Ecology

1.Ley RE, Hamady M, Lozupone C, Turnbaugh PJ, Ramey RR, Bircher JS, et al. Evolution of mammals and their gut microbes. Science. 2008;320:1647–51.CAS 
PubMed 
PubMed Central 

Google Scholar 
2.Song SJ, Sanders JG, Delsuc F, Metcalf J, Amato K, Taylor MW, et al. Comparative analyses of vertebrate gut microbiomes reveal convergence between birds and bats. mBio. 2020;11:e02901–19.CAS 
PubMed 
PubMed Central 

Google Scholar 
3.Godon J-J, Arulazhagan P, Steyer J-P, Hamelin J. Vertebrate bacterial gut diversity: size also matters. BMC Ecol. 2016;16:12.PubMed 
PubMed Central 

Google Scholar 
4.Lutz HL, Jackson EW, Webala PW, Babyesiza WS, Kerbis Peterhans JC, Demos TC, et al. Ecology and host identity outweigh evolutionary history in shaping the bat microbiome. mSystems. 2019;4:e00511–19.PubMed 
PubMed Central 

Google Scholar 
5.Nishida AH, Ochman H. Rates of gut microbiome divergence in mammals. Mol Ecol. 2018;27:1884–97.PubMed 
PubMed Central 

Google Scholar 
6.Groussin M, Mazel F, Sanders JG, Smillie CS, Lavergne S, Thuiller W, et al. Unraveling the processes shaping mammalian gut microbiomes over evolutionary time. Nat Commun. 2017;8:14319.CAS 
PubMed 
PubMed Central 

Google Scholar 
7.Lim SJ, Bordenstein SR. An introduction to phylosymbiosis. Proc Biol Sci. 2020;287:20192900.PubMed 
PubMed Central 

Google Scholar 
8.Ross AA, Müller KM, Weese JS, Neufeld JD. Comprehensive skin microbiome analysis reveals the uniqueness of human skin and evidence for phylosymbiosis within the class Mammalia. Proc Natl Acad Sci USA. 2018;115:E5786–E5795.CAS 
PubMed 
PubMed Central 

Google Scholar 
9.Ochman H, Worobey M, Kuo C-H, Ndjango J-BN, Peeters M, Hahn BH, et al. Evolutionary relationships of wild hominids recapitulated by gut microbial communities. PLoS Biol. 2010;8:e1000546.PubMed 
PubMed Central 

Google Scholar 
10.Amato KR, G Sanders J, Song SJ, Nute M, Metcalf JL, Thompson LR, et al. Evolutionary trends in host physiology outweigh dietary niche in structuring primate gut microbiomes. ISME J. 2018;13:576–87.PubMed 
PubMed Central 

Google Scholar 
11.Moeller AH, Caro-Quintero A, Mjungu D, Georgiev AV, Lonsdorf EV, Muller MN, et al. Cospeciation of gut microbiota with hominids. Science. 2016;353:380–2.CAS 
PubMed 
PubMed Central 

Google Scholar 
12.Brooks AW, Kohl KD, Brucker RM, van Opstal EJ, Bordenstein SR. Phylosymbiosis: relationships and functional effects of microbial communities across host evolutionary history. PLoS Biol. 2016;14:e2000225.PubMed 
PubMed Central 

Google Scholar 
13.Delsuc F, Metcalf JL, Wegener Parfrey L, Song SJ, González A, Knight R. Convergence of gut microbiomes in myrmecophagous mammals. Mol Ecol. 2014;23:1301–17.CAS 
PubMed 

Google Scholar 
14.Muegge BD, Kuczynski J, Knights D, Clemente JC, González A, Fontana L, et al. Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans. Science. 2011;332:970–4.CAS 
PubMed 
PubMed Central 

Google Scholar 
15.Turnbaugh PJ, Hamady M, Yatsunenko T, Cantarel BL, Duncan A, Ley RE, et al. A core gut microbiome in obese and lean twins. Nature. 2009;457:480–4.CAS 
PubMed 
PubMed Central 

Google Scholar 
16.Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature. 2012;486:207–14.
Google Scholar 
17.Weimer PJ. Redundancy, resilience, and host specificity of the ruminal microbiota: implications for engineering improved ruminal fermentations. Front Microbiol. 2015;6:296.PubMed 
PubMed Central 

Google Scholar 
18.Louca S, Parfrey LW, Doebeli M. Decoupling function and taxonomy in the global ocean microbiome. Science. 2016;353:1272–7.CAS 
PubMed 

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

Google Scholar 
20.Louca S, Polz MF, Mazel F, Albright MBN, Huber JA, O’Connor MI, et al. Function and functional redundancy in microbial systems. Nat Ecol Evol. 2018;2:936–43.PubMed 

Google Scholar 
21.Inkpen SA, Andrew Inkpen S, Douglas GM, Brunet TDP, Leuschen K, Ford Doolittle W, et al. The coupling of taxonomy and function in microbiomes. Biol Philos. 2017;32:1225–43.
Google Scholar 
22.Krautkramer KA, Fan J, Bäckhed F. Gut microbial metabolites as multi-kingdom intermediates. Nat Rev Microbiol. 2021;19:77–94.CAS 
PubMed 

Google Scholar 
23.Turnbaugh PJ, Gordon JI. An invitation to the marriage of metagenomics and metabolomics. Cell. 2008;134:708–13.CAS 
PubMed 

Google Scholar 
24.Moya A, Ferrer M. Functional redundancy-induced stability of gut microbiota subjected to disturbance. Trends Microbiol. 2016;24:402–13.CAS 
PubMed 

Google Scholar 
25.Wang M, Carver JJ, Phelan VV, Sanchez LM, Garg N, Peng Y, et al. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. Nat Biotechnol. 2016;34:828–37.CAS 
PubMed 
PubMed Central 

Google Scholar 
26.Wilson DE, Reeder DM Mammal species of the world: a taxonomic and geographic reference. 2005. JHU Press.27.Jami E, Israel A, Kotser A, Mizrahi I. Exploring the bovine rumen bacterial community from birth to adulthood. ISME J. 2013;7:1069–79.PubMed 
PubMed Central 

