Social organization and physical environment shape the microbiome of harvester ants

doi:10.21203/rs.3.rs-4938069/v1

Download PDF

Research Article

Social organization and physical environment shape the microbiome of harvester ants

https://doi.org/10.21203/rs.3.rs-4938069/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

All animals harbor microbiomes, which are obtained from the surrounding environment and are impacted by host behavior and life stage. To determine how the physical environment and social organization structure an organism's microbiome, we examined the microbial communities within and around nests of harvester ants (Veromessor andrei). We collected soil and nest content samples from five different nests. We used 16S rRNA gene sequencing and calculated alpha and beta diversity to compare microbial diversity and community composition across samples. We compared across i) sample types (ants, brood, seeds and reproductives, and soil), ii) soil inside and outside the nest, and iii) soil from different chamber types. Interestingly, we found support that both the environment and social organization structure the microbiome of V. andrei colonies. Soil from the five nests differed from one another in a way that mapped onto their geographical distance. Furthermore, soil from inside the nests resembled the surrounding soil, supporting the physical environment hypothesis. However, the microbiomes of the contents within the nest chambers, i.e., ants, brood, seeds, and reproductives, differed from one another in their microbiome and from the surrounding soil, supporting the social organization hypotheses. This study highlights the importance of considering environmental and social factors in understanding microbiome dynamics.

microbiome

social organization

physical environment

harvester ant

Veromessor andrei

All animals are associated with and are colonized by communities of microorganisms, known collectively as the microbiome. Animals obtain microbes from the environment and their behavior and life stage also have a large impact on their microbiome. Throughout their lives, animals are colonized by microbes from their surroundings [1–3]. Because microbes are acquired in various ways, composition of microbial communities depends on how they are obtained or transmitted. Studies in humans and non-human primates suggest that the surrounding environment including habitat, diet, or social group can significantly influence microbiome composition [4–6]. The composition of the gut microbiome varies across captive, urban, and rural environments in many organisms such as Ring-tailed lemurs [7], Tasmanian devils [8], deer mice [9], water dragons [10], and coyotes [11]. Host behaviors significantly impact host-microbiome dynamics. For example, communal nesting in four-toed salamanders (Hemidactylium scutatum) increases the transmission of beneficial, antifungal bacteria, enhancing hatchling survival compared to solitary nests [12, 13]. The diversity of host behaviors is further highlighted by the fact that both vertebrates and invertebrates, such as the Green iguana (Iguana iguana) and bumblebees (Bombus), engage in coprophagy, a behavior involving the consumption of feces to establish and regulate their gut microbiota [14, 15].

Most microbiome studies consider microbial communities inside or on the surface of the organism. However, many animals occupy stable burrows or construct nests [16], which may have a large impact on the microbial environment of the animals. In some cases, like social insects, nests can be considered to be part of the organism, as an extended phenotype [17, 18]. Thus, the microorganisms inside a nest can be considered as part of the microbiome of a colony of social insects. Little is known about the feedback between the microbiome of a nest and how it relates to the microbiome of its inhabitants. Furthermore, social insects are known for their intricate social organization, with a morphological division of reproductive labor, as well differentiation in the tasks that workers perform. Furthermore, ants use their nest to raise brood and store food leading to highly diverse contents within an ant colony’s nest. Therefore, social insects afford a unique opportunity to determine the role of the environment and social organization on microbiome diversity and composition.

Environmentally acquired microbes tend to be ephemeral and not host-specific due to the functional redundancy of bacterial species and the changing environmental conditions that both hosts and their microorganisms are exposed to (temperature, humidity, nutrients) [19]. Indeed, the microbiome of animals is often determined by the environment in which they live. For example, when the cuticular microbiomes of two arboreal ant species were compared, the physical location of their nest was a better predictor of their microbiome composition than the species of ant [20]. Similarly, when comparing the gut microbiome of deer mice (Peromyscus manuculatus) in captive and urban populations, individuals from each environment had distinct microbial compositions. Therefore, the relative impact of the environment on the microbiome of an animal is important to consider, especially for animals whose environment is an integral component of their phenotype, such as soil-nesting ants. Microbial diversity in soils is linked to soil pH, soil organic carbon, and oxygen [21, 22]. Furthermore, microbial biomass and diversity tend to decrease with soil depth [23]. Therefore, we expect that if the microbiome of animals that live in soil is impacted by the environment, such subterranean animals will have microbiomes that mirror the soil’s, including decreased microbial diversity with depth.

The behavior of an animal and the social organization of a society can impact the microbiome of an animal and the microbiome can provide information specific to the host. For example, in spotted hyaenas, the microbiome varies with sex and age-class and is specific among individuals [24]. The social organization of social insect colonies results in individuals performing different tasks and this division of labor can influence and structure the microbiome composition of individuals within colonies. For example, honeybee workers that perform different behavioral tasks, such as foraging, or nursing, show differences in gut bacterial community composition [25]. Additionally, there are differences in the gut microbiome composition of reproductives and non-reproductive workers in termites [26, 27], honey bees [28, 29], and ants [30, 31]. When comparing the gut microbiome of chimpanzees, individuals from the same communities had a similar microbiome composition, as expected, due to their shared diets and interactions. However, individuals who were considered long-term immigrants in that community showed more distinct gut microbiome composition, suggesting that the immigrant individuals retained characteristics of their gut microbial communities for extended periods regardless of the environment [32]. Thus, in some cases the behavioral role of individuals might have a stronger impact on their microbiome than the environment in which they live.

Ants are highly social animals that shape the environment in which they live - their nest. The microbial communities of the nest are shared with the microbiome of the ant colony [20]. However, most studies of ant microbiomes have focused on the gut microbiome, showing that ant species differ in the densities of bacterial communities in the gut according to diet type [33] and that ants can benefit from their microbiome via nutrient acquisition and defense against pathogens [34, 33]. However, to our knowledge, the role of the nest in shaping the microbiome of ant colonies has only been explored in arboreal ants that occupy exiting tree cavities [35] and not in subterranean ants that construct and shape their own nest. Each nest region has a different chemical signature that reflects the individuals that occupy that area [36]. Thus, it is likely that a similar relationship between the structure of the nest and the materials inside each chamber shape the microbiome of the colony and its nest, as seen in arboreal ants [35]. Behavioral tasks occur at specific locations within a nest, such that when not foraging, foragers are found near the entrance of the nest and brood nurses are found in the center, where the brood is located [37]. This spatial division of labor can influence and structure the microbiome composition of individuals within colonies of ants. The relationship between social organization and spatial position, as well as the nest being an extended phenotype of a colony suggests that the physical environment and social organization combine to influence the microbiome of ant colonies. For example, nest chambers might differ in their microbiome composition based on the behavioral tasks performed in them. Such potential differences in chamber microbiome can be driven by the chambers’ content (e.g., the seeds or the brood) or by the ants that tend to the chamber material (Fig. 1). Furthermore, the microbiome found inside ant nests might differ from the surrounding soil. Indeed, the mounds of harvester ant colonies can have a different nutrient composition compared to the surrounding soil [38], however, it is not known if such differences permeate the inside of the nest.

