Development of an automated method of detecting stereotyped feeding events in multisensor data from tagged rorqual whales

The introduction of animal‐borne, multisensor tags has opened up many opportunities for ecological research, making previously inaccessible species and behaviors observable. The advancement of tag technology and the increasingly widespread use of bio‐logging tags are leading to large volumes of sometimes extremely detailed data. With the increasing quantity and duration of tag deployments, a set of tools needs to be developed to aid in facilitating and standardizing the analysis of movement sensor data. Here, we developed an observation‐based decision tree method to detect feeding events in data from multisensor movement tags attached to fin whales (Balaenoptera physalus). Fin whales exhibit an energetically costly and kinematically complex foraging behavior called lunge feeding, an intermittent ram filtration mechanism. Using this automated system, we identified feeding lunges in 19 fin whales tagged with multisensor tags, during a total of over 100 h of continuously sampled data. Using movement sensor and hydrophone data, the automated lunge detector correctly identified an average of 92.8% of all lunges, with a false‐positive rate of 9.5%. The strong performance of our automated feeding detector demonstrates an effective, straightforward method of activity identification in animal‐borne movement tag data. Our method employs a detection algorithm that utilizes a hierarchy of simple thresholds based on knowledge of observed features of feeding behavior, a technique that is readily modifiable to fit a variety of species and behaviors. Using automated methods to detect behavioral events in tag records will significantly decrease data analysis time and aid in standardizing analysis methods, crucial objectives with the rapidly increasing quantity and variety of on‐animal tag data. Furthermore, our results have implications for next‐generation tag design, especially long‐term tags that can be outfitted with on‐board processing algorithms that automatically detect kinematic events and transmit ethograms via acoustic or satellite telemetry.     

Open population maximum likelihood spatial capture‐recapture

Open population capture‐recapture models are widely used to estimate population demographics and abundance over time. Bayesian methods exist to incorporate open population modeling with spatial capture‐recapture (SCR), allowing for estimation of the effective area sampled and population density. Here, open population SCR is formulated as a hidden Markov model (HMM), allowing inference by maximum likelihood for both Cormack‐Jolly‐Seber and Jolly‐Seber models, with and without activity center movement. The method is applied to a 12‐year survey of male jaguars (Panthera onca) in the Cockscomb Basin Wildlife Sanctuary, Belize, to estimate survival probability and population abundance over time. For this application, inference is shown to be biased when assuming activity centers are fixed over time, while including a model for activity center movement provides negligible bias and nominal confidence interval coverage, as demonstrated by a simulation study. The HMM approach is compared with Bayesian data augmentation and closed population models for this application. The method is substantially more computationally efficient than the Bayesian approach and provides a lower root‐mean‐square error in predicting population density compared to closed population models.     

Classifying movement behaviour in relation to environmental conditions using hidden Markov models

1.  Linking the movement and behaviour of animals to their environment is a central problem in ecology. Through the use of electronic tagging and tracking (ETT), collection of in situ data from free-roaming animals is now commonplace, yet statistical approaches enabling direct relation of movement observations to environmental conditions are still in development.
2.  In this study, we examine the hidden Markov model (HMM) for behavioural analysis of tracking data. HMMs allow for prediction of latent behavioural states while directly accounting for the serial dependence prevalent in ETT data. Updating the probability of behavioural switches with tag or remote-sensing data provides a statistical method that links environmental data to behaviour in a direct and integrated manner.
3.  It is important to assess the reliability of state categorization over the range of time-series lengths typically collected from field instruments and when movement behaviours are similar between movement states. Simulation with varying lengths of times series data and contrast between average movements within each state was used to test the HMMs ability to estimate movement parameters.
4.  To demonstrate the methods in a realistic setting, the HMMs were used to categorize resident and migratory phases and the relationship between movement behaviour and ocean temperature using electronic tagging data from southern bluefin tuna ( Thunnus maccoyii ). Diagnostic tools to evaluate the suitability of different models and inferential methods for investigating differences in behaviour between individuals are also demonstrated.     

