1. Ecology
  2. Microbiology and Infectious Disease

Environmental temperature is a strong driver of subspecies competition in the Drosophila microbiome

  1. Juan Bosco Gracia Alvira
  2. Stefanie Migotti
  3. Xiaomeng Tian
  4. Viola Nolte
  5. Christian Schlötterer  Is a corresponding author
  1. Institut für Populationsgenetik, Vetmeduni Vienna, Austria
  2. Vienna Graduate School of Population Genetics, Austria
https://doi.org/10.7554/eLife.110808.3
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  1. Juan Bosco Gracia Alvira
  2. Stefanie Migotti
  3. Xiaomeng Tian
  4. Viola Nolte
  5. Christian Schlötterer
(2026)
Environmental temperature is a strong driver of subspecies competition in the Drosophila microbiome
eLife 15:RP110808.
https://doi.org/10.7554/eLife.110808.3
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eLife Assessment

This valuable study explores changes in the Drosophila microbiome in response to environmental temperature over more than ten years. The evidence that temperature leads to diversification of bacterial clades is solid, despite the need for greater clarity in defining and tracking strain competition. The work will interest researchers working with microbiomes, microbial ecology, and evolutionary biology.

https://doi.org/10.7554/eLife.110808.3.sa0

Abstract

Most microbiome research focuses on the taxonomic composition at the species level to understand the impact of environmental factors, but intraspecific diversity has largely been ignored. To address this significant knowledge gap, we took advantage of the simple, culturable microbiome of Drosophila. First, we documented that natural populations of D. simulans harbor three diverged clades of Lactiplantibacillus plantarum, a key nutritional symbiont. We studied the distinct ecological roles of these three clades by exposing flies with their native microbiome to two temperature regimes in the laboratory. Tracking the three clades within the complete Drosophila microbiome over a period of more than 10 years at two temperatures, we identified strikingly distinct dynamics in response to the selection regime. We confirmed the functional differentiation of the three clades using in vitro growth measurements and in vivo mono-association assays. Our results highlight that environmental selection operates at the subspecies level. Therefore, we conclude that the functional diversification of the microbiome can only be understood when intra- and interspecific diversity is considered.

Introduction

Intraspecific diversity has largely been overlooked in microbial ecology surveys. This can be in part attributed to the success of 16 S rRNA profiling, which allows the characterization of species diversity with a very high efficiency (Langille et al., 2013; Douglas et al., 2020), but cannot identify intraspecific diversity (Caro-Quintero and Ochman, 2015). Although environmental variation has been identified as one of the major factors driving species composition, its role for intraspecific adaptation has been largely overlooked (Caro-Quintero and Ochman, 2015). This blind spot of microbiome research is particularly surprising as it is known that conspecific strains, i.e., strains from the same species, can display large phenotypic variation (Van Rossum et al., 2020). Clinically relevant species like Escherichia coli, in which different subspecies can be either pathogenic or commensal are a particularly illustrative example (Ohnishi et al., 2001).

Although a few studies in environmental microbial communities have provided evidence for the functional importance of intraspecific variation (Ansorge et al., 2019; Gould et al., 2023), such analyses are considerably more difficult. Therefore, descriptive correlative studies are more common than functional assays (Lange et al., 2023). In the gut-dwelling bacterium B. fragilis conspecific strains compete for the same niche leading to the mutual exclusion of toxicogenic and non-toxicogenic strains in the gut environment (Hecht et al., 2016). Not only in the human gut, but also in lakes, soil, and the sea conspecific strains frequently co-occur in sympatry (Wolff et al., 2023; Chen-Liaw et al., 2025; Kashtan et al., 2014). It has even been reported that strain richness, i.e., sympatric strain diversity within the same species, stabilizes microbial communities in fluctuating environments (García-García et al., 2019; Meziti et al., 2019).

In this work, we further explored the functional implications of intraspecific diversity within the same microbial population. We focused on the species Lactiplantibacillus plantarum, an extremely versatile lactic acid bacterium that has been associated with a variety of different habitats, including plants, the gastro-intestinal tracts of mammals and insects, as well as food, such as meat, dairy, and pickled products (Martino et al., 2016). L. plantarum is a facultative symbiont of Drosophila (Erkosar et al., 2013; Martino et al., 2018; Storelli et al., 2018; Lee et al., 2020). Drosophila larvae can enhance the growth of L. plantarum, which in response releases essential nutrients for the larvae under poor diet conditions (Storelli et al., 2018; Storelli et al., 2011). As a result of this nutritional symbiosis, L. plantarum is a prevalent taxon in the Drosophila microbiome (Martino et al., 2018).

To study the implications of intraspecific diversity of L. plantarum in fruit flies, we took advantage of an experimentally evolved Drosophila simulans population that has been adapting to two novel temperature regimes, hot and cold, for more than ten years. We detected three clades of L. plantarum co-occurring within the native microbiome of D. simulans population before the experimental temperature was modified. However, the exposure of the fly population to the novel temperature regimes altered the intraspecific composition of L. plantarum, revealing functional differences between the co-occurring clades. These functional differences were experimentally validated in vitro and in vivo. Our results provide a clear example of functional diversification of intraspecific diversity and demonstrate how experimental evolution can uncover this otherwise hidden functional diversity.

Results

Genome sequencing reveals the presence of three L. plantarum clades in the experimentally evolving populations

We sampled 33 isolates from the Florida experiment (See Materials and methods for details) to characterize the diversity of L. plantarum (Figure 1, Supplementary file 1A). The isolates formed three clades based on the pairwise average nucleotide identity (ANI). Between-group ANI values were 98.4–99.17%, below the ~99.5% threshold for conspecific strain identity (Rodriguez-R et al., 2024). On the other hand, the identity within groups was 99.48–99.99% (Figure 2). The clustering was consistent irrespective of whether we used SNPs in the core genome or presence/absence patterns of accessory genes. This indicates that the three clades are old with very limited genetic exchange.

Figure 1
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Experimental setup to study the long-term evolution of L. plantarum in Drosophila simulans populations.
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Pangenome of L. plantarum genomes isolated from the Florida experiment (Supplementary file 1A), sorted by phylogeny.