Colonies of the harvester ants, Veromessor andrei, live in grasslands, where they turn and aerate the soil, redistributing nutrients and potentially creating favorable conditions for microorganisms within and around their subterranean nests [38–41]. Veromessor andrei nests provide an opportunity to examine the effects of the physical environment and social organization on the microbiome of the colony. The structure of V. andrei nests affect their collective foraging [42] most likely through the impact of the nest structure on interactions among ants [43] which are known to regulate foraging activity in other harvester ants within the colony [44–46]. Thus nest structure impacts V. andrei behavior, and can potentially further segregate behavioral tasks such as brood care and food storage. However, it is not known how nest structure and social organization combine to impact the microbiome of harvester ant colonies.

To determine the roles of the physical environment and social organization in structuring an organism's microbiome we examined the microbial communities within and around nests of V. andrei. If the physical environment influences microbial communities we predict that: 1) the microbiome of the content of the nest (like ants, seeds, brood, etc) will be similar to the surrounding soil; 2) the microbiome of the soil inside nest chambers will not differ from the microbiome in the surrounding soil; 3) microbiome diversity in nest chambers will decrease with nest depth, similarly to the relationship between depth and microbiome that can be found in soils [23] and 4) nests in different locations will have different microbial diversity because soils change their microbial composition and diversity spatially [21, 22] (Fig. 1A). If social organization influences microbial communities of ant nests we predict that: 1) nest content (like ants, seeds, brood etc) will differ in their microbiome composition according to their biological classification and will be different from the surrounding soil; 2) microbiome diversity of chamber soil will differ across chambers according to the content found in them, regardless of the surrounding soil; 3) microbiome diversity of chamber soil will differ from the microbiome of the surrounding soil and 4) microbiome composition of soil inside nest chambers will be conserved by the content of the chamber in a way that is consistent across different nests (Fig. 1B).

Study site and sample collection

To examine the microbiome of ant nests’ soil and of the content of the nests, we collected samples from five colonies of V. andrei May 24–29, 2021 from Sedgwick Natural Reserve in southern California, US. Sedgwick Reserve is home to a thriving V. andrei population (> 100 colonies) and we selected five colonies that could be easily accessed, were far from other ant nests, to reduce any negative impact of excavation on other colonies, and whose nests were off the road - so that nest excavation would not disrupt access (Fig. 1a). To access the nest content we first dug a trench approximately 1-1.5m away from the nest entrance, using a tractor fitted with a post hole digger, pickaxes, and shovels. Once the trench was established, we began digging towards the nest until we reached a chamber from its side and sampled its content, as detailed below. Once we completed sampling a chamber we continued excavating in the direction of the tunnels leading out of the chamber, until we found another chamber and sampled it too. We proceeded to excavate and sample from all nest chambers until we could not find any more new chambers that were not sampled (Fig. 1b). When we reached a chamber, we collected with gloves or soft tweezers (sterilized with ethanol) samples of its content, which included ants, brood, seeds, and reproductives (Fig. 1e-h). Most chambers included ants, but not all chambers included brood, seeds, and reproductives. We placed each type of sample in a separate, labeled, 15ml tube. After sampling the chamber’s content, we used a small disposable plastic spoon to collect soil from inside the excavated chamber (Fig. 1d), referred to later as ‘nest soil’. Nest chamber soil samples were classified into ‘chamber types’ according to the chamber content (e.g., if they had ants inside, they were considered ‘ant’ chamber type). If a chamber had more than one type of content (e.g., both brood and reproductives were found in the same chamber) the chamber was assigned a type based on all the material in it (e.g., brood + reproductives). We then (using a new disposable plastic spoon) sampled soil from a location outside the nest, within approximately 5cm of the excavated chamber, and at the same depth as the chamber, which we referred to as ‘near soil control’ (Figs. 1b,c). We recorded the depth of the chamber from the ground surface using a measuring tape. Once all chambers were excavated, we obtained the ‘control far soil’ samples by collecting soil from the side of the trench that was opposite the nest (Fig. 1c). We used a measuring tape to sample soil from depths that matched those of the chambers we excavated. Thus, each chamber had several associated samples - three soil samples (from inside the chamber, near the chamber, and far (~ 1-2m) from the chamber - at the same depth) and samples of the content of the chamber (ranging from 1–3 additional samples - depending on the content we found). Each day we excavated one nest, with the first four nests (A, B, C, and D) excavated on consecutive days (May 24–27, 2021) and the fifth one (E) collected after a one-day break (on May 29th, 2021). Around noon and at the end of each day, around 5pm, we placed the samples we collected in a freezer at the field station. Nests differed in depth and number of chambers. All samples were transferred in a cooler to the UCLA campus (approximately a 2 hour drive from the field site), where they were stored in a -20 freezer until processing.

Sample processing

After collection, we stored and kept the samples at -20C until extractions. To prepare the soil samples for extractions we weighed and transferred up to 250 mg of soil to sterile 1.5ml tubes. To prepare the non-soil samples (ants, seeds, brood etc) for extractions we washed ant workers and alates in 70% ethanol and 5% bleach solutions and rinsed with sterile deionized water. We placed the washed samples in sterile 1.5ml tubes and used a sterile pestle to crush the ants. All seed and brood samples were crushed but not washed. Brood samples were not washed because the bleach and alcohol used in the wash protocol would have destroyed the samples. Seeds were not washed because we were interested in quantifying the microbiome on their exterior. After crushing, we added 800 ul of Solution C1 containing SDS for cell lysis (DNEasy PowerSoil kit) to all samples (including soil). To facilitate cell lysis, we vortexed and left the samples overnight at 56C. Microbial DNA was extracted using the Qiagen DNEasy PowerSoil Pro kit protocol. After DNA extraction, 285 samples were sent for sequencing at the UCLA Microbiome Center for 16S rRNA gene amplification and library sequencing. Amplicon sequencing of the bacterial community was performed using the V4 region of the 16S rRNA gene using the primers 515F (59-GTGCCAGCMGCCGCGGTAA-39) and 806R (59-GGACTACHVGGGTWTCTAAT-39) following the Earth Microbiome Project (EMP) protocol [47, 48].

Quantifying microbiome diversity

To determine the microbial composition of each sample type (soil, seeds, brood, adult ants), microbiome bioinformatics were conducted using QIIME2 version 2024.2.4 [49]. Initial raw sequence data underwent demultiplexing and quality filtering with the q2-demux plugin, followed by denoising using DADA2 [50] through the q2-dada2 plugin. Amplicon sequence variants (ASVs) were aligned using MAFFT [51] via q2-alignment, and a phylogeny was constructed with FastTree2 [52] through q2-phylogeny. Samples were rarefied (subsampled without replacement) to 3618 sequences per sample. This sampling depth of 3618 retained 955,152 features (25.73%) in 264 samples (92.65%). ASVs were assigned taxonomy using the q2-feature-classifier [53] classify-sklearn naive Bayes taxonomy classifier, referencing the Silva 13_8 99% OTU database [54]. ASVs are used as a proxy for bacterial species and are similar to OTUAs (operational taxonomic units) but at a finer-scale resolution (100% similarity). Once the quality filtering steps were completed, we estimated refraction, alpha and beta diversity measures using q2 diversity. We created a summary feature table (see supplemental materials) with information on how many sequences are associated with each sample. To create relative abundance plots and assess species composition, we exported the feature table and used the ‘phyloseq’ package in R [55].

