Philippa Kaur

Evaluation of the lithological, geochemical, and fossil diatom evidenceWe obtained a short, 270-cm sediment core ( 250 m40. Lake Chalco lies in the central region of the Trans-Mexican Volcanic Belt (Fig. 1). It is bounded to the north by the Sierra de Santa Catarina, to the east by the Sierra Nevada (Volcanoes Popocatepetl, Iztaccíhuatl, Tláloc and Telapón), and to the west, by Volcano Teuhtli, which is the closest volcano to the waterbody (~ 6.5 km)41. Lake sediments are composed mainly of clays that have been described as impermeable. The Lake Chalco Basin, however, is a highly complex system and seismically active. Therefore, the presence of active fractures during the Holocene is possible. Active fractures may result in inflow from the deep aquifer. Historical data indicate the existence of freshwater springs in upper parts of the aquifer, mainly at the eastern piedmont42. For instance, mineral and thermal waters at Peñón del los Baños, near the Mexico City airport (33 km from Lake Chalco), have been reported since Aztec times around AD 1,325. This spring is associated with a system of seismically active fractures. No thermal waters have been reported in modern Lake Chalco, however phreatomagmatic activity ( > 100,000 years BP) from the Xico Volcano has been documented43. Recent studies showed that the water table position changes from the upper parts of the watershed to central Texcoco, 45 km from Lake Chalco. In this study four components of the flow system were identified, including waters of recent infiltration and local circulation, evidencing intermediate chemical evolution, and waters more chemically evolved of large flow trajectories and of deep circulation44.The lithology of the studied sediment sequence is as follows: (1) Sediments from 270 to 250 cm are characterized by lapilli, ash and black to light brown silty ashes (massive to stratified); (2) Sediments in the interval 250–235 cm are composed of diatomite (yellow); (3) From 235 to 200 cm, sediments are characterized by massive black to brown sandy silts (brown); (4) From 200 to 70 cm, sandy brown to reddish, banded laminated silts, with scattered or banded pumice fragments (red) are present; (5) Sediments from 70 to 60 cm are black silty sands with organic material; (6) From 60 to 50 cm sediments are sandy brown to reddish, laminated silt, with scattered or banded pumice fragments (red); (7) Sediments from 50 to 40 cm are characterized by lapilli, ash and black to light brown silty ashes, massive to stratified, and (8) uppermost sediments from 40 to 0 cm are black silty sands with organic material (Fig. 3)36.Figure 3Taxonomic diversity revealed by metagenomic and fossil diatom analysis, and geochemical variables from the Lake Chalco Holocene sediment sequence. Each horizontal bar represents a collected sample, with the exception of the upper row, which shows the average of surface samples S1 (0 cm, i.e., modern) and S2 (0 cm, i.e., modern) (Supplementary Table S1). The lithology of the 270-cm sediment sequence is shown in the first column. The Upper Toluca Pumice (UTP) is represented as tephra underneath the first column (from left to right). Taxonomic diversity is depicted as the relative abundance of phyla Bacteria (green), Archaea (pink), and Eukarya (blue) (columns 2–4). Percent values correspond to the diversity of peptide sequences corresponding to each domain. Geochemical variables related to biological processes and past conditions are shown in columns 5–7. Results of fossil diatom analysis are shown in column 8. Dark horizontal lines show the boundaries for each delimited paleoenvironmental zone: (1) freshwater, (2) hyposaline and (3) subsaline. Edited in CorelDRAW 2020 version 22.0.Full size imageElemental geochemistry and fossil diatoms in sediment cores can be used as indicators of past wet and dry climate intervals. We measured geochemical indicators including element concentrations and ratios, and Total Organic Carbon (TOC) throughout the core (Supplementary Table S1). Total organic carbon is an important component of sediments and soils and can be used to assess the environmental status of terrestrial and aquatic ecosystems45. Maximum TOC values characterize the period of hyposaline conditions. The Mn/Fe ratio, often used to track past O2 content in bottom waters and changing redox conditions, was used as a proxy for water-column oxygen concentration46. The Mn/Fe ratios display highest values at depths of 200, 150 and 60 cm, suggesting periods of permanent anoxia during warmer conditions and excessive nutrient inputs47. The Fe/Ti ratio provides information about fluvial sediment sources. Changes in the abundance of iron oxides can be used to infer fluctuations in inputs of land‐derived detrital material48. We observed increasing Fe/Ti ratio values in superficial layers, and highest values at 100 cm. We performed cluster analysis based on Euclidean distance which revealed three groups of samples (Supplementary Fig. S1).We identified three zones in the Lake Chalco Holocene sediment sequence, based on geochemical analysis and diatom assemblages, which reflect different paleoenvironmental conditions: (1) a cool, freshwater lake (235–210 cm), (2) a warm, hyposaline lake (185–60 cm), and (3) a temperate, subsaline lake (50–0 cm) (Figs. 3, 5, Supplementary Fig. S1, Table S1).Fossil diatom assemblages provide information about past environmental changes and water quality7,49. Fossil diatom analysis, along with knowledge of species ecological preferences, enables inference of past limnological variables such as temperature, salinity, pH, electrical conductivity, and phosphorus concentration7. Inferences from our fossil diatom record concur with an earlier diatom-based paleoclimate study from Lake Chalco. Our studies revealed that during the last deglacial (~ 19,500–11,500 cal years BP), conditions were colder and much wetter than present. Assemblages are dominated by small araphid diatoms Gomphonema affine and Cocconeis placentula (Fig. 3). From 11,500 to 4,500 cal years BP, Lake Chalco was characterized by hyposaline conditions, with higher evaporation rates until ~ 6,500 cal years BP. Typical diatoms taxa include Anomoeoneis costata, Halamphora veneta and small araphids. After ~ 6,500 cal years BP, salinity in Lake Chalco declined, mean annual precipitation increased slightly, and summer insolation, seasonality, and evaporation decreased7. Assemblages are composed of H. veneta, Nitzschia frustulum, Cyclotella meneghiniana and small araphid diatoms.Meta-taxonomic analysis of Prokaryote and Eukaryote diversityOur metagenomic analysis identified 36,722 OTUs (genera) in the sediments of Lake Chalco. Among those genera, 81% correspond to bacteria (29,818 ± 106 identified to genera [ig]), 15% to Archaea (5,710 ± 118 ig), 3% to Eukarya (1,147 ± 6 ig), and 76. Iron is considered a potentially harmful element (PHE), which may be indicative of human-mediated contamination. For instance, high Fe concentrations in surficial sediments could be related to inputs of clastic sediments, and often reflect agricultural activities77. We determined 13 plant genera, five belonging to the family Poaceae (58%), including the genus Zea (corn), known to have been cultivated and consumed by early settlers78, and Oryza, a fast-growing weed that is indicative of human-mediated habitat disturbance. Twelve microscopic fungal genera were observed, including taxa that are pathogenic on wheat and rice (Gibberella and Cladochytrium), plants, keratin, and flies (Supplementary Fig. S10)79, and a protozoan that is pathogenic in humans (Giardia). We found high abundances of the family Culicidae (18%) (Supplementary Fig. S10) during the period of human occupation. The subsaline zone displays 290 unique pfams and the highest number of representatives of potassium metabolism throughout the entire Holocene (Supplementary Fig. S4B). The abundance of genes related to Cyanobacteria in this zone is much lower (9%), in contrast with findings from the Blastp against MetaProt database of the freshwater (30%) and hyposaline zones (60%) (Supplementary Fig. S10, Table S3).Our findings suggest that the biota in and around Lake Chalco during the Holocene responded mainly to changes in temperature, salinity, and trophic state, reflecting climate and human impacts over the last 6,000 years. This implies landscape modifications, agricultural activities and accelerated lake eutrophication. Furthermore, prokaryotic assemblages revealed gradual deposition of microbial communities capable of anaerobic fermentation of organic material and methanogenesis, as well as evidence for volcanic activity, inferred from the metabolic potential for sulfur cycling in the deeper zones (hyposaline and freshwater). This study generated information on Neotropical Prokaryote and Eukaryote diversity and microbial metabolic pathways during the Holocene. Nevertheless, we recommend that future studies focus on detailed characterization of microbial substrates and constrain post-depositional processes. Such studies should also consider selection processes that result from gradual depletion of substrates during burial52,59,63. Finally, we highlight the importance of including additional geochemical measures, such as porewater chemistry80. and sedimentology81 in future studies and measuring biomass by qPCR assay. More

Study site and soil samplingThe soil was collected at 5 − 20 cm (Ap horizon) from an agricultural field in Southern Germany (Freising, Bavaria, 48°23’53.8“N, 11°38’39.7“E) in December 2017. The sampling area is situated within the lower Bavarian upland, and characterized by a mean annual temperature of 7.8 °C and mean annual precipitation of 786 mm. The soil type is a Cambisol (silty clay loam; 32% clay, 53% silt, and 14% sand) with a considerable amount of loess mixed with underlying Neogene sandy sediments. The soil was selected to represent a widely distributed soil type and land use. The collected soil was oven-dried (2 days, 40 °C), sieved ( More

1.Rohwer F, Breitbart M, Jara J, Azam F, Knowlton N. Diversity of bacteria associated with the Caribbean coral Montastraea franksi. Coral Reefs. 2001;20:85–91.Article 

Google Scholar 
2.Kellogg CA. Tropical Archaea: diversity associated with the surface microlayer of corals. Mar Ecol Prog Ser. 2004;273:81–8.CAS 
Article 

Google Scholar 
3.Wegley L, Edwards R, Rodriguez-Brito B, Liu H, Rohwer F. Metagenomic analysis of the microbial community associated with the coral Porites astreoides. Environ Microbiol. 2007;9:2707–19.CAS 
PubMed 
Article 

Google Scholar 
4.Thurber RV, Payet JP, Thurber AR, Correa AMS. Virus–host interactions and their roles in coral reef health and disease. Nat Rev Microbiol. 2017;15:205–16.CAS 
PubMed 
Article 

Google Scholar 
5.Siboni N, Rasoulouniriana D, Ben-Dov E, Kramarsky-Winter E, Sivan A, Loya Y, et al. Stramenopile microorganisms associated with the massive coral favia sp. J Eukaryot Microbiol. 2010;57:236–44.CAS 
PubMed 

Google Scholar 
6.Harvell CD. Emerging marine diseases-climate links and anthropogenic factors. Science. 1999;285:1505–10.CAS 
PubMed 
Article 

Google Scholar 
7.Reshef L, Koren O, Loya Y, Zilber-Rosenberg I, Rosenberg E. The coral probiotic hypothesis. Environ Microbiol. 2006;8:2068–73.CAS 
PubMed 
Article 

Google Scholar 
8.Ainsworth TD, Thurber RV, Gates RD. The future of coral reefs: a microbial perspective. Trends Ecol Evol. 2010;25:233–40.PubMed 
Article 

Google Scholar 
9.Welsh RM, Rosales SM, Zaneveld JR, Payet JP, McMinds R, Hubbs SL, et al. Alien vs. predator: bacterial challenge alters coral microbiomes unless controlled by Halobacteriovorax predators. PeerJ. 2017;5:e3315.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
10.Rosenberg E, Koren O, Reshef L, Efrony R, Zilber-Rosenberg I. The role of microorganisms in coral health, disease and evolution. Nat Rev Microbiol. 2007;5:355–62.CAS 
PubMed 
Article 

Google Scholar 
11.Kimes NE, Van Nostrand JD, Weil E, Zhou J, Morris PJ. Microbial functional structure of Montastraea faveolata, an important Caribbean reef-building coral, differs between healthy and yellow-band diseased colonies. Environ Microbiol. 2010;12:541–56.CAS 
PubMed 
Article 

Google Scholar 
12.Raina JB, Tapiolas D, Willis BL, Bourne DG. Coral-associated bacteria and their role in the biogeochemical cycling of sulfur. Appl Environ Microbiol. 2009;75:3492–501.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
13.Rädecker N, Pogoreutz C, Voolstra CR, Wiedenmann J, Wild C. Nitrogen cycling in corals: the key to understanding holobiont functioning? Trends Microbiol. 2015;23:490–7.PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
14.Pogoreutz C, Rädecker N, Cárdenas A, Gärdes A, Wild C, Voolstra CR. Nitrogen fixation aligns with nifH abundance and expression in two coral trophic functional groups. Front Microbiol. 2017;8:1–7.Article 

Google Scholar 
15.Falkowski PG, Dubinsky Z, Muscatine L, McCloskey L. Population control in symbiotic corals. Bioscience. 1993;43:606–11.Article 

Google Scholar 
16.Falkowski PG. Evolution of the nitrogen cycle and its influence on the biological sequestration of CO2 in the ocean. Nature. 1997;387:272–5.CAS 
Article 

Google Scholar 
17.Tyrrell T. The relative influences of nitrogen and phosphorus on oceanic primary production. Nature. 1999;400:525–31.CAS 
Article 

Google Scholar 
18.Lesser MP, Mazel CH, Gorbunov MY, Falkowski PG. Discovery of symbiotic nitrogen fixing cyanobacteria in coral. Science. 2004;997:997–1000.Article 
CAS 

Google Scholar 
19.Wafar MM, Wafar S, David JJ. Nitrification in reef corals. Limnol Oceanogr. 1990;35:725–30.CAS 
Article 

Google Scholar 
20.Tilstra A, El-Khaled YC, Roth F, Rädecker N, Pogoreutz C, Voolstra CR, et al. Denitrification aligns with N2 fixation in Red Sea corals. Sci Rep. 2019;9:1–9.Article 
CAS 

Google Scholar 
21.Beraud E, Gevaert F, Rottier C, Ferrier-Pagès C. The response of the scleractinian coral Turbinaria reniformis to thermal stress depends on the nitrogen status of the coral holobiont. J Exp Biol. 2013;216:2665–74.PubMed 

Google Scholar 
22.Dubinsky Z, Jokiel PL. Ratio of energy and nutrient fluxes regulates symbiosis between zooxanthellae and corals. Pacific Sci. 1994;48:313–24.
Google Scholar 
23.Rädecker N, Pogoreutz C, Ziegler M, Ashok A, Barreto MM, Chaidez V, et al. Assessing the effects of iron enrichment across holobiont compartments reveals reduced microbial nitrogen fixation in the Red Sea coral Pocillopora verrucosa. Ecol Evol. 2017;7:6614–21.PubMed 
PubMed Central 
Article 

Google Scholar 
24.Middelburg JJ, Mueller CE, Veuger B, Larsson AI, Form A, Van Oevelen D. Discovery of symbiotic nitrogen fixation and chemoautotrophy in cold-water corals. Sci Rep. 2015;5:1–9.
Google Scholar 
25.Zhang Y, Ling J, Yang Q, Wen C, Yan Q, Sun H, et al. The functional gene composition and metabolic potential of coral-associated microbial communities. Sci Rep. 2015;5:16191.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
26.Bourne DG, Morrow KM, Webster NS. Insights into the coral microbiome: underpinning the health and resilience of reef ecosystems. Annu Rev Microbiol. 2016;70:317–40.CAS 
PubMed 
Article 