Left panel: Maximum likelihood tree built based on the core genome alignment. Tree leaves correspond to the name of the isolate and are colored based on isolation origin: ancestral population (gray), hot-evolved population (red) or cold-evolved population (blue). Nodes supported by bootstrap values ≥90% based on 1000 replicates are highlighted in yellow. Middle panel: Pattern of gene presence (black)/absence (white) in each genome. Right panel: pairwise average nucleotide identity between the isolates. The color ranges between 98.5% (purple) and 100% (yellow).

Since the isolates were sampled either from the unevolved, hot-evolved, or cold-evolved populations (Figure 1), we tested whether the clades were randomly distributed across the experimental treatments and observed a non-random distribution (Fisher’s exact test, 3×3 contingency table, p=5.5e-10). In combination with the clustering of clades (Figure 2), we conclude that each of the three clades is more prevalent either in the novel experimental populations or in the unevolved founder population at the beginning of the experiment. We, therefore, coined the terms H (hot-evolved), C (cold-evolved), and U (unevolved) clades which will be used throughout the manuscript.

In order to further investigate the association between L. plantarum genotype and temperature, we sampled from two additional experiments, obtaining seven isolates from hot-evolved Drosophila simulans Portugal and two isolates from constant-cold-evolved Drosophila simulans South Africa (Supplementary file 1A). In a phylogenetic tree, the hot-evolved isolates and the constant-cold-evolved isolates grouped into clades H and C, respectively (Figure 2—figure supplement 1). These results not only support the association between L. plantarum clade composition and environmental temperature (Fisher’s exact test, 3×3 contingency table, p=2.6e-12), but also suggest that clade C is favored irrespective of whether the temperature is fluctuating (20 °C during the day, 10 °C at night) or constant (15 °C). Furthermore, the grouping of isolates originating from different experiments to the same clades indicates that these are not restricted to a single natural D. simulans population, but represent global diversity.

The three sympatric clades independently adapted to their host

Our analyses suggest that all three clades are shared among natural D. simulans populations, but it is not clear whether they diverged in D. simulans or independently colonized their Drosophila host. We used 79 publicly available L. plantarum genomes from several environments and geographic locations (Supplementary file 1B) to reconstruct the phylogeny based on the core genome.

All three L. plantarum clades are more closely related to genomes from other sources than to each other, which implies that they diverged before colonizing D. simulans (Figure 3). Moreover, the genomes from the clade H are almost identical to public sequences isolated from global collections of Drosophila melanogaster (Figure 3, Supplementary file 1B). The clade U isolate B246 is closely related to the Drosophila-associated isolate LpWF originating from a wild D. melanogaster individual (Obadia et al., 2017). Clade C did not have a close relative in the collection. The high similarity of the L. plantarum sequences isolated from D. melanogaster and D. simulans highlights that both Drosophila species share this component of the microbiome. Most likely, a deeper sampling of the D. melanogaster microbiome will also detect clade C.

Figure 3 with 1 supplement see all
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Maximum likelihood tree of 92 L. plantarum genomes.

Tree leaves are colored by source of isolation. The genomes from our lab are colored according to experimental treatment: unevolved in gray, hot-evolved in red, and cold-evolved in blue. Nodes supported by bootstrap values based on 1000 replicates are highlighted in orange (≥70% bootstrap support) or yellow (≥90% bootstrap support).

Despite being phylogenetically divergent, the three sympatric clades could have converged functionally in the process of adaptation to a common environment. In order to test this, we built the L. plantarum pangenome including all the available isolates and performed hierarchical clustering based on the presence/absence of accessory genes. We found no grouping of the three sympatric clades (Figure 3—figure supplement 1), which suggests that they are not only phylogenetically diverged, but have the potential to be functionally different based on their pool of accessory genes.

Clade dynamics are temperature-specific and consistent between population replicates

Up to now, our analyses were restricted to isolates sampled at specific time points of the experiment. Since genomic DNA from flies with their microbiome was sequenced throughout the entire experiment (Figure 1), a metagenomic analysis could track the frequency trajectories of the three clades to shed more light on the adaptation process. We calculated the proportion of reads assigned to each clade in the population at a given time point. At the beginning of the experiment, the populations were dominated by clade U (54.5% mean relative abundance), followed by clade C (31.9%), and then clade H (13.6%). When the fly populations were subjected to the experimental treatments, clade H became dominant under the hot regime and clade C became dominant under the cold regime (Figure 4). Clade U greatly decreased in relative abundance in both regimes. This suggests that changes in environmental temperature alter the fitness landscape of the population and lead to a new adaptive state, in which the relative fitness of the clades changes.

Figure 4
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Clade composition over time in each replicate population from both temperature regimes.

C in light blue, clade H in red, and clade U in gray. Clade relative abundance was inferred by mapping competitively short reads against the three clades’ reference sequences.

A strong asset of the experimental evolution setup is the availability of 10 replicate populations that were generated from the same founder population and independently maintained throughout the entire experiment under the same conditions. Hence, the comparison of replicates provides an estimate for the strength of deterministic and stochastic forces during the experiment. In the cold regime, all ten replicates exhibited a rapid takeover of clade C (Figure 4). In contrast, the hot-evolved replicates were more variable in the rate at which clade H increased in abundance. The most extreme cases were replicates 8 and 10, in which clade C dominated the population for 90 generations before being replaced by clade H. Thus, the populations subjected to the hot regime took longer to reach a new equilibrium, which is also less reproducible than that of the cold regime. Overall, the temperature-specific dynamic is consistent across replicates in both regimes, confirming the association between environmental temperature and clade frequency that was observed during the isolation of the clones (Figures 1 and 2).

Clades differ significantly in their growth kinetics in liquid culture

The metagenomic analysis showed that a different clade dominated each novel temperature regime and this was highly consistent across replicates. The temperature-specific dynamics can arise from clade-specific growth kinetics, as observed for B. cereus groups (Carlin et al., 2013). Alternatively, temperature could affect L. plantarum indirectly, by acting on the host, the food, or other microbiome members. To determine if the observed clade dynamics were driven solely by differences in optimal growth temperature, we grew isolates from the three clades in liquid MRS medium at temperature conditions similar to the experimental settings (See Materials and methods for details). The selected isolates represented all sampling events across the two temperature regimes (hot and cold) as well as the unevolved population (Supplementary file 1A).

