Philippa Kaur

Overmann, J., Abt, B. & Sikorski, J. Present and future of culturing bacteria. Annual Review of Microbiology 71, 711–730 (2017).CAS 

Google Scholar 
O’Leary, N. A. et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Research 44, D733 (2016).
Google Scholar 
Bobay, L. M. & Ochman, H. Biological species are universal across life’s domains. Genome Biology and Evolution 9, 491–501 (2017).
Google Scholar 
Magnabosco, C., Moore, K., Wolfe, J. & Fournier, G. Dating phototrophic microbial lineages with reticulate gene histories. Geobiology 16, 179–189 (2018).CAS 

Google Scholar 
Louca, S. et al. Function and functional redundancy in microbial systems. Nature Ecology & Evolution 2, 936–943 (2018).ADS 

Google Scholar 
Jain, C., Rodriguez-R, L. M., Phillippy, A. M., Konstantinidis, K. T. & Aluru, S. High throughput ANI analysis of 90 K prokaryotic genomes reveals clear species boundaries. Nature Communications 9, 5114 (2018).ADS 

Google Scholar 
Zhu, Q. et al. Phylogenomics of 10,575 genomes reveals evolutionary proximity between domains bacteria and archaea. Nature Communications 10, 5477 (2019).ADS 
CAS 

Google Scholar 
Royalty, T.M. & Steen, A.D. Quantitatively partitioning microbial genomic traits among taxonomic ranks across the microbial tree of life. mSphere 4 (2019).Murray, C. S., Gao, Y. & Wu, M. Re-evaluating the evidence for a universal genetic boundary among microbial species. Nature Communications 12, 4059 (2021).ADS 
CAS 

Google Scholar 
Powell, S. et al. eggNOG v4.0: nested orthology inference across 3686 organisms. Nucleic Acids Research 42, D231–D239 (2014).CAS 

Google Scholar 
Stoddard, S. F., Smith, B. J., Hein, R., Roller, B. R. & Schmidt, T. M. rrnDB: improved tools for interpreting rRNA gene abundance in bacteria and archaea and a new foundation for future development. Nucleic Acids Research 43, D593–D598 (2014).
Google Scholar 
Douglas, G. M. et al. Picrust2 for prediction of metagenome functions. Nature Biotechnology 38, 685–688 (2020).CAS 

Google Scholar 
Wemheuer, F. et al. Tax4Fun2: prediction of habitat-specific functional profiles and functional redundancy based on 16S rRNA gene sequences. Environmental Microbiome 15, 1–12 (2020).
Google Scholar 
Louca, S., Parfrey, L. W. & Doebeli, M. Decoupling function and taxonomy in the global ocean microbiome. Science 353, 1272–1277 (2016).ADS 
CAS 

Google Scholar 
Wu, D. et al. A phylogeny-driven genomic encyclopaedia of bacteria and archaea. Nature 462, 1056–1060 (2009).ADS 
CAS 

Google Scholar 
Louca, S. & Pennell, M. W. A general and efficient algorithm for the likelihood of diversification and discrete-trait evolutionary models. Systematic Biology 69, 545–556 (2020).
Google Scholar 
Tyson, G. W. et al. Community structure and metabolism through reconstruction of microbial genomes from the environment. Nature 428, 37–43 (2004).ADS 
CAS 

Google Scholar 
Sharon, I. & Banfield, J. F. Genomes from metagenomics. Science 342, 1057–1058 (2013).ADS 
CAS 

Google Scholar 
Parks, D. H. et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nature Microbiology 2, 1533–1542 (2017).CAS 

Google Scholar 
Chen, L. X., Anantharaman, K., Shaiber, A., Eren, A. M. & Banfield, J. F. Accurate and complete genomes from metagenomes. Genome Research 30, 315–333 (2020).CAS 

Google Scholar 
Konstantinidis, K. T. & Tiedje, J. M. Genomic insights that advance the species definition for prokaryotes. Proceedings of the National Academy of Sciences 102, 2567–2572 (2005).ADS 
CAS 

Google Scholar 
Kim, M., Oh, H. S., Park, S. C. & Chun, J. Towards a taxonomic coherence between average nucleotide identity and 16S rRNA gene sequence similarity for species demarcation of prokaryotes. Journal of Systematic and Evolutionary Microbiology 64, 346–351 (2014).CAS 

Google Scholar 
Shapiro, B.J. What microbial population genomics has taught us about speciation. In Polz, M.F. & Rajora, O.P. (eds.) Population Genomics: Microorganisms, 31–47 (Springer International Publishing, Cham, Switzerland, 2019).Olm, M. R. et al. Consistent metagenome-derived metrics verify and delineate bacterial species boundaries. mSystems 5, e00731–19 (2020).CAS 

Google Scholar 
Lagkouvardos, I., Overmann, J. & Clavel, T. Cultured microbes represent a substantial fraction of the human and mouse gut microbiota. Gut Microbes 8, 493–503 (2017).
Google Scholar 
Zhang, Z., Wang, J., Wang, J., Wang, J. & Li, Y. Estimate of the sequenced proportion of the global prokaryotic genome. Microbiome 8, 1–9 (2020).
Google Scholar 
Aramaki, T. et al. KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics 36, 2251–2252 (2019).
Google Scholar 
Mira, A., Ochman, H. & Moran, N. A. Deletional bias and the evolution of bacterial genomes. Trends in Genetics 17, 589–596 (2001).CAS 

Google Scholar 
Morris, J. J., Lenski, R. E. & Zinser, E. R. The Black Queen Hypothesis: evolution of dependencies through adaptive gene loss. MBio 3, e00036–12 (2012).
Google Scholar 
Giovannoni, S. J., Cameron Thrash, J. & Temperton, B. Implications of streamlining theory for microbial ecology. ISME Journal 8, 1553–1565 (2014).
Google Scholar 
Nayfach, S., Shi, Z. J., Seshadri, R., Pollard, K. S. & Kyrpides, N. C. New insights from uncultivated genomes of the global human gut microbiome. Nature 568, 505–510 (2019).ADS 
CAS 

Google Scholar 
Gary, P.R. Adjusting for nonresponse in surveys. In Smart, J.C. (ed.) Higher Education: Handbook of Theory and Research, chap. 8, 411–449 (Springer, Dordrecht, Netherlands, 2007).Maguire, F. et al. Metagenome-assembled genome binning methods with short reads disproportionately fail for plasmids and genomic islands. Microbial Genomics 6, mgen000436 (2020).
Google Scholar 
Huerta-Cepas, J. et al. eggnog 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Research 47, D309–D314 (2019).CAS 

Google Scholar 
Abdel-Hamid, A.M., Solbiati, J.O., Cann, I.K.O., Sariaslani, S. & Gadd, G.M. Insights into lignin degradation and its potential industrial applications, vol. 82, chap. 1, 1–28 (Academic Press, 2013).El-Bondkly, A.M. Sequence analysis of industrially important genes from trichoderma. In Biotechnology and biology of Trichoderma, chap. 28, 377–392 (Elsevier, 2014).Dawood, A. & Ma, K. Applications of microbial β-mannanases. Frontiers in Bioengineering and Biotechnology 8 (2020).Khelaifia, S., Raoult, D. & Drancourt, M. A versatile medium for cultivating methanogenic archaea. PLOS ONE 8, e61563 (2013).ADS 
CAS 

Google Scholar 
Khelaifia, S. et al. Aerobic culture of methanogenic archaea without an external source of hydrogen. European Journal of Clinical Microbiology & Infectious Diseases 35, 985–991 (2016).CAS 

Google Scholar 
Michał, B. et al. Phymet2: a database and toolkit for phylogenetic and metabolic analyses of methanogens. Environmental Microbiology Reports 10, 378–382 (2018).
Google Scholar 
Albright, S. & Louca, S. Trait biases in microbial reference genomes, figshare., https://doi.org/10.6084/m9.figshare.c.6055004.v1 (2022).Castelle, C. J. & Banfield, J. F. Major new microbial groups expand diversity and alter our understanding of the tree of life. Cell 172, 1181–1197 (2018).CAS 

Google Scholar 
Murray, A. E. et al. Roadmap for naming uncultivated archaea and bacteria. Nature Microbiology 5, 987–994 (2020).CAS 

Google Scholar 
Palleroni, N. J. Prokaryotic diversity and the importance of culturing. Antonie van Leeuwenhoek 72, 3–19 (1997).CAS 

Google Scholar 
Langille, M. G. et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nature Biotechnology 31, 814–821 (2013).CAS 

Google Scholar 
Tran, P. Q. et al. Depth-discrete metagenomics reveals the roles of microbes in biogeochemical cycling in the tropical freshwater Lake Tanganyika. The ISME Journal 15, 1971–1986 (2021).CAS 

Google Scholar 
Kroeger, M. E. et al. New biological insights into how deforestation in amazonia affects soil microbial communities using metagenomics and metagenome-assembled genomes. Frontiers in Microbiology 9, 1635 (2018).
Google Scholar 
Nathani, N. M. et al. 309 metagenome assembled microbial genomes from deep sediment samples in the Gulfs of Kathiawar Peninsula. Scientific Data 8, 194 (2021).
Google Scholar 
Irazoqui, J. M., Eberhardt, M. F., Adjad, M. M., Amadio, A. F. & Collado, M. C. Identification of key microorganisms in facultative stabilization ponds from dairy industries, using metagenomics. PeerJ 10, e12772 (2022).
Google Scholar 
Hwang, Y. et al. Leave no stone unturned: individually adapted xerotolerant Thaumarchaeota sheltered below the boulders of the Atacama Desert hyperarid core. Microbiome 9, 234 (2021).CAS 

Google Scholar 
Tully, B., Wheat, C. G., Glazer, B. T. & Huber, J. A dynamic microbial community with high functional redundancy inhabits the cold, oxic subseafloor aquifer. ISME Journal 12, 1–16 (2018).CAS 

Google Scholar 
Vanwonterghem, I., Jensen, P. D., Rabaey, K. & Tyson, G. W. Genome-centric resolution of microbial diversity, metabolism and interactions in anaerobic digestion. Environmental Microbiology 18, 3144–3158 (2016).CAS 

Google Scholar 
Glasl, B. et al. Comparative genome-centric analysis reveals seasonal variation in the function of coral reef microbiomes. The ISME Journal 14, 1435–1450 (2020).
Google Scholar 
Robbins, S. J. et al. A genomic view of the reef-building coral Porites lutea and its microbial symbionts. Nature Microbiology 4, 2090–2100 (2019).
Google Scholar 
Engelberts, J. P. et al. Characterization of a sponge microbiome using an integrative genome-centric approach. The ISME Journal 14, 1100–1110 (2020).CAS 

Google Scholar 
Bowerman, K. L. et al. Disease-associated gut microbiome and metabolome changes in patients with chronic obstructive pulmonary disease. Nature Communications 11, 5886 (2020).ADS 
CAS 

Google Scholar 
Chen, Y. J. et al. Hydrodynamic disturbance controls microbial community assembly and biogeochemical processes in coastal sediments. The ISME Journal 16, 750–763 (2022).CAS 

Google Scholar 
Hugerth, L. W. et al. Metagenome-assembled genomes uncover a global brackish microbiome. Genome Biology 16, 279 (2015).
Google Scholar 
Alneberg, J. et al. Ecosystem-wide metagenomic binning enables prediction of ecological niches from genomes. Communications Biology 3, 119 (2020).
Google Scholar 
Di Cesare, A. et al. Genomic comparison and spatial distribution of different Synechococcus phylotypes in the Black Sea. Frontiers in Microbiology 11, 1979 (2020).
Google Scholar 
van Vliet, D. M. et al. The bacterial sulfur cycle in expanding dysoxic and euxinic marine waters. Environmental Microbiology 23, 2834–2857 (2021).
Google Scholar 
Dalcin Martins, P. et al. Enrichment of novel Verrucomicrobia, Bacteroidetes, and Krumholzibacteria in an oxygen-limited methane- and iron-fed bioreactor inoculated with Bothnian Sea sediments. MicrobiologyOpen 10, e1175 (2021).CAS 

Google Scholar 
Stewart, R. D. et al. Compendium of 4,941 rumen metagenome-assembled genomes for rumen microbiome biology and enzyme discovery. Nature Biotechnology 37, 953–961 (2019).CAS 

Google Scholar 
Segura-Wang, M., Grabner, N., Koestelbauer, A., Klose, V. & Ghanbari, M. Genome-resolved metagenomics of the chicken gut microbiome. Frontiers in Microbiology 12, 726923 (2021).
Google Scholar 
Ruuskanen, M. O. et al. Microbial genomes retrieved from High Arctic lake sediments encode for adaptation to cold and oligotrophic environments. Limnology and Oceanography 65, S233–S247 (2020).CAS 

Google Scholar 
Haas, S., Desai, D. K., LaRoche, J., Pawlowicz, R. & Wallace, D. W. R. Geomicrobiology of the carbon, nitrogen and sulphur cycles in Powell Lake: a permanently stratified water column containing ancient seawater. Environmental Microbiology 21, 3927–3952 (2019).CAS 

Google Scholar 
Spasov, E. et al. High functional diversity among Nitrospira populations that dominate rotating biological contactor microbial communities in a municipal wastewater treatment plant. The ISME Journal 14, 1857–1872 (2020).CAS 

Google Scholar 
Vigneron, A. et al. Genomic evidence for sulfur intermediates as new biogeochemical hubs in a model aquatic microbial ecosystem. Microbiome 9, 46 (2021).CAS 

Google Scholar 
Galambos, D., Anderson, R. E., Reveillaud, J. & Huber, J. A. Genome-resolved metagenomics and metatranscriptomics reveal niche differentiation in functionally redundant microbial communities at deep-sea hydrothermal vents. Environmental Microbiology 21, 4395–4410 (2019).CAS 

Google Scholar 
Stewart, R. D. et al. Assembly of 913 microbial genomes from metagenomic sequencing of the cow rumen. Nature Communications 9, 870 (2018).ADS 

Google Scholar 
Xing, P. et al. Stratification of microbiomes during the holomictic period of Lake Fuxian, an alpine monomictic lake. Limnology and Oceanography 65, S134–S148 (2020).
Google Scholar 
Zhang, S., Hu, Z. & Wang, H. Metagenomic analysis exhibited the co-metabolism of polycyclic aromatic hydrocarbons by bacterial community from estuarine sediment. Environment International 129, 308–319 (2019).CAS 

Google Scholar 
Lin, Y., Wang, L., Xu, K., Li, K. & Ren, H. Revealing taxon-specific heavy metal-resistance mechanisms in denitrifying phosphorus removal sludge using genome-centric metaproteomics. Microbiome 9, 67 (2021).CAS 

Google Scholar 
Liu, L. et al. High-quality bacterial genomes of a partial-nitritation/anammox system by an iterative hybrid assembly method. Microbiome 8, 155 (2020).CAS 

Google Scholar 
Kantor, R. S. et al. Bioreactor microbial ecosystems for thiocyanate and cyanide degradation unravelled with genome-resolved metagenomics. Environmental Microbiology 17, 4929–4941 (2015).CAS 

Google Scholar 
Zhou, Z. et al. Gammaproteobacteria mediating utilization of methyl-, sulfur- and petroleum organic compounds in deep ocean hydrothermal plumes. The ISME Journal 14, 3136–3148 (2020).CAS 

Google Scholar 
Reysenbach, A. L. et al. Complex subsurface hydrothermal fluid mixing at a submarine arc volcano supports distinct and highly diverse microbial communities. Proceedings of the National Academy of Sciences 117, 32627–32638 (2020).ADS 
CAS 

Google Scholar 
Hou, J. et al. Microbial succession during the transition from active to inactive stages of deep-sea hydrothermal vent sulfide chimneys. Microbiome 8, 102 (2020).CAS 

Google Scholar 
Campanaro, S. et al. Metagenomic analysis and functional characterization of the biogas microbiome using high throughput shotgun sequencing and a novel binning strategy. Biotechnology for Biofuels 9, 26 (2016).
Google Scholar 
Singleton, C. M. et al. Connecting structure to function with the recovery of over 1000 high-quality metagenome-assembled genomes from activated sludge using long-read sequencing. Nature Communications 12, 2009 (2021).CAS 

Google Scholar 
Diamond, S. et al. Mediterranean grassland soil C–N compound turnover is dependent on rainfall and depth, and is mediated by genomically divergent microorganisms. Nature Microbiology 4, 1356–1367 (2019).CAS 

Google Scholar 
Rasigraf, O. et al. Microbial community composition and functional potential in Bothnian Sea sediments is linked to Fe and S dynamics and the quality of organic matter. Limnology and Oceanography 65, S113–S133 (2020).CAS 

Google Scholar 
Rissanen, A. J. et al. Vertical stratification patterns of methanotrophs and their genetic controllers in water columns of oxygen-stratified boreal lakes. FEMS Microbiology Ecology 97, fiaa252 (2021).CAS 

Google Scholar 
Campanaro, S. et al. New insights from the biogas microbiome by comprehensive genome-resolved metagenomics of nearly 1600 species originating from multiple anaerobic digesters. Biotechnology for Biofuels 13, 25 (2020).CAS 

Google Scholar 
Almeida, A. et al. A unified catalog of 204,938 reference genomes from the human gut microbiome. Nature Biotechnology 39, 105–114 (2021).CAS 

Google Scholar 
Zhou, Z. et al. Genome- and community-level interaction insights into carbon utilization and element cycling functions of hydrothermarchaeota in hydrothermal sediment. mSystems 5 (2020).Pachiadaki, M. G. et al. Charting the complexity of the marine microbiome through single-cell genomics. Cell 179, 1623–1635.e11 (2019).CAS 

Google Scholar 
Martijn, J., Vosseberg, J., Guy, L., Offre, P. & Ettema, T. J. G. Deep mitochondrial origin outside the sampled alphaproteobacteria. Nature 557, 101–105 (2018).ADS 
CAS 

Google Scholar 
Greenlon, A. et al. Global-level population genomics reveals differential effects of geography and phylogeny on horizontal gene transfer in soil bacteria. Proceedings of the National Academy of Sciences 116, 15200–15209 (2019).ADS 
CAS 

