The question matters because the existing literature has been strikingly inconsistent. Researchers have long known that bats modify their echolocation behavior when flying in groups compared with flying alone, but studies have reported different, sometimes conflicting, patterns in exactly which call properties change and in which direction. Some of this confusion is methodological. Echolocation recordings from a colony are a mixture of many overlapping signals, and separating the influence of social context from the influence of physical surroundings is genuinely difficult. A bat flying through a cluttered forest edge will naturally adjust its calls whether or not any companions are nearby, so disentangling the two effects requires data collected in a setting where both vary and an analytical framework flexible enough to detect their influence without prejudging its form.

That is precisely the approach the team took. Working with a colony of wild gray bats, they recorded echolocation calls as animals emerged from the roost and flew through their environment, while also tracking how many bats were emerging at a given time and noting the presence of obstacles along the flight path. Gray bats are an ideal subject for this kind of work. They are highly social, roosting and emerging in large numbers, which means group size varies substantially across emergence events. Their cave-roosting lifestyle also means they routinely navigate around and through physical structures, so environmental clutter is a natural feature of their daily flights rather than an artificial laboratory condition.

The researchers focused on two call properties that capture the energetic and temporal character of echolocation: the number of calls a bat produces and the acoustic power of those calls. Call rate and call intensity are the levers a bat can pull to change how much information its sonar gathers and how far that sonar reaches. Producing more calls refreshes the acoustic picture of the world more frequently, which is useful when maneuvering quickly or flying through tight spaces. Increasing acoustic power extends the range of detection but also increases the chance that the call interferes with, or is masked by, the calls of nearby conspecifics. The central hypothesis was that both the number of flying companions and the geometry of the surroundings would leave measurable fingerprints on these two properties.

To test that hypothesis without assuming in advance what the relationship should look like, the team turned to a time-series analysis tool called transfer entropy. Transfer entropy quantifies how much knowing the past state of one variable reduces uncertainty about the future state of another, beyond what the variable’s own history already reveals. In this context, it allowed the researchers to ask whether group size carries information about future call properties, and whether the presence of obstacles does, without imposing a linear or any other predetermined functional form on the relationship. This is a crucial advantage in a system as complex as a flying bat colony, where the true dynamics are almost certainly nonlinear and shaped by feedback between individuals and their environment.

The analysis revealed significant transfer entropy values linking both social and environmental factors to call properties. In plain terms, the number of bats emerging together and the presence of obstacles each carried genuine predictive information about how many calls individual bats produced and how acoustically powerful those calls were. The influence was detected without the researchers assuming any particular mathematical shape for it, which strengthens the conclusion that the relationships are real features of the bats’ behavior rather than artifacts of a chosen model. This finding helps resolve the inconsistency in earlier literature by suggesting that reported differences across studies may reflect genuine context dependence: the direction and magnitude of call adjustments plausibly depend on the specific combination of group density and environmental clutter a colony experiences.

Transfer entropy established that the influences exist, but the team wanted to go further and sketch what the underlying dynamics might actually look like. For this they applied a data-driven algorithm known as sparse identification of nonlinear dynamics, or SINDy. The core idea behind SINDy is elegant: given time-series measurements, it searches an enormous library of candidate functions and terms for the simplest set of equations that can reproduce the observed dynamics. Rather than a human modeler guessing that call rate should depend linearly on group size, say, or quadratically on obstacle proximity, SINDy lets the data nominate the terms that matter and discard the rest. The result is a compact mathematical description of echolocation behavior assembled directly from recordings of wild bats going about their nightly business.

The SINDy-based models suggested concrete ways in which gray bats adjust their echolocation in response to conspecifics and obstacles, offering a candidate structure for the phenomenon that earlier descriptive studies could only gesture at. This modeling step is what elevates the study from a correlation hunt to a constructive contribution toward predictive theory. If the identified equations capture real behavioral rules, they can be tested against new recordings, compared across bat species with different social systems, and eventually incorporated into larger models of collective movement in which echolocation serves simultaneously as a sensory channel and, potentially, as a medium of unintended information sharing among group members.

