Avoidance of simultaneous patch use in Japanese large-footed bats

1 1 Avoidance of simultaneous patch use in Japanese 2 large-footed bats 3 4 short title: Avoidance of simultaneous patch use in bats 5 6 Emyo Fujioka1*, Masashi Shiraishi2, Tamao Hirao3, Yui Onishi3, Dai Fukui4, Shizuko 7 Hiryu3 8 9 1 Organization for Research Initiatives and Development, Doshisha University, 10 Kyotanabe, Kyoto 610-0321, Japan 11 2 Graduate School of Information Sciences Department of Intelligent Systems, 12 Hiroshima City University, Hiroshima 731-3194, Japan 13 3 Faculty of Life and Medical Sciences, Doshisha University, Kyotanabe, Kyoto 610- 14 0321, Japan 15 4 The University of Tokyo Fuji Iyashinomori Woodland Study Center, Graduate School 16 of Agricultural and Life Sciences, The University of Tokyo, Minamitsuru-gun, 17 Yamanashi 401-0501, Japan. 18 19 * Corresponding author 20 Email: [email protected] .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint 2 22 Abstract 23 Group foraging can enhance prey detection, but depending on resource 24 availability, it may also generate conflicts among conspecifics. To understand how 25 animals balance these benefits and costs, foraging performance must be evaluated 26 together with inter-individual interactions. However, under fully natural conditions, it 27 remains challenging to quantify both simultaneously. Here, we investigated how 28 individual foraging efficiency and pairwise interactions are shaped when more than one 29 individuals simultaneously exploit the same foraging patch, using the Japanese large- 30 footed bat ( Myotis macrodactylus) as a model system. We monitored an entire pond 31 functioning as a natural foraging patch using two thermal cameras and an eight-channel 32 microphone array, and reconstructed the arrival, prey-attack, and exit times of 33 individual bats. Using a Poisson generalized linear mixed model (GLMM), we found 34 that prey-attack rates were approximately 25% lower during paired flights than during 35 solitary flights. We then constructed a null model in which arrival, attack, and departure 36 events followed independent Poisson processes parameterized from the empirical data. 37 Compared with null-model predictions, both the total duration and the duration of 38 individual paired flights in the empirical data were significantly shorter, indicating that 39 bats limited the time spent co-using the same patch relative to solitary foraging. In 40 addition, the probability that the first exiting individual was the one that arrived earlier 41 or later did not deviate from chance levels, providing no evidence for a prior residence 42 advantage. Together, these results demonstrate that simultaneous patch use avoidance 43 occurs independently of arrival order and coincides with reduced prey-attack rate, 44 suggesting that bats leave shared patches and move to alternative foraging sites to 45 mitigate losses in prey-attack efficiency. Our findings highlight bats as an excellent 46 model system for non-invasively linking individual behavior and foraging performance .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint 3 47 via echolocation, and for elucidating the dynamics of foraging behavior and sensory 48 interference in the wild. 49 50 Introduction 51 Foraging behavior is one of the most fundamental activities in animals and 52 directly affects survival and fitness [1, 2]. Many predatory species do not forage 53 exclusively alone but instead adopt strategies involving temporary or permanent group 54 formation (e.g., fish[3, 4]; ants[5, 6]; birds[7, 8]). Such strategies confer multiple 55 advantages, including an expansion of individual perceptual ranges that enhances prey- 56 detection capabilities [9, 10], improved access to temporally and spatially ephemeral 57 food patches [11, 12], and increased foraging success through information transfer 58 among group members, which facilitates efficient food searching and hunting [13, 14]. 59 At the same time, group foraging entails costs, such as intensified intraspecific 60 competition and interference arising from resource sharing [15, 16], as well as 61 reductions in per capita food intake [17, 18]. Although these trade-offs have been 62 demonstrated in numerous studies at the individual level (e.g., [10, 12]) and under 63 controlled conditions (e.g., [14, 17]), quantitatively observing the foraging behavior 64 and interactions of multiple freely moving individuals in fully natural environments, 65 and understanding how predation is actually carried out under such conditions, remain 66 extremely challenging. 67 Bats rely on echolocation for navigation and prey detection in darkness [19, 20]. 68 Echolocation calls contain rich information about the direction of attention and the 69 behavioral state of an individual. For example, feeding buzzes emitted immediately 70 prior to prey capture indicate when a bat is capturing target prey, and the interval of .