Long-Read Amplicon Sequencing Uncovers Complex Microbial Communities in the European Brown Hare Gut: Methodological Implications for Wildlife Microbiome Research

Long-Read Amplicon Sequencing Uncovers Complex Microbial Communities in the European Brown Hare Gut: Methodological Implications for Wildlife Microbiome Research | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Long-Read Amplicon Sequencing Uncovers Complex Microbial Communities in the European Brown Hare Gut: Methodological Implications for Wildlife Microbiome Research Zbigniew Bełkot, Mateusz G. Adamski, Zuzanna J. Strzałkowska, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6277572/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The declining European brown hare ( Lepus europaeus ) population necessitates comprehensive microbiome studies to better understand factors affecting their health. This study employed Oxford Nanopore third-generation sequencing to characterize the hare gut microbiome through full-length 16S and 18S rRNA gene analysis. Intestinal contents from 30 hunted brown hares were pooled into three composite samples for sequencing. Comparative analysis using 80% and 95% sequence matching thresholds revealed dramatic differences in detected diversity. The 80% threshold identified substantially more taxa across all taxonomic levels (up to 10-fold more species) and revealed previously unreported microbiome components, including Spirochaetota (25.2% at 80%) and Ascomycota (4.3% at 95%). Statistical analysis confirmed significant differences between samples at genus and species levels for both thresholds (p < 0.05). Our approach identified 28 unique phyla, 360 unique families, 1,027 unique genera, and 3,373 unique species not reported in previous studies. These findings demonstrate how threshold selection fundamentally alters microbiome characterization and highlights the potential of long-read sequencing for expanding our understanding of wildlife microbiomes, which may contribute to improved conservation strategies for declining species. Biological sciences/Microbiology Biological sciences/Microbiology/Communities Biological sciences/Microbiology/Communities/Microbiome Third Generation Sequencing microbiome brown hare 16S and 18S rRNA gene Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction The brown hare ( Lepus europaeus Pall.) is a small game species that is very popular in Poland. However, its population has significantly decreased over the last 50 years. In the 1970s, the population was estimated to be over 3 million individuals. Since then, this number has declined fivefold, and the hunting acquisition of this species has also drastically decreased. The rapid decline in the hare population is attributed to a variety of factors [ 1 , 2 ]. The decline in the hare population can be attributed to several common factors. Firstly, the intensification of agriculture has led to the loss of essential habitats. Urbanization and the expansion of the road network also contribute to this issue. Additionally, pollution affecting soil, water, and air, along with ongoing climate change, negatively impacts reproductive processes in hares. Another critical factor in the decline is the significant increase in predation, particularly by foxes. This rise in fox numbers is linked to successful rabies vaccination programs. The presence of invasive species such as raccoon dogs and raccoons, as well as birds of prey and corvids, further affects hare populations. Moreover, poaching and hunting activities pose additional threats, along with the influence of synanthropic predators, especially outdoor domestic cats [ 3 – 7 ]. A significant factor that influences the hare population is the spread of infectious and parasitic diseases. These diseases can lead to a decline in their overall health and reproductive capacity, and they are often a direct cause of mortality [ 8 – 11 ] The brown hare is a typical herbivorous animal. It rarely drinks water; instead, it meets its hydration needs through the plants it consumes. In spring and summer, it primarily feeds on above-ground parts of plants. In autumn, its diet may include roots and other plant foods sourced from underground. During the day, brown hares gnaw on twigs from trees and shrubs, and they also enjoy the young shoots found in streams. Their favorite foods include young seedlings of fruit crops as well as a variety of agricultural crops [ 12 ]. Hares reach sexual maturity at approximately 8 months of age. A typical litter consists of 2 to 5 young; however, in areas influenced by human activity and agriculture, the actual reproductive capacity is often limited to 1 or 2 young. This limitation is primarily due to high levels of indicator metals that affect the reproduction of these animals, along with other factors impacting reproductive success. Studies indicate that out of 10 young born to a single female in a year (across four litters), only one typically survives to adulthood [ 13 ]. In Poland, the hunting season for hares lasts for 2 months, from November 1 to December 31. Hunting can only occur during organized collective hunts, which require the participation of at least six hunters. The carcasses of hunted hares are highly valuable for their culinary uses, as their meat is considered a prized food source. The brown hare ( Lepus europaeus ) plays a key role in forest and agricultural ecosystems, acting as a vital link in the trophic cascades and a potential reservoir of pathogens. Its gut microbiome is a dynamic ecosystem of microorganisms that play fundamental roles in metabolic processes, nutrient digestion, and ecological interactions. Studies of the intestinal microbiome of the brown hare reveal a dominance of bacteria from the phyla Firmicutes and Bacteroidetes . These bacteria are essential for digesting dietary fiber and for the host's energy metabolism [ 14 ]. The high abundance of these bacteria indicates that the hare has adapted to a diet rich in plant-based components, including difficult-to-digest polysaccharides. Additionally, the presence of bacteria from the genus Ruminococcus suggests that the hare is capable of efficiently fermenting cellulose, which may enhance its ability to extract energy from the plant material it consumes [ 14 ]. A comparative analysis of the gut microbiome in the brown hare and the European rabbit (Oryctolagus cuniculus) reveals significant differences in their microbiota composition [ 15 ]. Hares have been observed to possess a greater diversity of microorganisms that are involved in carbohydrate metabolism, indicating their capability to utilize a wider range of food resources. These differences may hold ecological significance regarding competition for food niches and could impact the population dynamics of both species in ecosystems where they coexist. Additional studies indicate an essential role for enzymes such as urease in metabolic processes occurring in the large intestine of the hare [ 16 ]. The high activity of this enzyme may be related to nitrogen recycling, which allows for more efficient use of nutrients and affects the nitrogen balance in ecosystems. This may be crucial for the trophic structure and functioning of habitats in which the hare occurs. The hare microbiome may also play a role in the transmission of pathogens with zoonotic potential. Studies show the presence of bacteria from the genera Escherichia, Listeria, Clostridium and Salmonella , which may be etiological factors of infections in other animals and humans (unpublished own studies). The research on the digestive microbiome of the brown hare ( Lepus europaeus ) is currently limited, and the existing scientific literature in this field remains insufficient. Next-generation sequencing (NGS) is widely used to study the microbiomes of humans and animals. [ 17 , 18 ]. This technology allows for high-throughput parallel sequencing of short DNA fragments. Key methodologies involved in NGS include the fragmentation of DNA into short reads (typically 100–300 base pairs for Illumina and up to 600 base pairs for Ion Torrent, with some specialized applications reaching up to 800 base pairs), polymerase chain reaction (PCR) amplification to increase the amount of genetic material (which can introduce amplification biases), and sequencing by synthesis, where fluorescent signals are detected during the incorporation of nucleotides. NGS is extensively applied in microbiome research due to its high sequencing depth, cost efficiency per sample, and high accuracy in short-read analysis. However, its primary limitation lies in the short read length, which poses challenges in the de novo assembly of complex bacterial genomes and the precise differentiation of closely related microbial taxa. This limitation typically restricts the sequencing of the 16S rRNA gene to partial coverage of 3–4 variable regions at most, depending on the platform and protocol used. Such read length constraints often make it difficult to distinguish between closely related bacterial species, although still allowing for accurate genus-level identification. While this approach has been the cornerstone of microbiome studies for years, the inability to consistently resolve species-level differences has been a persistent challenge for researchers seeking fine-scale taxonomic resolution.[ 19 ] Third-generation sequencing (TGS) technologies represent a paradigm shift by enabling long-read sequencing without length limitations for DNA fragments. This advancement allows for the sequencing of entire 16S rRNA genes (approximately 1,500 base pairs), providing significantly improved taxonomic resolution. Currently, two major platforms dominate the TGS landscape. Pacific Biosciences (PacBio) employs Single Molecule Real-Time (SMRT) sequencing, which utilizes zero-mode waveguides to detect fluorescent signals from single DNA molecules during synthesis, allowing for real-time observation of nucleotide incorporation without amplification bias. In contrast, Oxford Nanopore Technologies uses a fundamentally different approach based on ion flow through nanopores. This platform integrates electronic chips with sequencing proteins, where neural networks interpret nucleotides based on voltage differences as DNA molecules pass through the nanopores—a substantial departure from conventional sequencing methodologies that rely on optical detection systems.[ 20 ] The recent introduction of Oxford Nanopore's Q14 chemistry and improved flow cell designs has revolutionized the field, with quality and accuracy scores now comparable to other sequencing platforms (Q scores above 30). This technological advancement has effectively eliminated the previous accuracy concerns associated with nanopore sequencing, making it a viable option for high-resolution microbiome studies. This article presents how the use of third-generation sequencing can significantly expand the range of microorganisms detected in the microbiome based on sequencing the full-length 16S and 18S rRNA gene amplicons from the large intestine of the brown hare. By leveraging the capabilities of long-read sequencing, we demonstrate enhanced taxonomic resolution that provides deeper insights into microbial community structure and function than previously possible with short-read technologies. 2. Results 2.1. Comparative Analysis of Taxonomic Diversity at Different Sequence Matching Thresholds Key biodiversity metrics calculated for hare rectal microbiome samples at two different sequence matching thresholds (95% and 80%) were presented in Table 1. The indices include Shannon diversity (H), which measures both richness and evenness; Species Richness, representing the total number of unique species identified; Simpson diversity (1-D), which emphasizes species dominance; Pielou's evenness (J), indicating how evenly species are distributed; and Chao1, an estimator of true species richness accounting for undetected species. Table 1. Comparison of biodiversity indices between 95% and 80% accuracy thresholds across three samples (Z1, Z2, and Z3). Index Z1 (95%) Z1 (80%) Z2 (95%) Z2 (80%) Z3 (95%) Z3 (80%) Shannon H 3.21 4.66 3.14 5.09 4.18 4.89 Species Richness 168.00 2182.00 212.00 2405.00 328.00 3051.00 Simpson 1-D 0.91 0.94 0.87 0.96 0.97 0.94 Pielou J 0.63 0.61 0.59 0.65 0.72 0.61 Chao1 265.53 4450.33 332.44 4353.67 541.49 5984.93 This analysis is visually supported by Figure 1, where panel (b) illustrates the substantial differences in Shannon diversity indices across taxonomic levels, consistently showing higher values for the 80% threshold samples. As shown in Figure 1(A), the 80% threshold leads to a significant increase in taxonomic richness compared to the 95% threshold, particularly at the genus and species levels, where the differences are most evident. While the 95% threshold offers greater confidence in taxonomic assignments, the 80% threshold markedly broadens the detectable diversity, revealing a wider array of microorganisms that might otherwise remain undetected with stricter matching criteria. A detailed comparison of taxonomic assignments across seven levels, from Superkingdom to Species, is presented in Table 2. This table includes the total number of taxa identified, the number of assigned sequences, the percentage of assigned sequences, the taxa shared between thresholds, and the taxa uniquely identified at the 80% threshold. This systematic comparison demonstrates how the stringency of sequence matching impacts the depth and breadth of taxonomic classification. Table 2. Comprehensive taxonomic composition analysis across all hierarchical levels at 95% and 80% sequence matching thresholds for three microbiome samples (Z1, Z2, and Z3). Taxonomic Level Parameter Z1 (95%) Z1 (80%) Z2 (95%) Z2 (80%) Z3 (95%) Z3 (80%) Superkingdom Number of taxa 2 2 2 2 2 2 Assigned sequences 2471 33573 4105 41881 4595 58314 % of assigned sequences 100.00 99.99 100.00 99.99 100.00 97.53 Shared taxa between thresholds 2 - 2 - 2 - Unique taxa for 80% - 0 - 0 - 0 Phylum Number of taxa 19 28 18 29 17 33 Assigned sequences 2457 31204 4077 39278 4413 55625 % of assigned sequences 99.43 92.94 99.32 93.77 96.04 93.03 Shared taxa between thresholds 19 - 18 - 17 - Unique taxa for 80% - 9 - 11 - 16 Class Number of taxa 25 57 29 63 25 59 Assigned sequences 2414 29913 4005 36711 4215 52694 % of assigned sequences 97.69 89.09 97.56 87.64 91.73 88.13 Shared taxa between thresholds 25 - 29 - 25 - Unique taxa for 80% - 32 - 34 - 34 Order Number of taxa 31 112 35 117 38 126 Assigned sequences 2423 29851 4019 36668 4217 52348 % of assigned sequences 98.06 88.91 97.90 87.54 91.77 87.55 Shared taxa between thresholds 31 - 35 - 38 - Unique taxa for 80% - 81 - 82 - 88 Family Number of taxa 47 220 55 232 62 252 Assigned sequences 2408 28531 3929 34213 4056 49325 % of assigned sequences 97.45 84.98 95.71 81.68 88.27 82.49 Shared taxa between thresholds 47 - 55 - 62 - Unique taxa for 80% - 173 - 177 - 190 Genus Number of taxa 73 577 88 623 143 749 Assigned sequences 2403 27583 3867 33057 3879 47573 % of assigned sequences 97.25 82.15 94.20 78.92 84.42 79.56 Shared taxa between thresholds 73 - 88 - 143 - Unique taxa for 80% - 504 - 535 - 606 Species Number of taxa 168 1715 212 1848 328 2285 Assigned sequences 2471 33573 4105 41881 4595 58314 % of assigned sequences 100.00 99.99 100.00 99.99 100.00 97.53 Shared taxa between thresholds 168 - 212 - 328 - Unique taxa for 80% - 1547 - 1636 - 1957 A quantitative comparison of taxonomic overlap between samples Z1, Z2, and Z3 was presented in the table within Figure 2, at both 80% and 95% sequence matching thresholds. The table reports key metrics for each taxonomic level, including the total number of unique taxa across all samples, the taxa common to all three samples, and the individual taxonomic counts for each sample. This comparison is visually represented in Figure 2, which uses Venn diagrams