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Isolation by distance promotes gut microbial strain divergence in wild mouse populations

Nature Ecology & Evolution (2026) Cite this article

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Abstract

Bacterial species within the mammalian gut microbiota exhibit considerable strain diversity associated with both geography and host genetic ancestry. However, because geography and ancestry are typically confounded, disentangling their contributions to the divergence of gut bacterial strains has remained challenging. Here we show that isolation by distance (IBD) promotes gut bacterial strain divergence within host species independently of host ancestry. Joint profiling of gut bacterial and mitochondrial genomes from wild-living populations of deer mice (Peromyscus maniculatus) sampled across the USA revealed significant IBD in 27 predominant gut bacterial species, including Muribaculaceae and Lachnospiraceae spp., but limited evidence for co-inheritance of gut bacterial genomes with mitochondria during the diversification of mouse populations. Spore-forming gut bacterial species exhibited reduced IBD, suggesting that adaptations facilitating bacterial dispersal can lessen geographic structuring of strain diversity. In contrast to conspecific hosts sampled at the same field site, hosts of different rodent genera sampled in sympatry with deer mice harboured divergent strains within shared gut bacterial species. These results indicate that geographic distance mediates the early stages of gut bacterial strain divergence between conspecific hosts, whereas effects of host ancestry on strain-level microbiota composition emerge over longer evolutionary timescales, such as those separating host genera.

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Fig. 1: IBD shapes genetic diversity in deer mice and their gut microbiota.
The alternative text for this image may have been generated using AI.
Fig. 2: IBD drives strain divergence within gut bacterial species.
The alternative text for this image may have been generated using AI.
Fig. 3: Sporulation ability mitigates effects of IBD on strain divergence.
The alternative text for this image may have been generated using AI.
Fig. 4: Sympatric host genera maintain genomically distinct gut bacterial strains.
The alternative text for this image may have been generated using AI.

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Data availability

All sequence data generated in this article has been deposited in the National Center for Biotechnology Information Sequence Read Archive under the BioProject accession PRJNA1306187. All bacterial MAGs generated in this study are available via Dryad at https://doi.org/10.5061/dryad.95x69p8z4 (ref. 63).

Code availability

The code used to set up and run the SGB diversification analyses is available via Zenodo at https://doi.org/10.5281/zenodo.19262353 (ref. 64).

References

  1. Valles-Colomer, M. et al. The person-to-person transmission landscape of the gut and oral microbiomes. Nature 614, 125–135 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Andreu-Sánchez, S. et al. Global genetic diversity of human gut microbiome species is related to geographic location and host health. Cell 188, 3942–3959.e9 (2025).

    Article  PubMed  Google Scholar 

  3. Sanders, J. G. et al. Widespread extinctions of co-diversified primate gut bacterial symbionts from humans. Nat. Microbiol. 8, 1039–1050 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Levin, D. et al. Diversity and functional landscapes in the microbiota of animals in the wild. Science 372, eabb5352 (2021).

    Article  CAS  PubMed  Google Scholar 

  5. Olm, M. R. et al. inStrain profiles population microdiversity from metagenomic data and sensitively detects shared microbial strains. Nat. Biotechnol. 39, 727–736 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Zahavi, L. et al. Bacterial SNPs in the human gut microbiome associate with host BMI. Nat. Med. 29, 2785–2792 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Yang, C. et al. Fecal IgA levels are determined by strain-level differences in Bacteroides ovatus and are modifiable by gut Microbiota manipulation. Cell Host Microbe 27, 467–475.e6 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Park, S.-Y. et al. Strain-level fitness in the gut microbiome is an emergent property of glycans and a single metabolite. Cell 185, 513–529.e21 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Kujawska, M. et al. Bifidobacterium castoris strains isolated from wild mice show evidence of frequent host switching and diverse carbohydrate metabolism potential. ISME Commun. 2, 20 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Moeller, A. H. et al. Dispersal limitation promotes the diversification of the mammalian gut microbiota. Proc. Natl Acad. Sci. USA 114, 13768–13773 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Sprockett, D. D., Dillard, B. A., Landers, A. A., Sanders, J. G. & Moeller, A. H. Recent genetic drift in the co-diversified gut bacterial symbionts of laboratory mice. Nat. Commun. 16, 2218 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Suzuki, T. A. et al. Codiversification of gut microbiota with humans. Science 377, 1328–1332 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Truong, D. T., Tett, A., Pasolli, E., Huttenhower, C. & Segata, N. Microbial strain-level population structure and genetic diversity from metagenomes. Genome Res. 27, 626–638 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Dominguez-Bello, M. G. et al. Delivery mode shapes the acquisition and structure of the initial microbiota across multiple body habitats in newborns. Proc. Natl Acad. Sci. USA 107, 11971–11975 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Dubois, L. et al. Paternal and induced gut microbiota seeding complement mother-to-infant transmission. Cell Host Microbe 32, 1011–1024.e4 (2024).

    Article  CAS  PubMed  Google Scholar 

  16. Moeller, A. H., Suzuki, T. A., Phifer-Rixey, M. & Nachman, M. W. Transmission modes of the mammalian gut microbiota. Science 362, 453–457 (2018).

    Article  CAS  PubMed  Google Scholar 

  17. Groussin, M., Mazel, F. & Alm, E. J. Co-evolution and co-speciation of host–gut bacteria systems. Cell Host Microbe 28, 12–22 (2020).

    Article  CAS  PubMed  Google Scholar 

  18. Moeller, A. H. Partner fidelity, not geography, drives co-diversification of gut microbiota with hominids. Biol. Lett. 21, 20240454 (2025).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Good, B. H. Limited codiversification of the gut microbiota with humans. Preprint at bioRxiv https://doi.org/10.1101/2022.10.27.514143 (2022).

  20. Yang, D.-S. & Kenagy, G. J. Nuclear and mitochondrial DNA reveal contrasting evolutionary processes in populations of deer mice (Peromyscus maniculatus). Mol. Ecol. 18, 5115–5125 (2009).

    Article  CAS  PubMed  Google Scholar 

  21. Taylor, Z. S. & Hoffman, S. M. G. Microsatellite genetic structure and cytonuclear discordance in naturally fragmented populations of deer mice (Peromyscus maniculatus). J. Hered. 103, 71–79 (2012).

    Article  CAS  PubMed  Google Scholar 

  22. Upham, N. S., Esselstyn, J. A. & Jetz, W. Inferring the mammal tree: species-level sets of phylogenies for questions in ecology, evolution, and conservation. PLoS Biol. 17, e3000494 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Young, L. A. et al. Analysis of bone structure in PEROMYSCUS: effects of burrowing behavior. Anat. Rec. (Hoboken) 307, 3506–3518 (2024).

    Article  PubMed  Google Scholar 

  24. Kumar, S. et al. TimeTree 5: an expanded resource for species divergence times. Mol. Biol. Evol. 39, msac174 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Qian, Y. et al. fastMitoCalc: an ultra-fast program to estimate mitochondrial DNA copy number from whole-genome sequences. Bioinformatics 33, 1399–1401 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Kang, D. D. et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7, e7359 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Wu, Y.-W., Simmons, B. A. & Singer, S. W. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 32, 605–607 (2016).

    Article  CAS  PubMed  Google Scholar 

  28. Alneberg, J. et al. Binning metagenomic contigs by coverage and composition. Nat. Methods 11, 1144–1146 (2014).

    Article  CAS  PubMed  Google Scholar 

  29. Li, D. et al. MEGAHIT v1.0: a fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods 102, 3–11 (2016).

    Article  CAS  PubMed  Google Scholar 

  30. Chklovski, A., Parks, D. H., Woodcroft, B. J. & Tyson, G. W. CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning. Nat. Methods 20, 1203–1212 (2023).

    Article  CAS  PubMed  Google Scholar 

  31. Orakov, A. et al. GUNC: detection of chimerism and contamination in prokaryotic genomes. Genome Biol. 22, 178 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk v2: memory friendly classification with the genome taxonomy database. Bioinformatics 38, 5315–5316 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Enav, H., Paz, I. & Ley, R. E. Strain tracking in complex microbiomes using synteny analysis reveals per-species modes of evolution. Nat. Biotechnol. 43, 773–783 (2025).

    Article  CAS  PubMed  Google Scholar 

  35. Mazel, F., Guisan, A. & Parfrey, L. W. Transmission mode and dispersal traits correlate with host specificity in mammalian gut microbes. Mol. Ecol. https://doi.org/10.1111/mec.16862 (2023).

    Article  PubMed  Google Scholar 

  36. Ashburner, M. et al. Gene Ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Gene Ontology Consortium et al. The Gene Ontology knowledgebase in 2023. Genetics 224, iyad031 (2023).