An evaluation of behavior inferences from Bayesian state‐space models: A case study with the Pacific walrus

State‐space models offer researchers an objective approach to modeling complex animal location data sets, and state‐space model behavior classifications are often assumed to have a link to animal behavior. In this study, we evaluated the behavioral classification accuracy of a Bayesian state‐space model in Pacific walruses using Argos satellite tags with sensors to detect animal behavior in real time. We fit a two‐state discrete‐time continuous‐space Bayesian state‐space model to data from 306 Pacific walruses tagged in the Chukchi Sea. We matched predicted locations and behaviors from the state‐space model (resident, transient behavior) to true animal behavior (foraging, swimming, hauled out) and evaluated classification accuracy with kappa statistics (κ) and root mean square error (RMSE). In addition, we compared biased random bridge utilization distributions generated with resident behavior locations to true foraging behavior locations to evaluate differences in space use patterns. Results indicated that the two‐state model fairly classified true animal behavior (0.06 ≤ κ ≤ 0.26, 0.49 ≤ RMSE ≤ 0.59). Kernel overlap metrics indicated utilization distributions generated with resident behavior locations were generally smaller than utilization distributions generated with true foraging behavior locations. Consequently, we encourage researchers to carefully examine parameters and priors associated with behaviors in state‐space models, and reconcile these parameters with the study species and its expected behaviors.     

Animal movement tools (amt): R package for managing tracking data and conducting habitat selection analyses

Advances in tracking technology have led to an exponential increase in animal location data, greatly enhancing our ability to address interesting questions in movement ecology, but also presenting new challenges related to data management and analysis. Step‐selection functions (SSFs) are commonly used to link environmental covariates to animal location data collected at fine temporal resolution. SSFs are estimated by comparing observed steps connecting successive animal locations to random steps, using a likelihood equivalent of a Cox proportional hazards model. By using common statistical distributions to model step length and turn angle distributions, and including habitat‐ and movement‐related covariates (functions of distances between points, angular deviations), it is possible to make inference regarding habitat selection and movement processes or to control one process while investigating the other. The fitted model can also be used to estimate utilization distributions and mechanistic home ranges. Here, we present the R package amt (animal movement tools) that allows users to fit SSFs to data and to simulate space use of animals from fitted models. The amt package also provides tools for managing telemetry data. Using fisher (Pekania pennanti) data as a case study, we illustrate a four‐step approach to the analysis of animal movement data, consisting of data management, exploratory data analysis, fitting of models, and simulating from fitted models.     

Consumer movement dynamics as hidden drivers of stream habitat structure: suckers as ecosystem engineers on the night shift

Ecosystem engineering can control the spatial and temporal distribution of resources and movement by engineering organisms within an ecosystem can mobilize resources across boundaries and distribute engineering effects. Movement patterns of fishes can cause physical changes to aquatic habitats though nesting or feeding, both of which often vary in space and time. Here we present evidence of ecosystem engineering by the Sonora sucker Catostomus insignis, a dominant fish in streams of the southwestern United States, and show how cryptic nocturnal movement patterns and bioturbation activities control heterogeneity in benthic substrates, and in sediment and carbon export. Sonora suckers exhibit distinct diel movement patterns, spending daylight hours in refuge habitats (typically deep pools) while moving into shallow habitats at night to feed. Feeding by suckers creates substantial disturbance in soft sediments that are patchy in space and time. These disturbances moved up to 2.4 × 10⁴ cm³ of sediment per square meter per week in locations that are up to hundreds of meters away from sucker daytime refuges. The diel cycles in feeding activity (i.e. nocturnal digging in benthic substrates) caused nighttime pulses in suspended sediment that comprised up to 32% of the daily suspended load and organic matter transport of a stream reach. During the daytime, this particulate transport settles in habitats beyond the location of the initial disturbance, thus redistributing both sediment and organic matter. Our data indicate that cryptic movement by ecosystem engineers can distribute their effects in space and time generating heterogeneity in resources and suggest that habitat modifications restricting consumer movement may alter the impact of engineering activities.     