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint 4 71 ultrasonic emissions allows precise identification of the timing at which prey-approach 72 behavior is initiated. Thus, in bats, echolocation calls enable high-resolution 73 quantification of foraging behavior under fully natural conditions [21, 22]. In particular, 74 the Japanese large-footed bat (Myotis macrodactylus) uses echolocation to detect 75 aquatic insects near the water surface and captures prey by trawling flight over ponds 76 and streams [23, 24]. This behavioral specialization facilitates fixed-site observations 77 of a foraging patch, making this species an excellent wild model for quantitatively 78 analyzing foraging behavior and inter-individual interactions based on acoustic 79 recordings. 80 A distinctive feature of inter-individual interactions during bat foraging is that 81 echolocation calls and their echoes are audible not only to the emitting individual but 82 also to nearby conspecifics. Listening to the calls of others can yield both benefits and 83 costs during foraging. For example, it has been reported that bats—including both 84 conspecifics and heterospecifics—eavesdrop on the echolocation calls of others to 85 obtain profitable information about foraging sites [25-28]. At the same time, calls 86 emitted by nearby individuals can interfere with a bat’s own echoes, potentially 87 degrading echolocation performance and reducing foraging efficiency. Indeed, recent 88 studies have highlighted a trade-off in group foraging, whereby foraging with a small 89 number of conspecifics can facilitate prey detection, whereas the presence of too many 90 individuals can instead reduce efficiency [29]. Considering these opposing benefits and 91 costs, understanding bat foraging strategies when multiple individuals occupy the same 92 foraging patch requires quantitative evaluation not only of individual behavior but also 93 of its relationship with local prey resources. In Japanese large-footed bats, aquatic 94 insects targeted during foraging emerge at the water surface and exit shortly thereafter, 95 such that competition is expected among individuals attempting to exploit prey .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint 5 96 immediately after emergence. Accordingly, analyses of foraging behavior in this 97 species provide an opportunity to reveal dynamic inter-individual interactions and 98 strategic adjustments within a shared foraging patch. 99 In this study, we monitored the entire pond as a natural foraging site and 100 examined the foraging behavior of Japanese large-footed bats that arrived sequentially 101 at the site, with particular attention to periods in which more than one individual was 102 co-using the site simultaneously. At the focal pond, both solitary foraging events and 103 situations in which additional bats arrived and foraged simultaneously were observed. 104 In echolocation-based foraging targeting prey at the water surface, acoustic interference 105 arising from reflections off the water surface is expected to occur—an effect that is 106 absent during foraging in featureless three-dimensional open space. Consequently, 107 when the foraging patch is spatially restricted, sharing the patch with conspecifics is 108 predicted to impose substantial echolocation-related costs on Japanese large-footed bats. 109 Based on this reasoning, we hypothesized that the presence of other bats within the 110 same foraging patch reduces foraging efficiency and that individuals adopt behavioral 111 strategies to avoid co-using the patch with conspecifics. 112 To test this hypothesis, we deployed wide–field video cameras and a 113 microphone array configured to cover the entire pond, allowing us to record the arrival 114 and departure times of successive bats as well as their prey-capture behavior within the 115 foraging patch. We further modeled bat arrival, prey-attack, and exit events at the patch 116 as Poisson processes and estimated their occurrence rates from the empirical data to 117 construct a null model that excludes inter-individual interactions. By comparing the 118 empirical data with this null model, we evaluated whether bats adjust their behavior in 119 response to the presence of conspecifics when co-using the same patch—that is, .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint 6 120 whether patch staying times deviate from expectations under a simple probabilistic 121 process. The primary objective of this study was to investigate how Japanese large- 122 footed bats adjust their behavior under natural conditions when more than one 123 individual simultaneously exploits the same foraging patch and to clarify the 124 relationship between such behavioral adjustments and foraging efficiency. To further 125 address this objective, we additionally analyzed which individual was more likely to 126 leave first during simultaneous multi-individual foraging bouts, providing 127 complementary insights into inter-individual interactions during bat foraging. 