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    Genome-wide sequencing identifies a thermal-tolerance related synonymous mutation in the mussel, Mytilisepta virgata

    Orr, H. A. The genetic theory of adaptation: a brief history. Nat. Rev. Genet. 6, 119–127 (2005).Article 
    CAS 

    Google Scholar 
    Barrett, R. D. H. & Schluter, D. Adaptation from standing genetic variation. Trends Ecol. Evol. 23, 38–44 (2008).Article 

    Google Scholar 
    Exposito-Alonso, M., Burbano, H. A., Bossdorf, O., Nielsen, R. & Weigel, D. Natural selection on the Arabidopsis thaliana genome in present and future climates. Nature 573, 126–129 (2019).Article 
    CAS 

    Google Scholar 
    Han, G., Wang, W. & Dong, Y. Effects of balancing selection and microhabitat temperature variations on heat tolerance of the intertidal black mussel Septifer virgatus. Integr. Zool. 15, 416–427 (2020).Article 

    Google Scholar 
    Meester, L. D., Stoks, R. & Brans, K. I. Genetic adaptation as a biological buffer against climate change: Potential and limitations. Integr. Zool. 13, 372–391 (2018).Article 

    Google Scholar 
    Hoffmann, A. A. & Sgrò, C. M. Climate change and evolutionary adaptation. Nature 470, 479–485 (2011).Article 
    CAS 

    Google Scholar 
    Günter, F. et al. Genotype-environment interactions rule the response of a widespread butterfly to temperature variation. J. Evol. Biol. 33, 920–929 (2020).Article 

    Google Scholar 
    Lowry, D. B. et al. QTL × environment interactions underlie adaptive divergence in switchgrass across a large latitudinal gradient. Proc. Natl Acad. Sci. USA 116, 12933–12941 (2019).Article 
    CAS 

    Google Scholar 
    Bates, A. E. et al. Biologists ignore ocean weather at their peril. Nature 560, 299–301 (2018).Article 
    CAS 

    Google Scholar 
    Plotkin, J. B. & Kudla, G. Synonymous but not the same: the causes and consequences of codon bias. Nat. Rev. Genet. 12, 32–42 (2011).Article 
    CAS 

    Google Scholar 
    Zhao, F. et al. Genome-wide role of codon usage on transcription and identification of potential regulators. Proc. Natl Acad. Sci. USA 118, e2022590118 (2021).Hanson, G. & Coller, J. Codon optimality, bias and usage in translation and mRNA decay. Nat. Rev. Mol. Cell Bio. 19, 20–30 (2018).Article 
    CAS 

    Google Scholar 
    Chen, S. et al. Codon-resolution analysis reveals a direct and context-dependent impact of individual synonymous mutations on mRNA level. Mol. Biol. Evol. 34, 2944–2958 (2017).Article 
    CAS 

    Google Scholar 
    Wu, Z. et al. Expression level is a major modifier of the fitness landscape of a protein coding gene. Nat. Ecol. Evol. 6, 103–115 (2022).Article 

    Google Scholar 
    Lebeuf-Taylor, E., McCloskey, N., Bailey, S. F., Hinz, A. & Kassen, R. The distribution of fitness effects among synonymous mutations in a gene under directional selection. ELife. 8, e45952 (2019).Bailey, S. F., Hinz, A. & Kassen, R. Adaptive synonymous mutations in an experimentally evolved Pseudomonas fluorescens population. Nat. Commun. 5, 4076 (2014).Agashe, D. et al. Large-effect beneficial synonymous mutations mediate rapid and parallel adaptation in a bacterium. Mol. Biol. Evol. 33, 1542–1553 (2016).Article 
    CAS 

    Google Scholar 
    Zhao, Y. et al. Synonymous mutation in growth regulating factor 15 of miR396a target sites enhances photosynthetic efficiency and heat tolerance in poplar. J. Exp. Bot. 72, 4502–4519 (2021).Article 
    CAS 

    Google Scholar 
    Somero, G. N. The physiology of global change: linking patterns to mechanisms. Annu. Rev. Mar. Sci. 4, 39–61 (2012).Article 

