Exploring the development of wild microbiomes in the Eastern Fence Lizard

Exploring the development of wild microbiomes in the Eastern Fence Lizard | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 13 March 2025 V1 Latest version Share on Exploring the development of wild microbiomes in the Eastern Fence Lizard Authors : Michael Grapin , Justin Wright , Jeremy Chen See , Samantha Anderson , Regina Lamendella 0000-0002-0952-1326 [email protected] , and John Matter Authors Info & Affiliations https://doi.org/10.22541/au.174184938.88368057/v1 205 views 111 downloads Contents Abstract Introduction Discussion Data Accessibility Benefit-Sharing Statement Funding Author contributions Acknowledgments. Conflict of interest Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This study examines the gut microbiome from fecal samples of a reptilian model (Sceloporus undulatus). Vertebrates display a diverse range of variability in gut microbiota. The dynamic host-symbiote interactions between gut microbiota are essential in various metabolic processes and overall health of the host organism. We used 16S rRNA gene sequencing to profile the bacterial community from the guts of wild Eastern Fence Lizards. Microbiome profiles were collected at four distinct time points when lizards were actively foraging in the environment and across four age groups, aiming to characterize the natural variation in bacterial diversity among wild specimens. Bioinformatic analysis was conducted using QIIME2/DADA2 pipelines. Successional changes in the gut microbiome of lizards from the juvenile stage to adulthood revealed a pattern of increased functionality and diversity. Environmental bacteria such as Mycobacterium and Pantoea in young of year (YOY) lizards underwent successional microbiome changes to taxa including Caproiciproducens, Eubacterium, Fusobacterium, and Roseburia in adults, reflecting dietary shifts from maternal nutrients and early-stage prey to fiber-rich arthropod diets. Juvenile lizards exhibited a simple gut microbiome dominated by pioneer colonizers (Firmicutes, Bacteroidota, and Proteobacteria), with alpha diversity increasing rapidly within a month to near adult-levels. This research extends our understanding of reptilian microbiomes, capturing a snapshot of the ecological complexities of Eastern Fence Lizard microbiomes. Introduction Microbiomes are intricate communities of microorganisms living within and around organisms and play crucial roles in various aspects of biology, including health, ecology, and evolution. These bacterial communities interact with their host organisms in complex ways, influencing digestion, immunity, and even behavior. Furthermore, microbiomes impact ecosystem dynamics, nutrient cycling, and disease susceptibility across diverse environments (McFall-Ngai et al., 2013) and are directly intertwined with organismal ecology and evolution. Specifically, the vertebrate gut microbiome has been implicated in affecting host nutrition, growth, disease, and reproductive fitness (T. J. Colston & Jackson, 2016; Ley et al., 2008). Extensive studies have profiled mammalian microbiomes (Bleich & Fox, 2015; Proctor et al., 2019; Turnbaugh et al., 2007) and other model organisms; less is known about the diversity and unique roles of microbiomes within reptilian branches of the tree of life. Research has shown that reptilian microbiomes undergo constant fluctuations influenced by factors such as diet, environmental conditions, host physiology, and interactions with other organisms. For reptiles, whose habitats and diets can vary widely, these fluctuations in the microbiome play essential roles in maintaining health, aiding digestion, and potentially even defending against pathogens. Understanding the dynamics of reptilian microbiomes not only sheds light on the intricacies of reptile biology but also offers insights into broader ecological and evolutionary processes (Vasconcelos et al., 2023), diet (Jiang et al., 2017; Lemieux-Labonté et al., 2022; Montoya-Ciriaco et al., 2020) and environmental conditions (Liu et al., 2022) have been implicated in shaping lizard bacterial communities. Intrinsic factors such as reproductive events (Trevelline et al., 2019), evolutionary relatedness (Bunker & Weiss, 2022; Hong et al., 2011), and bacterial localization (Kohl et al., 2017; Smith et al., 2021) have also been shown to alter bacterial