Google Scholar 
Hervé, V. et al. Phylogenomic analysis of 589 metagenome-assembled genomes encompassing all major prokaryotic lineages from the gut of higher termites. PeerJ 8, e8614 (2020).
Google Scholar 
von Appen, W.J. The expedition PS114 of the research vessel POLARSTERN to the Fram Strait in 2018. Tech. Rep., Alfred Wegener Institute for Polar and Marine Research (2018).Dombrowski, N., Seitz, K. W., Teske, A. P. & Baker, B. J. Genomic insights into potential interdependencies in microbial hydrocarbon and nutrient cycling in hydrothermal sediments. Microbiome 5, 106 (2017).
Google Scholar 
Yu, J. et al. Dna-stable isotope probing shotgun metagenomics reveals the resilience of active microbial communities to biochar amendment in oxisol soil. Frontiers in Microbiology 11, 587972 (2020).
Google Scholar 
Forster, S. C. et al. A human gut bacterial genome and culture collection for improved metagenomic analyses. Nature Biotechnology 37, 186–192 (2019).CAS 

Google Scholar 
Gharechahi, J. et al. Metagenomic analysis reveals a dynamic microbiome with diversified adaptive functions to utilize high lignocellulosic forages in the cattle rumen. The ISME Journal 15, 1108–1120 (2021).CAS 

Google Scholar 
Meier, D. V., Imminger, S., Gillor, O. & Woebken, D. Distribution of mixotrophy and desiccation survival mechanisms across microbial genomes in an arid biological soil crust community. mSystems 6, e00786–20 (2021).CAS 

Google Scholar 
Haro-Moreno, J. M. et al. Dysbiosis in marine aquaculture revealed through microbiome analysis: reverse ecology for environmental sustainability. FEMS Microbiology Ecology 96, fiaa218 (2020).CAS 

Google Scholar 
Haro-Moreno, J. M. et al. Fine metagenomic profile of the Mediterranean stratified and mixed water columns revealed by assembly and recruitment. Microbiome 6, 128 (2018).
Google Scholar 
Dong, X. et al. Metabolic potential of uncultured bacteria and archaea associated with petroleum seepage in deep-sea sediments. Nature Communications 10, 1816 (2019).ADS 

Google Scholar 
Poghosyan, L. et al. Metagenomic profiling of ammonia- and methane-oxidizing microorganisms in two sequential rapid sand filters. Water Research 185, 116288 (2020).CAS 

Google Scholar 
Paula, D. M., Jeroen, F., Hugh, M. & Meng, M. L. & J., W.M. Wetland sediments host diverse microbial taxa capable of cycling alcohols. Applied and Environmental Microbiology 85, 00189–19 (2019).
Google Scholar 
Aromokeye, D. A. et al. Crystalline iron oxides stimulate methanogenic benzoate degradation in marine sediment-derived enrichment cultures. The ISME Journal 15, 965–980 (2021).CAS 

Google Scholar 
Borchert, E. et al. Deciphering a marine bone-degrading microbiome reveals a complex community effort. mSystems 6, e01218–20 (2021).CAS 

Google Scholar 
Osvatic, J. T. et al. Global biogeography of chemosynthetic symbionts reveals both localized and globally distributed symbiont groups. Proceedings of the National Academy of Sciences 118, e2104378118 (2021).CAS 

Google Scholar 
Boeuf, D. et al. Biological composition and microbial dynamics of sinking particulate organic matter at abyssal depths in the oligotrophic open ocean. Proceedings of the National Academy of Sciences 116, 11824–11832 (2019).ADS 
CAS 

Google Scholar 
Woodcroft, B. J. et al. Genome-centric view of carbon processing in thawing permafrost. Nature 560, 49–54 (2018).ADS 
CAS 

Google Scholar 
Alqahtani, M. F. et al. Enrichment of Marinobacter sp. and halophilic homoacetogens at the biocathode of microbial electrosynthesis system inoculated with Red Sea brine pool. Frontiers in Microbiology 10, 2563 (2019).
Google Scholar 
Haroon, M. F., Thompson, L. R., Parks, D. H., Hugenholtz, P. & Stingl, U. A catalogue of 136 microbial draft genomes from Red Sea metagenomes. Scientific Data 3, 160050 (2016).CAS 

Google Scholar 
Vavourakis, C. D. et al. A metagenomics roadmap to the uncultured genome diversity in hypersaline soda lake sediments. Microbiome 6, 1–18 (2018).
Google Scholar 
Cabello-Yeves, P. J. et al. Microbiome of the deep Lake Baikal, a unique oxic bathypelagic habitat. Limnology and Oceanography 65, 1471–1488 (2020).ADS 
CAS 

Google Scholar 
Vavourakis, C. D. et al. Metagenomes and metatranscriptomes shed new light on the microbial-mediated sulfur cycle in a siberian soda lake. BMC Biology 17, 69 (2019).
Google Scholar 
Waterworth, S. C., Isemonger, E. W., Rees, E. R., Dorrington, R. A. & Kwan, J. C. Conserved bacterial genomes from two geographically isolated peritidal stromatolite formations shed light on potential functional guilds. Environmental Microbiology Reports 13, 126–137 (2021).CAS 

Google Scholar 
Huddy, R. J. et al. Thiocyanate and organic carbon inputs drive convergent selection for specific autotrophic Afipia and Thiobacillus strains within complex microbiomes. Frontiers in Microbiology 12, 643368 (2021).
Google Scholar 
Emerson, J. B. et al. Diverse sediment microbiota shape methane emission temperature sensitivity in Arctic lakes. Nature Communications 12, 5815 (2021).ADS 
CAS 

Google Scholar 
Chiri, E. et al. Termite gas emissions select for hydrogenotrophic microbial communities in termite mounds. Proceedings of the National Academy of Sciences 118, e2102625118 (2021).CAS 

Google Scholar 
Gong, G., Zhou, S., Luo, R., Gesang, Z. & Suolang, S. Metagenomic insights into the diversity of carbohydrate-degrading enzymes in the yak fecal microbial community. BMC Microbiology 20, 302 (2020).
Google Scholar 
Zhou, S. et al. Characterization of metagenome-assembled genomes and carbohydrate-degrading genes in the gut microbiota of Tibetan pig. Frontiers in Microbiology 11, 595066 (2020).
Google Scholar 
Tully, B. J., Graham, E. D. & Heidelberg, J. F. The reconstruction of 2,631 draft metagenome-assembled genomes from the global oceans. Scientific Data 5, 170203 (2018).CAS 

Google Scholar 
Lavrinienko, A. et al. Two hundred and fifty-four metagenome-assembled bacterial genomes from the bank vole gut microbiota. Scientific Data 7, 312 (2020).CAS 

Google Scholar 
Peng, X. et al. Genomic and functional analyses of fungal and bacterial consortia that enable lignocellulose breakdown in goat gut microbiomes. Nature Microbiology 6, 499–511 (2021).CAS 

Google Scholar 
Dudek, N. K. et al. Novel microbial diversity and functional potential in the marine mammal oral microbiome. Current Biology 27, 3752–3762.e6 (2017).CAS 

Google Scholar 
Pinto, A. J. et al. Metagenomic evidence for the presence of comammox nitrospira-like bacteria in a drinking water system. mSphere 1, e00054–15 (2015).
Google Scholar 
Zaremba-Niedzwiedzka, K. et al. Asgard archaea illuminate the origin of eukaryotic cellular complexity. Nature 541, 353–358 (2017).ADS 
CAS 

Google Scholar 
Nobu, M. K. et al. Catabolism and interactions of uncultured organisms shaped by eco-thermodynamics in methanogenic bioprocesses. Microbiome 8, 111 (2020).CAS 

Google Scholar 
Butterfield, C. N. et al. Proteogenomic analyses indicate bacterial methylotrophy and archaeal heterotrophy are prevalent below the grass root zone. PeerJ 4, e2687 (2016).
Google Scholar 
Castelle, C. J. et al. Protein family content uncovers lineage relationships and bacterial pathway maintenance mechanisms in DPANN Archaea. Frontiers in Microbiology 12, 660052 (2021).
Google Scholar 
Alteio, L. V. et al. Complementary metagenomic approaches improve reconstruction of microbial diversity in a forest soil. mSystems 5, e00768–19 (2020).
Google Scholar 
Shaiber, A. et al. Functional and genetic markers of niche partitioning among enigmatic members of the human oral microbiome. Genome Biology 21, 292 (2020).
Google Scholar 
Jungbluth, S. P., Amend, J. P. & Rappé, M. S. Metagenome sequencing and 98 microbial genomes from Juan de Fuca Ridge flank subsurface fluids. Scientific Data 4, 170037 (2017).CAS 

Google Scholar 
Sheik, C. S. et al. Dolichospermum blooms in Lake Superior: DNA-based approach provides insight to the past, present and future of blooms. Journal of Great Lakes Research 48, 1191–1205 (2022).CAS 

Google Scholar 
Barnum, T. P. et al. Genome-resolved metagenomics identifies genetic mobility, metabolic interactions, and unexpected diversity in perchlorate-reducing communities. The ISME Journal 12, 1568–1581 (2018).CAS 

Google Scholar 
Julian, D. et al. Coastal ocean metagenomes and curated metagenome-assembled genomes from Marsh Landing, Sapelo Island (Georgia, USA). Microbiology Resource Announcements 8, e00934–19 (2019).
Google Scholar 
Breister, A. M. et al. Soil microbiomes mediate degradation of vinyl ester-based polymer composites. Communications Materials 1, 101 (2020).ADS 

Google Scholar 
Fu, H., Uchimiya, M., Gore, J. & Moran, M. A. Ecological drivers of bacterial community assembly in synthetic phycospheres. Proceedings of the National Academy of Sciences 117, 3656–3662 (2020).ADS 
CAS 

Google Scholar 
Nobu, M. K. et al. Thermodynamically diverse syntrophic aromatic compound catabolism. Environmental Microbiology 19, 4576–4586 (2017).CAS 

Google Scholar 
Pasolli, E. et al. Extensive unexplored human microbiome diversity revealed by over 150,000 genomes from metagenomes spanning age, geography, and lifestyle. Cell 176, 649–662 (2019).CAS 

Google Scholar 
Nayfach, S. et al. A genomic catalog of Earth’s microbiomes. Nature Biotechnology 39, 499–509 (2021).CAS 

Google Scholar 
Li, Z. et al. Deep sea sediments associated with cold seeps are a subsurface reservoir of viral diversity. The ISME Journal 15, 2366–2378 (2021).CAS 

Google Scholar 
Bay, S. K. et al. Trace gas oxidizers are widespread and active members of soil microbial communities. Nature Microbiology 6, 246–256 (2021).CAS 

Google Scholar 
Seyler, L. M., Trembath-Reichert, E., Tully, B. J. & Huber, J. A. Time-series transcriptomics from cold, oxic subseafloor crustal fluids reveals a motile, mixotrophic microbial community. The ISME Journal 15, 1192–1206 (2021).CAS 

Google Scholar 
Herold, M. et al. Integration of time-series meta-omics data reveals how microbial ecosystems respond to disturbance. Nature Communications 11, 5281 (2020).ADS 
CAS 

Google Scholar 
Dong, X. et al. Thermogenic hydrocarbon biodegradation by diverse depth-stratified microbial populations at a Scotian Basin cold seep. Nature Communications 11, 5825 (2020).ADS 
CAS 

Google Scholar 
Thompson, L. R. et al. Metagenomic covariation along densely sampled environmental gradients in the Red Sea. The ISME Journal 11, 138–151 (2017).CAS 

Google Scholar 
Dominik, S., Daniela, Z., Anja, P., Katharina, R. & Rolf, D. Metagenome-assembled genome sequences from different wastewater treatment stages in Germany. Microbiology Resource Announcements 10, e00504–21 (2021).
Google Scholar 
Langwig, M. V. et al. Large-scale protein level comparison of Deltaproteobacteria reveals cohesive metabolic groups. The ISME Journal 16, 307–320 (2022).CAS 

Google Scholar 
Rezaei Somee, M. et al. Distinct microbial community along the chronic oil pollution continuum of the Persian Gulf converge with oil spill accidents. Scientific Reports 11, 11316 (2021).ADS 
CAS 

Google Scholar 
Gilroy, R. et al. Metagenomic investigation of the equine faecal microbiome reveals extensive taxonomic diversity. PeerJ 10, e13084 (2022).
Google Scholar 
Bhattarai, B., Bhattacharjee, A. S., Coutinho, F. H. & Goel, R. K. Viruses and their interactions with bacteria and archaea of hypersaline Great Salt Lake. Frontiers in Microbiology 12, 701414 (2021).
Google Scholar 
Liu, L. et al. Microbial diversity and adaptive strategies in the Mars-like Qaidam Basin, North Tibetan Plateau, China. Environmental Microbiology Reports (2022).Lin, H. et al. Mercury methylation by metabolically versatile and cosmopolitan marine bacteria. The ISME Journal 15, 1810–1825 (2021).CAS 

Google Scholar 
Martnez-Pérez, C. et al. Lifting the lid: nitrifying archaea sustain diverse microbial communities below the Ross Ice Shelf. SSRN (2020).Zhang, L. et al. Metagenomic insights into the effect of thermal hydrolysis pre-treatment on microbial community of an anaerobic digestion system. Science of The Total Environment 791, 148096 (2021).ADS 
CAS 

Google Scholar 
Starr, E. P. et al. Stable-isotope-informed, genome-resolved metagenomics uncovers potential cross-kingdom interactions in rhizosphere soil. mSphere 6, e00085–21 (2021).CAS 

Google Scholar 
Matthew, C. et al. Archaeal and bacterial metagenome-assembled genome sequences derived from pig feces. Microbiology Resource Announcements 11, 01142–21 (2022).
Google Scholar 
Wang, Y., Zhao, R., Liu, L., Li, B. & Zhang, T. Selective enrichment of comammox from activated sludge using antibiotics. Water Research 197, 117087 (2021).CAS 

Google Scholar 
Gilroy, R. et al. Extensive microbial diversity within the chicken gut microbiome revealed by metagenomics and culture. PeerJ 9, e10941 (2021).
Google Scholar 
Chen, Y. H. et al. Salvaging high-quality genomes of microbial species from a meromictic lake using a hybrid sequencing approach. Communications Biology 4, 996 (2021).CAS 

Google Scholar 
Beach, N. K., Myers, K. S., Donohue, T. J. & Noguera, D. R. Metagenomes from 25 low-abundance microbes in a partial nitritation anammox microbiome. Microbiology Resource Announcements 11, 00212–22 (2022).CAS 

Google Scholar 
Solanki, V. et al. Glycoside hydrolase from the GH76 family indicates that marine Salegentibacter sp. Hel_I_6 consumes alpha-mannan from fungi. The ISME Journal 16, 1818–1830 (2022).CAS 

Google Scholar 
Hiraoka, S. et al. Diverse DNA modification in marine prokaryotic and viral communities. Nucleic Acids Research 50, 1531–1550 (2022).CAS 

Google Scholar 
Haryono, M.A.S. et al. Recovery of high quality metagenome-assembled genomes from full-scale activated sludge microbial communities in a tropical climate using longitudinal metagenome sampling. Frontiers in Microbiology 13 (2022).Rodrguez-Ramos, J.A. et al. Microbial genome-resolved metaproteomic analyses frame intertwined carbon and nitrogen cycles in river hyporheic sediments. Research Square (2021).Kim, M., Cho, H. & Lee, W. Y. Distinct gut microbiotas between southern elephant seals and Weddell seals of Antarctica. Journal of Microbiology 58, 1018–1026 (2020).CAS 

Google Scholar 
Voorhies, A. A. et al. Cyanobacterial life at low O2: community genomics and function reveal metabolic versatility and extremely low diversity in a Great Lakes sinkhole mat. Geobiology 10, 250–267 (2012).CAS 

Google Scholar 
McDaniel, E. A. et al. Tbasco: trait-based comparative ‘omics identifies ecosystem-level and niche-differentiating adaptations of an engineered microbiome. ISME Communications 2, 111 (2022).
Google Scholar 
Wang, W. et al. Contrasting bacterial and archaeal distributions reflecting different geochemical processes in a sediment core from the Pearl River Estuary. AMB Express 10, 16 (2020).
Google Scholar 
Mandakovic, D. et al. Genome-scale metabolic models of Microbacterium species isolated from a high altitude desert environment. Scientific Reports 10, 5560 (2020).ADS 
CAS 

Google Scholar 
Wang, Y. et al. Seasonal prevalence of ammonia-oxidizing archaea in a full-scale municipal wastewater treatment plant treating saline wastewater revealed by a 6-year time-series analysis. Environmental Science & Technology 55, 2662–2673 (2021).ADS 
CAS 

Google Scholar 
Bulzu, P. A. et al. Casting light on Asgardarchaeota metabolism in a sunlit microoxic niche. Nature Microbiology 4, 1129–1137 (2019).CAS 

Google Scholar 
Karen, J. et al. Hydrogen-oxidizing bacteria are abundant in desert soils and strongly stimulated by hydration. mSystems 5, e01131–20 (2020).
Google Scholar 
Rust, M. et al. A multiproducer microbiome generates chemical diversity in the marine sponge Mycale hentscheli. Proceedings of the National Academy of Sciences 117, 9508–9518 (2020).ADS 
CAS 

Google Scholar 
Podowski, J. C., Paver, S. F., Newton, R. J. & Coleman, M. L. Genome streamlining, proteorhodopsin, and organic nitrogen metabolism in freshwater nitrifiers. mBio 13, e02379–21 (2022).
Google Scholar 
Coutinho, F. H. et al. New viral biogeochemical roles revealed through metagenomic analysis of Lake Baikal. Microbiome 8, 163 (2020).CAS 

Google Scholar 
Philippi, M. et al. Purple sulfur bacteria fix N2 via molybdenum-nitrogenase in a low molybdenum Proterozoic ocean analogue. Nature Communications 12, 4774 (2021).ADS 
CAS 

Google Scholar 
Katie, S. et al. Eight metagenome-assembled genomes provide evidence for microbial adaptation in 20,000- to 1,000,000-year-old Siberian permafrost. Applied and Environmental Microbiology 87, e00972–21 (2021).
Google Scholar 
Mert, K. et al. Unexpected abundance and diversity of phototrophs in mats from morphologically variable microbialites in Great Salt Lake, Utah. Applied and Environmental Microbiology 86, e00165–20 (2020).
Google Scholar 
Patin, N. V. et al. Gulf of Mexico blue hole harbors high levels of novel microbial lineages. The ISME Journal 15, 2206–2232 (2021).CAS 