    Google Scholar 
    Helmuth, B. et al. Climate change and latitudinal patterns of intertidal thermal stress. Science 298, 1015–1017 (2002).Article 
    CAS 

    Google Scholar 
    Helmuth, B., Mieszkowska, N., Moore, P. & Hawkins, S. J. Living on the edge of two changing worlds: forecasting the responses of rocky intertidal ecosystems to climate change. Annu Rev. Ecol. Evol. Syst. 37, 373–404 (2006).Article 

    Google Scholar 
    Seabra, R., Wethey, D. S., Santos, A. M. & Lima, F. P. Side matters: microhabitat influence on intertidal heat stress over a large geographical scale. J. Exp. Mar. Biol. Ecol. 400, 200–208 (2011).Article 

    Google Scholar 
    Schmidt, P. S. & Rand, D. M. Intertidal microhabitat and selection at MPI: interlocus contrasts in the Northern Acorn Barnacle, Semibalanus balanoides. Evolution 53, 135 (1999).
    Google Scholar 
    Li, X., Tan, Y., Sun, Y., Wang, J. & Dong, Y. Microhabitat temperature variation combines with physiological variation to enhance thermal resilience of the intertidal mussel Mytilisepta virgata. Funct. Ecol. 35, 2497–2507 (2021).Article 
    CAS 

    Google Scholar 
    Dong, Y. et al. Untangling the roles of microclimate, behaviour and physiological polymorphism in governing vulnerability of intertidal snails to heat stress. Proc. Royal. Soc. B. 284, (2017).Li, X. & Dong, Y. Living on the upper intertidal mudflat: different behavioral and physiological responses to high temperature between two sympatric Cerithidea snails with divergent habitat-use strategies. Mar. Environ. Res. 159, 105015 (2020).Article 
    CAS 

    Google Scholar 
    Wang, J., Peng, X. & Dong, Y. High abundance and reproductive output of an intertidal limpet (Siphonaria japonica) in environments with high thermal predictability. Mar. Life. Sci. Tech. 2, 324–333 (2020).Article 

    Google Scholar 
    Dong, Y., Liao, M., Han, G. & Somero, G. N. An integrated, multi-level analysis of thermal effects on intertidal molluscs for understanding species distribution patterns. Biol. Rev. 97, 554–581 (2022).Article 

    Google Scholar 
    Georges, A., Gros, P. & Fodil, N. USP15: a review of its implication in immune and inflammatory processes and tumor progression. Genes Immun. 22, 12–23 (2021).Article 
    CAS 

    Google Scholar 
    Vlasschaert, C., Xia, X., Coulombe, J. & Gray, D. A. Evolution of the highly networked deubiquitinating enzymes USP4, USP15, and USP11. BMC Evol. Biol. 15, 230 (2015).Mallard, F., Nolte, V., Tobler, R., Kapun, M. & Schlötterer, C. A simple genetic basis of adaptation to a novel thermal environment results in complex metabolic rewiring in Drosophila. Genome Biol. 19, 119 (2018).Cornelissen, T. et al. The deubiquitinase USP15 antagonizes Parkin-mediated mitochondrial ubiquitination and mitophagy. Hum. Mol. Genet. 23, 5227–5242 (2014).Article 
    CAS 

    Google Scholar 
    Morton, B. The biology and functional morphology of Septifer bilocularis and Mytilisepta virgata (Bivalvia: Mytiloidea) from corals and the exposed rocky shores, respectively, of Hong Kong. Reg. Stud. Mar. Sci. 235, 485–500 (1995).
    Google Scholar 
    Boroda, A. V., Kipryushina, Y. O. & Odintsova, N. A. The effects of cold stress on Mytilus species in the natural environment. Cell Stress Chaperones 25, 821–832 (2020).Article 
    CAS 

    Google Scholar 
    Thayer, C. W. Brachiopods versus mussels: competition, predation, and palatability. Science 228, 1527–1528 (1985).Article 
    CAS 

