Table for five, please: Dietary partitioning in boreal bats.

Differences in diet can explain resource partitioning in apparently similar, sympatric species. Here, we analyzed 1,252 fecal droppings from five species (Eptesicus nilssonii, Myotis brandtii, M. daubentonii, M. mystacinus, and Plecotus auritus) to reveal their dietary niches using fecal DNA metabarcoding. We identified nearly 550 prey species in 13 arthropod orders. Two main orders (Diptera and Lepidoptera) formed the majority of the diet for all species, constituting roughly 80%-90% of the diet. All five species had different dietary assemblages. We also found significant differences in the size of prey species between the bat species. Our results on diet composition remain mostly unchanged when using either read counts as a proxy for quantitative diet or presence-absence data, indicating a strong biological pattern. We conclude that although bats share major components in their ecology (nocturnal life style, insectivory, and echolocation), species differ in feeding behavior, suggesting bats may have distinctive evolutionary strategies. Diet analysis helps illuminate life history traits of various species, adding to sparse ecological knowledge, which can be utilized in conservation planning.

INTRODUCTION

Coexistence of sympatric species is facilitated by differences in the use of resources, that is, resource partitioning (Schoener, 1974). Resource partitioning occurs in several dimensions, with regard to resources. Ultimately, the sum of these dimensions constitutes the ecological niche of an organism, that is, the set of biotic and abiotic conditions in which a species can persist (Holt, 2009). This includes both the distribution of a species and its interactions with other species, but also factors relevant to the fine‐scale distribution of species (e.g., microhabitats), their biotic interactions as well as their diet (Wiens et al., 2010).

With a notable adaptive radiation in their evolutionary history, and over 1,300 known species worldwide (Fenton & Simmons, 2015), bats have an important role in supporting global ecosystems through their dietary preferences. This is evidenced primarily through the consumption of nocturnal insects and dispersal of nutrients, pollen, and seeds (Patterson, Willig, & Stevens, 2003). Research on the feeding behavior of species is essential to understanding ecosystem function and the impacts of pollution, habitat destruction, and global climate change (Boyles & Storm, 2007; Kunz, Braun de Torrez, Bauer, Lobova, & Fleming, 2011; Vesterinen, 2015; Vesterinen et al., 2016). Furthermore, establishing factors influencing the extinction risk of bats is essential for their conservation, because they help identify endangered species and provide the basis for conservation (Safi & Kerth, 2004). However, these factors may be difficult to discern between species of bats, of which many appear to share portions of their ecological niches, such as habitat and apparently diet.

Even though some degree of food mixing is required for most species, it is thought that the diets of terrestrial mammals are generally highly specialized (Pineda‐Munoz & Alroy, 2014). Indeed, when viewed in its entirety, the dietary diversity in bats is huge, ranging from insectivores, frugivores, and nectarivores to piscivores, carnivores, and even sanguinivores (Kunz, 1998). However, closely related species often occupy similar ecological niches, suggesting that components of the diet overlap to a high degree (Lara, Pérez, Castillo‐Guevara, & Serrano‐Meneses, 2015; Losos, 2008; Münkemüller, Boucher, Thuiller, & Lavergne, 2015; Razgour et al., 2011; Wilson, 2010). This phylogenetic signal in food webs is associated with the tendency of related species to share habitat and body size (Rezende, Albert, Fortuna, & Bascompte, 2009). For instance, insectivorous bats are generally small, because of the negative correlation between size and echolocation frequency of a bat. High‐frequency echolocation calls are needed for the detection of small prey (Brigham, 1991). Nevertheless, species with identical niches rarely exist (Wiens et al., 2010).

Consisting of ca. 430 species sharing similar morphology, the insectivorous family Vespertilionidae [Gray 1821] is a useful group for research on resource partitioning (Aldridge & Rautenbach, 1987; Saunders & Barclay, 1992). Vespertilionidae exhibits only subtle interspecific morphological variation compared to members of the other bat families, even among distantly related species. This has posed a challenge in elucidating their evolutionary history (Jones, Purvis, MacLarnon, Bininda‐Emonds, & Simmons, 2002; Van Den Bussche & Lack, 2013). Similarities in morphology are mirrored in diet; the almost cosmopolitan vesper bats are primarily insectivorous (Hoofer & Bussche, 2003; Simmons, 2005; Van Den Bussche & Lack, 2013). However, based on feeding behavior, vesper bat species have been classified to guilds of either aerial‐hawking, gleaning, or trawling bats according to their foraging behavior (Norberg & Rayner, 1987). Recent advances in molecular methodology have begun to offer a deeper insight into the cryptic diet of these animals (Roslin, Majaneva, & Clare, 2016; Vesterinen et al., 2016; Vesterinen, Lilley, Laine, & Wahlberg, 2013). Vesper bats within the same feeding guild appear to share a great proportion of their diet (Roswag, Becker, & Encarnação, 2018). Because insectivorous bats opportunistically consume prey that may be periodically abundant (Vesterinen et al., 2013), this leads to significant temporal changes in the diet (Vesterinen et al., 2016), but could additionally result in a large overlap in dietary niches, suggesting resource partitioning occurs in other ecological dimensions.

Here, we unravel the resource partitioning of five resident vesper bats in southwestern Finland through deep dietary analysis, including prey species identification, an estimate for prey body size and temporal changes in diet using fecal DNA barcoding. At high northern latitudes, the distribution of bats is constrained by extreme environmental demands and prey availability is more seasonal than elsewhere in their range (Clare et al., 2014; Shively & Barboza, 2017; Shively, Barboza, Doak, & Jung, 2017). The ranges of these five species (Eptesicus nilssonii [Keyserling & Bläsius, 1839], Myotis daubentonii [Kuhl, 1817], M. mystacinus [Kuhl, 1817], M. brandtii [Eversmann, 1845], and Plecotus auritus [Linnaeus, 1758]) show considerable overlap, suggesting that trophic resource partitioning is important in supporting the species in Fennoscandia. We expect to see clear guild‐specific segregation in diet between the three different feeding guilds presented by our species, trawling (M. daubentonii), gleaning (P. auritus), and aerial hawking (Figure 1; M. brandtii, M. mystacinus, and E. nilssonii), and that we will see at least a partial dietary overlap among the members of the aerial hawkers. Because of the opportunistic foraging behavior of insectivorous bats (Vesterinen et al., 2013), we also predict significant temporal changes in diet throughout the sampling season (but see Vesterinen et al., 2016). Finally, we predict a positive correlation between predator and prey size, which could be due to the negative correlation between bat size and echolocation frequency, hindering the ability to detect small prey items (Brigham, 1991). To the best of our knowledge, of the species studied here, molecular data on diet exist only for M. daubentonii (Galan et al., 2018; Krüger, Clare, Greif, et al., 2014; Krüger, Clare, Symondson, Keišs, & Pētersons, 2014; Vesterinen et al., 2013, 2016 ), although the dietary contents of all species have previously been described through morphological analysis of fecal remains (Rydell, 1986; Vaughan, 1997).

Figure 1

One of the study species, Myotis brandtii, foraging in its natural environment near the study area in southwestern Finland. M. brandtii catches its prey mainly in flight in an open or semi‐open environment. The current study is the first ever published molecular analysis of its diet: Geometrid and tortricid moths constituted half of its diet, while mosquitos, midges, and flies formed another large part of the menu, approximately one‐third. Photograph credits: Mr. Risto Lindstedt

MATERIALS AND METHODS

Study species

Of the 13 species of bats occurring in Finland, the species sampled here represent the most common and accessible (Myotis daubentonii, Eptesicus nilssonii, M. brandtii, M. mystacinus, and Plecotus auritus). Based on both the Finnish Biodiversity Information Facility (http://www.laji.fi) databases and our own bat sampling, spanning for more than 10 years, these bat species constitute approximately 90%–98% of all bat occurrences in Finland, and have been the focus of most bat research in Finland so far (Jakava‐Viljanen, Lilley, Kyheröinen, & Huovilainen, 2010; Laine, Lilley, Norrdahl, & Primmer, 2013; Lilley et al., 2013; Lilley, Stauffer, Kanerva, & Eeva, 2014; Lilley, Veikkolainen, & Pulliainen, 2015; Veikkolainen, Vesterinen, Lilley, & Pulliainen, 2014).

Of the sampled species, only the Northern bat (Eptesicus nilssonii) has a range encompassing all of Finland, with records extending far above the Arctic Circle, all the way to Utsjoki at 69°45′27, 27°1′29 (Figure 2b; Iso‐Iivari, 1988; IUCN, 2016a). Although records of M. daubentonii extend to the Arctic Circle (Figure 2a; IUCN, 2008a; Siivonen & Wermundsen, 2008), the distributions of most of the other focal species, M. mystacinus, M. brandtii, and P. auritus, are considered to reach their northern limits in central Finland (Figure 2c–e; IUCN, 2008b, 2008c, 2016b). These five species, with the addition of the extremely rare M. nattererii and M. dasycneme, are most likely the only regularly hibernating species in Finland, whereas the other species migrate or are infrequent visitors (but see Ijäs, Kahilainen, Vasko, & Lilley, 2017).

Figure 2

The map showing the distribution of each studied bat species in northeastern Eurasia: (a) Myotis daubentonii, (b) Eptesicus nilssonii, (c) M. brandtii, (d) M. mystacinus, and (e) Plecotus auritus with a star denoting the focal area of the current study. (f) Locations of the roost sites for each bat species in the current study in southwestern Finland: NAU = Nautelankoski (M. daubentonii), RUI = Ruissalo (M. brandtii), SJÄ = Sahajärvi (E. nilssonii), SSA = Särkisalo (E. nilssonii), LAI = Laiterla (P. auritus and M. mystacinus), and ROT = Rotholma (P. auritus and M. brandtii)

Field sampling

Fecal pellets were collected between April and July 2014 (Table 1) from day roosts of five species of bats in southwestern Finland, and all these roosts were in buildings within approximately 60 km of each other (Figure 2f). The pellets were collected by placing a clean paper sheet under the roosting bats the day before the collection, and collecting the droppings the next day. The collection was repeated for two or three consecutive days within a period of two weeks. Pellets were stored in RNA later at −20°C until laboratory analysis.

