{"paper_id":"242dc83c-fd29-48d0-a50f-70906096ffe5","body_text":"1\n1 Avoidance of simultaneous patch use in Japanese \n2 large-footed bats\n3\n4 short title: Avoidance of simultaneous patch use in bats\n5\n6 Emyo Fujioka1*, Masashi Shiraishi2, Tamao Hirao3, Yui Onishi3, Dai Fukui4, Shizuko \n7 Hiryu3\n8\n9 1 Organization for Research Initiatives and Development, Doshisha University, \n10 Kyotanabe, Kyoto 610-0321, Japan\n11 2 Graduate School of Information Sciences Department of Intelligent Systems, \n12 Hiroshima City University, Hiroshima 731-3194, Japan\n13 3 Faculty of Life and Medical Sciences, Doshisha University, Kyotanabe, Kyoto 610-\n14 0321, Japan\n15 4 The University of Tokyo Fuji Iyashinomori Woodland Study Center, Graduate School \n16 of Agricultural and Life Sciences, The University of Tokyo, Minamitsuru-gun, \n17 Yamanashi 401-0501, Japan.\n18\n19 * Corresponding author\n20 Email: efujioka@mail.doshisha.ac.jp\n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint \n\n2\n22 Abstract \n23 Group foraging can enhance prey detection, but depending on resource \n24 availability, it may also generate conflicts among conspecifics. To understand how \n25 animals balance these benefits and costs, foraging performance must be evaluated \n26 together with inter-individual interactions. However, under fully natural conditions, it \n27 remains challenging to quantify both simultaneously. Here, we investigated how \n28 individual foraging efficiency and pairwise interactions are shaped when more than one \n29 individuals simultaneously exploit the same foraging patch, using the Japanese large-\n30 footed bat ( Myotis macrodactylus) as a model system. We monitored an entire pond \n31 functioning as a natural foraging patch using two thermal cameras and an eight-channel \n32 microphone array, and reconstructed the arrival, prey-attack, and exit times of \n33 individual bats. Using a Poisson generalized linear mixed model (GLMM), we found \n34 that prey-attack rates were approximately 25% lower during paired flights than during \n35 solitary flights. We then constructed a null model in which arrival, attack, and departure \n36 events followed independent Poisson processes parameterized from the empirical data. \n37 Compared with null-model predictions, both the total duration and the duration of \n38 individual paired flights in the empirical data were significantly shorter, indicating that \n39 bats limited the time spent co-using the same patch relative to solitary foraging. In \n40 addition, the probability that the first exiting individual was the one that arrived earlier \n41 or later did not deviate from chance levels, providing no evidence for a prior residence \n42 advantage. Together, these results demonstrate that simultaneous patch use avoidance \n43 occurs independently of arrival order and coincides with reduced prey-attack rate, \n44 suggesting that bats leave shared patches and move to alternative foraging sites to \n45 mitigate losses in prey-attack efficiency. Our findings highlight bats as an excellent \n46 model system for non-invasively linking individual behavior and foraging performance \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint \n\n3\n47 via echolocation, and for elucidating the dynamics of foraging behavior and sensory \n48 interference in the wild.\n49\n50 Introduction\n51 Foraging behavior is one of the most fundamental activities in animals and \n52 directly affects survival and fitness [1, 2]. Many predatory species do not forage \n53 exclusively alone but instead adopt strategies involving temporary or permanent group \n54 formation (e.g., fish[3, 4]; ants[5, 6]; birds[7, 8]). Such strategies confer multiple \n55 advantages, including an expansion of individual perceptual ranges that enhances prey-\n56 detection capabilities [9, 10], improved access to temporally and spatially ephemeral \n57 food patches [11, 12], and increased foraging success through information transfer \n58 among group members, which facilitates efficient food searching and hunting [13, 14]. \n59 At the same time, group foraging entails costs, such as intensified intraspecific \n60 competition and interference arising from resource sharing [15, 16], as well as \n61 reductions in per capita food intake [17, 18]. Although these trade-offs have been \n62 demonstrated in numerous studies at the individual level (e.g., [10, 12]) and under \n63 controlled conditions (e.g., [14, 17]), quantitatively observing the foraging behavior \n64 and interactions of multiple freely moving individuals in fully natural environments, \n65 and understanding how predation is actually carried out under such conditions, remain \n66 extremely challenging.