    Google Scholar 
    Iorio, R., Celenza, G. & Petricca, S. Mitophagy: molecular mechanisms, new concepts on Parkin activation and the emerging role of AMPK/ULK1 Axis. Cells 11, 30 (2022).Article 
    CAS 

    Google Scholar 
    Feidantsis, K. et al. Correlation between intermediary metabolism, Hsp gene expression, and oxidative stress-related proteins in long-term thermal-stressed Mytilus galloprovincialis. Am. J. Physiol. Regul. Integr. Comp. Physiol. 319, R264–R281 (2020).Article 
    CAS 

    Google Scholar 
    Heise, K., Puntarulo, S., Portner, H. O. & Abele, D. Production of reactive oxygen species by isolated mitochondria of the Antarctic bivalve Laternula elliptica (King and Broderip) under heat stress. Comp. Biochem. Physiol. C. Toxicol. Pharmacol. 134, 79–90 (2003).Article 
    CAS 

    Google Scholar 
    Abele, D., Heise, K., Portner, H. O. & Puntarulo, S. Temperature-dependence of mitochondrial function and production of reactive oxygen species in the intertidal mud clam Mya arenaria. J. Exp. Biol. 205, 1831–1841 (2002).Article 
    CAS 

    Google Scholar 
    Xiao, Q. et al. Transcriptome analysis reveals the molecular mechanisms of heterosis on thermal resistance in hybrid abalone. BMC Genom. 22, 650 (2021).Li, L. et al. Heat stress induces apoptosis through a Ca2+-mediated mitochondrial apoptotic pathway in human umbilical vein endothelial cells. PLoS ONE 9, e111083 (2014).Article 

    Google Scholar 
    Gu, Z. T. et al. Heat stress induced apoptosis is triggered by transcription-independent p53, Ca2+ dyshomeostasis and the subsequent Bax mitochondrial translocation. Sci. Rep. 5, 11497 (2015).Article 
    CAS 

    Google Scholar 
    Gerdol, M., De Moro, G., Venier, P. & Pallavicini, A. Analysis of synonymous codon usage patterns in sixty-four different bivalve species. Peer J. 3, e1520 (2015).Article 

    Google Scholar 
    Zhou, M. et al. Non-optimal codon usage affects expression, structure and function of clock protein FRQ. Nature 495, 111–115 (2013).Article 
    CAS 

    Google Scholar 
    Yu, C. et al. Codon usage influences the local rate of translation elongation to tegulate co-translational protein folding. Mol. Cell 59, 744–754 (2015).Article 
    CAS 

    Google Scholar 
    Spencer, P. S., Siller, E., Anderson, J. F. & Barral, J. M. Silent substitutions predictably alter translation elongation rates and protein folding efficiencies. J. Mol. Biol. 422, 328–335 (2012).Article 
    CAS 

    Google Scholar 
    Pechmann, S., Chartron, J. W. & Frydman, J. Local slowdown of translation by nonoptimal codons promotes nascent-chain recognition by SRP in vivo. Nat. Struct. Mol. Biol. 21, 1100–1105 (2014).Article 
    CAS 

    Google Scholar 
    Kimchi-Sarfaty, C. et al. A “silent” polymorphism in the MDR1 gene changes substrate specificity. Science 315, 525–528 (2007).Article 
    CAS 

    Google Scholar 
    Zhou, Z. et al. Codon usage is an important determinant of gene expression levels largely through its effects on transcription. Proc. Natl Acad. Sci. USA 113, E6117–E6125 (2016).Article 
    CAS 

    Google Scholar 
    Shabalina, S. A., Spiridonov, N. A. & Kashina, A. Sounds of silence: synonymous nucleotides as a key to biological regulation and complexity. Nucleic Acids Res. 41, 2073–2094 (2013).Article 
    CAS 

    Google Scholar 
    Liao, M., Dong, Y. & Somero, G. N. Thermal adaptation of mRNA secondary structure: stability versus lability. Proc. Natl Acad. Sci. USA 118, e2113324118 (2021).Article 
    CAS 

    Google Scholar 
    Wan, Y. et al. Genome-wide measurement of RNA folding energies. Mol. Cell. 48, 169–181 (2012).Article 

