Using on-board sound recordings to infer behaviour of free-moving wild animals

Time synchronisation for millisecond-precision on bio-loggers

Time-synchronised data streams from bio-loggers are becoming increasingly important for analysing and interpreting intricate animal behaviour including split-second decision making, group dynamics, and collective responses to environmental conditions. With the increased use of AI-based approaches for behaviour classification, time synchronisation between recording systems is becoming an essential challenge. Current solutions in bio-logging rely on manually removing time errors during post processing, which is complex and typically does not achieve sub-second timing accuracies.We first introduce an error model to quantify time errors, then optimise three wireless methods for automated onboard time (re)synchronisation on bio-loggers (GPS, WiFi, proximity messages). The methods can be combined as required and, when coupled with a state-of-the-art real time clock, facilitate accurate time annotations for all types of bio-logging data without need for post processing. We analyse time accuracy of our optimised methods in stationary tests and in a case study on 99 Egyptian fruit bats (Rousettus aegyptiacus). Based on the results, we offer recommendations for projects that require high time synchrony.During stationary tests, our low power synchronisation methods achieved median time accuracies of 2.72 / 0.43 ms (GPS / WiFi), compared to UTC time, and relative median time accuracies of 5 ms between tags (wireless proximity messages). In our case study with bats, we achieved a median relative time accuracy of 40 ms between tags throughout the entire 10-day duration of tag deployment. Using only one automated resynchronisation per day, permanent UTC time accuracies of ≤ 185 ms can be guaranteed in 95% of cases over a wide temperature range between 0 and 50 °C. Accurate timekeeping required a minimal battery capacity, operating in the nano- to microwatt range.Time measurements on bio-loggers, similar to other forms of sensor-derived data, are prone to errors and so far received little scientific attention. Our combinable methods offer a means to quantify time errors and autonomously correct them at the source (i.e., on bio-loggers). This approach facilitates sub-second comparisons of simultaneously recorded time series data across multiple individuals and off-animal devices such as cameras or weather stations. Through automated resynchronisations on bio-loggers, long-term sub-second accurate timestamps become feasible, even for life-time studies on animals. We contend that our methods have potential to greatly enhance the quality of ecological data, thereby improving scientific conclusions.

Advances in biologging can identify nuanced energetic costs and gains in predators

Foraging is a key driver of animal movement patterns, with specific challenges for predators which must search for mobile prey. These patterns are increasingly impacted by global changes, principally in land use and climate. Understanding the degree of flexibility in predator foraging and social strategies is pertinent to wildlife conservation under global change, including potential top-down effects on wider ecosystems. Here we propose key future research directions to better understand foraging strategies and social flexibility in predators. In particular, rapid continued advances in biologging technology are helping to record and understand dynamic behavioural and movement responses of animals to environmental changes, and their energetic consequences. Data collection can be optimised by calibrating behavioural interpretation methods in captive settings and strategic tagging decisions within and between social groups. Importantly, many species' social systems are increasingly being found to be more flexible than originally described in the literature, which may be more readily detectable through biologging approaches than behavioural observation. Integrating the effects of the physical landscape and biotic interactions will be key to explaining and predicting animal movements and energetic balance in a changing world.

Invertebrates as models of learning and memory: investigating neural and molecular mechanisms

In this Commentary, we shed light on the use of invertebrates as model organisms for understanding the causal and conserved mechanisms of learning and memory. We provide a condensed chronicle of the contribution offered by mollusks to the studies on how and where the nervous system encodes and stores memory and describe the rich cognitive capabilities of some insect species, including attention and concept learning. We also discuss the use of planarians for investigating the dynamics of memory during brain regeneration and highlight the role of stressful stimuli in forming memories. Furthermore, we focus on the increasing evidence that invertebrates display some forms of emotions, which provides new opportunities for unveiling the neural and molecular mechanisms underlying the complex interaction between stress, emotions and cognition. In doing so, we highlight experimental challenges and suggest future directions that we expect the field to take in the coming years, particularly regarding what we, as humans, need to know for preventing and/or delaying memory loss. This article has an associated ECR Spotlight interview with Veronica Rivi.