Google Scholar 
27.Thompson JR, Rivera HE, Closek CJ, Medina M. Microbes in the coral holobiont: partners through evolution, development, and ecological interactions. Front Cell Infect Microbiol. 2015;4:1–20.Article 

Google Scholar 
28.Agostini S, Suzuki Y, Higuchi T, Casareto BE, Yoshinaga K, Nakano Y, et al. Biological and chemical characteristics of the coral gastric cavity. Coral Reefs. 2012;31:147–56.Article 

Google Scholar 
29.Bythell JC, Wild C. Biology and ecology of coral mucus release. J Exp Mar Bio Ecol. 2011;408:88–93.Article 

Google Scholar 
30.Shashar N, Banaszak AT, Lesser MP, Amrami D. Coral endolithic algae: life in a protected environment. Pacific Sci. 1997;51:167–73.
Google Scholar 
31.Lesser MP, Morrow KM, Pankey SM, Noonan SHC. Diazotroph diversity and nitrogen fixation in the coral Stylophora pistillata from the Great Barrier Reef. ISME J. 2018;12:813–24.CAS 
PubMed 
Article 

Google Scholar 
32.Benavides M, Houlbrèque F, Camps M, Lorrain A, Grosso O, Bonnet S. Diazotrophs: a non-negligible source of nitrogen for the tropical coral Stylophora pistillata. J Exp Biol. 2016:jeb.139451. https://doi.org/10.1242/jeb.139451.33.Koop K, Booth D, Broadbent A, Brodie JE, Bucher D, Capone DG, et al. ENCORE: the effect of nutrient enrichment on coral reefs. Synthesis of results and conclusions. Mar Pollut Bull. 2001;42:91–120.CAS 
PubMed 
Article 

Google Scholar 
34.Hatcher AI, Hatcher BG. Seasonal and spatial variation in dissolved inorganic nitrogen in the one tree reef lagoon. Proc 4th Int Coral Reef Symp. 1981;1:419–24.
Google Scholar 
35.Mohr W, Großkopf T, Wallace DWR, LaRoche J. Methodological underestimation of oceanic nitrogen fixation rates. PLoS ONE. 2010;5:1–7.Article 
CAS 

Google Scholar 
36.Lewicka-Szczebak D, Well R, Giesemann A, Rohe L, Wolf U. An enhanced technique for automated determination of 15N signatures of N2, (N2+N2O) and N2O in gas samples. Rapid Commun Mass Spectrom. 2013;27:1548–58.CAS 
PubMed 
Article 

Google Scholar 
37.Sigman DM, Casciotti KL, Andreani M, Barford C, Galanter M, Böhlke JK. A bacterial method for the nitrogen isotopic analysis of nitrate in seawater and freshwater. Anal Chem. 2001;73:4145–53.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
38.Montoya JP, Voss M, Kahler P, Capone DG. A simple, high-precision, high-sensitivity tracer assay for N2 fixation. Appl Environ Microbiol. 1996;62:986–93.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
39.Grover R, Maguer JF, Reynaud-Vaganay S, Ferrier-Pagès C. Uptake of ammonium by the scleractinian coral Stylophora pistillata: effect of feeding, light, and ammonium concentrations. Limnol Oceanogr. 2002;47:782–90.Article 

Google Scholar 
40.Grover R, Maguer JF, Allemand D, Ferrier-Pagès C. Nitrate uptake in the scleractinian coral Stylophora pistillata. Limnol Oceanogr. 2003;48:2266–74.CAS 
Article 

Google Scholar 
41.Aalto SL, Suurnäkki S, von Ahnen M, Siljanen HMP, Pedersen PB, Tiirola M. Nitrate removal microbiology in woodchip bioreactors: a case-study with full-scale bioreactors treating aquaculture effluents. Sci Total Environ. 2020;723:138093.CAS 
PubMed 
Article 

Google Scholar 
42.Pandey CB, Kumar U, Kaviraj M, Minick KJ, Mishra AK, Singh JS. DNRA: a short-circuit in biological N-cycling to conserve nitrogen in terrestrial ecosystems. Sci Total Environ. 2020;738:139710.CAS 
PubMed 
Article 

Google Scholar 
43.Beman JM, Roberts KJ, Wegley L, Rohwer F, Francis CA. Distribution and diversity of archaeal ammonia monooxygenase genes associated with corals. Appl Environ Microbiol. 2007;73:5642–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
44.Yang S, Sun W, Zhang F, Li Z. Phylogenetically diverse denitrifying and ammonia-oxidizing bacteria in corals Alcyonium gracillimum and Tubastraea coccinea. Mar Biotechnol. 2013;15:540–51.CAS 
Article 

Google Scholar 
45.El-Khaled YC, Roth F, Tilstra A, Rädecker N, Karcher DB, Kürten B, et al. In situ eutrophication stimulates dinitrogen fixation, denitrification, and productivity in Red Sea coral reefs. Mar Ecol Prog Ser. 2020;645:55–66.CAS 
Article 

Google Scholar 
46.Siboni N, Ben-Dov E, Sivan A, Kushmaro A. Global distribution and diversity of coral-associated archaea and their possible role in the coral holobiont nitrogen cycle. Environ Microbiol. 2008;10:2979–90.CAS 
PubMed 
Article 

Google Scholar 
47.Shashar N, Cohen Y, Loya Y. Extreme diel fluctuations of oxygen in diffusive boundary layers surrounding stony corals. Biol Bull. 1993;185:455–61.CAS 
PubMed 
Article 

Google Scholar 
48.Risk MJ, Muller HR. Poreweater in coral heads: evidence for nutrient regeneration. Limnol Oceanogr. 1983;28:1004–8.Article 

Google Scholar 
49.Guerrero MA, Jones RD. Photoinhibition of marine nitrifying bacteria. II. Dark recovery after monochromatic or polychromatic irradiation. Mar Ecol Prog Ser. 1996;141:193–8.Article 

Google Scholar 
50.Merbt SN, Stahl DA, Casamayor EO, Martí E, Nicol GW, Prosser JI. Differential photoinhibition of bacterial and archaeal ammonia oxidation. FEMS Microbiol Lett. 2012;327:41–6.CAS 
PubMed 
Article 

Google Scholar 
51.Pernice M, Raina JB, Rädecker N, Cárdenas A, Pogoreutz C, Voolstra CR. Down to the bone: the role of overlooked endolithic microbiomes in reef coral health. ISME J. 2020;14:325–34.PubMed 
Article 

Google Scholar 
52.Shashar N, Cohen Y, Loya Y, Sar N. Nitrogen fixation (acetylene reduction) in stony corals—evidence for coral–bacteria interactions. Mar Ecol Prog Ser. 1994;111:259–64.CAS 
Article 

Google Scholar 
53.Lesser MP, Falcón LI, Rodríguez-Román A, Enríquez S, Hoegh-Guldberg O, Iglesias-Prieto R. Nitrogen fixation by symbiotic cyanobacteria provides a source of nitrogen for the scleractinian coral Montastraea cavernosa. Mar Ecol Prog Ser. 2007;346:143–52.CAS 
Article 

Google Scholar 
54.Lema KA, Willis BL, Bourne DG. Corals form characteristic associations with symbiotic nitrogen-fixing bacteria. Appl Environ Microbiol. 2012;78:3136–44.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
55.Crossland CJ, Barnes DJ. Acetylene reduction by coral skeletons. Limnol Oceanogr. 1976;21:153–6.Article 

Google Scholar 
56.Cai L, Zhou G, Tian R-M, Tong H, Zhang W, Sun J, et al. Metagenomic analysis reveals a green sulfur bacterium as a potential coral symbiont. Sci Rep. 2017;7:9320.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
57.Olson ND, Ainsworth TD, Gates RD, Takabayashi M. Diazotrophic bacteria associated with Hawaiian Montipora corals: diversity and abundance in correlation with symbiotic dinoflagellates. J Exp Mar Bio Ecol. 2009;371:140–6.CAS 
Article 

Google Scholar 
58.Cardini U, Bednarz VN, Naumann MS, van Hoytema N, Rix L, Foster RA, et al. Functional significance of dinitrogen fixation in sustaining coral productivity under oligotrophic conditions. Proc R Soc B Biol Sci. 2015;282:20152257.Article 
CAS 

Google Scholar 
59.Grover R, Ferrier-Pagès C, Maguer JF, Ezzat L, Fine M. Nitrogen fixation in the mucus of Red Sea corals. J Exp Biol. 2014;217:3962–3.PubMed 

Google Scholar 
60.Bednarz VN, Grover R, Maguer JF, Fine M. The assimilation of diazotroph-derived nitrogen by scleractinian corals. Amer Soc Microbiol. 2017;8:1–14.
Google Scholar 
61.Knapp AN. The sensitivity of marine N2 fixation to dissolved inorganic nitrogen. Front Microbiol. 2012;3:1–14.
Google Scholar 
62.Bednarz VN, Naumann MS, Cardini U, van Hoytema N, Rix L, Al-Rshaidat MMD, et al. Contrasting seasonal responses in dinitrogen fixation between shallow and deep-water colonies of the model coral Stylophora pistillata in the northern Red Sea. PLoS ONE. 2018;13:e0199022.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
63.Larkum AWD, Kennedy IR, Muller WJ. Nitrogen fixation on a coral reef. Mar Biol. 1988;98:143–55.Article 

Google Scholar 
64.Shashar N, Feldstein T, Cohen Y, Loya Y. Nitrogen fixation (acetylene reduction) on a coral reef. Coral Reefs. 1994;13:171–4.Article 

Google Scholar 
65.Santos HF, Carmo FL, Duarte G, Dini-Andreote F, Castro CB, Rosado AS, et al. Climate change affects key nitrogen-fixing bacterial populations on coral reefs. ISME J. 2014;8:2272–9.PubMed 
PubMed Central 
Article 

Google Scholar 
66.Bythell JC. Nutrient uptake in the reef-building coral Acropora palmata at natural environmental concentrations. Mar Ecol Prog Ser. 1990;68:65–9.Article 

Google Scholar 
67.Furnas MJ. Net in situ growth rates of phytoplankton in an oligotrophic, tropical shelf ecosystem. Limnol Oceanogr. 1991;36:13–29.Article 

Google Scholar 
68.Badgley BD, Lipschultz F, Sebens KP. Nitrate uptake by the reef coral Diploria strigosa: effects of concentration, water flow, and irradiance. Mar Biol. 2006;149:327–38.CAS 
Article 

Google Scholar 
69.Tanaka Y, Miyajima T, Koike I, Hayashibara T, Ogawa H. Translocation and conservation of organic nitrogen within the coral-zooxanthella symbiotic system of Acropora pulchra, as demonstrated by dual isotope-labeling techniques. J Exp Mar Bio Ecol. 2006;336:110–9.CAS 
Article 

Google Scholar 
70.Fernandes de Barros Marangoni L, Ferrier-Pagès C, Rottier C, Bianchini A, Grover R. Unravelling the different causes of nitrate and ammonium effects on coral bleaching. Sci Rep. 2020;10:1–14.Article 
CAS 

Google Scholar 
71.Munn CB. The role of vibrios in diseases of corals. Microbiol Spectr. 2015;3:1–12.CAS 
Article 

Google Scholar 
72.Rubio-Portillo E, Gago JF, Martínez-García M, Vezzulli L, Rosselló-Móra R, Antón J, et al. Vibrio communities in scleractinian corals differ according to health status and geographic location in the Mediterranean Sea. Syst Appl Microbiol. 2018;41:131–8.PubMed 
Article 

Google Scholar 
73.Tout J, Siboni N, Messer LF, Garren M, Stocker R, Webster NS, et al. Increased seawater temperature increases the abundance and alters the structure of natural Vibrio populations associated with the coral Pocillopora damicornis. Front Microbiol. 2015;6:1–12.Article 

Google Scholar 
74.Erler DV, Santos IR, Eyre BD. Inorganic nitrogen transformations within permeable carbonate sands. Cont Shelf Res. 2014;77:69–80.Article 

Google Scholar 
75.Tiedje JM, Sexstone AJ, Myrold DD, Robinson JA. Denitrification: ecological niches, competition and survival. Antonie Van Leeuwenhoek. 1983;48:569–83.Article 

Google Scholar 
76.Stremińska MA, Felgate H, Rowley G, Richardson DJ, Baggs EM. Nitrous oxide production in soil isolates of nitrate-ammonifying bacteria. Environ Microbiol Rep. 2012;4:66–71.PubMed 
Article 
CAS 

Google Scholar 
77.Gardner WS, McCarthy MJ, An S, Sobolev D, Sell KS, Brock D. Nitrogen fixation and dissimilatory nitrate reduction to ammonium (DNRA) support nitrogen dynamics in Texas estuaries. Limnol Oceanogr. 2006;51:558–68.CAS 
Article 

Google Scholar 
78.Giblin AE, Weston NB, Banta GT, Tucker J, Hopkinson CS. The effects of salinity on nitrogen losses from an oligohaline estuarine sediment. Estuaries Coasts. 2010;33:1054–68.CAS 
Article 

Google Scholar 
79.Giblin AE, Tobias CR, Song B, Weston N, Banta GT, Rivera-Monroy VH. The importance of dissimilatory nitrate reduction to ammonium (DNRA) in the nitrogen cycle of coastal ecosystems. Oceanography. 2013;26:124–31.Article 

Google Scholar  More

1.Le Quéré C, Moriarty R, Andrew RM, Canadell JG, Sitch S, Korsbakken JI, et al. Global carbon budget 2015. Earth Syst Sci Data. 2015;7:349–96.Article 

Google Scholar 
2.Hoegh-Guldberg O, Mumby PJ, Hooten AJ, Steneck RS, Greenfield P, Gomez E, et al. Coral reefs under rapid climate change and ocean acidification. Science. 2007;318:1737–42.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
3.Bell JJ, Bennett HM, Rovellini A, Webster NS. Sponges to be winners under near-future climate scenarios. Bioscience. 2018;68:955–68.Article 

Google Scholar 
4.Bell JJ. The functional roles of marine sponges. Estuar Coast Shelf Sci. 2008;79:341–53.Article 

Google Scholar 
5.Pita L, Rix L, Slaby BM, Franke A, Hentschel U. The sponge holobiont in a changing ocean: from microbes to ecosystems. Microbiome. 2018;6:46.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
6.Smith AM, Berman J, Key MM Jr, Winter DJ. Not all sponges will thrive in a high-CO2 ocean: Review of the mineralogy of calcifying sponges. Palaeogeogr Palaeoclimatol Palaeoecol. 2013;392:463–72.Article 

Google Scholar 
7.Webster NS, Thomas T. The sponge hologenome. MBio. 2016;7:e00135–16.PubMed 
PubMed Central 
Article 

