A microbial view on secondary contact between two Alpine butterflies

doi:10.21203/rs.3.rs-7637106/v1

Download PDF

Research Article

A microbial view on secondary contact between two Alpine butterflies

https://doi.org/10.21203/rs.3.rs-7637106/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background

Coexistence between sibling species can be limited if they are ecologically too close, leading to the formation of often narrow zones of secondary contact. While the ecological niche is commonly estimated using abiotic factors, the potential for differentiation in gut microbial communities is less well studied. We address this gap in research, focusing on two Alpine butterfly species of the genus Erebia that form a stable and very narrow contact zone.

Results

Using a metabarcoding approach to sequence the adult gut microbial communities of our two focal species as well as capturing the microbial diversity found on three nectar plant species, we found that the microbial community i) significantly differed between species but not between sexes, that ii) the abundance of the endosymbiont Wolbachia differed between species, where its high abundance resulted in the presence of fewer other microbial taxa, and that iii) microbes found on flowers largely overlap with the ones found in the butterfly hosts, suggesting that intestinal environmental filtering occurs only to some degree.

Conclusions

Unlike for abiotic environmental factors, we uncovered significant species specific differences in the gut microbial communities of our focal species, further highlighting the complex interactions between host biology and environmental factors in shaping the gut microbiota. The observed microbial differences could reflect potential adaptive mechanisms and evolutionary processes at play. Overall, our study highlights the utility to study cryptic niche differentiation during secondary contact, advancing our understanding of the ecological dynamics of alpine butterflies.

Erebia

metabarcoding

16S rRNA amplicon sequencing

gut microbiota

Wolbachia

The distributions of many species in the Northern temperate zone are shaped by recurrent range expansions and contractions as a result of the Pleistocene glacial cycles (Hewitt, 2000). In many cases, species have recolonized from different glacial refugia, where they often diverged in allopatry due to drift or selection. Where such lineages meet, they may form zones of secondary contact, whose outcomes vary depending on how strongly differentiated the lineages are. Outcomes may range from admixture to the formation of narrow hybrid zones, where hybrids are either sterile or selected against [1]. Contact zones between distinct lineages offer the opportunity to study the process of speciation, even in cases where strong phenotypic or genetic differentiation is already apparent but reproductive isolation remains incomplete [2]. The factors preventing co-existence are not fully understood. In passerine birds for example, the potential for secondary sympatry is positively associated with both the degree of phylogenetic and phenotypic differentiation between species, suggesting that competition between ecologically similar species may limit the potential for sympatry [3]. For butterflies different mechanisms have been suggested depending on the taxonomic group, including reproductive interference coupled with climatic preferences [4] or niche conservatism [5]. In addition to the host, niche conservatism can also be reflected by the gut microbial composition [6], which has, however, rarely been studied in the context of secondary contact [7, 8]. Here, we study such a case in a species pair of Alpine butterflies that form a very narrow contact zone [9–11].

All animals interact with microorganisms in various ways including mutual interactions between hosts and their microbiome to parasitism [12]. The diversity of microbiota is about a magnitude lower in insects than mammals, yet insects display a wide variety of potential host-symbiont interactions [13]. Potential interactions include the manipulation of host reproduction [14], the breakdown of toxic metabolites [15] or the production of nutrients essential to the host [16]. Endosymbionts can also protect their hosts against abiotic stressors and pathogens [17]. However, while experimental approaches provide insights into these interactions under laboratory conditions, the actual implications of many microorganisms in the wild is often not fully understood given the broad diversity of the microbial community and interactions [18]. Many factors can affect the microbiota, and our knowledge of the ecological drivers underlying individual variation in microbial communities in natural populations is limited. The gut microbial community of insects is often heterogeneous, and its composition is thought to be primarily driven by the food resources of a species or individual [19]. The Lepidopteran gut microbial community further varies among life stages, i.e. between the caterpillar and the imago and often reflects different feeding regimes, which may also be adaptive [20, 21]. However, relatively little is known about the microbial communities in non-model Lepidoptera organisms or how they may reflect different food resources.

Erebia is a genus of cold-adapted butterflies, whose diversification has been associated with differentiation in distinct glacial refugia during the glacial cycles [22–24]. Following postglacial range expansions, distantly related Erebia species often coexist by exploiting different microhabitats [25]. Conversely, closely related species or lineages are ecologically often similar, showing niche conservatism [5] and exclude each other or form very narrow secondary contact zones [11, 26–28]. One of the best studied examples are the two sibling species Erebia tyndarus and E. cassioides, whose contact zones in the central Swiss Alps extends only over a few hundred metres and are stable since at least 50 years [10, 11]. The two species form very steep phenotypic and genomic clines with only very few first generation hybrids across their contact zone [9, 10, 22]. The factors that prevent co-existence and interspecific gene flow are not fully understood. Consistent with niche conservatism, the two species seem to have similar ecological niches, which could prevent co-existence [9]. Differences in host-symbiont interactions moreover occur: While almost all E. cassioides individuals are infected by the endosymbiotic bacterium Wolbachia, E. tyndarus seems to have lost Wolbachia at the contact zone [10, 22] when it hosts a different lineage than E. cassioides in allopatric populations [29].

