Ecology

1.Hou, S. et al. A microbiota–root–shoot circuit favours Arabidopsis growth over defence under suboptimal light. Nat. Plants 7, 1078–1092 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
2.Durán, P. et al. Microbial interkingdom interactions in roots promote Arabidopsis survival. Cell 175, 973–983.e14 (2018).PubMed 
PubMed Central 

Google Scholar 
3.van der Heijden, M. G., Bruin, S., de, Luckerhoff, L., van Logtestijn, R. S. & Schlaeppi, K. A widespread plant-fungal-bacterial symbiosis promotes plant biodiversity, plant nutrition and seedling recruitment. ISME J. 10, 389–399 (2015).PubMed 
PubMed Central 

Google Scholar 
4.Carrión, V. J. et al. Pathogen-induced activation of disease-suppressive functions in the endophytic root microbiome. Science 366, 606–612 (2019).ADS 
PubMed 

Google Scholar 
5.Wagg, C., Schlaeppi, K., Banerjee, S., Kuramae, E. E. & van der Heijden, M. G. A. Fungal-bacterial diversity and microbiome complexity predict ecosystem functioning. Nat. Commun. 10, 1–10 (2019).CAS 

Google Scholar 
6.Martin, F. M., Uroz, S. & Barker, D. G. Ancestral alliances: Plant mutualistic symbioses with fungi and bacteria. Science 356 (2017).7.Nagy, L. G. et al. in The Fungal Kingdom 35–56 (ASM Press, 2017). https://doi.org/10.1128/9781555819583.ch2.8.Brundrett, M. C. & Tedersoo, L. Evolutionary history of mycorrhizal symbioses and global host plant diversity. N. Phytol. 220, 1108–1115 (2018).
Google Scholar 
9.Delavaux, C. S. et al. Mycorrhizal fungi influence global plant biogeography. Nat. Ecol. Evol. 3, 424–429 (2019).PubMed 

Google Scholar 
10.Soudzilovskaia, N. A. et al. Global mycorrhizal plant distribution linked to terrestrial carbon stocks. Nat. Commun. 10, 1–10 (2019).CAS 

Google Scholar 
11.Steidinger, B. S. et al. Climatic controls of decomposition drive the global biogeography of forest-tree symbioses. Nature 569, 404–408 (2019).ADS 
CAS 
PubMed 

Google Scholar 
12.Trivedi, P., Leach, J. E., Tringe, S. G., Sa, T. & Singh, B. K. Plant–microbiome interactions: from community assembly to plant health. Nat. Rev. Microbiol. 18, 607–621 (2020).CAS 
PubMed 

Google Scholar 
13.Lugtenberg, B. J. J., Caradus, J. R. & Johnson, L. J. Fungal endophytes for sustainable crop production. FEMS Microbiol. Ecol. 92, fiw194 (2016).PubMed 

Google Scholar 
14.Glynou, K. et al. The local environment determines the assembly of root endophytic fungi at a continental scale. Environ. Microbiol. 18, 2418–2434 (2016).CAS 
PubMed 

Google Scholar 
15.Glynou, K., Nam, B., Thines, M. & Maciá-Vicente, J. G. Facultative root-colonizing fungi dominate endophytic assemblages in roots of nonmycorrhizal Microthlaspi species. N. Phytol. 217, 1190–1202 (2018).
Google Scholar 
16.U’Ren, J. M. et al. Host availability drives distributions of fungal endophytes in the imperilled boreal realm. Nat. Ecol. Evol. 3, 1430–1437 (2019).PubMed 

Google Scholar 
17.Maciá-Vicente, J. G., Piepenbring, M. & Koukol, O. Brassicaceous roots as an unexpected diversity hot-spot of helotialean endophytes. IMA Fungus 11, 1–23 (2020).
Google Scholar 
18.Thiergart, T. et al. Root microbiota assembly and adaptive differentiation among European Arabidopsis populations. Nat. Ecol. Evol. 4, 122–131 (2020).PubMed 

Google Scholar 
19.Oita, S. et al. Climate and seasonality drive the richness and composition of tropical fungal endophytes at a landscape scale. Commun. Biol. 4, 1–11 (2021).
Google Scholar 
20.Vannier, N., Bittebiere, A. K., Mony, C. & Vandenkoornhuyse, P. Root endophytic fungi impact host plant biomass and respond to plant composition at varying spatio-temporal scales. Fungal Ecol. 44, 100907 (2020).
Google Scholar 
21.Jumpponen, A., Herrera, J., Porras-Alfaro, A. & Rudgers, J. Biogeography of root-associated fungal endophytes. Biogeography of Mycorrhizal Symbiosis 195–222. https://doi.org/10.1007/978-3-319-56363-3_10 (2017).22.Bokati, D., Herrera, J. & Poudel, R. Soil influences colonization of root-associated fungal endophyte communities of maize, wheat, and their progenitors. J. Mycol. 2016, 1–9 (2016).
Google Scholar 
23.Card, S. D. et al. Beneficial endophytic microorganisms of Brassica – A review. Biol. Control 90, 102–112 (2015).
Google Scholar 
24.Junker, C., Draeger, S. & Schulz, B. A fine line – endophytes or pathogens in Arabidopsis thaliana. Fungal Ecol. 5, 657–662 (2012).
Google Scholar 
25.Fesel, P. H. & Zuccaro, A. Dissecting endophytic lifestyle along the parasitism/mutualism continuum in Arabidopsis. Curr. Opin. Microbiol. 32, 103–112 (2016).PubMed 

Google Scholar 
26.Kia, S. H. et al. Influence of phylogenetic conservatism and trait convergence on the interactions between fungal root endophytes and plants. ISME J. 11, 777–790 (2017).PubMed 

Google Scholar 
27.Lahrmann, U. et al. Mutualistic root endophytism is not associated with the reduction of saprotrophic traits and requires a noncompromised plant innate immunity. N. Phytol. 207, 841–857 (2015).CAS 

Google Scholar 
28.Hacquard, S. et al. Survival trade-offs in plant roots during colonization by closely related beneficial and pathogenic fungi. Nat. Commun. 7, 1–13 (2016).
Google Scholar 
29.Hiruma, K. et al. Root endophyte Colletotrichum tofieldiae confers plant fitness benefits that are phosphate status dependent. Cell 165, 464–474 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
30.Almario, J. et al. Root-associated fungal microbiota of nonmycorrhizal Arabis alpina and its contribution to plant phosphorus nutrition. Proc. Natl Acad. Sci. USA 114, E9403–E9412 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
31.Kohler, A. et al. Convergent losses of decay mechanisms and rapid turnover of symbiosis genes in mycorrhizal mutualists. Nat. Genet. 47, 410–415 (2015).CAS 
PubMed 

Google Scholar 
32.Miyauchi, S. et al. Large-scale genome sequencing of mycorrhizal fungi provides insights into the early evolution of symbiotic traits. Nat. Commun. 11, 1–17 (2020).
Google Scholar 
33.Spatafora, J. W., Sung, G. H. J. M. S., Hywel-Jones, N. L. & White, J. F. Phylogenetic evidence for an animal pathogen origin of ergot and the grass endophytes. Mol. Ecol. 16, 1701–1711 (2007).CAS 
PubMed 

Google Scholar 
34.Xu, X. H. et al. The rice endophyte Harpophora oryzae genome reveals evolution from a pathogen to a mutualistic endophyte. Sci. Rep. 4, 1–9 (2014).CAS 

Google Scholar 
35.Weiß, M., Waller, F., Zuccaro, A. & Selosse, M. Sebacinales – one thousand and one interactions with land plants. N. Phytol. 211, 20–40 (2016).
Google Scholar 
36.Knapp, D. G. et al. Comparative genomics provides insights into the lifestyle and reveals functional heterogeneity of dark septate endophytic fungi. Sci. Rep. 8, 6321 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
37.Hettiarachchige, I. K. et al. Global changes in asexual Epichloë transcriptomes during the early stages, from seed to seedling, of symbiotum establishment. Microorg 9, 991 (2021).
Google Scholar 
38.Větrovský, T. et al. GlobalFungi, a global database of fungal occurrences from high-throughput-sequencing metabarcoding studies. Sci. Data 7, 1–14 (2020).
Google Scholar 
39.Agler, M. T. et al. Microbial hub taxa link host and abiotic factors to plant microbiome variation. PLoS Biol. 14, e1002352 (2016).PubMed 
PubMed Central 

Google Scholar 
40.Nguyen, N. H. et al. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 20, 241–248 (2016).
Google Scholar 
41.Selosse, M.-A., Schneider-Maunoury, L. & Martos, F. Time to re-think fungal ecology? Fungal ecological niches are often prejudged. N. Phytol. 217, 968–972 (2018).
Google Scholar 
42.Zuccaro, A. et al. Endophytic life strategies decoded by genome and transcriptome analyses of the mutualistic root symbiont Piriformospora indica. PLoS Pathog. 7, e1002290 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
43.David, A. S. et al. Draft genome sequence of Microdochium bolleyi, a dark septate fungal endophyte of beach grass. Genome Announc. 4, e00270-16 (2016).44.Walker, A. K. et al. Full genome of Phialocephala scopiformis DAOMC 229536, a fungal endophyte of spruce producing the potent anti-insectan compound rugulosin. Genome Announc. 4, e01768-15 (2016).45.Wu, W. et al. Characterization of four endophytic fungi as potential consolidated bioprocessing hosts for conversion of lignocellulose into advanced biofuels. Appl. Microbiol. Biotechnol. 101.6, 2603–2618 (2017).
Google Scholar 
46.Emms, D. M. & Kelly, S. OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biol. 20, 1–14 (2019).
Google Scholar 
47.Csűös, M. Count: evolutionary analysis of phylogenetic profiles with parsimony and likelihood. Bioinformatics 26, 1910–1912 (2010).
Google Scholar 
48.Shah, F. et al. Ectomycorrhizal fungi decompose soil organic matter using oxidative mechanisms adapted from saprotrophic ancestors. N. Phytol. 209, 1705–1719 (2016).CAS 

Google Scholar 
49.Pellegrin, C., Morin, E., Martin, F. M. & Veneault-Fourrey, C. Comparative analysis of secretomes from ectomycorrhizal fungi with an emphasis on small-secreted proteins. Front. Microbiol. 6, 1278 (2015).PubMed 
PubMed Central 

