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Animal species have evolved to enhance their survival by focusing their temporal activity on specific parts of the diurnal-nocturnal cycle. Various factors, including inter-specific competition and anti-predator behavior, as well as anthropogenic effects like light pollution, have prompted some species to expand or shift their temporal niches. Our study focuses on the temporal niche shift of the Egyptian fruit bat (Rousettus aegyptiacus) to diurnal activity in Israel.Through an extensive citizen-science study, we assessed the distribution of these bats’ diurnal activity across Israel. We also documented the sex and age of bats from a colony known for its diurnal activity and collected blood samples from them for metabolic analysis.Our findings indicate that the shift toward daytime activity predominantly takes place in urban settings and is mostly exhibited by females. We found a significant physiological effect of this temporal shift, namely: diurnal bats’ vitamin D levels were significantly higher, and their parathyroid hormone (PTH) levels were significantly lower than those of nocturnal bats.We suggest that the reproductive metabolic demands of female bats might be a key factor driving this shift to diurnal activity. We hypothesize that the increase in vitamin D, derived from sunlight hours, might play a crucial role in regulating calcium homeostasis, thus contributing to the bats’ physiological needs during the reproduction season.

Abstract The rate of sensory update is one of the most important parameters of any sensory system. The acquisition rate of most sensory systems is fixed and has been optimized by evolution to the needs of the animal. Echolocating bats have the ability to adjust their sensory update rate which is determined by the intervals between emissions - the inter-pulse intervals (IPI). The IPI is routinely adjusted, but the exact factors driving its regulation are unknown. We use on-board audio recordings to determine how four species of echolocating bats with different foraging strategies regulate their sensory update rate during commute flights. We reveal strong correlations between the IPI and various echolocation and movement parameters. Specifically, the update rate increases when the signals’ peak-energy frequency and intensity increases while the update rate decreases when flight speed and altitude increases. We suggest that bats control their information update rate according to the behavioral mode they are engaged in, while always maintaining sensory continuity. Specifically, we suggest that bats apply two modes of attention during commute flights. Our data moreover suggests that bats emit echolocation signals at accurate intervals without the need for external feedback.

Abstract One of the main functions of behavioral plasticity lies in the ability to contend with dynamic environments. Indeed, while numerous studies have shown that animals adapt their behavior to the environment, how they adapt their latent learning and decision strategies to changes in the environment is less understood. Here, we used a controlled experiment to examine the bats’ ability to adjust their decision strategy according to the environmental dynamics. Twenty-five Egyptian fruit bats were placed individually in either a stable or a volatile environment for four consecutive nights. In the stable environment, two feeders offered food, each with a different reward probability (0.2 vs. 0.8) that remained fixed over two nights and were then switched, while in the volatile environment, the positions of the more and the less rewarding feeders were changed every hour. We then fit two alternative commonly used models namely, reinforcement learning and win-stay-lose-shift strategies to the bats' behavior. We found that while the bats adapted their decision-making strategy to the environmental dynamics, they seemed to be limited in their responses based on natural priors. Namely, when the environment had changed slowly, at a rate that is natural for these bats, they seemed to rely on reinforcement learning and their performance was nearly optimal, but when the experimental environment changed much faster than in the natural environment, the bats stopped learning and switched to a random decision-making strategy. Together, these findings exemplify both the bats’ decision-making plasticity as well as its natural limitations.

IntroductionWhen 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.MethodsIn 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.ResultsAcoustic 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.DiscussionOur 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 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 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.

Covid-19 lockdowns provided ecologists with a rare opportunity to examine how animals behave when humans are absent. Indeed many studies reported various effects of lockdowns on animal activity, especially in urban areas and other human-dominated habitats. We explored how Covid-19 lockdowns in Israel have influenced bird activity in an urban environment by using continuous acoustic recordings to monitor three common bird species that differ in their level of adaptation to the urban ecosystem: (1) the hooded crow, an urban exploiter, which depends heavily on anthropogenic resources; (2) the rose-ringed parakeet, an invasive alien species that has adapted to exploit human resources; and (3) the graceful prinia, an urban adapter, which is relatively shy of humans and can be found in urban habitats with shrubs and prairies. Acoustic recordings provided continuous monitoring of bird activity without an effect of the observer on the animal. We performed dense sampling of a 1.3 square km area in northern Tel-Aviv by placing 17 recorders for more than a month in different micro-habitats within this region including roads, residential areas and urban parks. We monitored both lockdown and no-lockdown periods. We portray a complex dynamic system where the activity of specific bird species depended on many environmental parameters and decreases or increases in a habitat-dependent manner during lockdown. Specifically, urban exploiter species decreased their activity in most urban habitats during lockdown, while human adapter species increased their activity during lockdown especially in parks where humans were absent. Our results also demonstrate the value of different habitats within urban environments for animal activity, specifically highlighting the importance of urban parks. These species- and habitat-specific changes in activity might explain the contradicting results reported by others who have not performed a habitat specific analysis.