Google Scholar 
Wang, J., Tang, X., Mo, Z. & Mao, Y. Metagenome-assembled genomes from Pyropia haitanensis microbiome provide insights into the potential metabolic functions to the seaweed. Frontiers in Microbiology 13, 857901 (2022).
Google Scholar 
Burgsdorf, I. et al. Lineage-specific energy and carbon metabolism of sponge symbionts and contributions to the host carbon pool. The ISME Journal 16, 1163–1175 (2022).CAS 

Google Scholar 
Suarez, C. et al. Disturbance-based management of ecosystem services and disservices in partial nitritation-anammox biofilms. npj Biofilms and Microbiomes 8, 47 (2022).CAS 

Google Scholar 
Kumar, D. et al. Textile industry wastewaters from Jetpur, Gujarat, India, are dominated by Shewanellaceae, Bacteroidaceae, and Pseudomonadaceae harboring genes encoding catalytic enzymes for textile dye degradation. Frontiers in Environmental Science 9, 720707 (2021).ADS 

Google Scholar 
Seitz, V. A. et al. Variation in root exudate composition influences soil microbiome membership and function. Applied and Environmental Microbiology 88, e00226–22 (2022).
Google Scholar 
Lindner, B. G. et al. Toward shotgun metagenomic approaches for microbial source tracking sewage spills based on laboratory mesocosms. Water Research 210, 117993 (2022).CAS 

Google Scholar 
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. Applied and Environmental Microbiology 88, e02464–21 (2022).
Google Scholar 
Liu, L. et al. Charting the complexity of the activated sludge microbiome through a hybrid sequencing strategy. Microbiome 9, 205 (2021).CAS 

Google Scholar 
Speth, D. R. et al. Microbial communities of Auka hydrothermal sediments shed light on vent biogeography and the evolutionary history of thermophily. The ISME Journal 16, 1750–1764 (2022).CAS 

Google Scholar 
Blyton, M. D. J., Soo, R. M., Hugenholtz, P. & Moore, B. D. Maternal inheritance of the koala gut microbiome and its compositional and functional maturation during juvenile development. Environmental Microbiology 24, 475–493 (2022).CAS 

Google Scholar 
Nuccio, E. E. et al. Niche differentiation is spatially and temporally regulated in the rhizosphere. The ISME Journal 14, 999–1014 (2020).CAS 

Google Scholar 
Jaffe, A. L. et al. Long-term incubation of lake water enables genomic sampling of consortia involving planctomycetes and candidate phyla radiation bacteria. mSystems 7, e00223–22 (2022).
Google Scholar 
Cabral, L. et al. Gut microbiome of the largest living rodent harbors unprecedented enzymatic systems to degrade plant polysaccharides. Nature Communications 13, 629 (2022).ADS 
CAS 

Google Scholar 
Blyton, M. D. J., Soo, R. M., Hugenholtz, P. & Moore, B. D. Characterization of the juvenile koala gut microbiome across wild populations. Environmental Microbiology 24, 4209–4219 (2022).CAS 

Google Scholar 
Xu, B. et al. A holistic genome dataset of bacteria, archaea and viruses of the Pearl River estuary. Scientific Data 9, 49 (2022).MathSciNet 
CAS 

Google Scholar 
Royo-Llonch, M. et al. Compendium of 530 metagenome-assembled bacterial and archaeal genomes from the polar Arctic Ocean. Nature Microbiology 6, 1561–1574 (2021).CAS 

Google Scholar 
Sun, J., Prabhu, A., Aroney, S. T. N. & Rinke, C. Insights into plastic biodegradation: community composition and functional capabilities of the superworm (Zophobas morio) microbiome in styrofoam feeding trials. Microbial Genomics 8, 000842 (2022).CAS 

Google Scholar 
Kim, M. et al. Higher pathogen load in children from Mozambique vs. USA revealed by comparative fecal microbiome profiling. ISME Communications 2, 74 (2022).ADS 

Google Scholar 
Kelly, J. B., Carlson, D. E., Low, J. S. & Thacker, R. W. Novel trends of genome evolution in highly complex tropical sponge microbiomes. Microbiome 10, 164 (2022).CAS 

Google Scholar 
Bray, M. S. et al. Phylogenetic and structural diversity of aromatically dense pili from environmental metagenomes. Environmental Microbiology Reports 12, 49–57 (2020).CAS 

Google Scholar 
Cabello-Yeves, P. J. et al. α-cyanobacteria possessing form IA RuBisCO globally dominate aquatic habitats. The ISME Journal 16, 2421–2432 (2022).CAS 

Google Scholar 
Berben, T. et al. The Polar Fox Lagoon in Siberia harbours a community of Bathyarchaeota possessing the potential for peptide fermentation and acetogenesis. Antonie van Leeuwenhoek 115, 1229–1244 (2022).CAS 

Google Scholar 
Tamburini, F. B. et al. Short- and long-read metagenomics of urban and rural South African gut microbiomes reveal a transitional composition and undescribed taxa. Nature Communications 13, 926 (2022).ADS 
CAS 

Google Scholar 
Kantor, R. S., Miller, S. E. & Nelson, K. L. The water microbiome through a pilot scale advanced treatment facility for direct potable reuse. Frontiers in Microbiology 10, 993 (2019).
Google Scholar 
Muratore, D. et al. Complex marine microbial communities partition metabolism of scarce resources over the diel cycle. Nature Ecology & Evolution 6, 218–229 (2022).
Google Scholar 
Zhou, Y. L., Mara, P., Cui, G. J., Edgcomb, V. P. & Wang, Y. Microbiomes in the challenger deep slope and bottom-axis sediments. Nature Communications 13, 1515 (2022).ADS 
CAS 

Google Scholar 
Zhang, H. et al. Metagenome sequencing and 768 microbial genomes from cold seep in South China Sea. Scientific Data 9, 480 (2022).CAS 

Google Scholar 
Zhuang, J. L., Zhou, Y. Y., Liu, Y. D. & Li, W. Flocs are the main source of nitrous oxide in a high-rate anammox granular sludge reactor: insights from metagenomics and fed-batch experiments. Water Research 186, e116321 (2020).
Google Scholar 
Shiffman, M. E. et al. Gene and genome-centric analyses of koala and wombat fecal microbiomes point to metabolic specialization for eucalyptus digestion. PeerJ 5, 4075 (2017).
Google Scholar 
Murphy, S. M. C., Bautista, M. A., Cramm, M. A. & Hubert, C. R. J. Diesel and crude oil biodegradation by cold-adapted microbial communities in the Labrador Sea. Applied and Environmental Microbiology 87, e00800–21 (2021).ADS 
CAS 

Google Scholar 
Suarez, C. et al. Metagenomic evidence of a novel family of anammox bacteria in a subsea environment. Environmental Microbiology 24, 2348–2360 (2022).CAS 

Google Scholar 
Dharamshi, J.E. et al. Genomic diversity and biosynthetic capabilities of sponge-associated chlamydiae. The ISME Journal (2022).Florian, P. O., Hugo, R. & Mathieu, A. Recovery of metagenome-assembled genomes from a human fecal sample with pacific biosciences high-fidelity sequencing. Microbiology Resource Announcements 11, e00250–22 (2022).
Google Scholar 
Bloom, S. M. et al. Cysteine dependence of Lactobacillus iners is a potential therapeutic target for vaginal microbiota modulation. Nature Microbiology 7, 434–450 (2022).CAS 

Google Scholar 
Aylward, F. O. et al. Diel cycling and long-term persistence of viruses in the ocean’s euphotic zone. Proceedings of the National Academy of Sciences 114, 11446–11451 (2017).ADS 
CAS 

Google Scholar 
Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Research 25, 1043–1055 (2015).CAS 

Google Scholar 
Bowers, R. M. et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nature biotechnology 35, 725 (2017).CAS 

Google Scholar 
Chaumeil, P. A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925–1927 (2020).CAS 

Google Scholar 
Louca, S. The rates of global bacterial and archaeal dispersal. ISME Journal 16, 159–167 (2021).ADS 

Google Scholar 
Ondov, B. D. et al. Mash: fast genome and metagenome distance estimation using minhash. Genome Biology 17, 132 (2016).
Google Scholar 
Müllner, D. fastcluster: Fast hierarchical, agglomerative clustering routines for R and Python. Journal of Statistical Software 53, 1–18 (2013).
Google Scholar 
Kinene, T., Wainaina, J., Maina, S., Boykin, L.M. & Kliman, R.M. Methods for rooting trees, vol. 3, 489–493 (Academic Press, Oxford, 2016).Louca, S. & Doebeli, M. Efficient comparative phylogenetics on large trees. Bioinformatics 34, 1053–1055 (2018).CAS 

Google Scholar 
Rees, J. A. & Cranston, K. Automated assembly of a reference taxonomy for phylogenetic data synthesis. Biodiversity Data Journal 5, e12581 (2017).
Google Scholar 
Heck, K. et al. Evaluating methods for purifying cyanobacterial cultures by qPCR and high-throughput Illumina sequencing. Journal of Microbiological Methods 129, 55–60 (2016).CAS 

Google Scholar 
Cornet, L. et al. Consensus assessment of the contamination level of publicly available cyanobacterial genomes. PLOS ONE 13, e0200323 (2018).
Google Scholar 
Alneberg, J. et al. Genomes from uncultivated prokaryotes: a comparison of metagenome-assembled and single-amplified genomes. Microbiome 6, 173 (2018).
Google Scholar 
Eddy, S. R. Accelerated profile HMM searches. PLoS Computational Biology 7, e1002195 (2011).ADS 
MathSciNet 
CAS 

Google Scholar 
Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nature Methods 12, 59–60 (2014).
Google Scholar 
Pedregosa, F. et al. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011).MathSciNet 
MATH 

Google Scholar  More

Biodiversity on Earth is threatened by land-use changes, overexploitation of resources, pollution, biological invasions, and current and projected climate change. Understanding how species will respond to these stressors is difficult, in part because stressors don’t occur in isolation, and because responses can trickle through ecological networks due to interactions among species. More

Our approach uses hypothetical 30 cm fixed depth samples taken at three successive time points (t0, t1, and t2) with prescribed changes in SOC (1.4% to 1.6%) and BD (1.5–1.1 g cm−3) over these time points (Table 1). The 30 cm soil depth is the common international standard for sampling and analysis required for SOC stock assessment and adhered to by carbon accounting and market organizations6,18. The changes we adopted (a 27% decrease in BD and a 14% increase in SOC) while relatively large, are consistent with those reported in the literature. For example, Reganold and Palmer reported a 25% decrease in BD (1.2–0.9 g cm−3) in neighboring farms with differing management practices23, and Syswerda et al. observed a 17% increase in SOC concentration (10.4–12.2 g C kg soil−1) when converting from a conventionally to organically managed row crop rotation21.Table 1 Hypothetical changes in bulk density (BD) and soil organic carbon (SOC) concentration in 30 cm fixed depth samples at time points t0, t1 and t2 along with calculated values of SOC stock and total soil mass and mineral soil mass.Full size tableIn Table 1, the total soil mass, mineral soil mass, and the SOC stock of the fixed depth samples were calculated by equations as described in the introduction from our prescribed changes in BD and SOC values.ScenariosWe compared hypothetical ESM correction scenarios with our 30 cm fixed depth sample at each time point (Table 2, Figs. 2, 3).Table 2 Hypothetical ESM scenarios showing variation with depth for bulk density (BD) and soil organic carbon (SOC) at each sampling time point, along with the sample depth intervals investigated.Full size tableFigure 2Flow chart of the definition, sampling, and SOC stock correction for a theoretical data set at time points t0, t1, and t2 for scenarios s1 with linear distributions of BD and SOC and s2 with a linear increase in BD and exponential decrease in SOC with depth. Scenario s2 is sampled at (a) 10 cm, (b) 15 cm, and (c) 30 cm intervals.Full size imageFigure 3Scenarios (S1 and S2), showing (a) bulk density variation (BD, g cm−3), and (b) soil organic carbon (SOC, %) variation by depth (0–30 cm) at each time point (t0, t1, and t2). For scenario 2, the single 30 cm depth interval was used (2c). See Table 1 and 2 for details.Full size imageScenario 1We carried out the ESM correction on a 30 cm sample and assumed that the sample was homogenous throughout the profile, with constant SOC and BD values at each time point.To correct for the error in SOC stock estimation when using fixed depth soil sampling, we used  Eqs.2a, 2b and 2c that consider changes in BD28,35. The adjusted soil depth resulting from the change in BD is calculated as:$${mathrm{M}}_{mathrm{n}}= {mathrm{M}}_{mathrm{i}}$$
(2a)
$${mathrm{D}}_{mathrm{a}}*{mathrm{BD}}_{mathrm{n}}*left(1-mathrm{k}*{mathrm{SOC}}_{mathrm{n}}right)={mathrm{D}}_{mathrm{i}}*{mathrm{BD}}_{mathrm{i}}*left(1-mathrm{k}*{mathrm{SOC}}_{mathrm{i}}right)$$
(2b)
$${mathrm{D}}_{mathrm{a}}={mathrm{D}}_{mathrm{i}}*frac{{mathrm{BD}}_{mathrm{i}}}{{mathrm{BD}}_{mathrm{n}}}*frac{1-mathrm{k}*{mathrm{SOC}}_{mathrm{i}}}{1-mathrm{k}*{mathrm{SOC}}_{mathrm{n}}}$$
(2c)
where Mi = Initial mineral soil mass per area (left[frac{M}{{L}^{2}}right]) , Mn = New mineral soil mass per area (left[frac{M}{{L}^{2}}right]) , Da = Adjusted soil surface depth (left[Lright]) , BDi = Initial bulk density (left[frac{M}{{L}^{3}}right]) , BDn = New bulk density (left[frac{M}{{L}^{3}}right]) , SOCi = Initial SOC as a decimal percent (left[frac{M}{M}right]) , SOCn = New SOC as a decimal percent (left[frac{M}{M}right]) , Di = Initial depth (left[Lright]).To conform with Eq. (2a), an increase in SOC over time results in a displacement of some soil mineral mass from the sample, whereas a decrease in SOC over time requires some soil mineral mass to be replaced34. Multiplying the BD by the mineral fraction of the soil (left(1-mathrm{k}*{mathrm{SOC}}right)) for each time point allowed us to compare equivalent mineral mass28. The effect of a change in SOC on mineral mass is small, with a 1% change in SOC equating to approximately a 2% change in apparent depth. This adjustment relates SOC per unit of mineral mass of the fine fraction ( 2 mm)20. The corrected apparent depth can then be used to calculate the corrected SOC stock of a single layer, fixed depth sample (Eq. 3).$$SO{C}_{stock}={D}_{a}*BD*SOC$$
(3)
Scenario 2In ESM correction scenarios 2a, 2b, and 2c, we imposed variable, dynamic BD and SOC values with depth over time (Table 2, Figs. 2, 3). To investigate these profiles, we determined the SOC and BD values throughout the soil depth by separating the soil into one (1) cm depth increments (i.e., 0–1 cm, 1–2 cm, etc.). We refer to this calculated incremental profile as the scenario 2 baseline. We assumed that our prescribed SOC concentration varied with depth following an exponential decay. To represent this decay, we simulated the global average distribution of SOC concentration with depth on crop land36, following the distribution from Hobley and Wilson37 (Eq. 4),$$SOCleft(dright)=SO{C}_{infty }+left(SO{C}_{o}-SO{C}_{infty }right)times {e}^{-dk}$$
(4)
where SOC (d) is the SOC concentration at depth (d), ({SOC}_{infty }) is the infinity SOC concentration, SOC0 is the SOC concentration at the soil surface, and k is the decay rate. We solved for the decay rate, initial SOC0, and infinity ({SOC}_{infty }) to fit the global average distribution for the 30 cm profile36 and then scaled the SOC concentration to our 30 cm fixed depth sample’s average SOC (1.4%) at t0 (Fig. 3).In scenarios 2a, 2b, and 2c, the BD increased linearly with depth38,39. At the initial time point (t0), we varied the BD values by ± 10% of the BD average over the 30 cm depth, such that for example, BD at t0 (profile average of 1.5 g cm−3) was 1.35 g cm−3 and 1.65 g cm−3 for the upper (0–1 cm) and lower (29–30 cm) depth increment, respectively. For each sequential time point, as the average BD decreased, the soil expanded. To determine the expansion, the depth of the initial sample (e.g., at t0) that filled the 30 cm depth in the subsequent sample (e.g., at t1) was calculated as the initial depth multiplied by the ratio of the average initial BD over the average new BD (e.g., 1.5/1.3 = 1.15 for t0/t1).The linear increase in BD with depth of each following time point maintained the average BD of scenario 1. We then varied the new BD by ± the percent change in the average BD between the time periods (see annotated scripts “main.R” and “functions.R” in Supplementary Material 1 for the development of the theoretical dataset). We then divided each initial BD increment (using soil mass for every 1 cm depth increment) by the new BD in the expanded increment (using soil mass for every  > 1 cm depth increment) to determine the expanded depth of each increment. The SOC value at the initial time represented the same, now expanded, ( > 1 cm) increments, as SOC is a ratio of mass. We used a linear decay rate that was twice that of the percent change in BD between time points to maintain an average BD that was consistent with scenario 1. To model the subsequent fixed depth sample, the BD and SOC concentration values of this expanded soil profile were then interpolated back to the 30 × 1 cm increments of the scenario 2 baseline depth. This calculation preserved the prescribed average BD of the new time point by only expanding the initial SOC concentration.We adjusted the SOC concentration of the next time point to maintain the average SOC concentrations of the 30 cm fixed depth sample, (see annotated scripts “main.R” and “functions.R” in Supplementary Material 1). Because the BD changed between time points and because the SOC stock in the 30 cm fixed depth sample was known, we determined the change in SOC stock between time points by subtracting the average SOC stock in the prior sample from the new sample. We then weighted this change across the 30 cm profile using the distribution of the global soil SOC in the top 30 cm to simulate SOC stratification with reduced tillage or agricultural intensification40. We then multiplied this change by the BD to convert back to SOC concentration and added the delta ((Delta )) SOC value to the prior sample. A worked example is shown in Supplementary Material 2 “Correction Example”.At each time point we split the soil profile at 10 cm and 15 cm depth intervals to create samples for scenarios 2a (3 soil intervals) and 2b (2 soil intervals), respectively. Note that scenario 2c is mathematically equivalent to scenario 1—with only one sample depth interval (30 cm) the sample contains no data on varying SOC or BD. The samples for 2a and 2b were generated by summing the total mass per area and SOC stock values from the scenario 2 baseline to produce single sample values of total soil mass per area and SOC concentration values per depth interval (as would be determined in a laboratory) and calculating BD and mineral mass.In scenario 2, any required additional mineral mass and the associated SOC values were ‘placed’ at the base of the sample to represent a soil profile that had expanded below the fixed 30 cm depth. To account for this, we calculated the increase in adjusted sample depth and accumulated additional soil mineral mass with the lowest sample depth interval of each split sample (Eqs. 5 and 6).$$mathrm{Delta D}={D}_{a}-{D}_{i}$$
(5)
$$SO{C}_{stock}={(D}_{1}*B{D}_{1}*SO{C}_{1}+dots + {(D}_{j}+Delta D)*B{D}_{j}*SO{C}_{j}))*10^2 (mathrm{g}/mathrm{cm}^{2})/(mathrm{Mg}/mathrm{ha})$$
(6)
where (mathrm{ Delta D}) is the apparent change in depth needed to generate the same mineral mass of the initial sample and the subscript j is the number of sample depth intervals from 1 to j.Varying BD linearly with depth introduces additional complexity in the calculation of the apparent depth. Each sample depth interval may expand (or contract in cases not explored here) at differing rates. Here, the over or under sampling of soil mineral mass is no longer constant with depth and the correction for apparent depth (Da) is estimated with linear interpolation using the BD of each sampling depth interval (i.e., 10 cm, 15 cm, or 30 cm). To do so, we calculated the mineral mass in each depth interval, determined their difference between the initial sample time point and new sample time point, and converted the change in mineral mass to a depth, where:$${mathrm{D}}_{mathrm{a}}={mathrm{D}}_{mathrm{i}}+frac{left(mathrm{sum}left({mathrm{D}}_{mathrm{ij}}*{mathrm{BD}}_{mathrm{ij}}*(1-mathrm{k}*{mathrm{soc}}_{mathrm{ij}}right))- mathrm{sum}left({mathrm{D}}_{mathrm{nj}}*{mathrm{BD}}_{mathrm{nj}}*(1-mathrm{k}*{mathrm{soc}}_{nmathrm{j}})right)right)}{{mathrm{BD}}_{{mathrm{nj}}_{mathrm{bottom}}}*1-mathrm{k}*{mathrm{soc}}_{n{mathrm{j}}_{mathrm{bottom}}}}$$
(7)
where jbottom is the lowest sample depth interval, and other terms are as previous. Using Eqs. (5), (6), and (7), with variable BD and SOC values, SOC stock can be corrected using samples split into the 10 cm and 15 cm sampling depth intervals. More