The broader significance of the work lies in how it reframes the interplay between sociality and sensory ecology. Schools of fish and flocks of birds coordinate largely through vision and lateral-line or aerodynamic sensing, channels that do not degrade in the same way when many individuals use them at once. Echolocation is different: it is an active sensing modality in which every individual’s measurement apparatus is also a potential source of noise for everyone else. The new results indicate that gray bats manage this trade-off in a way that is jointly sensitive to who else is flying and what the physical environment demands. Sociality, in other words, is not just a background condition for echolocation but an active driver of its dynamics, intertwined with the geometry of caves, cliffs, and forest corridors through which the animals move.

For researchers studying collective behavior, bioacoustics, and sensory ecology, the study offers both a methodological template and a set of testable expectations. The combination of transfer entropy and SINDy provides a pipeline for extracting dynamical rules from noisy field recordings of animals whose signals overlap and interfere, a challenge that extends well beyond bats to any species using active sensing in groups. For the gray bats themselves, the findings paint a picture of animals continuously recalibrating their sonar against two competing pressures: the need to navigate a cluttered, obstacle-strewn world and the need to keep their acoustic measurements usable in the company of hundreds of calling companions. As the authors note, these results provide new insights into the role of sociality and physical surroundings in shaping echolocation dynamics, and they suggest that the nightly exodus from a gray bat cave is not just a mass of sound but a richly structured information environment, one the bats themselves are constantly measuring, adjusting to, and rewriting call by call.

Subject of Research: How group size and environmental obstacles influence echolocation call properties in flying gray bats

Article Title: Group size and environmental obstacles drive acoustic call properties for gray bats in flight: A data-driven analysis

Article References: Grey, M., Aung, E., & Abaid, N. (2026). Group size and environmental obstacles drive acoustic call properties for gray bats in flight: A data-driven analysis. PLOS Complex Systems, 3(4), e0000100. https://doi.org/10.1371/journal.pcsy.0000100

Image Credits: AI Generated

DOI: 10.1371/journal.pcsy.0000100

Keywords: gray bats, echolocation, Myotis grisescens, transfer entropy, SINDy, collective behavior, bioacoustics, group size, environmental obstacles, nonlinear dynamics, sensory ecology, PLOS Complex Systems

The macadamia is not an ordinary crop. Labour-intensive harvesting and complex post-harvest processing make it one of the costliest nuts in the global market, which means that every percentage point of yield lost to insects translates into substantial economic damage. Protecting the trees from pests is therefore a central concern for growers, and the conventional response has often been intensive chemical or mechanical intervention. The new research suggests that an alternative, ecologically grounded strategy may already be flying overhead every night. By examining how natural vegetation in and around plantations shapes the communities of birds and bats that forage among the trees, the team set out to quantify the pest control services these animals provide and to identify the landscape features that sustain them.

The study was conducted at ten intensively managed macadamia plantations in South Africa, a design that allowed the researchers to capture variation in both management intensity and surrounding land cover. At each site, the team compared trees growing at the edge of the plantation with trees in the centre, a comparison specifically chosen to analyse the influence of the surrounding vegetation on the animals visiting the crop. Edge trees sit closer to hedgerows, forest fragments and other natural features, while central trees are embedded more deeply within the monoculture, so any systematic differences in pest damage or animal communities between the two positions can reveal how strongly the wider landscape shapes what happens inside the orchard.

Surveying the fauna required two very different techniques. The researchers recorded 111 bird species through direct observation, walking the orchards and documenting the birds foraging among the trees. Bats, being nocturnal and difficult to observe visually, were detected through their echolocation calls, which were captured with acoustic recording equipment and used to identify 16 species. This acoustic monitoring allowed the team to build a detailed picture of which bat species were active over the plantations, how their activity varied between sites, and how that variation related to the structure of the surrounding landscape and the level of insect damage measured on the nuts.

The results pointed clearly to bats as the dominant pest control agents in the system. Bat activity was associated with reduced insect damage, and this effect was particularly pronounced in plantations that contained less natural habitat within their boundaries or were surrounded by natural habitat in the wider landscape. Natural habitat, in the sense used by the researchers, includes native forests, bushland and scrubland, the heterogeneous vegetation types that provide roosting sites, foraging grounds and refuge for insectivorous bats. In landscapes where these features persisted, the bats appeared to compensate for the scarcity of on-farm natural cover, patrolling the orchards and suppressing the insect populations that would otherwise feed on the developing nuts.

