Search

The continuous process of decision-making in animals is crucial for their survival. For example, when deciding when, where, and with whom to forage, they need to consider their internal state, previous experience, and social information in addition to external factors such as food distribution and weather conditions. Studying animal decision-making in the wild is a complicated task due to the complexity of the process, which requires continuous monitoring of the examined individual and its environment. Here, we review the most advanced methods to examine decision-making from an individual point of view, namely tracking technologies to monitor the movement of an individual, the sensory information available to it, the presence and behavior of other animals around it, and its surrounding environment. We provide examples for studying decision-making during competition, examining the ontogeny of decision-making, and describing the importance of long-term monitoring and field manipulation for understanding decision processes throughout different life stages. [ABSTRACT FROM AUTHOR]

Social foraging is very common in the animal kingdom. Numerous studies have documented collective foraging in various species and many reported the attraction of various species to foraging conspecifics. It is nonetheless difficult to quantify the benefits and costs of collective foraging, especially in the wild. We examined the benefits and costs of social foraging using on-board microphones mounted on freely foraging Molossus nigricans bats. This allowed us to quantify the bats' attacks on prey and to assess their success as a function of conspecific density. We found that the bats spent most of their time foraging at low conspecific densities, during which their attacks were most successful in terms of prey items captured per time unit. Notably, their capture rate dropped when conspecific density became either too high or too low. Our findings thus demonstrate a clear social foraging trade-off in which the presence of a few conspecifics probably improves foraging success, whereas the presence of too many impairs it. [ABSTRACT FROM AUTHOR]

Introduction: When studying bird intra- and inter-specific interactions it is crucial to accurately track which individual emits which vocalization. However, locating sounds of free moving birds (and other animals) in nature can be challenging, especially in situations when many individuals call in spatial and temporal vicinity. Methods: In this paper, we will introduce the use of a hand-held acoustic camera which allows solving this problem. The system comprises 64 microphones each sampling a wide angle at a rate of 200kHz. The system can localize up to dozens of nearby callers and it displays them on a visual image. We applied the system to localize and record vocalizations of Arabian babblers (Argya squamiceps) during snake-mobbing behavior to demonstrate its potential. As the recorded babblers are ringed and therefore identifiable, we could use the calling order to analyze the vocalizing mob and to reconstruct a vocal social network depicting which individuals call after which individuals. Results: Acoustic analysis revealed that Arabian babblers call in a periodic pattern and with age specific pause lengths between different individuals. Mobbing events with young babblers display a lower vocal network density compared to only adult groups. Discussion: Our approach demonstrates how this novel technology can be applied to reveal new insight about vocal events in group living wild animals. The approach can be elaborated to study intra- and inter-specific animal vocalizations. [ABSTRACT FROM AUTHOR]

The brain connectome is an embedded network of anatomically interconnected brain regions, and the study of its topological organization in mammals has become of paramount importance due to its role in scaffolding brain function and behavior. Unlike many other observable networks, brain connections incur material and energetic cost, and their length and density are volumetrically constrained by the skull. Thus, an open question is how differences in brain volume impact connectome topology. We address this issue using the MaMI database, a diverse set of mammalian connectomes reconstructed from 201 animals, covering 103 species and 12 taxonomy orders, whose brain size varies over more than 4 orders of magnitude. Our analyses focus on relationships between volume and modular organization. After having identified modules through a multiresolution approach, we observed how connectivity features relate to the modular structure and how these relations vary across brain volume. We found that as the brain volume increases, modules become more spatially compact and dense, comprising more costly connections. Furthermore, we investigated how spatial embedding shapes network communication, finding that as brain volume increases, nodes' distance progressively impacts communication efficiency. We identified modes of variation in network communication policies, as smaller and bigger brains show higher efficiency in routing- and diffusion-based signaling, respectively. Finally, bridging network modularity and communication, we found that in larger brains, modular structure imposes stronger constraints on network signaling. Altogether, our results show that brain volume is systematically related to mammalian connectome topology and that spatial embedding imposes tighter restrictions on larger brains. How did neural wiring evolve to accommodate different brain volumes? By characterizing the connectome modular organization in more than 200 mammals with brain size varying over four orders of magnitude, this study shows that spatial embedding imposes tighter restrictions on larger brains, resulting in more spatially compact and well defined modules. [ABSTRACT FROM AUTHOR]