Google Scholar 
8.Hentschel U, Piel J, Degnan SM, Taylor MW. Genomic insights into the marine sponge microbiome. Nat Rev Microbiol. 2012;10:641–54.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
9.Thompson JR, Rivera HE, Closek CJ, Medina M. Microbes in the coral holobiont: partners through evolution, development, and ecological interactions. Front Cell Infect Microbiol. 2014;4:176.PubMed 
PubMed Central 

Google Scholar 
10.Fan L, Liu M, Simister R, Webster NS, Thomas T. Marine microbial symbiosis heats up: the phylogenetic and functional response of a sponge holobiont to thermal stress. ISME J. 2013;7:991–1002.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
11.Egan S, Gardiner M. Microbial dysbiosis: rethinking disease in marine ecosystems. Front Microbiol. 2016;7:991.PubMed 
PubMed Central 

Google Scholar 
12.Voolstra CR, Ziegler M. Adapting with microbial help: microbiome flexibility facilitates rapid responses to environmental change. Bioessays. 2020;42:e2000004.PubMed 
Article 

Google Scholar 
13.Botte ES, Nielsen S, Abdul Wahab MA, Webster J, Robbins S, Thomas T, et al. Changes in the metabolic potential of the sponge microbiome under ocean acidification. Nat Commun. 2019;10:4134.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
14.Morrow KM, Bourne DG, Humphrey C, Botté ES, Laffy P, Zaneveld J, et al. Natural volcanic CO2 seeps reveal future trajectories for host–microbial associations in corals and sponges. ISME J. 2015;9:894–908.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
15.Pollock FJ, Lamb JB, van de Water J, Smith HA, Schaffelke B, Willis BL, et al. Reduced diversity and stability of coral-associated bacterial communities and suppressed immune function precedes disease onset in corals. R Soc Open Sci. 2019;6:190355.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
16.Pinzon JH, Kamel B, Burge CA, Harvell CD, Medina M, Weil E, et al. Whole transcriptome analysis reveals changes in expression of immune-related genes during and after bleaching in a reef-building coral. R Soc Open Sci. 2015;2:140214.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
17.Pita L, Hoeppner MP, Ribes M, Hentschel U. Differential expression of immune receptors in two marine sponges upon exposure to microbial-associated molecular patterns. Sci Rep. 2018;8:16081.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
18.Guzman C, Conaco C. Gene expression dynamics accompanying the sponge thermal stress response. PLoS ONE. 2016;11:e0165368.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
19.Riesgo A, Farrar N, Windsor PJ, Giribet G, Leys SP. The analysis of eight transcriptomes from all poriferan classes reveals surprising genetic complexity in sponges. Mol Biol Evol. 2014;31:1102–20.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
20.Germer J, Cerveau N, Jackson DJ. The holo-transcriptome of a calcified early branching metazoan. Front Mar Sci. 2017;4:81.21.Ryu T, Seridi L, Moitinho-Silva L, Oates M, Liew YJ, Mavromatis C, et al. Hologenome analysis of two marine sponges with different microbiomes. BMC Genomics. 2016;17:158.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
22.Hooper JNA, Van Soest RWM. Systema Porifera. A guide to the classification of sponges. In: Hooper JNA, Van Soest RWM, editors. Systema Porifera. New York, NY: Springer; 2002. p. 1–7.23.Pachauri RK, Allen MR, Barros VR, Broome J, Cramer W, Christ R, et al. Climate change 2014: synthesis report. Contribution of Working Groups I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change. Geneva: IPCC; 2014.24.Pierrot DE, Lewis E, Wallace DWR. MS Excel program developed for CO2 system calculations. Oak Ridge, TN: Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, US Department of Energy, ORNL/CDIAC-IOS; 2006.25.Herlemann DP, Labrenz M, Jurgens K, Bertilsson S, Waniek JJ, Andersson AF. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME J. 2011;5:1571–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
26.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 
Article 

Google Scholar 
27.Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
28.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 
Article 

Google Scholar 
29.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 
Article 

Google Scholar 
30.Dixon P. VEGAN, a package of R functions for community ecology. J Veg Sci. 2003;14:927–30.Article 

Google Scholar 
31.McMurdie PJ, Holmes S. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput Biol. 2014;10:e1003531.PubMed 
PubMed Central 
Article 
CAS 

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 
Article 

Google Scholar 
33.Asshauer KP, Wemheuer B, Daniel R, Meinicke P. Tax4Fun: predicting functional profiles from metagenomic 16S rRNA data. Bioinformatics. 2015;31:2882–4.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
34.Kanehisa M, Sato Y. KEGG Mapper for inferring cellular functions from protein sequences. Protein Sci. 2020;29:28–35.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
35.Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.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.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
37.Conesa A, Gotz S. Blast2GO: A comprehensive suite for functional analysis in plant genomics. Int J Plant Genomics. 2008;2008:619832.PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
38.Finn RD, Bateman A, Clements J, Coggill P, Eberhardt RY, Eddy SR, et al. Pfam: the protein families database. Nucleic Acids Res. 2014;42:D222–D30.CAS 
PubMed 
Article 

Google Scholar 
39.Eddy SR. Profile hidden Markov models. Bioinformatics. 1998;14:755–63.CAS 
PubMed 
Article 

Google Scholar 
40.Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics. 2011;12:323.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10:R25.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
42.Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40.CAS 
PubMed 
Article 

Google Scholar 
43.Alexa A, Rahnenführer J. Gene set enrichment analysis with topGO. Bioconductor Improv. 2009;27:1–26.44.Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47:D607–D13.CAS 
Article 

Google Scholar 
45.Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Moitinho-Silva L, Nielsen S, Amir A, Gonzalez A, Ackermann GL, Cerrano C, et al. The sponge microbiome project. Gigascience. 2017;6:1–7.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
47.Lurgi M, Thomas T, Wemheuer B, Webster NS, Montoya JM. Modularity and predicted functions of the global sponge-microbiome network. Nat Commun. 2019;10:992.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
48.Srivastava M, Simakov O, Chapman J, Fahey B, Gauthier ME, Mitros T, et al. The Amphimedon queenslandica genome and the evolution of animal complexity. Nature. 2010;466:720–6.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
49.Guzman C, Conaco C. Comparative transcriptome analysis reveals insights into the streamlined genomes of haplosclerid demosponges. Sci Rep. 2016;6:18774.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
50.Fortunato SA, Adamski M, Ramos OM, Leininger S, Liu J, Ferrier DE, et al. Calcisponges have a ParaHox gene and dynamic expression of dispersed NK homeobox genes. Nature. 2014;514:620–3.CAS 
PubMed 
Article 

Google Scholar 
51.Voigt O, Fradusco B, Gut C, Kevrekidis C, Vargas S, Wörheide G. Carbonic anhydrases: an ancient tool in calcareous sponge biomineralization. Front Genet. 2021;12:624533.52.Yuen B, Bayes JM, Degnan SM. The characterization of sponge NLRs provides insight into the origin and evolution of this innate immune gene family in animals. Mol Biol Evol. 2014;31:106–20.CAS 
PubMed 
Article 

Google Scholar 
53.Madeira F, Park YM, Lee J, Buso N, Gur T, Madhusoodanan N, et al. The EMBL-EBI search and sequence analysis tools APIs in 2019. Nucleic Acids Res. 2019;47:W636–41.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
54.Darriba D, Taboada GL, Doallo R, Posada D. ProtTest 3: fast selection of best-fit models of protein evolution. Bioinformatics. 2011;27:1164–5.CAS 
PubMed 
Article 

Google Scholar 
55.Ronquist F, Huelsenbeck JP. MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics. 2003;19:1572–4.CAS 
PubMed 
Article 

Google Scholar 
56.Wickham H. ggplot2: elegant graphics for data analysis. New York: Springer; 2016.57.McDevitt-Irwin JM, Baum JK, Garren M, Vega Thurber RL. Responses of coral-associated bacterial communities to local and global stressors. Front Mar Sci. 2017;4:262.58.Hori K, Matsumoto S. Bacterial adhesion: from mechanism to control. Biochem Eng J. 2010;48:424–34.CAS 
Article 

Google Scholar 
59.Yao J, Allen C. Chemotaxis is required for virulence and competitive fitness of the bacterial wilt pathogen Ralstonia solanacearum. J Bacteriol. 2006;188:3697–708.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
60.Chu H, Mazmanian SK. Innate immune recognition of the microbiota promotes host-microbial symbiosis. Nat Immunol. 2013;14:668–75.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Bazzoni F, Beutler B. The tumor necrosis factor ligand and receptor families. N Engl J Med. 1996;334:1717–25.CAS 
PubMed 
Article 

Google Scholar 
62.Hayden MS, Ghosh S. Regulation of NF-kappaB by TNF family cytokines. Semin Immunol. 2014;26:253–66.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
63.Parrish AB, Freel CD, Kornbluth S. Cellular mechanisms controlling caspase activation and function. Cold Spring Harb Perspect Biol. 2013;5:a008672.64.Wiens M, Korzhev M, Krasko A, Thakur NL, Perovic-Ottstadt S, Breter HJ, et al. Innate immune defense of the sponge Suberites domuncula against bacteria involves a MyD88-dependent signaling pathway. Induction of a perforin-like molecule. J Biol Chem. 2005;280:27949–59.CAS 
PubMed 
Article 

Google Scholar 
65.Muller WE, Muller IM. Origin of the metazoan immune system: identification of the molecules and their functions in sponges. Integr Comp Biol. 2003;43:281–92.PubMed 
Article 

Google Scholar 
66.Yuen B Deciphering the genomic toolkit underlying animal-bacteria interactions – insights through the demosponge Amphimedon queenslandica. Saint Lucia, QLD: School of Biological Sciences, The University of Queensland; 2016.67.Gauthier ME, Du Pasquier L, Degnan BM. The genome of the sponge Amphimedon queenslandica provides new perspectives into the origin of Toll-like and interleukin 1 receptor pathways. Evol Dev. 2010;12:519–33.CAS 
PubMed 
Article 

Google Scholar 
68.Roue M, Quevrain E, Domart-Coulon I, Bourguet-Kondracki ML. Assessing calcareous sponges and their associated bacteria for the discovery of new bioactive natural products. Nat Prod Rep. 2012;29:739–51.CAS 
PubMed 
Article 

Google Scholar 
69.Steinert G, Busch K, Bayer K, Kodami S, Arbizu PM, Kelly M, et al. Compositional and quantitative insights into bacterial and archaeal communities of South Pacific deep-sea sponges (Demospongiae and Hexactinellida). Front Microbiol. 2020;11:716.PubMed 
PubMed Central 
Article 

Google Scholar 
70.Thomas T, Moitinho-Silva L, Lurgi M, Bjork JR, Easson C, Astudillo-Garcia C, et al. Diversity, structure and convergent evolution of the global sponge microbiome. Nat Commun. 2016;7:11870.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
71.Yap NV, Whelan FJ, Bowdish DM, Golding GB. The evolution of the scavenger receptor cysteine-rich domain of the class a scavenger receptors. Front Immunol. 2015;6:342.PubMed 
PubMed Central 

Google Scholar 
72.Brown GD, Willment JA, Whitehead L. C-type lectins in immunity and homeostasis. Nat Rev Immunol. 2018;18:374–89.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
73.von Moltke J, Ayres JS, Kofoed EM, Chavarria-Smith J, Vance RE. Recognition of bacteria by inflammasomes. Annu Rev Immunol. 2013;31:73–106.Article 
CAS 

Google Scholar 
74.Robertson SJ, Rubino SJ, Geddes K, Philpott DJ. Examining host-microbial interactions through the lens of NOD: from plants to mammals. Semin Immunol. 2012;24:9–16.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
75.Ting JP, Lovering RC, Alnemri ES, Bertin J, Boss JM, Davis BK, et al. The NLR gene family: a standard nomenclature. Immunity. 2008;28:285–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
76.Messier-Solek C, Buckley KM, Rast JP. Highly diversified innate receptor systems and new forms of animal immunity. Semin Immunol. 2010;22:39–47.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
77.Bennett HM, Altenrath C, Woods L, Davy SK, Webster NS, Bell JJ. Interactive effects of temperature and pCO2 on sponges: from the cradle to the grave. Glob Chang Biol. 2017;23:2031–46.PubMed 
Article 
PubMed Central 

Google Scholar 
78.Luter HM, Andersen M, Versteegen E, Laffy P, Uthicke S, Bell JJ, et al. Cross-generational effects of climate change on the microbiome of a photosynthetic sponge. Environ Microbiol. 2020;22:4732–44.79.Girvan MS, Campbell CD, Killham K, Prosser JI, Glover LA. Bacterial diversity promotes community stability and functional resilience after perturbation. Environ Microbiol. 2005;7:301–13.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
80.Ziegler M, Grupstra CGB, Barreto MM, Eaton M, BaOmar J, Zubier K, et al. Coral bacterial community structure responds to environmental change in a host-specific manner. Nat Commun. 2019;10:3092.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
81.Ribes M, Calvo E, Movilla J, Logares R, Coma R, Pelejero C. Restructuring of the sponge microbiome favors tolerance to ocean acidification. Environ Microbiol Rep. 2016;8:536–44.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
82.Vega Thurber R, Willner-Hall D, Rodriguez-Mueller B, Desnues C, Edwards RA, Angly F, et al. Metagenomic analysis of stressed coral holobionts. Environ Microbiol. 2009;11:2148–63.PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
83.van de Water J, Chaib De Mares M, Dixon GB, Raina JB, Willis BL, Bourne DG, et al. Antimicrobial and stress responses to increased temperature and bacterial pathogen challenge in the holobiont of a reef-building coral. Mol Ecol. 2018;27:1065–80.PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
84.Weisz JB, Lindquist N, Martens CS. Do associated microbial abundances impact marine demosponge pumping rates and tissue densities? Oecologia. 2008;155:367–76.PubMed 
Article 
PubMed Central 

Google Scholar 
85.Ludeman DA, Reidenbach MA, Leys SP. The energetic cost of filtration by demosponges and their behavioural response to ambient currents. J Exp Biol. 2017;220:995–1007.PubMed 
Article 

Google Scholar 
86.Perea-Blazquez A, Davy SK, Bell JJ. Estimates of particulate organic carbon flowing from the pelagic environment to the benthos through sponge assemblages. PLoS ONE. 2012;7:e29569.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
87.Morganti TM, Ribes M, Yahel G, Coma R. Size is the major determinant of pumping rates in marine sponges. Front Physiol. 2019;10:1474.PubMed 
PubMed Central 
Article 

Google Scholar 
88.Peck LS, Clark MS, Power D, Reis J, Batista FM, Harper EM. Acidification effects on biofouling communities: winners and losers. Glob Chang Biol. 2015;21:1907–13.PubMed 
PubMed Central 
Article 

