Volk T, Hoffert M. Ocean carbon pumps: analysis of relative strengths and efficiencies in ocean-driven atmospheric CO2 changes. The carbon cycle and atmospheric CO2: natural variations Archean to present 1985;32:99–110. https://doi.org/10.1029/GM032p0099.
Boyd PW, Trull TW. Understanding the export of biogenic particles in oceanic waters: is there consensus? Prog Oceanogr. 2007;72(4):276–312. https://doi.org/10.1016/j.pocean.2006.10.007.
Google Scholar
Steinberg DK, Landry MR. Zooplankton and the ocean carbon cycle. Ann Rev Mar Sci. 2017;9:413–44. https://doi.org/10.1146/annurev-marine-010814-015924.
Google Scholar
Caron DA, Davis PG, Madin LP, Sieburth JM. Enrichment of microbial- populations in macroaggregates (marine snow) from surface waters of the North- Atlantic. J Mar Res. 1986;44:543–65 https://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=8283264.
Google Scholar
Agusti S, González-Gordillo JI, Vaqué D, Estrada M, Cerezo MI, Salazar G, et al. Ubiquitous healthy diatoms in the deep sea confirm deep carbon injection by the biological pump. Nat Commun. 2015;6:7608 https://www.nature.com/articles/ncomms8608.
Google Scholar
Weinbauer MG, Liu J, Motegi C, Maier C, Pedrotti ML, Dai M, et al. Seasonal variability of microbial respiration and bacterial and archaeal community composition in the upper twilight zone. Aquat Microb Ecol. 2013;71(2):99–115. https://doi.org/10.3354/ame01666.
Google Scholar
Alcolombri U, Peaudecerf FJ, Fernandez VI, Behrendt L, Lee KS, Stocker R. Sinking enhances the degradation of organic particles by marine bacteria. Nat Geosci. 2021;14(10):775–80 https://www.nature.com/articles/s41561-021-00817-x.
Google Scholar
Buesseler KO, Lamborg C, Boyd P, Lam P, Trull T, Bidigare R, et al. Revisiting carbon flux through the ocean’s twilight zone. Science. 2007;316:567–70. https://doi.org/10.1126/science.113795.
Google Scholar
Xu C, Xiang M, Chen B, Huang Y, Qiu G, Zhang Y, et al. Constraining the twilight zone remineralization in the South China Sea basin: insights from the multi-method intercomparison. Prog Oceanogr. 2024;228:103316. https://doi.org/10.1016/j.pocean.2024.103316.
Google Scholar
Giering SLC, Sanders R, Lampitt RS, Anderson TR, Tamburini C, Boutrif M, et al. Reconciliation of the carbon budget in the ocean’s twilight zone. Nature. 2014;507:480–517 https://www.nature.com/articles/nature13123.
Google Scholar
Steinberg DK, Van Mooy BAS, Buesseler KO, Boyd PW, Kobari T, Karl DM. Bacterial vs. zooplankton control of sinking particle flux in the ocean’s twilight zone. Limnol Oceanogr. 2008;53:1327–38. https://doi.org/10.4319/lo.2008.53.4.1327.
Google Scholar
Buesseler KO, Pike S, Maiti K, Lamborg CH, Siegel DA, Trull TW. Thorium-234 as a tracer of spatial, temporal and vertical variability in particle flux in the North Pacific. Deep Sea Res, Part I. 2009;56(7):1143–67. https://doi.org/10.1016/j.dsr.2009.04.001.
Google Scholar
Kong LF, He YB, Xie ZX, Luo X, Zhang H, Yi SH, et al. Illuminating key microbial players and metabolic processes involved in the remineralization of particulate organic carbon in the ocean’s twilight zone by metaproteomics. Appl Environ Microbiol. 2021;87(20):e00986-e1021. https://doi.org/10.1128/AEM.00986-21.