to illustrate the distribution and overlap of taxa between the samples at each taxonomic level. The comparative analysis of taxonomic overlap reveals significant differences in microbial community composition between the 80% and 95% sequence matching thresholds. At the 80% threshold, a substantially greater number of taxa were identified across all taxonomic levels compared to the 95% threshold. The most notable differences were observed at the species level (3,408 unique taxa at 80% vs. 465 unique taxa at 95%) and the genus level (1,041 unique taxa at 80% vs. 193 unique taxa at 95%). Additionally, the number of taxa common to all three samples was consistently higher at the 80% threshold, indicating that a broader core microbiome was detected using less stringent matching criteria (Table 3.). Table 3. Summary table quantifying the taxonomic distribution across samples at both thresholds, including total unique taxa, taxa common to all samples, and individual sample counts for each taxonomic level. Taxonomic Level Accuracy Total Unique Taxa Common in All Samples Z1 Count Z2 Count Z3 Count Phylum 80% 40 20 33 29 28 95% 23 10 17 18 19 Class 80% 81 38 59 63 57 95% 37 14 25 29 25 Order 80% 185 62 126 117 112 95% 55 17 38 35 31 Family 80% 383 117 252 232 220 95% 88 25 62 55 47 Genus 80% 1041 344 749 623 577 95% 193 32 143 88 73 Species 80% 3408 871 2285 1848 1715 95% 465 74 328 212 168 Notes: Total Unique Taxa: The total number of unique taxa across all three samples. Common in All Samples: The number of taxa found in all three samples. Z1, Z2, Z3 Count: The number of unique taxa in each sample. Sample Z1 consistently exhibited the highest taxonomic richness at both thresholds across most taxonomic levels, particularly at the species level, where it identified 2,285 taxa at an 80% accuracy threshold compared to 328 taxa at a 95% accuracy threshold. The Venn diagrams clearly illustrate that, while the 95% threshold results in fewer unique taxa per sample, the 80% threshold presents a more complex picture of both shared and sample specific microbial diversity. This analysis demonstrates that sequence matching stringency significantly impacts the detected taxonomic composition and the apparent relationships between samples. While the 95% threshold provides higher confidence in taxonomic assignments, it may underestimate the true diversity and commonality between samples, potentially obscuring important ecological patterns in microbial communities. The figure displays comparative Venn diagrams for each taxonomic level, ranging from Phylum to Species. On the left, the diagrams represent an 80% threshold, while the right side shows a 95% threshold. Each circle corresponds to a sample (Z1, Z2, or Z3), with numbers indicating the counts of unique and shared taxa between the samples. The taxonomic composition of microbial communities in samples Z1, Z2, and Z3 is presented in the table, which includes unique sequence identifiers, taxonomic classifications, and abundance metrics for each identified taxon. Only taxa with a minimum of 10 sequences (Count ≥ 10) are included in this table (Supplementary Table 1). To evaluate the impact of the sequence matching threshold on detecting variability between samples, we conducted Kruskal-Wallis tests to compare samples Z1, Z2, and Z3 at each taxonomic level, using both 80% and 95% thresholds (see Table 3). This non-parametric statistical method allowed us to determine whether the taxonomic differences observed among the samples were statistically significant (p < 0.05). Table 3. Statistical comparison of taxonomic composition between samples Z1, Z2, and Z3 at different sequence matching thresholds. Accuracy Taxonomic Level Kruskal-Wallis (H) p-value Significant 80% Phylum 0.2585 0.8788 No 80% Class 0.6067 0.7383 No 80% Order 0.0450 0.9778 No 80% Family 0.3128 0.8552 No 80% Genus 11.6245 0.0030 Yes 80% Species 29.9248 3.2×10 -7 Yes 95% Phylum 0.8734 0.6462 No 95% Class 0.1351 0.9347 No 95% Order 0.3459 0.8412 No 95% Family 2.5374 0.2812 No 95% Genus 37.9491 5.7×10 -9 Yes 95% Species 63.8668 1.4×10 -14 Yes Note: The Kruskal-Wallis test was performed to determine statistically significant differences between samples at each taxonomic level. A p-value < 0.05 indicates significant differences. At the 80% threshold, significant differences between samples were observed at two taxonomic levels: Genus (H = 11.6245, p = 0.0030) and Species (H = 29.9248, p = 3.2×10 -7 ). In contrast, at the 95% threshold, significant differences were also detected at the same two taxonomic levels, with particularly strong differences at the Genus (H = 37.9491, p = 5.7×10 -9 ) and Species (H = 63.8668, p = 1.4×10 -14 ) levels. No significant differences were found at the Phylum, Class, Order, or Family levels at either threshold. 2.2. Taxonomic Composition Analysis Across Classification Levels 2.2.1. Taxonomic Composition at Different Sequence Matching Thresholds The analysis of taxonomic composition across all major taxonomic levels (Phylum, Class, Order, Family, Genus, and Species) at both 80% and 95% sequence matching thresholds revealed significant differences in community structure and diversity. Importantly, the 80% threshold appears to mask certain taxa that become visible at the 95% threshold while simultaneously overrepresenting others. This differential detection significantly impacts the characterization of the microbiome. For example, Ascomycota (4.3%) is completely absent in the 80% analysis (likely falling below the 1% reporting threshold), while Spirochaetota (25.2%) is prominent at 80% but absent at 95%. These substantial differences demonstrate how threshold selection can fundamentally alter our understanding of microbial community composition. At the Phylum level, Bacteroidota shows a dramatic difference between thresholds, accounting for only 25.9% at 80% but dominating at 59.6% at the 95% threshold. This represents a 33.7% difference, emphasizing how threshold selection can significantly shift the perceived dominance patterns of major phyla. Conversely, Bacillota and Actinomycetota both show reduced representation at higher thresholds (39.7% vs 31.7% and 6.6% vs 1.5%, respectively). The Class-level analysis revealed even more pronounced threshold-dependent variations. Bacteroidia shows a 34.7% difference between thresholds (26.7% at 80% vs 61.4% at 95%). Three classes present at >1% in the 80% threshold were undetectable at 95%: Spirochaetia (26.5%), Erysipelotrichia (3.9%), and Negativicutes (1.0%). Conversely, Saccharomycetes (3.1%) was uniquely detected at the 95% threshold. Such substantial shifts in class representation demonstrate how threshold choice can profoundly influence our understanding of microbial community structure. At the Order level, similar patterns emerged with Bacteroidales showing a 34.6% difference between thresholds. Four orders present at >1% in the 80% threshold were undetectable at 95%, including Spirochaetales (26.6%), while three orders including Lactobacillales (4.1%) and Monoglobales (3.8%) were uniquely detected at 95%. Family-level composition revealed the most dramatic differences, with Bacteroidaceae showing a 40.3% difference between thresholds (11.2% at 80% vs 51.5% at 95%). Remarkably, 10 families detected at >1% in the 80% threshold were absent at 95%, including Spirochaetaceae (26.4%) and Odoribacteraceae (6.7%), while 5 families including Monoglobaceae (3.9%) and Streptococcaceae (3.4%) were uniquely detected at 95%. Genus-level analysis showed that Spirochaeta , the predominant genus at 80% (26.3%), was completely absent at 95%. Meanwhile, Phocaeicola and Bacteroides showed dramatically higher abundances at 95% (26.0% vs 3.6% and 26.7% vs 7.4%, respectively). Nine genera present at >1% in the 80% threshold were undetectable at 95%, while five genera were uniquely detected at the 95% threshold. At the Species level, which provides the most detailed taxonomic resolution, Spirochaeta sp. canine oral taxon 379 dominated at 80% (21.3%) but was completely absent at 95%. Conversely, Phocaeicola vulgatus (16.5%) and Bacteroides uniformis (15.9%) dominated at 95% but showed minimal presence at 80% (1.6% and 1.8%, respectively). A total of 9 species present at >1% in the 80% threshold were undetectable at 95%, while 11 species including Phocaeicola dorei (4.2%) and Monoglobus pectinilyticus (3.7%) were uniquely detected at 95%. These findings demonstrate that threshold selection substantially impacts microbial community characterization, with the 95% threshold generally revealing a more diverse community structure with different dominant taxa compared to the 80% threshold. The fact that many taxa can be completely missed or significantly under/overrepresented depending on threshold choice highlights the importance of careful parameter selection in microbiome studies, as these differences can lead to fundamentally different biological interpretations of the same sample. 2.2.2. Comparative Analysis of Taxa Richness Between Sequence Matching Thresholds To further investigate the impact of sequence matching thresholds on microbial community characterization, we compared the number and distribution of taxa detected at 80% and 95% thresholds across all taxonomic levels from superkingdom to species (Figure 3). This analysis revealed striking differences in taxonomic richness and composition between the two thresholds. As illustrated in Figure 3a, the average number of taxa per sample was consistently higher at the 80% threshold across all taxonomic levels. This difference became more pronounced as the taxonomic levels became more specific. At the species level, samples analyzed with the 80% threshold contained an average of about 2,000 taxa per sample, whereas those processed at the 95% threshold had only around 200 taxa per sample. This represents nearly a 10-fold difference in detected diversity. The comparison of taxonomic composition (Figure 3b) offers additional insights into the relationship between taxa identified at both thresholds. While there was a core set of taxa shared between the two thresholds across all taxonomic levels, the 80% threshold revealed a significantly larger number of unique taxa that were not detected at the 95% threshold. This distinction was particularly pronounced at the species level, where approximately 3,000 taxa were uniquely identified at the 80% threshold, compared to only around 400 unique taxa at the 95% threshold. Interestingly, the number of shared taxa remained relatively stable across most taxonomic levels, suggesting that the 95% threshold primarily captures a core subset of the microbial community that is also detected at the 80% threshold. The comparatively small number of taxa unique to the 95% threshold indicates that higher stringency rarely results in novel taxonomic assignments not captured by lower stringency analysis. These findings complement our previous taxonomic composition and statistical analyses, demonstrating that while the 95% threshold provides higher confidence in taxonomic assignments and better discriminatory power for detecting inter-sample differences, it does so at the cost of significantly reduced taxonomic breadth, potentially missing thousands of taxa that are detected at lower stringency thresholds. Each panel presents paired stacked bar charts with 80% threshold data (left) and 95% threshold data (right). Taxa contributing less than 1% to overall community composition are grouped as "Other (<1%)". NCBI Taxonomy IDs is provided for species in parentheses. Color coding is consistent within each taxonomic level to facilitate comparison between threshold values. 2.2.3. Comparison with Previously Published Microbiome Studies To evaluate the comprehensiveness of our taxonomic characterization approach, we compared the taxa identified in our study with those reported in two previous microbiome studies: a Stalder et al. 2019 publication in Scientific Reports and a Padula et al. 2021 publication in Biology (Figure 5). This comparative analysis was performed across four taxonomic levels (Phylum, Family, Genus, and Species) to assess the extent of taxonomic overlap and the identification of taxa. At the phylum level (Figure 5A), our analysis identified 40 phyla, of which 28 (70%) were unique to our study. We shared 8 phyla with the Stalder et al. 2019 study and 12 phyla with the Padula et al. 2021 study, while 1 phyla was exclusively shared between the two reference studies. The differences became more pronounced at higher taxonomic resolutions. At the family level (Figure 5B), our analysis identified 360 unique families compared to only one unique family in Stalder et al. 2019 study The genus-level comparison (Figure 5C) revealed even more striking differences, with our study identifying 1,027 unique genera with 7 unique to Biology study. At the species level (Figure 5D), the divergence was most dramatic, with our analysis identifying 3,373 unique species. The Stalder et al. 2019 study identified only one unique species The missing taxa report (Table 5) identified 37 taxa across all taxonomic levels that were reported in previous studies but not detected in our analysis. Of these, 21 taxa (56.8%) lacked defined entries in the NCBI taxonomy database. These undefined taxa, described using non-standard nomenclature, include 12 phyla from the Padula et al. 2021 publication and 1 phyla from publication Stalder et al. 2019, 1 family from publication Stalder et al. 2019 and 6 genera from Padula et al. 2021. Table 5. Taxa with NCBI database entries identified in previous publications but absent from the current dataset. Lp Level Source NCBI Name Published Name NCBI Tax ID 1 Phylum Padula et al. 2021 Armatimonadota Armatimonadetes 67819 2 Phylum Padula et al. 2021 Chlamydiota Chlamydiae 204428 3 Phylum Padula et al. 2021 Campylobacterota Epsilonbacteraeota 29547 4 Phylum Padula et al. 2021 Methanobacteriota Euryarchaeota 28890 5 Phylum Padula et al. 2021 Gemmatimonadota Gemmatimonadetes 142182 6 Phylum Padula et al. 2021 Kiritimatiellota Kiritimatiellaeota 134625 7 Phylum Padula et al. 2021 Nanobdellota Nanoarchaeota 192989 8 Phylum Padula et al. 2021 Candidatus Omnitrophota Omnitrophicaeota 67812 9 Phylum Padula et al. 2021 Planctomycetota Planctomycetes 203682 10 Phylum Padula et al. 2021 Nitrososphaerota Thaumarchaeota 651137 11 Phylum Padula et al. 2021 Verrucomicrobiota Verrucomicrobia 74201 12 Family Stalder et al. 2019 Brucellaceae Brucellaceae 118882 13 Genus Padula et al. 2021 Candidatus Saccharimonas Candidatus Saccharimonas 1331051 14 Species Stalder et al. 2019 Sphingobacterium wenxiniae Sphingobacterium wenxiniae 683125 15 Species Stalder et al. 2019 Selenomonas dianae Selenomonas dianae 135079 16 Species Stalder et al. 2019 Kyrpidia tusciae Kyrpidia tusciae 33943 Note: The table shows representative taxa with valid NCBI taxonomy entries that were reported in previous studies but not detected in our analysis. The full report identified 40 missing taxa, of which 19 (47.5%) lacked defined entries in the NCBI taxonomy database. The list of taxa found in reference sources but missing in the current study is provided in Supplementary table 3. 