To determine how the nest microbiome is influenced by the physical and social environment, we examined microbiome diversity within (alpha diversity) and among (beta diversity) samples. Alpha diversity indices provide information regarding the number of microbial taxa in a single sample. The alpha diversity indices we used include:

Shannon’s index - describes how evenly species are distributed, independent of species richness [56, 57]. A high Shannon index indicates more species diversity whereas a value of zero indicates that fewer species are present in the sample.

Faith’s phylogenetic diversity - a weighted measure of richness that describes the amount of the phylogenetic tree that is covered by the communities, i.e. more evolutionary branches would result in greater diversity [58].

Pielou’s evenness - provides information about the relative abundance of species in a sample, i.e., if some species are dominating others or if all species have similar abundances.

Observed amplicon sequence variants (Observed ASVs) - the number of observed unique sequences that are present in the sample [50]

Beta diversity provides information about the differences in microbiome composition among multiple samples classifying samples into groups according to similarities in their microbiome composition based on sequence abundances or the presence or absence of sequences [59]. Here we used the Bray-Curtis dissimilarity index as a beta diversity measure of compositional dissimilarity among microbial communities of samples [60]. We measured beta diversity differences between samples using a permutational multivariate analysis of variance (PERMANOVA) on Bray-Curtis dissimilarity matrices. Principal Coordinate Analysis (PCoA) ordination was calculated based on these matrices using the Adonis [61] and Vegan package [62], with 999 permutations for the PERMANOVA. The resulting PCoA plots were visualized using ggplot2 [63]. This analysis tested for differences in beta diversity among all sample types, all soil types, and nest soil samples.

Statistical analysis

All sample types: To determine if alpha diversity differed across all sample types we ran four linear models (LM) - one for each of the four alpha diversity measures (Shannon, Faith’s phylogenetic diversity, Pielou’s evenness, and ASV richness) as the response variable. The explanatory variable was the type of sample: ants, seeds, reproductive, brood, and soil. We used the lm() function in R [64] for these models. For specific comparisons of microbial diversity among sample types, we used a post hoc Tukey test by applying the Tukey HSD() function in R [64]. We further examined PCoA plots and used a PERMANOVA to examine beta diversity across sample types.

All soil samples: To determine if alpha diversity changed with soil depth and differed across locations we ran linear models (LM) implemented as detailed above. In each model one of the four alpha diversity measures (Shannon index, Faith’s phylogenetic diversity, Pielou’s evenness, and ASV richness) was the response variable. The explanatory variables included: depth, nest ID, and soil type (chamber soil, control near, and control far). For post hoc tests we used the package ‘emmeans’ [65]. We used a model selection approach to determine which interaction terms to include in our final statistical model. We ran each model with either no interactions among soil type, depth, and nest ID; with the three-way interaction among the three variables; and three additional models with just one interaction each between a different pair of variables each time, totaling five statistical models per alpha diversity measure. We then compared the models using AIC [66] and selected the best fit model, i.e., the one with the lowest AIC score. The best fit models for all diversity measures included no interaction terms among the explanatory variables. For specific comparisons of microbial diversity among soil types, we used a post hoc Tukey test [65]. We further examined PCoA plots and used a PERMANOVA to examine the beta diversity among soil samples and the five different nests. For specific comparisons of microbial diversity among soil types, we used pairwise PERMANOVA tests by applying the pairwise.adonis( ) function in the package ‘pairwiseAdonis’ [61].

Chamber soil samples: To determine if the microbial composition in the soil inside nest chambers differed based on chamber type (i.e., the content found in the chamber: ants, seeds, reproductives, and brood), we ran linear models (LM) and post hoc tests as detailed above. The response variable was one of the four diversity indices (Shannon index, Faith’s phylogenetic diversity, Pielou’s evenness, and ASV richness) and the explanatory variables included: nest ID, sample depth, and chamber type (based on the content listed above). We used the same model selection approach detailed above [66] and if the best fit model included an interaction term, but the collinearity was very high (VIF > 10), we removed the interaction term. Due to high collinearity among terms in the models, we ended up keeping only models with no interaction terms. We further examined PCoA plots and used a permanova to examine beta diversity across chamber types.

All analysis was conducted in R version 4.3.2 [64] and all of the best-fitting models met the required statistical assumptions – examined using the check_model() function in the ‘performance’ package [66]. Data and code are available in supplementary materials and DAlejandraG/nest-microbes.

We collected and sequenced 285 samples (see supplementary material). The 16S rRNA gene amplicon sequencing raw reads are available from NCBI via BioProject record PRJNA1147938. The raw dataset contained a total of 4,884,506 reads. We rarefied the dataset at a sampling depth of 3,618 and retained 955,152 features after refraction with a total of 264 samples after 22 samples were removed. We then removed 21 sample types that were obtained for only some nests, or did not have a large enough sample size to include in the analysis (e.g., entrance soil and soil from the mound). Lasty, we removed four samples due to errors in labeling during sample collection or during sample extraction. Code for this data cleaning is available in the supplementary material.

All sample types

The alpha, and beta diversity of all samples differed substantially by sample type. Ants, reproductives, brood, seeds, and soil had different alpha diversity, regardless of which measure we examined (Table 1, Figs. 3, 4). A post hoc Tukey test showed that ant samples had the lowest and soil samples had the highest alpha diversity compared to all other sample types, across all measures of alpha diversity. Brood and reproductives did not differ significantly in their alpha diversity across all diversity measures. Finally, seeds and reproductives showed significant differences in the Faith’s Phylogenetic distance measure (Fig. 4A).

Using a principal coordinate analysis (PCoA) for visualization, the Bray-Curtis distances demonstrated that the ASV samples clustered by sample type (Fig. 4B). We performed a PERMANOVA on Bray-Curtis distances calculated from the rarefied dataset to test for dissimilarities in microbial community composition among samples based on the ASVs by sample type (ants, reproductives, brood, seeds, and soil). Sample type explained a significant amount of variation in the dataset, explaining approximately 22.42% of the total variation (PERMANOVA: p-value = 0.001). Pairwise PERMANOVA results show a significant difference between most pairwise comparisons (Adjusted p < 0.01, Table S1. Only samples of brood and reproductives were not significantly different. The comparisons with ants (ants vs. soil, ants vs. seeds, ants vs. brood, ants vs. reproductives) show higher R2 values, indicating that ants explain a substantial proportion of the variance in these comparisons. Soil comparisons (soil vs. seeds, soil vs. brood, soil vs. reproductives) show lower R2 values, suggesting less variance explained (Table S1).