Google Scholar
Arístegui J, Agustí S, Middelburg JJ, Duarte CM. Respiration in the mesopelagic and bathypelagic zones of the oceans. Respiration in aquatic ecosystems. 2005;181(205):10–1093
Jiao N, Herndl GJ, Hansell DA, Benner R, Kattner G, Wilhelm SW, et al. Microbial production of recalcitrant dissolved organic matter: long-term carbon storage in the global ocean. Nat Rev Microbiol. 2010;8:593–9 https://www.nature.com/articles/nrmicro2386.
Google Scholar
Li T, Bai Y, He X, Chen X, Chen CTA, Tao B, et al. The relationship between POC export efficiency and primary production: opposite on the shelf and basin of the northern South China Sea. Sustainability. 2018;10(10):3634. https://doi.org/10.3390/su10103634.
Google Scholar
DeLong EF, Preston CM, Mincer T, Rich V, Hallam SJ, Frigaard NU, et al. Community genomics among stratified microbial assemblages in the ocean’s interior. Science. 2006;311(5760):496–503. https://doi.org/10.1126/science.1120250.
Google Scholar
Eloe EA, Shulse CN, Fadrosh DW, Williamson SJ, Allen EE, Bartlett DH. Compositional differences in particle-associated and free-living microbial assemblages from an extreme deep-ocean environment. Environ Microbiol Rep. 2010;3:449–58. https://doi.org/10.1111/j.1758-2229.2010.00223.x.
Google Scholar
Schapira M, McQuaid CD, Froneman PW. Metabolism of free-living and particle-associated prokaryotes: consequences for carbon flux around a Southern Ocean archipelago. J Marine Syst. 2012;90(1):58–66. https://doi.org/10.1016/j.jmarsys.2011.08.009.
Google Scholar
Ganesh S, Bristow LA, Larsen M, Sarode N, Thamdrup B, Stewart FJ. Size-fraction partitioning of community gene transcription and nitrogen metabolism in a marine oxygen minimum zone. ISME J. 2015;9(12):2682–96. https://doi.org/10.1038/ismej.2015.44.
Google Scholar
Eloe EA, Fadrosh DW, Novotny M, Zeigler Allen L, Kim M, Lombardo MJ, et al. Going deeper: metagenome of a hadopelagic microbial community. PLoS ONE. 2011;6:e20388. https://doi.org/10.1371/journal.pone.0020388.
Google Scholar
Lapoussière A, Michel C, Starr M, Gosselin M, Poulin M. Role of free-living and particle-attached bacteria in the recycling and export of organic material in the Hudson Bay system. J Marine Syst. 2011;88(3):434–45. https://doi.org/10.1016/j.jmarsys.2010.12.003.
Google Scholar
Fuchsman CA, Kirkpatrick JB, Brazelton WJ, Murray JW, Staley JT. Metabolic strategies of free-living and aggregate-associated bacterial communities inferred from biologic and chemical profiles in the Black Sea suboxic zone. FEMS Microbiol Ecol. 2011;78:586–603. https://doi.org/10.1111/j.1574-6941.2011.01189.x.
Google Scholar
Mestre M, Borrull E, Sala MM, Gasol JM. Patterns of bacterial diversity in the marine planktonic particulate matter continuum. ISME J. 2017;11(4):999–1010. https://doi.org/10.1038/ismej.2016.166.
Google Scholar
Cai PH, Dai MH, Chen WF, Tang TT, Zhou KB. On the importance of the decay of 234Th in determining size-fractionated C/234Th ratio on marine particles. Geophys Res Lett. 2006;33:L23602. https://doi.org/10.1029/2006GL027792.
Google Scholar
Hung CC, Gong GC, Santschi PH. 234Th in different size classes of sediment trap collected particles from the northwestern Pacific Ocean. Geochim Cosmochim Acta. 2012;91:60–74. https://doi.org/10.1016/j.gca.2012.05.017.