3. Discussion Next-generation sequencing (NGS) has become a cornerstone technology in microbiome research due to its high sequencing depth, cost efficiency per sample, and high accuracy in short-read analysis. However, its primary limitation lies in the short read length, which typically ranges from 100–300 base pairs for Illumina and up to 600 base pairs for Ion Torrent platforms. This constraint poses significant challenges in the de novo assembly of complex bacterial genomes and the precise differentiation of closely related microbial taxa. While NGS has been widely applied to study the microbiomes of both humans and animals, the short-read nature of the technology often restricts 16S rRNA gene sequencing to partial coverage of only 3–4 variable regions at most. This limitation frequently results in difficulties distinguishing between closely related bacterial species, typically allowing only for accurate genus-level identification rather than the species-level resolution often required for comprehensive microbiome characterization. Third-generation sequencing (TGS) technologies, represented by PacBio Single Molecule Real-Time (SMRT) sequencing and Oxford Nanopore Technology (ONT), address several fundamental limitations of NGS. The most significant advantage of TGS for microbiome analysis is its ability to generate long reads that can span the entire length of marker genes such as 16S rRNA (approximately 1,500 base pairs) and 18S rRNA. This capability provides significantly improved taxonomic resolution compared to the partial gene coverage achieved with NGS technologies. While both NGS and TGS approaches for amplicon sequencing utilize PCR amplification, the long-read capability of TGS enables complete coverage of taxonomically informative regions in a single read, eliminating the need for assembly of shorter fragments and reducing the ambiguity in taxonomic assignments. Additionally, the real-time data acquisition capability, particularly in ONT-based platforms, accelerates sequencing and downstream bioinformatics analyses, making it increasingly suitable for field applications and rapid microbiome assessments. For microbiome analysis, the targeted sequencing of marker genes that enable species identification, particularly 16S and 18S rRNA genes, represents the most efficient approach. These conserved genes contain hypervariable regions that permit taxonomic classification while maintaining sufficient conservation for universal primer binding. Typically, only a small fraction of a bacterial genome, often estimated at less than 1%, consists of species-specific sequences suitable for accurate taxonomic identification. This means that whole-genome shotgun sequencing would require approximately 100 times more sequencing depth to achieve the same taxonomic resolution as targeted amplicon sequencing. In practical terms, this translates to 5 hours of targeted sequencing versus several days for whole-genome approaches, making amplicon sequencing significantly more cost-effective and time-efficient for taxonomic profiling. While 16S and 18S rRNA genes remain the gold standard for bacterial and eukaryotic identification respectively, other marker genes are occasionally employed for specific taxonomic groups. These include rpoB (RNA polymerase beta subunit) for enhanced resolution in some bacterial genera [ 21 ], gyrB (DNA gyrase subunit B) for better discrimination of closely related species [ 22 ], and ITS (Internal Transcribed Spacer) regions for fungal identification. Despite widespread usage, 16S rRNA sequencing often lacks sufficient resolution for species and strain-level identification [ 23 ]. A significant limitation of amplicon-based approaches is their dependence on universal primers. Primer bias can lead to selective amplification of certain microbial groups while failing to capture others, resulting in skewed community representations. This challenge is particularly pronounced when studying diverse and less characterized microbiomes such as those found in wildlife species like the brown hare. In our study, we utilized proprietary primers developed by SPARK-TECH that were specifically designed to target a broad range of over 120,000 prokaryotic and eukaryotic species. These primers are the subject of a patent application and represent a significant advancement in reducing amplification bias in microbiome studies of non-model organisms. Our analysis of the brown hare microbiome reveals important considerations regarding sequence matching thresholds in taxonomic assignment. While human microbiome studies typically employ high similarity thresholds (≥ 95%) for taxonomic classification, our findings demonstrate that such stringent criteria may be suboptimal for wildlife microbiome analysis. At the 95% threshold, the number of sequences assigned was significantly lower compared to the 80% threshold. Our data showed that at the 80% threshold, the number of assigned sequences increased dramatically—by approximately 12.6-fold for sample Z1, 10.2-fold for sample Z2, and 12.7-fold for sample Z3 compared to the 95% threshold. This difference is particularly important when considering how the BLAST algorithm functions in taxonomic assignment, where alignment scores and percentage identity are used to determine the closest match in reference databases. The improved classification rate at the 80% threshold likely reflects the substantial diversity of microbial species in wildlife and the underrepresentation of these organisms in current reference databases. Organisms showing 80–95% similarity to reference sequences may represent novel strains, species variants adapted to the hare gut environment, or even entirely new taxa that have not been previously characterized. This finding underscores the potential of wildlife microbiome studies as fertile ground for the discovery of novel microbial diversity. Our results emphasize that achieving genus and species-level resolution is critical for understanding microbiome composition and function in wildlife. The significant differences observed between 80% and 95% thresholds at these taxonomic levels (504–606 unique genera and 1547–1957 unique species identified only at the 80% threshold) demonstrate that overly stringent matching criteria may obscure substantial portions of the microbial community. This is particularly evident in the detection of important taxa such as Spirochaetota (25.2% abundance at 80% threshold but completely absent at 95%) and Ascomycota (4.3% abundance at 95% but absent at 80%), highlighting how threshold selection can fundamentally alter our perception of community composition. The comprehensive nature of our approach is further validated by our comparative analysis with previous studies of the hare microbiome. Our findings encompass nearly all previously reported taxa while identifying thousands of additional organisms not detected in earlier investigations. Specifically, we identified 40 phyla (with 28 unique to our study), 360 unique families, 1,027 unique genera, and 3,373 unique species not reported in previous publications. This substantial expansion of the known hare microbiome composition directly results from the combination of third-generation sequencing technology and optimized taxonomic assignment parameters. The advantages of TGS make it particularly valuable for studying the microbiome of wildlife species like the brown hare. The ability to fully sequence 16S and 18S rRNA genes provides unprecedented taxonomic resolution, enabling the identification of rare or novel microbial strains that might play crucial roles in host health or disease. This comprehensive approach to taxonomic classification significantly enhances our understanding of microbial diversity in wildlife species. In summary, our study represents the most comprehensive characterization of the brown hare microbiome to date, made possible by the application of third-generation sequencing technology and optimized analytical approaches. Our findings not only encompass previously identified taxa but substantially expand our understanding of the microbial diversity associated with this important wildlife species. Furthermore, our methodological approach provides a valuable framework for future wildlife microbiome studies, emphasizing the importance of appropriate sequence matching thresholds and comprehensive taxonomic analysis based on full-length 16S and 18S rRNA gene sequencing. 4. Materials and Methods In Poland, according to the Regulation of the Minister of the Environment regarding hunting seasons for game animals, the hunting season for brown hares is from 1 November to 31 December [ 24 ] 4.1. Animals, Samples, and Localization The hares chosen for the study were selected during a two-day collective hunt organized in two hunting districts in the western part of the Lublin Upland. During the 2024 hare hunting season, a total of 30 healthy brown hares ( Lepus europaeus Pall.) were hunted, including both males and females. After conducting autopsies, it was determined that all the carcasses were healthy and showed no anatomopathological changes. The study material consisted of large intestine contents. A total of 30 hares were randomly divided into three groups (Z1, Z2, Z3), each comprising 10 individuals. Within each group, large intestinal content samples were randomly collected from five individual hares and subsequently pooled to create a composite sample. From each composite sample (Z1, Z2, and Z3), a 2.5 g sample was collected and stored at -20°C for further analysis. 4.2. Sequencing, Data Calculation, and Statistical Analysis The protocol for isolation, amplification, and sequencing was performed as previously published with modifications [ 25 ]. Prior to sequencing, the samples were thawed at a controlled temperature. DNA was then isolated using a silica column-based kit (QIAamp PowerFecal Pro DNA Kit, QIAGEN), according to the manufacturer's instructions. Following DNA isolation, polymerase chain reaction (PCR) was performed to amplify the 16S rRNA gene and 18S rRNA gene regions of the bacterial and eukaryotic DNA. This amplification was carried out using proprietary SPARKbiom primers (SPARK-TECH). After PCR amplification, the samples were prepared for sequencing according to the Oxford Nanopore Kit 14 chemistry library preparation protocol. Amplification was performed under the following conditions: the first step was Initial denaturation at 95°C for 1 min; the second step included 30 cycles (denaturation at 95°C for the 20s, annealing at 55°C for 30s and extension at 65°C for 2 min). The last step was a final extension at 65°C for 5 min. Nanopore sequencing was then carried out using the MinION device (Oxford Nanopore). Upon completion of the sequencing, the raw data was processed using the MinKNOW software (Oxford Nanopore) to assess the quality of the reads and perform base calling. Taxonomic classification of the sequences was carried out using a database of 1.5 million reference sequences from NCBI (querry16:16S [Title] NOT uncultured[All Fields] NOT unidentified[All Fields] NOT unknown[All Fields] AND (800:40000[Sequence Length]), querry18: 18S[Title] NOT (uncultured[All Fields] OR unidentified[All Fields] OR unknown[All Fields] OR environmental[All Fields] OR sample[All Fields]) AND (500:5000[Sequence Length]);). BLAST+ (version ncbi-blast-2.16.0) was employed to identify sequences with a minimum matching level of 95% and 80% for species assignment. Matches below 500 nucleotides length were excluded from the analysis. For each sample, two results were obtained with minimum matching thresholds of 95% and 80% (80% includes the 95% results). The pooled sample contains taxa from all samples with a summed count for each species, with the average based on the total count. Based on two publications, Stadler et al. (2019) and Padula et al. (2021), taxonomic groups present in the hare intestine were identified at the phylum, family, genus, and species levels. Taxonomic classification was mapped according to the current NCBI taxonomy. In instances where specific taxa could not be assigned, they were designated as "unclassified," followed by the original taxonomic designation reported in the respective publication. For the paired sample analysis, a systematic comparison of taxonomic results was conducted at accuracy thresholds of 95% and 80% using multiple diversity indices. Biodiversity metrics were calculated for each sample pair, including the Shannon diversity index (H'), Species Richness, Simpson diversity index (1-D), Pielou's evenness index (J), and the Chao1 species richness estimator. For the comparative analysis of taxonomic diversity, the number of unique taxa and the Shannon diversity index were calculated for each taxonomic level, ranging from Superkingdom to Species, across all samples at both accuracy thresholds. Data processing and analysis were conducted using Python, employing the pandas and NumPy libraries. Visualization was performed using Plotly to generate comparative bar charts illustrating taxonomic richness and Shannon diversity indices, facilitating a direct comparison between datasets at the 80% and 95% accuracy thresholds. Following the analysis of taxonomic diversity, Venn diagram visualization was applied to examine relationships between samples across accuracy thresholds. Using the matplotlib-venn library in Python, interactive side-by-side diagrams were generated for each taxonomic level, from Phylum to Species, to illustrate shared and unique taxa among samples (Z1, Z2, Z3) at both 80% and 95% accuracy thresholds. This analysis was supplemented with a comprehensive summary table presenting the total number of unique taxa, taxa common to all samples, and taxon counts specific to individual samples at each taxonomic level. For the assessment of taxonomic composition, relative abundance was aggregated and visualized using Python with the pandas and matplotlib libraries. Stacked bar charts were generated to display the top 15 taxa at each taxonomic level, while less abundant taxa (< 1%) were grouped into an "Other" category. NCBI Taxonomy IDs were incorporated to facilitate parallel comparisons between accuracy thresholds. To assess differences between classifications at the two accuracy thresholds, a comparative group analysis was conducted by calculating three key metrics: the average number of taxa identified per sample at each threshold, the number of taxa unique to each threshold, and the number of taxa shared between both thresholds. Visualizations included grouped bar charts representing average taxa counts and stacked charts illustrating the composition breakdown across taxonomic levels. Statistical evaluation was conducted using a non-parametric approach, applying Kruskal-Wallis H-tests to assess significant differences in taxonomic composition between samples at both accuracy thresholds. Statistical significance was defined as p < 0.05. The results were compiled into tables presenting H-statistic values, p-values, and significance determinations for each comparison. For comparative analysis with published literature, Venn diagram visualization was utilized to compare the 80% threshold dataset with taxonomic compositions reported in two reference publications [ 14 , 26 ]. The analysis focused on four taxonomic levels (Phylum, Family, Genus, Species) and incorporated both NCBI-standardized nomenclature and the original taxonomic names from the referenced studies. Diagrams illustrating taxonomic overlap were generated, and reports were compiled to document taxa identified in previous studies but absent from the analyzed dataset. Declarations Data availability: The sequences analyzed in this study can be found in the NCBI accession for these SRA data: PRJNA1238910, with a temporary Submission ID: SUB15187482. The release date is April 30, 2026. More information can be accessed at: https://www.ncbi.nlm.nih.gov/sra/PRJNA1238910. Supplementary Materials: Supplementary table 1; Supplementary table 2; Supplementary table 3. Author Contributions: Conceptualization, Z.B, M.G.A., & J.P-C.; methodology, Z.B, M.G.A., & J.P-C..; software, M.G.A., G.K. & J.P-C.; validation, Z.B, M.G.A., and J.P-C.; material collection: Z.J.S., J.P-Cz., E.D.D., DK; formal analysis, M.G.A., G.K., D.S., and J.P-C.; investigation, Z.B, Z.J.S, DK; resources, Z.B, Z.J.S, EDD.; data curation, Z.B, M.G.A., Z.J.S, EDD, J.P-C.; writing— Z.B, M.G.A., Z.J.S, EDD, J.P-C., DK, G.K., D.S; writing—review and editing Z.B, M.G.A., J.P-C.; visualization, M.A., Z.J.S, EDD, J.P-C.; supervision, Z.B and M.G.A., J.P-C.; project administration, Z.B, J.P-C.; funding acquisition, Z.B. All authors have read and agreed to the published version of the manuscript. Competing interests: The authors declare no competing interests. Funding: This research received no external funding. Institutional Review Board Statement: The study complied with Directive 2010/63/EU and the Act of the Polish Parliament dated 15 January 2015 on the protection of animals used for scientific purposes (Journal of Laws of the Republic of Poland 2015, item 266). The brown hares were not killed for the purposes of the study. The hunt took place in accordance with Polish hunting law (Act of the Polish Parliament dated 13 October 1995, item 713, the Hunting law, Chapter 3, Art. 8 Hunt and the Regulation of the Minister of the Environment of 16 March 2005 on the determination of hunting periods for game animals [Journal of Laws 2023, item 99]) during the 2024 hunting season. Informed Consent Statement: Not applicable. References Demirbaş Yasin 2015_Demirbaş_hares and foxes. ACTA Zool. Bulg. 2015 , 67 (4) , 515–520. Nasiadka, P.; Dziedzic, R. Podręcznik najlepszych praktyk ochrony kuropatwy i zająca. 