ecology. Although individual elements play a role, structure of the gut microbiome is ultimately influenced by a combination of multiple factors (T. Colston, 2017). The role of age in shaping the microbiome is still not entirely understood. From mammalian studies, the microbiome develops when bacteria colonize their host (Faith et al., 2013). However, within lizards, this maturation process has not been fully described. We present the fecal bacterial community profiles from Eastern Fence Lizard ( Sceloporus undulatus ), a medium-sized phrynosomatid lizard found throughout the central and eastern United States. This species lives in forest edge habitats and open and disturbed mixed hardwood ecosystems and is an insectivorous generalist with respect to dietary preference. Sampling of wild microbiomes is essential to understanding the complex ecological networks of microbes (Hird, 2017). Utilizing 16S rRNA gene Illumina-tag sequencing, our study delves into the intricacies of the gut microbiome of Sceloporus undulatus , offering a comprehensive exploration of bacterial communities at various stages of development throughout the maturation process of wild lizards. By employing 16S rRNA gene sequencing, the study characterizes and compares the composition and diversity of gut bacterial communities across different age groups of Eastern Fence Lizards, shedding light on how the microbiome shifts throughout their development. This research provides valuable insights into the dynamics of lizard-microbiome interactions and their potential implications for lizard health, ecology, and evolution. Materials and Methods Sample Collection Lizards were collected from an established study site adjacent to Raystown Lake (Huntingdon Co., PA, USA). The study site is a shale barren location with a unique ecology and a long history of lizard population assessment. Lizards were caught by hand or by a slip noose made of dental floss suspended from a collapsible rod. In the field, animals were weighed to the nearest 0.1 gram and their snout-vent length (SVL) was measured to the nearest millimeter. Lizards were then kept in ethanol-sterilized plastic containers and returned to the lab overnight to provide time for them to produce a fecal specimen. Sampling was repeated at four periods important to the lizard life cycle. 1) April-May (proximate to emergence from brumation); n=40, 2) June (includes peak reproductive activity); n=43, 3) July-August (post-reproductive period); n=34 and 4) September-October (includes hatchlings [young-of-the-year; YOY] and adults in preparation for over-winter dormancy); n=49. From each sampling period lizards were classified into their respective age groups and sorted into five groups. Adults were animals that had reached sexual maturity at the time of capture. Rising adults were individuals who had hatched the previous year but had not yet been predicted to participate in a reproductive event (i.e., they had not attained reproductive size during the period of reproductive activity). Female lizards in the study population annually produce two clutches of eggs. A first clutch is produced in early June, while a second clutch is released approximately four weeks later (early July). Both clutches require 60 days for embryonic development. This results in hatchlings entering the population in August (YOY1) and September (YOY2). Young of the year one (YOY1) were the first clutch hatchlings, and young of the year two (YOY2) were second clutch hatchlings. The animal study was reviewed and approved by Juniata’s IACUC committee (IACUC#2021-10-002 ). 16S RNA Gene Sequencing of Lizard Fecal Specimens DNA was extracted from 164 fecal samples using the Zymobiomics DNA Extraction Kit (Orange, California) following the manufacturer’s instructions. All 16S rRNA Illumina-tag PCR reactions were performed on the V4 hypervariable region per the Earth Microbiome Project’s protocol (Caporaso et al., 2018) using a T100 Thermal Cycler (Bio-Rad, Hercules, CA) with the following cycling conditions: 94°C 3 min; 35x cycle of 94°C 45 sec; 50°C 1 min; 72°C 1 min 30 sec; then final extension 72°C for 10 min, hold at 4 °C. PCR products were pooled, and gel purified on a 2% agarose gel using the QIAquick Gel Purification Kit (Qiagen, Frederick, Maryland). Prior to sequencing, the purified pool was quality-checked using a Qubit 4 Fluorometer with 1X dsDNA High Sensitivity