Social organization and physical environment shape the microbiome of harvester ants

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Flynn, Eva Sofia Horna Lowell, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4938069/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Mar, 2025 Read the published version in Animal Microbiome → Version 1 posted 11 You are reading this latest preprint version Abstract 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 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background 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. 1 A). 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. 1 B). Methods 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. 1 a). 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. 1 b). 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. 1 e-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. 1 d), 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. 1 b,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. 1 c). 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. Results 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. 4 A). Using a principal coordinate analysis (PCoA) for visualization, the Bray-Curtis distances demonstrated that the ASV samples clustered by sample type (Fig. 4 B). 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. 6 A). 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. 5 A). 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. 7 A). The only significant effect was of nest ID on the Shannon and Faith’s phylogenetic distance indexes (Table 3 ; Fig. 5 A). 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 Discussion 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. 4 B), 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. 2 A, 5 ). This similarity can be explained by the similar soil environments because nest microbiome differences mirrored the physical location of the nests (Fig. 2 A), 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. 2 C, 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. Conclusions 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. Declarations 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4938069","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":345367358,"identity":"a2fbc894-1893-4ae1-8fe3-8fbe343fa9a0","order_by":0,"name":"Denisse Alejandra Gamboa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYDCCAwwMzEBKjrG9AcS1ABEGRGkxZu45AOJKEK8lsX1GApFa+M4vfrq5oMKOsXfm8wvMPDUS8gzszdsk8GmRvPHM7PaMM8nMkrNzCph5jkkYNvAcK8OrxeDGAbPbvG3MbIazcxKYeRskGBskcswIaDn+Dailnsf+5hmwFvsG+TcEtJzvAdlyWIJxBvsBkJbEBgke/Fokb/CUAf1y3ICxJ4fh4JxjEsltPGnFFvi08J0/vu12QUV1fWP78YcP3tTY2PazH954A58WBokEGIvH4ACIYsOrHAT4D8BY7A8IKh4Fo2AUjIKRCQCiokzswfskFwAAAABJRU5ErkJggg==","orcid":"","institution":"University of California Los Angeles","correspondingAuthor":true,"prefix":"","firstName":"Denisse","middleName":"Alejandra","lastName":"Gamboa","suffix":""},{"id":345367359,"identity":"6daccab3-845e-4ea8-a888-6ebf3bcd92ad","order_by":1,"name":"Peter J. Flynn","email":"","orcid":"","institution":"Harvard University","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"J.","lastName":"Flynn","suffix":""},{"id":345367360,"identity":"d2075aff-6b38-4743-91bf-00ba507383b4","order_by":2,"name":"Eva Sofia Horna Lowell","email":"","orcid":"","institution":"San Diego Natural History Museum","correspondingAuthor":false,"prefix":"","firstName":"Eva","middleName":"Sofia Horna","lastName":"Lowell","suffix":""},{"id":345367361,"identity":"c3d1e13d-e306-4e88-9bcd-e0213eafedc5","order_by":3,"name":"Noa Pinter-Wollman","email":"","orcid":"","institution":"University of California Los Angeles","correspondingAuthor":false,"prefix":"","firstName":"Noa","middleName":"","lastName":"Pinter-Wollman","suffix":""}],"badges":[],"createdAt":"2024-08-19 10:51:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4938069/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4938069/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s42523-025-00390-3","type":"published","date":"2025-03-19T15:57:19+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":64741477,"identity":"4bd0deb6-d841-48be-ab2d-f0e285d1f1bf","added_by":"auto","created_at":"2024-09-18 08:57:43","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":545629,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVisualization of hypotheses. \u003c/strong\u003eA) The physical environment determines microbial community structure. We expect the contents within the nest chambers \u0026nbsp;(ants, reproductives, brood, and seeds) to have similar microbiome composition to the surrounding soil and that nests will differ from one another in their microbiome - based on differences in the surrounding soil. B) Social organization determines microbial composition. We expect the microbiome composition of contents within the nests chambers to differ across chambers regardless of the microbial composition of the surrounding soil. The colors of the circles represent different microbiome compositions of the nest chambers. The background colors represent the microbiome composition of the surrounding soil.\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4938069/v1/6720cc0aa2e53d776691f282.jpg"},{"id":64740574,"identity":"c6cdc84e-aa47-43e0-b8a1-20c46c5ad7da","added_by":"auto","created_at":"2024-09-18 08:49:46","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":172168,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSample collection. \u003c/strong\u003e(a) Map of the study site with the locations of the five nests that we excavated indicated with orange stars and the letter ID of each colony, is colored according to their representation in figures 4 and 5. (b) the excavated nest of colony D; the white box indicates the approximate location where we sampled ‘near soil control’ for the chamber immediately to the right of the white box. (c) EHL crouching in the trench we dug to reach the nest chambers from the side, sampling soil from one of the chambers. A white box indicates the general area from which a ‘near soil control’ sample would be taken and the yellow rectangle shows the approximate location where ‘far control soil’ samples were collected. The entrance of the nest is indicated at the top right in a gray circle. (d) sampling soil from inside a chamber with a plastic spoon. We collected each chamber’s content, including (e) ants, (f) seeds, (g) brood, and (h) reproductives. All photo credits: Noa Pinter-Wollman.\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4938069/v1/c2e8e5da0c076da3cc46836c.jpg"},{"id":64740571,"identity":"788d9c11-577b-4325-8f98-805775dad07c","added_by":"auto","created_at":"2024-09-18 08:49:43","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2031886,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundance of microbial phyla ordered by sample type: ants, reproductives (R), brood (Br), seeds (S), nest soil, control near soil, control far soil. Each vertical bar is an individual sample with color indicating the bacterial phyla according to ASV. The sampling depth was 3618 reads.