1. Ecology

Crickets evade bats via olfaction beyond acoustic cues

  1. Yannan Li
  2. Wenhao Zhang
  3. Jiaqi Wei
  4. Hanhong Xu
  5. Jiang Feng  Is a corresponding author
  6. Aiqing Lin  Is a corresponding author
  1. Jilin Provincial Key Laboratory of Animal Resource and Ecological Security, Northeast Normal University, China
  2. State Key Laboratory of Green Pesticide, College of Plant Protection, South China Agricultural University, China
  3. Jilin Provincial International Cooperation Key Laboratory for Biological Control of Agricultural Pests, Jilin Agricultural University, China
  4. Key Laboratory of Vegetation Ecology of Education Ministry, Institute of Grassland Science, Northeast Normal University, China
https://doi.org/10.7554/eLife.110936.3
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  1. Yannan Li
  2. Wenhao Zhang
  3. Jiaqi Wei
  4. Hanhong Xu
  5. Jiang Feng
  6. Aiqing Lin
(2026)
Crickets evade bats via olfaction beyond acoustic cues
eLife 15:RP110936.
https://doi.org/10.7554/eLife.110936.3
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eLife Assessment

This study provides important evidence that crickets use bat-associated odors as cues of predation risk, extending the classic bat-insect arms race beyond its traditional acoustic framework. The authors combine fecal metabarcoding, behavioral assays, electrophysiology, chemical analyses, and field observations to show that Loxoblemmus equestris avoids the odor of the insectivorous bat Scotophilus kuhlii, and that synthetic limonene can elicit antennal responses, avoidance in the laboratory, and reduced calling activity in the field. Together, the multidisciplinary results provide a coherent and convincing chain of evidence for an olfactory component of predator detection in this bat-insect system. The work will be of broad interest to researchers in sensory ecology, chemical ecology, predator-prey interactions, and bat-insect coevolution.

https://doi.org/10.7554/eLife.110936.3.sa0

Abstract

The evolutionary arms race between insectivorous bats and their insect prey is a classic paradigm of acoustic predation and evasion, with insects having evolved sophisticated auditory countermeasures. Both bats and insects also rely heavily on olfaction for key behaviors, such as social communication. Moreover, predator-derived odors are well established as risk cues in many other predator–prey systems. However, whether olfaction plays a role in the bat–insect arms race remains unknown. Here, we unveil a previously unknown olfactory dimension to this interaction. We demonstrated that the body odor of the insectivorous bat Scotophilus kuhlii triggered robust avoidance and electrophysiological antennal responses in a common cricket prey, Loxoblemmus equestris. We identified limonene as a behaviorally active volatile in bat odor that elicited electrophysiological responses in cricket antennae and was sufficient to elicit avoidance in crickets. Field experiments confirmed that limonene exposure reduced cricket calling activity, demonstrating the ecological relevance of this cue. Our findings establish that insects can detect and initiate avoidance of phylogenetically distant vertebrate predators via olfaction, a process that could be mediated by the elemental perception of individual odor compounds. This work broadens the sensory framework of a classic predator–prey system and highlights olfactory eavesdropping as a functional strategy in phylogenetically distant predator–prey systems.

Introduction

The evolutionary arms race between insectivorous bats and their insect prey represents a quintessential predator–prey system, characterized by the evolution of sophisticated acoustic countermeasures in insects (Conner and Corcoran, 2012; Ter Hofstede and Ratcliffe, 2016). As dominant nocturnal aerial insectivores, bats consume diverse taxa such as Lepidoptera, Coleoptera, and Diptera (Gong et al., 2025; Srilopan et al., 2025), providing vital biocontrol against agricultural pests like Spodoptera frugiperda and Helicoverpa armigera (Liu et al., 2024; Maine and Boyles, 2015). Over 50 million years of coevolutionary arms race, insects evolved sophisticated auditory countermeasures: ultrasound hearing, sound-absorbing scales, acoustic decoys, and ultrasonic jamming (Barber et al., 2022; Jacobs and Bastian, 2016; Zeng et al., 2011). Auditory detection, independently evolved in at least seven insect orders, is the most widespread adaptation (Ter Hofstede and Ratcliffe, 2016). Upon detecting bat echolocation calls, insects typically execute evasive flight maneuvers away from the predator source to reduce detection and predation risk (Jacobs and Bastian, 2016). However, the dominance of this acoustic narrative may have overshadowed other sensory channels (Stidsholt et al., 2025).

Notably, both bats and insects themselves rely heavily on olfaction for essential behaviors. Many bats (e.g. Hipposideros armiger, Lasiurus cinereus, Phyllostomus hastatus, and Saccopteryx bilineata) produce species-specific volatile organic compounds (VOCs) from specialized glands for social communication (Adams et al., 2018; Caspers et al., 2009; Stefaniak et al., 2025; Zhang et al., 2022). Conversely, insects possess exquisitely sensitive olfactory systems capable of detecting chemical cues (e.g. sex pheromones) over kilometers, critical for foraging, mate finding, and predator avoidance (Kannan et al., 2022). Thus, olfaction could, in principle, provide an additional sensory channel in the bat–insect dynamic. More broadly, predator-derived VOCs are well established as risk cues that elicit adaptive avoidance behaviors in diverse predator–prey systems (Apfelbach et al., 2005; Apfelbach et al., 2015; Ferrero et al., 2011; Rosen et al., 2015; Takahashi et al., 2005; Takahashi et al., 2008). Empirical studies demonstrate that prey organisms exhibit multifaceted anti-predator responses, including enhanced vigilance, activity suppression, and abandonment of non-defensive behaviors (e.g. foraging), when exposed to predator odors (Cornelis et al., 2019; Hegab et al., 2015; Pérez-Gómez et al., 2015). Such chemically cued defenses are well established within phylogenetic groups: vertebrate prey (e.g. rabbits, rodents, and ungulates) avoid mammalian predator odors (e.g. bobcats, foxes, and wolves), while invertebrate prey (e.g. various arthropods, including crustaceans and insects) evade arthropod predators (e.g. aquatic insect larvae and spiders) (Kempraj et al., 2020; Monclús et al., 2005; Osada et al., 2014; Swihart et al., 1991; Weiss et al., 2018). These intraphyletic frameworks elucidate core principles of chemical communication and inform biological control strategies (Apfelbach et al., 2005; Dicke and Grostal, 2001).

However, a fundamental gap exists in understanding whether such olfactory eavesdropping can operate across the vast phylogenetic divide separating vertebrate predators and invertebrate prey (Apfelbach et al., 2015; Dicke and Grostal, 2001; Schoeppner and Relyea, 2005). Although olfactory interactions across broad taxonomic boundaries are widespread in nature, such as mosquitoes using host odors to blood-feed, elephants and moths sharing pheromonal components, and aroids chemically mimicking carrion to attract pollinating flies (Kang et al., 2023; Zaremska et al., 2022; Zhao et al., 2022), these interactions are primarily shaped by selective pressures tied to foraging, reproduction, or mutualisms, rather than by predation-related selection. Given the pronounced differences between vertebrates and invertebrates in olfactory receptor architectures, neural processing, and behavioral ecology (Kaupp, 2010; Wang et al., 2024), two pivotal and interconnected questions arise: First, can invertebrates detect and respond to volatile cues from phylogenetically distant vertebrate predators? Second, what is the mechanistic basis of such recognition? Specifically, does it require the holistic perception of complex, multi-component odor blends (configural processing), or can it be triggered by the detection of simple, elemental compounds (Apfelbach et al., 2015; Clifford and Riffell, 2013; Coureaud et al., 2022; Lei and Vickers, 2008)? Resolving these questions is essential to advancing theories of sensory evolution and chemical ecology beyond intraphyletic paradigms.