The three clades differed significantly in growth rate, carrying capacity, and inflection time, i.e., the time it takes an isolate to reach the log phase, in both temperature conditions (Figure 5, Figure 5—figure supplement 1; Kruskal-Wallis test, p<0.005 for all parameters). However, the performance of the three clades did not fully match our expectations, based on the time series. In hot conditions, clade U outperformed clade C in growth rate, carrying capacity, and inflection time (Figure 5) despite clade C prevailed in the Drosophila populations for many fly generations (Figure 4). In cold conditions, clade C had significantly lower growth rate than clade H and no significant differences in carrying capacity and inflection time (Figure 5—figure supplement 1). These results reveal differences in growth kinetics of the three clades, but do not support the hypothesis that differences in optimal growth temperature caused the shifts in composition observed in the time-series experiments in flies.

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Clade-specific growth dynamics at hot fluctuating 28/23 °C.

(A) Overlapped curves of all replicates. Lines are colored by clade. Background color represents the growth temperature at a given time. (B–D) Boxplots depicting the carrying capacity, growth rate, and inflection time of each clade. We measured the growth of four isolates from clade U, nine isolates from clade C, and sixteen isolates from clade H (Supplementary file 1A). Each isolate was grown three times. Each dot corresponds to a technical replicate. Data are grouped and colored by clade. Statistical significance was determined using Dunn’s test with Holm-adjusted p-values. Only significant comparisons are indicated. ****p<0.0001; ***p<0.001; **p<0.01; *p<0.05.

Nevertheless, we caution that the environment in the time-series experiment is radically different to that of liquid MRS (Fink and Manhart, 2023). We confirmed this by agar-drop assays in solid fly food, which resulted in delayed growth of clade U, relative to clades H and C, in both focal temperature regimes (Figure 5—figure supplement 2). Furthermore, L. plantarum lives in a complex community in which the different clades interact with each other, with other members of the microbiome, and finally also with the host. Bacteria can persist in the community by thriving in the food or by stably colonizing the fly gut. The former strategy might be controlled by growth rate and carrying capacity, but the latter is affected by additional factors, such as death rate in the gut and defecation rate, that cannot be assessed in liquid medium (Obadia et al., 2017). Thus, observed differences in growth could indicate different strategies to persist in the community.

Fitness effects of L. plantarum strains on their host

Although growth dynamics differ significantly among the three clades in growth culture, this does not explain the observed changes in abundance during experimental evolution. Therefore, we turned to the Drosophila host to explore the fitness consequences of colonization by each clade.

L. plantarum can increase larval fitness of Drosophila melanogaster relative to germ-free flies under low protein conditions (Storelli et al., 2018; Storelli et al., 2011; Téfit and Leulier, 2017). Protein content is controlled by the amount of yeast in the fly food (Storelli et al., 2018; Storelli et al., 2011; Téfit and Leulier, 2017). L. plantarum-mediated benefits have been observed in low yeast (≤ 12 g/l) food, but not in protein-rich diets (50–80 g/l yeast). Because our experimental diet contains an intermediate yeast content (24.3 g/l), we wondered whether we could detect temperature-dependent fitness effects of the host in this food. To test this, we designed a set of inoculation experiments in both focal temperatures using two host species, D. melanogaster and D. simulans, and the three L. plantarum clades. While we could produce axenic D. melanogaster, attempts to produce axenic D. simulans were unsuccessful. Thus, D. simulans used in these experiments were dechorionated but not axenic.

Contrary to experiments with protein-poor diets, none of the L. plantarum clades provided a fitness advantage to the host relative to controls in any of the species. In the case of clades U and H, the number of offspring and developmental time did not differ significantly from the dechorionated controls (Figure 6; Dunn’s test, p>0.05 for all pairs). Surprisingly, in both host species we observed significant fitness reductions in the presence of clade C. While in D. melanogaster offspring number decreased and developmental time increased at both temperatures (Figure 6; Dunn’s test, p<0.05 for all significant comparisons), in D. simulans only developmental time increased in the cold (Figure 6; Dunn’s test, p<0.05 for all significant comparisons). We attribute the weaker effects in the non-axenic flies to a reduced L. plantarum load due to the presence of other taxa (Obadia et al., 2017), or to higher-order interactions with other members of the microbiome (Douglas, 2019; Jones et al., 2022; Ludington, 2022).

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Fitness effect of L. plantarum inoculation in axenic D. melanogaster and dechorionated D. simulans with microbiome in the first transfer (See Materials and methods).

(A, B) Total number of F1 flies eclosed normalized by day and female under the cold (A) and hot (B) regime. (C, D) Developmental time, estimated as the number of days it takes 50% of the offspring to eclose. Measurements were grouped by inoculation treatment and Drosophila species. Each dot corresponds to a biological replicate (n=10 for D. melanogaster and n=9 for D. simulans). Statistical significance was determined using Dunn’s test with Holm-adjusted p-values. Only significant comparisons are indicated. *** P<0.001; **p<0.01; *p<0.05.

We further explored the role of bacterial load on the host fitness by extending the experiment to a second transfer: the same parents laid eggs into a new set of sterile vials (See Materials and methods for details). The effect of clade C on the host disappeared for D. simulans and was diminished in D. melanogaster relative to the first transfer (Figure 6—figure supplement 1; Dunn’s test, p<0.05 for all significant comparisons). Since the bacterial load could be a major factor influencing these results, we performed an additional experiment in D. simulans to quantify the bacterial load of parental flies in the second transfer. Median bacterial load of flies inoculated with clade C was 4.1×105 colony-forming units (CFUs) per fly in the hot regime and 1.3×104 CFUs/fly in the cold one, at least 10-fold higher relative to the other clades in the same regime (Figure 6—figure supplement 2). These results suggest that the effect of clade C on the host depends on the bacterial load, and could be related to an excess of bacterial growth inside the host.

In summary, L. plantarum does not confer a fitness benefit to the host in any temperature regime. Moreover, clade C, which is dominant in the cold-evolved populations (Figure 4), is clearly detrimental for reproduction and development in D. melanogaster hosts. In D. simulans, though, slower development provided by clade C could be beneficial in the cold, as observed in other environmental conditions (Horváth and Kalinka, 2016; Borash et al., 2000).

Functional divergence on the genomic level

Our analyses clearly indicate functional divergence between the three clades at genetic and phenotypic levels (Figure 3—figure supplement 1, Figures 5 and 6). Therefore, we were interested in identifying the genes that contribute to this functional differentiation. A comparative KEGG pathway analysis showed that clade C contains the most KEGG Orthologs (KOs), 1163 KOs, followed by clade H (1157 KOs), and clade U (1131 KOs). A subset of 111 orthologs is unique to one or two clades. Of these, 41 are shared by clades H and C (Figure 7). These differential orthologs fell into 53 KEGG metabolic pathways.

