Ecology

Divergent CuMMOs identified in MAGs recovered from soil and sediment ecosystemsIn previous work we identified putative divergent amoA/pmoA homologues in 7 Thermoplasmatota genomes recovered from Mediterranean grassland soil [25]. This was intriguing, given that amo/pmo homologues had not been previously observed in archaea outside of the Nitrososphaerales. Here we searched for additional genomes encoding related (divergent) amo/pmo’s using a series of readily available, and custom built, hidden markov models (HMMs) across all archaeal genomes in the Genome Taxonomy Database (GTDB), and in all archaeal MAGs in our unpublished datasets from ongoing studies (Supplementary Fig. 1 and Supplementary Data 1). We found additional amoA/pmoA genes in genomes recovered from soils at the South Meadow and Rivendell sites of the Angelo Coast Range Reserve (CA) [25, 26], the nearby Sagehorn site [26], a hillslope of the East River watershed (CO) [27], and in sediments from the Rifle aquifer (CO) [28] and the deep ocean [29]. In total we identified 201 archaeal MAGs taxonomically placed using phylogenetically informative single copy marker genes outside of Nitrososphaerales containing divergent amo/pmo proteins (Supplementary Table 1 and Supplementary Data 1). Genome de-replication resulted in 34 species-level genome clusters, 20 of which encoded an amo/pmo homologue (Supplementary Table 2). Of these genomes, 11 are species not previously available in public databases. In all cases where assembled sequences were of sufficient length, the amoA/pmoA, B, and C protein coding genes were found co-located with each other and with a hypothetical protein here called amoX/pmoX in the order C-A-X-B (Fig. 1A, Supplementary Table 2, and Supplementary Fig. 2). The mean sequence identity of the novel amoA/pmoA, B, and C proteins to known bacterial sequences were 16.7, 8.0, and 14.2% and 13.8, 9.5, and 20.8% to known archaeal sequences. This level of divergent amino acid identity is typical for CuMMOs, as known bacterial and known archaeal amoA/pmoA, B, and C proteins share mean identities of 16.1, 9.7, and 16.5% respectively. As might be expected considering the large sequence divergence between the recovered sequences and known amo/pmo proteins, we found that no pair of typical primers used for bacterial and archaeal amoA/pmoA environmental surveys [30] matched any novel amoA/pmoA gene with More

1.Davies, J. & Davies, D. Origins and evolution of antibiotic resistance. Microbiol. Mol. Biol. Rev. 74, 417–433 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
2.European Centre for Disease Prevention and Control, European Medicines Agencies. The Bacterial Challenge: Time to React. A Call to Narrow the Gap Between Multidrug-Resistant Bacteria in the EU and the Development of New Antibacterial Agents https://ecdc.europa.eu/sites/portal/files/media/en/publications/Publications/0909_TER_The_Bacterial_Challenge_Time_to_React.pdf (2009).3.Jevons, M. P. “Celbenin”—resistant Staphylococci. Br. Med. J. 1, 124–125 (1961).PubMed Central 

Google Scholar 
4.Harkins, C. P. et al. Methicillin-resistant Staphylococcus aureus emerged long before the introduction of methicillin into clinical practice. Genome Biol. 18, 130 (2017).PubMed 
PubMed Central 

Google Scholar 
5.Chambers, H. F. & DeLeo, F. R. Waves of resistance: Staphylococcus aureus in the antibiotic era. Nat. Rev. Microbiol. 7, 629–641 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
6.Price, L. B. et al. Staphylococcus aureus CC398: host adaptation and emergence of methicillin resistance in livestock. mBio 3, e00305-11 (2012).PubMed 
PubMed Central 

Google Scholar 
7.Global Priority List of Antibiotic-Resistant Bacteria to Guide Research, Discovery, and Development of New Antibiotics http://www.who.int/medicines/publications/WHO-PPL-Short_Summary_25Feb-ET_NM_WHO.pdf?ua=1 (WHO, 2017).8.Rasmussen, S. L. et al. European hedgehogs (Erinaceus europaeus) as a natural reservoir of methicillin-resistant Staphylococcus aureus carrying mecC in Denmark. PLoS ONE 14, e0222031 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
9.Bengtsson, B. et al. High occurrence of mecC-MRSA in wild hedgehogs (Erinaceus europaeus) in Sweden. Vet. Microbiol. 207, 103–107 (2017).PubMed 

Google Scholar 
10.García-Álvarez, L. et al. Methicillin-resistant Staphylococcus aureus with a novel mecA homologue in human and bovine populations in the UK and Denmark: a descriptive study. Lancet Infect. Dis. 11, 595–603 (2011).PubMed 
PubMed Central 

Google Scholar 
11.Paterson, G. K., Harrison, E. M. & Holmes, M. A. The emergence of mecC methicillin-resistant Staphylococcus aureus. Trends Microbiol. 22, 42–47 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
12.Marples, M. J. & Smith, J. M. B. The hedgehog as a source of human ringworm. Nature 188, 867–868 (1960).ADS 
CAS 
PubMed 

Google Scholar 
13.English, M. P., Evans, C. D., Hewitt, M. & Warin, R. P. “Hedgehog ringworm”. Br. Med. J. 1, 149–151 (1962).CAS 
PubMed 
PubMed Central 

Google Scholar 
14.Smith, J. M. B. & Marples, M. J. A natural reservoir of penicillin-resistant strains of Staphylococcus aureus. Nature 201, 844 (1964).ADS 
CAS 
PubMed 

Google Scholar 
15.Smith, J. M. B. & Marples, M. J. Dermatophyte lesions in the hedgehog as a reservoir of penicillin-resistant staphylococci. J. Hyg. 63, 293–303 (1965).CAS 
PubMed 
PubMed Central 

Google Scholar 
16.Smith, J. M. B. Staphylococcus aureus strains associated with the hedgehog Erinaceus europaeus. J. Hyg. Camb. 63, 293–303 (1965).CAS 
PubMed 
PubMed Central 

Google Scholar 
17.Morris, P. & English, M. P. Trichophyton mentagrophytes var. erinacei in British hedgehogs. Sabouraudia 7, 122–128 (1969).CAS 
PubMed 

Google Scholar 
18.Le Barzic, C. et al. Detection and control of dermatophytosis in wild European hedgehogs (Erinaceus europaeus) admitted to a French wildlife rehabilitation centre. J. Fungi 7, 74 (2021).
Google Scholar 
19.Dube, F., Söderlund, R., Salomonsson, M. L., Troell, K. & Börjesson, S. Benzylpenicillin-producing Trichophyton erinacei and methicillin resistant Staphylococcus aureus carrying the mecC gene on European hedgehogs: a pilot-study. BMC Microbiol. 21, 212 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
20.Hewitt, G. The genetic legacy of the Quaternary ice ages. Nature 405, 907–913 (2000).ADS 
CAS 
PubMed 

Google Scholar 
21.Brockie, R. E. Distribution and abundance of the hedgehog (Erinaceus europaeus) L. in New Zealand, 1869–1973. N. Z. J. Zool. 2, 445–462 (1975).
Google Scholar 
22.van den Berg, M. A. et al. Genome sequencing and analysis of the filamentous fungus Penicillium chrysogenum. Nat. Biotechnol. 26, 1161–1168 (2008).CAS 
PubMed 

Google Scholar 
23.Ullán, R. V., Campoy, S., Casqueiro, J., Fernández, F. J. & Martín, J. F. Deacetylcephalosporin C production in Penicillium chrysogenum by expression of the isopenicillin N epimerization, ring expansion, and acetylation genes. Chem. Biol. 14, 329–339 (2007).PubMed 

Google Scholar 
24.Kitano, K. et al. A novel penicillin produced by strains of the genus Paecilomyces. J. Ferment. Technol. 54, 705–711 (1976).CAS 

Google Scholar 
25.Petersen, A. et al. Epidemiology of methicillin-resistant Staphylococcus aureus carrying the novel mecC gene in Denmark corroborates a zoonotic reservoir with transmission to humans. Clin. Microbiol. Infect. 19, E16–E22 (2013).CAS 
PubMed 

Google Scholar 
26.Richardson, E. J. et al. Gene exchange drives the ecological success of a multi-host bacterial pathogen. Nat. Ecol. Evol. 2, 1468–1478 (2018).PubMed 
PubMed Central 

Google Scholar 
27.Holden, M. T. G. et al. A genomic portrait of the emergence, evolution, and global spread of a methicillin-resistant Staphylococcus aureus pandemic. Genome Res. 23, 653–664 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
28.Strauß, L. et al. Origin, evolution, and global transmission of community-acquired Staphylococcus aureus ST8. Proc. Natl Acad. Sci. USA 114, E10596–E10604 (2017).PubMed 
PubMed Central 

Google Scholar 
29.Nübel, U. et al. Frequent emergence and limited geographic dispersal of methicillin-resistant Staphylococcus aureus. Proc. Natl Acad. Sci. USA 105, 14130–14135 (2008).ADS 
PubMed 
PubMed Central 

Google Scholar 
30.Rasmussen, S. L., Nielsen, J. L., Jones, O. R., Berg, T. B. & Pertoldi, C. Genetic structure of the European hedgehog (Erinaceus europaeus) in Denmark. PLoS ONE 15, e0227205 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
31.Hansen, J. E. et al. LA-MRSA CC398 in dairy cattle and veal calf farms indicates spillover from pig production. Front. Microbiol. 10, 2733 (2019).PubMed 
PubMed Central 

Google Scholar 
32.Eriksson, J. Espinosa-Gongora, C., Stamphøj, I., Larsen, A. R. & Guardabassi, L. Carriage frequency, diversity and methicillin resistance of in Danish small ruminants. Vet. Microbiol. 163, 110–115 (2013).CAS 
PubMed 

Google Scholar 
33.Danish Integrated Antimicrobial Resistance Monitoring and Research Programme. DANMAP 2019: Use of Antimicrobial Agents and Occurrence of Antimicrobial Resistance in Bacteria From Food Animals, Food, and Humans in DENMARK https://www.danmap.org/-/media/Sites/danmap/Downloads/Reports/2019/DANMAP_2019.ashx?la=da&hash=AA1939EB449203EF0684440AC1477FFCE2156BA5 (2020).34.Veterinary Medicines Directorate. UK Veterinary Antibiotic Resistance and Sales Surveillance Reporthttps://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/950126/UK-VARSS_2019_Report__2020-TPaccessible.pdf (2020).35.Harrison, E. M. et al. Whole genome sequencing identifies zoonotic transmission of MRSA isolates with the novel mecA homologue mecC. EMBO Mol. Med. 5, 509–515 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
36.Loncaric, I. et al. Characterization of mecC gene-carrying coagulase-negative Staphylococcus spp. isolated from various animals. Vet. Microbiol. 230, 138–144 (2019).CAS 
PubMed 

Google Scholar 
37.Gómez, P. et al. Detection of MRSA ST3061-t843-mecC and ST398-t011-mecA in white stork nestlings exposed to human residues. J. Antimicrob. Chemother. 71, 53–57 (2016).PubMed 

Google Scholar 
38.Kim, C. et al. Properties of a novel PBP2A protein homolog from Staphylococcus aureus strain LGA251 and its contribution to the β-lactam-resistant phenotype. J. Biol. Chem. 287, 36854–36863 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
39.Tahlan, K. & Jensen, S. E. Origins of the β-lactam rings in natural products. J. Antibiot. 66, 401–419 (2013).CAS 

Google Scholar 
40.Pantůček, R. et al. Staphylococcus edaphicus sp. nov. isolated in Antarctica harbors the mecC gene and genomic islands with a suspected role in adaptation to extreme environment. Appl. Environ. Microbiol. 84, e01746-17 (2018).PubMed 
PubMed Central 

Google Scholar 
41.D’Costa, V. M., et al. Antibiotic resistance is ancient. Nature 477, 457–461 (2011).ADS 
PubMed 

Google Scholar 
42.Allen, H. K., Moe, L. A., Rodbumrer, J., Gaarder, A. & Handelsman, J. Functional metagenomics reveals diverse beta-lactamases in a remote Alaskan soil. ISME J. 3, 243–251 (2009).CAS 

Google Scholar 
43.Forsberg, K. J. et al. The shared antibiotic resistome of soil bacteria and human pathogens. Science 337, 1107–1111 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
44.Forsberg, K. J. et al. Bacterial phylogeny structures soil resistomes across habitats. Nature 509, 612–616 (2014).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
45.Coll, F. et al. Definition of a genetic relatedness cutoff to exclude recent transmission of meticillin-resistant Staphylococcus aureus: a genomic epidemiology analysis. Lancet Microbe 1, e328–e335 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
46.Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its application to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).MathSciNet 
CAS 
PubMed 
PubMed Central 