\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4938069/v1/25b74731a961e6e679bccccb.jpg"},{"id":64740567,"identity":"2f9d62a0-5881-43c1-aea7-4fb938a86505","added_by":"auto","created_at":"2024-09-18 08:49:43","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":71868,"visible":true,"origin":"","legend":"\u003cp\u003eAlpha and beta diversity measures by sample type: ants, brood, reproductives (repro), seeds, and soil. (A) Box plots of microbiota alpha diversity measures (Shannon, Faith’s Phylogenetic distance, and Observed ASVs) by sample types. Here, and in all following figures, boxes indicate interquartile ranges, lines inside the boxes denote medians, whiskers extend to 1.5 times the interquartile range, and dots are outliers. Boxes that do not share letters are statistically different according to a post hoc Tukey test (p value \u0026lt; 0.05). (B) Beta diversity Principle coordinate analysis (PCoA) from Bray-Curtis dissimilarity matrix by sample types. Each point represents one sample and is color coded by sample type. The closeness of points indicates high community similarity.\u003c/p\u003e","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4938069/v1/9af17e23123d343bc906c274.jpg"},{"id":64740573,"identity":"4997f9f1-f345-4deb-877f-a6ee14116ad1","added_by":"auto","created_at":"2024-09-18 08:49:43","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":596603,"visible":true,"origin":"","legend":"\u003cp\u003eAlpha and beta diversity measures of all soil samples by nest. (A) Effect of nest ID (A, B, C, D, E) on alpha diversity measures (Shannon, Faith’s phylogenetic distance, Pielou’s Evenness, and Observed ASVs) of soil samples only. For measures in which nest ID was a significant effect, boxes that do not share a letter are statically significant according to a post hoc Tukey test. (B) PCoA plots from a Bray-Curtis dissimilarity distance matrix. Each point is a soil samples with colors corresponding to colony ID and point shape representing soil type (nest - circles, control near - triangles, and control far - squares).\u003c/p\u003e","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4938069/v1/f612e2ff9fa3c006d98a5bad.jpg"},{"id":64740569,"identity":"4c2d0619-34a8-4723-86f8-7d9c72c17f99","added_by":"auto","created_at":"2024-09-18 08:49:43","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":344273,"visible":true,"origin":"","legend":"\u003cp\u003eAlpha and beta diversity measures of all soil samples by soil type - same soil samples shown in Figure 5 with colors corresponding to soil type (nest soil, control near, and control far). \u0026nbsp;(A) Effect of soil type on alpha diversity measure: Pielou’s evenness. Boxes that do not share a letter are statistically significant according to a post hoc Tukey test. (B) PCoA plot with Bray-Curtis dissimilarity distance matrix. Each point represents one soil sample and colors correspond to soil type (nest, control near, and control far).\u003c/p\u003e","description":"","filename":"Fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4938069/v1/1c731e773b9d1a81847cd8a4.jpg"},{"id":64740572,"identity":"6f4dd4ab-4a38-417e-a9b9-9a9c0f0d8800","added_by":"auto","created_at":"2024-09-18 08:49:43","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":13912,"visible":true,"origin":"","legend":"\u003cp\u003eAlpha and beta diversity of soil samples only from inside the nest, by chamber type (ants, brood, brood + reproductive (Br + Rep), brood/seeds (Br + Seeds), and seeds). (A) Effect of chamber type on alpha diversity measures (Shannon, Faith’s Phylogenetic distance, Pielou’s Evenness, and Observed ASVs). Chamber type did not have a statistically significant effect on any of the alpha diversity measures. (B) PCoA plot with Bray-Curtis dissimilarity matrix for nest soil by chamber type and nest - colors represent chamber type (ants, brood, brood + seeds, brood + reproductives, and seeds) and point shape corresponds to nest ID.\u003c/p\u003e","description":"","filename":"Fig7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4938069/v1/2bcbc0a34ab9644f11c287bc.jpg"},{"id":79120616,"identity":"83e44d56-75d2-44bc-8a26-20b353d7111b","added_by":"auto","created_at":"2025-03-24 16:10:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4670259,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4938069/v1/89e1aea0-bdf7-45a7-b390-917a25e0a32f.pdf"},{"id":64740566,"identity":"e262df48-7481-407c-b366-9a73c9427634","added_by":"auto","created_at":"2024-09-18 08:49:42","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":320567,"visible":true,"origin":"","legend":"","description":"","filename":"FigS1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4938069/v1/70538b45da9df0dc588fe686.jpg"},{"id":64741478,"identity":"f9cfff0a-e89d-479b-828f-cb87b208c1ab","added_by":"auto","created_at":"2024-09-18 08:57:43","extension":"xls","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":31744,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xls","url":"https://assets-eu.researchsquare.com/files/rs-4938069/v1/92d978b0e26fe5d8899def20.xls"}],"financialInterests":"No competing interests reported.","formattedTitle":"Social organization and physical environment shape the microbiome of harvester ants","fulltext":[{"header":"Background","content":"\u003cp\u003eAll 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 [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. 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 [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The composition of the gut microbiome varies across captive, urban, and rural environments in many organisms such as Ring-tailed lemurs [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], Tasmanian devils [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], deer mice [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], water dragons [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and coyotes [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Host behaviors significantly impact host-microbiome dynamics. For example, communal nesting in four-toed salamanders (\u003cem\u003eHemidactylium scutatum\u003c/em\u003e) increases the transmission of beneficial, antifungal bacteria, enhancing hatchling survival compared to solitary nests [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The diversity of host behaviors is further highlighted by the fact that both vertebrates and invertebrates, such as the Green iguana (\u003cem\u003eIguana iguana\u003c/em\u003e) and bumblebees (\u003cem\u003eBombus\u003c/em\u003e), engage in coprophagy, a behavior involving the consumption of feces to establish and regulate their gut microbiota [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMost microbiome studies consider microbial communities inside or on the surface of the organism. However, many animals occupy stable burrows or construct nests [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], 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 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. 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\u0026rsquo;s nest. Therefore, social insects afford a unique opportunity to determine the role of the environment and social organization on microbiome diversity and composition.