The bat–insect system presents an ideal model to address these questions. Despite the clear importance of olfaction to both taxa and its established role in predator–prey ecology, whether it plays any functional role in the iconic bat–insect arms race remains unexplored. In the present study, we hypothesized that insects might eavesdrop on bat VOCs as an early warning. Using the Asiatic lesser yellow house bat (Scotophilus kuhlii) and its cricket prey (Loxoblemmus equestris) as a model system, we tested whether bat body odor elicits avoidance in insects and if individual odor components were sufficient to trigger such responses. S. kuhlii (Mammalia: Chiroptera: Vespertilionidae) is widely distributed across Asian tropics and subtropics. In China, this species typically forms dense colonies in palm trees (Livistona chinensis). S. kuhlii preys on a variety of insects, including members of the orders Coleoptera, Orthoptera, and Lepidoptera (Zhu et al., 2012). This species forages on the wing over forests, farmlands, and grasslands, habitats shared with L. equestris. This spatial overlap creates predation opportunities, as the bats frequently hunt near the ground. In our study area, L. equestris dominates cricket assemblages in the foraging habitats of S. kuhlii, producing intense mating calls (primarily for mate attraction and territory defense) and frequently relocating by flight, behaviors that heighten its vulnerability to predators (Ren et al., 2023; Stevenson and Rillich, 2012). Notably, S. kuhlii emits a strong and distinctive odor. Following confirmation of this specific predator–prey relationship by DNA metabarcoding, we integrated behavioral, electrophysiological, chemical, and field evidence to demonstrate olfactory-mediated avoidance in L. equestris and identify its underlying mechanism.

Results

L. equestris is a prey species of S. kuhlii

To establish the predatory relationship between S. kuhlii and L. equestris, we conducted DNA metabarcoding analysis on fecal samples from 30 individual bats. Using mitochondrial 16S rRNA gene (16S) and cytochrome c oxidase subunit I (COI) gene markers, we obtained 426,832 insect sequence reads (323 operational taxonomic units [OTUs]) and 1,805,577 insect sequence reads (534 OTUs), respectively. The diet of S. kuhlii was primarily composed of Coleoptera, Blattodea, Lepidoptera, Hemiptera, Orthoptera, Mantodea, Diptera, and Hymenoptera (Figure 1A). Within Orthoptera, Gryllidae was the dominant family, comprising 94% (16S) and 93.5% (COI) of Orthoptera sequences (Figure 1B), and was detected in 50% of bats (15/30 individuals). Phylogenetic analysis revealed at least seven cricket species among the prey (Figure 1C). Among these prey crickets, L. equestris was the most abundant species encountered at ground level in the bat foraging sites. This ground-level abundance was supported by insect surveys conducted over eight nights during fecal sample collection, which captured 80 crickets, 70 (87.5%) of which were L. equestris (Appendix 1—table 1). Based on the crickets’ well-developed olfactory capacity, the confirmed predation by S. kuhlii, the high local abundance and accessibility of L. equestris, and its tractability in behavioral settings, we selected L. equestris for subsequent experiments testing olfactory responses to bat body odor.

Figure 1
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Dietary composition of S. kuhlii.

(A) Order-level composition based on 16S gene sequences, analyzed using the weighted percentage of occurrence (wPOO). Individual bats are labeled S1–S30. (B) Proportions of Orthoptera families in the diet, detected using the cytochrome c oxidase subunit I (COI) and 16S markers and presented as both relative read abundance (RRA) and wPOO. Color intensity corresponds to proportion magnitude. (C) Maximum-likelihood phylogenetic tree of Gryllidae reconstructed from 16S sequences. Operational taxonomic units (OTUs) identified in this study are highlighted in orange; reference sequences from NCBI are shown in black. Associated bat individuals are listed after each corresponding scientific name. Bootstrap support values are indicated at the nodes.

Figure 1—source data 1

Identification of insect operational taxonomic units (OTUs) in bat feces using 16S rRNA gene markers in Figure 1A.

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Figure 1—source data 2

Identification of insect operational taxonomic units (OTUs) in bat feces using cytochrome c oxidase subunit I (COI) gene markers in Figure 1B.

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Figure 1—source data 3

Sequence file used to construct the 16S phylogenetic tree shown in Figure 1C.

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S. kuhlii body odor elicited behavioral avoidance and antennal responses in L. equestris

Behaviorally, in a two-choice olfactometer (Figure 2A), L. equestris strongly avoided the arm containing bat body odor. Upon release, 44 of 47 crickets (93.6%) chose the odor-free control arm, whereas only one entered the bat-odor arm and two shuttled without making a definitive choice (chi-square test comparing avoiding vs. non-avoiding individuals: χ²=35.76, df = 1, p<0.001, φ=0.87, 95% CI: 0.73–1.00; Figure 2B). Control trials with odor-free air in both arms showed no significant bias (13 of 24 vs. 11 of 24 crickets; chi-square test, χ²=0.17, df = 1, p=0.68, φ=0.08, 95% CI: 0–0.49; Figure 2B). Electrophysiological recordings using gas chromatography–electroantennographic detection (GC–EAD) confirmed that volatiles from S. kuhlii body odor are detected by L. equestris antennae. A mixture of bat odor volatiles from eight individuals elicited consistent antennal depolarizations (>0.1 mV) in all five crickets tested (Figure 2—figure supplement 1). Parallel analysis by gas chromatography–mass spectrometry (GC–MS) identified six candidate compounds in the same odor samples. Among these, two compounds, namely 2,2-dimethylheptane and limonene, consistently co-eluted with EAD-active peaks, identifying them as key antennal stimulants. No antennal responses were observed in odor-free controls tested on three crickets (Figure 2C and D). Together, these results establish that L. equestris perceives and avoids the body odor of its predator, S. kuhlii.

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Behavioral and electrophysiological responses of L. equestris to S. kuhlii body odor.

(A) Schematic of the Y-tube olfactometer used in two-choice assays. The cricket’s release position (junction) and airflow direction are indicated. (B) Choice percentages of crickets for the control vs. test arm across three treatments: bat body odor, air without bat odor (control for odor bias), and limonene. (C) Representative gas chromatography–electroantennographic detection (GC–EAD) recordings. Upper panel: flame ionization detection (FID) chromatograms of bat body odor extract (brown) and an odor-free control (blue). Lower panel: corresponding EAD response (black) of a L. equestris antenna. Peaks 1–6 correspond to compounds identified by GC–MS (see panel D); asterisks mark compounds that elicited consistent antennal depolarizations (>0.1 mV). (D) Gas chromatography–mass spectrometry (GC–MS) total ion chromatograms of the same bat body odor sample (upper) and an odor-free control (lower). Identified compounds are labeled (peaks 1–6). Axes indicate retention time (x) and relative ion intensity (y).

Figure 2—source data 1

Behavioral preference of crickets in Y-tube maze experiment with control in Figure 2B.

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Figure 2—source data 2

Behavioral preference of crickets in Y-tube maze experiment with bat odor in Figure 2B.

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Figure 2—source data 3

Behavioral preference of crickets in Y-tube maze experiment with limonene in Figure 2B.

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Figure 2—source data 4

Retention times and mass-spectral identification information for volatile compounds detected in the body odor of S. kuhlii by gas chromatography–mass spectrometry (GC–MS) in Figure 2D.

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Figure 2—source data 5

Experimental electron ionization (EI) mass spectra of compounds 1–6 detected in the body odor of S. kuhlii and the corresponding standard EI mass spectra used for their gas chromatography–mass spectrometry (GC–MS) identification in Figure 2D.