Assay (ThermoFisher Scientific, Waltham, MA) in conjunction with an Agilent 2100 BioAnalyzer and Agilent DNA High Sensitivity DNA kit (Agilent Technologies, Santa Clara, California). The purified pool was stored at −20 °C and then sequenced at Wright Labs, LLC on an Illumina MiSeq platform using v2 chemistry to produce paired-end 250 base pair reads. Bioinformatic and Statistical Analyses Forward sequences were trimmed at a length of 238 bp and reverse sequences were trimmed at a length of 219 bp. Additional quality filtration was performed at an average expected error of 1, and DADA2 was used to group the remaining sequences into Amplicon Sequence Variants (ASVs) (Callahan et al., 2016; Bolyen et al., 2019). ASV’s identified as mitochondria or chloroplasts were removed from the dataset. Samples containing less than 1,000 sequences were filtered out of the dataset due to poor coverage, resulting in analysis of 163 samples collected in this study. The quality-filtered reads were analyzed using the QIIME 2.0 software package (Bolyen et al., 2019). ASV taxonomy was assigned using a Naive Bayes classifier with taxonomy assigned using the Silva database (Quast et al., 2013). Adjusted p values (denoted as q values) using the Hockenberg method (Benjamini & Hochberg, 1995) to correct for false discovery rate were considered significant below a 0.05 alpha threshold. Alpha diversity metrics were constructed from the ASV table and rarefied to a maximum depth of 1500. A rooted phylogenetic tree was created using the ASV representative sequences aligned using MAFFT (Katoh & Standley, 2013), and the tree was developed based on that alignment using FastTree 2 (Price et al., 2010). Rarefaction curves were constructed to demonstrate alpha diversity values approaching a horizontal asymptote indicative of adequate depth (S1). Kruskal Wallis pairwise comparisons were performed and analyses of age groups were reported based on observed features and Faith’s phylogenetic diversity (Faith, 1992). The ASV table was normalized using metagenomeSeq’s Cumulative Sum Scaling (CSS) algorithm to account for between-sample differences in sequencing effectiveness (Paulson et al. 2013). Beta diversity metrics were constructed from the normalized ASV table and rooted phylogenetic tree artifact. Weighted Unifrac distance matrices (Chen et al., 2012) were calculated using QIIME2. Permutational Multivariate Analysis of Variance (PERMANOVA) comparisons were used to determine if significant differences exist based on age groups. Linear Discriminant Analysis Effect Size (LEfSe) (Segata et al., 2011) was performed to determine if bacteria taxa were more abundant in certain age groups. LEfSe analysis was conducted with the filtered ASV table after it underwent Counts per million (CPM) normalization. Taxa were summarized at the species level, and a Linear Discriminant Analysis (LDA) cutoff was set at 2. Differential abundant taxa were statistically significant at a p-value cutoff of 0.05. Partial Least Squares Discriminant Analysis (PLS-DA) was done with the mixOmics R package (Rohart et al., 2017), and the resulting model was validated using 10-fold cross-validation and 10 iterations. Classification error plots can be found in supplemental information (S2). Core microbiome analysis was conducted using the Qiime2 core features plugin. A threshold of 75% of samples having a recurrent feature was determined as a core feature (Neu et al., 2021). Sequencing Statistics Illumia sequencing generated 4,206,027 total sequences containing an average frequency of 24,741 sequences for both forward and reverse reads across our 164 samples. DADA2 retained 73% merged and denoised sequences per sample. Samples containing less than 1,000 sequences were filtered out of the dataset due to poor coverage, resulting in analysis of 163 samples for downstream analysis. These samples comprised 2,991,108 total sequences, encompassing 1,109 unique ASVs. Core Microbiome: At the family taxonomic level, Lachnospiracea, Enterobacteriaceae , and Bacteriodaceae were the most abundant bacterial families across all samples. These three taxa comprised 68% of the bacterial community for each age group. The relative abundance of bacterial taxa within the four age cohorts is consistent with bacterial taxa found in the lizard gut core microbiome (Figure 1). All other taxonomic families termed “other” in Figure 1 