Ecological effects on gut bacterial communities in wild bumblebee colonies


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1. Animal hosts harbour diverse and often specific bacterial communities (microbiota) in their gut. These microbiota can provide crucial services to the host such as aiding in digestion of food and immune defence. However, the ecological factors correlating with and eventually shaping these microbiota under natural conditions are poorly understood.

2. Bumblebees have recently been shown to possess simple and highly specific microbiota. We here examine the dynamics of these microbiota in field colonies of the bumblebee Bombus terrestris over one season. The gut bacteria were assessed with culture-independent methods, that is, with terminal restriction fragment length profiles of the 16S rRNA gene.

3. To further understand the factors that affect the microbiota, we experimentally manipulated field-placed colonies in a fully factorial experiment by providing additional food or by priming the workers’ immune system by injecting heat-killed bacteria. We furthermore looked at possible correlates of diversity and composition of the microbiota for (i) natural infections with the microbial parasites Crithidia bombi and Nosema bombi, (ii) bumblebee worker size, (iii) colony identity, and (iv) colony age.

4. We found an increase in diversity of the microbiota in individuals naturally infected with either C. bombi or N. bombi. Crithidia bombi infections, however, appear to be only indirectly linked with higher microbial diversity when comparing colonies. The treatments of priming the immune system with heat-killed bacteria and additional food supply, as well as host body size, had no effect on the diversity or composition of the microbiota. Host colony identity had only a weak effect on the composition of the microbiota at the level of resolution of our method. We found both significant increases and decreases in the relative abundance of selected bacterial taxa over the season.

5. We present the first study on the ecological dynamics of gut microbiota in bumblebees and identify parasite infections, colony identity and colony age as important factors influencing the diversity and composition of the bacterial communities. The absence of an effect of our otherwise effective experimental treatments suggests a remarkable ability of the host to maintain a homoeostasis in this community under widely different environments.


Specific microbial communities (microbiota) colonizing animal hosts are ubiquitous in nature and can play important roles, such as for digestive functions, immune defence and organ formation (Fraune & Bosch 2010). However, we know little about the dynamics and the ecological factors shaping these communities in the field. The field situation is of course especially interesting, as it can differ fundamentally from the situation in laboratory-reared hosts (e.g. Xiang et al. 2006). As a case study, we here investigate the bacterial microbiota of important pollinators in temperate regions, the bumblebees (Bombus spp.) (Bingham & Orthner 1998). The case is likely of practical relevance, as bumblebee populations have been declining world-wide (Goulson, Lye & Darvill 2008; Williams & Osborne 2009), and this has been linked to pathogens (Williams & Osborne 2009; Cameron et al. 2011). With regard to their microbiota, recent evidence shows that bumblebees and honeybees have a distinct and relatively species-poor microbiota in the gut (Koch & Schmid-Hempel 2011a; Martinson et al. 2011). These bacteria might play an important role in defence against pathogens (Forsgren et al. 2010; Koch & Schmid-Hempel 2011b) and the digestion of food (Gilliam 1997; Vásquez & Olofsson 2009). But the field situation, where different ecological factors affect the microbiota, and the respective functions remain poorly studied (Hamdi et al. 2011).

Plausible ecological factors that affect the microbiota include nutritional status of the host (Dillon et al. 2010) and the activation of the immune system upon parasitic infections (Ryu et al. 2008; Lazzaro & Rolff 2011). We tested these factors by experimentally manipulating colonies of B. terrestris (Linnaeus) either by stimulating the immune system with heat-killed bacteria or by providing additional food (sugar water) to field-placed colonies in a full-factorial design. We also looked at the following questions:

  • 1 At the individual level: (a) Does the microbiota change when natural infections by the two common microbial parasites C. bombi Lipa & Triggiani and N. bombi Fantham & Porter are present? An infection could alter the gut microbiota, either through direct interaction or through activation of the immune system by parasites, disturbing the homoeostasis with resident microbiota (Ryu et al. 2008; Lazzaro & Rolff 2011). (b) Does host body size influence the diversity of gut microbiota (allometry)? For macroscopic parasites, host size tends to be correlated with higher parasite diversity (Poulin & Morand 2000). Hosts size can be expected to be related to gut volume (Yang & Joern 1994) and therefore to the size of the potential habitat for gut microbiota within a host. The diversity of free-living bacteria shows a species–area relationship comparable to that found in macroscopic organisms (Bell et al. 2005). However, whether such a relationship also exists for microbial communities within hosts of different sizes has so far not been examined. Bumblebees present a good model to study this question, as highly related workers within one colony can vary up to 10-fold in body mass (Couvillon et al. 2010).
  • 2 At the colony level: (a) Do different colonies harbour distinct microbiota? The microbiota may be colony-specific as has been observed in termites (Minkley et al. 2006). Colony specificity of microbiota could arise through predominantly vertical transmission within the nest in contrast to environmental transmission in the field. If the host genotype can influence the microbiota (Ley, Peterson & Gordon 2006), microbiota should differ between colonies. (b) Does the microbiota change over the colony cycle? While the microbiota of isolated bumblebee individuals have been characterized (Koch & Schmid-Hempel 2011a; Martinson et al. 2011), there is no information on a possible change in microbiota throughout the colony cycle. We looked both at changes in overall microbial diversity and at changes in relative abundance of specific bacterial taxa.

Materials and methods

Description of Field Experiment/Sampling

The B. terrestris colonies used in this experiment originated from queens collected in Neunforn (Thurgau, Switzerland) near the experimental field site in spring 2009. The queens were first taken to the laboratory, fed ad libitum with pollen and sugar water and allowed to establish colonies of c. 10 workers. Forty colonies were then taken to the field site (around Ittingen abbey, TG, Switzerland, c. 6 km from the queen collection site) and kept in nest boxes (Schwegler, Schorndorf, Germany). Ten of the 40 colonies were each assigned arbitrarily to one of the following four treatment groups:

  • 1 Priming (Pr): Once every week, all workers from the colony were injected with 2 μL of a cocktail of heat-killed bacteria (Arthrobacter globiformis (Conn) and Escherichia coli (Escherich), at 0·5 × 108 cells mL−1 each in insect Ringer) into the haemolymph with a sterile glass capillary.
  • 2 Food supply (Fs): Colonies received 60 mL of 50% Apiinvert sugar water (Südzucker AG Mannheim/Ochsenfurt), offered inside the colony, every week.
  • 3 Primed and food supply (PrFs): Both treatments (i) and (ii) combined.
  • 4 Naïve (N): None of the above.

Individuals in treatment groups (2) and (4) were also pricked with a sterile needle but without injecting heat-killed bacteria, to control for the effect of experimental manipulation when injecting the bacteria. If excreted, the weekly injection of 2 μL insect Ringer may lead to a small increase in the amount of liquid passing through the gut. Nevertheless, this is unlikely to affect gut physiology, as bumblebees expel an amount of liquid close to their own body weight every day (c. 140 mg, Bertsch 1984). Every week, 10% of the workers from each colony with more than five individuals were collected randomly, removed and frozen for further analysis (total n = 354 individuals). A detailed analysis of the effects of the experimental manipulation on colony development and infection with C. bombi parasites will be published in an accompanying paper (Cisarovsky, Koch & Schmid-Hempel 2012).

To examine the relationship between host body size and gut size, we sampled 20 worker B. terrestris from two laboratory colonies. Workers were selected to be evenly distributed across the whole range of body sizes in these colonies. We used mass as a measure of body size and gut size. Bees were killed by freezing and weighed on a microbalance (model UMT2, Mettler-Toledo, Switzerland). Whole guts were then dissected out and weighed separately.

Bacterial Culture for the Immune Challenge

To activate the Toll and Imd pathways – two major pathways of insect immune defence (Hoffmann 2003) – heat-killed Gram-positive A. globiformis (DSM 20124) and Gram-negative E. coli (DSM 498) bacteria were injected into the haemolymph. For this, bacteria were cultured in liquid broth (10 g bacto-tryptone, 5 g yeast extract, 10 g NaCl in 1000 mL distilled water, pH 7) at 30 °C and 24 h for A. globiformis and 37 °C and overnight for E. coli. One millilitre of culture was washed three times with insect Ringer (previously autoclaved and filtered though a 0·2-μm filter) by centrifuging 10 min at 3000 r.p.m., removing the supernatant and replacing with fresh Ringer. Bacteria were then counted in Neubauer counting chambers and concentrations adjusted with insect Ringer to 108 cells mL−1 of each bacterium. Both bacterial suspensions were then mixed in equal proportions and stored in 2 mL aliquots at −80 °C. Bacteria were heat-killed by incubating thawed aliquots at 90 °C for 15 min before use.