\n67 Bats rely on echolocation for navigation and prey detection in darkness [19, 20]. \n68 Echolocation calls contain rich information about the direction of attention and the \n69 behavioral state of an individual. For example, feeding buzzes emitted immediately \n70 prior to prey capture indicate when a bat is capturing target prey, and the interval of \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint \n\n4\n71 ultrasonic emissions allows precise identification of the timing at which prey-approach \n72 behavior is initiated. Thus, in bats, echolocation calls enable high-resolution \n73 quantification of foraging behavior under fully natural conditions [21, 22]. In particular, \n74 the Japanese large-footed bat (Myotis macrodactylus) uses echolocation to detect \n75 aquatic insects near the water surface and captures prey by trawling flight over ponds \n76 and streams [23, 24]. This behavioral specialization facilitates fixed-site observations \n77 of a foraging patch, making this species an excellent wild model for quantitatively \n78 analyzing foraging behavior and inter-individual interactions based on acoustic \n79 recordings.\n80 A distinctive feature of inter-individual interactions during bat foraging is that \n81 echolocation calls and their echoes are audible not only to the emitting individual but \n82 also to nearby conspecifics. Listening to the calls of others can yield both benefits and \n83 costs during foraging. For example, it has been reported that bats—including both \n84 conspecifics and heterospecifics—eavesdrop on the echolocation calls of others to \n85 obtain profitable information about foraging sites [25-28]. At the same time, calls \n86 emitted by nearby individuals can interfere with a bat’s own echoes, potentially \n87 degrading echolocation performance and reducing foraging efficiency. Indeed, recent \n88 studies have highlighted a trade-off in group foraging, whereby foraging with a small \n89 number of conspecifics can facilitate prey detection, whereas the presence of too many \n90 individuals can instead reduce efficiency [29]. Considering these opposing benefits and \n91 costs, understanding bat foraging strategies when multiple individuals occupy the same \n92 foraging patch requires quantitative evaluation not only of individual behavior but also \n93 of its relationship with local prey resources. In Japanese large-footed bats, aquatic \n94 insects targeted during foraging emerge at the water surface and exit shortly thereafter, \n95 such that competition is expected among individuals attempting to exploit prey \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint \n\n5\n96 immediately after emergence. Accordingly, analyses of foraging behavior in this \n97 species provide an opportunity to reveal dynamic inter-individual interactions and \n98 strategic adjustments within a shared foraging patch.\n99 In this study, we monitored the entire pond as a natural foraging site and \n100 examined the foraging behavior of Japanese large-footed bats that arrived sequentially \n101 at the site, with particular attention to periods in which more than one individual was \n102 co-using the site simultaneously. At the focal pond, both solitary foraging events and \n103 situations in which additional bats arrived and foraged simultaneously were observed. \n104 In echolocation-based foraging targeting prey at the water surface, acoustic interference \n105 arising from reflections off the water surface is expected to occur—an effect that is \n106 absent during foraging in featureless three-dimensional open space. Consequently, \n107 when the foraging patch is spatially restricted, sharing the patch with conspecifics is \n108 predicted to impose substantial echolocation-related costs on Japanese large-footed bats. \n109 Based on this reasoning, we hypothesized that the presence of other bats within the \n110 same foraging patch reduces foraging efficiency and that individuals adopt behavioral \n111 strategies to avoid co-using the patch with conspecifics.\n112 To test this hypothesis, we deployed wide–field video cameras and a \n113 microphone array configured to cover the entire pond, allowing us to record the arrival \n114 and departure times of successive bats as well as their prey-capture behavior within the \n115 foraging patch. We further modeled bat arrival, prey-attack, and exit events at the patch \n116 as Poisson processes and estimated their occurrence rates from the empirical data to \n117 construct a null model that excludes inter-individual interactions. By comparing the \n118 empirical data with this null model, we evaluated whether bats adjust their behavior in \n119 response to the presence of conspecifics when co-using the same patch—that is, \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint \n\n6\n120 whether patch staying times deviate from expectations under a simple probabilistic \n121 process. The primary objective of this study was to investigate how Japanese large-\n122 footed bats adjust their behavior under natural conditions when more than one \n123 individual simultaneously exploits the same foraging patch and to clarify the \n124 relationship between such behavioral adjustments and foraging efficiency. To further \n125 address this objective, we additionally analyzed which individual was more likely to \n126 leave first during simultaneous multi-individual foraging bouts, providing \n127 complementary insights into inter-individual interactions during bat foraging.