128 129 Materials and methods 130 Target species and study site 131 Target species of this study is Myotis macrodactylus. This bat species performs 132 echolocation using frequency-modulated calls [23, 30]. When attacking insect prey, 133 individuals emit a feeding buzz, during which the call frequency decreases immediately 134 prior to the attack [23, 24]. This indicates that the timing of prey-attack events can be 135 identified from the recorded echolocation sounds. We defined foraging efficiency as 136 the frequency of attack attempts on prey, that is, the encounter rate with prey. 137 The study site was a single pond (approximately 20 × 20 m) located within the 138 Tomakomai Experimental Forest of Hokkaido University (Fig. 1), which is one of 139 several ponds distributed along Horonai stream within the forest. At this pond, M. 140 macrodactylus have been observed foraging just above the water surface alone or in the 141 presence of conspecifics [24]. Field recordings were conducted on three nights (12, 16, 142 and 17 September 2024). Data collection began shortly after sunset (approximately .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint 7 143 17:30), when the first bat appeared, and continued for approximately one hour each 144 night. 145 146 Fig. 1. Schematic diagram of the study site and measurement system. Blue arrows show 147 the direction of the stream. 148 149 Although the study site is designated as a wildlife reserve by the local government, 150 no specific permits were required for this study because it was a non-invasive 151 observational study that did not involve any endangered or protected species, animal 152 capture, or habitat disturbance. Experiments at the pond were conducted with 153 permission from the forest administration. 154 155 Video and acoustic recording 156 For video recordings, two FLIR A65 thermal video cameras (field of view: 90°, 157 frame rate: 30 fps; Teledyne FLIR LLC, Oregon, USA) were used to record bat arrivals 158 at and exits from the pond as monochrome video files on a personal computer (Fig. 1). 159 The recordings from the two cameras were temporally synchronized by clapping hands 160 within the overlapping fields of view of both cameras. Using the recorded videos, the 161 arrival and exit times (hh:mm:ss) of successive bats were extracted, and inter-arrival 162 intervals and patch residence times in patch for each bat were calculated. 163 For acoustic recordings, eight ultrasonic-band MEMS microphones (custom- 164 made, based on the SPU0410LR5H; Knowles) were deployed surrounding the pond 165 (Fig. 1). This configuration enabled comprehensive recording of all vocalizations 166 produced within the pond, including feeding buzzes emitted by individual bats. .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint 8 167 Acoustic signals captured by the microphones were recorded on a personal computer 168 via a data acquisition device (USB-6356; National Instruments, Inc., USA) at a 169 sampling rate of 500 kHz. Audio and video recordings were synchronized using the 170 handclap sounds that were also used for video synchronization. 171 The timing of prey-attack events was determined from the acoustic data by 172 identifying the terminal portion of feeding buzzes in which call frequency decreased, 173 with a temporal resolution of 0.1 s. When no frequency decrease was observed, the 174 event was recorded as a non-attack [24]. In this study, situations in which only a single 175 bat was present within the foraging area were defined as the single-bat context, whereas 176 situations involving two or more bats were defined as the multiple-bat context. In the 177 multiple-bat context, the individual flying near the microphone that recorded the 178 feeding buzz was determined to be the emitter of the feeding buzz. When it is hard to 179 identify the feeding-buzz emitter using such snapshot of video and acoustic recordings 180 because inter-individual distance was too small, the emitter was identified using time 181 differences of arrival (TDOA) in the microphone array. Specifically, the relative spatial 182 positions of the bats were determined from the temporal patterns of TDOA in acoustic 183 sequences containing a feeding buzz recorded by the microphones near the bats. Then, 184 the bat that emitted feeding buzz was identified by comparing the acoustically 185 determined relative positions with the bats’ flight positions observed in the 186 synchronized video recordings. 