Molecular Methods

The protocol for DNA extraction, PCR and terminal restriction fragment length polymorphism (TRFLP) analysis followed the method described in detail in Koch & Schmid-Hempel (2011a). Briefly, after weighing individual bees, the whole guts were dissected out and genomic DNA was extracted with a Qiagen DNAeasy kit (Qiagen, Hilden, Germany) following the protocol for blood and tissue samples with an additional digest with lysozyme. The bacterial 16S rRNA gene was then PCR-amplified with the nearly universal eubacterial primers 27f (AGA GTT TGA TCM TGG CTC AG, FAM labelled for TRFLP analysis) and 1492r (ACG GYT ACC TTG TTA CGA CTT) (Weisburg et al. 1991). PCR products were cleaned with Sephadex™ G-50 (GE Healthcare, Little Chalfont, UK), digested with HaeIII, cleaned again with Sephadex™ G-50 and run on a MegaBACE capillary sequencer.

Detecting Infections with Crithidia and Nosema

Infection status (infected vs. uninfected) with the intestinal parasite C. bombi and the intracellular parasite N. bombi was detected by PCR specific for either parasite using the DNA from the extracted guts as template. For C. bombi, a part of the 18S rRNA gene was amplified using the Crithidia-specific primers CB-SSUrRNA-F2 and CB-SSUrRNA-B4 following the study of Schmid-Hempel & Tognazzo (2010). To detect infections with N. bombi, the Nosema-specific primer pair 18f (CACCAGGTTGATTCTGCC) and 1537r (TTATGATCCTGCTAATGGTTC) (Baker et al. 1995) was used. For both reactions, the presence of a product of the right size was checked on a 1·5% agarose gel.

Data Analysis

Analysis of the TRFLP profiles followed the procedure outlined in the study of Koch & Schmid-Hempel (2011a). In short, after raw data are processed and sized in Fragment Profiler version 1.2 (MegaBACE, GE Healthcare, Little Chalfont, UK), the TRFLPs of a sample yield a profile with peaks corresponding to the various taxonomic units. Peaks are then filtered from baseline noise and binned between samples as described in the study of Abdo et al. (2006). The individual peak area was divided by the total peak area of all peaks in a sample to standardize between samples. A dissimilarity matrix was computed from these data using the Bray–Curtis coefficient (Bray & Curtis 1957) to compare the similarity of TRFLP profiles with a two-dimensional non-metric multidimensional scaling (NMDS) analysis with the PROXSCAL module in SPSS 19 (IBM). To assess the goodness-of-fit of the NMDS solution, a Shepard plot and Kruskal’s STRESS1 were examined. To test for significant differences between (i) the treatment groups, (ii) bumblebee colonies and (iii) individuals either infected or uninfected with Nosema or Crithidia, a one-way analysis of similarity (anosim) (Clarke 1993; Rees et al. 2004) was carried out in PAST (Hammer, Harper & Ryan 2001) with 10 000 permutations on the Bray–Curtis dissimilarity matrix; P-values were corrected for multiple testing by a Bonferroni’s correction. Positive R-values indicate a higher similarity between samples within one group than between groups, with values around R = 0 indicating no difference in similarity between samples within and between groups. Values of R > 0·75 are generally interpreted as indicating strong separation between groups, R > 0·5 as separation with overlap and R < 0·25 as barely separable (Ramette 2007). For reasons of simplicity and to exclude potential treatment effects, we restricted the between-colony comparison of the bumblebee microbiota to the largest treatment group (Fs). To compare diversities of microbial communities, the number of peaks in individual profiles was counted as a measure of the richness of bacterial taxa in the gut of an individual bee. Additionally, the Shannon diversity index (Krebs 1989) was calculated. For this, the peak areas were first standardized by dividing the area of each peak by the total peak area of all peaks in an individual profile to account for different quantities of labelled DNA in each sample. For each sample, the standardized peak area was then treated as abundance of a bacterial taxon, while the number of peaks in total was treated as the number of bacterial taxa. The Shannon diversity index was calculated according to the formula in the study of Kuehl et al. (2005).