\n128\n129 Materials and methods\n130 Target species and study site\n131 Target species of this study is Myotis macrodactylus. This bat species performs \n132 echolocation using frequency-modulated calls [23, 30]. When attacking insect prey, \n133 individuals emit a feeding buzz, during which the call frequency decreases immediately \n134 prior to the attack [23, 24]. This indicates that the timing of prey-attack events can be \n135 identified from the recorded echolocation sounds. We defined foraging efficiency as \n136 the frequency of attack attempts on prey, that is, the encounter rate with prey.\n137 The study site was a single pond (approximately 20 × 20 m) located within the \n138 Tomakomai Experimental Forest of Hokkaido University (Fig. 1), which is one of \n139 several ponds distributed along Horonai stream within the forest. At this pond, M. \n140 macrodactylus have been observed foraging just above the water surface alone or in the \n141 presence of conspecifics [24]. Field recordings were conducted on three nights (12, 16, \n142 and 17 September 2024). Data collection began shortly after sunset (approximately \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint \n\n7\n143 17:30), when the first bat appeared, and continued for approximately one hour each \n144 night. \n145\n146 Fig. 1. Schematic diagram of the study site and measurement system. Blue arrows show \n147 the direction of the stream.\n148\n149 Although the study site is designated as a wildlife reserve by the local government, \n150 no specific permits were required for this study because it was a non-invasive \n151 observational study that did not involve any endangered or protected species, animal \n152 capture, or habitat disturbance. Experiments at the pond were conducted with \n153 permission from the forest administration.\n154\n155 Video and acoustic recording\n156 For video recordings, two FLIR A65 thermal video cameras (field of view: 90°, \n157 frame rate: 30 fps; Teledyne FLIR LLC, Oregon, USA) were used to record bat arrivals \n158 at and exits from the pond as monochrome video files on a personal computer (Fig. 1). \n159 The recordings from the two cameras were temporally synchronized by clapping hands \n160 within the overlapping fields of view of both cameras. Using the recorded videos, the \n161 arrival and exit times (hh:mm:ss) of successive bats were extracted, and inter-arrival \n162 intervals and patch residence times in patch for each bat were calculated.\n163 For acoustic recordings, eight ultrasonic-band MEMS microphones (custom-\n164 made, based on the SPU0410LR5H; Knowles) were deployed surrounding the pond \n165 (Fig. 1). This configuration enabled comprehensive recording of all vocalizations \n166 produced within the pond, including feeding buzzes emitted by individual bats. \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint \n\n8\n167 Acoustic signals captured by the microphones were recorded on a personal computer \n168 via a data acquisition device (USB-6356; National Instruments, Inc., USA) at a \n169 sampling rate of 500 kHz. Audio and video recordings were synchronized using the \n170 handclap sounds that were also used for video synchronization.\n171 The timing of prey-attack events was determined from the acoustic data by \n172 identifying the terminal portion of feeding buzzes in which call frequency decreased, \n173 with a temporal resolution of 0.1 s. When no frequency decrease was observed, the \n174 event was recorded as a non-attack [24]. In this study, situations in which only a single \n175 bat was present within the foraging area were defined as the single-bat context, whereas \n176 situations involving two or more bats were defined as the multiple-bat context. In the \n177 multiple-bat context, the individual flying near the microphone that recorded the \n178 feeding buzz was determined to be the emitter of the feeding buzz. When it is hard to \n179 identify the feeding-buzz emitter using such snapshot of video and acoustic recordings \n180 because inter-individual distance was too small, the emitter was identified using time \n181 differences of arrival (TDOA) in the microphone array. Specifically, the relative spatial \n182 positions of the bats were determined from the temporal patterns of TDOA in acoustic \n183 sequences containing a feeding buzz recorded by the microphones near the bats. Then, \n184 the bat that emitted feeding buzz was identified by comparing the acoustically \n185 determined relative positions with the bats’ flight positions observed in the \n186 synchronized video recordings. \n187 Null model construction and statistical analysis\n188 We constructed a stochastic behavioral simulation model in MATLAB \n189 (MathWorks, USA), in which three events at the foraging site—arrival, prey attack, and \n190 exit—were treated as independent Poisson processes. This model was positioned as a \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint \n\n9\n191 null model that does not incorporate inter-individual interactions, and the event rates \n192 (occurrence probabilities per unit time) for all processes were derived entirely from the \n193 empirical