Climatic niche conservatism has recently been suggested to be prevalent in Erebia [5] and could at least in part account for the lack of co-existence between closely related Erebia species. While former studies support this scenario for abiotic ecological variables in E. tyndarus and E. cassioides [9, 10], only little is known about their biotic ecological environment, for example to which degree both species would use similar nectar resources. Using a metabarcoding approach we aimed to shed light on how the two species might differ in their gut microbial communities as a proxy for ecological and dietary niche differentiation at the adult stage. Sampling also flowers from plants that are used by both host species as nectar sources [11], we further compared the microbial diversity between nectar plants and Erebia.

Sample Collection and DNA Extraction

We collected a total of 75 E. cassioides and E. tyndarus individuals by hand netting from near the contact zone in Grindelwald, Switzerland [9, 11] during summer 2023 (Table S1). Overall, we obtained more males (E. cassioides: N = 38, E. tyndarus N = 29) than females (E. cassioides: N = 4, E. tyndarus N = 4) as females tend to fly less than males. In addition, we collected 11 flowers from three plant species (Crepis pyrenaica, Carduus defloratus, Scabiosa lucida) at the same location that were visited by the target species. All butterflies were taken alive to the laboratory and stored at − 80°C on the same day they were collected before DNA extraction.

Prior to DNA extraction, we clipped the wings and dipped each individual three times in 70% ethanol, followed by three washes with distilled water to remove potential microbial contamination. We extracted DNA from the abdomen, which we cut along the dorsal midline, carefully extracting the internal tissues. We extracted DNA with the Qiagen Blood & Tissue Kit (Qiagen AG, Hombrechtikon, Switzerland) following the manufacturer's recommendations for bacteria. For plant samples, we first visually inspected each flower and removed exogenous material before we crushed each flower separately in liquid nitrogen using sterile pestles and performed DNA extraction as for the gut samples.

Library Preparation and Sequencing

We amplified the V3-V4 region of the bacterial 16S rRNA gene following the Illumina guideline for 16S metagenomic sequence library preparation. Primers are described in [30] and comprised standard Illumina adapter overhangs (forward 5'-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG-3', reverse 5'-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC-3'). The expected amplicon length was approximately 460 base pairs.

We performed PCR amplification using Promega GoTaq® G2 DNA Polymerase (Promega, Madison, WI, USA) in a 50 µl reaction volume. We followed the manufacturer’s instructions but added 2 µl of 25mM MgCl₂ to each sample reaction. PCR conditions were as follows: initial denaturation at 94°C for 2 min, followed by 30 cycles of 94°C denaturation for 1 min, annealing at 53°C for 15 sec, and extension at 72°C for 1 min, and a final extension step at 72°C for 5 min. We checked amplification success on a 1.5% agarose gel. Amplicons were sequenced on a single Illumina MiSeq using the MiSeq Reagent Kit v3 for 600 cycles that allows for read lengths of 2 × 300 bp. Library preparation and sequencing were outsourced to the Lausanne Genomics Technologies Facility at the University of Lausanne, Switzerland.

Bioinformatic Processing and Statistical Analyses

Following [31], we first assembled the demultiplexed reads using PEAR 0.9.8 [32] allowing a minimum Phred quality score of 30. We further filtered the assembled FASTQ files with FASTX-Toolkit 0.0.14 [33] to retain only those reads with a Phred quality score ≥ 10 for all bases and ≥ 28 for 95% of all bases. We then removed reads that were less than 200 bps long from the retained merged read pairs and performed a dereplication and clustering step with VSEARCH 2.9.1 [34]. For the clustering, we allowed a sequence identity of 97% or higher. From the resulting operational taxonomic units (OTUs), we removed OTUs with less than five reads supporting them to reduce potential sequencing errors. Finally, we queried each OTU against the SILVA microbial 16S NR 99 database release 138.1, which clusters sequences with more than 99% sequence [35]. We employed blastn [36] allowing for each OTU up to four BLAST matches with an e-value threshold of 0.001. Because many OTUs could not be confidently assigned to a particular microbial species, we merged OTUs at the genus level. To further account for variation in read numbers among samples, we calculated the relative proportions of each genus within a sample.

We first assessed alpha diversity by calculating the Shannon diversity index in the R package vegan [37] and compared them among species, sexes and flowers using an ANOVA with a Tukey post hoc decomposition. We further estimated beta diversity using the Bray-Curtis dissimilarity matrix, which we used to perform a Principal Coordinates Analysis (PCoA). We then performed a PERMANOVA and pairwise PERMANOVA to determine significant differences between groups, i.e. between species and sexes as well as flowers. To further identify taxa contributing most to group dissimilarities, we employed a similarity percentage (SIMPER) analysis, as well as an Indicator Taxa Analysis (IndVal) to determine taxa specifically associated with groups. Core taxa, i.e. taxa shared among more than 80% of samples, were identified and Venn diagrams were constructed to visualize taxonomic overlap between groups. We log-transformed the data where necessary to reduce skewness prior to ordination and heatmap visualization. All statistical analyses were performed in R v4.4.3 [38].