The statistical analyses of the relationship of number of taxa and Shannon diversity with experimental treatment and infection with parasites were carried out on the residuals of the colonies, to account for the colony effect. The colony residuals for the respective variables were extracted from an anova with the colony of origin as a random effect. A histogram of the colony residuals for the number of taxa and the Shannon diversity index showed them to be approximately normally distributed. We then constructed a linear model including experimental treatment and parasite infection status with Crithidia and Nosema as factors and sampling week as a covariate. Non-significant interaction terms were removed from the model. We also looked at the correlation between the average diversity of the microbiota of individuals within one colony and the infection prevalence with either Nosema or Crithidia using a Spearman’s correlation test. For this, colonies that had died within the first 4 weeks of the experiment were excluded, as the number of sampled individuals for these colonies was very low.

For the analyses not looking at the effects of the experimental treatments, we restricted the analysis to the food-supplied groups (Fs & PrFs). The colonies of the two treatment groups without food supply (N & Pr) had mostly died in the first weeks, therefore not providing useful data on the change in the microbiota through the season and inflating the sampling for the first weeks. We combined the Fs and PrFs groups for the analyses, as the immune priming treatment did not show a significant effect on composition or diversity of the microbiota (see Results). To test the effect of individual body size and colony age on the diversity of microbiota, the residuals of the colonies were correlated with individual body mass or week of collection in a Spearman’s rank correlation analysis, respectively.

The identification of the bacterial taxa corresponding with the TRFLP peaks is based on the survey of bacterial communities in bumblebee guts in the study of Koch & Schmid-Hempel (2011a), which used the same protocol for the TRFLP analysis and identified the predominant taxa with 16S sequences from clone libraries. As some of the clone libraries were from bumblebee individuals collected from a site in the vicinity of the field site of this study and included B. terrestris, we assume the peak identifications of Koch & Schmid-Hempel (2011a) to be valid for this study as well. We correlated the colony residuals of the relative signal intensity of the identified peaks with the week of collection, to look for changes in relative abundance of these taxa over the season. To visualize potential changes in the contribution of these taxa to the whole community in the different treatment groups and individuals infected or uninfected with parasites, we displayed the relative signal intensities in a heat map. Only the data for the first 5 weeks are displayed for the different treatment groups, as we did not have samples for all treatment groups for the following weeks.


Analysing the average for all individuals from all experimental colonies, the number of bacterial taxa and bacterial diversity measured by the Shannon diversity varied such that both measures might have increased with the addition of food. However, the effects – if any – were not significantly different from controls with food supply and immune-priming treatments (Fig. 1a and Table 1). Also, neither additional food supply nor immune priming changed the composition of the microbiota significantly, as all pairwise comparisons of different treatment groups with an analysis of similarities (anosim) were non-significant with very low R-values (anosim: R < 0·001, > 0·01). A visualization of the relative signal intensity of the main TRFLP peaks in a heatmap similarly shows no differences between the treatment groups (Fig. 2a). Hence, these ecological factors seemed not to affect the microbiota in the average individual.

Figure 1.

 Mean number of bacterial taxa of the microbiota from individuals of different treatment groups and parasite infection statuses. (a) Treatments are: Fs: food supply; PrFs: immune priming and food supply; N: naïve (control); Pr: immune priming; data restricted to weeks 1–4. (b) Individuals infected (+) or uninfected (−) with Crithidia (Crith) and Nosema (Nos). Error bars: ±1 SE. Number of individuals in each group on top of each bar. Shannon diversity of bacterial communities showed the same patterns (not shown).

Table 1. anova (type II) of linear models for the number of bacterial taxa and Shannon diversity index of bacterial communities
FactorNumber of taxa (colony residuals)FactorShannon–Wiener diversity (colony residuals)
d.f. F P d.f. F P
  1. Factors are different treatments and infection statuses. Non-significant interaction terms were removed from the model. n = 308 individuals. Global models: no. of taxa: r= 0·02, F5,302 = 1·28, P = 0·27; Shannon diversity: r= 0·007, F5,302 = 0·42, P = 0·83.

  2. Factors: week: covariate sampling week (1–16), Fs: food supply (yes/no), Pr: immune priming (yes/no), Crithidia: infected/uninfected with Crithidia, Nosema: infected/uninfected with Nosema.

Crithidia 10·040·83 Crithidia 10·550·46
Nosema 13·530·06 Nosema 10·920·34
Figure 2.

