. Even so, animals tagged and tracked in one region or study may be detected unexpectedly at distant locations by other researchers using compatible equipment, who ideally share that information. Synergies through national and global acoustic tracking networks are facilitating significant discoveries and unexpected observations that yield novel insight into the movement ecology and habitat use of wild animals. Here, we present a selection of case studies that highlight unexpected tracking observations or absence of observations where we expected to find animals in aquatic systems around the globe. These examples span freshwater and marine systems across spatiotemporal scales ranging from adjacent watersheds to distant ocean regions. These unexpected movements showcase the power of collaborative telemetry networks and serendipitous observations. Unique and unexpected observations such as those presented here can capture the imagination of both researchers and members of the public, and improve understanding of movement and connectivity within aquatic ecosystems.
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.
We used the R package TMB to develop a new hidden Markov model (HMM) for identifying behavioral states from animal tracks with negligible error, which we call the hidden Markov movement model (HMMM). We show that the HMMM can make meaningful inference from animal movement data collected on multiple species. It additionally provides a groundwork for development of more complex movement modelling with TMB. To facilitate its uptake, we make the HMMM available through the R package swim.
Fine‐scale tracking with passive acoustic telemetry can yield great insights into the movement ecology of aquatic animals. To predict fine‐scale positions of tagged animals in continuous space from ...spatially‐discrete detection data, state‐space modelling through the R package YAPS provides a promising alternative to frequently used positioning algorithms. However, YAPS cannot currently classify multiple kinds of movement that may be used as proxies for individual behaviours of study animals (behavioural states), an endeavour that is of increasing interest to movement ecologists.
We advance YAPS by incorporating the functionality to predict behavioural states by using an iterative maximization framework. Our model, which we call YAMS, occurs in continuous time and therefore we adapt current hidden Markov model (HMM) machinery to accommodate this while remaining within a likelihood framework that provides rapid fitting. We test our model using simulations and approximately 6 days’ worth of Northern pike data from Hald Lake, Denmark.
YAMS is shown to produce accurate parameter estimates and random effect predictions when model results were compared to simulated data, with behavioural state accuracies of 0.94 and 0.79 for two‐ and three‐state models, respectively, and location state root mean squared errors of 1.8 m for both models. In addition, the behavioural states are shown to reflect varying speeds of the pike, yielding a highly interpretable classification.
This research has the potential to be broadly applicable to both ecologists interested in identifying fine‐scale space use and behavioural states from acoustic telemetry data, as well as to statisticians who may wish to use standard HMM machinery to fit continuous‐time HMMs to animal movement data.
Telemetry, or the remote monitoring of animals with electronic transmitters and receivers, has vastly enhanced our ability to study aquatic animals. Radio telemetry, acoustic telemetry and passive ...integrated transponders are three common technologies that generate detection data — time‐stamped, tag‐specific records that are logged by receivers.
We review current statistical methods and comment on potential future directions for analysing detection data derived from fixed telemetry receiver arrays.
To illustrate how different methods may be used to achieve diverse study objectives, we provide a case study dataset collected by an array of 42 acoustic telemetry receivers on 187 bull trout in the Kinbasket Reservoir of British Columbia. To close, we present a decision tree for guiding the selection of a method based on study objectives and sampling design.
This paper provides both experienced and novice telemetry researchers with the knowledge and tools to facilitate more comprehensive analysis of detection data and, in so doing, ask a wide variety of ecological questions that will enhance our understanding of aquatic organisms.
Smolt migration through lakes is hazardous, as the predation pressure can be extreme and the hydrology a great contrast to that of a riverine area. However, the mechanisms yielding these challenges ...have been scarcely investigated. We conducted an acoustic telemetry field study in Lake Evangervatnet, Voss, Norway, utilising Vemco V5 predation tags. Atlantic salmon (Salmo salar) smolts (N = 20) were tagged with the novel predation sensor tag to investigate mortality, the lacustrine migration behaviour of smolts, and the applicability of these tags for smolt studies. A total of 60% of tagged Atlantic salmon (Salmo salar) smolts perished in the lake. Half of the mortalities (30% of tagged fish) were directly attributed to predation by brown trout (Salmo trutta) based on predation sensors. The surviving smolts were slow to traverse the 6.5 km lake, with progression rate between lake inlet and outlet on average 0.016 m/s over a mean of 7.9 ± 6.2 (SD) days. Acoustic detections revealed a consistent pattern of nocturnal migration and multidirectional movements within the lake. By running a series of correlated random walks under varying parameters and comparing the simulated travel times to the observed travel time used by the tagged smolts, we emulated the observed behaviour of the smolts. These simulations suggested that smolts lacked the ability to efficiently navigate through the lake, instead swimming in random directions until they reached the lake outlet. Predation sensors can offer improved resolution when tracking the behaviour and fate of smolts and can facilitate better mitigation efforts by identifying survival bottlenecks and separating predation from non‐predatory mortality.
