Philippa Kaur

1.Doney S, Abbott MR, Cullen JJ, Karl DM, Rothstein L. From genes to ecosystems: the ocean’s new frontier. Ecol Environ. 2004;2:457–66.
Google Scholar 
2.Field CB, Behrenfeld MJ, Randerson JT, Falkowski P. Primary production of the biosphere: integrating terrestrial and oceanic components. Science. 1998;281:237–40.CAS 
PubMed 

Google Scholar 
3.Eppley RW, Petersen BJ. Particulate organic matter flux and planktonic new production in the deep ocean. Nature. 1979;282:677–80.
Google Scholar 
4.Ducklow H, Steinberg DK, Buessler KO. Upper ocean carbon export and the biological pump. Oceanography. 2001;14:56–58.
Google Scholar 
5.Carlson C, Ducklow H. Dissolved organic carbon in the upper ocean of the central equatorial Pacific Ocean, 1992: Daily and finescale vertical variations. Deep Sea Res II. 1995;42:639–56.CAS 

Google Scholar 
6.Cho BC, Azam F. Major role of bacteria in biogeochemical fluxes in the ocean’s interior. Nature. 1988;332:441–3.CAS 

Google Scholar 
7.Duarte CM, Cebrian J. The fate of marine autotrophic production. Limnol Oceanogr. 1996;41:1758–66.CAS 

Google Scholar 
8.Ducklow H. The bacterial component of the oceanic euphotic zone. FEMS Microbiol Ecol. 1999;30:1–30.CAS 

Google Scholar 
9.Herndl GJ, Reinthaler T. Microbial control of the dark end of the biological pump. Nat Geosci. 2013;6:718–24.CAS 
PubMed 
PubMed Central 

Google Scholar 
10.Worden AZ, Follows MJ, Giovannoni SJ, Wilken S, Zimmerman AE, Keeling PJ. Environmental science. Rethinking the marine carbon cycle: factoring in the multifarious lifestyles of microbes. Science. 2015;347:1257594.PubMed 

Google Scholar 
11.Grossart HP, Rojas-Jimenez K. Aquatic fungi: targeting the forgotten in microbial ecology. Curr Opin Microbiol. 2016;31:140–5.PubMed 

Google Scholar 
12.Richards TA, Jones MD, Leonard G, Bass D. Marine fungi: their ecology and molecular diversity. Ann Rev Mar Sci. 2012;4:495–522.PubMed 

Google Scholar 
13.Burgaud G, Arzur D, Durand L, Cambon-Bonavita MA, Barbier G. Marine culturable yeasts in deep-sea hydrothermal vents: species richness and association with fauna. FEMS Microbiol Ecol. 2010;73:121–33.CAS 
PubMed 

Google Scholar 
14.Burgaud G, Le Calvez T, Arzur D, Vandenkoornhuyse P, Barbier G. Diversity of culturable marine filamentous fungi from deep-sea hydrothermal vents. Environ Microbiol. 2009;11:1588–1600.PubMed 

Google Scholar 
15.Redou V, Navarri M, Meslet-Cladiere L, Barbier G, Burgaud G. Species richness and adaptation of marine fungi from deep-subseafloor sediments. Appl Environ Microbiol. 2015;81:3571–83.CAS 
PubMed 
PubMed Central 

Google Scholar 
16.Hyde KD, Jones EBG, Leao E, Pointing SB, Poonyth AD, Vrjmoed LLP. Role of fungi in marine ecosystems. Biodivers Conserv. 1998;7:1147–61.
Google Scholar 
17.Jones EB. Marine fungi: some factors influencing biodiversity. Fungal Diversity. 2000;4:53–73.
Google Scholar 
18.Priest T, Fuchs B, Amann R, Reich M. Diversity and biomass dynamics of unicellular marine fungi during a spring phytoplankton bloom. Environ Microbiol. 2021;23:448–63.CAS 
PubMed 

Google Scholar 
19.Gutierrez MH, Jara AM, Pantoja S. Fungal parasites infect marine diatoms in the upwelling ecosystem of the Humboldt current system off central Chile. Environ Microbiol. 2016;18:1646–53.PubMed 

Google Scholar 
20.Gutierrez MH, Pantoja S, Tejos E. The role of fungi in processing marine organic matter in the upwelling ecosystem off Chile. Mar Biol. 2011;158:205–19.
Google Scholar 
21.Bochdansky AB, Clouse MA, Herdl GJ. Eukaryotic microbes, principally fungi and labyrinthulomycetes, dominate biomass on bathypelagic marine snow. ISME J. 2017;11:362–73.PubMed 

Google Scholar 
22.Becker S, Tebben J, Coffinet S, Wittshire K, Iversen MH, Harder T, et al. Laminarin is a major molecule in the marine carbon cycle. Proc Natl Acad Sci USA. 2020;117:6599–607.CAS 
PubMed 
PubMed Central 

Google Scholar 
23.Seymour JR, Amin SA, Raina JB, Stocker R. Zooming in on the phycosphere: the ecological interface for phytoplankton-bacteria relationships. Nat Microbiol. 2017;2:17065.CAS 
PubMed 

Google Scholar 
24.Hassett BT, Gradinger R. Chytrids dominate arctic fungal communities. Environ Microbiol. 2016;18:2001–9.CAS 
PubMed 

Google Scholar 
25.Lavik G, Stuhrmann T, Bruchert V, Van der Plas A, Mohrholz V, Lam P, et al. Detoxification of sulphidic African shelf waters by blooming chemolithotrophs. Nature. 2009;457:581–4.CAS 
PubMed 

Google Scholar 
26.Ortega-Arbulu AS, Pichler M, Vuillemin A, Orsi WD. Effects of organic matter and low oxygen on the mycobenthos in a coastal lagoon. Environ Microbiol 2019;21:374–88.CAS 
PubMed 

Google Scholar 
27.Orsi WD, Morard R, Vuillemin A, Eitel M, Wörheide G, Milucka J, et al. Anaerobic metabolism of Foraminifera thriving below the seafloor. ISME J. 2020;14:2580–94.CAS 
PubMed 
PubMed Central 

Google Scholar 
28.Orsi WD, Vuillemin A, Rodriguez P, Coskun OK, Gomez-Saez GV, Lavik G, et al. Metabolic activity analyses demonstrate that Lokiarchaeon exhibits homoacetogenesis in sulfidic marine sediments. Nat Microbiol. 2020;5:248–55.CAS 
PubMed 

Google Scholar 
29.Dittmar T, Koch B, Hertkorn N, Kattner G. A simple and efficient method for the solid-phase extraction of dissolved organic matter (SPE-DOM) from seawater. Limnology and Oceanography. Methods. 2008;6:230–5.CAS 

Google Scholar 
30.Green NW, Perdue EM, Aiken GR, Butler KD, Chen H, Dittmar T, et al. An intercomparison of three methods for the large-scale isolation of oceanic dissolved organic matter. Mar Chem. 2014;161:14–19.CAS 

Google Scholar 
31.Riedel T, Dittmar T. A method detection limit for the analysis of natural organic matter via Fourier transform ion cyclotron resonance mass spectrometry. Anal Chem. 2014;86:8376–82.CAS 
PubMed 

Google Scholar 
32.Merder J, Freund JA, Feudel U, Hansen CT, Hawkes JA, Jacob B, et al. ICBM-OCEAN: processing ultrahigh-resolution mass spectrometry data of complex molecular mixtures. Anal Chem. 2020;92:6832–8.CAS 
PubMed 

Google Scholar 
33.Koch BP, Dittmar T. From mass to structure: an aromaticity index for high resolution mass data of natural organic matter. Rapid Commun Mass Spectrom. 2006;20:926–32.CAS 

Google Scholar 
34.Koch BP, Dittmar T. Erratum: from mass to structure: an aromaticity index for high resolution mass data of natural organic matter. Rapid Commun Mass Spectrom. 2016;20:250–250.
Google Scholar 
35.Oksanen J, Blanchen FG, Friendly M, Kindt R, Legendre R, McGlinn D, et al. Vegan: community ecology package. R package version 2 4-3 2017. (https://CRAN.R-project.org/package=vegan). Accessed June 2020.36.Hansen CT, Niggemann J, Giebel HA, Simon M, Bach W, Dittmar T. Biodegradability of hydrothermally altered deep-sea dissolved organic matter. Mar Chem. 2019;217. https://doi.org/10.1016/j.marchem.2019.103706.37.Orsi WD, Smith JM, Liu S, Liu Z, Sakamoto CM, Wilken S, et al. Diverse, uncultivated bacteria and archaea underlying the cycling of dissolved protein in the ocean. ISME J. 2016;10:2158–73.CAS 
PubMed 
PubMed Central 

Google Scholar 
38.Gardes M, Bruns TD. ITS primers with enhanced specificity for basidiomycetes-application to the identification of mycorrhizae and rusts. Mol Ecol. 1993;2:113–8.CAS 
PubMed 

Google Scholar 
39.White TJ, Bruns S, Lee S, Taylor J “Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics”. In: M Innis, D Gelfand, K Sninsky, T White, editors. PCR Protocols: a guide to methods and applications. Academic Pres, New York, NY; 1990. pp. 315–22.40.Tedersoo L, Lindahl B. Fungal identification biases in microbiome projects. Environ Microbiol Rep. 2016;8:774–9.PubMed 

Google Scholar 
41.Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10:996–8.CAS 
PubMed 

Google Scholar 
42.Nilsson RH, Larsson KH, Taylor AFS, Bengtsson-Palme J, Jeppesen TS, Schigel D, et al. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 2019;47:D259–D264.CAS 
PubMed 

Google Scholar 
43.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 
44.Coskun OK, Pichler M, Vargas S, Gilder S, Orsi WD. Linking uncultivated microbial populations and benthic carbon turnover by using quantitative stable isotope probing. Appl Environ Microbiol. 2018;84:e01083–18.CAS 
PubMed 
PubMed Central 

Google Scholar 
45.Chemidlin Prevost-Boure N, Christen R, Dequiedt S, Mougel C, Lellevre M, Jolivet C, et al. Validation and application of a PCR primer set to quantify fungal communities in the soil environment by real-time quantitative PCR. PLoS One. 2011;6:e24166.CAS 
PubMed 
PubMed Central 

Google Scholar 
46.Banos S, Lentendu G, Kopf A, Wubet T, Glockner FO, Reich M. A comprehensive fungi-specific 18S rRNA gene sequence primer toolkit suited for diverse research issues and sequencing platforms. BMC Microbiol. 2018;18:190.CAS 
PubMed 
PubMed Central 

Google Scholar 
47.Haas BJ, Papanicolaou A, Yassour M, Grabherr M, Blood PD, Bowden J, et al. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat Protoc. 2013;8:1494–512.CAS 
PubMed 

Google Scholar 
48.Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12:59–60.CAS 
PubMed 

Google Scholar 
49.Keeling PJ, Burki F, Wilcox HM, Allam B, Allen EE, Amaral-Zettler LA, et al. The marine microbial eukaryote transcriptome sequencing project (MMETSP): illuminating the functional diversity of eukaryotic life in the oceans through transcriptome sequencing. PLoS Biol. 2014;12:e1001889.PubMed 
PubMed Central 

Google Scholar 
50.Tatusov RL, Koonin EV, Lipman DJ. A genomic perspective on protein families. Science. 1997;278:631–7.CAS 
PubMed 

Google Scholar 
51.Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–7.CAS 
PubMed 
PubMed Central 

Google Scholar 
52.Guindon S, Dufayard JF, Lefort V, Anisimova M, Hordijk W, Gascuel O, et al. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst Biol. 2010;59:307–21.CAS 
PubMed 

Google Scholar 
53.Gouy M, Guindon S, Gascuel O. SeaView version 4: a multiplatform graphical user interface for sequence alignment and phylogenetic tree building. Mol Biol Evol. 2010;27:221–4.CAS 
PubMed 

Google Scholar 
54.Lombard V, Golaconda Ramulu H, Drula E, Coutinho PM, Henrissat B. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res. 2014;42:D490–495.CAS 
PubMed 
PubMed Central 

Google Scholar 
55.Tamames J, Puente-Sanchez F. SqueezeMeta, a highly portable, fully automatic metagenomic analysis pipeline. Front Microbiol. 2018;9:3349.PubMed 

Google Scholar 
56.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 
57.Li D, Liu CM, Luo R, Sadakane K, Lam TW. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics. 2015;31:1674–6.CAS 
PubMed 

Google Scholar 
58.Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–9.CAS 
PubMed 

Google Scholar 
59.Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–6.CAS 
PubMed 
PubMed Central 

Google Scholar 
60.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:D590–596.CAS 
PubMed 

Google Scholar 
61.Edgar RC. MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinforma. 2004;5:1–19.
Google Scholar 
62.Guillard RRL, Hargraves PE. Stichochrysis immobilis is a diatom, not a chrysophyte. Phycologia. 1993;32:234–6.
Google Scholar 
63.Inthorn M, Wagner T, Scheeder G, Zabel M. Lateral transport controls distribution, quality and burial of organic matter along continental slopes in high-productivity areas. Geology. 2006;34:205–8.CAS 

Google Scholar 
64.Hungate BA, Mau RL, Schwartz E, Caporaso JG, Dijkstra P, van Gestel N, et al. Quantitative microbial ecology through stable isotope probing. Appl Environ Microbiol. 2015;81:7570–81.CAS 
PubMed 
PubMed Central 

Google Scholar 
65.Igarza M, Dittmar T, Graco M, Niggemann J. Dissolved organic matter cycling in the coastal upwelling system off central Peru during an “El Niño” year. Front Mar Sci. 2019;6:198.
Google Scholar 
66.Kuypers MM, Lavik G, Woebken D, Schmid M, Fuchs BM, Amann R, et al. Massive nitrogen loss from the Benguela upwelling system through anaerobic ammonium oxidation. Proc Natl Acad Sci USA. 2005;102:6478–83.CAS 
PubMed 
PubMed Central 

Google Scholar 
67.Wright JJ, Konwar KM, Hallam SJ. Microbial ecology of expanding oxygen minimum zones. Nat Rev Microbiol. 2012;10:381–94.CAS 
PubMed 

Google Scholar 
68.Rossel PE, Stubbins A, Hach PF, Dittmar T. Bioavailability and molecular composition of dissolved organic matter from a diffuse hydrothermal system. Mar Chem. 2015;177:257–66.CAS 

Google Scholar 
69.Schmidt F, Koch BP, Goldhammer T, Elvert M, Witt M, Lin Y, et al. Unraveling signatures of biogeochemical processes and the depositional setting in the molecular composition of pore water DOM across different marine environments. Geochim Cosmochim Acta. 2017;207:57–80.CAS 

Google Scholar 
70.Gruninger RJ, Puniya AK, Callaghan TM, Edwards JE, Youssef N, Dagar SS, et al. Anaerobic fungi (phylum Neocallimastigomycota): advances in understanding their taxonomy, life cycle, ecology, role and biotechnological potential. FEMS Microbiol Ecol. 2014;90:1–17.CAS 
PubMed 