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.

Abstract The development of the two religions: Christianity and Judaism, is a topic of much debate. Whereas Judaism and Christianity are known as separate religions, in fact, these two religions developed side by side. While earlier researchers conceptualized a “parting-of-the-ways,” after which the two religions evolved independently, new studies reveal a multi-layered set of interactions throughout the first several centuries CE. Until recently, this question was explored with the limited source material and limited tools to analyze it. While working on a limited set of data, from a specific corpus, this project offers a new set of methodological tools, borrowed from computer sciences, that could ultimately serve for understanding the connections between Jews and Christians in late antiquity. We generated models of inter-religious Christian–Jewish networks that demonstrate the scope, nature, and advantages of network analysis for revealing the complex intertwined evolution of the two religions. The Jewish corpora chosen for this research are rabbinic writings from late antique Babylonia and Palestine. Christian texts range from the first through sixth centuries CE. Instead of representing interactions between people or places, as is typically done with social networks, we model literary interactions that, in our view, indicate historical connections between religious communities. This novel approach allows us to visually represent sets of temporal–spatial–contextual relationships, which evolved over hundreds of years, in single snapshots. It also reveals new insights about the relationships between the two communities. For example, we find that rabbinic sources exhibit a largely polemical approach towards earlier Christian traditions but a non-polemical attitude towards later ones. Moreover, network analysis suggests a temporal–spatial familiarity correlation. Namely, Jewish sources are familiar with early, eastern Christian sources and with both Eastern and Western Christian sources in later periods. The application of network analysis makes it possible to identify the most influential texts—that is, the key “nodes”—testifying to the importance of certain traditions for both religious communities. Finally, the network approach is a tool for pointing scholarly research in new directions, which only reveals itself as a result of this type of mapping. In other words, the network not only describes the known data, but it is itself a way to enlarge the network and lead us down new and exciting paths that are currently unknown.

Abstract Background Reproduction entails substantial demands throughout its distinct stages. The mammalian gestation period imposes various energetic costs and movement deficits, but its effects on the sensory system are poorly understood. Bats rely heavily on active sensing, using echolocation to forage in complete darkness, or when lighting is uncertain. We examined the effects of pregnancy on bat echolocation. Results We show that pregnant Kuhl’s pipistrelles (Pipistrellus kuhlii) altered their echolocation and flight behavior. Specifically, pregnant bats emitted longer echolocation signals at an ~ 15% lower rate, while flying more slowly and at a lower altitude compared to post-lactating females. A sensorimotor foraging model suggests that these changes could lead to an ~ 15% reduction in hunting performance during pregnancy. Conclusions Sensory deficits related to pregnancy could impair foraging in echolocating bats. Our study demonstrates an additional cost of reproduction of possible relevance to other sensory modalities and organisms.

A machine learning model is trained to navigate a visual environment in a similar way to a bat, demonstrating similarities between trained computer models and the mammalian brain.

Abstract Background As well known to any photographer, controlling the “field of view” offers an extremely powerful mechanism by which to adjust target acquisition. Only a few natural sensory systems can actively control their field of view (e.g., dolphins, whales, and bats). Bats are known for their active sensing abilities and modify their echolocation signals by actively controlling their spectral and temporal characteristics. Less is known about bats’ ability to actively modify their bio-sonar field of view. Results We show that Pipistrellus kuhlii bats rapidly narrow their sensory field of view (i.e., their bio-sonar beam) when scanning a target. On-target vertical sonar beams were twofold narrower than off-target beams. Continuous measurements of the mouth gape of free-flying bats revealed that they control their bio-sonar beam by a ~3.6 mm widening of their mouth gape: namely, bats open their mouth to narrow the beam and vice versa. Conclusions Bats actively and rapidly control their echolocation vertical beam width by modifying their mouth gape. We hypothesize that narrowing their vertical beam narrows the zone of ensonification when estimating the elevation of a target. In other words, bats open their mouth to improve sensory localization.

Abstract To examine the challenges faced by free-ranging Rousettus aegyptiacus living at the northern edge of their distribution, we performed a retrospective analysis of 2196 clinical cases reported by a bat rescue NGO over a period of 36 months, from throughout Israel. All cases of injured bats were evaluated and categorized according to date, place, sex, age, and etiology of the morbidity. The data analysis revealed an increase in all types of morbidity during the wintertime, with more than two-fold the number of cases per week compared to in the summer, over three consecutive years. Moreover, we found that the number of abandoned pups peaked during spring and summer, when adult morbidity is minimal. We characterized two prominent types of previously undescribed morbidities in R. aegyptiacus. We also employed GPS tracking to monitor the movement and foraging of dozens of bats, and to examine the potential correlates of elevated winter morbidity. Our results suggest that it is mainly harsh weather that drives the observed winter morbidity, with food limitations playing a minor-role. We hypothesize that R. aegyptiacus, of tropical origin, is facing major seasonal survival difficulties near the northern edge of its distribution, probably limiting its spread further northwards still.