 Heatmap with the relative contribution of the dominant bacterial taxa to the whole bacterial gut microbiota in different treatment groups (a) or infection statuses (b). (a) Treatments are: Fs: food supply; PrFs: immune priming and food supply; N: naïve (Control); Pr: immune priming; data restricted to weeks 1–4. (b) Individuals infected (+) or uninfected (−) with Crithidia (Crith) and Nosema (Nos). Putative identifications of TRFLP peaks are (with clade no. from the study of Koch & Schmid-Hempel 2011a,b: peak 38 bp: taxon Bacteroidetes (clade IV), 202 bp: ‘Candidatus Gilliamella apicola’ (I), 224 bp: ‘Candidatus Snodgrassella alvi’ (III), 246 bp: Lactobacillus sp. (VI), 257 bp: Bombiscardovia coagulans (IX), 307 bp: Fructobacillus sp. (VIII), 321 bp: Firmicutes (V). Numbers in peak labels indicate the terminal restriction fragment length of the respective peak in base pairs. The shade of the heatmap cells is the average proportion of the corresponding peak area (taxonomic group) relative to the peak area of all peaks in respective profiles.

Increased Diversity of Gut Microbiota in Parasite-Infected Individuals

Whereas no significant effects of the treatments on the microbiota were observed, natural infections with Crithidia and Nosema acquired by the colonies in the field were linked to both a higher number of bacterial taxa in the gut and a higher Shannon diversity index of the community (no. of bacterial taxa: Fig. 1b, data for Shannon diversity show identical pattern). However, when accounting for colony identity as a potentially confounding factor, this increased diversity was only linked to Nosema infections (Table 1). While Crithidia infections were not directly linked to higher gut bacterial diversity (Table 1), when comparing all colonies the average per-capita diversity of the microbiota for members of a given colony was positively and significantly correlated with the infection prevalence with Crithidia (i.e. percentage of workers infected) of the same colony (mean number of bacterial taxa: Spearman’s rs = 0·571, P = 0·0133; mean Shannon diversity index: rs = 0·576, = 0·0123). This correlation was not observed for the Nosema infection prevalence (mean number of bacterial taxa: Spearman’s rs = 0·159, = 0·529; mean Shannon diversity index: rs = 0·161, = 0·524).

The community composition significantly differed in Crithidia-infected individuals as compared to uninfected ones (anosim: < 0·0001). These two groups were, however, poorly separated as indicated by the very low R-value of the analysis of similarities (anosim: R < 0·072). By contrast, no difference in community composition was observed between Nosema-infected and uninfected bees (R < 0·001, > 0·01). In Fig. 2, we visualize this variation in a heatmap, showing the signal intensity (i.e. a measure of TFRLP peak areas) in relation to the main TRFLP peaks. In particular, the heatmap shows lower relative abundances of ‘Candidatus Gilliamella apicola’ and ‘Candidatus Snodgrassella alvi’ (sensuMartinson, Moy & Moran 2012) in Crithidia-infected bees, but no difference for these bacteria linked to Nosema infection status (Fig. 2b, see also Koch & Schmid-Hempel 2011b).

No effect of Worker Size on Diversity of Gut Microbiota

Worker body mass was highly linearly correlated with gut mass across the examined range (Pearson’s = 0·93, < 0·001, n = 20; body mass range: 73–217 mg). We therefore used body mass as a proxy for gut size, reflecting the habitat size of the gut microbiota. Although the workers examined in this study varied by almost sixfold in body mass (range: 59–309 mg), no correlations with the number of taxa or the Shannon diversity index of gut bacterial communities were found (number of bacterial taxa: Spearman’s rs = −0·027, = 0·64; Shannon diversity: rs = −0·025, = 0·68; n = 286 individuals).

Weak Effects of Colony Affiliation on Microbiota of Workers

When compared pairwise, some of the colonies differed significantly in terms of the gut microbiota of their workers, albeit with low R-values (R < 0·5), indicating some degree of overlap (Table 2). Overall colonies showed no clear separation (within the resolution provided by our method), as evident in two-dimensional NMDS plot of the gut bacterial communities of different worker individuals (Fig. 3). There were, however, highly significant differences in the average diversity of the microbiota per worker individual between different colonies (Fig. 4) both for the number of bacterial taxa (anova, d.f. = 9159, F = 18·21, < 0·0001) and for the Shannon diversity index of the bacterial community (anova, d.f. = 9159, F = 19·00, < 0·0001). The average bacterial diversity in a colony ranged from 4·4 taxa per individual (Shannon index = 0·92) to 13·6 taxa (Shannon index = 2·02).