wisepair: a computer program for individual matching in genetic tracking studies

Individual‐based data sets tracking organisms over space and time are fundamental to answering broad questions in ecology and evolution. A ‘permanent’ genetic tag circumvents a need to invasively mark or tag animals, especially if there are little phenotypic differences among individuals. However, genetic tracking of individuals does not come without its limits; correctly matching genotypes and error rates associated with laboratory work can make it difficult to parse out matched individuals. In addition, defining a sampling design that effectively matches individuals in the wild can be a challenge for researchers. Here, we combine the two objectives of defining sampling design and reducing genotyping error through an efficient Python‐based computer‐modelling program, wisepair . We describe the methods used to develop the computer program and assess its effectiveness through three empirical data sets, with and without reference genotypes. Our results show that wisepair outperformed similar genotype matching programs using previously published from reference genotype data of diurnal poison frogs (Allobates femoralis) and without‐reference (faecal) genotype sample data sets of harbour seals (Phoca vitulina) and Eurasian otters (Lutra lutra). In addition, due to limited sampling effort in the harbour seal data, we present optimal sampling designs for future projects. wisepair allows for minimal sacrifice in the available methods as it incorporates sample rerun error data, allelic pairwise comparisons and probabilistic simulations to determine matching thresholds. Our program is the lone tool available to researchers to define parameters a priori for genetic tracking studies.     

A finite-state continuous-time approach for inferring regional migration and mortality rates from archival tagging and conventional tag-recovery experiments

Spatially structured population dynamics models are important management tools for harvested, highly mobile species and although conventional tag recovery experiments remain useful for estimation of various demographic parameters of these models, archival tagging experiments are becoming an important data source for analyzing migratory behavior of mobile marine species. We provide a likelihood-based approach for estimating the regional migration and mortality rate parameters intrinsic to these models that may use information obtained from conventional tag recovery and archival tagging experiments. Specifically, we assume that the regional location and survival of animals through time is a finite-state continuous-time stochastic process. The stochastic process is the basis of probability models for observations provided by the different types of tags. Results from application to simulated tagging experiments for western Atlantic bluefin tuna show that maximum likelihood estimators based on archival tagging observations and corresponding confidence intervals perform similar to conventional tagging observations for a given number of tag releases and releasing tags in each region can improve the behavior of maximum likelihood estimators regardless of tag type. We provide an example application with Atlantic bluefin tuna released with conventional tags in 1990-1992.     

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 DCRWS, 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.     

State‐space modeling reveals habitat perception of a small terrestrial mammal in a fragmented landscape

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Telemetry tag effects on juvenile lingcod Ophiodon elongatus movement: a laboratory and field study

This study tested the behavioural effects of tagging subyearling and yearling lingcod Ophiodon elongatus with acoustic telemetry tags in laboratory tanks and in the natural environment (Puget Sound, WA). In the laboratory, tagged individuals showed less movement and feeding behaviour soon after tagging than untagged controls. The effect dissipated after c. 1 week, presumably as the tagged O. elongatus recovered from surgery or adjusted to the presence of the tags. This dissipation enabled a field study that compared early‐tagged individuals with a long recovery period after tagging to recently‐tagged individuals with a short recovery period after tagging. Consistent with findings from the laboratory experiment, recently tagged individuals showed less movement away from three release sites in Puget Sound than early‐tagged individuals. Together, the laboratory and field results provide evidence of temporary tag effects on actual movement in the natural environment and provide a method for testing tag effects in the field. This study suggests that subyearling and yearling O. elongatus should be held for a recovery period before release. If holding after tagging is not an option, then movement data collected during the first week should be interpreted cautiously.     

Bayesian Estimation of Animal Movement from Archival and Satellite Tags

The reliable estimation of animal location, and its associated error is fundamental to animal ecology. There are many existing techniques for handling location error, but these are often ad hoc or are used in isolation from each other. In this study we present a Bayesian framework for determining location that uses all the data available, is flexible to all tagging techniques, and provides location estimates with built-in measures of uncertainty. Bayesian methods allow the contributions of multiple data sources to be decomposed into manageable components. We illustrate with two examples for two different location methods: satellite tracking and light level geo-location. We show that many of the problems with uncertainty involved are reduced and quantified by our approach. This approach can use any available information, such as existing knowledge of the animal''s potential range, light levels or direct location estimates, auxiliary data, and movement models. The approach provides a substantial contribution to the handling uncertainty in archival tag and satellite tracking data using readily available tools.     