Google Scholar 
50.Tung Ho, L. S. & Ané, C. A linear-time algorithm for gaussian and non-gaussian trait evolution models. Syst. Biol. 63, 397–408 (2014).
Google Scholar 
51.Klopfenstein, D. V. et al. GOATOOLS: A Python library for Gene Ontology analyses. Sci. Rep. 8, 1–17 (2018).CAS 

Google Scholar 
52.Szklarczyk, D. et al. STRING v11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47, D607–D613 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
53.Schulz, B. & Boyle, C. The endophytic continuum. Mycol. Res. 109, 661–686 (2005).PubMed 

Google Scholar 
54.Kim, D., Paggi, J. M., Park, C., Bennett, C. & Salzberg, S. L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 37, 907–915 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
55.Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).PubMed 
PubMed Central 

Google Scholar 
56.Curran, D. M., Gilleard, J. S. & Wasmuth, J. D. MIPhy: identify and quantify rapidly evolving members of large gene fam. PeerJ 2018, e4873 (2018).
Google Scholar 
57.Atanasova, L. et al. Evolution and functional characterization of pectate lyase PEL12, a member of a highly expanded Clonostachys rosea polysaccharide lyase 1 family. BMC Microbiol. 18, 1–19 (2018).
Google Scholar 
58.Keim, J., Mishra, B., Sharma, R., Ploch, S. & Thines, M. Root-associated fungi of Arabidopsis thaliana and Microthlaspi perfoliatum. Fungal Divers 66, 99–111 (2014).
Google Scholar 
59.Vannier, N., Agler, M. & Hacquard, S. Microbiota-mediated disease resistance in plants. PLoS Pathog. 15, e1007740 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
60.Hassani, M. A., Durán, P. & Hacquard, S. Microbial interactions within the plant holobiont. Microbiome 6, 1–17 (2018).
Google Scholar 
61.Getzke, F., Thiergart, T. & Hacquard, S. Contribution of bacterial-fungal balance to plant and animal health. Curr. Opin. Microbiol. 49, 66–72 (2019).CAS 
PubMed 

Google Scholar 
62.Wolinska, K. W. et al. Tryptophan metabolism and bacterial commensals prevent fungal dysbiosis in Arabidopsis roots. Proc. Natl Acad Sci USA. 118, e2111521118 (2021).PubMed 

Google Scholar 
63.Lofgren, L. A. et al. Genome-based estimates of fungal rDNA copy number variation across phylogenetic scales and ecological lifestyles. Mol. Ecol. 28, 721–730 (2019).PubMed 

Google Scholar 
64.Karasov, T. L. et al. Arabidopsis thaliana and Pseudomonas pathogens exhibit stable associations over evolutionary timescales. Cell Host Microbe 24, 168–179.e4 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
65.Karasov, T. L. et al. The relationship between microbial population size and disease in the Arabidopsis thaliana phyllosphere. Preprint at https://doi.org/10.1101/828814 (2020).66.Benen, J. A. E., Kester, H. C. M., Pařenicová, L. & Visser, J. Characterization of Aspergillus niger pectate lyase A. Biochemistry 39, 15563–15569 (2000).CAS 
PubMed 

Google Scholar 
67.Bauer, S., Vasu, P., Persson, S., Mort, A. J. & Somerville, C. R. Development and application of a suite of polysaccharide-degrading enzymes for analyzing plant cell walls. Proc. Natl Acad. Sci. USA 103, 11417–11422 (2006).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
68.Bacic, A. Breaking an impasse in pectin biosynthesis. Proc. Natl Acad. Sci. USA 103, 5639–5640 (2006).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
69.Vogel, J. Unique aspects of the grass cell wall. Curr. Opin. Plant Biol. 11, 301–307 (2008).CAS 
PubMed 

Google Scholar 
70.Bacete, L. et al. Arabidopsis response reGUlator 6 (ARR6) modulates plant cell-wall composition and disease resistance. Mol. Plant-Microbe Interact. 33, 767–780 (2020).CAS 
PubMed 

Google Scholar 
71.Molina, A. et al. Arabidopsis cell wall composition determines disease resistance specificity and fitness. Proc. Natl Acad. Sci. USA 118, 2021 (2021).
Google Scholar 
72.Sun, Z.-B. et al. Biology and applications of Clonostachys rosea. J. Appl. Microbiol. 129, 486–495 (2020).PubMed 

Google Scholar 
73.Broberg, M. et al. Comparative genomics highlights the importance of drug efflux transporters during evolution of mycoparasitism in Clonostachys subgenus Bionectria (Fungi, Ascomycota, Hypocreales). Evol. Appl. 14, 476–497 (2021).CAS 
PubMed 

Google Scholar 
74.Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
75.Chin, C. S. et al. Phased diploid genome assembly with single-molecule real-time sequencing. Nat. Methods 13, 1050–1054 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
76.Grabherr, M. G. et al. Trinity: Reconstructing a full-length transcriptome without a genome from RNA-Seq data. Nat. Biotechnol. 29, 644 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
77.Grigoriev, I. V. et al. MycoCosm portal: gearing up for 1000 fungal genomes. Nucleic Acids Res. 42, D699–D704 (2014).CAS 
PubMed 

Google Scholar 
78.Nilsson, R. H. et al. The UNITE database for molecular identification of fungi: Handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 47, D259–D264 (2019).CAS 
PubMed 

Google Scholar 
79.Solovyev, V., Kosarev, P., Seledsov, I. & Vorobyev, D. Automatic annotation of eukaryotic genes, pseudogenes and promoters. Genome Biol. 7, S10 (2006).PubMed 
PubMed Central 

Google Scholar 
80.Cohen, O., Ashkenazy, H., Belinky, F., Huchon, D. & Pupko, T. GLOOME: gain-loss mapping engine. Bioinformatics 26, 2914–2915 (2010).CAS 
PubMed 

Google Scholar 
81.Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Machine Learning Res. http://scikit-learn.sourceforge.net. (2011).82.Seppey, M., Manni, M. & Zdobnov, E. M. BUSCO: Assessing genome assembly and annotation completeness. In Methods in Molecular Biology vol. 1962, 227–245 (Humana Press Inc., 2019).83.Morin, E. et al. Comparative genomics of Rhizophagus irregularis, R. cerebriforme, R. diaphanus and Gigaspora rosea highlights specific genetic features in Glomeromycotina. N. Phytol. 222, 1584–1598 (2019).CAS 

Google Scholar 
84.Cantarel, B. I. et al. The Carbohydrate-Active EnZymes database (CAZy): an expert resource for glycogenomics. Nucleic Acids Res. 37, 233–238 (2009).
Google Scholar 
85.Rawlings, N. D., Barrett, A. J. & Finn, R. Twenty years of the MEROPS database of proteolytic enzymes, their substrates and inhibitors. Nucleic Acids Res. 44, D343–D350 (2016).CAS 
PubMed 

Google Scholar 
86.Fischer, M. & Pleiss, J. The Lipase Engineering Database: a navigation and analysis tool for protein families. Nucleic Acids Res. 31, 319–321 (2003).CAS 
PubMed 
PubMed Central 

Google Scholar 
87.Cock, P. J. A. et al. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 25, 1422–1423 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
88.Deorowicz, S., Debudaj-Grabysz, A. & Gudys, A. FAMSA: Fast and accurate multiple sequence alignment of huge protein families. Sci. Rep. 6, 1–13 (2016).
Google Scholar 
89.Eddy, S. R. Accelerated profile HMM searches. PLoS Comput. Biol. 7, e1002195 (2011).90.Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).CAS 
PubMed 
PubMed Central 

Google Scholar 
91.Morris, J. H. et al. ClusterMaker: a multi-algorithm clustering plugin for Cytoscape. BMC Bioinforma. 12, 436 (2011).CAS 

Google Scholar 
92.Gruber, B. D., Giehl, R. F. H., Friedel, S. & von Wirén, N. Plasticity of the Arabidopsis root system under nutrient deficiencies. Plant Physiol. 163, 161–179 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
93.Hedges, L. V. Distribution Theory for Glass’s estimator of effect size and related estimators. J. Educ. Stat. 6, 107–128 (1981).
Google Scholar 
94.Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
95.Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).CAS 

Google Scholar 
96.Zhu, A., Ibrahim, J. G. & Love, M. I. Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences. Bioinformatics 35, 2084–2092 (2019).CAS 
PubMed 

Google Scholar 
97.Mesny, F. Genomic determinants of endophytism in the Arabidopsis root mycobiome. GitHub https://doi.org/10.5281/zenodo.5642698 (2021). More

1.Report of the Open-Ended Working Group on the Post-2020 Global Biodiversity Framework on its Third Meeting (Part I) CBD/WG2020/3/5 (CBD, 2021).2.Maxwell, S. L. et al. Nature 586, 217–227 (2020).CAS 
Article 

Google Scholar 
3.Protected Planet Live Report 2021 (UNEP-WCMC, IUCN, NGS, 2021).4.Díaz, S. et al. Science 366, eaax3100 (2019).Article 

Google Scholar 
5.Visconti, P. et al. Science 364, 239–241 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
6.Maron, M. et al. Conserv. Lett. 14, e12816 (2021).Article 

Google Scholar 
7.Pressey, R. L. et al. Trends Ecol. Evol. 36, 808–821 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
8.Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Service (IPBES Secretariat, 2019).9.Living Planet Report 2020 (WWF, 2020).10.Jetz, W. et al. Nat. Ecol. Evol. 3, 539–551 (2019).Article 
PubMed 

Google Scholar 
11.Powers, R. P. & Jetz, W. Nat. Clim. Change 9, 323–329 (2019).Article 

Google Scholar 
12.Wilson, E. O. Half-Earth: Our Planet’s Fight for Life (WW Norton & Company, 2016).13.Sala, E. et al. Nature 592, 397–402 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
14.Rinnan, D. S., Sica, Y., Ranipeta, A., Wilshire, J. & Jetz, W. Preprint at bioRxiv https://doi.org/10.1101/2020.02.05.936047 (2020).15.Beger, M. et al. Nat. Commun. 6, 8208 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
16.Armstrong, C. Conserv. Biol. 33, 554–560 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
17.Post-2020 Global Biodiversity Framework: Scientific and Technical Information to Support the Review of the Updated Goals and Targets, and Related Indicators and Baselines CBD/SBSTTA/24/3 (CBD, 2020).18.Moilanen, A., Wilson, K. A. & Possingham, H. Spatial Conservation Prioritization: Quantitative Methods and Computational Tools (Oxford Univ. Press, 2009).19.Jung, M. et al. Nat. Ecol. Evol. 5, 1499–1509 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
20.Navarro, L. M. et al. Curr. Opin. Environ. Sustain. 29, 158–169 (2017).Article 