Summary: Bats host a range of disease-causing viruses without displaying clinical symptoms. The mechanisms behind this are a continuous source of interest. Here, we studied the antiviral response in the Egyptian fruit bat and Kuhl’s pipistrelle, representing two subordinal clades. We profiled the antiviral response in fibroblasts using RNA sequencing and compared bat with primate and rodent responses. Both bats upregulate similar genes; however, a subset of these genes is transcriptionally divergent between them. These divergent genes also evolve rapidly in sequence, have specific promoter architectures, and are associated with programs underlying tolerance and resistance. Finally, we characterized antiviral genes that expanded in bats, with duplicates diverging in sequence and expression.Our study reveals a largely conserved antiviral program across bats and points to a set of genes that rapidly evolve through multiple mechanisms. These can contribute to bat adaptation to viral infection and provide directions to understanding the mechanisms behind it.

Abstract Background Various mammalian species emit ultrasonic vocalizations (USVs), which reflect their emotional state and mediate social interactions. USVs are usually analyzed by manual or semi-automated methodologies that categorize discrete USVs according to their structure in the frequency-time domains. This laborious analysis hinders the effective use of USVs as a readout for high-throughput analysis of behavioral changes in animals. Results Here we present a novel automated open-source tool that utilizes a different approach towards USV analysis, termed TrackUSF. To validate TrackUSF, we analyzed calls from different animal species, namely mice, rats, and bats, recorded in various settings and compared the results with a manual analysis by a trained observer. We found that TrackUSF detected the majority of USVs, with less than 1% of false-positive detections. We then employed TrackUSF to analyze social vocalizations in Shank3-deficient rats, a rat model of autism, and revealed that these vocalizations exhibit a spectrum of deviations from appetitive calls towards aversive calls. Conclusions TrackUSF is a simple and easy-to-use system that may be used for a high-throughput comparison of ultrasonic vocalizations between groups of animals of any kind in any setting, with no prior assumptions.

IntroductionInteractions between the gut microbiome (GM) and the immune system influence host health and fitness. However, few studies have investigated this link and GM dynamics during disease in wild species. Bats (Mammalia: Chiroptera) have an exceptional ability to cope with intracellular pathogens and a unique GM adapted to powered flight. Yet, the contribution of the GM to bat health, especially immunity, or how it is affected by disease, remains unknown.MethodsHere, we examined the dynamics of the Egyptian fruit bats’ (Rousettus aegyptiacus) GM during health and disease. We provoked an inflammatory response in bats using lipopolysaccharides (LPS), an endotoxin of Gram-negative bacteria. We then measured the inflammatory marker haptoglobin, a major acute phase protein in bats, and analyzed the GM (anal swabs) of control and challenged bats using high-throughput 16S rRNA sequencing, before the challenge, 24h and 48h post challenge.ResultsWe revealed that the antigen challenge causes a shift in the composition of the bat GM (e.g., Weissella, Escherichia, Streptococcus). This shift was significantly correlated with haptoglobin concentration, but more strongly with sampling time. Eleven bacterial sequences were correlated with haptoglobin concentration and nine were found to be potential predictors of the strength of the immune response, and implicit of infection severity, notably Weissella and Escherichia. The bat GM showed high resilience, regaining the colony’s group GM composition rapidly, as bats resumed foraging and social activities.ConclusionOur results demonstrate a tight link between bat immune response and changes in their GM, and emphasize the importance of integrating microbial ecology in ecoimmunological studies of wild species. The resilience of the GM may provide this species with an adaptive advantage to cope with infections and maintain colony health.

Summary: Many animals, humans included, rely on acoustic vocalizations for communication. The complexity of non-human vocal communication has been under debate one of the main open questions being: What could be the function of multi-syllabic vocal sequences? We address these questions by analyzing fruit-bat vocal communication. We use neural networks to encode the vocalizations, and statistical models to examine the information conveyed by sequences of vocalizations. We show that fruit-bat vocal sequences potentially convey more contextual information than individual syllables, but that the order of the syllables within the sequence is unimportant for context. Specifically, sequences are composed of slightly modified syllables, thus increasing the probability of context-specificity. We note that future behavioral, e.g., playback experiments are needed in order to validate the biological relevance of our statistical results. We hypothesize that such sequences might have served as pre-syntax precursors in the evolution of animal communication.