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Phylogenomic Analysis Substantiates the gyrB Gene as a Powerful Molecular Marker to Efficiently Differentiate the Most Closely Related Genera Myxococcus, Corallococcus, and Pyxidicoccus. Front. Microbiol. 2021 , 12 , doi:10.3389/fmicb.2021.763359. Johnson, J.S.; Spakowicz, D.J.; Hong, B.Y.; Petersen, L.M.; Demkowicz, P.; Chen, L.; Leopold, S.R.; Hanson, B.M.; Agresta, H.O.; Gerstein, M.; et al. Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis. Nat. Commun. 2019 , 10 , doi:10.1038/s41467-019-13036-1. Regulation of the Minister of the Environment of 16 March 2005 on the determination of hunting periods for game animals. J. Laws 2023 , 1–5. Węsierska, E.; Micek, P.; Adamski, M.G.; Gondek, K.; Lis, M.; Trela, M.; Wojtysiak, D.; Kowal, J.; Wyrobisz-Papiewska, A.; Kunstman, G.; et al. Changes in the intestinal microbiota of broiler chicken induced by dietary supplementation of the diatomite-bentonite mixture. BMC Vet. Res. 2025 , 21 , 13, doi:10.1186/s12917-024-04439-4. Padula, A.; Bambi, M.; Mengoni, C.; Greco, C.; Mucci, N.; Greco, I.; Masoni, A.; Del Duca, S.; Bacci, G.; Santini, G.; et al. Exploring the Gut Microbiome Alteration of the European Hare (Lepus europaeus) after Short-Term Diet Modifications. Biology (Basel). 2021 , 10 , 148, doi:10.3390/biology10020148. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.docx SupplementaryTable2.docx SupplementaryTable3.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-6277572","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":465410136,"identity":"612c2eb7-790b-47ba-b99f-a47b4c7eca81","order_by":0,"name":"Zbigniew Bełkot","email":"","orcid":"","institution":"University of Life Sciences in Lublin","correspondingAuthor":false,"prefix":"","firstName":"Zbigniew","middleName":"","lastName":"Bełkot","suffix":""},{"id":465410137,"identity":"32433951-75c9-4ee9-8a06-5c626bfae71a","order_by":1,"name":"Mateusz G. 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(a) Average number of taxa per sample at each taxonomic level. Green bars represent the 80% threshold, and purple bars represent the 95% threshold. (b) Taxonomic composition comparison showing the distribution of taxa across different taxonomic levels. Each bar is divided into three components: taxa unique to the 80% threshold (green), taxa shared between both thresholds (purple), and taxa unique to the 95% threshold (red). (\u003c/strong\u003eThe supplementary data for Figure 3 can be found in Supplementary Table 2.)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6277572/v1/990821e1b7d84c2d96e36028.png"},{"id":83832790,"identity":"f57547ff-fd07-45e2-a877-5a9409d2ea38","added_by":"auto","created_at":"2025-06-03 12:21:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":621988,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTaxonomic composition analysis at 80% and 95% sequence matching thresholds across six taxonomic levels.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: Symbols * and † indicate taxa unique to 80% threshold and 95% threshold, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003e Family composition showing the relative abundance (%) of dominant families at both 80% threshold (left) and 95% threshold (right).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(b)\u003c/strong\u003e Class composition illustrating the distribution of bacterial classes at both sequence matching thresholds.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(c)\u003c/strong\u003e Order composition comparing the relative abundances of bacterial orders between 80% and 95% thresholds.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(d)\u003c/strong\u003e Genus composition displaying the distribution of bacterial genera with different sequence matching stringencies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(e)\u003c/strong\u003e Phylum composition showing the major bacterial phyla detected at both sequence matching thresholds.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(f)\u003c/strong\u003e Species composition comparing the distribution of bacterial species between 80% and 95% thresholds.\u003c/p\u003e\n\u003cp\u003eEach panel presents paired stacked bar charts with 80% threshold data (left) and 95% threshold data (right). Taxa contributing less than 1% to overall community composition are grouped as \"Other (\u0026lt;1%)\". NCBI Taxonomy IDs is provided for species in parentheses. Color coding is consistent within each taxonomic level to facilitate comparison between threshold values.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6277572/v1/3cff2fc74ae7082422c1efcf.png"},{"id":83833477,"identity":"ed9134a3-85a4-4811-8f45-b7ff3fba60d4","added_by":"auto","created_at":"2025-06-03 12:29:58","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":125768,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of taxonomic composition across different studies of similar microbiomes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVenn diagrams comparing the taxonomic overlap at four levels: \u003cstrong\u003e(a)\u003c/strong\u003e Phylum - comparison between this study (red), Stalder et al. 2019 (blue), and Padula et al. 2021 (green); \u003cstrong\u003e(b) \u003c/strong\u003eFamily - comparison between this study and Stalder et al. 2019; \u003cstrong\u003e(c)\u003c/strong\u003e Genus - comparison between this study and Padula et al. 2021; \u003cstrong\u003e(d)\u003c/strong\u003e Species - comparison between this study and Stalder \u0026nbsp;et al. 2019. Numbers represent unique taxa counts.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6277572/v1/01525cd6b3cea90c6aa3a2e4.png"},{"id":86345426,"identity":"da5bb27c-c10e-4794-aa2a-4f9910a09261","added_by":"auto","created_at":"2025-07-09 14:54:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3106543,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6277572/v1/e0654bf0-0688-44b5-94df-dff80a37ef83.pdf"},{"id":83832784,"identity":"1cc5ca9f-7a6b-4cd1-a9ee-97a10d363618","added_by":"auto","created_at":"2025-06-03 12:21:58","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":117119,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6277572/v1/f1b663aea3656124bb9413fc.docx"},{"id":83834318,"identity":"48505859-eb95-45b3-a4cb-dc36a1b73b52","added_by":"auto","created_at":"2025-06-03 12:45:58","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":32804,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6277572/v1/ea8457ce2901d5395340152c.docx"},{"id":83833729,"identity":"85990800-0d6a-4831-810f-933ac45199ad","added_by":"auto","created_at":"2025-06-03 12:37:58","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":37789,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable3.docx","url":"https://assets-eu.researchsquare.com/files/rs-6277572/v1/d606cb21be316013da314501.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Long-Read Amplicon Sequencing Uncovers Complex Microbial Communities in the European Brown Hare Gut: Methodological Implications for Wildlife Microbiome Research","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe brown hare (\u003cem\u003eLepus europaeus\u003c/em\u003e Pall.) is a small game species that is very popular in Poland. However, its population has significantly decreased over the last 50 years. In the 1970s, the population was estimated to be over 3\u0026nbsp;million individuals. Since then, this number has declined fivefold, and the hunting acquisition of this species has also drastically decreased. The rapid decline in the hare population is attributed to a variety of factors [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The decline in the hare population can be attributed to several common factors. Firstly, the intensification of agriculture has led to the loss of essential habitats. Urbanization and the expansion of the road network also contribute to this issue. Additionally, pollution affecting soil, water, and air, along with ongoing climate change, negatively impacts reproductive processes in hares. Another critical factor in the decline is the significant increase in predation, particularly by foxes. This rise in fox numbers is linked to successful rabies vaccination programs. The presence of invasive species such as raccoon dogs and raccoons, as well as birds of prey and corvids, further affects hare populations. Moreover, poaching and hunting activities pose additional threats, along with the influence of synanthropic predators, especially outdoor domestic cats [\u003cspan additionalcitationids=\"CR4 CR5 CR6\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. A significant factor that influences the hare population is the spread of infectious and parasitic diseases. These diseases can lead to a decline in their overall health and reproductive capacity, and they are often a direct cause of mortality [\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] The brown hare is a typical herbivorous animal. It rarely drinks water; instead, it meets its hydration needs through the plants it consumes. In spring and summer, it primarily feeds on above-ground parts of plants. In autumn, its diet may include roots and other plant foods sourced from underground. During the day, brown hares gnaw on twigs from trees and shrubs, and they also enjoy the young shoots found in streams. Their favorite foods include young seedlings of fruit crops as well as a variety of agricultural crops [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Hares reach sexual maturity at approximately 8 months of age. A typical litter consists of 2 to 5 young; however, in areas influenced by human activity and agriculture, the actual reproductive capacity is often limited to 1 or 2 young. This limitation is primarily due to high levels of indicator metals that affect the reproduction of these animals, along with other factors impacting reproductive success. Studies indicate that out of 10 young born to a single female in a year (across four litters), only one typically survives to adulthood [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In Poland, the hunting season for hares lasts for 2 months, from November 1 to December 31. Hunting can only occur during organized collective hunts, which require the participation of at least six hunters. The carcasses of hunted hares are highly valuable for their culinary uses, as their meat is considered a prized food source.\u003c/p\u003e \u003cp\u003eThe brown hare (\u003cem\u003eLepus europaeus\u003c/em\u003e) plays a key role in forest and agricultural ecosystems, acting as a vital link in the trophic cascades and a potential reservoir of pathogens. Its gut microbiome is a dynamic ecosystem of microorganisms that play fundamental roles in metabolic processes, nutrient digestion, and ecological interactions.\u003c/p\u003e \u003cp\u003eStudies of the intestinal microbiome of the brown hare reveal a dominance of bacteria from the phyla \u003cem\u003eFirmicutes\u003c/em\u003e and \u003cem\u003eBacteroidetes\u003c/em\u003e. These bacteria are essential for digesting dietary fiber and for the host's energy metabolism [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The high abundance of these bacteria indicates that the hare has adapted to a diet rich in plant-based components, including difficult-to-digest polysaccharides. Additionally, the presence of bacteria from the genus \u003cem\u003eRuminococcus\u003c/em\u003e suggests that the hare is capable of efficiently fermenting cellulose, which may enhance its ability to extract energy from the plant material it consumes [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA comparative analysis of the gut microbiome in the brown hare and the European rabbit (Oryctolagus cuniculus) reveals significant differences in their microbiota composition [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Hares have been observed to possess a greater diversity of microorganisms that are involved in carbohydrate metabolism, indicating their capability to utilize a wider range of food resources. These differences may hold ecological significance regarding competition for food niches and could impact the population dynamics of both species in ecosystems where they coexist.\u003c/p\u003e \u003cp\u003eAdditional studies indicate an essential role for enzymes such as urease in metabolic processes occurring in the large intestine of the hare [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The high activity of this enzyme may be related to nitrogen recycling, which allows for more efficient use of nutrients and affects the nitrogen balance in ecosystems. This may be crucial for the trophic structure and functioning of habitats in which the hare occurs.\u003c/p\u003e \u003cp\u003eThe hare microbiome may also play a role in the transmission of pathogens with zoonotic potential. Studies show the presence of bacteria from the genera \u003cem\u003eEscherichia, Listeria, Clostridium\u003c/em\u003e and \u003cem\u003eSalmonella\u003c/em\u003e, which may be etiological factors of infections in other animals and humans (unpublished own studies). The research on the digestive microbiome of the brown hare (\u003cem\u003eLepus europaeus\u003c/em\u003e) is currently limited, and the existing scientific literature in this field remains insufficient.\u003c/p\u003e \u003cp\u003eNext-generation sequencing (NGS) is widely used to study the microbiomes of humans and animals. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This technology allows for high-throughput parallel sequencing of short DNA fragments. Key methodologies involved in NGS include the fragmentation of DNA into short reads (typically 100\u0026ndash;300 base pairs for Illumina and up to 600 base pairs for Ion Torrent, with some specialized applications reaching up to 800 base pairs), polymerase chain reaction (PCR) amplification to increase the amount of genetic material (which can introduce amplification biases), and sequencing by synthesis, where fluorescent signals are detected during the incorporation of nucleotides. NGS is extensively applied in microbiome research due to its high sequencing depth, cost efficiency per sample, and high accuracy in short-read analysis. However, its primary limitation lies in the short read length, which poses challenges in the de novo assembly of complex bacterial genomes and the precise differentiation of closely related microbial taxa. This limitation typically restricts the sequencing of the 16S rRNA gene to partial coverage of 3\u0026ndash;4 variable regions at most, depending on the platform and protocol used. Such read length constraints often make it difficult to distinguish between closely related bacterial species, although still allowing for accurate genus-level identification. While this approach has been the cornerstone of microbiome studies for years, the inability to consistently resolve species-level differences has been a persistent challenge for researchers seeking fine-scale taxonomic resolution.