187 Null model construction and statistical analysis 188 We constructed a stochastic behavioral simulation model in MATLAB 189 (MathWorks, USA), in which three events at the foraging site—arrival, prey attack, and 190 exit—were treated as independent Poisson processes. This model was positioned as a .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint 9 191 null model that does not incorporate inter-individual interactions, and the event rates 192 (occurrence probabilities per unit time) for all processes were derived entirely from the 193 empirical data obtained in this study. Specifically, the arrival rate was defined as the 194 number of bats arriving at the foraging site per unit time. The prey-attack rate was 195 estimated separately for the single-bat and multiple-bat contexts by modeling the 196 number of prey attacks per unit time for each bat using a Poisson generalized linear 197 mixed model (GLMM), with the contexts (i.e., single/multiple) treated as a fixed effect 198 (see Section 2.4 for details). The exit rate was defined as the inverse of the mean 199 seconds of patch residence time of bats at the foraging site. 200 Because no significant day-to-day differences were detected in inter-arrival 201 intervals or the patch residence time (all p > 0.05, Kolmogorov–Smirnov tests), these 202 rates were estimated using pooled data from all three days. In contrast, inter-attack 203 intervals differed significantly among days (p < 0.001, Kolmogorov–Smirnov test), and 204 therefore day-specific attack rates were used in the model. 205 Differences in prey-attack rate between the single-bat and multiple-bat contexts 206 were analyzed using a 　 Poisson GLMM, in which the number of prey attacks per 207 individual was treated as the response variable and patch residence time of individuals 208 was included as an offset term. Context (single-bat vs. multiple-bat) was specified as a 209 fixed effect, and the significance of its effect was evaluated using a Wald test. For 210 individuals that engaged in multiple-bat context, attack events were assigned separately 211 to the single-bat and multiple-bat contexts when the same individual flew alone before 212 or after multiple-bat context. Individual identity and experimental date were included 213 as random effects in the model. 214 Simulations of the mathematical null model were conducted with each trial .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint 10 215 covering the same duration as the empirical measurements (i.e., 1 h). A total of 10,000 216 trials were performed to generate the distributions of flight patch residence time under 217 the single-bat and multiple-bat contexts, which were then tested for differences from 218 the empirical data. Using the outputs of the mathematical model as the null distribution, 219 deviations of the empirical bat data from model expectations were evaluated using a 220 parametric bootstrap test. 221 To examine whether individuals tended to occupy the foraging patch in the 222 multiple-bat context, we analyzed which bat exited first during paired flights: the 223 individual that arrived earlier or the one that arrived later. When multiple bats arrived 224 at or exited from the foraging patch nearly simultaneously (with a time difference of 225 less than 1 s), the order of events could not be reliably determined; such cases were 226 therefore excluded from this analysis. To restrict the analysis to simple and 227 interpretable situations, only two-bat conditions (i.e., paired flights) were considered. 228 Individuals that experienced situations involving three or more bats were excluded from 229 this analysis (observed only in the case of numerical simulation, see Results). 230 231 Results 232 Over the three observation nights (12, 16, and 17 September), the first 233 individual appeared at approximately 18:30, and a total of 44, 75, and 61 individuals 234 were recorded within the subsequent 1 h, respectively (Fig. 2A, S1 Dataset). Bats 235 arrived at the foraging patch successively, repeatedly attacked prey, and then exited. 236 The inter-arrival interval, the patch residence time, and inter-attack interval averaged 237 57.6 ± 46.0 s, 17.3 ± 17.9 s, and 2.52 ± 2.14 s (mean ± SD), respectively, across the .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint 11 238 three days (Fig. 2B). A total of 31 flight events of multiple-bat context were observed 239 over the three days, and no cases involving three or more individuals were recorded. 240 241 Fig. 2. (A) Time series data of the bats foraging at the pond during 1-h measurement. 