Predators that target multiple prey types are predicted to switch foraging modes according to prey profitability to increase energy returns in dynamic environments. Here, we use bat-borne tags and DNA metabarcoding of feces to test the hypothesis that greater mouse-eared bats make immediate foraging decisions based on prey profitability and changes in the environment. We show that these bats use two foraging strategies with similar average nightly captures of 25 small, aerial insects and 29 large, ground-dwelling insects per bat, but with much higher capture success in the air (76%) vs ground (30%). However, owing to the 3–20 times larger ground prey, 85% of the nightly food acquisition comes from ground prey despite the 2.5 times higher failure rates. We find that most bats use the same foraging strategy on a given night suggesting that bats adapt their hunting behavior to weather and ground conditions. We conclude that these bats use high risk-high gain gleaning of ground prey as a primary foraging tactic, but switch to aerial hunting when environmental changes reduce the profitability of ground prey, showing that prey switching matched to environmental dynamics plays a key role in covering the energy intake even in specialized predators.

Network models of anatomical connections allow for the extraction of quantitative features describing brain organization, and their comparison across brains from different species. Such comparisons can inform our understanding of between-species differences in brain architecture and can be compared to existing taxonomies and phylogenies. Here we performed a quantitative comparative analysis using the MaMI database (Tel Aviv University), a collection of brain networks reconstructed from ex vivo diffusion MRI spanning 125 species and 12 taxonomic orders or superorders. We used a broad range of metrics to measure between-mammal distances and compare these estimates to the separation of species as derived from taxonomy and phylogeny. We found that within-taxonomy order network distances are significantly closer than between-taxonomy network distances, and this relation holds for several measures of network distance. Furthermore, to estimate the evolutionary divergence between species, we obtained phylogenetic distances across 10,000 plausible phylogenetic trees. The anatomical network distances were rank-correlated with phylogenetic distances 10,000 times, creating a distribution of coefficients that demonstrate significantly positive correlations between network and phylogenetic distances. Collectively, these analyses demonstrate species-level organization across scales and informational sources: we relate brain networks distances, derived from MRI, with evolutionary distances, derived from genotyping data.

Abstract Background Bats are remarkable in their dynamic control over body temperature, showing both hypothermia with torpor and hyperthermia during flight. Despite considerable research in understanding bats’ thermoregulation mechanisms, knowledge on the relationship between flight and body temperature in bats remains limited, possibly due to technological restraints. Results We used onboard dataloggers including a temperature sensor and an inertial sensor (accelerometers) and continuously recorded the flight behavior and skin temperature (T sk ) subcutaneously of a perch-hunting bat, Hipposideros armiger, both in the laboratory and in the field. We provide evidence that flight increases the body temperature of bats. The median of the maximum increase in the T sk caused by flight bouts was 3.4 °C (between 1.9 and 5.3 °C for different individuals) in the laboratory. The maximum T sk for the bats was narrowly centered around 40 °C (between 38.5 and 40.9 °C). Moreover, we found that the faster the T sk rises, the greater the maximum increase in T sk . Interestingly, bats can slow down the T sk rises with intermittent fights, during which they perch after brief flight bouts to allow the body temperature to drop rapidly. Similar data were collected from field recordings in free-ranging bats. Conclusions We suggest that perch-hunting behavior observed in approximately 200 species of bats that results in intermittent flights may function as a thermoregulatory strategy, in addition to optimizing energy efficiency as demonstrated by previous studies.

Abstract 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.

Abstract Background Urbanization is rapidly changing our planet and animals that live in urban environments must quickly adjust their behavior. One of the most prevalent behavioral characteristics of urban dwelling animals is an increased level of risk-taking. Here, we aimed to reveal how urban fruitbats become risk-takers, and how they differ behaviorally from rural bats, studying both genetic and non-genetic factors that might play a role in the process. We assessed the personality of newborn pups from both rural and urban colonies before they acquired experience outdoors, examining risk-taking, exploration, and learning rates. Results Urban pups exhibited significantly higher risk-taking levels, they were faster learners, but less exploratory than their rural counterparts. A cross-fostering experiment revealed that pups were more similar to their adoptive mothers, thus suggesting a non-genetic mechanism and pointing towards a maternal effect. We moreover found that lactating urban mothers have higher cortisol levels in their milk, which could potentially explain the transmission of some personality traits from mother to pup. Conclusions Young bats seem to acquire environment suitable traits via post-birth non-genetic maternal effects. We offer a potential mechanism for how urban pups can acquire urban-suitable behavioral traits through hormonal transfer from their mothers.