To test the contribution of flying vertebrates directly, the team conducted exclusion trials, a classic experimental approach in which nets are used to keep birds and bats away from the trees. The outcome was striking. When birds and bats were excluded, the proportion of nuts damaged by insects rose from 6.2 per cent to 10.7 per cent, an increase of roughly 70 per cent. This experiment provides some of the most direct evidence that these animals deliver a measurable, economically meaningful service to macadamia growers. Intriguingly, however, the researchers could not demonstrate a direct influence of bird numbers on pest infestation, suggesting that in this system the nocturnal bats, rather than the diurnal birds, carry the primary burden of biological pest control.

The landscape context proved to matter in more ways than one. As the proportion of natural habitats around the plantations increased, not only did the number of bats rise, but their diversity increased as well, indicating that intact vegetation supports a richer community of these aerial predators rather than merely a larger one dominated by a few generalist species. Even the yield of the macadamia trees was linked to the share of natural habitat in the landscape, reaching its highest level when natural habitats accounted for around 60 per cent of the surrounding area. This peak suggests a non-linear relationship in which a substantial but not exclusive share of natural cover best supports the ecological processes, from pest suppression to pollination, that underpin crop production.

Bird communities, by contrast, responded to fine-scale structure within the plantations rather than to the broader landscape. The researchers found that the species assemblages differed between the edges and the centres of the orchards: species that prefer dense and woody vegetation were more common at the plantation edges, while species of open habitats predominated in the interior. This edge-to-centre gradient shows that even within a single intensively managed plantation, the arrangement of vegetation creates distinct niches for different bird species, and it hints that the conservation value of an orchard depends not only on what surrounds it but also on how its own internal habitat mosaic is structured.

The implications extend well beyond macadamia. Lead author Dr Mina Anders of the Department of Functional Agrobiodiversity and Agroecology at the University of Göttingen emphasised the broader message of the findings. Natural habitats, she explained, are valuable refuges that support beneficial organisms providing pollination and pest control, which in turn benefits agricultural production and the farmers who depend on it. It is therefore essential, she argued, that natural habitats and their conservation are incorporated into the planning of agricultural land. In other words, the patches of native forest, bushland and scrubland that persist between and around fields should not be viewed as idle or expendable land, but as functional infrastructure that actively contributes to farm productivity.

For a crop as valuable as the macadamia, the economics of this ecological service are compelling. A 70 per cent increase in insect-damaged nuts when bats and birds are excluded illustrates how quickly quality, and with it market value, deteriorates without these natural allies. The study thus highlights a form of agriculture that combines biodiversity with production, in which conserving natural vegetation within and around farmland is not a concession to environmental ideals but a practical investment in yield and nut quality. As global demand for macadamias continues to grow, the research offers growers and land-use planners a concrete, evidence-based reason to protect the forests and scrublands that shelter the bats working the night shift over South Africa’s orchards, and it adds macadamia plantations to the growing list of cropping systems where the fate of the harvest is quietly tied to the health of the surrounding landscape.

Subject of Research: The role of bats, birds and natural habitat in biological pest control in macadamia orchards

Article Title: Bats as a form of biological pest control

Article References: Bats as a form of biological pest control. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: bats, biological pest control, macadamia, South Africa, agroecology, natural habitat, biodiversity, bird communities, echolocation, crop yield, landscape management, University of Göttingen

The study, led by Jianan Ding, Arjan Boonman, Ofri Eitan, Yuval Werber, David Troupin, Nir Sapir, and Yossi Yovel, deployed passive acoustic recorders at 27 sites, 12 urban and 15 rural, spread across nearly 300 kilometers of Israel. The sites ranged from temperate forests receiving roughly 800 millimeters of annual rainfall in the north to semi-arid desert zones receiving about 200 millimeters in the south. Each AudioMoth full-spectrum recorder was placed within 20 meters of a meteorological station, allowing the researchers to tie every recorded bat call to precise, locally measured weather conditions. From August 1 to October 13, the team logged 1,151 nights of recordings, capturing 95,473 bat occurrences in total.