Table 2. R-values of analysis of similarity (anosim) comparing the similarity of the composition of the microbiota between different colonies (only Fs treatment)
Colony no.1313416017117218196202225
  1. Higher R-values indicate stronger separation between microbiota of colonies. Overall R for all comparisons = 0·1467, < 0·0001. *< 0·01, **< 0·0001 (Bonferroni-corrected for multiple comparisons). For a non-metric multidimensional scaling plot visualizing the similarities of the microbiota of individuals from these colonies, see Fig. 3.

Figure 3.

 Two-dimensional non-metric multidimensional scaling (NMDS) plot for the similarity of the microbiota in the gut of workers of 10 different colonies. Each point is an individual worker, and the symbols indicate the different source colonies (colony ID) of workers (see legend). Proximity of points represents the degree of similarity between the gut bacteria of different individuals based on TRFLP profiles. Only individuals from the Fs treatment colonies are shown (total n = 169 individuals).

Figure 4.

 Boxplot of the number of bacterial taxa in individuals of the 10 colonies in the Fs treatment group. Colony IDs are identical to Fig. 3 and Table 2. Sample size for each colony on top of each boxplot (total n = 169 individuals). Boxes show median with upper and lower quartiles, and whiskers extend to 1·5 times the interquartile range.

Effect of Colony Age on Microbiota

An overall weak negative correlation of gut bacterial diversity with colony age was detected (age vs. number of bacterial taxa: Spearman’s rs = −0·167, = 0·0046; age vs. Shannon diversity: rs = −0·067, = 0·0256, n = 286). Figure 5 shows that the picture is more complex, as the diversity stays approximately constant for the first 10 weeks, with a visible decline towards the late season, roughly corresponding to the onset of reproduction in the colony. Looking at the relative contribution of individual bacterial taxa to the whole microbiota, we found a significant decrease in ‘Ca. Snodgrasella alvi’ and Bombiscardovia coagulans (Killer et al. 2010) over the season and a trend towards a decrease for ‘Ca. Gilliamella apicola’ (Fig. 6a). In contrast, peaks probably corresponding to Fructobacillus sp., Bacteroidetes and Firmicutes tended to increase in relative signal intensity (Fig. 6b). No change was detected for the relative signal intensity of Lactobacillus sp. (=fragment 246 bp; Spearman’s rank correlation rs = −0·046, = 0·44).

Figure 5.

 Mean number of gut bacterial taxa for all workers collected at each weekly sampling date across the season. Start of field placement of colonies defined as week 0. Error bars: ±1 SE. Data are restricted to the food-supplied treatment groups (Fs & PrFs, 20 colonies, n = 286 individuals). Sample size for each week on top of each bar.

Figure 6.

 Change in the mean standardized relative peak area for selected TRFLP peaks over the course of the season. Start of field placement of colonies is at week = 0. The standardized peak area represents the relative abundance of each bacterial taxon (area of individual peak divided by sum of the areas of all peaks of a profile). Analysis restricted to food-supplied treatment group (Fs and PrFs, 20 colonies, n = 286 individuals). Sample size for the different weeks is identical to the sample size reported in Fig. 5. Error bars are ±1 SE. See text for methods. The fragments represent (see the study of Koch & Schmid-Hempel 2011a,b, with the respective clade). (a) 202 bp corresponding to ‘Candidatus Gilliamella apicola’ (clade I), 224 bp: ‘Candidatus Snodgrassella alvi’ (clade III) and 257 bp: Bombiscardovia coagulans; These groups show a decrease with colony age (correlation on colony residuals: 202 bp: Spearman’s rs = −0·112, = 0·058; 224 bp: rs = −0·346, < 0·0001; 257 bp: rs = −0·216, = 0·00024). (b): Fragment 38 bp: Bacteroidetes (clade IV), 307 bp: Fructobacillus sp. (clade VIII), and 321 bp: Firmicutes (). These groups show an increase with colony age (38 bp: Spearman’s rs = 0·156, = 0·0081; 307 bp: rs = 0·180, = 0·0022; 321 bp: rs = 0·117, = 0·047).