Google Scholar 
71.Jones MD, Richards TA, Hawksworth DL, Bass D. Validation and justification of the phylum name Cryptomycota phyl. nov. IMA Fungus. 2011;2:173–5.PubMed 
PubMed Central 

Google Scholar 
72.Spatafora JW, Chang Y, Benny GL, Lazarus K, Smith ME, Berbee ML, et al. A phylum-level phylogenetic classification of zygomycete fungi based on genome-scale data. Mycologia. 2016;108:1028–46.CAS 
PubMed 
PubMed Central 

Google Scholar 
73.Morand SC, Bertignac M, Iltis A, Kolder ICRM, Pirovano W, Jourdain R, et al. Complete genome sequence of Malassezia restricta CBS 7877, an opportunist pathogen involved in dandruff and seborrheic dermatitis. Microbiol Resour Announc. 2019;8:e01543–18.PubMed 
PubMed Central 

Google Scholar 
74.Buckley DH, Huangyutitham V, Hsu SF, Nelson TA. Stable isotope probing with 15N achieved by disentangling the effects of genome G+C content and isotope enrichment on DNA density. Appl Environ Microbiol. 2007;73:3189–95.CAS 
PubMed 
PubMed Central 

Google Scholar 
75.Tedersoo L, Sanchez-Ramirez S, Kõljalg U, Bahram M, Döring M, Schigel D, et al. High-level classification of the Fungi and a tool for evolutionary ecological analyses. Fungal Diversity. 2018;90:135–59.
Google Scholar 
76.Walsh EA, Kirkpatrick JB, Rutherford SD, Smith DC, Sogin M, D’Hondt S, et al. Bacterial diversity and community composition from seasurface to subseafloor. ISME J. 2016;10:979–89.PubMed 

Google Scholar 
77.Karpov SA, Mamkaeva MA, Aleoshin VV, Nassonova E, Lilje O, Gleason FH. Morphology, phylogeny, and ecology of the aphelids (Aphelidea, Opisthokonta) and proposal for the new superphylum Opisthosporidia. Front Microbiol. 2014;5:112.PubMed 
PubMed Central 

Google Scholar 
78.Jones MD, Forn I, Gadelha C, Egan MJ, Bass D, Massana R, et al. Discovery of novel intermediate forms redefines the fungal tree of life. Nature. 2011;474:200–3.CAS 
PubMed 

Google Scholar 
79.Chang Y, Wang S, Sekimoto S, Aerts AL, Choi C, Clum A, et al. Phylogenomic analyses indicate that early Fungi evolved digesting cell walls of algal ancestors of land plants. Genome Biol Evol. 2015;7:1590–601.CAS 
PubMed 
PubMed Central 

Google Scholar 
80.Loron CC, Francois C, Rainbird RH, Turner EC, Borensztajn S, Javaux EJ. Early fungi from the Proterozoic era in Arctic Canada. Nature. 2019;570:232–5.CAS 
PubMed 

Google Scholar 
81.Lyons TW, Reinhard CT, Planavsky NJ. The rise of oxygen in Earth’s early ocean and atmosphere. Nature. 2014;506:307–15.CAS 
PubMed 

Google Scholar 
82.Passow U. Production of transparent exopolymer particles (TEP) by phyto- and bacterioplankton. Mar Ecol Prog Ser. 2002;236:1–12.
Google Scholar 
83.Takahashi E, Ledauphin J, Goux D, Orvain F. Optimising extraction of extracellular polymeric substances (EPS) from benthic diatoms: comparison of the efficiency of six EPS extraction methods. Mar Freshw Res. 2009;60:1201–10.CAS 

Google Scholar 
84.de Brouwer JFC, Wolfstein K, Stal J. Physical characterization and diel dynamics of different fractions of extracellular polysaccharides in an axenic culture of a benthic diatom. Eur J Phycol. 2002;37:37–44.
Google Scholar 
85.Bass D, Howe A, Brown N, Barton H, Demidova M, Michelle H, et al. Yeast forms dominate fungal diversity in the deep oceans. Proc R Soc B. 2007;274:3069–77.CAS 
PubMed 
PubMed Central 

Google Scholar 
86.Amend A. From dandruff to deep-sea vents: Malassezia-like fungi are ecologically hyper-diverse. PLoS Pathog. 2014;10:e1004277.PubMed 
PubMed Central 

Google Scholar 
87.Meeboon J, Takamatsu S. Microidium phyllanthi-reticulati sp. nov. on Phyllanthus reticulatus. Mycotaxon. 2017;132:289–97.
Google Scholar 
88.Lueders T, Wagner B, Claus P, Friedrich MW. Stable isotope probing of rRNA and DNA reveals a dynamic methylotroph community and trophic interactions with fungi and protozoa in oxic rice field soil. Environ Microbiol. 2004;6:60–72.CAS 
PubMed 

Google Scholar 
89.Kjeldsen KU, Schreiber L, Thorup CA, Boesen T, Bjerg JT, Yang T, et al. On the evolution and physiology of cable bacteria. Proc Natl Acad Sci USA. 2019;116:19116–25.CAS 
PubMed 
PubMed Central 

Google Scholar 
90.Dyksma S, Bischof K, Fuchs BM, Hoffmann K, Meier D, Meyerdierks A, et al. Ubiquitous Gammaproteobacteria dominate dark carbon fixation in coastal sediments. ISME J. 2016;10:1939–53.CAS 
PubMed 
PubMed Central 

Google Scholar 
91.Middelburg JJ. Chemoautotrophy in the ocean. Geophys Res Let. 2011;38:94–97.
Google Scholar 
92.Starzynska-Janiszewska A, Dulinski R, Stodolak B. Fermentation with edible Rhizopus strains to enhance the bioactive potential of hull-less pumpkin oil cake. Molecules. 2020;25:5782.CAS 
PubMed Central 

Google Scholar 
93.Dubovenko AG, Dunaevsky YE, Belozersky MA, Oppert B, Lord JC, Elpidina EN. Trypsin-like proteins of the fungi as possible markers of pathogenicity. Fungal Biol. 2010;114:151–9.CAS 
PubMed 

Google Scholar 
94.Arnosti C, Wietz M, Brinkhoff T, Hehemann JH, Probandt D, Zeugner L, et al. The biogeochemistry of marine polysaccharides: sources, inventories, and bacterial drivers of the carbohydrate cycle. Ann Rev Mar Sci. 2021;13:81–108.CAS 
PubMed 

Google Scholar 
95.Rossel PE, Bienhold C, Hehemann JH, Dittmar T, Boetius A. Molecular composition of dissolved organic matter in sediment porewater of the arctic deep-sea observatory HAUSGARTEN (Fram Strait). Front Mar Sci. 2020;7:428.
Google Scholar 
96.Fenchel T, Finlay BJ. Ecology and evolution in anoxic worlds. In: RM May, PH Harvey, editors. Oxford Series in Ecology and Evolution. Oxford University Press, Oxford; 1–288, 1995.97.Lynd LR, Weimer PJ, van Zyl WH, Pretorius IS. Microbial cellulose utilization: fundamentals and biotechnology. Microbiol Mol Biol Rev. 2002;66:506–77.CAS 
PubMed 
PubMed Central 

Google Scholar  More

1.Cavicchioli R, Ripple WJ, Timmis KN, Azam F, Bakken LR, Baylis M, et al. Scientists’ warning to humanity: microorganisms and climate change. Nat Rev Microbiol. 2019;17:569–86.CAS 
PubMed 
PubMed Central 

Google Scholar 
2.Bunse C, Pinhassi J. Marine Bacterioplankton seasonal succession dynamics. Trends Microbiol. 2017;25:494–505.CAS 
PubMed 

Google Scholar 
3.Buttigieg PL, Fadeev E, Bienhold C, Hehemann L, Offre P, Boetius A. Marine microbes in 4D-using time series observation to assess the dynamics of the ocean microbiome and its links to ocean health. Curr Opin Microbiol. 2018;43:169–85.PubMed 

Google Scholar 
4.Gilbert JA, Steele JA, Caporaso JG, Steinbrück L, Reeder J, Temperton B, et al. Defining seasonal marine microbial community dynamics. ISME J. 2012;6:298–308.CAS 
PubMed 

Google Scholar 
5.Cram JA, Chow C-ET, Sachdeva R, Needham DM, Parada AE, Steele JA, et al. Seasonal and interannual variability of the marine bacterioplankton community throughout the water column over ten years. ISME J. 2015;9:563–80.PubMed 

Google Scholar 
6.Auladell A, Barberán A, Logares R, Garcés E, Gasol JM, Ferrera I. Seasonal niche differentiation among closely related marine bacteria. ISME J. 2021.7.Alonso-Saez L, Sanchez O, Gasol JM, Balague V, Pedros-Alio C. Winter-to-summer changes in the composition and single-cell activity of near-surface Arctic prokaryotes. Environ Microbiol. 2008;10:2444–54.CAS 
PubMed 

Google Scholar 
8.Rokkan Iversen K, Seuthe L. Seasonal microbial processes in a high-latitude fjord (Kongsfjorden, Svalbard): I. Heterotrophic bacteria, picoplankton and nanoflagellates. Polar Biol. 2011;34:731–49.
Google Scholar 
9.Grzymski JJ, Riesenfeld CS, Williams TJ, Dussaq AM, Ducklow H, Erickson M, et al. A metagenomic assessment of winter and summer bacterioplankton from Antarctica Peninsula coastal surface waters. ISME J. 2012;6:1901–15.CAS 
PubMed 
PubMed Central 

Google Scholar 
10.Pedrós-Alió C, Potvin M, Lovejoy C. Diversity of planktonic microorganisms in the Arctic Ocean. Prog Oceanogr. 2015;139:233–43.
Google Scholar 
11.Wilson B, Müller O, Nordmann E-L, Seuthe L, Bratbak G, Øvreås L. Changes in marine prokaryote composition with season and depth over an Arctic polar year. Front Mar Sci. 2017;4:95.
Google Scholar 
12.Sandaa R-A, E Storesund J, Olesin E, Lund Paulsen M, Larsen A, Bratbak G, et al. Seasonality drives microbial community structure, shaping both eukaryotic and prokaryotic host−viral relationships in an Arctic marine ecosystem. Viruses. 2018;10:715.CAS 
PubMed Central 

Google Scholar 
13.Williams TJ, Long E, Evans F, Demaere MZ, Lauro FM, Raftery MJ, et al. A metaproteomic assessment of winter and summer bacterioplankton from Antarctic Peninsula coastal surface waters. ISME J. 2012;6:1883–900.CAS 
PubMed 
PubMed Central 

Google Scholar 
14.Freyria NJ, Joli N, Lovejoy C. A decadal perspective on north water microbial eukaryotes as Arctic Ocean sentinels. Sci Rep. 2021;11:8413.CAS 
PubMed 
PubMed Central 

Google Scholar 
15.Assmy P, Fernández-Méndez M, Duarte P, Meyer A, Randelhoff A, Mundy CJ, et al. Leads in Arctic pack ice enable early phytoplankton blooms below snow-covered sea ice. Sci Rep. 2017;7:40850.CAS 
PubMed 
PubMed Central 

Google Scholar 
16.Hegseth EN, Assmy P, Wiktor JM, Wiktor J, Kristiansen S, Leu E, et al. Phytoplankton seasonal dynamics in Kongsfjorden, Svalbard and the adjacent shelf. In: Hop H, Wiencke C (eds). The ecosystem of Kongsfjorden, Svalbard. 2019. Springer International Publishing, Cham, pp 173–227.17.Liu Y, Blain S, Crispi O, Rembauville M, Obernosterer I. Seasonal dynamics of prokaryotes and their associations with diatoms in the Southern Ocean as revealed by an autonomous sampler. Environ Microbiol. 2020;22:3968–84.CAS 
PubMed 

Google Scholar 
18.Randelhoff A, Lacour L, Marec C, Leymarie E, Lagunas J, Xing X, et al. Arctic mid-winter phytoplankton growth revealed by autonomous profilers. Sci Adv. 2020;6:eabc2678.PubMed 
PubMed Central 

Google Scholar 
19.Randelhoff A, Reigstad M, Chierici M, Sundfjord A, Ivanov V, Cape M, et al. Seasonality of the physical and biogeochemical hydrography in the inflow to the Arctic Ocean through Fram Strait. Front Mar Sci. 2018;5:224.
Google Scholar 
20.Berge J, Renaud PE, Darnis G, Cottier F, Last K, Gabrielsen TM, et al. In the dark: a review of ecosystem processes during the Arctic polar night. Prog Oceanogr. 2015;139:258–71.
Google Scholar 
21.Müller O, Wilson B, Paulsen ML, Rumińska A, Armo HR, Bratbak G, et al. Spatiotemporal dynamics of ammonia-oxidizing thaumarchaeota in distinct arctic water masses. Front Microbiol. 2018;9:24.PubMed 
PubMed Central 

Google Scholar 
22.Johnsen G, Leu E, Gradinger R. Marine micro- and macroalgae in the polar night. In: Berge J, Johnsen G, Cohen JH (eds). Polar night marine ecology: life and light in the dead of night. 2020. Springer International Publishing, Cham, pp 67–112.23.Vader A, Marquardt M, Meshram A, Gabrielsen T. Key Arctic phototrophs are widespread in the polar night. Polar Biol. 2014;38:13–21.
Google Scholar 
24.Leu E, Mundy CJ, Assmy P, Campbell K, Gabrielsen TM, Gosselin M, et al. Arctic spring awakening—steering principles behind the phenology of vernal ice algal blooms. Prog Oceanogr. 2015;139:151–70.
Google Scholar 
25.Soltwedel T, Bauerfeind E, Bergmann M, Bracher A, Budaeva N, Busch K, et al. Natural variability or anthropogenically-induced variation? Insights from 15 years of multidisciplinary observations at the arctic marine LTER site HAUSGARTEN. Ecol Indic. 2016;65:89–102.
Google Scholar 
26.Nöthig E-M, Ramondenc S, Haas A, Hehemann L, Walter A, Bracher A, et al. Summertime chlorophyll a and particulate organic carbon standing stocks in surface waters of the Fram Strait and the Arctic Ocean (1991–2015). Front Mar Sci. 2020;7:350.
Google Scholar 
27.Nöthig E-M, Bracher A, Engel A, Metfies K, Niehoff B, Peeken I, et al. Summertime plankton ecology in Fram Strait—a compilation of long- and short-term observations. Polar Res. 2015;34:23349.
Google Scholar 
28.Engel A, Bracher A, Dinter T, Endres S, Grosse J, Metfies K, et al. Inter-annual variability of organic carbon concentration in the Eastern Fram Strait during summer (2009-17). Front Mar Sci. 2019;6:187.
Google Scholar 
29.Fadeev E, Salter I, Schourup-Kristensen V, Nöthig E-M, Metfies K, Engel A, et al. Microbial communities in the east and west Fram Strait during sea ice melting season. Front Mar Sci. 2018;5:429.
Google Scholar 
30.von Jackowski A, Grosse J, Nöthig E-M, Engel A. Dynamics of organic matter and bacterial activity in the Fram Strait during summer and autumn. Philos Trans R Soc Math Phys Eng Sci. 2020;378:20190366.
Google Scholar 
31.Metfies K, Bauerfeind E, Wolf C, Sprong P, Frickenhaus S, Kaleschke L, et al. Protist communities in moored long-term sediment traps (Fram Strait, Arctic)–preservation with mercury chloride allows for PCR-based molecular genetic analyses. Front Mar Sci. 2017;4:301.
Google Scholar 
32.Cardozo-Mino MG, Fadeev E, Salman-Carvalho V, Boetius A. Spatial distribution of Arctic bacterioplankton abundance is linked to distinct water masses and summertime phytoplankton bloom dynamics (Fram Strait, 79°N). Front Microbiol. 2021;12:658803.PubMed 
PubMed Central 