Google Scholar 
47.Enright, M. C., Day, N. P., Davies, C. E., Peacock, S. J., Spratt, B. G. Multilocus sequence typing for characterization of methicillin-resistant and methicillin-susceptible clones of Staphylococcus aureus. J. Clin. Microbiol. 38, 1008–1015 (2000).CAS 
PubMed 
PubMed Central 

Google Scholar 
48.Van Wamel, W. J., Rooijakkers, S. H., Ruyken, M. van Kessel, K. P. & Strijp, J. A. The innate immune modulators staphylococcal complement inhibitor and chemotaxis inhibitory protein of Staphylococcus aureus are located on beta-hemolysin-converting bacteriophages. J. Bacteriol. 188, 1310–1315 (2006).PubMed 
PubMed Central 

Google Scholar 
49.Viana, D. et al. Adaptation of Staphylococcus aureus to ruminant and equine hosts involved SaPI-carried variants of von Willebrand factor-binding protein. Mol. Microbiol. 77, 1583–1594 (2010).50.Rooijakkers, S. H. M. et al. Staphylococcal complement inhibitor: structure and active sites. J. Immunol. 179, 2989–2998 (2007).CAS 
PubMed 

Google Scholar 
51.Arndt, D. et al. PHASTER: a better, faster version of the PHAST phage search tool. Nucleic Acids Res. 44, W16–W21 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
52.Bortolaia, V. et al. ResFinder 4.0 for predictions of phenotypes from genotypes. J. Antimicrob. Chemother. 75, 3491–3500 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
53.Clausen, P. T. L. C., Aarestrup, F. M. & Lund, O. Rapid and precise alignment of raw reads against redundant database with KMA. BMC Bioinform. 19, 397 (2018).
Google Scholar 
54.Sahl, J. W. et al. NASP: an accurate, rapid method for the identification of SNPs in WGS datasets that supports flexible input and output formats. Microb. Genom. 2, e000074 (2016).PubMed 
PubMed Central 

Google Scholar 
55.Li, H. & Durbin, R. Fast and accurate short read alignment with Burrow-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
56.McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
57.DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation sequencing data. Nat. Genet. 43, 491–498 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
58.Delcher, A. L., Phillippy, A., Carlton, J. & Salzberg, S. L. Fast algorithms for large-scale genome alignment and comparison. Nucleic Acids Res. 30, 2478–2483 (2002).PubMed 
PubMed Central 

Google Scholar 
59.Kurz, S. et al. Versatile and open software for comparing large genomes. Genome Biol. 5, R12 (2004).
Google Scholar 
60.Guindon, S. & Gasquel, O. A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst. Biol. 52, 696–704 (2003).PubMed 

Google Scholar 
61.Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst. Biol. 59, 307–321 (2010).CAS 

Google Scholar 
62.Didelot, X. & Wilson, D. J. ClonalFrameML: efficient inference of recombination in whole bacterial genome. PLoS Comput. Biol. 11, e1004041 (2015).ADS 
PubMed 
PubMed Central 

Google Scholar 
63.Didelot, X. et al. Bayesian inference of ancestral dates on bacterial phylogenetic trees. Nucleic Acids Res. 46, e134 (2018).PubMed 
PubMed Central 

Google Scholar 
64.Didelot, X., Siveroni, I. & Volz, E. M. Additive uncorrelated relaxed clock models for the dating of genomic epidemiology phylogenies. Mol. Biol. Evol. 38, 307–317 (2021).CAS 
PubMed 

Google Scholar 
65.Plummer, M., Best, N., Cowles, K. & Vines, K. CODA: convergence diagnosis and output analysis for MCMC. R News 6, 7–11 (2006).
Google Scholar 
66.Volz, E. M. & Frost, S. D. Scalable relaxed clock phylogenetic dating. Virus Evol. 3, vex025 (2017).
Google Scholar 
67.Wang, M. et al. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. Nat. Biotechnol. 34, 828–837 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
68.Adusumilli, R. & Mallick, P. Data conversion with ProteoWizard msConvert. Methods Mol. Biol. 1550, 339–368 (2017).CAS 
PubMed 

Google Scholar  More

Brown AHD, Feldman MW, Nevo E (1980) Multilocus structure of natural populations of Hordeum spontaneum. Genetics 96:523–536CAS 
Article 

Google Scholar 
Burow MD, Simpson CE, Starr JL, Paterson AH (2001) Transmission genetics of chromatin from a synthetic amphidiploid to cultivated peanut (Arachis hypogaea L.): broadening the gene pool of a monophyletic polyploid species. Genetics 159:823CAS 
Article 

Google Scholar 
Butruille DV, Boiteux LS (2000) Selection–mutation balance in polysomic tetraploids: Impact of double reduction and gametophytic selection on the frequency and subchromosomal localization of deleterious mutations. Proc Natl Acad Sci USA 97:6608–6613CAS 
Article 

Google Scholar 
Cockerham CC, Weir BS (1977) Digenic descent measures for finite populations. Genet Res 30:121–147Article 

Google Scholar 
Devlin B, Risch N (1995) A comparison of linkage disequilibrium measures for fine-scale mapping. Genomics 29:311–322CAS 
Article 

Google Scholar 
Do C, Waples RS, Peel D, Macbeth G, Tillett BJ, Ovenden JR (2014) NeEstimator v2: re‐implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Mol Ecol Resour 14:209–214CAS 
Article 

Google Scholar 
England PR, Cornuet J-M, Berthier P, Tallmon DA, Luikart G (2006) Estimating effective population size from linkage disequilibrium: severe bias in small samples. Conserv Genet 7:303Article 

Google Scholar 
Fisher RA (1947) The theory of linkage in polysomic inheritance. Philos Trans R Soc Lond Ser B Biol Sci 233:55–87
Google Scholar 
Gao XY, Starmer J, Martin ER (2008) A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms. Genet Epidemiol 32:361–369Article 

Google Scholar 
Hästbacka J, de la Chapelle A, Kaitila I, Sistonen P, Weaver A, Lander E (1992) Linkage disequilibrium mapping in isolated founder populations: diastrophic dysplasia in Finland. Nat Genet 2:204–211Article 

Google Scholar 
Hayes BJ, Visscher PM, McPartlan HC, Goddard ME (2003) Novel multilocus measure of linkage disequilibrium to estimate past effective population size. Genome Res 13:635–643CAS 
Article 

Google Scholar 
Hill WG (1974) Disequilibrium among several linked neutral genes in finite population I. Mean changes in disequilibrium. Theor Popul Biol 5:366–392CAS 
Article 

Google Scholar 
Hill WG (1975) Linkage disequilibrium among multiple neutral alleles produced by mutation in finite population. Theor Popul Biol 8:117–126CAS 
Article 

Google Scholar 
Hill WG (1981) Estimation of effective population size from data on linkage disequilibrium. Genet Res 38:209–216Article 

Google Scholar 
Hill WG, Robertson A (1968) Linkage disequilibrium in finite populations. Theor Appl Genet 38:226–231CAS 
Article 

Google Scholar 
Hill WG, Weir BS (1994) Maximum-likelihood estimation of gene location by linkage disequilibrium. Am J Hum Genet 54:705CAS 

Google Scholar 
Hollenbeck C, Portnoy D, Gold J (2016) A method for detecting recent changes in contemporary effective population size from linkage disequilibrium at linked and unlinked loci. Heredity 117:207–216CAS 
Article 

Google Scholar 
Hosking LK, Boyd PR, Xu CF, Nissum M, Cantone K, Purvis IJ, Khakhar R, Barnes MR, Liberwirth U, Hagen-Mann K (2002) Linkage disequilibrium mapping identifies a 390 kb region associated with CYP2D6 poor drug metabolising activity. Pharmacogenomics J 2:165CAS 
Article 

Google Scholar 
Huang K, Dunn DW, Ritland K, Li BG (2020) polygene: Population genetics analyses for autopolyploids based on allelic phenotypes. Methods Ecol Evol 11:448–456Article 

Google Scholar 
Jorde LB (1995) Linkage disequilibrium as a gene-mapping tool. Am J Hum Genet 56:11CAS 

Google Scholar 
Lewontin RC (1964) The interaction of selection and linkage. I. General considerations; heterotic models. Genetics 49:49CAS 
Article 

Google Scholar 
Maruyama T (1982) Stochastic integrals and their application to population genetics. In: Kimura M (ed) Molecular evolution, protein polymorphism and the neutral theory. Japan Scientific Societies Press, Tokyo, p 151–166
Google Scholar 
Nei M (1987) Molecular evolutionary genetics. Columbia university press, New YorkOhta T (1980) Linkage disequilibrium between amino acid sites in immunoglobulin genes and other multigene families. Genet Res 36:181–197CAS 
Article 

Google Scholar 
Ohta T, Kimura M (1969) Linkage disequilibrium at steady state determined by random genetic drift and recurrent mutation. Genetics 63:229CAS 
Article 

Google Scholar 
Otto SP (2007) The evolutionary consequences of polyploidy. Cell 131:452–462CAS 
Article 

Google Scholar 
Rieger R, Michaelis A, Green MM (1968) A glossary of genetics and cytogenetics: classical and molecular. Springer-Verlag, New York, NYBook 

Google Scholar 
Robert C (1991) Generalized inverse normal distributions. Stat Probabil Lett 11:37–41Article 

Google Scholar 
Santiago E, Novo I, Pardiñas AF, Saura M, Wang J, Caballero A (2020) Recent demographic history inferred by high-resolution analysis of linkage disequilibrium. Mol Biol Evol 37:3642–3653CAS 
Article 

Google Scholar 
Sattler MC, Carvalho CR, Clarindo WR (2016) The polyploidy and its key role in plant breeding. Planta 243:281–296CAS 
Article 

Google Scholar 
Slatkin M (2008) Linkage disequilibrium—understanding the evolutionary past and mapping the medical future. Nat Rev Genet 9:477CAS 
Article 

Google Scholar 
Stift M, Berenos C, Kuperus P, van Tienderen PH (2008) Segregation models for disomic, tetrasomic and intermediate inheritance in tetraploids: a general procedure applied to Rorippa (yellow cress) microsatellite data. Genetics 179:2113–2123Article 

Google Scholar 
Sved JA (1964) The relationship between diploid and tetraploid recombination frequencies. Heredity 19:585–596CAS 
Article 

Google Scholar 
Sved JA, Cameron EC, Gilchrist AS (2013) Estimating effective population size from linkage disequilibrium between unlinked loci: theory and application to fruit fly outbreak populations. PLoS ONE 8:e69078CAS 
Article 

Google Scholar 
Sved JA, Feldman MW (1973) Correlation and probability methods for one and two loci. Theor Popul Biol 4:129–132CAS 
Article 

Google Scholar 
Tenesa A, Navarro P, Hayes BJ, Duffy DL, Clarke GM, Goddard ME, Visscher PM (2007) Recent human effective population size estimated from linkage disequilibrium. Genome Res 17:520–526CAS 
Article 

Google Scholar 
Udall JA, Wendel JF (2006) Polyploidy and crop improvement. Crop Sci 46:S-3–S-14Article 

Google Scholar 
Waples RS (2006) A bias correction for estimates of effective population size based on linkage disequilibrium at unlinked gene loci. Conserv Genet 7:167–184. https://doi.org/10.1007/s10592-005-9100-yArticle 

Google Scholar 
Waples RS, Antao T, Luikart G (2014) Effects of overlapping generations on linkage disequilibrium estimates of effective population size. Genetics 197:769–780Article 

Google Scholar 
Waples RK, Larson WA, Waples RS (2016) Estimating contemporary effective population size in non-model species using linkage disequilibrium across thousands of loci. Heredity 117:233–240. https://doi.org/10.1038/hdy.2016.60CAS 
Article 

Google Scholar 
Weir BS (1979) Inferences about linkage disequilibrium. Biometrics 35:235–254Weir BS, Cockerham CC (1979) Estimation of linkage disequilibrium in randomly mating populations. Heredity 42:105Article 

Google Scholar 
Weir BS, Hill WG (1980) Effect of mating structure on variation in linkage disequilibrium. Genetics 95:477–488CAS 
Article 

Google Scholar  More

1.Tilman, D. et al. Science 277, 1300–1302 (1997).CAS 
Article 

Google Scholar 
2.Hughes, T. P. et al. Nature 556, 492–496 (2018).CAS 
Article 

Google Scholar 
3.Eddy, T. D. et al. One Earth 4, 1278–1285 (2021).Article 

Google Scholar 
4.Stoeckl, N., Condie, S. & Anthony, K. Ecosyst. Serv. 51, 101352 (2021).Article 