Overall microbial composition

Following filtering, we retained ~ 6.7 million bacterial sequences for all 86 sequenced samples, including both butterflies and flowers, that could be assigned to a total of 411 different OTUs (Table 1; S1). Interestingly, we found Wolbachia to be present in every specimen of both Erebia species. However, prevalence was much higher in E. cassioides (Fig. 1) as has previously been suggested [10, 22, 29]. Because Wolbachia can occur in the gut [39] as well as in other tissues, we retained it in our subsequent analyses. We further detected Wolbachia and Rickettsia on all flower samples, likely reflecting contamination from small invertebrates, insect residues, or organic matter that were not fully removed during visual inspection. We therefore repeated our analyses that included flowers excluding both Wolbachia and Rickettsia. Other dominant taxa that occurred in > 80% of all butterfly specimens included Cutibacterium (Actinomycetota, Actinomycetia) and Staphylococcus (Bacillota, Bacilli) (Table S2). The taxa with the highest total abundance, i.e. with the highest number of filtered reads included Wolbachia and Commensalibacter, followed by Serratia (Proteobacteria, Gammaproteobacteria), Enterococcus (Bacillota, Bacilli) and Acinetobacter (Proteobacteria, Gammaproteobacteria). Based on the presence/absence matrix, Wolbachia was the most prevalent genus, detected in all 75 butterfly specimens, followed by Cutibacterium and Staphylococcus, which were found in 70 and 69 butterflies, respectively (Table S4). Focusing on the 25 most abundant genera, host species specific differences occurred, where Staphylococcus, Commensalibacter, Cutibacterium and Corynebacterium showed a higher prevalence in E. tyndarus than E. cassioides (Fig. 1). Interestingly, E. cassioides females showed more overlap with the flowers but sample size was limited.

Table 1
Summary per sample group. The number of samples, total number of retained sequence reads, and observed operational taxonomic units (OTUs) are indicated.
Group	# Samples	# Filtered reads	OTUs
E. tyndarus males	29	1,529,524	351
E. tyndarus females	4	134,493	123
E. cassioides males	38	4,553,269	165
E. cassioides females	4	474,757	29
Flowers	11	31,916	66
Total	86	6,723,959	411

Alpha diversity of the microbial community, as measured by the Shannon index, differed significantly among sample groups (ANOVA: F₄,₈₁ = 28.63, p < 0.001). Post hoc Tukey tests indicated that both male and female E. tyndarus exhibited significantly higher microbial diversity than E. cassioides individuals (p < 0.001), particularly when compared to E. cassioides females, which consistently showed the lowest alpha diversity (Fig. 2). Plant samples also displayed significantly higher diversity than both E. cassioides groups (p < 0.010), but did not statistically differ from E. tyndarus (p > 0.300). No significant differences in alpha diversity was observed between male and female E. tyndarus (p = 0.590).

Beta diversity using Bray-Curtis differences and Principal Coordinates Analysis (PCoA) ordination revealed a clear clustering, separating the two Erebia species as well the flowers (Fig. 3). Consistent with the higher Shannon diversity, E. tyndarus individuals occupied a broader range of the multivariate space. Females and males largely overlapped for both E. tyndarus and E. cassioides, suggesting limited sex specific differences. The PCoA rank showed a good agreement with the data, given a stress value of 0.2, indicating a reliable representation. The PERMANOVA analysis supported the clustering with significant differences in community composition among groups (R² = 0.432, p = 0.001). These findings overall suggest host-specific structuring of microbial communities.

Species and sex related variation in butterfly microbiomes

Core microbial taxa differed between sexes in E. cassioides, i.e. being for males Commensalibacter, Cutibacterium, Staphylococcus, Wolbachia and for females Cutibacterium, Serratia, Staphylococcus, Wolbachia. Consistent with the overall higher number of OTUs (Table 1), the number of microbial core taxa was higher in E. tyndarus. They included for females Acinetobacter, Commensalibacter, Corynebacterium, Cutibacterium, Flavobacterium, Methylobacterium-Methylorubrum, Mycobacterium, Staphylococcus and Wolbachia and for males Corynebacterium, Cutibacterium, Sphingomonas, Staphylococcus and Wolbachia. However, the pairwise PERMANOVA revealed significant differences in the microbial community composition between species (R² = 0.37, p < 0.001) but not between sexes (R² = 0.01, p > 0.05), which could also reflect the limited sample sizes for females.

The indicator taxa analysis revealed 33 bacterial genera that were specifically associated with certain sample groups: Mycobacterium, Bacillus, Nannocystis, Flavobacterium, Tundrisphaera and Methylobacterium–Methylorubrum showed high indicator values (> 0.80) and were primarily associated with E. tyndarus females. A total of 15 bacteria, including bacteria such as Anaerococcus, Corynebacterium, Cutibacterium, Flavobacterium, Hymenobacter, were determined as indicators for E. tyndarus males. In contrast, Cutibacterium, Ochrobactrum, Pseudomonas and Staphylococcus were more associated with E. cassioides females (Fig. 4). However, E. cassioides males had no indicator genera (Table S2). The SIMPER analysis, determining which OTUs contributed most to the community differences observed between the groups, identified Wolbachia, Commensalibacter, Serratia, Enterococcus and Acinetobacter to separate E. tyndarus and E. cassioides (Fig. S1). The SIMPER analysis could not be performed for sex, because the respective pairwise PERMANOVA was not significant. Interestingly, the Indicator Taxa (IndVal) analysis identified almost only taxa in E. tyndarus females, highlighting their unique prevalence of bacterial genera (Fig. 5).