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThird-generation sequencing (TGS) technologies represent a paradigm shift by enabling long-read sequencing without length limitations for DNA fragments. This advancement allows for the sequencing of entire 16S rRNA genes (approximately 1,500 base pairs), providing significantly improved taxonomic resolution. Currently, two major platforms dominate the TGS landscape. Pacific Biosciences (PacBio) employs Single Molecule Real-Time (SMRT) sequencing, which utilizes zero-mode waveguides to detect fluorescent signals from single DNA molecules during synthesis, allowing for real-time observation of nucleotide incorporation without amplification bias. In contrast, Oxford Nanopore Technologies uses a fundamentally different approach based on ion flow through nanopores. This platform integrates electronic chips with sequencing proteins, where neural networks interpret nucleotides based on voltage differences as DNA molecules pass through the nanopores\u0026mdash;a substantial departure from conventional sequencing methodologies that rely on optical detection systems.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe recent introduction of Oxford Nanopore's Q14 chemistry and improved flow cell designs has revolutionized the field, with quality and accuracy scores now comparable to other sequencing platforms (Q scores above 30). This technological advancement has effectively eliminated the previous accuracy concerns associated with nanopore sequencing, making it a viable option for high-resolution microbiome studies.\u003c/p\u003e \u003cp\u003eThis article presents how the use of third-generation sequencing can significantly expand the range of microorganisms detected in the microbiome based on sequencing the full-length 16S and 18S rRNA gene amplicons from the large intestine of the brown hare. By leveraging the capabilities of long-read sequencing, we demonstrate enhanced taxonomic resolution that provides deeper insights into microbial community structure and function than previously possible with short-read technologies.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"2. Results","content":"\u003cp\u003e\u003cstrong\u003e2.1. Comparative Analysis of Taxonomic Diversity at Different Sequence Matching Thresholds\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKey biodiversity metrics calculated for hare rectal microbiome samples at two different sequence matching thresholds (95% and 80%) were presented in Table 1. The indices include Shannon diversity (H), which measures both richness and evenness; Species Richness, representing the total number of unique species identified; Simpson diversity (1-D), which emphasizes species dominance; Pielou\u0026apos;s evenness (J), indicating how evenly species are distributed; and Chao1, an estimator of true species richness accounting for undetected species.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Comparison of biodiversity indices between 95% and 80% accuracy thresholds across three samples (Z1, Z2, and Z3).\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"599\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eZ1 (95%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eZ1 (80%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eZ2 (95%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eZ2 (80%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eZ3 (95%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eZ3 (80%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eShannon H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e4.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e3.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e5.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e4.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e4.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eSpecies Richness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e168.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e2182.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e212.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e2405.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e328.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e3051.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eSimpson 1-D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003ePielou J\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eChao1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e265.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e4450.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e332.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e4353.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e541.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e5984.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThis analysis is visually supported by Figure 1, where panel (b) illustrates the substantial differences in Shannon diversity indices across taxonomic levels, consistently showing higher values for the 80% threshold samples.\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 1(A), the 80% threshold leads to a significant increase in taxonomic richness compared to the 95% threshold, particularly at the genus and species levels, where the differences are most evident. While the 95% threshold offers greater confidence in taxonomic assignments, the 80% threshold markedly broadens the detectable diversity, revealing a wider array of microorganisms that might otherwise remain undetected with stricter matching criteria. A detailed comparison of taxonomic assignments across seven levels, from Superkingdom to Species, is presented in Table 2. This table includes the total number of taxa identified, the number of assigned sequences, the percentage of assigned sequences, the taxa shared between thresholds, and the taxa uniquely identified at the 80% threshold. This systematic comparison demonstrates how the stringency of sequence matching impacts the depth and breadth of taxonomic classification.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Comprehensive taxonomic composition analysis across all hierarchical levels at 95% and 80% sequence matching thresholds for three microbiome samples (Z1, Z2, and Z3).\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"667\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTaxonomic Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eZ1 (95%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eZ1 (80%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eZ2 (95%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eZ2 (80%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eZ3 (95%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eZ3 (80%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eSuperkingdom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eNumber of taxa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eAssigned sequences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e2471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e33573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e4105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e41881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e4595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e58314\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003e% of assigned sequences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e99.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e99.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e97.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eShared taxa between thresholds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eUnique taxa for 80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003ePhylum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eNumber of taxa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eAssigned sequences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e2457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e31204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e4077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e39278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e4413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e55625\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003e% of assigned sequences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e99.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e92.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e99.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e93.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e96.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e93.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eShared taxa between thresholds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eUnique taxa for 80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eClass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eNumber of taxa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eAssigned sequences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e2414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e29913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e4005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e36711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e4215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e52694\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003e% of assigned sequences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e97.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e89.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e97.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e87.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e91.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e88.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eShared taxa between thresholds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eUnique taxa for 80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eOrder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eNumber of taxa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eAssigned sequences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e2423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e29851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e4019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e36668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e4217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e52348\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003e% of assigned sequences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e98.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e88.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e97.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e87.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e91.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e87.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eShared taxa between thresholds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eUnique taxa for 80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eFamily\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eNumber of taxa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e252\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eAssigned sequences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e2408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e28531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e3929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e34213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e4056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e49325\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003e% of assigned sequences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e97.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e84.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e95.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e81.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e88.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e82.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eShared taxa between thresholds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eUnique taxa for 80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e190\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eGenus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eNumber of taxa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e749\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eAssigned sequences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e2403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e27583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e3867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e33057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e3879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e47573\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003e% of assigned sequences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e97.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e82.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e94.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e78.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e84.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e79.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eShared taxa between thresholds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eUnique taxa for 80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e606\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eSpecies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eNumber of taxa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e2285\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eAssigned sequences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e2471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e33573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e4105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e41881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e4595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e58314\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003e% of assigned sequences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e99.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e99.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e97.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eShared taxa between thresholds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eUnique taxa for 80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1957\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eA quantitative comparison of taxonomic overlap between samples Z1, Z2, and Z3 was presented in the table within Figure 2, at both 80% and 95% sequence matching thresholds. The table reports key metrics for each taxonomic level, including the total number of unique taxa across all samples, the taxa common to all three samples, and the individual taxonomic counts for each sample. This comparison is visually represented in Figure 2, which uses Venn diagrams to illustrate the distribution and overlap of taxa between the samples at each taxonomic level.\u003c/p\u003e\n\u003cp\u003eThe comparative analysis of taxonomic overlap reveals significant differences in microbial community composition between the 80% and 95% sequence matching thresholds. At the 80% threshold, a substantially greater number of taxa were identified across all taxonomic levels compared to the 95% threshold. The most notable differences were observed at the species level (3,408 unique taxa at 80% vs. 465 unique taxa at 95%) and the genus level (1,041 unique taxa at 80% vs. 193 unique taxa at 95%). Additionally, the number of taxa common to all three samples was consistently higher at the 80% threshold, indicating that a broader core microbiome was detected using less stringent matching criteria (Table 3.).