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Snout secretions are the primary source of body odor in S. kuhlii

To identify the biological sources of bat body odor, we analyzed VOCs from hair, feces, and snout (pararhinal gland) secretions of nine bats using headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS–SPME–GC–MS). Snout secretions and hair shared similar hydrocarbon-rich VOC profiles, whereas fecal volatiles were distinct (Figure 3A). Individual-level terpenoid profiles further showed that limonene was detected in hair and snout secretion VOC collections but was absent from feces (Figure 3B and Appendix 1—table 2). Principal component analysis (PCA) revealed that the VOC profile of a pooled bat body odor sample (from eight bats) clustered more closely with those from snout secretions and hair than with feces (Figure 3C). As hair likely functions as a passive carrier, its surface VOCs probably originate from transferred snout secretions. These results point to snout secretions as the primary source of S. kuhlii body odor.

Figure 3
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Volatile organic compound (VOC) profiles of potential odor sources in S. kuhlii.

(A) Hierarchical clustering of VOC profiles from feces, hair, and snout secretions, based on compositional similarity (values normalized 0–1). All three odor sources were sampled from the same nine bats (27 samples total). (B) Individual-level relative abundance of terpenoid compounds in VOC collections from hair, snout secretions, and feces. Each stacked bar represents one VOC collection from an individual bat, and the segment corresponding to limonene indicates its presence and relative contribution in the sample. (C) Principal component analysis (PCA) of VOC profiles from the same samples, based on compound presence/absence data, with the addition of a pooled body odor composite (combined from eight bats) for comparison. Ellipses represent 90% confidence intervals, and marginal density plots are shown for each source.

Figure 3—source data 1

Volatile compounds identified from bat feces, hair, and secretions using headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS–SPME–GC–MS) analysis in Figure 3A–C.

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A single compound, limonene, triggers avoidance and reduces calling activity in L. equestris

To assess the behavioral relevance of identified volatiles, we measured antennal responses of L. equestris to four commercially available compounds previously detected in bat body odor: limonene, undecane, pentadecane, and hexadecane, using electroantennography (EAG). Compared to a hexane control, only limonene (10% vol/vol, 5.87×10–7 mol/µL) evoked a significant antennal response (repeated-measures ANOVA, F(4, 28)=89.77, p<0.001, η²p=0.93; limonene vs. control: Bonferroni-corrected paired t-test, t(7) = 10.70, p<0.001, Hedges’ g=3.41; Figure 4A). EAG responses to limonene were concentration-dependent (repeated-measures ANOVA, F(5, 25)=24.95, p<0.001, η²p=0.83; Figure 4B), with both 1% and 10% limonene solutions eliciting significantly stronger responses than the hexane control (Bonferroni-corrected paired t-tests, 1%: t(5) = 10.77, p<0.001, Hedges’ g=3.82; 10%: t(5) = 6.92, p=0.005, Hedges’ g=2.46).

Figure 4 with 2 supplements see all
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Electrophysiological and ecological responses of L. equestris to candidate bat body odor compounds.

(A) Electroantennographic (EAG) responses to four candidate compounds (n=8 individuals). Significance vs. the hexane control is indicated (***p<0.001; repeated-measures ANOVA with Bonferroni-corrected paired t-tests, see Results for exact F- and p-values and post hoc comparisons). (B) Concentration-dependent EAG responses to limonene (n=6 individuals). **p<0.01, ***p<0.001 vs. control (same statistical test, see Results). (C) Mean call rate over time (calls per minute) in field plots treated with limonene (n=8 datasets) or a hexane control (n=7 datasets). Shaded areas represent 95% confidence intervals (1200 recordings total).

Figure 4—source data 1

Electroantennographic (EAG) responses in crickets to four candidate compounds in Figure 4A.

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Figure 4—source data 2

Concentration-dependent effects of limonene on electroantennogram (EAG) responses in crickets in Figure 4B.

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Figure 4—source data 3

The variation in cricket chirping frequency within each quadrat before and after treatment analysis in Figure 4C.

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Notably, limonene alone was sufficient to elicit robust avoidance behavior in L. equestris. In a two-choice olfactometer assay, 34 of 38 L. equestris individuals (89.5%) chose the limonene-free control arm, demonstrating significant avoidance (chi-square test, χ²=23.68, df = 1, p<0.001, φ=0.79, 95% CI: 0.59–0.99; Figure 2B). Field experiments confirmed the ecological relevance of this avoidance. Prior to spray application during their peak nocturnal activity period, the mean call rate of L. equestris did not differ significantly between treatment and control plots (mean ± SD, 245±143 vs. 207±133 calls/min; generalized linear mixed model [GLMM], estimate = 0.10, SE = 0.25, 95% CI: –0.39 to 0.59, Z=0.38, p=0.720; Appendix 1—table 3). When exposed to limonene, the mean call rate of L. equestris in treatment plots significantly decreased over the 40 min trial (post- vs. pre-exposure: mean ± SD, 130±139 vs. 245±143 calls/min; GLMM, estimate = –0.68, SE = 0.06, 95% CI: –0.80 to –0.57, Z=–11.90, p<0.001; Figure 4C and Appendix 1—table 3). In contrast, call rates increased significantly in control plots sprayed with hexane alone (post- vs. pre-exposure: 298±126 vs. 207±133 calls/min; GLMM, estimate = 0.40, SE = 0.06, 95% CI: 0.28–0.51, Z=6.68, p<0.001; Figure 4C and Appendix 1—table 3), a pattern consistent with their natural crepuscular activity peak. Together, these results demonstrate that limonene, a single component of bat odor, is sufficient to induce avoidance and reduce call activity in L. equestris under natural conditions.

Discussion

Our study provides direct experimental evidence that an insect species can detect and avoid a phylogenetically distant vertebrate predator via olfaction. We demonstrate that the body odor of the insectivorous bat S. kuhlii triggers robust avoidance and electrophysiological responses in the cricket L. equestris, and that a single compound, limonene, is sufficient to elicit this avoidance in the laboratory and suppress calling in the field. These findings fundamentally expand the sensory narrative of a classic predator–prey system and offer novel insights into the mechanisms of olfactory detection and chemical eavesdropping in interactions across broad phylogenetic divides.

Olfaction as a novel and functional channel in bat–insect interactions

For decades, the bat–insect arms race has served as a paradigm for acoustic coevolution, with insect auditory adaptations to echolocation being meticulously documented (Conner and Corcoran, 2012; Ter Hofstede and Ratcliffe, 2016). This dominant narrative, however, may have inadvertently narrowed the research focus, contributing to relatively less exploration of alternative sensory channels beyond audition (Stidsholt et al., 2025). Many bats produce body odor in the form of VOCs from specialized glands for intraspecific communication (Adams et al., 2018; Caspers et al., 2009; Stefaniak et al., 2025; Zhang et al., 2022). Here, we found that the body odor of S. kuhlii, which originates primarily from snout secretions, elicits robust avoidance and antennal responses in the cricket L. equestris. This suggests that insects may eavesdrop on predators’ intraspecific olfactory signals to mitigate predation threat. Our discovery thus reveals a previously overlooked olfactory dimension to this classic predator–prey interaction. This finding aligns with the established importance of multimodal predator detection, challenging the presumed supremacy of acoustics in this particular system (Jacobs and Bastian, 2016).

While our laboratory assays demonstrate clear olfactory-mediated avoidance in crickets, the ecological dynamics of a direct bat–insect encounter in the wild are more complex. Nonetheless, our field experiment provides critical, ecologically relevant support for the anti-predator function of this cue. The significant reduction in cricket calling activity upon limonene exposure—whether due to dispersal from the limonene-treated plots or direct cessation of signaling—constitutes a functionally adaptive anti-predator response, as both outcomes would reduce acoustic attraction and lower predation risk (Belwood and Morris, 1987; Hedrick and Kortet, 2006). This behavioral response confirms the ecological relevance of the olfactory cue, consistent with the evolutionary logic of eavesdropping on predator signals (Hermann and Thaler, 2014).