Butterfly and flower-associated microbiota

Core microbial taxa on flowers were Commensalibacter, Serratia, Sphingomonas, Staphylococcus and Wolbachia (Table S3), which overlaps with the SIMPER analysis, identifying the determining groups between butterflies and flowers as Anaerococcus, Commensalibacter, Enterococcus, Serratia and Wolbachia (Fig. S1). The pairwise PERMANOVA analyses revealed significant differences in the microbial community composition between butterfly and flower-associated microbiota (R² = 0.130, p < 0.001) (Fig. 4). The majority of microbial genera (N = 224) were unique to E. tyndarus, while E. cassioides had only 26 unique genera (Fig. 6). There were 142 genera shared between the two butterfly species, of which 47 genera also occurred on flowers. Only nine genera were uniquely found on the flowers. Importantly, re-running the analyses without Wolbachia and Rickettsia yielded overall similar patterns (Fig. S2).

The secondary contact zone between the two sibling species Erebia cassioides and E. tyndarus represents an advanced stage of speciation, where the two species show strong phenotypic [9, 10] and genomic differentiation [22] but fail to co-exist. Ecological niche conservatism has been suggested to limit coexistence of closely related Erebia species [5] and both E. cassioides and E. tyndarus seem to use a similar abiotic ecological niche based on climate variables [9]. However, our knowledge on caterpillar host plants and adult nectar resources are limited [11]. Using a metabarcoding approach, we investigated the adult gut microbial community to assess whether it would differ between two focal Erebia species, also in relation to some of their nectar plants.

Despite their close phylogenetic relationship and ecological similarities, the two Erebia species exhibited significantly different gut bacterial communities (Fig. 1). The core microbiota and indicator taxa analyses further highlight the role of butterfly species identity in shaping microbial community composition (Fig. 5, Table S3). We nevertheless identified several core bacterial taxa, i.e. genera that occurred consistently in all butterflies, including Wolbachia, Staphylococcus, and Cutibacterium (Table S3). This suggests a stable association with the butterfly host regardless of individual or sex.

Interestingly, the gut microbiota composition largely overlapped between sexes for both species (Table S1), which could reflect a common ecology in terms of food resources and habitat use at the adult life stage [40, 41]. The relatively short adult lifespan of just a few weeks may further limit the opportunity for sex-specific microbial differentiation. In other Lepidoptera, microbiota structure was similarly found to be more strongly influenced by host species and developmental stage than sex [42–44]. However, the indicator taxon analysis revealed some potential sex-specific relationships, with Prevotella being significantly associated with E. tyndarus males, and several microbial genera, such as Acidibacter and Actinomycetospora, being significantly associated with E. tyndarus females (Fig. 5). Some indicator genera were also present in other groups but showed a less strong association (Table S2).

The composition of insect gut microbial communities is thought to be primarily driven by the food resources that species or individuals use [19]. Consistently, we found that the microbiota on flowers of plants that were used as nectar resources, largely overlapped with the microbiota of both Erebia species (Fig. 6). This overlap may not only reflect bacterial acquisition during feeding, but flowers could also serve as passive reservoirs for microbial exchange [45]. Horizontal transmission might occur through contact or defecation. Our sampled plant species have condensed capitulate inflorescences with extended blooming phases, potentially increasing their capacity to retain microbial communities over time, promoting an ecological trade-off between mutualistic microbial acquisition and pathogenic exposure. However, most of the OTUs that we detected in butterflies were absent in the analysed flowers. Erebia likely use a much broader range of flowering plants as nectar source without any clear specificity [11]. In addition, adult Erebia, as other butterflies, are exposed to a variety of microbial sources, for example during puddling, whereby individuals extract micronutrients from mineral-rich media, like mud puddles or animal excrements [46]. Given that we also observed OTUs unique to plants, may suggest that the conditions in the gut could also exert some ecological filtering on colonists [47, 48]. For instance, gut pH, redox conditions, or host immune responses may act as barriers for environmental microbes to establish. This was further supported by our PCoA analysis (Fig. 3), which revealed a clear clustering of samples by butterfly species, indicating compositional differences between the host species. Therefore, even when butterflies coexist in sympatry, species-specific filtering mechanisms may govern the acquisition and maintenance of specific microbial taxa. This aligns with previous findings in Lepidoptera, where host plant identity has been shown to be a key determinant of microbiota structure, often more important than environmental factors [40–42, 47, 49]. Interestingly, we also detected Wolbachia on all flowers, suggesting that we likely extracted DNA from some small invertebrates, insect residues, or organic matter that we failed to remove during visual inspection. To further test the robustness of our results, we repeated the multivariate analyses after removing Wolbachia and Rickettsia from the flower samples. The resulting ordination plots (Fig. S2) showed the same overall patterns, where butterflies and flowers remained clearly separated, E. tyndarus and E. cassioides were significantly distinct, while male and female butterflies did not differ in their gut microbiome. In the SIMPER analysis other bacterial taxa such as Commensalibacter, Serratia, and Enterococcus became more prominent contributors to the observed group differences (Fig. S2). In addition to plants, we detected the endosymbiotic bacterium Wolbachia in all butterfly specimens despite former studies suggesting that E. tyndarus lost Wolbachia at the contact zone [10, 22]. These studies used genomic data generated from thorax tissue, suggesting that in E. tyndarus Wolbachia either occurs only in the abdomen or the gut and at a lower abundance than in E. cassioides (Fig. 1). The presence of Wolbachia can itself change the gut microbial community by reducing alpha diversity, as has been found in Drosophila [50]. Here, competition for iron and amino acids between Wolbachia and other microbes together with oxidative stress generated by Wolbachia [51] has been suggested to reduce microbial diversity [50]. Consistently, we found a higher alpha diversity in the less infected E. tyndarus than in E. cassioides (Fig. 2).