\u003c/p\u003e\n\u003cp\u003eTable 3. Summary table quantifying the taxonomic distribution across samples at both thresholds, including total unique taxa, taxa common to all samples, and individual sample counts for each taxonomic level.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTaxonomic Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccuracy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Unique Taxa\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommon in All Samples\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eZ1 Count\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eZ2 Count\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eZ3 Count\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 96px;\"\u003e\n \u003cp\u003ePhylum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e95%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 96px;\"\u003e\n \u003cp\u003eClass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e95%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 96px;\"\u003e\n \u003cp\u003eOrder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e95%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 96px;\"\u003e\n \u003cp\u003eFamily\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e220\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e95%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 96px;\"\u003e\n \u003cp\u003eGenus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e577\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e95%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 96px;\"\u003e\n \u003cp\u003eSpecies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e3408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e2285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1715\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e95%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNotes: Total Unique Taxa: The total number of unique taxa across all three samples. Common in All Samples: The number of taxa found in all three samples. Z1, Z2, Z3 Count: The number of unique taxa in each sample.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSample Z1 consistently exhibited the highest taxonomic richness at both thresholds across most taxonomic levels, particularly at the species level, where it identified 2,285 taxa at an 80% accuracy threshold compared to 328 taxa at a 95% accuracy threshold. The Venn diagrams clearly illustrate that, while the 95% threshold results in fewer unique taxa per sample, the 80% threshold presents a more complex picture of both shared and sample specific microbial diversity.\u003c/p\u003e\n\u003cp\u003eThis analysis demonstrates that sequence matching stringency significantly impacts the detected taxonomic composition and the apparent relationships between samples. While the 95% threshold provides higher confidence in taxonomic assignments, it may underestimate the true diversity and commonality between samples, potentially obscuring important ecological patterns in microbial communities.\u003c/p\u003e\n\u003cp\u003eThe figure displays comparative Venn diagrams for each taxonomic level, ranging from Phylum to Species. On the left, the diagrams represent an 80% threshold, while the right side shows a 95% threshold. Each circle corresponds to a sample (Z1, Z2, or Z3), with numbers indicating the counts of unique and shared taxa between the samples. The taxonomic composition of microbial communities in samples Z1, Z2, and Z3 is presented in the table, which includes unique sequence identifiers, taxonomic classifications, and abundance metrics for each identified taxon. Only taxa with a minimum of 10 sequences (Count \u0026ge; 10) are included in this table (Supplementary Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo evaluate the impact of the sequence matching threshold on detecting variability between samples, we conducted Kruskal-Wallis tests to compare samples Z1, Z2, and Z3 at each taxonomic level, using both 80% and 95% thresholds (see Table 3). This non-parametric statistical method allowed us to determine whether the taxonomic differences observed among the samples were statistically significant (p \u0026lt; 0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Statistical comparison of taxonomic composition between samples Z1, Z2, and Z3 at different sequence matching thresholds.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"609\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccuracy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTaxonomic Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKruskal-Wallis (H)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSignificant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003ePhylum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e0.2585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e0.8788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eClass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e0.6067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e0.7383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eOrder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e0.0450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e0.9778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eFamily\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e0.3128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e0.8552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eGenus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e11.6245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e0.0030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eSpecies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e29.9248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e3.2\u0026times;10\u003csup\u003e-7\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e95%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003ePhylum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e0.8734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e0.6462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e95%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eClass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e0.1351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e0.9347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e95%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eOrder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e0.3459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e0.8412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e95%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eFamily\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e2.5374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e0.2812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e95%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eGenus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e37.9491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e5.7\u0026times;10\u003csup\u003e-9\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e95%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eSpecies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e63.8668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e1.4\u0026times;10\u003csup\u003e-14\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: The Kruskal-Wallis test was performed to determine statistically significant differences between samples at each taxonomic level. A p-value \u0026lt; 0.05 indicates significant differences.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt the 80% threshold, significant differences between samples were observed at two taxonomic levels: Genus (H = 11.6245, p = 0.0030) and Species (H = 29.9248, p = 3.2\u0026times;10\u003csup\u003e-7\u003c/sup\u003e). In contrast, at the 95% threshold, significant differences were also detected at the same two taxonomic levels, with particularly strong differences at the Genus (H = 37.9491, p = 5.7\u0026times;10\u003csup\u003e-9\u003c/sup\u003e) and Species (H = 63.8668, p = 1.4\u0026times;10\u003csup\u003e-14\u003c/sup\u003e) levels. No significant differences were found at the Phylum, Class, Order, or Family levels at either threshold.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2. Taxonomic Composition Analysis Across Classification Levels\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.1. Taxonomic Composition at Different Sequence Matching Thresholds\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe analysis of taxonomic composition across all major taxonomic levels (Phylum, Class, Order, Family, Genus, and Species) at both 80% and 95% sequence matching thresholds revealed significant differences in community structure and diversity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eImportantly, the 80% threshold appears to mask certain taxa that become visible at the 95% threshold while simultaneously overrepresenting others. This differential detection significantly impacts the characterization of the microbiome. For example, Ascomycota (4.3%) is completely absent in the 80% analysis (likely falling below the 1% reporting threshold), while Spirochaetota (25.2%) is prominent at 80% but absent at 95%. These substantial differences demonstrate how threshold selection can fundamentally alter our understanding of microbial community composition.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt the Phylum level, Bacteroidota shows a dramatic difference between thresholds, accounting for only 25.9% at 80% but dominating at 59.6% at the 95% threshold. This represents a 33.7% difference, emphasizing how threshold selection can significantly shift the perceived dominance patterns of major phyla. Conversely, Bacillota and Actinomycetota both show reduced representation at higher thresholds (39.7% vs 31.7% and 6.6% vs 1.5%, respectively).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Class-level analysis revealed even more pronounced threshold-dependent variations. Bacteroidia shows a 34.7% difference between thresholds (26.7% at 80% vs 61.4% at 95%). Three classes present at \u0026gt;1% in the 80% threshold were undetectable at 95%: Spirochaetia (26.5%), Erysipelotrichia (3.9%), and Negativicutes (1.0%). Conversely, Saccharomycetes (3.1%) was uniquely detected at the 95% threshold. Such substantial shifts in class representation demonstrate how threshold choice can profoundly influence our understanding of microbial community structure.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt the Order level, similar patterns emerged with Bacteroidales showing a 34.6% difference between thresholds. Four orders present at \u0026gt;1% in the 80% threshold were undetectable at 95%, including Spirochaetales (26.6%), while three orders including Lactobacillales (4.1%) and Monoglobales (3.8%) were uniquely detected at 95%.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFamily-level composition revealed the most dramatic differences, with Bacteroidaceae showing a 40.3% difference between thresholds (11.2% at 80% vs 51.5% at 95%). Remarkably, 10 families detected at \u0026gt;1% in the 80% threshold were absent at 95%, including Spirochaetaceae (26.4%) and Odoribacteraceae (6.7%), while 5 families including Monoglobaceae (3.9%) and Streptococcaceae (3.4%) were uniquely detected at 95%.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGenus-level analysis showed that \u003cem\u003eSpirochaeta\u003c/em\u003e, the predominant genus at 80% (26.3%), was completely absent at 95%. Meanwhile, Phocaeicola and Bacteroides showed dramatically higher abundances at 95% (26.0% vs 3.6% and 26.7% vs 7.4%, respectively). Nine genera present at \u0026gt;1% in the 80% threshold were undetectable at 95%, while five genera were uniquely detected at the 95% threshold.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt the Species level, which provides the most detailed taxonomic resolution, Spirochaeta sp. canine oral taxon 379 dominated at 80% (21.3%) but was completely absent at 95%. Conversely, \u003cem\u003ePhocaeicola vulgatus\u003c/em\u003e (16.5%) and \u003cem\u003eBacteroides uniformis\u003c/em\u003e (15.9%) dominated at 95% but showed minimal presence at 80% (1.6% and 1.8%, respectively). A total of 9 species present at \u0026gt;1% in the 80% threshold were undetectable at 95%, while 11 species including \u003cem\u003ePhocaeicola dorei\u003c/em\u003e (4.2%) and \u003cem\u003eMonoglobus pectinilyticus\u003c/em\u003e (3.7%) were uniquely detected at 95%.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese findings demonstrate that threshold selection substantially impacts microbial community characterization, with the 95% threshold generally revealing a more diverse community structure with different dominant taxa compared to the 80% threshold. The fact that many taxa can be completely missed or significantly under/overrepresented depending on threshold choice highlights the importance of careful parameter selection in microbiome studies, as these differences can lead to fundamentally different biological interpretations of the same sample.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.2.2. \u003cstrong\u003eComparative Analysis of Taxa Richness Between Sequence Matching Thresholds\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo further investigate the impact of sequence matching thresholds on microbial community characterization, we compared the number and distribution of taxa detected at 80% and 95% thresholds across all taxonomic levels from superkingdom to species (Figure 3). This analysis revealed striking differences in taxonomic richness and composition between the two thresholds.\u003c/p\u003e\n\u003cp\u003eAs illustrated in Figure 3a, the average number of taxa per sample was consistently higher at the 80% threshold across all taxonomic levels. This difference became more pronounced as the taxonomic levels became more specific. At the species level, samples analyzed with the 80% threshold contained an average of about 2,000 taxa per sample, whereas those processed at the 95% threshold had only around 200 taxa per sample. This represents nearly a 10-fold difference in detected diversity.\u003c/p\u003e\n\u003cp\u003eThe comparison of taxonomic composition (Figure 3b) offers additional insights into the relationship between taxa identified at both thresholds. While there was a core set of taxa shared between the two thresholds across all taxonomic levels, the 80% threshold revealed a significantly larger number of unique taxa that were not detected at the 95% threshold. This distinction was particularly pronounced at the species level, where approximately 3,000 taxa were uniquely identified at the 80% threshold, compared to only around 400 unique taxa at the 95% threshold.\u003c/p\u003e\n\u003cp\u003eInterestingly, the number of shared taxa remained relatively stable across most taxonomic levels, suggesting that the 95% threshold primarily captures a core subset of the microbial community that is also detected at the 80% threshold. The comparatively small number of taxa unique to the 95% threshold indicates that higher stringency rarely results in novel taxonomic assignments not captured by lower stringency analysis.\u003c/p\u003e\n\u003cp\u003eThese findings complement our previous taxonomic composition and statistical analyses, demonstrating that while the 95% threshold provides higher confidence in taxonomic assignments and better discriminatory power for detecting inter-sample differences, it does so at the cost of significantly reduced taxonomic breadth, potentially missing thousands of taxa that are detected at lower stringency thresholds.\u003c/p\u003e\n\u003cp\u003eEach panel presents paired stacked bar charts with 80% threshold data (left) and 95% threshold data (right). Taxa contributing less than 1% to overall community composition are grouped as \u0026quot;Other (\u0026lt;1%)\u0026quot;. NCBI Taxonomy IDs is provided for species in parentheses. Color coding is consistent within each taxonomic level to facilitate comparison between threshold values. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.3. Comparison with Previously Published Microbiome Studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the comprehensiveness of our taxonomic characterization approach, we compared the taxa identified in our study with those reported in two previous microbiome studies: a Stalder et al. 2019 publication in Scientific Reports and a Padula et al. 2021 publication in Biology (Figure 5). This comparative analysis was performed across four taxonomic levels (Phylum, Family, Genus, and Species) to assess the extent of taxonomic overlap and the identification of taxa.\u003c/p\u003e\n\u003cp\u003eAt the phylum level (Figure 5A), our analysis identified 40 phyla, of which 28 (70%) were unique to our study. We shared 8 phyla with the Stalder et al. 2019 study and 12 phyla with the Padula et al. 2021 study, while 1 phyla was exclusively shared between the two reference studies.\u003c/p\u003e\n\u003cp\u003eThe differences became more pronounced at higher taxonomic resolutions. At the family level (Figure 5B), our analysis identified 360 unique families compared to only one unique family in Stalder et al. 2019 study\u003c/p\u003e\n\u003cp\u003eThe genus-level comparison (Figure 5C) revealed even more striking differences, with our study identifying 1,027 unique genera with 7 unique to Biology study.\u003c/p\u003e\n\u003cp\u003eAt the species level (Figure 5D), the divergence was most dramatic, with our analysis identifying 3,373 unique species. The Stalder et al. 2019 study identified only one unique species\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe missing taxa report (Table 5) identified 37 taxa across all taxonomic levels that were reported in previous studies but not detected in our analysis. Of these, 21 taxa (56.8%) lacked defined entries in the NCBI taxonomy database. These undefined taxa, described using non-standard nomenclature, include 12 phyla from the Padula et al. 2021 publication and 1 phyla from publication Stalder et al. 2019, 1 family from publication Stalder et al. 2019 and 6 genera from Padula et al. 2021.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Taxa with NCBI database entries identified in previous publications but absent from the current dataset.\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"595\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLp\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNCBI Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePublished Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNCBI\u0026nbsp;\u003cbr\u003e\u0026nbsp;Tax ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003ePhylum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003ePadula et al. 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eArmatimonadota\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eArmatimonadetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e67819\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003ePhylum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003ePadula et al. 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eChlamydiota\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eChlamydiae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e204428\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003ePhylum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003ePadula et al. 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eCampylobacterota\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eEpsilonbacteraeota\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e29547\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003ePhylum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003ePadula et al. 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMethanobacteriota\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eEuryarchaeota\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e28890\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003ePhylum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003ePadula et al. 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eGemmatimonadota\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eGemmatimonadetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e142182\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003ePhylum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003ePadula et al. 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eKiritimatiellota\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eKiritimatiellaeota\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e134625\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003ePhylum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003ePadula et al. 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNanobdellota\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eNanoarchaeota\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e192989\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003ePhylum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003ePadula et al. 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eCandidatus Omnitrophota\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eOmnitrophicaeota\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e67812\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003ePhylum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003ePadula et al. 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003ePlanctomycetota\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003ePlanctomycetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e203682\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003ePhylum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003ePadula et al. 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNitrososphaerota\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eThaumarchaeota\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e651137\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003ePhylum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003ePadula et al. 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eVerrucomicrobiota\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eVerrucomicrobia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e74201\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003eFamily\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003eStalder et al. 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eBrucellaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eBrucellaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e118882\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003eGenus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003ePadula et al. 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eCandidatus Saccharimonas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eCandidatus Saccharimonas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1331051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003eSpecies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eStalder et al. 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eSphingobacterium wenxiniae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eSphingobacterium wenxiniae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e683125\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003eSpecies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eStalder et al. 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eSelenomonas dianae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eSelenomonas dianae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e135079\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003eSpecies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eStalder et al. 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eKyrpidia tusciae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eKyrpidia tusciae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e33943\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: The table shows representative taxa with valid NCBI taxonomy entries that were reported in previous studies but not detected in our analysis. The full report identified 40 missing taxa, of which 19 (47.5%) lacked defined entries in the NCBI taxonomy database. The list of taxa found in reference sources but missing in the current study is provided in Supplementary table 3.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"3. Discussion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eNext-generation sequencing (NGS) has become a cornerstone technology in microbiome research due to its high sequencing depth, cost efficiency per sample, and high accuracy in short-read analysis. However, its primary limitation lies in the short read length, which typically ranges from 100\u0026ndash;300 base pairs for Illumina and up to 600 base pairs for Ion Torrent platforms. This constraint poses significant challenges in the de novo assembly of complex bacterial genomes and the precise differentiation of closely related microbial taxa. While NGS has been widely applied to study the microbiomes of both humans and animals, the short-read nature of the technology often restricts 16S rRNA gene sequencing to partial coverage of only 3\u0026ndash;4 variable regions at most. This limitation frequently results in difficulties distinguishing between closely related bacterial species, typically allowing only for accurate genus-level identification rather than the species-level resolution often required for comprehensive microbiome characterization.\u003c/p\u003e \u003cp\u003eThird-generation sequencing (TGS) technologies, represented by PacBio Single Molecule Real-Time (SMRT) sequencing and Oxford Nanopore Technology (ONT), address several fundamental limitations of NGS. The most significant advantage of TGS for microbiome analysis is its ability to generate long reads that can span the entire length of marker genes such as 16S rRNA (approximately 1,500 base pairs) and 18S rRNA. This capability provides significantly improved taxonomic resolution compared to the partial gene coverage achieved with NGS technologies. While both NGS and TGS approaches for amplicon sequencing utilize PCR amplification, the long-read capability of TGS enables complete coverage of taxonomically informative regions in a single read, eliminating the need for assembly of shorter fragments and reducing the ambiguity in taxonomic assignments. Additionally, the real-time data acquisition capability, particularly in ONT-based platforms, accelerates sequencing and downstream bioinformatics analyses, making it increasingly suitable for field applications and rapid microbiome assessments.\u003c/p\u003e \u003cp\u003eFor microbiome analysis, the targeted sequencing of marker genes that enable species identification, particularly 16S and 18S rRNA genes, represents the most efficient approach. These conserved genes contain hypervariable regions that permit taxonomic classification while maintaining sufficient conservation for universal primer binding. Typically, only a small fraction of a bacterial genome, often estimated at less than 1%, consists of species-specific sequences suitable for accurate taxonomic identification. This means that whole-genome shotgun sequencing would require approximately 100 times more sequencing depth to achieve the same taxonomic resolution as targeted amplicon sequencing. In practical terms, this translates to 5 hours of targeted sequencing versus several days for whole-genome approaches, making amplicon sequencing significantly more cost-effective and time-efficient for taxonomic profiling.\u003c/p\u003e \u003cp\u003eWhile 16S and 18S rRNA genes remain the gold standard for bacterial and eukaryotic identification respectively, other marker genes are occasionally employed for specific taxonomic groups. These include rpoB (RNA polymerase beta subunit) for enhanced resolution in some bacterial genera [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], gyrB (DNA gyrase subunit B) for better discrimination of closely related species [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], and ITS (Internal Transcribed Spacer) regions for fungal identification. Despite widespread usage, 16S rRNA sequencing often lacks sufficient resolution for species and strain-level identification [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA significant limitation of amplicon-based approaches is their dependence on universal primers. Primer bias can lead to selective amplification of certain microbial groups while failing to capture others, resulting in skewed community representations. This challenge is particularly pronounced when studying diverse and less characterized microbiomes such as those found in wildlife species like the brown hare. In our study, we utilized proprietary primers developed by SPARK-TECH that were specifically designed to target a broad range of over 120,000 prokaryotic and eukaryotic species. These primers are the subject of a patent application and represent a significant advancement in reducing amplification bias in microbiome studies of non-model organisms.\u003c/p\u003e \u003cp\u003eOur analysis of the brown hare microbiome reveals important considerations regarding sequence matching thresholds in taxonomic assignment. While human microbiome studies typically employ high similarity thresholds (\u0026ge;\u0026thinsp;95%) for taxonomic classification, our findings demonstrate that such stringent criteria may be suboptimal for wildlife microbiome analysis. At the 95% threshold, the number of sequences assigned was significantly lower compared to the 80% threshold. Our data showed that at the 80% threshold, the number of assigned sequences increased dramatically\u0026mdash;by approximately 12.6-fold for sample Z1, 10.2-fold for sample Z2, and 12.7-fold for sample Z3 compared to the 95% threshold. This difference is particularly important when considering how the BLAST algorithm functions in taxonomic assignment, where alignment scores and percentage identity are used to determine the closest match in reference databases.\u003c/p\u003e \u003cp\u003eThe improved classification rate at the 80% threshold likely reflects the substantial diversity of microbial species in wildlife and the underrepresentation of these organisms in current reference databases. Organisms showing 80\u0026ndash;95% similarity to reference sequences may represent novel strains, species variants adapted to the hare gut environment, or even entirely new taxa that have not been previously characterized. This finding underscores the potential of wildlife microbiome studies as fertile ground for the discovery of novel microbial diversity.\u003c/p\u003e \u003cp\u003eOur results emphasize that achieving genus and species-level resolution is critical for understanding microbiome composition and function in wildlife. The significant differences observed between 80% and 95% thresholds at these taxonomic levels (504\u0026ndash;606 unique genera and 1547\u0026ndash;1957 unique species identified only at the 80% threshold) demonstrate that overly stringent matching criteria may obscure substantial portions of the microbial community. This is particularly evident in the detection of important taxa such as Spirochaetota (25.2% abundance at 80% threshold but completely absent at 95%) and Ascomycota (4.3% abundance at 95% but absent at 80%), highlighting how threshold selection can fundamentally alter our perception of community composition.\u003c/p\u003e \u003cp\u003eThe comprehensive nature of our approach is further validated by our comparative analysis with previous studies of the hare microbiome. Our findings encompass nearly all previously reported taxa while identifying thousands of additional organisms not detected in earlier investigations. Specifically, we identified 40 phyla (with 28 unique to our study), 360 unique families, 1,027 unique genera, and 3,373 unique species not reported in previous publications. This substantial expansion of the known hare microbiome composition directly results from the combination of third-generation sequencing technology and optimized taxonomic assignment parameters.\u003c/p\u003e \u003cp\u003eThe advantages of TGS make it particularly valuable for studying the microbiome of wildlife species like the brown hare. The ability to fully sequence 16S and 18S rRNA genes provides unprecedented taxonomic resolution, enabling the identification of rare or novel microbial strains that might play crucial roles in host health or disease. This comprehensive approach to taxonomic classification significantly enhances our understanding of microbial diversity in wildlife species.\u003c/p\u003e \u003cp\u003eIn summary, our study represents the most comprehensive characterization of the brown hare microbiome to date, made possible by the application of third-generation sequencing technology and optimized analytical approaches. Our findings not only encompass previously identified taxa but substantially expand our understanding of the microbial diversity associated with this important wildlife species. Furthermore, our methodological approach provides a valuable framework for future wildlife microbiome studies, emphasizing the importance of appropriate sequence matching thresholds and comprehensive taxonomic analysis based on full-length 16S and 18S rRNA gene sequencing.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"4. Materials and Methods","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn Poland, according to the Regulation of the Minister of the Environment regarding hunting seasons for game animals, the hunting season for brown hares is from 1 November to 31 December [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Animals, Samples, and Localization\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe hares chosen for the study were selected during a two-day collective hunt organized in two hunting districts in the western part of the Lublin Upland. During the 2024 hare hunting season, a total of 30 healthy brown hares (\u003cem\u003eLepus europaeus\u003c/em\u003e Pall.) were hunted, including both males and females. After conducting autopsies, it was determined that all the carcasses were healthy and showed no anatomopathological changes.\u003c/p\u003e \u003cp\u003eThe study material consisted of large intestine contents. A total of 30 hares were randomly divided into three groups (Z1, Z2, Z3), each comprising 10 individuals. Within each group, large intestinal content samples were randomly collected from five individual hares and subsequently pooled to create a composite sample. From each composite sample (Z1, Z2, and Z3), a 2.5 g sample was collected and stored at -20\u0026deg;C for further analysis.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Sequencing, Data Calculation, and Statistical Analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe protocol for isolation, amplification, and sequencing was performed as previously published with modifications [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrior to sequencing, the samples were thawed at a controlled temperature. DNA was then isolated using a silica column-based kit (QIAamp PowerFecal Pro DNA Kit, QIAGEN), according to the manufacturer's instructions. Following DNA isolation, polymerase chain reaction (PCR) was performed to amplify the 16S rRNA gene and 18S rRNA gene regions of the bacterial and eukaryotic DNA. This amplification was carried out using proprietary SPARKbiom primers (SPARK-TECH). After PCR amplification, the samples were prepared for sequencing according to the Oxford Nanopore Kit 14 chemistry library preparation protocol. Amplification was performed under the following conditions: the first step was Initial denaturation at 95\u0026deg;C for 1 min; the second step included 30 cycles (denaturation at 95\u0026deg;C for the 20s, annealing at 55\u0026deg;C for 30s and extension at 65\u0026deg;C for 2 min). The last step was a final extension at 65\u0026deg;C for 5 min. Nanopore sequencing was then carried out using the MinION device (Oxford Nanopore). Upon completion of the sequencing, the raw data was processed using the MinKNOW software (Oxford Nanopore) to assess the quality of the reads and perform base calling.\u003c/p\u003e \u003cp\u003eTaxonomic classification of the sequences was carried out using a database of 1.5\u0026nbsp;million reference sequences from NCBI (querry16:16S [Title] NOT uncultured[All Fields] NOT unidentified[All Fields] NOT unknown[All Fields] AND (800:40000[Sequence Length]), querry18: 18S[Title] NOT (uncultured[All Fields] OR unidentified[All Fields] OR unknown[All Fields] OR environmental[All Fields] OR sample[All Fields]) AND (500:5000[Sequence Length]);). BLAST+ (version ncbi-blast-2.16.0) was employed to identify sequences with a minimum matching level of 95% and 80% for species assignment. Matches below 500 nucleotides length were excluded from the analysis.\u003c/p\u003e \u003cp\u003eFor each sample, two results were obtained with minimum matching thresholds of 95% and 80% (80% includes the 95% results). The pooled sample contains taxa from all samples with a summed count for each species, with the average based on the total count.\u003c/p\u003e \u003cp\u003eBased on two publications, Stadler et al. (2019) and Padula et al. (2021), taxonomic groups present in the hare intestine were identified at the phylum, family, genus, and species levels. Taxonomic classification was mapped according to the current NCBI taxonomy. In instances where specific taxa could not be assigned, they were designated as \"unclassified,\" followed by the original taxonomic designation reported in the respective publication.\u003c/p\u003e \u003cp\u003eFor the paired sample analysis, a systematic comparison of taxonomic results was conducted at accuracy thresholds of 95% and 80% using multiple diversity indices. Biodiversity metrics were calculated for each sample pair, including the Shannon diversity index (H'), Species Richness, Simpson diversity index (1-D), Pielou's evenness index (J), and the Chao1 species richness estimator.\u003c/p\u003e \u003cp\u003eFor the comparative analysis of taxonomic diversity, the number of unique taxa and the Shannon diversity index were calculated for each taxonomic level, ranging from Superkingdom to Species, across all samples at both accuracy thresholds. Data processing and analysis were conducted using Python, employing the pandas and NumPy libraries. Visualization was performed using Plotly to generate comparative bar charts illustrating taxonomic richness and Shannon diversity indices, facilitating a direct comparison between datasets at the 80% and 95% accuracy thresholds.\u003c/p\u003e \u003cp\u003eFollowing the analysis of taxonomic diversity, Venn diagram visualization was applied to examine relationships between samples across accuracy thresholds. Using the matplotlib-venn library in Python, interactive side-by-side diagrams were generated for each taxonomic level, from Phylum to Species, to illustrate shared and unique taxa among samples (Z1, Z2, Z3) at both 80% and 95% accuracy thresholds. This analysis was supplemented with a comprehensive summary table presenting the total number of unique taxa, taxa common to all samples, and taxon counts specific to individual samples at each taxonomic level.\u003c/p\u003e \u003cp\u003eFor the assessment of taxonomic composition, relative abundance was aggregated and visualized using Python with the pandas and matplotlib libraries. Stacked bar charts were generated to display the top 15 taxa at each taxonomic level, while less abundant taxa (\u0026lt;\u0026thinsp;1%) were grouped into an \"Other\" category. NCBI Taxonomy IDs were incorporated to facilitate parallel comparisons between accuracy thresholds.\u003c/p\u003e \u003cp\u003eTo assess differences between classifications at the two accuracy thresholds, a comparative group analysis was conducted by calculating three key metrics: the average number of taxa identified per sample at each threshold, the number of taxa unique to each threshold, and the number of taxa shared between both thresholds. Visualizations included grouped bar charts representing average taxa counts and stacked charts illustrating the composition breakdown across taxonomic levels.\u003c/p\u003e \u003cp\u003eStatistical evaluation was conducted using a non-parametric approach, applying Kruskal-Wallis H-tests to assess significant differences in taxonomic composition between samples at both accuracy thresholds. Statistical significance was defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The results were compiled into tables presenting H-statistic values, p-values, and significance determinations for each comparison.\u003c/p\u003e \u003cp\u003eFor comparative analysis with published literature, Venn diagram visualization was utilized to compare the 80% threshold dataset with taxonomic compositions reported in two reference publications [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The analysis focused on four taxonomic levels (Phylum, Family, Genus, Species) and incorporated both NCBI-standardized nomenclature and the original taxonomic names from the referenced studies. Diagrams illustrating taxonomic overlap were generated, and reports were compiled to document taxa identified in previous studies but absent from the analyzed dataset.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003eThe sequences analyzed in this study can be found in the NCBI accession for these SRA data: PRJNA1238910, with a temporary Submission ID: SUB15187482. The release date is April 30, 2026. More information can be accessed at: https://www.ncbi.nlm.nih.gov/sra/PRJNA1238910.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Materials:\u0026nbsp; \u0026nbsp;\u003c/strong\u003eSupplementary table 1; Supplementary table 2; Supplementary table 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e Conceptualization, Z.B, M.G.A., \u0026amp; J.P-C.; methodology, Z.B, M.G.A., \u0026amp; J.P-C..; software, M.G.A., G.K. \u0026amp; J.P-C.; validation, Z.B, M.G.A., and J.P-C.; material collection: Z.J.S., J.P-Cz., E.D.D., DK; formal analysis, M.G.A., G.K., D.S., and J.P-C.; investigation, Z.B, Z.J.S,\u0026nbsp;DK; resources, Z.B, Z.J.S, EDD.; data curation, Z.B, M.G.A., Z.J.S, EDD, J.P-C.; writing\u0026mdash;\u0026nbsp;Z.B, M.G.A., Z.J.S, EDD, J.P-C., DK, G.K., D.S; writing\u0026mdash;review and editing\u0026nbsp;Z.B, M.G.A., J.P-C.; visualization, M.A., Z.J.S, EDD, J.P-C.; supervision,\u0026nbsp;Z.B and M.G.A., J.P-C.; project administration,\u0026nbsp;Z.B, J.P-C.; funding acquisition, Z.B.\u003c/p\u003e\n\u003cp\u003eAll authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003eCompeting interests: The authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u0026nbsp;\u003c/strong\u003eThe study complied with Directive 2010/63/EU and the Act of the Polish Parliament dated 15 January 2015 on the protection of animals used for scientific purposes (Journal of Laws of the Republic of Poland 2015, item 266). The brown hares were not killed for the purposes of the study. The hunt took place in accordance with Polish hunting law (Act of the Polish Parliament dated 13 October 1995, item 713, the Hunting law, Chapter 3, Art. 8 Hunt and the Regulation of the Minister of the Environment of 16 March 2005 on the determination of hunting periods for game animals [Journal of Laws 2023, item 99]) during the 2024 hunting season.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli dir=\"LTR\"\u003eDemirbaş Yasin 2015_Demirbaş_hares and foxes. \u003cem\u003eACTA Zool. Bulg.\u003c/em\u003e \u003cstrong\u003e2015\u003c/strong\u003e, \u003cem\u003e67 (4)\u003c/em\u003e, 515\u0026ndash;520.\u003c/li\u003e\n \u003cli dir=\"LTR\"\u003eNasiadka, P.; Dziedzic, R. \u003cem\u003ePodręcznik najlepszych praktyk ochrony kuropatwy i zająca. 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Res.\u003c/em\u003e \u003cstrong\u003e2025\u003c/strong\u003e, \u003cem\u003e21\u003c/em\u003e, 13, doi:10.1186/s12917-024-04439-4.\u003c/li\u003e\n \u003cli dir=\"LTR\"\u003e Padula, A.; Bambi, M.; Mengoni, C.; Greco, C.; Mucci, N.; Greco, I.; Masoni, A.; Del Duca, S.; Bacci, G.; Santini, G.; et al. Exploring the Gut Microbiome Alteration of the European Hare (Lepus europaeus) after Short-Term Diet Modifications. \u003cem\u003eBiology (Basel).\u003c/em\u003e \u003cstrong\u003e2021\u003c/strong\u003e, \u003cem\u003e10\u003c/em\u003e, 148, doi:10.3390/biology10020148.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Third Generation Sequencing, microbiome, brown hare, 16S and 18S rRNA gene","lastPublishedDoi":"10.21203/rs.3.rs-6277572/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6277572/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe declining European brown hare (\u003cem\u003eLepus europaeus\u003c/em\u003e) population necessitates comprehensive microbiome studies to better understand factors affecting their health. This study employed Oxford Nanopore third-generation sequencing to characterize the hare gut microbiome through full-length 16S and 18S rRNA gene analysis. Intestinal contents from 30 hunted brown hares were pooled into three composite samples for sequencing. Comparative analysis using 80% and 95% sequence matching thresholds revealed dramatic differences in detected diversity. The 80% threshold identified substantially more taxa across all taxonomic levels (up to 10-fold more species) and revealed previously unreported microbiome components, including Spirochaetota (25.2% at 80%) and Ascomycota (4.3% at 95%). Statistical analysis confirmed significant differences between samples at genus and species levels for both thresholds (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Our approach identified 28 unique phyla, 360 unique families, 1,027 unique genera, and 3,373 unique species not reported in previous studies. These findings demonstrate how threshold selection fundamentally alters microbiome characterization and highlights the potential of long-read sequencing for expanding our understanding of wildlife microbiomes, which may contribute to improved conservation strategies for declining species.\u003c/p\u003e","manuscriptTitle":"Long-Read Amplicon Sequencing Uncovers Complex Microbial Communities in the European Brown Hare Gut: Methodological Implications for Wildlife Microbiome Research","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-03 12:21:53","doi":"10.21203/rs.3.rs-6277572/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d8fe79f9-2b07-49b6-8489-d0505a5d963c","owner":[],"postedDate":"June 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49407676,"name":"Biological sciences/Microbiology"},{"id":49407677,"name":"Biological sciences/Microbiology/Communities"},{"id":49407678,"name":"Biological sciences/Microbiology/Communities/Microbiome"}],"tags":[],"updatedAt":"2025-07-09T14:53:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-03 12:21:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6277572","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6277572","identity":"rs-6277572","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}