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Two decades of bacterial ecology and evolution in a freshwater lake

Nature Microbiology volume 10, pages 246–257 (2025)Cite this article

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Abstract

Ecology and evolution are considered distinct processes that interact on contemporary time scales in microbiomes. Here, to observe these processes in a natural system, we collected a two-decade, 471-metagenome time series from Lake Mendota (Wisconsin, USA). We assembled 2,855 species-representative genomes and found that genomic change was common and frequent. By tracking strain composition via single nucleotide variants, we identified cyclical seasonal patterns in 80% and decadal shifts in 20% of species. In the dominant freshwater family Nanopelagicaceae, environmental extremes coincided with shifts in strain composition and positive selection of amino acid and nucleic acid metabolism genes. These genes identify organic nitrogen compounds as potential drivers of freshwater responses to global change. Seasonal and long-term strain dynamics could be regarded as ecological processes or, equivalently, as evolutionary change. Rather than as distinct interacting processes, we propose a conceptualization of ecology and evolution as a continuum to better describe change in microbial communities.

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Fig. 1: The TYMEFLIES dataset.
Fig. 2: Bacterial seasonality at the subspecies level.
Fig. 3: Long-term changes in strain composition.
Fig. 4: Abrupt changes in Nanopelagicaceae strain composition coincide with environmental extremes in 2012.
Fig. 5: Step change in strain composition coincides with more genes under selection.

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Data availability

Metagenome and MAG sequences are available from the NCBI SRA under Umbrella Project accession PRJNA1056043. Individual metagenome SRA accession numbers are listed in Supplementary Data 1 and individual MAG SRA accession numbers are listed in Supplementary Data 2. Most MAGs are available under the NCBI BioProject accession PRJNA1158976, but a few, detailed in Supplementary Data 2, are available from the Open Science Framework120. The filtered fastq files and single-sample assemblies used in this study are available through the JGI Genome Portal under ITS Proposal ID 504350. Environmental data is publicly available through the EDI (https://edirepository.org/)53,109,110,111,112,113,114,115,116,117 and the US Geological Survey’s Water Data for the Nation (https://waterdata.usgs.gov/nwis)54.

Code availability

Custom scripts used for data processing are available via GitHub at https://github.com/rrohwer/TYMEFLIES_manuscript and via Zenodo at https://doi.org/10.5281/zenodo.10663021 (ref. 121).

References

  1. Brennan, G. L. & Logares, R. Tracking contemporary microbial evolution in a changing ocean. Trends Microbiol. 31, 336–345 (2023).

    Article  CAS  PubMed  Google Scholar 

  2. Martiny, J. B. H. et al. Investigating the eco-evolutionary response of microbiomes to environmental change. Ecol. Lett. 26, S81–S90 (2023).

    Article  PubMed  Google Scholar 

  3. Lenski, R. E. Experimental evolution and the dynamics of adaptation and genome evolution in microbial populations. ISME J. 11, 2181–2194 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Rohwer, R. R. & McMahon, K. D. A two-decade microbial time series from a freshwater lake, introducing the limony and TYMEFLIES datasets. Preprint at https://doi.org/10.1101/2022.08.04.502869 (2024).

  5. Kane, M. D. Microbial observatories: exploring and discovering microbial diversity in the 21st century. Microb. Ecol. 48, 447–448 (2004).

    Article  CAS  PubMed  Google Scholar 

  6. Magnuson, J. J., Kratz, T. K. & Benson, B. J. (eds). Long-Term Dynamics of Lakes in the Landscape: Long-Term Ecological Research on North Temperate Lakes (Oxford Univ. Press, 2006).

  7. Magee, M. R., Wu, C. H., Robertson, D. M., Lathrop, R. C. & Hamilton, D. P. Trends and abrupt changes in 104 years of ice cover and water temperature in a dimictic lake in response to air temperature, wind speed, and water clarity drivers. Hydrol. Earth Syst. Sci. 20, 1681–1702 (2016).