Google Scholar 
5.Hoegh-Guldberg, O., Pendleton, L. & Kaup, A. Reg. Stud. Mar. Sci. 30, 100699 (2019).Article 

Google Scholar 
6.Teh, L. S. L., Teh, L. C. L. & Sumaila, U. R. PLoS ONE 8, e65397 (2013).CAS 
Article 

Google Scholar 
7.Robinson, J. P. W. et al. Nat. Ecol. Evol. 3, 183–190 (2019).Article 

Google Scholar 
8.Crain, C. M., Kroeker, K. & Halpern, B. S. Ecol. Lett. 11, 1304–1315 (2008).Article 

Google Scholar 
9.Schröter, M. et al. Conserv. Lett. 7, 514–523 (2014).Article 

Google Scholar 
10.Glanemann, N., Willner, S. N. & Levermann, A. Nat. Commun. 11, 110 (2020).CAS 
Article 

Google Scholar 
11.Emissions Gap Report 2021: The Heat is On – A World of Climate Promises Not Yet Delivered (UNEP, 2021).12.Bathiany, S., Dakos, V., Scheffer, M. & Lenton, T. M. Sci. Adv. 4, eaar5809 (2018).Article 

Google Scholar 
13.Berrang-Ford, L. et al. Nat. Clim. Change 11, 989–1000 (2021).Article 

Google Scholar 
14.Roberts, J. T. et al. Nat. Clim. Change 11, 180–182 (2021).Article 

Google Scholar  More

1.Muellner-Riehl, A. N. et al. Origins of global mountain plant biodiversity: Testing the ‘mountain-geobiodiversity hypothesis’. J. Biogeogr. 46, 2826–2838 (2019).
Google Scholar 
2.Antonelli, A. et al. Geological and climatic influences on mountain biodiversity. Nat. Geosci. 11, 718–725 (2018).ADS 
CAS 

Google Scholar 
3.Schrodt, F. et al. Opinion: To advance sustainable stewardship, we must document not only biodiversity but geodiversity. Proc. Natl. Acad. Sci. 116, 16155–16158 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
4.Alahuhta, J. et al. The role of geodiversity in providing ecosystem services at broad scales. Ecol. Indic. 91, 47–56 (2018).
Google Scholar 
5.Read, Q. D. et al. Beyond counts and averages: Relating geodiversity to dimensions of biodiversity. Glob. Ecol. Biogeogr. 29, 696–710 (2020).
Google Scholar 
6.Díaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science 366, eaax3100 (2019).PubMed 

Google Scholar 
7.Alahuhta, J., Toivanen, M. & Hjort, J. Geodiversity–biodiversity relationship needs more empirical evidence. Nat. Ecol. Evol. 4, 2–3 (2020).PubMed 

Google Scholar 
8.Boothroyd, A. & McHenry, M. Old processes, new movements: the inclusion of geodiversity in biological and ecological discourse. Diversity 11, 216 (2019).
Google Scholar 
9.Hunter, M. L., Jacobson, G. L. & Webb, T. Paleoecology and the coarse-filter approach to maintaining biological diversity. Conserv. Biol. 2, 375–385 (1988).
Google Scholar 
10.Hjort, J. & Luoto, M. Can geodiversity be predicted from space?. Geomorphology 153–154, 74–80 (2012).ADS 

Google Scholar 
11.Benito-Calvo, A., Pérez-González, A., Magri, O. & Meza, P. Assessing regional geodiversity: the Iberian Peninsula. Earth Surf. Process. Landf. 34, 1433–1445 (2009).ADS 

Google Scholar 
12.dos Santos, F. M., de La Corte Bacci, D., Saad, A. R. & da Silva Ferreira, A. T. Geodiversity index weighted by multivariate statistical analysis. Appl. Geomat. 12, 361–370 (2020).
Google Scholar 
13.Crisp, J. R., Ellison, J. C. & Fischer, A. Current trends and future directions in quantitative geodiversity assessment. Prog. Phys. Geogr. Earth Environ. https://doi.org/10.1177/0309133320967219 (2020).Article 

Google Scholar 
14.Pereira, D. I., Pereira, P., Brilha, J. & Santos, L. Geodiversity assessment of Paraná State (Brazil): An innovative approach. Environ. Manag. 52, 541–552 (2013).ADS 

Google Scholar 
15.Gray, M. Geodiversity and geoconservation: What, why, and how?. George Wright Forum 22, 4–12 (2005).
Google Scholar 
16.Ruban, D. A. Quantification of geodiversity and its loss. Proc. Geol. Assoc. 121, 326–333 (2010).
Google Scholar 
17.Hjort, J., Gordon, J. E., Gray, M. & Hunter, M. L. Why geodiversity matters in valuing nature’s stage: Why geodiversity matters. Conserv. Biol. 29, 630–639 (2015).PubMed 

Google Scholar 
18.Beier, P. & Brost, B. Use of land facets to plan for climate change: Conserving the arenas, not the actors. Conserv. Biol. J. Soc. Conserv. Biol. 24, 701–710 (2010).
Google Scholar 
19.Anderson, M. G. & Ferree, C. E. Conserving the stage: Climate change and the geophysical underpinnings of species diversity. PLoS ONE 5, e11554 (2010).ADS 
PubMed 
PubMed Central 

Google Scholar 
20.Knudson, C., Kay, K. & Fisher, S. Appraising geodiversity and cultural diversity approaches to building resilience through conservation. Nat. Clim. Change 8, 678–685 (2018).ADS 

Google Scholar 
21.Turner, J. A. Geodiversity: The natural support system of ecosystems. In Landscape Planning with Ecosystem Services: Theories and Methods for Application in Europe 253–265 (eds von Haaren, C. et al.) (Springer, 2019). https://doi.org/10.1007/978-94-024-1681-7_16.Chapter 

Google Scholar 
22.Fox, N., Graham, L. J., Eigenbrod, F., Bullock, J. M. & Parks, K. E. Incorporating geodiversity in ecosystem service decisions. Ecosyst. People 16, 151–159 (2020).
Google Scholar 
23.Parks, K. E. & Mulligan, M. On the relationship between a resource based measure of geodiversity and broad scale biodiversity patterns. Biodivers. Conserv. 19, 2751–2766 (2010).
Google Scholar 
24.Comer, P. J. et al. Incorporating geodiversity into conservation decisions: Geodiversity and conservation decisions. Conserv. Biol. 29, 692–701 (2015).PubMed 

Google Scholar 
25.Chakraborty, A. & Gray, M. A call for mainstreaming geodiversity in nature conservation research and praxis. J. Nat. Conserv. 56, 125862 (2020).
Google Scholar 
26.Lawler, J. et al. The theory behind, and the challenges of, conserving nature’s stage in a time of rapid change. Conserv. Biol. 29, 618–629 (2015).PubMed 

Google Scholar 
27.Beier, P. et al. A review of selection-based tests of abiotic surrogates for species representation. Conserv. Biol. J. Soc. Conserv. Biol. 29, 668–679 (2015).
Google Scholar 
28.Purvis, A. & Hector, A. Getting the Measure of Biodiversity. Nature 405, 212–219 (2000).CAS 
PubMed 

Google Scholar 
29.Moreno, C. et al. Measuring biodiversity in the Anthropocene: A simple guide to helpful methods. Biodivers. Conserv. 26, 2993–2998 (2017).
Google Scholar 
30.Roswell, M., Dushoff, J. & Winfree, R. A conceptual guide to measuring species diversity. Oikos 130, 321–338 (2021).
Google Scholar 
31.Chiarucci, A., Bacaro, G. & Scheiner, S. M. Old and new challenges in using species diversity for assessing biodiversity. Philos. Trans. R. Soc. B Biol. Sci. 366, 2426–2437 (2011).
Google Scholar 
32.Hooper, D. U. et al. Effects of biodiversity on ecosystem functioning: A consensus of current knowledge. Ecol. Monogr. 75, 3–35 (2005).
Google Scholar 
33.Hjort, J., Heikkinen, R. K. & Luoto, M. Inclusion of explicit measures of geodiversity improve biodiversity models in a boreal landscape. Biodivers. Conserv. 21, 3487–3506 (2012).
Google Scholar 
34.Bailey, J. J., Boyd, D. S., Hjort, J., Lavers, C. P. & Field, R. Modelling native and alien vascular plant species richness: At which scales is geodiversity most relevant?. Glob. Ecol. Biogeogr. 26, 763–776 (2017).
Google Scholar 
35.Zarnetske, P. L. et al. Towards connecting biodiversity and geodiversity across scales with satellite remote sensing. Glob. Ecol. Biogeogr. 28, 548–556 (2019).PubMed 
PubMed Central 

Google Scholar 
36.Bétard, F. Patch-scale relationships between geodiversity and biodiversity in hard rock quarries: Case study from a disused quartzite quarry in NW France. Geoheritage 5, 59–71 (2013).
Google Scholar 
37.Tukiainen, H. et al. Spatial relationship between biodiversity and geodiversity across a gradient of land-use intensity in high-latitude landscapes. Landsc. Ecol. 32, 1049–1063 (2017).
Google Scholar 
38.Anderson, M. G. et al. Case studies of conservation plans that incorporate geodiversity. Conserv. Biol. 29, 680–691 (2015).CAS 
PubMed 

Google Scholar 
39.Ren, Y., Lü, Y., Hu, J. & Yin, L. Geodiversity underpins biodiversity but the relations can be complex: Implications from two biodiversity proxies. Glob. Ecol. Conserv. 31, e01830 (2021).
Google Scholar 
40.Homeier, J., Breckle, S.-W., Günter, S., Rollenbeck, R. T. & Leuschner, C. Tree diversity, forest structure and productivity along altitudinal and topographical gradients in a species-rich Ecuadorian montane rain forest: Ecuadorian Montane forest diversity and structure. Biotropica 42, 140–148 (2010).
Google Scholar 
41.Krashevska, V., Bonkowski, M., Maraun, M. & Scheu, S. Testate amoebae (protista) of an elevational gradient in the tropical mountain rain forest of Ecuador. Pedobiologia 51, 319–331 (2007).
Google Scholar 
42.Zhalnina, K. et al. Soil pH determines microbial diversity and composition in the park grass experiment. Microb. Ecol. 69, 395–406 (2015).CAS 
PubMed 

Google Scholar 
43.Fierer, N., Craine, J. M., McLauchlan, K. & Schimel, J. P. Litter quality and the temperature sensiticity of decomposition. Ecology 86, 320–326 (2005).
Google Scholar 
44.Gibb, H. et al. Climate mediates the effects of disturbance on ant assemblage structure. Proc. R. Soc. B Biol. Sci. 282, 20150418 (2015).
Google Scholar 
45.Sanders, N. J., Lessard, J.-P., Fitzpatrick, M. C. & Dunn, R. R. Temperature, but not productivity or geometry, predicts elevational diversity gradients in ants across spatial grains. Glob. Ecol. Biogeogr. 16, 640–649 (2007).
Google Scholar 
46.Paaijmans, K. P. et al. Temperature variation makes ectotherms more sensitive to climate change. Glob. Change Biol. 19, 2373–2380 (2013).ADS 

Google Scholar 
47.McCain, C. M. Global analysis of bird elevational diversity. Glob. Ecol. Biogeogr. 18, 346–360 (2009).
Google Scholar 
48.Tews, J. et al. Animal species diversity driven by habitat heterogeneity/diversity: The importance of keystone structures: Animal species diversity driven by habitat heterogeneity. J. Biogeogr. 31, 79–92 (2004).
Google Scholar 
49.Rahbek, C. et al. Humboldt’s enigma: What causes global patterns of mountain biodiversity?. Science 365, 1108–1113 (2019).ADS 
CAS 
PubMed 

Google Scholar 
50.Hofhansl, F. et al. Climatic and edaphic controls over tropical forest diversity and vegetation carbon storage. Sci. Rep. 10, 5066 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
51.Peters, M. K. et al. Predictors of elevational biodiversity gradients change from single taxa to the multi-taxa community level. Nat. Commun. 7, 13736 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
52.Gagic, V. et al. Functional identity and diversity of animals predict ecosystem functioning better than species-based indices. Proc. R. Soc. B Biol. Sci. 282, 20142620 (2015).
Google Scholar 
53.Kraft, N. J. B., Godoy, O. & Levine, J. M. Plant functional traits and the multidimensional nature of species coexistence. Proc. Natl. Acad. Sci. 112, 797–802 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
54.Cadotte, M. W. Functional traits explain ecosystem function through opposing mechanisms. Ecol. Lett. 20, 989–996 (2017).PubMed 