Google Scholar 
89.Ribeiro B, Padua A, Barno A, Villela H, Duarte G, Rossi A, et al. Assessing skeleton and microbiome responses of a calcareous sponge under thermal and pH stresses. ICES J Mar Sci. 2020:fsaa231.90.Lanna E, Klautau M. Life history and reproductive dynamics of the cryptogenic calcareous sponge Sycettusa hastifera (Porifera, Calcarea) living in tropical rocky shores. J Mar Biol Assoc UK. 2018;98:505–14.Article 

Google Scholar 
91.Pörtner HO, Langenbuch M, Michaelidis B. Synergistic effects of temperature extremes, hypoxia, and increases in CO2 on marine animals: from Earth history to global change. J Geophys Res. 2005;110:C09S10.92.Emms DM, Kelly S. OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biol. 2019;20:238.PubMed 
PubMed Central 
Article 

Google Scholar  More

1.Carpenter LJ. Biogeochemical cycles | iodine. Encyclopedia of Atmospheric Sciences: Elsevier; United States; 2015. p. 205–19.2.Chemburkar SR, Deming KC, Reddy RE. Chemistry of thyroxine: an historical perspective and recent progress on its synthesis. Tetrahedron. 2010;66:1955–62.CAS 
Article 

Google Scholar 
3.Schweizer U, Steegborn C. Thyroid hormones—from crystal packing to activity to reactivity. Angew Chem. 2015;54:12856–8.CAS 
Article 

Google Scholar 
4.Küpper FC, Feiters MC, Olofsson B, Kaiho T, Yanagida S, Zimmermann MB, et al. Commemorating two centuries of iodine research: an interdisciplinary overview of current research. Angew Chem. 2011;50:11598–620.Article 
CAS 

Google Scholar 
5.Manley SL, Dastoor MN. Methyl iodide (CH3I) production by kelp and associated microbes. Mar Biol. 1988;98:477–82.CAS 
Article 

Google Scholar 
6.Lebel LS, Dickson RS, Glowa GA. Radioiodine in the atmosphere after the Fukushima Dai-ichi nuclear accident. J Environ Radioact. 2016;151:82–93.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Luther GW, Wu J, Cullen JB. Redox chemistry of iodine in seawater. Aquatic chemistry. Advances in chemistry. 244: American Chemical Society; Washington, DC; 1995. p. 135–55.8.Gonzales J, Tymon T, Küpper FC, Edwards MS, Carrano CJ. The potential role of kelp forests on iodine speciation in coastal seawater. PloS ONE. 2017;12:e0180755.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
9.Vedamati J, Goepfert T, Moffett JW. Iron speciation in the eastern tropical South Pacific oxygen minimum zone off Peru. Limnol Oceanogr. 2014;59:1945–57.Article 

Google Scholar 
10.Tsunogai S, Sase T. Formation of iodide-iodine in the ocean. Deep Sea Res Oceanogr Abstr. 1969;16:489–96.CAS 
Article 

Google Scholar 
11.Councell TB, Landa ER, Lovley DR. Microbial reduction of iodate. Water Air Soil Pollut. 1997;100:99–106.CAS 
Article 

Google Scholar 
12.Youngblut MD, Tsai C-L, Clark IC, Carlson HK, Maglaqui AP, Gau-Pan PS, et al. Perchlorate reductase is distinguished by active site aromatic gate residues. J Biol Chem. 2016;291:9190–202.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
13.Farrenkopf AM, Dollhopf ME, Chadhain SN, Luther GW, Nealson KH. Reduction of iodate in seawater during Arabian Sea incubations and in laboratory cultures of the marine Shewanella putrefaciens strain MR-4 shipboard bacterium. Mar Chem. 1997;57:347–54.CAS 
Article 

Google Scholar 
14.Amachi S, Kawaguchi N, Muramatsu Y, Tsuchiya S, Watanabe Y, Shinoyama H, et al. Dissimilatory iodate reduction by marine Pseudomonas sp. strain SCT. Appl Environ Microbiol. 2007;73:5725–30.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
15.Yamazaki C, Kashiwa S, Horiuchi A, Kasahara Y, Yamamura S, Amachi S. A novel dimethylsulfoxide reductase family of molybdenum enzyme, Idr, is involved in iodate respiration by Pseudomonas sp. SCT. Environ Microbiol. 2020;22:2196–212.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
16.Youngblut MD, Wang O, Barnum TP, Coates JD. (Per)chlorate in biology on earth and beyond. Annu Rev Microbiol. 2016;70:435–57.17.Toporek YJ, Mok JK, Shin HD, Lee BD, Lee MH, DiChristina TJ. Metal reduction and protein secretion genes required for Iodate Reduction by Shewanella oneidensis. Appl Environ Microbiol. 2019;85:e02115–18.18.Carlström CI, Lucas LN, Rohde RA, Haratian A, Engelbrektson AL, Coates JD. Characterization of an anaerobic marine microbial community exposed to combined fluxes of perchlorate and salinity. Appl Microbiol Biotechnol. 2016;100:9719–32.PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
19.Yip KC-W, Gu J-D. A novel bacterium involved in the degradation of 2-methylindole isolated from sediment of Inner Deep Bay of Hong Kong. Appl Environ Biotechnol. 2015;1:52–63.Article 

Google Scholar 
20.Glazyrina J, Materne EM, Dreher T, Storm D, Junne S, Adams T, et al. High cell density cultivation and recombinant protein production with Escherichia coli in a rocking-motion-type bioreactor. Micro Cell Fact. 2010;9:1–11.Article 
CAS 

Google Scholar 
21.Loferer-Krössbacher M, Klima J, Psenner R. Determination of bacterial cell dry mass by transmission electron microscopy and densitometric image analysis. Appl Environ Microbiol. 1998;64:688–94.PubMed 
PubMed Central 
Article 

Google Scholar 
22.McInerney MJ, Beaty PS. Anaerobic community structure from a nonequilibrium thermodynamic perspective. Can J Microbiol. 1988;34:487–93.CAS 
Article 

Google Scholar 
23.Stern JH, Passchier AA. The heats of formation of triiodide and iodate ions. J Phys Chem. 1962;66:752–3.CAS 
Article 

Google Scholar 
24.Weber KA, Achenbach LA, Coates JD. Microorganisms pumping iron: anaerobic microbial iron oxidation and reduction. Nat Rev Microbiol. 2006;4:752–64.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Leimkühler S, Iobbi-Nivol C. Bacterial molybdoenzymes: Old enzymes for new purposes. FEMS Microbiol Rev. 2016;40:1–18.PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
26.McEwan AG, Ridge JP, McDevitt CA, Hugenholtz P. The DMSO reductase family of microbial molybdenum enzymes: Molecular properties and role in the dissimilatory reduction of toxic elements. Geomicrobiol J. 2002;19:3–21.CAS 
Article 

Google Scholar 
27.Chaudhuri SK, O’Connor SM, Gustavson RL, Achenbach LA, Coates JD. Environmental factors that control microbial perchlorate reduction. Appl Environ Microbiol. 2002;68:4425–30.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
28.Snel B, Bork P, Huynen MA. Genomes in flux: the evolution of archaeal and proteobacterial gene content. Genome Res. 2002;12:17–25.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
29.Saunders JK, Fuchsman CA, McKay C, Rocap G. Complete arsenic-based respiratory cycle in the marine microbial communities of pelagic oxygen-deficient zones. Proc Natl Acad Sci USA. 2019;116:9925–30.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Dabir DV, Leverich EP, Kim SK, Tsai FD, Hirasawa M, Knaff DB, et al. A role for cytochrome c and cytochrome c peroxidase in electron shuttling from Erv1. EMBO J. 2007;26:4801–11.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
31.Martins D, Kathiresan M, English AM. Cytochrome c peroxidase is a mitochondrial heme-based H2O2 sensor that modulates antioxidant defense. Free Radic Biol Med. 2013;65:541–51.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
32.Almagro Armenteros JJ, Tsirigos KD, Sønderby CK, Petersen TN, Winther O, Brunak S, et al. SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat Biotechnol. 2019;37:420–3.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
33.Berks BC. The twin-arginine protein translocation pathway. Annu Rev Biochem. 2015;84:843–64.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
34.Toporek M, Michałowska-Kaczmarczyk AM, Michałowski T. Disproportionation reactions of HIO and NaIO in static and dynamic systems. Am J Anal Chem. 2014;5:1046.CAS 
Article 

Google Scholar 
35.Ellis KV, Van Vree HBRJ. Iodine used as a water-disinfectant in turbid waters. Water Res. 1989;23:671–6.CAS 
Article 

Google Scholar 
36.Alternative drinking-water disinfectants: bromine, iodine and silver. Geneva: World Health Organization; 2018. Licence: CC BY-NC-SA 3.0 IGO.37.Liebensteiner MG, Pinkse MWH, Schaap PJ, Stams AJM, Lomans BP. Archaeal (per)chlorate reduction at high temperature: An interplay of biotic and abiotic reactions. Science. 2013;340:85–7.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
38.Dudley M, Salamone A, Nerenberg R. Kinetics of a chlorate-accumulating, perchlorate-reducing bacterium. Water Res. 2008;42:2403–10.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
39.Melnyk RA, Youngblut MD, Clark IC, Carlson HK, Wetmore KM, Price MN, et al. Novel mechanism for scavenging of hypochlorite involving a periplasmic methionine-rich peptide and methionine sulfoxide reductase. MBio. 2015;6:e00233-15.40.Steinegger M, Söding J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat Biotechnol. 2017;35:1026–8.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
41.Ordoñez OF, Rasuk MC, Soria MN, Contreras M, Farías ME. Haloarchaea from the Andean Puna: biological role in the energy metabolism of arsenic. Microb Ecol. 2018;76:695–705.PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
42.Anantharaman K, Brown CT, Hug LA, Sharon I, Castelle CJ, Probst AJ, et al. Thousands of microbial genomes shed light on interconnected biogeochemical processes in an aquifer system. Nat Commun. 2016;7:1–11.Article 
CAS 

Google Scholar 
43.Becraft ED, Woyke T, Jarett J, Ivanova N, Godoy-Vitorino F, Poulton N, et al. Rokubacteria: genomic giants among the uncultured bacterial phyla. Front Microbiol. 2017;8:2264.44.He Z, Cai C, Wang J, Xu X, Zheng P, Jetten MSM, et al. A novel denitrifying methanotroph of the NC10 phylum and its microcolony. Sci Rep. 2016;6:1–10.Article 
CAS 

Google Scholar 
45.Melnyk RA, Engelbrektson A, Clark IC, Carlson HK, Byrne-Bailey K, Coates JD. Identification of a perchlorate reduction genomic island with novel regulatory and metabolic genes. Appl Environ Microbiol. 2011;77:7401–4.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Scornavacca C, Zickmann F, Huson DH. Tanglegrams for rooted phylogenetic trees and networks. Bioinformatics. 2011;27:i248–56.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
47.Juhas M, van der Meer JR, Gaillard M, Harding RM, Hood DW, Crook DW. Genomic islands: tools of bacterial horizontal gene transfer and evolution. FEMS Microbiol Rev. 2009;33:376–93.CAS 
PubMed 
Article 

Google Scholar 
48.Reiter WD, Palm P, Yeats S. Transfer RNA genes frequently serve as integration sites for prokaryotic genetic elements. Nucleic Acids Res. 1989;17:1907–14.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
49.Larbig KD, Christmann A, Johann A, Klockgether J, Hartsch T, Merkl R, et al. Gene islands integrated into tRNAGly genes confer genome diversity on a Pseudomonas aeruginosa clone. J Bacteriol. 2002;184:6665–80.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
50.Boyd E, Barkay T. The mercury resistance operon: From an origin in a geothermal environment to an efficient detoxification machine. Front Microbiol. 2012;3:349.PubMed 
PubMed Central 

Google Scholar 
51.Besaury L, Bodilis J, Delgas F, Andrade S, De la Iglesia R, Ouddane B, et al. Abundance and diversity of copper resistance genes cusA and copA in microbial communities in relation to the impact of copper on Chilean marine sediments. Mar Pollut Bull. 2013;67:16–25.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
52.Bertelli C, Laird MR, Williams KP, Simon Fraser University Research Computing Group, Lau BY, Hoad G, et al. IslandViewer 4: expanded prediction of genomic islands for larger-scale datasets. Nucleic Acids Res. 2017;45:W30–5.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
53.Jin HM, Lee HJ, Kim JM, Park MS, Lee K, Jeon CO. Litorimicrobium taeanense gen. nov., sp. nov., isolated from a sandy beach. Int J Syst Evol Microbiol. 2011;61:1392–6.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Alex A, Antunes A. Comparative genomics reveals metabolic specificity of Endozoicomonas isolated from a marine sponge and the genomic repertoire for host-bacteria symbioses. Microorganisms. 2019;7:635.CAS 
PubMed Central 
Article 

Google Scholar 
55.Kim Y-O, Park S, Nam B-H, Park J-M, Kim D-G, Yoon J-H. Litoreibacter ascidiaceicola sp. nov., isolated from the golden sea squirt Halocynthiaaurantium. Int J Syst Evol Microbiol. 2014;64:2545–50.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
56.Kupper FC, Carpenter LJ, McFiggans GB, Palmer CJ, Waite TJ, Boneberg EM, et al. Iodide accumulation provides kelp with an inorganic antioxidant impacting atmospheric chemistry. Proc Natl Acad Sci USA. 2008;105:6954–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
57.Jung HS, Jeong SE, Chun BH, Quan Z-X, Jeon CO. Rhodophyticola porphyridii gen. nov., sp. nov., isolated from a red alga, Porphyridium marinum. Int J Syst Evol Microbiol. 2019;69:1656–61.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
58.Wagner GP, Kin K, Lynch VJ. Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples. Theory Biosci. 2012;131:281–5.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
59.Ribicic D, Netzer R, Hazen TC, Techtmann SM, Drabløs F, Brakstad OG. Microbial community and metagenome dynamics during biodegradation of dispersed oil reveals potential key-players in cold Norwegian seawater. Mar Pollut Bull. 2018;129:370–8.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
60.Lachkar Z, Lévy M, Smith KS. Strong intensification of the Arabian Sea oxygen minimum zone in response to Arabian Gulf warming. Geophys Res Lett. 2019;46:5420–9.CAS 
Article 

Google Scholar 
61.Farrenkopf AM, Luther GW. Iodine chemistry reflects productivity and denitrification in the Arabian Sea: evidence for flux of dissolved species from sediments of western India into the OMZ. Deep-Sea Res Pt II. 2002;49:2303–18.CAS 
Article 

Google Scholar 
62.Bertagnolli AD, Stewart FJ. Microbial niches in marine oxygen minimum zones. Nat Rev Microbiol. 2018;16:723–9.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
63.Cutter GA, Moffett JW, Nielsdóttir MC, Sanial V. Multiple oxidation state trace elements in suboxic waters off Peru: In situ redox processes and advective/diffusive horizontal transport. Mar Chem. 2018;201:77–89.CAS 
Article 

Google Scholar 
64.Karstensen J, Stramma L, Visbeck M. Oxygen minimum zones in the eastern tropical Atlantic and Pacific oceans. Prog Oceanogr. 2008;77:331–50.Article 

Google Scholar 
65.Farrenkopf AM, Luther GW, Truesdale VW, Van Der Weijden CH. Sub-surface iodide maxima: evidence for biologically catalyzed redox cycling in Arabian Sea OMZ during the SW intermonsoon. Deep Sea Res Pt II. 1997;44:1391–409.CAS 
Article 

Google Scholar 
66.Kalvelage T, Lavik G, Jensen MM, Revsbech NP, Löscher C, Schunck H, et al. Aerobic microbial respiration in oceanic oxygen minimum zones. PLoS ONE. 2015;10:e0133526.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
67.Howarth RW. Nutrient limitation of net primary production in marine ecosystems. Annu Rev Ecol Syst. 1988;19:89–110.Article 

Google Scholar 
68.Shalel Levanon S, San K-Y, Bennett GN. Effect of oxygen on the Escherichia coli ArcA and FNR regulation systems and metabolic responses. Biotechnol Bioeng. 2005;89:556–64.Article 
CAS 

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

Google Scholar 
70.Hardisty DS, Horner TJ, Evans N, Moriyasu R, Babbin AR, Wankel SD, et al. Limited iodate reduction in shipboard seawater incubations from the Eastern Tropical North Pacific oxygen deficient zone. Earth Planet Sci Lett. 2021;554:116676.CAS 
Article 

Google Scholar 
71.Li H-P, Yeager CM, Brinkmeyer R, Zhang S, Ho Y-F, Xu C, et al. Bacterial production of organic acids enhances H2O2-dependent iodide oxidation. Environ Sci Technol. 2012;46:4837–44.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
72.Shiroyama K, Kawasaki Y, Unno Y, Amachi S. A putative multicopper oxidase, IoxA, is involved in iodide oxidation by Roseovarius sp. strain A-2. Biosci Biotechnol Biochem. 2015;79:1898–905.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
73.Lavik G, Stührmann T, Brüchert 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 
Article 
PubMed Central 

Google Scholar 
74.Wadley MR, Stevens DP, Jickells T, Hughes C, Chance R, Hepach H, et al. Modelling iodine in the ocean. Earth Space Sci Open Access Arch. 2020:46. https://doi.org/10.1002/essoar.10502078.1.75.Waite TJ, Truesdale VW. Iodate reduction by Isochrysis galbana is relatively insensitive to de-activation of nitrate reductase activity—are phytoplankton really responsible for iodate reduction in seawater? Mar Chem. 2003;81:137–48.CAS 
Article 

Google Scholar 
76.Coates JD, Achenbach LA. Microbial perchlorate reduction: rocket-fuelled metabolism. Nat Rev Microbiol. 2004;2:569–80.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
77.Jones DS, Bailey JV, Flood BE. Sedimenticola thiotaurini sp. nov., a sulfur-oxidizing bacterium isolated from salt marsh sediments, and emended descriptions of the genus Sedimenticola and Sedimenticola selenatireducens. Int J Syst Evol Microbiol. 2015;65:2522–30.PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
78.Kanehisa M, Sato Y. KEGG Mapper for inferring cellular functions from protein sequences. Protein Sci. 2020;29:28–35.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
79.Boden R, Hutt LP, Rae AW. Reclassification of Thiobacillus aquaesulis (Wood & Kelly, 1995) as Annwoodia aquaesulis gen. nov., comb. nov., transfer of Thiobacillus (Beijerinck, 1904) from the Hydrogenophilales to the Nitrosomonadales, proposal of Hydrogenophilalia class. nov. within the ‘Proteobacteria’, and four new families within the orders Nitrosomonadales and Rhodocyclales. Int J Syst Evol Microbiol. 2017;67:1191–205.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
80.Brinkmann T, Specht CH, Frimmel FH. Non-linear calibration functions in ion chromatography with suppressed conductivity detection using hydroxide eluents. J Chromatogr. 2002;957:99–109.CAS 
Article 

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

Google Scholar 
82.Wick RR, Schultz MB, Zobel J, Holt KE. Bandage: interactive visualization of de novo genome assemblies. Bioinformatics 2015;31:3350–2.CAS 
PubMed 
PubMed Central 
Article 

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

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

Google Scholar 
85.Finn RD, Clements J, Eddy SR. HMMER web server: Interactive sequence similarity searching. Nucleic Acids Res. 2011;39:W29–37.CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
87.Huerta-Cepas J, Serra F, Bork P. ETE 3: Reconstruction, analysis, and visualization of phylogenomic data. Mol Biol Evol. 2016;33:1635–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
88.Méheust R, Burstein D, Castelle CJ, Banfield JF. The distinction of CPR bacteria from other bacteria based on protein family content. Nat Commun. 2019;10:4173.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
89.Barnum TP, Figueroa IA, Carlström CI, Lucas LN, Engelbrektson AL, Coates JD. Genome-resolved metagenomics identifies genetic mobility, metabolic interactions, and unexpected diversity in perchlorate-reducing communities. ISME J. 2018;12:1568–81.CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
91.Karsenti E. The making of Tara Oceans: Funding blue skies research for our Blue Planet. Mol Syst Biol. 2015;11:811.PubMed 
PubMed Central 
Article 

Google Scholar 
92.Pesant S, Not F, Picheral M, Kandels-Lewis S, Le Bescot N, Gorsky G, et al. Open science resources for the discovery and analysis of Tara Oceans data. Sci Data. 2015;2:1–16.Article 
CAS 

Google Scholar 
93.Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
94.Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25:2078–9.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
95.Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: Machine learning in Python. J Mac Learn Res. 2011;12:2825–30.
Google Scholar 
96.Azur MJ, Stuart EA, Frangakis C, Leaf PJ. Multiple imputation by chained equations: what is it and how does it work? Int J methods Psychiatr Res. 2011;20:40–9.PubMed 
PubMed Central 
Article 

Google Scholar  More

1.Pfeffer C, Larsen S, Song J, Dong M, Besenbacher F, Meyer RL, et al. Filamentous bacteria transport electrons over centimetre distances. Nature. 2012;491:218–21.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.Meysman FJR, Cornelissen R, Trashin S, Bonné R, Martinez SH, van der Veen J, et al. A highly conductive fibre network enables centimetre-scale electron transport in multicellular cable bacteria. Nat Commun. 2019;10:4120.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
3.Nielsen LP, Risgaard-Petersen N, Fossing H, Christensen PB, Sayama M. Electric currents couple spatially separated biogeochemical processes in marine sediment. Nature. 2010;463:1071–4.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Malkin SY, Rao AM, Seitaj D, Vasquez-Cardenas D, Zetsche EM, Hidalgo-Martinez S, et al. Natural occurrence of microbial sulphur oxidation by long-range electron transport in the seafloor. Isme J. 2014;8:1843–54.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
5.Burdorf LDW, Tramper A, Seitaj D, Meire L, Hidalgo-Martinez S, Zetsche EM, et al. Long-distance electron transport occurs globally in marine sediments. Biogeosciences. 2017;14:683–701.CAS 
Article 

Google Scholar 
6.Marzocchi U, Bonaglia S, van de Velde S, Hall POJ, Schramm A, Risgaard-Petersen N, et al. Transient bottom water oxygenation creates a niche for cable bacteria in long-term anoxic sediments of the Eastern Gotland Basin. Environ Microbiol. 2018;20:3031–41.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Risgaard-Petersen N, Kristiansen M, Frederiksen RB, Dittmer AL, Bjerg JT, Trojan D, et al. Cable bacteria in freshwater sediments. Appl Environ Microbiol. 2015;81:6003–11.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
8.Risgaard-Petersen N, Revil A, Meister P, Nielsen LP. Sulfur, iron-, and calcium cycling associated with natural electric currents running through marine sediment. Geochim Cosmochim Acta. 2012;92:1–13.CAS 
Article 

Google Scholar 
9.Seitaj D, Schauer R, Sulu-Gambari F, Hidalgo-Martinez S, Malkin SY, Burdorf LD, et al. Cable bacteria generate a firewall against euxinia in seasonally hypoxic basins. Proc Natl Acad Sci USA. 2015;112:13278–83.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
10.Sulu-Gambari F, Seitaj D, Meysman FJR, Schauer R, Polerecky L, Slomp CP. Cable bacteria control iron–phosphorus dynamics in sediments of a coastal hypoxic basin. Environ Sci Technol. 2016;50:1227–33.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
11.Marzocchi U, Trojan D, Larsen S, Meyer RL, Revsbech NP, Schramm A, et al. Electric coupling between distant nitrate reduction and sulfide oxidation in marine sediment. ISME J. 2014;8:1682–90.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
12.Risgaard-Petersen N, Damgaard LR, Revil A, Nielsen LP. Mapping electron sources and sinks in a marine biogeobattery. J Geophys Res Biogeosci. 2014;119:1475–86.CAS 
Article 

Google Scholar 
13.Kessler AJ, Wawryk M, Marzocchi U, Roberts KL, Wong WW, Risgaard‐Petersen N, et al. Cable bacteria promote DNRA through iron sulfide dissolution. Limnol Oceanogr. 2018;64:1228–38.Article 
CAS 

Google Scholar 
14.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 
Article 

Google Scholar 
15.MacGregor BJ, Biddle JF, Siebert JR, Staunton E, Hegg EL, Matthysse AG, et al. Why orange Guaymas Basin Beggiatoa spp. are orange: single-filament-genome-enabled identification of an abundant octaheme cytochrome with hydroxylamine oxidase, hydrazine oxidase, and nitrite reductase activities. Appl Environ Microbiol. 2013;79:1183–90.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
16.Buckley A, MacGregor B, Teske A. Identification, expression and activity of candidate nitrite reductases from orange Beggiatoaceae, Guaymas Basin. Front Microbiol. 2019;10:644.PubMed 
PubMed Central 
Article 

Google Scholar 
17.Sandfeld T, Marzocchi U, Petro C, Schramm A, Risgaard-Petersen N. Electrogenic sulfide oxidation mediated by cable bacteria stimulates sulfate reduction in freshwater sediments. ISME J. 2020;14:1233–46.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
18.Damgaard LR, Risgaard‐Petersen N, Nielsen LP. Electric potential microelectrode for studies of electrobiogeophysics. J Geophys Res Biogeosci. 2014;119:1906–17.CAS 
Article 

Google Scholar 
19.Archie GE. The electrical resistivity log as an aid in determining some reservoir characteristics. T Am I Min Met Eng. 1942;146:54–61.
Google Scholar 
20.Nielsen LP. Denitrification in sediment determined from nitrogen isotope pairing. Fems Microbiol Ecol. 1992;86:357–62.CAS 
Article 

Google Scholar 
21.Risgaard-Petersen N, Rysgaard S. Nitrate reduction in sediments and waterlogged soil measured by 15N techniques. In: Alef K, Nannipieri P, editors. Methods in applied soil microbiology and biochemistry. Academic Press; 1995. p. 287–95.22.Risgaard-Petersen N, Rysgaard S, Revsbech NP. Combined microdiffusion-hypobromite oxidation method for determining N-15 isotope in ammonium. Soil Sci Soc Am J. 1995;59:1077–80.CAS 
Article 

Google Scholar 
23.Bower CE, Holmhansen T. A salicylate-hypochlorite method for determining ammonia in seawater. Can J Fish Aquat Sci. 1980;37:794–8.CAS 
Article 

Google Scholar 
24.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
25.Bushmanova E, Antipov D, Lapidus A, Prjibelski AD. rnaSPAdes: a de novo transcriptome assembler and its application to RNA-Seq data. Gigascience. 2019; 8:giz100.26.Rho M, Tang H, Ye Y. FragGeneScan: predicting genes in short and error-prone reads. Nucleic Acids Res. 2010;38:e191.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
27.Fish JA, Chai B, Wang Q, Sun Y, Brown CT, Tiedje JM, et al. FunGene: the functional gene pipeline and repository. Front Microbiol. 2013;4:291.PubMed 
PubMed Central 
Article 

Google Scholar 
28.Welsh A, Chee-Sanford JC, Connor LM, Löffler FE, Sanford RA. Refined NrfA phylogeny improves PCR-based nrfA gene detection. Appl Environ Micro. 2014;80:2110–9.Article 
CAS 

Google Scholar 
29.Jepson BJN, Marietou A, Mohan S, Cole JA, Butler CS, Richardson DJ. Evolution of the soluble nitrate reductase: defining the monomeric periplasmic nitrate reductase subgroup. Biochem Soc T. 2006;34:122–6.CAS 
Article 

Google Scholar 
30.Haase D, Hermann B, Einsle O, Simon J. Epsilonproteobacterial hydroxylamine oxidoreductase (epsilon Hao): characterization of a ‘missing link’ in the multihaem cytochrome c family. Mol Microbiol. 2017;105:127–38.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
31.Klotz MG, Schmid MC, Strous M, op den Camp HJ, Jetten MS, Hooper AB. Evolution of an octahaem cytochrome c protein family that is key to aerobic and anaerobic ammonia oxidation by bacteria. Environ Microbiol. 2008;10:3150–63.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
32.Betlach MR, Tiedje JM. Kinetic explanation for accumulation of nitrite, nitric-oxide, and nitrous-oxide during bacterial denitrification. Appl Environ Micro. 1981;42:1074–84.CAS 
Article 

Google Scholar 
33.Simon J, Klotz MG. Diversity and evolution of bioenergetic systems involved in microbial nitrogen compound transformations. BBA Bioenerg. 2013;1827:114–35.CAS 
Article 

Google Scholar 
34.Hermans M, Lenstra WK, Hidalgo-Martinez S, van Helmond N, Witbaard R, Meysman FJR, et al. Abundance and biogeochemical impact of cable bacteria in Baltic sea sediments. Environ Sci Technol. 2019;53:7494–503.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
35.Marshall IPG, Starnawski P, Cupit C, Fernandez Caceres E, Ettema TJG, Schramm A, et al. The novel bacterial phylum Calditrichaeota is diverse, widespread and abundant in marine sediments and has the capacity to degrade detrital proteins. Environ Microbiol Rep. 2017;9:397–403.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
36.Lefevre CT, Frankel RB, Abreu F, Lins U, Bazylinski DA. Culture-independent characterization of a novel, uncultivated magnetotactic member of the Nitrospirae phylum. Environ Microbiol. 2011;13:538–49.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
37.Logares R, Brate J, Bertilsson S, Clasen JL, Shalchian-Tabrizi K, Rengefors K. Infrequent marine-freshwater transitions in the microbial world. Trends Microbiol. 2009;17:414–22.CAS 
PubMed 
Article 