    Google Scholar 
    Seffens, W. & Digby, D. mRNAs have greater negative folding free energies than shuffled or codon choice randomized sequences. Nucleic Acids Res. 27, 1578–1584 (1999).Article 
    CAS 

    Google Scholar 
    Faure, G., Ogurtsov, A. Y., Shabalina, S. A. & Koonin, E. V. Role of mRNA structure in the control of protein folding. Nucleic Acids Res. 44, 10898–10911 (2016).Article 
    CAS 

    Google Scholar 
    Victor, M. P., Acharya, D., Begum, T. & Ghosh, T. C. The optimization of mRNA expression level by its intrinsic properties-Insights from codon usage pattern and structural stability of mRNA. Genomics 111, 1292–1297 (2019).Article 
    CAS 

    Google Scholar 
    Backlund, M. & Kulozik, A. E. Differential analysis of the nuclear and the cytoplasmic RNA interactomes in living cells. Methods Mol. Biol. 2428, 291–304 (2022).Article 

    Google Scholar 
    Zaghlool, A. et al. Characterization of the nuclear and cytosolic transcriptomes in human brain tissue reveals new insights into the subcellular distribution of RNA transcripts. Sci. Rep. 11, 4076 (2021).Clark, M. S. et al. Life in the intertidal: cellular responses, methylation and epigenetics. Funct. Ecol. 32, 1982–1994 (2018).Article 

    Google Scholar 
    Jeremias, G. et al. Synthesizing the role of epigenetics in the response and adaptation of species to climate change in freshwater ecosystems. Mol. Ecol. 27, 2790–2806 (2018).Article 

    Google Scholar 
    Li, L. et al. Divergence and plasticity shape adaptive potential of the Pacific oyster. Nat. Ecol. Evol. 2, 1751–1760 (2018).Article 

    Google Scholar 
    Chu, D. & Wei, L. Nonsynonymous, synonymous and nonsense mutations in human cancer-related genes undergo stronger purifying selections than expectation. BMC Cancer. 19, 359 (2019).Lima, F. P. & Wethey, D. S. Robolimpets: measuring intertidal body temperatures using biomimetic loggers. Limol. Oceanogr. Methods 7, 347–353 (2009).Article 

    Google Scholar 
    Dong, Y. & Williams, G. A. Variations in cardiac performance and heat shock protein expression to thermal stress in two differently zoned limpets on a tropical rocky shore. Mar. Biol. 158, 1223–1231 (2011).Article 

    Google Scholar 
    Vito, M. Segmented: An R Package to fit regression models with broken-line relationships. R. N. 8, 20–25 (2008).
    Google Scholar 
    R Core Team. R: a language and environment for statistical computing (R Foundation for Statistical Computing, 2021).Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).Article 

    Google Scholar 
    Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26, 589–595 (2010).Article 

    Google Scholar 
    Danecek, P. et al. Twelve years of SAMtools and BCFtools. GigaScience. 10, giab008 (2021).Rochette, N. C., Rivera Colón, A. G. & Catchen, J. M. Stacks 2: analytical methods for paired-end sequencing improve RADseq-based population genomics. Mol. Ecol. 28, 4737–4754 (2019).Article 
    CAS 

    Google Scholar 
    Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).Article 
    CAS 

    Google Scholar 
    Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).Article 

    Google Scholar 
    Devlin, B. & Roeder, K. Genomic control for association studies. Biometrics 55, 997–1004 (1999).Article 
    CAS 

    Google Scholar 
    Hao, Z. et al. RIdeogram: drawing SVG graphics to visualize and map genome-wide data on the idiograms. PeerJ Comput. Sci. 6, e251 (2020).Article 

    Google Scholar 
    Rozas, J. et al. DnaSP 6: DNA sequence polymorphism analysis of large data sets. Mol. Biol. Evol. 34, 3299–3302 (2017).Article 
    CAS 

    Google Scholar 
    Letunic, I., Khedkar, S. & Bork, P. SMART: recent updates, new developments and status in 2020. Nucleic Acids Res. 49, D458–D460 (2021).Article 
    CAS 