Google Scholar 
55.Hillebrand, H. et al. Biodiversity change is uncoupled from species richness trends: Consequences for conservation and monitoring. J. Appl. Ecol. 55, 169–184 (2018).
Google Scholar 
56.Whittaker, R. H. Evolution and measurement of species diversity. Taxon 21, 213–251 (1972).
Google Scholar 
57.Socolar, J. B., Gilroy, J. J., Kunin, W. E. & Edwards, D. P. How should beta-diversity inform biodiversity conservation?. Trends Ecol. Evol. 31, 67–80 (2016).PubMed 

Google Scholar 
58.Legendre, P. & De Cáceres, M. Beta diversity as the variance of community data: Dissimilarity coefficients and partitioning. Ecol. Lett. 16, 951–963 (2013).PubMed 

Google Scholar 
59.Lichstein, J. W. Multiple regression on distance matrices: A multivariate spatial analysis tool. Plant Ecol. 188, 117–131 (2007).
Google Scholar 
60.Tuomisto, H. & Ruokolainen, K. Analyzing or explaining beta diversity? Understanding the targets of different methods of analysis. Ecology 87, 2697–2708 (2006).PubMed 

Google Scholar 
61.Peres-Neto, P. R. & Jackson, D. A. How well do multivariate data sets match? The advantages of a Procrustean superimposition approach over the Mantel test. Oecologia 129, 169–178 (2001).ADS 
PubMed 

Google Scholar 
62.Peres-Neto, P. R., Legendre, P., Dray, S. & Borcard, D. Variation partitioning of species data matrices: Estimation and comparison of fractions. Ecology 87, 2614–2625 (2006).PubMed 

Google Scholar 
63.Hillebrand, H. & Matthiessen, B. Biodiversity in a complex world: Consolidation and progress in functional biodiversity research: Consolidation and progress in BDEF research. Ecol. Lett. 12, 1405–1419 (2009).PubMed 

Google Scholar 
64.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 
PubMed 

Google Scholar 
65.Bendix, J. et al. A research framework for projecting ecosystem change in highly diverse tropical mountain ecosystems. Oecologia 195, 589–600 (2021).ADS 
PubMed 
PubMed Central 

Google Scholar 
66.Beck, E., Bendix, J., Kottke, I., Makeschin, F. & Mosandl, R. Gradients in a Tropical Mountain Ecosystem of Ecuador. ISBN: 978-3-540-73525-0
(Springer, 2008).
Google Scholar 
67.Landscape Restoration, Sustainable Use and Cross-Scale Monitoring of Biodiversity and Ecosystem Functions – A Science-directed Approach for South Ecuador (PAK823–825 Platform for Biodiversity and Ecosystem Monitoring and Research in South Ecuador, 2017).68.Beck, E. et al. Ecosystem Services, Biodiversity and Environmental Change in a Tropical Mountain Ecosystem of South Ecuador. ISBN: 978-3-642-38136-2 (Springer, 2013).
Google Scholar 
69.Homeier, J. & Leuschner, C. Factors controlling the productivity of tropical Andean forests: Climate and soil are more important than tree diversity. Biogeosciences 18, 1525–1541 (2021).ADS 
CAS 

Google Scholar 
70.Krashevska, V., Sandmann, D., Maraun, M. & Scheu, S. Consequences of exclusion of precipitation on microorganisms and microbial consumers in montane tropical rainforests. Oecologia 170, 1067–1076 (2012).ADS 
PubMed 
PubMed Central 

Google Scholar 
71.Krashevska, V., Sandmann, D., Maraun, M. & Scheu, S. Moderate changes in nutrient input alter tropical microbial and protist communities and belowground linkages. ISME J. 8, 1126–1134 (2014).CAS 
PubMed 

Google Scholar 
72.Tiede, Y. et al. Ants as indicators of environmental change and ecosystem processes. Ecol. Indic. 83, 527–537 (2017).
Google Scholar 
73.Santillán, V. et al. Spatio-temporal variation in bird assemblages is associated with fluctuations in temperature and precipitation along a tropical elevational gradient. PLoS ONE 13, e0196179 (2018).PubMed 
PubMed Central 

Google Scholar 
74.Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: An R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods Ecol. Evol. 7, 1451–1456 (2016).
Google Scholar 
75.Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: Interpolation and Extrapolation for Species Diversity. R package version 2.0.20, http://chao.stat.nthu.edu.tw/wordpress/software_download/ (2020).76.Chao, A. et al. Rarefaction and extrapolation with Hill numbers: A framework for sampling and estimation in species diversity studies. Ecol. Monogr. 84, 45–67 (2014).
Google Scholar 
77.Wallis, C. I. B. et al. Modeling tropical montane forest biomass, productivity and canopy traits with multispectral remote sensing data. Remote Sens. Environ. 225, 77–92 (2019).ADS 

Google Scholar 
78.Keuskamp, J. A., Dingemans, B. J. J., Lehtinen, T., Sarneel, J. M. & Hefting, M. M. Tea Bag Index: A novel approach to collect uniform decomposition data across ecosystems. Methods Ecol. Evol. 4, 1070–1075 (2013).
Google Scholar 
79.Quitián, M. et al. Elevation-dependent effects of forest fragmentation on plant-bird interaction networks in the tropical Andes. Ecography 41, 1497–1506 (2018).
Google Scholar 
80.Fries, A. et al. Thermal structure of a megadiverse Andean mountain ecosystem in southern Ecuador and its regionalization. Erdkunde 63, 321–335 (2009).
Google Scholar 
81.Fries, A., Rollenbeck, R., Nauß, T., Peters, T. & Bendix, J. Near surface air humidity in a megadiverse Andean mountain ecosystem of southern Ecuador and its regionalization. Agric. For. Meteorol. 152, 17–30 (2012).ADS 

Google Scholar 
82.Zvoleff, A. glcm: calculate textures from grey-level co-occurrence matrices (GLCMs). R package version 1.6.1 (2016).83.Wallis, C. I. B. et al. Remote sensing improves prediction of tropical montane species diversity but performance differs among taxa. Ecol. Indic. 83, 538–549 (2017).
Google Scholar 
84.Wolf, K., Veldkamp, E., Homeier, J. & Martinson, G. O. Nitrogen availability links forest productivity, soil nitrous oxide and nitric oxide fluxes of a tropical montane forest in southern Ecuador: N2 O + NO flux of tropical montane forests. Glob. Biogeochem. Cycles https://doi.org/10.1029/2010GB003876 (2011).Article 

Google Scholar 
85.Fisher, W. D. On Grouping for Maximum Homogeneity. J. Am. Stat. Assoc. 53, 789–798 (1958).MathSciNet 
MATH 

Google Scholar 
86.Bivand, R. classInt: Choose Univariate Class Intervals (2020).87.Oksanen, J. et al. vegan: Community Ecology Package (2020).88.vegan: Community Ecology Package. https://CRAN.R-project.org/package=vegan.89.Wood, S. N. Generalized Additive Models: An Introduction with R (Chapman and Hall/CRC, 2017).MATH 

Google Scholar 
90.Barbosa, A. M., Real, R., Munoz, A. R. & Brown, J. A. New measures for assessing model equilibrium and prediction mismatch in species distribution models. Divers. Distrib. 19, 1333–1338 (2013).
Google Scholar 
91.Lotz, T., Nieschulze, J., Bendix, J., Dobbermann, M. & König-Ries, B. Diverse or uniform? Intercomparison of two major German project databases for interdisciplinary collaborative functional biodiversity research. Ecol. Inform. 8, 10–19 (2012).
Google Scholar 
92.Göttlicher, D. et al. Land-cover classification in the Andes of southern Ecuador using Landsat ETM+ data as a basis for SVAT modelling. Int. J. Remote Sens. 30, 1867–1886 (2009).
Google Scholar 
93.Deng, Y., Wilson, J. P. & DEM Bauer, B. O. resolution dependencies of terrain attributes across a landscape. Int. J. Geogr. Inf. Sci. 21, 187–213 (2007).
Google Scholar 
94.Weiss, M. & Baret, F. S2ToolBox Level 2 products: LAI, FAPAR, FCOVER Version 1.1. in S2 Toolbox Level 2 Product algorithms v1.1 53.95.Unger, M., Homeier, J. & Leuschner, C. Relationships among leaf area index, below-canopy light availability and tree diversity along a transect from tropical lowland to montane forests in NE Ecuador. Trop. Ecol. 54, 33–45 (2013).
Google Scholar 
96.Krashevska, V., Maraun, M. & Scheu, S. Micro- and macroscale changes in density and diversity of Testate amoebae of tropical montane rain forests of southern Ecuador. Acta Protozool. 49, 17–28 (2010).
Google Scholar  More

1.Frolking, S. et al. Peatlands in the earth’s 21st century climate system. Environ. Rev. 19, 371–396 (2011).CAS 

Google Scholar 
2.Loisel, J. et al. A database and synthesis of northern peatland soil properties and Holocene carbon and nitrogen accumulation. Holocene 24(9), 1028–1042 (2014).ADS 

Google Scholar 
3.Belyea, L. R. & Malmer, N. Carbon sequestration in peatland: Patterns and mechanisms of response to climate change. Glob. Change Biol. 10(7), 1043–1052 (2004).ADS 

Google Scholar 
4.Gallego-Sala, A. V. et al. Latitudinal limits to the predicted increase of the peatland carbon sink with warming. Nat. Clim. Change 8(10), 907–913 (2018).ADS 
CAS 

Google Scholar 
5.Harden, J. W., Sundquist, E. T., Stallard, R. F. & Mark, R. K. Dynamics of soil carbon during deglaciation of the Laurentide ice-sheet. Science 258(5090), 1921–1924 (1992).ADS 
CAS 
PubMed 

Google Scholar 
6.Gorham, E., Lehman, C., Dyke, A., Janssens, J. & Dyke, L. Temporal and spatial aspects of peatland initiation following deglaciation in North America. Quat. Sci. Rev. 26(3–4), 300–311 (2007).ADS 

Google Scholar 
7.Indermühle, A. et al. Holocene carbon-cycle dynamics based on CO2 trapped in ice at Taylor Dome, Antarctica. Nature 398(6723), 121–126 (1999).ADS 

Google Scholar 
8.IPCC. In Climate Change (2013): The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Stocker T.F. et al.) (Cambridge University Press, 2013).9.Charman, D. J. et al. Climate-related changes in peatland carbon accumulation during the last millennium. Biogeosciences 10(2), 929–944 (2013).ADS 

Google Scholar 
10.Loisel, J. & Yu, Z. Recent acceleration of carbon accumulation in a boreal peatland, south central Alaska. J. Geophys. Res. Biogeosci. 118, 41–53 (2013).CAS 

Google Scholar 
11.Lund, M. et al. Variability in exchange of CO2 across 12 northern peatland and tundra sites. Glob. Change Biol. https://doi.org/10.1111/j.1365-2486.2009.02104.x (2010).Article 

Google Scholar 
12.Yang, G. et al. Responses of CO2 emission and pore water DOC concentration to soil warming and water table drawdown in Zoige Peatlands. Atmos. Environ. 152, 323–329 (2017).ADS 
CAS 

Google Scholar 
13.Laine, A. M. et al. Warming impacts on boreal fen CO2 exchange under wet and dry conditions. Glob. Change Biol. 25(6), 1995–2008 (2019).ADS 

Google Scholar 
14.Pancotto, V., Holl, D., Escobar, J., Castagnani, M. F. & Kutzbach, L. Cushion bog plant community responses to passive warming in southern Patagonia. Biogeosciences 18(16), 4817–4839 (2020).ADS 

Google Scholar 
15.Gunnarsson, U. Global patterns of Sphagnum productivity. J. Bryol. 27, 269–279 (2005).
Google Scholar 
16.Limpens, J. & Berendse, F. How litter quality affects mass loss and N loss from decomposing Sphagnum. Oikos 103(3), 537–547 (2003).CAS 

Google Scholar 
17.Hajek, T., Ballance, S., Limpens, J., Zijlstra, M. & Verhoeven, J. T. A. Cell-wall polysaccharides play an important role in decay resistance of Sphagnum and actively depressed decomposition in vitro. Biogeochemistry 103, 45–57 (2011).CAS 

Google Scholar 
18.Dorrepaal, E. et al. Carbon respiration from subsurface peat accelerated by climate warming in the subarctic. Nature 460, 616–619. https://doi.org/10.1038/nature08216 (2009).ADS 
CAS 
Article 

Google Scholar 
19.Loisel, J. et al. Expert assessment of future vulnerability of the global peatland carbon sink. Nat. Clim. Change 11, 70–77 (2021).ADS 