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Venn diagram depicting the overlap in KEGG Orthologs between the three clades.

Segments were colored by number of KOs.

Interestingly, clades C and H encode a shared genetic repertoire related to sugar metabolism that is absent in clade U, which also does not encode any private sugar-related function (Supplementary file 1C). Clade U cannot use sorbitol as carbon source, since it does not encode the PTS transporter to internalize it (EC: 2.7.1.198; Figure 7—figure supplement 1) nor the enzyme sorbitol dehydrogenase (EC: 1.1.1.140), that converts sorbitol into fructose (El-Kabbani et al., 2004). Clade U also lacks transaldolase activity (EC: 2.2.1.2), that connects the Embden–Meyerhof–Parnass pathway with the non-oxidative pentose phosphate pathway (Samland and Sprenger, 2009). Finally, clade U is unable to hydrolyze chitobiose into N-acetyl-glucosamine monomers because it lacks the enzyme hexosaminidase (EC: 3.2.1.52).

Additionally, clade H and C harbor different sugar-related functions (Supplementary file 1C), indicating that, despite partial overlap, these strains possess distinct sugar metabolic capacities. We also identified functional differences between clades in cofactor biosynthesis and respiratory metabolism. These differences do not explain the clade-specific selection, but reflect the different evolutionary histories of the clades (Supplementary file 1C, Figure 7—figure supplement 1, Figure 7—figure supplement 2, Figure 7—figure supplement 3, Figure 7—figure supplement 4). A detailed description of the functional differences between clades can be found in Appendix 1.

To sum up, among the clade-specific genes we found an enrichment of sugar-related functions in clades C and H. This, summed to the delayed growth of clade U in solid fly food (Figure 5—figure supplement 2), suggests that diet, in addition to temperature, affected the intraspecific dynamics of L. plantarum.

Discussion

Intraspecific diversity within the same environment has been reported to be ecologically important to maintain the stability of the entire microbial community (Chen-Liaw et al., 2025; García-García et al., 2019; Meziti et al., 2019). This parameter has been studied in lakes, soils, seas, and the human gut (Wolff et al., 2023; Chen-Liaw et al., 2025; Kashtan et al., 2014). However, little attention has been given to intraspecific variation in Drosophila, and insect microbiomes in general (Lange et al., 2023). Based on our results in experimentally evolved fruit flies, we propose that within-species competition, thus far largely overlooked, could contribute to ecological adaptation and evolution of the host, as it has been observed at the species level in Drosophila (Rudman et al., 2019) and other animals (Montaño-Salazar et al., 2023; Petersen et al., 2023; Zhang et al., 2024).

Our finding of three co-occurring subspecies of L. plantarum in our populations is particularly surprising, given the low microbiome richness of Drosophila at the species level, which is estimated to be orders of magnitude lower than in humans (Wong et al., 2011; Ludington and Ja, 2020). However, the intraspecific richness of L. plantarum in our flies was three times higher than that estimated in human gut microbiomes (Chen-Liaw et al., 2025). We note that a recent study also found several co-occurring L. plantarum variants within a Drosophila population (González et al., 2026). Our work further expands this observation by demonstrating that this diversity is not neutral, but rather driven by external factors. This discrepancy between inter- and intra-species diversity suggests that the Drosophila microbiome is more complex than previously thought (Erkosar et al., 2013; Douglas, 2019; Matos and Leulier, 2014). However, this diversity exists below the species level and has, therefore, remained cryptic due to the limitations of amplicon sequencing (Caro-Quintero and Ochman, 2015).

By altering the experimental temperature at which the populations were maintained, we discovered consistent shifts in the composition of L. plantarum subspecies, that inevitably led to the dominance of a single clade. Further in vivo experiments revealed that clade composition significantly impacts the fitness of the host. It was previously shown that nutritional symbiosis in L. plantarum is strain-specific (Lee et al., 2020), and that some genotypes can be detrimental for the host (Gould et al., 2018; Fast et al., 2018). In our study, we also showed that clade C was detrimental to the host, but nevertheless in the cold regime, this clade outcompeted the other clades, which did not have a negative effect on host fitness. This demonstrates that the benefits of parasitism or mutualism in the Drosophila microbiome are context-dependent, as previously observed in flies and humans (Gould et al., 2018; Nysten et al., 2024; Armistead et al., 2019; Henry et al., 2025).

Finally, we also gained insight into how the microbiome of the fly could have adapted to laboratory conditions. The two clades, C and H, which are favored in the time-series, harbor unique sugar-related metabolic functions, consistent with faster growth in the sugar-rich laboratory diet. Besides the novel temperature regimes, introduction to the laboratory changes the diet and mobility of the host and the microbiome relative to the wild (Chandler et al., 2011; Winans et al., 2017). This explains the rapid decrease of clade U in both temperature regimes, despite showing high fitness in liquid MRS medium. Our findings are consistent with microbiome laboratory adaptation, and agree with previous observations in L. plantarum (Martino et al., 2018) and Acetobacter (Winans et al., 2017).

Overall, our work emphasizes the importance of intraspecific diversity in maintaining the stability of microbial communities and the host’s ability to adapt to environmental changes. The current climate crisis has sparked a significant interest in how environmental temperature affects microbial communities at the species level (Moghadam et al., 2018; Li et al., 2023; Bååth and Kritzberg, 2024). Our study highlights that even subspecies diversity plays a key role in adaptation to environmental temperature, and probably other abiotic factors. Therefore, we propose that a comprehensive understanding of adaptive processes and ecological dynamics in the case of host-microbiome interactions depends critically on including diversity at the subspecies level.

Materials and methods

Experimental evolution

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Twenty replicate populations were set up using 202 isofemale lines from a natural D. simulans population collected in Florida (Barghi et al., 2017). The populations were maintained in a 12 hr photoperiod and at two different novel temperature regimes: ten replicates were kept in a fluctuating hot environment (28 °C during the day and 18 °C at night), whereas the other ten were kept in a fluctuating cold environment (20 °C during the day and 10 °C at night). The census population size of the replicates was 1000–1250 with a 50:50 sex ratio. The flies in each replicate were equally distributed across five 300 ml bottles containing 60 ml of standard Drosophila medium (300 g Agar +990 g sugar beet syrup +1000 g malt syrup +2,310 g corn flour +390 g soy flour +900 g yeast in 37.5 L water) (Lai and Schlötterer, 2022). The evolved D. simulans populations were sequenced in 10-generation intervals using Pool-Seq (Schlötterer et al., 2014; Barghi et al., 2019). A subset of these sequencing reads has been used to study the evolutionary dynamics of fruit flies (Barghi et al., 2019). Similar pool-seq time series data have been previously used to analyze long-term endosymbiont and microbiome dynamics in D. melanogaster populations evolving in the same experimental conditions (Versace et al., 2014; Mazzucco et al., 2020; Mazzucco and Schlötterer, 2021).