    Article  Google Scholar 

  8. Kucharik, C. J., Serbin, S. P., Vavrus, S., Hopkins, E. J. & Motew, M. M. Patterns of climate change across wisconsin from 1950 to 2006. Phys. Geogr. 31, 1–28 (2010).

    Article  Google Scholar 

  9. Robertson, D. M., Ragotzkie, R. A. & Magnuson, J. J. Lake ice records used to detect historical and future climatic changes. Clim. Change 21, 407–427 (1992).

    Article  Google Scholar 

  10. Lathrop, R. C. Perspectives on the eutrophication of the Yahara lakes. Lake Reserv. Manag. 23, 345–365 (2007).

    Article  Google Scholar 

  11. Carpenter, S. R., Booth, E. G., Kucharik, C. J. & Lathrop, R. C. Extreme daily loads: role in annual phosphorus input to a north temperate lake. Aquat. Sci. 77, 71–79 (2015).

    Article  CAS  Google Scholar 

  12. Spear, M. J. et al. Early changes in the benthic community of a eutrophic lake following zebra mussel (Dreissena polymorpha) invasion. Inland Waters 12, 311–329 (2022).

    Article  CAS  Google Scholar 

  13. Walsh, J. R., Carpenter, S. R. & Zanden, M. J. V. Invasive species triggers a massive loss of ecosystem services through a trophic cascade. Proc. Natl Acad. Sci. USA 113, 4081–4085 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Rohwer, R. R., Hale, R. J., Vander Zanden, M. J., Miller, T. R. & McMahon, K. D. Species invasions shift microbial phenology in a two-decade freshwater time series. Proc. Natl Acad. Sci. USA 120, e2211796120 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Beal, M. R. W., O’Reilly, B., Hietpas, K. R. & Block, P. Development of a sub-seasonal cyanobacteria prediction model by leveraging local and global scale predictors. Harmful Algae 108, 102100 (2021).

    Article  PubMed  Google Scholar 

  16. Gilbert, J. A. et al. Defining seasonal marine microbial community dynamics. ISME J. 6, 298–308 (2012).

    Article  CAS  PubMed  Google Scholar 

  17. Yeh, Y.-C. & Fuhrman, J. A. Effects of phytoplankton, viral communities, and warming on free-living and particle-associated marine prokaryotic community structure. Nat. Commun. 13, 7905 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Ferrera, I. et al. Seasonal and interannual variability of the free-living and particle-associated bacteria of a coastal microbiome. Environ. Microbiol. Rep. 16, e13299 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Cordero, O. X. & Polz, M. F. Explaining microbial genomic diversity in light of evolutionary ecology. Nat. Rev. Microbiol. 12, 263–273 (2014).

    Article  CAS  PubMed  Google Scholar 

  20. Rossum, T. V., Ferretti, P., Maistrenko, O. M. & Bork, P. Diversity within species: interpreting strains in microbiomes. Nat. Rev. Microbiol.https://doi.org/10.1038/s41579-020-0368-1 (2020).

  21. 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 

  22. Delmont, T. O. et al. Single-amino acid variants reveal evolutionary processes that shape the biogeography of a global SAR11 subclade. eLife 8, e46497 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Ilicic, D., Ionescu, D., Woodhouse, J. & Grossart, H.-P. Temperature-related short-term succession events of bacterial phylotypes in Potter Cove, Antarctica. Genes 14, 1051 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Bendall, M. L. et al. Genome-wide selective sweeps and gene-specific sweeps in natural bacterial populations. ISME J. 10, 1589–1601 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Garcia, S. L. et al. Contrasting patterns of genome-level diversity across distinct co-occurring bacterial populations. ISME J. 12, 742–755 (2018).

    Article  CAS  PubMed  Google Scholar 

  26. Hwang, Y. & Girguis, P. R. Differentiated evolutionary strategies of genetic diversification in Atlantic and Pacific thaumarchaeal populations. mSystems 7, e01477-21 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Hoetzinger, M. et al. Geographic population structure and distinct intra-population dynamics of globally abundant freshwater bacteria. ISME J. 18, wrae113 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Bassar, R. D., Coulson, T., Travis, J. & Reznick, D. N. Towards a more precise—and accurate—view of eco-evolution. Ecol. Lett. 24, 623–625 (2021).

    Article  PubMed  Google Scholar 

  29. Hendry, A. P. Eco-Evolutionary Dynamics (Princeton Univ. Press, 2017).

  30. Ware, I. M. et al. Feedbacks link ecosystem ecology and evolution across spatial and temporal scales: empirical evidence and future directions. Funct. Ecol. 33, 31–42 (2019).

    Article  Google Scholar 

  31. Rosselló-Móra, R. & Amann, R. Past and future species definitions for Bacteria and Archaea. Syst. Appl. Microbiol. 38, 209–216 (2015).

    Article  PubMed  Google Scholar 

  32. 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 

  33. 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 

  34. Varghese, N. J. et al. Microbial species delineation using whole genome sequences. Nucleic Acids Res. 43, 6761–6771 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Jain, C., Rodriguez-R, L. M., Phillippy, A. M., Konstantinidis, K. T. & Aluru, S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat. Commun. 9, 1–8 (2018).

    Article  Google Scholar 

  36. Olm, M. R. et al. Consistent metagenome-derived metrics verify and delineate bacterial species boundaries. mSystems https://doi.org/10.1128/msystems.00731-19 (2020).

  37. Chiriac, M.-C., Haber, M. & Salcher, M. M. Adaptive genetic traits in pelagic freshwater microbes. Environ. Microbiol. 25, 606–641 (2023).

    Article  PubMed  Google Scholar 

  38. Kim, S., Kang, I., Seo, J.-H. & Cho, J.-C. Culturing the ubiquitous freshwater actinobacterial acI lineage by supplying a biochemical ‘helper’ catalase. ISME J. 13, 2252–2263 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Neuenschwander, S. M., Ghai, R., Pernthaler, J. & Salcher, M. M. Microdiversification in genome-streamlined ubiquitous freshwater Actinobacteria. ISME J. 12, 185–198 (2018).

    Article  CAS  PubMed  Google Scholar 

  40. Magee, M. R. & Wu, C. H. Effects of changing climate on ice cover in three morphometrically different lakes. Hydrol. Process. 31, 308–323 (2017).

    Article  Google Scholar 

  41. Magee, M. R. & Wu, C. H. Response of water temperatures and stratification to changing climate in three lakes with different morphometry. Hydrol. Earth Syst. Sci. 21, 6253–6274 (2017).

    Article  Google Scholar 

  42. Snortheim, C. A. et al. Meteorological drivers of hypolimnetic anoxia in a eutrophic, north temperate lake. Ecol. Modell. 343, 39–53 (2017).

    Article  CAS  Google Scholar 

  43. Matsuzaki, S.-I. S. et al. Climate and food web effects on the spring clear-water phase in two north-temperate eutrophic lakes. Limnol. Oceanogr. 66, 30–46 (2020).

  44. Zhou, Z. et al. A 20-year time-series of a freshwater lake reveals seasonal dynamics and environmental drivers of viral diversity, ecology, and evolution. Nat. Microbiol. https://doi.org/10.1038/s41564-024-01876-7 (2025)

  45. Krinos, A. I. et al. Time-series metagenomics reveals changing protistan ecology of a temperate dimictic lake. Microbiome 12, 133 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Okazaki, Y., Nakano, S., Toyoda, A. & Tamaki, H. Long-read-resolved, ecosystem-wide exploration of nucleotide and structural microdiversity of lake bacterioplankton genomes. mSystems 7, e00433-22 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Meziti, A. et al. Quantifying the changes in genetic diversity within sequence-discrete bacterial populations across a spatial and temporal riverine gradient. ISME J. 13, 767–779 (2019).

    Article  PubMed  Google Scholar 

  48. Schmidt, M. L. et al. Microhabitats are associated with diversity–productivity relationships in freshwater bacterial communities. FEMS Microbiol. Ecol. 96, fiaa029 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Magnuson, J. J. Long-term ecological research and the invisible present. BioScience 40, 495–501 (1990).

    Article  Google Scholar 

  50. Turner, M. G. et al. Climate change, ecosystems and abrupt change: science priorities. Philos. Trans. R. Soc. B 375, 20190105 (2020).

    Article  Google Scholar 

  51. Scheffer, M., Carpenter, S., Foley, J. A., Folke, C. & Walker, B. Catastrophic shifts in ecosystems. Nature 413, 591–596 (2001).

    Article  CAS  PubMed  Google Scholar 

  52. Denef, V. J. & Banfield, J. F. In situ evolutionary rate measurements show ecological success of recently emerged bacterial hybrids. Science 336, 462–466 (2012).

    Article  CAS  PubMed  Google Scholar 

  53. Magnuson, J. J., Carpenter, S. R. & Stanley, E. H. North temperate lakes LTER: ice duration—Madison lakes area 1853–current. Environmental Data Initiative https://doi.org/10.6073/PASTA/69B3391E13955392587413ECBFC7C298 (2023).

  54. USGS 05427718 Yahara River at Windsor, WI, Daily Discharge 00060 (US Geological Survey National Water Information System, 2023).

  55. Rohwer, R. R. et al. Increased anoxia following species invasion of a eutrophic lake. Limnol. Oceanogr. Lett. 9, 33–42 (2024).

    Article  CAS  Google Scholar 

  56. Bailey, J. & Hood, J. M. Biotic and thermal drivers alter zooplankton phenology in western Lake Erie. Limnol. Oceanogr. Lett. 9, 219–228 (2024).

    Article  Google Scholar 

  57. Carpenter, S. R., Booth, E. G. & Kucharik, C. J. Extreme precipitation and phosphorus loads from two agricultural watersheds. Limnol. Oceanogr. 63, 1221–1233 (2018).

    Article  CAS  Google Scholar 

  58. Carey, C. C., Hanson, P. C., Lathrop, R. C., Amand, S. & L, A. Using wavelet analyses to examine variability in phytoplankton seasonal succession and annual periodicity. J. Plankton Res. 38, 27–40 (2016).

    Article  CAS  Google Scholar 

  59. Berg, S. M., Peterson, B. D., McMahon, K. D. & Remucal, C. K. Spatial and temporal variability of dissolved organic matter molecular composition in a stratified eutrophic lake. J. Geophys. Res. Biogeosci. 127, e2021JG006550 (2022).

    Article  CAS  Google Scholar 

  60. Woolway, R. I. et al. Lake heatwaves under climate change. Nature 589, 402–407 (2021).

    Article  CAS  PubMed  Google Scholar 

  61. Walsh, J. R., Munoz, S. E. & Vander Zanden, M. J. Outbreak of an undetected invasive species triggered by a climate anomaly. Ecospherehttps://doi.org/10.1002/ecs2.1628 (2016).

  62. Rastetter, E. B. et al. Time lags: insights from the US Long Term Ecological Research Network. Ecosphere 12, e03431 (2021).

    Article  Google Scholar 

  63. Kanehisa, M., Furumichi, M., Sato, Y., Kawashima, M. & Ishiguro-Watanabe, M. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 51, D587–D592 (2023).

    Article  CAS  PubMed  Google Scholar 

  64. Ramoneda, J., Jensen, T. B. N., Price, M. N., Casamayor, E. O. & Fierer, N. Taxonomic and environmental distribution of bacterial amino acid auxotrophies. Nat. Commun. 14, 7608 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Castelle, C. J. et al. Biosynthetic capacity, metabolic variety and unusual biology in the CPR and DPANN radiations. Nat. Rev. Microbiol. 16, 629–645 (2018).