Google Scholar 
33.Richter ME, von Appen W-J, Wekerle C. Does the East Greenland Current exist in the northern Fram Strait? Ocean Sci. 2018;14:1147–65.CAS 

Google Scholar 
34.Tuerena RE, Hopkins J, Buchanan PJ, Ganeshram RS, Norman L, von Appen W-J, et al. An Arctic strait of two halves: the changing dynamics of nutrient uptake and limitation across the Fram Strait. Glob Biogeochem Cycles. 2021;35:e2021GB006961.35.Polyakov IV, Pnyushkov AV, Alkire MB, Ashik IM, Baumann TM, Carmack EC, et al. Greater role for Atlantic inflows on sea-ice loss in the Eurasian Basin of the Arctic Ocean. Science. 2017;356:285–91.CAS 
PubMed 

Google Scholar 
36.Lannuzel D, Tedesco L, van Leeuwe M, Campbell K, Flores H, Delille B, et al. The future of Arctic sea-ice biogeochemistry and ice-associated ecosystems. Nat Clim Change. 2020;10:983–92.
Google Scholar 
37.Carter-Gates M, Balestreri C, Thorpe SE, Cottier F, Baylay A, Bibby TS, et al. Implications of increasing Atlantic influence for Arctic microbial community structure. Sci Rep. 2020;10:19262.CAS 
PubMed 
PubMed Central 

Google Scholar 
38.Parada AE, Needham DM, Fuhrman JA. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol. 2016;18:1403–14.CAS 
PubMed 

Google Scholar 
39.Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 2011;17:10–2.
Google Scholar 
40.Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
PubMed 
PubMed Central 

Google Scholar 
41.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:D590–596.CAS 
PubMed 

Google Scholar 
42.Guillou L, Bachar D, Audic S, Bass D, Berney C, Bittner L, et al. The Protist Ribosomal Reference database (PR2): a catalog of unicellular eukaryote Small Sub-Unit rRNA sequences with curated taxonomy. Nucleic Acids Res. 2013;41:D597–D604.CAS 
PubMed 

Google Scholar 
43.Hsieh TC, Ma KH, Chao A. iNEXT: an R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods Ecol Evol. 2016;7:1451–6.
Google Scholar 
44.Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, et al. Welcome to the Tidyverse. J Open Source Softw. 2019;4:1686.
Google Scholar 
45.McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLOS ONE. 2013;8:e61217.CAS 
PubMed 
PubMed Central 

Google Scholar 
46.Andersen KSS, Kirkegaard RH, Karst SM, Albertsen M. ampvis2: an R package to analyse and visualise 16S rRNA amplicon data. bioRxiv. 2018.47.Lawlor J. PNWColors: A Pacific Northwest inspired R color palette package. 2020 https://github.com/jakelawlor/PNWColors.48.von Appen W-J, Schauer U, Hattermann T, Beszczynska-Möller A. Seasonal cycle of mesoscale instability of the west Spitsbergen Current. J Phys Oceanogr. 2016;46:1231–54.
Google Scholar 
49.Wekerle C, Wang Q, von Appen W-J, Danilov S, Schourup-Kristensen V, Jung T. Eddy-resolving simulation of the Atlantic water circulation in the Fram Strait with focus on the seasonal cycle. J Geophys Res Oceans. 2017;122:8385–405.
Google Scholar 
50.Giner CR, Balagué V, Krabberød AK, Ferrera I, Reñé A, Garcés E, et al. Quantifying long-term recurrence in planktonic microbial eukaryotes. Mol Ecol. 2019;28:923–35.PubMed 

Google Scholar 
51.Royo-Llonch M, Sánchez P, Ruiz-González C, Salazar G, Pedrós-Alió C, Sebastián M, et al. Compendium of 530 metagenome-assembled bacterial and archaeal genomes from the polar Arctic Ocean. Nat. Microbiol. 2021;6:1561–74.52.Priest T, Orellana LH, Huettel B, Fuchs BM, Amann R. Microbial metagenome-assembled genomes of the Fram Strait from short and long read sequencing platforms. PeerJ. 2021;9:e11721.PubMed 
PubMed Central 

Google Scholar 
53.Sunagawa S, Coelho LP, Chaffron S, Kultima JR, Labadie K, Salazar G, et al. Structure and function of the global ocean microbiome. Science. 2015;348:1261359.PubMed 

Google Scholar 
54.Teeling H, Fuchs BM, Becher D, Klockow C, Gardebrecht A, Bennke CM, et al. Substrate-controlled succession of marine bacterioplankton populations induced by a phytoplankton bloom. Science. 2012;336:608–11.CAS 
PubMed 

Google Scholar 
55.Monier A, Comte J, Babin M, Forest A, Matsuoka A, Lovejoy C. Oceanographic structure drives the assembly processes of microbial eukaryotic communities. ISME J. 2015;9:990–1002.CAS 
PubMed 

Google Scholar 
56.Leeuwe M, van, Tedesco L, Arrigo KR, Assmy P, Campbell K, Meiners KM, et al. Microalgal community structure and primary production in Arctic and Antarctic sea ice: a synthesis. Elem Sci Anth. 2018;6:4.
Google Scholar 
57.Fadeev E, Rogge A, Ramondenc S, Nöthig E-M, Wekerle C, Bienhold C, et al. Sea ice presence is linked to higher carbon export and vertical microbial connectivity in the Eurasian Arctic Ocean. Commun Biol. 2021;4:1–13.
Google Scholar 
58.Wasmund N, Göbel J, von Bodungen B. 100-years-changes in the phytoplankton community of Kiel Bight (Baltic Sea). J Mar Syst. 2008;73:300–22.
Google Scholar 
59.Stoecker DK, Lavrentyev PJ. Mixotrophic plankton in the polar seas: a pan-Arctic review. Front Mar Sci. 2018;5:292.
Google Scholar 
60.Lampe V, Nöthig E-M, Schartau M. Spatio-temporal variations in community size structure of Arctic protist plankton in the Fram Strait. Front Mar Sci. 2021;7:579880.
Google Scholar 
61.Brichta M, Nöthig E-M. The role of life cycle stages of diatoms in decoupling carbon and silica cycles in polar regions. In: Proceedings of SCAR Open Science Conference Bremen, Germany. 2004.62.Not F, Siano R, Kooistra WHCF, Simon N, Vaulot D, Probert I. Diversity and ecology of eukaryotic marine phytoplankton. In: Piganeau G (ed). Advances in botanical research. 2012. Academic Press, pp 1–53.63.Raghukumar S. Ecology of the marine protists, the Labyrinthulomycetes (Thraustochytrids and Labyrinthulids). Eur J Protistol. 2002;38:127–45.
Google Scholar 
64.Scholz B, Guillou L, Marano AV, Neuhauser S, Sullivan BK, Karsten U, et al. Zoosporic parasites infecting marine diatoms—a black box that needs to be opened. Fungal Ecol. 2016;19:59–76.PubMed 
PubMed Central 

Google Scholar 
65.Choi DH, Park K-T, An SM, Lee K, Cho J-C, Lee J-H, et al. Pyrosequencing revealed SAR116 clade as dominant dddP-containing bacteria in oligotrophic NW Pacific Ocean. PLOS One. 2015;10:e0116271.PubMed 
PubMed Central 

Google Scholar 
66.Wemheuer B, Wemheuer F, Hollensteiner J, Meyer F-D, Voget S, Daniel R. The green impact: bacterioplankton response toward a phytoplankton spring bloom in the southern North Sea assessed by comparative metagenomic and metatranscriptomic approaches. Front Microbiol. 2015;6:805.PubMed 
PubMed Central 

Google Scholar 
67.Delpech L-M, Vonnahme TR, McGovern M, Gradinger R, Præbel K, Poste A. Terrestrial inputs shape coastal bacterial and archaeal communities in a high Arctic Fjord (Isfjorden, Svalbard). Front Microbiol. 2021;12:614634.PubMed 
PubMed Central 

Google Scholar 
68.Alldredge AL, Gotschalk CC. Direct observations of the mass flocculation of diatom blooms: characteristics, settling velocities and formation of diatom aggregates. Deep Sea Res Part A. Oceanogr Res Pap. 1989;36:159–71.CAS 

Google Scholar 
69.Lundholm N, Hansen PJ, Kotaki Y. Effect of pH on growth and domoic acid production by potentially toxic diatoms of the genera Pseudo-nitzschia and Nitzschia. Mar Ecol Prog Ser. 2004;273:1–15.CAS 

Google Scholar 
70.Underwood GJC, Michel C, Meisterhans G, Niemi A, Belzile C, Witt M, et al. Organic matter from Arctic sea-ice loss alters bacterial community structure and function. Nat Clim Change. 2019;9:170–6.
Google Scholar 
71.Graham E, Tully BJ. Marine Dadabacteria exhibit genome streamlining and phototrophy-driven niche partitioning. ISME J. 2021;15:1248–56.72.Clarke LJ, Bestley S, Bissett A, Deagle BE. A globally distributed Syndiniales parasite dominates the Southern Ocean micro-eukaryote community near the sea-ice edge. ISME J. 2019;13:734–7.CAS 
PubMed 

Google Scholar 
73.Randelhoff A, Sundfjord A, Reigstad M. Seasonal variability and fluxes of nitrate in the surface waters over the Arctic shelf slope. Geophys Res Lett. 2015;42:3442–9.CAS 

Google Scholar 
74.García FC, Alonso-Sáez L, Morán XAG, López-Urrutia Á. Seasonality in molecular and cytometric diversity of marine bacterioplankton: the re-shuffling of bacterial taxa by vertical mixing. Environ Microbiol. 2015;17:4133–42.PubMed 

Google Scholar 
75.Jousset A, Bienhold C, Chatzinotas A, Gallien L, Gobet A, Kurm V, et al. Where less may be more: how the rare biosphere pulls ecosystems strings. ISME J. 2017;11:853–62.PubMed 
PubMed Central 

Google Scholar 
76.Giner CR, Pernice MC, Balagué V, Duarte CM, Gasol JM, Logares R, et al. Marked changes in diversity and relative activity of picoeukaryotes with depth in the world ocean. ISME J. 2020;14:437–49.PubMed 

Google Scholar 
77.Lehtovirta-Morley LE. Ammonia oxidation: ecology, physiology, biochemistry and why they must all come together. FEMS Microbiol Lett. 2018;365:fny058.
Google Scholar 
78.Williams TJ, Lefevre CT, Zhao W, Beveridge TJ, Bazylinski DA. Magnetospira thiophila gen. nov., sp. nov., a marine magnetotactic bacterium that represents a novel lineage within the Rhodospirillaceae (Alphaproteobacteria). Int J Syst Evol Microbiol. 2012;62:2443–50.CAS 
PubMed 

Google Scholar 
79.von Friesen LW, Riemann L. Nitrogen fixation in a changing Arctic Ocean: an overlooked source of nitrogen? Front Microbiol. 2020;11:596426.
Google Scholar 
80.Alonso-Saez L, Waller AS, Mende DR, Bakker K, Farnelid H, Yager PL, et al. Role for urea in nitrification by polar marine archaea. Proc Natl Acad Sci USA. 2012;109:17989–94.CAS 
PubMed 
PubMed Central 

Google Scholar 
81.Martínez-Pérez C, Greening C, Zhao Z, Lappan RJ, Bay SK, De Corte D, et al. Lifting the lid: nitrifying archaea sustain diverse microbial communities below the Ross Ice Shelf. Cell Rev. 2020; SSRN: https://ssrn.com/abstract=3677479 or https://doi.org/10.2139/ssrn.3677479.82.Mohamed NM, Saito K, Tal Y, Hill RT. Diversity of aerobic and anaerobic ammonia-oxidizing bacteria in marine sponges. ISME J. 2010;4:38–48.CAS 
PubMed 

Google Scholar 
83.Mussmann M, Pjevac P, Kruger K, Dyksma S. Genomic repertoire of the Woeseiaceae/JTB255, cosmopolitan and abundant core members of microbial communities in marine sediments. ISME J. 2017;11:1276–81.CAS 
PubMed 
PubMed Central 

Google Scholar 
84.Burow LC, Kong Y, Nielsen JL, Blackall LL, Nielsen PH. Abundance and ecophysiology of Defluviicoccus spp., glycogen-accumulating organisms in full-scale wastewater treatment processes. Microbiology. 2007;153:178–85.CAS 
PubMed 

Google Scholar 
85.Lucas J, Koester I, Wichels A, Niggemann J, Dittmar T, Callies U, et al. Short-term dynamics of North Sea Bacterioplankton-dissolved organic matter coherence on molecular level. Front Microbiol. 2016;7:321.86.Stecher A, Neuhaus S, Lange B, Frickenhaus S, Beszteri B, Kroth PG, et al. rRNA and rDNA based assessment of sea ice protist biodiversity from the central Arctic Ocean. Eur J Phycol. 2016;51:31–46.CAS 

Google Scholar 
87.Lalande C, Nöthig E-M, Somavilla R, Bauerfeind E, Shevchenko V, Okolodkov Y. Variability in under-ice export fluxes of biogenic matter in the Arctic Ocean. Glob Biogeochem Cycles. 2014;28:571–83.CAS 

Google Scholar 
88.Hoffmann K, Hassenrück C, Salman-Carvalho V, Holtappels M, Bienhold C. Response of bacterial communities to different detritus compositions in Arctic deep-sea sediments. Front Microbiol. 2017;8:266.PubMed 
PubMed Central 

Google Scholar 
89.Kappelmann L, Krüger K, Hehemann J-H, Harder J, Markert S, Unfried F, et al. Polysaccharide utilization loci of North Sea Flavobacteriia as basis for using SusC/D-protein expression for predicting major phytoplankton glycans. ISME J. 2019;13:76–91.CAS 
PubMed 

Google Scholar 
90.Izaguirre I, Unrein F, Schiaffino MR, Lara E, Singer D, Balagué V, et al. Phylogenetic diversity and dominant ecological traits of freshwater Antarctic Chrysophyceae. Polar Biol. 2021;44:941–57.
Google Scholar 
91.Humphry DR, George A, Black GW, Cummings SP. Flavobacterium frigidarium sp. nov., an aerobic, psychrophilic, xylanolytic and laminarinolytic bacterium from Antarctica. Int J Syst Evol Microbiol. 2001;51:1235–43.CAS 
PubMed 