Google Scholar 
38.Trojan D, Schreiber L, Bjerg JT, Boggild A, Yang T, Kjeldsen KU, et al. A taxonomic framework for cable bacteria and proposal of the candidate genera Electrothrix and Electronema. Syst Appl Microbiol. 2016;39:297–306.PubMed 
PubMed Central 
Article 

Google Scholar 
39.Dam AS, Marshall IPG, Risgaard-Petersen N, Burdorf LDW, Marzocchi U. Effect of salinity on cable bacteria species composition and diversity. Environ Microbiol. 2021;23:2605–16.CAS 
PubMed 
Article 

Google Scholar 
40.Jones CM, Stres B, Rosenquist M, Hallin S. Phylogenetic analysis of nitrite, nitric oxide, and nitrous oxide respiratory enzymes reveal a complex evolutionary history for denitrification. Mol Biol Evol. 2008;25:1955–66.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
41.Graf DR, Jones CM, Hallin S. Intergenomic comparisons highlight modularity of the denitrification pathway and underpin the importance of community structure for N2O emissions. PLoS ONE. 2014;9:e114118.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
42.Kraft B, Strous M, Tegetmeyer HE. Microbial nitrate respiration – Genes, enzymes and environmental distribution. J Biotechnol. 2011;155:104–17.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
43.Simon J, Sänger M, Schuster SC, Gross R. Electron transport to periplasmic nitrate reductase (NapA) of Wolinella succinogenes is independent of a NapC protein. Mol Microbiol. 2003;49:69–79.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
44.Bjerg JT, Boschker HTS, Larsen S, Berry D, Schmid M, Millo D, et al. Long-distance electron transport in individual, living cable bacteria. Proc Natl Acad Sci USA. 2018;115:5786–91.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
45.Venceslau SS, Lino RR, Pereira IAC. The Qrc membrane complex, related to the alternative complex III, is a menaquinone reductase involved in sulfate respiration. J Biol Chem. 2010;285:22774–83.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Meysman FJR, Risgaard-Petersen N, Malkin SY, Nielsen LP. The geochemical fingerprint of microbial long-distance electron transport in the seafloor. Geochim Cosmochim Ac. 2015;152:122–42.CAS 
Article 

Google Scholar 
47.Kern M, Simon J. Electron transport chains and bioenergetics of respiratory nitrogen metabolism in Wolinella succinogenes and other Epsilonproteobacteria. Biochim Biophys Acta. 2009;1787:646–56.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
48.Chen Y, Wang F. Insights on nitrate respiration by Shewanella. Front Mar Sci. 2015; 1:80.49.Gao H, Yang ZK, Barua S, Reed SB, Romine MF, Nealson KH, et al. Reduction of nitrate in Shewanella oneidensis depends on atypical NAP and NRF systems with NapB as a preferred electron transport protein from CymA to NapA. ISME J. 2009;3:966–76.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
50.Geerlings NMJ, Karman C, Trashin S, As KS, Kienhuis MVM, Hidalgo-Martinez S, et al. Division of labor and growth during electrical cooperation in multicellular cable bacteria. Proc Natl Acad Sci USA. 2020;117:5478–85.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.Scilipoti S, Koren K, Risgaard-Petersen N, Schramm A, Nielsen LP. Oxygen consumption of individual cable bacteria. Sci Adv. 2021; 7:eabe1870.52.Bjerg JT, Damgaard LR, Holm SA, Schramm A, Nielsen LP. Motility of electric cable bacteria. Appl Environ Microbiol. 2016;82:3816–21.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
53.Dam A-S, Marshall IPG, Petersen NR, Burdorf LDW, Marzocchi U. Effect of salinity on cable bacteria species composition and diversity. Environ Microbiol. 2021;23:2605–16.54.Westram R, Bader K, Pruesse E, Kumar Y, Meier H, Glöckner FO, et al. ARB: a software environment for sequence data. In: de Bruijn FJ, editor. Handbook of molecular microbial ecology I: metagenomics and complementary approaches. John Wiley & Sons, Inc.; Hoboken, New Jersey; 2011. p. 399–406. More

Research areaThe study was conducted in the Yunyang Experimental Station (108° 54′ E, 30° 55′ N; altitude of 700 m), Southwest University, Chongqing, China. The study area has a subtropical monsoon humid climate with an average annual sunshine duration of 1500 h, average annual temperature of 18.4 °C average annual rainfall of 1100.1 mm, and the rain period predominantly prolongs from June to September. Local soil type is clay loam in texture and Dystric Purple-Udic Cambosols according to the Chinese Soil Taxonomy (CRGCST 2001). Basic properties of 0–20 cm soil layer were as follows: pH 7.29, total N 0.94 g kg−1, total C 7.14 g kg−1, available N 37.45 mg kg−1, available P 2.36 mg kg−1, and available K 72.58 mg kg−1, respectively.The tested biochar was purchased from the Nanjing Qinfeng Straw Technology Co., Ltd. (Nanjing, China), which was made by pyrolysis of the rice (Oryza sativa L.) straw with limited oxygen supply at 500 °C for 2 h. Its properties were as follows: total N 0.61 g kg−1, total P 1.99 g kg−1, total K 27.15 g kg−1, total C 537.97 g kg−1 and pH 8.70.Experimental designA two-year filed experiment (2017–2019) was performed in a completely randomized design with twelve treatments in triplicates including two factors. The first factor was the application of biochar including B0 (0 t ha−1), B10 (10 t ha−1), B20 (20 t ha−1) and B40 (40 t ha−1); and the second factor is the application level N fertilizer including conventional rate (application amount by local farmers)-180 kg N ha−1 (N100), 80% of conventional rate-144 kg N ha−1 (N80) and 60% of conventional rate-108 kg N ha−1 (N60). The plot size was 3 m × 6 m with a border (0.5 m wide) between plots. Biochar was applied to soil only in the first year before the sowing of rapeseed. Each treatment plot received the same amount of potassium (90 kg K2O ha−1) and phosphorus (90 kg P2O5 ha−1). Further details of fertilizer application have been reported by Tian et al.24, being the same for the two-year experiment. Weed, pesticide, and pest management kept the same with the local farmers’ rapeseed management practices. Winter rapeseed (Sanxiayou No.5) was used in the experiment, which was sowed on 21 October 2017 and on 16 October 2018, respectively, and was harvested on 1 May in both years (2018 and 2019).Sampling and analysis of soil and cropCrop yieldRapeseed was hand-harvested when 70–80% of total seeds changed their color from green to black on 1 May 2019, and each plot was separately harvested for seed yield. Seed yield was calculated using 6% as standard seed moisture content.Soil indicesAfter the rapeseed harvest, soil samples were collected from all plots. Five sampling points were randomly selected within each plot. At each point, twenty soil cores of 2.5 cm diameter and 20.0 cm depth were taken in a 1 m radius of the point. All soil cores from each point were put in a plastic bag and thoroughly bulked, crumbled and mixed for physical, chemical and biological analyses. By dividing each soil sample into two subsamples, one subsample was ground, passed through a 2-mm sieve and was air-dried for the analyses of soil organic matter (SOM), total nitrogen (TN), total phosphorus (TP), total potassium (TK), alkali-hydrolyzale nitrogen (AN), available phosphorus (AP), available potassium (AK)25, particulate organic carbon (POC), water-soluble organic carbon (DOC), easily oxidized organic carbon (AOC)26, sucrase (SUC) and urease (URE)27, and another one was ground, passed through a 2-mm sieve and was stored in a refrigerator at − 20 °C for the analyses of structural and functional characteristics of soil microbial community28. At the same time, mixed soil samples (0–20 cm) from five points in each plot were taken using a shovel for soil aggregates analyses24.Drying method was used to determine soil water content (SWC); soil temperature (ST) was measured by temperature probe on the LI6400–09 (LI-COR Inc., Lincoln, NE); potassium dichromate oxidation method was used to determine SOM and DOC content; TN was measured by the Kjeldahl method; TP was determined by Mo-Sb colorimetric method; TK was determined by NaOH melting and analyzed using an atomic spectrophotometry; AN was determined by diffusion-absorption method; AP was quantified by colorimetric analysis following extraction of soil with 0.5 mol L−1 NaHCO3; AK was measured using 1.0 mol L−1 CH3COONH4 extraction; POC was determined by sodium hexametaphosphate dispersion method; AOC was measured by potassium permanganate oxidation method; SUC was measured by 3,5-dinitrosalicylic acid colorimetric determination method; URE was measured by phenol-sodium hypochlorite indophenol colorimetry method; amount of bacteria (B), fungi (F), actinomycetes (A), gram-positive bacteria (GP), gram-negative bacteria (GN) was measured by the Bligh–Dyer method; utilization of sugars (S), amino acids (AA), phenolic acids (PA), carboxylic acids (CA), amines (AM) and polymers (P) by microorganism was measured using commercial Biolog EcoPlate (Biolog Inc., CA, USA).Shannon index (H), Simpson index (D), and evenness index (E) were calculated by the following equations:$$ {text{AWCD}} = sum {(C_{i} – R_{i} )} /n $$$$ {text{H}} = – sum {P_{i} } (ln P_{i} )quad P_{i} = (C_{i} – R_{i} )/sum {(C_{i} – R_{i} } ) $$$$ {text{D}} = 1 – sum P _{i}^{2} $$$$ {text{E}} = {text{H}}/ln {text{S}} $$where n is the 31 carbon sources on the ECO board; Ci and Ri and are the optical density values of the microwell and the control well respectively; Pi is the ratio of the absorbance of a particular well i to the sums of absorbance of all 31well at 120 h; S is the number of color change holes, which represents the number of carbon source used by the microbial community; Average well color development (AWCD), representing the overall carbon substrate utilization potential of cultural microbial communities across all wells per plate.In order to investigate the aggregate structure, all bulk clod samples from each plot were carefully mixed and then gently sieved to pass through a 10-mm sieve. According to the wet-sieving and dry-sieving protocol, the tested soil was fractionated into  > 5, 2 ~ 5, 1 ~ 2, 0.25 ~ 1 and  0.25} right)} }}{{sumnolimits_{{i = 1}}^{n} {(w_{i} )} }} times 100% $$$$ {text{D – MWD}}left( {{text{W – MWD}}} right) = sumlimits_{{i = 1}}^{n} {(bar{d}_{i} w_{i} )} $$$$ {text{D – GMD}}left( {{text{W – GMD}}} right) = exp left[ {frac{{sumlimits_{{i = 1}}^{n} {m_{i} ln bar{d}_{i} } }}{{sumlimits_{{i = 1}}^{n} {m_{i} } }}} right] $$where DR0.25 and WR0.25 are the proportion of  > 0.25 mm soil mechanical-stable aggregates and water-stable aggregates, respectively; D-MWD and W-MWD are the mean weight diameter of mechanical-stable aggregates and water-stable aggregates (mm), respectively; D-GMD and W-GMD are the mean geometric diameter of mechanical-stable aggregates and water-stable aggregates (mm), respectively; mi is mass in size fraction i; and wi is the proportion (%) of the total sample mass in size fraction i and di is mean diameter of size fraction i.Evaluation of soil fertilityGrey correlation analysisGrey correlation analysis refers to a method of quantitative description and comparison of a system’s development and change. The basic idea is to determine whether they are closely connected by determining the geometric similarity of the reference data column and several comparison data columns, which reflects the degree of correlation between the curves29. The grey relational coefficient ξi (k) can be expressed as follows:$$ xi (k) = frac{{mathop {min }limits_{i} mathop {min }limits_{k} left| {x_{0} (k) – x_{i} (k)} right| + rho mathop {max }limits_{i} mathop {max }limits_{k} left| {x_{0} (k) – x_{i} (k)} right|}}{{left| {x_{0} (k) – x_{i} (k)} right| + rho mathop {max }limits_{i} max left| {mathop {x_{0} (k)}limits_{k} – x_{i} (k)} right|}} $$$$ x_{i}^{k} = frac{{x_{i}^{k} }}{{mathop {max }limits_{i} x_{i}^{k} }} $$$$ gamma _{i} = frac{1}{n}sumlimits_{{k = i}}^{n} {xi _{i} } (k) $$$$ omega _{{i(gamma )}} = frac{1}{n}sumlimits_{{i = 1}}^{n} {gamma _{i} } $$$$ G_{i}^{k} = sumlimits_{{i = 1}}^{n} {left( {xi _{i} times omega _{{i(gamma )}} } right),quad k = 1,2,3, ldots ,n;quad i = 1,2,3, ldots ,n} $$where (x_{i}^{k}) The i trait observation value of treatment k; (mathop {max }limits_{i} x_{i}^{k}) The maximum value of the i trait in all treatments; (mathop {min }limits_{i} x_{i}^{k}) The minimum value of the i trait in all treatments; (mathop {min }limits_{i} mathop {min }limits_{k} left| {x_{0} (k) – x_{i} (k)} right|) Second level minimum difference; (mathop {max }limits_{i} mathop {max }limits_{k} left| {x_{0} (k) – x_{i} (k)} right|) Second level maximum difference; (rho) Resolution coefficient (0.5).Principal component analysisPrincipal component analysis refers to a multivariate statistical method that converts multiple indicators into several comprehensive indicators by the idea of dimensionality under the premise of losing little information. It simplifies the complexity in high-dimensional data while retaining trends and patterns30.Cluster analysisCluster analysis comprises a range of methods for classifying multivariate data into subgroups. Using the euclidean distance as a measure of the difference in the fertility of each treatment, the shortest distance method was used to systematically cluster according to the degree of intimacy and similarity of soil fertility levels. By organizing multivariate data into such subgroups, clustering can help reveal the characteristics of any structure or patterns present31.Statistical analysisCorrelation analysis was performed to assess the relationships between rapeseed yield and soil attributes. Grey correlation analysis and principal component analysis were performed to establish comprehensive score for soil fertility and the main soil factors affecting rapeseed yield. Cluster analysis was used to cluster the soil fertility of each treatment. All the statistical analyses were performed using Excel 2018 (Office Software, Inc., Beijing, China) and SPSS 17.0 (SPSS Inc., Chicago, Illinois, USA). The comparisons of treatment means were based on LSD test at the P  More