242 The length of the line indicates the time spent at the pond. Red lines indicate the bats 243 exiting the pond alone, whereas the blue and yellow lines show the bats exiting the 244 pond in group; the latter bats entered before (blue) and after (yellow) the entrance of 245 the former bats. The vertical lines across the horizontal lines indicate the timing of 246 attacking prey. (B) Proportional distribution of arrival intervals (left), patch residence 247 time (middle) and attack intervals (right) of the bats during the measurement periods 248 for each day. 249 250 The Poisson GLMM of prey-attack counts indicated that bats attacked prey at a 251 mean rate of 0.224 attacks s⁻¹ in the single-bat context (estimate = −1.498 ± 0.113 SE, 252 z = −13.21, p < 0.001), and that this rate was significantly reduced in the multiple-bat 253 context (estimate = −0.285 ± 0.131 SE, z = −2.17, p < 0.05; Fig. 3). Consequently, the 254 prey-attack rate in the multiple-bat context was reduced by 25% relative to the single- 255 bat context (exp[−0.285] = 0.75; S2 Table). 256 257 Fig. 3. Prey attack rates during single and multiple flights estimated by a GLMM; The 258 vertical lines show the standard errors. The slope of the fixed effect for multiple vs. 259 single was -0.285 (p < 0.05 (*), Wald test). Number of data was 217 (single: 160, group: 260 57). .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint 12 261 262 Using the parameters estimated from the empirical data, we conducted foraging 263 simulations (Fig. 4A). The simulated outputs—inter-arrival intervals, patch residence 264 time, and prey-attack rates per simulation run (i.e., 1 h)—closely matched the 265 corresponding empirical values used as model inputs (S3 Table). In the multiple-bat 266 context, such events occurred 13.1 ± 4.0 times per hour (mean ± SD, N = 10,000 267 simulations), with a total duration of 130.5 ± 52.6 s per hour. In contrast, the total 268 duration of the multiple-bat context in the empirical data was 49 s (Day 1, N = 6), 79 s 269 (Day 2, N = 13), and 61 s (Day 3, N = 12), which was significantly shorter than 270 predicted by the simulations (Z ≈ −2.35, p < 0.001, one-sample Z test; Fig. 4B). 271 Furthermore, the duration of individual multiple-bat contexts averaged 9.9 ± 10.0 s in 272 the simulations (N = 131,374 events from 10,000 simulations), whereas the 273 corresponding values in the empirical data were 5.5 ± 3.7 s (Day 1, N = 6), 6.2 ± 6.9 s 274 (Day 2, N = 13), and 5.1 ± 3.0 s (Day 3, N = 12; mean ± SD). Parametric bootstrap tests 275 revealed that the durations of individual multiple-bat contexts in the empirical data were 276 significantly shorter than those predicted by the model for all days (p < 0.001; Fig. 4C). 277 278 Fig. 4. (A) Time series data of the 1-h foraging simulation of the null model (drawing 279 rule is same as Fig. 2A). (B) Total time spent in the foraging patch of model and 280 measured (three different days) bats while in multiple contexts. Vertical lines on the 281 orange bar indicate the standard deviation based on 10,000 numerical simulations. One- 282 sample Z test was conducted; *** p < 0.001. (C) Boxplots (0.025, 0.25, 0.5, 0.75, and 283 0.975 quantiles) showing the consecutive durations of each multiple flight obtained 284 from the numerical simulations (orange) and from three days of field observations of .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint 13 285 bats (blue). Parametric bootstrap tests were conducted; *** p < 0.001. 286 287 In paired flights, we examined whether the individual that exited first was the 288 one that arrived earlier (Bat A) or the one that arrived later (Bat B). In the simulations, 289 once a paired flight began, both bats had identical departure probabilities, resulting in 290 an equal likelihood of Bat A or Bat B exiting first (Fig. 5, left). Analysis of the empirical 291 data revealed an exactly even split, with the first exit occurring equally often for Bat A 292 and Bat B (Fig. 5, right; N = 20; Bat A = 10 cases, Bat B = 10 cases). 293 294 Fig. 5. The bat that exited the pond first when in paired flights for the case of model 295 simulation (red) and the experimental data (blue). Bat A is the bat that was staying 296 before the paired flight together with the latter bat (Bat B). The numerical simulation 297 was exhibited 10,000 times. 298 299 Discussion 300 In this study, we used a microphone array and video cameras to quantify the 301 sequential arrival, foraging, and exiting of bats within a foraging patch. Our results 302 demonstrate that when two individuals were present simultaneously within the patch, 303 prey-attack rate was reduced relative to single-bat flight context (Fig. 3). Because the 304 pond we measured was sufficiently small that flight areas overlapped during paired 305 flights, this reduction is likely attributable to the presence of conspecifics acting as 306 moving obstacles and/or to acoustic interference between the ultrasonic signals emitted 307 for echolocation, which may have decreased prey-detection efficiency. Furthermore, .