Fire as a driver and mediator of predator-prey interactions

Both fire and predators have strong influences on the population dynamics and behaviour of animals, and the effects of predators may either be strengthened or weakened by fire. However, knowledge of how fire drives or mediates predator–prey interactions is fragmented and has not been synthesised. Here, we review and synthesise knowledge of how fire influences predator and prey behaviour and interactions. We develop a conceptual model based on predator–prey theory and empirical examples to address four key questions: (i) how and why do predators respond to fire; (ii) how and why does prey vulnerability change post‐fire; (iii) what mechanisms do prey use to reduce predation risk post‐fire; and (iv) what are the outcomes of predator–fire interactions for prey populations? We then discuss these findings in the context of wildlife conservation and ecosystem management before outlining priorities for future research. Fire‐induced changes in vegetation structure, resource availability, and animal behaviour influence predator–prey encounter rates, the amount of time prey are vulnerable during an encounter, and the conditional probability of prey death given an encounter. How a predator responds to fire depends on fire characteristics (e.g. season, severity), their hunting behaviour (ambush or pursuit predator), movement behaviour, territoriality, and intra‐guild dynamics. Prey species that rely on habitat structure for avoiding predation often experience increased predation rates and lower survival in recently burnt areas. By contrast, some prey species benefit from the opening up of habitat after fire because it makes it easier to detect predators and to modify their behaviour appropriately. Reduced prey body condition after fire can increase predation risk either through impaired ability to escape predators, or increased need to forage in risky areas due to being energetically stressed. To reduce risk of predation in the post‐fire environment, prey may change their habitat use, increase sheltering behaviour, change their movement behaviour, or use camouflage through cryptic colouring and background matching. Field experiments and population viability modelling show instances where fire either amplifies or does not amplify the impacts of predators on prey populations, and vice versa. In some instances, intense and sustained post‐fire predation may lead to local extinctions of prey populations. Human disruption of fire regimes is impacting faunal communities, with consequences for predator and prey behaviour and population dynamics. Key areas for future research include: capturing data continuously before, during and after fires; teasing out the relative importance of changes in visibility and shelter availability in different contexts; documenting changes in acoustic and olfactory cues for both predators and prey; addressing taxonomic and geographic biases in the literature; and predicting and testing how changes in fire‐regime characteristics reshape predator–prey interactions. Understanding and managing the consequences for predator–prey communities will be critical for effective ecosystem management and species conservation in this era of global change.

Big-data approaches lead to an increased understanding of the ecology of animal movement

Understanding animal movement is essential to elucidate how animals interact, survive, and thrive in a changing world. Recent technological advances in data collection and management have transformed our understanding of animal "movement ecology" (the integrated study of organismal movement), creating a big-data discipline that benefits from rapid, cost-effective generation of large amounts of data on movements of animals in the wild. These high-throughput wildlife tracking systems now allow more thorough investigation of variation among individuals and species across space and time, the nature of biological interactions, and behavioral responses to the environment. Movement ecology is rapidly expanding scientific frontiers through large interdisciplinary and collaborative frameworks, providing improved opportunities for conservation and insights into the movements of wild animals, and their causes and consequences.

Quantifying song behavior in a free-living, light-weight, mobile bird using accelerometers

To acquire a fundamental understanding of animal communication, continuous observations in a natural setting and at an individual level are required. Whereas the use of animal-borne acoustic recorders in vocal studies remains challenging, light-weight accelerometers can potentially register individuals' vocal output when this coincides with body vibrations. We collected one-dimensional accelerometer data using light-weight tags on a free-living, crepuscular bird species, the European Nightjar (Caprimulgus europaeus). We developed a classification model to identify four behaviors (rest, sing, fly, and leap) from accelerometer data and, for the purpose of this study, validated the classification of song behavior. Male nightjars produce a distinctive "churring" song while they rest on a stationary song post. We expected churring to be associated with body vibrations (i.e., medium-amplitude body acceleration), which we assumed would be easy to distinguish from resting (i.e., low-amplitude body acceleration). We validated the classification of song behavior using simultaneous GPS tracking data (i.e., information on individuals' movement and proximity to audio recorders) and vocal recordings from stationary audio recorders at known song posts of one tracked individual. Song activity was detected by the classification model with an accuracy of 92%. Beyond a threshold of 20 m from the audio recorders, only 8% of the classified song bouts were recorded. The duration of the detected song activity (i.e., acceleration data) was highly correlated with the duration of the simultaneously recorded song bouts (correlation coefficient = 0.87, N = 10, S = 21.7, p = .001). We show that accelerometer-based identification of vocalizations could serve as a promising tool to study communication in free-living, small-sized birds and demonstrate possible limitations of audio recorders to investigate individual-based variation in song behavior.