Google Scholar 
28.Stevenson DM, Weimer PJ. Dominance of Prevotella and low abundance of classical ruminal bacterial species in the bovine rumen revealed by relative quantification real-time PCR. Appl Microbiol Biotechnol. 2007;75:165–74.CAS 
PubMed 

Google Scholar 
29.Caporaso JG, Gregory Caporaso J, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 2012;6:1621–4.CAS 
PubMed 
PubMed Central 

Google Scholar 
30.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 
31.Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.CAS 
PubMed 

Google Scholar 
32.Douglas GM, Maffei VJ, Zaneveld JR, Yurgel SN, Brown JR, Taylor CM, et al. PICRUSt2 for prediction of metagenome functions. Nat Biotechnol. 2020;38:685–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
33.Gawlik-Dziki U, Dziki D, Baraniak B, Lin R. The effect of simulated digestion in vitro on bioactivity of wheat bread with Tartary buckwheat flavones addition. LWT. 2009;42:137–43.CAS 

Google Scholar 
34.Melnik AV, da Silva RR, Hyde ER, Aksenov AA, Vargas F, Bouslimani A, et al. Coupling targeted and untargeted mass spectrometry for metabolome-microbiome-wide association studies of human fecal samples. Anal Chem. 2017;89:7549–59.CAS 
PubMed 

Google Scholar 
35.Giavalisco P, Li Y, Matthes A, Eckhardt A, Hubberten H-M, Hesse H, et al. Elemental formula annotation of polar and lipophilic metabolites using 13C, 15N and 34S isotope labelling, in combination with high-resolution mass spectrometry. Plant J. 2011;68:364–76.CAS 
PubMed 

Google Scholar 
36.Lisec J, Schauer N, Kopka J, Willmitzer L, Fernie AR. Gas chromatography mass spectrometry–based metabolite profiling in plants. Nat Protoc. 2006;1:387–96.CAS 
PubMed 

Google Scholar 
37.Hochberg U, Degu A, Toubiana D, Gendler T, Nikoloski Z, Rachmilevitch S, et al. Metabolite profiling and network analysis reveal coordinated changes in grapevine water stress response. BMC Plant Biol. 2013;13:184.PubMed 
PubMed Central 

Google Scholar 
38.Shabat SKB, Sasson G, Doron-Faigenboim A, Durman T, Yaacoby S, Berg Miller ME, et al. Specific microbiome-dependent mechanisms underlie the energy harvest efficiency of ruminants. ISME J. 2016;10:2958–72.CAS 
PubMed 
PubMed Central 

Google Scholar 
39.Pluskal T, Castillo S, Villar-Briones A, Orešič M MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics. 2010;11;1–11.40.Nothias L-F, Petras D, Schmid R, Dührkop K, Rainer J, Sarvepalli A, et al. Feature-based molecular networking in the GNPS analysis environment. Nat Methods. 2020;17:905–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
41.da Silva RR, Wang M, Nothias L-F, van der Hooft JJJ, Caraballo-Rodríguez AM, Fox E, et al. Propagating annotations of molecular networks using in silico fragmentation. PLoS Comput Biol. 2018;14:e1006089.PubMed 
PubMed Central 

Google Scholar 
42.Ernst M, Kang KB, Caraballo-Rodríguez AM, Nothias L-F, Wandy J, Chen C, et al. MolNetEnhancer: enhanced molecular networks by integrating metabolome mining and annotation tools. Metabolites. 2019;9:144.CAS 
PubMed Central 

Google Scholar 
43.Djoumbou Feunang Y, Eisner R, Knox C, Chepelev L, Hastings J, Owen G, et al. ClassyFire: automated chemical classification with a comprehensive, computable taxonomy. J Cheminform. 2016;8:61.PubMed 
PubMed Central 

Google Scholar 
44.Chambers MC, Maclean B, Burke R, Amodei D, Ruderman DL, Neumann S, et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat Biotechnol. 2012;30:918–20.CAS 
PubMed 
PubMed Central 

Google Scholar 
45.Kessner D, Chambers M, Burke R, Agus D, Mallick P. ProteoWizard: open source software for rapid proteomics tools development. Bioinformatics. 2008;24:2534–6.CAS 
PubMed 
PubMed Central 

Google Scholar 
46.Aksenov AA, Laponogov I, Zhang Z, Doran SLF, Belluomo I, Veselkov D, et al. Auto-deconvolution and molecular networking of gas chromatography–mass spectrometry data. Nat Biotechnol. 2021;39:169–73.CAS 
PubMed 

Google Scholar 
47.Kiela PR, Ghishan FK. Physiology of intestinal absorption and secretion. Best Pr Res Clin Gastroenterol. 2016;30:145–59.CAS 

Google Scholar 
48.Karasov WH, Diamond JM. Interplay between physiology and ecology in digestion. Bioscience. 1988;38:602–11.CAS 

Google Scholar 
49.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 
50.Dixon P. VEGAN, a package of R functions for community ecology. J Veg Sci. 2003;14:927–30.
Google Scholar 
51.Wickham H, ggplot2: elegant graphics for data analysis. Springer; 2016.52.Hulsen T, de Vlieg J, Alkema W. BioVenn—a web application for the comparison and visualization of biological lists using area-proportional Venn diagrams. BMC Genomics. 2008;9:488.PubMed 
PubMed Central 

Google Scholar 
53.Anderson MJ. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 2001;26:32–46.
Google Scholar 
54.Anderson MJ, Walsh DCI. PERMANOVA, ANOSIM, and the Mantel test in the face of heterogeneous dispersions: what null hypothesis are you testing? Ecol Monogr. 2013;83:557–74.
Google Scholar 
55.Galili T. dendextend: an R package for visualizing, adjusting and comparing trees of hierarchical clustering. Bioinformatics. 2015;31:3718–20.CAS 
PubMed 
PubMed Central 

Google Scholar 
56.Paradis E, Schliep K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics. 2019;35:526–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
57.Hedges SB, Dudley J, Kumar S. TimeTree: a public knowledge-base of divergence times among organisms. Bioinformatics. 2006;22:2971–2.CAS 
PubMed 