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint 14 308 comparison with the null model we constructed revealed that the duration of paired 309 flights was significantly shorter in the empirical data than predicted by the model (Fig. 310 4). This finding indicates that bats actively exited from the foraging patch to avoid 311 simultaneous foraging with conspecifics. The study site was located within a university 312 experimental forest, where multiple ponds and stream pools are distributed in the 313 surrounding area. It is therefore plausible that bats avoided simultaneous foraging with 314 conspecifics at a single patch and instead moved to alternative nearby foraging patches. 315 When two individuals are co-using a foraging patch, an important question is 316 which individual leaves first in order to avoid simultaneous foraging. Previous studies 317 have frequently reported behaviors in which individuals expel others from feeding sites 318 (food patch defense), with the resident typically displacing the intruder—a pattern 319 known as the prior residence effect [31, 32]. In the present study, however, the 320 probability that the first individual to exit was the resident or the intruder did not differ 321 from chance levels (Fig. 5), indicating that avoidance of simultaneous patch use does 322 not depend on the order of arrival at the foraging patch. Accordingly, a prior residence 323 effect does not appear to operate in the foraging behavior of Japanese large-footed bats. 324 More generally, avoidance of simultaneous foraging with conspecifics at a feeding 325 patch may be influenced not only by prior residence effects but also by kin selection 326 [33] and dominance hierarchies [34]. High relatedness can promote tolerance or food 327 sharing among individuals, whereas dominance relationships may lead to displacement 328 or exclusion from feeding sites [35, 36]. To gain a deeper understanding of the 329 mechanisms underlying collective foraging in bats, future studies should jointly 330 examine foraging behavior, social hierarchy, and relatedness within colonies. .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint 15 331 Japanese large-footed bats have been reported to emit various types of social 332 calls at foraging sites [37], some of which are suggested to function in repelling 333 conspecifics [38]. Thus, social calls could potentially influence the patch residence time 334 and prey-attack rate during paired flights in the present study. However, social calls 335 were observed infrequently, occurring in only 5 of the 31 paired flights (16.1%). When 336 we reanalyzed the data using a Poisson GLMM after excluding flights in which social 337 calls were observed (including those individuals during solitary flights), bats attacked 338 prey at a mean rate of 0.225 attacks/s during solitary flights (estimate = −1.491 ± 0.112 339 SE, z = −13.36, p < 0.001), and this rate was significantly reduced during paired flights 340 (estimate = −0.332 ± 0.147 SE, z = −2.26, p < 0.05). Consequently, prey-attack rate 341 during paired flights was reduced by 28% relative to solitary flights (exp[−0.332] = 342 0.72; S2 Table). These results indicate that, regardless of the presence or absence of 343 social calls, prey-attack rate was significantly lower during paired flights than during 344 solitary flights, and the magnitude of this reduction was largely unchanged. This 345 suggests that social calls likely had little effect on foraging efficiency in the present 346 study. 347 Foraging efficiency during multiple-bat flights with conspecifics is likely to 348 depend on the size of the foraging patch. If bats can fly within a patch while 349 experiencing minimal interference from other individuals—such as overlap in flight 350 paths or sensory interference—then the presence of conspecifics may not necessarily 351 reduce prey-attack rate. Even in large patches, however, increasing the number of 352 individuals can intensify competition, prompting predators to move to alternative 353 foraging patches; as a result, the benefits gained at each patch tend to become 354 approximately equal among individuals. Such distributions of predators across feeding 355 sites are described by the ideal free distribution [39]. In bat research, it has been .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint 16 356 reported that increases in bat density at a given foraging patch lead to competition and 357 subsequent shifts to alternative patches [40]. In the present study, because multiple 358 ponds are distributed along the river within the experimental forest where our study 359 pond is located, Japanese large-footed bats may similarly compare prey availability 360 between the focal pond and other nearby foraging patches and avoid paired flights in 361 order to maintain foraging efficiency. 