Google Scholar 
21.Jantke, K., Kuempel, C. D., McGowan, J., Chauvenet, A. L. M. & Possingham, H. P. Divers. Distrib. 25, 170–175 (2019).Article 

Google Scholar 
22.Bhola, N. et al. Conserv. Biol. 35, 168–178 (2021).Article 
PubMed 

Google Scholar 
23.Hansen, A. J. et al. Conserv. Lett. 14, e12822 (2021).Article 

Google Scholar 
24.Measuring Ecosystem Integrity (Goal A) in the Post-2020 Global Biodiversity Framework: The Geo Bon Species Habitat Index CBD/WG2020/3/INF/6 (CBD Secretariat, 2021).25.Rondinini, C. & Visconti, P. Conserv. Biol. 29, 1028–1036 (2015).Article 

Google Scholar 
26.McGeoch, M. A. et al. Preprint at bioRxiv https://doi.org/10.1101/2021.08.26.457851 (2021).27.Hoskins, A. J. et al. Environ. Model. Softw. 132, 104806 (2020).Article 

Google Scholar 
28.Adams, V. M., Visconti, P., Graham, V. & Possingham, H. P. One Earth 4, 901–906 (2021).Article 

Google Scholar 
29.Heiner, M. et al. Conserv. Sci. Pract. 1, e110 (2019).
Google Scholar  More

1.Tian, H. et al. Proc. Natl Acad. Sci. USA 108, 14521–14526 (2011).ADS 
CAS 
Article 

Google Scholar 
2.Sugihara, G. Nature 378, 559–560 (1995).ADS 
CAS 
Article 

Google Scholar 
3.Bebber, D. P., Ramotowski, M. A. T. & Gurr, S. J. Nat. Clim. Change 3, 985–988 (2013).ADS 
Article 

Google Scholar 
4.Bebber, D. P. et al. Glob. Change Biol. 25, 2703–2713 (2019).ADS 
Article 

Google Scholar 
5.Wang, C. et al. Nat. Food https://doi.org/10.1038/s43016-021-00428-0 (2021).6.Pasiecznik, N. M. et al. EPPO Bull. 35, 1–7 (2005).Article 

Google Scholar 
7.Paini, D. R. et al. Proc. Natl Acad. Sci. USA 113, 7575–7579 (2016).CAS 
Article 

Google Scholar 
8.Chaloner, T. M., Gurr, S. J. & Bebber, D. P. Nat. Clim. Change 11, 710–715 (2021).ADS 
Article 

Google Scholar 
9.Deutsch, C. A. et al. Science 361, 916–919 (2018).ADS 
CAS 
Article 

Google Scholar 
10.Delgado-Baquerizo, M. et al. Nat. Clim. Change 10, 550–554 (2020).ADS 
Article 

Google Scholar 
11.Wright, B. D. Appl. Econ. Perspect. Policy 33, 32–58 (2011).Article 

Google Scholar  More

1.Barbosa, L. C., Alves, M. A. S. & Grelle, C. E. V. Land Use Policy 104, 105384 (2021).Article 

Google Scholar 
2.PL 6024/2019 (Câmara dos Deputados, 2021); https://go.nature.com/3p8ygLo3.Serra do Divisor National Park. https://go.nature.com/3rcbdSg (UNESCO, 2021).4.F. A. Obermüller et al. Lista de espécies de plantas vasculares do Parque Nacional da Serra do Divisor. Catálogo de Plantas das Unidades de Conservação do Brasil https://go.nature.com/3HTJjAs (Jardim Botânico do Rio de Janeiro, 2020).5.Livro Temático/Recursos naturais: Biodiversidade e ambientes do Acre (ACRE, 2010).6.Hansen, M. C. et al. Sci. Adv. 6, eaax8574 (2020).Article 

Google Scholar 
7.Grilli, M. Base de dados do DNIT prevê expansão da BR-364 dentro de unidade de conservação. Revista Globo Rural https://go.nature.com/3DUgQYX (2021).8.Orlando, S. A Estrada do Pacífico no comércio exterior do Acre. ac24horas.com https://go.nature.com/3raofzL (2020).9.Mascarenhas, F. et al. Desenvolv e Meio Ambient 48, 236–262 (2018).Article 

Google Scholar 
10.Castro, W. Reserva Extrativista Chico Mendes lidera lista de Áreas Protegidas que mais perdem floresta por desmatamento desde Agosto de 2020. SOS Amazonia https://go.nature.com/3CU5jra (2021).11.Fá, J. E. et al. Front. Ecol. Environ. 18, 135–140 (2020).Article 

Google Scholar 
12.Bernard, E., Penna, L. A. & Araújo, E. Conserv. Biol. 28, 939–950 (2014).CAS 
Article 

Google Scholar 
13.Kroner, R. E. G. et al. Science 364, 881–886 (2019).Article 

Google Scholar 
14.Ferrante, L. & Fearnside, P. M. Science 369, 634 (2020).Article 

Google Scholar 
15.Laurance, W. F. & Balmford, A. Nature 495, 308–309 (2013).CAS 
Article 

Google Scholar 
16.Kehoe, L. et al. One Earth 3, 268–272 (2020).Article 

Google Scholar  More

1.Foley, J. A. et al. Solutions for a cultivated planet. Nature 478, 337–342 (2011).ADS 
CAS 

Google Scholar 
2.The Future of Food and Agriculture—Alternative Pathways to 2050 (Food and Agriculture Organization of the United Nations, 2018).3.Tilman, D., Balzer, C., Hill, J. & Befort, B. L. Global food demand and the sustainable intensification of agriculture. Proc. Natl Acad. Sci. USA 108, 20260–20264 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
4.Mueller, N. D. et al. Closing yield gaps through nutrient and water management. Nature 490, 254–257 (2012).ADS 
CAS 

Google Scholar 
5.Zhang, W. et al. Closing yield gaps in China by empowering smallholder farmers. Nature 537, 671–674 (2016).ADS 
CAS 

Google Scholar 
6.Chakraborty, S. & Newton, A. C. Climate change, plant diseases and food security: an overview. Plant Pathol. 60, 2–14 (2011).
Google Scholar 
7.Oerke, E. C. Crop losses to pests. J. Agri. Sci. 144, 31–43 (2005).
Google Scholar 
8.Bebber, D. P., Ramotowski, M. A. T. & Gurr, S. J. Crop pests and pathogens move polewards in a warming world. Nat. Clim. Change 3, 985–988 (2013).ADS 

Google Scholar 
9.Deutsch, C. A. et al. Increase in crop losses to insect pests in a warming climate. Science 361, 916–919 (2018).ADS 
CAS 
Article 

Google Scholar 
10.Delcour, I., Spanoghe, P. & Uyttendaele, M. Literature review: impact of climate change on pesticide use. Food Res. Int. 68, 7–15 (2015).
Google Scholar 
11.Ziska, L. H. Increasing minimum daily temperatures are associated with enhanced pesticide use in cultivated soybean along a latitudinal gradient in the mid-western United States. PLoS ONE 9, e98516 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
12.Lamichhane, J. R. et al. Robust cropping systems to tackle pests under climate change. A review. Agron. Sustain. Dev. 35, 443–459 (2014).
Google Scholar 
13.Bebber, D. P. et al. Many unreported crop pests and pathogens are probably already present. Glob. Change Biol. 25, 2703–2713 (2019).ADS 

Google Scholar 
14.Bale, J. S. et al. Herbivory in global climate change research: direct effects of rising temperature on insect herbivores. Glob. Change Biol. 8, 1–16 (2002).ADS 

Google Scholar 
15.Garrett, K. A., Dendy, S. P., Frank, E. E., Rouse, M. N. & Travers, S. E. Climate change effects on plant disease: genomes to ecosystems. Annu. Rev. Phytopathol. 44, 489–509 (2006).CAS 

Google Scholar 
16.Hruska, A. J. Fall armyworm (Spodoptera frugiperda) management by smallholders. CAB Rev. 14, 1–11 (2019).
Google Scholar 
17.Sutherst, R. W. et al. Adapting to crop pest and pathogen risks under a changing climate. Wiley Interdiscip. Rev. Clim. Change 2, 220–237 (2011).
Google Scholar 
18.Donatelli, M. et al. Modelling the impacts of pests and diseases on agricultural systems. Agric. Syst. 155, 213–224 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
19.Jones, J. W. et al. Toward a new generation of agricultural system data, models, and knowledge products: state of agricultural systems science. Agric. Syst. 155, 269–288 (2017).PubMed 
PubMed Central 

Google Scholar 
20.Miller, S. A., Beed, F. D. & Harmon, C. L. Plant disease diagnostic capabilities and networks. Annu. Rev. Phytopathol. 47, 15–38 (2009).CAS 

Google Scholar 
21.Bebber, D. P., Holmes, T., Smith, D. & Gurr, S. J. Economic and physical determinants of the global distributions of crop pests and pathogens. New Phytol. 202, 901–910 (2014).PubMed 
PubMed Central 

Google Scholar 
22.Savary, S. et al. The global burden of pathogens and pests on major food crops. Nat. Ecol. Evol. 3, 430–439 (2019).
Google Scholar 
23.An early warning news about the mirgating condition of Fall Armyworm in China from National Agro-Tech Extension and Service Center https://www.natesc.org.cn/News/des?id=eaf064ae-6582-47c1-a9f3-a58969fd47b3&kind=HYTX (in Chinese, available in Nov.2021).24.Piao, S. et al. The impacts of climate change on water resources and agriculture in China. Nature 467, 43–51 (2010).ADS 
CAS 

Google Scholar 
25.Chown, S. L., Sorensen, J. G. & Terblanche, J. S. Water loss in insects: an environmental change perspective. J. Insect Physiol. 57, 1070–1084 (2011).CAS 

Google Scholar 
26.Bjorkman, A. D. et al. Plant functional trait change across a warming tundra biome. Nature 562, 57–62 (2018).ADS 
CAS 

Google Scholar 
27.National Agricultural Technology Extension and Service Center. Technical Specification Manual of Major Crop Pest and Disease Observation and Forecast in China (China Agriculture Press, 2010).28.Olfert, O., Weiss, R. M. & Elliott, R. H. Bioclimatic approach to assessing the potential impact of climate change on wheat midge (Diptera: Cecidomyiidae) in North America. Can. Entomol. 148, 52–67 (2015).
Google Scholar 
29.Savary, S., Teng, P. S., Willocquet, L. & Nutter, F. W. Quantification and modeling of crop losses: a review of purposes. Annu. Rev. Phytopathol. 44, 89–112 (2006).CAS 