Google Scholar
Hung CC, Gong GC. POC/234Th ratios in particles collected in sediment traps in the northern South China Sea. Estuarine, Coastal Shelf Sci. 2010;88(3):303–10. https://doi.org/10.1016/j.ecss.2010.04.008.
Google Scholar
Durkin CA, Estapa ML, Buesseler KO. Observations of carbon export by small sinking particles in the upper mesopelagic. Mar Chem. 2015;175:72–81. https://doi.org/10.1016/j.marchem.2015.02.011.
Google Scholar
Ganesh S, Parris DJ, DeLong EF, Stewart FJ. Metagenomic analysis of size-fractionated picoplankton in a marine oxygen minimum zone. ISME J. 2014;8(1):187–211. https://doi.org/10.1038/ismej.2013.144.
Google Scholar
Heins A, Reintjes G, Amann RI, Harder J. Particle collection in Imhoff sedimentation cones enriches both motile chemotactic and particle-attached bacteria. Front Microbiol. 2021;12:643730. https://doi.org/10.3389/fmicb.2021.643730.
Google Scholar
Milici M, Vital M, Tomasch J, Badewien TH, Giebel HA, Plumeier I, et al. Diversity and community composition of particle-associated and free-living bacteria in mesopelagic and bathypelagic Southern Ocean water masses: evidence of dispersal limitation in the Bransfield Strait. Limnol Oceanogr. 2017;62(3):1080–95. https://doi.org/10.1002/lno.10487.
Google Scholar
Turner JT. Zooplankton fecal pellets, marine snow, phytodetritus and the ocean’s biological pump. Prog Oceanogr. 2015;130:205–48. https://doi.org/10.1016/j.pocean.2014.08.005.
Google Scholar
Hemsley V, Füssel J, Duret MT, Rayne RR, Iversen MH, Henson SA, et al. Suspended particles are hotspots of microbial remineralization in the ocean’s twilight zone. Deep Sea Res, Part II. 2023;212:105339. https://doi.org/10.1016/j.dsr2.2023.105339.
Google Scholar
Jørgensen BB, Boetius A. Feast and famine-microbial life in the deep-sea bed. Nat Rev Microbiol. 2007;5(10):770–81 https://www.nature.com/articles/nrmicro1745.
Google Scholar
Harms NC, Lahajnar N, Gaye B, Rixen T, Schwarz-Schampera U, Emeis KC. Sediment trap-derived particulate matter fluxes in the oligotrophic subtropical gyre of the South Indian Ocean. Deep Sea Res, Part II. 2021;183:104924. https://doi.org/10.1016/j.dsr2.2020.104924.
Google Scholar
Smith KL, Ruhl HA, Huffard CL, Messié M, Kahru M. Episodic organic carbon fluxes from surface ocean to abyssal depths during long-term monitoring in NE Pacific. Proc Natl Acad Sci USA. 2018;115(48):12235–40. https://doi.org/10.1073/pnas.1814559115.
Google Scholar
Boyd PW, Claustre H, Levy M, Siegel DA, Weber T. Multi-faceted particle pumps drive carbon sequestration in the ocean. Nature. 2019;568(7752):327–35 https://www.nature.com/articles/s41586-019-1098-2.
Google Scholar
Smith KL, Ruhl HA, Bett BJ, Billett DSM, Lampitt RS, Kaufmann RS. Climate, carbon cycling, and deep-ocean ecosystems. Proc Natl Acad Sci USA. 2009;106(46):19211–8. https://doi.org/10.1073/pnas.0908322106.
Google Scholar
Siegel MW, Silver DK, Steinberg J, Valdes B, Van Mooy S. Wilson Revisiting the carbon flux through the ocean’s twilight zone. Science. 2007;316:567–70. https://doi.org/10.1126/science.113795.
Google Scholar
Guidi L, Jackson GA, Stemmann L, Mique JC, Picheral M, Gorsky G. Relationship between particle size distribution and flux in the mesopelagic zone. Deep Sea Res, Part I. 2008;55(10):1364–74. https://doi.org/10.1016/j.dsr.2008.05.014.