The technical setup was demanding. Recorders operated on a 50 percent duty cycle, capturing 30-second audio sessions every minute for 12 hours each night from sunset to sunrise, generating nearly 720 audio files per site per night. A custom-written MATLAB program then detected echolocation calls and automatically classified them to species based on signal frequency, duration, and harmonic content. To validate the automated pipeline, the researchers manually reviewed complete recordings from 30 nights at each site. Automatic classification was used only where accuracy exceeded 90 percent; at sites with intermediate accuracy, every identification was manually verified, and at two sites with accuracy below 70 percent, all files were classified by hand. Three sites yielded no bat detections at all.

Of the 23 bat species detectable in the region with the equipment’s sampling constraints, the team recorded 11 species from 7 genera and 4 families across 24 sites. Rural sites proved richer, hosting all 11 species, while urban sites supported 8, all of which also occurred in rural areas. For seven of the species found in both habitat types, activity was significantly higher at rural sites, measured as encounters per hour. The sole exception was Tadarida teniotis, the European free-tailed bat, which was significantly more active in urban areas, where it typically roosts. Species richness also correlated positively with latitude, reflecting the region’s rainfall gradient, with the five most species-rich sites all clustered in the wetter north.

The study’s central insight emerged when the researchers examined how environmental variables influenced bat activity. They divided species into two functional groups: generalists, detected at both urban and rural sites, and rural specialists, found exclusively outside cities. Using zero-inflated negative binomial models, they tested 511 predictor combinations per species and selected the best-supported models by Akaike Information Criterion. The predictors included temperature, humidity, wind speed, ambient illumination, the proportion of built-up area, average building height, environmental noise, hours from sunset, and latitude. The pattern was strikingly consistent: rural specialists were the most sensitive to weather, generalists at rural sites showed intermediate sensitivity, and generalists at urban sites were the least affected of all.

On average, bat activity rose with temperature and humidity and fell with wind speed and illumination, but the strength of these effects varied dramatically by habitat. Rural bats strongly preferred low wind speeds, a preference the authors attribute to thermoregulatory costs, acoustic interference with echolocation, and the behavior of insect prey, whose own flight activity drops in windy conditions. Urban bats, by contrast, showed little to no wind aversion. When the analysis isolated moonlight as the sole illumination variable, activity at rural sites generally declined as moonlight intensified, consistent with heightened perceived predation risk, whereas most urban species showed no significant lunar response, likely because city lights wash out the moon’s signal entirely.

Comparing the environmental conditions under which bats actually flew revealed the buffering effect in vivid detail. Rural specialists were the most restricted animals in the dataset, active only at higher temperatures and lower humidities than generalist species foraging at the very same sites. Generalists, meanwhile, operated across a much wider range of conditions, but remarkably, the same species extended their activity to lower temperatures, higher humidities, and stronger winds when hunting in cities than when hunting in the countryside. A Kullback-Leibler divergence analysis, which quantifies how different the weather during bat activity was from the weather across the entire night, confirmed that this mismatch was greatest for rural specialists and smallest for urban generalists.

The physical environment itself helps explain the pattern. During the study period, urban sites averaged roughly 2 degrees Celsius warmer and about 5 percent more humid-differing in humidity by about 5 percent lower-than nearby rural sites, a signature of the urban heat island effect and the reduced weather variability that cities create. The authors suggest several mechanisms that might allow urban bats to tolerate harsher conditions: the proximity of reliable food, the availability of irrigated parks, ponds, and lakes that attract both insects and bats in a water-scarce region, and the microclimatic shelter provided by buildings. Seasonal trends reinforced the story, as species richness and activity declined markedly toward winter at rural sites but remained comparatively stable in cities, hinting that urban environments may extend the active season for city-dwelling bats.

The researchers are careful to note the study’s limitations. Acoustic monitoring counts call encounters rather than individual bats, so differences in activity could partly reflect differences in the number of animals present. The monitoring window covered only 2.5 months from midsummer to autumn, leaving other seasons unexamined. And because the work was entirely remote, it cannot definitively establish that urban buffering causes bats to broaden their activity windows; unmeasured factors such as prey availability and fine-scale habitat structure may also contribute. The microphone setup, sampling at 96 kilohertz, could reliably detect only relatively loud calls below 50 kilohertz, excluding nine local species including all horseshoe bats, though previous work consistently identifies those species as rural specialists unlikely to alter the conclusions.