data obtained in this study. Specifically, the arrival rate was defined as the \n194 number of bats arriving at the foraging site per unit time. The prey-attack rate was \n195 estimated separately for the single-bat and multiple-bat contexts by modeling the \n196 number of prey attacks per unit time for each bat using a Poisson generalized linear \n197 mixed model (GLMM), with the contexts (i.e., single/multiple) treated as a fixed effect \n198 (see Section 2.4 for details). The exit rate was defined as the inverse of the mean \n199 seconds of patch residence time of bats at the foraging site. \n200 Because no significant day-to-day differences were detected in inter-arrival \n201 intervals or the patch residence time (all p > 0.05, Kolmogorov–Smirnov tests), these \n202 rates were estimated using pooled data from all three days. In contrast, inter-attack \n203 intervals differed significantly among days (p < 0.001, Kolmogorov–Smirnov test), and \n204 therefore day-specific attack rates were used in the model.\n205 Differences in prey-attack rate between the single-bat and multiple-bat contexts \n206 were analyzed using a 　 Poisson GLMM, in which the number of prey attacks per \n207 individual was treated as the response variable and patch residence time of individuals \n208 was included as an offset term. Context (single-bat vs. multiple-bat) was specified as a \n209 fixed effect, and the significance of its effect was evaluated using a Wald test. For \n210 individuals that engaged in multiple-bat context, attack events were assigned separately \n211 to the single-bat and multiple-bat contexts when the same individual flew alone before \n212 or after multiple-bat context. Individual identity and experimental date were included \n213 as random effects in the model.\n214 Simulations of the mathematical null model were conducted with each trial \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint \n\n10\n215 covering the same duration as the empirical measurements (i.e., 1 h). A total of 10,000 \n216 trials were performed to generate the distributions of flight patch residence time under \n217 the single-bat and multiple-bat contexts, which were then tested for differences from \n218 the empirical data. Using the outputs of the mathematical model as the null distribution, \n219 deviations of the empirical bat data from model expectations were evaluated using a \n220 parametric bootstrap test.\n221 To examine whether individuals tended to occupy the foraging patch in the \n222 multiple-bat context, we analyzed which bat exited first during paired flights: the \n223 individual that arrived earlier or the one that arrived later. When multiple bats arrived \n224 at or exited from the foraging patch nearly simultaneously (with a time difference of \n225 less than 1 s), the order of events could not be reliably determined; such cases were \n226 therefore excluded from this analysis. To restrict the analysis to simple and \n227 interpretable situations, only two-bat conditions (i.e., paired flights) were considered. \n228 Individuals that experienced situations involving three or more bats were excluded from \n229 this analysis (observed only in the case of numerical simulation, see Results).\n230\n231 Results\n232 Over the three observation nights (12, 16, and 17 September), the first \n233 individual appeared at approximately 18:30, and a total of 44, 75, and 61 individuals \n234 were recorded within the subsequent 1 h, respectively (Fig. 2A, S1 Dataset). Bats \n235 arrived at the foraging patch successively, repeatedly attacked prey, and then exited. \n236 The inter-arrival interval, the patch residence time, and inter-attack interval averaged \n237 57.6 ± 46.0 s, 17.3 ± 17.9 s, and 2.52 ± 2.14 s (mean ± SD), respectively, across the \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint \n\n11\n238 three days (Fig. 2B). A total of 31 flight events of multiple-bat context were observed \n239 over the three days, and no cases involving three or more individuals were recorded.\n240\n241 Fig. 2. (A) Time series data of the bats foraging at the pond during 1-h measurement. \n242 The length of the line indicates the time spent at the pond. Red lines indicate the bats \n243 exiting the pond alone, whereas the blue and yellow lines show the bats exiting the \n244 pond in group; the latter bats entered before (blue) and after (yellow) the entrance of \n245 the former bats. The vertical lines across the horizontal lines indicate the timing of \n246 attacking prey. (B) Proportional distribution of arrival intervals (left), patch residence \n247 time (middle) and attack intervals (right) of the bats during the measurement periods \n248 for each day.\n249\n250 The Poisson GLMM of prey-attack counts indicated that bats attacked prey at a \n251 mean rate of 0.224 attacks s⁻¹ in the single-bat context (estimate = −1.498 ± 0.113 SE, \n252 z = −13.21, p < 0.001), and that this rate was significantly reduced in the multiple-bat \n253 context (estimate = −0.285 ± 0.131 SE, z = −2.17, p < 0.05; Fig. 3). Consequently, the \n254 prey-attack rate in the multiple-bat context was reduced by 25% relative to the single-\n255 bat context (exp[−0.285] = 0.75; S2 Table).