Google Scholar 
30.Chakraborty, S. Migrate or evolve: options for plant pathogens under climate change. Glob. Change Biol. 19, 1985–2000 (2013).ADS 

Google Scholar 
31.Deutsch, C. A. et al. Impacts of climate warming on terrestrial ectotherms across latitude. Proc. Natl Acad. Sci. USA 105, 6668–6672 (2008).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
32.Chaloner, T. M., Gurr, S. J. & Bebber, D. P. Plant pathogen infection risk tracks global crop yields under climate change. Nat. Clim. Change 11, 710–715 (2021).ADS 

Google Scholar 
33.Carvalho, J. L. N. et al. Agronomic and environmental implications of sugarcane straw removal: a major review. Glob. Change Biol. Bioenergy 9, 1181–1195 (2017).CAS 

Google Scholar 
34.Savary, S., Horgan, F., Willocquet, L. & Heong, K. L. A review of principles for sustainable pest management in rice. Crop Prot. 32, 54–63 (2012).
Google Scholar 
35.Frolking, S. et al. Combining remote sensing and ground census data to develop new maps of the distribution of rice agriculture in China. Glob. Biogeochem. Cycles 16, 38-31–38-10 (2002).
Google Scholar 
36.Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 dataset. Int. J. Climatol. 34, 623–642 (2014).
Google Scholar 
37.Harvell, C. D. et al. Climate warming and disease risks for terrestrial and marine biota. Science 296, 2158–2162 (2002).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
38.Scherm, H. Climate change: can we predict the impacts on plant pathology and pest management? Can. J. Plant Pathol. 26, 267–273 (2004).
Google Scholar 
39.Cheke, R. A. & Tratalos, J. A. Migration, patchiness, and population processes illustrated by two migrant pests. Bioscience 57, 145–154 (2007).
Google Scholar 
40.Eyring, V. et al. Overview of the Coupled Model Intercomparison Project phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).ADS 

Google Scholar 
41.O’Neill, B. C. et al. The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 9, 3461–3482 (2016).ADS 

Google Scholar 
42.van Vuuren, D. P. et al. The representative concentration pathways: an overview. Climatic Change 109, 5–31 (2011).ADS 

Google Scholar 
43.Lange, S. Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0). Geosci. Model Dev. 12, 3055–3070 (2019).ADS 

Google Scholar 
44.Gregory, P. J., Johnson, S. N., Newton, A. C. & Ingram, J. S. Integrating pests and pathogens into the climate change/food security debate. J. Exp. Bot. 60, 2827–2838 (2009).CAS 

Google Scholar 
45.Allen, R. G., Pereira, L. S., Raes, D. & Smith, M. Crop Evapotranspiration—Guidelines for Computing Crop Water Requirements FAO irrigation and drainage paper 56 (FAO, 1998).46.Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data 7, 109 (2020).PubMed 
PubMed Central 

Google Scholar 
47.Kahiluoto, H. et al. Decline in climate resilience of European wheat. Proc. Natl Acad. Sci. USA 116, 123–128 (2019).CAS 

Google Scholar 
48.Folke, C. et al. Regime shifts, resilience, and biodiversity in ecosystem management. Annu. Rev. Ecol. Evol. Syst. 35, 557–581 (2004).
Google Scholar 
49.Renard, D. & Tilman, D. National food production stabilized by crop diversity. Nature 571, 257–260 (2019).ADS 
CAS 

Google Scholar 
50.Clark, J. S. Why environmental scientists are becoming Bayesians. Ecol. Lett. 8, 2–14 (2005).
Google Scholar 
51.Clark, J. S. & Gelfand, A. E. A future for models and data in environmental science. Trends Ecol. Evol. 21, 375–380 (2006).
Google Scholar 
52.Gelfand, A. E. & Smith, A. F. M. Sampling-based approaches to calculating marginal densities. J. Am. Stat. Assoc. 85, 398–409 (1990).MathSciNet 
MATH 

Google Scholar 
53.Lunn, D., Spiegelhalter, D., Thomas, A. & Best, N. The BUGS project: evolution, critique and future directions. Stat. Med. 28, 3049–3067 (2009).MathSciNet 

Google Scholar 
54.Brooks, S. P. & Gelman, A. General methods for monitoring convergence of iterative simulations. J. Comput. Graph. Stat. 7, 434–455 (1998).MathSciNet 