consisted of an average of 8.7%. Comparisons between adults and rising adults showed there were 12 taxonomic families considered core, while including YOY only resulted in 5 core families. The additional families were Marinifilaceae, Clostridia, Oscillospiraceae, Peptococcaceae, Ruminococcaceae, Rikenellaceae, Erysipelotrichaceae, Selenomonadaceae, Eggerthellaceae, Butyricicoccaceae, Veillonellales-Selenomonadales, Sporomusaceae, and Anaerovoracaceae . Summarizing findings can be found below(Table 1). Figure 1.) Relative Abundance of Taxonomic Families Summarized by Sceloporus Age Class. Stacked bar charts show the top ten most abundant families between the four age classes. Resampled YOY1 were condensed into one group to display, and the remaining brackets included all time points. Taxonomic families outside the top ten are included in the “other” category, as seen in gray. Table 1.) Core Microbiome Taxonomic Comparisons at the Family Level. Adult & Rising Adult 75% 12 Lachnospiraceae, Enterobacteriaceae, Bacteroidaceae, Tannerellaceae, Marinifilaceae, Clostridia, Oscillospiraceae, Peptococcaceae, Ruminococcaceae, Rikenellaceae, Erysipelotrichaceae, Selenomonadaceae, Eggerthellaceae, Butyricicoccaceae, Veillonellales-Selenomonadales, Sporomusaceae, Anaerovoracaceae Adult, Rising Adult, YOY 75% 5 Lachnospiraceae, Bacteroidaceae, Enterobacteriaceae, Tannerellaceae, Ruminococcaceae Alpha Diversity of Gut Microbiota Across Age Groups Alpha diversity analysis of observed features revealed a significant difference when comparing across all age groups (Kruskal-Wallis; p=0.002), with younger lizards having fewer observed features (unique ASVs) than adult lizards (Figure 2A). The August YOY1 group had the least observed features, with a median of 21, ranging from 7 - 73 features. When the same age cohort was sampled again (September YOY1) we observed a median value of 101 features, with 22 - 137 features (Kruskal Wallis; q=0.016). Contrastingly, no significant difference was observed in the comparisons between the YOY1 and YOY2 cohorts. It should be noted that August YOY1 had significantly lower observed features than all adult cohorts (S3). The rising adult cohort had a comparable number of median features to adults, yet this cohort also had the most variability compared to all other cohorts. The analysis of phylogenetic diversity significantly differed across age groups (Kruskal-Wallis; p=0.002). Adult cohorts consistently displayed higher bacterial diversity, with median values ranging from 6.4 to 7.3 (Figure 2B). In contrast, Young-of-Year (YOY) groups showed a marked increase in diversity from August YOY1 (median = 2.57) to September YOY1 (median = 7.24). Pairwise comparisons revealed that August YOY1 had significantly lower bacterial diversity than the adult groups (Table 3). However, there were no significant differences in diversity between the YOY1 and YOY2 groups. Additionally, a significant increase in diversity was observed between the August and September YOY1 groups (q=0.015), suggesting changes within the same cohort over time. The August Rising Adults group had notably lower median diversity (median = 2.91) and displayed significant differences compared to both adult and YOY groups (Table 3). Figure 2. Alpha Diversity of Gut bacterial Communities in Sceloporus . (A) Box plot of observed features (ASVs) from YOY cohorts and Adult cohorts at these longitudinal time points from youngest age to oldest. (B) Box plot of Faith’s phylogenetic diversity from YOY and Adult gut bacterial communities. Observed features and Faith’s phylogenetic diversity metrics were calculated on a rarefied ASV table. Both observed features and Faith’s phylogenetic diversity comparisons were evaluated for significance using Kruskal-Wallis tests, and all bolded labels represent significant q values below the 0.05 threshold. A full list of significant q values can be found in Tables 2 and 3. Table 2. Alpha Diversity q-value table for significant observed features pairwise comparisons. August YOY1 (n=10) September YOY1 (n=12) 0.016 August YOY1 (n=10) August Adult (n=12) 0.016 August YOY1 (n=10) June Adult (n=25) 0.016 August YOY1 (n=10) May Adult (n=26) 0.017 August YOY1 (n=10) September Adult (n=13) 0.017 August YOY1 (n=10) May Rising Adult (n=14) 0.023 August Rising Adult (n=12) September YOY1 (n=12) 0.020 August Rising Adult (n=12) June Adult (n=25) 0.029 