While our study provides novel insights into the microbiota composition of alpine butterflies, we acknowledge several limitations. First, the sample size for females was limited, which may restrict our ability to detect potential subtle sex-based differences as has been found in other Lepidoptera [52]. Second, our study focused exclusively on adult butterflies, leaving out potential shifts in microbial communities during the larval stages [20, 53]. Lepidoptera may generally lack strong bacterial associations, meaning they do not rely on stable, host-specific symbiotic microbiota for essential physiological functions, possibly due to the ecological and developmental factors disrupting long-term bacterial colonization [20]. A lack of bacterial associations may be due to changes in the gut during metamorphosis that prevent the growth and establishment of the microbiome, as well as the development of diverse and effective digestive enzymes during feeding in butterflies, allowing them to digest host plants [20, 54, 55]. Third, while 16S rRNA amplicon sequencing is effective for taxonomic profiling, it does not provide direct information on microbial function. Finally, environmental variables such as microhabitat conditions, floral resources, or seasonal effects could not be controlled for or be quantified in detail. Future research integrating metagenomics, functional assays, and longitudinal sampling across life stages and habitats can help clarify the ecological roles of microbial core and indicator taxa and further illuminate the dynamics of host–microbiota interactions in natural butterfly populations. This is important as butterflies can develop different relationships with microbes independent of their host plants, where microbiomes may play an important role in immune functions at different stages of their lives [48, 56, 57]. Microbiome dependence can also affect foraging [58], fertility [59], and lifespan [60] in many insects, which are additional avenues to explore. Integrating metabolomic or transcriptomic approaches could provide deeper insights into such functional contributions, especially of core microbial taxa to butterfly physiology, such as their roles in digestion, detoxification, or immune modulation.

Using a metabarcoding approach, we uncovered cryptic differentiation between our two focal species, which otherwise show no separation in their abiotic environment at their zone of secondary contact [10, 22]. However, while metabarcoding offers valuable insights into microbial diversity, it does not directly reflect dietary intake as it can be affected by other factors including microhabitat use [25] or foraging behaviour [46]. Given that we recovered microbial taxa that were unique to nectar flowers further suggests that the host gut exerts some microbial filtering. The presence of unique taxa in butterflies may similarly indicate that we did not sample the breadth of nectar plants used. To which degree the observed pattern differs within a species across their ranges requires further explorations, ideally incorporating larval stages and more females. Interestingly, we found a strong difference in the abundance of the endosymbiont Wolbachia, which is higher in E. cassioides, corroborating former studies [29]. E. cassioides also show a reduction in alpha diversity, which could be a result of the increased Wolbachia abundance [50], highlighting a more complex interplay of Wolbachia and its host. Overall, our study highlights the utility of microbial diversity to uncover cryptic ecological differentiation between closely related species, which are common in alpine butterflies [61].

Acknowledgement: We are indebted to Irena Klečková for her invaluable help in the field.

Author Contributions: PT conducted the fieldwork, labwork and analysis and wrote the first draft. AM assisted the statistical analyses and interpretations and revised the manuscript. KL obtained the funding for the study, designed the study and revised the manuscript.

Conflicts of Interest: The authors declare no conflict of interest.

Consent for Publication: Not applicable.

Data Availability Statement: All raw sequence data is deposited on NCBI SRA BioProject PRJNA1293317.

Ethics declaration: Sample collection followed cantonal law and did not require a specific permit.

Funding: This study was supported by the Swiss National Science Foundation grant 202869 awarded to KL. Financial support from the Erasmus+ program France-Turkey was awarded to PT and AM.