The functional advantages of this olfactory channel likely complement, rather than replace, established auditory defenses. The high sensitivity and long-range nature of insect olfaction may facilitate earlier predator detection, particularly against bats employing low-intensity or ‘stealth’ echolocation strategies that challenge acoustic detection alone (Corcoran and Conner, 2017; Goerlitz et al., 2010). More broadly, it could provide a vital, generalized defense for insect taxa lacking specialized ultrasonic hearing, including many beetles and non-hearing moths that are common bat prey (Göpfert and Hennig, 2016; Lin et al., 2023). The suppression of calling activity we observed exemplifies a key benefit: reducing acoustic conspicuousness. Insect calls can serve as a potential cue for certain bats, either through passive hearing or by enhancing the prey’s detectability within the returning echo (Alem et al., 2011; Falk et al., 2015; Prakash et al., 2021). We propose that olfaction in this system acts as a general alert mechanism, priming vigilance and enabling more informed deployment of evasive maneuvers triggered by closer-range acoustic (including echolocation) cues. In this integrated model, olfactory and auditory channels might operate across complementary spatial and temporal scales, forming a more robust, layered sensory defense system. Ultimately, direct observations of bat–insect encounters within manipulated olfactory environments will be crucial to fully quantify the survival benefit conferred by this eavesdropping channel under real predation pressure.

Implications for cross-phylum sensory ecology and the mechanism of eavesdropping

Beyond the specific bat–insect model, our work addresses a central question in sensory ecology: how chemical eavesdropping operates within predator–prey systems between phylogenetically distant taxa with fundamentally divergent olfactory systems (Adams et al., 2020; Emerson and Johnson, 2024; Kaupp, 2010). While intraphyletic kairomone detection is well established (e.g. rodents avoiding carnivore odors, aphids responding to ladybug chemicals), compelling experimental evidence for such olfaction-mediated recognition across broad phylogenetic divides has been limited (Apfelbach et al., 2005; Ferrari et al., 2007; Tanis et al., 2018). By linking physiological detection to behavioral avoidance within a confirmed predator–prey relationship, we demonstrate that functional cross-phylum olfactory eavesdropping is attainable, proving this capacity persists despite profound divergence in underlying olfactory circuitry.

A key question concerns the perceptual mechanism underlying such olfactory eavesdropping: does it require a complex, species-specific odor blend (‘configural processing’), or can it be initiated by simple, ‘elemental’ compounds (Apfelbach et al., 2015; Coureaud et al., 2022; Lei and Vickers, 2008)? Our identification of limonene as a single, sufficient elicitor of avoidance supports an elemental perception strategy in this cross-phylum context. This mechanism is evolutionarily parsimonious. Evolving high-sensitivity receptors for a reliable kairomone bypasses the need to decode complex vertebrate odor profiles (Schoeppner and Relyea, 2005), likely being more efficient than evolving sophisticated neural circuits for configural processing (Haverkamp et al., 2018; Niven and Laughlin, 2008). Functionally, elemental perception enables a rapid, high-sensitivity response to trace amounts (Stengl, 2010), potentially allowing prey to identify threats at lower concentrations or greater distances than required for complex blends (Cardé and Willis, 2008; Minor and Kaissling, 2003). Thus, elemental perception emerges as a plausible and efficient strategy for initiating anti-predator behavior against phylogenetically remote threats.

Although limonene reliably induced avoidance behavior in crickets, two related questions still merit careful consideration. One question is whether the limonene we identified genuinely originates from bats or reflects contamination during sampling. Limonene is common in plants and numerous consumer products (Boncan et al., 2020; Schuman, 2023), making its endogenous production by a mammal seem unusual. Nevertheless, multiple lines of evidence militate against contamination. First, we adhered to rigorous protocols. For example, all instruments were cleaned with ethanol and oven-dried before each use; bats were housed in stainless-steel cages, and cloth bags had been rinsed with purified water. Second, limonene was absent from all blank controls, including empty-chamber air samples and clean swabs, and was not detected in bat fecal samples. In contrast, it was consistently identified in hair and snout secretion samples from bats. Third, independent studies have similarly identified limonene in the secretions of other bat species (Faulkes et al., 2019; Zhang et al., 2022). Furthermore, emerging evidence indicates that skin-associated microbes may contribute to bat volatile profiles, with some taxa possessing enzymes involved in terpene biosynthesis (Sun et al., 2026). Taken together, these observations point toward an endogenous or microbe-mediated source, although the exact biosynthetic pathway remains to be determined.

The second question is how crickets might distinguish bat-derived limonene from environmental sources of this compound, given its prevalence in mint, citrus peel, pine, and other non-threatening plants (Boncan et al., 2020; Schuman, 2023). It seems implausible that crickets could simply rely on limonene per se to differentiate a bat from a leaf. Two non-exclusive mechanisms could help resolve this issue. First, limonene need not be the only olfactory cue mediating risk perception. Our findings establish the sufficiency of limonene as an avoidance trigger, but do not preclude a role for other odor components. The crickets’ antennal responses to other bat volatiles in our GC–EAD analyses suggest more complex peripheral perception. Additional compounds, either alone or in synergistic blends, may modulate the full behavioral response in nature. Therefore, elemental perception via limonene likely represents one effective strategy within a broader olfactory toolkit available to insects. Second, crickets may discriminate bat-derived limonene through context-specific cues (e.g. temporal and spatial patterning, co-occurrence with other bat-specific compounds) to minimize false alarms. Comparative studies on enantiomeric specificity and detection thresholds of cricket olfactory sensory neurons will be essential. Equally critical will be future efforts to quantify natural bat odor composition, limonene release rates, ambient exposure concentrations, and odor-plume dynamics, which together will inform ecologically valid stimulus design in controlled assays. Critically, our field data confirm that limonene exposure in nature robustly triggers an adaptive anti-predator response, irrespective of the precise discrimination mechanism.

In conclusion, our integrated approach reveals that the body odor of S. kuhlii triggers avoidance in L. equestris and that a single component, limonene, is sufficient to elicit this behavior in the laboratory and suppress calling in the field. We have uncovered a novel olfactory axis in the classic bat–insect arms race, demonstrating that chemical cues constitute a functional sensory channel for predator detection. More broadly, it provides evidence that olfactory eavesdropping can operate across vast phylogenetic distances within predator–prey systems. Elemental perception of individual odor compounds could represent a parsimonious strategy for such cross-phylum threat detection. Future work on the neural circuitry, phylogenetic distribution, and ecological contingencies of this capability will elucidate how such olfactory-mediated eavesdropping evolves and operates across the tree of life. The identification of behaviorally active volatiles like limonene may offer a starting point for the bioinspired development of novel insect repellents (Rasmann et al., 2005), but such applications must be informed by a deeper understanding of the ecological context in which these cues are perceived and interpreted.