Talking to animals is a fundamental human desire. The emergence of powerful AI algorithms, and specifically Large Language Models, has driven many to suggest that we are on the verge of fulfilling this wish. A few large scientific consortia have been formed around this topic and several commercial entities even offer such services. We frame the task of communicating with animals as 'The Doctor Dolittle challenge' and identify three main obstacles on the route to doing so. First, although generative AI models can create novel animal communication samples, it is very difficult to determine their context, and we will forever be biased by our human umwelt when doing so. Second, using AI to extract context in an unsupervised manner must be validated through controlled experiments aiming to measure the animals' response. This is difficult, and moreover, AI algorithms tend to cling on to any available information and are thus prone to finding spurious correlations. And third, animal communication focuses on a restricted set of contexts, such as alarm and courtship, highly limiting our ability to communicate regarding other contexts. Nevertheless, using the tremendous power of novel AI methods to decipher and mimic animal communication is both fascinating and important. We thus define the criteria for passing the Doctor Dolittle challenge and call upon scientists to take on the mission. Would AI be able, like Doctor Dolittle, to 'talk' with animals? Yovel and Rechavi argue that, to ace the 'The Doctor Dolittle challenge', we would need to pass three obstacles that would be very difficult to overcome. [ABSTRACT FROM AUTHOR]

Bat movement and behaviour are still mostly understudied over large scales. High‐altitude, nocturnal activity makes visual identification of bats from the ground virtually impossible, dramatically hindering our ability to study their movement ecology. Despite the wide use of radar in aeroecology, its application to study specific taxa is limited due to incomplete target classification abilities. BATScan is a bat classifier for vertical‐looking radar data, which enables identifying bats and characterizing their unique aeroecology.We constructed the classifier using data from 10 radar deployments, covering a wide range of habitats on a central bird migration flyway over a 7‐year period, comprising ~18 million observations. We analysed animal migration above the Hula Valley, home to over 30 species of bats spanning a range of 5–150 g in size and exhibiting a variety of ecological characteristics. We distinguished bat‐labelled radar echoes for training according to phenology, morphology and movement ecology of bats, birds and insects. Several non‐bat datasets were constructed and joined to train classifiers under increasing levels of difficulty. Class imbalance in the resulting training data was handled using a generative adversarial network for up‐sampling the much smaller bat dataset.The resulting classification tool reached a high level of accuracy and precision, and was further scrutinized with an extensive set of ecological validations.Bats perform seasonal migrations over long distances, but little is known about the spatial and temporal characteristics of this movement, and the ability to study it at a large scale has so far been limited. We present the Israeli BATScan dataset, containing over 60,000 bat observations spanning the entire country and representing multiple habitats. Using this data, we produce an unprecedented large scale, highly detailed documentation of the yearly movements of bats on a major migration flyway, and distinguish this pattern from bird migration over space and time.So far, radar aeroecology dealt primarily with birds, increasingly with insects, and only rarely with bats. We present BATScan, a classification tool that can incorporate bats into the framework of radar aeroecology to finally enable a comprehensive description of animal aeroecology. [ABSTRACT FROM AUTHOR]

Wind energy is a major and rapidly expanding renewable energy source. Horizontal‐axis wind turbines, the main tool in this industry, induce mortality in flying animals and consequently bring about conservation concerns and regulatory restrictions. We utilized a unique combination of RADAR, LIDAR and ultrasonic acoustic recorders to test the utility of a novel technology meant to prevent wind turbine‐related mortality in bats. Our drone‐mounted deterrent device produces a pulsating combination of strong auditory and visual signals while moving through the air. LIDAR was used to assess the device's impact below its flight altitude and RADAR to assess its influence above its flight altitude. Continuous acoustic recordings from ground level to ~400 m above‐ground‐level were used to monitor bat activity in the research site. We recorded the nightly altitudinal distributions of multiple bat species throughout the experiment. Analysis revealed a significant change in activity while the deterrent was flying compared to baseline conditions. We also recorded a significant ~40% decrease below and a significant ~50% increase above the deterrent's flight altitude during its operation compared to the post‐flight control. The tested technology is independent of wind farm activities and does not require modifying wind turbine form or operation procedures. The device differs from previously proposed solutions by being dynamic – moving in the airspace and emitting constantly changing signals – thus decreasing the probability of animal habituation. Our findings suggest that the deterrent could dramatically decrease wind turbine‐related mortality by deterring bats from approaching rotor‐swept airspace. Focused implementation in conditions where bat activity and energy production are in conflict may provide a practical, cost‐effective mortality mitigation solution compared to current alternatives. Thus, our results should be considered by the wind‐turbine industry and environmental monitoring and animal conservation organizations, as well as by regulatory agencies, when pursuing alleviation of wind turbine‐related mortality. [ABSTRACT FROM AUTHOR]