Google Scholar 
92.Rapp JZ, Fernández-Méndez M, Bienhold C, Boetius A. Effects of ice-algal aggregate export on the connectivity of bacterial communities in the central Arctic Ocean. Front Microbiol. 2018;9:1035.93.Ardyna M, Mundy CJ, Mayot N, Matthes LC, Oziel L, Horvat C, et al. Under-ice phytoplankton blooms: shedding light on the “invisible” part of Arctic primary production. Front Mar Sci. 2020;7:608032.
Google Scholar 
94.Alonso-Sáez L, Zeder M, Harding T, Pernthaler J, Lovejoy C, Bertilsson S, et al. Winter bloom of a rare betaproteobacterium in the Arctic Ocean. Front Microbiol. 2014;5:425.95.Hawley AK, Nobu MK, Wright JJ, Durno WE, Morgan-Lang C, Sage B, et al. Diverse Marinimicrobia bacteria may mediate coupled biogeochemical cycles along eco-thermodynamic gradients. Nat Commun. 2017;8:1507.PubMed 
PubMed Central 

Google Scholar 
96.Berdjeb L, Parada A, Needham DM, Fuhrman JA. Short-term dynamics and interactions of marine protist communities during the spring–summer transition. ISME J. 2018;12:1907–17.PubMed 
PubMed Central 

Google Scholar 
97.Singh A, Divya DT, Tripathy SC, Naik RK. Interplay of regional oceanography and biogeochemistry on phytoplankton bloom development in an Arctic fjord. Estuar Coast Shelf Sci. 2020;243:106916.CAS 

Google Scholar 
98.Engel A, Piontek J, Metfies K, Endres S, Sprong P, Peeken I, et al. Inter-annual variability of transparent exopolymer particles in the Arctic Ocean reveals high sensitivity to ecosystem changes. Sci Rep. 2017;7:4129.PubMed 
PubMed Central 

Google Scholar 
99.Nejstgaard JC, Tang KW, Steinke M, Dutz J, Koski M, Antajan E, et al. Zooplankton grazing on Phaeocystis: a quantitative review and future challenges. Biogeochemistry. 2007;83:147–72.
Google Scholar 
100.Lampitt RS, Salter I, Johns D. Radiolaria: major exporters of organic carbon to the deep ocean. Glob Biogeochem Cycles. 2009;23:GB1010.
Google Scholar 
101.Luria CM, Amaral-Zettler LA, Ducklow HW, Rich JJ. Seasonal succession of free-living bacterial communities in coastal waters of the western Antarctic Peninsula. Front Microbiol. 2016;7:1731.PubMed 
PubMed Central 

Google Scholar 
102.Taylor JD, Cunliffe M. Coastal bacterioplankton community response to diatom-derived polysaccharide microgels. Environ Microbiol Rep. 2017;9:151–7.CAS 
PubMed 

Google Scholar 
103.Gómez-Gutiérrez J, Kawaguchi S, Nicol S. Epibiotic suctorians and enigmatic ecto- and endoparasitoid dinoflagellates of euphausiid eggs (Euphausiacea) off Oregon, USA. J Plankton Res. 2009;31:777–85.
Google Scholar 
104.Cardman Z, Arnosti C, Durbin A, Ziervogel K, Cox C, Steen AD, et al. Verrucomicrobia: candidates for polysaccharide-degrading bacterioplankton in an Arctic fjord of Svalbard. Appl Environ Microbiol. 2014;80:3749–56.CAS 
PubMed 
PubMed Central 

Google Scholar 
105.Landa M, Blain S, Harmand J, Monchy S, Rapaport A, Obernosterer I. Major changes in the composition of a Southern Ocean bacterial community in response to diatom-derived dissolved organic matter. FEMS Microbiol Ecol. 2018;94:fiy034.
Google Scholar 
106.Fahrbach E, Meincke J, Østerhus S, Rohardt G, Schauer U, Tverberg V, et al. Direct measurements of volume transports through Fram Strait. Polar Res. 2001;20:217–24.
Google Scholar 
107.Comeau AM, Li WK, Tremblay JE, Carmack EC, Lovejoy C. Arctic Ocean microbial community structure before and after the 2007 record sea ice minimum. PLOS ONE. 2011;6:e27492.CAS 
PubMed 
PubMed Central 

Google Scholar 
108.Lalande C, Bauerfeind E, Nöthig E-M, Beszczynska-Möller A. Impact of a warm anomaly on export fluxes of biogenic matter in the eastern Fram Strait. Prog Oceanogr. 2013;109:70–7.
Google Scholar 
109.Dybwad C, Assmy P, Olsen LM, Peeken I, Nikolopoulos A, Krumpen T, et al. Carbon export in the seasonal sea ice zone north of Svalbard from winter to late summer. Front Mar Sci. 2021;7:525800.
Google Scholar 
110.Glud RN, Rysgaard S, Turner G, McGinnis DF, Leakey RJG. Biological- and physical-induced oxygen dynamics in melting sea ice of the Fram Strait. Limnol Oceanogr. 2014;59:1097–111.CAS 

Google Scholar 
111.Shiozaki T, Ijichi M, Fujiwara A, Makabe A, Nishino S, Yoshikawa C, et al. Factors regulating nitrification in the Arctic Ocean: potential impact of sea ice reduction and ocean acidification. Glob Biogeochem Cycles. 2019;33:1085–99.CAS 

Google Scholar  More

1.Wright, I. J. et al. The worldwide leaf economics spectrum. Nature 428, 821–827 (2004).ADS 
CAS 
PubMed 

Google Scholar 
2.Lourens, P. & Frans, B. Leaf traits are good predictors of plant performance across 53 rain forest species. Ecology 87, 1733–1743 (2006).
Google Scholar 
3.Domínguez, M. T. et al. Relationships between leaf morphological traits, nutrient concentrations and isotopic signatures for Mediterranean woody plant species and communities. Plant Soil 357, 407–424 (2012).
Google Scholar 
4.Tian, M., Yu, G., He, N. & Hou, J. Leaf morphological and anatomical traits from tropical to temperate coniferous forests Mechanisms and influencing factors. Sci. Rep. 6, 19703 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
5.Paź-Dyderska, S. et al. Leaf traits and aboveground biomass variability of forest understory herbaceous plant species. Ecosystems 23, 555–569 (2020).
Google Scholar 
6.Lusk, C. H. Leaf functional trait variation in a humid temperate forest, and relationships with juvenile tree light requirements. PeerJ 7, e6855 (2019).PubMed 
PubMed Central 

Google Scholar 
7.Liu, C., Li, Y., Xu, L., Chen, Z. & He, N. Variation in leaf morphological, stomatal, and anatomical traits and their relationships in temperate and subtropical forests. Sci. Rep. 9, 1–8 (2019).ADS 

Google Scholar 
8.Qin, J. & Shangguan, Z. Effects of forest types on leaf functional traits and their interrelationships of Pinus massoniana coniferous and broad-leaved mixed forests in the subtropical mountain, Southeastern China. Ecol. Evol. 9, 6922–6932 (2019).PubMed 
PubMed Central 

Google Scholar 
9.Smart, S. M. et al. Leaf dry matter content is better at predicting above-ground net primary production than specific leaf area. Funct. Ecol. 31, 1336–1344 (2017).
Google Scholar 
10.Osnas, J. L. D., Lichstein, J. W., Reich, P. B. & Pacala, S. W. Global leaf trait relationships: Mass, area, and the leaf economics spectrum. Science 340, 741–744 (2013).ADS 
CAS 
PubMed 

Google Scholar 
11.Pierce, S. et al. A global method for calculating plant CSR ecological strategies applied across biomes world-wide. Funct. Ecol. 31, 444–457 (2017).
Google Scholar 
12.Grime, J. P. Plant strategy theories: A comment on Craine (2005). J. Ecol. 95, 227–230 (2007).
Google Scholar 
13.Nam, K. J. & Lee, E. J. Variation in leaf functional traits of the Korean maple (Acer pseudosieboldianum) along an elevational gradient in a montane forest in Southern Korea. J. Ecol. Environ. 42, 33 (2018).
Google Scholar 
14.Li, Y. et al. Spatiotemporal variation in leaf size and shape in response to climate. J. Plant Ecol. 13, 87–96 (2020).
Google Scholar 
15.Liu, W., Zheng, L. & Qi, D. Variation in leaf traits at different altitudes reflects the adaptive strategy of plants to environmental changes. Ecol. Evol. 10, 8166–8175 (2020).PubMed 
PubMed Central 

Google Scholar 
16.Zhu, Z., Wang, X., Li, Y., Wang, G. & Guo, H. Predicting plant traits and functional types response to grazing in an alpine shrub meadow on the Qinghai-Tibet Plateau. Sci. China Earth Sci. 55, 837–851 (2012).ADS 

Google Scholar 
17.Wang, J. et al. Response of plant functional traits to grazing for three dominant species in alpine steppe habitat of the Qinghai-Tibet Plateau, China. Ecol. Res. 31, 515–524 (2016).
Google Scholar 
18.Negi, G. C. S. Leaf and bud demography and shoot growth in evergreen and deciduous trees of central Himalaya, India. Trees 20, 416–429 (2006).
Google Scholar 
19.Osnas, J. L. D. et al. Divergent drivers of leaf trait variation within species, among species, and among functional groups. PNAS 115, 5480–5485 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
20.Liu, C., Li, Y., Xu, L., Chen, Z. & He, N. Variation in leaf morphological, stomatal, and anatomical traits and their relationships in temperate and subtropical forests. Sci. Rep. 9, 5803 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
21.Zobel, D. B. & Singh, S. P. Himalayan forests and ecological generalizations. Bioscience 47, 735–745 (1997).
Google Scholar 
22.Kattge, J. et al. TRY—A global database of plant traits. Glob. Change Biol. 17, 2905–2935 (2011).ADS 

Google Scholar 
23.Pérez-Harguindeguy, N. et al. New handbook for standardised measurement of plant functional traits worldwide. Aust. J. Bot. 61, 167 (2013).
Google Scholar 
24.Reich, P. B., Walters, M. B. & Ellsworth, D. S. From tropics to tundra: Global convergence in plant functioning. PNAS 94, 13730–13734 (1997).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
25.Güsewell, S. & Verhoeven, J. T. A. Litter N:P ratios indicate whether N or P limits the decomposability of graminoid leaf litter. Plant Soil 287, 131–143 (2006).
Google Scholar 
26.Niinemets, U. Is there a species spectrum within the world-wide leaf economics spectrum? Major variations in leaf functional traits in the Mediterranean sclerophyll Quercus ilex. New Phytol. 205, 79–96 (2015).PubMed 

Google Scholar 
27.Devi, A. F. & Garkoti, S. C. Variation in evergreen and deciduous species leaf phenology in Assam, India. Trees 27, 985–997 (2013).
Google Scholar 
28.Givnish, T. Adaptive significance of evergreen vs. deciduous leaves: Solving the triple paradox. Silva Fenn. 36, 703–743 (2002).
Google Scholar 
29.Liu, Y. et al. Does greater specific leaf area plasticity help plants to maintain a high performance when shaded?. Ann. Bot. 118, 1329–1336 (2016).PubMed 
PubMed Central 

Google Scholar 
30.Derroire, G., Powers, J. S., Hulshof, C. M., Varela, L. E. C. & Healey, J. R. Contrasting patterns of leaf trait variation among and within species during tropical dry forest succession in Costa Rica. Sci. Rep. 8, 285 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
31.Bai, K., He, C., Wan, X. & Jiang, D. Leaf economics of evergreen and deciduous tree species along an elevational gradient in a subtropical mountain. AoB Plants 7, plv064. https://doi.org/10.1093/aobpla/plv064 (2015).
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
32.Ma, S. et al. Variations and determinants of carbon content in plants: A global synthesis. Biogeosciences 15, 693–702 (2018).ADS 
CAS 

Google Scholar 
33.Singh, N. D. Leaf litter decomposition of evergreen and deciduous Dillenia species in humid tropics of north-east India. J. Trop. For. Sci. 14, 105–115 (2002).
Google Scholar 
34.Liang, X., Liu, S., Wang, H. & Wang, J. Variation of carbon and nitrogen stoichiometry along a chronosequence of natural temperate forest in northeastern China. J. Plant Ecol. 11, 339–350 (2018).
Google Scholar 
35.Lübbe, T., Schuldt, B. & Leuschner, C. Acclimation of leaf water status and stem hydraulics to drought and tree neighbourhood: Alternative strategies among the saplings of five temperate deciduous tree species. Tree Physiol. 37, 456–468 (2017).PubMed 

Google Scholar 
36.Young-Robertson, J. M., Bolton, W. R., Bhatt, U. S., Cristóbal, J. & Thoman, R. Deciduous trees are a large and overlooked sink for snowmelt water in the boreal forest. Sci. Rep. 6, 1–10 (2016).
Google Scholar 
37.Hogan, K. P., Smith, A. P. & Samaniego, M. Gas exchange in six tropical semi-deciduous forest canopy tree species during the wet and dry seasons. Biotropica 27, 324–333 (1995).
Google Scholar 
38.Keel, S. G., Pepin, S., Leuzinger, S. & Körner, C. Stomatal conductance in mature deciduous forest trees exposed to elevated CO2. Trees 21, 151 (2006).
Google Scholar 
39.Kosugi, Y. & Matsuo, N. Seasonal fluctuations and temperature dependence of leaf gas exchange parameters of co-occurring evergreen and deciduous trees in a temperate broad-leaved forest. Tree Physiol. 26, 1173–1184 (2006).PubMed 

Google Scholar 
40.Medlyn, B. E. et al. Stomatal conductance of forest species after long-term exposure to elevated CO2 concentration: A synthesis. New Phytol. 149, 247–264 (2001).CAS 
PubMed 

Google Scholar 
41.Catovsky, S., Holbrook, N. M. & Bazzaz, F. A. Coupling whole-tree transpiration and canopy photosynthesis in coniferous and broad-leaved tree species. Can. J. For. Res. 32, 295–309 (2002).
Google Scholar 
42.Rawat, M., Arunachalam, K., Arunachalam, A., Alatalo, J. & Pandey, R. Associations of plant functional diversity with carbon accumulation in a temperate forest ecosystem in the Indian Himalayas. Ecol. Ind. 98, 861–868 (2019).
Google Scholar 
43.Weraduwage, S. M. et al. The relationship between leaf area growth and biomass accumulation in Arabidopsis thaliana. Front. Plant Sci. 6, 167 (2015).PubMed 
PubMed Central 

Google Scholar 
44.Sirisampan, S., Hiyama, T., Takahashi, A., Hashimoto, T. & Fukushima, Y. Diurnal and seasonal variations of stomatal conductance in a secondary temperate forest. J. Jpn. Soc. Hydrol. Water Resour. 16, 113–130 (2003).
Google Scholar 
45.Ghimire, C. P. et al. Transpiration and stomatal conductance in a young secondary tropical montane forest: Contrasts between native trees and invasive understorey shrubs. Tree Physiol. 38, 1053–1070 (2018).PubMed 

