ABSTRACT: Urbanization significantly impacts wild populations, favoring urban dweller species over those that are unable to adapt to rapid and abrupt changes. One possible explanation for differential adaptation between these species is that the microbiota could modulate the host phenotype rapidly through a high degree of flexibility. Conversely, under such anthropic perturbations, the microbiota composition of some species could be disrupted, resulting in dysbiosis and negative impacts on host fitness, potentially causing local extirpation. The links between the impact of urbanization on host communities and their gut microbiota have only been scarcely explored. In this study, we tested the hypothesis that the gut microbiota (GM) could play a role in host adaptation to urban environments considering several species. We addressed this question by studying small terrestrial mammals sampled in forested areas along a forested gradient of urbanization (from rural forests to urban parks) during 2020 fall. The gut was collected and bacteria were described using a 16S metabarcoding approach. We tested whether urbanization led to changes in small mammal communities and in their GM. We analyzed these changes in terms of the presence and abundance of taxa and functions to decipher the processes underlying these changes. We found that urbanization had marked impacts on small mammal communities and also their GM, either directly or indirectly through small mammal species categories. The urban dweller species had a lower taxonomic diversity but a higher functional diversity and a different composition compared to the urban adapter species. Their GM assembly was mostly governed by stochastic effects, which could indicate dysbiosis in these urban species. Selection and new functions were detected that could be associated with adaptation to urban environments despite potential dysbiosis. On the contrary, urbanization could impact the diversity and taxonomic composition of GM in urban adapter species. However, their functional diversity and composition remained relatively stable. This can be explained by functional redundancy, where certain taxa, regardless of their competitiveness in a specific environment, express the same function. This could explain the adaptation of urban adapter species in various environments, including urban settings. We can therefore assume that there are feedback loops between the gut microbiota and the host species within communities, enabling rapid and flexible adaptation. FILE DESCRIPTION: Information concerning the small mammal samples and the positive and negative controls multiplexed in the 16Sv4 MiSeq sequencing run This CSV file contains the PCR_ID, PCR_replicate, Sequence file name (read 1 fastq file), Sequence file name (read 2 fastq file), Sample_ID, and others informations for the 759 PCR products multiplexed in the Illumina MiSeq run. Only the sequences from 556 PCR products were used in this study (see “Sample_included_in_this_study”), corresponding to 103 negative & positive controls and 222 individuals showing results on colon bacteriome at the four sampling sites in autumn 2022. File name: Sequencing_informations.csv MiSeq raw sequences of the 16Sv4 rRNA gene from colons of small mammal samples This ZIP file contains FASTQ files of the paired-end reads (R1: reads 1; R2: reads 2) produced for each small mammal sample using the MiSeq platform. The 759 multiplexed PCR products were indexed using both forward and reverse indices. The list of the multiplexed samples and positive & negative controls are provided in the following CSV file titled: Sequencing_informations.csv. File name: MiSeq_Reads_16S_Colons_Run01.zip Bash script used to analyse the 16Sv4 sequences to ASVs (Amplicon Sequence Variants) using the Qiime 2 package This SH file contains the bash command lines to analyse the 16Sv4 sequences to ASVs (Amplicon Sequence Variants) using the Qiime 2 package. File name: qiimeRG.sh Individual information on small mammal samples whose colons have been sequenced This CSV file contains data on 380 small mammal individuals whose DNA extracted from the colon organ was successfully sequenced using the 16S rRNA V4 gene. The table is structured with the sequencing code as rows and various individual variables as columns. The variables include: "Id_Code": Unique identifier for each individual. "IdSamplesRun01ASV": Individual code corresponding to MiSeq sequencing. "NbReadsCol_ASVs": Number of reads for each ASV (Amplicon Sequence Variant). "Species" and "Species code": Small mammal species information, including genus, species, or abbreviation. "PhylogenyFam": Family of each species based on phylogeny. "Sex": Sex of the individual (F for female, M for male). "Code_Loc": Sampling locations, with codes such as FRFMIG (Mignovillard), FRFCOR (Cormaranche-en-Bugey), FRPDLL (Domaine Lacroix Laval), FRPLTO (Lyon Tête d’Or). "LocSpec": Combination of species and sites with their names abbreviated. "Maturity": Maturity status (1 for mature, 0 for immature). "Period": Period of sampling. "Dead": Indicates whether the individual was found dead in the trap before cervical dislocation and dissection. "Niche1Score" and "Niche2Score": Coordinates of species in Correspondence Analysis (CA) for the first two dimensions. "CompoComm1" and "CompoComm2": Coordinates of sites in CA for the first two dimensions. "UrbanisationScore" and "ManagementScore": PCA scores of sites based on spatial variables in dimensions 1 and 2, respectively. "CommRichness" and "CommShannon": Species richness and Shannon diversity index of small mammal communities. "TrapSuccess": Trapping success calculated for each species, site, and period. These variables provide comprehensive information for further analysis and interpretation. File name: Sampling_ColonRun01_Fall2020.csv Table of abundance of ASVs for small mammals samples whose colons have been sequenced This CSV file contains the number of reads obtained after data filtering for each ASVof the MiSeq runs and each small mammal sample whose colon has been sequenced. The results of the two PCR replicates from the same sample have been summed and filtered using negative and positive controls. File name: Abund_ASVs_ColonRun01_Fall2020_Silva138.1p100.csv Affiliation table of ASVs This CSV file contains the 5973 ASVs corresponding to the filtered ASVs. Each ASVs has been affiliated to the SILVA database. Phylum, Class, Order, Family, Genus and Species correspond to the different affiliation levels. "Total" refers to the concatenated affiliation of each level. File name: Taxa_ASVs_ColonRun01_Fall2020_Silva138.1p100.csv Phylogenetic Tree of ASVs The phylogenetic tree of each ASV. File name: Tree_ASVs_ColonRun01_Fall2020_Silva138.1p100.nhx Tables of abundance of gut bacteriome for rodent samples whose colons have been sequenced These CSV files contain the number of reads obtained after data filtering for each enzyme (EC) or Patway (ptw) obtained with Picrust2 and each small mammal sample. Files name: Abund_EC_ColonRun01_Fall2020_Picrust2.csv Abund_Ptw_ColonRun01_Fall2020_Picrust2.csv Tables of class description for each function These CSV files contain the descriptions for each EC or pathway obtained using Picrust2. The descriptions for each level correspond to the MetaCyc database. Files name: TableMetacyc_EC_describ.csv TableMetacyc_Ptw_describ.csv Scripts The zip file contains various scripts corresponding to each step of the analysis, available in .rmd (Rmarkdown format) for R scripts, and a sh script for the Slurm script. File name: Scripts_GM_Fall2020_BouilloudMarie.zip