Materials and methods

Key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Biological sample (Scotophilus kuhlii)BatThis paperSee Materials and methods, ‘Diet composition and prey availability of S. kuhlii’
Biological sample (Loxoblemmus equestris)CricketThis paperSee Materials and methods, ‘Behavioral and electrophysiological assays of cricket responses to S. kuhlii body odor’
Sequence-based reagentLCO-1490Lin et al., 2023
PCR primerGGTCAACAAATCATAAAGATATTGG
Sequence-based reagentZBJ-ArtR2cLin et al., 2023PCR primerWACTAATCAATTWCCAAATCCTCC
Sequence-based reagentColeop_16ScLin et al., 2023
PCR primerTGCAAAGGTAGCATAATMATTAG
Sequence-based reagentColeop_16SdLin et al., 2023
PCR primerTCCATAGGGTCTTCTCGTC
Commercial assay or kitDNA extraction kitOmega Bio-tek
Chemical compound, drugLimoneneMacklinCAS:5989-54-8Purity: ≥95%
Chemical compound, drugUndecaneMacklinCAS:1120-21-4Purity: ≥98%
Chemical compound, drugPentadecaneMacklinCAS:629-62-9Purity: ≥99%
Chemical compound, drugHexadecaneMacklinCAS:544-76-3Purity: ≥98%
Chemical compound, drugHexaneMREDACAS:110-54-3Purity: ≥98%
Chemical compound, drugDichloromethaneMREDACAS:75-09-2Purity: ≥99%
OtherMiSeq sequencing platformIlluminaMiSeq PE300;
RRID:SCR_016379		Used for sequencing COI and 16S amplicons from bat fecal samples
OtherGas chromatography system (GC)Agilent Technologies, USAAgilent 7820A; HP-5 column;
RRID:SCR_019445		Used for GC–EAD separation of volatile compounds from bat body odor
Other Electroantennographic detection system (EAD)Syntech, GermanyIDAC-4; MP-15; PRG-3Used for both GC–EAD and EAG experiments
OtherGas chromatography–mass spectrometry system (GC–MS)Agilent Technologies, USAAgilent 6850 GC-5975 MS; HP-5 columnUsed to identify volatile compounds in bat body odor extracts
OtherHeadspace solid-phase microextraction–gas chromatography–mass spectrometry system (HS–SPME–GC–MS)Agilent Technologies, USAAgilent 7890B GC-7000D MS; DVB/CWR/PDMS fiber; DB-5MS columnUsed to analyze volatile organic compounds in bat hair, snout secretions, and feces
OtherDynamic headspace sampling systemThis paperVacuum pump (QC-1S); Adsorbent columns (Porapak-Q)Used to collect volatile organic compounds from the body odor of adult S. kuhlii for subsequent GC–EAD and GC–MS analyses
OtherY-tube olfactometerShelai Instrument PlatformUsed for two-choice behavioral assays of cricket responses to bat body odor and limonene
OtherAudioMoth acoustic recorderOpen Acoustic DevicesVersion: 1.2.0Used to record cricket calling activity during field experiments
Software, algorithmUsearchEdgar, 2016
Version: 11.0; RRID:SCR_027438
Software, algorithmMEGAKumar et al., 2016Version: 7.0; RRID:SCR_000667
Software, algorithmIQ-TREENguyen et al., 2015Version: 1.6.12; RRID:SCR_017254
Software, algorithmGcEadSyntechVersion: 1.2.5
Software, algorithmEagProSyntechVersion: 2.0
Software, algorithmMassHunterAgilent TechnologiesVersion: B.08.00
Software, algorithmAvisoft SASLab ProAvisoft BioacousticsVersion: 5.2.07; RRID:SCR_014438
Software, algorithmRR Foundation for Statistical ComputingVersion: 4.3.2; RRID:SCR_001905
Software, algorithmPythonPython Software FoundationVersion: 3.9; RRID:SCR_008394

Diet composition and prey availability of S. kuhlii

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To confirm the predator–prey relationship between S. kuhlii and L. equestris, we analyzed the diet of S. kuhlii via DNA metabarcoding of fecal samples. On July 10, 2022, we captured 30 individuals of S. kuhlii (21 females and 9 males) from a roosting colony in a Chinese fan palm in Guangzhou, China, using hand nets. Sampling occurred during the bats’ initial return to the roost following nightly foraging activity, a period associated with high insect consumption. Each bat was placed individually in a thoroughly washed cotton bag, and fecal samples were collected after 2 hr. The samples were immediately transferred to 2 mL Eppendorf tubes (Corning, Cat# 430659) and stored at –20°C. Bats were released unharmed after sample collection.

From each fecal sample, a 100 mg subsample was taken for DNA extraction using an Omega Bio-tek DNA extraction kit according to the manufacturer’s instructions. We targeted a 225 bp fragment of the mitochondrial COI gene with primers LCO-1490 (5′-GGTCAACAAATCATAAAGATATTGG-3′) and ZBJ-ArtR2c (5′-WACTAATCAATTWCCAAATCCTCC-3′), and a 105 bp fragment of the mitochondrial 16S ribosomal DNA with primers Coleop_16Sc (5′-TGCAAAGGTAGCATAATMATTAG-3′) and Coleop_16Sd (5′-TCCATAGGGTCTTCTCGTC-3′) (Lin et al., 2023). Polymerase chain reaction conditions followed published protocols (Lin et al., 2023). Amplified products of all 64 samples (60 from bat feces and four extraction blanks as negative controls) were sequenced on an Illumina MiSeq PE300 platform following the standard operating procedures at Majorbio Bio-Pharm Technology Co., Ltd. Ultimately, using the 16S molecular marker, we obtained insect sequences from 29 bats; no insect sequences were detected for one individual (bat identity: S28). Using the COI molecular marker, we obtained dietary sequences from 30 individual bats.

Dietary analysis was performed following established bioinformatic protocols (Chang et al., 2019; Lin et al., 2023). Briefly, after quality filtering and merging paired-end reads, DNA sequences were clustered into OTUs at a 97% similarity threshold using Usearch v11.0 (Edgar, 2016). Taxonomic assignment was conducted by comparing representative sequences from each OTU against the GenBank and BOLD databases, based on the following criteria: (a) sequences with >98% identity to a single species were assigned to that species; (b) those matching multiple congeners within the same genus (>98%) were assigned to the genus level; and (c) sequences matching multiple genera within a family or only family-level references were assigned to the family level. No contaminant insect sequences were detected in the negative controls. Only insect sequences were retained for diet analysis. We quantified bat diet composition using the relative read abundance (RRA) and the weighted percentage of occurrence (wPOO) of prey orders, families, and species (Deagle et al., 2019). For RRA, total read counts were normalized to 80,000 reads per sample. The wPOO was calculated from occurrence frequency tables standardized to a total occurrence frequency of 100 across all samples.

To achieve species-level resolution within Gryllidae (crickets), we implemented a phylogenetic approach (Ross et al., 2008). Representative sequences of each Gryllidae OTU were aligned with their top five BLAST matches from NCBI, and duplicate sequences were removed. Multiple sequence alignment was performed in MEGA v7.0 (Kumar et al., 2016), and a maximum-likelihood (ML) phylogeny was reconstructed using IQ-TREE v1.6.12 under the best-fit substitution model (16S: TPM3u+F+G4; COI: TIM2+F+I+G4) (Nguyen et al., 2015), with Gampsocleis sinensis sequences as the outgroup. OTUs clustering within established species-level clades were assigned to the corresponding species.

To assess prey availability, we surveyed insects in a grassland foraging site of S. kuhlii approximately 1 km from its roost. Sampling was carried out on eight nights between July and September 2022 (19:30–21:30) using one ultraviolet light trap (BY201, Beijing Baoyuan Xingye Technology Co., Ltd., China) and one Malaise trap (BY201, Beijing Baoyuan Xingye Technology Co., Ltd., China). Lepidoptera (moths) and Gryllidae were the most abundant taxa collected. Preliminary Y-tube olfactometer trials indicated that moths exhibited minimal movement in the Y-tube, precluding reliable assessment of odor-mediated behavior, whereas crickets showed consistent locomotor activity. We therefore focused on estimating the relative abundance of Gryllidae species. L. equestris was selected for subsequent olfactory experiments due to its high local abundance and confirmed status as a natural prey item of S. kuhlii (see Results).

Behavioral and electrophysiological assays of cricket responses to S. kuhlii body odor

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We employed two-choice tests in a Y-tube olfactometer (Shelai Instrument Platform, China) to assess whether the body odor of S. kuhlii elicits avoidance behavior in the cricket L. equestris. Individuals of L. equestris were collected nightly (19:30–21:30) from the bat’s foraging habitat (see the section above) and acclimated in insect cages with water provided in a temporary laboratory until testing.