Mammalian taxonomies are conventionally defined by morphological traits and genetics. How species differ in terms of neural circuits and whether inter-species differences in neural circuit organization conform to these taxonomies is unknown. The main obstacle to the comparison of neural architectures has been differences in network reconstruction techniques, yielding species-specific connectomes that are not directly comparable to one another. Here, we comprehensively chart connectome organization across the mammalian phylogenetic spectrum using a common reconstruction protocol. We analyse the mammalian MRI (MaMI) data set, a database that encompasses high-resolution ex vivo structural and diffusion MRI scans of 124 species across 12 taxonomic orders and 5 superorders, collected using a unified MRI protocol. We assess similarity between species connectomes using two methods: similarity of Laplacian eigenspectra and similarity of multiscale topological features. We find greater inter-species similarities among species within the same taxonomic order, suggesting that connectome organization reflects established taxonomic relationships defined by morphology and genetics. While all connectomes retain hallmark global features and relative proportions of connection classes, inter-species variation is driven by local regional connectivity profiles. By encoding connectomes into a common frame of reference, these findings establish a foundation for investigating how neural circuits change over phylogeny, forging a link from genes to circuits to behaviour.

Abstract Background Learning to adapt to changes in the environment is highly beneficial. This is especially true for echolocating bats that forage in diverse environments, moving between open spaces to highly complex ones. Bats are known for their ability to rapidly adjust their sensing according to auditory information gathered from the environment within milliseconds but can they also benefit from longer adaptive processes? In this study, we examined adult bats’ ability to slowly adapt their sensing strategy to a new type of environment they have never experienced for such long durations, and to then maintain this learned echolocation strategy over time. Results We show that over a period of weeks, Pipistrellus kuhlii bats gradually adapt their pre-takeoff echolocation sequence when moved to a constantly cluttered environment. After adopting this improved strategy, the bats retained an ability to instantaneously use it when placed back in a similarly cluttered environment, even after spending many months in a significantly less cluttered environment. Conclusions We demonstrate long-term adaptive flexibility in sensory acquisition in adult animals. Our study also gives further insight into the importance of sensory planning in the initiation of a precise sensorimotor behavior such as approaching for landing.

Abstract Background Urbanization is one of the most influential processes on our globe, putting a great number of species under threat. Some species learn to cope with urbanization, and a few even benefit from it, but we are only starting to understand how they do so. In this study, we GPS tracked Egyptian fruit bats from urban and rural populations to compare their movement and foraging in urban and rural environments. Because fruit trees are distributed differently in these two environments, with a higher diversity in urban environments, we hypothesized that foraging strategies will differ too. Results When foraging in urban environments, bats were much more exploratory than when foraging in rural environments, visiting more sites per hour and switching foraging sites more often on consecutive nights. By doing so, bats foraging in settlements diversified their diet in comparison to rural bats, as was also evident from their choice to often switch fruit species. Interestingly, the location of the roost did not dictate the foraging grounds, and we found that many bats choose to roost in the countryside but nightly commute to and forage in urban environments. Conclusions Bats are unique among small mammals in their ability to move far rapidly. Our study is an excellent example of how animals adjust to environmental changes, and it shows how such mobile mammals might exploit the new urban fragmented environment that is taking over our landscape.

Summary: While bats are often referred to as reservoirs of viral pathogens, a meta-analysis of the literature reveals many cases in which there is not enough evidence to claim so. In many cases, bats are able to confront viruses, recover, and remain immune by developing a potent titer of antibodies, often without becoming a reservoir. In other cases, bats might have carried an ancestral virus that at some time point might have mutated into a human pathogen. Moreover, bats exhibit a balanced immune response against viruses that have evolved over millions of years. Using genomic tools, it is now possible to obtain a deeper understanding of that unique immune system and its variability across the order Chiroptera. We conclude, that with the exception of a few viruses, bats pose little zoonotic danger to humans and that they operate a highly efficient anti-inflammatory response that we should strive to understand.

On the social scale, the blind mole rat (BMR; Spalax ehrenbergi) is an extreme. It is exceedingly solitary, territorial, and aggressive. BMRs reside underground, in self-excavated tunnels that they rarely leave. They possess specialized sensory systems for social communication and navigation, which allow them to cope with the harsh environmental conditions underground. This review aims to present the blind mole rat as an ideal, novel neuroethological model for studying aggressive and solitary behaviors. We discuss the BMR’s unique behavioral phenotype, particularly in the context of ‘anti-social’ behaviors, and review the available literature regarding its specialized sensory adaptations to the social and physical habitat. To date, the neurobiology of the blind mole rat remains mostly unknown and holds a promising avenue for scientific discovery. Unraveling the neural basis of the BMR’s behavior, in comparison to that of social rodents, can shed important light on the underlying mechanisms of psychiatric disorders in humans, in which similar behaviors are displayed.