We also isolated L. plantarum from two other experimental evolution experiments, one originated from a South African population reared at a constant 15 °C and the other from Portugal, which was reared in the fluctuating hot regime as described earlier (Mallard et al., 2018). Flies in both experimental evolution studies followed the same maintenance regime as the Florida populations, with temperature being the only experimentally altered variable (Otte et al., 2021).

Bacterial isolation

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We primarily obtained data from the experimental evolution studies using the Florida D. simulans founder population and collected 33 L. plantarum genomes from five different time points; two time points from each experimental temperature and one time point corresponding to the ancestral flies (Supplementary file 1A). In addition, we obtained two and seven genomes from experimental evolution studies originating from South African and Portuguese D. simulans populations.

Around 100 flies from each population were homogenized in sterile PBS using an autoclaved pestle. The homogenate was diluted to avoid physical contact between colonies and streaked on agar plates with three different media: De Man, Rogosa, and Sharpe (MRS), mannitol, and tryptic soy. The plates were incubated at 28 °C for 48 hr. We identified L. plantarum using PCR with custom Lactiplantibacillus-specific primers (in 5′–3′ orientation, LacF: GATGGGCGCTTACCCGATTA, LacR: CTGCCCCGCAAATTGTTTCA). Colonies which could be successfully amplified were picked and regrown to stationary phase in the same medium from which they were isolated, but in liquid format. A glycerol stock of each isolate was made by mixing the culture with glycerol (50% v/v) in 1:1 proportion. The proportion of L. plantarum colonies, relative to those of other taxa, varied across growth media and fly populations. A list of other microbial taxa isolated from the flies can be found in Supplementary file 1D.

A representative isolate from each clade was deposited in DMSZ with the accession numbers DSM 120997, DSM 120998 and DSM 120999 (Supplementary file 1A). The same representatives were used in the in vivo inoculation experiments.

DNA extraction and sequencing

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Genomic DNA was extracted from all the replicate populations using the high salt method (Miller et al., 1988). The fly populations were sampled in 10 generations intervals starting from generation 0. At sampling, the age of the flies varied between four and eight days for the hot environment and between nine and 16 days for the cold environment. Pools of flies were sequenced at various time points, using a range of library kits, insert sizes, and read lengths (Supplementary file 1E). We expanded the genomic dataset used in Barghi et al., 2019 (Barghi et al., 2019) with a new set of pools covering the hot-evolved populations up to generation 250 and the cold-evolved population until generation 100.

For sequencing, the L. plantarum isolates were grown to stationary phase in MRS medium. gDNA was isolated with the high salt extraction method (Miller et al., 1988). Additionally, a lysozyme pre-treatment was used to degrade Gram-positive cell wall (Alimolaei and Golchin, 2016). Briefly, the pellet was resuspended in 480 µl of EDTA 50 mM. The suspension was treated with 120 µl of lysozyme (20 mg/ml dissolved in NaCl 30 mM–EDTA 2 mM) and incubated for 2 hr at 37 °C. After this, the samples were centrifuged at maximum speed, the supernatant was discarded, and the pellet was further treated following the high-salt gDNA extraction. Genomic DNA was quality-controlled on an Agilent Bioanalyzer (Agilent Technologies, Inc, Santa Clara, CA) and subsequently used to prepare DNBSEQ short-read libraries, and 2×150 bp reads were sequenced on a DNBSEQ-G400 (MGI Tech Co., Ltd., Shenzhen, China).

Genomic assemblies

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Sequencing reads were processed using BBDuk (Bushnell, 2025) to remove adapters, ΦX174 sequences and low-quality bases with the following parameters: ktrim = r, k=23, mink = 11, hdist = 1, tbo, tpe. Reads were assembled using SPAdes v4 (Prjibelski et al., 2020) with kmer sizes 21, 33, 55, 77, 99, and 127. Subsequently, CheckM2 v1.0.1 (Chklovski et al., 2023) was used to estimate the completeness and contamination of each assembly, and the taxonomy was determined with GTDB-Tk v2.1.1 (Chaumeil et al., 2022). Ten assemblies had more than 5% contamination, probably due to accidentally picking two adjacent colonies. We used Centrifuge v1.0.4 (Kim et al., 2016) to classify and exclude contigs with exogenous origin from these assemblies.

Comparative genomics and phylogeny

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We built the pangenome and phylogenetic tree of the collection of genomes isolated from our lab (Supplementary file 1A). First, we used Prokka v1.14.6 (Seemann, 2014) to predict the genes in all the genomes. Roary v3.13 (Page et al., 2015) was used to construct the isolates’ pangenome and extract the aligned genes belonging to the core genome, i.e., those that are present in all genomes. SNP-sites v2.5.1 (Page et al., 2016) was used to extract the variable positions from the concatenated core genes. A maximum likelihood tree of the aforementioned variable positions was built using the model GTR + ASC and 1000 bootstraps with IQ-TREE v3.0.1 (Minh et al., 2020). Additionally, pairwise average nucleotide identity between all the isolates was computed using the program PyANI v0.2.13.1 (Pritchard et al., 2016), with ANIb as alignment method using the whole genomes. This workflow was carried out for the subset of 33 genomes isolated from the Florida experiment and for the whole dataset of 42 genomes, including those isolated from South Africa and Portugal experiments. Unless specified otherwise, subsequent analyses were conducted using the whole dataset pangenome.

The same phylogenetic and pangenomic methods were used for the extended L. plantarum genomes collection, including 79 publicly available genomes (Supplementary file 1B). In order to root the tree, an additional tree was made including the reference genome from the sister species Lactiplantibacillus paraplantarum (GCA_003641145.1) as an outgroup. The L. plantarum isolate that was most closely related to L. paraplantarum, NIZO2776, was used to root the tree.