Google Scholar 
20.Van der Heijden, E., Verbeek, S. K. & Kuiper, P. J. C. Elevated atmospheric CO2 and increased nitrogen deposition: Effects on C and N metabolism and growth of the peat moss Sphagnum recurvum P. Beauv. Var. mucronatum (Russ.) Warnst. Glob. Change Biol. 6(2), 201–212 (2000).ADS 

Google Scholar 
21.Berendse, F. et al. Raised atmospheric CO2 levels and increased N deposition cause shifts in plant species composition and production in Sphagnum bogs. Glob. Change Biol. 7(5), 591–598 (2001).ADS 

Google Scholar 
22.Heijmans, M. M. P. D. et al. Effects of elevated carbon dioxide and increased nitrogen deposition on bog vegetation in the Netherlands. J. Ecol. 89(2), 268–279 (2001).CAS 

Google Scholar 
23.Heijmans, M. M. P. D., Klees, H., de Visser, W. & Berendse, F. Response of a Sphagnum bog plant community to elevated CO2 and N supply. Plant Ecol. 162(1), 123–134 (2002).
Google Scholar 
24.Mitchell, E. A. D. et al. Contrasted effects of increased N and CO2 supply on two keystone species in peatland restoration and implications for global change. J. Ecol. 90(3), 529–533 (2002).CAS 

Google Scholar 
25.Toet, S. et al. Moss responses to elevated CO2 and variation in hydrology in a temperate lowland peatland. Plant Ecol. 182(1–2), 27–40 (2006).
Google Scholar 
26.Ehlers, I. et al. Detecting long-term metabolic shifts using isotopomers: CO2-driven suppression of photorespiration in C3 plants over the 20th century. Proc. Natl. Acad. Sci. USA 112(51), 15585–15590 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
27.Serk, H., Nilsson, M. B., Figueira, J., Wieloch, T. & Schleucher, J. CO2 fertilization of Sphagnum peat mosses is modulated by water table level and other environmental factors. Plant Cell Environ. https://doi.org/10.1111/pce.14043 (2021).Article 
PubMed 

Google Scholar 
28.Walker, A. P. et al. Integrating the evidence for a terrestrial carbon sink caused by increasing atmospheric CO2. New Phytol. https://doi.org/10.1111/nph.16866 (2020).Article 
PubMed 

Google Scholar 
29.Schipperges, B. & Rydin, H. Response of photosynthesis of Sphagnum species from contrasting microhabitats to tissue water content and repeated desiccation. New Phytol. 140(4), 677–684 (1998).CAS 
PubMed 

Google Scholar 
30.Robroek, B. J. M., Schouten, M. G. C., Limpens, J., Berendse, F. & Poorter, H. Interactive effects of water table and precipitation on net CO2 assimilation of three co-occurring Sphagnum mosses differing in distribution above the water table. Glob. Change Biol. 15, 680–691 (2009).ADS 

Google Scholar 
31.Weston, D. J. et al. Sphagnum physiology in the context of changing climate: Emergent influences of genomics, modelling and host–microbiome interactions on understanding ecosystem function. Plant Cell Environ. 38(9), 1737–1751 (2015).PubMed 

Google Scholar 
32.Bengtsson, F., Granath, G. & Rydin, H. Photosynthesis, growth, and decay traits in Sphagnum—A multispecies comparison. Ecol. Evol. 6(19), 3325–3341 (2016).PubMed 
PubMed Central 

Google Scholar 
33.Luterbacher, J., Dietrich, D., Xoplaki, E., Grosjean, M. & Wanner, H. European seasonal and annual temperature variability, trends, and extremes since 1500. Science 303, 1499–1503 (2004).ADS 
CAS 
PubMed 

Google Scholar 
34.Pauling, A., Luterbacher, J., Casty, C. & Wanner, H. Five hundred years of gridded high-resolution precipitation reconstructions over Europe and the connection to large-scale circulation. Clim. Dyn. 26, 387–405 (2006).
Google Scholar 
35.Willmot, C.J., & Matsuura, K. Terrestrial air temperature and precipitation: Gridded monthly time series (1900–2017), (V 5.01 added 6/1/18). http://climate.geog.udel.edu/~climate/html_pages/Global2017/README.GlobalTsT2017.html and README.GlobalTsP2017.html (2018).36.Loisel, J., Garneau, M. & Hélie, J.-F. Sphagnum δ13C values as indicators of palaeohydrological changes in a peat bog. Holocene 20(2), 285–291 (2010).ADS 

Google Scholar 
37.Swindles, G. T. et al. Widespread drying of European peatlands in recent centuries. Nat. Geosci. 12, 922–928 (2019).ADS 
CAS 

Google Scholar 
38.Pelletier, N. et al. Influence of Holocene permafrost aggradation and thaw on the paleoecology and carbon storage of a peatland complex in northwestern Canada. Holocene 27(9), 1391–1405 (2017).ADS 

Google Scholar 
39.Talbot, J., Richard, P. J. H., Roulet, N. T. & Booth, R. K. Assessing long-term hydrological and ecological responses to drainage in a raised bog using paleoecology and a hydrosequence. J. Veg. Sci. 21, 143–156 (2010).
Google Scholar 
40.Kopp, B. J. et al. Impact of long-term drainage on summer groundwater flow patterns in the Mer Bleue peatland, Ontario, Canada. Hydrol. Earth Sci. 17, 3485–3498 (2013).
Google Scholar 
41.Van Bellen, S. et al. Late-Holocene climate dynamics recorded in the peat bogs of Tierra del Fuego, South America. Holocene 26(3), 489–501 (2016).ADS 

Google Scholar 
42.De Jong, R., Schoning, K. & Björck, S. Increased aeolian acitivty during humidity shifts as recorded in a raised bog in south-west Sweden during the past 1700 years. Clim. Past 3, 411–422 (2007).
Google Scholar 
43.Kunshan, B. et al. A 100-year history of water level change and driving mechanism in Heilongjiang River basin wetlands. Quat. Sci. 38(4), 981–995 (2018).
Google Scholar 
44.Zheng, X. The reconstruction of moisture availability in south-eastern Australia during the Holocene. PhD thesis, University of New South Wales, Sydney (2018).45.Loader, N. J. et al. Measurements of hydrogen, oxygen and carbon isotope variability in Sphagnum moss along a micro-topographical gradient in a southern Patagonian peatland. J. Quat. Sci. 31(4), 426–435 (2016).
Google Scholar 
46.Xia, Z. et al. Environmental controls on the carbon and water (H and O) isotopes in peatland Sphagnum mosses. Geochim. Cosmochim. Acta 277, 265–284 (2020).ADS 
CAS 

Google Scholar 
47.Sharkey, T. D. Estimating the rate of photorespiration in leaves. Physiol. Plant. 73, 147–152 (1988).CAS 

Google Scholar 
48.Flamholz, A. I. et al. Revisiting trade-offs between Rubisco kinetic properties. Biochemistry 58, 3365–3376 (2019).CAS 
PubMed 

Google Scholar 
49.Wu, J. H. & Roulet, N. T. Climate change reduces the capacity of northern peatlands to absorb the atmospheric carbon dioxide: The different responses of bogs and fens. Glob. Biogeochem. Cycles https://doi.org/10.1002/2014GB004845 (2014).Article 

Google Scholar 
50.Fenner, N. & Freeman, C. Drought-induced carbon loss in peatlands. Nat. Geosci. 4, 895–900 (2011).ADS 
CAS 

Google Scholar 
51.Lund, M., Chrsitensen, T. R., Lindroth, A. & Schubert, P. Effects of drought conditions on the carbon dioxide dynamics in a temperate peatland. Environ. Res. Lett. 7, 045704. https://doi.org/10.1088/1748-9326/7/4/045704 (2012).ADS 
Article 

Google Scholar 
52.Chong, M., Humphreys, E. R. & Moore, T. R. Microclimatic response to increasing shrub cover and its effect on Sphagnum CO2 exchange in a bog. Ecoscience 19, 89–97 (2012).
Google Scholar 
53.Fritz, C. et al. Nutrient additions in pristine Patagonian Sphagnum bog vegetation: Can phosphorus addition alleviate (the effects of) increased nitrogen loads. Plant Biol. 14, 491–499 (2012).CAS 
PubMed 

Google Scholar 
54.Farquhar, G. D., Ehleringer, J. R. & Hubick, K. T. Carbon isotope discrimination and photosynthesis. Annu. Rev. Plant Physiol. Plant Mol. Biol. 40, 503–537 (1989).CAS 

Google Scholar 
55.Bengtsson, F., Granath, G., Cronberg, N. & Rydin, H. Mechanisms behind species-specific water economy responses to water level drawdown in peat mosses. Ann. Bot. 126(2), 219–230 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
56.Nijp, J. J. et al. Can frequent precipitation moderate the impact of drought on peatmoss carbon uptake in northern peatlands?. New Phytol. 203(1), 70–80 (2014).PubMed 

Google Scholar 
57.Limpens, J., Berendse, F. & Klees, H. How phosphorous availability affects the impact of nitrogen deposition on Sphagnum and vascular plants in bogs. Ecosystems 7, 793–804 (2004).CAS 

Google Scholar 
58.Wu, J. H., Roulet, N. T., Nilsson, M., Lafleur, P. & Humphreys, E. Simulating the carbon cycling of Northern peat lands using a land surface scheme coupled to a Wetland Carbon Model (CLASS3W-MWM). Atmos. Ocean 50(4), 487–506 (2012).CAS 

Google Scholar 
59.Etheridge D. M., Steele L. P., Langenfelds R. L., Francey R. J., Barnola J. M., & Morgan V. I. Historical CO2 records from the law dome DE08, DE08-2, and DSS ice cores (1006 A.D.–1978 A.D). https://doi.org/10.3334/CDIAC/ATG.011 (Carbon Dioxide Information Analysis Center (CDIAC); Oak Ridge National Laboratory (ORNL), 1998).60.Laine, J. et al. The intrinsic beauty of Sphagnum Mosses—A Finnish guide to Identification. University of Helsinki. Dept. For. Sci. Publ. 2, 1–191 (2011).
Google Scholar 
61.Grover, S. P. P., Baldock, J. A. & Jacobsen, G. E. Accumulation and attrition of peat soils in the Australian Alps: Isotopic dating evidence. Austral. Ecol. 37, 510–517 (2012).
Google Scholar 
62.Kleinbecker, T., Hölzel, N. & Vogel, A. Gradients of continentality and moisture in south Patagonian ombrotrophic peatland vegetation. Folia Geobotanica 42, 363–382 (2007).
Google Scholar 
63.Hassel, K. et al. Sphagnum divinum (sp. nov.) and S. medium Limpr. and their relationship to S. magellanicum Brid. J. Bryol. 40, 197–222 (2018).
Google Scholar 
64.Betson, T. R., Augusti, A. & Schleucher, J. Quantification of deuterium isotopomers of tree-ring cellulose using nuclear magnetic resonance. Anal. Chem. 78(24), 8406–8411 (2006).CAS 
PubMed 

Google Scholar 
65.Schleucher, J., Vanderveer, P., Markley, J. L. & Sharkey, T. D. Intramolecular deuterium distributions reveal disequilibrium of chloroplast phosphoglucose isomerase. Plant Cell Environ. 22(5), 525–533 (1999).CAS 

Google Scholar 
66.Werner, R. A., Bruch, B. A. & Brand, W. A. ConFlo III—An interface for high precision δ13C and δ15N analysis with an extended dynamic range. Rapid Commun. Mass Spectrom. 13(13), 1237–1241 (1999).ADS 
CAS 
PubMed 

Google Scholar 
67.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67(1), 1–48 (2015).
Google Scholar 
68.Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).MathSciNet 
MATH 

Google Scholar 
69.Gareth, J., Witten, D., Hastie, T. & Tibshirani, R. An Introduction to Statistical Learning: With Applications in R (Springer Science+Business Media, 2013).MATH 

Google Scholar 
70.Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S 4th edn. (Springer, 2002).MATH 

Google Scholar 
71.Lüning, S., Galka, M., Bamonte, F. P., Rodríguez, F. G. & Vahrenholt, F. The medieval climate anomaly in South America. Quat. Int. 508, 70–87 (2019).
Google Scholar 
72.Schimpf, D. et al. The significance of chemical isotopic and detrital components in three coeval stalagmites from the superhumid southernmost Andes (53°S) as high-resolution paleo-climate proxies. Quat. Sci. Rev. 30, 443–459 (2011).ADS 

Google Scholar  More

1.Thompson LR, Sanders JG, McDonald D, Amir A, Ladau J, Locey KJ, et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature. 2017;551:457.CAS 
PubMed 
PubMed Central 

Google Scholar 
2.Nelson MB, Martiny AC, Martiny JBH. Global biogeography of microbial nitrogen-cycling traits in soil. Proc Natl Acad Sci USA. 2016;113:8033–40.CAS 
PubMed 
PubMed Central 