Table 1

Information on the sampling details and characteristics of the field and molecular data. Time/roost sampling points per bat species denote how many times per roost the species was sampled: M. daubentonii was sampled from only a single roost (NAU; see Figure 2 for locations of the roost sites in the current study), E. nilssonii was sampled separately from two roosting sites (SJÄ, SSA), M. mystacinus and P. auritus were sampled from the same roost (LAI), and M. brandtii was sampled at two locations (RUI), one of which was shared by P. auritus (ROT). We found no statistical differences between samples from different bat species in the total reads, total prey species richness, or the average number of prey in each pellet

	All samples	Myotis daubentonii	Eptesicus nilssonii	M. brandtii	M. mystacinus	Plecotus auritus
Sampling period	29th Apr–7th Aug 2014	30th Apr–7th Aug	15th May–18th Jul	27th May–19th Jul	18th Jul	29th Apr–19th Jul
Pooled samples	51	20	9	10	1	11
Pellets in total	1,215	453	225	250	25	262
Avg. prey species per pellet	3.1 ± 1.4	3.0 ± 1.7	2.9 ± 1.1	3.3 ± 0.9	4.2	3.1 ± 1.6
Total prey reads	5,449,755	1,768,337	1,030,783	1,128,927	119,416	1,402,292
Avg. reads per sample	106,858 ± 52,134	88,417 ± 42,780	114,531 ± 69,513	112,893 ± 50,648	119,416	127,481 ± 51,818
Prey species	547	340	301	329	105	277
Avg. prey species per sample	69.7 ± 23.8	60.6 ± 22.6	71.8 ± 26.9	83.3 ± 23.2	105.0	69.2 ± 17.7

Laboratory work

We aimed to pool 25 droppings (from the same roost and same time point) into each sample to maximize the number of droppings without the need to analyze hundreds of fecal pellets individually. Only four samples included less than 25 droppings, and for these, we pooled every available pellet for the given time point per roost. We focused sampling on roosts inhabited by a single species, and likewise, we intended to pool pellets from a single species into a single pooled sample. In total, we initially sampled 1,252 fecal pellets from the five bat species in this study (Table 1). The DNA was extracted using NucleoSpin® DNA Stool Kit (product nr 740472, Macherey‐Nagel, Düren, Germany) following the manual (version April 2016/Rev. 01) “Protocol for fresh or frozen stool samples” with following modifications: step 1) we used on average 360 mg (±91 mg) of starting material per sample (samples dried only briefly on paper prior to the weighing), and we increased the amount of lysis buffer ST1 to 1,000 µl to increase the amount of supernatant in the subsequent stages; step 2) we used Tissue Lyser II (Cat No. 85300, Qiagen, Hilden, Germany) 2 × 30 s at full speed; step 3) we centrifuged the samples at 13,000 g for 5 min, after which the supernatant was transferred into a new tube; and in the final step DNA was eluted into 100 µl of SE buffer.

We used a single primer pair (SFF‐145f: 5′‐GTHACHGCYCAYGCHTTYGTAATAAT‐3′ and SFF‐351r: 5′‐CTCCWGCRTGDGCWAGRTTTCC‐3′; primers and PCR setup from Walker, Williamson, Sanchez, Sobek, & Chambers, 2016) to test the DNA extraction success in the pooled samples and confirm the bat species by molecular analysis and another primer pair to amplify the potential prey (ZBJ‐ArtF1c: 5′‐AGATATTGGAACWTTATATTTTATTTTTGG‐3′ and ZBJ‐ArtR2c: 5′‐WACTAATCAATTWCCAAATCCTCC‐3′; primers and PCR setup from Zeale, Butlin, Barker, Lees, & Jones, 2011). Despite the proposed bias in Zeale primers toward Diptera and Lepidoptera (Clarke, Soubrier, Weyrich, & Cooper, 2014), we chose these for several reasons: (a) These are the most widely applied markers, (b) many species have been detected using exactly the same primers, even though claimed to be nonamplifiable in the earlier criticism, and (c) we wanted to allow comparison of our results with those of other studies using the same primers (Clare et al., 2014; Kaunisto, Roslin, Sääksjärvi, & Vesterinen, 2017; Koskinen et al., 2018; Krüger, Clare, Greif, et al., 2014; Krüger, Clare, Symondson, et al., 2014; Vesterinen et al., 2013, 2016 ; Wirta et al., 2015; Eitzinger et al., 2018). The PCR and library construction closely followed Kaunisto et al. (2017), except we used MyTaq HS Red Mix (product nr BIO‐25048, Bioline, UK) polymerase throughout the protocol. In short, the first‐step PCR reactions included tagged locus‐specific primers targeting either predator or prey COI gene, and the second‐step PCR followed directly after this including Illumina‐specific adapters with a unique dual‐index combination for each single reaction. After this, the individual libraries were pooled (SFF and ZBJ in separate pools at this stage) by equal volume (2 µl each library) and each pool was purified using dual‐SPRI (solid‐phase reversible immobilization) beads as in Vesterinen et al. (2016). To summarize the SPRI method, 80 µl SPRI was added on top of 100 µl library pool, vortexed thoroughly and incubated at room temperature for 5 min. The mix was then briefly centrifuged and placed on a strong magnet until clear, after which the supernatant was removed (shorter than 600 bp fragments in the beads, longer in the supernatant) and 20 µl SPRI was added to the pellet, and then once again vortexed, incubated, centrifuged, and placed on magnet. Supernatant was removed (shorter than 250 bp in the supernatant, longer in the beads), and pellet was washed twice with freshly prepared 70% ethanol and then dried. Then, 100 µl of MQ‐H2O was added, vortexed, incubated, centrifuged, and placed on magnet, and subsequently, the purified pool was transferred into a clean Lo‐Bind 1.5 ml Eppendorf tube. We then combined ZBJ (90% of the final pool volume) and SFF (10%) pools into one. See Vesterinen et al. (2016) and Koskinen et al. (2018) for further instructions for how to prepare and use SPRI. The pool included a smaller set of samples (approximately one‐third of the input DNA in the pool) to be used in another study. Sequencing was performed on the Illumina MiSeq platform (Illumina Inc., San Diego, California, USA) by the Turku Centre for Biotechnology, Turku, Finland, using v2 chemistry with 300 cycles and 2 × 150 bp paired‐end read length.

Bioinformatics and prey list construction

The Illumina sequencing yielded 13,219,213 paired‐end reads (SFF: 2,480,440 reads; ZBJ: 10,738,773 reads) identified to samples with unique dual‐index combinations. The reads were uploaded directly from the sequencing facility to CSC servers (IT Center for Science, http://www.csc.fi) for trimming and further analysis. Trimming and quality control of the sequences were conducted according to Kaunisto et al. (2017). Consequently, paired‐end reads were merged (SFF: ~90% reads successfully merged; ZBJ: ~85%) and trimmed for quality using program USEARCH with “fastq_maxee_rate” algorithm with threshold 1 (Edgar, 2010). Primers were removed using python program cutadapt (SFF: ~99% reads passed; ZBJ: ~96%) (Martin, 2011). We then dereplicated reads using USEARCH “fastx_uniques” algorithm with option “minuniquesize 2”, and then, we applied USEARCH UNOISE3 algorithm to cluster these unique reads into ZOTUs (zero‐radius operational taxonomical units; Edgar, 2016). In short, UNOISE algorithm allows the simultaneous a) detection and removal of chimeras (PCR artifacts where two fragments of different origin bind together), point errors (substitutions due to incorrect base calls and gaps due to omitted or spurious base calls), and b) results in ZOTUs (zero‐radius OTUs) that are superior to conventional 97% OTUs for most purposes, because they provide the maximum possible biological resolution given the data available (Edgar, 2016). Finally, reads were mapped back to the original trimmed reads to establish the total number of reads in each sample using USEARCH “otutab” algorithm. After processing, our datasets for this study consisted of 5,449,755 prey reads (produced with primers ZBJ‐ArtF1c and ZBJ‐ArtR2c) and 1,452,602 bat reads (produced with primers SFF‐145f and SFF‐351r). The remaining reads (roughly 30% of total output of the sequencing run; ZBJ: 2,618,342 + SFF: 721,684) were used in another study.

We used the following strict criteria for including prey species in the data: (a) Sequence similarity with the reference sequence had to be at least 98% for the ZOTU to be given any (even higher taxa) assignation, and (b) at least ten reads of the final assigned prey species were required to be present in the final data. We assigned the ZOTUs to species as accurately as possible, utilizing a large reference sequence collection orchestrated by the Finnish Barcode of Life campaign (FinBOL: http://www.finbol.org) and BOLD database (Ratnasingham & Hebert, 2007), and confirmed that all the prey species were actually recorded from (southern) Finland. After the above trimming, we were able to identify and retain 93% of all the prey reads. To account for the even distribution of reads into separate samples, we used ANOVA to test samples from different bat species for differences in the total reads per sample, total prey species richness per sample, and the average number of prey in each pellet (prey richness divided by the number of pooled pellets). The reads originating from bats in the second dataset were used to confirm the bat species identity. The molecular confirmation of bat species revealed a switch in roost occupancy (M. mystacinus to E. nilssonii) in the middle of the sampling season, which resulted in only one pooled sample of M. mystacinus. Also, we removed two mixed samples, containing DNA from two distinct bat species. Labeled raw reads and ZOTUs are available in the Dryad Digital Repository: http://https://doi.org/10.5061/dryad.6880rf1.

A number of metric measurements strongly correlate with the biomass in insects (García‐Barros, 2015; Gruner, 2003). Thus, for data on taxon‐specific prey size (wingspan for Lepidoptera and thorax length for all the other prey taxa) we referred to earlier dietary studies from Finland (Kaunisto et al., 2017; Vesterinen et al., 2016), or to literature or pictures from reference databases. Wingspan for lepidopteran prey was chosen as it was highly available, accessible, and reliable. The prey taxa where the size could not be determined (e.g., due to a compound taxon that was too large to be reliable or informative, such as “Orthoptera sp.”) were omitted from the prey size analysis. For the predator size analysis, we extracted forearm (FA) length measurements from bat banding data collected from the study area. Forearm length is a standard measurement for bats, and it has been shown to highly correlate with the full body length (R 2 = 0.933; Meng, Zhu, Huang, Irwin, & Zhang, 2016). After discarding repeatedly encountered bat individuals, as well as those with unclear identification or no data on size, we ended up with 1,553 distinct individuals from the bat banding data.