    Google Scholar 
    Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).Article 
    CAS 

    Google Scholar 
    Suchard, M. A. et al. Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10. Virus Evol. 4, vey016 (2018).Bailey, T. L., Johnson, J., Grant, C. E. & Noble, W. S. The MEME suite. Nucleic Acids Res. 43, W39–W49 (2015).Article 
    CAS 

    Google Scholar 
    Mathews, D. H. et al. Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure. Proc. Natl Acad. Sci. USA 101, 7287–7292 (2004).Article 
    CAS 

    Google Scholar 
    Mathews, D. H., Sabina, J., Zuker, M. & Turner, D. H. Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure. J. Mol. Biol. 288, 911–940 (1999).Article 
    CAS 

    Google Scholar 
    Moyen, N. E., Somero, G. N. & Denny, M. W. Mussels’ acclimatization to high, variable temperatures is lost slowly upon transfer to benign conditions. J. Exp. Biol. 223, Pt 13 (2020).
    Google Scholar 
    Havird, J. C. et al. Distinguishing between active plasticity due to thermal acclimation and passive plasticity due to Q10 effects: why methodology matters. Funct. Ecol. 34, 1015–1028 (2020).Article 

    Google Scholar 
    Panova, M. et al. DNA extraction protocols for whole-genome sequencing in marine organisms. Methods Mol. Biol. 1452, 13–44 (2016).Article 
    CAS 

    Google Scholar 
    Bustin, S. A. et al. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin. Chem. 55, 611–622 (2009).Article 
    CAS 

    Google Scholar 
    Gerdol, M. et al. The purplish bifurcate mussel Mytilisepta virgata gene expression atlas reveals a remarkable tissue functional specialization. BMC Genomics. 18, 590 (2017).Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2-ΔΔCT method. Methods 25, 402–408 (2001).Article 
    CAS 

    Google Scholar  More

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    Image dataset for benchmarking automated fish detection and classification algorithms

    Cheung, W. W. L. et al. Shrinking of fishes exacerbates impacts of global ocean changes on marine ecosystems. Nat. Clim. Chang. 3, 254–258, https://doi.org/10.1038/nclimate1691 (2013).Article 
    ADS 

    Google Scholar 
    Cheung, W. W. L., Watson, R. & Pauly, D. Signature of ocean warming in global fisheries catch. Nature 497, 365–368, https://doi.org/10.1038/nature12156 (2013).Article 
    ADS 
    CAS 

    Google Scholar 
    Hilborn, R. et al. Global status of groundfish stocks. Fish Fish. 00, 1–18, https://doi.org/10.1111/faf.12560 (2021).Article 

    Google Scholar 
    Aguzzi, J. et al. Challenges to the assessment of benthic populations and biodiversity as a result of rhythmic behaviour: video solutions from cabled observatories. Oceanography and Marine Biology: An Annual Review 50, 233–284 (2012).
    Google Scholar 
    Aguzzi, J. et al. Coastal observatories for monitoring of fish behaviour and their responses to environmental changes. Reviews in fish biology and fisheries 25, 463–483, https://doi.org/10.1007/s11160-015-9387-9 (2015).Article 

    Google Scholar 
    Doya, C. et al. Diel behavioral rhythms in sablefish (Anoplopoma fimbria) and other benthic species, as recorded by the Deep-sea cabled observatories in Barkley canyon (NEPTUNE-Canada). Journal of Marine Systems 130, 69–78, https://doi.org/10.1016/j.jmarsys.2013.04.003 (2014).Article 
    ADS 

    Google Scholar 
    Aguzzi, J. et al. Ecological video monitoring of Marine Protected Areas by underwater cabled surveillance cameras. Marine Policy 119, 104052, https://doi.org/10.1016/j.marpol.2020.104052 (2020).Article 

    Google Scholar 
    Milligan, R. J. et al. Evidence for seasonal cycles in deep‐sea fish abundances: A great migration in the deep SE Atlantic? Journal of Animal Ecology 89, 1593–1603, https://doi.org/10.1111/1365-2656.13215 (2020).Article 