Google Scholar 
3.Tringe SG, von Mering C, Kobayashi A, Salamov AA, Chen K, Chang HW, et al. Comparative metagenomics of microbial communities. Science. 2005;308:554–7.CAS 
PubMed 

Google Scholar 
4.Ottesen EA, Young CR, Gifford SM, Eppley JM, Marin R, Schuster SC, et al. Ocean microbes. Multispecies diel transcriptional oscillations in open ocean heterotrophic bacterial assemblages. Science. 2014;345:207–12.CAS 
PubMed 

Google Scholar 
5.Fierer N. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat Rev Microbiol. 2017;15:579–90.CAS 
PubMed 

Google Scholar 
6.Starnawski P, Bataillon T, Ettema TJG, Jochum LM, Schreiber L, Chen X, et al. Microbial community assembly and evolution in subseafloor sediment. Proc Natl Acad Sci USA. 2017;114:2940–5.CAS 
PubMed 
PubMed Central 

Google Scholar 
7.Huttenhower C, Gevers D, Knight R, Abubucker S, Badger JH, Chinwalla AT, et al. Structure, function and diversity of the healthy human microbiome. Nature. 2012;486:207–14.CAS 

Google Scholar 
8.Brulc JM, Antonopoulos DA, Miller MEB, Wilson MK, Yannarell AC, Dinsdale EA, et al. Gene-centric metagenomics of the fiber-adherent bovine rumen microbiome reveals forage specific glycoside hydrolases. Proc Natl Acad Sci USA. 2009;106:1948–53.CAS 
PubMed 
PubMed Central 

Google Scholar 
9.Schloss PD, Handelsman J. Metagenomics for studying unculturable microorganisms: cutting the Gordian knot. Genome Biol. 2005;6:229.PubMed 
PubMed Central 

Google Scholar 
10.Fierer N, Leff JW, Adams BJ, Nielsen UN, Bates ST, Lauber CL, et al. Cross-biome metagenomic analyses of soil microbial communities and their functional attributes. Proc Natl Acad Sci USA. 2012;109:21390–5.CAS 
PubMed 
PubMed Central 

Google Scholar 
11.Almeida A, Mitchell AL, Boland M, Forster SC, Gloor GB, Tarkowska A, et al. A new genomic blueprint of the human gut microbiota. Nature. 2019;568:499–504.CAS 
PubMed 
PubMed Central 

Google Scholar 
12.Almeida A, Nayfach S, Boland M, Strozzi F, Beracochea M, Shi ZJ, et al. A unified catalog of 204,938 reference genomes from the human gut microbiome. Nat Biotechnol. 2021;39:105–14.CAS 
PubMed 

Google Scholar 
13.Anantharaman K, Brown CT, Hug LA, Sharon I, Castelle CJ, Probst AJ, et al. Thousands of microbial genomes shed light on interconnected biogeochemical processes in an aquifer system. Nat Commun. 2016;7:13219.CAS 
PubMed 
PubMed Central 

Google Scholar 
14.Delmont TO, Quince C, Shaiber A, Esen OC, Lee STM, Lucker S, et al. Nitrogen-fixing populations of planctomycetes and proteobacteria are abundant in the surface ocean. bioRxiv. 2017. https://doi.org/10.1101/129791.15.Parks DH, Rinke C, Chuvochina M, Chaumeil P-A, Woodcroft BJ, Evans PN, et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat Microbiol. 2017;2:1533–42.CAS 
PubMed 

Google Scholar 
16.Nayfach S, Shi ZJ, Seshadri R, Pollard KS, Kyrpides NC. New insights from uncultivated genomes of the global human gut microbiome. Nature. 2019;568:505–10.CAS 
PubMed 
PubMed Central 

Google Scholar 
17.Nayfach S, Roux S, Seshadri R, Udwary D, Varghese N, Schulz F, et al. A genomic catalog of Earth’s microbiomes. Nat Biotechnol. 2021;39:499–509.CAS 
PubMed 

Google Scholar 
18.Eloe-Fadrosh EA, Paez-Espino D, Jarett J, Dunfield PF, Hedlund BP, Dekas AE, et al. Global metagenomic survey reveals a new bacterial candidate phylum in geothermal springs. Nat Commun. 2016;7:10476.CAS 
PubMed 
PubMed Central 

Google Scholar 
19.Schulz F, Eloe-Fadrosh EA, Bowers RM, Jarett J, Nielsen T, Ivanova NN, et al. Towards a balanced view of the bacterial tree of life. Microbiome. 2017;5:140.PubMed 
PubMed Central 

Google Scholar 
20.Brown CT, Hug LA, Thomas BC, Sharon I, Castelle CJ, Singh A, et al. Unusual biology across a group comprising more than 15% of domain Bacteria. Nature. 2015;523:208–11.CAS 
PubMed 

Google Scholar 
21.Jay ZJ, Inskeep WP. The distribution, diversity, and importance of 16S rRNA gene introns in the order Thermoproteales. Biol Direct. 2015;10:1–10.CAS 

Google Scholar 
22.Albertsen M, Hugenholtz P, Skarshewski A, Nielsen KL, Tyson GW, Nielsen PH. Genome sequences of rare, uncultured bacteria obtained by differential coverage binning of multiple metagenomes. Nat Biotechnol. 2013;31:533–8.CAS 
PubMed 

Google Scholar 
23.Chen L-X, Anantharaman K, Shaiber A, Eren AM, Banfield JF. Accurate and complete genomes from metagenomes. Genome Res. 2020;30:315–33.CAS 
PubMed 
PubMed Central 

Google Scholar 
24.Tully BJ, Graham ED, Heidelberg JF. The Reconstruction of 2,631 draft metagenome-assembled genomes from the global oceans. bioRxiv. 2017. https://doi.org/10.1101/162503.25.Sczyrba A, Hofmann P, Belmann P, Koslicki D, Janssen S, Dröge J, et al. Critical assessment of metagenome interpretation—a benchmark of metagenomics software. Nat Methods. 2017;14:1063–71.CAS 
PubMed 
PubMed Central 

Google Scholar 
26.Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.CAS 
PubMed 
PubMed Central 

Google Scholar 
27.Bowers RM, Doud DFR, Woyke T. Analysis of single-cell genome sequences of bacteria and archaea. Emerg Top Life Sci. 2017;1:249–55.CAS 
PubMed 

Google Scholar 
28.Labonté JM, Swan BK, Poulos B, Luo H, Koren S, Hallam SJ, et al. Single-cell genomics-based analysis of virus-host interactions in marine surface bacterioplankton. ISME J. 2015;9:2386–99.PubMed 
PubMed Central 

Google Scholar 
29.Roux S, Hawley AK, Torres Beltran M, Scofield M, Schwientek P, Stepanauskas R, et al. Ecology and evolution of viruses infecting uncultivated SUP05 bacteria as revealed by single-cell- and meta-genomics. Elife. 2014;3:e03125.PubMed 
PubMed Central 

Google Scholar 
30.Jarett JK, Džunková M, Schulz F, Roux S, Paez-Espino D, Eloe-Fadrosh E, et al. Insights into the dynamics between viruses and their hosts in a hot spring microbial mat. ISME J. 2020;14:2527–41.CAS 
PubMed 
PubMed Central 

Google Scholar 
31.Woyke T, Doud DFR, Schulz F. The trajectory of microbial single-cell sequencing. Nat Methods. 2017;14: 1045–54.32.Lasken RS. Genomic sequencing of uncultured microorganisms from single cells. Nat Rev Microbiol. 2012;10:631–40.CAS 
PubMed 

Google Scholar 
33.Bowers RM, Kyrpides NC, Stepanauskas R, Harmon-Smith M, Doud D, Reddy TBK, et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat Biotechnol. 2017;35:725–31.CAS 
PubMed 
PubMed Central 

Google Scholar 
34.Zaremba-Niedzwiedzka K, Viklund J, Zhao W, Ast J, Sczyrba A, Woyke T, et al. Single-cell genomics reveal low recombination frequencies in freshwater bacteria of the SAR11 clade. Genome Biol. 2013;14:R130.PubMed 
PubMed Central 

Google Scholar 
35.Kashtan N, Roggensack SE, Rodrigue S, Thompson JW, Biller SJ, Coe A, et al. Single-cell genomics reveals hundreds of coexisting subpopulations in wild Prochlorococcus. Science. 2014;344:416–20.CAS 
PubMed 

Google Scholar 
36.Pachiadaki MG, Brown JM, Brown J, Bezuidt O, Berube PM, Biller SJ, et al. Charting the complexity of the marine microbiome through single-cell genomics. Cell. 2019;179:1623–35.e11.CAS 
PubMed 
PubMed Central 

Google Scholar 
37.Ellegaard KM, Klasson L, Andersson SGE. Testing the reproducibility of multiple displacement amplification on genomes of clonal endosymbiont populations. PLoS ONE. 2013;8:e82319.PubMed 
PubMed Central 

Google Scholar 
38.Luo C, Knight R, Siljander H, Knip M, Xavier RJ, Gevers D. ConStrains identifies microbial strains in metagenomic datasets. Nat Biotechnol. 2015;33:1045–52.CAS 
PubMed 
PubMed Central 

Google Scholar 
39.Milanese A, Mende DR, Paoli L, Salazar G, Ruscheweyh HJ, Cuenca M, et al. Microbial abundance, activity and population genomic profiling with mOTUs2. Nat Commun. 2019;10:1–11.
Google Scholar 
40.Nayfach S, Rodriguez-Mueller B, Garud N, Pollard KS. An integrated metagenomics pipeline for strain profiling reveals novel patterns of bacterial transmission and biogeography. Genome Res. 2016;26:1612–25.CAS 
PubMed 
PubMed Central 

Google Scholar 
41.Schloissnig S, Arumugam M, Sunagawa S, Mitreva M, Tap J, Zhu A, et al. Genomic variation landscape of the human gut microbiome. Nature. 2013;493:45–50.PubMed 

Google Scholar 
42.Garud NR, Pollard KS. Population genetics in the human microbiome. Trends Genet. 2020;36:53–67.43.Bushnell B, Rood J, Singer E. BBMerge—accurate paired shotgun read merging via overlap. PLoS ONE. 2017;12:e0185056.PubMed 
PubMed Central 

Google Scholar 
44.Grasby SE, Hutcheon I. Controls on the distribution of thermal springs in the southern Canadian Cordillera. Can J Earth Sci. 2001;38:427–40.CAS 

Google Scholar 
45.Brady AL, Sharp CE, Grasby SE, Dunfield PF. Anaerobic carboxydotrophic bacteria in geothermal springs identified using stable isotope probing. Front Microbiol. 2015;6:897.PubMed 
PubMed Central 

Google Scholar 
46.Rinke C, Lee J, Nath N, Goudeau D, Thompson B, Poulton N, et al. Obtaining genomes from uncultivated environmental microorganisms using FACS-based single-cell genomics. Nat Protoc. 2014;9:1038–48.CAS 
PubMed 

Google Scholar 
47.Tremblay J, Singh K, Fern A, Kirton ES, He S, Woyke T, et al. Primer and platform effects on 16S rRNA tag sequencing. Front Microbiol. 2015;6:771.PubMed 
PubMed Central 

Google Scholar 
48.Bowers RM, Clum A, Tice H, Lim J, Singh K, Ciobanu D, et al. Impact of library preparation protocols and template quantity on the metagenomic reconstruction of a mock microbial community. BMC Genomics. 2015;16:856.PubMed 
PubMed Central 

Google Scholar 
49.Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37:852–7.CAS 
PubMed 
PubMed Central 

Google Scholar 
50.Amir A, McDonald D, Navas-Molina JA, Kopylova E, Morton JT, Zech Xu Z, et al. Deblur rapidly resolves single-nucleotide community sequence patterns. mSystems. 2017;2:e00191–16.PubMed 
PubMed Central 

Google Scholar 
51.Bokulich NA, Kaehler BD, Rideout JR, Dillon M, Bolyen E, Knight R, et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome. 2018;6:1–17.
Google Scholar 
52.Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.CAS 
PubMed 

Google Scholar 
53.Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19:455–77.CAS 
PubMed 
PubMed Central 

Google Scholar 
54.Kang DD, Froula J, Egan R, Wang Z. A robust statistical framework for reconstructing genomes from metagenomic data. bioRxiv. 2014. https://doi.org/10.1101/011460.55.Chen IMA, Chu K, Palaniappan K, Ratner A, Huang J, Huntemann M, et al. The IMG/M data management and analysis system v.6.0: New tools and advanced capabilities. Nucleic Acids Res. 2021;49:D751–63.CAS 
PubMed 