Gompert Z, Mandeville EG, Buerkle CA. Analysis of population genomic data from hybrid zones. Annu Rev Ecol Evol Syst. 2017;48:207–29. https://doi.org/10.1146/annurev-ecolsys-110316-022652.
Kulmuni J, Butlin RK, Lucek K, Savolainen V, Westram AM. Towards the completion of speciation: the evolution of reproductive isolation beyond the first barriers. Phil Trans R Soc B. 2020;375:20190528. https://doi.org/10.1098/rstb.2019.0528.
Pigot AL, Tobias JA. Species interactions constrain geographic range expansion over evolutionary time. Ecol Lett. 2013;16:330–8. https://doi.org/10.1111/ele.12043.
Vodă R, Dapporto L, Dincă V, Vila R. Why do cryptic species tend not to co-occur? A case study on two cryptic pairs of butterflies. PLoS ONE. 2015;10:e0117802. https://doi.org/10.1371/journal.pone.0117802.
Klečková I, Klečka J, Fric ZF, Česánek M, Dutoit L, Pellissier L, et al. Climatic niche conservatism and ecological diversification in the Holarctic cold-dwelling butterfly genus Erebia. Insect Syst Divers. 2023;7:2.
Youngblut ND, Reischer GH, Walters W, Schuster N, Walzer C, Stalder G, et al. Host diet and evolutionary history explain different aspects of gut microbiome diversity among vertebrate clades. Nat Commun. 2019;10:2200. https://doi.org/10.1038/s41467-019-10191-3.
Groussin M, Mazel F, Alm EJ. Co-evolution and co-speciation of host-gut bacteria systems. Cell Host & Microbe. 2020;28:12–22. https://doi.org/10.1016/j.chom.2020.06.013.
Sottas C, Schmiedová L, Kreisinger J, Albrecht T, Reif J, Osiejuk TS, et al. Gut microbiota in two recently diverged passerine species: evaluating the effects of species identity, habitat use and geographic distance. BMC Ecol Evo. 2021;21:41. https://doi.org/10.1186/s12862-021-01773-1.
Augustijnen H, Patsiou T, Lucek K. Secondary contact rather than coexistence— Erebia butterflies in the Alps. Evolution. 2022:evo.14615. https://doi.org/10.1111/evo.14615.
Lucek K, Butlin RK, Patsiou T. Secondary contact zones of closely‐related Erebia butterflies overlap with narrow phenotypic and parasitic clines. J Evol Biol. 2020;33:1152–63. https://doi.org/10.1111/jeb.13669.
Sonderegger P. Die Erebien der Schweiz: (Lepidoptera: Satyrinae, Genus Erebia). P. Sonderegger; 2005.
McFall-Ngai M, Hadfield MG, Bosch TCG, Carey HV, Domazet-Lošo T, Douglas AE, et al. Animals in a bacterial world, a new imperative for the life sciences. Proc Natl Acad Sci USA. 2013;110:3229–36. https://doi.org/10.1073/pnas.1218525110.
Douglas AE. Lessons from studying insect cymbioses. Cell Host & Microbe. 2011;10:359–67. https://doi.org/10.1016/j.chom.2011.09.001.
Engelstädter J, Hurst GDD. The ecology and evolution of microbes that manipulate host reproduction. Annu Rev Ecol Evol Syst. 2009;40:127–49. https://doi.org/10.1146/annurev.ecolsys.110308.120206.
Kikuchi Y, Hayatsu M, Hosokawa T, Nagayama A, Tago K, Fukatsu T. Symbiont-mediated insecticide resistance. Proc Natl Acad Sci USA. 2012;109:8618–22. https://doi.org/10.1073/pnas.1200231109.
Salem H, Bauer E, Strauss AS, Vogel H, Marz M, Kaltenpoth M. Vitamin supplementation by gut symbionts ensures metabolic homeostasis in an insect host. Proc R Soc B. 2014;281:20141838. https://doi.org/10.1098/rspb.2014.1838.
King KC, Brockhurst MA, Vasieva O, Paterson S, Betts A, Ford SA, et al. Rapid evolution of microbe-mediated protection against pathogens in a worm host. ISME J. 2016;10:1915–24. https://doi.org/10.1038/ismej.2015.259.
Antwis RE, Griffiths SM, Harrison XA, Aranega-Bou P, Arce A, Bettridge AS, et al. Fifty important research questions in microbial ecology. FEMS Microbiology Ecology. 2017;93. https://doi.org/10.1093/femsec/fix044.
Douglas AE. Multiorganismal Insects: diversity and function of resident microorganisms. Annu Rev Entomol. 2015;60:17–34. https://doi.org/10.1146/annurev-ento-010814-020822.
Hammer TJ, Janzen DH, Hallwachs W, Jaffe SP, Fierer N. Caterpillars lack a resident gut microbiome. Proc Natl Acad Sci USA. 2017;114:9641–6. https://doi.org/10.1073/pnas.1707186114.
Hammer TJ, Dickerson JC, McMillan WO, Fierer N. Heliconius butterflies host characteristic and phylogenetically structured adult-stage microbiomes. Appl Environ Microbiol. 2020;86.
Augustijnen H, Lucek K. Beyond gene flow: (non)‐parallelism of secondary contact in a pair of highly differentiated sibling species. Mol Ecol. 2024;33:e17488.