Table 1

Statistical output of the effect of sample type on the four alpha diversity measures using a linear model.
Diversity measure	Sum of squares	DF	F value	P value
Shannon	1363.2	4	691.59	< 0.0001
Faith	6041.3	4	186.99	< 0.0001
Pielou Evenness	16.924	4	387.15	< 0.0001
Observed ASVs	972137	4	161.61	< 0.0001

All soil samples

When examining the alpha diversity of the various soil samples the best fit model did not include any interaction terms (Table 2). Soil type (nest soil, control near, and control far) significantly impacted only the Pielou Evenness index but none of the other alpha diversity measures (Table 2, Fig. 6A). Soil depth did not have a statistically significant effect on any of the alpha diversity measures (Table 2). Finally, nest ID significantly impacted all alpha diversity measures except for Faith’s phylogenetic distance (Table 2)(Fig. 5A). Microbiome beta diversity was explained both by soil type (nest soil, control far, control near) and nest ID (A, B, C, D, E). The PERMANOVA yielded significant p-values (0.001) for both nest and soil type indicating that these factors play a significant role in explaining the variance in the dataset (PERMANOVA). All pairwise comparisons among soil samples (A, B, C, D, E) are statistically significant except D vs E, with adjusted p-values ≤ 0.01. The R2 values vary, with the highest being 0.228 and smallest being 0.051. Comparisons of nest soil to control (near and far) were statistically significant but with lower R2 values - ranging from 0.012 to 0.042 - suggesting that grouping by soil type explains a smaller proportion of the variance compared to grouping by nest ID. PERMANOVA results indicate that control soils - near and far were not significantly different from one another (Table 4).

Table 2

Statistical output for the effect of nest, depth, and soil type chamber soil, control near, and control far) on diversity measures using a linear model.
Diversity measure	Effect	Sum of Squares	DF	F value	P value
Shannon	Nest	2.6748	4	5.5244	< 0.001
	Depth	0.037	1	0.3057	0.5811
	Soil type	0.4824	2	1.9925	0.14
Faith	Nest	49.53	4	1.8846	0.1161
	Depth	13.96	1	2.1246	0.1471
	Soil type	9.65	2	0.7346	0.4814
Pielou Evenness	Nest	0.0136	4	9.3684	< 0.0001
	Depth	0.00002	1	0.0801	0.777
	Soil type	0.0047	2	6.376	0.002
Observed ASVs	Nest	19929	4	3.3403	0.0119
	Depth	135	1	0.093	0.7642
	Soil type	665	2	0.2228	0.8005

Nest soil samples

When comparing the alpha diversity of soil samples from inside the nest by chamber type we used statistical models without any interaction terms. While models with interaction terms had a better fit to the data according to AIC, these models had very high levels of collinearity (VIF > 10) and therefore we only examined models without interaction terms. Chamber type and sample depth did not have a significant effect on any of the alpha diversity measures (Table 3; Fig. 7A). The only significant effect was of nest ID on the Shannon and Faith’s phylogenetic distance indexes (Table 3; Fig. 5A).

The beta diversity of soil from inside the nest was not explained by chamber type. There was no significant effect of chamber type in the PERMANOVA (Table S1). Similar to all soil samples, when examining just the soil from inside the nest, The five nests significantly differed in their ASV composition according to the PERMANOVA of nest soil by nest ID (Table 4).

Table 3

Statistical output for the effect of chamber type on diversity measures using an LM
Diversity measure	Effect	Sum of Squares	DF	F value	P value
Shannon	Nest	2.054	4	3.4925	0.1649
	Depth	0.2516	1	1.7106	0.1991
	Chamber type	0.8335	4	1.4167	0.2481
Faith	Nest	55.438	4	1.6478	0.1836
	Depth	5.455	1	0.6485	0.4259
	Chamber type	34.502	4	1.0255	0.4073
Pielou Evenness	Nest	0.005297	4	2.3454	0.0730
	Depth	0.0008984	1	1.5912	0.2152
	Chamber type	0.0042393	4	1.8771	0.1357
Observed ASVs	Nest	13147	4	2.3933	0.0685
	Depth	665	1	0.4840	0.4910
	Chamber type	6806	4	1.2390	0.3118

Our study suggests that both social and environmental factors are crucial in shaping the microbiome of V. andrei colonies. In support of the physical environment hypothesis (Fig. 1) we found that the microbiome of nests in different locations varied significantly across alpha and beta diversity (Fig. 5) and the relative abundance of the nest soil microbiome was similar to that of the control soil samples (Fig. 3). In support of the social organization hypothesis, we found that the microbiome of the nest contents differed according to biological classification and was different from the microbiome of the surrounding soil (Fig. 3, Fig. 4). Furthermore, the beta diversity and evenness of the soil microbiome inside the nest was significantly different from the control soil samples (Fig. 6, Table 4). However, the microbiome diversity of the nest soil did not differ significantly across chambers according to the contents found in them (Fig. 7).

Differences in the microbiome composition within V. andrei nests (biotic and soil samples) provide partial support for the physical environment hypothesis. Overall, the microbiome of the biotic content of the nest (ants, reproductives, seeds and brood) was significantly different from the soil inside the nest and the surrounding soil (Figs. 3, 4). These findings align with previous research indicating that the microbiomes of Formica exsecta ants are different from those present in the nest and alpha and beta diversity were lower in ant samples compared to the nest material [67]. However, this work did not differentiate between inside chambers and surrounding soils and only sampled nest material from the top layer of the soil (0-20cm). Further, our findings are consistent with other work on social insects, such as termites [27], honey bees [25], and ants [68, 30, 31, 69] that show differences in the microbiome of brood, reproductives, and workers. Only brood samples were similar to soil samples according to Faith’s PD and Observed ASVs and reproductives were not significantly different from soil for Pilou’s evenness. This similarity between brood and soil can be explained by the fact that we did not wash the brood during processing because they would have disintegrated due to the lack of outer protection and contact with harsh chemicals [70]. In addition, we did not wash the seed samples during processing, because we were interested in sequencing the microbes that were found on their exterior, and we still found significant differences between the seed and soil samples. Therefore, not washing the brood samples might not be the only explanation for not finding differences between brood and soil samples. The beta diversity of seeds, reproductives, and brood, were all similar (Fig. 4B), which might be explained by the fact that brood are the primary consumers of protein [71, 72], which comes from seeds. Furthermore, reproductives are most likely recently eclosed, being closer in developmental stage to brood than workers. Thus, it is possible that some bacterial species from seeds are present in the developmental stages that feed on them (brood) and the ants that recently fed on them (reproductives). These findings are consistent with other work on microbiome of honey bees [73] and ants [74] that have highlighted the role of developmental stage on microbiome composition and with studies that found an impact of diet on microbiome composition of ants and honeybees [75–77]

In further support of the physical environment hypothesis, most alpha diversity measures of the soil microbiome inside the nest did not differ from the surrounding soil, either near, or far from the nest. This result suggests that the bacterial species inside the nest come from the surrounding environment, as seen in nests of arboreal ant species [20, 78]. Indeed, we also found that geographic location impacts the nest microbiome. As we predicted, nests in closer proximity had more similar microbial communities than nests farther apart (Fig. 2A, 5). This similarity can be explained by the similar soil environments because nest microbiome differences mirrored the physical location of the nests (Fig. 2A), with colonies that were physically closer to each other exhibiting similar alpha and beta diversity (Fig. 5). Such geographic clustering of microbial communities is seen in studies of soil microbiome [79, 80] where microbial communities impact the soil's physical structure, chemical properties, and water content [79, 81]. Future work might examine how geographical differences in soil microbial composition may affect the behavior of ant colonies and the structure of their nests.