Data analysis

Traditionally, the read count (or read abundance) data produced in metabarcoding studies are directly transformed into presence/absence data, considered to be more cautious and less biased than using read counts. However, the latest opinion on the field seems to suggest that using normalized read abundance data could be even less biased than mere converting to p/a data (Deagle et al., 2018; see also Vesterinen, 2015; Vesterinen et al., 2016). For this reason, we chose to use relative read abundance (RRA: calculated as the proportion of reads per each prey item in each sample). To make the comparison to earlier studies possible, we also prepared the secondary set of analysis using p/a data or more precisely the modified frequency of occurrence (MFO) data throughout the analysis. MFO was calculated as the proportion of occurrences of each prey taxa in each sample scaled to 100% across all prey items (see Deagle et al. (2018) for the terminology and further discussion on the topic).

To begin our data analysis, we calculated prey species accumulation curves to account for sampling adequacy (Colwell & Coddington, 1994). We used R package “iNEXT” to resample the prey reads and frequencies for each bat species and plotted these against accumulated prey species richness (Hsieh, Ma, & Chao, 2016; R Core Team, 2013).

In order to unfold the trophic interactions resolved by the DNA analysis, we used package bipartite (Dormann, Gruber, & Fründ, 2008) implemented in program R to draw interaction webs for each bat predator species using both RRA and MFO data. For those two cases, where two different bat species were observed in the same roost, we constructed additional webs to analyze the diet between separate samples in each location using RRA data. To further estimate patterns among the dietary assemblages of the five species, we used principal coordinates analysis (PCoA) based on Bray–Curtis dissimilarity (Jaccard similarity for presence/absence data) between samples (Davis, 2002; Podani & Miklós, 2002).

Then, to study the effects of predator species and temporal variation (as week number) on variation in prey species composition in each sample, we conducted a permutational multivariate analysis of variance (with Bray–Curtis for RRA and Jaccard for presence/absence data), using 9,999 random permutations to evaluate statistical significance (Anderson, 2001)(PERMANOVA; Anderson, 2001). Analysis of variance was carried out using “adonis” in software R with package “vegan” (Oksanen et al., 2013). Variation was further dissembled using pairwise analysis of variance with package “pairwise.adonis” between all bat species using Bonferroni correction for p‐values (Martinez Arbizu, 2017).

Finally, we used information on predator and prey sizes to add dimensions to our attempt to segregate the ecological guilds and predator species. The bat banding data (n = 1,553) consisted of unequal sample sizes for the five bat species with unequal variances (Levene's test for homogeneity of variance: p = 0.0012), and thus, to compare the forearm lengths (size) of the five bat species, we used a Kruskal–Wallis analysis of variance (nonparametric ANOVA) procedure to compare body size (FA length) as a function of predator size using command “kruskal.test” in R (Kruskal & Wallis, 1952). To further study the difference between bat species pairs, we applied the Tukey and Kramer (Nemenyi) test with Tukey‐Dist approximation for independent samples with R package “PMCMR” (Pohlert, 2014; Sach, 1997, pp. 395–397, 662–664). The same tests were applied to test prey size (wingspan or thorax length as explained above) differences between the bat species.

RESULTS

General aspects of the diet and the study

Altogether, we identified 547 distinct prey species in 13 arthropod orders (Table 1). The main prey order for M. daubentonii and E. nilssonii was Diptera (56% and 77% of all reads, respectively). For M. brandtii, M. mystacinus, and P. auritus, Lepidoptera was the largest prey order (65%, 74%, and 72%, respectively). The only other very abundant prey orders included Trichoptera (15% of reads in M. daubentonii diet) and Coleoptera (19% in P. auritus). The observed summed prey species richness per bat species varied from 105 prey species to 340 prey species (Tables 1 and 2). From technical point of view, our data show even average distribution of reads across samples (although with high variation), and the average number of prey species per pellet calculated across samples did not differ between bat species (Table 1). The species accumulation curves showed that for M. mystacinus the sampling was rather inadequate, but for others more comparable to each other in terms of reads per bat species (Figure 3a), although when using presence/absence data, the curves did not seem to reach the plateau yet (Figure 3b). Nevertheless, we kept M. mystacinus in all the analysis, but interpret the results with relevant caution.

Table 2

Prey species observed in the current study. For simplicity, prey species are reported as presence or absence for each bat species. First column stands for the prey number used in the plotweb analysis (Figures 3 and 4). If species name was not available in the molecular species assignation, the BIN cluster number is reported, as listed in Barcode of Life Database (http://https://v4.boldsystems.org). The bat species are abbreviated as follows: Md = Myotis daubentonii, En = Eptesicus nilssonii, Mb = M. brandtii, Mm = M. mystacinus, and Pa = Plecotus auritus