Google Scholar 
46.Kirschbaum, M. U. F. & McMillan, A. M. S. Warming and elevated CO2 have opposing influences on transpiration. Which is more important?. Curr. For. Rep. 4, 51–71 (2018).
Google Scholar 
47.Saha, S., Rajwar, G. S. & Kumar, M. Soil properties along altitudinal gradient in Himalayan temperate forest of Garhwal region. Acta Ecol. Sin. 38, 1–8 (2018).ADS 

Google Scholar 
48.Raina, A. K. & Gupta, M. K. Soil characteristics in relation to vegetation and parent material under different forest covers in Kempty forest range, Uttarakhand. Indian Forester 135, 331–341 (2009).CAS 

Google Scholar 
49.Champion, S. H. G. & Seth, S. K. A Revised Survey of the Forest Types of India. (1968).50.Belluau, M. & Shipley, B. Linking hard and soft traits: Physiology, morphology and anatomy interact to determine habitat affinities to soil water availability in herbaceous dicots. PLoS ONE 13, e0193130 (2018).PubMed 
PubMed Central 

Google Scholar 
51.Rita, A. et al. Coordination of morphological and physiological traits in naturally recruited Abies alba Mill. saplings: Insights from a structural equation modeling approach. Ann. For. Sci. 74, 49 (2017).
Google Scholar 
52.Kumar, U., Singh, P. & Boote, K. J. Chapter two—effect of climate change factors on processes of crop growth and development and yield of groundnut (Arachis hypogaea L.). In Advances in Agronomy Vol. 116 (ed. Sparks, D. L.) 41–69 (Academic Press, 2012).
Google Scholar 
53.Gratani, L., Pesoli, P. & Crescente, M. F. Relationship between photosynthetic activity and chlorophyll content in an isolated Quercus ilex L. tree during the year. Photosynthetica 35, 445–451 (1998).
Google Scholar 
54.Lin, H., Chen, Y., Zhang, H., Fu, P. & Fan, Z. Stronger cooling effects of transpiration and leaf physical traits of plants from a hot dry habitat than from a hot wet habitat. Funct. Ecol. 31, 2202–2211 (2017).
Google Scholar 
55.Damm, A., Haghighi, E., Paul-Limoges, E. & van der Tol, C. On the seasonal relation of sun-induced chlorophyll fluorescence and transpiration in a temperate mixed forest. Agric. For. Meteorol. 304–305, 108386 (2021).ADS 

Google Scholar 
56.Zhang, X. et al. Stomatal conductance bears no correlation with transpiration rate in wheat during their diurnal variation under high air humidity. PeerJ 8, e8927 (2020).PubMed 
PubMed Central 

Google Scholar 
57.Wang, C., Zhou, J., Xiao, H., Liu, J. & Wang, L. Variations in leaf functional traits among plant species grouped by growth and leaf types in Zhenjiang, China. J. For. Res. https://doi.org/10.1007/s11676-016-0290-6 (2016).Article 

Google Scholar 
58.Cornelissen, J. H. C., Castro Diez, P. & Hunt, R. Seedling growth, allocation and leaf attributes in a wide range of woody plant species and types. J. Ecol. 84, 755–765 (1996).
Google Scholar 
59.Zhang, S., Zhang, Y. & Ma, K. The association of leaf lifespan and background insect herbivory at the interspecific level. Ecology 98, 425–432 (2017).PubMed 

Google Scholar 
60.Cunningham, S., Summerhayes, B. & Westoby, M. Evolutionary divergences in leaf structure and chemistry, comparing rainfall and soil nutrient gradients. Ecol. Monogr. 69(4), 569–588. https://doi.org/10.1890/0012-9615(1999)069[0569:EDILSA]2.0.CO;2 (1999).Article 

Google Scholar 
61.Reich, P. B. et al. Generality of leaf trait relationships: A test across six biomes. Ecology 80, 1955–1969 (1999).
Google Scholar 
62.Fyllas, N. M. et al. Functional trait variation among and within species and plant functional types in mountainous Mediterranean forests. Front. Plant Sci. 11, 212 (2020).PubMed 
PubMed Central 

Google Scholar 
63.De Long, J. R. et al. Relationships between plant traits, soil properties and carbon fluxes differ between monocultures and mixed communities in temperate grassland. J. Ecol. 107, 1704–1719 (2019).PubMed 
PubMed Central 

Google Scholar  More

1.Baker, A. & Brooks, R. Terrestrial higher plants which hyperaccumulate metallic elements, a review of their distribution, ecology and phytochemistry. Biorecovery 1, 81–126 (1989).CAS 

Google Scholar 
2.Reeves, R. D. et al. A global database for plants that hyperaccumulate metal and metalloid trace elements. New Phytol. 218, 407–411 (2018).PubMed 

Google Scholar 
3.Reeves, R. D., Baker, A. J. M., Borhidi, A. & Berazaín, R. Nickel-accumulating plants from the ancient serpentine soils of Cuba. New Phytol. 133, 217–224 (1996).CAS 
PubMed 

Google Scholar 
4.Reeves, R., Baker, A., Borhidi, A. & Berazaín Iturralde, R. Nickel hyperaccumulation in the serpentine flora of Cuba. Ann. Bot. 83, 29–38 (1999).CAS 

Google Scholar 
5.Whiting, S. N. et al. Research priorities for conservation of metallophyte biodiversity and their potential for restoration and site remediation. Restor. Ecol. 12, 106–116 (2004).
Google Scholar 
6.Jaffré, T., Pillon, Y., Thomine, S. & Merlot, S. The metal hyperaccumulators from New Caledonia can broaden our understanding of nickel accumulation in plants. Front. Plant Sci. 4, 279 (2013).PubMed 
PubMed Central 

Google Scholar 
7.Losfeld, G. et al. Leaf-age and soil–plant relationships: Key factors for reporting trace-elements hyperaccumulation by plants and design applications. Environ. Sci. Pollut. Res. Int. 22, 5620–5632 (2015).CAS 
PubMed 

Google Scholar 
8.Gei, V. et al. Tools for the discovery of hyperaccumulator plant species and understanding their ecophysiology. In Agromining: Farming for metals: Extracting unconventional resources using plants (eds Van der Ent, A. et al.) 117–133 (Springer International Publishing, 2018). https://doi.org/10.1007/978-3-319-61899-9_7.Chapter 

Google Scholar 
9.Gei, V. et al. A systematic assessment of the occurrence of trace element hyperaccumulation in the flora of New Caledonia. Bot. J. Linn. Soc. 194, 1–22 (2020).
Google Scholar 
10.Grison, C., Escande, V. & Biton, J. Ecocatalysis: A New Integrated Approach to Scientific Ecology (Elsevier, 2015).
Google Scholar 
11.Grison, C. Special issue in environmental science and pollution research: Combining phytoextraction and ecocatalysis: an environmental, ecological, ethic and economic opportunity. Environ. Sci. Pollut. Res. 22, 5589–5698 (2015).
Google Scholar 
12.Grison, C., Escande, V. & Olszewski, T. K. Ecocatalysis: A new approach toward bioeconomy, chapter 25. In Bioremediation and Bioeconomy (ed. Prasad, M. N. V.) 629–663 (Elsevier, 2016). https://doi.org/10.1016/B978-0-12-802830-8.00025-3.Chapter 

Google Scholar 
13.Deyris, P.-A. & Grison, C. Nature, ecology and chemistry: An unusual combination for a new green catalysis, ecocatalysis. Curr. Opin. Green Sustain. Chem. 10, 6–10 (2018).
Google Scholar 
14.Grison, C. & LockToyKi, Y. Ecocatalysis, a new vision of green and sustainable chemistry. Curr. Opin. Green Sustain. Chem. 29, 100461 (2021).
Google Scholar 
15.Chaney, R. L., Angle, J. S., Li, Y.-M. & Baker, A. J. M. Recuperation de metaux presents dans des sols (2000).16.Chaney, R. L. et al. Improved understanding of hyperaccumulation yields commercial phytoextraction and phytomining technologies. J. Environ. Qual. 36, 1429–1443 (2007).CAS 
PubMed 

Google Scholar 
17.Li, Y.-M. et al. Development of a technology for commercial phytoextraction of nickel: Economic and technical considerations. Plant Soil 249, 107–115 (2003).CAS 

Google Scholar 
18.Strawn, K. Unearthing the habitat of a hyperaccumulator: Case study of the invasive plant yellowtuft (Alyssum; Brassicaceae) in Southwest Oregon, USA. Manag. Biol. Invasions 4, 249–259 (2013).
Google Scholar 
19.Grison, C. et al. Psychotria douarrei and Geissois pruinosa, novel resources for the plant-based catalytic chemistry. RSC Adv. 3, 22340–22345 (2013).ADS 
CAS 

Google Scholar 
20.Lange, B. et al. Copper and cobalt mobility in soil and accumulation in a metallophyte as influenced by experimental manipulation of soil chemical factors. Chemosphere 146, 75–84 (2016).ADS 
CAS 
PubMed 

Google Scholar 
21.Grison, C. M. et al. The leguminous species Anthyllis vulneraria as a Zn-hyperaccumulator and eco-Zn catalyst resources. Environ. Sci. Pollut. Res. 22, 5667–5676 (2015).CAS 

Google Scholar 
22.Escande, V. et al. Ecological catalysis and phytoextraction: Symbiosis for future. Appl. Catal. B 146, 279–288 (2014).CAS 

Google Scholar 
23.Liu, C. et al. Element case studies: Rare earth elements. In Agromining: Farming for Metals (Springer, 2018). https://doi.org/10.1007/978-3-319-61899-9_1924.Lahl, U. & Hawxwell, K. A. REACH—The new European chemicals law. Environ. Sci. Technol. 40, 7115–7121 (2006).ADS 
CAS 
PubMed 

Google Scholar 
25.Sarrailh, J.-M. La revégétalisation des exploitations minières: l’exemple de la Nouvelle-Calédonie. Bois For. Trop. (2002).26.Losfeld, G. et al. Phytoextraction from mine spoils: Insights from New Caledonia. Environ. Sci. Pollut. Res. 22, 5608–5619 (2015).CAS 

Google Scholar 
27.Garel, C. et al. Structure and composition of first biosourced Mn-rich catalysts with a unique vegetal footprint. Mater. Today Sustain. https://doi.org/10.1016/j.mtsust.2019.100020 (2019).Article 

Google Scholar 
28.Jaffré, T. Accumulation du manganèse par les Protéacées de Nouvelle Calédonie. Compt. Rend. Acad. Sci. (Paris) Sér. D 289, 425–428 (1979).
Google Scholar 
29.Jaffré, T. Plantes de Nouvelle Calédonie permettant de revégétaliser des sites miniers (SLN, 1992).
Google Scholar 
30.Jaffré, T. Accumulation du manganèse par des espèces associées aux terrains ultrabasiques de Nouvelle Calédonie. Compt. Rend. Acad. Sci. Paris Sér. D 284, 1573–1575 (1977).
Google Scholar 
31.Luçon, S., Marion, F., Niel, J. F. & Pelletier, B. Réhabilitation des sites miniers sur roches ultramafiques en Nouvelle-Calédonie. In Ecologie des milieux sur roches ultramafiques et sur sols métallifères: actes de la deuxième conférence internationale sur l’écologie des milieux serpentiniques Vol. III (eds Jaffré, T. et al.) 297–303 (ORSTOM, 1997).
Google Scholar 
32.Reeves, R. D. Tropical hyperaccumulators of metals and their potential for phytoextraction. Plant Soil 249, 57–65 (2003).CAS 

Google Scholar 
33.L’Huillier, L. et al. La restauration des sites miniers. In Mines et environnement en Nouvelle Calédonie: les milieux sur substrats ultramafiques et leur restauration (eds L’Huillier, L. et al.) 147–230 (IAC, 2010).
Google Scholar 
34.Udo, H., Barrault, J. & Gâteblé, G. Multiplication et valorisation horticole de plantes indigènes à la Nouvelle-Calédonie: Compte-rendu des essais 2011 (2011).35.Jaffré, T. Etude écologique du peuplement végétal des sols dérivés de roches ultrabasiques en Nouvelle Calédonie (ORSTOM, 1980).
Google Scholar 
36.Baker, A., Mcgrath, S., Reeves, R. & Smith, J. A. C. Metal hyperaccumulator plants: A review of the ecology and physiology of a biological resource for phytoremediation of metal-polluted soils. Phytoremediat. Contamin. Soil Water. https://doi.org/10.1201/9780367803148-5 (2000).Article 

Google Scholar 
37.Bihanic, C., Richards, K., Olszewski, T. K. & Grison, C. Eco-Mn ecocatalysts: Toolbox for sustainable and green Lewis acid catalysis and oxidation reactions. ChemCatChem 12, 1529–1545 (2020).CAS 

Google Scholar 
38.Pillon, Y., Munzinger, J., Amir, H. & Lebrun, M. Ultramafic soils and species sorting in the flora of New Caledonia. J. Ecol. 98, 1108–1116 (2010).
Google Scholar 
39.Bidwell, S. D., Woodrow, I. E., Batianoff, G. N. & Sommer-Knudsen, J. Hyperaccumulation of manganese in the rainforest tree Austromyrtus bidwillii (Myrtaceae) from Queensland, Australia. Funct. Plant Biol. 29, 899–905 (2002).CAS 
PubMed 

Google Scholar 
40.Fernando, D. R. et al. Foliar Mn accumulation in eastern Australian herbarium specimens: Prospecting for ‘new’ Mn hyperaccumulators and potential applications in taxonomy. Ann. Bot. 103, 931–939 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
41.Mizuno, T. et al. Age-dependent manganese hyperaccumulation in Chengiopanax sciadophylloides (Araliaceae). J. Plant Nutr. 31, 1811–1819 (2008).CAS 

Google Scholar 
42.Xue, S. G. et al. Manganese uptake and accumulation by the hyperaccumulator plant Phytolacca acinosa Roxb. (Phytolaccaceae). Environ. Pollut. 131, 393–399 (2004).CAS 
PubMed 

Google Scholar 
43.Yang, S. X., Deng, H. & Li, M. S. Manganese uptake and accumulation in a woody hyperaccumulator, Schima superba. Plant Soil Environ. 54, 441–446 (2008).CAS 

Google Scholar 
44.Proctor, J., Phillipps, C., Duff, G. K., Heaney, A. & Robertson, F. M. Ecological studies on Gunung Silam, a small ultrabasic Mountain in Sabah, Malaysia. II. Some Forest Processes. J. Ecol. 77, 317–331 (1989).CAS 