362 In conclusion, our study demonstrates that prey-attack rate of M. macrodactylus 363 declines during paired flights within a foraging patch and that bats actively avoid 364 simultaneous foraging with conspecifics within the patch. By quantitatively 365 characterizing foraging behavior that is otherwise difficult to capture under natural 366 conditions, this study highlights the Japanese large-footed bat as an excellent model 367 system for elucidating the dynamics of collective foraging and the mechanisms of 368 sensory interference in the wild. These findings provide a foundation for future studies 369 incorporating multiple foraging patches and temporal variation in prey availability. 370 Acknowledgments 371 We gratefully acknowledge Fumiya Hamai, Yuna Yabuta, Yuuka Mizuguchi, 372 Yasumasa Kitamura, Kana Matsuoka, Shoko Fujitani, Kiho Fujii, Akito Nomi, Yuto 373 Wakasa, Ryota Yao, Mikuto Hori, Tsubasa Sakamoto, for their support in fieldwork 374 and its analysis. Also, we thank Yuichi Mizutani for his help in improving our 375 manuscript. 376 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint 17 377 References 378 1. MacArthur RH, Pianka ER. On Optimal Use of a Patchy Environment. The 379 American Naturalist. 1966;100(916):603-9. doi: 10.1086/282454. 380 2. Charnov EL. Optimal foraging, the marginal value theorem. 1976. 381 3. Couzin ID. Collective cognition in animal groups. Trends in Cognitive 382 Sciences. 2009;13(1):36-43. doi: 10.1016/j.tics.2008.10.002. 383 4. Katz Y, Tunstrøm K, Ioannou CC, Huepe C, Couzin ID. Inferring the structure 384 and dynamics of interactions in schooling fish. Proceedings of the National Academy 385 of Sciences. 2011;108(46):18720-5. doi: doi:10.1073/pnas.1107583108. 386 5. Czaczkes TJ, Grüter C, Ratnieks FL. Trail pheromones: an integrative view of 387 their role in social insect colony organization. Annu Rev Entomol. 2015;60:581-99. 388 Epub 20141024. doi: 10.1146/annurev-ento-010814-020627. PubMed PMID: 389 25386724. 390 6. Doering GN, Prebus MM, Suresh S, Greer JN, Bowden R, Linksvayer TA. 391 Emergent collective behavior evolves more rapidly than individual behavior among 392 acorn ant species. Proceedings of the National Academy of Sciences. 393 2024;121(48):e2420078121. doi: doi:10.1073/pnas.2420078121. 394 7. Cavagna A, Cimarelli A, Giardina I, Parisi G, Santagati R, Stefanini F, et al. 395 Scale-free correlations in starling flocks. Proceedings of the National Academy of 396 Sciences. 2010;107(26):11865-70. doi: doi:10.1073/pnas.1005766107. 397 8. Bialek W, Cavagna A, Giardina I, Mora T, Silvestri E, Viale M, et al. Statistical 398 mechanics for natural flocks of birds. Proceedings of the National Academy of 399 Sciences. 2012;109(13):4786-91. doi: doi:10.1073/pnas.1118633109. 400 9. Kenward R. Hawks and doves: factors affecting success and selection in 401 goshawk attacks on woodpigeons. The Journal of Animal Ecology. 1978:449-60. .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint 18 402 10. Sutton GJ, Hoskins AJ, Arnould JPY. Benefits of Group Foraging Depend on 403 Prey Type in a Small Marine Predator, the Little Penguin. PLOS ONE. 404 2015;10(12):e0144297. doi: 10.1371/journal.pone.0144297. 405 11. Clark CW, Mangel M. Foraging and flocking strategies: information in an 406 uncertain environment. The American Naturalist. 1984;123(5):626-41. 407 12. Egert-Berg K, Hurme ER, Greif S, Goldstein A, Harten L, Herrera Montalvo 408 LG, et al. Resource Ephemerality Drives Social Foraging in Bats. Current Biology. 409 2018;28(22):3667-73. doi: 10.1016/j.cub.2018.09.064. 410 13. Ward P, Zahavi A. The importance of certain assemblages of birds as 411 “information‐centres” for food‐finding. Ibis. 1973;115(4):517-34. 412 14. Bijleveld AI, van Gils JA, Jouta J, Piersma T. Benefits of foraging in small 413 groups: An experimental study on public information use in red knots Calidris canutus. 414 Behavioural Processes. 2015;117:74-81. doi: 415 https://doi.org/10.1016/j.beproc.2014.09.003. 416 15. Corcoran AJ, Conner WE. Bats jamming bats: Food competition through sonar 417 interference. Science. 2014;346(6210):745-7. doi: doi:10.1126/science.1259512. 418 16. Beauchamp G. Social predation: how group living benefits predators and prey: 419 Elsevier; 2013. 420 17. Sansom A, Cresswell W, Minderman J, Lind J. Vigilance benefits and 421 competition costs in groups: do individual redshanks gain an overall foraging benefit? 422 Animal Behaviour. 2008;75(6):1869-75. doi: 423 https://doi.org/10.1016/j.anbehav.2007.11.005. 424 18. Bijleveld AI, Folmer EO, Piersma T. Experimental evidence for cryptic 425 interference among socially foraging shorebirds. Behavioral Ecology. 2012;23(4):806- 426 14. doi: 10.1093/beheco/ars034. .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint 19 427 19. Griffin DR. Listening in the dark: the acoustic orientation of bats and men. New 428 Haven: Yale University Press; 1958. 