No	Prey taxa	Md	En	Mb	Mm	Pa
	ARACHNIDA
	Araneae
	Anyphaenidae
1	Anyphaena accentuata	1	1	1	1	1
	Araneidae
2	Larinioides patagiatus	0	1	0	0	0
	Linyphiidae
3	Diplostyla concolor	0	1	0	0	0
4	Erigone sp.	0	0	0	0	1
	Philodromidae
5	Philodromus cespitum	0	1	1	0	0
	Theridiidae
6	Cryptachaea riparia	1	0	0	0	0
	Thomisidae
7	Xysticus sp. 1	0	0	1	0	0
8	Xysticus sp. 2	1	0	1	0	0
	INSECTA
	Blattodea
	Ectobiidae
9	Ectobius sp.	0	0	1	1	1
	Coleoptera
	Cantharidae
10	Podabrus alpinus	0	1	0	0	0
	Carabidae
11	Acupalpus parvulus	0	1	0	0	1
12	Badister dilatatus	0	1	0	0	0
13	Pterostichus adstrictus	1	1	1	0	1
14	Pterostichus melanarius	1	1	1	1	1
15	Pterostichus nigrita	1	0	0	0	0
	Cerambycidae
16	Acanthocinus aedilis	0	1	0	0	1
17	Coleoptera sp.	0	1	0	0	0
	Curculionidae
18	Brachyderes incanus	0	0	0	0	1
19	Strophosoma capitatum	0	0	0	0	1
	Dytiscidae
20	Laccophilus comes	0	0	1	0	0
	Gyrinidae
21	Orectochilus villosus	1	1	1	0	1
	Melyridae
22	Dasytes plumbeus	1	0	1	0	0
	Oedemeridae
23	Calopus serraticornis	0	1	1	0	1
	Staphylinidae
24	Dropephylla ioptera	0	1	0	0	0
25	Nudobius lentus	0	0	0	0	1
	Diptera
	Anisopodidae
26	Sylvicola cinctus	1	1	1	0	0
27	Sylvicola fenestralis	0	1	1	1	1
	Anthomyiidae
28	Delia florilega	0	1	0	0	0
29	Delia platura	1	1	0	0	1
30	Pegomya rubivora	1	0	0	0	1
31	Pegomya sp.	0	1	0	0	0
32	Pegoplata annulata	1	1	0	0	1
33	Zaphne ambigua	0	1	0	0	0
	Anthomyzidae
34	Anthomyza sp.	1	0	1	0	1
	Calliphoridae
35	Melinda viridicyanea	1	0	0	0	0
	Cecidomyiidae
36	CecidInt35 sp. BOLD:ACB9926	0	0	1	0	0
37	Cecidomyiidae sp.	1	0	1	0	0
	Ceratopogonidae
38	Palpomyia lineata	1	1	1	0	0
	Chaoboridae
39	Chaoborus flavicans	1	1	0	0	0
40	Chaoborus sp. BOLD:AAG5462	1	1	1	0	0
	Chironomidae
41	Ablabesmyia aspera	1	0	1	0	0
42	Ablabesmyia monilis	1	0	0	0	0
43	Arctopelopia barbitarsis	1	1	1	0	1
44	Chironomidae sp. BOLD:ACP1316	1	0	0	0	0
45	Chironomidae sp. BOLD:ACQ8800	1	1	1	0	1
46	Chironomidae sp. BOLD:ACU9532	1	0	0	0	1
47	Chironominae sp.	1	1	0	0	0
48	Chironomus sp. BOLD:AAI4299	1	1	1	0	0
49	Chironomus sp. BOLD:AAI4301	1	1	1	0	0
50	Chironomus sp.1	1	1	1	0	1
51	Chironomus sp.2	1	1	1	0	1
52	Cladopelma sp.	1	1	0	0	0
53	Cladopelma sp. 1TE	1	1	0	1	0
54	Conchapelopia melanops	1	1	1	0	0
55	Conchapelopia sp. BOLD:ACQ3496	1	0	0	0	0
56	Cricotopus bicinctus	1	1	1	0	1
57	Cricotopus sp.	1	1	1	0	1
58	Cricotopus sylvestris	1	0	1	0	0
59	Cricotopus triannulatus	1	0	0	0	0
60	Cryptochironomus supplicans	1	1	0	0	0
61	Demicryptochironomus sp.	0	1	0	0	0
62	Dicrotendipes lobiger	0	1	1	0	0
63	Dicrotendipes nervosus	1	1	1	0	0
64	Dicrotendipes tritomus	0	1	0	0	0
65	Endochironomus tendens	1	1	0	0	0
66	Glyptotendipes barbipes	0	1	0	0	0
67	Glyptotendipes cauliginellus	1	1	1	0	1
68	Glyptotendipes lobiferus	1	1	1	0	1
69	Glyptotendipes sp.	1	0	0	0	0
70	Glyptotendipes sp. BOLD:ACG4324	1	1	0	0	0
71	Heterotrissocladius marcidus	1	0	0	0	1
72	Kiefferulus sp.	1	0	1	0	0
73	Metriocnemus sp. 3ES	0	0	1	0	0
74	Microchironomus tener	0	0	1	0	0
75	Microtendipes chloris	1	1	1	0	1
76	Microtendipes pedellus	1	1	1	0	1
77	Microtendipes sp.	1	1	1	0	1
78	Orthocladiinae sp.	1	1	1	0	1
79	Parachironomus digitalis	1	0	1	0	1
80	Parachironomus monochromus	1	0	0	0	0
81	Paracladopelma sp.1	1	0	0	0	1
82	Paracladopelma sp.2	1	0	0	0	0
83	Paratanytarsus dissimilis	0	0	1	0	0
84	Polypedilum convictum	1	0	0	0	0
85	Polypedilum nubeculosum	1	1	1	0	1
86	Polypedilum pedestre	0	0	1	0	0
87	Polypedilum sordens	1	1	1	0	0
88	Polypedilum sp.	1	0	1	0	0
89	Polypedilum sp. BOLD:ACR0701	1	0	0	0	0
90	Procladius culiciformis	1	1	1	0	0
91	Procladius nigriventris	1	1	0	0	0
92	Procladius sp. 1ES	1	1	1	0	1
93	Procladius sp. BOLD:AAG5487	1	1	1	0	1
94	Psectrocladius limbatellus	0	1	0	0	0
95	Psectrocladius octomaculatus	0	1	0	0	0
96	Psectrocladius sp.	1	0	0	0	0
97	Psectrotanypus varius	1	0	0	0	0
98	Stictochironomus sp. 3TE	1	1	1	0	1
99	Tanytarsus eminulus	1	0	1	0	0
100	Tanytarsus mendax	1	1	1	0	0
101	Thienemannimyia carnea	1	1	1	1	1
102	Xenochironomus xenolabis	1	1	0	0	1
103	Zavrelimyia sp.	1	0	1	0	0
	Chloropidae
104	Thaumatomyia notata	0	1	0	0	0
105	Thaumatomyia sp.	0	1	0	0	1
106	Thaumatomyia sp. BOLD:ACX2752	0	1	0	0	0
	Culicidae
107	Aedes cinereus	1	1	1	0	0
108	Aedes vexans	0	1	0	0	1
109	Anopheles claviger	0	0	1	0	0
110	Anopheles messeae	1	1	1	1	0
111	Culex pipiens	1	1	1	1	1
112	Culicidae sp. 1	0	1	0	0	0
113	Culicidae sp. 2	0	1	0	0	0
114	Culiseta annulata	0	1	1	0	0
115	Culiseta morsitans	0	1	1	0	1
116	Culiseta ochroptera	0	1	0	0	1
117	Ochlerotatus cataphylla	0	0	1	0	0
118	Ochlerotatus communis	1	1	1	0	1
119	Ochlerotatus excrucians	0	1	0	0	0
120	Ochlerotatus punctor	0	1	1	0	0
	Dolichopodidae
121	Gymnopternus sp.	0	0	1	1	0
	Drosophilidae
122	Drosophilidae sp.	0	0	1	0	1
123	Scaptomyza pallida	0	1	0	0	0
	Empididae
124	Rhamphomyia anaxo	1	0	1	0	0
125	Rhamphomyia caesia	0	0	1	0	0
126	Rhamphomyia nigripennis	1	1	1	0	1
127	Rhamphomyia nr. anaxo	1	0	1	0	0
128	Rhamphomyia sp.	0	1	0	0	0
129	Rhamphomyia umbripennis	0	0	1	0	0
130	Rhamphomyia valga	0	1	1	0	0
	Fanniidae
131	Fannia minutipalpis	0	0	1	0	0
132	Fannia sociella	1	1	0	0	0
	Heleomyzidae
133	Suillia bicolor	0	0	0	0	1
	Hippoboscidae
134	Nycteribia kolenati	1	1	1	0	1
	Hybotidae
135	Bicellaria simplicipes	0	1	0	0	1
	Keroplatidae
136	Macrocera stigma	0	0	1	0	0
	Limoniidae
137	Austrolimnophila unica	0	1	1	0	1
138	Dicranomyia didyma	1	0	0	0	0
139	Dicranomyia frontalis	0	0	0	1	0
140	Dicranomyia modesta	1	1	1	0	0
141	Dicranomyia sp.	1	1	0	1	0
142	Eloeophila maculata	1	0	1	1	0
143	Erioptera divisa	1	0	1	0	0
144	Erioptera sp.	1	1	1	0	0
145	Gonomyia tenella	0	1	0	0	0
146	Helius longirostris	1	1	1	0	1
147	Limonia nubeculosa	1	0	0	0	0
148	Limonia trivittata	1	0	1	1	0
149	Metalimnobia bifasciata	1	0	0	1	1
150	Metalimnobia quadrinotata	1	1	1	1	1
151	Molophilus sp.	0	0	0	0	1
152	Phylidorea squalens	0	1	0	0	0
153	Rhipidia maculata	1	1	1	0	1
154	Symplecta stictica	1	0	1	0	0
	Muscidae
155	Helina evecta	1	1	1	0	0
156	Hydrotaea armipes	0	0	0	1	0
157	Hydrotaea irritans	0	0	0	1	1
158	Muscina levida	0	0	0	0	1
159	Mydaea new sp. nr urbana	0	1	0	0	0
160	Polietes lardarius	1	0	1	0	1
161	Thricops diaphanus	0	0	0	0	1
162	Thricops rufisquamus	0	1	1	0	0
	Mycetophilidae
163	Exechia seriata	0	1	0	0	0
164	Phronia sp.	0	0	1	1	0
165	Sciophila lutea	1	1	1	0	0
166	Sciophila pseudoflexuosa	0	0	1	0	0
	Pediciidae
167	Pedicia rivosa	0	1	0	0	1
168	Pediciidae sp.	1	1	1	0	1
169	Ula mixta	1	1	1	0	1
170	Ula sylvatica	1	1	1	0	1
	Psychodidae
171	Psychoda lobata	1	1	1	0	1
172	Psychoda sp.	1	1	1	1	1
173	Telmatoscopus advena	1	0	1	0	0
	Rhagionidae
174	Rhagio scolopaceus	1	1	1	0	1
	Rhinophoridae
175	Paykullia maculata	1	0	1	0	0
	Scathophagidae
176	Scathophaga suilla	0	1	1	0	0
	Sciaridae
177	Sciaridae sp.	1	1	1	0	0
	Simuliidae
178	Simulium equinum	1	0	0	0	1
179	Simulium noelleri	1	1	1	0	1
180	Simulium ornatum	1	0	1	0	0
181	Simulium vernum	0	1	1	0	1
	Stratiomyidae
182	Beris chalybata	0	0	1	0	0
	Syrphidae
183	Meliscaeva cinctella	0	0	0	0	1
184	Syrphus torvus	0	1	1	0	0
185	Syrphus vitripennis	1	1	1	0	1
186	Xanthandrus comtus	0	1	1	0	0
	Tachinidae
187	Bactromyia aurulenta	1	1	1	0	1
188	Ceromya silacea	0	0	1	1	1
189	Cyzenis albicans	1	0	0	0	0
190	Eloceria delecta	0	0	0	0	1
191	Loewia foeda	1	1	1	0	1
192	Macquartia dispar	1	0	0	0	0
193	Oswaldia muscaria	0	0	1	0	1
194	Pales pavida	0	0	0	0	1
195	Phorocera obscura	1	1	1	0	0
196	Siphona geniculata	0	0	1	0	0
	Tipulidae
197	Nephrotoma aculeata	1	1	1	0	1
198	Nephrotoma lunulicornis	1	1	0	0	0
199	Tipula fascipennis	1	1	1	1	1
200	Tipula fulvipennis	0	1	1	0	0
201	Tipula lateralis	1	0	0	0	0
202	Tipula lunata	0	1	1	1	1
203	Tipula maxima	1	1	0	0	1
204	Tipula nubeculosa	0	1	0	0	1
205	Tipula paludosa	1	1	0	0	1
206	Tipula pierrei	1	1	1	0	1
207	Tipula scripta	1	1	1	1	1
208	Tipula sp. BOLD:AAF9041	1	1	0	0	0
209	Tipula truncorum	1	1	1	1	1
210	Tipulidae sp.	0	1	0	0	1
	Trichoceridae
211	Trichocera regelationis	1	1	1	0	1
212	Trichocera sp.	1	0	1	0	0
	Ephemeroptera
	Baetidae
213	Procloeon bifidum	1	0	0	0	0
	Caenidae
214	Caenis horaria	1	1	1	0	1
	Ephemeridae
215	Ephemera vulgata	1	1	0	0	0
	Heptageniidae
216	Heptagenia sulphurea	1	1	1	0	1
	Siphlonuridae
217	Siphlonurus alternatus	1	0	1	0	0
	Hemiptera
	Aphididae
218	Euceraphis betulae	0	1	1	0	1
219	Euceraphis punctipennis	0	1	0	0	1
	Cicadellidae
220	Fagocyba douglasi	0	0	1	0	0
	Miridae
221	Lygus pratensis	0	1	1	0	1
222	Neolygus contaminatus	1	0	1	1	0
	Hymenoptera
	Braconidae
223	Choeras jft30	0	1	1	0	0
224	Hymenoptera sp.	1	0	1	0	0
	Ichneumonidae
225	Astiphromma splenium	0	0	1	0	1
226	Diadegma majale	0	0	1	0	0
227	Hyposoter PRO‐3	0	0	1	0	0
228	Mesochorus sp.	1	0	0	0	0
229	Mesochorus vitticollis	0	1	1	0	1
230	Pleolophus sp.	0	0	0	0	1
	Tenthredinidae
231	Dolerus vestigialis	1	0	0	0	1
232	Pachyprotasis rapae	1	1	0	0	0
	Lepidoptera
	Adelidae
233	Nematopogon swammerdamellus	1	1	1	0	0
	Arctiidae
234	Atolmis rubricollis	1	0	1	0	1
235	Eilema depressum	0	0	0	1	0
	Argyresthiidae
236	Argyresthia abdominalis	1	0	0	0	0
237	Argyresthia bergiella	1	1	1	1	1
238	Argyresthia goedartella	1	1	1	1	1
239	Argyresthia retinella	0	1	1	0	1
	Batrachedridae
240	Batrachedra pinicolella	1	0	1	0	1
	Bucculatricidae
241	Bucculatrix cidarella	0	0	1	0	0
242	Bucculatrix thoracella	1	0	1	0	0
243	Bucculatrix ulmella	1	1	1	0	1
	Coleophoridae
244	Coleophora betulella	1	1	1	0	1
245	Coleophora kuehnella	0	1	1	0	0
246	Coleophora spinella	1	1	1	1	1
247	Coleophora versurella	1	1	0	1	1
	Cosmopterigidae
248	Limnaecia phragmitella	1	0	0	0	0
249	Sorhagenia janiszewskae	1	0	0	0	0
	Crambidae
250	Acentria ephemerella	1	0	0	0	1
251	Agriphila inquinatella	1	0	0	0	1
252	Agriphila selasella	1	1	0	0	1
253	Agriphila straminella	1	0	0	0	0
254	Calamotropha paludella	1	1	0	0	0
255	Chrysoteuchia culmella	0	1	1	0	1
256	Crambus lathoniellus	1	0	0	0	0
257	Crambus pascuellus	0	0	0	1	1
258	Donacaula mucronella	1	1	1	0	1
259	Elophila nymphaeata	1	0	0	1	1
260	Evergestis extimalis	1	0	1	0	1
261	Nymphula nitidulata	1	0	1	0	0
262	Ostrinia nubilalis	1	0	1	0	0
263	Scoparia ancipitella	1	0	1	1	1
264	Scoparia subfusca	1	0	0	0	0
265	Udea lutealis	1	0	0	1	0
	Depressariidae
266	Agonopterix angelicella	1	1	1	0	1
267	Agonopterix arenella	1	0	1	0	1
268	Agonopterix ciliella	1	0	1	1	1