    Article  CAS  PubMed  Google Scholar 

  66. Garcia, S. L. et al. Auxotrophy and intrapopulation complementary in the ‘interactome’ of a cultivated freshwater model community. Mol Ecol 24, 4449–4459 (2015).

    Article  CAS  PubMed  Google Scholar 

  67. Garcia, S. L. et al. Metabolic potential of a single cell belonging to one of the most abundant lineages in freshwater bacterioplankton. ISME J. 7, 137–147 (2013).

    Article  CAS  PubMed  Google Scholar 

  68. Hamilton, J. J. et al. Metabolic network analysis and metatranscriptomics reveal auxotrophies and nutrient sources of the cosmopolitan freshwater microbial lineage acI. MSystems 2, e00091-17 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Drake, T. W., Raymond, P. A. & Spencer, R. G. M. Terrestrial carbon inputs to inland waters: a current synthesis of estimates and uncertainty. Limnol. Oceanogr. Lett. 3, 132–142 (2018).

    Article  CAS  Google Scholar 

  70. Grant, L. et al. Attribution of global lake systems change to anthropogenic forcing. Nat. Geosci. 14, 849–854 (2021).

    Article  CAS  Google Scholar 

  71. Roy, H. E. et al. IPBES invasive alien species assessment: summary for policymakers. Zenodo https://zenodo.org/record/8314303 (2023).

  72. Zwart, J. A. et al. Cross‐scale interactions dictate regional lake carbon flux and productivity response to future climate. Geophys. Res. Lett. https://doi.org/10.1029/2019GL083478 (2019).

  73. Finlay, K. et al. Decrease in CO2 efflux from northern hardwater lakes with increasing atmospheric warming. Nature 519, 215–218 (2015).

    Article  CAS  PubMed  Google Scholar 

  74. Azam, F. et al. The ecological role of water-column microbes in the sea. Mar. Ecol. Prog. Ser. 10, 257–263 (1983).

    Article  Google Scholar 

  75. Tiedje, J. M. et al. Microbes and climate change: a research prospectus for the future. mBio https://doi.org/10.1128/mbio.00800-22 (2022).

  76. Ghiotto, G., Zampieri, G., Campanaro, S. & Treu, L. Strain-resolved metagenomics approaches applied to biogas upgrading. Environ. Res. 240, 117414 (2024).

    Article  CAS  PubMed  Google Scholar 

  77. Koch, H. et al. Genomic, metabolic and phenotypic variability shapes ecological differentiation and intraspecies interactions of Alteromonas macleodii. Sci. Rep. 10, 809 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Ustick, L. J., Larkin, A. A. & Martiny, A. C. Global scale phylogeography of functional traits and microdiversity in Prochlorococcus. ISME J. 17, 1671–1679 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  79. Keller, L. M., Colman, D. R. & Boyd, E. S. An active microbiome in Old Faithful geyser. Proc. Natl Acad. Sci. USA Nexus 2, pgad066 (2023).

    Google Scholar 

  80. Yancey, C. E. et al. Metagenomic and metatranscriptomic insights into population diversity of microcystis blooms: spatial and temporal dynamics of mcy genotypes, including a partial operon that can be abundant and expressed. Appl. Environ. Microbiol. 88, e02464-21 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  81. Liao, J. et al. Microdiversity of the vaginal microbiome is associated with preterm birth. Nat. Commun. 14, 4997 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Ma, C. et al. Cross-cohort single-nucleotide-variant profiling of gut microbiota suggests a novel gut-health assessment approach. mSystems 8, e00828-23 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  83. Niccum, B. A., Kastman, E. K., Kfoury, N., Robbat, A. & Wolfe, B. E. Strain-level diversity impacts cheese rind microbiome assembly and function. mSystems https://doi.org/10.1128/msystems.00149-20 (2020).