Google Scholar 
58.Kumar S, Stecher G, Suleski M, Hedges SB. TimeTree: a resource for timelines, timetrees, and divergence times. Mol Biol Evol. 2017;34:1812–9.CAS 
PubMed 

Google Scholar 
59.Baker FB. Stability of two hierarchical grouping techniques case I: sensitivity to data errors. J Am Stat Assoc. 1974;69:440–5.
Google Scholar 
60.De Cáceres M, Legendre P. Associations between species and groups of sites: indices and statistical inference. Ecology. 2009;90:3566–74.
Google Scholar 
61.Sumner LW, Amberg A, Barrett D, Beale MH, Beger R, Daykin CA, et al. Proposed minimum reporting standards for chemical analysis chemical analysis working group (CAWG) metabolomics standards initiative (MSI). Metabolomics. 2007;3:211–21.CAS 
PubMed 
PubMed Central 

Google Scholar 
62.Jarmusch AK, Wang M, Aceves CM, Advani RS, Aguirre S, Aksenov AA, et al. ReDU: a framework to find and reanalyze public mass spectrometry data. Nat Methods. 2020;17:901–4.CAS 
PubMed 
PubMed Central 

Google Scholar 
63.Ridlon JM, Kang D-J, Hylemon PB. Bile salt biotransformations by human intestinal bacteria. J Lipid Res. 2006;47:241–59.CAS 
PubMed 

Google Scholar 
64.Winston JA, Theriot CM. Diversification of host bile acids by members of the gut microbiota. Gut Microbes. 2019;11:1–14.65.Quinn RA, Melnik AV, Vrbanac A, Fu T, Patras KA, Christy MP, et al. Global chemical effects of the microbiome include new bile-acid conjugations. Nature. 2020;579:123–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
66.Haslewood GA. Bile salt evolution. J Lipid Res. 1967;8:535–50.CAS 
PubMed 

Google Scholar 
67.Hofmann AF, Hagey LR, Krasowski MD. Bile salts of vertebrates: structural variation and possible evolutionary significance. J Lipid Res. 2010;51:226–46.CAS 
PubMed 
PubMed Central 

Google Scholar 
68.Hofmann AF. Bile acids: the good, the bad, and the ugly. N. Physiol Sci. 1999;14:24–29.CAS 

Google Scholar 
69.Bergman EN. Energy contributions of volatile fatty acids from the gastrointestinal tract in various species. Physiol Rev. 1990;70:567–90.CAS 
PubMed 

Google Scholar 
70.Engelhardt W von, Rechkemmer G. The physiological effects of short-chain fatty acids in the hind gut. Fibre in human and animal nutrition. 1983. The Royal Society of New Zealand, Palmerston North, New Zealand, pp 149-55.71.Reichardt N, Duncan SH, Young P, Belenguer A, McWilliam Leitch C, Scott KP, et al. Phylogenetic distribution of three pathways for propionate production within the human gut microbiota. ISME J. 2014;8:1323–35.CAS 
PubMed 
PubMed Central 

Google Scholar 
72.Clemens ET, Stevens CE. Sites of organic acid production and patterns of digesta movement in the gastro-intestinal tract of the raccoon. J Nutr. 1979;109:1110–6.CAS 
PubMed 

Google Scholar 
73.Schwab C, Cristescu B, Boyce MS, Stenhouse GB, Gänzle M. Bacterial populations and metabolites in the feces of free roaming and captive grizzly bears. Can J Microbiol. 2009;55:1335–46.CAS 
PubMed 

Google Scholar 
74.Schwab C, Gänzle M. Comparative analysis of fecal microbiota and intestinal microbial metabolic activity in captive polar bears. Can J Microbiol. 2011;57:177–85.CAS 
PubMed 

Google Scholar 
75.Kanehisa M. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28:27–30.CAS 
PubMed 
PubMed Central 

Google Scholar 
76.Tofalo R, Cocchi S, Suzzi G. Polyamines and gut microbiota. Front Nutr. 2019;6:16.PubMed 
PubMed Central 

Google Scholar 
77.Matsumoto M, Kibe R, Ooga T, Aiba Y, Kurihara S, Sawaki E, et al. Impact of intestinal microbiota on intestinal luminal metabolome. Sci Rep. 2012;2:233.PubMed 
PubMed Central 

Google Scholar 
78.Pugin B, Barcik W, Westermann P, Heider A, Wawrzyniak M, Hellings P, et al. A wide diversity of bacteria from the human gut produces and degrades biogenic amines. Micro Ecol Health Dis. 2017;28:1353881.
Google Scholar 
79.Nakamura A, Ooga T, Matsumoto M. Intestinal luminal putrescine is produced by collective biosynthetic pathways of the commensal microbiome. Gut Microbes. 2019;10:159–71.CAS 
PubMed 

Google Scholar 
80.Aura A-M, O’Leary KA, Williamson G, Ojala M, Bailey M, Puupponen-Pimiä R, et al. Quercetin derivatives are deconjugated and converted to hydroxyphenylacetic acids but not methylated by human fecal flora in vitro. J Agric Food Chem. 2002;50:1725–30.CAS 
PubMed 

Google Scholar 
81.Booth AN, Deeds F, Jones FT, Murray CW. The metabolic fate of rutin and quercetin in the animal body. J Biol Chem. 1956;223:251–7.CAS 
PubMed 

Google Scholar 
82.Jaganath IB, Mullen W, Edwards CA, Crozier A. The relative contribution of the small and large intestine to the absorption and metabolism of rutin in man. Free Radic Res. 2006;40:1035–46.CAS 
PubMed 

Google Scholar 
83.Mena P, Calani L, Bruni R, Del Rio D. Bioactivation of high-molecular-weight polyphenols by the gut microbiome. Diet-Microbe Interactions in the Gut. Academic Press; 2015. pp 73–101.84.Serra A, Macià A, Romero M-P, Reguant J, Ortega N, Motilva M-J. Metabolic pathways of the colonic metabolism of flavonoids (flavonols, flavones and flavanones) and phenolic acids. Food Chem. 2012;130:383–93.CAS 