Google Scholar
Bach LT, Boxhammer T, Larsen A, Hildebrandt N, Schulz KG, Riebesell U. Influence of plankton community structure on the sinking velocity of marine aggregates. Global Biogeochem Cycles. 2016;30(8):1145–65. https://doi.org/10.1002/2016GB005372.
Google Scholar
Simon M, Grossart HP, Schweitzer B, Ploug H. Microbial ecology of organic aggregates in aquatic ecosystems. Aquat Microb Ecol. 2002;28:175–211. https://doi.org/10.3354/ame028175.
Google Scholar
Bauer M, Kube M, Teeling H, Richter M, Lombardot T, Allers E, et al. Whole genome analysis of the marine Bacteroidetes ‘Gramella forsetii’ reveals adaptations to degradation of polymeric organic matter. Environ Microbiol. 2006;8(12):2201–13. https://doi.org/10.1111/j.1462-2920.2006.01152.x.
Google Scholar
Kirchman DL. The ecology of Cytophaga-Flavobacteria in aquatic environments. FEMS Microbiol Ecol. 2002;39(2):91–100. https://doi.org/10.1111/j.1574-6941.2002.tb00910.x.
Google Scholar
Zhao Z, Baltar F, Herndl GJ. Linking extracellular enzymes to phylogeny indicates a predominantly particle-associated lifestyle of deep-sea prokaryotes. Sci Adv. 2020;6(16):eaaz4354 https://www.science.org/doi/full/10.1126/sciadv.aaz4354.
Google Scholar
Mestre M, Ruiz-González C, Logares R, Duarte CM, Gasol JM, Sala MM. Sinking particles promote vertical connectivity in the ocean microbiome. Proc Natl Acad Sci USA. 2018;115(29):E6799–807. https://doi.org/10.1073/pnas.1802470115.
Google Scholar
Li M, Jain S, Dick GJ. Genomic and transcriptomic resolution of organic matter utilization among deep-sea bacteria in Guaymas Basin hydrothermal plumes. Front Microbiol. 2016;7:1125. https://doi.org/10.3389/fmicb.2016.01125.
Google Scholar
Garel M, Panagiotopoulos C, Boutrif M, Repeta D, Sempéré R, Santinelli C, et al. Contrasting degradation rates of natural dissolved organic carbon by deep-sea prokaryotes under stratified water masses and deep-water convection conditions in the NW Mediterranean Sea. Mar Chem. 2021;231:103932. https://doi.org/10.1016/j.marchem.2021.103932.
Google Scholar
Alldredge AL, Silver MW. Characteristics, dynamics and significance of marine snow. Prog Oceanogr. 1988;20:41–82. https://doi.org/10.1016/0079-6611(88)90053-5.
Google Scholar
Jiao N, Robinson C, Azam F, Thomas H, Baltar F, Dang H, et al. Mechanisms of microbial carbon sequestration in the ocean-future research directions. Biogeosciences. 2014;11(19):5285–306. https://doi.org/10.5194/bg-11-5285-2014.
Google Scholar
Kamalanathan M, Doyle SM, Xu C, Achberger AM, Wade TL, Schwehr K, et al. Exoenzymes as a signature of microbial response to marine environmental conditions. mSystems. 2020;5(2):20. https://doi.org/10.1128/msystems.00290-20.
Google Scholar
Bergauer K, Fernandez-Guerra A, Garcia JA, Sprenger RR, Stepanauskas R, Pachiadaki MG, et al. Organic matter processing by microbial communities throughout the Atlantic water column as revealed by metaproteomics. Proc Natl Acad Sci. 2018;115(3):E400–8. https://doi.org/10.1073/pnas.1708779115.
Google Scholar
Gutierrez T, Berry D, Yang T, Mishamandani S, McKay L, Teske A, et al. Role of bacterial exopolysaccharides (EPS) in the fate of the oil released during the Deepwater Horizon oil spill. PLoS ONE. 2013;8(6):e67717. https://doi.org/10.1371/journal.pone.0067717.