\u003c/p\u003e \u003cp\u003eEnvironmentally 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) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Similarly, when comparing the gut microbiome of deer mice (\u003cem\u003ePeromyscus manuculatus\u003c/em\u003e) 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 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Furthermore, microbial biomass and diversity tend to decrease with soil depth [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. 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\u0026rsquo;s, including decreased microbial diversity with depth.\u003c/p\u003e \u003cp\u003eThe 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 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Additionally, there are differences in the gut microbiome composition of reproductives and non-reproductive workers in termites [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], honey bees [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], and ants [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Thus, in some cases the behavioral role of individuals might have a stronger impact on their microbiome than the environment in which they live.\u003c/p\u003e \u003cp\u003eAnts 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 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] and that ants can benefit from their microbiome via nutrient acquisition and defense against pathogens [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] 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 [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. 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\u0026rsquo; content (e.g., the seeds or the brood) or by the ants that tend to the chamber material (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). 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 [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], however, it is not known if such differences permeate the inside of the nest.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eColonies of the harvester ants, \u003cem\u003eVeromessor andrei\u003c/em\u003e, 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 [\u003cspan additionalcitationids=\"CR39 CR40\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. \u003cem\u003eVeromessor andrei\u003c/em\u003e nests provide an opportunity to examine the effects of the physical environment and social organization on the microbiome of the colony. The structure of \u003cem\u003eV. andrei\u003c/em\u003e nests affect their collective foraging [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] most likely through the impact of the nest structure on interactions among ants [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] which are known to regulate foraging activity in other harvester ants within the colony [\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Thus nest structure impacts \u003cem\u003eV. andrei\u003c/em\u003e 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.\u003c/p\u003e \u003cp\u003eTo 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 \u003cem\u003eV. andrei\u003c/em\u003e. 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 [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and 4) nests in different locations will have different microbial diversity because soils change their microbial composition and diversity spatially [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). 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.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy site and sample collection\u003c/h2\u003e \u003cp\u003eTo examine the microbiome of ant nests\u0026rsquo; soil and of the content of the nests, we collected samples from five colonies of \u003cem\u003eV. andrei\u003c/em\u003e May 24\u0026ndash;29, 2021 from Sedgwick Natural Reserve in southern California, US. Sedgwick Reserve is home to a thriving \u003cem\u003eV. andrei\u003c/em\u003e population (\u0026gt;\u0026thinsp;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.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). 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.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). 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.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee-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\u0026rsquo;s content, we used a small disposable plastic spoon to collect soil from inside the excavated chamber (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed), referred to later as \u0026lsquo;nest soil\u0026rsquo;. Nest chamber soil samples were classified into \u0026lsquo;chamber types\u0026rsquo; according to the chamber content (e.g., if they had ants inside, they were considered \u0026lsquo;ant\u0026rsquo; 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\u0026thinsp;+\u0026thinsp;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 \u0026lsquo;near soil control\u0026rsquo; (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb,c). We recorded the depth of the chamber from the ground surface using a measuring tape. Once all chambers were excavated, we obtained the \u0026lsquo;control far soil\u0026rsquo; samples by collecting soil from the side of the trench that was opposite the nest (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). 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 (~\u0026thinsp;1-2m) from the chamber - at the same depth) and samples of the content of the chamber (ranging from 1\u0026ndash;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\u0026ndash;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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSample processing\u003c/h2\u003e \u003cp\u003eAfter 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 [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eQuantifying microbiome diversity\u003c/h2\u003e \u003cp\u003eTo determine the microbial composition of each sample type (soil, seeds, brood, adult ants), microbiome bioinformatics were conducted using QIIME2 version 2024.2.4 [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Initial raw sequence data underwent demultiplexing and quality filtering with the q2-demux plugin, followed by denoising using DADA2 [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] through the q2-dada2 plugin. Amplicon sequence variants (ASVs) were aligned using MAFFT [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] via q2-alignment, and a phylogeny was constructed with FastTree2 [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] 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 [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] classify-sklearn naive Bayes taxonomy classifier, referencing the Silva 13_8 99% OTU database [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. 