429 20. Simmons JA, Fenton MB, O'Farrell MJ. Echolocation and pursuit of prey by 430 bats. Science. 1979;203(4375):16-21. PubMed PMID: 758674. 431 21. Surlykke A, Moss CF. Echolocation behavior of big brown bats, Eptesicus 432 fuscus, in the field and the laboratory. J Acoust Soc Am. 2000;108(5 Pt. 1):2419-29. 433 PubMed PMID: 11108382. 434 22. Fujioka E, Aihara I, Sumiya M, Aihara K, Hiryu S. Echolocating bats use 435 future-target information for optimal foraging. Proc Natl Acad Sci USA. 436 2016;113(17):4848-52. doi: 10.1073/pnas.1515091113. 437 23. Luo J-h, Ou W, Liu Y, Wang J, Wang L, Feng J. Plasticity in echolocation calls 438 of Myotis macrodactylus (Chiroptera: Vespertilionidae): implications for acoustic 439 identification. Acta Theriologica. 2012;57(2):137-43. 440 24. Mizuguchi Y, Fujioka E, Heim O, Fukui D, Hiryu S. Discriminating predation 441 attempt outcomes during natural foraging using the post-buzz pause in the Japanese 442 large-footed bat, Myotis macrodactylus. Journal of Experimental Biology. 443 2022;225(7). doi: 10.1242/jeb.243402. 444 25. Balcombe JP, Fenton MB. Eavesdropping by bats: the influence of echolocation 445 call design and foraging strategy. Ethology. 1988;79(2):158-66. 446 26. Gillam E. Eavesdropping by bats on the feeding buzzes of conspecifics. 447 Canadian Journal of Zoology. 2007;85:795-801. doi: 10.1139/Z07-060. 448 27. Dechmann DKN, Heucke SL, Giuggioli L, Safi K, Voigt CC, Wikelski M. 449 Experimental evidence for group hunting via eavesdropping in echolocating bats. 450 Proceedings of the Royal Society B: Biological Sciences. 2009;276(1668):2721-8. doi: 451 10.1098/rspb.2009.0473. .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint 20 452 28. Roeleke M, Schlägel UE, Gallagher C, Pufelski J, Blohm T, Nathan R, et al. 453 Insectivorous bats form mobile sensory networks to optimize prey localization: The 454 case of the common noctule bat. Proceedings of the National Academy of Sciences. 455 2022;119(33):e2203663119. doi: doi:10.1073/pnas.2203663119. 456 29. Krivoruchko K, Koblitz JC, Goldshtein A, Biljman K, Guillén-Servent A, 457 Yovel Y. A social foraging trade-off in echolocating bats reveals that they benefit from 458 some conspecifics but are impaired when many are around. Proceedings of the National 459 Academy of Sciences. 2024;121(30):e2321724121. doi: 460 doi:10.1073/pnas.2321724121. 461 30. Fukui D, Agetsuma N, Hill DA. Acoustic Identification of Eight Species of Bat 462 (Mammalia: Chiroptera) Inhabiting Forests of Southern Hokkaido, Japan: Potential for 463 Conservation Monitoring. Zoological Science. 2004;21(9):947-55. doi: 464 10.2108/zsj.21.947. 465 31. Eberhard JR, Ewald PW. Food availability, intrusion pressure and territory size: 466 an experimental study of Anna's hummingbirds (Calypte anna). Behavioral Ecology 467 and Sociobiology. 1994;34(1):11-8. doi: 10.1007/BF00175453. 468 32. Götze S, Denzinger A, Schnitzler H-U. High frequency social calls indicate 469 food source defense in foraging Common pipistrelle bats. Scientific Reports. 470 2020;10(1). doi: 10.1038/s41598-020-62743-z. 471 33. Hamilton WD. The genetical evolution of social behaviour. I. Journal of 472 Theoretical Biology. 1964;7(1):1-16. doi: https://doi.org/10.1016/0022- 473 5193(64)90038-4. 474 34. Tibbetts EA, Pardo-Sanchez J, Weise C. The establishment and maintenance of 475 dominance hierarchies. Philosophical Transactions of the Royal Society B: Biological 476 Sciences. 2022;377(1845). doi: 10.1098/rstb.2020.0450. .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint 21 477 35. Wilkinson GS. Reciprocal food sharing in the vampire bat. Nature. 478 1984;308(5955):181-4. 479 36. Oro D. LIVING IN A GHETTO WITHIN A LOCAL POPULATION: AN 480 EMPIRICAL EXAMPLE OF AN IDEAL DESPOTIC DISTRIBUTION. Ecology. 481 2008;89(3):838-46. doi: 10.1890/06-1936.1. 482 37. Guo D, Luo B, Zhang K, Liu M, Metzner W, Liu Y, et al. Social vocalizations 483 of big‐footed myotis ( Myotis macrodactylus ) during foraging. Integrative Zoology. 484 2019;14(5):446-59. doi: 10.1111/1749-4877.12367. 485 38. Guo D, Ding J, Liu H, Zhou L, Feng J, Luo B, et al. Social calls influence the 486 foraging behavior in wild big-footed myotis. Frontiers in Zoology. 2021;18(1). doi: 487 10.1186/s12983-020-00384-8. 488 39. Fretwell SD. Populations in a seasonal environment: Princeton University 489 Press; 1972. 490 40. Encarnacao JA, Becker NI, Ekschmitt K. When do Daubenton’s bats (Myotis 491 daubentonii) fly far for dinner? Canadian Journal of Zoology. 2010;88(12):1192-201. 492 493 Supporting Information 494 S1 Dataset. Times of pond entry, pond exit, and prey attacks by bats. 495 S2 Table. Prey-attack rates calculated by the GLMM for each day. 496 S3 Table. The three parameters calculated using experiment and simulation data. .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint