Microbiome-based tracking of diet shifts in ectotherms: a new approach to monitor effects of global changes on food webs?

Microbiome-based tracking of diet shifts in ectotherms: a new approach to monitor effects of global changes on food webs? | 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 Research Article Microbiome-based tracking of diet shifts in ectotherms: a new approach to monitor effects of global changes on food webs? Paula Cabral Eterovick, Katharina Ruthsatz, Selma Vieira, Johannes Sikorski, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8959905/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 Background Global warming and other human-driven impacts are reshaping food webs, compromising both food quality and availability. Ectotherms are particularly challenged under these conditions because they simultaneously face elevated energetic demands and unstable food supply. Their gut microbiomes respond strongly to diet and may either enhance host adaptive potential or undergo dysbiosis, contributing to adaptive failure. Understanding how diet affects ectotherm microbiomes is therefore fundamental for predicting the consequences of environmentally driven dietary change. However, studies on ectotherm diet-microbiome interactions remain relatively scarce, taxonomically biased, and methodologically heterogeneous. Results Here, we present a systematic literature review and meta-analysis quantifying diet-driven changes in gut microbiome diversity in ectothermic vertebrates while accounting for taxonomic/phylogenetic, ecological, and methodological sources of variation. Methodological heterogeneity hampered robust comparisons of microbiome alpha and phylogenetic diversity across gradients of diet nutritional composition. Across studies, however, we identified several bacterial genera and families that increased in relative abundance with higher insect consumption. These taxa are known to degrade chitin and other complex insect-derived compounds, generating metabolites that act as signaling molecules along hypothalamic–pituitary and related neuroendocrine axes to modulate host growth, development, reproduction, and senescence. Conclusions Our findings highlight the potential of diet-sensitive microbial groups as microbiome-based indicators and as agents that may promote ectotherm resilience to environmental change through physiological regulation of host metabolism. We outline priorities for improving data collection, reducing methodological heterogeneity, and ensuring open availability of sequence data. Bacteria gut microbiome fishes amphibia global warming food quality Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Animal guts are inhabited by a diverse microbiome that includes Archaea, Bacteria, Fungi, Protists, and Viruses [ 1 ]. Diet is a major determinant of gut microbiome composition and structure [ 2 ], and variation in dietary nutritional composition is widely recognized to influence bacterial communities in animal guts [e.g., 3, 4]. In turn, gut bacteria break down complex molecules into metabolites that can be assimilated by their hosts. Among these metabolites, short-chain fatty acids (SCFAs), polyamines, and neuropeptides can be recognized by host receptors and act as signaling molecules that induce cellular responses and regulate hormonal activity, thereby influencing host growth, development, reproduction, and senescence [ 5 ]. Within this tightly linked host–microbe system, bacteria act as important mediators of host physiological responses to environmental change, affecting performance, stress tolerance, and overall resilience under altered environmental conditions [ 6 , 7 ]. Conversely, disruption of diet–microbiome interactions can lead to dysbiosis, with downstream negative consequences for host health, growth, and survival [ 8 ]. Thus, microbiome plasticity and functional redundancy may buffer hosts against dietary stress, but in some contexts may instead reflect instability and declining host condition, underscoring the need to distinguish adaptive from maladaptive microbiome responses [ 9 , 10 ]. Global warming, pollution, eutrophication, and other anthropogenic stressors alter food webs, resource availability, and feeding opportunities and preferences across ecosystems [ 11 , 12 , 13 , 14 ]. These stressors modify not only food quantity but also nutritional composition, including macronutrient ratios, micronutrient availability, and the presence of secondary compounds or contaminants [ 14 ]. Changes in primary producers and lower trophic levels (e.g., phytoplankton, zooplankton, and insect communities) propagate through food webs, ultimately reshaping the diets of higher trophic consumers across aquatic and riparian habitats [ 15 ]. Such diet shifts are expected to have cascading effects on gut microbiome structure and function; however, these effects remain poorly comparable across systems [ 16 , 17 , 18 ]. Nonetheless, microbiomes of wild animals are known to reflect diet and life-history traits [ 19 ] and represent an important component of animal adaptation to environmental change [ 6 ]. Ectotherms dominate many aquatic and terrestrial food webs and play a central role in ecosystem functioning and energy transfer [ 20 , 21 , 22 , 23 ]. Because ectotherm metabolic rates and energetic demands increase with temperature [ 24 ], they are particularly impacted by the combined effects of warming and reduced food quality. Under such conditions, ectotherms may struggle to obtain sufficiently high-quality food to meet elevated energetic demands [ 14 ]. This mismatch between energy demand and resource quality poses a major challenge for ectotherms and can ultimately lead to asymmetric changes in food webs relative to endotherms [ 22 ]. Climate warming also alters both the requirements for and availability of specific nutrients in complex ways, such that even small decreases in nutrient availability can have amplified effects when consumer demand for those nutrients increases concurrently [ 25 ]. Despite their ecological importance and vulnerability, ectotherms remain underrepresented in meta-analyses of diet-microbiome interactions compared with those of endothermic vertebrates [ 19 , 26 ]. Moreover, the strength and direction of dietary effects on gut microbiomes in ectotherms likely vary across taxa, trophic strategies, and developmental stages, limiting generalization from single-species studies. Existing studies on dietary effects on ectotherm gut microbiomes vary widely in experimental design, dietary manipulations, microbiome metrics, and taxonomic resolution. Methodological challenges include the use of different sample types (e.g., gut mucosa and/or contents; sampling location along the gut; [ 3 , 27 ]), as well as variation in laboratory protocols and sequencing technologies, all of which influence the precision and comparability of results [e.g., 28, 29, 30, this study]. Such methodological heterogeneity hampers the identification of general patterns and limits predictive capacity by introducing additional sources of variation, thereby reducing statistical power to detect effects related to phylogenetic and ecological differences among host taxa. In this context, a meta-analysis provides a powerful framework to quantitatively assess the magnitude and consistency of diet-driven microbiome responses across taxa and ecosystems while accounting for confounding factors. Diet and trophic alterations are in the core of global changes [ 31 ] and identifying keystone microbial taxa, diversity metrics, or functional groups associated with dietary change could yield early-warning biomarkers of altered nutrient intake in ectotherms. Such microbiome-based indicators may enable the early detection of sublethal impacts of global change before declines in host performance or population viability become apparent [ 32 ]. In this study, we conducted a meta-analysis to quantify how dietary changes influence gut microbiome diversity in vertebrate ectotherms and to identify consistent microbial responses that may serve as indicators of diet alteration under global change. Microbiome biomarkers have been useful tools in varied approaches from human disease diagnostics to prediction of ecosystem functions and ecological features [e.g., 33, 34, 35, 36, 37]. Identifying gut microbiome biomarkers that signal marked changes in the intake of key nutrients could enable early detection of dietary shifts that may ultimately compromise host health. We performed a systematic literature review following PRISMA guidelines [ 38 ] to identify studies assessing the effects of dietary variation on gut microbiomes of vertebrate ectotherms. From these studies, we retrieved raw bacterial sequence data generated via 16S rRNA gene metabarcoding and quantified changes in microbial diversity patterns and indicator taxa in response to diet nutritional composition (i.e., crude protein, lipid, carbohydrate, and fiber content, as well as vertebrate-, insect-, and plant-derived protein and fat sources). Analyses accounted for methodological differences (e.g., experiment duration, sample type and preservation, DNA extraction kit, amplified 16S rRNA region, and sequencing technology), as well as host developmental stage, habitat, and taxonomic identity (species, genus, family, and order). Methods Systematic literature review and sequence retrieval A systematic literature review was conducted following PRISMA guidelines ([ 38 ]; Fig. 1) to identify studies investigating the effects of dietary nutritional variation on gut microbiome composition in ectothermic vertebrates (classes Agnatha, Chondrichthyes, Osteichthyes, Amphibia, and Reptilia). The databases Web of Science, Scopus, and PubMed were searched using the following query: “microbiom* OR microbiot*; AND diet* OR nutrition* OR food*; NOT human OR child* OR man OR woman OR men OR women OR adolescent* OR infant* OR clinical OR patient*; NOT obesity OR cancer OR Alzheimer”. Explicit specification of vertebrate ectotherms in the search query was not feasible given the taxonomic diversity of the group and the frequent use of species-specific or common names in study titles and abstracts. Because studies on humans constitute the vast majority of publications examining diet-microbiome interactions, exclusion terms were used to reduce retrieval of human-focused studies, as well as studies addressing well-established human health conditions (e.g., obesity, cancer, Alzheimer’s disease). Such studies were found to be disproportionately abundant in preliminary searches and often focused on human patients, genetically modified mice, or experimentally induced disease models. Records retrieved from the three databases were subsequently merged, and duplicate entries were removed. During the screening process (Fig. 1), records were excluded based on predefined eligibility criteria, applied sequentially at the title and abstract screening stage and during full-text assessment. Studies were excluded if they: (1) did not involve ectothermic vertebrates (i.e., focused on endothermic vertebrates or non-vertebrate taxa); (2) investigated specific human diseases, whether conducted in humans or in disease-model organisms; (3) evaluated the benefits or drawbacks of processed foods intended for human consumption; or (4) involved experimentally induced disease states, genetically modified organisms, or hybrids. Reports retained after initial screening were assessed for eligibility based on the following inclusion criteria, applied at both the title/abstract and full-text levels: (1) use of vertebrate ectotherms as the study organism (host); (2) assessment of the entire bacterial community (excluding studies targeting specific bacterial taxa only); (3) use of barcode amplification and sequencing of the 16S rRNA gene (excluding studies based on bacterial cultivation or denaturing gradient gel electrophoresis [DGGE]); (4) comparison of at least two distinct diets; (5) availability of diet composition data, including any combination of the variables crude protein, lipid, carbohydrate, or fiber content, and/or the proportional contribution of protein or fat derived from vertebrates, primary producers (plants or algae), or insects; and (6) availability of raw sequencing data that could be linked to host diet treatments in public repositories, specifically the National Center for Biotechnology Information (NCBI; [ 39 ]) or the European Nucleotide Archive (ENA; [ 40 ]). Corresponding authors were contacted when sequences were unavailable, inaccessible, or lacked sufficient metadata to assign them to diet treatments, in order to obtain the necessary sequence data or associated metadata. Data extraction Metadata retrieved for analyses included host taxonomy (order, family, genus, and species), developmental stage (larvae, juvenile, or adult), habitat (freshwater or saltwater), experimental features (temperature and duration in days), diet composition, and laboratory methods. Diet features comprised percent dry weight of crude protein, fat, carbohydrate, and fiber, as well as the proportional (mg/g) contribution of protein and fat sources derived from animals, primary producers (plants or algae), or insects (obtained from detailed diet compositions when provided in the study). Laboratory methods included sample type (whole digestive tract, gut mucosa, and/or gut contents representing the whole gut or specific portions: foregut, midgut, or hindgut), sample preservation (fresh, frozen at − 20°C or − 80°C, or chemically preserved), sequencing kit/protocol, amplified region of the 16S rRNA gene, sequencing technology, and whether sequencing was done in a paired-end or single-end run. In studies testing treatments in addition to diet, only the corresponding control group was used for data extraction. Additional host information, including mass, total length, condition factor, and survival, was also retrieved and included in the metadata (available at FigShare; doi: 10.6084/m9.figshare.31079137 ); however, these variables were mostly available only as treatment means rather than individual-level data, preventing statistical analysis. Experimental temperature was extracted for reference but not analyzed, as in the selected studies it was adjusted to the optimal rearing conditions for each ectotherm model and thus could not be statistically separated from host taxonomy or habitat. Bioinformatic analyses We developed a bioinformatic workflow to standardize datasets generated using different methodologies (e.g., gut sample type, amplified region of the 16S rRNA gene, sample preservation, extraction kit, sequencing platform). Analyses began with sequence quality filtering performed separately for each study. Paired-end and single-end demultiplexed FASTQ sequences corresponding to different 16S rRNA gene regions were imported into QIIME2 [ 41 ] and denoised using the q2-deblur algorithm, following the quality filtering approach of [ 42 ]. The q2-deblur algorithm is well-suited for meta-analyses involving multiple datasets, as it associates erroneous reads with the true biological sequences, reducing dataset-specific sequence error profiles [ 43 ]. Merged forward and reverse sequences, or single sequences, were trimmed to retain high-quality reads with a median Illumina quality score above 30 (Q30), corresponding to lengths of 100–440 bp. Trimming length was selected to maximize sequence retention while maintaining the desired quality threshold. Six studies that did not meet these quality criteria were excluded. Rarefaction analyses were performed for each of the 37 remaining studies. From these, nine had samples with number of reads below the sequencing depth threshold at which alpha diversity (Shannon entropy) stabilized. These samples were thus removed. For taxonomic assignment, a single classifier was applied to all Amplicon Sequence Variants (ASVs) to ensure standardization across studies. This classifier was built using the full-length 16S rRNA gene, as different studies amplified different gene regions. A phylogenetic tree was prepared based on the SILVA database version 138.2 [ 44 , 45 ] and processed with RESCRIPt [ 46 ]; script “process_silva_database.sh” in https://github.com/PaulaEterovick/meta-analysis-ectotherm-diet-and-microbiome/upload/main ). Although using environment-based sequence weights are recommended [ 47 ], we avoided this because our datasets included sequences from different gut sections and amplified 16S rRNA gene regions. To verify classification accuracy, we built an alternative classifier using sequence weights and classified sequences from one whole-gut dataset targeting the V4 region (our dataset on the European common frog, Rana temporaria ). Classification results were identical down to the family level, with minor differences at the genus level; species-level assignments remained incomplete and inconsistent for both classifiers. After classification, ASV identities were standardized across studies by replacing them with the SILVA Accession Number assigned to the Operational Taxonomic Units (OTUs) to match each ASV in the SILVA database ([ 44 , 45 ]; https://docs.qiime2.org/2024.10/plugins/available/feature-classifier/classify-consensus-blast/ ). SILVA is a high-quality curated database linking sequences to bacterial taxonomy and nomenclature. This approach also increased the reliability of sequence count data, as ASVs with minor differences that likely representing sequencing errors were assigned to the same reference sequence. Duplicate SILVA Accession Numbers (hereafter referred to as OTUs) were merged within each study, and the resulting files were then combined across studies (codes available at https://github.com/PaulaEterovick/metanalysis-ectotherm-diet-and-microbiome/upload/main , scripts classify_with_silva_accessions.sh, merge_taxonomy_with_accession.py, merge_duplicate_otus_complete.py, filter_taxonomy.py). The combined dataset included 37 studies, 1,393 samples, 10,750 OTUs and 26,772,009 reads. For downstream analyses, data were also aggregated to genus and family levels to increase classification precision and improve representativeness across samples and studies (code available at https://github.com/PaulaEterovick/metanalysis-ectotherm-diet-and-microbiome/upload/main , script merge_otus_by_family.py) . Statistical analyses Effects of diet on microbiome alpha- and phylogenetic diversity We aimed to assess the effects of diet on microbiome diversity while accounting for host life history traits (developmental stage, habitat), taxonomy, and methodological variation, which may also influence diversity. Alpha-diversity was calculated using Hill numbers with the incidence approach (order of diversity q = 2, which is based on the Simpson diversity index and thus gives greater weight to abundant OTUs) using the R package iNext [ 48 , 49 ]. We focused on abundant OTUs as they are likely easier to detect and quantify, thus minimizing differences among sample diversities caused by detection failure of rare OTUs. Because sample depth varied across studies, simulations were used to assess the coverage of the real number of species based on the slope of the rarefaction curve [ 50 ]. This approach is robust to variation in sample depth and is recommended for datasets that are zero-inflated, biased, or otherwise insufficient [ 51 ]. Observed and estimated Hill numbers for alpha diversity were very similar (results available at FigShare; doi: 10.6084/m9.figshare.31079137 ), indicating sample completeness despite variation in sampling depth. Thus, estimated diversity values were used in subsequent analyses. Phylogenetic diversity was calculated using Faith’s Phylogenetic Diversity (Faith’s PD = the sum of branch lengths separating taxa in a community) with standardized effect size (SES, which corrects for effects of different richness) in the picante package [ 52 ]. This produces standardized z-scores based on 999 simulations, testing whether samples are more or less diverse than expected by chance. It therefore accounts for differences in species richness arising from methodological variation among studies, correcting for unequal sampling effort. For this, a taxonomic tree including the 10,725 OTUs identified across studies was prepared. The sequences corresponding to these OTUs were extracted from the SILVA 138.2 database (SILVA_138.2_SSURef_NR99_tax_silva.fasta) based on their accession numbers, aligned with MAFFT [ 53 ], and a phylogenetic tree was built with FastTree ([ 54 ]; code “build_silva_tree_for_Hill_numbers.py” available at https://github.com/PaulaEterovick/meta-analysis-ectotherm-diet-and-microbiome/upload/main ). To test whether alpha- and phylogenetic diversity were primarily influenced by diet or by methodological, taxonomic, or ecological factors, we constructed conditional inference trees using the R package partykit [ 55 ]. This method generates a hierarchical structure of predictive variables, splitting groups at each node based on the variable with the greatest explanatory power. It applies recursive partitioning for numerical or categorical variables, is robust to datasets with many zeros, and reduces overfitting [ 56 ]. Before tree construction, Spearman correlations among diet variables were tested to identify and remove highly co-varying variables, as these might cause unwanted variance inflation hampering statistical tests. Only total carbohydrate percentage and total fiber percentage were highly correlated (rho = − 0.825, p < 0.001); total fiber percentage was excluded because it was reported in fewer studies. All other correlations had absolute rho ≤ 0.605 and were retained (following [ 57 ]). A total of 23 explanatory variables were included in the conditional inference trees describing diet composition (9 variables): crude protein percentage (CPP), crude lipid percentage (CLP), total carbohydrate percentage (TCP), vertebrate protein (VP, mg/g), insect protein (IP, mg/g), plant protein (PP, mg/g), vertebrate fat (VF), insect fat (IS), and plant fat (PF), host taxonomy (4 levels): species, genus, family, and order, host traits (2 variables): developmental stage (larvae, juvenile, adult) and habitat (freshwater or saltwater), and methodological variables (8 variables): duration of the experiment (days), sample type (digestive tract, gut with contents, gut mucosa, gut contents, foregut mucosa, foregut contents, midgut mucosa, midgut contents, midgut with contents, hindgut mucosa, hindgut contents, hindgut with contents), sample preservation (fresh, -20°C, -80°C, RNAlater, or Zymo Xpedition™ Lysis/Stabilization Solution), DNA extraction method (15 methods; see metadata at FigShare; doi: 10.6084/m9.figshare.31079137 ), amplified 16S rRNA gene region (V1-V2, V3-V4, V4, V4-V5, V6-V8), sequencing technology (Illumina MiSeq, HiSeq, NovaSeq, NovaSeq 6000, Ion Torrent Personal Genome Machine), sequencing type (single- or paired-end), and trimming length (100, 230, 250, 310, 400, or 440 bp). Microbiome indicators of dietary changes We assumed that a reliable bacterial indicator of dietary changes would be a taxonomic group that is widespread across vertebrate hosts, varies with specific diet nutritional parameters, and is robust to methodological variation in gut microbiome characterization. To identify such indicators, we first searched for strong correlations between gut bacterial genera and families and the nine diet composition variables. We then assessed whether these correlations were spurious - arising from methodological variation - or genuinely related to diet. OTUs were first analyzed using the FLORAL package [ 58 ] to identify potential indicators of the nine diet variables. OTU abundances (read counts) were correlated with diet variables, and those with p < 0.01 and an absolute correlation coefficient ≥ 0.4 were selected. The same procedure was applied after aggregating OTUs to the genus and family levels to detect consistent patterns at higher taxonomic resolution. To confirm that diet features were the main drivers of variation in the candidate microbiome biomarkers, we constructed conditional inference trees for genera and families showing significant correlations with diet. These taxonomic levels were chosen because most OTUs could not be reliably identified at species level, and genera and families are more likely to be widespread across hosts and comparable across studies, independent of the reference database used for taxonomic assignment. If the correlation between a diet feature and the number of reads assigned to a taxon was not confounded by other variables, we expected the diet feature to appear at the first node of the tree, indicating it as the primary explanatory variable. These partition analyses included the same 23 explanatory variables used in the microbiome diversity analyses. Analysis focused on the V3-V4 and V4 regions of the 16S rRNA gene and filtered dataset To evaluate the robustness of our method in detecting indicators of ectotherm dietary changes despite variation in the amplified 16S rRNA gene regions, we repeated the microbiome indicator analyses using only studies that targeted the V3–V4 or V4 regions. Different 16S rRNA regions can yield varying results in microbiome composition and taxonomic assignment accuracy [ 29 , 30 , 59 ]. Although the accuracy of specific regions depends on sample origin [ 29 , 30 , 60 ], we focused on V3-V4 and V4 because they are widely used in gut microbiome research and were amplified in the majority of selected studies (30 of 37). We also excluded OTUs present in less than 5% of samples to reduce potential bias from underrepresented taxa. These rare OTUs may reflect host- or habitat-specific taxa or may result from methodological differences, including sampling depth, DNA extraction protocols, or sequencing technologies [ 61 , 62 ]. The 5% threshold, although lower than the recommended 10% [ 63 ], represented a compromise between retaining representative taxa and maximizing the number of samples. This filter removed 12 individual samples spread across studies, but no study was entirely excluded. This resulted in a data set we refer to as “filtered” and