Google Scholar 
45.Graham, R. D., Hannam, R. J. & Uren, N. C. Manganese in Soils and Plants. https://doi.org/10.1007/978-94-009-2817-6 (Springer Netherlands, 1988).46.Loneragan, J. F. Distribution and movement of manganese in plants. In Manganese in Soils and Plants (eds Graham, R. D. et al.) 113–124 (Springer Netherlands, 1988). https://doi.org/10.1007/978-94-009-2817-6_9.Chapter 

Google Scholar 
47.Taiz, L. & Zeiger, E. Plant Physiology 3rd edn. (Sinauer Associates Inc., 2002).
Google Scholar 
48.Burnell, J. N. The biochemistry of manganese in plants. In Manganese in Soils and Plants (eds Graham, R. D. et al.) 125–137 (Springer Netherlands, 1988). https://doi.org/10.1007/978-94-009-2817-6_10.Chapter 

Google Scholar 
49.Lidon, F. C., Barreiro, M. G. & Ramalho, J. C. Manganese accumulation in rice: Implications for photosynthetic functioning. J. Plant Physiol. 161, 1235–1244 (2004).CAS 
PubMed 

Google Scholar 
50.Rengel, Z. Availability of Mn, Zn and Fe in the rhizosphere. J. Soil Sci. Plant Nutr. 15, 397–409 (2015).
Google Scholar 
51.Schmidt, S. B., Jensen, P. E. & Husted, S. Manganese deficiency in plants: The impact on photosystem II. Trends Plant Sci. 21, 622–632 (2016).CAS 
PubMed 

Google Scholar 
52.Wissemeier, A. H. & Horst, W. J. Simplified methods for screening cowpea cultivars for manganese leaf-tissue tolerance. Crop Sci. 31, 435–439 (1991).CAS 

Google Scholar 
53.Joardar Mukhopadhyay, M. & Sharma, A. Manganese in cell metabolism of higher plants. Bot. Rev. 57, 117–149 (1991).
Google Scholar 
54.Lynch, J. & St. Clair, S. Mineral stress: The missing link in understanding how global climate change will affect plants in real world soils. Field Crops Res. 90, 101–115 (2004).
Google Scholar 
55.Alejandro, S., Höller, S., Meier, B. & Peiter, E. Manganese in plants: From acquisition to subcellular allocation. Front. Plant Sci 11, 300 (2020).PubMed 
PubMed Central 

Google Scholar 
56.Shao, J. F., Yamaji, N., Shen, R. F. & Ma, J. F. The key to Mn homeostasis in plants: Regulation of Mn transporters. Trends Plant Sci. 22, 215–224 (2017).CAS 
PubMed 

Google Scholar 
57.Millaleo, R., Reyes-Diaz, M., Ivanov, A. G., Mora, M. L. & Alberdi, M. Manganese as essential and toxic element for plants: Transport, accumulation and resistance mechanisms. J. Soil Sci. Plant Nutr. 10, 470–481 (2010).
Google Scholar 
58.Vázquez, M. D. et al. Localization of zinc and cadmium in Thlaspi caerulescens (Brassicaceae), a metallophyte that can hyperaccumulate both metals. J. Plant Physiol. 140, 350–355 (1992).
Google Scholar 
59.Krämer, U., Grime, G. W., Smith, J. A. C., Hawes, C. R. & Baker, A. J. M. Micro-PIXE as a technique for studying nickel localization in leaves of the hyperaccumulator plant Alyssum lesbiacum. Nucl. Instrum. Methods Phys. Res. Sect. B 130, 346–350 (1997).ADS 

Google Scholar 
60.Küpper, H., Lombi, E., Zhao, F.-J. & McGrath, S. P. Cellular compartmentation of cadmium and zinc in relation to other elements in the hyperaccumulator Arabidopsis halleri. Planta 212, 75–84 (2000).PubMed 

Google Scholar 
61.Küpper, H., Lombi, E., Zhao, F.-J., Wieshammer, G. & McGrath, S. P. Cellular compartmentation of nickel in the hyperaccumulators Alyssum lesbiacum, Alyssum bertolonii and Thlaspi goesingense. J. Exp. Bot. 52, 2291–2300 (2001).PubMed 

Google Scholar 
62.Mesjasz-Przybyłowicz, J., Przybyłowicz, W. & Pineda, C. Nuclear microprobe studies of elemental distribution in apical leaves of the Ni hyperaccumulator Berkheya coddii. S. Afr. J. Sci. 97, 591 (2001).
Google Scholar 
63.Robinson, B. H., Lombi, E., Zhao, F. J. & McGrath, S. P. Uptake and distribution of nickel and other metals in the hyperaccumulator Berkheya coddii. New Phytol. 158, 279–285 (2003).CAS 

Google Scholar 
64.Bidwell, S. D., Crawford, S. A., Woodrow, I. E., Sommer-Knudsen, J. & Marshall, A. T. Sub-cellular localization of Ni in the hyperaccumulator, Hybanthus floribundus (Lindley) F. Muell. Plant Cell Environ. 27, 705–716 (2004).CAS 

Google Scholar 
65.Memon, A. R., Chino, M., Takeoka, Y., Hara, K. & Yatazawa, M. Distribution of manganese in leaf tissues of manganese accumulator: Acanthopanax sciadophylloides as revealed by Electronprobe X-Ray Microanalyzer. J. Plant Nutr. 2, 457–476 (1980).CAS 

Google Scholar 
66.Memon, A. R., Chino, M., Hara, K. & Yatazawa, M. Microdistribution of manganese in the leaf tissues of different plant species as revealed by X-ray microanalyzer. Physiol. Plant. 53, 225–232 (1981).CAS 

Google Scholar 
67.Xu, X. et al. Distribution and mobility of manganese in the hyperaccumulator plant Phytolacca acinosa Roxb. (Phytolaccaceae). Plant Soil 285, 323–331 (2006).CAS 

Google Scholar 
68.Fernando, D. R. et al. Novel pattern of foliar metal distribution in a manganese hyperaccumulator. Funct. Plant Biol. 35, 193 (2008).CAS 
PubMed 

Google Scholar 
69.Fernando, D. R. et al. Foliar manganese accumulation by Maytenus founieri (Celastraceae) in its native New Caledonian habitats: Populational variation and localization by X-ray microanalysis. New Phytol. 177, 178–185 (2008).CAS 
PubMed 

Google Scholar 
70.Fernando, D. R. et al. Manganese accumulation in the leaf mesophyll of four tree species: A PIXE/EDAX localization study. New Phytol. 171, 751–757 (2006).CAS 
PubMed 

Google Scholar 
71.Fernando, D. R. et al. Variability of Mn hyperaccumulation in the Australian rainforest tree Gossia bidwillii (Myrtaceae). Plant Soil 293, 145–152 (2007).CAS 

Google Scholar 
72.Fernando, D. R., Marshall, A., Baker, A. J. M. & Mizuno, T. Microbeam methodologies as powerful tools in manganese hyperaccumulation research: present status and future directions. Front. Plant Sci. 4, 319 (2013).73.Fernando, D. R., Woodrow, I. E., Baker, A. J. M. & Marshall, A. T. Plant homeostasis of foliar manganese sinks: Specific variation in hyperaccumulators. Planta 236, 1459–1470 (2012).CAS 
PubMed 

Google Scholar 
74.Fernando, D. R., Marshall, A. T. & Green, P. T. Cellular ion interactions in two endemic tropical rainforest species of a novel metallophytic tree genus. Tree Physiol. 38, 119–128 (2018).CAS 
PubMed 

Google Scholar 
75.Bihanic, C. et al. Eco-CaMnOx: A greener generation of eco-catalysts for eco-friendly oxidation processes. ACS Sustain. Chem. Eng. 8, 4044–4057 (2020).CAS 

Google Scholar 
76.Park, Y. J. & Doeff, M. M. Synthesis and electrochemical characterization of M2Mn3O8 (M = Ca, Cu) compounds and derivatives. Solid State Ion. 177, 893–900 (2006).CAS 

Google Scholar 
77.Harper, F. A. et al. Metal coordination in hyperaccumulating plants studied using EXAFS. In Synchrotron Radiation Department Scientific Reports 102 (eds Murphy, B. et al.) (Central Laboratory of Research Councils, 1999).
Google Scholar 
78.Rabier, J., Laffont-Schwob, I., Notonier, R., Fogliani, B. & Bouraïma-Madjèbi, S. Anatomical element localization by EDXS in Grevillea exul var. exul under nickel stress. Environ. Pollut. 156, 1156–1163 (2008).CAS 
PubMed 

Google Scholar 
79.Fernando, D. R., Mizuno, T., Woodrow, I. E., Baker, A. J. M. & Collins, R. N. Characterization of foliar manganese (Mn) in Mn (hyper)accumulators using X-ray absorption spectroscopy. New Phytol. 188, 1014–1027 (2010).CAS 
PubMed 

Google Scholar 
80.Fritsch, E. Les sols. In Atlas de la Nouvelle Calédonie (eds Bonvallot, J. et al.) 73–76 (IRD, 2012).
Google Scholar 
81.Isnard, S., L’huillier, L., Rigault, F. & Jaffré, T. How did the ultramafic soils shape the flora of the New Caledonian hotspot?. Plant Soil 403, 53–76 (2016).CAS 

Google Scholar 
82.Jaffré, T. Composition chimique et conditions de l’alimentation minérale des plantes sur roches ultrabasiques (Nouvelle Calédonie). Cah. ORSTOM. Sér. Biol. 11, 53–63 (1976).
Google Scholar 
83.Majourau, P. & Pillon, Y. A review of Grevillea (Proteaceae) from New Caledonia with the description of two new species. Phytotaxa 477, 243–252 (2020).
Google Scholar 
84.Jaffré, T. & Latham, M. Contribution à l’étude des relations sol-végétation sur un massif de roches ultrabasiques de la côte Ouest de la Nouvelle Calédonie: le Boulinda. Adansonia. Série 2(14), 311–336 (1974).
Google Scholar 
85.L’Huillier, L. et al. Mines et environnement en Nouvelle-Caledonie: les milieux sur substrats ultramafiques et leur restauration (IAC, 2010).
Google Scholar 
86.Purnell, H. M. Studies of the family Proteaceae. I. Anatomy and morphology of the roots of some Victorian species. Aust. J. Bot. 8, 38–50 (1960).
Google Scholar 
87.Lamont, B. B. Structure, ecology and physiology of root clusters—A review. Plant Soil 248, 1–19 (2003).CAS 

Google Scholar 
88.Shane, M. W. & Lambers, H. Manganese accumulation in leaves of Hakea prostrata (Proteaceae) and the significance of cluster roots for micronutrient uptake as dependent on phosphorus supply. Physiol. Plant. 124, 441–450 (2005).CAS 

Google Scholar 
89.Dinkelaker, B., Hengeler, C. & Marschner, H. Distribution and function of proteoid roots and other root clusters. Bot. Acta 108, 183–200 (1995).
Google Scholar 
90.Castillo-Michel, H. A., Larue, C., Pradas del Real, A. E., Cotte, M. & Sarret, G. Practical review on the use of synchrotron based micro- and nano- X-ray fluorescence mapping and X-ray absorption spectroscopy to investigate the interactions between plants and engineered nanomaterials. Plant Physiol. Biochem. 110, 13–32 (2017).CAS 
PubMed 

Google Scholar 
91.Vantelon, D. et al. The LUCIA beamline at SOLEIL. J. Synchrotron Radiat. 23, 635–640 (2016).CAS 
PubMed 

Google Scholar 
92.Solé, V. A., Papillon, E., Cotte, M., Walter, P. & Susini, J. A multiplatform code for the analysis of energy-dispersive X-ray fluorescence spectra. Spectrochim. Acta Part B 62, 63–68 (2007).ADS 

Google Scholar 
93.Ravel, B. & Newville, M. ATHENA, ARTEMIS, HEPHAESTUS: Data analysis for X-ray absorption spectroscopy using IFEFFIT. J. Synchrotron Radiat. 12, 537–541 (2005).CAS 
PubMed 

Google Scholar 
94.Schindelin, J. et al. Fiji: An open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).CAS 
PubMed 

Google Scholar 
95.Losfeld, G. L’association de la phytoextraction et de l’écocatalyse : un nouveau concept de chimie verte, une opportunité pour la remédiation de sites miniers. (Montpellier 2, 2014).96.van der Ent, A. et al. X-ray fluorescence elemental mapping of roots, stems and leaves of the nickel hyperaccumulators Rinorea cf. bengalensis and Rinorea cf. javanica (Violaceae) from Sabah (Malaysia), Borneo. Plant Soil. https://doi.org/10.1007/s11104-019-04386-2 (2020).Article 

Google Scholar 
97.Belli, M. et al. X-ray absorption near edge structures (XANES) in simple and complex Mn compounds. Solid State Commun. 35, 355–361 (1980).ADS 
CAS 

Google Scholar 
98.van der Ent, A. et al. X-ray elemental mapping techniques for elucidating the ecophysiology of hyperaccumulator plants. New Phytol. 218, 432–452 (2018).PubMed 

Google Scholar 
99.Neumann, G. & Martinoia, E. Cluster roots—An underground adaptation for survival in extreme environments. Trends Plant Sci. 7, 162–167 (2002).CAS 
PubMed 

Google Scholar 
100.Memon, A. R. & Yatazawa, M. Nature of manganese complexes in manganese accumulator plant—Acanthopanax sciadophylloides. J. Plant Nutr. 7, 961–974 (1984).CAS 

Google Scholar 
101.Xu, X., Shi, J., Chen, X., Chen, Y. & Hu, T. Chemical forms of manganese in the leaves of manganese hyperaccumulator Phytolacca acinosa Roxb. (Phytolaccaceae). Plant Soil 318, 197 (2008).
Google Scholar 
102.Fernando, D. R., Baker, A. J. M. & Woodrow, I. E. Physiological responses in Macadamia integrifolia on exposure to manganese treatment. Aust. J. Bot. 57, 406 (2009).CAS 

Google Scholar 
103.Fernando, D. R., Batianoff, G. N., Baker, A. J. & Woodrow, I. E. In vivo localization of manganese in the hyperaccumulator Gossia bidwillii (Benth.) N. Snow & Guymer (Myrtaceae) by cryo-SEM/EDAX. Plant Cell Environ. 29, 1012–1020 (2006).CAS 
PubMed 

Google Scholar 
104.Léon, V. et al. Effects of three nickel salts on germinating seeds of Grevillea exul var. rubiginosa, an endemic serpentine Proteaceae. Ann. Bot. https://doi.org/10.1093/aob/mci066 (2005).Article 
PubMed 
PubMed Central 

Google Scholar 
105.Jaffré, T., Latham, M. & Schmid, M. Aspects de l’influence de l’extraction du minerai de nickel sur la végétation et les sols en Nouvelle-Calédonie. Cah. ORSTOM. Sér. Biol. 12, 307–321 (1977).
Google Scholar 
106.Boyd, R. S. & Martens, S. The raison d’etre for metal hyperaccumulation by plants (1992).107.Krämer, U., Pickering, I. J., Prince, R. C., Raskin, I. & Salt, D. E. Subcellular localization and speciation of nickel in hyperaccumulator and non-accumulator Thlaspi species. Plant Physiol. 122, 1343–1353 (2000).PubMed 
PubMed Central 