In contrast to the physical environment hypothesis, the beta diversity and evenness of the soil microbiome inside the nest was significantly different from the control soil samples (Fig. 6, Table S2, 4). This finding suggests that there are differences in the identity of the taxa observed in soil samples collected from within the nest and soil samples collected approximately one meter away from the nest (Fig. 2C, 6, Table S2). These findings align with previous research indicating that ant nests serve as unique microhabitats with distinct microbial activity and soil nutrient composition [40]. However, this previous work used core samples that cut through the nest, and do not distinguish between soil inside the nest and the soil immediately outside the nest chambers - as we did here. Furthermore, they only examined the very top layer of the soil (0-20cm), whereas, our study did not include samples from the surface of the soil, and most of our samples were from deeper than 20cm. Interestingly, in contrast with other studies of soil microbiome [21, 82], we did not find a relationship between soil depth and microbial diversity (Table 3, Supplemental Figure S1). One possible explanation for this discrepancy could be the unique structure and activity within ant nests, creating microenvironments that sustain higher microbial diversity even at greater depths. This unexpected result highlights the complexity of microbial dynamics within ant nests and suggests that additional factors, such as nest architecture and ant activity, may mitigate the typical depth-related decline in microbial diversity.

The social organization hypothesis was supported by the distinct microbiome composition of each type of biotic nest content (ants, reproductives, seeds, brood). Each of these sample types had different bacterial composition and different alpha diversity (Figs. 3, 4). The Firmicutes bacterial phylum dominated ant samples whereas Actinobacteria, Proteobacteria, and Firmicutes were more evenly distributed in brood and reproductives (Fig. 3). The presence of Actinobacteria, Proteobacteria, and Firmicutes is typical of herbivorous and omnivorous ant species and larvae [75, 83]. However, Firmicutes is dominant in V. andrei adult worker ants, similar to what has been observed in copious predatory ant species such as army ants (Eciton), bullet ants (Paraponera clavata) [83, 84]. Considering past work found that in Azteca ants the microbiome inside chambers matches their content [35], and that the chemical signature of nest chambers is determined by their content [36] and that ants use certain nest chambers as latrines [85] it was surprising that we did not find a match between the content of a chamber and the microbiome of its soil (i.e. chamber type, Fig. 7). Thus, we did not find support for the idea that spatial division of labor influences and structures the microbiome composition of the nest itself, only that of the biotic content within it. As discussed above, the difference in evenness and beta diversity between nest and control soils suggests that there is some influence of the ants on their nest soil microbiome, however, it does not relate directly to the content of the chambers.

Our results contribute to a growing body of evidence that social insect nests are intricate ecosystems influenced by both intrinsic and extrinsic factors [86–90]. Our study highlights the significant roles of both social organization and the physical environment in shaping the microbiome of V. andrei colonies. The influence of the surrounding soil microbiome on the nest microbiome especially underscores the intricate interplay between environmental and social factors in structuring nest microbiome. Thus, future work examining microbial ecology of animals should consider both the physical environment and social organization when studying the animal holobiont.

Ethics approval and consent to participate: Not applicable.

Consent for publication: Not applicable.

Availability of data and materials: The datasets analysed during the current study are available in this published article and its supplementary information files and in the repository DAlejandraG/nest-microbes. All sequence data analysed during this study are available on NCBI BioProject record SUB14655354, https://www.ncbi.nlm.nih.gov/sra/PRJNA1147938.

Competing interests: These authors declare that they have no competing interests.

Funding: DAG and ESHL were supported by a National Science Foundation-Graduate Research Fellowships Program while conducting this work. ESHL was funded by the Mathias Graduate Student Research Grant to conduct fieldwork at the UC Natural Reserve, Sedgwick. The National Institutes of Health (NIH) grant GM115509 to NPW provided sequencing funding for this work.

Authors’ contributions: ESHL, NPW, and DAG contributed to the conceptualization of the study questions and design; ESHL and NPW collected the samples; DAG and PJF processed and analyzed the samples; PJF wrote code for data analysis; DAG and PJF produced data visualization; DAG, PJF, and NPW performed statistical analysis and wrote the manuscript. All authors contributed critically to the drafts and gave final approval for publication.

Acknowledgements: We would like to thank Lyza Johnsen, Adam Wollman, Michael Capeder, and the Sedgwick Reserve staff for their help in the field; Angelica Soriano and Monica Lu for their help in the lab with DNA extractions; the Goodman-Luskin Microbiome Center for 16S ribosomal RNA gene sequencing, V4 region; and Elvira D’Bastiani, Emily Laub, Kaija Gahm, and Sean O’Fallon for helpful feedback. This work was performed (in part) at the University of California Natural Reserve System, Sedgwick Reserve DOI: 10.21973/N3C08R.