Google Scholar 
56.Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High-throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. bioRxiv. 2017. https://doi.org/10.1101/225342.57.Chen I-MA, Markowitz VM, Chu K, Palaniappan K, Szeto E, Pillay M, et al. IMG/M: integrated genome and metagenome comparative data analysis system. Nucleic Acids Res. 2017;45:D507–16.CAS 
PubMed 

Google Scholar 
58.Eveleigh RJM, Meehan CJ, Archibald JM, Beiko RG. Being Aquifex aeolicus: untangling a hyperthermophile’s checkered past. Genome Biol Evol. 2013;5:2478.PubMed 
PubMed Central 

Google Scholar 
59.Fuchsman CA, Collins RE, Rocap G, Brazelton WJ. Effect of the environment on horizontal gene transfer between bacteria and archaea. PeerJ. 2017;5:e3865.PubMed 
PubMed Central 

Google Scholar 
60.Boussau B, Guéguen L, Gouy M. Accounting for horizontal gene transfers explains conflicting hypotheses regarding the position of aquificales in the phylogeny of Bacteria. BMC Evol Biol. 2008;8:272.PubMed 
PubMed Central 

Google Scholar 
61.Yu FB, Blainey PC, Schulz F, Woyke T, Horowitz MA, Quake SR. Microfluidic-based mini-metagenomics enables discovery of novel microbial lineages from complex environmental samples. Elife. 2017;6:e26580.PubMed 
PubMed Central 

Google Scholar 
62.Hug LA, Baker BJ, Anantharaman K, Brown CT, Probst AJ, Castelle CJ, et al. A new view of the tree of life. Nat Microbiol. 2016;1:16048.CAS 
PubMed 

Google Scholar 
63.Case RJ, Boucher Y, Dahllöf I, Holmström C, Doolittle WF, Kjelleberg S. Use of 16S rRNA and rpoB genes as molecular markers for microbial ecology studies. Appl Environ Microbiol. 2007;73:278–88.CAS 
PubMed 

Google Scholar 
64.Roux S, Enault F, Hurwitz BL, Sullivan MB. VirSorter: mining viral signal from microbial genomic data. PeerJ. 2015;3:e985.PubMed 
PubMed Central 

Google Scholar 
65.Krawczyk PS, Lipinski L, Dziembowski A. PlasFlow: predicting plasmid sequences in metagenomic data using genome signatures. Nucleic Acids Res. 2018;46:e35.PubMed 
PubMed Central 

Google Scholar 
66.Cock PJA, Antao T, Chang JT, Chapman BA, Cox CJ, Dalke A, et al. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics. 2009;25:1422–3.CAS 
PubMed 
PubMed Central 

Google Scholar 
67.Paez-Espino D, Chen I-MA, Palaniappan K, Ratner A, Chu K, Szeto E, et al. IMG/VR: a database of cultured and uncultured DNA Viruses and retroviruses. Nucleic Acids Res. 2017;45:D457–65.CAS 
PubMed 

Google Scholar 
68.Bland C, Ramsey TL, Sabree F, Lowe M, Brown K, Kyrpides NC, et al. CRISPR Recognition Tool (CRT): a tool for automatic detection of clustered regularly interspaced palindromic repeats. BMC Bioinforma. 2007;8:209.
Google Scholar 
69.Edgar RC. PILER-CR: Fast and accurate identification of CRISPR repeats. BMC Bioinforma. 2007;8:18.
Google Scholar 
70.Sharp CE, Brady AL, Sharp GH, Grasby SE, Stott MB, Dunfield PF. Humboldt’s spa: microbial diversity is controlled by temperature in geothermal environments. ISME J. 2014;8:1166–74.CAS 
PubMed 
PubMed Central 

Google Scholar 
71.Eloe-Fadrosh EA, Ivanova NN, Woyke T, Kyrpides NC. Metagenomics uncovers gaps in amplicon-based detection of microbial diversity. Nat Microbiol. 2016;1:15032.CAS 
PubMed 

Google Scholar 
72.Clingenpeel S, Clum A, Schwientek P, Rinke C, Woyke T. Reconstructing each cell’s genome within complex microbial communities-dream or reality? Front Microbiol. 2014;5:771.PubMed 

Google Scholar 
73.Westoby M, Nielsen DA, Gillings MR, Litchman E, Madin JS, Paulsen IT, et al. Cell size, genome size, and maximum growth rate are near-independent dimensions of ecological variation across bacteria and archaea. Ecol Evol. 2021;11:3956–76.PubMed 
PubMed Central 

Google Scholar 
74.Bendall ML, Stevens SL, Chan L-K, Malfatti S, Schwientek P, Tremblay J, et al. Genome-wide selective sweeps and gene-specific sweeps in natural bacterial populations. ISME J. 2016;10:1589–601.PubMed 
PubMed Central 

Google Scholar 
75.Meziti A, Tsementzi D, Rodriguez-R LM, Hatt JK, Karayanni H, Kormas KA, et al. Quantifying the changes in genetic diversity within sequence-discrete bacterial populations across a spatial and temporal riverine gradient. ISME J. 2019;13:767–79.PubMed 

Google Scholar 
76.Achtman M, Wagner M. Microbial diversity and the genetic nature of microbial species. Nat Rev Microbiol. 2008;6:431–40.77.Reysenbach A-L. Aquificales ord. nov. Bergey’s manual of systematics of archaea and bacteria. Chichester: John Wiley & Sons, Ltd; 2015. p. 1.78.McKay LJ, Nigro OD, Dlakić M, Luttrell KM, Rusch DB, Fields MW, et al. Sulfur cycling and host-virus interactions in Aquificales-dominated biofilms from Yellowstone’s hottest ecosystems. ISME J. 2021;2021:1–14.
Google Scholar 
79.Hügler M, Huber H, Molyneaux SJ, Vetriani C, Sievert SM. Autotrophic CO2 fixation via the reductive tricarboxylic acid cycle in different lineages within the phylum Aquificae: evidence for two ways of citrate cleavage. Environ Microbiol. 2007;9:81–92.PubMed 

Google Scholar 
80.Alneberg J, Karlsson CMG, Divne AM, Bergin C, Homa F, Lindh MV, et al. Genomes from uncultivated prokaryotes: a comparison of metagenome-assembled and single-amplified genomes. Microbiome. 2018;6:173.PubMed 
PubMed Central 

Google Scholar 
81.Nelson WC, Tully BJ, Mobberley JM. Biases in genome reconstruction from metagenomic data. PeerJ. 2020;8:e10119.PubMed 
PubMed Central 

Google Scholar 
82.Golicz AA, Bayer PE, Bhalla PL, Batley J, Edwards D. Pangenomics comes of age: from bacteria to plant and animal applications. Trends Genet. 2020;36:132–45.83.Maguire F, Jia B, Gray KL, Lau WYV, Beiko RG, Brinkman FSL. Metagenome-assembled genome binning methods with short reads disproportionately fail for plasmids and genomic islands. Micro Genomics. 2020;6:1–12.CAS 

Google Scholar 
84.Shmakov SA, Sitnik V, Makarova KS, Wolf YI, Severinov KV, Koonin EV. The CRISPR spacer space is dominated by sequences from species-specific mobilomes. mBio. 2017;8:e01397–17.CAS 
PubMed 
PubMed Central 

Google Scholar 
85.Weinberger AD, Wolf YI, Lobkovsky AE, Gilmore MS, Koonin EV. Viral diversity threshold for adaptive immunity in prokaryotes. MBio. 2012;3:e00456–12.CAS 
PubMed 
PubMed Central 

Google Scholar 
86.Drake JW. Avoiding dangerous missense: thermophiles display especially low mutation rates. PLoS Genet. 2009;5:1000520.
Google Scholar 
87.Wozniak RA, Waldor MK. Integrative and conjugative elements: mosaic mobile genetic elements enabling dynamic lateral gene flow. Nat Rev Microbiol. 2010;8:552–63.CAS 
PubMed 

Google Scholar 
88.Soto-Perez P, Bisanz JE, Berry JD, Lam KN, Bondy-Denomy J, Turnbaugh PJ. CRISPR-Cas system of a prevalent human gut bacterium reveals hyper-targeting against phages in a human virome catalog. Cell Host Microbe. 2019;26:325–35.e5.CAS 
PubMed 
PubMed Central 

Google Scholar 
89.Stern A, Keren L, Wurtzel O, Amitai G, Sorek R. Self-targeting by CRISPR: gene regulation or autoimmunity? Trends Genet. 2010;26:335–40.CAS 
PubMed 
PubMed Central 

Google Scholar 
90.Nobrega FL, Walinga H, Dutilh BE, Brouns SJJ. Prophages are associated with extensive CRISPR-Cas auto-immunity. Nucleic Acids Res. 2020;48:12074–84.CAS 
PubMed 
PubMed Central 

Google Scholar 
91.Edgar R, Qimron U. The Escherichia coli CRISPR system protects from λ lysogenization, lysogens, and prophage induction. J Bacteriol. 2010;192:6291–4.CAS 
PubMed 
PubMed Central 

Google Scholar 
92.Vercoe RB, Chang JT, Dy RL, Taylor C, Gristwood T, Clulow JS, et al. Cytotoxic chromosomal targeting by CRISPR/Cas systems can reshape bacterial genomes and expel or remodel pathogenicity islands. PLoS Genet. 2013;9:e1003454.CAS 
PubMed 
PubMed Central 

Google Scholar 
93.Wiedenbeck J, Cohan FM. Origins of bacterial diversity through horizontal genetic transfer and adaptation to new ecological niches. FEMS Microbiol Rev. 2011;35:957–76.94.Cheng L, Connor TR, Siren J, Aanensen DM, Corander J. Hierarchical and spatially explicit clustering of DNA sequences with BAPS software. Mol Biol Evol. 2013;30:1224–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
95.Tonkin-Hill G, Lees JA, Bentley SD, Frost SDW, Corander J. RhierBAPS: An R implementation of the population clustering algorithm hierBAPS. Wellcome Open Res. 2018;3:93.PubMed 
PubMed Central 

Google Scholar 
96.Rosen MJ, Davison M, Bhaya D, Fisher DS. Fine-scale diversity and extensive recombination in a quasisexual bacterial population occupying a broad niche. Science. 2015;348:1019–23.CAS 
PubMed 

Google Scholar 
97.Bubendorfer S, Krebes J, Yang I, Hage E, Schulz TF, Bahlawane C, et al. Genome-wide analysis of chromosomal import patterns after natural transformation of Helicobacter pylori. Nat Commun. 2016;7:1–12.
Google Scholar 
98.Hanage WP, Fraser C, Spratt BG. Fuzzy species among recombinogenic bacteria. BMC Biol. 2005;3:6.PubMed 
PubMed Central 

Google Scholar 
99.Sakoparnig T, Field C, van Nimwegen E. Whole genome phylogenies reflect the distributions of recombination rates for many bacterial species. Elife. 2021;10:1–61.
Google Scholar 
100.Koonin EV, Makarova KS, Wolf YI, Krupovic M. Evolutionary entanglement of mobile genetic elements and host defence systems: guns for hire. Nat Rev Genet. 2020;21:119–31.CAS 
PubMed 

Google Scholar 
101.Iranzo J, Cuesta JA, Manrubia S, Katsnelson MI, Koonin EV. Disentangling the effects of selection and loss bias on gene dynamics. Proc Natl Acad Sci USA. 2017;114:E5616–24.CAS 
PubMed 
PubMed Central 

Google Scholar  More

1.Douglas, A. E. Multiorganismal insects: Diversity and function of resident microorganisms. Annu. Rev. Entomol. 60, 17–34 (2015).CAS 
PubMed 

Google Scholar 
2.Dillon, R. J. & Dillon, V. M. THE gut bacteria of insects: Nonpathogenic interactions. Annu. Rev. Entomol. 49, 71–92 (2004).CAS 
PubMed 

Google Scholar 
3.Duplouy, A., Hursts, G. D. D., O’neill, S. L. & Charlat, S. Rapid spread of male-killing Wolbachia in the butterfly Hypolimnas bolina. J. Evol. Biol. 23, 231–235 (2010).CAS 
PubMed 

Google Scholar 
4.Altizer, S. M., Oberhauser, K. S. & Brower, L. P. Associations between host migration and the prevalence of a protozoan parasite in natural populations of adult monarch butterflies. Ecol. Entomol. 25, 125–139 (2000).
Google Scholar 
5.Jiggins, X., Hurst, X., Dolman, X. & Majerus, X. High-prevalence male-killing Wolbachia in the butterfly Acraea encedana. J. Evol. Biol. 13, 495–501 (2000).
Google Scholar 
6.Xu, P., Liu, Y., Graham, R. I., Wilson, K. & Wu, K. Densovirus is a mutualistic symbiont of a global crop pest (Helicoverpa armigera) and protects against a baculovirus and Bt Biopesticide. PLoS Pathog. 10, e1004490 (2014).PubMed 
PubMed Central 