which included 198 OTUs, 1,073 samples, and 10,614,500 reads. We then repeated the microbiome indicator analyses described above using this dataset. With this approach, we aimed to reduce noise in our dataset by minimizing variation in two key aspects: the targeted 16S rRNA region and taxonomic representativeness across hosts and habitats. We expected to obtain consistent results from both the complete and the filtered datasets if the findings were robust to variation in these parameters. Results Systematic literature review and sequence retrieval The initial literature search retrieved 31,400 papers from Web of Science, 24,825 from Scopus, and 22,311 from PubMed, resulting in 41,899 unique papers after removal of duplicates across databases. Titles were screened first, yielding 865 studies for further assessment (Fig. 1). Of these, 138 studies met inclusion criteria (as described in Materials and Methods), and we could access raw sequences with associated diet treatment information from 42 of them (Fig. 1). Six from these 42 studies, however, were excluded because their sequences did not meet our quality standards, and one was excluded because it had no treatment replicates. For 70 studies that lacked available sequences, the corresponding authors were contacted, and only three shared their data. On the other hand, three studies reported sequence availability, but the sequences could not be retrieved with the provided information. Twenty-nine studies deposited raw sequences but did not provide metadata linking sequences to host diet treatments. Contacting the corresponding authors yielded four additional datasets. The final dataset included 37 studies, comprising 10,725 OTUs (Silva Accession Numbers) and 1,393 samples from 13 fish species (larvae, juveniles, and adults) and three anuran amphibian species (larvae or juveniles; Table S1 ). The amplified regions of the 16S rRNA gene were V1-V2 (201 samples), V3-V4 (761 samples), V4 (359 samples), V4-V5 (10 samples), and V6-V8 (78 samples). Microbiome samples originated from the whole digestive tract, the whole gut, or from fore-, mid-, and hindgut sections, either including contents, mucosa only, or contents only. Records excludedBased on title: Studies did not include ectothermic vertebrates, focused on humans, or food for human consumption, used models genetically modified, with induced disease or hybrids (n = 41,034) Records screened(n = 41,899) Reports not retrievedBased on whole paper, studies NOT attending the criteria: Focus on ectothermic vertebrates, 16S rRNA gene barcoding of the whole bacterial community, comparison of at least two diets with associated nutritional information (n = 793) Reports sought for retrieval(n = 865) Reports excluded:Raw sequences not available neither shared by authors (n = 70)Sequence not associated to sample information neither after author request (n = 25)Low sample size (n = 1)Low sequence quality (n = 6) Reports assessed for eligibility(n = 138) Source: Page MJ, et al. BMJ 2021;372:n71. doi: 10.1136/bmj.n71 . This work is licensed under CC BY 4.0. To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/ Figure 1. PRISMA diagram (following [ 38 ]) showing the screening of studies for the metanalysis. All screening was conducted manually. The selection of papers for data retrieval was based on titles (titles that contained information showing the paper did not adhere to the inclusion criteria were discarded). Among the reports sought for retrieval, additional examination of the abstract and text were performed to certify that all inclusion criteria were met (see text for details on inclusion and exclusion criteria). From the 138 papers considered eligible, sequence availability and sample size represented the last screening criteria, resulting in the inclusion of 36 papers. Additionally, our own dataset on larvae of the European common frog ( Rana temporaria ) was included. Effects of diet on microbiome alpha- and phylogenetic diversity The variable that most strongly explained microbiome alpha diversity in the partition tree was the targeted region of the 16S rRNA gene (node 1). The V3–V4, V4, and V4–V5 regions showed lower alpha diversity than the V1–V2 and V6–V8 regions (Figs. 2 ). Sample type (node 2) and DNA extraction kit (node 19) were the next most important variables explaining variation in microbiome diversity within the resulting groups, and sample type also appeared frequently in subsequent nodes of the partition tree. Sequencing technology (node 24, n = 208 samples) and trimming size (node 47, n = 147) explained variation in specific nodes including a reduced number of samples. Sample preservation method and whether sequencing was single or paired-end did not appear in the tree. Host genus (nodes 21, 53; n = 252, 154, respectively), family (node 52, n = 198) and habitat (nodes 4, 32; n = 154, 113, respectively) explained variation only in nodes containing a limited number of samples, whereas experiment duration, host species, order, and life stage did not appear in the tree. Also, diet-related variables explained variation within individual groups of samples. Higher alpha diversity was associated with crude protein > 44% (node 5, n = 81) and > 44.6% (node 12, n = 63); absence of plant protein in the diet (node 10, n = 73); vertebrate protein > 42.2 mg/g (node 16, n = 49); total carbohydrate ≤ 12.6 mg/g (node 39, n = 290), vertebrate fat > 10.7 mg/g (node 40, n = 193), presence of insect protein (node 41, n = 145), and crude lipid ≤ 20.3% (node 43, n = 62). For phylogenetic diversity, genus was the most important explanatory variable (node 1), with no clear separation among higher taxonomic levels; both frogs and fishes occurred in branches with higher and lower phylogenetic diversity (Fig. S1 ). Sample type (node 2) and DNA extraction kit (node 37) were the next most important variables explaining phylogenetic diversity in the resulting nodes, both appearing again frequently throughout the partition tree. Amplified region of the 16S rRNA gene (nodes 10, 19, 62; n = 79, 92, 129, respectively) and trimming length (node 28, n = 184) explained variation in phylogenetic diversity within limited subsets of samples. Sequencing technology, whether sequencing was single or double stranded, and sample preservation were not included in the partition tree. Host species (node 4, n = 347), and order (node 44, n = 215) explained variation within nodes with a number of samples relatively higher than experiment duration (nodes 5, 15, 54; n = 95, 29, 59, respectively), host habitat (node 52, n = 72) and life stage (node 46, n = 107). Higher phylogenetic diversity was associated with the absence of insect protein sources in the diet (node 3, n = 439 samples) or amounts ≤ 11 mg/g (node 64, n = 77); crude protein > 47.3% (node 9, n = 171); plant fat ≤ 13.6 mg/g (node 13, n = 92) and > 4.8 mg/g (node 31, n = 115); total carbohydrate > 12.2% (nodes 20–21, n = 69); vertebrate protein > 40.59 mg/g (node 30, n = 159) or > 60 mg/g (node 50, n = 82); crude lipid > 20.2% (node 34, n = 44); vertebrate fat > 10.7 mg/g (node 39, n = 488); and plant protein ≤ 47.6 mg/g (node 41, n = 99) or > 33.4 mg/g (node 49, n = 174). Microbiome indicators of dietary changes OTU abundances from the complete dataset (10,125 OTUs) showed 11,667 significant correlations (p 0.4 (Fig. 3 ). Aggregating OTUs to the genus level yielded 959 genera (including both genera with valid names and genera with non-published names but labelled and assigned to specific publications), with 1,771 significant correlations, 30 of which had absolute correlation > 0.4. At the family level, 356 classified families exhibited 1,771 significant correlations, of which 12 had absolute correlation > 0.4. In the filtered dataset (V3-V4 or V4 regions, OTUs present in ≥ 5% of samples), there were 1,073 OTUs grouped into 74 classified genera and 43 classified families. Significant correlations (p < 0.01) with diet features totaled 927, 385, and 246 at the OTU, genus, and family levels, respectively, with 70, 24, and 20 correlations exceeding an absolute coefficient of 0.4. Among the 28 genera which were correlated with diet variables (Table 1 , Figs. S2-29), 15 had the correlated variable as the first node in the partition tree, 14 of which showed a positive correlation with insect protein sources ( Brevicacterium, Cellulosimicrobium, Corynebacterium, Heyndrickxia, Lederbergia, Oceanobacillus, Ornithibacillus, Metabacillus, Shouchella, Siminovitchia, Lysinibacillus, Globicatella, Enterococcus, Mamaliicoccus ; Figs. S5-7, S9-11, S13, S16, S19-20, S23-24, S27-28). The remaining genus, Alkalinohalinophilus (Fig. S3), correlated with insect fat sources (Table 1 , Fig. 3 ), while Lederbergia had insect fat as the second node (Fig. S13). Actinomyces , although having sample type as the first node, showed insect protein sources as the main driver of variation in the subsequent nodes (Table 1 , Fig. S2 ). The remaining genera had methodological variables (extraction kit, n = 5; sample type, n = 2; amplified region, n = 2; sequencing platform, n = 1) or host species (n = 1) as the first node. In all cases, diet features appeared elsewhere in the partition trees, except for Weissella and total carbohydrate percentage, where the correlation was not reflected in the tree (Fig. S2 9). Table 1 Microbiome taxa selected as indicators of nutritional features of diets (CLP = crude lipid percentage; CPP = crude protein percentage; IF = insect fat sources, mg/g; IP = insect protein sources, mg/g; PF = plant fat sources, mg/g; TCP = total carbohydrate percentage; VP = vertebrate protein sources, mg/g) fed to ectothermic hosts. Analyses were based on 37 studies (all) which amplified different regions of the bacterial 16S rRNA gene including 13 species of fishes and three amphibians as hosts, as well as on a filtered dataset (filt) composed of 30 studies that amplified just the V3-V4 or V4 region including 13 species of fishes and two amphibians. Positive correlations are indicated with + and negative correlations with -. Microbiome indicators were searched based on read counts at the OTU (o), genus (g or G), and family (f or F) levels, all the significant results (p < 0.001) with absolute correlation coefficients higher than 0.4 are shown in the table with letters corresponding to the corresponding taxonomic level. OTUs classified above the family level were not included. Families and genera marked with an * were not represented in the filtered dataset. Genera and families whose first node in the partitioning tree are the correlated diet descriptors are represented in capital boldfaced G or F, respectively. Genera with capital letter not boldfaced were the second node but also considered as robust indicators (see text for explanations). Phylum Actinomycetota CLP CPP IF IP PF TCP VP Class Order Family Genus filt filt all filt all filt all all filt filt Actinomycetota Actinomycetales Actinomycetaceae Actinomyces o+ ogf+ oG F + o GF + Bifidobacteriales Bifidobacteriaceae* Bifidobacterium* gf+ Micrococcales Brevibacteriaceae Brevibacterium o+ o+ o GF + o GF + Microbacteriaceae Microbacterium o+ Leucobacter og+ Micrococcaceae Enteractinococcus g+ o GF + Promicromonosporaceae* Cellulosimicrobium* o GF + Mycobacteriales Corynebacteriaceae Corynebacterium o+ o+ o GF + oG F + Propionibacteriales Proprionibacteriaceae Cutibacterium o+ o+ Phylum Bacillota Bacilli Bacillales Bacillaceae Alkalihalophilus* o G + F + F+ Bacillus o+ o+ Heyndrickxia* o G + Lederbergia oG+ o+ o G + oG+ Oceanobacillus o+ o+ o G + oG+ Ornithinibacillus* o G + Metabacillus* o G + Pseudogracilibacillus og+ oG+ og+ o G + Shouchella* o G + Siminovitchia o G + Planococcaceae Lysinibacillus o G + Exiguobacterales Exiguobacteraceae Exiguobacterium o+ Lactobacillales Aerococcaceae Globicatella GF + o GF + Carnobacteriaceae Atopostipes o G + Enterococcaceae Enterococcus o+ o+ o GF + oGF+ Lactobacillaceae Companilactobacillus o+ Lacticaseibacillus o+ o+ Lactiplantibacillus o+ Latilactobacillus og+ Lactobacillus o+ Leuconostoc o+ Levilactobacillus g+ Limosilactobacillus o+ Pediococcus o+ Schleiferilactobacillus o+ Weissella g- Streptococcaceae Lactococcus o+ Vagococcaceae Vagococcus o+ o+ Mycoplasmatales Mycoplasmataceae Malacoplasma o+ ogf+ ogf+ Mycoplasma og+ og+ Paenibacillales Paenibacillaceae Paenibacillus o+ o GF + ogf+ oGF+ Staphylococcales Staphylococcaceae Macrococcus gf+ f+ Mammaliicoccus o G + og+ Nosocomiicoccus g+ Clostridia Clostridiales Clostridiaceae Clostridium o+ o+ Peptostreptococcales-Tissierellales Family XI Peptoniphilus f+ o+ f+ Tepidimicrobium og+ Peptostreptococcaceae Peptostreptococcus o+ Phylum Bacteroidota Bacteroidia Bacteroidales Bacteroidaceae Bacteroides ogf- Chitinophagales Chitinophagaceae Sediminibacterium o+ Phylum Fusobacteriota Fusobacteriia Fusobacteriales Fusobacteriaceae Cetobacterium f+ g+ o+ ogf+ Phylum Pseudomonadota Gammaproteobacteria Burkholderiales Burkholderiaceae Ralstonia f+ g+ Comamonadaceae Delftia o+ Enterobacterales Aeromonadaceae Aeromonas ogf- Enterobacteriaceae Citrobacter ogf- Escherichia-Shigella o+ Vibrionaceae Photobacterium o+ Phylum Thermodesulfobacteriota Desulfovibrionia Desulfovibrionales Desulfovibrionaceae of+ In the filtered dataset (Figs. S30-48), Actinomyces, Brevibacterium , and Globicatella (Figs. S30, S34, S40) retained insect protein sources as the first node. Lederbergia and Oceanobacillus had insect protein sources as the second node, with insect fat sources as the first (Figs. S41, S48). Enterococcus also had insect protein sources as the second node, following sample type (Fig. S39). Several genera with insect protein as the first node in the complete dataset ( Cellulosimicrobium, Heyndrickxia, Ornithibacillus, Metabacillus, Shouchella ) were not present in the filtered dataset. Atopostipes correlated with insect protein sources only in the filtered dataset, and insect protein was its first node (Fig. S32). Enteractinococcus and Pseudogracilibacillus had insect protein as the first node in the filtered dataset (Figs. S38, S47) but not in the full dataset (Figs. S8, S26), where methodological variables were more influential. Paenibacillus correlated with insect fat sources, with insect protein as the second node (Fig. S46). Other genera had sample type (n = 5), host species (n = 2), host family or order (n = 1 each), or sequencing platform (n = 1) as the first node. Bacteroides (Fig. S33), Cetobacterium (Fig. S35), Citrobacter (Fig. S36), Malacoplasma (Fig. S42), Mycoplasma (Fig. S44), and Ralstonia (Fig. S48) did not show the correlated diet variables in their partition trees, indicating spurious correlations. Of the 12 families positively correlated with diet features (Figs. S49-60), seven had insect protein sources as the first node ( Actinomycetaceae, Brevibacteriaceae, Promicromonosporaceae, Corynebacteriaceae, Bacillaceae, Aerococcaceae, Enterococcaceae ; Table 1 , Figs. S49-S51, S53-S54, S56), while the remaining five had first nodes represented by sample type (n = 4), host species, family, or order (n = 1 each), or sequencing platform (n = 1). In the filtered dataset, Actinomycetaceae, Brevibacteriaceae, Corynebacteriaceae , and Aerococcaceae retained insect protein as the first node (Figs. S61-S62, S66-S67). Bacillaceae and Enterococcaceae had insect protein as the second node, with the first nodes being sample type and insect fat, respectively (Figs. S64, S69). Promicromonosporaceae was absent in the filtered dataset. Micrococcaceae and Paenibacillaceae showed positive correlations only in the filtered dataset (Figs. S71, S73), with insect protein and fat as first nodes, respectively. The remaining families had first nodes corresponding to sample type (n = 4), host species, family, or order (n = 1 each), or sequencing platform (n = 1). Four families ( Aeromonadaceae, Bacteroidaceae, Enterobacteriaceae, Mycoplasmataceae , Figs. S63, S65, S68, S72) lacked the correlated diet variables in their partition trees, indicating spurious correlations. Discussion Microbiomes may play an important role in enhancing the adaptive capacity of ectothermic hosts [ 6 , 7 ]. Understanding how microbiomes respond to changes in food availability and consumption is therefore a crucial first step toward elucidating microbial contributions to host adaptation under environmental change. In this context, the identification of microbiome indicator taxa and use as a monitoring tool could be very useful. Microbiome indicators are useful in many different contexts, such as indicating healthy gut ecosystems in humans [ 33 ], environmental properties along river courses [ 34 ], anthropogenic disturbances in ecosystem health [ 35 , 36 ], soil structure and productivity [ 37 ]. The available studies that include both information on ectotherm gut microbiome composition (with raw sequences available) and diet nutritional features were conducted under controlled conditions and with diets that attended the nutritional demand of the experimental animals (Table S1 ). Thus, our results likely apply to plastic changes in the microbiome within its healthy state. When hosts are submitted to highly stressful conditions that could lead to dysbiosis, however, they may suffer drastic and non-adaptive microbiome changes. Our aim, however, is to find indicators that could show changes before dysbiosis occurs, what makes the available data appropriate. On the other hand, our results show that methodological heterogeneity represents a major challenge for identifying diet- or ecology-driven patterns of microbiome diversity across studies. Nevertheless, despite the relatively small number of suitable studies and substantial methodological variation, we were able to identify microbial indicators associated with specific diet-related changes in ectotherms and relevant to host health. In addition, we provide recommendations aimed at accelerating knowledge acquisition and improving the monitoring of ectotherm health under environmental change using microbiome-based indicators. Effects of diet on microbiome alpha- and phylogenetic diversity Our results show that methodological approaches significantly affect microbiome diversity estimates, highlighting the need for methodological standardization to enable robust comparisons among biologically relevant scenarios. Among the variables examined, the targeted region of the 16S rRNA gene was the most important source of variation in alpha diversity, corroborating previous studies reporting differences among target regions [ 30 , 64 ]. Sample type also emerged as an important variable explaining both alpha and phylogenetic diversity, in agreement with earlier findings [e.g. 27]. The type of DNA extraction kit was likewise influential and is likely more difficult to standardize, given the wide range of extraction kits currently available on the market [ 65 ]. Low to mid throughput sequencing (MiSeq) provided lower alpha diversity than high-throughput sequencing technologies, what may be related to lower numbers of reads provided to describe bacterial community diversity resulting in the failure to detect less abundant taxa. Trimming length depends on both the extent of the amplified region and sequencing quality; however, its influence on diversity was limited. In contrast, sample preservation method and whether sequencing was single- or paired-end did not explain microbiome diversity, indicating that variation in these methodological steps is less problematic for diversity comparisons. Ectothermic host genus was the most important variable explaining bacterial phylogenetic diversity; however, the limited number of ectotherm species included and the presence of both fishes and amphibians in both resulting branches suggest that “genus” (i.e., host phylogeny) itself is unlikely to be the true driver of this variation. Instead, the observed patterns may reflect differences in original habitats or sources of microbiome acquisition. Although the variable “habitat” appeared in restricted nodes of the partition trees explaining either alpha or phylogenetic diversity, our classification was limited to freshwater versus saltwater habitats. Substantial habitat variability occurs at finer spatial and ecological scales within these broad categories, and such variation is known to strongly influence microbiome structure [ 66 ]. The effects of methodological variables were similar to alpha diversity, except that sequencing technology did not appear in the partition tree for phylogenetic diversity. Variables describing diet nutritional composition explained variation in both alpha and phylogenetic diversity, but only within restricted subsets of samples, making it difficult to generalize the results. The high degree of methodological heterogeneity limited our ability to robustly assess the effects of diet and other ecological variables on overall microbiome diversity using the available data. Microbiome indicators of dietary changes and their role on host health Our results confirm that identifying indicator taxa associated with dietary variation in ectothermic vertebrates is feasible, supporting the ecological expectation that selection favors increased the abundance of specialized microorganisms capable of digesting dominant food types [ 67 ]. We identified several bacterial indicator taxa whose abundances varied with the relative contribution of insects to ectotherm diets. Notably, these relationships were detectable despite substantial methodological heterogeneity, including variation in sample type, DNA preservation and extraction methods, targeted 16S rRNA regions, sequencing technologies, experimental designs, and taxonomic and life-history differences among the studied ectotherms. Several indicator microbial groups matched between the complete and the filtered datasets, whereas others with likely equivalent ecological functions were selected in each dataset. Moreover, existing evidence indicates that insects are a highly nutritious food source [ 68 ] and that their digestion by the microbiome generates metabolites that interact with the host’s neuroendocrine system and energy balance [ 5 ], potentially helping ectotherms cope with the physiological challenges imposed by global warming [ 69 ]. Insects are rich in protein and also provide beneficial fats, minerals, and vitamins [ 68 ]. In addition, they contain bioactive compounds such as chitin, which can be degraded by gut bacteria, leading to the production of or short-chain fatty acids (SCFAs) with immunostimulatory and anti-inflammatory properties [ 70 ]. Increased insect consumption has been associated with enhanced growth, antioxidant capacity, and immune responses in ectotherms [ 71 ]. These effects are likely mediated by microbiome shifts that enhance the degradation of complex compounds such as chitin, producing metabolites that can be assimilated by the host as energy sources and signaling molecules [ 5 , 72 ]. Chitin is abundant in insects and other organisms such as crustaceans, algae, and fungi. In chitin-containing tissues, the polymer is typically associated with other structural components, including proteins or glucans. The capacity to degrade chitin is widespread among bacteria and can be inferred from the presence of chitinase genes, which are thought to have spread through horizontal gene transfer. These genes are found in Actinobacteria and several representatives of Bacillota , as well as other taxa [ 72 ]. Our metanalysis showed that bacterial taxa whose abundance consistently increased with higher proportions of insect protein in ectotherm diets included, within the phylum Actinomycetota , the genera and families Actinomyces ( Actinomycetaceae ), Brevibacterium ( Brevibacteriaceae ), and Corynebacterium ( Corynebacteriaceae ). Within the phylum Bacillota , taxa showing similar patterns included Lederbergia and Oceanobacillus ( Bacillaceae ), Globicatella ( Aerococcaceae ), and Enterococcus ( Enterococcaceae ). “Insect protein sources” was the most important variable explaining the abundance of all these taxa, and the second most important—after “insect fat sources”—for Lederbergia and Oceanobacillus ( Bacillaceae ). Insect fat sources also correlated positively with, and explained most of the variation in, the abundance of Alkalihalophilus (present only in the complete dataset), and Paenibacillus ( Paenibacillaceae ). As an insoluble polymer, chitin is initially degraded extracellularly, followed by uptake of the hydrolysis products, which are further broken down into simple sugars and used as energy sources [ 72 ]. In natural habitats, chitin availability is closely linked to chitin hydrolysis rates [ 72 ], suggesting that efficient degraders increase in abundance under higher chitin supply. A similar pattern was observed in the guts of ectotherms fed diets rich in insect protein in our metanalysis. Consistent with these findings, increased abundance of Actinomyces has also been reported in the guts of rainbow trout ( Oncorhynchus mykiss ) fed insect-rich diets [ 73 ]. We likewise observed an increase in Corynebacterium with higher insect protein intake, a genus that includes Corynebacterium glutamicum , which has been associated with increased SCFA levels, reduced inflammation, and restoration of gut barrier function in mice [ 74 ]. Among microbial metabolites derived from ingested food, SCFAs, polyamines, and neuropeptides can be sensed by host receptors and act as signaling molecules that regulate cellular responses and hormonal activity [ 5 ]. By acting on the hypothalamic–pituitary and related neuroendocrine axes, these metabolites influence the expression of insulin-like growth factor I (IGF-I), thereby affecting development, growth, reproduction, and senescence. Although IGF-I can exert pro-inflammatory effects that tend to increase with age and reduced microbiome diversity, SCFAs and polyamines have anti-inflammatory and antioxidant properties that may delay senescence [ 5 ]. The hypothalamic–pituitary axis is a critical mediator of vertebrate adaptive physiological responses to environmental stressors, emphasizing its role in mobilizing energy substrates during challenging conditions [ 75 ]. Mitochondrial functions play a central role and are compromised by high temperatures in ectotherms, causing excessive production of reactive oxygen species (ROS) and increasing energetic costs for antioxidant defense [ 69 ]. Finally, Heyndrickxia coagulans (formerly Bacillus coagulans ) promotes the growth of other beneficial bacteria such as Bifidobacterium and Lactobacillus through its metabolic products, enhancing host absorption of trace inorganic elements [ 76 ]. In humans, these effects translate into improved gastrointestinal health, immune modulation, and increased energy availability [ 76 ]. Heyndrickxia coagulans also produces both pro- and anti-inflammatory regulators, as well as reactive oxygen species (ROS), however it supports immune regulation by modulating cytokine expression and enhancing phagocytosis [ 76 ]. We detected an increase in Heyndrickxia and an OTU classified within the genus Bacillus associated with insect protein sources in the complete dataset (Table 1 ), suggesting that these bacteria may similarly contribute to host health in ectotherms - an inference that will become clearer as more data become available and methodological heterogeneity is reduced. Methodological heterogeneity and biological complexity challenge the detection of diet-related microbiome indicators The lack of robust indicator taxa for most dietary variables tested likely reflects both biological and methodological factors (Fig. 4 ). The limited number of studies with suitable data restricts our ability to simultaneously account for multiple biological sources of variation, such as host diet, taxonomy, life history, and ecology. Moreover, even when biological variables other than diet are controlled for, microbiome responses to food intake may not always be captured by discrete taxonomic indicators. Although resource availability imposed by host diet strongly influences microbial communities [e.g., 72], similar functional profiles can emerge from taxonomically distinct microbiomes. This functional redundancy highlights the flexibility of microbiome assembly [ 77 ] and its adaptive role in enabling animals to cope with changing environments [ 66 ]. In addition, microbiome resilience may contribute to resistance against disturbance, as more plastic bacterial taxa are less likely to be lost under environmental change [ 78 , 79 ]. Host taxonomy had an increased importance as explanatory variable in the filtered dataset, likely because our filtering procedures reduced to some extent the differences resulting from different methods among studies. Differences in microbiomes among related species are expected to be influenced by both their phylogenetic relationships (phylogenetic inertia may occur among closely related species) as well as by their trophic niches, as increase in specialized microorganisms able to digest main food types should be favored by selection [ 67 ]. Methodological variation also played a substantial role in microbiome composition. Previous studies have documented strong effects of sample type on gut microbiome profiles [ 27 ], as well as biases associated with the targeted region of the 16S rRNA gene [ 30 , 64 ]. Consistent with these findings, for many bacterial genera and families that were strongly correlated with dietary variables, the primary factor explaining read abundance was methodological rather than diet-related. Finally, the available dataset on ectotherm gut microbiomes remains limited, and many published studies have not followed open science practices by making raw sequencing data publicly accessible, what seems to be a common problem [ 80 ]. This lack of data availability, combined with insufficient methodological standardization, represents a major obstacle to a comprehensive understanding of how dietary features shape gut microbiomes. Even among bacterial taxa that showed strong overall correlations with diet, dietary variables often appeared near the terminal nodes of the partition trees, whereas other factors exerted stronger effects. Ignoring these confounding variables would likely have led to misleading conclusions. Prospects for microbiome-informed conservation of ectothermic vertebrates We aimed to identify microbiome-based indicators that could enable early detection of sublethal impacts of global change on the diets of ectothermic vertebrates. Such indicators might help define thresholds beyond which the microbiome can no longer buffer hosts against changes in food quality or availability. We found that insect consumption had the most pronounced effects on ectotherm gut microbiomes, demonstrating that such indicators can indeed be detected. However, the nutritional value of insects varies widely among taxa and life stages, depending on factors such as chitin and fat content [ 71 ]. While laboratory diets are designed to meet nutritional requirements in accordance with animal ethics guidelines [ 81 ], wild animals may experience malnutrition and adverse conditions due to human impacts that reduce food availability, quality, and feeding opportunities [ 14 , 82 ]. Therefore, greater effort is needed to understand the microbiome-mediated effects of insect consumption and other food types under natural and human-altered conditions of food availability and selection (Fig. 4 ). Despite their high potential for discovery, the microbiomes of wild animals remain poorly characterized [ 19 ]. For instance, increases in xenobiotic-degradation orthologs may indicate pollutant bioaccumulation along food chains, whereas changes in the abundance of specific bacterial taxa may signal dietary shifts [19, this study]. Given the strong influence of microbiomes on host physiology and health, expanding knowledge of wild animal microbiomes and their shaping factors is essential to identify thresholds beyond which dysbiosis occurs, potentially increasing host mortality risk ([ 7 ]; Fig. 4 ). Assessing detailed nutritional composition of diets in the wild – comparable to that obtained in controlled feeding experiments – is often impractical, as free-ranging animals consume diverse resources that vary among individuals, locations, and seasons [ 82 ]. One promising approach to detect microbial indicators of dietary change in natural habitats is to combine microbiome analyses with stable isotope data from hosts, which can provide robust insights into both short- and long-term dietary composition [ 82 ]. Another major challenge is the standardization of methodological approaches to reduce unwanted variation in microbiome characterization (Fig. 4 ). Our systematic review showed that most studies on ectotherm gut microbiomes amplified the V3–V4 or V4 regions of the 16S rRNA gene, making these regions suitable for comparative analyses. When amplification of alternative regions is necessary, approaches such as the Short MUltiple Regions Framework (SMURF), which combines data from multiple regions, can improve taxonomic resolution [ 83 ]. Simulations suggest that detection probabilities for specific taxa using SMURF are similar to those obtained with the V4 region alone [ 83 ]. When this is not possible, our adopted strategy of focusing on genus- and family-level taxonomic assignments provides a robust framework for comparing studies despite methodological heterogeneity [ 84 ]. Finally, we strongly encourage improved data availability practices by researchers and scientific journals. Public access to raw sequencing data is essential for enabling reanalyses with updated bioinformatic pipelines, alternative taxonomic definitions (e.g., OTUs versus species), and standardized diversity metrics, thereby facilitating meaningful comparisons across studies [ 51 ]. Conclusions Our goal was to investigate the feasibility of detecting dietary changes in ectotherms (such as those triggered by global changes) using gut microbiome bacterial indicators. Here, we show that meta-analyses are a useful tool for synthesizing available microbiome data and for supporting the feasibility of this approach. However, additional data collected under more standardized conditions are needed to reduce the noise caused by substantial variability in experimental design and laboratory procedures. Our meta-analysis provides the first step towards identifying and addressing these issues in order to pursue scientifically robust conclusions. Species monitoring programs frequently focus on demographic parameters, as animal welfare is harder to assess [ 32 ]. Animal microbiomes likely contribute to host welfare by helping individuals cope with changing environmental conditions up to certain thresholds, beyond which dysbiosis may occur, with marked consequences for health and survival [ 7 , 8 , 9 ]. It is therefore important to develop monitoring tools capable of detecting changes before such thresholds are reached. Here, we demonstrate that specific dietary shifts in ectotherms are reflected in their gut microbiomes. Our results highlight the potential of microbiome-based indicators to monitor changes in food quality, availability, and/or consumption in ectotherms. This approach could be particularly valuable for wild populations, where detailed information on consumed food items is often unavailable. By identifying diet-related microbiome shifts—potentially linked to changes in food webs—it may be possible to investigate underlying causes before they ultimately contribute to population declines. Declarations Ethics approval Sample collection and experiment permits for our raw sequence dataset (European common frog, Rana temporaria) were obtained from the Niedersächsisches Landesamt für Verbraucherschutz und Lebensmittelsicherheit, Germany (Gz. 33.19-42502-04-20/3590 and 33.19-42502-04-22-00274). Fieldwork was carried out with permits from the Stadt Braunschweig (Stadt Braunschweig - Fachbereich Umwelt und Naturschutz, Willy-Brandt-Platz 13, 38102 Braunschweig; Gz. 68.11-11.8-3.3). The corresponding animal experiments were approved by the Animal Ethics Comittee, which is the Landesamt für Verbraucherschutz und Lebensmittelsicherheit in Niedersachsen, Germany. Ethics approval Sample collection and experiment permits for our raw sequence dataset (European common frog, Rana temporaria ) were obtained from the Niedersächsisches Landesamt für Verbraucherschutz und Lebensmittelsicherheit, Germany (Gz. 33.19-42502-04-20/3590 and 33.19-42502-04-22-00274). Fieldwork was carried out with permits from the Stadt Braunschweig (Stadt Braunschweig - Fachbereich Umwelt und Naturschutz, Willy-Brandt-Platz 13, 38102 Braunschweig; Gz. 68.11-11.8-3.3). Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding PCE was supported by the Deutsche Forschungsgemeinschaft (DFG; GZ: CA 3427/2 − 1, project number: 546565602; The role of diet on microbiome shaping and outcomes in host defensive behaviour and immune defence unveiled through a multiomic approach). Rana temporaria sample acquisition was funded by the DFG (Project number: 459850971; granted to KR). Author Contribution PCE was responsible for conceptualization, fund acquisition, leading data curation, formal analyses, validation, visualization, and manuscript writing. KR was responsible for fund acquisition, contributed to data curation and manuscript writing. SV, JS, and KWV contributed to formal analyses and manuscript writing. JO contributed with resources and manuscript writing. All authors read and approved the final manuscript. Acknowledgement We are thankful to the authors who made their data available in public repositories or under personal request. We used AI tools from Warp [Warp Dev, Inc. (2026). Warp: The Agentic Development Environment (Version: v0.2026.02.18.08.22.stable_02; Computer software. https://www.warp.dev/] to review and adapt R codes that presented issues during figure formatting and to generate codes to merge large data files. All resulting codes and produced files/figures/results were checked for accuracy, in accordance with guidelines from Springer Nature and the German Research Foundation. Data Availability Raw sequences of our dataset ( *Rana temporaria* ) included in the meta-analysis are deposited in the NCBI (BioProject PRJNA1304763, sample names are preceded by “PCE”). Codes and datafiles are available at GitHub ( [https://github.com/PaulaEterovick/meta-analysis-ectotherm-diet-and-microbiome](https:/github.com/PaulaEterovick/meta-analysis-ectotherm-diet-and-microbiome) ) and FigShare (doi:10.6084/m9.figshare.31079137). References Tuddenham S, Sears CL. The intestinal microbiome and health. Curr Opin Infect Dis. 2015;28(5):464–470; doi: 10.1097/QCO.0000000000000196 . Huang F, Shi X, Chen P, Hu Q, Zhao Y, Chen Z, … Zhang X. Dietary drivers of gut microbiota diversity and function in wildlife of Wolong Nature Reserve: a metagenomic study. Curr Zool. 2025;zoaf018; doi: 10.1093/cz/zoaf018 . Kashinskaya EN, Simonov EP, Kabilov MR, Izvekova GI, Andree KB, Solovyev MM. Diet and other environmental factors shape the bacterial communities of fish gut in an eutrophic lake. J Appl Microbiol. 2018;125(6):1626–1641; doi: 10.1111/jam.14064 . Escalas, A., Auguet, J. C., Avouac, A., Seguin, R., Gradel, A., Borrossi, L., & Villéger, S. (2021). Ecological specialization within a carnivorous fish family is supported by a herbivorous microbiome shaped by a combination of gut traits and specific diet. Front Mar Sci. 2021;8:622883; doi: 10.3389/fmars.2021.622883 . Warne RW, Dallas J. Microbiome mediation of animal life histories via metabolites and insulin-like signaling. Biol Rev. 2022;97(3):1118–1130; doi: 10.1111/brv.12833 . Henry LP, Bruijning M, Forsberg SK, Ayroles JF. The microbiome extends host evolutionary potential. Nat Commun. 2021;12(1):5141; doi: 10.1038/s41467-021-25315-x . Fontaine SS, Kohl KD. Ectotherm heat tolerance and the microbiome: current understanding, future directions and potential applications. J Exp Biol. 2023;226(12):jeb245761; doi: 10.1242/jeb.245761 . Guo J, Li Z, Liu X, Jin Y, Sun Y, Yuan Z, … Zhang M. Response of the gut microbiota to changes in the nutritional status of red deer during winter. Sci Rep. 2024;14(1):24961; doi:10.1038/s41598-024-76142-1. Eterovick PC, Schmidt R, Sabino-Pinto J, Yang C, Künzel S, Ruthsatz K. The microbiome at the interface between environmental stress and animal health: an example from the most threatened vertebrate group. Proc R Soc B. 2024;291(2031):20240917; doi: 10.1098/rspb.2024.0917 . Eterovick PC, Glos J, Burkart F, Overmann J, Ruthsatz K. 2026. Interplay of diet, heat stress, and the microbiome shapes health and escape behavior in amphibian larvae. EcoEvoRxiv 2026; doi: 10.32942/X2CW81 . O'Connor MI, Piehler MF, Leech DM, Anton A, Bruno JF. Warming and resource availability shift food web structure and metabolism. PLoS Biol. 2009;7(8):e1000178; doi: 10.1371/journal.pbio.1000178 . Seifert LI, de Castro F, Marquart A, Gaedke U, Weithoff G, Vos M. Heated relations: temperature-mediated shifts in consumption across trophic levels. PLoS One 2014;9(5):e95046; doi: 10.1371/journal.pone.0095046 . Carreira BM, Segurado P, Orizaola G, Gonçalves N, Pinto V, Laurila A, Rebelo R. Warm vegetarians? Heat waves and diet shifts in tadpoles. Ecology 2016;97(11):2964–2974; doi: 10.1002/ecy.1541 . Hardison EA, Eliason EJ. Diet effects on ectotherm thermal performance. Biol Rev. 2024;99(4):1537–1555; doi: 10.1111/brv.13081 . Ward CA, Tunney TD, Donohue I, Bieg C, Hale KR, McMeans BC, … McCann KS. Global Change Asymmetrically Rewires Ecosystems. Ecol Lett. 2025;28(7):e70174; doi:10.1111/ele.70174. Amato KR, Yeoman CJ, Kent A, Righini N, Carbonero F, Estrada A., … Leigh SR. Habitat degradation impacts black howler monkey ( Alouatta pigra ) gastrointestinal microbiomes. ISME J. 2013;7(7):1344–1353; doi:10.1038/ismej.2013.16. Baniel A, Amato KR, Beehner JC, Bergman TJ, Mercer A, Perlman RF, … Snyder-Mackler N. Seasonal shifts in the gut microbiome indicate plastic responses to diet in wild geladas. Microbiome 2021;9(1):26; doi:10.1186/s40168-020-00977-9. Li Q, Fei HL, Luo ZH, Gao SM, Wang PD, Lan LY, … Fan PF. Gut microbiome responds compositionally and functionally to the seasonal diet variations in wild gibbons.NPJ Biofilms Microbiomes 2023;9(1):21; doi:10.1038/s41522-023-00388-2. Levin D, Raab N, Pinto Y, Rothschild D, Zanir G, Godneva A, … Segal E. Diversity and functional landscapes in the microbiota of animals in the wild. Science 2021;372(6539):eabb5352;doi:10.1126/science.abb5352. Worm B, Barbier EB, Beaumont N, Duffy JE, Folke C, Halpern BS, … Watson R. Impacts of biodiversity loss on ocean ecosystem services. Science 2006;314(5800):787–790;doi:10.1126/science.1132294. Cardinale BJ, Duffy JE, Gonzalez A, Hooper DU, Perrings C, Venail P, … Naeem S. Biodiversity loss and its impact on humanity. Nature 2012;486(7401):59–67; doi:10.1038/nature11148. Gibert JP, Grady JM, Dell AI. Food web consequences of thermal asymmetries. Funct Ecol. 2022;36(8):1887–1899; doi: 10.1111/1365-2435.14091 . Rosenberg Y, Bar-On YM, Fromm A, Ostikar M, Shoshany A, Giz O, Milo R. The global biomass and number of terrestrial arthropods. Sci Adv. 2023;9(5):eabq4049; doi: 10.1126/sciadv.abq40 . Riemer K, Anderson-Teixeira KJ, Smith FA, Harris DJ, Ernest SM. Body size shifts influence effects of increasing temperatures on ectotherm metabolism. Glob Ecol Biogeogr. 2018;27(8):958–967; doi: 10.1111/geb.12757 . Laspoumaderes C, Meunier CL, Magnin A, Berlinghof J, Elser JJ, Balseiro E, … Boersma M. A common temperature dependence of nutritional demands in ectotherms. Ecol Lett. 2022;25(10):2189–2202; doi:10.1111/ele.14093. Song SJ, Sanders JG, Delsuc F, Metcalf J, Amato K, Taylor MW, … Knight R. (2020).Comparative analyses of vertebrate gut microbiomes reveal convergence between birds and bats. MBio 2020;11(1):10-1128; doi: 10.1128/mbio.02901-19. Rangel F, Enes P, Gasco L, Gai F, Hausmann B, Berry D, … Pereira FC. Differential modulation of the European sea bass gut microbiota by distinct insect meals. Front Microbiol. 2022;13:831034; doi:10.3389/fmicb.2022.831034. Cornejo-Granados F, Gallardo-Becerra L, Leonardo-Reza M, Ochoa-Romo JP, Ochoa-Leyva A. A meta-analysis reveals the environmental and host factors shaping the structure and function of the shrimp microbiota. PeerJ 2018;6:e5382; doi: 10.7717/peerj.5382 . Sirichoat A, Sankuntaw N, Engchanil C, Buppasiri P, Faksri K, Namwat W, … Lulitanond V. (2021). Comparison of different hypervariable regions of 16S rRNA for taxonomic profiling of vaginal microbiota using next-generation sequencing. Arch Microbiol. 2021;203(3):1159–1166; doi:10.1007/s00203-020-02114-4) López-Aladid R, Fernández-Barat L, Alcaraz-Serrano V, Bueno-Freire L, Vázquez N,Pastor-Ibáñez R, … Torres A. Determining the most accurate 16S rRNA hypervariable region for taxonomic identification from respiratory samples. Sci Rep. 2023;13(1):3974; doi:10.1038/s41598-023-30764-z. Hallam J, Harris NC. What's going to be on the menu with global environmental changes? Glob Change Biol. 2023;29(20):5744–5759; doi: 10.1111/gcb.16866 . Sonnega S, Sheriff MJ. Harnessing the gut microbiome: a potential biomarker for wild animal welfare. Front Vet Sci. 2024;11:1474028; doi: 10.3389/fvets.2024.1474028 . Gupta VK, Kim M, Bakshi U, Cunningham KY, Davis III JM, Lazaridis KN,… Sung J. A predictive index for health status using species-level gut microbiome profiling. Nat Commun. 2020;11(1):4635; doi:10.1038/s41467-020-18476-8. Fontaine L, Pin L, Savio D, Friberg N, Kirschner AK, Farnleitner AH, Eiler A. Bacterial bioindicators enable biological status classification along the continental Danube river. Commun Biol. 2023;6(1):862; doi: 10.1038/s42003-023-05237-8 . Ribas MP, García-Ulloa M, Espunyes J, Cabezón O. Improving the assessment of ecosystem and wildlife health: microbiome as an early indicator. Curr Opin Biotechnol. 2023;81:102923; doi: 10.1016/j.copbio.2023.102923 Terzin M, Laffy PW, Robbins S, Yeoh YK, Frade PR, Glasl B, … Bourne DG. The road forward to incorporate seawater microbes in predictive reef monitoring. Environ Microbiome 2024;19(1):5; doi:10.1186/s40793-023-00543-4. Romero F, Labouyrie M, Orgiazzi A, Ballabio C, Panagos P, Jones A, … van der Heijden MG. The soil microbiome as an indicator of ecosystem multifunctionality in European soils.Nat Commun. 2026;17:705; doi:10.1038/s41467-025-67353-9. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, … Moher D. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71; doi:10.1136/bmj.n71. Sayers EW, Beck J, Bolton EE, Brister JR, Chan J, Connor R., … Pruitt KD. Database resources of the National Center for Biotechnology Information in 2025. Nucleic Acids Res. 2025;53(D1):D20-D29; doi:10.1093/nar/gkae979. Leinonen R, Akhtar R, Birney E, Bower L, Cerdeno-Tárraga A, Cheng Y, … Cochrane G. The European nucleotide archive. Nucleic Acids Res. 2010;39(suppl_1):D28-D31;doi:10.1093/nar/gkq967. Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, … Caporaso JG. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37(8):852–857; doi:10.1038/s41587-019-0209-9. Bokulich NA, Subramanian S, Faith JJ, Gevers D, Gordon JI, Knight R, … Caporaso JG.Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing.Nat Methods 2013;10(1):57–59; doi:10.1038/nmeth.2276. Estaki M, Jiang L, Bokulich NA, McDonald D, González A, Kosciolek T, Martino C, …Knight R. QIIME 2 enables comprehensive end-to‐end analysis of diverse microbiome data and comparative studies with publicly available data. Curr Protoc Bioinformatics 2020;70:e100; doi:10.1002/cpbi.100. Pruesse E, Quast C, Knittel K, Fuchs BM, Ludwig W, Peplies J, Glöckner FO. “SILVA: A comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB.” Nucleic Acids Res. 2007;35 (21):7188–96; doi: 10.1093/nar/gkm864 . Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glöckner FO. “The SILVA Ribosomal RNA Gene Database Project: Improved data processing and web-based tools.” Nucleic Acids Res. 2013;41:D590–96; doi: 10.1093/nar/gks1219 . Robeson MS, O’Rourke DR, Kaehler BD, Ziemski M, Dillon MR, Foster JT, Bokulich NA. RESCRIPt: Reproducible sequence taxonomy reference database management. PLoS Comput Biol. 2021;17(11):e1009581; doi: 10.1371/journal.pcbi.1009581 . Kaehler BD, Bokulich NA, Caporaso JG, Huttley GA. Species-level microbial sequence classification is improved by source-environment information. Nat Commun. 2019;10:4643; doi: 10.1038/s41467-019-12669-6 . R Core Team. R: A language and Environment for Statistical Computing. R Foundation for Statistical Computing. Version 4.5.1, Vienna, Austria; 2025. https://cran.r-project.org/ Chao A, Gotelli NJ, Hsieh TC, Sander EL, Ma KH, Colwell RK, Ellison AM. Rarefaction and extrapolation with Hill numbers: a framework for sampling and estimation in species diversity studies. Ecol Monogr. 2014;84(1):45–67; doi: 10.1890/13-0133.1 . Chao A, Jost L. Coverage-based rarefaction and extrapolation: standardizing samples by completeness rather than size. Ecology 2012;93(12):2533–2547; doi: 10.1890/11-1952.1 . Alberdi A., Gilbert MTP. A guide to the application of Hill numbers to DNA-based diversity analyses. Mol Ecol Res. 2019;19(4):804–817; doi: 10.1111/1755-0998.13014 . Kembel SW, Cowan PD, Helmus MR, Cornwell WK, Morlon H, Ackerly DD, Blomberg SP, Webb CO. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 2010;26:1463–1464; doi: 10.1093/bioinformatics/btq166 . Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30(4):772–780; doi: 10.1093/molbev/mst010 . Price MN, Dehal PS, Parkin AP. FastTree: computing large minimum evolution trees with profiles instead of a distance matrix. Mol Biol Evol. 2009;26(7):1641–1650; doi: 10.1093/molbev/msp077 . Hothorn T, Zeileis A. “partykit: A Modular Toolkit for Recursive Partytioning in R.” J Mach Learn Res. 2015;16:3905–3909; https://jmlr.org/papers/v16/hothorn15a.html Hothorn T, Hornik K, Zeileis A. Unbiased recursive partitioning: A conditional inference framework. J Comput Graph Stat. 2006;15(3):651–674; doi: 10.1198/106186006X133933 . Dormann CF, Elith J, Bacher S, Buchmann C, Carl G, Carré G, … Lautenbach S. Collinearity:a review of methods to deal with it and a simulation study evaluating their performance.Ecography 2013;36(1):27–46; doi:10.1111/j.1600-0587.2012.07348.x. Fei T, Funnell T, Waters NR, Raj SS, Baichoo M, Sadeghi K, … van den Brink MR. Scalable log-ratio lasso regression for enhanced microbial feature selection with FLORAL. Cel Rep Methods 2024;4(11); doi:0.1016/j.crmeth.2024.100899. Sperling JL, Silva-Brandão KL, Brandão MM, Lloyd VK, Dang S, Davis CS, … Magor KE.Comparison of bacterial 16S rRNA variable regions for microbiome surveys of ticks.Ticks Tick-Borne Dis. 2017;8(4):453–461; doi:10.1016/j.ttbdis.2017.02.002. Yang B, Wang Y, Qian PY. Sensitivity and correlation of hypervariable regions in 16S rRNA genes in phylogenetic analysis. BMC Bioinform. 2016;17(1):135; doi: 10.1186/s12859-016-0992-y . Nikodemova M, Holzhausen EA, Deblois CL, Barnet JH, Peppard PE, Suen G, Malecki KM. The effect of low-abundance OTU filtering methods on the reliability and variability of microbial composition assessed by 16S rRNA amplicon sequencing. Front Cell Infect Microbiol. 2023;13:1165295; doi: 10.3389/fcimb.2023.1165295 . Zoruk P, Morozov M, Veselovsky V, Strokach A, Babenko V, Klimina K. Impact of DNA extraction techniques and sequencing approaches on microbial community profiling accuracy. Front Microbiomes 2025;4:1688681; doi: 10.3389/frmbi.2025.1688681 . Nearing JT, Douglas GM, Hayes MG, MacDonald J, Desai DK, Allward N, … Langille MGI.Microbiome differential abundance methods produce different results across 38 datasets.Nat Commun. 2022;13:342; doi:10.1038/s41467-022-28034-z. Hrovat K, Dutilh BE, Medema MH, Melkonian C. Taxonomic resolution of different 16S rRNA variable regions varies strongly across plant-associated bacteria. ISME Commun. 2024;4(1):ycae034; doi: 10.1093/ismeco/ycae034 . Galla G, Praeg N, Rzehak T, Sprecher E, Colla F, Seeber J,… Hauffe HC. Comparison of DNA extraction methods on different sample matrices within the same terrestrial ecosystem. Sci Rep. 2024;14(1):8715; doi:10.1038/s41598-024-59086-4. Bletz MC, Goedbloed DJ, Sanchez E, Reinhardt T, Tebbe CC, Bhuju S, … Steinfartz S.Amphibian gut microbiota shifts differentially in community structure but converges on habitat-specific predicted functions. Nat Commun. 2016;7(1):13699; doi:10.1038/ncomms13699. Baldo L, Pretus JL, Riera JL, Musilova Z, Bitja Nyom AR, Salzburger W. Convergence of gut microbiotas in the adaptive radiations of African cichlid fishes. The ISME J. 2017;11(9):1975–1987; doi: 10.1038/ismej.2017.62 . Pinheiro NA, Silva LJ, Pena A, Pereira AM. Entomophagy: nutritional value, benefits, regulation and food safety. Foods 2025;14(13):2380; doi: 10.3390/foods14132380 . Sokolova IM. Ectotherm mitochondrial economy and responses to global warming. Acta Physiol. 2023;237(4):e13950; doi: 10.1111/apha.13950 . Gasco L, Józefiak A, Henry M. Beyond the protein concept: health aspects of using edible insects on animals. J Insects Food Feed 2021;7(5):715–741; doi: 10.3920/JIFF2020.0077 . Maulu S, Langi S, Hasimuna OJ, Missinhoun D, Munganga BP, Hampuwo BM, … Dawood MA.Recent advances in the utilization of insects as an ingredient in aquafeeds: A review.Anim Nutr. 2022;11:334–349; doi:0.1016/j.aninu.2022.07.013. Beier S, Bertilsson S. Bacterial chitin degradation—mechanisms and ecophysiological strategies. Front Microbiol. 2013;4:149; doi: 10.3389/fmicb.2013.00149 . Gaudioso G, Marzorati G, Faccenda F, Weil T, Lunelli F, Cardinaletti G,… Fava F.Processed animal proteins from insect and poultry by-products in a fish meal-free diet for rainbow trout: Impact on intestinal microbiota and inflammatory markers.Int J Mol Sci. 2021;22(11):5454; doi:10.3390/ijms22115454. Ye J, Li Y, Wang X, Yu M, Liu X, Zhang H, … Yue T. Positive interactions among Corynebacterium glutamicum and keystone bacteria producing SCFAs benefited T2D mice to rebuild gut eubiosis.Food Res Int. 2023;172:113163; doi:10.1016/j.foodres.2023.113163. Pecoraro N, Dallman MF, Warne JP, Ginsberg AB, Laugero KD, la Fleur SE, … Akana SF.From Malthus to motive: how the HPA axis engineers the phenotype, yoking needs to wants. Prog Neurobiol. 2006;79(5–6):247–340; doi:10.1016/j.pneurobio.2006.07.004. Liang J, Li C, Chen Z, Guo F, Dou J, Wang T, Xu ZS. Progress of research and application of Heyndrickxia coagulans ( Bacillus coagulans ) as probiotic bacteria. Front Cell Infect Microbiol. 2024;14:1415790; doi: 10.3389/fcimb.2024.1415790 . Louca S, Polz MF, Mazel F, Albright MB, Huber JA, O’Connor MI, … Parfrey LW. Function and functional redundancy in microbial systems. Nat Ecol Evol. 2018;2(6):936–943;doi:10.1038/s41559-018-0519-1. Allison SD, Martiny JB. Resistance, resilience, and redundancy in microbial communities. Proc Nat Acad Sci. 2008;105(supplement_1):11512–11519; doi: 10.1073/pnas.0801925105 . Shade A, Peter H, Allison SD, Baho DL, Berga M, Bürgmann H, … Handelsman J. Fundamentals of microbial community resistance and resilience. Front Microbiol. 2012;3:417;doi:10.3389/fmicb.2012.00417. Jurburg SD, Konzack M, Eisenhauer N, Heintz-Buschart A. The archives are half-empty: an assessment of the availability of microbial community sequencing data. Commun Biol. 2020;3(1):474; doi: 10.1038/s42003-020-01204-9 . Kiani AK, Pheby D, Henehan G, Brown R, Sieving P, Sykora P, … International Bioethics Study Group. Ethical considerations regarding animal experimentation. J Prev Med Hyg.2022;63(2 Suppl 3):E255; doi:10.15167/2421-4248/jpmh2022.63.2S3.2768. Birnie-Gauvin K, Peiman KS, Raubenheimer D, Cooke SJ. Nutritional physiology and ecology of wildlife in a changing world. Conserv Physiol. 2017;5(1):cox030; doi: 10.1093/conphys/cox030 . Fuks G, Elgart M, Amir A, Zeisel A, Turnbaugh PJ, Soen Y, Shental N. Combining 16S rRNA gene variable regions enables high-resolution microbial community profiling. Microbiome 2018;6(1):17; doi: 10.1186/s40168-017-0396-x . Soriano-Lerma A, Pérez-Carrasco V, Sánchez-Marañón M, Ortiz-González M, Sánchez-Martín V, Gijón J, … Soriano M. Influence of 16S rRNA target region on the outcome of microbiome studies in soil and saliva samples. Sci Rep. 2020;10(1):13637; doi:10.1038/s41598-020-70141-8. Additional Declarations No competing interests reported. Supplementary Files TableS1.xlsx SupplementaryFigures.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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8959905","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":596921035,"identity":"f524ef98-508c-45bb-a4b9-cb8fd10b623d","order_by":0,"name":"Paula Cabral Eterovick","email":"data:image/png;base64,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","orcid":"","institution":"Leibniz Institute DSMZ - German Collection of Microorganisms and Cell Cultures GmbH","correspondingAuthor":true,"prefix":"","firstName":"Paula","middleName":"Cabral","lastName":"Eterovick","suffix":""},{"id":596921036,"identity":"1be9621c-fe8e-474c-af02-d4fc254ec01a","order_by":1,"name":"Katharina Ruthsatz","email":"","orcid":"","institution":"Doñana Biological Station, CSIC","correspondingAuthor":false,"prefix":"","firstName":"Katharina","middleName":"","lastName":"Ruthsatz","suffix":""},{"id":596921037,"identity":"90a37168-5293-493b-abfb-65e859aa46f9","order_by":2,"name":"Selma Vieira","email":"","orcid":"","institution":"Leibniz Institute DSMZ - German Collection of Microorganisms and Cell Cultures GmbH","correspondingAuthor":false,"prefix":"","firstName":"Selma","middleName":"","lastName":"Vieira","suffix":""},{"id":596921038,"identity":"c5098ad2-a458-4f32-9f50-87f9d2804f24","order_by":3,"name":"Johannes Sikorski","email":"","orcid":"","institution":"Leibniz Institute DSMZ - German Collection of Microorganisms and Cell Cultures GmbH","correspondingAuthor":false,"prefix":"","firstName":"Johannes","middleName":"","lastName":"Sikorski","suffix":""},{"id":596921039,"identity":"43d321cf-4037-4093-be52-89fc631b81a7","order_by":4,"name":"Katharina Wollenberg Valero","email":"","orcid":"","institution":"University College Dublin","correspondingAuthor":false,"prefix":"","firstName":"Katharina","middleName":"Wollenberg","lastName":"Valero","suffix":""},{"id":596921041,"identity":"dc866b03-4a63-4975-92dc-cf0414c8ac02","order_by":5,"name":"Jörg Overmann","email":"","orcid":"","institution":"Leibniz Institute DSMZ - German Collection of Microorganisms and Cell Cultures GmbH","correspondingAuthor":false,"prefix":"","firstName":"Jörg","middleName":"","lastName":"Overmann","suffix":""}],"badges":[],"createdAt":"2026-02-24 17:23:17","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8959905/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8959905/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104263252,"identity":"44a05ce0-0f31-484b-a6a9-5a06abd79469","added_by":"auto","created_at":"2026-03-09 19:05:09","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":114211,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA diagram (following [38]) showing the screening of studies for the metanalysis. All screening was conducted manually. The selection of papers for data retrieval was based on titles (titles that contained information showing the paper did not adhere to the inclusion criteria were discarded). Among the reports sought for retrieval, additional examination of the abstract and text were performed to certify that all inclusion criteria were met (see text for details on inclusion and exclusion criteria). From the 138 papers considered eligible, sequence availability and sample size represented the last screening criteria, resulting in the inclusion of 36 papers. Additionally, our own dataset on larvae of the European common frog (\u003cem\u003eRana temporaria\u003c/em\u003e) was included.\u003c/p\u003e\n\u003cp\u003eSource: Page MJ, et al. BMJ 2021;372:n71. doi: 10.1136/bmj.n71.\u003c/p\u003e\n\u003cp\u003eThis work is licensed under CC BY 4.0. To view a copy of this license, visit \u003ca href=\"https://creativecommons.org/licenses/by/4.0/\"\u003ehttps://creativecommons.org/licenses/by/4.0/\u003c/a\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8959905/v1/bf5be83d5269da7daaab9eea.jpg"},{"id":104263253,"identity":"7647e71d-448a-45ac-a299-52cedcee2275","added_by":"auto","created_at":"2026-03-09 19:05:09","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":278956,"visible":true,"origin":"","legend":"\u003cp\u003eRecursive partitioning tree showing the hierarchical importance of 23 variables (described in the Statistical Analyses section) to explain variation in gut bacteria alpha diversity (Hill numbers) of ectotherms.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8959905/v1/b3bbde4e8d808c4d340157b2.jpg"},{"id":104263254,"identity":"f489d0be-9761-46ba-9896-e7f045582886","added_by":"auto","created_at":"2026-03-09 19:05:09","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":343149,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmaps showing microbiome indicators strongly correlated with diet variables (absolute correlation \u0026gt; 0.4; p \u0026lt; 0.01) for the complete (A, C, E) and the filtered (B, D, F) dataset, respectively, at the OTU (i.e., SILVA Accession Number; A, B), genus (C, D), and family (E, F) levels. The heatmap for OTU indicators of the complete dataset (A) shows only the 15 (at most) highest correlation coefficients for each diet descriptor variable (from a total of 138 selected indicators), the other pannels show all bioindicators. Diet variables are abbreviated as in Table 1.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8959905/v1/0f73801a9ecaeea7d80178f1.jpg"},{"id":104263255,"identity":"93c5c218-8807-4934-9da8-131c8714a38e","added_by":"auto","created_at":"2026-03-09 19:05:10","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":314094,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of challenges and recommendations to advance knowledge on effects of diet changes in ectotherms and their effects on host health mediated by the microbiome.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8959905/v1/e0a47b1a4abe662d99bd749b.jpg"},{"id":104784220,"identity":"3d9cb96f-8763-4440-b17a-f76511d91a04","added_by":"auto","created_at":"2026-03-17 08:05:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2435655,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8959905/v1/b3d8b81b-d93b-48f0-8bf3-7d5682ae17f1.pdf"},{"id":104779960,"identity":"3654350b-ca6f-43e4-8173-88c84a67099d","added_by":"auto","created_at":"2026-03-17 07:48:32","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18851,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8959905/v1/851c9bcb3b0bd6bd72dbf1ea.xlsx"},{"id":104263257,"identity":"fc3f12f3-6780-4ed0-9508-ac63ece1eaee","added_by":"auto","created_at":"2026-03-09 19:05:10","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":19154174,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-8959905/v1/8e330b7d11fcc618e8ea6f44.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Microbiome-based tracking of diet shifts in ectotherms: a new approach to monitor effects of global changes on food webs?","fulltext":[{"header":"Background","content":"\u003cp\u003eAnimal guts are inhabited by a diverse microbiome that includes Archaea, Bacteria, Fungi, Protists, and Viruses [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Diet is a major determinant of gut microbiome composition and structure [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], and variation in dietary nutritional composition is widely recognized to influence bacterial communities in animal guts [e.g., 3, 4]. In turn, gut bacteria break down complex molecules into metabolites that can be assimilated by their hosts. Among these metabolites, short-chain fatty acids (SCFAs), polyamines, and neuropeptides can be recognized by host receptors and act as signaling molecules that induce cellular responses and regulate hormonal activity, thereby influencing host growth, development, reproduction, and senescence [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Within this tightly linked host\u0026ndash;microbe system, bacteria act as important mediators of host physiological responses to environmental change, affecting performance, stress tolerance, and overall resilience under altered environmental conditions [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Conversely, disruption of diet\u0026ndash;microbiome interactions can lead to dysbiosis, with downstream negative consequences for host health, growth, and survival [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Thus, microbiome plasticity and functional redundancy may buffer hosts against dietary stress, but in some contexts may instead reflect instability and declining host condition, underscoring the need to distinguish adaptive from maladaptive microbiome responses [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGlobal warming, pollution, eutrophication, and other anthropogenic stressors alter food webs, resource availability, and feeding opportunities and preferences across ecosystems [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These stressors modify not only food quantity but also nutritional composition, including macronutrient ratios, micronutrient availability, and the presence of secondary compounds or contaminants [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Changes in primary producers and lower trophic levels (e.g., phytoplankton, zooplankton, and insect communities) propagate through food webs, ultimately reshaping the diets of higher trophic consumers across aquatic and riparian habitats [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Such diet shifts are expected to have cascading effects on gut microbiome structure and function; however, these effects remain poorly comparable across systems [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Nonetheless, microbiomes of wild animals are known to reflect diet and life-history traits [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and represent an important component of animal adaptation to environmental change [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEctotherms dominate many aquatic and terrestrial food webs and play a central role in ecosystem functioning and energy transfer [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Because ectotherm metabolic rates and energetic demands increase with temperature [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], they are particularly impacted by the combined effects of warming and reduced food quality. Under such conditions, ectotherms may struggle to obtain sufficiently high-quality food to meet elevated energetic demands [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This mismatch between energy demand and resource quality poses a major challenge for ectotherms and can ultimately lead to asymmetric changes in food webs relative to endotherms [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Climate warming also alters both the requirements for and availability of specific nutrients in complex ways, such that even small decreases in nutrient availability can have amplified effects when consumer demand for those nutrients increases concurrently [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Despite their ecological importance and vulnerability, ectotherms remain underrepresented in meta-analyses of diet-microbiome interactions compared with those of endothermic vertebrates [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Moreover, the strength and direction of dietary effects on gut microbiomes in ectotherms likely vary across taxa, trophic strategies, and developmental stages, limiting generalization from single-species studies.\u003c/p\u003e \u003cp\u003eExisting studies on dietary effects on ectotherm gut microbiomes vary widely in experimental design, dietary manipulations, microbiome metrics, and taxonomic resolution. Methodological challenges include the use of different sample types (e.g., gut mucosa and/or contents; sampling location along the gut; [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]), as well as variation in laboratory protocols and sequencing technologies, all of which influence the precision and comparability of results [e.g., 28, 29, 30, this study]. Such methodological heterogeneity hampers the identification of general patterns and limits predictive capacity by introducing additional sources of variation, thereby reducing statistical power to detect effects related to phylogenetic and ecological differences among host taxa. In this context, a meta-analysis provides a powerful framework to quantitatively assess the magnitude and consistency of diet-driven microbiome responses across taxa and ecosystems while accounting for confounding factors. Diet and trophic alterations are in the core of global changes [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and identifying keystone microbial taxa, diversity metrics, or functional groups associated with dietary change could yield early-warning biomarkers of altered nutrient intake in ectotherms. Such microbiome-based indicators may enable the early detection of sublethal impacts of global change before declines in host performance or population viability become apparent [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we conducted a meta-analysis to quantify how dietary changes influence gut microbiome diversity in vertebrate ectotherms and to identify consistent microbial responses that may serve as indicators of diet alteration under global change. Microbiome biomarkers have been useful tools in varied approaches from human disease diagnostics to prediction of ecosystem functions and ecological features [e.g., 33, 34, 35, 36, 37]. Identifying gut microbiome biomarkers that signal marked changes in the intake of key nutrients could enable early detection of dietary shifts that may ultimately compromise host health. We performed a systematic literature review following PRISMA guidelines [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] to identify studies assessing the effects of dietary variation on gut microbiomes of vertebrate ectotherms. From these studies, we retrieved raw bacterial sequence data generated via 16S rRNA gene metabarcoding and quantified changes in microbial diversity patterns and indicator taxa in response to diet nutritional composition (i.e., crude protein, lipid, carbohydrate, and fiber content, as well as vertebrate-, insect-, and plant-derived protein and fat sources). Analyses accounted for methodological differences (e.g., experiment duration, sample type and preservation, DNA extraction kit, amplified 16S rRNA region, and sequencing technology), as well as host developmental stage, habitat, and taxonomic identity (species, genus, family, and order).\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSystematic literature review and sequence retrieval\u003c/h2\u003e \u003cp\u003eA systematic literature review was conducted following PRISMA guidelines ([\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]; Fig.\u0026nbsp;1) to identify studies investigating the effects of dietary nutritional variation on gut microbiome composition in ectothermic vertebrates (classes Agnatha, Chondrichthyes, Osteichthyes, Amphibia, and Reptilia). The databases Web of Science, Scopus, and PubMed were searched using the following query:\u003c/p\u003e \u003cp\u003e\u0026ldquo;microbiom* OR microbiot*; AND diet* OR nutrition* OR food*; NOT human OR child* OR man OR woman OR men OR women OR adolescent* OR infant* OR clinical OR patient*; NOT obesity OR cancer OR Alzheimer\u0026rdquo;.\u003c/p\u003e \u003cp\u003eExplicit specification of vertebrate ectotherms in the search query was not feasible given the taxonomic diversity of the group and the frequent use of species-specific or common names in study titles and abstracts. Because studies on humans constitute the vast majority of publications examining diet-microbiome interactions, exclusion terms were used to reduce retrieval of human-focused studies, as well as studies addressing well-established human health conditions (e.g., obesity, cancer, Alzheimer\u0026rsquo;s disease). Such studies were found to be disproportionately abundant in preliminary searches and often focused on human patients, genetically modified mice, or experimentally induced disease models. Records retrieved from the three databases were subsequently merged, and duplicate entries were removed.\u003c/p\u003e \u003cp\u003eDuring the screening process (Fig.\u0026nbsp;1), records were excluded based on predefined eligibility criteria, applied sequentially at the title and abstract screening stage and during full-text assessment. Studies were excluded if they: (1) did not involve ectothermic vertebrates (i.e., focused on endothermic vertebrates or non-vertebrate taxa); (2) investigated specific human diseases, whether conducted in humans or in disease-model organisms; (3) evaluated the benefits or drawbacks of processed foods intended for human consumption; or (4) involved experimentally induced disease states, genetically modified organisms, or hybrids.\u003c/p\u003e \u003cp\u003eReports retained after initial screening were assessed for eligibility based on the following inclusion criteria, applied at both the title/abstract and full-text levels: (1) use of vertebrate ectotherms as the study organism (host); (2) assessment of the entire bacterial community (excluding studies targeting specific bacterial taxa only); (3) use of barcode amplification and sequencing of the 16S rRNA gene (excluding studies based on bacterial cultivation or denaturing gradient gel electrophoresis [DGGE]); (4) comparison of at least two distinct diets; (5) availability of diet composition data, including any combination of the variables crude protein, lipid, carbohydrate, or fiber content, and/or the proportional contribution of protein or fat derived from vertebrates, primary producers (plants or algae), or insects; and (6) availability of raw sequencing data that could be linked to host diet treatments in public repositories, specifically the National Center for Biotechnology Information (NCBI; [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]) or the European Nucleotide Archive (ENA; [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]). Corresponding authors were contacted when sequences were unavailable, inaccessible, or lacked sufficient metadata to assign them to diet treatments, in order to obtain the necessary sequence data or associated metadata.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData extraction\u003c/h3\u003e\n\u003cp\u003eMetadata retrieved for analyses included host taxonomy (order, family, genus, and species), developmental stage (larvae, juvenile, or adult), habitat (freshwater or saltwater), experimental features (temperature and duration in days), diet composition, and laboratory methods. Diet features comprised percent dry weight of crude protein, fat, carbohydrate, and fiber, as well as the proportional (mg/g) contribution of protein and fat sources derived from animals, primary producers (plants or algae), or insects (obtained from detailed diet compositions when provided in the study). Laboratory methods included sample type (whole digestive tract, gut mucosa, and/or gut contents representing the whole gut or specific portions: foregut, midgut, or hindgut), sample preservation (fresh, frozen at \u0026minus;\u0026thinsp;20\u0026deg;C or \u0026minus;\u0026thinsp;80\u0026deg;C, or chemically preserved), sequencing kit/protocol, amplified region of the 16S rRNA gene, sequencing technology, and whether sequencing was done in a paired-end or single-end run. In studies testing treatments in addition to diet, only the corresponding control group was used for data extraction. Additional host information, including mass, total length, condition factor, and survival, was also retrieved and included in the metadata (available at FigShare; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.6084/m9.figshare.31079137\u003c/span\u003e\u003cspan address=\"10.6084/m9.figshare.31079137\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e); however, these variables were mostly available only as treatment means rather than individual-level data, preventing statistical analysis. Experimental temperature was extracted for reference but not analyzed, as in the selected studies it was adjusted to the optimal rearing conditions for each ectotherm model and thus could not be statistically separated from host taxonomy or habitat.\u003c/p\u003e\n\u003ch3\u003eBioinformatic analyses\u003c/h3\u003e\n\u003cp\u003eWe developed a bioinformatic workflow to standardize datasets generated using different methodologies (e.g., gut sample type, amplified region of the 16S rRNA gene, sample preservation, extraction kit, sequencing platform). Analyses began with sequence quality filtering performed separately for each study. Paired-end and single-end demultiplexed FASTQ sequences corresponding to different 16S rRNA gene regions were imported into QIIME2 [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] and denoised using the q2-deblur algorithm, following the quality filtering approach of [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The q2-deblur algorithm is well-suited for meta-analyses involving multiple datasets, as it associates erroneous reads with the true biological sequences, reducing dataset-specific sequence error profiles [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Merged forward and reverse sequences, or single sequences, were trimmed to retain high-quality reads with a median Illumina quality score above 30 (Q30), corresponding to lengths of 100\u0026ndash;440 bp. Trimming length was selected to maximize sequence retention while maintaining the desired quality threshold. Six studies that did not meet these quality criteria were excluded. Rarefaction analyses were performed for each of the 37 remaining studies. From these, nine had samples with number of reads below the sequencing depth threshold at which alpha diversity (Shannon entropy) stabilized. These samples were thus removed.\u003c/p\u003e \u003cp\u003eFor taxonomic assignment, a single classifier was applied to all Amplicon Sequence Variants (ASVs) to ensure standardization across studies. This classifier was built using the full-length 16S rRNA gene, as different studies amplified different gene regions. A phylogenetic tree was prepared based on the SILVA database version 138.2 [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] and processed with RESCRIPt [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]; script \u0026ldquo;process_silva_database.sh\u0026rdquo; in \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/PaulaEterovick/meta-analysis-ectotherm-diet-and-microbiome/upload/main\u003c/span\u003e\u003cspan address=\"https://github.com/PaulaEterovick/meta-analysis-ectotherm-diet-and-microbiome/upload/main\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Although using environment-based sequence weights are recommended [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], we avoided this because our datasets included sequences from different gut sections and amplified 16S rRNA gene regions. To verify classification accuracy, we built an alternative classifier using sequence weights and classified sequences from one whole-gut dataset targeting the V4 region (our dataset on the European common frog, \u003cem\u003eRana temporaria\u003c/em\u003e). Classification results were identical down to the family level, with minor differences at the genus level; species-level assignments remained incomplete and inconsistent for both classifiers.\u003c/p\u003e \u003cp\u003eAfter classification, ASV identities were standardized across studies by replacing them with the SILVA Accession Number assigned to the Operational Taxonomic Units (OTUs) to match each ASV in the SILVA database ([\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://docs.qiime2.org/2024.10/plugins/available/feature-classifier/classify-consensus-blast/\u003c/span\u003e\u003cspan address=\"https://docs.qiime2.org/2024.10/plugins/available/feature-classifier/classify-consensus-blast/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). SILVA is a high-quality curated database linking sequences to bacterial taxonomy and nomenclature. This approach also increased the reliability of sequence count data, as ASVs with minor differences that likely representing sequencing errors were assigned to the same reference sequence. Duplicate SILVA Accession Numbers (hereafter referred to as OTUs) were merged within each study, and the resulting files were then combined across studies (codes available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/PaulaEterovick/metanalysis-ectotherm-diet-and-microbiome/upload/main\u003c/span\u003e\u003cspan address=\"https://github.com/PaulaEterovick/metanalysis-ectotherm-diet-and-microbiome/upload/main\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, scripts classify_with_silva_accessions.sh, merge_taxonomy_with_accession.py, merge_duplicate_otus_complete.py, filter_taxonomy.py).\u003c/p\u003e \u003cp\u003eThe combined dataset included 37 studies, 1,393 samples, 10,750 OTUs and 26,772,009 reads. For downstream analyses, data were also aggregated to genus and family levels to increase classification precision and improve representativeness across samples and studies (code available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/PaulaEterovick/metanalysis-ectotherm-diet-and-microbiome/upload/main\u003c/span\u003e\u003cspan address=\"https://github.com/PaulaEterovick/metanalysis-ectotherm-diet-and-microbiome/upload/main\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, script merge_otus_by_family.py) .\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eEffects of diet on microbiome alpha- and phylogenetic diversity\u003c/h2\u003e \u003cp\u003eWe aimed to assess the effects of diet on microbiome diversity while accounting for host life history traits (developmental stage, habitat), taxonomy, and methodological variation, which may also influence diversity. Alpha-diversity was calculated using Hill numbers with the incidence approach (order of diversity q\u0026thinsp;=\u0026thinsp;2, which is based on the Simpson diversity index and thus gives greater weight to abundant OTUs) using the R package iNext [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. We focused on abundant OTUs as they are likely easier to detect and quantify, thus minimizing differences among sample diversities caused by detection failure of rare OTUs. Because sample depth varied across studies, simulations were used to assess the coverage of the real number of species based on the slope of the rarefaction curve [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. This approach is robust to variation in sample depth and is recommended for datasets that are zero-inflated, biased, or otherwise insufficient [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Observed and estimated Hill numbers for alpha diversity were very similar (results available at FigShare; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.6084/m9.figshare.31079137\u003c/span\u003e\u003cspan address=\"10.6084/m9.figshare.31079137\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), indicating sample completeness despite variation in sampling depth. Thus, estimated diversity values were used in subsequent analyses.\u003c/p\u003e \u003cp\u003ePhylogenetic diversity was calculated using Faith\u0026rsquo;s Phylogenetic Diversity (Faith\u0026rsquo;s PD\u0026thinsp;=\u0026thinsp;the sum of branch lengths separating taxa in a community) with standardized effect size (SES, which corrects for effects of different richness) in the picante package [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. This produces standardized z-scores based on 999 simulations, testing whether samples are more or less diverse than expected by chance. It therefore accounts for differences in species richness arising from methodological variation among studies, correcting for unequal sampling effort. For this, a taxonomic tree including the 10,725 OTUs identified across studies was prepared. The sequences corresponding to these OTUs were extracted from the SILVA 138.2 database (SILVA_138.2_SSURef_NR99_tax_silva.fasta) based on their accession numbers, aligned with MAFFT [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], and a phylogenetic tree was built with FastTree ([\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]; code \u0026ldquo;build_silva_tree_for_Hill_numbers.py\u0026rdquo; available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/PaulaEterovick/meta-analysis-ectotherm-diet-and-microbiome/upload/main\u003c/span\u003e\u003cspan address=\"https://github.com/PaulaEterovick/meta-analysis-ectotherm-diet-and-microbiome/upload/main\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo test whether alpha- and phylogenetic diversity were primarily influenced by diet or by methodological, taxonomic, or ecological factors, we constructed conditional inference trees using the R package partykit [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. This method generates a hierarchical structure of predictive variables, splitting groups at each node based on the variable with the greatest explanatory power. It applies recursive partitioning for numerical or categorical variables, is robust to datasets with many zeros, and reduces overfitting [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBefore tree construction, Spearman correlations among diet variables were tested to identify and remove highly co-varying variables, as these might cause unwanted variance inflation hampering statistical tests. Only total carbohydrate percentage and total fiber percentage were highly correlated (rho = \u0026minus;\u0026thinsp;0.825, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); total fiber percentage was excluded because it was reported in fewer studies. All other correlations had absolute rho\u0026thinsp;\u0026le;\u0026thinsp;0.605 and were retained (following [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]).\u003c/p\u003e \u003cp\u003eA total of 23 explanatory variables were included in the conditional inference trees describing diet composition (9 variables): crude protein percentage (CPP), crude lipid percentage (CLP), total carbohydrate percentage (TCP), vertebrate protein (VP, mg/g), insect protein (IP, mg/g), plant protein (PP, mg/g), vertebrate fat (VF), insect fat (IS), and plant fat (PF), host taxonomy (4 levels): species, genus, family, and order, host traits (2 variables): developmental stage (larvae, juvenile, adult) and habitat (freshwater or saltwater), and methodological variables (8 variables): duration of the experiment (days), sample type (digestive tract, gut with contents, gut mucosa, gut contents, foregut mucosa, foregut contents, midgut mucosa, midgut contents, midgut with contents, hindgut mucosa, hindgut contents, hindgut with contents), sample preservation (fresh, -20\u0026deg;C, -80\u0026deg;C, RNAlater, or Zymo Xpedition\u0026trade; Lysis/Stabilization Solution), DNA extraction method (15 methods; see metadata at FigShare; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.6084/m9.figshare.31079137\u003c/span\u003e\u003cspan address=\"10.6084/m9.figshare.31079137\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), amplified 16S rRNA gene region (V1-V2, V3-V4, V4, V4-V5, V6-V8), sequencing technology (Illumina MiSeq, HiSeq, NovaSeq, NovaSeq 6000, Ion Torrent Personal Genome Machine), sequencing type (single- or paired-end), and trimming length (100, 230, 250, 310, 400, or 440 bp).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMicrobiome indicators of dietary changes\u003c/h2\u003e \u003cp\u003eWe assumed that a reliable bacterial indicator of dietary changes would be a taxonomic group that is widespread across vertebrate hosts, varies with specific diet nutritional parameters, and is robust to methodological variation in gut microbiome characterization. To identify such indicators, we first searched for strong correlations between gut bacterial genera and families and the nine diet composition variables. We then assessed whether these correlations were spurious - arising from methodological variation - or genuinely related to diet.\u003c/p\u003e \u003cp\u003eOTUs were first analyzed using the FLORAL package [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e] to identify potential indicators of the nine diet variables. OTU abundances (read counts) were correlated with diet variables, and those with p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and an absolute correlation coefficient\u0026thinsp;\u0026ge;\u0026thinsp;0.4 were selected. The same procedure was applied after aggregating OTUs to the genus and family levels to detect consistent patterns at higher taxonomic resolution.\u003c/p\u003e \u003cp\u003eTo confirm that diet features were the main drivers of variation in the candidate microbiome biomarkers, we constructed conditional inference trees for genera and families showing significant correlations with diet. These taxonomic levels were chosen because most OTUs could not be reliably identified at species level, and genera and families are more likely to be widespread across hosts and comparable across studies, independent of the reference database used for taxonomic assignment. If the correlation between a diet feature and the number of reads assigned to a taxon was not confounded by other variables, we expected the diet feature to appear at the first node of the tree, indicating it as the primary explanatory variable. These partition analyses included the same 23 explanatory variables used in the microbiome diversity analyses.\u003c/p\u003e \u003cp\u003e \u003cem\u003eAnalysis focused on the V3-V4 and V4 regions of the 16S rRNA gene and filtered dataset\u003c/em\u003e \u003c/p\u003e \u003cp\u003eTo evaluate the robustness of our method in detecting indicators of ectotherm dietary changes despite variation in the amplified 16S rRNA gene regions, we repeated the microbiome indicator analyses using only studies that targeted the V3\u0026ndash;V4 or V4 regions. Different 16S rRNA regions can yield varying results in microbiome composition and taxonomic assignment accuracy [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Although the accuracy of specific regions depends on sample origin [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], we focused on V3-V4 and V4 because they are widely used in gut microbiome research and were amplified in the majority of selected studies (30 of 37).\u003c/p\u003e \u003cp\u003eWe also excluded OTUs present in less than 5% of samples to reduce potential bias from underrepresented taxa. These rare OTUs may reflect host- or habitat-specific taxa or may result from methodological differences, including sampling depth, DNA extraction protocols, or sequencing technologies [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. The 5% threshold, although lower than the recommended 10% [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], represented a compromise between retaining representative taxa and maximizing the number of samples. This filter removed 12 individual samples spread across studies, but no study was entirely excluded. This resulted in a data set we refer to as \u0026ldquo;filtered\u0026rdquo; and which included 198 OTUs, 1,073 samples, and 10,614,500 reads. We then repeated the microbiome indicator analyses described above using this dataset. With this approach, we aimed to reduce noise in our dataset by minimizing variation in two key aspects: the targeted 16S rRNA region and taxonomic representativeness across hosts and habitats. We expected to obtain consistent results from both the complete and the filtered datasets if the findings were robust to variation in these parameters.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSystematic literature review and sequence retrieval\u003c/h2\u003e \u003cp\u003eThe initial literature search retrieved 31,400 papers from Web of Science, 24,825 from Scopus, and 22,311 from PubMed, resulting in 41,899 unique papers after removal of duplicates across databases. Titles were screened first, yielding 865 studies for further assessment (Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eOf these, 138 studies met inclusion criteria (as described in Materials and Methods), and we could access raw sequences with associated diet treatment information from 42 of them (Fig.\u0026nbsp;1). Six from these 42 studies, however, were excluded because their sequences did not meet our quality standards, and one was excluded because it had no treatment replicates. For 70 studies that lacked available sequences, the corresponding authors were contacted, and only three shared their data. On the other hand, three studies reported sequence availability, but the sequences could not be retrieved with the provided information. Twenty-nine studies deposited raw sequences but did not provide metadata linking sequences to host diet treatments. Contacting the corresponding authors yielded four additional datasets.\u003c/p\u003e \u003cp\u003eThe final dataset included 37 studies, comprising 10,725 OTUs (Silva Accession Numbers) and 1,393 samples from 13 fish species (larvae, juveniles, and adults) and three anuran amphibian species (larvae or juveniles; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The amplified regions of the 16S rRNA gene were V1-V2 (201 samples), V3-V4 (761 samples), V4 (359 samples), V4-V5 (10 samples), and V6-V8 (78 samples). Microbiome samples originated from the whole digestive tract, the whole gut, or from fore-, mid-, and hindgut sections, either including contents, mucosa only, or contents only.\u003c/p\u003e \u003cp\u003eRecords excludedBased on title: Studies did not include ectothermic vertebrates, focused on humans, or food for human consumption, used models genetically modified, with induced disease or hybrids (n = 41,034)\u003c/p\u003e \u003cp\u003eRecords screened(n = 41,899)\u003c/p\u003e \u003cp\u003eReports not retrievedBased on whole paper, studies NOT attending the criteria: Focus on ectothermic vertebrates, 16S rRNA gene barcoding of the whole bacterial community, comparison of at least two diets with associated nutritional information (n = 793)\u003c/p\u003e \u003cp\u003eReports sought for retrieval(n = 865)\u003c/p\u003e \u003cp\u003eReports excluded:Raw sequences not available neither shared by authors (n = 70)Sequence not associated to sample information neither after author request (n = 25)Low sample size (n = 1)Low sequence quality (n = 6)\u003c/p\u003e \u003cp\u003eReports assessed for eligibility(n = 138)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSource: Page MJ, et al. BMJ 2021;372:n71. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmj.n71\u003c/span\u003e\u003cspan address=\"10.1136/bmj.n71\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThis work is licensed under CC BY 4.0. To view a copy of this license, visit \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://creativecommons.org/licenses/by/4.0/\u003c/span\u003e\u003cspan address=\"https://creativecommons.org/licenses/by/4.0/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;1. PRISMA diagram (following [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]) showing the screening of studies for the metanalysis. All screening was conducted manually. The selection of papers for data retrieval was based on titles (titles that contained information showing the paper did not adhere to the inclusion criteria were discarded). Among the reports sought for retrieval, additional examination of the abstract and text were performed to certify that all inclusion criteria were met (see text for details on inclusion and exclusion criteria). From the 138 papers considered eligible, sequence availability and sample size represented the last screening criteria, resulting in the inclusion of 36 papers. Additionally, our own dataset on larvae of the European common frog (\u003cem\u003eRana temporaria\u003c/em\u003e) was included.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEffects of diet on microbiome alpha- and phylogenetic diversity\u003c/h2\u003e \u003cp\u003eThe variable that most strongly explained microbiome alpha diversity in the partition tree was the targeted region of the 16S rRNA gene (node 1). The V3\u0026ndash;V4, V4, and V4\u0026ndash;V5 regions showed lower alpha diversity than the V1\u0026ndash;V2 and V6\u0026ndash;V8 regions (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Sample type (node 2) and DNA extraction kit (node 19) were the next most important variables explaining variation in microbiome diversity within the resulting groups, and sample type also appeared frequently in subsequent nodes of the partition tree. Sequencing technology (node 24, n\u0026thinsp;=\u0026thinsp;208 samples) and trimming size (node 47, n\u0026thinsp;=\u0026thinsp;147) explained variation in specific nodes including a reduced number of samples. Sample preservation method and whether sequencing was single or paired-end did not appear in the tree. Host genus (nodes 21, 53; n\u0026thinsp;=\u0026thinsp;252, 154, respectively), family (node 52, n\u0026thinsp;=\u0026thinsp;198) and habitat (nodes 4, 32; n\u0026thinsp;=\u0026thinsp;154, 113, respectively) explained variation only in nodes containing a limited number of samples, whereas experiment duration, host species, order, and life stage did not appear in the tree. Also, diet-related variables explained variation within individual groups of samples. Higher alpha diversity was associated with crude protein\u0026thinsp;\u0026gt;\u0026thinsp;44% (node 5, n\u0026thinsp;=\u0026thinsp;81) and \u0026gt;\u0026thinsp;44.6% (node 12, n\u0026thinsp;=\u0026thinsp;63); absence of plant protein in the diet (node 10, n\u0026thinsp;=\u0026thinsp;73); vertebrate protein\u0026thinsp;\u0026gt;\u0026thinsp;42.2 mg/g (node 16, n\u0026thinsp;=\u0026thinsp;49); total carbohydrate\u0026thinsp;\u0026le;\u0026thinsp;12.6 mg/g (node 39, n\u0026thinsp;=\u0026thinsp;290), vertebrate fat\u0026thinsp;\u0026gt;\u0026thinsp;10.7 mg/g (node 40, n\u0026thinsp;=\u0026thinsp;193), presence of insect protein (node 41, n\u0026thinsp;=\u0026thinsp;145), and crude lipid\u0026thinsp;\u0026le;\u0026thinsp;20.3% (node 43, n\u0026thinsp;=\u0026thinsp;62).\u003c/p\u003e \u003cp\u003eFor phylogenetic diversity, genus was the most important explanatory variable (node 1), with no clear separation among higher taxonomic levels; both frogs and fishes occurred in branches with higher and lower phylogenetic diversity (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Sample type (node 2) and DNA extraction kit (node 37) were the next most important variables explaining phylogenetic diversity in the resulting nodes, both appearing again frequently throughout the partition tree. Amplified region of the 16S rRNA gene (nodes 10, 19, 62; n\u0026thinsp;=\u0026thinsp;79, 92, 129, respectively) and trimming length (node 28, n\u0026thinsp;=\u0026thinsp;184) explained variation in phylogenetic diversity within limited subsets of samples. Sequencing technology, whether sequencing was single or double stranded, and sample preservation were not included in the partition tree. Host species (node 4, n\u0026thinsp;=\u0026thinsp;347), and order (node 44, n\u0026thinsp;=\u0026thinsp;215) explained variation within nodes with a number of samples relatively higher than experiment duration (nodes 5, 15, 54; n\u0026thinsp;=\u0026thinsp;95, 29, 59, respectively), host habitat (node 52, n\u0026thinsp;=\u0026thinsp;72) and life stage (node 46, n\u0026thinsp;=\u0026thinsp;107).\u003c/p\u003e \u003cp\u003eHigher phylogenetic diversity was associated with the absence of insect protein sources in the diet (node 3, n\u0026thinsp;=\u0026thinsp;439 samples) or amounts\u0026thinsp;\u0026le;\u0026thinsp;11 mg/g (node 64, n\u0026thinsp;=\u0026thinsp;77); crude protein\u0026thinsp;\u0026gt;\u0026thinsp;47.3% (node 9, n\u0026thinsp;=\u0026thinsp;171); plant fat\u0026thinsp;\u0026le;\u0026thinsp;13.6 mg/g (node 13, n\u0026thinsp;=\u0026thinsp;92) and \u0026gt;\u0026thinsp;4.8 mg/g (node 31, n\u0026thinsp;=\u0026thinsp;115); total carbohydrate\u0026thinsp;\u0026gt;\u0026thinsp;12.2% (nodes 20\u0026ndash;21, n\u0026thinsp;=\u0026thinsp;69); vertebrate protein\u0026thinsp;\u0026gt;\u0026thinsp;40.59 mg/g (node 30, n\u0026thinsp;=\u0026thinsp;159) or \u0026gt;\u0026thinsp;60 mg/g (node 50, n\u0026thinsp;=\u0026thinsp;82); crude lipid\u0026thinsp;\u0026gt;\u0026thinsp;20.2% (node 34, n\u0026thinsp;=\u0026thinsp;44); vertebrate fat\u0026thinsp;\u0026gt;\u0026thinsp;10.7 mg/g (node 39, n\u0026thinsp;=\u0026thinsp;488); and plant protein\u0026thinsp;\u0026le;\u0026thinsp;47.6 mg/g (node 41, n\u0026thinsp;=\u0026thinsp;99) or \u0026gt;\u0026thinsp;33.4 mg/g (node 49, n\u0026thinsp;=\u0026thinsp;174).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMicrobiome indicators of dietary changes\u003c/h2\u003e \u003cp\u003eOTU abundances from the complete dataset (10,125 OTUs) showed 11,667 significant correlations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) with diet features, with individual OTUs potentially correlating with more than one feature. However, only 138 correlations had an absolute correlation coefficient\u0026thinsp;\u0026gt;\u0026thinsp;0.4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Aggregating OTUs to the genus level yielded 959 genera (including both genera with valid names and genera with non-published names but labelled and assigned to specific publications), with 1,771 significant correlations, 30 of which had absolute correlation\u0026thinsp;\u0026gt;\u0026thinsp;0.4. At the family level, 356 classified families exhibited 1,771 significant correlations, of which 12 had absolute correlation\u0026thinsp;\u0026gt;\u0026thinsp;0.4.\u003c/p\u003e \u003cp\u003eIn the filtered dataset (V3-V4 or V4 regions, OTUs present in \u0026ge;\u0026thinsp;5% of samples), there were 1,073 OTUs grouped into 74 classified genera and 43 classified families. Significant correlations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) with diet features totaled 927, 385, and 246 at the OTU, genus, and family levels, respectively, with 70, 24, and 20 correlations exceeding an absolute coefficient of 0.4.\u003c/p\u003e \u003cp\u003eAmong the 28 genera which were correlated with diet variables (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Figs. S2-29), 15 had the correlated variable as the first node in the partition tree, 14 of which showed a positive correlation with insect protein sources (\u003cem\u003eBrevicacterium, Cellulosimicrobium, Corynebacterium, Heyndrickxia, Lederbergia, Oceanobacillus, Ornithibacillus, Metabacillus, Shouchella, Siminovitchia, Lysinibacillus, Globicatella, Enterococcus, Mamaliicoccus\u003c/em\u003e; Figs. S5-7, S9-11, S13, S16, S19-20, S23-24, S27-28). The remaining genus, \u003cem\u003eAlkalinohalinophilus\u003c/em\u003e (Fig. S3), correlated with insect fat sources (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e), while \u003cem\u003eLederbergia\u003c/em\u003e had insect fat as the second node (Fig. S13). \u003cem\u003eActinomyces\u003c/em\u003e, although having sample type as the first node, showed insect protein sources as the main driver of variation in the subsequent nodes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). The remaining genera had methodological variables (extraction kit, n\u0026thinsp;=\u0026thinsp;5; sample type, n\u0026thinsp;=\u0026thinsp;2; amplified region, n\u0026thinsp;=\u0026thinsp;2; sequencing platform, n\u0026thinsp;=\u0026thinsp;1) or host species (n\u0026thinsp;=\u0026thinsp;1) as the first node. In all cases, diet features appeared elsewhere in the partition trees, except for \u003cem\u003eWeissella\u003c/em\u003e and total carbohydrate percentage, where the correlation was not reflected in the tree (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e9).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMicrobiome taxa selected as indicators of nutritional features of diets (CLP\u0026thinsp;=\u0026thinsp;crude lipid percentage; CPP\u0026thinsp;=\u0026thinsp;crude protein percentage; IF\u0026thinsp;=\u0026thinsp;insect fat sources, mg/g; IP\u0026thinsp;=\u0026thinsp;insect protein sources, mg/g; PF\u0026thinsp;=\u0026thinsp;plant fat sources, mg/g; TCP\u0026thinsp;=\u0026thinsp;total carbohydrate percentage; VP\u0026thinsp;=\u0026thinsp;vertebrate protein sources, mg/g) fed to ectothermic hosts. Analyses were based on 37 studies (all) which amplified different regions of the bacterial 16S rRNA gene including 13 species of fishes and three amphibians as hosts, as well as on a filtered dataset (filt) composed of 30 studies that amplified just the V3-V4 or V4 region including 13 species of fishes and two amphibians. Positive correlations are indicated with +\u0026thinsp;and negative correlations with -. Microbiome indicators were searched based on read counts at the OTU (o), genus (g or G), and family (f or F) levels, all the significant results (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with absolute correlation coefficients higher than 0.4 are shown in the table with letters corresponding to the corresponding taxonomic level. OTUs classified above the family level were not included. Families and genera marked with an * were not represented in the filtered dataset. Genera and families whose first node in the partitioning tree are the correlated diet descriptors are represented in capital boldfaced G or F, respectively. Genera with capital letter not boldfaced were the second node but also considered as robust indicators (see text for explanations).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePhylum \u003cem\u003eActinomycetota\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCLP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCPP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eTCP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eVP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClass\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eOrder\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eFamily\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eGenus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003efilt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003efilt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003efilt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003efilt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003efilt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003efilt\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eActinomycetota\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eActinomycetales\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eActinomycetaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eActinomyces\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eo+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eogf+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eoG\u003cb\u003eF\u003c/b\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eo\u003cb\u003eGF\u003c/b\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBifidobacteriales\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eBifidobacteriaceae*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eBifidobacterium*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003egf+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMicrococcales\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eBrevibacteriaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eBrevibacterium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eo+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eo+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eo\u003cb\u003eGF\u003c/b\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eo\u003cb\u003eGF\u003c/b\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eMicrobacteriaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMicrobacterium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eo+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eLeucobacter\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eog+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eMicrococcaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEnteractinococcus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eg+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eo\u003cb\u003eGF\u003c/b\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePromicromonosporaceae*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eCellulosimicrobium*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eo\u003cb\u003eGF\u003c/b\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMycobacteriales\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCorynebacteriaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eCorynebacterium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eo+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eo+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eo\u003cb\u003eGF\u003c/b\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eoG\u003cb\u003eF\u003c/b\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePropionibacteriales\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eProprionibacteriaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eCutibacterium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eo+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eo+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhylum\u003c/b\u003e \u003cb\u003eBacillota\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBacilli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBacillales\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eBacillaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAlkalihalophilus*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eo\u003cb\u003eG\u003c/b\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eF\u003c/b\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eF+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eBacillus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eo+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eo+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eHeyndrickxia*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eo\u003cb\u003eG\u003c/b\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eLederbergia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eoG+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eo+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eo\u003cb\u003eG\u003c/b\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eoG+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eOceanobacillus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eo+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eo+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eo\u003cb\u003eG\u003c/b\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eoG+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eOrnithinibacillus*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eo\u003cb\u003eG\u003c/b\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMetabacillus*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eo\u003cb\u003eG\u003c/b\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePseudogracilibacillus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eog+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eoG+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eog+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eo\u003cb\u003eG\u003c/b\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eShouchella*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eo\u003cb\u003eG\u003c/b\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eSiminovitchia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eo\u003cb\u003eG\u003c/b\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePlanococcaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eLysinibacillus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eo\u003cb\u003eG\u003c/b\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eExiguobacterales\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eExiguobacteraceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eExiguobacterium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eo+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLactobacillales\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAerococcaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eGlobicatella\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eGF\u003c/b\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eo\u003cb\u003eGF\u003c/b\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCarnobacteriaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAtopostipes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eo\u003cb\u003eG\u003c/b\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eEnterococcaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEnterococcus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eo+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eo+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eo\u003cb\u003eGF\u003c/b\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eoGF+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eLactobacillaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eCompanilactobacillus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eo+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eLacticaseibacillus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eo+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eo+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eLactiplantibacillus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eo+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eLatilactobacillus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eog+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eLactobacillus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eo+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eLeuconostoc\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eo+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eLevilactobacillus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eg+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eLimosilactobacillus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eo+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePediococcus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eo+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eSchleiferilactobacillus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eo+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eWeissella\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eg-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eStreptococcaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eLactococcus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eo+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eVagococcaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eVagococcus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eo+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eo+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMycoplasmatales\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eMycoplasmataceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMalacoplasma\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eo+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eogf+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eogf+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMycoplasma\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eog+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eog+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePaenibacillales\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePaenibacillaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePaenibacillus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eo+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eo\u003cb\u003eGF\u003c/b\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eogf+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eoGF+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eStaphylococcales\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eStaphylococcaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMacrococcus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003egf+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ef+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMammaliicoccus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eo\u003cb\u003eG\u003c/b\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eog+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eNosocomiicoccus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eg+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eClostridia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eClostridiales\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eClostridiaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eClostridium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eo+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eo+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePeptostreptococcales-Tissierellales\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFamily XI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePeptoniphilus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ef+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eo+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003ef+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eTepidimicrobium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eog+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePeptostreptococcaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePeptostreptococcus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eo+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhylum\u003c/b\u003e \u003cb\u003eBacteroidota\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBacteroidia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBacteroidales\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eBacteroidaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eBacteroides\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eogf-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eChitinophagales\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eChitinophagaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eSediminibacterium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eo+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhylum\u003c/b\u003e \u003cb\u003eFusobacteriota\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFusobacteriia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFusobacteriales\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eFusobacteriaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eCetobacterium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ef+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eg+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eo+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eogf+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhylum\u003c/b\u003e \u003cb\u003ePseudomonadota\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGammaproteobacteria\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBurkholderiales\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eBurkholderiaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eRalstonia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ef+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eg+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eComamonadaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eDelftia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eo+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEnterobacterales\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAeromonadaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAeromonas\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eogf-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eEnterobacteriaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eCitrobacter\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eogf-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEscherichia-Shigella\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eo+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eVibrionaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePhotobacterium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eo+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhylum\u003c/b\u003e \u003cb\u003eThermodesulfobacteriota\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDesulfovibrionia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eDesulfovibrionales\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eDesulfovibrionaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eof+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the filtered dataset (Figs. S30-48), \u003cem\u003eActinomyces, Brevibacterium\u003c/em\u003e, and \u003cem\u003eGlobicatella\u003c/em\u003e (Figs. S30, S34, S40) retained insect protein sources as the first node. \u003cem\u003eLederbergia\u003c/em\u003e and \u003cem\u003eOceanobacillus\u003c/em\u003e had insect protein sources as the second node, with insect fat sources as the first (Figs. S41, S48). \u003cem\u003eEnterococcus\u003c/em\u003e also had insect protein sources as the second node, following sample type (Fig. S39). Several genera with insect protein as the first node in the complete dataset (\u003cem\u003eCellulosimicrobium, Heyndrickxia, Ornithibacillus, Metabacillus, Shouchella\u003c/em\u003e) were not present in the filtered dataset. \u003cem\u003eAtopostipes\u003c/em\u003e correlated with insect protein sources only in the filtered dataset, and insect protein was its first node (Fig. S32). \u003cem\u003eEnteractinococcus\u003c/em\u003e and \u003cem\u003ePseudogracilibacillus\u003c/em\u003e had insect protein as the first node in the filtered dataset (Figs. S38, S47) but not in the full dataset (Figs. S8, S26), where methodological variables were more influential. \u003cem\u003ePaenibacillus\u003c/em\u003e correlated with insect fat sources, with insect protein as the second node (Fig. S46). Other genera had sample type (n\u0026thinsp;=\u0026thinsp;5), host species (n\u0026thinsp;=\u0026thinsp;2), host family or order (n\u0026thinsp;=\u0026thinsp;1 each), or sequencing platform (n\u0026thinsp;=\u0026thinsp;1) as the first node. \u003cem\u003eBacteroides\u003c/em\u003e (Fig. S33), \u003cem\u003eCetobacterium\u003c/em\u003e (Fig. S35), \u003cem\u003eCitrobacter\u003c/em\u003e (Fig. S36), \u003cem\u003eMalacoplasma\u003c/em\u003e (Fig. S42), \u003cem\u003eMycoplasma\u003c/em\u003e (Fig. S44), and \u003cem\u003eRalstonia\u003c/em\u003e (Fig. S48) did not show the correlated diet variables in their partition trees, indicating spurious correlations.\u003c/p\u003e \u003cp\u003eOf the 12 families positively correlated with diet features (Figs. S49-60), seven had insect protein sources as the first node (\u003cem\u003eActinomycetaceae, Brevibacteriaceae, Promicromonosporaceae, Corynebacteriaceae, Bacillaceae, Aerococcaceae, Enterococcaceae\u003c/em\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Figs. S49-S51, S53-S54, S56), while the remaining five had first nodes represented by sample type (n\u0026thinsp;=\u0026thinsp;4), host species, family, or order (n\u0026thinsp;=\u0026thinsp;1 each), or sequencing platform (n\u0026thinsp;=\u0026thinsp;1). In the filtered dataset, \u003cem\u003eActinomycetaceae, Brevibacteriaceae, Corynebacteriaceae\u003c/em\u003e, and \u003cem\u003eAerococcaceae\u003c/em\u003e retained insect protein as the first node (Figs. S61-S62, S66-S67). \u003cem\u003eBacillaceae\u003c/em\u003e and \u003cem\u003eEnterococcaceae\u003c/em\u003e had insect protein as the second node, with the first nodes being sample type and insect fat, respectively (Figs. S64, S69). \u003cem\u003ePromicromonosporaceae\u003c/em\u003e was absent in the filtered dataset. \u003cem\u003eMicrococcaceae\u003c/em\u003e and \u003cem\u003ePaenibacillaceae\u003c/em\u003e showed positive correlations only in the filtered dataset (Figs. S71, S73), with insect protein and fat as first nodes, respectively. The remaining families had first nodes corresponding to sample type (n\u0026thinsp;=\u0026thinsp;4), host species, family, or order (n\u0026thinsp;=\u0026thinsp;1 each), or sequencing platform (n\u0026thinsp;=\u0026thinsp;1). Four families (\u003cem\u003eAeromonadaceae, Bacteroidaceae, Enterobacteriaceae, Mycoplasmataceae\u003c/em\u003e, Figs. S63, S65, S68, S72) lacked the correlated diet variables in their partition trees, indicating spurious correlations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eMicrobiomes may play an important role in enhancing the adaptive capacity of ectothermic hosts [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Understanding how microbiomes respond to changes in food availability and consumption is therefore a crucial first step toward elucidating microbial contributions to host adaptation under environmental change. In this context, the identification of microbiome indicator taxa and use as a monitoring tool could be very useful. Microbiome indicators are useful in many different contexts, such as indicating healthy gut ecosystems in humans [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], environmental properties along river courses [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], anthropogenic disturbances in ecosystem health [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], soil structure and productivity [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe available studies that include both information on ectotherm gut microbiome composition (with raw sequences available) and diet nutritional features were conducted under controlled conditions and with diets that attended the nutritional demand of the experimental animals (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Thus, our results likely apply to plastic changes in the microbiome within its healthy state. When hosts are submitted to highly stressful conditions that could lead to dysbiosis, however, they may suffer drastic and non-adaptive microbiome changes. Our aim, however, is to find indicators that could show changes before dysbiosis occurs, what makes the available data appropriate.\u003c/p\u003e \u003cp\u003eOn the other hand, our results show that methodological heterogeneity represents a major challenge for identifying diet- or ecology-driven patterns of microbiome diversity across studies. Nevertheless, despite the relatively small number of suitable studies and substantial methodological variation, we were able to identify microbial indicators associated with specific diet-related changes in ectotherms and relevant to host health. In addition, we provide recommendations aimed at accelerating knowledge acquisition and improving the monitoring of ectotherm health under environmental change using microbiome-based indicators.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eEffects of diet on microbiome alpha- and phylogenetic diversity\u003c/h2\u003e \u003cp\u003eOur results show that methodological approaches significantly affect microbiome diversity estimates, highlighting the need for methodological standardization to enable robust comparisons among biologically relevant scenarios. Among the variables examined, the targeted region of the 16S rRNA gene was the most important source of variation in alpha diversity, corroborating previous studies reporting differences among target regions [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Sample type also emerged as an important variable explaining both alpha and phylogenetic diversity, in agreement with earlier findings [e.g. 27]. The type of DNA extraction kit was likewise influential and is likely more difficult to standardize, given the wide range of extraction kits currently available on the market [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Low to mid throughput sequencing (MiSeq) provided lower alpha diversity than high-throughput sequencing technologies, what may be related to lower numbers of reads provided to describe bacterial community diversity resulting in the failure to detect less abundant taxa. Trimming length depends on both the extent of the amplified region and sequencing quality; however, its influence on diversity was limited. In contrast, sample preservation method and whether sequencing was single- or paired-end did not explain microbiome diversity, indicating that variation in these methodological steps is less problematic for diversity comparisons.\u003c/p\u003e \u003cp\u003eEctothermic host genus was the most important variable explaining bacterial phylogenetic diversity; however, the limited number of ectotherm species included and the presence of both fishes and amphibians in both resulting branches suggest that \u0026ldquo;genus\u0026rdquo; (i.e., host phylogeny) itself is unlikely to be the true driver of this variation. Instead, the observed patterns may reflect differences in original habitats or sources of microbiome acquisition. Although the variable \u0026ldquo;habitat\u0026rdquo; appeared in restricted nodes of the partition trees explaining either alpha or phylogenetic diversity, our classification was limited to freshwater versus saltwater habitats. Substantial habitat variability occurs at finer spatial and ecological scales within these broad categories, and such variation is known to strongly influence microbiome structure [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. The effects of methodological variables were similar to alpha diversity, except that sequencing technology did not appear in the partition tree for phylogenetic diversity.\u003c/p\u003e \u003cp\u003eVariables describing diet nutritional composition explained variation in both alpha and phylogenetic diversity, but only within restricted subsets of samples, making it difficult to generalize the results. The high degree of methodological heterogeneity limited our ability to robustly assess the effects of diet and other ecological variables on overall microbiome diversity using the available data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMicrobiome indicators of dietary changes and their role on host health\u003c/h2\u003e \u003cp\u003eOur results confirm that identifying indicator taxa associated with dietary variation in ectothermic vertebrates is feasible, supporting the ecological expectation that selection favors increased the abundance of specialized microorganisms capable of digesting dominant food types [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. We identified several bacterial indicator taxa whose abundances varied with the relative contribution of insects to ectotherm diets. Notably, these relationships were detectable despite substantial methodological heterogeneity, including variation in sample type, DNA preservation and extraction methods, targeted 16S rRNA regions, sequencing technologies, experimental designs, and taxonomic and life-history differences among the studied ectotherms. Several indicator microbial groups matched between the complete and the filtered datasets, whereas others with likely equivalent ecological functions were selected in each dataset. Moreover, existing evidence indicates that insects are a highly nutritious food source [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e] and that their digestion by the microbiome generates metabolites that interact with the host\u0026rsquo;s neuroendocrine system and energy balance [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], potentially helping ectotherms cope with the physiological challenges imposed by global warming [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInsects are rich in protein and also provide beneficial fats, minerals, and vitamins [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. In addition, they contain bioactive compounds such as chitin, which can be degraded by gut bacteria, leading to the production of or short-chain fatty acids (SCFAs) with immunostimulatory and anti-inflammatory properties [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Increased insect consumption has been associated with enhanced growth, antioxidant capacity, and immune responses in ectotherms [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. These effects are likely mediated by microbiome shifts that enhance the degradation of complex compounds such as chitin, producing metabolites that can be assimilated by the host as energy sources and signaling molecules [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eChitin is abundant in insects and other organisms such as crustaceans, algae, and fungi. In chitin-containing tissues, the polymer is typically associated with other structural components, including proteins or glucans. The capacity to degrade chitin is widespread among bacteria and can be inferred from the presence of chitinase genes, which are thought to have spread through horizontal gene transfer. These genes are found in \u003cem\u003eActinobacteria\u003c/em\u003e and several representatives of \u003cem\u003eBacillota\u003c/em\u003e, as well as other taxa [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur metanalysis showed that bacterial taxa whose abundance consistently increased with higher proportions of insect protein in ectotherm diets included, within the phylum \u003cem\u003eActinomycetota\u003c/em\u003e, the genera and families \u003cem\u003eActinomyces\u003c/em\u003e (\u003cem\u003eActinomycetaceae\u003c/em\u003e), \u003cem\u003eBrevibacterium\u003c/em\u003e (\u003cem\u003eBrevibacteriaceae\u003c/em\u003e), and \u003cem\u003eCorynebacterium\u003c/em\u003e (\u003cem\u003eCorynebacteriaceae\u003c/em\u003e). Within the phylum \u003cem\u003eBacillota\u003c/em\u003e, taxa showing similar patterns included \u003cem\u003eLederbergia\u003c/em\u003e and \u003cem\u003eOceanobacillus\u003c/em\u003e (\u003cem\u003eBacillaceae\u003c/em\u003e), \u003cem\u003eGlobicatella\u003c/em\u003e (\u003cem\u003eAerococcaceae\u003c/em\u003e), and \u003cem\u003eEnterococcus\u003c/em\u003e (\u003cem\u003eEnterococcaceae\u003c/em\u003e). \u0026ldquo;Insect protein sources\u0026rdquo; was the most important variable explaining the abundance of all these taxa, and the second most important\u0026mdash;after \u0026ldquo;insect fat sources\u0026rdquo;\u0026mdash;for \u003cem\u003eLederbergia\u003c/em\u003e and \u003cem\u003eOceanobacillus\u003c/em\u003e (\u003cem\u003eBacillaceae\u003c/em\u003e). Insect fat sources also correlated positively with, and explained most of the variation in, the abundance of \u003cem\u003eAlkalihalophilus\u003c/em\u003e (present only in the complete dataset), and \u003cem\u003ePaenibacillus\u003c/em\u003e (\u003cem\u003ePaenibacillaceae\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eAs an insoluble polymer, chitin is initially degraded extracellularly, followed by uptake of the hydrolysis products, which are further broken down into simple sugars and used as energy sources [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. In natural habitats, chitin availability is closely linked to chitin hydrolysis rates [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e], suggesting that efficient degraders increase in abundance under higher chitin supply. A similar pattern was observed in the guts of ectotherms fed diets rich in insect protein in our metanalysis. Consistent with these findings, increased abundance of \u003cem\u003eActinomyces\u003c/em\u003e has also been reported in the guts of rainbow trout (\u003cem\u003eOncorhynchus mykiss\u003c/em\u003e) fed insect-rich diets [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. We likewise observed an increase in \u003cem\u003eCorynebacterium\u003c/em\u003e with higher insect protein intake, a genus that includes \u003cem\u003eCorynebacterium glutamicum\u003c/em\u003e, which has been associated with increased SCFA levels, reduced inflammation, and restoration of gut barrier function in mice [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong microbial metabolites derived from ingested food, SCFAs, polyamines, and neuropeptides can be sensed by host receptors and act as signaling molecules that regulate cellular responses and hormonal activity [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. By acting on the hypothalamic\u0026ndash;pituitary and related neuroendocrine axes, these metabolites influence the expression of insulin-like growth factor I (IGF-I), thereby affecting development, growth, reproduction, and senescence. Although IGF-I can exert pro-inflammatory effects that tend to increase with age and reduced microbiome diversity, SCFAs and polyamines have anti-inflammatory and antioxidant properties that may delay senescence [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The hypothalamic\u0026ndash;pituitary axis is a critical mediator of vertebrate adaptive physiological responses to environmental stressors, emphasizing its role in mobilizing energy substrates during challenging conditions [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. Mitochondrial functions play a central role and are compromised by high temperatures in ectotherms, causing excessive production of reactive oxygen species (ROS) and increasing energetic costs for antioxidant defense [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFinally, \u003cem\u003eHeyndrickxia coagulans\u003c/em\u003e (formerly \u003cem\u003eBacillus coagulans\u003c/em\u003e) promotes the growth of other beneficial bacteria such as \u003cem\u003eBifidobacterium\u003c/em\u003e and \u003cem\u003eLactobacillus\u003c/em\u003e through its metabolic products, enhancing host absorption of trace inorganic elements [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. In humans, these effects translate into improved gastrointestinal health, immune modulation, and increased energy availability [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. \u003cem\u003eHeyndrickxia coagulans\u003c/em\u003e also produces both pro- and anti-inflammatory regulators, as well as reactive oxygen species (ROS), however it supports immune regulation by modulating cytokine expression and enhancing phagocytosis [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. We detected an increase in \u003cem\u003eHeyndrickxia\u003c/em\u003e and an OTU classified within the genus \u003cem\u003eBacillus\u003c/em\u003e associated with insect protein sources in the complete dataset (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), suggesting that these bacteria may similarly contribute to host health in ectotherms - an inference that will become clearer as more data become available and methodological heterogeneity is reduced.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eMethodological heterogeneity and biological complexity challenge the detection of diet-related microbiome indicators\u003c/h2\u003e \u003cp\u003eThe lack of robust indicator taxa for most dietary variables tested likely reflects both biological and methodological factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The limited number of studies with suitable data restricts our ability to simultaneously account for multiple biological sources of variation, such as host diet, taxonomy, life history, and ecology. Moreover, even when biological variables other than diet are controlled for, microbiome responses to food intake may not always be captured by discrete taxonomic indicators. Although resource availability imposed by host diet strongly influences microbial communities [e.g., 72], similar functional profiles can emerge from taxonomically distinct microbiomes. This functional redundancy highlights the flexibility of microbiome assembly [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e] and its adaptive role in enabling animals to cope with changing environments [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. In addition, microbiome resilience may contribute to resistance against disturbance, as more plastic bacterial taxa are less likely to be lost under environmental change [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHost taxonomy had an increased importance as explanatory variable in the filtered dataset, likely because our filtering procedures reduced to some extent the differences resulting from different methods among studies. Differences in microbiomes among related species are expected to be influenced by both their phylogenetic relationships (phylogenetic inertia may occur among closely related species) as well as by their trophic niches, as increase in specialized microorganisms able to digest main food types should be favored by selection [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMethodological variation also played a substantial role in microbiome composition. Previous studies have documented strong effects of sample type on gut microbiome profiles [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], as well as biases associated with the targeted region of the 16S rRNA gene [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Consistent with these findings, for many bacterial genera and families that were strongly correlated with dietary variables, the primary factor explaining read abundance was methodological rather than diet-related.\u003c/p\u003e \u003cp\u003eFinally, the available dataset on ectotherm gut microbiomes remains limited, and many published studies have not followed open science practices by making raw sequencing data publicly accessible, what seems to be a common problem [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. This lack of data availability, combined with insufficient methodological standardization, represents a major obstacle to a comprehensive understanding of how dietary features shape gut microbiomes. Even among bacterial taxa that showed strong overall correlations with diet, dietary variables often appeared near the terminal nodes of the partition trees, whereas other factors exerted stronger effects. Ignoring these confounding variables would likely have led to misleading conclusions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eProspects for microbiome-informed conservation of ectothermic vertebrates\u003c/h2\u003e \u003cp\u003eWe aimed to identify microbiome-based indicators that could enable early detection of sublethal impacts of global change on the diets of ectothermic vertebrates. Such indicators might help define thresholds beyond which the microbiome can no longer buffer hosts against changes in food quality or availability. We found that insect consumption had the most pronounced effects on ectotherm gut microbiomes, demonstrating that such indicators can indeed be detected. However, the nutritional value of insects varies widely among taxa and life stages, depending on factors such as chitin and fat content [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. While laboratory diets are designed to meet nutritional requirements in accordance with animal ethics guidelines [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e], wild animals may experience malnutrition and adverse conditions due to human impacts that reduce food availability, quality, and feeding opportunities [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. Therefore, greater effort is needed to understand the microbiome-mediated effects of insect consumption and other food types under natural and human-altered conditions of food availability and selection (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite their high potential for discovery, the microbiomes of wild animals remain poorly characterized [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. For instance, increases in xenobiotic-degradation orthologs may indicate pollutant bioaccumulation along food chains, whereas changes in the abundance of specific bacterial taxa may signal dietary shifts [19, this study]. Given the strong influence of microbiomes on host physiology and health, expanding knowledge of wild animal microbiomes and their shaping factors is essential to identify thresholds beyond which dysbiosis occurs, potentially increasing host mortality risk ([\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAssessing detailed nutritional composition of diets in the wild \u0026ndash; comparable to that obtained in controlled feeding experiments \u0026ndash; is often impractical, as free-ranging animals consume diverse resources that vary among individuals, locations, and seasons [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. One promising approach to detect microbial indicators of dietary change in natural habitats is to combine microbiome analyses with stable isotope data from hosts, which can provide robust insights into both short- and long-term dietary composition [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAnother major challenge is the standardization of methodological approaches to reduce unwanted variation in microbiome characterization (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Our systematic review showed that most studies on ectotherm gut microbiomes amplified the V3\u0026ndash;V4 or V4 regions of the 16S rRNA gene, making these regions suitable for comparative analyses. When amplification of alternative regions is necessary, approaches such as the Short MUltiple Regions Framework (SMURF), which combines data from multiple regions, can improve taxonomic resolution [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. Simulations suggest that detection probabilities for specific taxa using SMURF are similar to those obtained with the V4 region alone [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. When this is not possible, our adopted strategy of focusing on genus- and family-level taxonomic assignments provides a robust framework for comparing studies despite methodological heterogeneity [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFinally, we strongly encourage improved data availability practices by researchers and scientific journals. Public access to raw sequencing data is essential for enabling reanalyses with updated bioinformatic pipelines, alternative taxonomic definitions (e.g., OTUs versus species), and standardized diversity metrics, thereby facilitating meaningful comparisons across studies [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur goal was to investigate the feasibility of detecting dietary changes in ectotherms (such as those triggered by global changes) using gut microbiome bacterial indicators. Here, we show that meta-analyses are a useful tool for synthesizing available microbiome data and for supporting the feasibility of this approach. However, additional data collected under more standardized conditions are needed to reduce the noise caused by substantial variability in experimental design and laboratory procedures. Our meta-analysis provides the first step towards identifying and addressing these issues in order to pursue scientifically robust conclusions.\u003c/p\u003e \u003cp\u003eSpecies monitoring programs frequently focus on demographic parameters, as animal welfare is harder to assess [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Animal microbiomes likely contribute to host welfare by helping individuals cope with changing environmental conditions up to certain thresholds, beyond which dysbiosis may occur, with marked consequences for health and survival [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. It is therefore important to develop monitoring tools capable of detecting changes before such thresholds are reached. Here, we demonstrate that specific dietary shifts in ectotherms are reflected in their gut microbiomes.\u003c/p\u003e \u003cp\u003eOur results highlight the potential of microbiome-based indicators to monitor changes in food quality, availability, and/or consumption in ectotherms. This approach could be particularly valuable for wild populations, where detailed information on consumed food items is often unavailable. By identifying diet-related microbiome shifts\u0026mdash;potentially linked to changes in food webs\u0026mdash;it may be possible to investigate underlying causes before they ultimately contribute to population declines.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval Sample collection and experiment permits for our raw sequence dataset (European common frog, Rana temporaria) were obtained from the Nieders\u0026auml;chsisches Landesamt f\u0026uuml;r Verbraucherschutz und Lebensmittelsicherheit, Germany (Gz. 33.19-42502-04-20/3590 and 33.19-42502-04-22-00274). Fieldwork was carried out with permits from the Stadt Braunschweig (Stadt Braunschweig - Fachbereich Umwelt und Naturschutz, Willy-Brandt-Platz 13, 38102 Braunschweig; Gz. 68.11-11.8-3.3).\u003c/p\u003e\n\u003cp\u003eThe corresponding animal experiments were approved by the Animal Ethics Comittee, which is the Landesamt f\u0026uuml;r Verbraucherschutz und Lebensmittelsicherheit in Niedersachsen, Germany.\u0026nbsp;\u003c/p\u003e\u003cp\u003e \u003cstrong\u003eEthics approval\u003c/strong\u003e \u003cp\u003eSample collection and experiment permits for our raw sequence dataset (European common frog, \u003cem\u003eRana temporaria\u003c/em\u003e) were obtained from the Nieders\u0026auml;chsisches Landesamt f\u0026uuml;r Verbraucherschutz und Lebensmittelsicherheit, Germany (Gz. 33.19-42502-04-20/3590 and 33.19-42502-04-22-00274). Fieldwork was carried out with permits from the Stadt Braunschweig (Stadt Braunschweig - Fachbereich Umwelt und Naturschutz, Willy-Brandt-Platz 13, 38102 Braunschweig; Gz. 68.11-11.8-3.3).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003ePCE was supported by the Deutsche Forschungsgemeinschaft (DFG; GZ: CA 3427/2\u0026thinsp;\u0026minus;\u0026thinsp;1, project number: 546565602; The role of diet on microbiome shaping and outcomes in host defensive behaviour and immune defence unveiled through a multiomic approach). \u003cem\u003eRana temporaria\u003c/em\u003e sample acquisition was funded by the DFG (Project number: 459850971; granted to KR).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003ePCE was responsible for conceptualization, fund acquisition, leading data curation, formal analyses, validation, visualization, and manuscript writing. KR was responsible for fund acquisition, contributed to data curation and manuscript writing. SV, JS, and KWV contributed to formal analyses and manuscript writing. JO contributed with resources and manuscript writing. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe are thankful to the authors who made their data available in public repositories or under personal request. We used AI tools from Warp [Warp Dev, Inc. (2026). Warp: The Agentic Development Environment (Version: v0.2026.02.18.08.22.stable_02; Computer software. https://www.warp.dev/] to review and adapt R codes that presented issues during figure formatting and to generate codes to merge large data files. All resulting codes and produced files/figures/results were checked for accuracy, in accordance with guidelines from Springer Nature and the German Research Foundation.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eRaw sequences of our dataset ( *Rana temporaria* ) included in the meta-analysis are deposited in the NCBI (BioProject PRJNA1304763, sample names are preceded by \u0026ldquo;PCE\u0026rdquo;). Codes and datafiles are available at GitHub ( [https://github.com/PaulaEterovick/meta-analysis-ectotherm-diet-and-microbiome](https:/github.com/PaulaEterovick/meta-analysis-ectotherm-diet-and-microbiome) ) and FigShare (doi:10.6084/m9.figshare.31079137).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTuddenham S, Sears CL. The intestinal microbiome and health. Curr Opin Infect Dis. 2015;28(5):464\u0026ndash;470; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/QCO.0000000000000196\u003c/span\u003e\u003cspan address=\"10.1097/QCO.0000000000000196\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang F, Shi X, Chen P, Hu Q, Zhao Y, Chen Z, \u0026hellip; Zhang X. Dietary drivers of gut microbiota diversity and function in wildlife of Wolong Nature Reserve: a metagenomic study. Curr Zool. 2025;zoaf018; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/cz/zoaf018\u003c/span\u003e\u003cspan address=\"10.1093/cz/zoaf018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKashinskaya EN, Simonov EP, Kabilov MR, Izvekova GI, Andree KB, Solovyev MM. Diet and other environmental factors shape the bacterial communities of fish gut in an eutrophic lake. J Appl Microbiol. 2018;125(6):1626\u0026ndash;1641; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/jam.14064\u003c/span\u003e\u003cspan address=\"10.1111/jam.14064\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEscalas, A., Auguet, J. C., Avouac, A., Seguin, R., Gradel, A., Borrossi, L., \u0026amp; Vill\u0026eacute;ger, S. (2021). Ecological specialization within a carnivorous fish family is supported by a herbivorous microbiome shaped by a combination of gut traits and specific diet. Front Mar Sci. 2021;8:622883; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fmars.2021.622883\u003c/span\u003e\u003cspan address=\"10.3389/fmars.2021.622883\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWarne RW, Dallas J. Microbiome mediation of animal life histories via metabolites and insulin-like signaling. Biol Rev. 2022;97(3):1118\u0026ndash;1130; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/brv.12833\u003c/span\u003e\u003cspan address=\"10.1111/brv.12833\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHenry LP, Bruijning M, Forsberg SK, Ayroles JF. The microbiome extends host evolutionary potential. Nat Commun. 2021;12(1):5141; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41467-021-25315-x\u003c/span\u003e\u003cspan address=\"10.1038/s41467-021-25315-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFontaine SS, Kohl KD. Ectotherm heat tolerance and the microbiome: current understanding, future directions and potential applications. J Exp Biol. 2023;226(12):jeb245761; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1242/jeb.245761\u003c/span\u003e\u003cspan address=\"10.1242/jeb.245761\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo J, Li Z, Liu X, Jin Y, Sun Y, Yuan Z, \u0026hellip; Zhang M. Response of the gut microbiota to changes in the nutritional status of red deer during winter. Sci Rep. 2024;14(1):24961; doi:10.1038/s41598-024-76142-1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEterovick PC, Schmidt R, Sabino-Pinto J, Yang C, K\u0026uuml;nzel S, Ruthsatz K. The microbiome at the interface between environmental stress and animal health: an example from the most threatened vertebrate group. Proc R Soc B. 2024;291(2031):20240917; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1098/rspb.2024.0917\u003c/span\u003e\u003cspan address=\"10.1098/rspb.2024.0917\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEterovick PC, Glos J, Burkart F, Overmann J, Ruthsatz K. 2026. Interplay of diet, heat stress, and the microbiome shapes health and escape behavior in amphibian larvae. EcoEvoRxiv 2026; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.32942/X2CW81\u003c/span\u003e\u003cspan address=\"10.32942/X2CW81\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO'Connor MI, Piehler MF, Leech DM, Anton A, Bruno JF. Warming and resource availability shift food web structure and metabolism. PLoS Biol. 2009;7(8):e1000178; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pbio.1000178\u003c/span\u003e\u003cspan address=\"10.1371/journal.pbio.1000178\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeifert LI, de Castro F, Marquart A, Gaedke U, Weithoff G, Vos M. Heated relations: temperature-mediated shifts in consumption across trophic levels. PLoS One 2014;9(5):e95046; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0095046\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0095046\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarreira BM, Segurado P, Orizaola G, Gon\u0026ccedil;alves N, Pinto V, Laurila A, Rebelo R. Warm vegetarians? Heat waves and diet shifts in tadpoles. Ecology 2016;97(11):2964\u0026ndash;2974; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/ecy.1541\u003c/span\u003e\u003cspan address=\"10.1002/ecy.1541\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHardison EA, Eliason EJ. Diet effects on ectotherm thermal performance. Biol Rev. 2024;99(4):1537\u0026ndash;1555; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/brv.13081\u003c/span\u003e\u003cspan address=\"10.1111/brv.13081\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWard CA, Tunney TD, Donohue I, Bieg C, Hale KR, McMeans BC, \u0026hellip; McCann KS. Global Change Asymmetrically Rewires Ecosystems. Ecol Lett. 2025;28(7):e70174; doi:10.1111/ele.70174.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmato KR, Yeoman CJ, Kent A, Righini N, Carbonero F, Estrada A., \u0026hellip; Leigh SR. Habitat degradation impacts black howler monkey (\u003cem\u003eAlouatta pigra\u003c/em\u003e) gastrointestinal microbiomes. ISME J. 2013;7(7):1344\u0026ndash;1353; doi:10.1038/ismej.2013.16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaniel A, Amato KR, Beehner JC, Bergman TJ, Mercer A, Perlman RF, \u0026hellip; Snyder-Mackler N. Seasonal shifts in the gut microbiome indicate plastic responses to diet in wild geladas. Microbiome 2021;9(1):26; doi:10.1186/s40168-020-00977-9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Q, Fei HL, Luo ZH, Gao SM, Wang PD, Lan LY, \u0026hellip; Fan PF. Gut microbiome responds compositionally and functionally to the seasonal diet variations in wild gibbons.NPJ Biofilms Microbiomes 2023;9(1):21; doi:10.1038/s41522-023-00388-2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevin D, Raab N, Pinto Y, Rothschild D, Zanir G, Godneva A, \u0026hellip; Segal E. Diversity and functional landscapes in the microbiota of animals in the wild. Science 2021;372(6539):eabb5352;doi:10.1126/science.abb5352.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorm B, Barbier EB, Beaumont N, Duffy JE, Folke C, Halpern BS, \u0026hellip; Watson R. Impacts of biodiversity loss on ocean ecosystem services. Science 2006;314(5800):787\u0026ndash;790;doi:10.1126/science.1132294.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCardinale BJ, Duffy JE, Gonzalez A, Hooper DU, Perrings C, Venail P, \u0026hellip; Naeem S. Biodiversity loss and its impact on humanity. Nature 2012;486(7401):59\u0026ndash;67; doi:10.1038/nature11148.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGibert JP, Grady JM, Dell AI. Food web consequences of thermal asymmetries. Funct Ecol. 2022;36(8):1887\u0026ndash;1899; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/1365-2435.14091\u003c/span\u003e\u003cspan address=\"10.1111/1365-2435.14091\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosenberg Y, Bar-On YM, Fromm A, Ostikar M, Shoshany A, Giz O, Milo R. The global biomass and number of terrestrial arthropods. Sci Adv. 2023;9(5):eabq4049; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1126/sciadv.abq40\u003c/span\u003e\u003cspan address=\"10.1126/sciadv.abq40\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiemer K, Anderson-Teixeira KJ, Smith FA, Harris DJ, Ernest SM. Body size shifts influence effects of increasing temperatures on ectotherm metabolism. Glob Ecol Biogeogr. 2018;27(8):958\u0026ndash;967; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/geb.12757\u003c/span\u003e\u003cspan address=\"10.1111/geb.12757\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaspoumaderes C, Meunier CL, Magnin A, Berlinghof J, Elser JJ, Balseiro E, \u0026hellip; Boersma M. A common temperature dependence of nutritional demands in ectotherms. Ecol Lett. 2022;25(10):2189\u0026ndash;2202; doi:10.1111/ele.14093.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong SJ, Sanders JG, Delsuc F, Metcalf J, Amato K, Taylor MW, \u0026hellip; Knight R. (2020).Comparative analyses of vertebrate gut microbiomes reveal convergence between birds and bats. MBio 2020;11(1):10-1128; doi: 10.1128/mbio.02901-19.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRangel F, Enes P, Gasco L, Gai F, Hausmann B, Berry D, \u0026hellip; Pereira FC. Differential modulation of the European sea bass gut microbiota by distinct insect meals. Front Microbiol. 2022;13:831034; doi:10.3389/fmicb.2022.831034.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCornejo-Granados F, Gallardo-Becerra L, Leonardo-Reza M, Ochoa-Romo JP, Ochoa-Leyva A. A meta-analysis reveals the environmental and host factors shaping the structure and function of the shrimp microbiota. PeerJ 2018;6:e5382; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7717/peerj.5382\u003c/span\u003e\u003cspan address=\"10.7717/peerj.5382\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSirichoat A, Sankuntaw N, Engchanil C, Buppasiri P, Faksri K, Namwat W, \u0026hellip; Lulitanond V. (2021). Comparison of different hypervariable regions of 16S rRNA for taxonomic profiling of vaginal microbiota using next-generation sequencing. Arch Microbiol. 2021;203(3):1159\u0026ndash;1166; doi:10.1007/s00203-020-02114-4)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL\u0026oacute;pez-Aladid R, Fern\u0026aacute;ndez-Barat L, Alcaraz-Serrano V, Bueno-Freire L, V\u0026aacute;zquez N,Pastor-Ib\u0026aacute;\u0026ntilde;ez R, \u0026hellip; Torres A. Determining the most accurate 16S rRNA hypervariable region for taxonomic identification from respiratory samples. Sci Rep. 2023;13(1):3974; doi:10.1038/s41598-023-30764-z.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHallam J, Harris NC. What's going to be on the menu with global environmental changes? Glob Change Biol. 2023;29(20):5744\u0026ndash;5759; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/gcb.16866\u003c/span\u003e\u003cspan address=\"10.1111/gcb.16866\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSonnega S, Sheriff MJ. Harnessing the gut microbiome: a potential biomarker for wild animal welfare. Front Vet Sci. 2024;11:1474028; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fvets.2024.1474028\u003c/span\u003e\u003cspan address=\"10.3389/fvets.2024.1474028\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGupta VK, Kim M, Bakshi U, Cunningham KY, Davis III JM, Lazaridis KN,\u0026hellip; Sung J. A predictive index for health status using species-level gut microbiome profiling. Nat Commun. 2020;11(1):4635; doi:10.1038/s41467-020-18476-8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFontaine L, Pin L, Savio D, Friberg N, Kirschner AK, Farnleitner AH, Eiler A. Bacterial bioindicators enable biological status classification along the continental Danube river. Commun Biol. 2023;6(1):862; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s42003-023-05237-8\u003c/span\u003e\u003cspan address=\"10.1038/s42003-023-05237-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRibas MP, Garc\u0026iacute;a-Ulloa M, Espunyes J, Cabez\u0026oacute;n O. Improving the assessment of ecosystem and wildlife health: microbiome as an early indicator. Curr Opin Biotechnol. 2023;81:102923; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.copbio.2023.102923\u003c/span\u003e\u003cspan address=\"10.1016/j.copbio.2023.102923\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTerzin M, Laffy PW, Robbins S, Yeoh YK, Frade PR, Glasl B, \u0026hellip; Bourne DG. The road forward to incorporate seawater microbes in predictive reef monitoring. Environ Microbiome 2024;19(1):5; doi:10.1186/s40793-023-00543-4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRomero F, Labouyrie M, Orgiazzi A, Ballabio C, Panagos P, Jones A, \u0026hellip; van der Heijden MG. The soil microbiome as an indicator of ecosystem multifunctionality in European soils.Nat Commun. 2026;17:705; doi:10.1038/s41467-025-67353-9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePage MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, \u0026hellip; Moher D. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71; doi:10.1136/bmj.n71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSayers EW, Beck J, Bolton EE, Brister JR, Chan J, Connor R., \u0026hellip; Pruitt KD. Database resources of the National Center for Biotechnology Information in 2025. Nucleic Acids Res. 2025;53(D1):D20-D29; doi:10.1093/nar/gkae979.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeinonen R, Akhtar R, Birney E, Bower L, Cerdeno-T\u0026aacute;rraga A, Cheng Y, \u0026hellip; Cochrane G. The European nucleotide archive. Nucleic Acids Res. 2010;39(suppl_1):D28-D31;doi:10.1093/nar/gkq967.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, \u0026hellip; Caporaso JG. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37(8):852\u0026ndash;857; doi:10.1038/s41587-019-0209-9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBokulich NA, Subramanian S, Faith JJ, Gevers D, Gordon JI, Knight R, \u0026hellip; Caporaso JG.Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing.Nat Methods 2013;10(1):57\u0026ndash;59; doi:10.1038/nmeth.2276.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEstaki M, Jiang L, Bokulich NA, McDonald D, Gonz\u0026aacute;lez A, Kosciolek T, Martino C, \u0026hellip;Knight R. QIIME 2 enables comprehensive end-to‐end analysis of diverse microbiome data and comparative studies with publicly available data. Curr Protoc Bioinformatics 2020;70:e100; doi:10.1002/cpbi.100.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePruesse E, Quast C, Knittel K, Fuchs BM, Ludwig W, Peplies J, Gl\u0026ouml;ckner FO. \u0026ldquo;SILVA: A comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB.\u0026rdquo; Nucleic Acids Res. 2007;35 (21):7188\u0026ndash;96; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gkm864\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkm864\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Gl\u0026ouml;ckner FO. \u0026ldquo;The SILVA Ribosomal RNA Gene Database Project: Improved data processing and web-based tools.\u0026rdquo; Nucleic Acids Res. 2013;41:D590\u0026ndash;96; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gks1219\u003c/span\u003e\u003cspan address=\"10.1093/nar/gks1219\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRobeson MS, O\u0026rsquo;Rourke DR, Kaehler BD, Ziemski M, Dillon MR, Foster JT, Bokulich NA. RESCRIPt: Reproducible sequence taxonomy reference database management. PLoS Comput Biol. 2021;17(11):e1009581; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pcbi.1009581\u003c/span\u003e\u003cspan address=\"10.1371/journal.pcbi.1009581\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaehler BD, Bokulich NA, Caporaso JG, Huttley GA. Species-level microbial sequence classification is improved by source-environment information. Nat Commun. 2019;10:4643; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41467-019-12669-6\u003c/span\u003e\u003cspan address=\"10.1038/s41467-019-12669-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR Core Team. R: A language and Environment for Statistical Computing. R Foundation for Statistical Computing. Version 4.5.1, Vienna, Austria; 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.r-project.org/\u003c/span\u003e\u003cspan address=\"https://cran.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChao A, Gotelli NJ, Hsieh TC, Sander EL, Ma KH, Colwell RK, Ellison AM. Rarefaction and extrapolation with Hill numbers: a framework for sampling and estimation in species diversity studies. Ecol Monogr. 2014;84(1):45\u0026ndash;67; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1890/13-0133.1\u003c/span\u003e\u003cspan address=\"10.1890/13-0133.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChao A, Jost L. Coverage-based rarefaction and extrapolation: standardizing samples by completeness rather than size. Ecology 2012;93(12):2533\u0026ndash;2547; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1890/11-1952.1\u003c/span\u003e\u003cspan address=\"10.1890/11-1952.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlberdi A., Gilbert MTP. A guide to the application of Hill numbers to DNA-based diversity analyses. Mol Ecol Res. 2019;19(4):804\u0026ndash;817; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/1755-0998.13014\u003c/span\u003e\u003cspan address=\"10.1111/1755-0998.13014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKembel SW, Cowan PD, Helmus MR, Cornwell WK, Morlon H, Ackerly DD, Blomberg SP, Webb CO. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 2010;26:1463\u0026ndash;1464; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/bioinformatics/btq166\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/btq166\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKatoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30(4):772\u0026ndash;780; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/molbev/mst010\u003c/span\u003e\u003cspan address=\"10.1093/molbev/mst010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrice MN, Dehal PS, Parkin AP. FastTree: computing large minimum evolution trees with profiles instead of a distance matrix. Mol Biol Evol. 2009;26(7):1641\u0026ndash;1650; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/molbev/msp077\u003c/span\u003e\u003cspan address=\"10.1093/molbev/msp077\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHothorn T, Zeileis A. \u0026ldquo;partykit: A Modular Toolkit for Recursive Partytioning in R.\u0026rdquo; J Mach Learn Res. 2015;16:3905\u0026ndash;3909; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://jmlr.org/papers/v16/hothorn15a.html\u003c/span\u003e\u003cspan address=\"https://jmlr.org/papers/v16/hothorn15a.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHothorn T, Hornik K, Zeileis A. Unbiased recursive partitioning: A conditional inference framework. J Comput Graph Stat. 2006;15(3):651\u0026ndash;674; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1198/106186006X133933\u003c/span\u003e\u003cspan address=\"10.1198/106186006X133933\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDormann CF, Elith J, Bacher S, Buchmann C, Carl G, Carr\u0026eacute; G, \u0026hellip; Lautenbach S. Collinearity:a review of methods to deal with it and a simulation study evaluating their performance.Ecography 2013;36(1):27\u0026ndash;46; doi:10.1111/j.1600-0587.2012.07348.x.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFei T, Funnell T, Waters NR, Raj SS, Baichoo M, Sadeghi K, \u0026hellip; van den Brink MR. Scalable log-ratio lasso regression for enhanced microbial feature selection with FLORAL. Cel Rep Methods 2024;4(11); doi:0.1016/j.crmeth.2024.100899.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSperling JL, Silva-Brand\u0026atilde;o KL, Brand\u0026atilde;o MM, Lloyd VK, Dang S, Davis CS, \u0026hellip; Magor KE.Comparison of bacterial 16S rRNA variable regions for microbiome surveys of ticks.Ticks Tick-Borne Dis. 2017;8(4):453\u0026ndash;461; doi:10.1016/j.ttbdis.2017.02.002.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang B, Wang Y, Qian PY. Sensitivity and correlation of hypervariable regions in 16S rRNA genes in phylogenetic analysis. BMC Bioinform. 2016;17(1):135; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12859-016-0992-y\u003c/span\u003e\u003cspan address=\"10.1186/s12859-016-0992-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNikodemova M, Holzhausen EA, Deblois CL, Barnet JH, Peppard PE, Suen G, Malecki KM. The effect of low-abundance OTU filtering methods on the reliability and variability of microbial composition assessed by 16S rRNA amplicon sequencing. Front Cell Infect Microbiol. 2023;13:1165295; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fcimb.2023.1165295\u003c/span\u003e\u003cspan address=\"10.3389/fcimb.2023.1165295\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZoruk P, Morozov M, Veselovsky V, Strokach A, Babenko V, Klimina K. Impact of DNA extraction techniques and sequencing approaches on microbial community profiling accuracy. Front Microbiomes 2025;4:1688681; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/frmbi.2025.1688681\u003c/span\u003e\u003cspan address=\"10.3389/frmbi.2025.1688681\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNearing JT, Douglas GM, Hayes MG, MacDonald J, Desai DK, Allward N, \u0026hellip; Langille MGI.Microbiome differential abundance methods produce different results across 38 datasets.Nat Commun. 2022;13:342; doi:10.1038/s41467-022-28034-z.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHrovat K, Dutilh BE, Medema MH, Melkonian C. Taxonomic resolution of different 16S rRNA variable regions varies strongly across plant-associated bacteria. ISME Commun. 2024;4(1):ycae034; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ismeco/ycae034\u003c/span\u003e\u003cspan address=\"10.1093/ismeco/ycae034\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGalla G, Praeg N, Rzehak T, Sprecher E, Colla F, Seeber J,\u0026hellip; Hauffe HC. Comparison of DNA extraction methods on different sample matrices within the same terrestrial ecosystem. Sci Rep. 2024;14(1):8715; doi:10.1038/s41598-024-59086-4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBletz MC, Goedbloed DJ, Sanchez E, Reinhardt T, Tebbe CC, Bhuju S, \u0026hellip; Steinfartz S.Amphibian gut microbiota shifts differentially in community structure but converges on habitat-specific predicted functions. Nat Commun. 2016;7(1):13699; doi:10.1038/ncomms13699.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaldo L, Pretus JL, Riera JL, Musilova Z, Bitja Nyom AR, Salzburger W. Convergence of gut microbiotas in the adaptive radiations of African cichlid fishes. The ISME J. 2017;11(9):1975\u0026ndash;1987; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/ismej.2017.62\u003c/span\u003e\u003cspan address=\"10.1038/ismej.2017.62\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePinheiro NA, Silva LJ, Pena A, Pereira AM. Entomophagy: nutritional value, benefits, regulation and food safety. Foods 2025;14(13):2380; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/foods14132380\u003c/span\u003e\u003cspan address=\"10.3390/foods14132380\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSokolova IM. Ectotherm mitochondrial economy and responses to global warming. Acta Physiol. 2023;237(4):e13950; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/apha.13950\u003c/span\u003e\u003cspan address=\"10.1111/apha.13950\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGasco L, J\u0026oacute;zefiak A, Henry M. Beyond the protein concept: health aspects of using edible insects on animals. J Insects Food Feed 2021;7(5):715\u0026ndash;741; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3920/JIFF2020.0077\u003c/span\u003e\u003cspan address=\"10.3920/JIFF2020.0077\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaulu S, Langi S, Hasimuna OJ, Missinhoun D, Munganga BP, Hampuwo BM, \u0026hellip; Dawood MA.Recent advances in the utilization of insects as an ingredient in aquafeeds: A review.Anim Nutr. 2022;11:334\u0026ndash;349; doi:0.1016/j.aninu.2022.07.013.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeier S, Bertilsson S. Bacterial chitin degradation\u0026mdash;mechanisms and ecophysiological strategies. Front Microbiol. 2013;4:149; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fmicb.2013.00149\u003c/span\u003e\u003cspan address=\"10.3389/fmicb.2013.00149\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGaudioso G, Marzorati G, Faccenda F, Weil T, Lunelli F, Cardinaletti G,\u0026hellip; Fava F.Processed animal proteins from insect and poultry by-products in a fish meal-free diet for rainbow trout: Impact on intestinal microbiota and inflammatory markers.Int J Mol Sci. 2021;22(11):5454; doi:10.3390/ijms22115454.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYe J, Li Y, Wang X, Yu M, Liu X, Zhang H, \u0026hellip; Yue T. Positive interactions among \u003cem\u003eCorynebacterium glutamicum\u003c/em\u003e and keystone bacteria producing SCFAs benefited T2D mice to rebuild gut eubiosis.Food Res Int. 2023;172:113163; doi:10.1016/j.foodres.2023.113163.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePecoraro N, Dallman MF, Warne JP, Ginsberg AB, Laugero KD, la Fleur SE, \u0026hellip; Akana SF.From Malthus to motive: how the HPA axis engineers the phenotype, yoking needs to wants. Prog Neurobiol. 2006;79(5\u0026ndash;6):247\u0026ndash;340; doi:10.1016/j.pneurobio.2006.07.004.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiang J, Li C, Chen Z, Guo F, Dou J, Wang T, Xu ZS. Progress of research and application of \u003cem\u003eHeyndrickxia coagulans\u003c/em\u003e (\u003cem\u003eBacillus coagulans\u003c/em\u003e) as probiotic bacteria. Front Cell Infect Microbiol. 2024;14:1415790; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fcimb.2024.1415790\u003c/span\u003e\u003cspan address=\"10.3389/fcimb.2024.1415790\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLouca S, Polz MF, Mazel F, Albright MB, Huber JA, O\u0026rsquo;Connor MI, \u0026hellip; Parfrey LW. Function and functional redundancy in microbial systems. Nat Ecol Evol. 2018;2(6):936\u0026ndash;943;doi:10.1038/s41559-018-0519-1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAllison SD, Martiny JB. Resistance, resilience, and redundancy in microbial communities. Proc Nat Acad Sci. 2008;105(supplement_1):11512\u0026ndash;11519; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1073/pnas.0801925105\u003c/span\u003e\u003cspan address=\"10.1073/pnas.0801925105\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShade A, Peter H, Allison SD, Baho DL, Berga M, B\u0026uuml;rgmann H, \u0026hellip; Handelsman J. Fundamentals of microbial community resistance and resilience. Front Microbiol. 2012;3:417;doi:10.3389/fmicb.2012.00417.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJurburg SD, Konzack M, Eisenhauer N, Heintz-Buschart A. The archives are half-empty: an assessment of the availability of microbial community sequencing data. Commun Biol. 2020;3(1):474; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s42003-020-01204-9\u003c/span\u003e\u003cspan address=\"10.1038/s42003-020-01204-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKiani AK, Pheby D, Henehan G, Brown R, Sieving P, Sykora P, \u0026hellip; International Bioethics Study Group. Ethical considerations regarding animal experimentation. J Prev Med Hyg.2022;63(2 Suppl 3):E255; doi:10.15167/2421-4248/jpmh2022.63.2S3.2768.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBirnie-Gauvin K, Peiman KS, Raubenheimer D, Cooke SJ. Nutritional physiology and ecology of wildlife in a changing world. Conserv Physiol. 2017;5(1):cox030; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/conphys/cox030\u003c/span\u003e\u003cspan address=\"10.1093/conphys/cox030\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFuks G, Elgart M, Amir A, Zeisel A, Turnbaugh PJ, Soen Y, Shental N. Combining 16S rRNA gene variable regions enables high-resolution microbial community profiling. Microbiome 2018;6(1):17; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s40168-017-0396-x\u003c/span\u003e\u003cspan address=\"10.1186/s40168-017-0396-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSoriano-Lerma A, P\u0026eacute;rez-Carrasco V, S\u0026aacute;nchez-Mara\u0026ntilde;\u0026oacute;n M, Ortiz-Gonz\u0026aacute;lez M, S\u0026aacute;nchez-Mart\u0026iacute;n V, Gij\u0026oacute;n J, \u0026hellip; Soriano M. Influence of 16S rRNA target region on the outcome of microbiome studies in soil and saliva samples. Sci Rep. 2020;10(1):13637; doi:10.1038/s41598-020-70141-8.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":"Bacteria, gut microbiome, fishes, amphibia, global warming, food quality","lastPublishedDoi":"10.21203/rs.3.rs-8959905/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8959905/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eGlobal warming and other human-driven impacts are reshaping food webs, compromising both food quality and availability. Ectotherms are particularly challenged under these conditions because they simultaneously face elevated energetic demands and unstable food supply. Their gut microbiomes respond strongly to diet and may either enhance host adaptive potential or undergo dysbiosis, contributing to adaptive failure. Understanding how diet affects ectotherm microbiomes is therefore fundamental for predicting the consequences of environmentally driven dietary change. However, studies on ectotherm diet-microbiome interactions remain relatively scarce, taxonomically biased, and methodologically heterogeneous.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eHere, we present a systematic literature review and meta-analysis quantifying diet-driven changes in gut microbiome diversity in ectothermic vertebrates while accounting for taxonomic/phylogenetic, ecological, and methodological sources of variation. Methodological heterogeneity hampered robust comparisons of microbiome alpha and phylogenetic diversity across gradients of diet nutritional composition. Across studies, however, we identified several bacterial genera and families that increased in relative abundance with higher insect consumption. These taxa are known to degrade chitin and other complex insect-derived compounds, generating metabolites that act as signaling molecules along hypothalamic\u0026ndash;pituitary and related neuroendocrine axes to modulate host growth, development, reproduction, and senescence.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur findings highlight the potential of diet-sensitive microbial groups as microbiome-based indicators and as agents that may promote ectotherm resilience to environmental change through physiological regulation of host metabolism. We outline priorities for improving data collection, reducing methodological heterogeneity, and ensuring open availability of sequence data.\u003c/p\u003e","manuscriptTitle":"Microbiome-based tracking of diet shifts in ectotherms: a new approach to monitor effects of global changes on food webs?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-09 19:05:05","doi":"10.21203/rs.3.rs-8959905/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":"bd05e19d-673f-420a-82e6-c73261b76845","owner":[],"postedDate":"March 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-14T07:40:26+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-09 19:05:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8959905","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8959905","identity":"rs-8959905","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}