A memory‐based foraging tactic reveals an adaptive mechanism for restricted space use

The restricted area of space used by most mobile animals is thought to result from fitness‐rewarding decisions derived from gaining information about the environment. Yet, assessments of how animals deal with uncertainty using memory have been largely theoretical, and an empirically derived mechanism explaining restricted space use in animals is still lacking. Using a patch‐to‐patch movement analysis, we investigated predictions of how free‐ranging bison (Bison bison) living in a meadow‐forest matrix use memory to reduce uncertainty in energy intake rate. Results indicate that bison remembered pertinent information about location and quality of meadows, and they used this information to selectively move to meadows of higher profitability. Moreover, bison chose profitable meadows they had previously visited, and this choice was stronger after visiting a relatively poor quality meadow. Our work demonstrates a link between memory, energy gains and restricted space use while establishing a fitness‐based integration of movement, cognitive and spatial ecology.     

Application of harmonic radar to analyze dispersal behavior of the Japanese pine sawyer beetle,Monochamus alternatus (Coleoptera: Cerambycidae)

Monochamus alternatus (Hope) is a severe wood‐boring pest in coniferous forests and a major vector of pine wilt disease in East Asia. Harmonic radar is a powerful tool for studying the dispersal behavior of insects and it could be applied to control pine wilt disease. In this study, we validated the application of harmonic radar for analyzing the dispersal behavior of M. alternatus beetles in a field environment. We determined the wing capacities of the beetles and the effects of electronic tagging and marking on their movement, flight ability, survivorship, and food consumption in the laboratory to confirm the suitability of this technique. The detection rate and recovery rate for beetles were analyzed separately using radar on caged pine stands and in the field environment. The results showed that the minimum wing capacity of the Japanese pine sawyer was 24.9 mg, which was seven times the weight of the electronic tag (3.5 mg). Marking and tagging the beetles had no significant adverse effects on their movement, flight capacity, food consumption, and survivorship. The detection rate using the radar system and recovery rate based on visual observations of the beetles in caged pines were both 95.6%. However, in the field environment, the detection and recovery rates were only 55.6% and 37.8% after one week, respectively, and 33.3% and 7.8% after two weeks. Harmonic radar is a promising technique for studying the dispersal behavior of the Japanese pine sawyer, but its performance is not satisfactory and major improvements are required for both the radar system and electronic tags.     

Evaluation of passive integrated transponder tag retention from two tagging locations in juvenile pallid sturgeon

Population augmentation and propagation of pallid sturgeon (Scaphirhynchus albus) have been a focus of scientists since the mid‐1990s. Various tag types have been used to gain a better understanding of population characteristics, stocking success, and movement patterns. Passive integrated transponder (PIT) tags have been consistently used since the inception of recovery efforts to mark age‐1 and older pallid sturgeon. This tag has been successfully used with other sturgeon species, and tagging protocols for pallid sturgeon were developed in response to those successes. Tag retention rates in pallid sturgeon have been reported as variable or unknown and there has been no formal study to determine PIT tag retention in pallid sturgeon, particularly with age‐1 individuals that are propagated in hatcheries to be stocked into the wild. The objective was to evaluate retention rates of PIT tags inserted into the operculum and along the base of the dorsal fin of age‐1 pallid sturgeon (total fork length [FL] range = 214–358 mm). Tagged sturgeon (n = 80) were held in a large holding tank and inspected for tag loss twice a month for a total of 189 days. Final tag retention was 83% for tags inserted into the operculum and 85% for tags inserted near the dorsal fin. Tags shed from the operculum were from smaller fish (mean FL = 265 mm) and losses occurred during the first 60 days of the experiment. Tags shed from the dorsal site were predominantly from larger individuals (mean FL = 305 mm) and losses were continual throughout the experiment. Future research should determine size‐specific tag retention rates so that hatcheries can maximize retention in either tag placement location.     

The development of an intermediate‐duration tag to characterize the diving behavior of large whales