269	Agonopterix heracliana	1	1	1	0	1
270	Agonopterix propinquella	1	0	1	0	0
271	Depressaria daucella	1	1	1	0	1
272	Depressaria emeritella	1	1	1	0	1
273	Depressaria libanotidella	1	1	1	0	1
274	Depressaria olerella	1	1	1	0	1
275	Depressaria radiella	1	0	0	0	0
276	Depressaria sordidatella	1	1	0	0	1
	Drepanidae
277	Drepana falcataria	1	0	0	0	0
278	Falcaria lacertinaria	1	0	1	0	0
279	Tethea or	0	0	0	0	1
280	Tetheella fluctuosa	1	1	1	0	1
	Elachistidae
281	Elachista adscitella	0	0	1	1	1
	Endromidae
282	Endromis versicolora	0	1	1	0	1
	Epermeniidae
283	Epermenia illigerella	1	0	0	0	0
	Erebidae
284	Calliteara pudibunda	0	1	1	0	1
285	Diacrisia sannio	1	1	0	0	1
286	Herminia tarsipennalis	0	0	1	0	1
287	Hypena crassalis	0	1	0	0	1
288	Macrochilo cribrumalis	1	1	1	1	1
289	Rivula sericealis	0	0	1	1	0
290	Scoliopteryx libatrix	0	0	0	0	1
291	Spilarctia luteum	1	0	0	0	1
	Gelechiidae
292	Carpatolechia fugitivella	0	0	1	0	0
293	Carpatolechia proximella	1	1	1	0	1
294	Caryocolum vicinella	1	1	1	1	1
295	Chionodes electella	1	1	1	1	1
296	Chionodes lugubrella	1	1	1	0	1
297	Dichomeris alacella	0	0	1	0	0
298	Exoteleia dodecella	1	1	1	1	1
299	Gelechia muscosella	1	0	0	0	0
300	Gelechia nigra	1	1	0	1	0
301	Gelechia sororculella	1	0	0	0	1
302	Helcystogramma rufescens	1	1	0	0	1
303	Monochroa lutulentella	1	1	1	0	1
304	Neofriseria peliella	1	1	1	1	0
305	Psoricoptera gibbosella	1	1	1	0	1
306	Recurvaria leucatella	1	0	1	0	0
307	Scrobipalpa atriplicella	1	0	1	0	0
308	Teleiopsis diffinis	0	0	1	0	0
	Geometridae
309	Aethalura punctulata	1	0	0	0	1
310	Agriopis aurantiaria	1	1	1	1	1
311	Alcis repandata	0	0	1	0	1
312	Bupalus piniaria	0	1	0	1	1
313	Cabera pusaria	0	0	1	0	1
314	Cleora cinctaria	0	0	1	1	1
315	Crocallis elinguaria	1	1	1	0	1
316	Deileptenia ribeata	1	1	1	0	1
317	Ectropis crepuscularia	1	1	0	0	1
318	Epirrhoe alternata	0	1	0	0	0
319	Epirrita autumnata	0	0	1	0	1
320	Eupithecia abietaria	0	1	0	0	1
321	Eupithecia indigata	0	1	0	0	1
322	Eupithecia lanceata	1	1	1	1	1
323	Eupithecia plumbeolata	0	1	1	1	1
324	Eupithecia subfuscata	1	0	0	0	1
325	Eupithecia tantillaria	0	1	0	0	1
326	Eupithecia tenuiata	1	0	0	0	1
327	Eupithecia virgaureata	1	0	0	0	0
328	Gandaritis pyraliata	1	0	0	0	0
329	Geometridae sp.	1	1	1	0	1
330	Idaea dimidiata	1	0	0	1	1
331	Idaea emarginata	1	1	1	0	0
332	Lomaspilis marginata	0	1	0	0	0
333	Macaria liturata	1	1	1	1	1
334	Odontopera bidentata	1	0	0	0	1
335	Paradarisa consonaria	0	1	0	0	1
336	Pasiphila rectangulata	0	0	1	0	0
337	Plagodis pulveraria	0	1	0	0	1
338	Rheumaptera undulata	0	0	0	0	1
339	Scopula floslactata	1	0	0	0	1
340	Scopula immutata	1	0	0	0	0
341	Selenia dentaria	1	0	1	0	1
342	Xanthorhoe montanata	1	1	1	0	0
343	Xanthorhoe quadrifasciata	1	0	1	1	1
344	Xanthorhoe spadicearia	0	1	0	0	0
	Glyphipterigidae
345	Orthotelia sparganella	1	0	1	0	0
	Gracillariidae
346	Caloptilia alchimiella	0	0	1	0	0
347	Caloptilia betulicola	0	1	1	0	0
348	Caloptilia elongella	0	1	1	0	0
349	Caloptilia hemidactylella	1	0	1	0	0
350	Caloptilia populetorum	0	1	1	0	0
351	Parornix betulae	1	1	1	0	0
352	Parornix devoniella	1	1	1	0	1
353	Phyllonorycter harrisella	0	0	1	0	0
	Hepialidae
354	Pharmacis fusconebulosa	0	0	1	1	1
	Lasiocampidae
355	Dendrolimus pini	1	1	1	0	1
356	Lasiocampa quercus	1	1	1	0	1
357	Macrothylacia rubi	1	1	1	0	1
	Lyonetiidae
358	Lyonetia clerkella	0	1	1	0	0
	Lypusidae
359	Pseudatemelia elsae	0	1	0	0	0
360	Pseudatemelia josephinae	1	1	0	1	1
	Momphidae
361	Mompha sturnipennella	1	0	1	0	0
362	Mompha subbistrigella	1	1	1	0	0
	Noctuidae
363	Acronicta auricoma	1	0	0	0	0
364	Acronicta rumicis	1	0	0	0	1
365	Agrochola helvola	0	0	0	0	1
366	Agrotis clavis	1	1	1	1	1
367	Agrotis exclamationis	1	1	1	0	1
368	Allophyes oxyacanthae	0	0	1	0	1
369	Apamea crenata	0	0	1	0	1
370	Apamea remissa	1	1	1	1	1
371	Apamea scolopacina	0	0	0	0	1
372	Apamea sordens	1	1	1	0	1
373	Autographa gamma	1	1	1	0	1
374	Autographa pulchrina	0	0	0	0	1
375	Brachionycha nubeculosa	1	0	0	0	1
376	Caradrina morpheus	1	1	1	1	1
377	Cerastis rubricosa	1	1	1	1	1
378	Charanyca ferruginea	1	1	1	1	1
379	Chloantha hyperici	0	0	1	0	0
380	Colocasia coryli	0	1	0	0	1
381	Conistra rubiginea	1	1	1	1	1
382	Conistra vaccinii	1	1	1	1	1
383	Diarsia rubi	1	0	0	0	1
384	Eurois occultus	1	1	1	1	1
385	Hada plebeja	1	0	1	1	1
386	Helotropha leucostigma	1	1	1	1	1
387	Hoplodrina octogenaria	1	1	1	1	1
388	Hydraecia micacea	1	0	1	0	1
389	Hyppa rectilinea	1	0	0	0	1
390	Lenisa geminipuncta	0	1	0	0	1
391	Lithophane furcifera	0	0	0	0	1
392	Lithophane socia	1	1	0	1	1
393	Mesapamea secalis	0	1	1	1	1
394	Mniotype bathensis	0	1	0	0	1
395	Oligia latruncula	0	0	0	1	1
396	Orthosia gothica	1	1	1	1	1
397	Orthosia opima	1	1	1	1	1
398	Panolis flammea	1	1	1	1	1
399	Panthea coenobita	1	1	0	0	1
400	Polia hepatica	1	0	1	0	1
401	Protolampra sobrina	0	0	0	0	1
402	Subacronicta megacephala	0	1	0	0	1
403	Trachea atriplicis	0	0	0	0	1
404	Xestia triangulum	1	0	1	1	1
405	Xylena vetusta	1	1	1	1	1
	Nolidae
406	Nycteola degenerana	0	1	1	1	1
407	Nycteola revayana	1	0	1	0	1
	Notodontidae
408	Cerura vinula	0	0	0	0	1
409	Notodonta dromedarius	1	1	0	0	1
410	Ptilodon capucinus	1	0	0	0	0
	Nymphalidae
411	Argynnis paphia	0	1	1	0	1
	Oecophoridae
412	Crassa tinctella	1	0	1	1	0
413	Denisia obscurella	1	0	1	0	1
414	Denisia stipella	0	1	1	0	0
	Pieridae
415	Colias palaeno	0	1	0	0	0
	Plutellidae
416	Plutella xylostella	1	1	1	0	1
	Praydidae
417	Prays fraxinella	0	0	1	0	0
	Psychidae
418	Taleporia tubulosa	0	1	0	0	0
	Pterophoridae
419	Gillmeria pallidactyla	1	0	1	1	0
	Pyralidae
420	Dioryctria abietella	0	0	0	1	1
	Saturniidae
421	Aglia tau	0	1	0	0	1
422	Saturnia pavonia	0	0	1	0	1
	Sphingidae
423	Deilephila elpenor	0	0	0	0	1
	Tineidae
424	Morophaga choragella	0	0	1	0	0
425	Nemapogon nigralbella	0	0	1	0	0
426	Nemaxera betulinella	0	0	1	0	0
427	Niditinea striolella	0	0	1	0	0
428	Triaxomera fulvimitrella	1	0	1	0	0
	Tischeriidae
429	Tischeria ekebladella	0	1	1	0	0
	Tortricidae
430	Acleris forsskaleana	1	0	1	1	1
431	Acleris lipsiana	1	0	1	1	1
432	Acleris logiana	1	1	1	0	1
433	Acleris notana	1	0	1	0	1
434	Adoxophyes orana	1	1	1	1	1
435	Aethes smeathmanniana	1	1	1	0	1
436	Agapeta hamana	0	1	0	0	0
437	Aleimma loeflingiana	0	1	1	1	1
438	Ancylis badiana	1	0	0	0	0
439	Ancylis laetana	0	0	1	0	0
440	Ancylis mitterbacheriana	1	0	1	0	0
441	Ancylis myrtillana	1	1	0	0	1
442	Aphelia paleana	0	1	0	0	0
443	Apotomis fraterculana	1	1	0	1	0
444	Apotomis infida	1	0	0	0	0
445	Archips podanus	1	0	1	0	1
446	Bactra lancealana	1	0	0	0	0
447	Celypha rivulana	1	0	0	0	0
448	Clepsis spectrana	1	0	0	0	0
449	Cnephasia asseclana	0	0	1	0	0
450	Cnephasia stephensiana	1	1	1	1	1
451	Cochylis nana	1	1	1	0	0
452	Eana argentana	1	0	1	0	0
453	Eana incanana	1	1	1	1	1
454	Enarmonia formosana	0	0	1	0	0
455	Epiblema scutulana	1	0	1	0	1
456	Epinotia bilunana	0	1	1	0	0
457	Epinotia cinereana	0	0	0	1	0
458	Epinotia nisella	1	0	1	1	0
459	Epinotia signatana	1	0	1	0	0
460	Epinotia solandriana	1	0	0	0	0
461	Epinotia tedella	0	0	1	1	0
462	Epinotia tenerana	0	1	1	1	0
463	Epinotia tetraquetrana	0	1	1	0	0
464	Eucosma cana	1	0	1	0	1
465	Eucosma hohenwartiana	1	0	1	0	0
466	Eudemis porphyrana	1	0	1	0	0
467	Gypsonoma dealbana	0	1	1	1	0
468	Hedya nubiferana	1	1	1	1	0
469	Hedya ochroleucana	1	0	0	0	0
470	Lobesia reliquana	0	0	1	0	0
471	Metendothenia atropunctana	0	1	1	1	0
472	Orthotaenia undulana	1	1	1	1	1
473	Pandemis cerasana	0	1	1	0	0
474	Pandemis cinnamomeana	0	1	1	1	0
475	Paramesia gnomana	1	0	1	0	1
476	Phalonidia udana	0	0	1	0	0
477	Piniphila bifasciana	0	1	0	0	0
478	Ptycholoma lecheana	0	0	1	0	1
479	Rhopobota naevana	1	1	1	1	1
480	Rhyacionia buoliana	0	1	1	0	1
481	Syndemis musculana	0	1	1	0	0
482	Thiodia citrana	1	1	1	0	1
483	Tortrix viridana	1	1	1	1	1
484	Zeiraphera isertana	0	0	1	0	0
485	Zeiraphera ratzeburgiana	1	0	0	1	0
	Yponomeutidae
486	Argyresthia arceuthina	1	0	1	0	0
487	Argyresthia brockeella	0	0	0	1	0
488	Argyresthia conjugella	0	1	1	0	1
489	Argyresthia glabratella	1	1	1	0	1
490	Cedestis gysseleniella	1	1	1	1	1
491	Paraswammerdamia conspersella	1	0	1	0	1
492	Paraswammerdamia nebulella	1	0	1	0	1
	Ypsolophidae
493	Ypsolopha asperella	0	1	0	0	0
494	Ypsolopha falcella	1	1	1	0	0
495	Ypsolopha parenthesella	1	1	1	0	1
496	Ypsolopha scabrella	1	0	1	0	1
497	Ypsolopha sylvella	1	0	1	0	0
498	Ypsolopha ustella	1	0	1	0	1
	Megaloptera
	Sialidae
499	Sialis lutaria	1	0	0	0	0
	Neuroptera
	Chrysopidae
500	Chrysopa pallens	1	1	1	0	1
501	Chrysoperla carnea	1	1	0	0	1
502	Cunctochrysa albolineata	0	0	0	1	0
	Hemerobiidae
503	Hemerobius contumax	1	1	1	1	1
504	Hemerobius fenestratus	0	1	1	1	1
505	Hemerobius humulinus	1	1	1	1	1
506	Hemerobius pini	0	1	0	1	1
507	Hemerobius stigma	1	1	1	1	1
508	Wesmaelius concinnus	1	1	1	1	1
509	Neuroptera sp.	0	1	1	0	0
	Sisyridae
510	Sisyra nigra	1	0	0	0	1
	Orthoptera
511	Orthoptera sp.	0	0	0	0	1
	Psocodea
	Peripsocidae
512	Peripsocus subfasciatus	1	0	1	0	1
	Trichoptera
	Goeridae
513	Goera pilosa	1	1	1	0	1
	Lepidostomatidae
514	Lepidostoma hirtum	1	1	1	0	0
	Leptoceridae
515	Athripsodes cinereus	1	1	0	0	0
516	Ceraclea albimacula	1	0	1	0	0
517	Ceraclea annulicornis	1	0	0	0	0
518	Ceraclea dissimilis	1	0	0	0	0
519	Ceraclea excisa	1	0	0	0	0
520	Ceraclea fulva	1	1	0	1	0
521	Ceraclea senilis	0	1	0	0	0
522	Mystacides azureus	1	1	0	0	0
523	Mystacides longicornis	0	1	0	0	0
524	Mystacides nigra	1	1	0	0	0
525	Oecetis furva	1	1	0	0	0
526	Oecetis lacustris	1	1	1	0	0
527	Oecetis ochracea	0	1	0	0	0
528	Oecetis testacea	1	1	0	0	0
529	Triaenodes detruncatus	1	0	0	0	0
	Limnephilidae
530	Glyphotaelius pellucidus	1	1	0	0	1
531	Limnephilus affinis	1	1	1	0	1
532	Limnephilus flavicornis	0	1	0	0	0
533	Limnephilus fuscicornis	1	1	0	0	1
534	Micropterna sequax	0	0	1	0	1
535	Rhadicoleptus alpestris	0	1	1	0	1
536	Stenophylax lateralis	0	0	0	0	1
	Molannidae
537	Molanna angustata	1	0	0	0	0
	Phryganeidae
538	Agrypnia obsoleta	1	0	1	0	0
539	Agrypnia pagetana	0	1	0	0	1
540	Agrypnia varia	0	1	1	0	0
541	Phryganea grandis	1	1	1	0	0
	Polycentropodidae
542	Cyrnus trimaculatus	1	0	0	0	0
543	Plectrocnemia conspersa	1	1	1	0	0
544	Polycentropus flavomaculatus	1	0	1	0	0
	Psychomyiidae
545	Lype phaeopa	1	0	0	0	0
546	Psychomyia pusilla	1	1	0	0	0
	Rhyacophilidae
547	Rhyacophila nubila	1	0	0	0	0