Sampling sites and sample preparationCulms of three- to four-year-old of Bambusa vulgaris Schrad. ex J.C., B. maculata Widjaja, and Gigantochloa atter (Hassk) Kurz ex Munro were collected from naturally bamboo forests at four sites in Lombok Island, Indonesia23. The culm age was estimated based on some morphological features (the presence of culm sheath, color, and sound created by tapping with fingers) checked by an experienced bamboo farmer. Figure 1 shows the map of sampling sites and climatic conditions of the sites. Ten individual culms in each species at each site were collected from different clumps and cut 20 cm above the ground (Fig. 2). A total of 120 culms (three species × four sites × 10 individual culms from 10 individual clumps) were collected in the present study (Fig. 2). To determine the longitudinal variations of the anatomical characteristics and mechanical properties, the internode section was collected at 2-m intervals from 2 to 8 m above the ground; a total of 480 internode sections. (120 culms × four heights) were obtained from three species (Fig. 2). The collection of bamboo culms was permitted by Indonesian Institute of Science (Reference no. B-206/SKIKH/KS.02.04/X/2020) and complied with relevant guidelines and regulations of Indonesian CITES Management Authority, Ministry of Environment and Forestry, Indonesia. In addition, the voucher specimen was deposited at the Herbarium Lesser Sunda, University of Mataram, Indonesia under the voucher number of DSR01, 02, and 03 (specimens were identified by Mr. Niechi Valentino). Table 1 shows the culm diameter at 1.3 m above the ground, total culm height, and mean value of culm thickness at four positions23.Figure 1Locations and climate conditions of sampling sites in the present study23. Note: Site I, Tempos (8°41′59″ S, 116°8′40″ E); Site II, Kabul (8°47′21″ S, 116°10′21″ E); Site III, Keruak (8°45′45″ S, 116°28′54″ E); Site IV, Genggelang (8°23′16″ S, 116°15′35″ E). *, mean annual precipitation. The value in the bracket is the mean annual temperature. Climate data were provided from Nusa Tenggara River Basin Management I, Indonesia. Mean monthly temperature and precipitation were calculated by averaging monthly values from 2016 to 2018. Bars indicate the mean values of precipitation. Circles indicate the mean values of temperature. The graph was originally created by R27 (version 4.0.3, https://www.R-project.org/).Full size imageFigure 2Photographs of the clumps in three bamboo species (a–c) and schematic diagrams of experimental procedures (d). Note: a, B. vulgaris; b, B. maculata; c, G. atter. The specimens of fiber area measurement and mechanical properties have the whole culm thickness (including the cortex and inner part of the culm) in the radial direction.Full size imageTable 1 Mean values and standard deviations of growth characteristics in three bamboo species at each site23.Full size tableAnatomical characteristicsThe internode sections were split into two parts: the strips (10 mm in the longitudinal direction) and the small blocks (10 [T] mm by 10 [L] mm by culm thickness in the radial direction) (Fig. 2). The strips and small blocks were the samples for measuring fiber length and fiber area, respectively. In the present study, the fiber area was defined as the sheaths area around the vascular bundles24.To determine the fiber length, small sticks (not including the cortex and the most inner part of the culm) were obtained from the strips with a razor blade (Fig. 2). Randomly selected sticks from each height position (without separation of collected positions of the samples within the radial direction of the culm in a height) were macerated with Schultze’s solution (100 mL of 35% nitric acid containing 6 g potassium chloride) at 70 °C for two hours. The length of 50 fibers was measured in each sample with a digital caliper (CD-15CX, Mitutoyo, Kawasaki, Japan) on a microprojector (V-12B, Nikon, Tokyo, Japan).To measure the fiber area, one block was taken at each height position on each individual culm (Fig. 2). The transverse sections of the blocks were polished with sandpaper sheet (#180, 3 M Japan, Tokyo, Japan), and then their images were captured using a microscope digital camera (DS-2210, Sato Shouji Inc., Kawasaki, Japan) attached to a stereo microscope (SZX12, Olympus, Tokyo, Japan). The fiber area was determined by ImageJ25 (version 1.53e). Binarized images were prepared by ImageJ to distinguish as clearly as possible between the vascular bundle and the background (Fig. 3). The darker area of binarized images in Fig. 3 was identified as fiber sheaths. The fiber area was calculated as follows:$$FAleft( % right) , = A_{fs} /A_{c} times {1}00$$
(1)
where FA = fiber area (%), Afs = the transverse-sectional area of fiber sheath in bamboo culm (mm2), and Ac = the transverse-sectional area of bamboo culm (mm2).Figure 3The photomicrographs of transverse section in B. vulgaris (a and d), B. maculata (b and e), and G. atter (c and f). Note: a, b and c, original image; d, e and f, binarized image processed by ImageJ25 (version 1.53e, https://imagej.nih.gov/ij/). The darker area in photomicrographs (d, e and f) is fiber sheath area.Full size imageMechanical propertiesThe following mechanical properties of culm were measured: bending properties (MOE and MOR), CS, and tensile properties (TM and TS). A total of 480 specimens (one specimen × four heights in an individual × ten individuals × three species × four sites) without node were obtained in each property (Fig. 2).The strips (10 [T] mm × 200 [L] mm × varied culm thickness in the radial direction) were prepared as the specimens for the static bending test (Fig. 2). The static bending test was conducted using a universal testing machine (MSC 5/500–2, Tokyo Testing Machine, Tokyo, Japan). A load was applied to the center of the specimen on the outer cortex surface with 180 mm span and 3 mm min−1 load speed. Due to larger thickness (exceeded 12.9 mm = 180 mm of span / 14) in the radial direction, the span / depth ratio in some specimens was less than 14, indicating that MOR in some specimens might be underestimated due to the occurrence of the shearing strength26. Of 480 specimens, the large culm thickness exceeded 12.9 mm was total 19 specimens from B. vulgaris species collected at 2 m height position from different sites (Site I = four specimens, Site II = six specimens, Site III = four specimens, and Site IV = five specimens). However, all these 19 specimens were broken at the tension side of the specimens during static bending test, which was the normal breaking forms of bending specimens with span / depth ratio less than 14.The load and deflection were recorded with a personal computer, and then MOE and MOR were calculated by the following formulae:$$MOE , left( {GPa} right) , = Delta Pl^{3} / , 4Delta Ybh^{3} , times 10^{ – 3}$$
(2)
$$MOR , left( {MPa} right) , = , 3Pl/ , 2bh^{2}$$
(3)
where ΔP = difference between upper and lower proportional limit within the range of elasticity (N), l = length of the span (mm), ∆Y = deflection due to ∆P (mm), b = width of the specimen (mm), h = height of the specimen (mm), and P = maximum load (N).The compressive test specimen (10 [T] mm × 20 [L] mm × culm thickness in the radial direction) was also prepared (Fig. 2). The test was conducted using a universal testing machine (RTF-2350, A&D, Tokyo, Japan) with a load speed of 0.3 mm min−1. The compressive strength parallel to grain (CS) was calculated by the following formula:$${text{CS }}left( {{text{MPa}}} right) , = P/A_{0}$$
(4)
where P = maximum load (N), and A0 = the cross-sectional area of the specimen (mm2).The tensile tests were conducted using bone-shaped specimens (Fig. 2). The specimen length was 230 (L) mm with a 20 (T) mm width of the specimen grip. The cross-sectional area of the specimen was 2 mm in the tangential direction by culm thickness in the radial direction. A strain gage type extensometer (SG25-10A, A&D, Tokyo, Japan) was used to detect the elongation in the test specimen. The specimen grip sections were attached to small boards (75 mm in length × 40 mm in width × 5 mm in thickness) and then were clamped between the metal grip of a universal testing machine (RTC-2410, A&D, Tokyo, Japan). The tensile load was applied at 1 mm min−1. The tensile strength (TS) and Young’s modulus (TM) were calculated by the following formulae:$${text{TS }}left( {{text{MPa}}} right) , = P/A_{0}$$
(5)
$${text{TM }}left( {{text{GPa}}} right) , = Delta Pl/A_{0} Delta l times {1}0^{{ – 3}}$$
(6)
where P = maximum load (N), A0 = the cross-sectional area of the specimen (mm2), ∆P = difference between upper and lower proportional limit within the range of elasticity (N), l = gauge length (mm), and ∆l = elongation of the original gauge length (mm).The moisture content and air-dry density of each specimen were measured after each mechanical testing by the oven-dry method. The moisture content and air-dry density of the specimen at testing were listed in Table S1.Statistical analysisThe statistical analyses were conducted using R software (version 4.0.3)27. To evaluate the longitudinal variations of the measured properties in each species, the y-intercept, linear, and nonlinear mixed-effects models with each measured property value as a responsible variable, the height position as a fixed effect, and site and individual culm as random effects were developed by the “lmer” function in “lme4” packages28 and the “nlme” function in the “nlme” package29. The following four full models were developed and compared:Model I (y-intercept model):$$Y_{ijk} = alpha_{{1}} + Site_{{{1}k}} + Culm_{{{1}jk}} + e_{ijk}$$
(7)
Model II (linear model):$$Y_{ijk} = , (beta_{0} + Site_{0k} + Culm_{0jk} )X_{ijk} + beta_{{1}} + Site_{{{1}k}} + Culm_{{{1}jk}} + e_{ijk}$$
(8)
Model III (logarithmic model):$$Y_{ijk} = , (gamma_{0} + Site_{0k} + Culm_{0jk} ){text{ ln }}left( {X_{ijk} } right) + gamma_{{1}} + Site_{{{1}k}} + Culm_{{{1}jk}} + e_{ijk}$$
(9)
Model IV (quadratic model):$$begin{gathered} Y_{ijk} = , (zeta_{0} + Site_{0k} + Culm_{0jk} )X_{ijk}^{{2}} + , (zeta_{{1}} + Site_{{{1}k}} + Culm_{{{1}jk}} )X_{ijk} hfill \ + zeta_{{2}} + Site_{{{2}k}} + Culm_{{{2}jk}} + e_{ijk} hfill \ end{gathered}$$
(10)

where Yijk is measured property at the ith height position from the jth individual culm within the kth site, Xijk is the ith height position from the jth individual culm within the kth site, α1, β0, β1, γ0, γ1, ζ0, ζ1, and ζ2 are the fixed effects, Site0k, Site1k, and Site2k are the random effect at the site level, Culm0jk, Culm1jk, and Culm2jk are the random effects at the individual culm level, and eijk is residual. Total 36 derived models (three y-intercept models, 15 linear models, nine logarithmic models, and nine quadratic models) were developed. The model selection was conducted using the Akaike information criterion30. The model with the minimum AIC value was regarded as the most parsimonious model among developed models. In addition, the differences in AIC (ΔAIC) ≤ 2 indicate no significant differences between models, and a simpler model with fewer parameters is preferred31. To evaluate the longitudinal variation, estimated values of each property was calculated at 0.1 m interval from 2.0 to 8.0 m above the ground using fixed-effect parameters of the selected models. Mean value and standard deviation were obtained from the estimated values from 2.0 to 8.0 m in each property. In addition, the coefficient of variation was also calculated from the mean value and standard deviation. The longitudinal variation patterns were classified into four types (Types A to D) based on the model selection (Fig. 4). Although model II to IV was selected, longitudinal variation with the coefficient of variation less than 3.0% was regarded as stable (Type A in Fig. 4).Figure 4Classification of longitudinal variation of bamboo culm property. Note: Lines or curves indicate formulae with fixed-effect parameters in the selected mixed-effect model for explaining longitudinal variation (Tables 3, 4, 5). Coefficient of variation calculated from mean values and standard deviation from 2 to 8 m above the ground estimated by fixed-effect parameters values less than 3.0% is regard as stable variation (Type A), even in selected model is Model II to IV.Full size imageGeographic variations in each bamboo property were estimated by evaluating the variance component of sites and culms as random effects by using the intercept-only linear mixed-effects model. The full model is described as follows:$$Y_{ijk} = mu + Site_{k} + Culm_{jk} + e_{ijk}$$
(11)
where Yijk is the bamboo property at the ith height position of the jth individual culm within kth site, μ is the model intercept or grand mean, Sitek is the random effect of the kth site, Culmjk is random effect of jth individual culm within kth site, and eijk is the residual. The contribution of each level of variation was calculated as a percentage of the total random variation in the best model32,33. More