Google Scholar 
85.Peng X, Zhang Z, Zhang N, Liu L, Li S, Wei H. In vitro catabolism of quercetin by human fecal bacteria and the antioxidant capacity of its catabolites. Food Nutr Res. 2014;58:23406.86.Feng X, Li Y, Brobbey Oppong M, Qiu F. Insights into the intestinal bacterial metabolism of flavonoids and the bioactivities of their microbe-derived ring cleavage metabolites. Drug Metab Rev. 2018;50:343–56.CAS 
PubMed 

Google Scholar 
87.Maini Rekdal V, Bess EN, Bisanz JE, Turnbaugh PJ, Balskus EP. Discovery and inhibition of an interspecies gut bacterial pathway for Levodopa metabolism. Science. 2019;364:1055.
Google Scholar 
88.Maini Rekdal V, Nol Bernadino P, Luescher MU, Kiamehr S, Le C, Bisanz JE, et al. A widely distributed metalloenzyme class enables gut microbial metabolism of host- and diet-derived catechols. Elife. 2020;9:e50845.PubMed 
PubMed Central 

Google Scholar 
89.Davenport ER, Sanders JG, Song SJ, Amato KR, Clark AG, Knight R. The human microbiome in evolution. BMC Biol. 2017;15:127.PubMed 
PubMed Central 

Google Scholar 
90.Steiner CC, Ryder OA. Molecular phylogeny and evolution of the Perissodactyla. Zool J Linn Soc. 2011;163:1289–303.
Google Scholar 
91.McKenzie VJ, Song SJ, Delsuc F, Prest TL, Oliverio AM, Korpita TM, et al. The effects of captivity on the mammalian gut microbiome. Integr Comp Biol. 2017;57:690–704.PubMed 
PubMed Central 

Google Scholar 
92.Frankel JS, Mallott EK, Hopper LM, Ross SR, Amato KR. The effect of captivity on the primate gut microbiome varies with host dietary niche. Am J Primatol. 2019;81:e23061.PubMed 

Google Scholar 
93.Dührkop K, Nothias L-F, Fleischauer M, Reher R, Ludwig M, Hoffmann MA, et al. Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra. Nat Biotechnol. 2021;39:462–71.PubMed 

Google Scholar 
94.Tripathi A, Vázquez-Baeza Y, Gauglitz JM, Wang M, Dührkop K, Nothias-Esposito M, et al. Chemically informed analyses of metabolomics mass spectrometry data with Qemistree. Nat Chem Biol. 2021;17:146–51.CAS 
PubMed 

Google Scholar 
95.Hehemann J-H, Correc G, Barbeyron T, Helbert W, Czjzek M, Michel G. Transfer of carbohydrate-active enzymes from marine bacteria to Japanese gut microbiota. Nature. 2010;464:908–12.CAS 
PubMed 

Google Scholar 
96.Pudlo NA, Pereira GV, Parnami J, Cid M, Markert S, Tingley JP, et al. Extensive transfer of genes for edible seaweed digestion from marine to human gut bacteria. bioRxiv. 2020. https://doi.org/10.1101/2020.06.09.142968.97.Scheline RR Metabolism of higher terpenoids. CRC Handbook of Mammalian Metabolism of Plant Compounds. CRC Press; 1991. pp 197–241.98.Saha JR, Butler VP Jr, Neu HC, Lindenbaum J. Digoxin-inactivating bacteria: identification in human gut flora. Science. 1983;220:325–7.CAS 
PubMed 

Google Scholar 
99.Koppel N, Bisanz JE, Pandelia M-E, Turnbaugh PJ, Balskus EP. Discovery and characterization of a prevalent human gut bacterial enzyme sufficient for the inactivation of a family of plant toxins. Elife. 2018;7:e33953.PubMed 
PubMed Central 

Google Scholar 
100.Louis P, Flint HJ. Formation of propionate and butyrate by the human colonic microbiota. Environ Microbiol. 2017;19:29–41.CAS 
PubMed 

Google Scholar 
101.Ridlon JM, Kang DJ, Hylemon PB, Bajaj JS. Bile acids and the gut microbiome. Curr Opin Gastroenterol. 2014;30:332–8.PubMed 
PubMed Central 

Google Scholar 
102.Begley M, Gahan CGM, Hill C. The interaction between bacteria and bile. FEMS Microbiol Rev. 2005;29:625–51.CAS 
PubMed 

Google Scholar 
103.Lee M-T, Le HH, Johnson EL. Dietary sphinganine is selectively assimilated by members of the mammalian gut microbiome. J Lipid Res. 2021;62:100034.CAS 
PubMed 
PubMed Central 

Google Scholar 
104.Johnson EL, Heaver SL, Waters JL, Kim BI, Bretin A, Goodman AL, et al. Sphingolipids produced by gut bacteria enter host metabolic pathways impacting ceramide levels. Nat Commun. 2020;11:2471.CAS 
PubMed 
PubMed Central 

Google Scholar  More

1.Gaston, K. J. et al. Personalised ecology. Trends Ecol. Evol. 33, 916–925 (2018).
Google Scholar 
2.Soga, M. & Gaston, K. J. The ecology of human–nature interactions. Proc. R. Soc. B 287, 20191882 (2020).
Google Scholar 
3.Leong, M., Dunn, R. R. & Trautwein, M. D. Biodiversity and socioeconomics in the city: a review of the luxury effect. Biol. Lett. 14, 20180082 (2018).
Google Scholar 
4.Mace, G. M. Whose conservation? Science 345, 1558–1560 (2014).CAS 

Google Scholar 
5.Soga, M. & Gaston, K. J. Extinction of experience: the loss of human–nature interactions. Front. Ecol. Environ. 14, 94–101 (2016).
Google Scholar 
6.Hartig, T., Mitchell, R., De Vries, S. & Frumkin, H. Nature and health. Annu. Rev. Public Health 35, 207–228 (2014).
Google Scholar 
7.Chippaux, J. P. Incidence and mortality due to snakebite in the Americas. PLoS Negl. Trop. Dis. 11, e0005662 (2017).
Google Scholar 
8.Markevych, I. et al. Exploring pathways linking greenspace to health: theoretical and methodological guidance. Environ. Res. 158, 301–317 (2017).CAS 