In order to investigate which metabolic functions were differentially encoded in each clade, we followed the Anvi’o v7.1 pangenomic workflow (Eren et al., 2015; Delmont and Eren, 2018) to identify ‘gene clusters’ that are unique or shared across the genomes isolated in our lab. These gene clusters were further annotated against the KEGG database using the built-in Anvi’o function ‘anvi-run-kegg-kofams.’ The script ‘anvi-compute-functional-enrichment’ (Shaiber et al., 2020) was then used to screen for KEGG functions that are differentially enriched between the clades. Briefly, this script estimates the fraction of genomes from each clade that encode each KEGG Ortholog (KO) and tests whether they are significantly associated to one or more groups. We used the R package ggKegg v1.4.1 (Sato et al., 2023) to visualize the KEGG pathways in which at least one gene was enriched.

Competitive mapping and clade relative abundance calculation

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First, we pre-filtered the Drosophila pool-seq reads by removing the reads that have eukaryotic or Wolbachia origin. We used Bowtie v2.5.4 (Langmead and Salzberg, 2012) with stringent settings (-D 500 R 40 N 0 L 20 -I S,1,0.50 --no-mixed --no-discordant) to map each pool-seq data set against a collection of reference genomes that included D. simulans (GCF_016746395.2), D. melanogaster (GCF_000001215.4), D. mauritiana (GCF_004382145.1), H. sapiens (GRCh38), M. musculus (GCF_000001635.27), A. thaliana (GCF_000001735.4), S. cerevisiae (GCF_000146045.2), C. lupus (GCF_011100685.1) and several insect-infecting Wolbachia strains (GCF_000008025.1, GCF_000022285.1, GCF_000376585.1, GCF_000376605.1, GCF_000475015.1). We kept the read pairs in which none of the reads mapped to any of the genomes in the collection, which should predominantly represent prokaryotic sequences.

Then, we used SuperPang v0.9.6 (Puente-Sánchez et al., 2023) to build the reference graph pangenome of each L. plantarum clade using all the genomes belonging to the clade as input (Supplementary file 1A, Figure 2). In order to calculate the clades relative abundance in each pre-filtered pool-seq, we used BBMap (Bushnell, 2025) with ‘perfect mode’ settings to competitively map each read against the three concatenated clade pangenomes. For each sample, the number of reads that mapped uniquely to each clade was counted and normalized to Reads Per Million (RPM) in order to account for differences in genome size and sample depth. L. plantarum represented between 0.0438% and 17.2246% of relative abundance of the reads in all time points.

We benchmarked our approach to confirm the relative abundance estimates from our competitive mapping pipeline. Briefly, we simulated 200 k HiSeq reads from each reference graph pangenome using InSilicoSeq v2.0.1 (Gourlé et al., 2019), and used ‘seqkit sample’ v2.3.0 (Shen et al., 2024) to mix them in different ratios, maintaining the original size of 200 k reads: 1:0:0, 0:1:0, 0:0:1, 1:1:1, 2:1:7, and 8:1:1. Each of the combined read sets was randomly subsampled from 150 k to 10 reads (10, 20, 40, 60, 80, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1 k, 2 k, 5 k, 10 k, 150 k reads) and mapped competitively to the three clades. We confirmed that the relative abundances were correctly estimated with as few as 100–1000 reads mapping the references (Appendix 1—figure 1).

Growth assays

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The glycerol stocks from all the available L. plantarum isolates were re-grown in agar MRS. A single colony per isolate was grown in liquid MRS and passaged daily for five days in order to remove the influence of freezing. On the fifth day, each suspension was normalized to an optical density (OD600) of 0.01, and serially diluted 1:100 in MRS. We selected 31 isolates that represented samples from all the available time points and temperature regimes, and plated them in triplicates in a flat-bottom 96-well plate. Three wells were filled with sterile MRS medium as blank controls. Later, we detected contamination in two of the isolates, leaving us with 29 isolates. In total, we grew four isolates from clade U, nine isolates from clade C, and sixteen isolates from clade H (Supplementary file 1A). The plate was incubated for 72 hr and OD600 was measured every 15 min in a Synergy H1 plate reader. The temperature regime was chosen to be similar to the hot conditions of the fly populations, but due to limitations of the spectrophotometer’s cooling capacity, instead of 28 °C during the day and 18 °C at night, we set it to 28 °C during the day and 23 °C at night. Orbital shaking was set to 425 cpm.

Growth in cold conditions could not be measured with the plate reader as it does not have cooling capacity and the experiment would have lasted more than a week, so we used an alternative approach. We grew twelve isolates, four from each clade (Supplementary file 1A), in glass test tubes using 5 ml MRS. We normalized each suspension to an OD600 of 0.05 and then diluted 1:100 in 5 ml of MRS. We also prepared an MRS blank. We prepared six replicates per isolate and blank. Three replicates were set at 17:00 and three the next day at 10:00. All tubes were incubated at a constant 20 °C and 168 rpm in a shaker (Excella E24, New Brunswick, USA). The OD of all tubes was measured for 12 days at 10:00 and at 17:00 using a spectrophotometer (Ultrospec 10; Biochrom, Cambridge, UK). By using the replicates at two different days, we effectively duplicated the measured time points without the need to increase the sampling frequency.

The R package GrowthCurver v0.3.1 (Sprouffske and Wagner, 2016) was used to infer the growth parameters from the growth curves. All statistical analyses were conducted in R v4.4.2 (R Development Core Team, 2024) using the package rstatix v0.7.2 (Kassambara, 2023). We used the Kruskal-Wallis test to assess overall differences in growth parameters among the clades. For post-hoc pairwise comparisons, Dunn’s test was applied, with p-value adjustments based on the Holm method. Differences were considered significant at p<0.05.

Agar-drop assays

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One representative isolate from each clade (Supplementary file 1A, B89 for ancestral, S103 for cold, and S239 for hot) was grown overnight in MRS medium from glycerol stocks. OD600 was normalized to 0.2, and subsequently diluted 1:10 six times. We plated on fly food Petri dishes 10 µl drops of the original suspension and of 10–2, 10–4, and 10–6 dilutions. We incubated three plates at 28/18 °C and three at 20/10 °C for 10 days. Pictures of all plates were taken at incubation days 3, 6, and 10.