    Article  Google Scholar 

  38. Weimann, A. et al. From genomes to phenotypes: Traitar, the microbial trait analyzer. mSystems 1, e00101–e00116 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Wang, Z., Zhang, C., Li, G. & Yi, X. The influence of species identity and geographic locations on gut microbiota of small rodents. Front. Microbiol. 13, 983660 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Moeller, A. H. et al. Sympatric chimpanzees and gorillas harbor convergent gut microbial communities. Genome Res. 23, 1715–1720 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Linnenbrink, M. et al. The role of biogeography in shaping diversity of the intestinal microbiota in house mice. Mol. Ecol. 22, 1904–1916 (2013).

    Article  PubMed  Google Scholar 

  42. Goertz, S. et al. Geographical location influences the composition of the gut microbiota in wild house mice (Mus musculus domesticus) at a fine spatial scale. PLoS ONE 14, e0222501 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Weldon, L. et al. The gut microbiota of wild mice. PLoS ONE 10, e0134643 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Browne, H. P. et al. Host adaptation in gut Firmicutes is associated with sporulation loss and altered transmission cycle. Genome Biol. 22, 204 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  45. NEON Biorepository mammal collection (fecal samples). National Ecological Observatory Network https://doi.org/10.15468/PX9KDP (2025).

  46. Thibault, K. M., Laney, C. M., Yule, K. M., Franz, N. M. & Mabee, P. M. The US National Ecological Observatory Network and the Global Biodiversity Framework: national research infrastructure with a global reach. J. Ecol. Environ. 47, 530–555 (2023).

    Article  Google Scholar 

  47. Mölder, F. et al. Sustainable data analysis with Snakemake. F1000Res. 10, 33 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10 (2011).

    Article  Google Scholar 

  49. FastQC a quality control tool for high throughput sequence data. Babraham Bioinformatics https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (2010).

  50. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Gurevich, A., Saveliev, V., Vyahhi, N. & Tesler, G. QUAST: quality assessment tool for genome assemblies. Bioinformatics 29, 1072–1075 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Zhu, Q. et al. Phylogenomics of 10,575 genomes reveals evolutionary proximity between domains Bacteria and Archaea. Nat. Commun. 10, 5477 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Titus Brown, C. & Irber, L. sourmash: a library for MinHash sketching of DNA. J. Open Source Softw. 1, 27 (2016).

    Article  Google Scholar 

  54. Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Sieber, C. M. K. et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat Microbiol 3, 836–843 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Minh, B. Q. et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol. Biol. Evol. 37, 1530–1534 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Olm, M. R., Brown, C. T., Brooks, B. & Banfield, J. F. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 11, 2864–2868 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Dixon, P. VEGAN, a package of R functions for community ecology. J. Veg. Sci. 14, 927–930 (2003).

    Article  Google Scholar 

  59. Goslee, S. & Urban, D. The ecodist package for dissimilarity-based analysis of ecological data. J. Stat. Softw. 22, 1–19 (2007).

    Article  Google Scholar 

  60. Joseph, T. A., Chlenski, P., Litman, A., Korem, T. & Pe’er, I. Accurate and robust inference of microbial growth dynamics from metagenomic sequencing reveals personalized growth rates. Genome Res. 32, 558–568 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).

    Article  CAS  PubMed  Google Scholar 

  62. Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2019).

    Article  CAS  PubMed  Google Scholar 

  63. Dillard, B. A. et al. Data for article—Isolation by distance promotes gut microbial strain divergence in wild mouse populations. Dryad https://doi.org/10.5061/dryad.95x69p8z4 (2026).

  64. Dillard, B. A. et al. Code for article—Isolation by distance promotes gut microbial strain divergence in wild mouse populations. Zenodo https://doi.org/10.5281/zenodo.19262353 (2026).

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Acknowledgements

We thank W. Yan for assistance with DNA extractions from rodent faecal samples. The National Ecological Observatory Network is a programme sponsored by the US National Science Foundation and operated under cooperative agreement by Battelle. This material uses specimens and/or samples collected as part of the NEON Program and provided by the NEON Biorepository at Arizona State University. Funding was provided by the National Institutes of Health grants R35 GM138284 and R01 DK139214 to A.H.M.

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Authors and Affiliations

  1. Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY, USA

    Brian A. Dillard, Jon G. Sanders & Andrew H. Moeller

  2. School of Life Sciences, Arizona State University, Tempe, AZ, USA

    Azhar P. Husain & Kelsey M. Yule

  3. Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA

    Andrew H. Moeller

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  1. Brian A. Dillard
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Contributions

B.A.D designed the study, performed analyses and wrote and edited the paper. J.G.S designed the study and edited the paper. A.P.H and K.M.Y facilitated sample selection and provisioning and edited the paper. A.H.M supervised the project, designed the study, and wrote and edited the paper.