    Google Scholar 
    Hutchingson, G. E. Concluding remarks. Cold Spring Harbor Symp. 22, 415–427, https://doi.org/10.1101/SQB.1957.022.01.039 (1957).Article 

    Google Scholar 
    Hut, R. A., Kronfeld-Schor, N., Van Der Vinne, V. & De la Iglesia, H. In search of a temporal niche: environmental factors. Progress in brain research 199, 281–304, https://doi.org/10.1016/B978-0-444-59427-3.00017-4 (2012).Article 

    Google Scholar 
    Aguzzi, J. et al. The hierarchic treatment of marine ecological information from spatial networks of benthic platforms. Sensors 20, 1751, https://doi.org/10.3390/s20061751 (2020).Article 
    ADS 

    Google Scholar 
    Danovaro, R. et al. A new international ecosystem-based strategy for the global deep ocean. Science 355, 452–454, https://doi.org/10.1126/science.aah7178 (2017).Article 
    ADS 
    CAS 

    Google Scholar 
    Aguzzi, J. et al. The potential of video imagery from worldwide cabled observatory networks to provide information supporting fish-stock and biodiversity assessment. ICES Journal of Marine Science 77, 2396–2410, https://doi.org/10.1093/icesjms/fsaa169 (2020).Article 

    Google Scholar 
    Aguzzi, J. et al. New high-tech flexible networks for the monitoring of deep-sea ecosystems. Environmental science and technology 53, 6616–6631, https://doi.org/10.1021/acs.est.9b00409 (2019).Article 
    ADS 
    CAS 

    Google Scholar 
    Rountree, R. A. et al. Towards an optimal design for ecosystem-level ocean observatories. In Oceanography and Marine Biology. Taylor and Francis, pp. 79–106 (2020).Aguzzi, J. et al. Developing technological synergies between deep-sea and space research. Elementa: Science of the Anthropocene 10, 00064, https://doi.org/10.1525/elementa.2021.00064 (2022).Article 

    Google Scholar 
    Aguzzi, J. et al. Multiparametric monitoring of fish activity rhythms in an Atlantic coastal cabled observatory. Journal of Marine Systems 212, 103424, https://doi.org/10.1016/j.jmarsys.2020.103424 (2020).Article 

    Google Scholar 
    Matabos et al. Expert, Crowd, Students or Algorithm: who holds the key to deep-sea imagery ‘big data’ processing? Methods in Ecology and Evolution 8, 996–1004, https://doi.org/10.1111/2041-210X.12746 (2017).Article 

    Google Scholar 
    Zuazo, A. et al. An automated pipeline for image processing and data treatment to track activity rhythms of Paragorgia arborea in relation to hydrographic conditions. Sensors 20, 6281, https://doi.org/10.3390/s20216281 (2020).Article 
    ADS 

    Google Scholar 
    Dibattista, J. D. et al. Community-based citizen science projects can support the distributional monitoring of fishes. Aquatic Conservation: Marine and Freshwater Ecosystems 31, 3580–3593, https://doi.org/10.1002/aqc.3726 (2021).Article 

    Google Scholar 
    Malde, K., Handegard, N. O., Eikvil, L. & Salberg, A. B. Machine intelligence and the data-driven future of marine science. ICES Journal of Marine Science 77, 1274–1285, https://doi.org/10.1093/icesjms/fsz057 (2020).Article 

    Google Scholar 
    European Marine Board. Big Data in Marine Science. European Marine Broad Advencing Seas & Ocean Science. https://www.marineboard.eu/publications/big-data-marine-science (2020).Aguzzi, J. et al. The new SEAfloor OBservatory (OBSEA) for remote and long-term coastal ecosystem monitoring. Sensors-Basel 11, 5850–5872, https://doi.org/10.3390/s110605850 (2011).Article 
    ADS 

    Google Scholar 
    Del Rio, J. et al. Obsea: a decadal balance for a cabled observatory deployment. IEEE Access 8, 33163–33177, https://doi.org/10.1109/ACCESS.2020.2973771 (2020).Article 