This study investigated the dynamics of bacterial gut communities in bumblebee host colonies in the field. Remarkably, neither experimentally altering the food supply nor immune priming of the host had a significant effect on the diversity and composition of the gut microbiota. As shown in a companion study, the food supply treatment strongly affected the development of our experimental colonies as a whole though. Non-food-supplied colonies mostly starved after several weeks in the field, indicating harsh conditions that were ameliorated with food supplementation (Cisarovsky, Koch & Schmid-Hempel 2012). In this analysis, we also found a significant increase in antimicrobial activity of the haemolymph of workers 7 days after the experimental immune challenge in comparison with the control group (Cisarovsky, Koch & Schmid-Hempel 2012). Therefore, both of our experimental treatments can be assumed to have had a significant effect on individual worker condition. Because we here report no change, this indicates an astonishing resilience of the gut microbiota in the face of different host environments. Similarly, laboratory-based studies with Drosophila melanogaster have shown a sophisticated tuning of the immune system towards different gut bacterial burdens and thereby the maintenance of an immune homoeostasis in the gut (Leulier & Royet 2009). Our results suggest that bumblebees may also be able to keep up a high degree of gut homoeostasis under different conditions in the field. Perhaps, they thereby maintain a ‘healthy’ relationship with their specific resident gut flora (Koch & Schmid-Hempel 2011a,b), in the sense of maintaining a functionally competent microbiota. This homoeostasis might be of potentially great importance for functionalities such as the defence against natural pathogens (Koch & Schmid-Hempel 2011b; Lazzaro & Rolff 2011).

Looking at the potential effects of food addition, it seems plausible that the gut of better-fed individuals may represent an environment richer in nutrients and therefore a more productive one for bacterial growth. Extrapolating from observations on free-living microbial communities (Abrams 1995; Horner-Devine et al. 2003), such higher productivity may increase the diversity of microbes able to coexist in the gut. However, diversity–productivity relationships are often found to be hump-shaped (Smith 2007), which could lead to the observation of both positive and negative relationships in nature if only two states are compared (as was the case in our factorial experiment). In addition, a better nutritional status may also positively influence the host immune system (Tyler, Adams & Mallon 2006), which in turn may alter the community of gut bacteria (Ryu et al. 2008). Regardless, we here found no significant effect (Fig. 1). Our results contrast the study by Dillon et al. (2010) who found an increase in gut bacterial diversity for starving desert locusts as compared to normally fed individuals. Clearly, more experimental work is needed to understand the relationship of microbial diversity within hosts and nutritional status. As a caveat, we did not investigate the population size of the microbiota quantitatively, but rather focused on the community composition and diversity regardless of the overall population sizes. Thus, a change in the population size owing to our experimental treatments would have gone unnoticed if it were not also linked to a change in the diversity or composition of the microbiota.

Infection with the parasites N. bombi and C. bombi were inevitable and happened naturally in our field experiment. These infections were correlated with an increase in bacterial diversity in the gut. On closer examination, the two parasitic infections showed different patterns, however. Infections with the intracellular parasite N. bombi may be directly linked to a higher diversity of bacterial taxa in the gut of an individual (though marginally non-significant with = 0·06, Table 1 and Fig. 1). Nosema bombi infects various body tissues but is only found in the gut in the form of inactive spores. This may point towards an indirect interaction, perhaps mediated through the host immune system. For example, infection could start an immune response, which in turn may disrupt the homoeostasis of the commensal bacteria in the gut and alter the diversity of the community. In line with this hypothesis, an overexpression of antimicrobial peptides has previously shown to lead to a disturbance of the mutualistic gut microbial community in Drosophila (Ryu et al. 2008). Whatever the potential mechanisms, our experimental challenge of the host immune system with heat-killed bacteria (treatment immune-primed) did not significantly affect the microbiota (Fig. 1) even though it had previously been shown that such priming upregulates the bumblebee immune response for up to 14 days (Korner & Schmid-Hempel 2004) under laboratory conditions. Yet, it may not present a strong enough stimulus under field conditions in contrast to an active infection with a highly virulent parasite such as N. bombi (Otti & Schmid-Hempel 2007).

Crithidia bombi, by contrast, is an intestinal parasite residing in the hindgut and that therefore may potentially directly interact with the gut microbiota. Surprisingly, however, we only find an indirect link between higher diversity of gut microbiota and C. bombi infections, as colonies with higher C. bombi prevalence had – on average – more diverse gut bacterial communities. No such trend was observed when comparing C. bombi-infected and uninfected single individuals within a given colony by, at the same time, correcting for the colony effect (Table 1). This points towards variation among colonies in the ability to both limit the infections with higher numbers of bacterial taxa and contain the parasite C. bombi in the gut. Note that this view may suggest that the bacterial species colonize and grow to the best of their capacities, rather than forming a tightly regulated community. By implication, the observed homoeostasis in the bacterial community would result from a dynamic process that reflects the – not necessarily convergent – interests of hosts and microbiota.