\n256\n257 Fig. 3. Prey attack rates during single and multiple flights estimated by a GLMM; The \n258 vertical lines show the standard errors. The slope of the fixed effect for multiple vs. \n259 single was -0.285 (p < 0.05 (*), Wald test). Number of data was 217 (single: 160, group: \n260 57).\n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint \n\n12\n261\n262 Using the parameters estimated from the empirical data, we conducted foraging \n263 simulations (Fig. 4A). The simulated outputs—inter-arrival intervals, patch residence \n264 time, and prey-attack rates per simulation run (i.e., 1 h)—closely matched the \n265 corresponding empirical values used as model inputs (S3 Table). In the multiple-bat \n266 context, such events occurred 13.1 ± 4.0 times per hour (mean ± SD, N = 10,000 \n267 simulations), with a total duration of 130.5 ± 52.6 s per hour. In contrast, the total \n268 duration of the multiple-bat context in the empirical data was 49 s (Day 1, N = 6), 79 s \n269 (Day 2, N = 13), and 61 s (Day 3, N = 12), which was significantly shorter than \n270 predicted by the simulations (Z ≈ −2.35, p < 0.001, one-sample Z test; Fig. 4B). \n271 Furthermore, the duration of individual multiple-bat contexts averaged 9.9 ± 10.0 s in \n272 the simulations (N = 131,374 events from 10,000 simulations), whereas the \n273 corresponding values in the empirical data were 5.5 ± 3.7 s (Day 1, N = 6), 6.2 ± 6.9 s \n274 (Day 2, N = 13), and 5.1 ± 3.0 s (Day 3, N = 12; mean ± SD). Parametric bootstrap tests \n275 revealed that the durations of individual multiple-bat contexts in the empirical data were \n276 significantly shorter than those predicted by the model for all days (p < 0.001; Fig. 4C).\n277\n278 Fig. 4. (A) Time series data of the 1-h foraging simulation of the null model (drawing \n279 rule is same as Fig. 2A). (B) Total time spent in the foraging patch of model and \n280 measured (three different days) bats while in multiple contexts. Vertical lines on the \n281 orange bar indicate the standard deviation based on 10,000 numerical simulations. One-\n282 sample Z test was conducted; *** p < 0.001. (C) Boxplots (0.025, 0.25, 0.5, 0.75, and \n283 0.975 quantiles) showing the consecutive durations of each multiple flight obtained \n284 from the numerical simulations (orange) and from three days of field observations of \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint \n\n13\n285 bats (blue). Parametric bootstrap tests were conducted; *** p < 0.001.\n286\n287 In paired flights, we examined whether the individual that exited first was the \n288 one that arrived earlier (Bat A) or the one that arrived later (Bat B). In the simulations, \n289 once a paired flight began, both bats had identical departure probabilities, resulting in \n290 an equal likelihood of Bat A or Bat B exiting first (Fig. 5, left). Analysis of the empirical \n291 data revealed an exactly even split, with the first exit occurring equally often for Bat A \n292 and Bat B (Fig. 5, right; N = 20; Bat A = 10 cases, Bat B = 10 cases).\n293\n294 Fig. 5. The bat that exited the pond first when in paired flights for the case of model \n295 simulation (red) and the experimental data (blue). Bat A is the bat that was staying \n296 before the paired flight together with the latter bat (Bat B). The numerical simulation \n297 was exhibited 10,000 times.\n298\n299 Discussion\n300 In this study, we used a microphone array and video cameras to quantify the \n301 sequential arrival, foraging, and exiting of bats within a foraging patch. Our results \n302 demonstrate that when two individuals were present simultaneously within the patch, \n303 prey-attack rate was reduced relative to single-bat flight context (Fig. 3). Because the \n304 pond we measured was sufficiently small that flight areas overlapped during paired \n305 flights, this reduction is likely attributable to the presence of conspecifics acting as \n306 moving obstacles and/or to acoustic interference between the ultrasonic signals emitted \n307 for echolocation, which may have decreased prey-detection efficiency. Furthermore, \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint \n\n14\n308 comparison with the null model we constructed revealed that the duration of paired \n309 flights was significantly shorter in the empirical data than predicted by the model (Fig. \n310 4). This finding indicates that bats actively exited from the foraging patch to avoid \n311 simultaneous foraging with conspecifics. The study site was located within a university \n312 experimental forest, where multiple ponds and stream pools are distributed in the \n313 surrounding area. It is therefore plausible that bats avoided simultaneous foraging with \n314 conspecifics at a single patch and instead moved to alternative nearby foraging patches.