Production of axenic flies

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We generated germ-free flies, using a modified version of the dechorionation protocol described by Kietz et al., 2018. Specifically, we used 2.8% active chlorine bleach instead of 1% and supplemented bleach, EtOH, and deionized H2O with TritonX (1% v/v). The addition of this detergent prevents dechorionated eggs from sticking to the walls of the tubes, thus facilitating their transfer to bottles. The whole process was carried out in sterile conditions using a laminar flow hood. Axenic flies were maintained via periodic transfers to new sterile food inside the laminar flow hood. We first carried out this procedure with several D. simulans isofemale lines, but it was not possible to fully eliminate the residual microbiome despite several rounds of treatment. However, we could produce and maintain axenic D. melanogaster Oregon-R flies. For this reason, we decided to use D. melanogaster as an axenic host in the inoculation experiments.

Inoculation experiments and measurement of bacterial load

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The representative isolate from each clade (Supplementary file 1A, B89 for ancestral, S103 for cold, and S239 for hot) was re-grown from the glycerol stocks in MRS medium. After three transfers, OD600 was normalized to 0.05. Autoclaved vials with axenic fly food were inoculated with 50 µl of each bacterial suspension. This volume corresponds to ~2.5×105 CFUs based on colony counting of serial dilutions. In total, five treatments were used: the pure culture from each genotype, a mixture of the three genotypes in equal proportions (‘mix’), and control, in which the vials were inoculated with 50 µl of fresh MRS medium. After overnight absorption of the liquid, twenty axenic flies (ten female and ten male flies) were added to each vial and allowed to lay eggs for either 24 hr (hot conditions) or 48 hr (cold conditions). After this step, the flies were transferred to a new set of vials with axenic, uninoculated food, and allowed to lay eggs for another 24 hr (hot conditions) or 72 hr (cold conditions). The adults were discarded and the vials were incubated at hot (28 °C during the day and 18 °C at night) or cold conditions (20 °C during the day and 10 °C at night) until the new generation eclosed. The number of eclosed flies was recorded daily. We estimated the total number of flies per vial and the developmental time as proxies of fitness. We calculated developmental time as the day from the start of the experiment until the day at which 50% of the flies in the vial were eclosed. The inoculation experiment was carried out with axenic D. melanogaster Oregon-R (ten replicates per treatment) and dechorionated, but non-axenic, D. simulans from Florida (nine replicates per treatment).

To quantify bacterial load of individual flies, we transferred four D. simulans males to two sets of inoculated vials as in the previous experiment. We allowed the flies to feed for 24 hr in either hot or cold conditions before transfer to sterile food and maintained in the same regime for 72 hr. Each fly was then placed in a 2 ml tube with 200 µl of PBS and a sterile 4 mm glass bead. The tubes were shaken at 1450 rpm for 4 min in a MiniG 1600 vertical shaker (SPEXSamplePrep). Homogenates were diluted 1:10 (cold-treated) or 1:100 (hot-treated) in PBS, and 200 µl of each dilution was plated on an MRS plate. After overnight incubation at 28 °C, CFUs were counted in each plate. Based on colony morphology, most CFUs corresponded to L. plantarum in inoculated flies, despite using D. simulans-harboring microbiome.

We used the Kruskal-Wallis test to assess overall differences among the treatments. For post-hoc pairwise comparisons, Dunn’s test was applied, with p-values adjusted using the Holm method. Differences were considered significant at p<0.05.

Appendix 1

Extended clade-specific differences in KEGG metabolic pathways

Clades C and H encode a shared genetic repertoire related to sugar metabolism that is lacking in clade U. In contrast, the latter does not encode any unique sugar-related function. Clade U is unable to hydrolyze chitobiose into N-acetyl-glucosamine monomers because it lacks the enzyme hexosaminidase (EC: 3.2.1.52). It also lacks transaldolase activity (EC: 2.2.1.2), that connects the Embden–Meyerhof–Parnass pathway with the non-oxidative pentose phosphate pathway (Samland and Sprenger, 2009). Finally, it cannot use sorbitol as a carbon source, since it does not encode the PTS transporter to internalize it (EC: 2.7.1.198; Figure 7—figure supplement 1) nor the enzyme sorbitol dehydrogenase (EC: 1.1.1.140), that converts sorbitol into fructose. These metabolic capacities could play a role in the rapid decrease in abundance of clade U observed in both temperature regimes (Supplementary file 1C). Chitobiose is the main product of chitin degradation, that makes up the flies’ exoskeleton (32). We have detected the presence of microbial chitinases in the metagenomic reads (unpublished observation), which suggests that chitobiose might be available in the community. Thus, we hypothesized that the ability to exploit this ubiquitous source of carbon and nitrogen could be very advantageous in the fly microbiome context (Figure 4), but would not affect the fitness in liquid MRS culture (Figure 5). Competitive advantage should be reflected in higher bacterial loads within individual flies, as bacteria in the gut are in contact with the peritrophic matrix (Beier and Bertilsson, 2013). However, we found no significant differences between bacterial loads upon inoculation with clade H (harbours exosaminidase) and clade U (lacks hexosaminidase) (Figure S6). Thus, experimental data do not support that chitobiose consumption confers a competitive advantage to clades C and H. We also found other sugar-related functions are differentially present between clades C and H (Supplementary file 1C), suggesting that, despite having overlapping functions, these clades also encode unique sugar-related functions.

We also found differences in cofactor biosynthesis pathways. Although all the analyzed genomes can import riboflavin, the capacity to produce it de novo is enriched in clades C and H (Supplementary file 1C; Figure 7—figure supplement 2). This vitamin is essential in many physiological processes (García-Angulo, 2017). Therefore, the possibility to synthesize it de novo under low extracellular riboflavin conditions is an advantage. In contrast, clade U has the unique capacity to synthesize de novo guanylyl molybdenum cofactor (Figure 7—figure supplement 3), that is essential in molybdenum-dependent enzymes (Schwarz et al., 2009). One of such enzymes is the nitrate reductase system Nar. Interestingly, clade U also harbors the operons nreABC and narGHIJ, that encode for genes that sense anoxic conditions and use nitrate as terminal electron donor instead of oxygen, respectively (Moreno-Vivián et al., 1999; Fedtke et al., 2002; Sohaskey and Wayne, 2003). The presence of this molybdenum-dependent alternative respiratory system exclusively in clade U suggests a different evolutionary history, in which this clade was exposed to anaerobic conditions before adapting to Drosophila (Figure 7—figure supplement 4). Similarly, the enzyme sulfur oxidoreductase (EC: 1.8.1.18) is highly enriched in clade C, which suggests that it can potentially use sulfur as terminal electron acceptor in absence of oxygen (Figure 7—figure supplement 4).