The diversity of macroscopic organisms generally follows a species–area relationship with habitats of greater size supporting a greater number of species (MacArthur & Wilson 1967). This relationship has also been found for free-living microorganisms, for example for bacteria in water-filled tree holes (Bell et al. 2005). If one considers the gut of a host as a similar ‘island’ habitat, one might predict bigger individuals of the same species (with larger gut volume) to harbour a greater diversity of gut microbiota. We did, however, not find a significant effect of bumblebee worker size (correlated with gut size) on bacterial diversity despite a nearly sixfold difference in the body mass among adult workers. This suggests that other factors are more important in shaping the diversity of the gut microbiota in the current case.

In contrast to a study on termites that also looked at colonies from the same population under identical environmental conditions (Minkley et al. 2006), we did not find a clear separation of the gut microbiota between colonies. We did, nevertheless, find significant differences for pairwise comparisons between some of the colonies. This is remarkable as colonies foraged in the same area under identical environmental conditions. The distinct microbiota of some colonies may be due to the close proximity of bees within a nest, which would facilitate within-nest transmission over transmission between individuals of different colonies. Workers are probably colonized after pupal eclosion with gut bacteria from their nest mates (Koch & Schmid-Hempel 2011b; Martinson, Moy & Moran 2012). Generally though, most bacterial taxa seem to be shared between colonies, at least at the level of resolution offered by our method, which cannot distinguish between different strains of the same bacterial species. These results are in line with previous findings (Koch & Schmid-Hempel 2011a) showing the most abundant bacterial taxa to be shared even between different bumblebee species and separate geographical localities. Future studies should try to disentangle the effects of host genotype and social transmission mode on microbiota composition.

An increase has been observed within a colony over the season for the diversity of strains of the intestinal parasite C. bombi (Imhoof & Schmid-Hempel 1998), which is horizontally transmitted at flowers (Durrer & Schmid-Hempel 1994). In this study, we observed an overall slight trend towards a decrease in gut bacterial diversity over the season, but this trend only became apparent in the last weeks. This decrease should be treated with caution, as the low number of individuals sampled at the end of the season makes this observation less reliable. The observed change in the relative abundance of certain bacterial taxa may be related to an accumulation of environmentally transmitted bacteria over the season, which would increase the relative contribution of these taxa over the bacteria preferentially vertically transmitted and potentially originating from the founder queen. We did observe this general pattern, as on the one hand the peak representing the probably unspecific acidophilic Fructobacillus sp. found on flowers (Endo & Okada 2008) significantly increased in relative abundance in the gut over the season. This finding is in line with the results of McFrederick et al. (2012), suggesting that a number of acidophilic bacteria may be transmitted between flowers and bees. On the other hand, the highly specific and potentially vertically transmitted ‘Ca. Snodgrassella alvi’ (Koch & Schmid-Hempel 2011a,b; Martinson, Moy & Moran 2012) decreased in relative abundance over the season.

In summary, bumblebees appear to be able to maintain a homoeostasis of their gut microbiota even in the face of strong environmental disturbances such as immune priming by non-specific bacteria and drastically altered nutritional states of the colony. Furthermore, even a sixfold difference in host size was not associated with changes in the specific microbiota. In contrast to these factors, parasite infections were associated with a higher diversity of the gut microbiota. Because these are correlations, the causal relationship remains unclear and may differ between C. bombi and N. bombi. Surprisingly, only a weak effect of colony affiliation on the taxonomic composition of the gut microbiota was detected, but a better resolving method may be needed to distinguish between different strains of the dominant bacteria in the bumblebee gut. Colonies however widely differed in the diversity of the microbiota associated with them. Finally, we observed that the composition of the microbiota changes through the colony cycle, potentially caused by a stronger representation of environmentally acquired bacteria in contrast to vertically transmitted bumblebee-specific bacteria later in the season.


We thank N. Rossel, R. Schmid-Hempel and M. Jales Hon for help in the field and Kartause Ittingen for the permit to work on their premises. Data analysed in this paper were generated in the Genetic Diversity Centre of ETH Zurich. This study was financially supported by the Swiss NSF (grant no. 31003A-116057 to PSH) and an ERC grant (no. 268853 to PSH).