  84. Anderson, R. E., Graham, E. D., Huber, J. A. & Tully, B. J. Microbial populations are shaped by dispersal and recombination in a low biomass subseafloor habitat. mBio 13, e00354-22 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  85. Wolff, R., Shoemaker, W. & Garud, N. Ecological stability emerges at the level of strains in the human gut microbiome. mBio 14, e02502–e02522 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  86. Goyal, A., Bittleston, L. S., Leventhal, G. E., Lu, L. & Cordero, O. X. Interactions between strains govern the eco-evolutionary dynamics of microbial communities. eLife 11, e74987 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Sukumaran, J. & Knowles, L. L. Multispecies coalescent delimits structure, not species. Proc. Natl Acad. Sci. USA 114, 1607–1612 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Coates, D. J., Byrne, M. & Moritz, C. Genetic diversity and conservation units: dealing with the species-population continuum in the age of genomics. Front. Ecol. Evol. 6 (2018).

  89. Brock, T. D. A Eutrophic Lake: Lake Mendota, Wisconsin (Springer, 1985); https://doi.org/10.1007/978-1-4419-8700-6_7

  90. Clum, A. et al. DOE JGI metagenome workflow. mSystems https://doi.org/10.1128/msystems.00804-20 (2021).

  91. Bushnell, B. BBMap short read aligner and other bioinformatic tools. OSTI.GOV https://www.osti.gov/biblio/1241166 (2014).

  92. Nurk, S., Meleshko, D., Korobeynikov, A. & Pevzner, P. A. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 27, 824–834 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Wilson, L. A. et al. Launcher: a simple tool for executing high throughput computing workloads. J. Open Source Softw. 2, 289 (2017).

    Article  Google Scholar 

  94. Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  95. 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 

  96. Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics https://doi.org/10.1093/bioinformatics/btz848 (2019).

  97. Parks, D. H. et al. GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy. Nucleic Acids Res. 50, D785–D794 (2022).

    Article  CAS  PubMed  Google Scholar 

  98. Aroney, S. T. N. et al. CoverM: read coverage calculator for metagenomics. Zenodo https://doi.org/10.5281/zenodo.10531254 (2024).

  99. Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11, 119 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  100. Aramaki, T. et al. KofamKOALA: KEGG ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics 36, 2251–2252 (2020).

    Article  CAS  PubMed  Google Scholar 

  101. R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2022).

  102. Barrett, T. et al. Data.table: extension of ‘data.Frame’. R package version 1.16.99 (2024); https://Rdatatable.gitlab.io/data.table

  103. Grolemund, G. & Wickham, H. Dates and times made easy with lubridate. J. Stat. Softw. 40, 1–25 (2011).

    Article  Google Scholar 

  104. Tange, O. GNU Parallel 20230822 (‘Chandrayaan’). Zenodo https://doi.org/10.5281/zenodo.8278274 (2023).

  105. van den Boogaart, K. G., Tolosana-Delgado, R. & Bren, M. compositions: compositional data analysis (v.2.0-6). (2023).

  106. Oksanen, J. et al. vegan: community ecology package (v.2.6-4). (2022).

  107. Zeileis, A., Leisch, F., Hornik, K. & Kleiber, C. strucchange: An R Package for Testing for Structural Change in Linear Regression Models. J. Stat. Soft. 7, 1–38 (2002).

    Article  Google Scholar 

  108. DeCicco, L., Hirsch, R., Lorenz, D., Watkins, D. & Johnson, M. dataRetrieval: R packages for discovering and retrieving water data available from US Federal Hydrologic web services (v.2.7.14). https://doi.org/10.5066/P9X4L3GE (2023).

  109. Magnuson, J. J., Carpenter, S. R. & Stanley, E. H. North temperate lakes LTER: chemical limnology of primary study lakes: nutrients, pH and carbon 1981–current. Environmental Data Initiative https://doi.org/10.6073/PASTA/325232E6E4CD1CE04025FA5674F7B782 (2023).

