Introduction

The study of prey selection by insectivorous bats has been approached primarily from two points of view: i) the morpho-physiological and ultrasonic ability of bats as predators to capture, handle and digest the insects they consume (Evans and Sanson 2005 and references therein); and ii) the ultrasonic capacity of certain types of insects as potential prey to evade bats (Fullard and Yack 1993, Corcoran and Moss 2017, Corcoran and Conner 2017). Since foraging decisions are directed by many external and internal factors, this remains an understudied area of bat ecology.

Insectivorous bats make foraging decisions choosing for prey traits such as size, hardness and mode of locomotion, among others, which have been mainly explained by considering bats’ morphology (e.g. body size, body weight, forearm ratio, wing morphology and cranial adaptations), foraging strategies and echolocation signal structure (Freeman 1981, Barclay and Brigham 1991, Jones 1992, Rydell and Waters 1995, Vaughan 1997, Bogdanowicz et al. 1999, Evans & Sanson 2005, Zeale et al. 2010, Koselj et al. 2011, Weterings and Umponstira 2014). Also, seasonal changes in consumption of certain types of prey are known to change in response to energy demands related to pregnancy, lactation, molt, and the availability of food resources (Kunz 1974, Clare et al. 2011). In regard to cranial adaptations, a general relationship between the bats’ morphology and diet has been recognized, where small bats with more delicate skulls usually take more soft-bodied prey as moths, and bigger bat species with robust skulls that have larger masseter muscles, great sagittal crests, longer coronoid processes and condyle lengths eat harder insects such as beetles (Freeman 1979, Jacobs 1996, Bogdanowicz et al. 1999, Dumont and Herrel 2003, Dumont 2007, Anderson et al. 2008, Krüger et al. 2014).

Additionally, insectivorous bats use echolocation for orientation, as well as to detect, locate, classify and capture prey. Bats emit intense pulses of sound when hunting (up to 140 dB SPL source levels) and analyze the reflected echoes returning by the target prey (Surlykke and Kalko 2008). However, in order to evade bats, many species of insects – principally moths, but also beetles, lacewings, and mantises – have evolved ears which allow them to hear the ultrasonic bat calls and avoid predation. In a more recent study, it was found that the acoustic warning and mimicry are the purpose for sound production in moths, although some moth species use high-duty-cycle ultrasound able to jam bat sonar (Barber et al. 2022). Further, it has been shown that bats with calls above or below frequencies between 30 and 60 kHz tend to capture eared insects more frequently than others (i.e. the so-called allotonic frequency hypothesis; Fullard 1987, Rydell and Arlettaz 1994, Schoeman and Jacobs 2003, Goertlitz et al. 2010, Corcoran and Conner 2017). Additionally, a general trend has been found where small-sized bats, which usually emit calls with higher maximum frequency (Fmax), feed on softer insects than do larger bats with lower Fmax (Jones 1992, Pavey and Burwell 1998, Bogdanowicz et al. 1999, Fenton et al. 1998, Jacobs 2000, Schoeman and Jacobs 2003, Weterings and Umponstira 2014).

Most of the studies addressing the relationship between diet and ultrasonic call structure within bat communities have been performed from a compilation of published data from the literature with different methodologies and at different study sites around the world (Jones 1992, Bogdanowicz et al. 1999, Weterings and Umponstira 2014). However, few studies have reported the bats’ diet and ultrasonic signal structure from bat communities captured during the same time and at the same location (Pavey and Burwell 1998, Fenton et al. 1998, Jacobs 2000, Schoeman and Jacobs 2003). Here, we correlated weather, morphological and call variables to prey hardness index with and without correcting for phylogenetic relationships in seven insectivore bat species (Vespertilionidae) in a mountain ecosystem of central Mexico. For this purpose, traditional correlations and Phylogenetically Independent Contrasts (PIC’s) were performed to better explain results by ecological factors (traditional correlations) or either by phylogenetic bats history (PIC’s).