\n315 When two individuals are co-using a foraging patch, an important question is \n316 which individual leaves first in order to avoid simultaneous foraging. Previous studies \n317 have frequently reported behaviors in which individuals expel others from feeding sites \n318 (food patch defense), with the resident typically displacing the intruder—a pattern \n319 known as the prior residence effect [31, 32]. In the present study, however, the \n320 probability that the first individual to exit was the resident or the intruder did not differ \n321 from chance levels (Fig. 5), indicating that avoidance of simultaneous patch use does \n322 not depend on the order of arrival at the foraging patch. Accordingly, a prior residence \n323 effect does not appear to operate in the foraging behavior of Japanese large-footed bats. \n324 More generally, avoidance of simultaneous foraging with conspecifics at a feeding \n325 patch may be influenced not only by prior residence effects but also by kin selection \n326 [33] and dominance hierarchies [34]. High relatedness can promote tolerance or food \n327 sharing among individuals, whereas dominance relationships may lead to displacement \n328 or exclusion from feeding sites [35, 36]. To gain a deeper understanding of the \n329 mechanisms underlying collective foraging in bats, future studies should jointly \n330 examine foraging behavior, social hierarchy, and relatedness within colonies.\n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint \n\n15\n331 Japanese large-footed bats have been reported to emit various types of social \n332 calls at foraging sites [37], some of which are suggested to function in repelling \n333 conspecifics [38]. Thus, social calls could potentially influence the patch residence time \n334 and prey-attack rate during paired flights in the present study. However, social calls \n335 were observed infrequently, occurring in only 5 of the 31 paired flights (16.1%). When \n336 we reanalyzed the data using a Poisson GLMM after excluding flights in which social \n337 calls were observed (including those individuals during solitary flights), bats attacked \n338 prey at a mean rate of 0.225 attacks/s during solitary flights (estimate = −1.491 ± 0.112 \n339 SE, z = −13.36, p < 0.001), and this rate was significantly reduced during paired flights \n340 (estimate = −0.332 ± 0.147 SE, z = −2.26, p < 0.05). Consequently, prey-attack rate \n341 during paired flights was reduced by 28% relative to solitary flights (exp[−0.332] = \n342 0.72; S2 Table). These results indicate that, regardless of the presence or absence of \n343 social calls, prey-attack rate was significantly lower during paired flights than during \n344 solitary flights, and the magnitude of this reduction was largely unchanged. This \n345 suggests that social calls likely had little effect on foraging efficiency in the present \n346 study.\n347 Foraging efficiency during multiple-bat flights with conspecifics is likely to \n348 depend on the size of the foraging patch. If bats can fly within a patch while \n349 experiencing minimal interference from other individuals—such as overlap in flight \n350 paths or sensory interference—then the presence of conspecifics may not necessarily \n351 reduce prey-attack rate. Even in large patches, however, increasing the number of \n352 individuals can intensify competition, prompting predators to move to alternative \n353 foraging patches; as a result, the benefits gained at each patch tend to become \n354 approximately equal among individuals. Such distributions of predators across feeding \n355 sites are described by the ideal free distribution [39]. In bat research, it has been \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint \n\n16\n356 reported that increases in bat density at a given foraging patch lead to competition and \n357 subsequent shifts to alternative patches [40]. In the present study, because multiple \n358 ponds are distributed along the river within the experimental forest where our study \n359 pond is located, Japanese large-footed bats may similarly compare prey availability \n360 between the focal pond and other nearby foraging patches and avoid paired flights in \n361 order to maintain foraging efficiency.\n362 In conclusion, our study demonstrates that prey-attack rate of M. macrodactylus \n363 declines during paired flights within a foraging patch and that bats actively avoid \n364 simultaneous foraging with conspecifics within the patch. By quantitatively \n365 characterizing foraging behavior that is otherwise difficult to capture under natural \n366 conditions, this study highlights the Japanese large-footed bat as an excellent model \n367 system for elucidating the dynamics of collective foraging and the mechanisms of \n368 sensory interference in the wild. 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It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint \n\n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint \n\n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint \n\n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint \n\n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint \n\n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted February 11, 2026. ; https://doi.org/10.64898/2026.02.09.704905doi: bioRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}