Vaishampayan PA, Kuehl JV, Froula JL, Morgan JL, Ochman H, Francino MP. Comparative metagenomics and population dynamics of the gut microbiota in mother and infant. Genome Biol Evol. 2010;2:53–66. https://doi.org/10.1093/gbe/evp057.
Desbonnet L, Clarke G, Traplin A, O’Sullivan O, Crispie F, Moloney RD, et al. Gut microbiota depletion from early adolescence in mice: Implications for brain and behaviour. Brain Behav Immun. 2015;48:165–73. https://doi.org/10.1016/j.bbi.2015.04.004.
Schwarz RS, Moran NA, Evans JD. Early gut colonizers shape parasite susceptibility and microbiota composition in honey bee workers. Proc Natl Acad Sci. 2016;113:9345–50. https://doi.org/10.1073/pnas.1606631113.
Yildirim S, Yeoman CJ, Sipos M, Torralba M, Wilson BA, Goldberg TL, et al. Characterization of the fecal microbiome from non-human wild primates reveals species specific microbial communities. PLoS ONE. 2010;5:e13963. https://doi.org/10.1371/journal.pone.0013963.
Kuthyar S, Manus MB, Amato KR. Leveraging non-human primates for exploring the social transmission of microbes. Curr Opin Microbiol. 2019;50:8–14. https://doi.org/10.1016/j.mib.2019.09.001.
Ying C, Siao Y-S, Chen W-J, Chen Y-T, Chen S-L, Chen Y-L, et al. Host species and habitats shape the bacterial community of gut microbiota of three non-human primates: Siamangs, white-handed gibbons, and Bornean orangutans. Front Microbiol. 2022;13:920190. https://doi.org/10.3389/fmicb.2022.920190.
Bornbusch SL, Greene LK, Rahobilalaina S, Calkins S, Rothman RS, Clarke TA, et al. Gut microbiota of ring-tailed lemurs (Lemur catta) vary across natural and captive populations and correlate with environmental microbiota. Anim Microbiome. 2022;4:29. https://doi.org/10.1186/s42523-022-00176-x.
Chong R, Grueber CE, Fox S, Wise P, Barrs VR, Hogg CJ, et al. Looking like the locals - gut microbiome changes post-release in an endangered species. Anim Microbiome. 2019;1:8. https://doi.org/10.1186/s42523-019-0012-4.
Diaz J, Redford KH, Reese AT. Captive and urban environments are associated with distinct gut microbiota in deer mice (Peromyscus maniculatus). Biol Lett. 2023;19:20220547. https://doi.org/10.1098/rsbl.2022.0547.
Littleford-Colquhoun BL, Weyrich LS, Jackson N, Frere CH. City life alters the gut microbiome and stable isotope profiling of the eastern water dragon (Intellagama lesueurii). Mol Ecol. 2019;28:4592–607. https://doi.org/10.1111/mec.15240.
Sugden S, Sanderson D, Ford K, Stein LY. St. Clair CC. An altered microbiome in urban coyotes mediates relationships between anthropogenic diet and poor health. Sci Rep. 2020;10:22207. https://doi.org/10.1038/s41598-020-78891-1.
Banning JL, Weddle AL, Wahl GW III, Simon MA, Lauer A, Walters RL, et al. Antifungal skin bacteria, embryonic survival, and communal nesting in four-toed salamanders, Hemidactylium scutatum. Oecologia. 2008;156:423–9. https://doi.org/10.1007/s00442-008-1002-5.
Lauer A, Simon MA, Banning JL, Lam BA, Harris RN. Diversity of cutaneous bacteria with antifungal activity isolated from female four-toed salamanders. ISME J. 2008;2:145–57. https://doi.org/10.1038/ismej.2007.110.
Troyer K. Microbes, herbivory and the evolution of social behavior. J Theor Biol. 1984;106:157–69. https://doi.org/10.1016/0022-5193(84)90016-X.
Koch H, Schmid-Hempel P. Socially transmitted gut microbiota protect bumble bees against an intestinal parasite. Proc Natl Acad Sci U S A. 2011;108:19288–92. https://doi.org/10.1073/pnas.1110474108.
Hansell MH. Animal architecture. 1st ed. Oxford: OUP Oxford; 2005.
Järvinen P, Brommer J. Nest ornaments and feather composition form an extended phenotype syndrome in a wild bird. Behav Ecol Sociobiol. 2020;74. https://doi.org/10.1007/s00265-020-02912-2.
Hill MS, Gilbert JA. Microbiology of the built environment: harnessing human-associated built environment research to inform the study and design of animal nests and enclosures. Microbiol Mol Biol Rev. 2023;87:e00121–21. https://doi.org/10.1128/mmbr.00121-21.
Rosenberg E. Holistic Fitness: Microbiomes are Part of the Holobiont’s Fitness. In: Rosenberg E, editor. Microbiomes Curr. Knowl. Unanswered Quest. Cham: Springer International Publishing; 2021. pp. 101–60. https://doi.org/10.1007/978-3-030-65317-0_4.
Birer C, Moreau CS, Tysklind N, Zinger L, Duplais C. Disentangling the assembly mechanisms of ant cuticular bacterial communities of two Amazonian ant species sharing a common arboreal nest. Mol Ecol. 2020;29:1372–85. https://doi.org/10.1111/mec.15400.
Eilers KG, Debenport S, Anderson S, Fierer N. Digging deeper to find unique microbial communities: The strong effect of depth on the structure of bacterial and archaeal communities in soil. Soil Biol Biochem. 2012;50:58–65. https://doi.org/10.1016/j.soilbio.2012.03.011.
Bach EM, Williams RJ, Hargreaves SK, Yang F, Hofmockel KS. Greatest soil microbial diversity found in micro-habitats. Soil Biol Biochem. 2018;118:217–26. https://doi.org/10.1016/j.soilbio.2017.12.018.
Lehmann A, Zheng W, Rillig MC. Soil biota contributions to soil aggregation. Nat Ecol Evol. 2017;1:1828–35. https://doi.org/10.1038/s41559-017-0344-y.
Rojas CA, Holekamp KE, Winters AD, Theis KR. Body site-specific microbiota reflect sex and age-class among wild spotted hyenas. FEMS Microbiol Ecol. 2020;96:fiaa007. https://doi.org/10.1093/femsec/fiaa007.
Jones JC, Fruciano C, Marchant J, Hildebrand F, Forslund S, Bork P, et al. The gut microbiome is associated with behavioural task in honey bees. Insectes Sociaux. 2018;65:419–29. https://doi.org/10.1007/s00040-018-0624-9.
Shimada K, Maekawa K. Gene expression and molecular phylogenetic analyses of beta-glucosidase in the termite Reticulitermes speratus (Isoptera: Rhinotermitidae). J Insect Physiol. 2014;65:63–9. https://doi.org/10.1016/j.jinsphys.2014.05.006.
Otani S, Zhukova M, Koné NA, da Costa RR, Mikaelyan A, Sapountzis P, et al. Gut microbial compositions mirror caste-specific diets in a major lineage of social insects. Environ Microbiol Rep. 2019;11:196–205. https://doi.org/10.1111/1758-2229.12728.
Kapheim KM, Rao VD, Yeoman CJ, Wilson BA, White BA, Goldenfeld N, et al. Caste-Specific Differences in Hindgut Microbial Communities of Honey Bees (Apis mellifera). PLoS ONE. 2015;10:e0123911. https://doi.org/10.1371/journal.pone.0123911.
Anderson KE, Ricigliano VA, Mott BM, Copeland DC, Floyd AS, Maes P. The queen’s gut refines with age: longevity phenotypes in a social insect model. Microbiome. 2018;6:108. https://doi.org/10.1186/s40168-018-0489-1.