Google Scholar  More

The Chaihuín stratigraphyCore transects (Fig. 2b) reveal three sand layers, intercalated between herbaceous peats, that are laterally extensive over 600 m across the marsh (Fig. 3a). In all cases, the sand layers have sharp lower contacts and transitional upper contacts. Ten accelerator mass spectrometric (AMS) radiocarbon dates modelled using a Bayesian phased sequence model provide the chronology (Fig. 3c and Supplementary Table 1). The age of plant macrofossils immediately beneath the upper layer, sand A, are consistent with burial by the 1960 tsunami. The age model places the deposition of the middle sand B at 1600–1820 and lower layer, sand C, at 1486–1616 CE. The calibrated age ranges for sands B and C are reasonably broad due to plateaux in the radiocarbon calibration curve, which affect dates from the seventeenth to twentieth centuries21.Fig. 3: Geological evidence from Chaihuín.a Stratigraphy of selected coring transects showing three laterally extensive sand sheets. Transect locations X–X’ and Y–Y’ shown on Fig. 2; b sedimentology of sand sheets, including grain size, sorting and clastic composition (%) classified relative to six modern environments established by discriminant analysis (see Supplementary Discussion), with images of sands A and B in CN17/8. Box-and-whisker plots show the statistical parameters measured in sand samples with the horizontal line inside the box representing the median, the box representing the upper and lower quartiles, the whiskers representing the minimum and maximum values excluding any outliers and the crosses the extreme outlier values. The number within each box indicates the number of samples in each group; c probability density functions (95.4%) of radiocarbon dates and modelled ages for the three earthquakes. Full radiocarbon results in Supplementary Table 1.Full size imageThe sedimentology and mineralogical signatures of the sand sheets are described in detail elsewhere based on over 100 hand-driven cores22 and summarised in Supplementary Discussion; here we analyse diatoms in three representative cores and present reconstructions of marsh surface elevation change over time from a diatom-based transfer function (Fig. 4 and Supplementary Data 1). From diatom analysis of the three cores, we identified 170 species indicative of differing tolerances to tidal inundation. Only 14 species were absent from a previously published modern training set that includes 29 samples from Chaihuín20, and 9 of these species constituted 2% of any sample (comprising 4–5% in 2 non-sand samples).Fig. 4: Diatom assemblages and estimates of land-level change derived from a regional south-central Chile transfer function for three cores from Chaihuín.a–c Diatom assemblage summaries and dominant taxa in cores CN14/5 (a), CN17/8 (b) and CN18/11 (c) at elevations of 0.88, 0.89 and 1.10 m above mean sea level (MSL), respectively. Elevation optima of diatom species are classified based on weighted averaging of the modern training set and reported relative to mean higher high water (MHHW). The modern analogue technique was used to calculate the squared chord distance to the closest modern analogue, and the threshold for a fossil sample having a close modern analogue is defined as the 20th percentile of the dissimilarity values (MinDC) for the modern training set44. Reconstructed palaeomarsh surface elevations (PMSE) and coseismic subsidence are shown from the weighted averaging partial least squares (WA-PLS) model only. d Estimates of coseismic subsidence in 1737 from three cores and three different diatom-based transfer function approaches, showing 95.4% uncertainties.Full size imageThe laterally extensive uppermost coarse to medium-grained sand sheet (A) is mid grey, varies in thickness between 1 and 19 cm, has a median grain size of 0.49 mm and is upwards fining (0.27–0.71 mm) in 61 cores (80% of those in which A is preserved, massive in the others). The marsh grades steeply into freshwater scrub, and there is no sand unit in cores just above the high marsh limit. There is an abrupt contact between the sand and dark brown silty herbaceous peat below, which contains plant material including below-ground stems (rhizomes) of Scirpus americanus. In many cores, there are rip-up clasts (~2 cm) of peat encased in the sand sheet, as well as vegetation rooted in the peat below. The peat below the sand sheet contains a diatom assemblage that is almost entirely composed of species found on the contemporary high marsh above mean higher high water (MHHW) (e.g. Eunotia praerupta, Nitzschia acidoclinata), with higher elevation optima than the diatoms found in the herbaceous peat above the sand unit (e.g. Rhopalodia constricta) (Fig. 4a). The overlying peat also contains low, albeit important, percentages (5–24%) of taxa with elevation optima below MHHW. By contrast to the peats, sand A is dominated by species with lower elevation optima (59–72% of the total assemblage have optima below MHHW), including Achnanthes reversa and Planothidium delicatulum.The middle brown-grey to dark grey mica-rich coarse to medium-grained sand sheet (B) is similarly laterally extensive across the entire marsh, varying in thickness between 2 and 32 cm. It has a median grain size of 0.47 mm and is upwards-fining (0.38–0.68 mm) in 31 cores (50% of those in which B is preserved, massive in others), but rip-up clasts of peat were only occasionally observed. In some cases, we observe a 2–4-cm-thick cap of horizontally bedded detrital plant fragments and wood at the top of the sand layer. The sand sheet abruptly overlays a red-brown to dark brown silty herbaceous peat with variable silt content and humification. Humidophila contenta dominates the diatom assemblage in the peat below sand B (up to 37% of the assemblage) and is also present in the peat overlying the sand sheet, which remains dominated by species with elevation optima above MHHW. In the core from the lowest contemporary marsh elevation (CN14/5, Fig. 4a), there is an increase in low marsh diatom species (elevation optima below MHHW) above the sand compared to below (e.g. A. reversa, P. delicatulum). Diatom assemblages are relatively consistent across the five samples from the sand unit, with 54–76% of the assemblages being species with elevation optima below MHHW, including A. reversa, Fallacia tenera and P. delicatulum.A third sand deposit (C) is found in 16 cores at the southern end of the marsh, although still traceable over 200 m and across most cores that penetrated deep enough to potentially sample sand C. The deposit is a dark grey fine to medium-grained massive sand (median grain size 0.25 mm, range 0.22-0.29 mm), with a maximum thickness of 51 cm and contains occasional rip-up clasts from the buried organic unit below encased in the sand. The basal contact is abrupt, with the sand overlying a brown clayey silt with occasional herbaceous plant remains, humified organic matter and woody plant material. The organic horizon below sand C contains more diatom species typically found at lower elevations in the tidal frame than the peats below A and B (Fig. 4a). There is also a change in species composition approaching the top of the peat, with abundances of Opephora pacifica and Pseudostaurosira perminuta decreasing and H. contenta and E. perpusilla increasing from the base to top of the peat below sand C. Also in contrast to the other two buried organic deposits, there is a change in species composition approaching the top of the peat and samples immediately above and below sand unit C have very similar diatom assemblages, dominated by H. contenta and E. perpusilla. Diatom preservation in the sand unit was very poor, and it was not possible to obtain representative counts from this unit.Brown silty herbaceous peats separate the three sand sheets, deposited intertidally on the basis of their diatom composition. In addition to the relative variations in freshwater and brackish diatom composition of peats described above, the peat units also vary in their degree of humification. While peats below sands A and C contain humified organic matter, the peat below sand B is unhumified. Additionally, two layers of highly humified black peat were observed immediately above and below sand A in low marsh cores from the southwest of the marsh, varying in thickness between 1 and 15 cm.Evidence for a locally sourced tsunamiWe interpret all three sand sheets as being deposited by locally sourced tsunamis, rather than far-field tsunamis or non-seismic processes (e.g. storms, river floods or aeolian processes). This is based primarily on coincident land deformation, and also upon their lateral extent, diatom composition, and sedimentological signatures. Dealing first with the latter lines of reasoning, sands A and B are not only dominated by marine sublittoral and epipsammic diatom species but also contain substantial numbers of benthic silty intertidal mudflat and freshwater taxa, which also dominate the underlying peats. This is consistent with mixed diatom assemblages in tsunami deposits worldwide and indicative of tsunamis eroding, transporting and redepositing diatoms from diverse environments as they cross coastal and inland areas23,24,25,26. The presence of marine and tidal flat diatoms excludes deposition of sand by river flooding25,27, and statistical comparison of the sedimentological and mineralogical signatures of the sands with modern depositional environments, reported by Aedo et al.22 and summarised in Supplementary Discussion, further supports a seaward rather fluvial sediment source. We observe a maximum sedimentary contribution of 12% from upstream fluvial sources (Fig. 3b) and do not observe erosional or depositional features characteristic of fluvial flood deposits, such as a high basal mud content reflective of suspended loads during the initial stages of flooding or inverse grading as energy increases28.Meteorologically driven deposition of the sands, either during storm surges or other transient sea-level fluctuation events (e.g. El Niño), is discounted as the diatoms in the overlying organic units demonstrate lasting ecological change27,29. While a non-tsunamigenic earthquake followed closely in time by a large storm surge may impact diatom assemblages in the same way, there are several further characteristics of the three sand sheets which are consistent with a tsunami origin, even though these, in themselves, are not diagnostic. These include the lateral extent (traceable across 230 m), upwards-fining grain size of sand sheets A and B, and clasts of underlying peats observed within sands A and C and occasionally within B. The absence of extreme climatic phenomena, such as hurricanes and tropical storms, in the Chaihuín area during the historic period also minimises the possibility of finding storm deposits. However, while it is recognised that the above criteria cannot be used individually to confirm tsunami deposition, it is the combination of all sedimentological and diatom evidence that we use here in support of the most compelling evidence for tsunami deposition, which comes from the accompanying abrupt land-level change. The latter rules out deposition by tsunamis sourced in the far-field, storms or aeolian processes.Evidence for coseismic land-level changeFollowing established criteria30,31, we use the sedimentary and diatom evidence to propose that the Chaihuín sequence records three earthquake events, associated with vertical coseismic deformation and tsunami deposition. Diatom assemblages from immediately below sand layers A and B are characterised by species with higher elevation preferences than those found immediately above the sands, suggesting decreases in marsh surface elevation consistent with coseismic subsidence (Fig. 4). Diatom assemblages show minimal change across sand layer C; instead a transition occurs prior to event C whereby species with lower elevation preferences are replaced by those with higher elevation preferences, indicating net emergence prior to event C followed by minimal coseismic subsidence.The transfer function reconstructs 0.35 ± 0.42 m of subsidence occurred in event A, which local testimony and radiocarbon dating confirm to be the 1960 earthquake. Compared to our previous estimate for this event20, refining the transfer function method and expanding the modern training set here, reduces the uncertainty by 0.26 m. Reconstructed subsidence agrees with observations of 0.7 ± 0.4 m19. By contrast, the transfer function reconstructs very minor subsidence of 0.10 ± 0.36 m occurred in event C, but this needs confirmation from analyses of additional cores.The transfer function predicts that coseismic subsidence occurred in event B, with reconstructions varying between 0.10 ± 0.33 and 0.52 ± 0.39 m, and averaging 0.22 ± 0.38 m (Fig. 4d). While this is close to the detection limit of coseismic land-level change30 and the error term is large compared to the amount of deformation, we interpret event B as being associated with net submergence for two reasons. First, changes in diatom-inferred marsh elevations between pre- and post-earthquake samples are greater than other sample-to-sample changes. Second, all nine reconstructions, regardless of core location or transfer function approach, indicate submergence rather than a mixture of submergence and emergence (Fig. 4d).Linking the geologic and historical recordsDespite the broad modelled age ranges for events B and C of 1600–1820 and 1486–1616 CE, respectively, each range only includes one historically reported earthquake. If the historical catalogue is complete, sands B and C represent tsunamis accompanying the 1737 and 1575 earthquakes, respectively. Although other great tsunamigenic earthquakes occurred in the time range of event B (1657, 1730, 1751), their rupture areas have been placed much further north8,32 and therefore are very unlikely sources for the observed deformation. Age ranges do not include 1837; therefore, absence of evidence for this earthquake at Chaihuín supports the chronicle-based interpretation that the 1837 rupture area lies further south11,16. The preservation of turbidites from 1837 at sites to the north of Chaihuín14 is consistent with observations of earthquake-triggered turbidites some distance outside the rupture zone, as observed for the Mw 8.8 2010 Maule earthquake14.Implications for the rupture depth in 1737The Chaihuín record provides the first evidence for crustal deformation during the 1737 earthquake and the first evidence for the earthquake being tsunamigenic. While the nearshore bathymetry and orientation of the coastline may amplify tsunami inundation and the abundant sediment source may enhance the potential for evidence creation during even moderate tsunamis, the direction of land-level change at Chaihuín (subsidence) calls for reconsideration of the associated rupture depth. While correlation with evidence of shaking-induced turbidites from Calafquén and Riñihue lakes14, along with the absence of a 1737 event in sedimentary records from Río Maullín and Chucalén to the south9,11, supports the hypothesis that a smaller section of the plate interface ruptured in 1737 (between 39 and 41°S) than in 1960 and 157514, the Chaihuín record also forms an important constraint on the depth of local slip in 1737.By combining deformation and tsunami modelling, we show that our evidence of coastal subsidence and tsunami inundation at Chaihuín is better explained by offshore, shallow megathrust slip rather than by deeper slip below land as previously suggested16 (Fig. 5 and Supplementary Fig. 1). This is demonstrated by a simple numerical experiment designed to find the most likely depth range of the causative earthquake rupture that can explain the coastal subsidence inferred at Chaihuín and also the tsunami inundation.Fig. 5: Results of model tests to show that the 1737 rupture must have been confined to the offshore region at shallower fault depths than previously proposed.a The lower panel shows the trench-normal section of the megathrust and seafloor geometry at the latitude of Chaihuín used in the modelling experiment. The upper panel shows the bell-shaped slip distributions for a suite of eight earthquake ruptures and the middle panel shows the modelled vertical surface deformations using an elastic dislocation model (see “Methods”). The red and blue curves are the deep and shallow ruptures used as illustrative examples in the text. In this suite of models, the rupture width and peak slip are fixed at 100 km and 1 m, respectively, and the rupture location is systematically shifted horizontally in the trench-normal direction to represent ruptures at different depths. b Summary plot showing the modelled coastal uplift (left vertical axis) and tsunami runup (right vertical axis) predicted by the suite of models. Note that coastal subsidence can only be produced by offshore ruptures, with slip shallower than ~20 km. Ruptures deeper than this produce uplift at the coast. This opposing pattern of coastal deformation between shallow versus deeper ruptures is insensitive to how much slip is prescribed at the fault. Supplementary Fig. 1 shows the results for two different suite of models, in which the rupture width varies by fixing the updip (Supplementary Fig. 1a) and downdip (Supplementary Fig. 1b) limits.Full size imageOur numerical approach (see also “Methods”) leverages the sensitivity of the deformation sign (uplift or subsidence) and tsunami size at the Chaihuín coast to the depth of megathrust slip33 (Fig. 5). An earthquake rupture with maximum slip at 33 km fault depth (Fig. 5a, red model), as previously inferred from historical records16, will result in coastal uplift and a relatively small tsunami. Instead, if the rupture occurs offshore (Fig. 5a, blue model), the deformation will result in coastal subsidence and a much larger tsunami. From a systematic analysis in which the hypothetical rupture models are shifted horizontally in the trench-normal direction or vertically in the depth direction (Fig. 5a, upper panel), we conclude that subsidence at the Chaihuín coast could only be produced by ruptures placed mainly offshore, at average megathrust depths shallower than 20 km (Fig. 5b, downward triangles). Deeper ruptures will produce coastal uplift and consequent smaller tsunamis (Fig. 5b). The same conclusion is reached by varying the rupture width with fixed updip and downdip limits (Supplementary Fig. 1).Our conclusions are independent of the use of a normalised unit displacement in all models (i.e. 1 m at the centre of its corresponding bell-shaped rupture) because the opposing effects of deep versus shallow ruptures at Chaihuín are insensitive to the magnitude of slip involved and depend on its locus. The amount of slip determines the magnitude of deformation but not its sign due to the elastic response of the crust during earthquakes34. However, with evidence at only one location we only feel confident to constrain the depth range but not the magnitude nor along-strike extent of the causative slip. Therefore, from our numerical experiment we conclude that to produce subsidence at the Chaihuín coast, an offshore rupture likely shallower than 20 km is required as a deeper source would result in coastal uplift. This is also consistent with the inferred tsunami heights (Fig. 5b), which are larger for a shallower rupture and therefore more likely to produce inundation on land independent of the local topography. This geologically-based inference of an offshore rupture (blue curve in Fig. 5b) contrasts with the deeper rupture below land (red curve in Fig. 5b) previously inferred from historical observations alone16.Implications for tsunami recurrence intervalsThe average interval between the three events preserved at Chaihuín, 193 years, is shorter than the interval proposed for full segment 1960-style ruptures of 270-280 years9,11,14. This supports the notion that the Chilean subduction zone displays a variable rupture mode, in which the size, depth, tsunamigenic potential and recurrence interval vary between earthquakes10. Of greatest importance, however, is the shorter average recurrence interval of tsunami inundation than previously reported. With the addition of the 1737 tsunami alongside previously known events in 1960, 1837 and 1575, the historical recurrence interval for tsunamis generated anywhere along the Valdivia segment of the Chilean subduction zone is reduced to 130 years. This holds even if the inferred tsunami inundation is not associated with the 1737 earthquake, but with another earthquake of similar age missed in the historical catalogue. More