Bio-acoustic tracking and localization using heterogeneous, scalable microphone arrays

Microphone arrays are an essential tool in the field of bioacoustics as they provide a non-intrusive way to study animal vocalizations and monitor their movement and behavior. Microphone arrays can be used for passive localization and tracking of sound sources while analyzing beamforming or spatial filtering of the emitted sound. Studying free roaming animals usually requires setting up equipment over large areas and attaching a tracking device to the animal which may alter their behavior. However, monitoring vocalizing animals through arrays of microphones, spatially distributed over their habitat has the advantage that unrestricted/unmanipulated animals can be observed. Important insights have been achieved through the use of microphone arrays, such as the convergent acoustic field of view in echolocating bats or context-dependent functions of avian duets. Here we show the development and application of large flexible microphone arrays that can be used to localize and track any vocalizing animal and study their bio-acoustic behavior. In a first experiment with hunting pallid bats the acoustic data acquired from a dense array with 64 microphones revealed details of the bats' echolocation beam in previously unseen resolution. We also demonstrate the flexibility of the proposed microphone array system in a second experiment, where we used a different array architecture allowing to simultaneously localize several species of vocalizing songbirds in a radius of 75 m. Our technology makes it possible to do longer measurement campaigns over larger areas studying changing habitats and providing new insights for habitat conservation. The flexible nature of the technology also makes it possible to create dense microphone arrays that can enhance our understanding in various fields of bioacoustics and can help to tackle the analytics of complex behaviors of vocalizing animals.

Silence and reduced echolocation during flight are associated with social behaviors in male hoary bats (Lasiurus cinereus)

Bats are renowned for their sophisticated echolocation. However, recent research has indicated that bats may be less reliant on echolocation than has long been assumed. To test the hypothesis that bats reduce their use of echolocation to avoid eavesdropping by conspecifics, we deployed miniature tags that recorded ultrasound and accelerations on 10 wild hoary bats (Lasiurus cinereus) for one or two nights. This resulted in 997 10-s recordings. Bats switched between periods predominated by their typical high-intensity echolocation, or periods predominated by micro calls (unusually short, quiet calls), or no detectable calls (“silence”). Periods of high-intensity echolocation included high rates of feeding buzzes, whereas periods of micro calls and silence included high rates of social interactions with other bats. Bats switched back to high-intensity echolocation during actual social interactions. These data support the hypothesis that bats use reduced forms of echolocation and fly in silence to avoid eavesdropping from conspecifics, perhaps in the context of mating-related behavior. They also provide the strongest demonstration to date that bats fly for extended periods of time without the use of echolocation.

From Paths to Routes: A Method for Path Classification

Many animals establish, learn and optimize routes between locations to commute efficiently. One step in understanding route following is defining measures of similarities between the paths taken by the animals. Paths have commonly been compared by using several descriptors (e.g., the speed, distance traveled, or the amount of meandering) or were visually classified into categories by the experimenters. However, similar quantities obtained from such descriptors do not guarantee similar paths, and qualitative classification by experimenters is prone to observer biases. Here we propose a novel method to classify paths based on their similarity with different distance functions and clustering algorithms based on the trajectories of bumblebees flying through a cluttered environment. We established a method based on two distance functions (Dynamic Time Warping and Fréchet Distance). For all combinations of trajectories, the distance was calculated with each measure. Based on these distance values, we grouped similar trajectories by applying the Monte Carlo Reference-Based Consensus Clustering algorithm. Our procedure provides new options for trajectory analysis based on path similarities in a variety of experimental paradigms.