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 \u0026lsquo;\u003cem\u003ephyloseq\u003c/em\u003e\u0026rsquo; package in R [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo 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:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eShannon\u0026rsquo;s index - describes how evenly species are distributed, independent of species richness [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. A high Shannon index indicates more species diversity whereas a value of zero indicates that fewer species are present in the sample.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eFaith\u0026rsquo;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 [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePielou\u0026rsquo;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.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eObserved amplicon sequence variants (Observed ASVs) - the number of observed unique sequences that are present in the sample [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eBeta 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 [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Here we used the Bray-Curtis dissimilarity index as a beta diversity measure of compositional dissimilarity among microbial communities of samples [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] and Vegan package [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], with 999 permutations for the PERMANOVA. The resulting PCoA plots were visualized using ggplot2 [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. This analysis tested for differences in beta diversity among all sample types, all soil types, and nest soil samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003e \u003cem\u003eAll sample types\u003c/em\u003e: 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\u0026rsquo;s phylogenetic diversity, Pielou\u0026rsquo;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 [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e] 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 [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. We further examined PCoA plots and used a PERMANOVA to examine beta diversity across sample types.\u003c/p\u003e \u003cp\u003e \u003cem\u003eAll soil samples\u003c/em\u003e: 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\u0026rsquo;s phylogenetic diversity, Pielou\u0026rsquo;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 \u0026lsquo;emmeans\u0026rsquo; [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e] 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 [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. 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 \u0026lsquo;pairwiseAdonis\u0026rsquo; [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cem\u003eChamber soil samples\u003c/em\u003e: 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\u0026rsquo;s phylogenetic diversity, Pielou\u0026rsquo;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 [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e] and if the best fit model included an interaction term, but the collinearity was very high (VIF\u0026thinsp;\u0026gt;\u0026thinsp;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.\u003c/p\u003e \u003cp\u003eAll analysis was conducted in R version 4.3.2 [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e] and all of the best-fitting models met the required statistical assumptions \u0026ndash; examined using the check_model() function in the \u0026lsquo;performance\u0026rsquo; package [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Data and code are available in supplementary materials and \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eDAlejandraG/nest-microbes.\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eWe 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.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAll sample types\u003c/h2\u003e \u003cp\u003eThe 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\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). 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\u0026rsquo;s Phylogenetic distance measure (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUsing a principal coordinate analysis (PCoA) for visualization, the Bray-Curtis distances demonstrated that the ASV samples clustered by sample type (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). 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\u0026thinsp;=\u0026thinsp;0.001). Pairwise PERMANOVA results show a significant difference between most pairwise comparisons (Adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. 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 \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistical output of the effect of sample type on the four alpha diversity measures using a linear model.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiversity measure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSum of squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShannon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1363.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e691.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFaith\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6041.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e186.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePielou Evenness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e387.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObserved ASVs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e972137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e161.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eAll soil samples\u003c/h2\u003e \u003cp\u003eWhen examining the alpha diversity of the various soil samples the best fit model did not include any interaction terms (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). 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\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Soil depth did not have a statistically significant effect on any of the alpha diversity measures (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Finally, nest ID significantly impacted all alpha diversity measures except for Faith\u0026rsquo;s phylogenetic distance (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). 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\u0026thinsp;\u0026le;\u0026thinsp;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\u0026nbsp;4).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistical output for the effect of nest, depth, and soil type chamber soil, control near, and control far) on diversity measures using a linear model.