Peña C, Witthauer H, Klečková I, Fric Z, Wahlberg N. Adaptive radiations in butterflies: evolutionary history of the genus Erebia (Nymphalidae: Satyrinae). Biol J Linn Soc. 2015;116:449–67. https://doi.org/10.1111/bij.12597.
Schmitt T, Habel JC, Rödder D, Louy D. Effects of recent and past climatic shifts on the genetic structure of the high mountain Yellow-spotted ringlet butterfly Erebia manto (Lepidoptera, Satyrinae): a conservation problem. Glob Change Biol. 2014;20:2045–61. https://doi.org/10.1111/gcb.12462.
Klečková I, Konvicka M, Klecka J. Thermoregulation and microhabitat use in mountain butterflies of the genus Erebia: Importance of fine-scale habitat heterogeneity. J Therm Biol. 2014;41:50–8. https://doi.org/10.1016/j.jtherbio.2014.02.002.
Bouaouina S, Chittaro Y, Willi Y, Lucek K. Asynchronous life cycles contribute to reproductive isolation between two Alpine butterflies. Evol Lett. 2023;:qrad046. https://doi.org/10.1093/evlett/qrad046.
Cupedo F. Reproductive isolation and intraspecific structure in Alpine populations of Erebia euryale (Esper, 1805) (Lepidoptera, Nymphalidae, Satyrinae). Nota Lep. 2014;37:19–36. https://doi.org/10.3897/nl.37.7960.
Schmitt T, Müller P. Limited hybridization along a large contact zone between two genetic lineages of the butterfly Erebia medusa (Satyrinae, Lepidoptera) in Central Europe. J Zool Syst Evol Res. 2007;:8.
Lucek K, Bouaouina S, Jospin A, Grill A, de Vos JM. Prevalence and relationship of endosymbiotic Wolbachia in the butterfly genus Erebia. BMC Ecol Evo. 2021;21:95. https://doi.org/10.1186/s12862-021-01822-9.
Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nuc Acids Res. 2013;41:e1–e1. https://doi.org/10.1093/nar/gks808.
Winiger N, Seibold S, Lucek K, Müller J, Segelbacher G. Lost in dead wood? Environmental DNA sequencing from dead wood shows little signs of saproxylic beetles. Env DNA. 2022;4:654–60. https://doi.org/10.1002/edn3.284.
Zhang J, Kobert K, Flouri T, Stamatakis A. PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics. 2014;30:614–20. https://doi.org/10.1093/bioinformatics/btt593.
Gordon, A. FASTX-Toolkit (0.0.14). 2014. [Computer software]. http:// hannonlab.cshl.edu/fastx_toolkit/index.html
Rognes T, Flouri T, Nichols B, Quince C, Mahé F. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016;4:e2584. https://doi.org/10.7717/peerj.2584.
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nuc Acids Res. 2012;41:D590–6. https://doi.org/10.1093/nar/gks1219.
Boratyn GM, Camacho C, Cooper PS, Coulouris G, Fong A, Ma N, et al. BLAST: a more efficient report with usability improvements. Nuc Acids Res. 2013;41:W29–33. https://doi.org/10.1093/nar/gkt282.
Oksanen J, Simpson GL, Blanchet FG, Kindt R, Legendre P, Minchin PR, et al. vegan: Community Ecology Package. 2022.
R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2025.
Kaur R, Shropshire JD, Cross KL, Leigh B, Mansueto AJ, Stewart V, et al. Living in the endosymbiotic world of Wolbachia: A centennial review. Cell Host & Microbe. 2021;29:879–93. https://doi.org/10.1016/j.chom.2021.03.006.
Chen B, Du K, Sun C, Vimalanathan A, Liang X, Li Y, et al. Gut bacterial and fungal communities of the domesticated silkworm (Bombyx mori) and wild mulberry-feeding relatives. ISME J. 2018;12:2252–62. https://doi.org/10.1038/s41396-018-0174-1.
Phalnikar K, Kunte K, Agashe D. Disrupting butterfly caterpillar microbiomes does not impact their survival and development. Proc R Soc B. 2019;286:20192438. https://doi.org/10.1098/rspb.2019.2438.
Chen Y-P, Li Y-H, Sun Z-X, Du E-W, Lu Z-H, Li H, et al. Effects of host plants on bacterial community structure in larvae midgut of Spodoptera frugiperda. Insects. 2022;13:373. https://doi.org/10.3390/insects13040373.
Han S, Zhou Y, Wang D, Qin Q, Song P, He Y. Effect of different host plants on the diversity of gut bacterial communities of Spodoptera frugiperda (J. E. Smith, 1797). Insects. 2023;14:264. https://doi.org/10.3390/insects14030264.
Li D-D, Li J-Y, Hu Z-Q, Liu T-X, Zhang S-Z. Fall armyworm gut bacterial diversity associated with different developmental stages, environmental habitats, and diets. Insects. 2022;13:762. https://doi.org/10.3390/insects13090762.
Lignon VA, Mas F, Jones EE, Kaiser C, Dhami MK. The floral interface: a playground for interactions between insect pollinators, microbes, and plants. New Zealand J Zool. 2025;52:218–37. https://doi.org/10.1080/03014223.2024.2353285.