Among the genes that were uniquely present in clade C, we found sbnA and sbnB, that mediate the synthesis of L-2,3-diaminopropionic acid (Kobylarz et al., 2014). This unusual amino acid serves as precursor of antibiotics and siderophores (Kobylarz et al., 2014), but the rest of the biosynthesis pathways are absent in the genomes. However, it is also a potent enzymatic inhibitor (Garcia-Hernandez and Kun, 1957; Kalyani et al., 2012). Although it goes beyond the scope of this work, we hypothesize that the synthesis of this compound by clade C could be responsible for the observed dose-dependent toxicity upon inoculation in the axenic host (Figure 6). As a sanity check, we blasted the two protein sequences against the clustered non-redundant NCBI database. The best matches had identities of maximum 60%, and belonged to Streptococcus, Bacillus, and Chitinophaga. No publicly available L. plantarum encodes these genes, which further supports the exceptionality of clade C observed in the phylogeny (Figure 3).

Appendix 1—figure 1
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Relative abundance of each L. plantarum clade across different reads depths in simulated reads sets with known clade composition.

Dashed vertical lines show the values of 100, 500, and 1000 reads. Dashed horizontal lines show the true simulated relative abundances of each clade in the reads set. The top row titles reference the ratio of reads from each clade following descending order: C:H:U. The bottom plots depict scenarios in which all the reads belong to a single taxon. Diamonds at the bottom of each panel show the sampled number of reads. The x-axis was log10-scaled to better show the sampling distribution.

Data availability

The sequencing raw data used in this study are deposited in the European Nucleotide Archive (ENA) under the BioProject accessions: PRJEB96332, PRJEB29281, PRJEB20780, PRJEB20533, and PRJEB15225 (See Supplementary file 1A and E). Accessions to publicly available genomes used in this study can be found in Supplementary file 1B. All codes and material needed for data processing and reproduction can be found in https://github.com/bosco-gracia-alvira/Lpla_intraspecific_diversity (copy archived at Gracia Alvira, 2026).

The following data sets were generated
    1. Gracia-Alvira B
    2. Migotti S
    3. Tian X
    4. Nolte V
    5. Schlötterer C
    (2026) EBI European Nucleotide Archive
    ID PRJEB96332. Intraspecific diversity of Lactiplantibacillus plantarum reflects functional diversification in the microbiome of Drosophila simulans.
    https://www.ebi.ac.uk/ena/browser/view/PRJEB96332
The following previously published data sets were used
    1. Barghi N
    2. Tobler R
    3. Nolte V
    4. Schlötterer C
    (2018) EBI European Nucleotide Archive
    ID PRJEB20780. Drosophila simulans: a species with improved resolution in Evolve and Resequence studies.
    https://www.ebi.ac.uk/ena/browser/view/PRJEB20780
    1. Nouhaud P
    2. Tobler R
    3. Nolte V
    4. Schlötterer C
    (2016) EBI European Nucleotide Archive
    ID PRJEB15225. Ancestral population reconstitution from isofemale lines as a tool for experimental evolution.
    https://www.ebi.ac.uk/ena/browser/view/PRJEB15225

References

  73. Software
    1. R Development Core Team
    (2024) R: a language and environment for statistical computing
    R Foundation for Statistical Computing, Vienna, Austria.
    https://www.r-project.org

Article and author information

Author details

  1. Juan Bosco Gracia Alvira

    1. Institut für Populationsgenetik, Vetmeduni Vienna, Vienna, Austria
    2. Vienna Graduate School of Population Genetics, Vienna, Austria
    Contribution
    Conceptualization, Resources, Data curation, Software, Formal analysis, Investigation, Visualization, Methodology, Writing – original draft
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0009-0002-2879-4079

  2. Stefanie Migotti

    Institut für Populationsgenetik, Vetmeduni Vienna, Vienna, Austria
    Contribution
    Resources, Writing – review and editing
    Competing interests
    No competing interests declared

  3. Xiaomeng Tian

    Institut für Populationsgenetik, Vetmeduni Vienna, Vienna, Austria
    Present address
    Department of Biology, University of Konstanz, Konstanz, Germany
    Contribution
    Resources, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3858-9300

  4. Viola Nolte

    Institut für Populationsgenetik, Vetmeduni Vienna, Vienna, Austria
    Contribution
    Resources, Data curation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6159-3418

  5. Christian Schlötterer

    Institut für Populationsgenetik, Vetmeduni Vienna, Vienna, Austria
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Project administration, Writing – review and editing
    For correspondence
    schlotc@gmail.com
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4710-6526

Funding

Austrian Science Fund

https://doi.org/10.55776/W1225
  • Christian Schlötterer

Austrian Science Fund

https://doi.org/10.55776/PAT7786824
  • Christian Schlötterer

Austrian Science Fund

https://doi.org/10.55776/F91
  • Christian Schlötterer

European Research Council (Achadapt)

  • Christian Schlötterer

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

Acknowledgements

We thank Cameron Strachan and Evelyne Selberherr from the Zentrum für Lebensmittelmikrobiologie (Veterinärmedizinische Universität Wien) for granting us access to a plate reader for the growth experiment. We also wish to thank Martin Polz, William Ludington, Cameron Strachan, and Xiaoqian Annie Yu for their feedback regarding experimental design and interpretation of the results. We thank Rupert Mazzucco for his help with the processing and management of the genomic data from the lab. We thank Susamman Biswas for his help with the molecular characterization of the L. plantarum isolates. Finally, we are grateful to all members of the Institut für Populationsgenetik (Veterinärmedizinische Universität Wien) for helpful discussions and feedback throughout the project. Part of the Illumina sequencing was performed by the Next Generation Sequencing Facility at Vienna BioCenter Core Facilities (VBCF), member of the Vienna BioCenter (VBC), Austria (https://www.viennabiocenter.org/vbcf/).

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  1. Juan Bosco Gracia Alvira
  2. Stefanie Migotti
  3. Xiaomeng Tian
  4. Viola Nolte
  5. Christian Schlötterer
(2026)
Environmental temperature is a strong driver of subspecies competition in the Drosophila microbiome
eLife 15:RP110808.
https://doi.org/10.7554/eLife.110808.3

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