Google Scholar 
9.Bratman, G. N. et al. Nature and mental health: an ecosystem service perspective. Sci. Adv. 5, eaax0903 (2019).
Google Scholar 
10.Marselle, M. R. et al. Pathways linking biodiversity to human health: a conceptual framework. Environ. Int. 150, 106420 (2021).CAS 

Google Scholar 
11.Hanski, I. et al. Environmental biodiversity, human microbiota, and allergy are interrelated. Proc. Natl Acad. Sci. USA 109, 8334–8339 (2012).CAS 

Google Scholar 
12.Rook, G. A. Regulation of the immune system by biodiversity from the natural environment: an ecosystem service essential to health. Proc. Natl Acad. Sci. USA 110, 18360–18367 (2013).CAS 

Google Scholar 
13.Tzoulas, K. et al. Promoting ecosystem and human health in urban areas using Green Infrastructure: a literature review. Landsc. Urban Plann. 81, 167–178 (2007).
Google Scholar 
14.Balmford, A. et al. A global perspective on trends in nature-based tourism. PLoS Biol. 7, e1000144 (2009).
Google Scholar 
15.Nisbet, E. K., Zelenski, J. M. & Murphy, S. A. The nature relatedness scale: linking individuals’ connection with nature to environmental concern and behavior. Environ. Behav. 41, 715–740 (2009).
Google Scholar 
16.Chawla, L. Childhood nature connection and constructive hope: a review of research on connecting with nature and coping with environmental loss. People Nat. 2, 619–642 (2020).
Google Scholar 
17.Shanahan, D. F. et al. Nature-based interventions for improving health and wellbeing: the purpose, the people and the outcomes. Sports 7, 141 (2019).
Google Scholar 
18.Chapman, B. K. & McPhee, D. Global shark attack hotspots: identifying underlying factors behind increased unprovoked shark bite incidence. Ocean Coast. Manag. 133, 72–84 (2016).
Google Scholar 
19.Penteriani, V. et al. Human behaviour can trigger large carnivore attacks in developed countries. Sci. Rep. 6, 20552 (2016).CAS 

Google Scholar 
20.Ives, C. D. et al. Reconnecting with nature for sustainability. Sustain. Sci. 13, 1389–1397 (2018).
Google Scholar 
21.Cox, D. T. C. & Gaston, K. J. Human-nature interactions and the consequences and drivers of provisioning wildlife. Phil. Trans. R. Soc. B 373, 20170092 (2018).
Google Scholar 
22.Michie, S., Van Stralen, M. M. & West, R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement. Sci. 6, 42 (2011).
Google Scholar 
23.Soga, M., Evans, M. J., Cox, D. T. & Gaston, K. J. Impacts of the COVID‐19 pandemic on human–nature interactions: pathways, evidence and implications. People Nat. 3, 518–527 (2021).
Google Scholar 
24.Shaw, L. M., Chamberlain, D. & Evans, M. The house sparrow Passer domesticus in urban areas: reviewing a possible link between post-decline distribution and human socioeconomic status. J. Ornith. 149, 293–299 (2008).
Google Scholar 
25.Gaston, K. J. & Evans, K. L. Birds and people in Europe. Proc. R. Soc. B 271, 1649–1655 (2004).
Google Scholar 
26.Soga, M. & Gaston, K. J. Shifting baseline syndrome: causes, consequences, and implications. Front. Ecol. Environ. 16, 222–230 (2018).
Google Scholar 
27.Pauly, D. Anecdotes and the shifting baseline syndrome of fisheries. Trends Ecol. Evol. 10, 430 (1995).CAS 

Google Scholar 
28.Kellert, S. R. & Wilson, E. O. The Biophilia Hypothesis (Island, 1993).29.Balling, J. D. & Falk, J. H. Development of visual preference for natural environments. Environ. Behav. 14, 5–28 (1982).
Google Scholar 
30.Ulrich, R. S. in The Biophilia Hypothesis (eds Kelbert, S. R. & Wilson, E. O.) 73–137 (Island, 1993).31.Fukano, Y. & Soga, M. Why do so many modern people hate insects? The urbanization-disgust hypothesis. Sci. Total Environ. 777, 146229 (2021).CAS 