The ecological baseline in TirezThe geology and the climate of the Tirez region favored the generation and maintenance of a type of hypersaline habitat characterized by extreme seasonality: the sulfate-chlorine waters, with sodium and magnesium cations, showed significant seasonal variations15. The alkaline pH, the low oxidant value for the redox potential of the water column and the highly reduced sediments imposed extreme conditions (see Table 1 and Supplementary Information for details). This extreme seasonality requires to define a valid representative ecological baseline to compare the ecology of the lagoon between 2002 and 2021 and, in this way, set the basis to proposing our model of ecological succession with increasing dryness as a “time-analog” for early Mars. Taxonomic data from 2002 is a snapshot of the community during one season, so we include in our discussion the results presented by Montoya et al. (2013) from a sample campaign carried out in 2005, because they16 analyzed both water and sediment and during both the wet and dry seasons.We consider here only the results obtained by Montoya et al.16 by gene cloning, since those obtained by isolation and sequencing are not comparable. At the level of large groups, no major seasonal differences were observed: Pseudomonadota, followed by Bacteroidetes, were the dominant phyla, in both water and sediments, and both in the dry and the rainy seasons; although Alphaproteobacteria was the dominant class in water, while Gammaproteobacteria was dominant in sediments (in both dry and rainy seasons). With respect to the archaeal domain, all the identified sequences were affiliated to Halobacteriales order, mainly Halorubrum (water) and Halobacterium (sediment), both within Halobacteriaceae family. We can consider these results presented in Montoya et al.16 as the “ecological baseline” for Tirez, however taken with a grain of salt, because only 43 bacterial and 35 archaeal sequences, including rainy and dry seasons and water and sediments, were considered for analysis.Prokaryotic diversity in 2002As can be expected for an extreme environment, the bacterial diversity detected in 2002 was low, although we cannot exclude the possibility that this may reflect the limitation of DNA sequencing techniques at the time. 59% of the obtained clones in the then-wet sediments corresponded to the Malaciobacter genus. Malaciobacter (previously named17 Arcobacter) is an aerotolerant Epsilonproteobacteria. Species within this genus are moderately halophilic, e.g., M. halophilus, capable to grow in up to 4% NaCl. Even though the role that Malaciobacter can play in the environment is not known, it seems to thrive in aquatic systems, like sewage, with a high organic matter content17: e.g., M. canalis, M. cloacae, or M. defluvii.After Malaciobacter-like clones, the next most numerous group belongs to the phylum Bacillota (27% of the sequenced clones; Table 2). Under stressful environmental conditions, members of the genus Virgibacillus produce endospores, a very useful property in an extreme and variable environment (ionic strength, temperature, light intensity), easy to compare with early Mars. Endospores facilitate species survival, allowing them to overcome drastic negative changes, like dry periods, and to germinate when the conditions are favorable again. The closest identified species was the halotolerant V. halodenitrificans, but with low homology, not far from other halotolerant (e.g., V. dokdonensis) or halophilic (e.g., V. marismortui) species within the same genus. The other Gram-positive clones belong to the order Clostridiales. These clones cluster in two taxonomic units related with the strictly anaerobic genus Tissierella.Despite the abundance of Pseudomonadota, their biodiversity was very low, reduced to only two genera within the Epsilon- and Delta-proteobacteria. Six sequences affiliated to Deltaproteobacteria, and clustered in one OTU (salB38, similarity 96.6% with Desulfotignum), were retrieved. Its presence in anaerobic media rich in sulfates (Table 1) seems reasonable. In fact, sulfate-reducing activity was detected using a specific enrichment assay.Finally, one taxon belonging to the phylum Spirochaetota (previously named Spirochaetes) was identified. The presence of Spirochaetota in this system is not strange because members of the genus Spirochaeta are very often found in mud and anaerobic marine environments rich in sulfates18. Moreover, the closest species to SalB63, although with a low similarity of 87%, was Spirochaeta bajacaliforniensis, a spirochete isolated19 from a microbial mat in Laguna Figueroa (Baja California), an extensive hypersaline lagoon with high gypsum content, very similar, although much bigger, than Tirez lagoon.The diversity within the domain Archaea was very low in 2002. The phylogenetic analysis of 96 clones indicate that they correspond to one specie belonging to the obligate halophile genus Methanohalophilus. Their high similarity (99.3%) with several species of Methanohalophilus, such as M. portucalensis (isolated from sediments of a solar saltern in Portugal), M. mahii (isolated from sediments of the Great Salt Lake), or M. halophilus (isolated20 from a cyanobacterial mat at Hamelin Pool, Australia), makes impossible its adscription to any particular species level. Methanohalophilus is strictly methylotrophic, which is consistent with this environment, given that the methylotrophic methanogenesis pathway, non-competitive at low-salt conditions, is predominant at high saline concentrations21. We further confirmed methanogenic activity in Tirez by the measurement of methane by gas chromatography in enrichment cultures.Prokaryotic diversity in context of other studies between 2002 and 2021It was challenging to establish a timeline for the succession of the populations involved, because the scarcity of data harvested and published so far from Tirez. However, combining our results with the few data available in Montoya et al.16 and Preston et al.22 on samplings carried out on 2005 and 2017, respectively, we can see a clear predominance of the phylum Pseudomonadota: Epsilonproteobacteria, i.e. Arcobacter-like, and Deltaproteobacteria, mainly sulfate-reducing bacteria (this work, sampling 2002), and Gammaproteobacteria16 when Tirez maintained a water film, to eventually a final predominance of Gammaproteobacteria, e.g. Chromatiales and Pseudomonadales, in the dry Tirez (this work, 2020 sampling). The Bacillales order has remained widely represented both in the wet and dry Tirez.Regarding the archaeal domain, the few references available (Refs.16,22; this work) confirm that the members of the Halobacteriaceae family are well adapted to both the humid and dry ecosystems of Tirez, being predominant in both conditions. Preston et al.22 found that the second most abundant group of archaea in the dry sediments of Tirez was the Methermicoccaceae family, within the Methanosarcinales order, Methanomicrobia class. Taking into account the results obtained in the dry Tirez (Preston et al.22; and this work, sample 2020), the methanogenic archaea have decreased drastically through time, probably due to salt stress and the competition with sulfate-reducing bacteria.Prokaryotic diversity in 2021From a metabolic point of view, most of the bacteria present today in the sediment are chemoorganotrophs, anaerobes, and halophilic or halotolerant. Scarce information is available about the predominant OTU, Candidate Division OP1. The OP1 division was one of the main bacterial phyla in a sulfur-rich sample in the deepest analyzed samples from the Red Sea sediments under brine pools23. In addition, the phylogenetically related Candidate division KB1 has been observed in deep-sea hypersaline anoxic basins at Orca Basin (Gulf of Mexico), and other hypersaline environments24. Eight of the nine genera identified show coverage greater than 1% of the sequences: i.e., Rubinisphaera, Halothiobacillus, Thiohalophilus, Anaerobacillus/Halolactibacillus, Halomonas, Halothermothrix, and Aliifodinibius are halophilic or halotolerant genera13,25.Regarding archaea, our analyses reveal archaeal groups that seem to thrive in sediments from extreme environments, e.g., marine brine pools/deep water anoxic basins or hypersaline lakes. The most abundant OTU, Thermoplasmata KTK4A, was found prominent and active in the sediment of Lake Strawbridge, a hypersaline lake in Western Australia26, and in soda-saline lakes in China27. The creation of a Candidatus Haloplasmatales, a novel order to include KTK4A-related Thermoplasmata, has been proposed27. On the other hand, both in the aforementioned soda-saline lakes in China27 and in a sulfur-rich section of the sediments from below the Red Sea brine pools23, retrieved sequences were assigned to Marine Benthic Groups B, D, and E. Finally, in the section of nitrogen-rich sediments from the aforementioned Red Sea brine pools, the unclassified lineage ST-12K10A represented the most abundant archaeal group. In the Tirez Lagoon sediment after desiccation, all Methanomicrobia readings belonged to this group.The significance and implications of an ecosystem characterized in 2021 by high diversity, high inequality, and lack of isolated representatives, resides in that Tirez is today an ecosystem in which many (most) of the species/OTUs present are dormant, and they do not play any metabolic role. Hence the high percentage of raretons, greater than 80% for both bacteria and archaea, which are actually present in the lagoon but with only one or two copies each. Only those species adapted to the conditions imposed by the extreme environment are able to actually thrive, and consequently only a few species carry out all the metabolic activity. We conclude that the microbiota in Tirez today represents an ecosystem with a high resilience capacity in the face of environmental changes that may occur.We want to clearly highlight that the technique available in 2002 to study the microbiota of the Tirez lagoon only allowed to obtain a low-resolution image, but that was the state-of-the-art procedure at the time, and the Tirez lagoon cannot be sampled again with the conditions back in 2002, which no longer exist and are not expected to return. Although we have kept in storage several samples of water and sediment from the 2002 Tirez lagoon, it is reasonable to assume that those laboratory microcosms would have chemically and microbiologically changed during the last 20 years, and as such no longer represent reliable replicas of the original lagoon, so we cannot use them for the purposes of this work. Therefore, we are aware that any comparisons of the 2002 laboratory results with the much more robust results obtained by Illumina in 2021 need to be taken with a grain of salt. With all the precautions required, in a high-level, first-order comparison, the most noticeable difference between 2002 and 2021 is a drastic change in the microbial Tirez population. Only some OTUs within Bacillales (Virgibacillus/Anaerobacillus), sulfate-reducing Deltaproteobacteria (Desulfotignum/Desulfobacteraceae-Desulfovibrio), and Spirochaetes are shared among the 2002 and 2021 samples. This comparison is enough for the purposes of this work, as we are interested in the evolution of the lagoon system as a whole to establish a “time-analog” with the wet-to-dry transition on early Mars, and not in the particular outcome of each and every OTU in Tirez. With the results at hand, we conclude that, since 2002, the lacustrine microbiota has shifted to one more adapted to the extreme conditions in the dry sediments, derived from the gradual and persistent desiccation concluding ca. 7 years ago (i.e., completely desiccated in 2015), such as lack of light, absence of oxygen, and lack of water availability. This shift has likely been triggered because organisms that were originally in the lagoon but at low abundance in 2002 became dominant as they were better adapted to desiccation, and because the incoming of new microorganisms transported by birds or wind28.Lipid biomarkers analysis of the desiccated lake sedimentsThe analysis of cell membrane-derived lipid compounds on the dry lake sediments at present allow to provide another perspective of the microbial communities inhabiting the Tirez lagoon, by contributing additional information about the ecosystem and depositional environment. It is important to note that, analyzing only the 2021 lake sediments, we cannot differentiate between lipidic biomarkers of the microorganisms inhabiting Tirez in 2002 and before from those left behind by the microorganisms living in the dried sediments today. Instead, the analyses of lipid biomarkers provide clues about the different microorganisms that have populated Tirez through time, including both older communities inhabiting the former aqueous system and also younger communities better adapted to the present dry conditions. Thus, the lipid biomarkers analysis can be considered as a time-integrative record of the microbial community inhabiting Tirez during the last decades.Based on the molecular distribution of lipid biomarkers, the presence of gram-positive bacteria was inferred from the relative abundance of the monounsaturated alkanoic acid C18:1[ω9], or iso/anteiso pairs of alkanoic acids from 12 to 17 carbons29 with dominance of i/a-C15:0 and i/a-C17:0 (Fig. 3B). In contrast, generally ubiquitous alkanoic acids such as C16:1[ω7], C18:1[ω7], or C18:2[ω6] suggested a provenance rather related to gram-negative bacteria30. The combined detection of the i/a-C15:0 and i/a-C17:0 acids, with dominance of the iso over the anteiso congeners, together with other biomarkers such as the mid-chain branched 10Me16:0, the monounsaturated C17:1, or the cyclopropyl Cy17:0 and Cy19:0 acids, may be associated with a community of SRB31 in today´s dry sediments of Tirez. Specifically, most of those alkanoic acids have been found in a variety of Deltaproteobacteria and/or Bacteroidota (previously named Bacteroidetes). The presence of archaea was deduced from the detection of prominent peaks of archaeol in the polar fraction32 (Fig. 3C), as well as squalene and relatives (dihydrosqualene and tetrahydrosqualene) in the apolar fraction33 (Fig. 3A). Squalene and a variety of unsaturated derivatives are present in the neutral lipid fractions of many archaea with high abundances in saline lakes34. The relative abundance of autotrophs over heterotrophs35 can be estimated by the ratio of the autotrophically-related pristane and phytane over the both autotrophically- and heterotrophically-produced n-C17 and n-C18 alkanes ([Pr + Ph]/[n-C17 + n-C18]). A ratio of 0.56 in the Tirez sediments suggest the presence of a relevant proportion of heterotrophs in the ancient lacustrine system.Furthermore, the lipid biomarkers analysis was able to detect compounds specific of additional microbial sources, such as cyanobacteria36 (n-C17, C17:1, or 7Me-C15 and 7Me-C17), microalgae and/or diatoms (phytosterols37; or C20:5, and C22:6 alkanoic acids30), and other photoautotrophs (phytol and potentially degradative compounds such as pristane and phytane31). A relatively higher preservation of the cell-membrane remnants (i.e., lipids) compared to the DNA-composing nucleic acids may contribute to explain the lack of detection of cyanobacteria, diatoms and microalgae, and other phototrophs by DNA analysis (a deficit in our results shared with Montoya et al.16, and Preston et al.22). Although abundant in higher plants38, sterols such as those detected here (i.e., the sterols campesterol, stigmasterol, and β-sitosterol, as well as ergosterol) are also major sterols in some microalgal classes37 (such as Bacillariophyceae, Chrysophyceae, Euglenophyceae, Eustigmatophyceae, Raphidophyceae, Xanthophyceae, and Chlorophyceae), cyanobacteria (β-sitosterol), and fungi (ergosterol39).The carbon isotopic composition of lipid biomarkers provides a rapid screening of the carbon metabolism in a system, by recognizing the principal carbon fixation pathways used by autotrophs. The range of δ13C values measured in the Tirez sediments (from − 33.9 to − 16.1‰) denotes a mixed use of different carbon assimilation pathways, involving mostly the reductive pentose phosphate (a.k.a. Calvin–Benson–Bassham or just Calvin) cycle (from − 19 to − 30‰), and in lesser extent the reductive acetyl-CoA (a.k.a. Wood–Ljungdahl) pathway (from − 28 to − 44‰), and/or the reverse tricarboxylic acid (rTCA) cycle (from − 12 to − 21‰).The lipids synthesized by microorganisms using the Calvin or reductive acetyl-CoA pathway are typically depleted relative to the bulk biomass, particularly those produced via de latter pathway. In the dry Tirez sediments, the majority of the lipid compounds are more depleted in 13C than the bulk biomass (Fig. 4). In particular, the branched alkane DiMeC18 (Fig. 4A) and the SRB-indicative 10Me16:0 acid (Fig. 4B) showed the most depleted δ13C values and suggested the use of the reductive acetyl-CoA pathway. The rest of lipid compounds showed isotopic signatures (from − 16.1 to − 31.4‰) compatible with the prevalence of the Calvin pathway. These values may directly reflect the autotrophic activity of microorganisms fixing carbon via the Calvin cycle or heterotrophic activity of microorganisms growing on their remnants. Thus, the saturated and linear alkyl chains of lipids (i.e., n-alkanes, n-alkanoic acids, and n-alkanols) showing the most negative δ13C values (e.g., alkanes n-C17 and C17:1; or acid C18:1[ω7]) reflect prokaryotic sources of Calvin-users autotrophs (e.g., cyanobacteria or purple sulfur bacteria), while the rest of compounds with slightly less negative δ13C values instead stem from the autotrophic activity of eukaryotes also users of the Calvin cycle (unsaturated fatty acids and sterols) or from the metabolism of heterotrophs such as SRB (iso/anteiso-, other branched, and cyclopropyl fatty acids) and haloarchaea (isoprenoids, phytanol, and archaeol). All in all, the compound-specific isotope composition of the dry sediments in the today´s Tirez lagoon may indirectly reflect the prevailing autotrophic mechanisms in the present lacustrine system of Tirez, by showing isotopic signatures of secondary lipids similar to their carbon source40.In addition, the use of a number of lipid molecular ratios or proxies allow further characterization of the lacustrine ecosystem and depositional environment. For example, the average chain length of the n-alkanes (24.1) suggests a relevant presence of eukaryotic biomass in the lacustrine sediments, since long-chained alkanes ( > C20) are known to originate from epicuticular leaf waxes in higher plants41. Highlighting the relevance of eukaryotes and their ecological roles is one of the major contributions of this work, because previous studies on the microbial ecology of hypersaline environments have been focused primarily on prokaryotes42.The proportion of odd n-alkanes of high molecular chain (i.e., n-C27, n-C29, and n-C31) over even n-alkanes of low molecular chain (i.e., n-C15, n-C17, and n-C19) provides an estimate of the relative abundance of terrigenous over aqueous biomass43, which in Tirez is TAR = 1.8. The Paq index may also be used to differentiate the proportion of terrigenous versus aquatic (emergent and submerged) plant biomass44. A Paq of 0.3 in the Tirez sediments from 2021 supported the relative abundance of land plants. Finally, the depositional environment in the lacustrine system of Tirez may be also characterized analyzing the ratio of pristane over phytane (Pr/Ph), which is higher than 1 when phytol degrades to pristane under oxic conditions45. Assuming that both isoprenoids in the Tirez sediments derived from phytol31, according to their similarly depleted δ13C (Fig. 4A), we can conclude that the sediments in the Tirez lagoon were deposited under predominantly oxic conditions (i.e., Pr/Ph ratio of 1.1).In summary, the lipid biomarkers study revealed useful information about the depositional environment and lacustrine ecosystem, including the presence of active or past autotrophic metabolisms involving prokaryotes (e.g., cyanobacteria and purple sulfur bacteria) and eukaryotes (plants, diatoms and other microalgae), as well as heterotrophic metabolisms of likely SRB and haloarchaea growing on Calvin-users exudates. These results are quite in agreement with the microbial community previously reported16 in sediments from the wet and dry seasons: abundant Gammaproteobacteria and Alphaproteobacteria, together with Algae and Cyanobacteria, dinoflagellates and filamentous fungi, Bacillota, Actinomycetota (previously named Actinomycetes), and a halophilic sulfate-reducing Deltaproteobacteria.Tirez as the first astrobiological “time-analog” for early MarsEarly Mars most likely had a diversity of environments in terms of pH, redox conditions, geochemistry, temperature, and so on. Field research in terrestrial analog environments contribute to understand the habitability of this diversity of environments on Mars in the past, because terrestrial analogues are places on Earth characterized by environmental, mineralogical, geomorphological, or geochemical conditions similar to those observed on present or past Mars9. Therefore, so far analogs have been referred to terrestrial locations closely similar to any of the geochemical environments that have been inferred on Mars, i.e., they are “site-analogs” that represent snapshots in time: one specific environmental condition at a very specific place and a very specific time. Because of this, each individual field analog site cannot be considered an adequate representation of the changing martian environmental conditions through time. Here we introduce the concept of astrobiological “time-analog”, referred to terrestrial analogs that may help understand environmental transitions and the related possible ecological successions on early Mars. In this sense, they should be “time-resolved analogs”: dynamic analog environments where we can analyze changes over time. To the best of our knowledge, this is the first study that looks at the environmental microbiology of a Mars astrobiological analog site over a significant and long period of change, and try to understand the ecological successions to put them in the context of martian environmental evolution.As Mars lost most of its surface water at the end of the Hesperian5,9,12, this wet-to-dry global transition can be considered the major environmental perturbation in the geological history of Mars, and therefore merits to be the first one to be assigned a “time-analog” for its better understanding and characterization. The drying of Mars was probably a stepwise process, characterized by multiple transitions between drier and wetter environments12,47, and therefore the seasonal fluctuations and eventual full desiccation of Tirez represent a suitable analog to better understand possible ecological transitions during the global desiccation of most of the Mars’s surface before the Amazonian (beginning 3.2 Ga).To introduce Tirez as the first Mars astrobiological “time-analog” of the wet-to-dry transition on early Mars, the objective of this study was threefold: first, we wanted to identify the dominant prokaryotic microorganisms in the active Tirez lagoon 20 years ago, a unique hypersaline ecosystem with an ionic composition different from that of marine environments, and therefore potentially analogous to ancient saline lacustrine environments on Mars during the Noachian and into the Hesperian46,47. Our results provide a preliminary basis to hypothesize how the microbial communities on the Noachian Mars could have developed in salty environments with dramatically fluctuating water availability. The requirement to deal with important variations in ionic strength and water availability, involving at times the complete evaporation of the water, could have represented additional constraints48 for microorganisms on early Mars.The second objective of this investigation was the identification of the microbial community inhabiting the desiccated Tirez sediments today, after all the water was lost, as a potential analog to desiccated basins on Mars at the end of the Hesperian1,3,4,47. Our results suggest that hypothetical early microbial communities on early Mars, living with relative abundance of liquid water during the Noachian, would have been forced to adapt to increasingly desiccating surface environments, characterized by extreme conditions derived from the persistent dryness and lack of water availability. Our investigation in Tirez suggest that hypothetical microorganisms at the end of the Hesperian would have needed to evolve strategies similar to those of microorganisms on Earth adapted to living at very low water activity49, to thrive in the progressively desiccating sediments.And the third objective of this investigation was the identification of the lipidic biomarkers left behind by the microbial communities in Tirez, as a guide to searching and identifying the potential leftovers of a hypothetical ancient biosphere on Mars. Lipids (i.e., fatty acids and other biosynthesized hydrocarbons) are structural components of cell membranes bearing recognized higher resistance to degradation relative to other biomolecules, thus with potential to reconstruct paleobiology in a broader temporal scale than more labile molecules50. Our results reinforce the notion that lipidic biomarkers should be preferred targets in the search for extinct and/or extant life on Mars precisely because they are so recalcitrant. More