1.Landschützer, P., Gruber, N. & Bakker, D. C. E. Decadal variations and trends of the global ocean carbon sink. Glob. Biogeochem. Cycles 30, 1396–1417 (2016).ADS 

Google Scholar 
2.Gruber, N., Landschützer, P. & Lovenduski, N. S. The variable Southern Ocean carbon sink. Annu. Rev. Mar. Sci. 11, 159–186 (2019).ADS 

Google Scholar 
3.Passow, U. & Carlson, C. A. The biological pump in a high CO2 world. Mar. Ecol. Prog. Ser. 470, 249–271 (2012).ADS 
CAS 

Google Scholar 
4.Henson, S. A., Sanders, R. & Madsen, E. Global patterns in efficiency of particulate organic carbon export and transfer to the deep ocean. Glob. Biogeochem. Cycles 26, GB1028 (2012).ADS 

Google Scholar 
5.Eppley, R. W. & Peterson, B. J. Particulate organic matter flux and planktonic new production in the deep ocean. Nature 282, 677–680 (1979).ADS 

Google Scholar 
6.Iversen, M. H., Nowald, N., Ploug, H., Jackson, G. A. & Fischer, G. High resolution profiles of vertical particulate organic matter export off Cape Blanc, Mauritania: degradation processes and ballasting effects. Deep Sea Res. Pt. I 57, 771–784 (2010).CAS 

Google Scholar 
7.Steinberg, D. K. & Landry, M. R. Zooplankton and the ocean carbon cycle. Annu. Rev. Mar. Sci. 9, 413–444 (2017).ADS 

Google Scholar 
8.Manno, C., Stowasser, G., Enderlein, P., Fielding, S. & Tarling, G. A. The contribution of zooplankton faecal pellets to deep-carbon transport in the Scotia Sea (Southern Ocean). Biogeosciences 12, 1955–1965 (2015).ADS 

Google Scholar 
9.Archibald, K. M., Siegel, D. A. & Doney, S. C. Modeling the impact of zooplankton diel vertical migration on the carbon export flux of the biological pump. Glob. Biogeochem. Cycles 33, 181–199 (2019).ADS 
CAS 

Google Scholar 
10.Whitehouse, M. J. et al. Role of krill versus bottom-up factors in controlling phytoplankton biomass in the northern Antarctic waters of South Georgia. Mar. Ecol. Prog. Ser. 393, 69–82 (2009).ADS 
CAS 

Google Scholar 
11.Atkinson, A., Schmidt, K., Fielding, S., Kawaguchi, S. & Geissler, P. A. Variable food absorption by Antarctic krill: Relationships between diet, egestion rate and the composition and sinking rates of their fecal pellets. Deep Sea Res. 59–60, 147–158 (2012). Pt. II.ADS 

Google Scholar 
12.Cavan, E. L. et al. The importance of Antarctic krill in biogeochemical cycles. Nat. Commun. 10, 4742 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
13.Belcher, A. et al. The potential role of Antarctic krill faecal pellets in efficient carbon export at the marginal ice zone of the South Orkney Islands in spring. Polar Biol. 40, 2001–2013 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
14.Belcher, A. et al. Krill faecal pellets drive hidden pulses of particulate organic carbon in the marginal ice zone. Nat. Commun. 10, 889 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
15.Gleiber, M. R., Steinberg, D. K. & Ducklow, H. W. Time series of vertical flux of zooplankton fecal pellets on the continental shelf of the western Antarctic Peninsula. Mar. Ecol. Prog. Ser. 471, 23–36 (2012).ADS 

Google Scholar 
16.Belcher, A. et al. The role of particle associated microbes in remineralization of fecal pellets in the upper mesopelagic of the Scotia Sea, Antarctica. Limnol. Oceanogr. 61, 1049–1064 (2016).ADS 

Google Scholar 
17.Siegel, V. & Watkins, J. L. Distribution, Biomass and Demography of Antarctic Krill, Euphausia Superba in Biology and Ecology of Antarctic Krill 21-100 (Springer International Publishing, Switzerland, 2016).18.Cavan, E. L. et al. Attenuation of particulate organic carbon flux in the Scotia Sea, Southern Ocean, is controlled by zooplankton fecal pellets. Geophys. Res. Lett. 42, 821–830 (2015).ADS 
CAS 

Google Scholar 
19.Bathmann, U., Fischer, G., Müller, P. J. & Gerdes, D. Short-term variations in particulate matter sedimentation off Kapp Norvegia, Weddell Sea, Antarctica: relation to water mass advection, ice cover, plankton biomass and feeding activity. Polar Biol. 11, 185–195 (1991).
Google Scholar 
20.Ducklow, H. W. et al. Marine pelagic ecosystems: The West Antarctic Peninsula. Philos. Trans. R. Soc., B. 362, 67–94 (2007).
Google Scholar 
21.Clarke, A. et al. Climate change and the marine ecosystem of the western Antarctic Peninsula. Philos. Trans. R. Soc., B. 362, 149–166 (2007).
Google Scholar 
22.Vaughan, D. G. et al. Recent rapid regional climate warming on the Antarctic Peninsula. Clim. Change 60, 243–274 (2003).
Google Scholar 
23.Atkinson, A. et al. Krill (Euphausia superba) distribution contracts southward during rapid regional warming. Nat. Clim. Change 9, 142–147 (2019).ADS 

Google Scholar 
24.Atkinson, A., Siegel, V., Pakhomov, E. & Rothery, P. Long-term decline in krill stock and increase in salps within the Southern Ocean. Nature 432, 100–103 (2004).ADS 
CAS 
PubMed 

Google Scholar 
25.Bernard, K. S., Steinberg, D. K. & Schofield, O. M. E. Summertime grazing impact of the dominant macrozooplankton off the Western Antarctic Peninsula. Deep Sea Res. Pt. I 62, 111–122 (2012).
Google Scholar 
26.Pakhomov, E. A., Dubischar, C. D., Strass, V., Brichta, M. & Bathmann, U. V. The tunicate Salpa thompsoni ecology in the Southern Ocean. I. Distribution, biomass, demography and feeding ecophysiology. Mar. Biol. 149, 609–623 (2006).
Google Scholar 
27.Fischer, G. et al. Seasonal variability of particle flux in the Weddell Sea and its relation to ice cover. Nature 335, 426–428 (1988).ADS 

Google Scholar 
28.Schmidt, K. & Atkinson, A. Feeding and Food Processing in Antarctic Krill (Euphausia superba Dana) in Biology and Ecology of Antarctic Krill 175-224 (Springer International Publishing, Switzerland, 2016).29.Bone, Q., Carré, C. & Chang, P. Tunicate feeding filters. J. Mar. Biol. Assoc. 83, 907–919 (2003).
Google Scholar 
30.Pakhomov, E. A., Froneman, P. W. & Perissinotto, R. Salp/krill interactions in the Southern Ocean: spatial segregation and implications for the carbon flux. Deep Sea Res. 49, 1881–1907 (2002). Pt. II.ADS 
CAS 

Google Scholar 
31.Iversen, M. H. et al. Sinkers or floaters? Contribution from salp pellets to the export flux during a large bloom event in the Southern Ocean. Deep Sea Res. 138, 116–125 (2017). Pt. II.CAS 

Google Scholar 
32.Loeb, V. et al. Effects of sea-ice extent and krill or salp dominance on the Antarctic food web. Nature 387, 897–900 (1997).ADS 
CAS 

Google Scholar 
33.Dubischar, C. D. & Bathmann, U. V. The occurrence of faecal material in relation to different pelagic systems in the Southern Ocean and its importance for vertical flux. Deep Sea Res. 49, 3229–3242 (2002). Pt. II.ADS 

Google Scholar 
34.Manno, C. et al. Continuous moulting by Antarctic krill drives major pulses of carbon export in the north Scotia Sea, Southern Ocean. Nat. Commun. 11, 6051 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
35.Thiele, S., Fuchs, B. M., Amann, R. & Iversen, M. H. Colonization in the photic zone and subsequent changes during sinking determine bacterial community composition in marine snow. Appl. Environ. Microbiol. 81, 1463–1471 (2015).ADS 
PubMed 
PubMed Central 

Google Scholar 
36.Pauli, N.-C. et al. Selective feeding in Southern Ocean key grazers—diet composition of krill and salps. Commun. Biol. 4, 1061 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
37.Siegel, V. Introducing Antarctic Krill Euphausia Superba Dana, 1850 in Biology and Ecology of Antarctic Krill 23-41 (Springer International Publishing, Switzerland, 2016).38.Pakhomov, E. A. Salp/krill interactions in the eastern Atlantic sector of the Southern Ocean. Deep Sea Res. 51, 2645–2660 (2004). Pt. II.ADS 
CAS 

Google Scholar 
39.Phillips, B., Kremer, P. & Madin, L. P. Defecation by Salpa thompsoni and its contribution to vertical flux in the Southern Ocean. Mar. Biol. 156, 455–467 (2009).
Google Scholar 
40.Perissinotto, R. & Pakhomov, E. A. Contribution of salps to carbon flux of marginal ice zone of the Lazarev Sea, Southern Ocean. Mar. Biol. 131, 25–32 (1998).CAS 

Google Scholar 
41.Iversen, M. H. & Ploug, H. Temperature effects on carbon-specific respiration rate and sinking velocity of diatom aggregates—potential implications for deep ocean export processes. Biogeosciences 10, 4073–4085 (2013).ADS 

Google Scholar 
42.Ploug, H., Iversen, M. H. & Fischer, G. Ballast, sinking velocity, and apparent diffusivity within marine snow and zooplankton fecal pellets: Implications for substrate turnover by attached bacteria. Limnol. Oceanogr. 53, 1878–1886 (2008).ADS 