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiversity measure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSum of Squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eShannon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNest\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.5244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.5811\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoil type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.9925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eFaith\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.8846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.1246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1471\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoil type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4814\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePielou Evenness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNest\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e9.3684\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSoil type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e6.376\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eObserved ASVs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNest\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3.3403\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.0119\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.7642\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoil type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.8005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eNest soil samples\u003c/h2\u003e \u003cp\u003eWhen 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\u0026thinsp;\u0026gt;\u0026thinsp;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\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). The only significant effect was of nest ID on the Shannon and Faith\u0026rsquo;s phylogenetic distance indexes (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe 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 \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). 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\u0026nbsp;4).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistical output for the effect of chamber type on diversity measures using an LM\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiversity measure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSum of Squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eShannon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNest\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.4925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.1649\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.7106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1991\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChamber type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.4167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.2481\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eFaith\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNest\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.6478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1836\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4259\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChamber type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.0255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePielou Evenness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.3454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0730\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0008984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.5912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.2152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChamber type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0042393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.8771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1357\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eObserved ASVs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.3933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0685\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4910\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChamber type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.2390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study suggests that both social and environmental factors are crucial in shaping the microbiome of V. \u003cem\u003eandrei\u003c/em\u003e colonies. In support of the physical environment hypothesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) we found that the microbiome of nests in different locations varied significantly across alpha and beta diversity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e) and the relative abundance of the nest soil microbiome was similar to that of the control soil samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). 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.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Furthermore, the beta diversity and evenness of the soil microbiome inside the nest was significantly different from the control soil samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Table\u0026nbsp;4). However, the microbiome diversity of the nest soil did not differ significantly across chambers according to the contents found in them (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDifferences in the microbiome composition within \u003cem\u003eV. andrei\u003c/em\u003e 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.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These findings align with previous research indicating that the microbiomes of \u003cem\u003eFormica exsecta\u003c/em\u003e ants are different from those present in the nest and alpha and beta diversity were lower in ant samples compared to the nest material [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], honey bees [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and ants [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e] that show differences in the microbiome of brood, reproductives, and workers. Only brood samples were similar to soil samples according to Faith\u0026rsquo;s PD and Observed ASVs and reproductives were not significantly different from soil for Pilou\u0026rsquo;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 [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. 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.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), which might be explained by the fact that brood are the primary consumers of protein [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e], 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 [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e] and ants [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e] 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 [\u003cspan additionalcitationids=\"CR76\" citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eIn 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 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. 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.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This similarity can be explained by the similar soil environments because nest microbiome differences mirrored the physical location of the nests (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), with colonies that were physically closer to each other exhibiting similar alpha and beta diversity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Such geographic clustering of microbial communities is seen in studies of soil microbiome [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e] where microbial communities impact the soil's physical structure, chemical properties, and water content [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. Future work might examine how geographical differences in soil microbial composition may affect the behavior of ant colonies and the structure of their nests.