Social learning exploits the available auditory or visual cues

The ability to acquire a behavior can be facilitated by exposure to a conspecific demonstrator. Such social learning occurs under a range of conditions in nature. Here, we tested the idea that social learning can benefit from any available sensory cue, thereby permitting learning under different natural conditions. The ability of naïve gerbils to learn a sound discrimination task following 5 days of exposure adjacent to a demonstrator gerbil was tested in the presence or absence of visual cues. Naïve gerbils acquired the task significantly faster in either condition, as compared to controls. We also found that exposure to a demonstrator was more potent in facilitating learning, as compared to exposure to the sounds used to perform the discrimination task. Therefore, social learning was found to be flexible and equally efficient in the auditory or visual domains.

Genetic monogamy despite frequent extrapair copulations in "strictly monogamous" wild jackdaws

"Monogamy" refers to different components of pair exclusiveness: the social pair, sexual partners, and the genetic outcome of sexual encounters. Avian monogamy is usually defined socially or genetically, whereas quantifications of sexual behavior remain scarce. Jackdaws (Corvus monedula) are considered a rare example of strict monogamy in songbirds, with lifelong pair bonds and little genetic evidence for extrapair (EP) offspring. Yet jackdaw copulations, although accompanied by loud copulation calls, are rarely observed because they occur visually concealed inside nest cavities. Using full-day nest-box video surveillance and on-bird acoustic bio-logging, we directly observed jackdaw sexual behavior and compared it to the corresponding genetic outcome obtained via molecular parentage analysis. In the video-observed nests, we found genetic monogamy but frequently detected forced EP sexual behavior, accompanied by characteristic male copulation calls. We, thus, challenge the long-held notion of strict jackdaw monogamy at the sexual level. Our data suggest that male mate guarding and frequent intrapair copulations during the female fertile phase, as well as the forced nature of the copulations, could explain the absence of EP offspring. Because EP copulation behavior appeared to be costly for both sexes, we suggest that immediate fitness benefits are an unlikely explanation for its prevalence. Instead, sexual conflict and dominance effects could interact to shape the spatiotemporal pattern of EP sexual behavior in this species. Our results call for larger-scale investigations of jackdaw sexual behavior and parentage and highlight the importance of combining social, sexual, and genetic data sets for a more complete understanding of mating systems.

LG, Flores-Martínez JJ, Wilkinson GS, Yovel Y. Acoustic evaluation of behavioral states predicted from GPS tracking: a case study of a marine fishing bat

BACKGROUND

Multiple methods have been developed to infer behavioral states from animal movement data, but rarely has their accuracy been assessed from independent evidence, especially for location data sampled with high temporal resolution. Here we evaluate the performance of behavioral segmentation methods using acoustic recordings that monitor prey capture attempts.

METHODS

We recorded GPS locations and ultrasonic audio during the foraging trips of 11 Mexican fish-eating bats, Myotis vivesi, using miniature bio-loggers. We then applied five different segmentation algorithms (k-means clustering, expectation-maximization and binary clustering, first-passage time, hidden Markov models, and correlated velocity change point analysis) to infer two behavioral states, foraging and commuting, from the GPS data. To evaluate the inference, we independently identified characteristic patterns of biosonar calls ("feeding buzzes") that occur during foraging in the audio recordings. We then compared segmentation methods on how well they correctly identified the two behaviors and if their estimates of foraging movement parameters matched those for locations with buzzes.

RESULTS

While the five methods differed in the median percentage of buzzes occurring during predicted foraging events, or true positive rate (44-75%), a two-state hidden Markov model had the highest median balanced accuracy (67%). Hidden Markov models and first-passage time predicted foraging flight speeds and turn angles similar to those measured at locations with feeding buzzes and did not differ in the number or duration of predicted foraging events.

CONCLUSION

The hidden Markov model method performed best at identifying fish-eating bat foraging segments; however, first-passage time was not significantly different and gave similar parameter estimates. This is the first attempt to evaluate segmentation methodologies in echolocating bats and provides an evaluation framework that can be used on other species.