Figure 3

(a) Read‐count‐based and (b) frequency‐of‐occurrence‐based rarefaction (solid line segment) and extrapolation (dotted line segments) sampling curves with 95% confidence intervals (shaded areas) for the five bat's prey species. The solid shapes represent the reference samples

Figure 4

Food webs of the bat predator species and their prey species visualizing the differences in the diet. The pictures in the upper row represent predators in each web and the blocks in the lower row the prey species. A line connecting a predator with a prey represents a detected predation record, and the thickness of the line represents (a) the relative read abundance (RRA) or (b) modified proportional frequency (MFO) of each predation record. See the “Data analysis” in the main text for details on the RRA and MFO. The numbers below the lower blocks correspond to the prey numbers in the Table 2

Dietary patterns of the studied bats

The quantitative prey assemblages (RRA) seem to be very different for all the bat species, as revealed by the bipartite analysis (Figure 4a). However, when using frequencies (MFO), these patterns are not that clear (Figure 4b). In the current study, different bat species were mainly sampled in different roosts, but luckily prey use does not seem to be vastly related to the roost site, as can be seen from the bipartite analysis from the two sites where two different bat species were sampled from the same roost (Figure 5a,b). The prey use patterns were further illustrated in the PCoA ordinations: Both RRA and presence/absence data ordinations grouped the bat species according to their respective feeding guilds based on differences in the prey species assemblages (Figure 6a,b). In the RRA plotting, first coordinate explained 10.5% and the second coordinate 7.5% of the variation in the data (Figure 6a), and in the plot using presence/absence data, the first and the second coordinates explained 15% and 9.9% of the variation (Figure 6b), respectively, so for both data types a large part of the variation remained unexplained. Altogether 44 common prey species were shared by all the bat species, and 90 more equally common prey species were shared by four bat species (Table 2; Silvonen, Top‐Jensen, & Fibiger, 2014).

Figure 5

Food webs in the two roosting sites where two different bat species were sampled to show that the bat species consumed dissimilar prey, even when collected on the same site during same time. (a) Laiterla roost food web shows that M. mystacinus is fond of soft‐bodied insects, such as Neuroptera, whereas P. auritus diet consists of larger carabid beetles. (b) Rotholma roost, where the two M. brandtii sample contains different Diptera and Hymenoptera prey, compared to the P. auritus. The numbers below the lower blocks correspond to the prey numbers in the Table 2

Figure 6

PCoA ordination based on composition of prey species in the diet of each bat species using (a) the Bray‐Curtis dissimilarity with relative reads abundances (see Methods for details) and (b) the Jaccard similarity between samples with presence/absence data in each sample. Circle = Myotis daubentonii; asterisk = Eptesicus nilssonii; square = M. brandtii; plus = M. mystacinus; and triangle = Plecotus auritus

Dietary patterns in the feeding guilds

The feeding guilds are also easily separated by looking the diet at the prey family level (here using percentages from relative read abundance data, but approximately the same ratios can be drawn from the presence–absence data; Table 2): The trawling species (M. daubentonii) predominantly consumes a single prey family, Chironomidae (45.8% of all the reads), which is a highly abundant and species‐rich family in southwestern Finland (Lilley, Ruokolainen, Vesterinen, Paasivirta, & Norrdahl, 2012; Paasivirta, 2012, 2014 ), but constrained to the vicinity of aquatic environment, where the bat collects its prey from the water surface (Nilsson, 1997). The gleaner (P. auritus) relies on the plentiful moth family Noctuidae (57.2%), which is either caught in flight or from surfaces on vegetation, as some of the prey species are mainly diurnal (Silvonen et al., 2014). The other largely consumed prey family for P. auritus was the coleopteran family Carabidae (18.7%), which is most probably foraged from the ground. The third guild, hawkers, consists of three bat species (E. nilssonii, M. brandtii, and M. mystacinus), which all have distinct prey family spectrum. E. nilssonii is known to be Nematocera specialist (Rydell, 1986), and we can confirm this observation: E. nilssonii preyed upon Pediciidae (21.3%), Trichoceridae (18.4%), Tipulidae (13.0%), and also on chironomids (10.7%). The other two hawkers relied solely on moths: M. brandtii's menu included Tortricidae (26.5%) and Geometridae (24.3%). Interestingly, at least one very abundant prey species Agriopis aurantiaria (Geometridae) only flies during October and after that, so this moth must have been caught by M. brandtii as larvae on leafs or while hanging from the tree (Silvonen et al., 2014). On the other hand, M. mystacinus foraged on the moth families Argyresthiidae (21.0%), Geometridae (16.5%), and Lypusidae (11.3%), which all have distinct life strategies and behavioral ecologies (Silvonen et al., 2014).