August Rising Adult (n=12) September Adult (n=13) 0.035 August Rising Adult (n=12) May Adult (n=26) 0.038 August Adult (n=12) August Rising Adult (n=12) 0.021 Table 3. Alpha Diversity q-value table for significant Faith's phylogenetic diversity pairwise comparisons. August YOY1 (n=10) September YOY1 (n=12) 0.015 August YOY1 (n=10) August Adult (n=12) 0.018 August YOY1 (n=10) June Adult (n=25) 0.018 August YOY1 (n=10) May Adult (n=26) 0.018 August YOY1 (n=10) September Adult (n=13) 0.018 August Rising Adult (n=12) September YOY1 (n=12) 0.018 August YOY1 (n=10) May Rising Adult (n=14) 0.018 August Rising Adult (n=12) June Adult (n=25) 0.040 August Adult (n=12) August Rising Adult (n=12) 0.040 August Rising Adult (n=12) September Adult (n=13) 0.049 Beta Diversity: Comparisons between age groups Beta diversity significantly differed when among the age groups (PERMANOVA; p-value=0.001). The most substantial source of variation was observed when a YOY cohort was compared to other sampled groups (Table 4). Distinguishing age groups by their respective microbiome illustrates a clear distinction between Adult and YOY individuals, with both the X and Y axis predictors explaining 4% of the variation (Figure 3). However, when comparing YOY1 and YOY2, there is substantial overlap, indicating slight variation between the age cohorts compared to adult lizards. Error rates for the PLS-DA model were approximately 30%, suggesting that 70% of the time, observed variation can help explain differences in bacterial communities between adult and YOY lizards. This indicates a reasonably effective model for discriminating between the microbiomes of adult and young of the year lizards based on the provided predictor variables. Figure 3. Partial Least Squares Discriminant Analysis (PLS-DA) percentage explained variation of bacterial communities between Adults, YOY1 hatchlings, and YOY2 hatchlings. Plotted values are from a normalized taxonomy table using the PLS-DA method to compare the variation of microbiomes between Adult, YOY1, and YOY2 aged lizards. Eclipse identity borders between-group comparisons and lines to points show distance to the centroid. The X and Y axes explain the percent variation between age classes. Table 4.) Pairwise Permanova Results with significant q-values between age classes. August YOY1 June Adult 5.130 0.044 August YOY1 September YOY1 6.260 0.044 August YOY1 September Adult 4.547 0.048 August YOY1 May Rising Adult 4.972 0.048 August Rising Adult September YOY1 8.685 0.044 August Rising Adult September Adult 5.127 0.044 August Rising Adult May Adult 4.540 0.047 August Rising Adult May Rising Adult 5.915 0.047 August Adults August Rising Adult 5.238 0.044 Differentially-abundant bacterial taxa: Significantly differentially-abundant (LDA > 2.0, P < 0.05) bacterial taxa were observed between each age group using LeFSe analysis, and 38 taxonomic bacterial classifications were enriched amongst the different age groups. YOY2 has the most enriched taxa, having 18 classifications belonging to seven different orders. Rising adults had 10 enriched taxa among three different orders. Adult lizards have 10 enriched taxa across three orders. YOY1 was found to have no differentially abundant bacteria. Among taxa found at YOY2, there were multiple classifications in Mycobacteriaceae, Rickettsiales, Caprociciproducens , and Eubacterium . Rising adults had limited phylogenetic resolution, but taxa were classified as multiple Veillonella and Fusobacterium species. Adults had taxa within the genus Roseburia in addition to other commonly found anaerobes to the gut microbiome. Figure 4.) LeFSe shows enriched bacterial taxa between adults, rising adults, YOY1, and YOY2. Plotted taxa are from a minimum LDA score cutoff of 2 and have unique phylogenetic classification. No taxa were found to be differentially abundant in the YOY1. Discussion This study leveraged 16S rRNA gene sequencing to describe the development of wild microbiomes from Eastern Fence Lizards, Sceloporus undulatus. Successional changes in the gut microbiome of lizards from the juvenile stage to adulthood involve dynamic shifts in bacterial composition, diversity, and functional capacity. Our study took an exploratory approach to capture this bacterial diversity and explain trends in the context of fence lizards’ life-history ecology. In the early stages of development, juvenile lizards