Materials and methods

Study site and bat collection

Vespertilionid bat species were collected in La Malinche National Park (LMNP), a mountain ecosystem belonging to the Transvolcanic Belt, in Central Mexico (19°13′34.08" N, 98° 1′28.92" W; Acosta and Kong 1991). Climate is temperate sub-humid with a rainy season in summer. Ambient temperature ranges between 5 and 12 ºC throughout the year. The dominant vegetation type is composed of pine, pine-fir and high mountain grasses (INEGI 1987). Bats’ feeding habits were determined on the basis of fecal samples, collected from April 2014 to July 2015. Monthly captures were carried out using a total of 6 mist nets (3 × 2 and 6 × 2 m) located at 3,020; 3,089 and 3,156 m a.s.l., respectively), for two nights. Nets were open at dusk and closed at 01:00 am. Because mountain ecosystems are in general quite cold during night, we checked the nets every 20–30 min to guarantee the good health of bats. Bats were captured under permission of the Dirección General de Vida Silvestre (SEMARNAT 07,019 to MMG). We obtained monthly weather variables, incluiding: minimum, maximum and mean monthly temperature (ºC), mean monthly precipitation (mm), and minimum, maximum and mean monthly humidity (%) from La Malinche Biological Station, which is located 300-600 m apart from the capture sites.

Fecal samples

Bats’ feces collected in this study corresponded to the same individuals studied by Ayala-Berdon et al. (2017). We collected 79 feces samples from 8 bat species (Myotis melanorhinus n = 21; Myotis californicus n = 6; Myotis Volans n = 25; Myotis thysanodes n = 2; Myotis velifer n = 5; Corynorhinus mexicanus n = 3; Idionicteris phylotis n = 1; Eptesicus fuscus n = 16). In brief, bats were individually introduced in cotton bags during two hours after capture. We placed bat feces in paper envelopes until analyses. Fecal analyses were performed in laboratory, following Whitaker (1988). We softened the fecal samples in water; next, we teased pellets with dissecting needles under a dissection microscope. Then, we fixed the insects remains on slides with adhesive mucilage. Insect remains were subsequently analyzed and identified to the order level following Whitaker (1988) and Mc Aney et al. (1991), and with the direct recognition of undigested structures.

Index of prey hardness

Since the invertebrate prey of bats exhibit a range of body sizes and exoskeleton hardness related to insect type, to ranks them on a qualitative scale of hardness we followed Freeman (1981). In this scale, bats prey was ranked from the softest (1) to the hardest (5), to arrive an average hardness scale of eaten invertebrates. In brief, percent volume of diet items in feces was calculated as percentage of the slide area covered with a specific prey item using Image J Version 1.53a Software® (Schneider et al. 2012). Then, the index of prey hardness was ranked from soft to hard; since in this study, the insect orders found in feces were represented by Diptera, Neuroptera, Lepidoptera and Coleoptera, then the correspondent hardness values were 1 for Diptera and Neuroptera; two for Lepidoptera and 5 for Coleoptera (for further details see Freeman 1981). Finally, the whole diet of each individual was obtained by multiplying the hardness value for each dietary item and their percent volume. For individuals containing multiple diet items, index of hardness was averaged (Ghazali and Dzeverin 2013, Table 1).

Table 1 Prey items in diet of vespertilionid bat species. Index of hardness for the whole diet of each bat species, was obtained multiplying the hardness value of each item as suggested by Freeman (1981) and Ghazali and Dzeverin (2013) by its own percent volume
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Bats’ body and cranial measures

Bat’s body measures were obtained from our own collected bats in field, and cranial measures were obtained from museum specimens. In brief, data of body mass (g), total body length (mm) and forearm length (mm) published by Ayala-Berdon et al. (2017). These individuals were the same we used to collect the fecal samples analyzed here (see above). Regarding cranial measures, we compiled mandibular length, height of the coronoid process, zygomatic breadth, C-M3 inferior row length, lower molar width, C-M3 superior row length, and upper molar width (Fig. 1). Measures were taken to the nearest 0.01 mm. Cranial measures of 7 vespertilionid bat species were taken from 100 specimens stored at the Mexican Mammal National Collection of the Biological Institute UNAM, Mexico (M. melanorhinus n = 4, M. volans n = 1, M. thysanodes n = 8, C. mexicanus n = 13, I. phyllotis n = 5, M. velifer n = 33 and E. fuscus n = 36, Additional file 1. Appendix A).