Abstract Background Sensory systems acquire both external and internal information to guide behavior. Adjustments based on external input are much better documented and understood than internal-based sensory adaptations. When external input is not available, idiothetic—internal—cues become crucial for guiding behavior. Here, we take advantage of the rapid sensory adjustments exhibited by bats in order to study how animals rely on internal cues in the absence of external input. Constant frequency echolocating bats are renowned for their Doppler shift compensation response used to adjust their emission frequency in order to optimize sensing. Previous studies documented the importance of external echoes for this response. Results We show that the Doppler compensation system works even without external feedback. Bats experiencing accelerations in an echo-free environment exhibited an intact compensation response. Moreover, using on-board GPS tags on free-flying bats in the wild, we demonstrate that the ability to perform Doppler shift compensation response based on internal cues might be essential in real-life when echo feedback is not available. Conclusions We thus show an ecological need for using internal cues as well as an ability to do so. Our results illustrate the robustness of one particular sensory behavior; however, we suggest this ability to rely on different streams of information (i.e., internal or external) is probably relevant for many sensory behaviors.

One of the most difficult sensorimotor behaviors exhibited by flying animals is the ability to track another flying animal based on its sound emissions. From insects to mammals, animals display this ability in order to localize and track conspecifics, mate or prey. The pursuing individual must overcome multiple non-trivial challenges including the detection of the sounds emitted by the target, matching the input received by its (mostly) two sensors, localizing the direction of the sound target in real time and then pursuing it. All this has to be done rapidly as the target is constantly moving. In this project, we set to mimic this ability using a physical bio-mimetic autonomous drone. We equipped a miniature commercial drone with our in-house 2D sound localization electronic circuit which uses two microphones (mimicking biological ears) to localize sound signals in real-time and steer the drone in the horizontal plane accordingly. We focus on bat signals because bats are known to eavesdrop on conspecifics and follow them, but our approach could be generalized to other biological signals and other man-made signals. Using two different experiments, we show that our fully autonomous aviator can track the position of a moving sound emitting target and pursue it in real-time. Building an actual robotic-agent, forced us to deal with real-life difficulties which also challenge animals. We thus discuss the similarities and differences between our and the biological approach.

Abstract 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.

Summary: Fireflies are known for emitting light signals for intraspecific communication. However, in doing so, they reveal themselves to many potential nocturnal predators from a large distance. Therefore, many fireflies evolved unpalatable compounds and probably use their light signals as anti-predator aposematic signals. Fireflies are occasionally attacked by predators despite their warning flashes. Bats are among the most substantial potential firefly predators. Using their echolocation, bats might detect a firefly from a short distance and attack it in between two flashes. We thus aimed to examine whether fireflies use additional measures of warning, specifically focusing on sound signals. We recorded four species from different genera of fireflies in Vietnam and Israel and found that all of them generated ultrasonic clicks centered around bats' hearing range. Clicks were synchronized with the wingbeat and are probably produced by the wings. We hypothesize that ultrasonic clicks can serve as part of a multimodal aposematic display.

For decades, researchers have speculated how echolocating bats deal with masking by conspecific calls when flying in aggregations. To date, only a few attempts have been made to mathematically quantify the probability of jamming, or its effects. We developed a comprehensive sensorimotor predator-prey simulation, modeling numerous bats foraging in proximity. We used this model to examine the effectiveness of a spectral Jamming Avoidance Response (JAR) as a solution for the masking problem. We found that foraging performance deteriorates when bats forage near conspecifics, however, applying a JAR does not improve insect sensing or capture. Because bats constantly adjust their echolocation to the performed task (even when flying alone), further shifting the signals' frequencies does not mitigate jamming. Our simulations explain how bats can hunt successfully in a group despite competition and despite potential masking. This research demonstrates the advantages of a modeling approach when examining a complex biological system.

Abstract Animals execute sensorimotor sequences to optimize performance of complex actions series. However, the sensory aspects of these sequences and their dynamic control are often poorly understood. We trained bats to fly to targets at different distances, and analysed their sensory behavior before and during flight to test whether they assess target distance before flight and how they adapt sensory acquisition in different situations. We demonstrate that bats’ sensory acquisition during approach-flight is more flexible than previously described. We identified acoustic parameters that illustrate that bats assess target distance before takeoff. We show that bats adapt their echolocation approach-sequences to target distance - ignoring closer background objects. At shorter distances, bats initiated their echolocation approach-sequence with distance-appropriate parameters, thus entering the approach sensory sequence “in step”. Our results suggest that in order to perform fine flight-manoeuvres, bats must maintain their sensorimotor plan in phase. To do this, they adapt acquisition according to target distance before initiating a complex sensory sequence based on a sensorimotor feedback-loop, even in complex acoustic environments, which impose other sensory reactions and restrictions. Though studying this in non-echolocating animals may prove difficult, such mechanisms are probably widely used in nature whenever complex series of sensorimotor actions are required.