  110. Magnuson, J. J., Carpenter, S. R. & Stanley, E. H. North temperate lakes LTER: chemical limnology of primary study lakes: major ions 1981–current. Environmental Data Initiative https://doi.org/10.6073/pasta/bb563f16c7338fdb3ddf82057ef43cc6 (2023).

  111. Rohwer, R. R. & McMahon, K. D. Lake Mendota microbial observatory temperature, dissolved oxygen, pH, and conductivity data, 2006–present. Environmental Data Initiative https://doi.org/10.6073/PASTA/7E533C197ED8EBD27777A89A2C8D7DFE (2022).

  112. Magnuson, J. J., Carpenter, S. R. & Stanley, E. H. North temperate lakes LTER: physical limnology of primary study lakes 1981–current. Environmental Data Initiative https://doi.org/10.6073/PASTA/316203040EA1B8ECE89673985AB431B7 (2021).

  113. Magnuson, J., Carpenter, S. & Stanley, E. North temperate lakes LTER: high frequency water temperature data—Lake Mendota Buoy 2006–current. Environmental Data Initiative https://doi.org/10.6073/PASTA/8CEFF296AD68FA8DA6787076E0A5D992 (2020).

  114. Robertson, D. Lake Mendota water temperature secchi depth snow depth ice thickness and meterological conditions 1894–2007. Environmental Data Initiative https://doi.org/10.6073/PASTA/F20F9A644BD12E4B80CB288F1812C935 (2016).

  115. Magnuson, J. J., Carpenter, S. R. & Stanley, E. H. Lake Mendota multiparameter sonde profiles: 2017–current. Environmental Data Initiative https://doi.org/10.6073/PASTA/5F15BF453851987FC030B2F07A110B21 (2021).

  116. Magnuson, J. J., Carpenter, S. R. & Stanley, E. H. North Temperate Lakes LTER: phytoplankton—Madison Lakes Area 1995–current. Environmental Data Initiative https://doi.org/10.6073/PASTA/43D3D401AF88CC05C6595962BDB1AB5C (2022).

  117. Magnuson, J., Carpenter, S. & Stanley, E. North Temperate Lakes LTER: zooplankton—Madison Lakes Area 1997–current. Environmental Data Initiative https://doi.org/10.6073/PASTA/D5ABE9009D7F6AA87D1FCF49C8C7F8C8 (2022).

  118. Schlegel, R. W. & Smit, A. J. heatwaveR: a central algorithm for the detection of heatwaves and cold-spells. J. Open Source Softw. 3, 821 (2018).

    Article  Google Scholar 

  119. McDonald, J. H. & Kreitman, M. Adaptive protein evolution at the Adh locus in Drosophila. Nature 351, 652–654 (1991).

    Article  CAS  PubMed  Google Scholar 

  120. Rohwer, R. TYMEFLIES Robinning MAGs not included in NCBI SRA BioProject PRJNA1158976. OSF https://doi.org/10.17605/OSF.IO/TX2JG (2024).