Fig. 1
figure 1

Cranial measures of vespertilionid bat species of specimens sheltered at the Mammal National Collection of the Biological Institute, UNAM, Mexico (Original figure). Mandibular length (ML), height of the coronoid process (HCP), zygomathic breadth (ZB), C-M3 inferior row length (IRL), lower molar width (LMW), C-M3 superior row length (SRL), and uper molar width (UMW)

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Bats’ ultrasonic signal variables

In order to determine whether there was a relationship between bat echolocation signal structure and prey hardness, we considered 6 ultrasonic call variables (call duration, low frequency, high frequency, band width, frequency max power and total slope) belonging to 592 pulses obtained from 7 vespertilionid bat species taken from Ayala-Berdon et al. (2021, Additional file 2. Appendix B).

Statistical and phylogenetic analysis

In order to assess correlations among prey hardness and weather, bats’ body, cranial and ultrasonic variables, we performed conventional non-phylogenetic Spearman´s correlations using the Prism 8 ® Version 8.4.0 for Mac OS, GraphPad Software. For phylogenetic analyses, the matrix was constructed with DNA sequences of two mitochondrial (COI and Cyt-b) and one nuclear gene (RAG2) housed in GenBank (July 2016), which has been used in previous works on Phyllostomidae phylogeny with different taxon sampling and combinations (Table 2, Baker et al. 2016, Datzmann et al. 2010, Monteiro and Nogueira 2011, Dávalos et al. 2012). The sequences were extracted with the search code “Phyllostomidae + name of gene” and saved as the GenBank (full) format. The data matrix was constructed with the GenBank to TNT software (GB2TNT) with all the instructions recommended by the authors; muscle as the software for alignment with the “-maxiters 1” instruction, and finally obtain a matrix for TNT software (Goloboff and Catalano 2012, 2016). The complete analysis comprised of a total of 127 species and 7401 characters. Phylogenetic analyses were implemented with New Technology Search in TNT v.1.5 (Goloboff et al. 2008, Goloboff and Catalano 2016). The New Technology search options were: a) Sectorial Search (Consensus 5 rounds, fuse trees 3 times), b) Ratchet with default values, c) Drift with default values, d) Tree fusing (five rounds), and getting trees from Stabilize consensus 5 times with factor 75, random seed = 1,300 random addition sequences, swapping algorithm tree bisection and reconnection (TBR), holding 200 trees per replication. The phylogeny was pruned in Mesquite to include only the species of interest (Additional file 3. Appendix C). The phylogenetically independent contrasts (PIC´s) were calculated using the R package caper (Orme et al. 2013). The phylogenetic signal in variables data was calculated with the K statistic value (Blomberg et al. 2003) with the picante package (Kembel et al. 2010) in R, testing the significance of the K value that differs by chance with the “phylosignal” function with 1000 randomly repetitions.

Table 2 GenBank numbers for Vespertilionidae sequences used in phylogenetic analyses
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Results

Feeding habits of vespertilionid bats

Prey consumed by these bats were mainly composed by four orders of insects: Diptera, Neuroptera, Lepidoptera and Coleoptera (Fig. 2). All bat species consumed Lepidoptera and Coleoptera in different proportions, with the exception of Myotis thysanodes (Miller 1897) that only consumed Coleoptera. While, Lepidoptera was found in Myotis melanorhinus (Merriam 1886), Myotis californicus (Audubon and Bachman 1842), Myotis volans (Allen 1866) and Eptesicus fuscus (Palisot de Beauvois 1796), Neuroptera was only consumed by M. volans. Besides, mean prey hardness index ranked our bat species from soft (1) to hard (5) as follows: Myotis melanorhinus, Corynorhinus mexicanus, Myotis volans, Myotis californicus (< 3); Myotis velifer (< 4); Eptesicus fuscus, Idionycteris phyllotis and Myotis thysanodes (> 4.2, Table 1).