typically exhibit a simple gut microbiome dominated by pioneer colonizers (Lee et al., 2023; Milani et al., 2017; van Best et al., 2015). Alpha diversity results revealed notable differences in bacterial community structure, with newly hatched YOY lizards displaying lower diversity than adults (Figure 1). However, within about a month, the microbiome of YOY lizards diversified to near adult-level diversity. Establishing a diverse and balanced bacterial community is crucial for providing functional redundancy and ecosystem stability, enabling the gut microbiome to maintain its composition and function under varying conditions. Like other vertebrates, we observed core microbiota in Eastern Fence Lizards, primarily consisting of Firmicutes, Bacteroidota, and Proteobacteria (Figure 1). As juvenile lizards transition from a diet of maternal nutrients (abdominal yolk) and early-stage prey to more diverse adult diets, a corresponding shift in gut microbiome composition occurs. The observed developmental patterns in bacterial composition align with findings in other vertebrates. For example, microbiome maturation typically begins with an initial increase in diversity in newborns, followed by stabilization in adulthood, a trend mirrored in human microbiome development (Bäckhed et al., 2015). Initial exposure to colonizing microbes is foundational in shaping bacterial community development (Milani et al., 2017). Our observations in wild lizards reflect this conserved function of a balanced microbiome. The faster timescale of microbiome maturation in lizards compared to mammals may be attributed to multiple factors. One key aspect is their shorter intestinal tracts, which likely facilitate a more rapid establishment of bacterial homeostasis. Shorter gut lengths correlate with faster transit times and reduced microbial residence time, thereby selecting bacterial taxa capable of rapid colonization and adaptation (Stevens & Hume, 1998; McWhorter et al., 2009). Evidence suggests that gut size influences microbiome diversity, with larger gastrointestinal tracts supporting greater microbial richness and functional capacity (Godon et al., 2016; Karasov et al., 2011). In lizards, the small size of the gastrointestinal tract may impose constraints on the microbiome’s carrying capacity, limiting both the overall abundance of microorganisms and the availability of specific ecological niches. Additionally, smaller body size is closely tied to an organism’s lifespan and growth rate (Angilletta et al., 2004; Ringsby et al., 2015; Savage et al., 2004). Lizards exhibit accelerated developmental timelines, with young individuals reaching adulthood in 18–24 months. This rapid pace of development may drive microbiome maturation over a compressed timeframe, potentially favoring bacterial taxa foundational to gut microbiome structure and function. Such foundational taxa often play critical roles in host physiology, including nutrient absorption, immune modulation, and pathogen resistance, and may reflect long-standing symbiotic relationships (Hooper & Gordon, 2001; Shapira, 2016). Environmental exposure is another critical factor influencing the diversification of the gut microbiome in young lizards. YOY (young-of-year) lizards undergo a swift nutritional transition from yolk-based sustenance to an insectivorous diet within a few weeks of hatching. This dietary shift likely induces substantial changes in gut microbiome composition and functionality, as demonstrated in other vertebrates undergoing dietary transitions (Kohl & Dearing, 2012; David et al., 2014). The smaller prey items accessible to YOY lizards further shape their microbiome’s trajectory. Due to their limited gape width and smaller body size, YOY lizards are constrained to consume smaller, less nutritionally complex prey items, which may result in a diet lower in microbial diversity and functional breadth than adults. This dietary bottleneck could influence the rate and direction of microbiome development, selecting bacterial taxa that thrive under such resource-limited conditions (Hawlena et al., 2010; Kohl et al., 2015). Moreover, lizards’ reliance on environmental sources for bacterial inoculation, such as soil, plants, and prey, underscores the importance of ecological interactions in shaping gut microbial communities. Variability in habitat types, prey availability, and environmental microbiota may