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn 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.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, 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.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). These findings align with previous research indicating that ant nests serve as unique microhabitats with distinct microbial activity and soil nutrient composition [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e], we did not find a relationship between soil depth and microbial diversity (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Supplemental Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). 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.\u003c/p\u003e \u003cp\u003eThe 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.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The Firmicutes bacterial phylum dominated ant samples whereas Actinobacteria, Proteobacteria, and Firmicutes were more evenly distributed in brood and reproductives (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The presence of Actinobacteria, Proteobacteria, and Firmicutes is typical of herbivorous and omnivorous ant species and larvae [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. However, Firmicutes is dominant in \u003cem\u003eV. andrei\u003c/em\u003e adult worker ants, similar to what has been observed in copious predatory ant species such as army ants (Eciton), bullet ants (\u003cem\u003eParaponera clavata\u003c/em\u003e) [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]. Considering past work found that in Azteca ants the microbiome inside chambers matches their content [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], and that the chemical signature of nest chambers is determined by their content [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and that ants use certain nest chambers as latrines [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e] 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.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e). 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.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur results contribute to a growing body of evidence that social insect nests are intricate ecosystems influenced by both intrinsic and extrinsic factors [\u003cspan additionalcitationids=\"CR87 CR88 CR89\" citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]. Our study highlights the significant roles of both social organization and the physical environment in shaping the microbiome of \u003cem\u003eV. andrei\u003c/em\u003e 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.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThese authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eDAG 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions:\u0026nbsp;\u003c/strong\u003eESHL, 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eWe 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\u0026rsquo;Bastiani, Emily Laub, Kaija Gahm, and Sean O\u0026rsquo;Fallon for helpful feedback. This work was performed (in part) at the University of California Natural Reserve System, Sedgwick Reserve DOI: 10.21973/N3C08R.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVaishampayan PA, Kuehl JV, Froula JL, Morgan JL, Ochman H, Francino MP. Comparative metagenomics and population dynamics of the gut microbiota in mother and infant. 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Philos Trans R Soc B Biol Sci. 2023;378:20220146. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1098/rstb.2022.0146\u003c/span\u003e\u003cspan address=\"10.1098/rstb.2022.0146\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"animal-microbiome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"amic","sideBox":"Learn more about [Animal Microbiome](http://animalmicrobiome.biomedcentral.com)","snPcode":"42523","submissionUrl":"https://submission.nature.com/new-submission/42523/3","title":"Animal Microbiome","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"microbiome, social organization, physical environment, harvester ant, Veromessor andrei","lastPublishedDoi":"10.21203/rs.3.rs-4938069/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4938069/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAll 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 (\u003cem\u003eVeromessor andrei\u003c/em\u003e). 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 \u003cem\u003eV. andrei\u003c/em\u003e 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.\u003c/p\u003e","manuscriptTitle":"Social organization and physical environment shape the microbiome of harvester ants","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-18 08:49:28","doi":"10.21203/rs.3.rs-4938069/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-18T14:42:42+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-17T21:01:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-31T16:54:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-22T14:47:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"217395070664097556633343494611440825305","date":"2024-10-11T21:52:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"185994565668693540731383108501066425468","date":"2024-10-11T14:09:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"202668578095838002224228191849758879143","date":"2024-10-10T23:38:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-09T20:16:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-21T09:51:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-20T06:54:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Animal Microbiome","date":"2024-08-19T10:48:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"animal-microbiome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"amic","sideBox":"Learn more about [Animal Microbiome](http://animalmicrobiome.biomedcentral.com)","snPcode":"42523","submissionUrl":"https://submission.nature.com/new-submission/42523/3","title":"Animal Microbiome","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"03307904-ae12-413e-bf33-d4194d2bdec3","owner":[],"postedDate":"September 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-24T16:05:20+00:00","versionOfRecord":{"articleIdentity":"rs-4938069","link":"https://doi.org/10.1186/s42523-025-00390-3","journal":{"identity":"animal-microbiome","isVorOnly":false,"title":"Animal Microbiome"},"publishedOn":"2025-03-19 15:57:19","publishedOnDateReadable":"March 19th, 2025"},"versionCreatedAt":"2024-09-18 08:49:28","video":"","vorDoi":"10.1186/s42523-025-00390-3","vorDoiUrl":"https://doi.org/10.1186/s42523-025-00390-3","workflowStages":[]},"version":"v1","identity":"rs-4938069","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4938069","identity":"rs-4938069","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}