  121. Rohwer, R. R. rrohwer/TYMEFLIES_manuscript: v1.0. Zenodo https://doi.org/10.5281/zenodo.10663021 (2024).

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Acknowledgements

Long-term datasets such as TYMEFLIES rely on researchers who contribute a portion of their time and effort to future projects they may not be involved in. This work would not be possible without the generosity of many, including Lake Mendota sampling leads A. Kent, T. Yannarell, A. Shade, S. Jones, R. Newton, G. Wolfe, T. Miller, E. K. Read, L. Beversdorf, J. Mutschler and the original Microbial Observatory lead E. W. Triplett. We thank S. Stevens for her early input into the ideas pursued here, P. Golightly for advice on genes under selection data, T. Butts for advice on environmental data, and W. Ratcliff and V. Denef for advice on framing. R.R.R. acknowledges support from the E. Michael and Winona Foster Wisconsin Alumni Research Foundation (WARF) Wisconsin Idea Fellowship, the US National Science Foundation (NSF) (DBI-2011002), and the Texas Advanced Computing Center at The University of Texas at Austin that provided high performance computing resources that contributed to the research results reported within this paper (http://www.tacc.utexas.edu). M. Kirk acknowledges support from the US National Institutes of Health (NIH) (R01-GM116853) and the US NSF (DEB-1831730). M. Kell acknowledges that the work (proposal: https://doi.org/10.46936/10.25585/60001198) conducted by the US Department of Energy (DOE) Joint Genome Institute (JGI) (https://ror.org/04xm1d337), a DOE Office of Science User Facility, is supported by the Office of Science of the US DOE operated under contract no. DE-AC02-05CH11231. K.D.M. acknowledges support from the US DOE JGI (CSP 504350), the US Department of Agriculture (USDA) (WIS01516 and WIS01789), the US NSF (DEB-0702395, DEB-1344254) and the US NSF Microbial Observatory program (MCB-9977903, DEB-0702395). B.J.B. acknowledges support from the Simons Foundation Investigator in Aquatic Microbial Ecology Award (LI-SIAME-00002001). This work would not be possible without the long-term support of the US NSF NTL-LTER (DEB-9632853, DEB-0217533, DEB-0822700, DEB-1440297 and DEB-2025982).

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Authors and Affiliations

  1. Department of Integrative Biology, The University of Texas at Austin, Austin, TX, USA

    Robin R. Rohwer, Mark Kirkpatrick & Brett J. Baker

  2. Institute for Chemistry and Biology of the Marine Environment, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany

    Sarahi L. Garcia

  3. Science for Life Laboratory, Department of Ecology, Environment, and Plant Sciences, Stockholm University, Stockholm, Sweden

    Sarahi L. Garcia

  4. DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Matthew Kellom

  5. Department of Bacteriology, University of Wisconsin–Madison, Madison, WI, USA

    Katherine D. McMahon

  6. Department of Civil and Environmental Engineering, University of Wisconsin–Madison, Madison, WI, USA

    Katherine D. McMahon

  7. Department of Marine Science, The University of Texas at Austin, Austin, TX, USA

    Brett J. Baker

Authors
  1. Robin R. Rohwer
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  3. Sarahi L. Garcia
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  4. Matthew Kellom
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  5. Katherine D. McMahon
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Contributions

R.R.R. and K.D.M. conceptualized the research and obtained initial funding. K.D.M. and B.J.B. provided resources. R.R.R. conducted field and laboratory work and curated data. R.R.R. performed analyses and created visualizations. M. Kirk. advised statistical approaches. S.L.G., M. Kell., K.D.M. and B.J.B. advised analysis approaches. R.R.R. wrote the first draft, and R.R.R., K.D.M. and B.J.B. wrote the final draft incorporating edits provided by M. Kirk., S.L.G. and M. Kell.

Corresponding authors

Correspondence to Robin R. Rohwer, Katherine D. McMahon or Brett J. Baker.

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Nature Microbiology thanks Timothy Ghaly, David Pearce 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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Supplementary Data 1, 2 and 3.

TYMEFLIES metagenome metadata. Includes metadata for metagenome samples including JGI, GOLD and NCBI sample identifiers as well as McMahon Lab identifiers that pair metagenome samples with previous 16S rRNA gene sequencing2. TYMEFLIES MAG metadata. NCBI identifiers corresponding to each species-representative genome, as well as genome quality calculated by CheckM219, taxonomy assigned by GTDB-Tk87, and average relative abundance calculated by coverM89. Consistently selected gene annotations. KEGG annotations of consistently positively selected genes in a Nanopelagicus species that experienced a step change in strain composition in 2012 (ME2011-09-21_3300043464_group3_bin69). Table row order matches heat map row order in Fig. 5f.

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Rohwer, R.R., Kirkpatrick, M., Garcia, S.L. et al. Two decades of bacterial ecology and evolution in a freshwater lake. Nat Microbiol 10, 246–257 (2025). https://doi.org/10.1038/s41564-024-01888-3

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