contribute to interspecific differences in microbiome composition and maturation rates among lizards (Brucker & Bordenstein, 2013). These factors highlight the dynamic interplay between host physiology, life history, and environmental influences driving the rapid microbiome maturation observed in lizards. Future studies examining how these factors influence lizards’ microbial community assembly and functionality could provide broader insights into vertebrate microbiome evolution and ecology. It is known that core taxa in the phyla Firmicutes, Bacteroidota, Proteobacteria, and Actinobacteria are dominant taxa in gut microbiomes (Fujisaka et al., 2023). These bacteria have been selected for the gut ecosystem because of their functional roles in regulating metabolism. This redundancy is evolutionarily conserved and is seen across both reptilian and other vertebrate models (Ley et al., 2008; McFall-Ngai et al., 2013). Our findings align with previous studies on lizard gut microbiomes, which reported similar bacterial phyla distributions (Alemany et al., 2022; Zhou et al., 2020). Among these, the analysis of Sceloporus showed the greatest concordance with our data (Bunker et al., 2022). Specifically, the most abundant taxa identified in both our study and others include Lachnospiraceae , Enterobacteriaceae , Bacteroidaceae , Akkermansiaceae , Desulfovibrionaceae , Marinifilaceae , and Tannerellaceae . These shared taxa underscore common bacterial community structures in lizard models, providing robust comparability across studies. Similarly, the core microbiome analysis revealed bacterial taxa belonging to the phyla Firmicutes, Baceroidota, and Pseudomodota to which a representative human sample (Falony et al., 2016) saw similar core taxa. As lizards mature and are exposed to a broader range of environmental microbes and dietary substrates, the gut microbiome rapidly diversifies, reflecting a more complex and stable bacterial community. Young of year lizards transition from a diet primarily composed of maternal nutrients or early-stage prey items to more diverse adult diets of insect arthropods. Differential abundance analysis poses a narrative of successional changes that occur in gut ecosystems. First young lizards are seeded heavily by environmental bacteria. Enriched taxa such as Mycobacterium and Pantoae are commonly found in soil (Janssen, 2006; Walterson & Stavrinides, 2015), water (Delghandi et al., 2020; Walterson & Stavrinides, 2015), and plant material (Tian & Li, 2017; Walterson & Stavrinides, 2015). Additionally, recently hatched lizards might consume substrates or small prey items that harbor these bacteria. Metabolism of carbohydrates and fiber is critical to sustenance; bacteria like Caproiciproducens (Kim et al., 2015) and Eubacterium (Duncan et al. 2007; Dworkin et al., 2006) ) being enriched suggests metabolism of these early substrates. YOY2 had the most enriched taxa, suggesting their microbiome is still fluctuating. Transitioning to a broadening insectivorous diet, rising adults show taxa that thrive on lactates (Reichardt et al., 2014; Wang et al., 2020) (Negativicutes) from complex carbohydrate fermentation and amino acid-rich environments ( Fusobacterium ) indicative of protein consumption. As adult lizard microbiomes stabilize, we see taxa representing consistent metabolic pathways. Roseburia, known as short-chain fatty acid (SCFA)-producing bacteria (Tamanai-Shacoori et al., 2017) which ferment high fiber substrates, were enriched in adult lizards. The consumption of larger arthropods whose chitin exoskeleton provides a possible source of fiber that bacteria can metabolize into critical energy-ready SCFAs. Our study advances the understanding of microbiome development in a reptilian model, highlighting the dynamic process of bacterial colonization, diversification, and adaptation as lizards mature. Future research should address the limitations of 16S rRNA gene sequencing, which provides taxonomic resolution for bacteria and archaea but cannot capture functional potential or detect non-bacterial members of the microbiome, such as fungi and single-celled eukaryotes. These organisms play critical roles in maintaining the complex ecological balance of the microbiome and are integral to a more comprehensive understanding of microbial interactions. Future studies should leverage shotgun metagenomics sequencing to bridge