Fig. 2
figure 2

Insect orders percentage of occurrence in fecal samples of eight vespertilionid bat species captured in a Mountain ecosystem (LMNP), Tlaxcala, Mexico (Original figure)

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Conventional non-phylogenetic correlations between prey hardness and weather, bat’s body, cranial and ultrasonic cranial correlates

Prey hardness positively correlated with weather variables as minimum and mean temperatures (p < 0.05), and was not statistically significant when correlated to altitude, Tº max, and min, max and mean H (p > 0.05). Regarding to bat’s body variables, prey hardness positively correlated with body weight, total and forearm length (p < 0.0001) and no significant correlates were found with uropatagium (p > 0.05). Concerning to cranial variables, prey hardness positively correlated with zygomatic breadth (p < 0.005), C-M3 inferior row length (p < 0.001), mandibular length, height of the coronoid process, lower molar width, C-M3 superior row length and upper molar width (p < 0.0001). About ultrasonic signal variables, prey hardness negatively correlated to total slope (p < 0.05), call duration and low frequency (p < 0.005), high frequency, band width and frequency maximum power (p < 0.0001, Additional file 4. Appendix D).

Phylogenetic signal and correlates corrected for phylogenies

Phylogenetic signal (K) was significant for body weight and total slope ultrasonic variables (p < 0.05, Additional file 5. Appendix E). However, for hardness (p = 0.31) and most of the variables tested here, K was not significant (p < 0.05). When correlates were corrected for phylogenies, significant correlations were found between prey hardness and mandibular length, C-M3 inferior and superior row length (p < 0.05). No significant correlates were found between hardness and weather, body and ultrasonic signal variables (p > 0.05, Additional file 5. Appendix E).

Discussion

Bats preferences for a certain prey type or common traits in prey have been explained considering different approaches related to both, prey and bats (Freeman 1981, Fullard 1987, Vesterinen et al. 2016, Dumont 2007, Ter Hofstede et al. 2016, Arrizabalaga-Escudero et al. 2019, Barber et al. 2022). For example, variations in bat’s bite force has been found to result in variations in the range of food items bat species can consume (Santana and Dumont 2009). However, the fact that bats specialize in consuming certain prey does not mean that they exclude other type from their diet (Weterings and Umponstira 2014). Here, we found that in a community of vespertilionid bats inhabiting a mountain ecosystem of central Mexico: i) diet of studied bat species was mainly composed by 4 orders of insects; ii) when analyzed by conventional nonphylogenetic correlations, prey hardness was positively correlated with minimum and mean monthly temperature, bat body weight, total and forearm lengths, the 7 bat cranial variables studied here; and negatively correlated with the six ultrasonic variables here explored; and iii) when corrected for phylogenies, prey hardness was only correlated to cranial variables related to mandibular and tooth rows length, even so, their phylogenetic signal was not significant. Regarding to insectivore bats, to our knowledge this is the second attempt at determining correlates of prey choice within a phylogenetical framework, however, in this study and in the one previously published by Ghazali and Dzeverin (2013), phylogenetic signal of hardness (Blomberg’s K statistic value) was not significant, probably due to the low number of bat species, as phylogenetic signal is difficult to detect with fewer than 20 species (Bloomberg et al. 2003). Therefore, in this section, we will discuss our findings based on standard statistic methods without phylogenetic corrections.