The cell modelGrowth rate of a cellThe growth rate of a bacteria cell depends on the acquisition of C (from the particle) and N (from the particle and through ({{rm{N}}}_{2}) fixation), as well as on metabolic expenses in terms of C.Uptake of C and NBacteria get C from glucose and both C and N from amino acids. The total amount of C available for the cell from monomers is (units of C per time)$${J}_{{rm{DOC}}}={f}_{{rm{G,C}}}J_{G}+{f}_{{rm{A}},{rm{C}}}{J}_{{rm{A}}},$$
(8)
and the amount of N available from monomer is (N per time)$${J}_{{rm{DON}}}={f}_{{rm{A}},{rm{N}}}{J}_{{rm{A}}},$$
(9)
where ({J}_{rm{G}}) and ({J}_{rm{A}}) are uptake rates of glucose and amino acids, ({f}_{rm{G,C}}) is the fraction of C in glucose, and ({f}_{rm{A,C}}) and ({f}_{rm{A,N}}) are fractions of C and N in amino acids.The rate of obtaining N through ({{rm{N}}}_{2}) fixation is:$${J}_{{{rm{N}}}_{2}}({{psi }})={{psi }}{M}_{{{rm{N}}}_{2}},$$
(10)
where ({psi },(0 < {{psi }} < 1)) regulates ({{rm{N}}}_{2}) fixation rate and fixation can happen at a maximum rate ({M}_{{{rm{N}}}_{2}}). ({{rm{N}}}_{2}) fixation is only limited by the maximum ({{rm{N}}}_{2}) fixation rate as dissolved dinitrogen (({{rm{N}}}_{2})) gas in seawater is assumed to be unlimited70.The total uptake of C and N from different sources becomes$${J}_{{rm{C}}}={J}_{{rm{DOC}}}$$ (11) $${J}_{{rm{N}}}({{psi }})={J}_{{rm{DON}}}+{J}_{{{rm{N}}}_{2}}({{psi }})$$ (12) CostsRespiratory costs of cellular processes together with ({{rm{N}}}_{2}) fixation and its associated ({{rm{O}}}_{2}) removal cost depend on the cellular ({{rm{O}}}_{2}) concentration. Two possible scenarios can be observed: Case 1: When ({O}_{2}) concentration is sufficient to maintain aerobic respiration Respiratory costs for bacterial cellular maintenance can be divided into two parts: one dependent on limiting substrates and the other one is independent of substrate concentration71. Here we consider only the basal respiratory cost ({R}_{rm{B}}{x}_{rm{B}}), which is independent of the limiting substrates and is assumed as proportional to the mass of the cell ({x}_{B}) (μg C). In order to solubilize particles, particle-attached bacteria produce ectoenzymes that cleave bonds to make molecules small enough to be transported across the bacterial cell membrane. Cleavage is represented by a biomass-specific ectoenzyme production cost ({R}_{rm{E}})72. The metabolic costs associated with the uptake of hydrolysis products and intracellular processing are assumed to be proportional to the uptake (({J}_{i})): ({R}_{{rm{G}}}{J}_{{rm{G}}}) and ({R}_{{rm{A}}}{J}_{{rm{A}}}) where the ({R}_{i})’s are costs per unit of resource uptake. In a similar way, the metabolic cost of ({{rm{N}}}_{2}) fixation is assumed as proportional to the ({{rm{N}}}_{2}) fixation rate: ({R}_{{{rm{N}}}_{2}}{rho }_{{rm{CN}},{rm{B}}}{J}_{{{rm{N}}}_{2}}), where ({rho }_{{rm{CN}},{rm{B}}}) is the bacterial C:N ratio. If we define all the above costs as direct costs, then the total direct respiratory cost becomes$${R}_{{rm{D}}}({{psi }})={R}_{{rm{B}}}{x}_{{rm{B}}}+{R}_{{rm{E}}}{x}_{{rm{B}}}+{R}_{{rm{G}}}{J}_{{rm{G}}}+{R}_{{rm{A}}}{J}_{{rm{A}}}+{R}_{{{rm{N}}}_{2}}{rho }_{{rm{CN}},{rm{B}}}{J}_{{{rm{N}}}_{2}}({{psi }}).$$ (13) Indirect costs related to ({{rm{N}}}_{2}) fixation arises from the removal of ({{rm{O}}}_{2}) from the cell and the production/replenishment of nitrogenase as the enzyme is damaged by ({{rm{O}}}_{2}). The cell can remove ({{rm{O}}}_{2}) either by increasing respiration73 or by increasing the production of nitrogenase enzyme itself74. Here we consider only the process of ({{rm{O}}}_{2}) removal by increasing respiration. To calculate this indirect cost, the concentration of ({{rm{O}}}_{2}) present in the cell needs to be estimated.Since the time scale of ({{rm{O}}}_{2}) concentration inside a cell is short, we have assumed a pseudo steady state inside the cell; the ({{rm{O}}}_{2}) diffusion rate inside a cell is always balanced by the respiration rate14, which can be expressed as$${rho }_{{rm{CO}}}{F}_{{{rm{O}}}_{2}}={R}_{{rm{D}}}left({{psi }}right).$$ (14) Here ({rho }_{{rm{CO}}}) is the conversion factor of respiratory ({{rm{O}}}_{2}) to C equivalents and ({F}_{{{rm{O}}}_{2}}) is the actual ({{rm{O}}}_{2}) diffusion rate into a cell from the particle and can be calculated as$${F}_{{{rm{O}}}_{2}}=4{rm{pi }}{r}_{{rm{B}}}{K}_{{{rm{O}}}_{2}}left({X}_{{{rm{O}}}_{2}}-{X}_{{{rm{O}}}_{2},{rm{C}}}right),$$ (15) where ({r}_{{rm{B}}}) is the cell radius, ({X}_{{{rm{O}}}_{2}}) is the local ({{rm{O}}}_{2}) concentration inside the particle, ({X}_{rm{{O}}_{2},{rm{C}}}) is the cellular ({{rm{O}}}_{2}) concentration, and ({K}_{{{rm{O}}}_{2}}) is the effective diffusion coefficient of ({{rm{O}}}_{2}) over cell membrane layers. The effective diffusion coefficient can be calculated according to Inomura et al.14 in terms of diffusion coefficient inside particles (({bar{D}}_{{{rm{O}}}_{2}})), the diffusivity of cell membrane layers relative to water (({varepsilon }_{{rm{m}}})), the radius of cellular cytoplasm (({r}_{{rm{C}}})), and the thickness of cell membrane layers (({L}_{{rm{m}}})) as$${K}_{{{rm{O}}}_{2}}={bar{D}}_{{{rm{O}}}_{2}}frac{{varepsilon }_{{rm{m}}}({r}_{{rm{C}}}+{L}_{{rm{m}}})}{{varepsilon }_{{rm{m}}}{r}_{{rm{C}}}+{L}_{{rm{m}}}}.$$ (16) The apparent diffusivity inside particles (({bar{D}}_{{{rm{O}}}_{2}})) is considered as a fraction ({f}_{{{rm{O}}}_{2}}) of the diffusion coefficient in seawater (({D}_{{{rm{O}}}_{2}}))$${bar{D}}_{{{rm{O}}}_{2}}={f}_{{{rm{O}}}_{2}}{D}_{{{rm{O}}}_{2}}.$$ (17) Combining (14) and (15) gives the cellular ({{rm{O}}}_{2}) concentration ({X}_{{rm{O}}_{2},{rm{C}}}) as$${X}_{{{rm{O}}}_{2},{rm{C}}}={{max }}left[0,{X}_{{{rm{O}}}_{2}}-frac{{R}_{{rm{D}}}left({{psi }}right)}{4{rm{pi }}{r}_{{rm{B}}}{K}_{{{rm{O}}}_{2}}{rho }_{{rm{CO}}}}right].$$ (18) If there is excess ({{rm{O}}}_{2}) present in the cell after respiration (({X}_{{rm{O}}_{2},{rm{C}}} , > , 0)), then the indirect cost of removing the excess ({{rm{O}}}_{2}) to be able to perform ({{rm{N}}}_{2}) fixation can be written as$${R}_{{{rm{O}}}_{2}}left({{psi }}right)=Hleft({{psi }}right){rho }_{{rm{CO}}}4{rm{pi }}{r}_{{rm{B}}}{K}_{{{rm{O}}}_{2}}{X}_{rm{{O}}_{2},{rm{C}}},$$
(19)
where (H({{psi }})) is the Heaviside function:$$Hleft({{psi }}right)=left{begin{array}{cc}0,&{rm{if}}{,}{{psi }}=0\ 1, &{rm{if}}{,}{{psi }} , > , 0end{array}right..$$
(20)
Therefore, the total aerobic respiratory cost becomes:$${R}_{{rm{tot}},{rm{A}}}left({{psi }}right)={R}_{{rm{D}}}left({{psi }}right)+{R}_{{{rm{O}}}_{2}}left({{psi }}right).$$
(21)