Castillo, C. P. et al. Agricultural Land Abandonment in the EU within 2015-2030. (Joint Research Centre (Seville site), 2018).van der Zanden, E. H., Verburg, P. H., Schulp, C. J. E. & Verkerk, P. J. Trade-offs of European agricultural abandonment. Land Use Policy 62, 290–301 (2017).Article 

Google Scholar 
Sun, Z. et al. Dietary change in high-income nations alone can lead to substantial double climate dividend. Nat. Food 3, 29–37 (2022).Article 
CAS 

Google Scholar 
Rasmussen, L. V. et al. Social-ecological outcomes of agricultural intensification. Nat. Sustainability 1, 275–282 (2018).Article 

Google Scholar 
Arneth, A. et al. Post-2020 biodiversity targets need to embrace climate change. Proc. Natl. Acad. Sci. 117, 30882–30891 (2020).Article 
CAS 

Google Scholar 
Seddon, N., Turner, B., Berry, P., Chausson, A. & Girardin, C. A. J. Grounding nature-based climate solutions in sound biodiversity science. Nat. Clim. Change 9, 84–87 (2019).Article 

Google Scholar 
Rey Benayas, J. M. & Bullock, J. M. Restoration of biodiversity and ecosystem services on agricultural land. Ecosystems 15, 883–899 (2012).Article 

Google Scholar 
Malhi, Y. et al. The role of large wild animals in climate change mitigation and adaptation. Current Biology 32, R181–R196 (2022).Article 
CAS 

Google Scholar 
Perino, A. et al. Rewilding complex ecosystems. Science 364, eaav5570 (2019).Article 
CAS 

Google Scholar 
Zhou, Y. et al. Limited increases in savanna carbon stocks over decades of fire suppression. Nature 603, 445–449 (2022).Article 
CAS 

Google Scholar 
Anderegg, W. R. L. et al. Climate-driven risks to the climate mitigation potential of forests. Science 368, eaaz7005 (2020).Article 
CAS 

Google Scholar 
Svenning, J.-C. Rewilding should be central to global restoration efforts. One Earth 3, 657–660 (2020).Article 

Google Scholar 
Dandy, N. & Wynne-Jones, S. Rewilding forestry. Forest Policy Econ. 109, 101996 (2019).Article 

Google Scholar 
Reino, L. et al. Does afforestation increase bird nest predation risk in surrounding farmland. Forest Ecol. Manag. 260, 1359–1366 (2010).Article 

Google Scholar 
Pausas, J. G. & Bond, W. J. Alternative biome states in terrestrial ecosystems. Trends Plant Sci. 25, 250–263 (2020).Article 
CAS 

Google Scholar 
Bakker, E. S. et al. Combining paleo-data and modern exclosure experiments to assess the impact of megafauna extinctions on woody vegetation. Proc. Natl. Acad. Sci. 113, 847–855 (2016).Article 
CAS 

Google Scholar 
Johnston, C. M. T. & Radeloff, V. C. Global mitigation potential of carbon stored in harvested wood products. Proceedings of the National Academy of Sciences 116, 14526–14531 (2019).Article 
CAS 

Google Scholar 
Shukla, P. R. et al. Climate Change and Land: an IPCC special report on climate change, desertification, landdegradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems. Intergovernmental Panel on Climate Change (2019).Hong, S. et al. Divergent responses of soil organic carbon to afforestation. Nat. Sustainability 3, 694–700 (2020).Article 

Google Scholar 
Bala, G. et al. Combined climate and carbon-cycle effects of large-scale deforestation. Proc. Natl. Acad. Sci. 104, 6550–6555 (2007).Article 
CAS 

Google Scholar 
Rohatyn, S., Yakir, D., Rotenberg, E. & Carmel, Y. Limited climate change mitigation potential through forestation of the vast dryland regions. Science 377, 1436–1439 (2022).Article 
CAS 

Google Scholar 
Beer, C., Zimov, N., Olofsson, J., Porada, P. & Zimov, S. Protection of permafrost soils from thawing by increasing herbivore density. Sci Rep-Uk 10, 4170 (2020).Article 
CAS 

Google Scholar 
Johnson, C. N. et al. Can trophic rewilding reduce the impact of fire in a more flammable world. Philos. Trans. R. Soc. B: Biological Sci. 373, 20170443 (2018).Article 

Google Scholar 
Kristensen, J. A., Svenning, J.-C., Georgiou, K. & Malhi, Y. Can large herbivores enhance ecosystem carbon persistence? Trends Ecol. Evol. https://doi.org/10.1016/j.tree.2021.09.006 (2021).Granado-Díaz, R., Villanueva, A. J. & Gómez-Limón, J. A. Willingness to accept for rewilding farmland in environmentally sensitive areas. Land Use Policy 116, 106052 (2022).Article 

Google Scholar 
Broughton, R. K. et al. Long-term woodland restoration on lowland farmland through passive rewilding. PloS one 16, e0252466 (2021).Article 
CAS 

Google Scholar 
Carver, S. et al. Guiding principles for rewilding. Conserv. Biology 35, 1882–1893 (2021).Article 

Google Scholar  More

Gartland, L. A., Firth, J. A., Laskowski, K. L., Jeanson, R. & Ioannou, C. C. Sociability as a personality trait in animals: Methods, causes and consequences. Biol. Rev. https://doi.org/10.1111/brv.12823 (2021).Article 

Google Scholar 
Bergmüller, R. & Taborsky, M. Adaptive behavioural syndromes due to strategic niche specialization. BMC Ecol. 7, 12. https://doi.org/10.1186/1472-6785-7-12 (2007).Article 

Google Scholar 
Massen, J. J. M., Sterck, E. H. M. & de Vos, H. Close social associations in animals and humans: Functions and mechanisms of friendship. Behaviour 147, 1379–1412. https://doi.org/10.1163/000579510X528224 (2010).Article 

Google Scholar 
Haller, J., Harold, G., Sandi, C. & Neumann, I. D. Effects of adverse early-life events on aggression and anti-social behaviours in animals and humans. J. Neuroendocrinol. 26, 724–738. https://doi.org/10.1111/jne.12182 (2014).Article 
CAS 

Google Scholar 
Carlson Bruce, A. Early life experiences have complex and long-lasting effects on behavior. Proc. Natl. Acad. Sci. 114, 11571–11573. https://doi.org/10.1073/pnas.1716037114 (2017).Article 
ADS 
CAS 

Google Scholar 
Zablocki-Thomas, P. B. et al. Personality and performance are affected by age and early life parameters in a small primate. Ecol. Evol. 8, 4598–4605. https://doi.org/10.1002/ece3.3833 (2018).Article 

Google Scholar 
Langenhof, M. R. & Komdeur, J. Why and how the early-life environment affects development of coping behaviours. Behav. Ecol. Sociobiol. 72, 34–34. https://doi.org/10.1007/s00265-018-2452-3 (2018).Article 

Google Scholar 
Daros, R. R., Costa, J. H. C., von Keyserlingk, M. A. G., Hötzel, M. J. & Weary, D. M. Separation from the dam causes negative judgement bias in dairy calves. PLoS One 9, e98429. https://doi.org/10.1371/journal.pone.0098429 (2014).Article 
ADS 
CAS 

Google Scholar 
Grześkowiak, ŁM. et al. Impact of early-life events on the susceptibility to Clostridium difficile colonisation and infection in the offspring of the pig. Gut Microbes 10, 251–259. https://doi.org/10.1080/19490976.2018.1518554 (2019).Article 

Google Scholar 
Schmauss, C., Lee-McDermott, Z. & Medina, L. R. Trans-generational effects of early life stress: The role of maternal behavior. Sci. Rep. 4, 4873. https://doi.org/10.1038/srep04873 (2014).Article 
ADS 
CAS 

Google Scholar 
Brask, J. B., Ellis, S. & Croft, D. P. Animal social networks: An introduction for complex systems scientists. J. Complex Netw. 9, cnab001. https://doi.org/10.1093/comnet/cnab001 (2021).Article 

Google Scholar 
Almeling, L., Hammerschmidt, K., Sennhenn-Reulen, H., Freund, A. M. & Fischer, J. Motivational shifts in aging monkeys and the origins of social selectivity. Curr. Biol. 26, 1744–1749. https://doi.org/10.1016/j.cub.2016.04.066 (2016).Article 
CAS 

Google Scholar 
Borgeaud, C., Sosa, S., Sueur, C. & Bshary, R. The influence of demographic variation on social network stability in wild vervet monkeys. Anim. Behav. 134, 155–165. https://doi.org/10.1016/j.anbehav.2017.09.028 (2017).Article 

Google Scholar 
Cantor, M. et al. The importance of individual-to-society feedbacks in animal ecology and evolution. J. Anim. Ecol. 90, 27–44. https://doi.org/10.1111/1365-2656.13336 (2021).Article 

Google Scholar 
Sosa, S., Sueur, C. & Puga-Gonzalez, I. Network measures in animal social network analysis: Their strengths, limits, interpretations and uses. Methods Ecol. Evol. 12, 10–21. https://doi.org/10.1111/2041-210X.13366 (2021).Article 

Google Scholar 
Farine, D. R. & Whitehead, H. Constructing, conducting and interpreting animal social network analysis. J. Anim. Ecol. 84, 1144–1163. https://doi.org/10.1111/1365-2656.12418 (2015).Article 

Google Scholar 
Neethirajan, S. & Kemp, B. Social network analysis in farm animals: Sensor-based approaches. Animals 11, 434. https://doi.org/10.3390/ani11020434 (2021).Article 

Google Scholar 
Whitehead, H. Analyzing Animal Societies. University of Chicago Press, Chicago, IL, USA (2008).
Smith, J. E. & Pinter-Wollman, N. Observing the unwatchable: Integrating automated sensing, naturalistic observations and animal social network analysis in the age of big data. J. Anim. Ecol. 90, 62–75. https://doi.org/10.1111/1365-2656.13362 (2021).Article 

Google Scholar 
Chen, S., Ilany, A., White, B. J., Sanderson, M. W. & Lanzas, C. Spatial-temporal dynamics of high-resolution animal networks: What can we learn from domestic animals?. PLoS One 10, e0129253. https://doi.org/10.1371/journal.pone.0129253 (2015).Article 
CAS 

Google Scholar 
Atton, N., Galef, B. J., Hoppitt, W., Webster, M. M. & Laland, K. N. Familiarity affects social network structure and discovery of prey patch locations in foraging stickleback shoals. Proc. R. Soc. B Biol. Sci. 281, 20140579. https://doi.org/10.1098/rspb.2014.0579 (2014).Article 
CAS 

Google Scholar 
Ilany, A. & Akçay, E. Personality and social networks: A generative model approach. Integr. Comp. Biol. 56, 1197–1205. https://doi.org/10.1093/icb/icw068 (2016).Article 

Google Scholar 
Romano, V. et al. Modeling infection transmission in primate networks to predict centrality-based risk. Am. J. Primatol. 78, 767–779. https://doi.org/10.1002/ajp.22542 (2016).Article 

Google Scholar 
Ren, K., Bernes, G., Hetta, M. & Karlsson, J. Tracking and analysing social interactions in dairy cattle with real-time locating system and machine learning. J. Syst. Archit. 116, 102139. https://doi.org/10.1016/j.sysarc.2021.102139 (2021).Article 

Google Scholar 
Boyland, N. K., Mlynski, D. T., James, R., Brent, L. J. N. & Croft, D. P. The social network structure of a dynamic group of dairy cows: From individual to group level patterns. Appl. Anim. Behav. Sci. 174, 1–10. https://doi.org/10.1016/j.applanim.2015.11.016 (2016).Article 

Google Scholar 
Chopra, K. et al. Proximity interactions in a permanently housed dairy herd: Network structure, consistency, and individual differences. Front. Vet. Sci. 7, 583715 (2020).Article 

Google Scholar 
Šárová, R. et al. Pay respect to the elders: Age, more than body mass, determines dominance in female beef cattle. Anim. Behav. 86, 1315–1323. https://doi.org/10.1016/j.anbehav.2013.10.002 (2013).Article 

Google Scholar 
Foris, B., Haas, H. G., Langbein, J. & Melzer, N. Familiarity influences social networks in dairy cows after regrouping. J. Dairy Sci. 104, 3485–3494. https://doi.org/10.3168/jds.2020-18896 (2021).Article 
CAS 

Google Scholar 
Foris, B., Zebunke, M., Langbein, J. & Melzer, N. Comprehensive analysis of affiliative and agonistic social networks in lactating dairy cattle groups. Appl. Anim. Behav. Sci. 210, 60–67. https://doi.org/10.1016/j.applanim.2018.10.016 (2019).Article 

Google Scholar 
de Freslon, I., Martínez-López, B., Belkhiria, J., Strappini, A. & Monti, G. Use of social network analysis to improve the understanding of social behaviour in dairy cattle and its impact on disease transmission. Appl. Anim. Behav. Sci. 213, 47–54. https://doi.org/10.1016/j.applanim.2019.01.006 (2019).Article 

Google Scholar 
Bolt, S. L., Boyland, N. K., Mlynski, D. T., James, R. & Croft, D. P. Pair housing of dairy calves and age at pairing: Effects on weaning stress, health production and social networks. PLoS One 12, e0166926. https://doi.org/10.1371/journal.pone.0166926 (2017).Article 
CAS 

Google Scholar 
Koene, P. & Ipema, B. Social networks and welfare in future animal management. Animals (Basel) 4, 93–118. https://doi.org/10.3390/ani4010093 (2014).Article 

Google Scholar 
Raussi, S. et al. The formation of preferential relationships at early age in cattle. Behav. Proc. 84, 726–731. https://doi.org/10.1016/j.beproc.2010.05.005 (2010).Article 

Google Scholar 
Weary, D. M., Jasper, J. & Hötzel, M. J. Understanding weaning distress. Appl. Anim. Behav. Sci. 110, 24–41. https://doi.org/10.1016/j.applanim.2007.03.025 (2008).Article 

Google Scholar 
Lopes, P. C., Block, P. & König, B. Infection-induced behavioural changes reduce connectivity and the potential for disease spread in wild mice contact networks. Sci. Rep. 6, 31790. https://doi.org/10.1038/srep31790 (2016).Article 
ADS 
CAS 

Google Scholar 
Ripperger, S. P., Stockmaier, S. & Carter, G. G. Tracking sickness effects on social encounters via continuous proximity sensing in wild vampire bats. Behav. Ecol. 31, 1296–1302. https://doi.org/10.1093/beheco/araa111 (2020).Article 

Google Scholar 
McGuirk, S. M. & Peek, S. F. Timely diagnosis of dairy calf respiratory disease using a standardized scoring system. Anim. Health Res. Rev. 15, 145–147. https://doi.org/10.1017/s1466252314000267 (2014).Article 

Google Scholar 
Callan, R. J. & Garry, F. B. Biosecurity and bovine respiratory disease. Vet. Clin. N. Am. Food Anim. Pract. 18, 57–77. https://doi.org/10.1016/S0749-0720(02)00004-X (2002).Article 

Google Scholar 
Sewio. Tag Leonardo iMU/Personal. https://docs.sewio.net/docs/tag-leonardo-imu-personal-30146967.html (2022).Barker, Z. E. et al. Use of novel sensors combining local positioning and acceleration to measure feeding behavior differences associated with lameness in dairy cattle. J. Dairy Sci. 101, 6310–6321. https://doi.org/10.3168/jds.2016-12172 (2018).Article 
CAS 

Google Scholar 
Team, R. C. R: A Language and Environment for Statistical Computing.Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: The MCMCglmm R package. J. Stat. Softw. 33, 1–22. https://doi.org/10.18637/jss.v033.i02 (2010).Article 

Google Scholar 
Franks, D. W., Weiss, M. N., Silk, M. J., Perryman, R. J. Y. & Croft, D. P. Calculating effect sizes in animal social network analysis. Methods Ecol. Evol. 12, 33–41. https://doi.org/10.1111/2041-210X.13429 (2021).Article 

Google Scholar 
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
Bell, D. C., Atkinson, J. S. & Carlson, J. W. Centrality measures for disease transmission networks. Soc. Netw. 21, 1–21. https://doi.org/10.1016/S0378-8733(98)00010-0 (1999).Article 

Google Scholar 
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B (Methodol.) 57, 289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x (1995).Article 
MathSciNet 
MATH 

Google Scholar 
PercieduSert, N. et al. Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 2.0. PLoS Biol. 18, e3000411. https://doi.org/10.1371/journal.pbio.3000411 (2020).Article 
CAS 

Google Scholar 
Hulbert, L. E. & Moisá, S. J. Stress, immunity, and the management of calves. J. Dairy Sci. 99, 3199–3216. https://doi.org/10.3168/jds.2015-10198 (2016).Article 
CAS 

Google Scholar 
Sweeney, B. C., Rushen, J., Weary, D. M. & de Passillé, A. M. Duration of weaning, starter intake, and weight gain of dairy calves fed large amounts of milk. J. Dairy Sci. 93, 148–152. https://doi.org/10.3168/jds.2009-2427 (2010).Article 
CAS 

Google Scholar 
Rault, J.-L. Friends with benefits: Social support and its relevance for farm animal welfare. Appl. Anim. Behav. Sci. 136, 1–14. https://doi.org/10.1016/j.applanim.2011.10.002 (2012).Article 