Our results showed that bats’ diet composition mainly contained Diptera, Neuroptera, Lepidoptera and Coleoptera, and all bat species included hard prey (Coleoptera) as well as softer prey (either / or Diptera, Lepidoptera, Neuroptera) with the exception of Myotis thysanodes. The feces of both individuals of M. thysanodes in this study only contained remains of hard insects (Coleoptera). Despite the low number of individuals analyzed here, the present results are consistent with some other studies of this species, in which a diet dominated by beetles was also reported (Coleoptera, Black 1974, Rainey and Pierson 1996). However, unlike as what we found for this species, other studies have reported the presence of other orders (arachnids and orthopterans), albeit with a low percentage of occurrence (Black 1974), and hemipterans (Rainey and Pierson 1996). In contrast, Ober and Hayes (2008) found M. thysanodes to mainly consume Aranae, Lepidoptera and Homoptera over Coleoptera. Whitaker et al. (1977) found that the dominant prey in three out of four bats was lepidopterans with the presence of phalangids, gryllids, tipulids and araneids. Diet composition for the other studied bat species was similar to those previously reported (Kunz 1974, O’Farrel and Studier 1980, Czaplewski 1983, Warner and Czaplewski 1984, Warner 1985, Kurta and Baker 1990, Holloway and Barclay 2001, Ober and Hayes 2008, Clare et al. 2014).

Results showed that prey hardness positively correlated to minimum and mean monthly temperature. Climate parameters such as temperature are well known to influence insect dispersal in the environment, phenology, growing length development, flight behaviour and population dynamics (Netherer and Schopf 2010, Jaworski and Hilszczanski 2013, Hails 1982, Kingslover 1989, Tulp and Schekkerman 2008) and arthropod biomass (Whitaker 1952, Foley et al. 1996). It has been observed that at higher temperature conditions, the developmental time of egg, larval and pupal stages shorten, which is the characteristic phenomenon for a large group of forest species (Szujecki 1998). Faster development of these stages implies shorter time of exposure to adverse environment conditions such as low temperature, excessively high or insufficient humidity, among others, which in turn results in reproductive success of many insect species (Jaworski and Hilszczanski 2013), suggesting a greater abundance of larger and harder insects in the environment. In another study, significant variation in the amounts of coleopterans and lepidopterans in the diet of Eptesicus fuscus was found to be related with mean monthly temperature (Moosman et al. 2012), and in the same line, changes in temperature have been related to declines in prey availability for Myotis lucifugus, since the activity of aerial insects was negatively correlated to drops in temperature past a certain threshold (Taylor 1963). Other external variables as season, local insect community composition and geographic range have found to be important in determining traits in bat diet (Brian et al. 1996, Kurta et al. 1998, Leelapaibul et al. 2005, Moosman et al. 2012).

We found that prey hardness positively correlated with body weight, forearm and total bat lengths. These results showed that bat body size and mass might be important factors to explain prey hardness in the diet of insectivore bats, suggesting that the bigger an animal is, the harder the prey it can consume. Our findings agree with those found by Aguirre et al., (2003), Freeman and Whitaker (1952), Lemen (2007), and Ghazali and Dzeverin (2013). A similar trend in Stenodermatinae frugivorous bats has been recently reported (García-Herrera et al. 2021). Further, bats absolute size has been widely associated with bite force and specific traits of skull (Whitaker 1952, Stevens 2005, Dzeverin 2013, Weterings 2014). Accordingly, since body weight and forearm ratio represent wing loading, and has been found to be related with agility and maneuverability of flight, therefore, higher body weight and forearm ratio have been related to the presence of harder and larger insects in bat diets (Salsamendi et al. 2005, Weterings 2014).