Beyond its findings on bats, the study showcases the power of passive acoustic monitoring combined with automated species classification for biodiversity research at scales that would be impossible with traditional surveys. As urbanization accelerates worldwide, the results add nuance to a usually grim picture: while rural specialists avoid cities and overall diversity is lower in urban areas, the generalist bats that do persist there appear to gain a measure of climatic freedom, operating under conditions that would ground their rural relatives. In an era of rapid environmental change, the city, for some species at least, may be less a threat than a shelter.

Subject of Research: The effects of urbanization and weather conditions on the activity of insectivorous bats

Article Title: Urban habitats offer buffered environmental conditions for city-dwelling bats

Article References: Ding, J., Boonman, A., Eitan, O., Werber, Y., Troupin, D., Sapir, N., & Yovel, Y. (2026). Urban habitats offer buffered environmental conditions for city-dwelling bats. iScience, 29(10), Article 117767. https://doi.org/10.1016/j.isci.2026.117767

Image Credits: AI Generated

DOI: 10.1016/j.isci.2026.117767

Keywords: bats, urbanization, passive acoustic monitoring, echolocation, urban heat island, weather, moonlight, wildlife behavior, Israel, iScience, biodiversity, urban ecology

The study, published in the journal Discover Conservation, is a systematic review of more than three decades of bat bioacoustics research. Researchers Luna Pino-Aedo and Andrés Muñoz-Sáez of the Universidad de Chile searched the ISI Web of Science database for studies published between 1990 and 2022, the period since passive acoustic monitoring of terrestrial ecosystems began in earnest. Their initial queries returned 531 candidate articles, and after screening each one at full text, 207 studies met the criteria: they analyzed bat echolocation calls and specified exactly how those calls were analyzed. From this body of work the authors catalogued 34 distinct echolocation analysis methods, spanning commercial software packages and a growing arsenal of supervised machine learning techniques.

The review reveals a field that has expanded explosively. Most of the included studies appeared after 2010, a trend the authors attribute to the falling cost and rising availability of automated ultrasonic recorders, with a notable peak in publications in 2021 that may reflect pandemic-era restrictions pushing researchers toward data analysis rather than fieldwork. Geographically, the science is heavily skewed: the United States accounted for 36.28 percent of the articles, followed by China with 12.56 percent. That concentration matters, because reference libraries of bat calls and the classifiers trained on them are richest for Nearctic and Palearctic species, leaving tropical hotspots of bat diversity comparatively underserved.

Echolocation itself is a remarkable biological technology. Bats generate ultrasonic pulses in the larynx and emit them through the mouth or nose; the returning echoes build an acoustic image of the surroundings that the animals use to navigate, forage and communicate. Because each species tends to produce calls with characteristic frequencies, durations and shapes, researchers realized decades ago that recordings could be used to identify species without ever catching them. Bat detectors from manufacturers such as Pettersson, Titley Scientific, Wildlife Acoustics and Avisoft dominated the reviewed studies, capturing calls at high sampling frequencies for later analysis through spectrograms and oscillograms.

But turning a recording into a species name is where things get complicated. Among the 118 studies whose primary goal was species identification, only 29 relied on fully automated identification; 55 used manual identification by a specialist, and 34 used a mixed approach in which experts verified or complemented machine output. The most frequently used software packages were Kaleidoscope, Avisoft, Analook, BatSound and SonoBat, often made by the same companies that build the detectors. While all of these tools visualize calls and filter noise, only some, notably Kaleidoscope and SonoBat, were actually used for automated species classification; the rest served mainly for manual inspection and parameter measurement.