Google Scholar 
Ishiwata, T., Kilgour, R. J., Uetake, K., Eguchi, Y. & Tanaka, T. Choice of attractive conditions by beef cattle in a Y-maze just after release from restraint. J. Anim. Sci. 85, 1080–1085. https://doi.org/10.2527/jas.2006-405 (2007).Article 
CAS 

Google Scholar 
Ede, T., von Keyserlingk, M. A. G. & Weary, D. M. Social approach and place aversion in relation to conspecific pain in dairy calves. PLoS One 15, e0232897. https://doi.org/10.1371/journal.pone.0232897 (2020).Article 
CAS 

Google Scholar 
Cantor, M. C., Renaud, D. L., Neave, H. W. & Costa, J. H. C. Feeding behavior and activity levels are associated with recovery status in dairy calves treated with antimicrobials for Bovine Respiratory Disease. Sci. Rep. 12, 4854. https://doi.org/10.1038/s41598-022-08131-1 (2022).Article 
ADS 
CAS 

Google Scholar 
Kappeler, P. M., Cremer, S. & Nunn, C. L. Sociality and health: Impacts of sociality on disease susceptibility and transmission in animal and human societies. Philos. Trans. R. Soc. Lond. B Biol. Sci. 370, 20140116. https://doi.org/10.1098/rstb.2014.0116 (2015).Article 

Google Scholar 
Ezenwa, V. O. et al. Host behaviour–parasite feedback: An essential link between animal behaviour and disease ecology. Proc. R. Soc. B Biol. Sci. 283, 20153078. https://doi.org/10.1098/rspb.2015.3078 (2016).Article 
CAS 

Google Scholar 
Klein, S. L. Parasite manipulation of the proximate mechanisms that mediate social behavior in vertebrates. Physiol. Behav. 79, 441–449. https://doi.org/10.1016/S0031-9384(03)00163-X (2003).Article 
ADS 
CAS 

Google Scholar 
LavistaFerres, J. M. et al. Social connectedness and movements among communities of giraffes vary by sex and age class. Anim. Behav. 180, 315–328. https://doi.org/10.1016/j.anbehav.2021.08.008 (2021).Article 

Google Scholar 
VanderWaal, K. L., Wang, H., McCowan, B., Fushing, H. & Isbell, L. A. Multilevel social organization and space use in reticulated giraffe (Giraffa camelopardalis). Behav. Ecol. 25, 17–26. https://doi.org/10.1093/beheco/art061 (2014).Article 

Google Scholar 
Sato, S. & Wood-Gush, D. G. Observations on creche behaviour in suckler calves. Behav. Process. 15, 333–343. https://doi.org/10.1016/0376-6357(87)90017-9 (1987).Article 
CAS 

Google Scholar 
Lecorps, B., Kappel, S., Weary, D. M. & von Keyserlingk, M. A. G. Social proximity in dairy calves is affected by differences in pessimism. PLoS One 14, e0223746. https://doi.org/10.1371/journal.pone.0223746 (2019).Article 
CAS 

Google Scholar 
Carslake, C., Occhiuto, F., Vázquez-Diosdado, J. A. & Kaler, J. Repeatability and predictability of calf feeding behaviors—Quantifying between- and within-individual variation for precision livestock farming. Front. Vet. Sci. 9, 827124 (2022).Article 

Google Scholar 
Occhiuto, F., Vázquez-Diosdado, J. A., Carslake, C. & Kaler, J. Personality and predictability in farmed calves using movement and space-use behaviours quantified by ultra-wideband sensors. R. Soc. Open Sci. 9, 212019. https://doi.org/10.1098/rsos.212019 (2022).Article 
ADS 

Google Scholar 
Carslake, C., Occhiuto, F., Vázquez-Diosdado, J. A. & Kaler, J. Indication of a personality trait in dairy calves and its link to weight gain through automatically collected feeding behaviours. Sci. Rep. 12, 19425. https://doi.org/10.1038/s41598-022-24076-x (2022).Article 
ADS 
CAS 

Google Scholar 
Planas-Sitjà, I., Deneubourg, J.-L. & Cronin, A. L. Variation in personality can substitute for social feedback in coordinated animal movements. Commun. Biol. 4, 469. https://doi.org/10.1038/s42003-021-01991-9 (2021).Article 

Google Scholar 
Stockmaier, S., Bolnick, D. I., Page, R. A. & Carter, G. G. Sickness effects on social interactions depend on the type of behaviour and relationship. J. Anim. Ecol. 89, 1387–1394. https://doi.org/10.1111/1365-2656.13193 (2020).Article 

Google Scholar 
Smith, L. A., Swain, D. L., Innocent, G. T., Nevison, I. & Hutchings, M. R. Considering appropriate replication in the design of animal social network studies. Sci. Rep. 9, 7208. https://doi.org/10.1038/s41598-019-43764-9 (2019).Article 
ADS 
CAS 

Google Scholar 
Shin, D. H., Kang, H. M. & Seo, S. Social relationships enhance the time spent eating and intake of a novel diet in pregnant Hanwoo (Bos taurus coreanae) heifers. PeerJ 5, e3329. https://doi.org/10.7717/peerj.3329 (2017).Article 
CAS 

Google Scholar  More

Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).ADS 
CAS 

Google Scholar 
Spehn, E. M., Rudmann-Maurer, K. & Körner, C. Mountain biodiversity. Plant Ecol. Divers. 4, 301–302 (2011).
Google Scholar 
Körner, C. et al. A global inventory of mountains for bio-geographical applications. Alp Bot. 127, 1–15 (2017).
Google Scholar 
Hoorn, C. et al. (eds) Mountains, Climate and Biodiversity (Wiley, 2018).
Google Scholar 
Huang, S., Meijers, M. J. M., Eyres, A., Mulch, A. & Fritz, S. A. Unravelling the history of biodiversity in mountain ranges through integrating geology and biogeography. J. Biogeogr. 46, 1777–1791 (2019).
Google Scholar 
Perrigo, A., Hoorn, C. & Antonelli, A. Why mountains matter for biodiversity. J. Biogeogr. 47, 315–325 (2020).
Google Scholar 
Rahbek, C. et al. Humboldt’s enigma: What causes global patterns of mountain biodiversity?. Science 365, 1108–1113 (2019).ADS 
CAS 

Google Scholar 
Antonelli, A. et al. Amazonia is the primary source of Neotropical biodiversity. Proc. Natl. Acad. Sci. USA 115, 6034–6039 (2018).ADS 
CAS 

Google Scholar 
Merckx, V. S. F. T. et al. Evolution of endemism on a young tropical mountain. Nature 524, 347–350 (2015).ADS 
CAS 

Google Scholar 
Fjeldsa, J., Bowie, R. C. K. & Rahbek, C. The role of mountain ranges in the diversification of birds. Annu. Rev. Ecol. Evol. Syst. 43, 249–265 (2012).
Google Scholar 
Badgley, C. et al. Biodiversity and topographic complexity: Modern and geohistorical perspectives. Trends Ecol. Evol. 32, 211–226 (2017).
Google Scholar 
Körner, C. Mountain biodiversity, its causes and function. Ambio 33, 11 (2004).
Google Scholar 
Antonelli, A. et al. An engine for global plant diversity: Highest evolutionary turnover and emigration in the American tropics. Front. Genet. 6, 130 (2015).
Google Scholar 
Chazot, N. et al. Into the Andes: Multiple independent colonizations drive montane diversity in the Neotropical clearwing butterflies Godyridina. Mol. Ecol. 25, 5765–5784 (2016).
Google Scholar 
Stebbins, G. L. Flowering Plants: Evolution Above the Species Level (Belknap Press of Harvard University Press, 1974).
Google Scholar 
Rangel, T. F. et al. Modeling the ecology and evolution of biodiversity: Biogeographical cradles, museums, and graves. Science 361, 5452 (2018).
Google Scholar 
Chapman, F. M. The relationships and distribution of the warblers of the genus Compsothlypis: A contribution to the study of the origin of Andean bird life. Auk 42(2), 193–208 (1925).
Google Scholar 
Endler, J. A. Geographic variation, speciation, and clines. Genet. Res. 1, b1–b3 (1978).
Google Scholar 
Baert, L. & Maelfait, J. P. A contribution to the knowledge of the spider fauna of Galápagos (Ecuador). Bull. Koninklijk Belg. Instit. Nat. Entomol. 56, 93–123 (1986).
Google Scholar 
Desender, K., Baert, L. & Maelfait, J. P. Distribution and speciation of carabid beetles in the Galápagos Archipelago (Ecuador). Bull. Inst. R. Sci. Natl. Belg. 62, 57–65 (1992).
Google Scholar 
Patton, J. L. & Smith, M. F. mtDNA phylogeny of Andean mice: A test of diversification across ecological gradients. Evolution 46, 174 (1992).CAS 

Google Scholar 
Nevado, B., Contreras-Ortiz, N., Hughes, C. & Filatov, D. A. Pleistocene glacial cycles drive isolation, gene flow and speciation in the high-elevation Andes. New Phytol 219, 779–793 (2018).
Google Scholar 
Winger, B. M. & Bates, J. M. The tempo of trait divergence in geographic isolation: Avian speciation across the Marañon Valley of Peru. Evolution 69, 772–787 (2015).
Google Scholar 
Hazzi, N. A., Moreno, J. S., Ortiz-Movliav, C. & Palacio, R. D. Biogeographic regions and events of isolation and diversification of the endemic biota of the tropical Andes. Proc. Natl. Acad. Sci. USA. 115, 7985–7990 (2018).ADS 
CAS 

Google Scholar 
Palma, R. E., Marquet, P. A. & Boric-Bargetto, D. Inter-and intraspecific phylogeography of small mammals in the Atacama Desert and adjacent areas of northern Chile. J. Biogeogr. 32(11), 1931–1941 (2005).
Google Scholar 
Beckman, E. J. & Witt, C. C. Phylogeny and biogeography of the New World siskins and goldfinches: Rapid, recent diversification in the Central Andes. Mol. Phylogenet. Evol. 87, 28–45 (2015).
Google Scholar 
Drummond, C. S., Eastwood, R. J., Miotto, S. T. S. & Hughes, C. E. Multiple continental radiations and correlates of diversification in Lupinus (Leguminosae): Testing for key innovation with incomplete taxon sampling. Syst. Biol. 61, 443–460 (2012).
Google Scholar 
Hutter, C. R., Lambert, S. M. & Wiens, J. J. Rapid diversification and time explain amphibian richness at different scales in the tropical Andes, Earth’s most biodiverse hotspot. Am. Nat. 190, 828–843 (2017).
Google Scholar 
Toussaint, E. F. A. et al. Flight over the Proto-Caribbean seaway: Phylogeny and macroevolution of Neotropical Anaeini leafwing butterflies. Mol. Phylogenet. Evol. 137, 86–103 (2019).
Google Scholar 
Acevedo, A. A. Historical biogeography, phylogenetic diversity and evolution of body size in Pristimantis, the world’s most diverse amphibian genus. Doctoral thesis, Fac. Ciencias Biológicas, Pontificia Universidad Católica de Chile (2021).Lagomarsino, L. P., Condamine, F. L., Antonelli, A., Mulch, A. & Davis, C. C. The abiotic and biotic drivers of rapid diversification in A ndean bellflowers (Campanulaceae). New Phytol. 210, 1430–1442 (2016).
Google Scholar 
Fjeldsa, J. & Rahbek, C. Diversification of tanagers, a species rich bird group, from lowlands to montane regions of South America. Integr. Comp. Biol. 46(1), 72–81 (2006).CAS 

Google Scholar 
Struwe, L., Haag, S., Heiberg, E. & Grant, J. R. Andean speciation and vicariance in Neotropical Macrocarpaea (Gentianaceae-Helieae). Ann. Mol. Bot. Gard. 96, 450–469 (2009).
Google Scholar 
Hutter, C. R., Guayasamin, J. M. & Wiens, J. J. Explaining Andean megadiversity: The evolutionary and ecological causes of glassfrog elevational richness patterns. Ecol. Lett. 16, 1135–1144 (2013).
Google Scholar 
Santos, J. C. et al. Amazonian amphibian diversity is primarily derived from late Miocene Andean lineages. PLoS Biol. 7, e1000056 (2009).
Google Scholar 
Luebert, F. & Weigend, M. Phylogenetic insights into Andean plant diversification. Front. Ecol. Evol. 2, 27 (2014).
Google Scholar 
Chazot, N. et al. Renewed diversification following Miocene landscape turnover in a Neotropical butterfly radiation. Glob. Ecol. Biogeogr. 28, 1118–1132 (2019).
Google Scholar 
Esquerré, D., Brennan, I. G., Catullo, R. A., Torres-Pérez, F. & Keogh, J. S. How mountains shape biodiversity: The role of the Andes in biogeography, diversification, and reproductive biology in South America’s most species-rich lizard radiation (Squamata: Liolaemidae). Evolution 73, 214–230 (2019).
Google Scholar 
Garzione, C. N. et al. Rise of the Andes. Science 320, 1304–1307 (2008).ADS 
CAS 

Google Scholar 
Hoorn, C. et al. Amazonia through time: Andean uplift, climate change, landscape evolution, and biodiversity. Science 330, 927–931 (2010).ADS 
CAS 

Google Scholar 
Brumfield, R. T. & Edwards, S. V. Evolution into and out of the Andes: A Bayesian analysis of historical diversification in Thamnophilus antshrikes. Evolution 61, 346–367 (2007).CAS 

Google Scholar 
Pennington, R. T. et al. Contrasting plant diversification histories within the Andean biodiversity hotspot. Proc. Natl. Acad. Sci. USA. 107, 13783–13787 (2010).ADS 
CAS 

Google Scholar 
Antonelli, A. & Sanmartín, I. Why are there so many plant species in the Neotropics?. Taxon 60, 403–414 (2011).
Google Scholar 
Hughes, C. & Eastwood, R. Island radiation on a continental scale: Exceptional rates of plant diversification after uplift of the Andes. Proc. Natl. Acad. Sci. USA. 103, 10334–10339 (2006).ADS 
CAS 

Google Scholar 
Madriñán, S., Cortés, A. J. & Richardson, J. E. Páramo is the world’s fastest evolving and coolest biodiversity hotspot. Front. Genet. 4, 192 (2013).
Google Scholar 
Upham, N. S., Ojala-Barbour, R., Brito, M. J., Velazco, P. M. & Patterson, B. D. Transitions between Andean and Amazonian centers of endemism in the radiation of some arboreal rodents. BMC Evol. Biol. 13, 191 (2013).
Google Scholar 
Hughes, C. E. & Atchison, G. W. The ubiquity of alpine plant radiations: From the Andes to the Hengduan mountains. New Phytol. 207, 275–282 (2015).
Google Scholar 
Givnish, T. J. et al. Adaptive radiation, correlated and contingent evolution, and net species diversification in Bromeliaceae. Mol. Phylogenet. Evol. 71, 55–78 (2014).
Google Scholar 
Horton, B. K. Sedimentary record of Andean Mountain building. Earth Sci. Rev. 178, 279–309 (2018).ADS 
CAS 

Google Scholar 
Gianni, G. M. et al. Northward propagation of Andean genesis: Insights from Early Cretaceous synorogenic deposits in the Aysén-Río Mayo basin. Gondwana Res. 77, 238–259 (2020).ADS 
CAS 

Google Scholar 
Boschman, L. M. Andean Mountain building since the Late Cretaceous: A paleoelevation reconstruction. Earth Sci. Rev. 220, 103640 (2021).
Google Scholar 
Gentry, A. H. Patterns of neotropical plant species diversity. Evol. Biol. 15, 1–84 (1982).
Google Scholar 
Gregory-Wodzicki, K. M. Uplift history of the Central and Northern Andes: A review. Geol. Soc. Am. Bull. 112, 1091–1105 (2000).ADS 

Google Scholar 
Pérez-Escobar, O. A. et al. Recent origin and rapid speciation of Neotropical orchids in the world’s richest plant biodiversity hotspot. New Phytol. 215(2), 891–905 (2017).
Google Scholar 
Alhajeri, B. H., Schenk, J. J. & Steppan, S. J. Ecomorphological diversification following continental colonization in muroid rodents (Rodentia: Muroidea). Biol. J. Linn. Soc. 117, 463–481 (2016).
Google Scholar 
Burgin, C. J., Colella, J. P., Kahn, P. L. & Upham, N. S. How many species of mammals are there?. J. Mammal. 99, 1–14 (2018).
Google Scholar 
Parada, A., Pardiñas, U. F. J., Salazar-Bravo, J., D’Elía, G. & Palma, R. E. Dating an impressive Neotropical radiation: Molecular time estimates for the Sigmodontinae (Rodentia) provide insights into its historical biogeography. Mol. Phylogenet. Evol. 66, 960–968 (2013).
Google Scholar 
Schenk, J. J. & Steppan, S. J. The role of geography in adaptive radiation. Am. Nat. 192, 415–431 (2018).
Google Scholar 
Pardiñas, U. F. J. et al. Morphological disparity in a hyperdiverse mammal clade: A new morphotype and tribe of Neotropical cricetids. Zool. J. Linn. Soc. 196, 1013–1038 (2022).
Google Scholar 
Reig, O. A. Distribuição geográfica e história evolutiva dos roedores muroideos sulamericanos (Cricetidae: Sigmodontinae). Rev. Bras. Genét. 7, 333–365 (1984).
Google Scholar 
Reig, O. A. Diversity Patterns and Differentiation of High Andean Rodents. High Altitude Tropical Biogeography 404–438 (Oxford University Press, 1986).
Google Scholar 
Maestri, R., Upham, N. S. & Patterson, B. D. Tracing the diversification history of a Neogene rodent invasion into South America. Ecography 42, 683–695 (2019).
Google Scholar 
Engel, S. R., Hogan, K. M., Taylor, J. F. & Davis, S. K. Molecular systematics and paleobiogeography of the South American sigmodontine rodents. Mol. Biol. Evol. 15(1), 35–49 (1998).CAS 

Google Scholar 
Parada, A., D’Elía, G. & Palma, R. E. The influence of ecological and geographical context in the radiation of Neotropical sigmodontine rodents. BMC Evol. Biol. 15(1), 1–17 (2015).
Google Scholar 
Leite, R. N. et al. In the wake of invasion: Tracing the historical biogeography of the South American cricetid radiation (Rodentia, Sigmodontinae). PLoS ONE 9, e100687 (2014).ADS 