Temporal aspects and predator‐prey size analysis

The strong assorting patterns of different bat species seen in plotwebs and PCoA were confirmed when comparing all bat species’ diet's together in the analysis of variance (Table 3: Predator: RRA data, df = 4, R 2 = 0.12, p = 0.0001; PA data, df = 4, R 2 = 0.05, p = 0.0033). Despite the limited temporal span of the sampling for each bat (Table 1: 8 weeks for M. daubentonii and P. auritus; 5 weeks for M. brandtii and E. nilssonii), we tested the dietary variation in time, but found no significant variation between weeks (Table 3: Week). Temporal pattern was same for all bat species (Table 3: Predator × Week).

Table 3

Permutational multivariate analysis of variance (adonis) for prey communities for the studied bat species using Bray–Curtis dissimilarity matrix (for RRA) or Jaccard similarity (for presence–absence data) of presence or absence of prey species in each sample. Terms added sequentially (first to last) to the model. The only significant Bonferroni‐corrected p‐value (p b) is denoted with an asterisk, indicating that as a whole, the diet changes during the sampling season, although this effect was only observed with the PA data, but not in the RRA data

Predictor	df	F	R 2	p b
Relative read abundance data
Predator	4	1.46	0.12	0.0001*
Week	10	0.92	0.18	0.9544
Predator × Week	7	0.96	0.13	0.7598
Residuals	29		0.57
Total	50		1.00
Presence/absence data
Predator	4	1.77	0.13	0.0001*
Week	10	1.06	0.20	0.1372
Predator × Week	7	0.99	0.13	0.5561
Residuals	29		0.54
Total	50		1.00

When the prey assemblages were analyzed separately in pairwise PERMANOVA between species, the diet was significantly different in all compared pairs, except those with M. mystacinus, which was present in the sample with only one sample (Table 4). The same pattern occurred in both RRA and PA data (Table 4). The diet explained only 6%–13% of the total variance (Table 4).

Table 4

Pairwise permutational multivariate analysis of variance (pairwise.adonis) for prey communities for each of the studied bat species using Bray–Curtis dissimilarity matrix (for RRA) or Jaccard similarity (for presence–absence data) of presence or absence of prey species in each sample. Significant Bonferroni‐corrected p‐values (p b) are denoted with an asterisk. All the bat species pairs significantly differ in their prey species composition, except comparisons with M. mystacinus, which was represented with only one sample

Pairs	df	F	R 2	p b
Relative read abundance data
Plecotus auritus versus Myotis mystacinus	11	1.29	0.11	1.00
P. auritus versus M. daubentonii	30	3.07	0.10	0.01*
P. auritus versus M. brandtii	20	2.35	0.11	0.01*
P. auritus versus Eptesicus nilssonii	19	2.34	0.12	0.01*
M. mystacinus versus M. daubentonii	20	1.19	0.06	0.49
M. mystacinus versus M. brandtii	10	1.03	0.10	1.00
M. mystacinus versus E. nilssonii	9	1.10	0.12	1.00
M. daubentonii versus M. brandtii	29	2.24	0.07	0.01*
M. daubentonii versus E. nilssonii	28	1.60	0.06	0.05*
M. brandtii versus E. nilssonii	18	1.59	0.09	0.04*
Presence/absence data
P. auritus versus M. mystacinus	11	1.16	0.10	1.00
P. auritus versus M. daubentonii	30	3.83	0.12	0.01*
P. auritus versus M. brandtii	20	2.81	0.13	0.01*
P. auritus versus E. nilssonii	19	2.52	0.12	0.01*
M. mystacinus versus M. daubentonii	20	1.44	0.07	1.00
M. mystacinus versus M. brandtii	10	1.21	0.12	0.88
M. mystacinus versus E. nilssonii	9	1.22	0.13	1.00
M. daubentonii versus M. brandtii	29	2.55	0.08	0.01*
M. daubentonii versus E. nilssonii	28	2.63	0.09	0.01*
M. brandtii versus E. nilssonii	18	1.65	0.09	0.01*

The bat species differed significantly in size according to the banding data (Figure 7a, Kruskal–Wallis H = 867.29, df = 4, p < 0.0001), further confirmed by the pairwise analysis, where all the bats differed from each other significantly (Table 5). Similarly, the prey size differed significantly between bat species (Lepidoptera prey: H = 118.58, df = 4, p < 0.0001; other prey H = 34.5, df = 4, p < 0.0001). The pairwise analysis indicated that the diet of P. auritus consisted of lepidopteran prey of larger size than any of the other bat species. A similar, but not identical, pattern was observed for other than lepidopteran prey, in which P. auritus diet size was similar only to M. mystacinus. For M. brandtii, the lepidopteran prey size was significantly smaller compared to the other species, except for M. mystacinus, but other prey taxa differed in size with P. auritus only (Table 6). On average, P. auritus consumed the largest prey (Figure 7b,c; Table 6), whereas M. brandtii consumed the smallest prey (Figure 7b,c; Table 6).

Figure 7

(a) Size of adult bats (measured by the length of forearm), (b) size of lepidopteran prey taxa (measured by the wingspan), and (c) size of other than lepidopteran prey taxa (measured by the body length) for each of bat species in the current study. The number of records is denoted for each group

Table 5

Tukey and Kramer (Nemenyi) test with Tukey‐Dist approximation for independent samples with R package “PMCMR” between all the bat species for bat forearm length, Lepidoptera prey wing span, or other prey body length. The number of records is listed for each group. The significant p‐values are bolded (chi‐square was corrected for ties)

Compared pairs	Bats n = 1,553 pchisq	Lepidoptera n = 1,807 pchisq	Other prey n = 1,642 pchisq
Plecotus auritus versus Myotis mystacinus	<0.0001	0.0008	0.9980
P. auritus versus M. daubentonii	<0.0001	<0.0001	<0.0001
P. auritus versus M. brandtii	<0.0001	<0.0001	<0.0001
P. auritus versus E. nilssonii	0.5700	0.0003	0.0040
M. mystacinus versus M. daubentonii	<0.0001	0.6635	0.2240
M. mystacinus versus M. brandtii	0.4800	0.8516	0.1590
M. mystacinus versus Eptesicus nilssonii	<0.0001	0.7223	0.3680
M. daubentonii versus M. brandtii	<0.0001	<0.0001	0.9810
M. daubentonii versus E. nilssonii	<0.0001	1.0000	0.9580
M. brandtii versus E. nilssonii	<0.0001	0.0010	<0.0001

Table 6

Average sizes with standard deviations for all the bat species (bat forearm length), prey size (Lepidoptera prey wing span and for other prey body length) with standard deviations for each group

Bat species	Bats	Lepidoptera	Other prey
Myotis daubentonii	37.75 ± 1.03	26.12 ± 11.73	6.68 ± 3.62
Eptesicus nilssonii	39.49 ± 1.65	27.05 ± 14.10	7.05 ± 4.20
M. brandtii	35.01 ± 1.16	22.54 ± 11.99	6.62 ± 3.78
M. mystacinus	33.86 ± 1.34	23.86 ± 10.85	8.59 ± 4.68
Plecotus auritus	38.80 ± 1.57	30.85 ± 13.17	8.98 ± 5.52

DISCUSSION

Co‐occurring species with a relatively short active season offer an excellent setup for the study of dietary strategies. Here, we identified 547 prey species in the diet of five common and abundant boreal vespertilionid bat species. All species fed mainly on two insect orders (Diptera or Lepidoptera), which undoubtedly are among the most available dietary groups (with Coleoptera) in terms of species richness (Erwin, 1982; Stork, 2018) and probably for biomass, although reliable biomass estimates are lacking. The three feeding guilds (trawlers, hawkers, and gleaners) are clearly separated by diet in the data. Moreover, the dietary composition between all bat species differed significantly, a pattern that persisted throughout the results. This pattern was strong enough to be observed in all the interpretations of the molecular data (presence/absence, frequencies, and read count data analysis). The sampling week did not explain the diet for any bat species, but we found differences in average prey size consumed by the bat species, and a positive correlation between bat species size and size of prey, although with a fine marginal.

In concordance with dietary studies on insectivorous bats, we also revealed a high frequency of lepidopteran and Dipteran species in the diets of the sampled species (Clare et al., 2014; Shively et al., 2017; Vesterinen et al., 2016). In fact, combined, these two orders constitute the majority of all predation records in the whole study, regardless of the data type (read counts, frequency, or presence/absence). Especially, P. auritus appears to utilize lepidopteran prey species to a higher degree compared to the other species, although rather surprisingly, ~20% of the diet (in terms of relative read abundance) of P. auritus appears to consist of Coleoptera, particularly ground beetles. All other invertebrate orders are less relied on, although Trichoptera and Neuroptera constitute a small part of the diet in some species. This is expected, seeing as these orders include mass‐emerging species, such as Oecetis ochraea (Trichoptera, Leptoceridae), or species which are active and available as prey throughout the season, such as Brachyderes incanus (Coleoptera, Curculionidae), or otherwise very common and abundant species, such as Chrysoperla carnea (Neuroptera), are all found in this study (Vesterinen et al., 2013, 2016 ). This primarily highlights the huge biomass and species diversity found in Lepidoptera and Diptera, but secondly, also further establishes the importance of these orders to bat species diversity. Because of the huge biomass of insects worldwide, there are numerous predators in addition to bats, such as fish, birds and even predatory insects, consuming these, and other arthropods as their primary food source (fish: Jakubavičiūtė, Bergström, Eklöf, Haenel, & Bourlat, 2017; dragonflies: Kaunisto et al., 2017; birds, spiders: Wirta et al., 2015). Surprisingly, the prey order‐level similarity between different predator taxa is surprisingly high when comparing our results to the aforementioned studies, especially between bats and other flying insectivores.