Google Scholar 
108.Asemaneh, T., Ghaderian, S. M., Crawford, S. A., Marshall, A. T. & Baker, A. J. M. Cellular and subcellular compartmentation of Ni in the Eurasian serpentine plants Alyssum bracteatum, Alyssum murale (Brassicaceae) and Cleome heratensis (Capparaceae). Planta 225, 193–202 (2006).CAS 
PubMed 

Google Scholar 
109.Küpper, H., Jie Zhao, F. & McGrath, S. P. Cellular compartmentation of zinc in leaves of the hyperaccumulator Thlaspi caerulescens. Plant Physiol. 119, 305–312 (1999).PubMed Central 

Google Scholar 
110.Abubakari, F. et al. Incidence of hyperaccumulation and tissue-level distribution of manganese, cobalt and zinc in the genus Gossia (Myrtaceae). Metallomics https://doi.org/10.1093/mtomcs/mfab008 (2021).Article 
PubMed 

Google Scholar 
111.White, P. J. Long-distance transport in the xylem and phloem, chapter 3. In Marschner’s Mineral Nutrition of Higher Plants 3rd edn (ed. Marschner, P.) 49–70 (Academic Press, 2012). https://doi.org/10.1016/B978-0-12-384905-2.00003-0.Chapter 

Google Scholar 
112.Marschner, H. Marschner’s Mineral Nutrition of Higher Plants (Academic Press, 2012). https://doi.org/10.1016/C2009-0-63043-9.Book 

Google Scholar 
113.Fernando, D. R. et al. Does foliage metal accumulation influence plant-insect interactions? A field study of two sympatric tree metallophytes. Funct. Plant Biol. 45, 945–956 (2018).CAS 
PubMed 

Google Scholar 
114.Pearson, R. G. Hard and soft acids and bases, HSAB, part 1: Fundamental principles. J. Chem. Educ. 45, 581 (1968).CAS 

Google Scholar 
115.Alejandro, S., Höller, S., Meier, B. & Peiter, E. Manganese in plants: From acquisition to subcellular allocation. Front. Plant Sci. 11, 300 (2020).PubMed 
PubMed Central 

Google Scholar 
116.Hirschi, K. D., Korenkov, V. D., Wilganowski, N. L. & Wagner, G. J. Expression of Arabidopsis CAX2 in tobacco. Altered metal accumulation and increased manganese tolerance. Plant Physiol. 124, 125–134 (2000).CAS 
PubMed 
PubMed Central 

Google Scholar 
117.Wu, Z. et al. An endoplasmic reticulum-bound Ca(2+)/Mn(2+) pump, ECA1, supports plant growth and confers tolerance to Mn(2+) stress. Plant Physiol. 130, 128–137 (2002).CAS 
PubMed 
PubMed Central 

Google Scholar 
118.Pittman, J. K. Managing the manganese: Molecular mechanisms of manganese transport and homeostasis. New Phytol. 167, 733–742 (2005).CAS 
PubMed 

Google Scholar 
119.Mills, R. F. et al. ECA3, a Golgi-localized P2A-type ATPase, plays a crucial role in manganese nutrition in Arabidopsis. Plant Physiol. 146, 116–128 (2008).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
120.Mizuno, T., Emori, K. & Ito, S. Manganese hyperaccumulation from non-contaminated soil in Chengiopanax sciadophylloides Franch. et Sav. and its correlation with calcium accumulation. Soil Sci. Plant Nutr. 59, 591–602 (2013).CAS 

Google Scholar 
121.Tordoff, G. M., Baker, A. J. M. & Willis, A. J. Current approaches to the revegetation and reclamation of metalliferous mine wastes. Chemosphere 41, 219–228 (2000).ADS 
CAS 
PubMed 

Google Scholar 
122.Grossnickle, S. & Ivetic, V. Direct seeding in reforestation—A field performance review. REFORESTA https://doi.org/10.21750/REFOR.4.07.46 (2017).Article 

Google Scholar 
123.Bermúdez-Contreras, A. I., Ede, F., Waymouth, V., Miller, R. & Aponte, C. Revegetation technique changes root mycorrhizal colonisation and root fungal communities: The advantage of direct seeding over transplanting tube-stock in riparian ecosystems. Plant Ecol. https://doi.org/10.1007/s11258-020-01031-2 (2020).Article 

Google Scholar  More

OverviewWe used data from the national forest inventory conducted by the US Department of Agriculture, Forest Service, Forest Inventory and Analysis (FIA) program to quantify tree biomass and growth along forest edges and within the forest interior. We estimated the causal impact of the forest edge environment on patterns of tree biomass and growth, while accounting for potentially confounding variables. We then used the regression models to estimate the aggregate difference in growth attributable to forest edges throughout the northeastern U.S. Finally, to better understand the implications of our findings, we quantified the degree of forest fragmentation throughout temperate and tropical forest biomes world-wide, using a 30 m forest cover map.Study areaOur analyses of edge impacts on forest biomass and growth were conducted throughout twenty-states (1.7 million km2) in the northeastern and upper mid-west of the United States (Supplementary Fig. 1). This region contains 765,000 km2 of forest and encompasses gradients of dominant land-uses, climatic conditions, and forest composition while remaining within deciduous, coniferous, and mixed temperate forest ecosystems.Identifying edges in forest inventory dataThe FIA collects measurements of tree size, growth, and land-use within a nested plot design across the country19. Each FIA plot is composed of four individual subplots; within each subplot, the diameter at breast height (dbh) of every tree >12.7 cm is measured during each measurement period. The re-measurement frequency for FIA plots in our study area is between 5 and 7 years, but this can differ between Forest Service regions. In addition to tree measurements, the database details land-use condition data that includes the proportion of the area that is forested and, on some plots, the land-cover class of the non-forest area (FIA User’s Manual, Condition Table). FIA plots are considered forested if some portion of the plot includes a contiguous forest patch (including potentially outside of the plot area) of greater than 4047 m2 that has more than 10% canopy cover. With a memorandum of understanding between the USFS and Harvard University, we had access to the true, unfuzzed plot coordinates, which are not publicly available. Evaluating >48,000 plots in the USFS Northern Region sampled from 2010 to 2020 and selecting the most recent measurement cycle for each plot, we identified subplots that contained both a forest and a non-forest condition and categorized these as edges (Supplementary Table 1). Only subplots that included a forest condition in both the most recent and previous measurement were included. Subplots where the mapped condition changed from forest to non-forest were excluded. Changes in the amount of mapped forest condition were included and are incorporated into the calculation of response variables using the most recent condition area. We identified FIA plots where all four subplots were fully forested as interior plots to be used for comparison. Subplots located within the same plot as an edge subplot (i.e., edge-proximate subplots) were excluded from this study due to limitations in our ability to quantify their distance from an edge. The spatial configuration of subplots is such that a fully forested subplot may be up to ~65 m away from an identified forest edge within another subplot. Studies suggest that the distance of edge influence in temperate forest does not extend more than 30 m into the forest interior15,33. Since the FIA does not contain information about the geometry of non-forest conditions beyond the subplot boundary, we deemed that the large uncertainty in the relationship between these subplots to a non-forest edge precluded their inclusion in the study. The FIA plot configuration prevented quantification of the distance of edge influence in our analysis; the exclusion of subplots adjacent to edge-subplots may limit direct comparisons with other fragmentation studies.We used the FIA condition data to characterize the non-forest land use in edge subplots. Information on adjacent non-forest land cover is not collected on all FIA plots (4327 of 6607 edge subplots). We aggregated FIA land-cover classification to a binary anthropogenic or unknown edge type designation and present results from all edge subplots and the anthropogenic edge subset (FIA User’s Manual Condition Table, Section 2.4.50).For each subplot (168 m2 in area), we calculated two primary response variables of interest: total live tree BA and BAI. Notably, trees smaller than 12.7 cm dbh) in m2. BAI was calculated on a per-tree basis as the difference in radial growth of live adult trees between the most recent and previous measurements, and then divided by the number of years between measurements (m2 yr−1). In addition, we aggregated individual tree diameter measurements to calculate mean stem density (stems ha−1) and mean tree diameter for each subplot (Fig. 2).We accounted for variable subplot area by normalizing both BA and BAI to a per-hectare of forested area basis, resulting in units of m2 ha−1 and m2 ha−1 yr−1, respectively. To account for potential small-area bias, we performed a sensitivity analysis on the relationship between BA and subplot forested area (Supplementary Fig. 2). We subsequently excluded 1284 subplots under 30 m2 in area as the area to BA relationship asymptotes relationship above this threshold. Finally, we accounted for errors in field dbh measurements, sometimes resulting in negative BAI values, by excluding the 97.5% quantiles of both BA and BAI distributions.Given their spatial configuration, FIA subplots are not fully independent measurements, potentially introducing issues with pseudo-replication and spatial autocorrelation within our dataset. To test for spatial autocorrelation we examined the semivariance of model residuals36, and found that there was high correlation only at distances of less than 1 km. The spatial stratification of the FIA plot design minimizes issues of plot–plot proximity within our study. However, to account for autocorrelation between subplots, we filtered our pre-matched dataset to only including one subplot from each FIA plot. For plots containing multiple edge subplots, we selected the subplot with the largest forested area. For interior plots, we selected the central subplot and excluded all others.Isolating the effect of edges on growthAbiotic controlsTo account for environmental controls on forest growth we included the most critical abiotic predictors of terrestrial vegetation productivity (light, water, temperature, and nitrogen deposition) as covariates in the regression models (Supplementary Fig. 4, Supplementary Table 2). Light, water, and temperature data were drawn from spatial raster maps (0.5° resolution) as unit-less indices of relative limitation on vegetation productivity, ranging from 0 to 13. Nitrogen data were drawn from the 2018 NADP gridded inorganic wet nitrogen deposition product (4 km spatial resolution; kg of N ha−1)37. To interpolate across small gaps in the raster data (usually along water bodies), we used the Nibble tool from ArcGis Pro (ESRI Team). We then used FIA plot locations to extract values from each raster layer for all FIA subplots.Forest compositionTree species may vary in their responses to biogeochemical changes that occur on forest edges. Overall forest community response emerges from complex interactions between species. We used aggregations of tree species, termed forest composition groups (or forest types)38, to assess if species composition influenced the response to altered edge condition. Forest type classifications for each subplot are provided by the FIA (FIA User’s Manual, Condition Table) and are defined in Appendix D therein. We aggregated the FIA forest types into eight broader species groups, following Thompson et al.23, and defined in Supplementary Table 1.Matching, GLM regressions, and model selectionAll statistical analyses and most of the data processing were conducted in R, version 3.439. Using a causal inference framework, we created a quasi-experimental statistical design that included pre-matching followed by a GLM regression analysis40. Matching emulates an experimental design using observational data by identifying control groups of untreated (forest interior) plots that were as similar as possible to treated (forest edge) plots in terms of observable confounders. By capturing key differences in abiotic variables we control for the fundamental drivers of forest productivity, allowing for a direct estimation of the average treatment effect of edges. Similarity was defined by nearest-neighbor covariate matching determined by Malahanobis distance, implemented in the MatchIt library in R41, the simplest and best method when the dataset is robust enough to find a match for every treated plot20. This method excludes forest interior plots that are not matched with an edge plot. Given differences in sample size between the full edge dataset and the subset designated as anthropogenic edges, we performed matching separately on the two datasets. To assess the efficacy of matching on reducing the differences in covariate distributions, we used summary statistics calculated with the MatchIt library and report the pre- and post-matched covariate balance in Supplementary Table 4 and Supplementary Table 5 (sensu Schleicher et al.42). Matching was highly successful, largely eliminating differences in all covariate distributions in both datasets.Our primary response variables of interest, BA and BAI, were right-skewed, non-normally distributed and violated the assumptions of normality necessary for ordinary least squares regression43. We, therefore, used a GLM to better fit the structure of our data. GLMs are an extension of linear regression that allow more freedom in the choice of probability distribution function through the use of a link function to model relationships between predictors and response variables44. The gamma probability distribution is frequently chosen to model BA, given its assumptions of positive, continuous values and flexible model form23,45. We performed a series of GLM regressions on our post-matched datasets, using a gamma probability distribution with an inverse link function to model the relationship of BA and BA with a suite of predictor variables, using the glm function as implemented in the R Core stats package39. Due to differences in sample size between the all-edge dataset and the anthropogenic-edge subset, we modeled these two datasets separately for each of BA and BAI, resulting in four separate regression analyses. We used a model selection framework to identify the most parsimonious model within each of the model sets based on the Akaike Information Criterion (AIC) and residual deviance statistic46,47. We report the model-selection and model-fit results for each of our separate analyses, including model forms, AIC, Nagelkerke Pseudo-R2, and residual deviance in Supplementary Table 2. Across all four regression analyses, the best-performing model was one that included an interaction between the edge-status and forest type categorical variables, as well as the variables of temperature-limitation, light-limitation, water-limitation, and nitrogen deposition.We then used the best performing model from each analysis to compare the differences in BA and BAI between forest edge and interior across each forest type. We estimated the treatment effect of edge-state within each forest type using the ggeffects package48 to calculate marginal effects with the continuous predictors (temperature, light, water, and nitrogen deposition) held at their within-forest type regional means. The results of this analysis are displayed in Fig. 1 and Supplementary Table 3; primary error bars on the interior point show the 95% confidence interval of the marginal effect from the full edge model, while secondary error bars show the CI from the anthropogenic edge model. Due to the smaller sample size in the anthropogenic model, estimates of the mean marginal effect of the interior plots vary slightly (though non-significantly) from those from the full dataset. The main text description reports outputs from both models, calculated from separate interior mean estimates. For visual clarity, we only display one set of interior means in Fig. 1.Mortality and timber harvestIn tropical forests, large reductions in productivity along edges are associated with increased tree mortality.9 To assess differences in tree mortality across our study region, we applied a simplified GLM analysis, including edge-state as our only predictor variable. The FIA differentiates between mortality attributed to timber harvest and that attributed to other, non-harvest causes. The results of this analysis are presented as marginal effects of each edge category in Supplementary Fig. 3. There are no significant differences in biogenic mortality between edge groups and no difference in overall mortality (combined biogenic and anthropogenic); there is a small, but statistically significant (p  More

1.Wilson, E. O. Introduction. in Biodiversity II: understanding and protecting our biological resources (eds. Reaka-Kudla, M. L., Wilson, D. E. & Wilson, E. O.) 1–3 (Joseph Henry Press, 1997).2.Lovejoy, T. E. Biodiversity: what is it? in Biodiversity II: Understanding and protecting our biological resources (eds. Reaka-Kudla, M. L., Wilson, D. E. & Wilson, E. O.) 7–14 (Joseph Henry Press, 1997).3.Ehrlich, P. R. & Wilson, E. O. Biodiversity studies: Science and policy. Science 253, 758–762 (1991).ADS 
CAS 
Article 