One of the central interests of animal movement ecology is relating movement characteristics to behavioural characteristics. The traditional discrete-time statistical tool for inferring unobserved ...behaviours from movement data is the hidden Markov model (HMM). While the HMM is an important and powerful tool, sometimes it is not flexible enough to appropriately fit the data. Data for marine animals often exhibit conditional autocorrelation, self-dependence of the step length process that cannot be explained solely by the behavioural state, which violates one of the main assumptions of the HMM. Using a grey seal track as an example we motivate and develop the conditionally autoregressive hidden Markov model (CarHMM), a generalization of the HMM designed specifically to handle conditional autocorrelation. In addition to introducing and examining the new CarHMM with numerous simulation studies, we provide guidelines for all stages of an analysis using either an HMM or CarHMM. These include guidelines for pre-processing location data to obtain deflection angles and step lengths, model selection, and model checking. In addition to these practical guidelines, we link estimated model parameters to biologically relevant quantities such as activity budget and residency time. We also provide interpretations of traditional “foraging” and “transiting” behaviours in the context of the new CarHMM parameters.
State-space models (SSM) are often used for analyzing complex ecological processes that are not observed directly, such as marine animal movement. When outliers are present in the measurements, ...special care is needed in the analysis to obtain reliable location and process estimates. Here we recommend using the Laplace approximation combined with automatic differentiation (as implemented in the novel R package Template Model Builder; TMB) for the fast fitting of continuous-time multivariate non-Gaussian SSMs. Through Argos satellite tracking data, we demonstrate that the use of continuous-time
t
-distributed measurement errors for error-prone data is more robust to outliers and improves the location estimation compared to using discretized-time
t
-distributed errors (implemented with a Gibbs sampler) or using continuous-time Gaussian errors (as with the Kalman filter). Using TMB, we are able to estimate additional parameters compared to previous methods, all without requiring a substantial increase in computational time. The model implementation is made available through the R package argosTrack.
•Comparison of deep hooking and capture success with different hook-set intensities.•Rates of capture success and deep hooking depend on hook-set technique.•Circle hooks set with no force yielded low ...odds of deep hooking and bleeding.•Bobbers can increase rates of deep hooking.
One of the primary factors associated with mortality in catch-and-release recreational fisheries is depth of hook position relative to the snout, with deeper hooking locations (i.e., gullet) increasing risk of injury to vital tissues. As a result, there have been attempts to develop angling methods and gear that are less likely to result in deep hooking. Circle hooks represent an alternative to conventional “J” style hooks (J-hooks), and in general circle hooks have been shown to reduce the tendency for deep hooking in a variety of species, which can significantly improve post-release survival. Relative to fishing with J-hooks, circle hook manufacturers typically recommend that anglers use a rod movement (i.e., hook-set) of reduced intensity and force (i.e., a light hook-set), thereby maximizing the benefit of circle hooks by reducing the tendency for deep hooking and injury. To evaluate whether hook-set technique can affect hooking and injury in fish, we tested different combinations of hooks (circle hooks and J-hooks) and hook-set techniques (e.g., light, moderate, or heavy force, or with a bobber) in an angling study for bluegill (Lepomis macrochirus) in Lake Opinicon, Ontario, Canada. Binary responses of capture success and deep hooking were analysed with logistic regression. There was no significant interaction between hook type and hook-set, but overall, J-hooks increased the odds of successfully capturing a bluegill and also the odds of deep hooking a bluegill relative to circle hooks. The bobber hook-set technique increased the odds of deep hooking a bluegill relative to the active hook-setting techniques. This study suggests both deep hooking and capture of bluegill are significantly affected by both hook types and hook-set techniques.
One of the central interests of animal movement ecology is relating movement characteristics to behavioural characteristics. The traditional discrete-time statistical tool for inferring unobserved ...behaviours from movement data is the hidden Markov model (HMM). While the HMM is an important and powerful tool, sometimes it is not flexible enough to appropriately fit the data. Data for marine animals often exhibit conditional autocorrelation, self-dependence of the step length process which cannot be explained solely by the behavioural state, which violates one of the main assumptions of the HMM. Using a grey seal track as an example, along with multiple simulation scenarios, we motivate and develop the conditionally autoregressive hidden Markov model (CarHMM), which is a generalization of the HMM designed specifically to handle conditional autocorrelation. In addition to introducing and examining the new CarHMM, we provide guidelines for all stages of an analysis using either an HMM or CarHMM. These include guidelines for pre-processing location data to obtain deflection angles and step lengths, model selection, and model checking. In addition to these practical guidelines, we link estimated model parameters to biologically meaningful quantities such as activity budget and residency time. We also provide interpretations of traditional "foraging" and "transiting" behaviours in the context of the new CarHMM parameters.
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