The olfactometer consisted of a Y-shaped glass tube (inner diameter: 35 mm; main arm length: 300 mm; side arm length: 200 mm). One side arm was connected in series to a glass chamber housing one of eight adult S. kuhlii individuals (randomly selected for each trial) and a charcoal filter bottle; the other side arm was connected to an empty glass chamber and a charcoal filter bottle. Both charcoal bottles contained activated carbon for air purification. An air pump supplied airflow, regulated to 1 L/min in each arm by flowmeters.

Behavioral trials commenced at 23:00 under complete darkness. Before each trial, the Y-tube was cleaned with ethanol and oven-dried. Odorless sponges (diameter: 35 mm, thickness: 15 mm; Wuhan Hemida Technology Co., Ltd., China) were placed at the end of each arm to prevent the crickets from escaping. After confirming system airtightness, a single cricket was introduced at the junction (center) of the Y-tube, and its behavior was recorded using an infrared video camera (4K Ultra; Sony, China). Preliminary observations confirmed that crickets typically initiated exploration of the apparatus arms within 3 s of being introduced. Therefore, video recording began once a cricket started exploring the two side arms. A cricket was scored as ‘avoiding’ the bat odor if it entered the control arm and remained there continuously for 10 s. It was scored as ‘not avoiding’ the bat odor if it entered the test arm and remained there continuously for 10 s. Individuals that moved back and forth between the two arms without remaining in either arm for at least 10 s were also classified as ‘not avoiding’. We selected a 10 s duration as the criterion because the crickets were highly mobile, and this period was sufficient for them to fully explore the olfactometer. A total of 47 crickets were tested, with each individual used in a single trial. The left/right position of the bat odor source was randomized between trials to control for side bias. A separate control experiment with 24 crickets was performed under identical conditions, except that no bat was placed in the glass chamber. Behavioral responses were recorded following the same procedure. To minimize observer bias, the video recordings were analyzed by an experimenter who was blinded to the treatment conditions.

We collected the body odor of S. kuhlii (i.e. its VOCs) for chemical characterization and electrophysiological bioassay using GC–MS and GC–EAD with cricket antennae, respectively. Samples were collected from eight adult individuals (four females and four males) using a dynamic headspace sampling system equipped with Porapak-Q adsorbent columns. We selected Porapak-Q because bat body odor is rich in hydrocarbons, which are non-polar or only weakly polar, and Porapak-Q has a high adsorption capacity for these compounds. The system consisted of a vacuum pump (QC-1S; Beijing Institute of Labour Protection Science, China), a charcoal filter for air purification, a 5 L glass chamber housing the bats, and a Porapak-Q column (glass tube, 4 mm internal diameter, 200 mg of Porapak-Q 80/100 mesh; Supelco, USA) (Wang et al., 2023). Prior to sampling, all glassware was thoroughly rinsed with ethanol and dried in an oven at 120°C, and volatile odor collection was conducted in a dedicated odor-free room to minimize environmental contamination. Each adsorbent column was cleaned by rinsing sequentially with 3 mL of methanol and dichloromethane (DCM), followed by thermal conditioning under nitrogen at 180°C for 30 min. During VOC collection, purified air from the pump was passed through the system at a flow rate of 500 mL/min for 2 hr. Control samples were collected from empty glass chambers following the same procedure. After sampling, VOCs were eluted from each column with 1 mL of DCM, and the extracts were concentrated under a gentle nitrogen stream to a final volume of 1 mL. All samples were stored at –20°C until subsequent analysis by GC–EAD and GC–MS. The eight S. kuhlii used for odor collection and behavioral assays were captured from a roosting palm tree on October 1, 2023. Bats were housed socially in cages (50 cm×50 cm×50 cm) in a temporary laboratory maintained at 28°C and 60% relative humidity. Mealworm larvae (Tenebrio molitor) and water were provided daily following nightly experiments.

The antennae are a primary sensory organ for detecting both chemical and mechanical cues in crickets. To determine if L. equestris can physiologically perceive bat body odor, we employed a GC–EAD system following established methods (Barbosa-Cornelio et al., 2019; Ma et al., 2025) to test the electrophysiological sensitivity of L. equestris antennae to bat-derived VOCs. The system consisted of an Agilent 7820A gas chromatograph (Agilent Technologies, USA) and EAD equipment (Syntech, Germany). The EAD setup comprised an IDAC 4 signal recording controller, an MP-15 micromanipulator with high-impedance PRG-3 electrodes.

The tips and bases of cricket antennae were trimmed and mounted on a PRG-3 electrode using an MP-15 micromanipulator. After achieving a stable baseline, odor samples were separated on an Agilent 7820A gas chromatograph. A 4 μL aliquot was injected in splitless mode onto an HP-5 column (30 m×0.32 mm × 0.25 μm). We selected the HP-5 column because its non-polar stationary phase achieves excellent chromatographic separation of the non-polar to weakly polar hydrocarbons that dominate bat body odor, which is essential for subsequent detection. The GC oven temperature was programmed as follows: held at 50°C for 5 min, increased at 10 °C/min to 200°C (held 1 min), then raised at 20 °C/min to 250°C (held 5 min). Nitrogen carrier gas flow was 2.0 mL/min. Injector and detector temperatures were 250°C and 300°C, respectively. The GC effluent was split at a 1:2 ratio using a Y-shaped splitter. Two-thirds of the effluent was directed to a flame ionization detector (FID) for compound detection, and one-third was transferred through a heated transfer line into a humidified airstream (400 mL/min) before being delivered to the cricket antenna. Antennal signals were acquired using an IDAC-4 interface and GcEad software (v1.2.5; Syntech, Germany), allowing simultaneous recording of FID and EAD responses. Each chromatographic peak in the FID trace had a corresponding EAD trace, showing the depolarization of the antennal potential.

A total of eight antennae from individual crickets were tested. Five antennae were exposed to bat odor extracts, and three were exposed to empty-chamber controls (one antenna per injection). The empty-chamber controls were used to account for potential background signals from the experimental system and to provide a baseline for comparison with bat odor extracts. Snout secretions were not included in the GC–EAD analyses because the present study focused on bat whole-body odor as the ecologically relevant airborne odor source, and sufficient secretion samples for standardized volatile extraction were not available for electrophysiological testing. A compound was considered EAD-active if its GC peak coincided with an antennal depolarization >0.1 mV, and this response was reproducible in at least three individual antennae. Active compounds were identified by aligning the retention times and peak profiles of EAD-active signals with those from parallel GC–MS analyses.

Chemical profiling and source analysis of S. kuhlii body odor

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To characterize the major volatile constituents of S. kuhlii body odor, we analyzed the collected VOC extracts by GC–MS using an Agilent 6850–5975 mass spectrometer operated in electron ionization (EI) mode at 70 eV. The GC was equipped with the same HP-5 column used for GC–EAD, with helium as the carrier gas at a constant flow of 1 mL/min. A 1 μL aliquot of each sample was injected in splitless mode (0.75 min splitless period) at an injector temperature of 250°C. The oven temperature program matched that used for GC–EAD. Compounds were tentatively identified by comparison of their mass spectra with the NIST 2017 library. The identities of these compounds were confirmed by matching both retention times and mass spectra to those of authentic standards, including injections of a mixture containing 100 ng of each compound. However, co-injection with authentic standards could not be performed because the total extract volume from each sample was insufficient for additional runs after the primary GC–MS and GC–EAD analyses, owing to the limited amount of biological material available.