Brown BP, Wernegreen JJ. Deep divergence and rapid evolutionary rates in gut-associated Acetobacteraceae of ants. BMC Microbiol. 2016;16:140. https://doi.org/10.1186/s12866-016-0721-8.
Sinotte VM, Renelies-Hamilton J, Taylor BA, Ellegaard KM, Sapountzis P, Vasseur-Cognet M et al. Synergies Between Division of Labor and Gut Microbiomes of Social Insects. Front Ecol Evol 2020;7.
Degnan PH, Pusey AE, Lonsdorf EV, Goodall J, Wroblewski EE, Wilson ML, et al. Factors associated with the diversification of the gut microbial communities within chimpanzees from Gombe National Park. Proc Natl Acad Sci. 2012;109:13034–9. https://doi.org/10.1073/pnas.1110994109.
Moreau CS. Intestinal Symbionts. In: Starr C, editor. Encycl. Soc. Insects. Cham: Springer International Publishing; 2019. pp. 1–5. https://doi.org/10.1007/978-3-319-90306-4_65-1.
Engel P, Moran NA. The gut microbiota of insects – diversity in structure and function. FEMS Microbiol Rev. 2013;37:699–735. https://doi.org/10.1111/1574-6976.12025.
Lucas JM, Madden AA, Penick CA, Epps MJ, Marting PR, Stevens JL, et al. Azteca ants maintain unique microbiomes across functionally distinct nest chambers. Proc R Soc B Biol Sci. 2019;286:20191026. https://doi.org/10.1098/rspb.2019.1026.
Heyman Y, Shental N, Brandis A, Hefetz A, Feinerman O. Ants regulate colony spatial organization using multiple chemical road-signs. Nat Commun. 2017;8:15414. https://doi.org/10.1038/ncomms15414.
Mersch DP, Crespi A, Keller L. Tracking individuals shows spatial fidelity is a key regulator of ant social organization. Science. 2013;340:1090–3. https://doi.org/10.1126/science.1234316.
Brown MJF, Human KG. Effects of Harvester Ants on Plant Species Distribution and Abundance in a Serpentine Grassland. Oecologia. 1997;112:237–43.
Jouquet P, Dauber J, Lagerlöf J, Lavelle P, Lepage M. Soil invertebrates as ecosystem engineers: Intended and accidental effects on soil and feedback loops. Appl Soil Ecol. 2006;32:153–64. https://doi.org/10.1016/j.apsoil.2005.07.004.
Ginzburg O, Whitford WG, Steinberger Y. Effects of harvester ant (Messor spp.) activity on soil properties and microbial communities in a Negev Desert ecosystem. Biol Fertil Soils. 2008;45:165–73. https://doi.org/10.1007/s00374-008-0309-z.
Farji-Brener AG, Werenkraut V. The effects of ant nests on soil fertility and plant performance: a meta-analysis. J Anim Ecol. 2017;86:866–77. https://doi.org/10.1111/1365-2656.12672.
Pinter-Wollman N. Nest architecture shapes the collective behaviour of harvester ants. Biol Lett. 2015;11:20150695. https://doi.org/10.1098/rsbl.2015.0695.
Pinter-Wollman N. Persistent variation in spatial behavior affects the structure and function of interaction networks. Curr Zool. 2015;61:98–106. https://doi.org/10.1093/czoolo/61.1.98.
Pinter-Wollman N, Bala A, Merrell A, Queirolo J, Stumpe MC, Holmes S, et al. Harvester ants use interactions to regulate forager activation and availability. Anim Behav. 2013;86:197–207. https://doi.org/10.1016/j.anbehav.2013.05.012.
Greene MJ, Gordon DM. Cuticular hydrocarbons inform task decisions. Nature. 2003;423:32–32. https://doi.org/10.1038/423032a.
Greene MJ, Gordon DM. Interaction rate informs harvester ant task decisions. Behav Ecol. 2007;18:451–5. https://doi.org/10.1093/beheco/arl105.
Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ, et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc Natl Acad Sci. 2011;108:4516–22. https://doi.org/10.1073/pnas.1000080107.
Jacobs JP, Gupta A, Bhatt RR, Brawer J, Gao K, Tillisch K, et al. Cognitive behavioral therapy for irritable bowel syndrome induces bidirectional alterations in the brain-gut-microbiome axis associated with gastrointestinal symptom improvement. Microbiome. 2021;9:236. https://doi.org/10.1186/s40168-021-01188-6.
Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37:852–7. https://doi.org/10.1038/s41587-019-0209-9.
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3. https://doi.org/10.1038/nmeth.3869.
Katoh K, Misawa K, Kuma K, Miyata T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 2002;30:3059–66. https://doi.org/10.1093/nar/gkf436.
Price MN, Dehal PS, Arkin AP. FastTree 2 – Approximately Maximum-Likelihood Trees for Large Alignments. PLoS ONE. 2010;5:e9490. https://doi.org/10.1371/journal.pone.0009490.
Bokulich NA, Kaehler BD, Rideout JR, Dillon M, Bolyen E, Knight R, et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome. 2018;6:90. https://doi.org/10.1186/s40168-018-0470-z.
Robeson MS, O’Rourke DR, Kaehler BD, Ziemski M, Dillon MR, Foster JT, et al. RESCRIPt: Reproducible sequence taxonomy reference database management. PLOS Comput Biol. 2021;17:e1009581. https://doi.org/10.1371/journal.pcbi.1009581.
McMurdie PJ, Holmes S. phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLoS ONE. 2013;8:e61217. https://doi.org/10.1371/journal.pone.0061217.
Lemos LN, Fulthorpe RR, Triplett EW, Roesch LFW. Rethinking microbial diversity analysis in the high throughput sequencing era. J Microbiol Methods. 2011;86:42–51. https://doi.org/10.1016/j.mimet.2011.03.014.
Kim B-R, Shin J, Guevarra R, Lee JH, Kim DW, Seol K-H, et al. Deciphering Diversity Indices for a Better Understanding of Microbial Communities. J Microbiol Biotechnol. 2017;27:2089–93. https://doi.org/10.4014/jmb.1709.09027.
Faith DP. The Role of the Phylogenetic Diversity Measure, PD, in Bio-informatics: Getting the Definition Right. Evol Bioinforma Online. 2007;2:277–83.
Borcard D, Gillet F, Legendre P. Community Diversity. In: Borcard D, Gillet F, Legendre P, editors. Numer. Ecol. R. Cham: Springer International Publishing; 2018. pp. 369–412. https://doi.org/10.1007/978-3-319-71404-2_8.
Bray JR, Curtis JT. An Ordination of the Upland Forest Communities of Southern Wisconsin. Ecol Monogr. 1957;27:325–49. https://doi.org/10.2307/1942268.
Martinez Arbizu P, pairwiseAdonis. Pairwise multilevel comparison using adonis. R Package Version. 2020;04:1.
Oksanen J, Simpson GL, Blanchet FG, Kindt R, Legendre P, Minchin PR et al. Vegan: Community Ecology Package 2001:2.6–6.1. https://doi.org/10.32614/CRAN.package.vegan
Wickham H. ggplot2: Elegant Graphics for Data Analysis. 2nd ed. Incorporated: Springer Publishing Company; 2016.
R Core Team. R: A Language and Environment for Statistical Computing 2024.