Alongside commercial software, the review documented a parallel rise of supervised learning methods implemented in platforms such as Python and RStudio. Discriminant function analysis, a statistical technique that models predefined classes from numerical acoustic features, was among the most common, followed by artificial neural networks, random forests and support vector machines. Each brings distinct trade-offs. In a benchmark study by Armitage and Ober, artificial neural networks achieved the highest training accuracy at 86.1 percent of calls correctly identified but demanded the most computational resources and training time. Random forests delivered the best overall combination of accuracy, sensitivity and specificity at 85.5 percent while remaining fast to train. Discriminant function analysis was quickest but least accurate on average at 75.9 percent. In Uruguay, Botto Nuñez and colleagues found random forests outperformed both support vector machines and neural networks, correctly identifying 92.9 percent of calls with lower variance than either alternative.

Head-to-head comparisons under controlled conditions expose the gap between machine learning and commercial packages most starkly. Tabak and colleagues built a deep learning model based on convolutional neural networks that achieved an average accuracy of 91 percent, while Kaleidoscope Pro and BCID managed only 65 percent and 61 percent respectively on the same task. Earlier discriminant function and neural network approaches also outperformed the modern software evaluated by Rydell and colleagues, whose study found that the best automated program still fell short of the top human expert, though the software did reliably classify calls at the genus level and distinguished species with highly distinctive calls. Brabant and colleagues, testing four programs on nine European species, found BatIdent achieved the highest proportion of correct species-level identifications but also produced more misclassifications, while Kaleidoscope left the largest share of calls unidentified. Sensitivity settings materially changed results: Kaleidoscope’s accuracy rose under more sensitive configurations, whereas SonoChiro stayed relatively stable across settings.

Perhaps most disquieting is what happens when there is no ground truth at all. Studies by Lemen and by Nocera that assessed agreement between software packages without a reference identification found concordance rates of only around 40 percent, varying by species and sampling period. Agreement between programs, the authors conclude, is not a reliable proxy for accuracy. Even software version matters in unpredictable ways: Goodwin and Gillam found the best performance from Kaleidoscope Pro version 4.3.0 and SonoBat version 3.2.2, meaning newer releases were not necessarily better classifiers. Accuracy reporting itself remains rare, with only 28 of the 207 studies quantifying how often their methods classified calls correctly.

The review also catalogues the tangled web of factors that influence identification beyond the algorithms themselves. Call type matters, since search, approach, feeding and social calls differ within a single species, and many species produce calls so similar that even experts struggle. Recording context matters, whether calls come from free-flying animals or hand-released individuals. Weather matters, with temperature shifting peak frequencies. So do geography, age, morphology and even the roost a population occupies, all of which inject intraspecific variation that classifiers trained on narrow libraries cannot handle. Habitat structure and detector placement also shape outcomes: models trained on calls recorded at structurally complex forest edges outperformed those trained in open spaces, and recording angle significantly affected correct classification.

The authors’ prescription is a hybrid future. Automated classifiers should be treated as accelerators that triage enormous audio volumes and flag candidate events for expert review, not as stand-alone oracles, and they should be deployed unsupervised only for taxa with highly distinctive calls. Capture-based methods such as mist-netting remain indispensable, both for species with highly directional sound beams that evade standard detectors and for building verified reference datasets; combined acoustic-and-capture designs consistently detect more species than either method alone. The researchers call for open, regionally representative call libraries with detailed metadata on habitat and recording context, versioned benchmark datasets spanning detectors and ecoregions, and transparent reporting of complete analysis pipelines. With bat populations declining worldwide amid accelerating biodiversity loss, the stakes of getting this right could hardly be higher: misidentified calls can translate directly into misinformed conservation decisions for one of the planet’s most important, and most imperiled, groups of mammals.

Subject of Research: Systematic review of bat echolocation call analysis methods for species identification and conservation monitoring

Article Title: A systematic review of bat echolocation analysis methods and their implications for monitoring and conservation

Article References: Pino-Aedo, L., & Muñoz-Sáez, A. (2026). A systematic review of bat echolocation analysis methods and their implications for monitoring and conservation. Discover Conservation, 3(1), Article 17. https://doi.org/10.1007/s44353-026-00087-x

Image Credits: AI Generated

DOI: 10.1007/s44353-026-00087-x

Keywords: bats, echolocation, bioacoustics, machine learning, acoustic monitoring, species identification, conservation, neural networks, random forests, Kaleidoscope, passive acoustic monitoring, biodiversity