Predation on swarms of prey, especially using visual information, has drawn much interest in studies of collective movement. Surprisingly, in the field of biosonar this aspect of prey detection, which is probably very common, has received little to no attention. Here, we combine computer simulations and actual echo measurements to accurately estimate the echo sound pressure of insect swarms of different size and density. We show that swarm echo sound pressure increases with 3dB for every doubling of insect number, irrespective of swarm density. Thus swarms will be much easier to detect than single insects. Many of the insects bats eat are so small that they are only detectable by echolocation at very short distances. By focusing on detection of swarms of insects, a bat may increase its operating range and diversify its diet. Interestingly, interference between the sound waves reflected from a swarm of insects can sometimes result in echoes that are much weaker than echoes from single insects. We show that bats can reduce this problem by increasing the bandwidth of their echolocation calls. Specifically, a bandwidth of 3-8 kHz would guarantee receiving loud echoes from any angle relative to the swarm. Indeed, many bat species, and specifically bats hunting in open spaces, where swarms are abundant, use echolocation signals with a bandwidth of several kHz. Our results might also explain how the first echolocating bats that probably had limited echolocation abilities, could detect insects through swarm hunting.

During hundreds of millions of years of evolution, insects have evolved some of the most efficient and robust sensing organs, often far more sensitive than their man-made equivalents. In this study, we demonstrate a hybrid bio-technological approach, integrating a locust tympanic ear with a robotic platform. Using an Ear-on-a-Chip method, we manage to create a long-lasting miniature sensory device that operates as part of a bio-hybrid robot. The neural signals recorded from the ear in response to sound pulses, are processed and used to control the robot’s motion. This work is a proof of concept, demonstrating the use of biological ears for robotic sensing and control.

Echolocating bats rely on active sound emission (echolocation) for mapping novel environments and navigating through them. Many theoretical frameworks have been suggested to explain how they do so, but few attempts have been made to build an actual robot that mimics their abilities. Here, we present the 'Robat'-a fully autonomous bat-like terrestrial robot that relies on echolocation to move through a novel environment while mapping it solely based on sound. Using the echoes reflected from the environment, the Robat delineates the borders of objects it encounters, and classifies them using an artificial neural-network, thus creating a rich map of its environment. Unlike most previous attempts to apply sonar in robotics, we focus on a biological bat-like approach, which relies on a single emitter and two ears, and we apply a biological plausible signal processing approach to extract information about objects' position and identity.

Vocal learning, the substrate of human language acquisition, has rarely been described in other mammals. Often, group-specific vocal dialects in wild populations provide the main evidence for vocal learning. While social learning is often the most plausible explanation for these intergroup differences, it is usually impossible to exclude other driving factors, such as genetic or ecological backgrounds. Here, we show the formation of dialects through social vocal learning in fruit bats under controlled conditions. We raised 3 groups of pups in conditions mimicking their natural roosts. Namely, pups could hear their mothers' vocalizations but were also exposed to a manipulation playback. The vocalizations in the 3 playbacks mainly differed in their fundamental frequency. From the age of approximately 6 months and onwards, the pups demonstrated distinct dialects, where each group was biased towards its playback. We demonstrate the emergence of dialects through social learning in a mammalian model in a tightly controlled environment. Unlike in the extensively studied case of songbirds where specific tutors are imitated, we demonstrate that bats do not only learn their vocalizations directly from their mothers, but that they are actually influenced by the sounds of the entire crowd. This process, which we term "crowd vocal learning," might be relevant to many other social animals such as cetaceans and pinnipeds.

Animal flight requires fine motor control. However, it is unknown how flying animals rapidly transform noisy sensory information into adequate motor commands. Here we developed a sensorimotor control model that explains vertebrate flight guidance with high fidelity. This simple model accurately reconstructed complex trajectories of bats flying in the dark. The model implies that in order to apply appropriate motor commands, bats have to estimate not only the angle-to-target, as was previously assumed, but also the angular velocity ("proportional-derivative" controller). Next, we conducted experiments in which bats flew in light conditions. When using vision, bats altered their movements, reducing the flight curvature. This change was explained by the model via reduction in sensory noise under vision versus pure echolocation. These results imply a surprising link between sensory noise and movement dynamics. We propose that this sensory-motor link is fundamental to motion control in rapidly moving animals under different sensory conditions, on land, sea, or air.