The patterns detected in this study indicate the dominance of Diptera and Lepidoptera (Dip&Lep) in the diet of boreal bats. At first glance, this pattern could in theory be caused by the so‐called primer bias, which means that the chosen primers amplify some taxa (such as Dip&Lep) more than others (such as Coleoptera or arachnids). The primers used in this study, the most widely applied and very functional Zeale primers (Zeale et al., 2011), have received some (in vitro) criticism claiming they may over‐estimate Dip&Lep (Clarke et al., 2014). However, we feel that these two orders, Diptera and Lepidoptera, are arguably among the most species‐rich and abundant insect orders in Finland and especially in the study area (see, e.g., Supplement 1 in Vesterinen et al., 2016), and thus, the dietary patterns found by these markers seem very intuitive and logical. Furthermore, we found a large proportion of Coleoptera in the diet of P. auritus, suggesting that the claimed bias is not too strong to detect abundant prey outside Dip&Lep orders. At the time of conducting this study, no other primer pair has been shown to amplify a short target (to enable detection of highly fragmented prey DNA), and at the same time exclude bats, while including (mostly) all arthropod prey. This said, in future studies, other primers along Zeale primers and possibly more than one (mitochondrial) loci should be used, as no primer is totally free of bias (Alberdi, Aizpurua, Gilbert, & Bohmann, 2018; Clarke et al., 2014).

The diet of each bat species remained unchanged throughout the season. This, together with the high number of different species consumed, suggests the role of insectivorous bats as (perhaps habitat‐related) specialists (Vesterinen et al., 2016), although some opportunistic generalism has been observed (Salinas‐Ramos, Herrera Montalvo, León‐Regagnon, Arrizabalaga‐Escudero, & Clare, 2015; Vesterinen et al., 2013). This suggests that the diets of our study species could be determined by the abundance and availability of insect prey instead of any particular predator‐specific characteristic. In fact, it has previously been reported that bat diet responds to local insect population fluctuations (Aizpurua et al., 2018; Clare, Barber, Sweeney, Hebert, & Fenton, 2011; Sedlock, Krüger, & Clare, 2014; Vesterinen et al., 2016). Razgour et al. (2011) reported temporal shifts in the proportional frequencies of Lepidoptera and Diptera prey of P. auritus. We found no evidence of shift in these frequencies in our P. auritus samples. At the latitude where our study was conducted, there is only a two‐ to four‐week difference between the highest abundance peaks for Diptera and Lepidoptera, and furthermore, it may be that even during the period of low abundance, there are still more than enough prey items available for bats (Vesterinen et al., 2016).

Diet comparisons between sympatric bat species using molecular methods are still relatively scarce, but often show considerable overlap in diet, even at the lower taxon level (Krüger, Clare, Greif, et al., 2014; Krüger, Clare, Symondson, et al., 2014; Salinas‐Ramos et al., 2015; Ware, 2016). Most studies focus on either closely related species, or species which share a feeding guild, such as the two trawling bats (M. daubentonii and M. dasycneme) in a study by Krüger, Clare, Greif, et al., 2014; Krüger, Clare, Symondson, et al., 2014. In the current study, we compared the diet of five vespertilionid bats, representing three different guilds. According to our analysis, all three guilds are clearly evident, with little overlap between the aerial hawkers (M. brandtii and E. nilssonii) and the trawling bat (M. daubentonii). These dietary overlaps are likely to be explained by the opportunistic and sporadic consumption of a very few prey items, such as mass‐emerging chironomids, moths, mayflies, and caddisflies. Plecotus auritus, the species considered a gleaner and moth specialist, showed a marked difference in PCoA ordination compared to the other two groups. We also discovered a significant difference in the size of prey consumed, with the larger P. auritus consuming larger prey species, whereas the smaller bat, M. brandtii, consumed smaller prey items. This is not surprising as it is generally accepted that the echolocation used by aerial insectivorous bats renders smaller prey items unavailable to larger bats (Brigham, 1991; Waters, Rydell, & Jones, 1995). Additionally, P. auritus, among other members of the genus, possesses a suite of morphological characters (low wing‐loading, large pinna, low‐frequency hearing), which allow them to use both acoustic gleaning and aerial‐hawking foraging strategies to capture prey (Coles, Guppy, Anderson, & Schlegel, 1989; Norberg & Rayner, 1987). It is possible that some noctuid prey individuals have been foraged as larvae, as the flight peak of most noctuid prey in the current study is later than the sampling period.(Finnish Biodiversity Information Facility/FinBIF. http://https://tun.fi/HBF.31668; accessed 2018‐08‐26). These strategies permit the genus to occupy a specialized feeding niche within European bat assemblages (Roswag et al., 2018).

Interestingly, the two aerial‐hawking species studied here, E. nilssonii and M. brandtii, showed considerable overlap in diet according to the PCoA, analogous to M. dasycneme and M. daubentonii (Krüger, Harms, Fichtner, Wolz, & Sommer, 2012), despite representing two different genera. Taking a closer look at the diets of the two species, we notice that regardless of both species relying heavily on Lepidoptera and Diptera, the proportions of these taxa in the diets differ considerably and the diets consist of entirely different prey families. Whereas the majority of the E. nilssonii diet consists of nematoceran Diptera (>60% of reads are either Pediciidae, Trichoceridae, Tipulidae, or Chironomidae for E. nilssonii), the M. brandtii diet reveals a greater proportion of Lepidoptera (>50% of reads are Geometridae and Tortricidae for M. brandtii). In addition to this, the lepidopteran diet consumed by M. brandtii is considerably smaller in size compared to E. nilssonii. These finer scale differences in the diet of these two aerial‐hawking species could be explained by differences in other dimensions of their respective ecological niches. For instance, E. nilssonii forages in relatively open spaces (forest edges, clearings, open gardens, etc.), whereas M. brandtii prefers more confined spaces with forest cover (Dietz, Nill, & Helversen, 2009). This is resource partitioning that could be further dissected by looking at isotopic niches, for instance, to give a complementary scenery to dietary ecology besides DNA‐based analysis (Schmidt, Mosbacher, Vesterinen, Roslin, & Michelsen, 2018). Another option would be to increase sampling effort to obtain an even more robust overview of the main prey items. Information on the identified major dietary taxa could then be used to deduct the main foraging habitat, as presented by Alberdi, Garin, Aizpurua, and Aihartza (2012).

The molecular work carried out in this analysis not only highlights the deep insight offered by metabarcoding, but also underlines the dynamic and complementary nature of DNA‐based analysis. Based on our earlier field work, we had chosen species‐specific roosting sites for the diet analysis of five bat species, to obtain an equal sampling effort. However, when confirming the fecal “donor” by the means of metabarcoding, we noticed some discrepancies between the field data and confirmed data, that is, our M. mystacinus roost was confirmed as an E. nilssonii roost. In future, the molecular confirmation of noninvasively collected samples should be a standard approach, either by traditional Sanger sequencing or cost‐effective next‐generation sequencing (NGS), depending on the number of samples and the predator and prey species. Also, the importance of a comprehensive reference library (Mutanen et al., 2012; Pentinsaari, Hebert, & Mutanen, 2014; Pilipenko, Salmela, & Vesterinen, 2012), which allows the correct and reliable identification of most prey items, needs to be pointed out once more. This offers the possibility of deeper ecological dietary studies, such as prey size analysis (Pentinsaari et al., 2014). While some prey items had not been described with a scientific species‐level name in this study, a reliable estimate of their size could be inferred using the so‐called barcode index numbers (BIN; Ratnasingham & Hebert, 2013) to trace the images for measurements. This emphasizes the significance of public and easy‐accessible reference library systems, such as BOLD (Ratnasingham & Hebert, 2007). Although some studies still rely on OTUs (operational taxonomical units) instead of biological species, we highlight the importance of actual prey species determination, which allows a deeper and more robust insight into dietary ecology.

The main drawbacks of the molecular methods are the highly challenging interpretations of the quantitative aspects of the diet, that is, are the most frequently consumed prey items also the most important in terms of biomass and energy gain? While the current practice in many molecular ecological dietary studies using metabarcoding appears to mostly rely on frequency of occurrence (but see Vesterinen et al., 2016), the read counts may actually hold some important quantitative information (Deagle et al., 2018). Here, we tested our data using both frequency of occurrence and read count data and found no major differences in the outcome of the analysis, or more importantly, in the interpretation of the results. This suggests our data have strong ecological message that holds despite the methodological approach used.

Our study supports the existence of dietary flexibility in generalist bats and dietary niche overlapping, especially in bats of the same feeding guild in a highly seasonal ecosystem (Roswag et al., 2018). In fact, it could be the flexibility in feeding strategies which allows species to sustain populations in arctic and subarctic regions (Shively et al., 2017). Additionally, a great proportion of niche differentiation most likely also occurs outside the diet dimension where an almost infinite number of possible axes exist for competing species in the n‐dimensional niche hyper‐volume (Hutchinson, 1957). Even minor differences in a number of different axes can result in a substantial overall difference (Privitera et al., 2008). Clearly, the next logical step is to utilize deep dietary analysis, alongside other ecological (LIDAR: light detection and ranging method, etc.) and behavioral (GPS‐tracking) datasets to begin to understand niche realization and resource partitioning in species to a far higher accuracy than has been available to date.

AUTHOR CONTRIBUTIONS

EJV and TML designed the study, collected the data, and wrote the first version of manuscript. ASB collected samples in the field and gathered prey species measurements and the map data. AIEP and EJV conducted the molecular work and data analysis. All authors contributed to the final version of the manuscript.

DATA ACCESSIBILITY

Labeled raw reads and OTUs are available in the Dryad Digital Repository: http://https://doi.org/10.5061/dryad.6880rf1.