Google Scholar 
43.Ploug, H., Iversen, M. H., Koski, M. & Buitenhuis, E. T. Production, oxygen respiration rates, and sinking velocity of copepod fecal pellets: Direct measurements of ballasting by opal and calcite. Limnol. Oceanogr. 53, 469–476 (2008).ADS 
CAS 

Google Scholar 
44.Iversen, M. H. & Poulsen, L. K. Coprorhexy, coprophagy, and coprochaly in the copepods Calanus helgolandicus, Pseudocalanus elongatus, and Oithona similis. Mar. Ecol. Prog. Ser. 350, 79–89 (2007).ADS 

Google Scholar 
45.Cavan, E. L., Kawaguchi, S. & Boyd, P. W. Implications for the mesopelagic microbial gardening hypothesis as determined by experimental fragmentation of Antarctic krill fecal pellets. Ecol. Evol. 11, 1023–1036 (2021).PubMed 

Google Scholar 
46.Briggs, N., Dall’Olmo, G. & Claustre, H. Major role of particle fragmentation in regulating biological sequestration of CO2 by the oceans. Science 367, 791–793 (2020).ADS 
CAS 
PubMed 

Google Scholar 
47.DeMott, W. R. Retention Efficiency, Perceptual Bias, and Active Choice As Mechanisms of Food Selection by Suspension-Feeding Zooplankton in Behavioural Mechanisms of Food Selection 569–594 (Springer, Berlin, Heidelberg, Germany, 1990).48.Suh, H. L. & Nemoto, T. Morphology of the gastric mill in ten species of euphausiids. Mar. Biol. 97, 79–85 (1988).
Google Scholar 
49.Gauld, D. T. A peritrophic membrane in calanoid copepods. Nature 179, 325–326 (1957).ADS 

Google Scholar 
50.Bruland, K. W. & Silver, M. W. Sinking rates of fecal pellets from gelatinous zooplankton (salps, pteropods, doliolids). Mar. Biol. 63, 295–300 (1981).
Google Scholar 
51.von Harbou, L. Trophodynamics of Salps in the Atlantic Southern Ocean. PhD thesis, University of Bremen, (2009).52.Poulsen, L. K. & Iversen, M. H. Degradation of copepod fecal pellets: Key role of protozooplankton. Mar. Ecol. Prog. Ser. 367, 1–13 (2008).ADS 

Google Scholar 
53.Böckmann, S. et al. Salp fecal pellets release more bioavailable iron to Southern Ocean phytoplankton than krill fecal pellets. Curr. Biol. 31, 2737–2746.e2733 (2021).PubMed 

Google Scholar 
54.Alcaraz, M. et al. Changes in the C, N, and P cycles by the predicted salps-krill shift in the Southern Ocean. Front. Mar. Sci. 1, 45 (2014).
Google Scholar 
55.Fielding, S., Watkins, J. L., Collins, M. A., Enderlein, P. & Venables, H. J. Acoustic determination of the distribution of fish and krill across the Scotia Sea in spring 2006, summer 2008 and autumn 2009. Deep Sea Res. 59-60, 173–188 (2012). Pt. II.ADS 

Google Scholar 
56.Chiba, S., Horimoto, N., Satoh, R., Yamaguchi, Y. & Ishimaru, T. Macrozooplankton distribution around the Antarctic Divergence off Wilkes Land in the 1996 austral summer: With reference to high abundance of Salpa thompsoni. in: Proceedings of NIPR Symposium on Polar Biology, 33–50 (1998).57.Henschke, N. & Pakhomov, E. A. Latitudinal variations in Salpa thompsoni reproductive fitness. Limnol. Oceanogr. 64, 575–584 (2018).ADS 

Google Scholar 
58.Atkinson, A. et al. Oceanic circumpolar habitats of Antarctic krill. Mar. Ecol. Prog. Ser. 362, 1–23 (2008).ADS 
CAS 

Google Scholar 
59.Foxton, P. The Distribution and Life-History of Salpa thompsoni Foxton with Observations on a Related Species, Salpa gerlachei Foxton (Cambridge University Press, UK, Cambridge, 1966).60.Meyer, B. et al. Successful ecosystem-based management of Antarctic krill should address uncertainties in krill recruitment, behaviour and ecological adaptation. Commun. Earth Environ. 1, 28 (2020).ADS 

Google Scholar 
61.Atkinson, A., Siegel, V., Pakhomov, E. A., Jessopp, M. J. & Loeb, V. A re-appraisal of the total biomass and annual production of Antarctic krill. Deep Sea Res. Pt. I 56, 727–740 (2009).
Google Scholar 
62.Montes-Hugo, M. et al. Recent changes in phytoplankton communities associated with rapid regional climate change along the western Antarctic Peninsula. Science 323, 1470–1473 (2009).ADS 
CAS 
PubMed 

Google Scholar 
63.Fielding, S. et al. A Condensed History and Document of the Method Used by CCAMLR to Estimate Krill Biomass (B0) in 2010. (CCAMLR, 2016).64.Chu, D., Foote, K. G. & Stanton, T. K. Further analysis of target strength measurements of Antarctic krill at 38 and 120 kHz: comparison with deformed cylinder model and inference of orientation distribution. J. Acoust. Soc. Am. 93, 2985–2988 (1993).ADS 

Google Scholar 
65.McGehee, D. E., O’Driscoll, R. L. & Traykovski, L. V. M. Effects of orientation on acoustic scattering from Antarctic krill at 120 kHz. Deep Sea Res. 45, 1273–1294 (1998). Pt. II.ADS 

Google Scholar 
66.Demer, D. A. & Conti, S. G. Reconciling theoretical versus empirical target strengths of krill: effects of phase variability on the distorted-wave Born approximation. ICES J. Mar. Sci. 60, 429–434 (2003).
Google Scholar 
67.Conti, S. G. & Demer, D. A. Improved parameterization of the SDWBA for estimating krill target strength. ICES J. Mar. Sci. 63, 928–935 (2006).
Google Scholar 
68.Calise, L. & Skaret, G. Sensitivity investigation of the SDWBA Antarctic krill target strength model to fatness, material contrasts and orientation. CCAMLR Sci. 18, 97–122 (2011).
Google Scholar 
69.Hewitt, R. P. et al. Biomass of Antarctic krill in the Scotia Sea in January/February 2000 and its use in revising an estimate of precautionary yield. Deep Sea Res. 51, 1215–1236 (2004). Pt. II.ADS 

Google Scholar 
70.Flintrop, C. M. et al. Embedding and slicing of intact in situ collected marine snow. Limnol. Oceanogr. Methods 16, 339–355 (2018).
Google Scholar 
71.Markussen, T. N. et al. Tracks in the snow—advantage of combining optical methods to characterize marine particles and aggregates. Front. Mar. Sci. 7, 476 (2020).
Google Scholar 
72.Ploug, H. & Jorgensen, B. B. A net-jet flow system for mass transfer and microsensor studies of sinking aggregates. Mar. Ecol. Prog. Ser. 176, 279–290 (1999).ADS 
CAS 

Google Scholar 
73.Ploug, H., Terbrüggen, A., Kaufmann, A., Wolf-Gladrow, D. & Passow, U. A novel method to measure particle sinking velocity in vitro, and its comparison to three other in vitro methods. Limnol. Oceanogr. Methods 8, 386–393 (2010).
Google Scholar 
74.R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, Austria, 2019).75.ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag New York, 2016). More

1.Kislyuk AO, Haegeman B, Bergman NH, Weitz JS. Genomic fluidity: an integrative view of gene diversity within microbial populations. BMC Genom. 2011;12:1–10.
Google Scholar 
2.Tettelin H, Riley D, Cattuto C, Medini D. Comparative genomics: the bacterial pan-genome. Curr Opin Microbiol. 2008;11:472–7.CAS 

Google Scholar 
3.Tettelin H, Masignani V, Cieslewicz MJ, Donati C, Medini D, Ward NL, et al. Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae: implications for the microbial “pan-genome”. Proc Natl Acad Sci USA. 2005;102:13950–5.CAS 
PubMed 
PubMed Central 

Google Scholar 
4.Medini D, Donati C, Tettelin H, Masignani V, Rappuoli R. The microbial pan-genome. Curr Opin Genet Dev. 2005;15:589–94.CAS 

Google Scholar 
5.Vernikos G, Medini D, Riley DR, Tettelin H. Ten years of pan-genome analyses. Curr Opin Microbiol. 2015;23:148–54.CAS 
PubMed 

Google Scholar 
6.Caro-Quintero A, Konstantinidis KT. Bacterial species may exist, metagenomics reveal. Environ Microbiol. 2012;14:347–55.CAS 
PubMed 

Google Scholar 
7.Garcia SL, Stevens SLR, Crary B, Martinez-Garcia M, Stepanauskas R, Woyke T, et al. Contrasting patterns of genome-level diversity across distinct co-occurring bacterial populations. ISME J. 2018;12:742–55.CAS 
PubMed 

Google Scholar 
8.Olm MR, Crits-Christoph A, Diamond S, Lavy A, Matheus Carnevali PB, Banfield JF. Consistent metagenome-derived metrics verify and delineate bacterial species boundaries. mSystems. 2020;5:e00731–19.9.Konstantinidis KT, DeLong EF. Genomic patterns of recombination, clonal divergence and environment in marine microbial populations. ISME J. 2008;2:1052–65.CAS 
PubMed 

Google Scholar 
10.Bendall ML, Stevens SL, Chan LK, 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 
11.Johnston ER, Rodriguez RL, Luo C, Yuan MM, Wu L, He Z, et al. Metagenomics reveals pervasive bacterial populations and reduced community diversity across the Alaska tundra ecosystem. Front Microbiol. 2016;7:579.PubMed 
PubMed Central 

Google Scholar 
12.Meziti A, Tsementzi D, Rodriguez RL, 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 
13.Orellana LH, Ben Francis T, Kruger K, Teeling H, Muller MC, Fuchs BM, et al. Niche differentiation among annually recurrent coastal Marine Group II Euryarchaeota. ISME J. 2019;13:3024–36.CAS 
PubMed 
PubMed Central 

Google Scholar 
14.Jain C, Rodriguez RL, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9:5114.PubMed 
PubMed Central 

Google Scholar 
15.Shapiro BJ, Polz MF. Ordering microbial diversity into ecologically and genetically cohesive units. Trends Microbiol. 2014;22:235–47.CAS 
PubMed 
PubMed Central 

Google Scholar 
16.Andreani NA, Hesse E, Vos M. Prokaryote genome fluidity is dependent on effective population size. ISME J. 2017;11:1719–21.CAS 
PubMed 
PubMed Central 