Google Scholar 
32.Pergams, O. R. & Zaradic, P. A. Is love of nature in the US becoming love of electronic media? 16-year downtrend in national park visits explained by watching movies, playing video games, internet use, and oil prices. J. Environ. Manag. 80, 387–393 (2006).
Google Scholar 
33.Kesebir, S. & Kesebir, P. A growing disconnection from nature is evident in cultural products. Perspect. Psychol. Sci. 12, 258–269 (2017).
Google Scholar 
34.Soga, M. et al. How can we mitigate against increasing biophobia among children during the extinction of experience? Biol. Conserv. 242, 108420 (2020).
Google Scholar 
35.Soga, M., Yamanoi, T., Tsuchiya, K., Koyanagi, T. F. & Kanai, T. What are the drivers of and barriers to children’s direct experiences of nature? Landsc. Urban Plann. 180, 114–120 (2018).
Google Scholar 
36.Pett, T. J., Shwartz, A., Irvine, K. N., Dallimer, M. & Davies, Z. G. Unpacking the people–biodiversity paradox: a conceptual framework. BioScience 66, 576–583 (2016).
Google Scholar 
37.Balding, M. & Williams, K. J. Plant blindness and the implications for plant conservation. Conserv. Biol. 30, 1192–1199 (2016).
Google Scholar 
38.Gerl, T., Randler, C. & Neuhaus, B. J. Vertebrate species knowledge: an important skill is threatened by extinction. Int. J. Sci. Educ. 43, 928–948 (2021).
Google Scholar 
39.Cheng, J. C. H. & Monroe, M. C. Connection to nature: children’s affective attitude toward nature. Environ. Behav. 44, 31–49 (2012).
Google Scholar 
40.Pyle, R. M. The Thunder Tree: Lessons from an Urban Wildland (Houghton Mifflin, 1993).41.Wells, N. M. & Lekies, K. S. Nature and the life course: pathways from childhood nature experiences to adult environmentalism. Child. Youth Environ 16, 41663 (2006).
Google Scholar 
42.Wilson, E. O. in The Biophilia Hypothesis (Island, 1993).43.Nisbet, E. K., Zelenski, J. M. & Murphy, S. A. Happiness is in our nature: exploring nature relatedness as a contributor to subjective well-being. J. Happiness Stud. 12, 303–322 (2011).
Google Scholar 
44.Lin, B. B. et al. How green is your garden? Urban form and socio-demographic factors influence yard vegetation, visitation, and ecosystem service benefits. Landsc. Urban Plann. 157, 239–246 (2017).
Google Scholar 
45.Uitto, A., Juuti, K., Lavonen, J. & Meisalo, V. Students’ interest in biology and their out-of-school experiences. J. Biol. Educ. 40, 124–129 (2006).
Google Scholar 
46.Pretty, J. et al. Green exercise in the UK countryside: effects on health and psychological well-being, and implications for policy and planning. J. Environ. Plann. Manag. 50, 211–231 (2007).
Google Scholar 
47.Strachan, D. P. Family size, infection and atopy: the first decade of the ‘hygiene hypothesis’. Thorax 55, S2–S10 (2000).
Google Scholar 
48.Mills, J. G. et al. Urban habitat restoration provides a human health benefit through microbiome rewilding: the Microbiome Rewilding Hypothesis. Restor. Ecol. 25, 866–872 (2017).
Google Scholar 
49.Ulrich, R. S. et al. Stress recovery during exposure to natural and urban environments. J. Environ. Psychol. 11, 201–230 (1991).
Google Scholar 
50.Kaplan, R. & Kaplan, S. The Experience of Nature: A Psychological Perspective (Cambridge Univ. Press, 1989).51.Fuller, R. A., Irvine, K. N., Devine-Wright, P., Warren, P. H. & Gaston, K. J. Psychological benefits of greenspace increase with biodiversity. Biol. Lett. 3, 390–394 (2007).
Google Scholar 
52.Kuo, F. E. Nature-deficit disorder: evidence, dosage, and treatment. J. Policy Res. Tour. Leis. Events 5, 172–186 (2013).
Google Scholar 
53.Louv, R. Last Child in the Woods (Algonquin Books, 2005).54.Mygind, L. et al. Mental, physical and social health benefits of immersive nature-experience for children and adolescents: a systematic review and quality assessment of the evidence. Health Place 58, 102136 (2019).
Google Scholar 
55.Nyhus, P. J. Human–wildlife conflict and coexistence. Annu. Rev. Environ. Res. 41, 143–171 (2016).
Google Scholar 
56.von Döhren, P. & Haase, D. Ecosystem disservices research: a review of the state of the art with a focus on cities. Ecol. Indic. 52, 490–497 (2015).
Google Scholar 
57.Geffroy, B., Samia, D. S., Bessa, E. & Blumstein, D. T. How nature-based tourism might increase prey vulnerability to predators. Trends Ecol. Evol. 30, 755–765 (2015).
Google Scholar 
58.Richardson, M. et al. The green care code: how nature connectedness and simple activities help explain pro‐nature conservation behaviours. People Nat. 2, 821–839 (2020).
Google Scholar 
59.Van der Wal, A. J., Schade, H. M., Krabbendam, L. & Van Vugt, M. Do natural landscapes reduce future discounting in humans? Proc. R. Soc. B 280, 20132295 (2013).
Google Scholar 
60.Zelenski, J. M., Dopko, R. L. & Capaldi, C. A. Cooperation is in our nature: nature exposure may promote cooperative and environmentally sustainable behavior. J. Environ. Psychol. 42, 24–31 (2015).
Google Scholar 
61.Barua, M., Bhagwat, S. A. & Jadhav, S. The hidden dimensions of human-wildlife conflict: health impacts, opportunity and transaction costs. Biol. Conserv. 157, 309–316 (2013).
Google Scholar  More