Here, we found that prey hardness positively correlated with bat cranial variables. Bats cranium and dental shapes have been widely related to hardness in food they are used to consuming in the field (Freeman 1981, 2000, Aguirre et al. 2002, Gianninni and Kalko 2005, Monteiro and Nogueira 2009). Several studies have shown that bats consuming harder insects have evolved skulls with a higher coronoid process, greater development of the cranial crests, and posterior projection of the interparietal region that allow for greater muscle size and higher mechanical advantage of the temporalis (Freeman 1979, Nogueira et al. 2009, Ospina-Garcés et al. 2016). The height of the coronoid process influences the mechanics of the masticatory system (Santana et al. 2010). Among bats, stronger bite forces have been associated with an increase in cross-sectional areas of the temporalis and masseter, along with an increase of the temporalis arm which implicates a relative increase of coronoid process and a rostrum and mandible shortening (Nogueira et al. 2009). Accordingly, Ghazali and Dzeverin (2013) reported that within Myotis species, those that eat harder prey had relatively higher coronoid processes. Freeman (1979) found that molossid bats that are able to consume hard-shelled prey present large coronoid process. This characteristic allows the temporal muscle to exert more force at the back of the jaw, letting animals to crush harder material. Although the height of the coronoid process has been previously recognized as an important mandibular feature of bat prey selection, all of the cranial and mandibular features reported to be involved in previous studies (Freeman 1981, Ghazali and Dzeverin 2013) were not found to be significant in the present study. Regardless, the bite force required to process hard foods is likely a determining factor in dietary choices (Aguirre et al. 2003). In bat species with larger crowns, larger teeth have been found to be related with hardness in diet (Self 2015). All in all, the present study further supports the hypothesis that the more force bat´s bite exerts, the greater the capacity the bat will have to consume harder prey.

Results showed that prey hardness negatively correlated to ultrasonic variables. Accordingly, the peak frequency of bat´s echolocation has been strongly related to different prey types in food. As peak frequencies become higher, the amount of lepidoptera in the diet increases; by contrast, when peak frequencies are lower, the amount of Coleoptera tends to increase (Bogdanowicz et al. 1999). Moreover, Weterings and Umponstira (2014) showed that frequency of maximum intensity (Fmaxe) was positively related to Lepidoptera and negatively to coleoptera content in 92 insectivore bat species. High frequency calls are actually needed to feed efficiently on Lepidoptera. Several moth species are known to easily detect lower call frequencies and subsequently avoid the bats (Miller and Surlykke 2001, Schoeman and Jacobs 2003, Jones 2005, Weterings and Umponstira 2014). Bats that use higher call frequencies are capable of detecting much smaller insects in comparison to bats that use low frequencies (Houston et al. 2004, Jones 2005). Vespertilonid bats are classified within the oral-emitting bat taxa, meaning that echolocation calls are either produced through the mouth and no through the nasal passages. Due to the evolution of bite force, oral-emitting bats mostly feeding on hard-bodied prey, are known to have shorter, wider faces, whereas nasal-emitting bats have longer nasal areas and taller heads, in order to preserve their echolocation ability (Freeman 2000, Nogueira et al. 2009, Odendaal and Jacobs 2011). Moreover, the interactions between bats and moths have been termed as ‘an arms race’, suggesting that moths have evolved ears adapted to avoid the signals of those bats that prey on them. In response, bats have altered their echolocation calls, by using high frequencies, in order to overcome the moths’ defenses, leading to evolutionary changes that improve the moth’s ability to detect bats (Waters 2003, Barber et al. 2022). In contrast with our findings, echolocation frequency in the African Horseshoe bat (Rhinolophidae, Jacobs et al. 2014) has been positively related with bite force, suggesting that its evolution is influenced by a trade-off between the masticatory and sensory functions of the skull.

Overall, vespertilionid bat species studied here mainly fed on Lepidoptera, Diptera, Coleoptera and Neuroptera. Prey hardness positively correlated with temperature variables, bat’s body size and cranial variables and negatively with ultrasonic signal variables. Future studies should include a greater number of bat species to maintain the phylogenetic signal and be able to achieve the phylogenies influence in hardness of bats prey. Our findings clearly showed that ecological factors drive bat’s selection of feeding sources in field and supports the hypothesis that bat´s foraging decisions to select hard and soft prey are directed by weather, bat body and cranial characteristics, as well as ultrasonic traits.