Lenth RV, Bolker B, Buerkner P, Giné-Vázquez I, Herve M, Jung M et al. emmeans: Estimated Marginal Means, aka Least-Squares Means. 2024.
Lüdecke D, Ben-Shachar MS, Patil I, Waggoner P, Makowski D. performance: An R Package for Assessment, Comparison and Testing of Statistical Models. J Open Source Softw. 2021;6:3139. https://doi.org/10.21105/joss.03139.
Lindström S, Timonen SS, Sundström L. Microbial communities of the ant Formica exsecta and its nest material. Eur J Soil Sci. 2023;74:e13364. https://doi.org/10.1111/ejss.13364.
Johansson H, Dhaygude K, Lindström S, Helanterä H, Sundström L, Trontti K. A Metatranscriptomic Approach to the Identification of Microbiota Associated with the Ant Formica exsecta. PLoS ONE. 2013;8:e79777. https://doi.org/10.1371/journal.pone.0079777.
Shimoji H, Itoh H, Matsuura Y, Yamashita R, Hori T, Hojo MK, et al. Worker-dependent gut symbiosis in an ant. ISME Commun. 2021;1:1–10. https://doi.org/10.1038/s43705-021-00061-9.
Shu Z, Shahen M, College of Life Science, Northwest A, University F, Hegazi Y, Al-Sharkaw MAM et al. IM, Physiological response of Culex pipiens larvae to sublethal concentrations of sodium and calcium hypochlorite. J Environ Biol. 2018;39:314–23. https://doi.org/10.22438/jeb/39/3/MRN-710
Lee Cassill D, Tschinkel WR. Regulation of Diet in the Fire Ant, Solenopsis invicta. J Insect Behav. 1999;12:307–28. https://doi.org/10.1023/A:1020835304713.
Csata E, Dussutour A. Nutrient regulation in ants (Hymenoptera: Formicidae): a review. Myrmecol News n d;29:111–24. https://doi.org/10.25849/MYRMECOL.NEWS_029:111
Kapheim KM, Pan H, Li C, Salzberg SL, Puiu D, Magoc T, et al. Genomic signatures of evolutionary transitions from solitary to group living. Science. 2015;348:1139–43. https://doi.org/10.1126/science.aaa4788.
Ramalho MO, Bueno OC, Moreau CS. Species-specific signatures of the microbiome from Camponotus and Colobopsis ants across developmental stages. PLoS ONE. 2017;12:e0187461. https://doi.org/10.1371/journal.pone.0187461.
Russell JA, Moreau CS, Goldman-Huertas B, Fujiwara M, Lohman DJ, Pierce NE. Bacterial gut symbionts are tightly linked with the evolution of herbivory in ants. Proc Natl Acad Sci. 2009;106:21236–41. https://doi.org/10.1073/pnas.0907926106.
Rubin BER, Kautz S, Wray BD, Moreau CS. Dietary specialization in mutualistic acacia-ants affects relative abundance but not identity of host-associated bacteria. Mol Ecol. 2019;28:900–16. https://doi.org/10.1111/mec.14834.
Anderson KE, Copeland DC. The honey bee hive microbiota: meta-analysis reveals a native and aerobic microbiota prevalent throughout the social resource niche. Front Bee Sci 2024;2. https://doi.org/10.3389/frbee.2024.1410331
Dejean A, Compin A, Delabie JHC, Azémar F, Corbara B, Leponce M. Biotic and abiotic determinants of the formation of ant mosaics in primary Neotropical rainforests. Ecol Entomol. 2019;44:560–70. https://doi.org/10.1111/een.12735.
Siegwart L, Piton G, Jourdan C, Piel C, Sauze J, Sugihara S, et al. Carbon and nutrient colimitations control the microbial response to fresh organic carbon inputs in soil at different depths. Geoderma. 2023;440:116729. https://doi.org/10.1016/j.geoderma.2023.116729.
Philippot L, Chenu C, Kappler A, Rillig MC, Fierer N. The interplay between microbial communities and soil properties. Nat Rev Microbiol. 2024;22:226–39. https://doi.org/10.1038/s41579-023-00980-5.
Zhou W, Zhan P, Zeng M, Chen T, Zhang X, Yang G, et al. Effects of ant bioturbation and foraging activities on soil mechanical properties and stability. Glob Ecol Conserv. 2023;46:e02575. https://doi.org/10.1016/j.gecco.2023.e02575.
Xin Y, Wu Y, Zhang H, Li X, Qu X. Soil depth exerts a stronger impact on microbial communities and the sulfur biological cycle than salinity in salinized soils. Sci Total Environ. 2023;894:164898. https://doi.org/10.1016/j.scitotenv.2023.164898.
Anderson KE, Russell JA, Moreau CS, Kautz S, Sullam KE, Hu Y, et al. Highly similar microbial communities are shared among related and trophically similar ant species. Mol Ecol. 2012;21:2282–96. https://doi.org/10.1111/j.1365-294X.2011.05464.x.
Valdivia C, Newton JA, von Beeren C, O’Donnell S, Kronauer DJC, Russell JA, et al. Microbial symbionts are shared between ants and their associated beetles. Environ Microbiol. 2023;25:3466–83. https://doi.org/10.1111/1462-2920.16544.
Czaczkes TJ, Heinze J, Ruther J. Nest Etiquette—Where Ants Go When Nature Calls. PLoS ONE. 2015;10:e0118376. https://doi.org/10.1371/journal.pone.0118376.
Bollazzi M, Kronenbitter J, Roces F. Soil temperature, digging behaviour, and the adaptive value of nest depth in South American species of Acromyrmex leaf-cutting ants. Oecologia. 2008;158:165–75. https://doi.org/10.1007/s00442-008-1113-z.
Hoggard SJ, Wilson PD, Beattie AJ, Stow AJ. Social Complexity and Nesting Habits Are Factors in the Evolution of Antimicrobial Defences in Wasps. PLoS ONE. 2011;6:e21763. https://doi.org/10.1371/journal.pone.0021763.
Tschinkel WR. The architecture of subterranean ant nests: beauty and mystery underfoot. J Bioecon. 2015;17:271–91. https://doi.org/10.1007/s10818-015-9203-6.
DiRienzo N, Dornhaus A. Temnothorax rugatulus ant colonies consistently vary in nest structure across time and context. PLoS ONE. 2017;12:e0177598. https://doi.org/10.1371/journal.pone.0177598.
O’Fallon S, Drager K, Zhao A, Suarez A, Pinter-Wollman N. Foraging behaviour affects nest architecture in a cross-species comparison of ant nests. Philos Trans R Soc B Biol Sci. 2023;378:20220146. https://doi.org/10.1098/rstb.2022.0146.

No competing interests reported.

Download PDF

Reviews received at journal
31 Oct, 2024
Reviews received at journal
22 Oct, 2024
Reviewers agreed at journal
11 Oct, 2024
Reviewers agreed at journal
11 Oct, 2024
Reviewers agreed at journal
10 Oct, 2024
Reviewers invited by journal
09 Oct, 2024
Editor assigned by journal
21 Aug, 2024
Submission checks completed at journal
20 Aug, 2024
First submitted to journal
19 Aug, 2024

You are reading this latest preprint version

Social organization and physical environment shape the microbiome of harvester ants

Status:

Version 1

Abstract

Figures

Background

Methods

Study site and sample collection

Sample processing

Quantifying microbiome diversity

Statistical analysis

Results

All sample types

All soil samples

Nest soil samples

Discussion

Conclusions

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1