Google Scholar
Woebken D, Teeling H, Wecker P, Dumitriu A, Kostadinov I, Delong EF, et al. Fosmids of novel marine Planctomycetes from the Namibian and Oregon coast upwelling systems and their cross-comparison with planctomycete genomes. ISME J. 2007;1:419–35. https://doi.org/10.1038/ismej.2007.63.
Google Scholar
Cardman Z, Arnosti C, Durbin A, Ziervogel K, Cox C, Steen AD, et al. Verrucomicrobia are candidates for polysaccharide-degrading bacterioplankton in an arctic fjord of Svalbard. Appl Environ Microbiol. 2014;80:3749–56. https://doi.org/10.1128/AEM.00899-14.
Google Scholar
Teeling H, Fuchs BM, Becher D, Klockow C, Gardebrecht A, Bennke CM, et al. Substrate-controlled succession of marine bacterioplankton populations induced by a phytoplankton bloom. Science. 2012;336:608–11. https://doi.org/10.1126/science.1218344.
Google Scholar
McKee LS, La Rosa SL, Westereng B, Eijsink VG, Pope PB, Larsbrink J. Polysaccharide degradation by the Bacteroidetes: mechanisms and nomenclature. Environ Microbiol Rep. 2021;13:559–81. https://doi.org/10.1111/1758-2229.12980.
Google Scholar
Herlemann DPR, Lundin D, Labrenz M, Jurgens K, Zheng ZL, Aspeborg H, et al. Metagenomic de novo assembly of an aquatic representative of the verrucomicrobial class Spartobacteria. MBio. 2013;4:e00569-e612. https://doi.org/10.1128/mbio.00569-12.
Google Scholar
Michoud G, Kohler TJ, Ezzat L, Peter H, Nattabi JK, Nalwanga R, et al. The dark side of the moon: first insights into the microbiome structure and function of one of the last glacier-fed streams in Africa. R Soc Open Sci. 2023;10(8):230329. https://doi.org/10.1098/rsos.230329.
Google Scholar
Bishop JKB, Schupack D, Sherrell RM, Conte M. A multiple-unit large-volume in situ filtration system for sampling oceanic particulate matter in mesoscale environments. Adv Chem. 1985;5(209):155–75. https://doi.org/10.1021/ba-1985-0209.ch009.
Google Scholar
Call M, Sanders CJ, Macklin PA, Santos IR, Maher DT. Carbon outwelling and emissions from two contrasting mangrove creeks during the monsoon storm season in Palau, Micronesia. Estuarine, Coastal Shelf Sci. 2019;218:340–8. https://doi.org/10.1016/j.ecss.2019.01.002.
Google Scholar
Gao C, Yu F, Chen J, Huang Z, Jiang Y, Zhuang Z, et al. Anthropogenic impact on the organic carbon sources, transport and distribution in a subtropical semi-enclosed bay. Sci Total Environ. 2021;767:145047. https://doi.org/10.1016/j.scitotenv.2021.145047.
Google Scholar
Pearson WR, Wood T, Zhang Z, Miller W. Comparison of DNA sequences with protein sequences. Genomics. 1997;46(1):24–36. https://doi.org/10.1006/geno.1997.4995.
Google Scholar
Li D, Liu C, Luo R, Sadakane K, Lam T. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics. 2015;31:1674–6. https://doi.org/10.1093/bioinformatics/btv033.
Google Scholar
Bengtsson-Palme J, Hartmann M, Eriksson KM, Pal C, Thorell K, Larsson DG, et al. METAXA2: improved identification and taxonomic classification of small and large subunit rRNA in metagenomic data. Mol Ecol Resour. 2015;15(6):1403–14. https://doi.org/10.1111/1755-0998.12399.
Google Scholar
Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–6 https://www.nature.com/articles/nmeth.f.303.