Our results show that Chl a alone is not an adequate proxy for prey growth rates in dilution grazing experiments when mixoplankton are present5,10. Chlorophyll is, in any case, a poor proxy for phototrophic plankton biomass31 because of inter-species variations, and also for the photoacclimation abilities of some species (for which very significant changes can occur within a few hours). The problem extends to the involvement of mixoplanktonic prey and grazers. Nevertheless, even very recent studies continue to rely on this parameter for quantifications of grazing despite acknowledging the dominance, both in biomass and abundance, of mixoplanktonic predators in their system30. Moreover, the detailed analysis of the species-specific dynamics revealed that different prey species are consumed at very different rates. In our experiments, and contrary to expectations (see32,33, and Fig. S1 in the Supplementary Information), C. weissflogii was only actively ingested in the ciliate experiment and, according to the results from the control bottles (Table 2), not by M. rubrum (see Fig. 4 and Fig. S1a).Certainly, it is not the first time that a negative selection against diatoms has been seen; for example, Burkill et al.34 noticed that diatoms were less grazed by protist grazers than other phytoplankton species, as assessed by a dilution technique paired with High-Performance Liquid Chromatography for pigment analysis. Using the same method, Suzuki et al.35 reported that diatoms became the dominant phytoplankton group, which suggests that other groups were preferentially fed upon. Calbet et al.36, in the Arctic, also found only occasional grazing over the local diatoms. In our study, diatoms were not only not consumed, but the presence of dinoflagellates appeared to contribute to their growth (Fig. 4), this relationship being partly dependent on the concentration of the predator (see Fig. 2c, d). This result could be a direct consequence of assimilation and use of compounds (e.g.,37,38) released by microplankton such as ammonium (e.g.,39,40) and urea (e.g.,41), which were not supplied in the growth medium, but which would have supported prey growth. Alternatively, this unexpected outcome may have been a consequence of the selective ingestion of R. salina by the two predators, relieving the competition for nutrients and light and resulting in a higher growth rate of the diatom in the presence of the predators. We cannot rule out the fact that diatoms sink faster than flagellates which, as the bottles were not mixed during most of the incubation period (although gently mixed at every sampling point), may have also involuntarily decreased ingestion rates on C. weissflogii. Still, one C. weissflogii cell contains, on average, ca. 2.5 times more Chl a than one R. salina cell (initial value excluded, see Table 3). Taken together with the preference for R. salina it is not surprising that the proportion of total Chl a represented by the diatoms increased over time, in particular in the L/D treatment (Figs. 6a, c and 7a, c).Table 3 Chl a content (pg Chl a cell−1) of the target species at each sampling point as calculated from the control bottles.Full size tableAnother factor clearly highlighted by our experiments, is that protozooplankton themselves contribute a significant portion of the total chlorophyll of the system (due to ingested Chl a), in particular at the beginning of the incubation (see Figs. 6 and 7); this being invariably ignored in a traditional dilution experiment. The high Chl a detected inside the protozooplanktonic grazers at the beginning of the incubations could suggest that the system was initially not in equilibrium, and that this was the result of superfluous feeding (e.g.,42). This would, nevertheless, be surprising since we required ca. 1 h to collect the initial samples (t = 0 h) after joining all the organisms together (see the section “Dilution grazing experiments” in the “Methods” section); previous studies, like the one on G. dominans and Oxyrrhis marina by Calbet et al.42, showed that the hunger response and consequent vacuole replenishment occurred in ca. 100 min for very high prey concentrations and it is expected to decrease at lower prey concentrations as the ones used in our study. Therefore, even if one assumes that the first 4 h of incubation are a result of superfluous feeding, after 24 h, the “estimated”, “observed”, and “from dilution slope” grazing estimates are not significantly different to those displayed in Fig. 5 (P  > 0.05 in all instances) and, therefore, we can assume that the hunger response was likely irrelevant (e.g.,43) and did not mask our results. In any case, as stated before, an actual field grazing dilution experiment also suffers from similar problems, because grazers and prey are suddenly diluted and not pre-adapted to distinct food concentrations. Nevertheless, this is not novel information, since Chl a and its degradation products have been found inside several protozooplankton species from different phylogenetic groups immediately after feeding44 and even after some days without food45. An increase in intracellular Chl a concentrations immediately after feeding has also been found in mixoplankton46,47, on which this increase is derived both from ingested prey as well as from new synthesis of their own Chl a. Additionally, several experiments with Live Fluorescently Labelled Algae (LFLA) show that predators (irrespective of their trophic mode) seem to maximise the concentration of intracellular prey shortly after the initiation of the incubation (e.g.,48; Ferreira et al., submitted). Indeed, some authors have even been able to measure photosynthesis in protozooplankton, like the ciliates Mesodinium pulex49 and Strombidinopsis sp.50.The fact that Chl a is a poor indicator of phytoplankton biomass and the inherent consequences discussed so far can be solved by the quantification of the prey community abundance (e.g.,51) by microscopy or by the use of signature pigments for each major phytoplankton group. The latter method, however, is not as thorough as the former, since rare are the cases where one pigment is exclusively associated with a single group of organisms (see52 and references therein). In any case, any pigment-based proxy is subject to the same problems, as identified by Kruskopf & Flynn31. Irrespective of the quantification method, it has been made evident that the different algae are consumed at different rates (e.g., pigments10,34,35; microscopy5,36).Prey selection in protistan grazers is a common feature (e.g.,23,26,27,28). Given the diversity of grazers in natural communities and the array of preferred prey that each particular species possesses, it is logical to think that dilution experiments will capture the net community response properly. Likewise, grazers interact with each other through toxins, competition, and intraguild predation among other factors. An example of intraguild predation could be the observed on K. armiger by G. dominans (see Figs. 2f and 4 and Table 1), which caused an average loss of ca. 18.72 pg of K. armiger carbon per G. dominans per hour in the D treatment. Interestingly, in the same treatment, a slight negative effect of K. armiger on its predator G. dominans can also be deduced (i.e., positive g, Table 1), resulting in an average loss of ca. 0.33 pg G. dominans carbon per K. armiger per hour. This could be a consequence of algal toxins, since K. armiger is a known producer of karmitoxin22, whose presence may have negative effects even on metazoan grazers21. Regarding ciliates, none of the species used is a known producer of toxic compounds, which suggests that the average loss of ca. 1.25 pg M. rubrum carbon per hour in the D treatment was due to S. arenicola predation. Altogether, it seems clear from our data that intraguild predation cannot be ignored when analysing dilution experiments (Fig. 4). Furthermore, our results clearly show that single functional responses cannot be used to extrapolate community grazing impacts, as evidenced by the differences in estimated and measured ingestion rates based on the disappearance of prey in combined grazers experiments (Fig. 5). Nevertheless, this is a relatively common procedure (e.g.,53 and references therein). Often in modelling approaches, individual predator’s functional responses have been used to extrapolate prey selectivity and community grazing responses27; in reality complex prey selectivity functions are required to satisfactorily describe prey selectivity and inter-prey allelopathic interactions54.It is, however, also evident that the measured ingestion rates in combined grazers experiments were not the same as those calculated from the slope of the dilution grazing experiment. This raises the question of why was that the case. It is well known that phytoplankton cultures, when extremely diluted, show a lag phase of different duration55 which has been attributed to the net leakage of metabolites56. Assuming that the duration of the lag phase will be dependent on the level of dilution, it seems reasonable to deduce that after ca. 24 h the instantaneous growth rates (µ) in the most diluted treatments will be lower than that of the undiluted treatments. This has consequences, not only for the estimated prey growth rates but also for the whole assessment of the grazing rate, due to the flattening of the regression line (i.e., the