Case 2: Anaerobic respiration
When available ({{rm{O}}}_{2}) is insufficient to maintain aerobic respiration (({R}_{{rm{tot}}}left({{psi }}right) , > , {rho }_{{rm{CO}}}{F}_{{{rm{O}}}_{2},{{max }}})), cells use ({{{rm{NO}}}_{3}}^{-}) and ({{{rm{SO}}}_{4}}^{2-}) for respiration. The potential ({{{rm{NO}}}_{3}}^{-}) uptake, ({J}_{{{rm{NO}}}_{3},{rm{pot}}}), is$${J}_{{{rm{NO}}}_{3},{rm{pot}}}={M}_{{{rm{NO}}}_{3}}frac{{A}_{{{rm{NO}}}_{3}}{X}_{{{rm{NO}}}_{3}}}{{A}_{{{rm{NO}}}_{3}}{X}_{{{rm{NO}}}_{3}}+{M}_{{{rm{NO}}}_{3}}},$$
(22)
where ({M}_{{{rm{NO}}}_{3}}) and ({A}_{{{rm{NO}}}_{3}}) are maximum uptake rate and affinity for ({{{rm{NO}}}_{3}}^{-}) uptake, respectively. However, the actual rate of ({{{rm{NO}}}_{3}}^{-}) uptake, ({J}_{{{rm{NO}}}_{3}}), is determined by cellular respiration and can be written as$${J}_{{{rm{NO}}}_{3}}={{min }}left({J}_{{{rm{NO}}}_{3},{rm{pot}}},{{max }}left(0,frac{{R}_{{rm{tot}},{rm{A}}}left({{psi }}right)-{rho }_{{rm{CO}}}{F}_{{{rm{O}}}_{2},{{max }}}}{{rho }_{{rm{C}}{{rm{NO}}}_{3}}}right)right),$$
(23)
where ({rho }_{{rm{C}}{{rm{NO}}}_{3}}) is the conversion factor of respiratory ({{{rm{NO}}}_{3}}^{-}) to C equivalents and the maximum ({{rm{O}}}_{2}) diffusion rate into a cell ({F}_{{{rm{O}}}_{2},{{max }}}) can be obtained by making cellular ({{rm{O}}}_{2}) concentration ({X}_{{{rm{O}}}_{2},{rm{c}}}) zero in (15) as$${F}_{{{rm{O}}}_{2},{{max }}}=4{rm{pi }}{r}_{{rm{B}}}{K}_{{{rm{O}}}_{2}}{X}_{{{rm{O}}}_{2}},$$
(24)
Further, in the absence of sufficient ({{{rm{NO}}}_{3}}^{-}), the cell uses ({{{rm{SO}}}_{4}}^{2-}) as an electron acceptor for respiration. Since the average concentration of ({{{rm{SO}}}_{4}}^{2-}) in seawater is 29 mmol L−1 75, ({{{rm{SO}}}_{4}}^{2-}) is a nonlimiting nutrient for cell growth and the potential uptake rate of ({{{rm{SO}}}_{4}}^{2-}) is mainly governed by the maximum uptake rate as$${J}_{{{rm{SO}}}_{4},{rm{pot}}}={M}_{{{rm{SO}}}_{4}},$$
(25)
where ({M}_{{{rm{SO}}}_{4}}) is the maximum uptake rate for ({{{rm{SO}}}_{4}}^{2-}) uptake. The actual rate of ({{{rm{SO}}}_{4}}^{2-}) uptake, ({J}_{{{rm{SO}}}_{4}}), can be written as$${J}_{{{rm{SO}}}_{4}}={{min }}left({J}_{{{rm{SO}}}_{4},{rm{pot}}},{{max }}left(0,frac{{R}_{{rm{tot}},{rm{A}}}left({{psi }}right)-{rho }_{{rm{CO}}}{F}_{{{rm{O}}}_{2},{{max }}}-{rho }_{{rm{CN}}{{rm{O}}}_{3}}{F}_{{{rm{NO}}}_{3},{rm{pot}}}}{{rho }_{{rm{C}}{{rm{SO}}}_{4}}}right)right),$$
(26)
where ({rho }_{{rm{C}}{{rm{SO}}}_{4}}) is the conversion factor of respiratory ({{{rm{SO}}}_{4}}^{2-}) to C equivalents.According to formulations (23) and (26), ({{{rm{NO}}}_{3}}^{-}) and ({{{rm{SO}}}_{4}}^{2-}) uptake occurs only when the diffusive flux of ({{rm{O}}}_{2}), and both ({{rm{O}}}_{2}) and ({{{rm{NO}}}_{3}}^{-}) are insufficient to maintain respiration(.) Moreover, the uptake rates of ({{{rm{NO}}}_{3}}^{-}) and ({{{rm{SO}}}_{4}}^{2-}) are regulated according to the cells’ requirements.Uptakes of ({{{rm{NO}}}_{3}}^{-}) and ({{{rm{SO}}}_{4}}^{2-}) incur extra metabolic costs ({R}_{{{rm{NO}}}_{3}}{rho }_{{rm{C}}{{rm{NO}}}_{3}}{J}_{{{rm{NO}}}_{3}}) and ({R}_{{{rm{SO}}}_{4}}{rho }_{{rm{C}}{{rm{SO}}}_{4}}{J}_{{{rm{SO}}}_{4}}), where ({R}_{{{rm{NO}}}_{3}}) and ({R}_{{{rm{SO}}}_{4}}) are costs per unit of ({{{rm{NO}}}_{3}}^{-}) and ({{{rm{SO}}}_{4}}^{2-}) uptake. The total respiratory cost can be written as$${R}_{{rm{tot}}}left({{psi }}right)={R}_{{rm{tot}},{rm{A}}}left({{psi }}right)+{R}_{{{rm{NO}}}_{3}}{rho }_{{rm{C}}{{rm{NO}}}_{3}}{J}_{{{rm{NO}}}_{3}}+{R}_{{{rm{SO}}}_{4}}{rho }_{{rm{C}}{{rm{SO}}}_{4}}{J}_{{{rm{SO}}}_{4}}.$$
(27)
Synthesis and growth rateThe assimilated C and N are combined to synthesize new structure. The synthesis rate is constrained by the limiting resource (Liebig’s law of the minimum) and by available electron acceptors such that the total flux of C available for growth ({J}_{{rm{tot}}}) (μg C d−1) is:$${J}_{{rm{tot}}}left({{psi }}right)={{min }}left[{J}_{{rm{C}}}-{R}_{{rm{tot}}}left({{psi }}right),{rho }_{{rm{CN,B}}}{J}_{{rm{N}}}left({{psi }}right),{rho }_{{rm{CO}}}{F}_{{{rm{O}}}_{2}}+{rho }_{{rm{C}}{{rm{NO}}}_{3}}{J}_{{{rm{NO}}}_{3}}+{rho }_{{rm{C}}{{rm{SO}}}_{4}}{J}_{{{rm{SO}}}_{4}}right].$$
(28)
Here, the total available C for growth is ({J}_{{rm{C}}}-{R}_{{rm{tot}}}({{psi }})), the C required to synthesize biomass from N source is ({rho }_{{rm{CN}},B}{J}_{rm{N}}), and the C equivalent inflow rate of electron acceptors to the cell is ({rho }_{{rm{CO}}}{F}_{{rm{O}}_{2}}+{rho }_{{rm{C}}{{rm{NO}}}_{3}}{J}_{{{rm{NO}}}_{3}}+{rho }_{{rm{C}}{{rm{SO}}}_{4}}{J}_{{{rm{SO}}}_{4}}). We assume that excess C or N is released from the cell instantaneously.Synthesis is not explicitly limited by a maximum synthesis capacity; synthesis is constrained by the C and N uptake in the functional responses (Eqs. 34 and 35). The division rate (mu) of the cell (d−1) is the total flux of C available for growth divided by the C mass of the cell (({x}_{rm{B}})):$$mu ({{psi }})={J}_{{rm{tot}}}({{psi }})/{x}_{rm{B}}.$$
(29)
The resulting division rate, (mu), is a measure of the bacterial fitness and we assume that the cell regulates its ({{rm{N}}}_{2}) fixation rate depending on the environmental conditions to gain additional N while maximizing its growth rate. The optimal value of the parameter regulating ({{rm{N}}}_{2}) fixation ({{psi }}) ((0le {{psi }}le 1)) then becomes:$${{{psi }}}^{ast }={{arg }}mathop{{{max }}}limits_{{{psi }}}{mu ({{psi }})},$$
(30)
and the corresponding optimal division rate becomes$${mu }^{ast }=mu left({{{psi }}}^{ast }right).$$
(31)
The particle modelWe consider a sinking particle of radius ({r}_{{rm{P}}}) (cm) and volume ({V}_{{rm{P}}}) (cm3) (Supplementary Fig. S1). The particle contains facultative nitrogen-fixing bacterial population (B(r)) (cells L−1), polysaccharides ({C}_{{rm{P}}}(r)) (μg G L−1), and polypeptides ({P}_{{rm{P}}}(r)) (μg A L−1) at a radial distance (r) (cm) from the center of the particle, where G and A stand for glucose and amino acids. We assume that only fractions ({f}_{{rm{C}}}) and ({f}_{{rm{P}}}) of these polymers are labile (({C}_{{rm{L}}}(r)={f}_{{rm{C}}}{C}_{{rm{P}}}(r),) ({P}_{{rm{L}}}(r)={f}_{{rm{P}}}{P}_{{rm{P}}}(r))), i.e., accessible by bacteria. Bacterial enzymatic hydrolysis converts the labile polysaccharides and polypeptides into monosaccharides (glucose) ((G) μg G L−1) and amino acids ((A) μg A L−1) that are efficiently taken up by bacteria. Moreover, the particle contains ({{rm{O}}}_{2}), ({{{rm{NO}}}_{3}}^{-}), and ({{{rm{SO}}}_{4}}^{2-}) with concentrations ({X}_{{{rm{O}}}_{2}}(r)) (μmol O2 L−1), ({X}_{{{rm{NO}}}_{3}}(r)) (μmol NO3 L−1), and ({X}_{{{rm{SO}}}_{4}}(r)) (μmol SO4 L−1). Glucose and amino acids diffuse out of the particle whereas ({{rm{O}}}_{2}) and ({{{rm{NO}}}_{3}}^{-}) diffuse into the particle from the surrounding environment. Due to the high concentration of ({{{rm{SO}}}_{4}}^{2-}) in ocean waters, we assume that ({{{rm{SO}}}_{4}}^{2-}) is not diffusion limited inside particles, its uptake is limited by the maximum uptake capacity due to physical constraint. The interactions between particle, cells, and the surrounding environment are explained in Supplementary Fig. S1 and equations are provided in Table 1 of the main text.We assume that labile polysaccharide (({C}_{{rm{L}}})) and polypeptide (({P}_{{rm{L}}})) are hydrolyzed into glucose and amino acids at rates ({J}_{{rm{C}}}) and ({J}_{{rm{P}}}) with the following functional form$${J}_{{rm{C}}}={h}_{{rm{C}}}frac{{A}_{{rm{C}}}{C}_{{rm{L}}}}{{h}_{{rm{C}}}+{A}_{{rm{C}}}{C}_{{rm{L}}}}$$
(32)
$${J}_{{rm{P}}}={h}_{{rm{P}}}frac{{A}_{{rm{P}}}{P}_{{rm{L}}}}{{h}_{{rm{P}}}+{A}_{{rm{P}}}{P}_{{rm{L}}}}$$
(33)
where ({h}_{{rm{C}}}) and ({h}_{{rm{P}}}) are maximum hydrolysis rates of the carbohydrate and peptide pool, and ({A}_{{rm{C}}}) and ({A}_{{rm{P}}}) are respective affinities. ({J}_{{rm{G}}}) and ({J}_{{rm{A}}}) represent uptake of glucose and amino acids:$${J}_{{rm{G}}}={M}_{{rm{G}}}frac{{A}_{{rm{G}}}G}{{A}_{{rm{G}}}G+{M}_{{rm{G}}}}$$
(34)
$${J}_{{rm{A}}}={M}_{{rm{A}}}frac{{A}_{{rm{A}}}A}{{A}_{{rm{A}}}A+{M}_{{rm{A}}}}$$
(35)
where ({M}_{{rm{G}}}) and ({M}_{{rm{A}}}) are maximum uptake rates of glucose and amino acids, whereas ({A}_{{rm{G}}}) and ({A}_{{rm{A}}}) are corresponding affinities. Hydrolyzed monomers diffuse out of the particle at a rate ({D}_{{rm{M}}}).({mu }^{ast }) is the optimal division rate of cells (Eq. 31) and ({m}_{rm{B}}) represents the mortality rate (including predation) of bacteria. ({F}_{{{rm{O}}}_{2}}) and ({J}_{{{rm{NO}}}_{3}}) represent the diffusive flux of ({{rm{O}}}_{2}) and the consumption rate of ({{{rm{NO}}}_{3}}^{-}), respectively, through the bacterial cell membrane. ({bar{D}}_{{{rm{O}}}_{2}}) and ({bar{D}}_{{{rm{NO}}}_{3}}) are diffusion coefficients of ({{rm{O}}}_{2}) and ({{{rm{NO}}}_{3}}^{-}) inside the particle.At the center of the particle ((r=0)) the gradient of all quantities vanishes:$${left.frac{partial G}{partial r}right|}_{r=0}={left.frac{partial A}{partial r}right|}_{r=0}={left.frac{partial {X}_{{{rm{O}}}_{2}}}{partial r}right|}_{r=0}={left.frac{partial {X}_{{rm{N}}{{rm{O}}}_{3}}}{partial r}right|}_{r=0}=0$$
(36)
At the surface of the particle ((r={r}_{{rm{P}}})) concentrations are determined by the surrounding environment:$${left.Gright|}_{r={r}_{{rm{P}}}}={G}_{infty },{left.Aright|}_{r={r}_{{rm{P}}}}={A}_{infty },{left.{X}_{{{rm{O}}}_{2}}right|}_{r={r}_{{rm{P}}}}={X}_{{{rm{O}}}_{2},infty },{left.{X}_{{{rm{NO}}}_{3}}right|}_{r={r}_{{rm{P}}}}={X}_{{{rm{NO}}}_{3},infty }$$
(37)
where ({G}_{infty },) ({A}_{infty ,}) ({X}_{{{rm{O}}}_{2},infty }) and ({X}_{{{rm{NO}}}_{3},infty }) are concentrations of glucose, amino acids, ({{rm{O}}}_{2}), and ({{{rm{NO}}}_{3}}^{-}) in the environment.Calculation of total N2 fixation rateThe total amount of fixed ({{rm{N}}}_{2}) in a specific size class of particle, ({{rm{N}}}_{{rm{fix}},{rm{P}}}) (({rm{mu }})g N particle−1), is calculated as$${{rm{N}}}_{{rm{fix}},{rm{P}}}=int int 4pi {{r}_{{rm{B}}}}^{2}B{J}_{{{rm{N}}}_{2}}{rm{d}}{r}_{{rm{P}}}{rm{dz}},$$
(38)
where ({r}_{{rm{P}}}) (cm) is the particle radius and z (m) represents the water column depth.({{rm{N}}}_{2}) fixation rate per unit volume of water, ({{rm{N}}}_{{rm{fix}},{rm{V}}}left(tright)) (({rm{mu }}{rm{mol}}) N m−3 d−1), is calculated as$${{rm{N}}}_{{rm{fix}},{rm{V}}}=int int 4pi {{r}_{{rm{B}}}}^{2}rho B{J}_{{{rm{N}}}_{2}}n(x){rm{d}}{r}_{{rm{P}}}{rm{d}}x,$$
(39)
Here (x) (cm) represents the size range (radius) of particles, (rho) is the fraction of diazotrophs of the total heterotrophic bacteria, and (n(x)) (number of particles per unit volume of water per size increment) is the size spectrum of particles that is most commonly approximated by a power law distribution of the form$$n(x)={n}_{0}{(2x)}^{xi }$$
(40)
where ({n}_{0}) is a constant that controls total particle abundance and the slope (xi) represents the relative concentration of small to large particles: the steeper the slope, the greater the proportion of smaller particles and the flatter the slope, and the greater the proportion of larger particles34.Depth-integrated ({{rm{N}}}_{2}) fixation rate, ({{rm{N}}}_{{rm{fix}},{rm{D}}}) (({rm{mu }}{rm{mol}}) N m−2 d−1), can be obtained by$${{rm{N}}}_{{rm{fix}},{rm{D}}}left(tright)=int {{rm{N}}}_{{rm{fix}},{rm{V}}}{rm{d}}z.$$
(41)
Assumptions and simplification in the modeling approachAccording to our current model formulation, the particle size remains constant while sinking. However, in nature, particle size is dynamic due to processes like bacterial remineralization, aggregation, and disaggregation. We neglect these complications to keep the model simple and to focus on revealing the coupling between particle-associated environmental conditions and ({{rm{N}}}_{2}) fixation by heterotrophic bacteria. These factors can, however, possibly be incorporated by using in situ data or by using the relationship between carbon content and the diameter of particles48 and including terms for aggregation and disaggregation55.Our model represents a population of facultative heterotrophic diazotrophs that grow at a rate similar to other heterotrophic bacteria but the whole community initiates ({{rm{N}}}_{2}) fixation when conditions become suitable. However, under natural conditions, diazotrophs may only constitute a fraction of the bacterial community, and their proliferation may be gradual21, presumably affected by multiple factors. In such case, our approach will overestimate diazotroph cell concentration and consequently the ({{rm{N}}}_{2}) fixation rate.For simplicity, our approach includes only aerobic respiration, ({{{rm{NO}}}_{3}}^{-}) and ({{{rm{SO}}}_{4}}^{2-}) respiration, although many additional aerobic and anaerobic processes likely occur on particles (e.g Klawonn et al.19). To our knowledge, a complete picture of such processes, their interactions and effects on particle biochemistry is unavailable. For example, we have assumed that when ({{rm{O}}}_{2}) and ({{{rm{NO}}}_{3}}^{-}) are insufficient to maintain respiration, heterotrophic bacteria start reducing ({{{rm{SO}}}_{4}}^{2-}). However, ({{{rm{SO}}}_{4}}^{2-}) reduction has been detected only with a significant lag after the occurrence of anaerobic conditions, suggesting it as a slow adapted process76, whereas we assume it to be instantaneous. On the other hand, the lag may not be real but due to a so called cryptic sulfur cycle, where ({{{rm{SO}}}_{4}}^{2-}) reduction is accompanied by concurrent sulfide oxidation effectively masking sulfide production77. Hopefully, future insights into interactions between diverse aerobic and anaerobic microbial processes can refine our modelling approach and fine-tune predictions of biochemistry in marine particles.Procedure of numerically obtaining optimal N2 fixation rateTo avoid making the optimization in Eq. (30) at every time step during the simulation, a lookup table of ({mu }^{ast }) (Eq. 31) over realistic ranges of the four resources (glucose, amino acids, ({{rm{O}}}_{2}), and ({{{rm{NO}}}_{3}}^{-})) and the parameter determining ({{rm{N}}}_{2}) fixation rate (({{psi }})) was created at the beginning of the simulation.The effects of temperature on N2 fixation rateTo examine the role of temperature variation on ({{rm{N}}}_{2}) fixation rate in sinking particles, we consider hydrolysis of polysaccharide and polypeptide, uptake of glucose and amino acids, uptake of ({{{rm{NO}}}_{3}}^{-}), respiration, and diffusion dependent on temperature. Apart from diffusion, all other processes are multiplied by a factor ({Q}_{10}) that represents the factorial increase in rates with ({10}^{0})C temperature increase. The rate (R) at a given temperature (T) is then$$R={R}_{{rm{ref}}}{{Q}_{10}}^{(T-{T}_{{rm{ref}}})/10}.$$
(42)
Here the reference rate ({R}_{{rm{ref}}}) is defined as the rate at the reference temperature ({T}_{{rm{ref}}}.) We set the reference temperature ({T}_{{rm{ref}}}) at room temperature of 20 °C. The effect of temperature on the diffusion coefficient D for glucose, amino acids, ({{rm{O}}}_{2}), and ({{{rm{NO}}}_{3}}^{-}) is described by Walden’s rule:$$D={D}_{{rm{ref}}}{eta }_{{rm{ref}}}T/(eta {T}_{{rm{ref}}})$$
(43)
where (eta) is the viscosity of water at the given temperature (T), and ({D}_{{rm{ref}}}) and ({eta }_{{rm{ref}}}) are diffusion coefficient and viscosity at ({T}_{{rm{ref}}}).({Q}_{10}) values for different enzyme classes responsible for hydrolysis (({Q}_{10,{rm{h}}})) lie within the range 1.1–2.978. Here, we have chosen ({Q}_{10,{rm{h}}}=2) for hydrolysis from the middle of the prescribed range. The ({Q}_{10}) values for uptake affinities (({Q}_{10,{rm{A}}})) are taken as 1.579. ({Q}_{10,{rm{R}}}=2) is chosen for all parameters related to respiration (({R}_{{rm{B}}}), ({R}_{{rm{E}}}), ({R}_{{rm{G}}}), ({R}_{{rm{A}}}), ({R}_{{{rm{N}}}_{2}}), ({R}_{{{rm{NO}}}_{3}}), ({R}_{{{rm{SO}}}_{4}}))80. ({R}_{{rm{ref}}}) and ({D}_{{rm{ref}}}) are the values of (R)’s and (D)’s provided in Table S1. The reference viscosity (({eta }_{{rm{ref}}})) and viscosities ((eta)) at different temperatures are taken from Jumars et al.80. More