Google Scholar 
4.Myers, R. A. & Ottensmeyers, C. A. Extinction risk in marine species. in Marine Conservation Biology: The Science of Maintaining the Sea’s Biodiversity (eds. Norse, E. A. & Crowder, L. B.) 58–79 (Island Press, 2005).5.Reaka-Kudla, M. L. The global biodiversity of coral reefs: a comparison with rain forests. in Biodiversity II: understanding and protecting our biological resources (eds. Reaka-Kudla, M. L., Wilson, D. E. & Wilson, E. O.) 83–108 (Joseph Henry Press, 1997).6.Briggs, J. C. Marine extinctions and conservation. Mar. Biol. 158, 485–488 (2011).Article 

Google Scholar 
7.Harley, C. D. G. et al. The impacts of climate change in coastal marine systems: Climate change in coastal marine systems. Ecol. Lett. 9, 228–241 (2006).ADS 
Article 

Google Scholar 
8.Dupont, S., Dorey, N. & Thorndyke, M. What meta-analysis can tell us about vulnerability of marine biodiversity to ocean acidification?. Estuar. Coast. Shelf Sci. 89, 182–185 (2010).ADS 
Article 

Google Scholar 
9.Stork, N. E. Measuring global biodiversity and its decline. in Biodiversity II: understanding and protecting our biological resources (eds. Reaka-Kudla, M. L., Wilson, D. E. & Wilson, E. O.) 41–68 (Joseph Henry Press, 1997).10.Richards, Z. T. & Day, J. C. Biodiversity of the Great Barrier Reef—How adequately is it protected? PeerJ 6, e4747 (2018).11.Pyle, R. L. & Copus, J. M. Mesophotic Coral Ecosystems: introduction and overview. in Mesophotic Coral Ecosystems (eds. Loya, Y., Puglise, K. A. & Bridge, T. C. L.) vol. 12 3–27 (Springer International Publishing, 2019).12.Hinderstein, L. M. et al. Theme section on ‘Mesophotic coral ecosystems: Characterization, ecology, and management’. Coral Reefs 29, 247–251 (2010).ADS 
Article 

Google Scholar 
13.Bongaerts, P., Ridgway, T., Sampayo, E. M. & Hoegh-Guldberg, O. Assessing the ‘deep reef refugia’ hypothesis: Focus on Caribbean reefs. Coral Reefs 29, 309–327 (2010).Article 

Google Scholar 
14.Bongaerts, P. & Smith, T. B. Beyond the “Deep Reef Refuge” hypothesis: a conceptual framework to characterize persistence at depth. in Mesophotic Coral Ecosystems (eds. Loya, Y., Puglise, K. A. & Bridge, T. C. L.) vol. 12, 881–895 (Springer International Publishing, 2019).15.Vermeij, G. J. Survival during biotic crises: the properties and evolutionary significance of refuges. Dyn. Extinct. 231–246 (1986).16.Glynn, P. W. Coral reef bleaching: Facts, hypotheses and implications. Glob. Change Biol. 2, 495–509 (1996).ADS 
Article 

Google Scholar 
17.Riegl, B. & Piller, W. E. Possible refugia for reefs in times of environmental stress. Int. J. Earth Sci. 92, 520–531 (2003).Article 

Google Scholar 
18.Halfar, J., Godinez-Orta, L., Riegl, B., Valdez-Holguin, J. E. & Borges, J. M. Living on the edge: high-latitude Porites carbonate production under temperate eutrophic conditions. Coral Reefs 24, 582–592 (2005).ADS 
Article 

Google Scholar 
19.Loya, Y., Eyal, G., Treibitz, T., Lesser, M. P. & Appeldoorn, R. Theme section on mesophotic coral ecosystems: Advances in knowledge and future perspectives. Coral Reefs 35, 1–9 (2016).ADS 
Article 

Google Scholar 
20.Laverick, J. H. et al. To what extent do mesophotic coral ecosystems and shallow reefs share species of conservation interest? A systematic review. Environ. Evid. 7, 15 (2018).Article 

Google Scholar 
21.Smith, T. B., Glynn, P. W., Maté, J. L., Toth, L. T. & Gyory, J. A depth refugium from catastrophic coral bleaching prevents regional extinction. Ecology 95, 1663–1673 (2014).Article 

Google Scholar 
22.Smith, T. B. et al. Caribbean mesophotic coral ecosystems are unlikely climate change refugia. Glob. Change Biol. 22, 2756–2765 (2016).ADS 
Article 

Google Scholar 
23.Holstein, D. M., Smith, T. B., Gyory, J. & Paris, C. B. Fertile fathoms: Deep reproductive refugia for threatened shallow corals. Sci. Rep. 5 (2015).24.Holstein, D. M., Paris, C. B., Vaz, A. C. & Smith, T. B. Modeling vertical coral connectivity and mesophotic refugia. Coral Reefs 35, 23–37 (2016).ADS 
Article 

Google Scholar 
25.Holstein, D. M., Smith, T. B. & Paris, C. B. Depth-independent reproduction in the reef coral Porites astreoides from shallow to mesophotic zones. PLoS ONE 11, e0146068 (2016).26.Assis, J. et al. Deep reefs are climatic refugia for genetic diversity of marine forests. J. Biogeogr. 43, 833–844 (2016).Article 

Google Scholar 
27.Bongaerts, P. et al. Deep reefs are not universal refuges: Reseeding potential varies among coral species. Sci. Adv. 3, e1602373 (2017).28.Muir, P. R., Marshall, P. A., Abdulla, A. & Aguirre, J. D. Species identity and depth predict bleaching severity in reef-building corals: Shall the deep inherit the reef?. Proc. R. Soc. B. 284, 20171551 (2017).Article 

Google Scholar 
29.Semmler, R. F., Hoot, W. C. & Reaka, M. L. Are mesophotic coral ecosystems distinct communities and can they serve as refugia for shallow reefs?. Coral Reefs 36, 433–444 (2017).ADS 
Article 

Google Scholar 
30.Kavousi, J. & Keppel, G. Clarifying the concept of climate change refugia for coral reefs. ICES J. Mar. Sci. 75, 43–49 (2018).Article 

Google Scholar 
31.Morais, J. & Santos, B. A. Limited potential of deep reefs to serve as refuges for tropical Southwestern Atlantic corals. Ecosphere 9, e02281 (2018).32.Pereira, P. H. C., Macedo, C. H., Nunes, J. de A. C. C., Marangoni, L. F. de B. & Bianchini, A. Effects of depth on reef fish communities: Insights of a “deep refuge hypothesis” from Southwestern Atlantic reefs. PLoS ONE 13, e0203072 (2018).33.Rocha, L. A. et al. Mesophotic coral ecosystems are threatened and ecologically distinct from shallow water reefs. Science 361, 281–284 (2018).ADS 
CAS 
Article 

Google Scholar 
34.Slattery, M. et al. The Pulley Ridge deep reef is not a stable refugia through time. Coral Reefs 37, 391–396 (2018).ADS 
Article 

Google Scholar 
35.Kavousi, J. Biological interactions: The overlooked aspects of marine climate change refugia. Glob. Change Biol. 25, 3571–3573 (2019).ADS 
Article 

Google Scholar 
36.Baselga, A. The relationship between species replacement, dissimilarity derived from nestedness, and nestedness: Species replacement and nestedness. Glob. Ecol. Biogeogr. 21, 1223–1232 (2012).Article 

Google Scholar 
37.Fisher, R. et al. Species richness on coral reefs and the pursuit of convergent global estimates. Curr. Biol. 25, 500–505 (2015).CAS 
Article 

Google Scholar 
38.Montgomery, A. D., Fenner, D. & Toonen, R. J. Annotated checklist for stony corals of American Sāmoa with reference to mesophotic depth records. ZK 849, 1–170 (2019).39.Colwell, R. K. et al. Models and estimators linking individual-based and sample-based rarefaction, extrapolation and comparison of assemblages. J. Plant Ecol. 5, 3–21 (2012).Article 

Google Scholar 
40.Rooney, J. et al. Mesophotic coral ecosystems in the Hawaiian Archipelago. Coral Reefs 29, 361–367 (2010).ADS 
Article 

Google Scholar 
41.Bridge, T. C. L. et al. Diversity of Scleractinia and Octocorallia in the mesophotic zone of the Great Barrier Reef, Australia. Coral Reefs 31, 179–189 (2012).ADS 
Article 

Google Scholar 
42.Pyle, R. L. et al. A comprehensive investigation of mesophotic coral ecosystems in the Hawaiian Archipelago. PeerJ 4, e2475 (2016).43.Muir, P. R. & Pichon, M. Biodiversity of reef-building, Scleractinian corals. in Mesophotic Coral Ecosystems (eds. Loya, Y., Puglise, K. A. & Bridge, T. C. L.) vol. 12, 589–620 (Springer International Publishing, 2019).44.Spalding, H. L. et al. The Hawaiian Archipelago. in Mesophotic Coral Ecosystems (eds. Loya, Y., Puglise, K. A. & Bridge, T. C. L.) vol. 12, 445–464 (Springer International Publishing, 2019).45.Turak, E. & DeVantier, L. Reef-building corals of the upper mesophotic zone of the Central Indo-West Pacific. in Mesophotic Coral Ecosystems (eds. Loya, Y., Puglise, K. A. & Bridge, T. C. L.) vol. 12, 621–651 (Springer International Publishing, 2019).46.Vermeij, G. J. & Grosberg, R. K. Rarity and persistence. Ecol. Lett. 21, 3–8 (2018).Article 

Google Scholar 
47.Kammer, T. W., Baumiller, T. K. & Ausich, W. I. Evolutionary significance of differential species longevity in Osagean-Meramecian (Mississippian) crinoid clades. Paleobiology 24, 155–176 (1998).
Google Scholar 
48.Jones, G. P., Julian, C. M. & Munday, P. L. Rarity in coral reef fish communities. in Coral reef fishes: dynamics and diversity in a complex ecosystem (ed. Sale, P. F.) 81–102 (Academic Press, 2006).49.Yang, Q., Liu, G., Casazza, M., Gonella, F. & Yang, Z. Three dimensions of biodiversity: New perspectives and methods. Ecol. Indic. 130, 108099 (2021).50.Richards, Z. T. Rarity in the coral genus Acropora: Implications for biodiversity conservation. (James Cook University, 2009).51.Soares, M. de O. Marginal reef paradox: A possible refuge from environmental changes? Ocean Coast. Manag. 185, 105063 (2020).52.Soares, M. de O. et al. Why do mesophotic coral ecosystems have to be protected? Sci. Total Environ. 726, 138456 (2020).53.White, K. N. et al. Typhoon damage on a shallow mesophotic reef in Okinawa, Japan. PeerJ 1, e151 (2013).54.Smith, T. B., Holstein, D. M. & Ennis, R. S. Disturbance in mesophotic coral ecosystems and linkages to conservation and management. in Mesophotic Coral Ecosystems (eds. Loya, Y., Puglise, K. A. & Bridge, T. C. L.) vol. 12, 911–929 (Springer International Publishing, 2019).55.Pinheiro, H. T., Eyal, G., Shepherd, B. & Rocha, L. A. Ecological insights from environmental disturbances in mesophotic coral ecosystems. Ecosphere 10, e02666 (2019).56.Veron, J. E. N. Corals of the world. (Australian Institute of Marine Science, 2000).57.Luzon, K. S., Lin, M.-F., Ablan Lagman, Ma. C. A., Licuanan, W. R. Y. & Chen, C. A. Resurrecting a subgenus to genus: molecular phylogeny of Euphyllia and Fimbriaphyllia (order Scleractinia; Family Euphyllidae; clade V). PeerJ 5, e4074 (2017).58.Eyal, G. et al. Euphyllia paradivisa, a successful mesophotic coral in the northern Gulf of Eilat/Aqaba, Red Sea. Coral Reefs 35, 91–102 (2016).ADS 
Article 

Google Scholar 
59.Eyal, G., Tamir, R., Kramer, N., Eyal-Shaham, L. & Loya, Y. The Red Sea: Israel. in Mesophotic Coral Ecosystems (eds. Loya, Y., Puglise, K. A. & Bridge, T. C. L.) vol. 12, 199–214 (Springer International Publishing, 2019).60.Tamir, R., Eyal, G., Kramer, N., Laverick, J. H. & Loya, Y. Light environment drives the shallow‐to‐mesophotic coral community transition. Ecosphere 10 (2019).61.Fujii, T., Kitano, Y. F. & Tachikawa, H. New distributional records of three species of Euphylliidae (Cnidaria, Anthozoa, Hexacorallia, Scleractinia) from the Ryukyu Islands, Japan. Spec. Div. 25, 275–282 (2020).Article 

Google Scholar 
62.Longenecker, K., Roberts, T. E. & Colin, P. L. Papua New Guinea. in Mesophotic Coral Ecosystems (eds. Loya, Y., Puglise, K. A. & Bridge, T. C. L.) vol. 12 321–336 (Springer International Publishing, 2019).63.NOAA, [National Oceanic and Atmospheric Administration]. Endangered and threatened species; Critical habitat for the threatened Indo-Pacific corals. 85 FR 76262 (50 CFR Part 223 and 226) 76262–76299 (2020).64.Maragos, J. E., Hunter, C. L. & Meier, K. Z. Reefs and corals observed during the 1991–92 American Samoa coastal resources inventory. 50 (1994).65.Coles, S. et al. Introduced marine species in Pago Pago Harbor, Fagatele Bay and the National Park Coast, American Samoa. 182 (2003).66.Montgomery, A. D. et al. American Samoa. in Mesophotic Coral Ecosystems (eds. Loya, Y., Puglise, K. A. & Bridge, T. C. L.) vol. 12 387–407 (Springer International Publishing, 2019).67.Wallace, C. C. Staghorn corals of the world: A revision of the coral genus Acropora (Scleractinia; Astrocoeniina; Acroporidae) worldwide, with emphasis on morphology, phylogeny and biogeography. (Csiro Publishing, 1999).68.Hoeksema, B. W. Taxonomy, phylogeny and biogeography of mushroom corals (Scleractinina: Fungiidae). Zoologische Verhandelingen 254, 1–295 (1989).
Google Scholar 
69.World Register of Marine Species: WoRMS. Available online: http://www.marinespecies.org/. Accessed on 9/9/2020 (2020). https://doi.org/10.14284/170.70.Hsieh, T. C., Ma, K. H. & Chao, A. Interpolation and extrapolation for species diversity. (2020).71.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).Article 

Google Scholar 
72.Baselga, A. et al. Partitioning beta diversity into turnover and nestedness components ver. 1.5.2. (2020).73.Anderson, M. J., Gorley, R. N. & Clarke, K. R. PERMANOVA+ for Primer: Guide to software and statistical methods. 218 (2008).74.Clarke, K. R. & Gorley, R. N. Getting started with PRIMER 7. 18 http://updates.primer-e.com/primer7/manuals/Getting_started_with_PRIMER_7.pdf (2015).75.Gaston, K. What is rarity? in Rarity 1–21 (Chapman & Hall, 1994). More

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.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