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Correspondence to Brian A. Dillard or Andrew H. Moeller.

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Nature Ecology & Evolution thanks Oren Kolodny, Ruth Ley and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Consistent evidence for IBD in deer mice across field sites.

Scatter plots show IBD of deer mouse MT lineages across field sites. MT genetic distances and geographic distances are shown on the y- and x-axes, respectively. Each facet corresponds to a focal field site for which comparisons including that field site are shown. Points represent pairs of deer mice, with color denoting the non-focal field site. In each facet, trendline indicates best-fit linear regression and shading around trendline denotes standard error centered around the mean value predicted by regression. Asterisks indicate Mantel test FDR-corrected two-sided p-values; * < 0.05, ** < 0.01, *** < 0.001. All individual p-values are provided in Supplementary Table 2.

Extended Data Fig. 2 Minimal evidence for IBL on strain divergence within individual gut bacterial species.

Scatterplots and trendlines show lack of relationships between SGB conANI distances (residualized against geographic distances) and host MT distances for individual SGBs derived from deer mouse hosts. SGB ANI distances were residualized against the residuals from a GLS model with the formula: SGB ANI distance ~ geographic distance. Each facet represents an SGB. Colors denote the phylum to which each SGB belongs, as indicated by the key. Shading around each trendline shows standard error from the best-fit linear model. Standard error is centered around the mean value predicted by regression. No SGBs reached significance at the MRM FDR-corrected two-sided p-values < 0.05 threshold for either Pearson or Spearman based tests. All individual p-values are provided in Supplementary Table 4.

Extended Data Fig. 3 No difference in PTR ratio between SGBs showing or not showing significant evidence of IBD within deer mice.

Box plots the peak-to-trough ratios of SGBs (n = 248) either showing or not showing significant evidence of IBD within deer mice based on both Pearson and Spearman methods. Each boxplot shows minimum and maximum (whiskers), inner quartile range (lower and upper sides of box), and median (center line). Results of permutation t-test are shown; n.s. two-sided p-value > 0.05 (p-value = 0.4262).

Extended Data Fig. 4 Aggregating gut bacterial strain comparisons across SGBs indicates widespread IBD and relatively weaker IBL.

a) Scatter plot shows the relationship between strain genetic distance within SGBs (1 – conANI) and geographic distance (x-axis), after accounting for the effects of host MT genetic distance. Y-axis shows SGB genetic distance residualized against host MT genetic distances. b) Scatter plot shows the relationship between genetic distance similarity within SGBs (1 – conANI) and host MT genetic distance (x-axis), after accounting for the effects of geographic distance. Y-axis shows genetic distance (1 – conANI) residualized against geographic distances. In (A) and (B), trendlines and shading indicate best-fit linear regressions with standard errors. Asterisks indicate linear regression p-value; *** < 0.001 (p-value < 2.2e-16 in both A and B). Within each panel (a or b), p-values were derived from a single test and uncorrected.

Extended Data Fig. 5 Synteny scores indicate widespread IBD and relatively weaker IBL.

a) Scatter plot shows the relationship between strain average pairwise synteny score (APSS) distance (1-APSS) within SGBs and geographic distance (x-axis), after accounting for the effects of host MT genetic distance. Y-axis shows APSS distance residualized against host MT genetic distances. b) Scatter plot shows the relationship between strain APSS distance within SGBs and host MT genetic distance (x-axis), after accounting for the effects of geographic distance. Y-axis shows APSS distance within SGBs residualized against geographic distances. In (A) and (B), trendlines and shading indicate best-fit linear regressions with standard errors. Asterisks indicate regression p-value; *** < 0.001 (p-value < 2.2e-16 in A and p-value = 0.08148 in B). Within each panel (a or b), p-values were derived from a single test and uncorrected.

Extended Data Fig. 6 Traitar analyses confirm negative relationship between sporulation ability and strength of IBD.

Volcano plot shows the significance (y-axis, uncorrected p-values) of association between the presence of bacterial traits (points) and the strength of IBD (x-axis). The strength of IBD was measured as the MRM Pearson correlation coefficient describing the relationship between geographic distance and strain genomic divergence between mice within SGBs residualized against host MT genetic distances.

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Dillard, B.A., Sanders, J.G., Husain, A.P. et al. Isolation by distance promotes gut microbial strain divergence in wild mouse populations. Nat Ecol Evol (2026). https://doi.org/10.1038/s41559-026-03073-7

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