Google Scholar 
Vilela, J. F., Mello, B., Voloch, C. M. & Schrago, C. G. Sigmodontine rodents diversified in South America prior to the complete rise of the Panamanian Isthmus. J. Zool. Syst. Evol. Res. 52, 249–256 (2014).
Google Scholar 
Ronez, C., Martin, R. A., Kelly, T. S., Barbière, F. & Pardiñas, U. F. J. A brief critical review of sigmodontine rodent origins, with emphasis on paleontological data. Mastozool. Neotrop 28, 001–026 (2021).
Google Scholar 
Maestri, R. & Patterson, B. D. Patterns of species richness and turnover for the South American Rodent Fauna. PLoS ONE 11, e0151895 (2016).
Google Scholar 
Smith, M. F. & Patton, J. L. Phylogenetic relationships and the radiation of sigmodontine rodents in South America: Evidence from cytochrome b. J. Mamm. Evol. 6(2), 89–128 (1999).
Google Scholar 
Udvardy, M. D. & Udvardy, M. D. F. A Classification of the Biogeographical Provinces of the World Vol. 8 (International Union for Conservation of Nature and Natural Resources, 1975).
Google Scholar 
Olson, D. M. et al. Terrestrial ecoregions of the world: A new map of life on earth. Bioscience 51, 933 (2001).
Google Scholar 
Kreft, H. & Jetz, W. A framework for delineating biogeographical regions based on species distributions: Global quantitative biogeographical regionalizations. J. Biogeogr. 37, 2029–2053 (2010).
Google Scholar 
Patton, J. L. et al. (eds) Mammals of South America, Volume 2: Rodents (University of Chicago Press, 2015).
Google Scholar 
Marsh, C. J. et al. Expert range maps of global mammal distributions harmonised to three taxonomic authorities. J. Biogeogr. 49, 979–992 (2022).
Google Scholar 
Edgar, R. C. MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).CAS 

Google Scholar 
Tamura, K., Stecher, G. & Kumar, S. MEGA11: Molecular evolutionary genetics analysis version 11. Mol. Biol. Evol. 38, 3022–3027 (2021).CAS 

Google Scholar 
Parada, A., Hanson, J. & D’Eiía, G. Ultraconserved elements improve the resolution of difficult nodes within the rapid radiation of neotropical Sigmodontine Rodents (Cricetidae: Sigmodontinae). Syst. Biol. 70, 1090–1100 (2021).
Google Scholar 
Steppan, S. J. & Schenk, J. J. Muroid rodent phylogenetics: 900-species tree reveals increasing diversification rates. PLoS ONE 12, e0183070 (2017).
Google Scholar 
Gonçalves, P. R. et al. Unraveling deep branches of the Sigmodontinae Tree (Rodentia: Cricetidae) in Eastern South America. J Mammal Evol 27, 139–160 (2020).
Google Scholar 
Steppan, S. J., Adkins, R. M. & Anderson, J. Phylogeny and divergence-date estimates of rapid radiations in muroid rodents based on multiple nuclear genes. Syst. Biol. 53, 533–553 (2004).
Google Scholar 
Nguyen, L.-T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: A fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).CAS 

Google Scholar 
Chernomor, O., von Haeseler, A. & Minh, B. Q. Terrace aware data structure for phylogenomic inference from supermatrices. Syst. Biol. 65, 997–1008 (2016).
Google Scholar 
Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K. F., von Haeseler, A. & Jermiin, L. S. ModelFinder: Fast model selection for accurate phylogenetic estimates. Nat. Methods 14, 587–589 (2017).CAS 

Google Scholar 
Bouckaert, R. et al. BEAST 2: A software platform for bayesian evolutionary analysis. PLoS Comput. Biol. 10, e1003537 (2014).
Google Scholar 
Gavryushkina, A., Welch, D., Stadler, T. & Drummond, A. J. Bayesian inference of sampled ancestor trees for epidemiology and fossil calibration. PLoS Comput. Biol. 10, e1003919 (2014).ADS 

Google Scholar 
Heath, T. A., & Moore, B. R. Bayesian inference of species divergence times. Bayesian phylogenetics: Methods, algorithms, and applications, 277–318 (2014).Douglas, J., Zhang, R. & Bouckaert, R. Adaptive dating and fast proposals: Revisiting the phylogenetic relaxed clock model. PLoS Comput Biol 17, e1008322 (2021).ADS 
CAS 

Google Scholar 
Bouckaert, R. R. & Drummond, A. J. bModelTest: Bayesian phylogenetic site model averaging and model comparison. BMC Evol. Biol. 17, 42 (2017).
Google Scholar 
Barido-Sottani, J., Aguirre-Fernández, G., Hopkins, M. J., Stadler, T. & Warnock, R. Ignoring stratigraphic age uncertainty leads to erroneous estimates of species divergence times under the fossilized birth–death process. Proc. R. Soc. B. 286, 20190685 (2019).
Google Scholar 
Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarization in Bayesian phylogenetics using tracer 1.7. Syst. Biol. 67, 901–904 (2018).CAS 

Google Scholar 
Maliet, O., Hartig, F. & Morlon, H. A model with many small shifts for estimating species-specific diversification rates. Nat. Ecol. Evol. 3, 1086–1092 (2019).
Google Scholar 
Maliet, O. & Morlon, H. Fast and accurate estimation of species-specific diversification rates using data augmentation. Syst. Biol. 71, 353–366 (2022).CAS 

Google Scholar 
Gelman, A. & Rubin, D. B. A single series from the Gibbs sampler provides a false sense of security. Bayesian Stat. 4(1), 625–631 (1992).
Google Scholar 
Rabosky, D. L. et al. BAMMtools: An R package for the analysis of evolutionary dynamics on phylogenetic trees. Methods Ecol. Evol. 5, 701–707 (2014).
Google Scholar 
Ree, R. H., Moore, B. R., Webb, C. O. & Donoghue, M. J. A likelihood framework for inferring the evolution of geographic range on phylogenetic trees. Evolution 59, 2299–2311 (2005).
Google Scholar 
Ree, R. H. & Sanmartín, I. Conceptual and statistical problems with the DEC +J model of founder-event speciation and its comparison with DEC via model selection. J. Biogeogr. 45, 741–749 (2018).
Google Scholar 
Matzke, N. J. Model selection in historical biogeography reveals that founder-event speciation is a crucial process in Island Clades. Syst. Biol. 63, 951–970 (2014).
Google Scholar 
Matzke, N. J. Statistical comparison of DEC and DEC +J is identical to comparison of two ClaSSE submodels, and is therefore valid. J. Biogeogr. 49, 1805–1824 (2022).
Google Scholar 
Matzke, N. J. Probabilistic Historical Biogeography: New Models for Founder-Event Speciation, Imperfect Detection, and Fossils Allow Improved Accuracy and Model-Testing (University of California, 2013).
Google Scholar 
Tripp, E. A. & McDade, L. A. A rich fossil record yields calibrated phylogeny for acanthaceae (lamiales) and evidence for marked biases in timing and directionality of intercontinental disjunctions. Syst. Biol. 63, 660–684 (2014).
Google Scholar 
Matos-Maraví, P. et al. Mesoamerica is a cradle and the Atlantic Forest is a museum of Neotropical butterfly diversity: Insights from the evolution and biogeography of Brassolini (Lepidoptera: Nymphalidae). Biol. J. Lin. Soc. 133, 704–724 (2021).
Google Scholar 
Caetano, D. S., O’Meara, B. C. & Beaulieu, J. M. Hidden state models improve state-dependent diversification approaches, including biogeographical models. Evolution 72, 2308–2324 (2018).
Google Scholar 
Beaulieu, J. M. & O’Meara, B. C. Detecting hidden diversification shifts in models of trait-dependent speciation and extinction. Syst. Biol. 65, 583–601 (2016).
Google Scholar 
Schenk, J. J., Rowe, K. C. & Steppan, S. J. Ecological opportunity and incumbency in the diversification of repeated continental colonizations by muroid rodents. Syst. Biol. 62(6), 837–864 (2013).
Google Scholar 
Percequillo, A. R. et al. Tempo and mode of evolution of oryzomyine rodents (Rodentia, Cricetidae, Sigmodontinae): A phylogenomic approach. Mol. Phylogenet. Evol. 159, 107120 (2021).
Google Scholar 
Pacheco, V. R., Patton, J. L. & D’elía, G. Tribe Thomasomyini Steadman and Ray, 1982. In Mammals of South America Vol. 2 (eds Patton, J. L. et al.) 571–574 (The University of Chicago Press, 2015).
Google Scholar 
Salazar-Bravo, J., Pardiñas, U. F., Zeballos, H., & Teta, P. Description of a new tribe of sigmodontine rodents (Cricetidae: Sigmodontinae) with an updated summary of valid tribes and their generic contents. Museum of Texas Tech University 338 (2016).Pardiñas, U. F. et al. Morphological disparity in a hyperdiverse mammal clade: A new morphotype and tribe of Neotropical cricetids. Zool. J. Linnean Soc. 196, 1013–1038 (2022).
Google Scholar 
Pardiñas, U. F., Voglino, D. & Galliari, C. A. Miscellany on Bibimys (Rodentia, Sigmodontinae), a unique akodontine cricetid. Mastozool. Neotrop. 24(1), 241–250 (2017).
Google Scholar 
Salazar-Bravo, J., Pardiñas, U. F. J. & D’Elía, G. A phylogenetic appraisal of Sigmodontinae (Rodentia, Cricetidae) with emphasis on phyllotine genera: Systematics and biogeography. Zool. Scr. 42, 250–261 (2013).
Google Scholar 
Pardiñas, U. F. J., Lessa, G., Teta, P., Salazar-Bravo, J. & Câmara, E. M. V. C. A new genus of sigmodontine rodent from eastern Brazil and the origin of the tribe Phyllotini. J. Mamm. 95, 201–215 (2014).
Google Scholar 
Edler, D., Guedes, T., Zizka, A., Rosvall, M. & Antonelli, A. Infomap bioregions: Interactive mapping of biogeographical regions from species distributions. Syst. Biol. 1, 087 (2016).
Google Scholar 
Johnson, T. C. et al. Late pleistocene desiccation of lake victoria and rapid evolution of cichlid fishes. Science 273, 1091–1093 (1996).ADS 
CAS 

Google Scholar 
Azevedo, J. A. R. et al. Museums and cradles of diversity are geographically coincident for narrowly distributed Neotropical snakes. Ecography 43, 328–339 (2020).
Google Scholar 
Rosauer, D. F. & Jetz, W. Phylogenetic endemism in terrestrial mammals: Mammal phylogenetic endemism. Glob. Ecol. Biogeogr. 24, 168–179 (2015).
Google Scholar 
Peyton, B. Ecology, distribution, and food habits of spectacled bears, Tremarctos ornatus, in Peru. J. Mammal. 61, 639–652 (1980).
Google Scholar 
Patterson, B. D., Solari, S. & Velazco, P. M. The role of the Andes in the diversification and biogeography of Neotropical mammals. In Bones, Clones, and Biomes: The History and Geography of Recent Neotropical Mammals (eds Patterson, B. D. & Costa, L. P.) (Springer, 2012).
Google Scholar 
Tribe, C. J. The Neotropical Rodent Genus’ Rhipidomys’(Cricetidae: Sigmodontinae): A Taxonomic Revision (University of London, 1996).
Google Scholar 
Percequillo, A. R. Sistemática de Oryzomys Baird, 1858: Definição dos Grupos de Espécies e Revisão do Grupo Albigularis (Rodentia, Sigmodontinae) (Doctoral dissertation, Tese de Doutorado) (Universidade de São Paulo, 2003).
Google Scholar 
Brito, J. et al. A new genus of oryzomyine rodents (Cricetidae, Sigmodontinae) with three new species from montane cloud forests, western Andean cordillera of Colombia and Ecuador. PeerJ 8, e10247 (2020).
Google Scholar 
Valencia-Pacheco, E., Avaria-Llautureo, J., Munoz-Escobar, C., Boric-Bargetto, D. & Hernandez, C. E. Geographic patterns of richness distribution of rodents species from the Oryzomyini tribe (Rodentia: Sigmodontinae) in South America: Evaluating the importance of colonization and extinction processes. Rev. Chil. Hist. Nat. 84(3), 365–377 (2011).
Google Scholar 
Pine, R. H., Timm, R. M. & Weksler, M. A newly recognized clade of trans-Andean Oryzomyini (Rodentia: Cricetidae), with description of a new genus. J. Mammal. 93(3), 851–870 (2012).
Google Scholar 
Prado, J. R. & Percequillo, A. R. Geographic distribution of the genera of the tribe Oryzomyini (Rodentia: Cricetidae: Sigmodontinae) in South America: Patterns of distribution and diversity. Arq. Zool. 44(1), 1–120 (2013).
Google Scholar 
Prado, J. R. et al. Species richness and areas of endemism of oryzomyine rodents (Cricetidae, Sigmodontinae) in South America: An NDM/VNDM approach. J. Biogeogr. 42(3), 540–551 (2015).
Google Scholar 
Voss, R. S. A new species of Thomasomys (Rodentia: Muridae) from eastern Ecuador, with remarks on mammalian diversity and biogeography in the Cordillera Oriental. Am. Mus. Novit. 2003(3421), 1–47 (2003).
Google Scholar 
Brito, J. et al. Diversidad insospechada en los Andes de Ecuador: Filogenia del grupo “cinereus” de Thomasomys y descripción de una nueva especie (Rodentia, Cricetidae). Mastozool. Neotrop. 26(2), 308–330 (2019).
Google Scholar 
Rodríguez-Serrano, E., Palma, R. E. & Hernández, C. E. The evolution of ecomorphological traits within the Abrothrichini (Rodentia: Sigmodontinae): A Bayesian phylogenetics approach. Mol. Phylogenet. Evol. 48(2), 473–480 (2008).
Google Scholar 
Villagrán, C. & Hinojosa, L. F. Historia de los bosques del sur de Sudamérica, II: Análisis fitogeográfico. Rev. Chil. Hist. Nat. 70(2), 1–267 (1997).ADS 

Google Scholar 
Pardinas, U. F., Teta, P., D’elía, G. & Lessa, E. P. The evolutionary history of sigmodontine rodents in Patagonia and Tierra del Fuego. Biol. J. Lin. Soc. 103(2), 495–513 (2011).
Google Scholar 
Yepes, J. Consideraciones sobre el género “Andinomys” (Cricetinae) y descripción de una forma nueva. In Anales del Museo Argentino de Ciencias Naturales “Bernardino Rivadavia” (Vol. 38, 333–348) (1935).Salazar-Bravo, J. & Jayat, J. P. Genus Andinomys Thomas, 1902. Mamm. S. Am. 2, 75–77 (2015).
Google Scholar 
Pacheco, V. & Patton, J. L. A new species of the Puna mouse, genus Punomys Osgood, 1943 (Muridae, Sigmodontinae) from the Southeastern Andes of Peru. Z. Saugetierkunde 60(2), 85–96 (1995).
Google Scholar 
Salazar-Bravo, J., Miralles-Salazar, J., Rico-Cernohorska, A. & Vargas, J. First record of Punomys (Rodentia: Sigmodontinae) in Bolivia. Mastozool. Neotrop. 18(1), 143–146 (2011).
Google Scholar 
Rolland, J., Condamine, F. L., Beeravolu, C. R., Jiguet, F. & Morlon, H. Dispersal is a major driver of the latitudinal diversity gradient of C arnivora. Glob. Ecol. Biogeogr. 24(9), 1059–1071 (2015).
Google Scholar 
Pyron, R. A. & Wiens, J. J. Large-scale phylogenetic analyses reveal the causes of high tropical amphibian diversity. Proc. R. Soc. B Biol. Sci. 280(1770), 20131622 (2013).
Google Scholar 
Meseguer, A. S. et al. Reconstructing deep-time palaeoclimate legacies in the clusioid Malpighiales unveils their role in the evolution and extinction of the boreotropical flora. Glob. Ecol. Biogeogr. 27(5), 616–628 (2018).
Google Scholar 
Brumfield, R. T. & Edwards, S. V. Evolution into and out of the Andes: A Bayesian analysis of historical diversification in Thamnophilus antshrikes. Evolution 61(2), 346–367 (2007).CAS 

Google Scholar 
Ribas, C. C., Moyle, R. G., Miyaki, C. Y. & Cracraft, J. The assembly of montane biotas: Linking Andean tectonics and climatic oscillations to independent regimes of diversification in Pionus parrots. Proc. R. Soc. B 274(1624), 2399–2408 (2007).
Google Scholar 
Bonaccorso, E. Historical biogeography and speciation in the Neotropical highlands: Molecular phylogenetics of the jay genus Cyanolyca. Mol. Phylogenet. Evol. 50(3), 618–632 (2009).CAS 

Google Scholar 
McGuire, J. A., Witt, C. C., Altshuler, D. L. & Remsen, J. V. Phylogenetic systematics and biogeography of hummingbirds: Bayesian and maximum likelihood analyses of partitioned data and selection of an appropriate partitioning strategy. Syst. Biol. 56(5), 837–856 (2007).CAS 

Google Scholar 
Rheindt, F. E., Christidis, L. & Norman, J. A. Habitat shifts in the evolutionary history of a Neotropical flycatcher lineage from forest and open landscapes. BMC Evol. Biol. 8(1), 1–18 (2008).
Google Scholar 
Percequillo, A. R., Weksler, M. & Costa, L. P. Comments on oryzomyine biogeography. Zool. J. Linn. Soc. 161(2), 357–390 (2011).
Google Scholar 
Weksler, M. Tribe Oryzomyini Vorontsov, 1959. Mamm. S. Am. 2, 291–293 (2015).
Google Scholar 
Haag, T. et al. Phylogenetic relationships among species of the genus Calomys with emphasis on South American lowland taxa. J. Mammal. 88(3), 769–776 (2007).
Google Scholar 
Badgley, C. Tectonics, topography, and mammalian diversity. Ecography 33(2), 220–231 (2010).
Google Scholar 
Simpson, G. G. Species density of North American recent mammals. Syst. Zool. 13(2), 57–73 (1964).
Google Scholar  More