To investigate potential biological sources and characterize the volatile chemical profiles of bat body odor, we performed a broadly targeted volatile metabolomic analysis using HS–SPME–GC–MS on hair, pararhinal gland (snout) secretions, and feces from nine bats (four females, five males). These bats were collected and maintained as described above. Upon capture, bats were placed in clean stainless-steel cages and kept in groups consistent with their natural social associations during the brief interval prior to immediate odor sampling. Hair samples (10 mg per individual) were clipped from dorsal and ventral regions. Snout secretions were collected using sterile cotton swabs (CS15-005, Shenzhen SihuaBo Technology Co., Ltd., China), with two blank swabs as controls. These blank swab controls were included to account for potential volatile contamination from ambient air or the swab material itself. Fecal samples (100 mg per individual) were collected as in the dietary analysis. All samples were stored in 2 mL Eppendorf tubes at −80°C until analysis.

Samples were thawed on ice, transferred to headspace vials, and spiked with 10 µL of an internal standard (50 µg/mL). Volatiles were extracted by HS–SPME: after incubation at 60°C for 5  min, a 120 μm DVB/CWR/PDMS fiber was exposed to the headspace for 15 min for adsorption, then thermally desorbed at 250°C for 5 min in the GC injector. Analysis was performed on an Agilent 7890B 7000D GC–MS system equipped with a DB-5MS capillary column (30 m × 0.25 mm × 0.25 μm), with helium at 1.2 mL/min. The oven temperature program was: 40°C for 3.5 min, then increased at 10 °C /min to 100°C, 7 °C/min to 180°C, and 25 °C/min to 280°C (hold 5 min). The MS operated in EI mode at 70 eV, with ion source, quadrupole, and interface temperatures set at 230°C, 150°C, and 280°C, respectively. Data were acquired in selected ion monitoring mode. Each compound was identified by matching the retention time and the presence of one quantitative and two to three qualitative ions to those of a standard reference (Yuan et al., 2022). Data were integrated, corrected, and processed using MassHunter software (B.08.00; Agilent), and metabolites were qualified and quantified against an in-house database. Retention times for non-target compounds are not fully disclosed due to the proprietary policy of the commercial analytical service provider.

To identify the biological source of the characteristic body odor, we compared the VOC profiles from hair, feces, and snout secretions. Volatiles were classified into mutually exclusive chemical classes for comparison; note that ‘Hydrocarbons’ here refers only to aliphatic hydrocarbons, and ‘Nitrogen compounds’ excludes amines. To objectively assess the overall compositional similarity among hair, feces, snout secretions, and body odor, we performed a PCA based on binary (presence/absence) VOC data.

Identifying avoidance-eliciting odor compounds

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Chemical profiling of S. kuhlii body odor by GC–MS identified six volatile compounds: 2,2-dimethylheptane, limonene, undecane, 2,6,7-trimethyldecane, pentadecane, and hexadecane, two of which elicited EAD responses. Because GC–EAD provides a physiological readout of antennal sensitivity but may not fully capture behavioral relevance across different concentrations or odor contexts, we additionally tested antennal responses to commercially available standards of compounds identified by GC–MS. We selected four compounds (limonene, undecane, pentadecane, and hexadecane) and evaluated antennal responses using EAG. Each compound was diluted to 10% (vol/vol) in hexane for screening, resulting in a final limonene concentration of 5.87×10–7 mol/µL (Appendix 1—table 4). Hexane alone served as the solvent control. Stimuli were prepared by applying 10 μL of a test solution to a filter-paper strip (10 mm × 30 mm), which was then placed inside a glass Pasteur pipette. The pipette tip was positioned in a humidified airstream (1500 mL/min) directed at the antenna through a stainless-steel tube (7 mm inner diameter, 1 cm from the antenna). Cricket antennae were prepared as for GC–EAD. For each antenna, a hexane control was first presented to establish baseline antennal activity. For the initial screening, limonene, undecane, pentadecane, and hexadecane were diluted to 10% (vol/vol) in hexane and delivered individually in a randomized order. To assess dose-dependent responses, limonene was further tested at five concentrations (0.001%, 0.01%, 0.1%, 1%, and 10%, vol/vol in n-hexane), following the concentration gradient used in a previous study (Tang et al., 2024). Following the initial hexane control, the five limonene concentrations were tested in a randomized order across trials. Each stimulus lasted 0.5 s, with an inter-stimulus interval of 1 min to allow full recovery of antennal responses. EAG responses were recorded from eight antennae for the initial screening and from six antennae for the dose–response assay, with each antenna obtained from a different individual, and analyzed using EagPro software (v2.0; Syntech, Germany).

To examine the behavioral response of L. equestris to limonene, a two-choice assay was conducted using 10% (vol/vol) limonene (5.87×10–7 mol/µL) as the odor source. A 10 μL aliquot was applied to a filter-paper strip (10 mm × 30 mm) inside a glass chamber. A total of 38 L. equestris individuals were tested under the same experimental conditions as in the bat-odor assay.

To ecologically validate that a single odor compound can elicit avoidance, we tested the effect of 10% (vol/vol) limonene (5.87×10–7 mol/µL) on the calling activity of L. equestris within the bats’ foraging habitat. Over nine nights between June 24 to July 16, 2024, we established four sampling plots (5 m × 5 m) in a linear array at 100 m intervals within the known foraging range of S. kuhlii, at the same location where crickets were originally captured (Figure 4—figure supplement 1). Each night, two plots were randomly assigned to the limonene treatment and two to hexane control. To assess avoidance, we monitored changes in calling activity of L. equestris as a proxy, using passive acoustic monitoring during their peak nocturnal activity. At each plot, an AudioMoth (v1.2.0, Open Acoustic Devices, UK) was positioned 0.5 m above ground with the microphone facing downward. Audio was recorded in 55 s files separated by 5 s intervals (1 min duty cycle) at a 384 kHz sampling rate (medium gain). A 40 min pre-exposure recording started at 19:50. Between 20:30 and 20:40, 1 mL of 10% limonene (treatment) or hexane (control) was evenly sprayed over the 25 m2 area centered on the recorder. A 40 min post-exposure recording started at 20:50. Experimental nights were separated by at least 1 day, and rainy conditions were avoided.

During acoustic preprocessing, we excluded plot–night datasets that failed quality-control criteria: no cricket calls detected, >20% of 1 min files with zero calls, or total call count <10,000. This quality-control procedure yielded a final sample of 15 datasets: 8 from the limonene-treated (experimental) plots and 7 from the hexane-treated (control) plots. In total, 1200 recordings (80 files/plot/night) were analyzed. Using Avisoft SASLab Pro (v5.2.07, Avisoft Bioacoustics, Germany), recordings were band-pass filtered (3–6.5 kHz) to match the calling frequency of L. equestris (dominant frequency ~5 kHz, range 4.5–5.5 kHz). This frequency band does not overlap with sympatric cricket species, and L. equestris is the dominant local cricket (see Results), allowing reliable identification. Calls were identified via spectrogram matching using 10 templates of L. equestris calls. A ‘call’ was defined as a train of successive sound elements (syllables). Typical calls of L. equestris last 60–200 ms (Figure 4—figure supplement 2), with inter-syllable intervals <10 ms within a call, and >60 ms between consecutive calls from the same individual. The number of calls per minute was quantified for each plot and recording period. Our assays were designed to test physiological detectability and functional sufficiency, rather than to establish concentration thresholds or mimic natural emissions exactly.