Active-sensing systems abound in nature, but little is known about systematic strategies that are used by these systems to scan the environment. Here, we addressed this question by studying echolocating bats, animals that have the ability to point their biosonar beam to a confined region of space. We trained Egyptian fruit bats to land on a target, under conditions of varying levels of environmental complexity, and measured their echolocation and flight behavior. The bats modulated the intensity of their biosonar emissions, and the spatial region they sampled, in a task-dependant manner. We report here that Egyptian fruit bats selectively change the emission intensity and the angle between the beam axes of sequentially emitted clicks, according to the distance to the target, and depending on the level of environmental complexity. In so doing, they effectively adjusted the spatial sector sampled by a pair of clicks-the "field-of-view." We suggest that the exact point within the beam that is directed towards an object (e.g., the beam's peak, maximal slope, etc.) is influenced by three competing task demands: detection, localization, and angular scanning-where the third factor is modulated by field-of-view. Our results suggest that lingual echolocation (based on tongue clicks) is in fact much more sophisticated than previously believed. They also reveal a new parameter under active control in animal sonar-the angle between consecutive beams. Our findings suggest that acoustic scanning of space by mammals is highly flexible and modulated much more selectively than previously recognized.

Echo-based object classification is a fundamental task of animals that use a biosonar system. Dolphins and porpoises should be able to rely on echoes to discriminate a predator from a prey or to select a desired prey from an undesired object. Many studies have shown that dolphins and porpoises can discriminate between objects according to their echoes. All of these studies however, used unnatural objects that can be easily characterized in human terminologies (e.g., metallic spheres, disks, cylinders). In this work, we collected real fish echoes from many angles of acquisition using a sonar system that mimics the emission properties of dolphins and porpoises. We then tested two alternative statistical approaches in classifying these echoes. Our results suggest that fish species can be classified according to echoes returning from porpoise- and dolphin-like signals. These results suggest how dolphins and porpoises can classify fish based on their echoes and provide some insight as to which features might enable the classification.

A critical step on the way to understanding a sensory system is the analysis of the input it receives. In this work we examine the statistics of natural complex echoes, focusing on vegetation echoes. Vegetation echoes constitute a major part of the sensory world of more than 800 species of echolocating bats and play an important role in several of their daily tasks. Our statistical analysis is based on a large collection of plant echoes acquired by a biomimetic sonar system. We explore the relation between the physical world (the structure of the plant) and the characteristics of its echo. Finally, we complete the story by analyzing the effect of the sensory processing of both the echolocation and the auditory systems on the echoes and interpret them in the light of information maximization. The echoes of all different plant species we examined share a surprisingly robust pattern that was also reproduced by a simple Poisson model of the spatial reflector arrangement. The fine differences observed between the echoes of different plant species can be explained by the spatial characteristics of the plants. The bat's emitted signal enhances the most informative spatial frequency range where the species-specific information is large. The auditory system filtering affects the echoes in a similar way, thus enhancing the most informative spatial frequency range even more. These findings suggest how the bat's sensory system could have evolved to deal with complex natural echoes.

Echolocating bats use the echoes from their echolocation calls to perceive their surroundings. The ability to use these continuously emitted calls, whose main function is not communication, for recognition of individual conspecifics might facilitate many of the social behaviours observed in bats. Several studies of individual-specific information in echolocation calls found some evidence for its existence but did not quantify or explain it. We used a direct paradigm to show that greater mouse-eared bats (Myotis myotis) can easily discriminate between individuals based on their echolocation calls and that they can generalize their knowledge to discriminate new individuals that they were not trained to recognize. We conclude that, despite their high variability, broadband bat-echolocation calls contain individual-specific information that is sufficient for recognition. An analysis of the call spectra showed that formant-related features are suitable cues for individual recognition. As a model for the bat's decision strategy, we trained nonlinear statistical classifiers to reproduce the behaviour of the bats, namely to repeat correct and incorrect decisions of the bats. The comparison of the bats with the model strongly implies that the bats are using a prototype classification approach: they learn the average call characteristics of individuals and use them as a reference for classification.

Classification of plants according to their echoes is an elementary component of bat behavior that plays an important role in spatial orientation and food acquisition. Vegetation echoes are, however, highly complex stochastic signals: from an acoustical point of view, a plant can be thought of as a three-dimensional array of leaves reflecting the emitted bat call. The received echo is therefore a superposition of many reflections. In this work we suggest that the classification of these echoes might not be such a troublesome routine for bats as formerly thought. We present a rather simple approach to classifying signals from a large database of plant echoes that were created by ensonifying plants with a frequency-modulated bat-like ultrasonic pulse. Our algorithm uses the spectrogram of a single echo from which it only uses features that are undoubtedly accessible to bats. We used a standard machine learning algorithm (SVM) to automatically extract suitable linear combinations of time and frequency cues from the spectrograms such that classification with high accuracy is enabled. This demonstrates that ultrasonic echoes are highly informative about the species membership of an ensonified plant, and that this information can be extracted with rather simple, biologically plausible analysis. Thus, our findings provide a new explanatory basis for the poorly understood observed abilities of bats in classifying vegetation and other complex objects.