Lamie E, Morton ER, Parzer HF. Puddling in butterflies: current knowledge and new directions. Ann Entomol Soc America. 2025;118:110–8. https://doi.org/10.1093/aesa/saaf007.
Adair KL, Wilson M, Bost A, Douglas AE. Microbial community assembly in wild populations of the fruit fly Drosophila melanogaster. ISME J. 2018;12:959–72. https://doi.org/10.1038/s41396-017-0020-x.
Engel P, Moran NA. The gut microbiota of insects – diversity in structure and function. FEMS Microbiol Rev. 2013;37:699–735. https://doi.org/10.1111/1574-6976.12025.
Leech T, McDowall L, Hopkins KP, Sait SM, Harrison XA, Bretman A. Social environment drives sex and age‐specific variation in Drosophila melanogaster microbiome composition and predicted function. Molecular Ecology. 2021;30:5831–43. https://doi.org/10.1111/mec.16149.
Detcharoen M, Jiggins FM, Schlick-Steiner BC, Steiner FM. Wolbachia endosymbiotic bacteria alter the gut microbiome in the fly Drosophila nigrosparsa. J Invert Path. 2023;198:107915. https://doi.org/10.1016/j.jip.2023.107915.
Ye YH, Seleznev A, Flores HA, Woolfit M, McGraw EA. Gut microbiota in Drosophila melanogaster interacts with Wolbachia but does not contribute to Wolbachia -mediated antiviral protection. J Invert Path. 2017;143:18–25. https://doi.org/10.1016/j.jip.2016.11.011.
Chen B, Teh B-S, Sun C, Hu S, Lu X, Boland W, et al. Biodiversity and activity of the gut microbiota across the life history of the insect herbivore Spodoptera littoralis. Sci Rep. 2016;6:29505. https://doi.org/10.1038/srep29505.
Johnston PR, Rolff J. Host and symbiont jointly control gut microbiota during complete metamorphosis. PLoS Pathog. 2015;11:e1005246. https://doi.org/10.1371/journal.ppat.1005246.
Li X, Shi L, Zhou Y, Xie H, Dai X, Li R, et al. Molecular evolutionary mechanisms driving functional diversification of α-glucosidase in Lepidoptera. Sci Rep. 2017;7:45787. https://doi.org/10.1038/srep45787.
Tang X, Freitak D, Vogel H, Ping L, Shao Y, Cordero EA, et al. Complexity and variability of gut commensal microbiota in polyphagous Lepidopteran larvae. PLoS ONE. 2012;7:e36978. https://doi.org/10.1371/journal.pone.0036978.
Dillon RJ, Vennard CT, Buckling A, Charnley AK. Diversity of locust gut bacteria protects against pathogen invasion. Ecol Lett. 2005;8:1291–8. https://doi.org/10.1111/j.1461-0248.2005.00828.x.
Koch H, Schmid-Hempel P. Socially transmitted gut microbiota protect bumble bees against an intestinal parasite. Proc Natl Acad Sci USA. 2011;108:19288–92. https://doi.org/10.1073/pnas.1110474108.
Akami M, Andongma AA, Zhengzhong C, Nan J, Khaeso K, Jurkevitch E, et al. Intestinal bacteria modulate the foraging behavior of the oriental fruit fly Bactrocera dorsalis (Diptera: Tephritidae). PLoS ONE. 2019;14:e0210109. https://doi.org/10.1371/journal.pone.0210109.
Elgart M, Stern S, Salton O, Gnainsky Y, Heifetz Y, Soen Y. Impact of gut microbiota on the fly’s germ line. Nat Comm. 2016;7:11280.
Westfall S, Lomis N, Prakash S. Longevity extension in Drosophila through gut-brain communication. Sci Rep. 2018;8:8362. https://doi.org/10.1038/s41598-018-25382-z.
Descimon H, Mallet J. Bad species. In Ecology of butterflies in Europe. 2009;500 C:219.

No competing interests reported.

Download PDF

Editorial decision: Revision requested
30 Oct, 2025
Reviews received at journal
27 Oct, 2025
Reviews received at journal
21 Oct, 2025
Reviewers agreed at journal
05 Oct, 2025
Reviewers agreed at journal
02 Oct, 2025
Reviewers invited by journal
02 Oct, 2025
Editor assigned by journal
29 Sep, 2025
Editor invited by journal
29 Sep, 2025
Submission checks completed at journal
26 Sep, 2025
First submitted to journal
23 Sep, 2025

You are reading this latest preprint version

A microbial view on secondary contact between two Alpine butterflies

Status:

Version 1

Abstract

Background

Results

Conclusions

Figures

Introduction

Materials and Methods

Sample Collection and DNA Extraction

Library Preparation and Sequencing

Bioinformatic Processing and Statistical Analyses

Results

Overall microbial composition

Species and sex related variation in butterfly microbiomes

Butterfly and flower-associated microbiota

Discussion

Conclusions

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1