Data analysis

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All analyses were performed in R v4.3.2 unless specified otherwise. For the two-choice olfactory assay, we used a chi-square goodness-of-fit test to compare the number of crickets exhibiting ‘avoiding’ vs. ‘not avoiding’ behavior. Effect sizes are reported as the phi coefficient (φ) with 95% confidence intervals (estimated via the delta method). For chemical profiling, we performed multivariate analysis on VOC data. Hierarchical clustering with heatmap visualization was conducted in Python (v3.9) using the pandas, seaborn, and matplotlib libraries, based on data normalized to a 0–1 range, Euclidean distance, and average linkage. PCA based on a binary (presence/absence) matrix was performed using the vegan package in R to compare profiles from hair, feces, snout secretions, and bat body odor. For the EAG experiment, EAG responses were analyzed using repeated-measures ANOVA (sphericity checked with the rstatix package), followed by Bonferroni-corrected paired t-tests. Effect sizes are reported as partial eta-squared (η²p) for the ANOVA and Hedges’ g for the t-tests. For field calling activity, a GLMM was fitted to calls per minute using the glmmTMB package. Model selection compared zero-inflated and standard negative binomial distributions using AIC, and evaluated alternative random effects structures and fixed effects using AICc. The final model included a Group × Phase interaction as a fixed effect, with a random intercept for the plot-by-date combination to account for spatial and temporal non-independence. Model diagnostics were performed with the DHARMa package, including tests for residual uniformity, overdispersion, and zero-inflation.

Animal welfare

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The bats used in this study were carefully handled and maintained under controlled conditions (28°C, 60% relative humidity) with ad libitum access to food and water. Bats were housed socially in cages (50 cm×50 cm×50 cm). No bat mortality occurred during the study, and all bats were released at their original roost sites upon completion of experiments. Crickets were kept at densities ≤5 individuals per container to minimize aggression. Crickets not used in EAG assays were returned to their collection sites.

Appendix 1

Appendix 1—table 1
Composition of Gryllidae (crickets) in the insect prey community at a foraging site of S. kuhlii.

Species identified from grassland samples collected between July and September 2022 are listed with their corresponding order, family, and number of individuals.

Insect speciesOrderFamilyNumber
Loxoblemmus equestrisOrthopteraGryllidae70
Polionemobius taprobanensisOrthopteraGryllidae1
Svercacheta siamensisOrthopteraGryllidae5
Amusurgus genjiOrthopteraGryllidae1
Modicogryllus consobrinusOrthopteraGryllidae1
Teleogryllus emmaOrthopteraGryllidae2
Appendix 1—table 2
Annotation information for terpenoid compounds detected in volatile organic compound (VOC) samples from S. kuhlii.

Key parameters used for VOC annotation are summarized, including the NIST retention index (RI), quantitative ion, qualitative ion(s), and molecular weight (MW, Da). Compound identification was based on matching retention time and the presence of one quantitative ion and two to three qualitative ions with standard references, followed by qualification and quantification against an in-house database.

CompoundNIST RIQuant. ionQual. ionMW (Da)
Limonene102393136136.125
ar-Curcumene1524119132202.172
α-Phellandrene9699391136.125
δ-Elemene137712193204.188
trans-Farnesol17104169222.198
Terpinolene105293121136.125
Nerolidol15649369222.198
Phytol20457143296.308
δ-Cadinene1469161134204.188
Pulegone121215281152.12
Cedrol154395150222.198
β-Elemene13988168204.188
Safranal118610791150.104
7-Octylidene-bicycloheptane152213593206.203
Appendix 1—table 3
Generalized linear mixed models examining the effects of limonene (experimental) vs. hexane (control) treatments on call activity in the cricket L. equestris.

The model was fitted using a zero-inflated negative binomial distribution via the glmmTMB package in R. Fixed effects included the four-level treatment–phase combination (Control–Pre-exposure, Control–Post-exposure, Experimental–Pre-exposure, Experimental–Post-exposure); random effects comprised a random intercept for plot-night (plot:date). Results are presented twice with different reference categories: first with ‘Control–Pre-exposure’, then with ‘Experimental–Pre-exposure’. Both presentations derive from the same fitted model, yielding two intercept estimates but identical coefficients.

TermEstimateSE95% CIZp
Control–Pre-exposure (Intercept)5.300.18[4.94, 5.66]29.02<0.001
Control–Post-exposure0.400.06[0.28, 0.51]6.68<0.001
Experimental–Post-exposure–0.590.25[-1.08,–0.10]–2.350.019
Experimental–Pre-exposure0.100.25[–0.39, 0.59]0.380.720
Experimental–Pre-exposure (Intercept)5.400.17[5.06, 5.73]31.59<0.001
Experimental–Post-exposure–0.680.06[-0.80,–0.57]–11.90<0.001
Control–Post-exposure0.300.25[–0.19, 0.79]1.210.228
Control–Pre-exposure–0.100.25[–0.59, 0.39]–0.380.702
Appendix 1—table 4
Synthetic chemical standards and solvents used in the study.
CompoundSourceCAS Registry NumberPurity (%)
UndecaneMacklin1120-21-4≥98%
PentadecaneMacklin629-62-9≥99%
HexadecaneMacklin544-76-3≥98%
LimoneneMacklin5989-54-8≥95%
DichloromethaneMREDA75-09-2≥99%
HexaneMREDA110-54-3≥98%

Data availability

The raw behavioral, electrophysiological, and ecological datasets have been deposited in figshare and are publicly available at https://doi.org/10.6084/m9.figshare.31077256.

The following data sets were generated
    1. Li Y
    2. Zhang W
    3. Wei J
    4. Xu H
    5. Feng J
    6. Lin A
    (2026) figshare
    Crickets evade bats via olfaction beyond acoustic cues.
    https://doi.org/10.6084/m9.figshare.31077256

References

Article and author information

Author details

  1. Yannan Li

    Jilin Provincial Key Laboratory of Animal Resource and Ecological Security, Northeast Normal University, Changchun, China
    Contribution
    Conceptualization, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0009-0009-3014-1633

  2. Wenhao Zhang

    Jilin Provincial Key Laboratory of Animal Resource and Ecological Security, Northeast Normal University, Changchun, China
    Contribution
    Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0009-0002-2327-8024

  3. Jiaqi Wei

    State Key Laboratory of Green Pesticide, College of Plant Protection, South China Agricultural University, Guangzhou, China
    Contribution
    Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0009-0004-9656-7737

  4. Hanhong Xu

    State Key Laboratory of Green Pesticide, College of Plant Protection, South China Agricultural University, Guangzhou, China
    Contribution
    Investigation
    Competing interests
    No competing interests declared

  5. Jiang Feng

    1. Jilin Provincial Key Laboratory of Animal Resource and Ecological Security, Northeast Normal University, Changchun, China
    2. Jilin Provincial International Cooperation Key Laboratory for Biological Control of Agricultural Pests, Jilin Agricultural University, Changchun, China
    Contribution
    Conceptualization, Methodology
    For correspondence
    fengj@nenu.edu.cn
    Competing interests
    No competing interests declared

  6. Aiqing Lin

    1. Jilin Provincial Key Laboratory of Animal Resource and Ecological Security, Northeast Normal University, Changchun, China
    2. Key Laboratory of Vegetation Ecology of Education Ministry, Institute of Grassland Science, Northeast Normal University, Changchun, China
    Contribution
    Conceptualization, Formal analysis, Methodology, Writing – original draft, Writing – review and editing
    For correspondence
    linaq376@nenu.edu.cn
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2832-2605

Funding

National Natural Science Foundation of China (32430066)

  • Jiang Feng

National Natural Science Foundation of China (32571749)

  • Aiqing Lin

National Natural Science Foundation of China (32271558)

  • Aiqing Lin

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We are grateful to Jiqian Li, Yinli Hu, and Maojun Zhong for their advice on diet and behavioral analysis, and to Weiwei Wang and Pengji Li for their assistance with the field experiments.

Ethics

All procedures involving live animals were approved by the Science and Technology Ethics Committee of Northeast Normal University, Changchun, China (permit ID: NENU-2022-0308).

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Copyright

© 2026, Li et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Yannan Li
  2. Wenhao Zhang
  3. Jiaqi Wei
  4. Hanhong Xu
  5. Jiang Feng
  6. Aiqing Lin
(2026)
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eLife 15:RP110936.
https://doi.org/10.7554/eLife.110936.3

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