decrease in the computed growth rate). This artefact may be more evident in cultures acclimated to very particular conditions (as the laboratory cultures used in this study) than in nature.Another important finding of our research is the importance of light on the correct expression of the feeding activity by both mixoplankton and protozooplankton. We noticed that irrespective of the light conditions, all species exhibited a diurnal feeding rhythm (R. salina panels in Figs. 2 and 3), which is in accordance with earlier observations on protists (e.g.,29,57,58). The presence of light typically increased the ingestion rates. Additionally, the ingestion rates differed during the night period between L/D and D treatments, which implies that receiving light during the day is also vital in modulating the night behaviour of protoozoo- and mixoplankton. In particular, mixoplankton grazing is usually affected by light conditions, typically increasing (e.g.,32,59), but also sometimes decreasing(e.g.,60) in the presence of light. Different irradiance levels can also affect the magnitude of ingestion rates both in protozoo- and mixoplankton (see61 and references therein).For those reasons, we hoped for a rather consistent pattern among our protists that would help us discriminate mixoplankton in dilution grazing experiments. As a matter of fact, based on the results from Arias et al.29, we expected that in the dinoflagellate experiment, the D treatment would have inhibited only the grazing of K. armiger, enabling a simple discrimination between trophic modes. The reality did not meet the expectations since the day and night-time carbon-specific ingestion rates (as assessed using the control bottles, Table 2) of K. armiger were respectively higher and equal than those of G. dominans. Conversely, in the ciliate experiment, protozooplankton were the major grazers in our incubations regardless of the day period and light conditions. This response was not as straightforward as one would expect it to be because M. rubrum has been recently suggested to be a species complex containing at least 7 different species (62 and references therein), which hinders any possible conjecture on their grazing impact. Indeed, the uneven responses found between and within trophic modes precluded such optimistic hypothetical procedure.The D treatment in the present paper illustrated the importance of mimicking natural light conditions, a factor also addressed in the original description of the technique by Landry and Hassett1. It is crucial for the whole interpretation of the dilution technique that incubations should be conducted in similar light (and temperature) conditions as the natural ones to allow for the continued growth of the phototrophic prey. However, here we want to stress another aspect of the incubations: should they start during the day or the night? Considering our (and previous) results on diel feeding rhythms, and on the contribution of each species to the total Chl a pool, it is clear that different results will be obtained if the incubations are started during the day or the night. Besides, whether day or night, organisms are also likely to be in a very different physiological state (either growing or decreasing). Therefore, we recommend that dilution experiments conducted in the field should always be started at the same period of the day to enable comparisons (see also Anderson et al.14 for similar conclusions on bacterivory exerted by small flagellates). Ideally, incubations would be started at different times of the day to capture the intricacies of the community dynamics on a diel cycle. Nevertheless, should the segmented analysis be impossible, we argue that the right time to begin the incubations would be during the night, as this is the time where ingestion rates by protozooplankton are typically lower (e.g.,29,57,58, this study) and would, consequently, reduce their quota of Chl a in the system.Lastly, we want to stress that we are aware that our study does not represent natural biodiversity because our experiments were conducted in the laboratory with a few species. Nevertheless, we attempted to use common species of wide distribution for each major group of protists to provide a better institutionalisation of our conclusions. Further to the choice of predator and prey is their concentrations and proportions. Being a laboratory experiment designed to understand fundamental mechanisms within a dilution grazing experiment, we departed from near saturating food conditions from where we started the dilution series. In nature, the concentrations that we used may be high but are not unrealistic, and actually lower than in many bloom scenarios. We included diatoms at high concentrations, even knowing that they are not the preferred prey of most grazers34, because diatoms are very abundant in many natural ecosystems and to stress the point of food selection within the experiment. For sure, using different proportions of prey would have rendered different results. However, as previously mentioned, our aim was not to seek flaws in the dilution technique, but to understand the role of mixoplankton in these experiments and the complex trophic interactions that may occur within. Ultimately, with our choice of prey and their concentrations, we have proven that when there is no selection for a massively abundant prey, the use of Chl a as a proxy for community abundances may underestimate actual grazing rates.Some other aspects of our experiments may also be criticised because they do not fully match a standard dilution experiment. For instance, we manipulated light, adding complexity to the study. However, this manipulation enabled the deepening into the drivers of the mixoplanktonic and protozooplanktonic grazing responses. Another characteristic, perhaps awkward, of our study is that we allowed the grazers to deplete their prey before starting the experiment. One may argue this procedure does not mimic the natural previous trophic history a grazer may have in nature. Yet, in nature, when facing a dilution experiment, it is impossible to ascertain whether the organisms are encountering novel prey or not. Indeed, they (prey and predator) could have just migrated into such conditions, or be subject to famine, or just moved from a food patch. In any case, it is true that a consistent “hunger response” would have affected our initial grazing values, biasing grazing rate estimates. To overcome this artefact, we let the grazers feed for about one hour before starting the actual dilution assay (see the “Methods” section). From that point on, any dilution is, in fact, an abrupt alteration of the food scenario, which is likely more important than the previous trophic history of the grazer.In summary, with these laboratory experiments, we have presented evidence calling for a revision of the use of chlorophyll in dilution grazing experiments5,10, and we have highlighted the need to observe the organismal composition of both initial and final communities to better understand the dynamics during the dilution grazing experiments51. This approach will not incorporate mixoplanktonic activity into the dilution technique per se however if combined with LFLA (see5,17), a semi-quantitative approach to disentangle the contribution of mixoplankton to community grazing could be achieved (although not perfect). An alternative (and perhaps more elegant) solution could be the integration of the experimental technique with in silico modelling. The modelling approaches of the dilution technique have already been used, for example, to disentangle niche competition63 and to explore nonlinear grazer responses20. We believe that our experimental design and knowledge of the previously indicated data could be of use for the configuration of a dilution grazing model, which could then be validated in the field (and, optimistically, coupled to the ubiquitous application of the dilution technique across the globe). We cannot guarantee that having a properly constructed model that mimics the dilution technique will be the solution to the mixoplankton paradigm. However, it may provide a step towards that goal as it could finally shed much-needed light on the mixo- and heterotrophic contributions to the grazing pressure of a given system. To quote from the commentary of Flynn et al.6, it could provide the answer to the question of whether mixoplankton are de facto “another of the Emperor’s New Suit of Clothes” or, “on the other hand (…) collectively worthy of more detailed inclusion in models”. More

Systemic co-infections of commensal Pseudomonas with an individual pathogenTo examine the ability of commensal Pseudomonas strains to protect host plants from members of a pathogenic Pseudomonas lineage, we made use of a local isolate collection [16]. We henceforth refer to an operational taxonomic unit (OTU) as reported in that study as “ATUE” (isolates from Around TUEbingen), and following previous findings [16, 17], we refer to the lineage ATUE5 as pathogenic, and to all non-ATUE5 lineages as commensals.We grew plants on MS agar and monitored plant growth and health by extracting the number of green pixels from images over time (illustration in Fig. 1A). Green pixel count and rosette fresh weight were strongly correlated (Supplementary Fig. S1; R2 = 0.92, p value  More

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