this gap by enabling functional gene profiling and the inclusion of diverse microbial taxa beyond bacteria and archaea. Future investigations should incorporate early microbiome sampling in controlled laboratory environments to refine our understanding of the microbial inoculation processes and identify their primary sources. Additionally, we encourage more studies on wild microbiomes to capture the full diversity of bacterial communities in organisms across ecosystems. Physiological changes during growth, such as shifts in gut pH, mucosal structure, and immune function, create niche opportunities for specific bacterial taxa to establish and persist in the gut microbiome. As lizards mature, their gut microbiome becomes more stable and resilient to environmental disturbances, developing a diverse and balanced bacterial community that supports functional redundancy and ecosystem stability. Overall, successional changes in the gut microbiome of lizards from the juvenile stage to adulthood reflect a dynamic process of bacterial colonization, diversification, and adaptation influenced by host development, dietary transitions, and environmental factors. Data Accessibility The datasets generated for this study can be found in the NCBI Short Read Archives Bioproject PRJNA1215967. Benefit-Sharing Statement The animal study was approved by Juniata College IACUC. The study was conducted in accordance with the local legislation and institutional requirements. Benefits from this research accrue from the sharing of our data and results on public databases as described above. Funding The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was funded by Juniata College’s Student Scholarly Endeavor Committee. Data were processed on a computational cluster funded by the Howard Hughes Medical Institute through the Precollege and Undergraduate Science Education Program, as well as by the National Science Foundation through NSF award DBI-1248096. Author contributions Michael S. Grapin- Conceptualization, sample collection and preparation, data analysis, drafted manuscript Regina Lamendella- Conceptualization, sample processing, manuscript revision Justin Wright- Data analysis, manuscript revision Jeremy Chen See- Data analysis, manuscript revision Samantha Anderson- Sample processing, manuscript revision John Matter- Conceptualization, sample collection, manuscript revision Acknowledgments. The authors would like to acknowledge Sam Benham for assistance in specimen collection and Jillian Leister for wet lab preprocessing and training. This project was funded by Juniata College’s George and Cynthia Valko Endowment. The study also received support for computational resources from the Howard Hughes Medical Institute through the Precollege and Undergraduate Science Education Program, as well as the National Science Foundation through award DBI-1248096. Conflict of interest The authors declare that the research was conducted without any commercial or financial relationships that could potentially create a conflict of interest. Supplementary Material File (table1.docx) Download 14.21 KB File (table2.docx) Download 14.26 KB File (table3.docx) Download 14.22 KB File (table4.docx) Download 14.34 KB Information & Authors Information Version history V1 Version 1 13 March 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords 16s rrna gene bacterial succession gut microbiome sceloporus wild microbiome Authors Affiliations Michael Grapin Juniata College View all articles by this author Justin Wright Wright Labs LLC View all articles by this author Jeremy Chen See Wright Labs LLC View all articles by this author Samantha Anderson Wright Labs LLC View all articles by this author Regina Lamendella 0000-0002-0952-1326 [email protected] Juniata College View all articles by this author John Matter Juniata College View all articles by this author Metrics & Citations Metrics Article Usage 205 views 111 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Michael Grapin, Justin Wright, Jeremy Chen See, et al. Exploring the development of wild microbiomes in the Eastern Fence Lizard. Authorea . 13 March 2025. DOI: https://doi.org/10.22541/au.174184938.88368057/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.174184938.88368057/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9ff4a9c738f3df94',t:'MTc3OTM3Nzg1NA=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();