Google Scholar 
17.Konstantinidis KT, Ramette A, Tiedje JM. The bacterial species definition in the genomic era. Philos Trans R Soc B 2006;361:1929–40.
Google Scholar 
18.McInerney JO, McNally A, O’Connell MJ. Why prokaryotes have pangenomes. Nat Microbiol. 2017;2:17040.CAS 
PubMed 

Google Scholar 
19.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 
20.Tully BJ, Graham ED, Heidelberg JF. The reconstruction of 2,631 draft metagenome-assembled genomes from the global oceans. Sci Data. 2018;5:170203.CAS 
PubMed 
PubMed Central 

Google Scholar 
21.Chen LX, 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 
22.Shaiber A, Eren AM. Composite metagenome-assembled genomes reduce the quality of public genome repositories. mBio. 2019;10:e00725–19.23.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 

Google Scholar 
24.Meziti A, Rodriguez-R LM, Hatt JK, Peña-Gonzalez A, Levy K, Konstantinidis KT. The reliability of metagenome-assembled genomes (MAGs) in representing natural populations: Insights from comparing MAGs against isolate genomes derived from the same fecal sample. Appl Environ Microbiol. 2021;87:e02593–20.25.Meziti A, Tsementzi D, Ar Kormas K, Karayanni H, Konstantinidis KT. Anthropogenic effects on bacterial diversity and function along a river-to-estuary gradient in Northwest Greece revealed by metagenomics. Environ Microbiol. 2016;18:4640–52.PubMed 

Google Scholar 
26.Arevalo P, VanInsberghe D, Elsherbini J, Gore J, Polz MF. A reverse ecology approach based on a biological definition of microbial populations. Cell. 2019;178:820–34.e14.CAS 
PubMed 

Google Scholar 
27.Delmont TO, Eren AM. Linking pangenomes and metagenomes: the Prochlorococcus metapangenome. PeerJ. 2018;6:e4320.PubMed 
PubMed Central 

Google Scholar 
28.Delmont TO, Kiefl E, Kilinc O, Esen OC, Uysal I, Rappe MS, et al. Single-amino acid variants reveal evolutionary processes that shape the biogeography of a global SAR11 subclade. Elife. 2019;8:e46497.29.Berube PM, Biller SJ, Hackl T, Hogle SL, Satinsky BM, Becker JW, et al. Single cell genomes of Prochlorococcus, Synechococcus, and sympatric microbes from diverse marine environments. Sci Data. 2018;5:1–11.
Google Scholar 
30.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 
31.Viver T, Orellana LH, Diaz S, Urdiain M, Ramos-Barbero MD, Gonzalez-Pastor JE, et al. Predominance of deterministic microbial community dynamics in salterns exposed to different light intensities. Environ Microbiol. 2019;21:4300–15.CAS 
PubMed 

Google Scholar 
32.Viver T, Cifuentes A, Diaz S, Rodriguez-Valdecantos G, Gonzalez B, Anton J, et al. Diversity of extremely halophilic cultivable prokaryotes in Mediterranean, Atlantic and Pacific solar salterns: evidence that unexplored sites constitute sources of cultivable novelty. Syst Appl Microbiol. 2015;38:266–75.CAS 
PubMed 

Google Scholar 
33.Viver T, Conrad RE, Orellana LH, Urdiain M, González-Pastor JE, Hatt JK, et al. Distinct ecotypes within a natural haloarchaeal population enable adaptation to changing environmental conditions without causing population sweeps. ISME J. 2020:15:1–14.34.Konstantinidis KT, Tiedje JM. Trends between gene content and genome size in prokaryotic species with larger genomes. Proc Natl Acad Sci USA. 2004;101:3160–5.CAS 
PubMed 
PubMed Central 

Google Scholar 
35.Rodriguez‐R LM, Tsementzi D, Luo C, Konstantinidis KT. Iterative subtractive binning of freshwater chronoseries metagenomes identifies over 400 novel species and their ecologic preferences. Environ Microbiol. 2020;22:3394–412.PubMed 

Google Scholar 
36.Pena A, Teeling H, Huerta-Cepas J, Santos F, Yarza P, Brito-Echeverria J, et al. Fine-scale evolution: genomic, phenotypic and ecological differentiation in two coexisting Salinibacter ruber strains. ISME J. 2010;4:882–95.CAS 
PubMed 

Google Scholar 
37.Maistrenko OM, Mende DR, Luetge M, Hildebrand F, Schmidt TSB, Li SS, et al. Disentangling the impact of environmental and phylogenetic constraints on prokaryotic within-species diversity. ISME J. 2020;14:1247–59.PubMed 
PubMed Central 

Google Scholar 
38.Anton J, Lucio M, Pena A, Cifuentes A, Brito-Echeverria J, Moritz F, et al. High metabolomic microdiversity within co-occurring isolates of the extremely halophilic bacterium Salinibacter ruber. PLOS ONE. 2013;8:e64701.CAS 
PubMed 
PubMed Central 

Google Scholar 
39.Luley-Goedl C, Nidetzky B. Glycosides as compatible solutes: biosynthesis and applications. Nat Prod Rep. 2011;28:875–96.CAS 
PubMed 

Google Scholar 
40.Antón J, Oren A, Benlloch S, Rodríguez-Valera F, Amann R, Rosselló-Mora R. Salinibacter ruber gen. nov., sp. nov., a novel, extremely halophilic member of the bacteria from saltern crystallizer ponds. IJSEM. 2002;52:485–91.PubMed 

Google Scholar 
41.Antón J, Rosselló-Mora R, Rodríguez-Valera F, Amann R. Extremely halophilic bacteria in crystallizer ponds from solar salterns. Appl Environ Microbiol. 2000;66:3052–7.PubMed 
PubMed Central 

Google Scholar 
42.Viver T, Orellana L, Gonzalez-Torres P, Diaz S, Urdiain M, Farias ME, et al. Genomic comparison between members of the Salinibacteraceae family, and description of a new species of Salinibacter (Salinibacter altiplanensis sp. nov.) isolated from high altitude hypersaline environments of the Argentinian Altiplano. Syst Appl Microbiol. 2018;41:198–212.PubMed 

Google Scholar 
43.Oren A, Rodríguez-Valera F. The contribution of halophilic Bacteria to the red coloration of saltern crystallizer ponds. FEMS Microbiol Ecol. 2001;36:123–30.CAS 
PubMed 

Google Scholar 
44.Santos F, Moreno-Paz M, Meseguer I, Lopez C, Rossello-Mora R, Parro V, et al. Metatranscriptomic analysis of extremely halophilic viral communities. ISME J. 2011;5:1621–33.CAS 
PubMed 
PubMed Central 

Google Scholar 
45.Kuo CH, Ochman H. Deletional bias across the three domains of life. Genome Biol Evol. 2009;1:145–52.PubMed 
PubMed Central 

Google Scholar 
46.Lane N, Martin W. The energetics of genome complexity. Nature. 2010;467:929–34.CAS 
PubMed 

Google Scholar 
47.Vos M, Hesselman MC, Te Beek TA, van Passel MWJ, Eyre-Walker A. Rates of lateral gene transfer in prokaryotes: high but why? Trends Microbiol. 2015;23:598–605.CAS 
PubMed 

Google Scholar 
48.Gogarten JP, Townsend JP. Horizontal gene transfer, genome innovation and evolution. Nat Rev Microbiol. 2005;3:679–87.CAS 
PubMed 

Google Scholar 
49.Sczyrba A, Hofmann P, Belmann P, Koslicki D, Janssen S, Droge 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 
50.Munoz R, Lopez-Lopez A, Urdiain M, Moore ER, Rossello-Mora R. Evaluation of matrix-assisted laser desorption ionization-time of flight whole cell profiles for assessing the cultivable diversity of aerobic and moderately halophilic prokaryotes thriving in solar saltern sediments. Syst Appl Microbiol. 2011;34:69–75.CAS 
PubMed 

Google Scholar 
51.Urdiain M, López-López A, Gonzalo C, Busse H-J, Langer S, Kämpfer P, et al. Reclassification of Rhodobium marinum and Rhodobium pfennigii as Afifella marina gen. nov. comb. nov. and Afifella pfennigii comb. nov., a new genus of photoheterotrophic Alphaproteobacteria and emended descriptions of Rhodobium, Rhodobium orientis and Rhodobium gokarnense. Syst Appl Microbiol. 2008;31:339–51.CAS 
PubMed 

Google Scholar 
52.Andrews S. FastQC: a quality control tool for high throughput sequence data. Cambridge, United Kingdom: Babraham Bioinformatics, Babraham Institute; 2010.53.Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.CAS 
PubMed 
PubMed Central 

Google Scholar 
54.Hyatt D, Chen G-L, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 2010;11:119.
Google Scholar 
55.Rodriguez-R LM, Gunturu S, Harvey WT, Rosselló-Mora R, Tiedje JM, Cole JR, et al. The Microbial Genomes Atlas (MiGA) webserver: taxonomic and gene diversity analysis of Archaea and Bacteria at the whole genome level. Nucleic Acids Res. 2018;46:W282–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
56.Sievers F, Wilm A, Dineen D, Gibson TJ, Karplus K, Li W, et al. Fast, scalable generation of high‐quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol. 2011;7:539.57.Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–3.CAS 
PubMed 
PubMed Central 

Google Scholar 
58.Price MN, Dehal PS, Arkin AP. FastTree 2–approximately maximum-likelihood trees for large alignments. PLOS ONE. 2010;5:e9490.PubMed 
PubMed Central 

Google Scholar 
59.Rambaut A. FigTree v1.4.4. http://tree.bio.ed.ac.uk/software/figtree/ 2018.60.Letunic I, Bork P. Interactive Tree Of Life (iTOL): an online tool for phylogenetic tree display and annotation. Bioinformatics. 2007;23:127–8.CAS 
PubMed 

Google Scholar 
61.Fu L, Niu B, Zhu Z, Wu S, Li W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics. 2012;28:3150–2.CAS 
PubMed 
PubMed Central 

Google Scholar 
62.Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: architecture and applications. BMC Bioinform. 2009;10:421.
Google Scholar 
63.Aramaki T, Blanc-Mathieu R, Endo H, Ohkubo K, Kanehisa M, Goto S, et al. KofamKOALA: KEGG ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics. 2020;36:2251–2.CAS 
PubMed 

Google Scholar 
64.Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28:27–30.CAS 
PubMed 
PubMed Central 

Google Scholar  More