Google Scholar
Pruesse E, Quast C, Knittel K, Fuchs BM, Ludwig W, Peplies J, et al. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res. 2007;35(21):7188–96. https://doi.org/10.1093/nar/gkm864.
Google Scholar
Hammer Ø, Harper DA. PAST: Paleontological Statistics Software Package for educaton and data anlysis. Palaeontol Electron. 2001;4(1):1
Zhu W, Lomsadze A, Borodovsky M. Ab initio gene identification in metagenomic sequences. Nucleic Acids Res. 2010;38(12):e132. https://doi.org/10.1093/nar/gkq275.
Google Scholar
Fu L, Niu B, Zhu Z, Wu S, Li W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics. 2012;28(23):3150–2. https://doi.org/10.1093/bioinformatics/bts565.
Google Scholar
Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9(4):357–9 https://www.nature.com/articles/nmeth.1923.
Google Scholar
Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M, et al. KEGG for linking genomes to life and the environment. Nucleic Acids Res. 2008;36:480–4. https://doi.org/10.1093/nar/gkm882.
Google Scholar
Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12(1):59 https://www.nature.com/articles/nmeth.3176.
Google Scholar
Cantarel BL, Coutinho PM, Rancurel C, Bernard T, Lombard V, Henrissat B. The Carbohydrate-Active EnZymes database (CAZy): an expert resource for glycogenomics. Nucleic Acids Res. 2009;37:D233. https://doi.org/10.1093/nar/gkn663.
Google Scholar
Liu N, Zhang L, Zhou H, Zhang M, Yan X, Wang Q, et al. Metagenomic insights into metabolic capacities of the gut microbiota in a fungus-cultivating termite (Odontotermes yunnanensis). PLoS ONE. 2013;8(7):e69184. https://doi.org/10.1371/journal.pone.0069184.
Google Scholar
Uritskiy GV, DiRuggiero J, Taylor J. MetaWRAP-a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome. 2018;6:1–13 https://link.springer.com/article/10.1186/S40168-018-0541-1.
Google Scholar
Olm MR, Brown CT, Brooks B, Banfield JF. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 2017;11:2864. https://doi.org/10.1038/ismej.2017.126.
Google Scholar
Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25(7):1043–55. https://doi.org/10.1101/gr.186072.114.
Google Scholar
Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9(1):5114 https://www.nature.com/articles/s41467-018-07641-9.
Google Scholar
Chaumeil PA, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics. 2020;36(6):1925–7. https://doi.org/10.1093/bioinformatics/btz848.
Google Scholar
Zhang H, Yohe T, Huang L, Entwistle S, Wu P, Yang Z, et al. DbCAN2: a meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 2018;46:W95–101. https://doi.org/10.1093/nar/gky418.
Google Scholar
Clarke KR, Warwick RM. Change in marine communities: an approach to statistical analysis and interpretation. primer-e ltd: plymouth, UK. 2001. https://www.researchgate.net/profile/Aimeric-Blaud/post/Can_anyone_help_me_in_understanding_and_clearly_interpreting_ANOSIM_Analysis_of_Similarityand_SIMPER_Similarity_percentage_analysisresults/attachment/59d63f3ec49f478072ea9897/AS%3A273777156395016%401442284968397/download/PRIMER-E.pdf.
Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12:R60 https://link.springer.com/article/10.1186/gb-2011-12-6-r60.
Google Scholar
Parks DH, Tyson GW, Hugenholtz P, Beiko RG. STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics. 2014;30:3123–4. https://doi.org/10.1093/bioinformatics/btu494.
Google Scholar
Revelle W. Package ‘psych’. The Comprehensive R Archive Network, 2015; August 30. https://cran.r-project.org/web/packages/psych/psych.pdf.
Bastian M, Heymann S, Jacomy M. Gephi: an open source software for exploring and manipulating networks. In International AAAI conference on weblogs and social media: San Jose, California. 2009.
link