Google Scholar 
7.Bapatla, K. G., Singh, A., Yeddula, S. & Patil, R. H. Annotation of gut bacterial taxonomic and functional diversity in Spodoptera litura and Spilosoma obliqua. J. Basic Microbiol. 58, 217–226 (2018).CAS 
PubMed 

Google Scholar 
8.Chen, F. et al. Effects of Wolbachia on mitochondrial DNA variation in populations of Athetis lepigone (Lepidoptera: Noctuidae) in China. Mitochondrial DNA Part A 28, 826–834 (2017).CAS 

Google Scholar 
9.van Nieukerken, E. J. et al. Order Lepidoptera Linnaeus, 1758. In: Zhang, Z.-Q. (Ed.) Animal biodiversity: An outline of higher-level classification and survey of taxonomic richness. Zootaxa 1758, 212–221 (2011).
Google Scholar 
10.Duplouy, A. & Hornett, E. A. Uncovering the hidden players in Lepidoptera biology: The heritable microbial endosymbionts. PeerJ 6, e4629 (2018).PubMed 
PubMed Central 

Google Scholar 
11.Werren, J. H., Windsor, D. & Guo, L. Distribution of Wolbachia among neotropical arthropods. Proc. R. Soc. Lond. Ser. B Biol. Sci. 262, 197–204 (1995).ADS 

Google Scholar 
12.Salunke, B. K. et al. Determination of Wolbachia diversity in butterflies from Western Ghats, India, by a multigene approach. Appl. Environ. Microbiol. 78, 4458–4467 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
13.Duplouy, A. & Brattström, O. Wolbachia in the genus Bicyclus: A forgotten player. Microb. Ecol. 75, 255–263 (2018).PubMed 

Google Scholar 
14.Jiggins, F. M., Bentley, J. K., Majerus, M. E. & Hurst, G. D. How many species are infected with Wolbachia ? Cryptic sex ratio distorters revealed to be common by intensive sampling. Proc. R. Soc. Lond. Ser. B Biol. Sci. 268, 1123–1126 (2001).CAS 

Google Scholar 
15.Tagami, Y. & Miura, K. Distribution and prevalence of Wolbachia in Japanese populations of Lepidoptera. Insect Mol. Biol. 13, 359–364 (2004).CAS 
PubMed 

Google Scholar 
16.Ilinsky, Y. & Kosterin, O. E. Molecular diversity of Wolbachia in Lepidoptera: Prevalent allelic content and high recombination of MLST genes. Mol. Phylogenet. Evol. 109, 164–179 (2017).CAS 
PubMed 

Google Scholar 
17.Sazama, E. J., Ouellette, S. P. & Wesner, J. S. Bacterial endosymbionts are common among, but not necessarily within, insect species. Environ. Entomol. 48, 127–133 (2019).PubMed 

Google Scholar 
18.Zaspel, J. M. Systematics, biology, and behavior of fruit-piercing and blood-feeding moths in the subfamily calpinae (lepidoptera: noctuidae). (2008).19.Mason, C. J. & Raffa, K. F. Acquisition and structuring of midgut bacterial communities in gypsy moth (Lepidoptera: Erebidae) larvae. Environ. Entomol. 43, 25 (2014).
Google Scholar 
20.Ilinsky, Y. et al. Detection of bacterial symbionts (Wolbachia, Spiroplasma)and eukaryotic pathogen (Microsporidia) in Japanese populationsof gypsy moth species (Lymantria spp.). Euroasian Entomol. J. 16, 1–5 (2017).
Google Scholar 
21.Boonsit, P. & Wiwatanaratanabutr, I. Infection density, diversity, and distribution of Wolbachia bacteria in moths (Order Lepidoptera): First systematic report from Thailand. J. Asia-Pac. Entomol. 24, 20 (2021).
Google Scholar 
22.Gavotte, L. et al. A survey of the bacteriophage WO in the endosymbiotic bacteria Wolbachia. Mol. Biol. Evol. 24, 427–435 (2006).PubMed 

Google Scholar 
23.Wang, G. H. et al. Bacteriophage WO can mediate horizontal gene transfer in endosymbiotic wolbachia genomes. Front. Microbiol. 7, 1–16 (2016).
Google Scholar 
24.Wang, N., Jia, S., Xu, H., Liu, Y. & Huang, D. Multiple horizontal transfers of bacteriophage WO and host wolbachia in fig wasps in a closed community. Front. Microbiol. 7, 1–10 (2016).
Google Scholar 
25.Tanaka, K., Furukawa, S., Nikoh, N., Sasaki, T. & Fukatsu, T. Complete WO phage sequences reveal their dynamic evolutionary trajectories and putative functional elements required for integration into the wolbachia genome. Appl. Environ. Microbiol. 75, 5676–5686 (2009).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
26.Kaushik, S., Sharma, K. K., Ramani, R. & Lakhanpaul, S. Detection of Wolbachia phage (WO) in Indian Lac insect [Kerria lacca (Kerr)] and its implications. Indian J. Microbiol. 59, 237–240 (2019).CAS 
PubMed 

Google Scholar 
27.LePage, D. P. et al. Prophage WO genes recapitulate and enhance Wolbachia-induced cytoplasmic incompatibility. Nature 543, 243–247 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
28.Shropshire, J. D., On, J., Layton, E. M., Zhou, H. & Bordenstein, S. R. One prophage WO gene rescues cytoplasmic incompatibility in Drosophila melanogaster. Proc. Natl. Acad. Sci. 115, 4987 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
29.Bordenstein, S. R. & Bordenstein, S. R. Eukaryotic association module in phage WO genomes from Wolbachia. Nat. Commun. 7, 13155 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
30.Kent, B. N. & Bordenstein, S. R. Phage WO of Wolbachia: Lambda of the endosymbiont world. Trends Microbiol. 18, 173–181 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
31.Dale, C., Young, S. A., Haydon, D. T. & Welburn, S. C. The insect endosymbiont Sodalis glossinidius utilizes a type III secretion system for cell invasion. Proc. Natl. Acad. Sci. 98, 1883–1888 (2001).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
32.Boyd, B. M. et al. Two bacterial genera, sodalis and rickettsia, associated with the seal louse Proechinophthirus fluctus (Phthiraptera: Anoplura). Appl. Environ. Microbiol. 82, 3185–3197 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
33.Fukatsu, T. et al. Bacterial endosymbiont of the slender pigeon louse, Columbicola columbae, allied to endosymbionts of grain weevils and tsetse flies. Appl. Environ. Microbiol. 73, 6660–6668 (2007).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
34.Šochová, E., Husník, F., Nováková, E., Halajian, A. & Hypša, V. Arsenophonus and Sodalis replacements shape evolution of symbiosis in louse flies. PeerJ 5, e4099 (2017).PubMed 
PubMed Central 

Google Scholar 
35.Burke, G. R., Normark, B. B., Favret, C. & Moran, N. A. Evolution and diversity of facultative symbionts from the aphid subfamily lachninae. Appl. Environ. Microbiol. 75, 5328–5335 (2009).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
36.Santos-Garcia, D., Silva, F. J., Morin, S., Dettner, K. & Kuechler, S. M. The all-rounder sodalis: A new bacteriome-associated endosymbiont of the lygaeoid bug henestaris halophilus (Heteroptera: Henestarinae) and a critical examination of its evolution. Genome Biol. Evol. 9, 2893–2910 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
37.Toju, H. & Fukatsu, T. Diversity and infection prevalence of endosymbionts in natural populations of the chestnut weevil: Relevance of local climate and host plants. Mol. Ecol. 20, 853–868 (2011).PubMed 

Google Scholar 
38.Conord, C. et al. Long-term evolutionary stability of bacterial endosymbiosis in curculionoidea: Additional evidence of symbiont replacement in the dryophthoridae family. Mol. Biol. Evol. 25, 859–868 (2008).CAS 
PubMed 

Google Scholar 
39.Kaiwa, N. et al. Bacterial symbionts of the giant jewel stinkbug Eucorysses grandis (Hemiptera: Scutelleridae). Zool. Sci. 28, 169–174 (2011).
Google Scholar 
40.Rubin, B. E. R., Sanders, J. G., Turner, K. M., Pierce, N. E. & Kocher, S. D. Social behaviour in bees influences the abundance of Sodalis (Enterobacteriaceae) symbionts. R. Soc. Open Sci. 5, 180369 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
41.Sameshima, S., Hasegawa, E., Kitade, O., Minaka, N. & Matsumoto, T. Phylogenetic comparison of endosymbionts with their host ants based on molecular evidence. Zool. Sci. 16, 993–1000 (1999).CAS 

Google Scholar 
42.Oishi, S., Moriyama, M., Koga, R. & Fukatsu, T. Morphogenesis and development of midgut symbiotic organ of the stinkbug Plautia stali (Hemiptera: Pentatomidae). Zool. Lett. 5, 16 (2019).
Google Scholar 
43.Mason, C. J. & Raffa, K. F. Acquisition and structuring of midgut bacterial communities in gypsy moth (Lepidoptera: Erebidae) larvae. Environ. Entomol. 43, 595–604 (2014).PubMed 

Google Scholar 
44.Khojandi, N., Haselkorn, T. S., Eschbach, M. N., Naser, R. A. & DiSalvo, S. Intracellular Burkholderia Symbionts induce extracellular secondary infections; driving diverse host outcomes that vary by genotype and environment. ISME J. 13, 2068–2081 (2019).PubMed 
PubMed Central 

Google Scholar 
45.Itoh, H. et al. Host–symbiont specificity determined by microbe–microbe competition in an insect gut. Proc. Natl. Acad. Sci. USA 116, 22673–22682 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
46.Ohbayashi, T., Itoh, H., Lachat, J., Kikuchi, Y. & Mergaert, P. Burkholderia gut symbionts associated with European and Japanese populations of the dock bug Coreus marginatus (Coreoidea: Coreidae). Microbes Environ. 34, 219–222 (2019).PubMed 
PubMed Central 

Google Scholar 
47.Itoh, H. et al. Evidence of environmental and vertical transmission of Burkholderia symbionts in the oriental chinch bug, Cavelerius saccharivorus (Heteroptera: Blissidae). Appl. Environ. Microbiol. 80, 5974–5983 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
48.Kikuchi, Y. et al. Symbiont-mediated insecticide resistance. Proc. Natl. Acad. Sci. USA 109, 8618–8622 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
49.Louis, F. et al. The bracovirus genome of the parasitoid wasp Cotesia congregata is amplified within 13 replication units, including sequences not packaged in the particles. J. Virol. 87, 9649–9660 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
50.Ghanavi, H. R., Twort, V., Hartman, T. J., Zahiri, R. & Wahlberg, N. The (non) accuracy of mitochondrial genomes for family level phylogenetics: The case of erebid moths (Lepidoptera; Erebidae). bioRxiv https://doi.org/10.1101/2021.07.14.452330 (2021).Article 

Google Scholar 
51.Rigaud, T. & Juchault, P. Success and failure of horizontal transfers of feminizing Wolbachia endosymbionts in woodlice. J. Evol. Biol. 8, 25 (1995).
Google Scholar 
52.Zahiri, R. et al. Molecular phylogenetics of Erebidae (Lepidoptera, Noctuoidea). Syst. Entomol. 37, 102–124 (2012).
Google Scholar 
53.Andrews, S. FastQC: A Quality Control Tool for High Throughput Sequence Data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (2010).54.Schmieder, R. & Edwards, R. Quality control and preprocessing of metagenomic datasets. Bioinformatics https://doi.org/10.1093/bioinformatics/btr026 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
55.Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics https://doi.org/10.1093/bioinformatics/btu170 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
56.Wood, D. E. & Salzberg, S. L. Kraken: Ultrafast metagenomic sequence classification using exact alignments. Genome Biol. 15, R46 (2014).PubMed 
PubMed Central 

Google Scholar 
57.Segata, N. et al. Metagenomic microbial community profiling using unique clade-specific marker genes. Nat. Methods 9, 811–814 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
58.Kikuchi, Y. & Yumoto, I. Efficient colonization of the bean bug Riptortus pedestris by an environmentally transmitted Burkholderia Symbiont. Appl. Environ. Microbiol. 79, 2088–2091 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
59.Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 20 (2012).
Google Scholar 
60.Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 20 (2009).CAS 

Google Scholar 
61.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).MATH 

Google Scholar  More