The development of high‐resolution archival tag technologies has revolutionized our understanding of diving behavior in marine taxa such as sharks, turtles, and seals during their wide‐ranging movements. However, similar applications for large whales have lagged behind due to the difficulty of keeping tags on the animals for extended periods of time. Here, we present a novel configuration of a transdermally attached biologging device called the Advanced Dive Behavior (ADB) tag. The ADB tag contains sensors that record hydrostatic pressure, three‐axis accelerometers, magnetometers, water temperature, and light level, all sampled at 1 Hz. The ADB tag also collects Fastloc GPS locations and can send dive summary data through Service Argos, while staying attached to a whale for typical periods of 3–7 weeks before releasing for recovery and subsequent data download. ADB tags were deployed on sperm whales (Physeter macrocephalus; N = 46), blue whales (Balaenoptera musculus; N = 8), and fin whales (B. physalus; N = 5) from 2007 to 2015, resulting in attachment durations from 0 to 49.6 days, and recording 31 to 2,539 GPS locations and 27 to 2,918 dives per deployment. Archived dive profiles matched well with published dive shapes of each species from short‐term records. For blue and fin whales, feeding lunges were detected using peaks in accelerometer data and matched corresponding vertical excursions in the depth record. In sperm whales, rapid orientation changes in the accelerometer data, often during the bottom phase of dives, were likely related to prey pursuit, representing a relative measure of foraging effort. Sperm whales were documented repeatedly diving to, and likely foraging along, the seafloor. Data from the temperature sensor described the vertical structure of the water column in all three species, extending from the surface to depths >1,600 m. In addition to providing information needed to construct multiweek time budgets, the ADB tag is well suited to studying the effects of anthropogenic sound on whales by allowing for pre‐ and post‐exposure monitoring of the whale's dive behavior. This tag begins to bridge the gap between existing long‐duration but low‐data throughput tags, and short‐duration, high‐resolution data loggers.     

In situ tagging technique for fishes provides insight into growth and movement of invasive lionfish

Information on fish movement and growth is primarily obtained through the marking and tracking of individuals with external tags, which are usually affixed to anesthetized individuals at the surface. However, the quantity and quality of data obtained by this method is often limited by small sample sizes owing to the time associated with the tagging process, high rates of tagging‐related mortality, and displacement of tagged individuals from the initial capture location. To address these issues, we describe a technique for applying external streamer and dart tags in situ, which uses SCUBA divers to capture and tag individual fish on the sea floor without the use of anesthetic. We demonstrate this method for Indo‐Pacific lionfish (Pterois volitans/P. miles), species which are particularly vulnerable to barotrauma when transported to and handled at the surface. To test our method, we tagged 161 individuals inhabiting 26 coral reef locations in the Bahamas over a period of 3 years. Our method resulted in no instances of barotrauma, reduced handling and recovery time, and minimal post‐tagging release displacement compared with conventional ex situ tag application. Opportunistic resighting and recapture of tagged individuals reveals that lionfish exhibit highly variable site fidelity, movement patterns, and growth rates on invaded coral reef habitats. In total, 24% of lionfish were resighted between 29 and 188 days after tagging. Of these, 90% were located at the site of capture, while the remaining individuals were resighted between 200 m and 1.1 km from initial site of capture over 29 days later. In situ growth rates ranged between 0.1 and 0.6 mm/day. While individuals tagged with streamer tags posted slower growth rates with increasing size, as expected, there was no relationship between growth rate and fish size for individuals marked with dart tags, potentially because of large effects of tag presence on the activities of small bodied lionfish (i.e., <150 mm), where the tag was up to 7.6% of the lionfish's mass. Our study offers a novel in situ tagging technique that can be used to provide critical information on fish site fidelity, movement patterns, and growth in cases where ex situ tagging is not feasible.     

Short‐finned pilot whales (Globicephala macrorhynchus) of the Mariana Archipelago: Individual affiliations,movements, and spatial use

Little is known about short‐finned pilot whales (Globicephala macrorhynchus) in the western North Pacific outside of Japanese coastal waters. To expand understanding of short‐finned pilot whale ecology in the region, we conducted small‐boat surveys in 2010?2016 within the Mariana Archipelago to investigate individual associations, movements, spatial use, and dive behavior of short‐finned pilot whales. We collected genetic, photo‐identification, and satellite‐tag data and identified 191 distinctive individuals. A preliminary social network diagram of photo‐cataloged individuals revealed a main cluster that comprised 82% of individuals, representing all five mitochondrial DNA haplotypes identified within the population. Kernel density estimates for tagged short‐finned pilot whales (n = 11) during summer were used to identify areas with the highest probability of use (10% probability density contour), core area (50%) and home range (95%). The area with highest probability of use by short‐finned pilot whales was off the northwest side of Guam. Satellite tag data also suggest that some individuals are island‐associated year‐round. Data from five location‐dive tags demonstrated that the short‐finned pilot whales dove more often to intermediate depths at twilight and night, suggesting they may target prey that forage on the deep scattering layer as it migrates to and from the surface.