    Google Scholar 
    Condal, F. et al. Seasonal rhythm in a Mediterranean coastal fish community as monitored by a cabled observatory. Marine Biology 159, 2809–2817, https://doi.org/10.1007/s00227-012-2041-3 (2012).Article 

    Google Scholar 
    Naylor, E. Chronobiology of marine organisms (Cambridge University Press, 2010).Weis, J. S., Smith, G., Zhou, T., Santiago-Bass, C. & Weis, P. Effects of contaminants on behavior: biochemical mechanisms and ecological consequences: killifish from a contaminated site are slow to capture prey and escape predators; altered neurotransmitters and thyroid may be responsible for this behavior, which may produce population changes in the fish and their major prey, the grass shrimp. Bioscience 51, 209–217 https://doi.org/10.1641/0006-3568(2001)051[0209:EOCOBB]2.0.CO;2 (2001).Bellido, J. M. et al. Identifying essential fish habitat for small pelagic species in Spanish Mediterranean waters. In Essential Fish Habitat Mapping in the Mediterranean. Springer Netherlands, 171–184 https://doi.org/10.1007/978-1-4020-9141-4_13 (2008).Brander, K. Impacts of climate change on fisheries. Journal of Marine Systems 79, 389–402, https://doi.org/10.1016/j.jmarsys.2008.12.015 (2010).Article 
    ADS 

    Google Scholar 
    Viehman, H. A. & Zydlewski, G. B. Multi-scale temporal patterns in fish presence in a high-velocity tidal channel. PLoS One 12, e0176405, https://doi.org/10.1371/journal.pone.0176405 (2017).Article 
    CAS 

    Google Scholar 
    Van Der Walt, K. A., Porri, F., Potts, W. M., Duncan, M. I. & James, N. C. Thermal tolerance, safety margins and vulnerability of coastal species: Projected impact of climate change induced cold water variability in a temperate African region. Marine Environmental Research 169, 105346, https://doi.org/10.1016/j.marenvres.2021.105346 (2021).Article 
    CAS 

    Google Scholar 
    Marini, S. et al. Tracking fish abundance by underwater image recognition. Scientific reports 8, 1–12, https://doi.org/10.1038/s41598-018-32089-8 (2018).Article 
    ADS 
    CAS 

    Google Scholar 
    Sbragaglia, V. et al. Annual rhythms of temporal niche partitioning in the Sparidae family are correlated to different environmental variables. Scientific reports 9, 1–11, https://doi.org/10.1038/s41598-018-37954-0 (2019).Article 
    ADS 
    CAS 

    Google Scholar 
    Francescangeli, M. et al. Long-Term Monitoring of Diel and Seasonal Rhythm of Dentex dentex at an Artificial Reef. Frontier in Marine Science 9, 1–17, https://doi.org/10.3389/fmars.2022.801033 (2022).Article 

    Google Scholar 
    Knausgård, K. M. et al. Temperate fish detection and classification: a deep learning based approach. Applied Intelligence 52, 6988–7001, https://doi.org/10.1007/s10489-020-02154-9 (2022).Article 

    Google Scholar 
    Wu, J. et al. Multi-Label Active Learning Algorithms for Image Classification: Overview and Future Promise. ACM Computing Surveys (CSUR) 53, 1–35, https://doi.org/10.1145/3379504 (2020).Article 

    Google Scholar 
    He J., Mao R., Shao Z. & Zhu F. Incremental Learning in Online Scenario. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 13923–13932 https://doi.org/10.1109/CVPR42600.2020.01394 (2020).Zhou, D. W., Yang, Y., & Zhan, D. C. Learning to Classify with Incremental New Class. In IEEE Transactions on Neural Networks and Learning Systems https://doi.org/10.1109/TNNLS.2021.3104882 (2021).Hashmani, M. A., Jameel, S. M., Alhussain, H., Rehman, M. & Budiman, A. Accuracy performance degradation in image classification models due to concept drift. International Journal of Advanced Computer Science and Applications 10, 422–425, https://doi.org/10.14569/ijacsa.2019.0100552 (2019).Article 

    Google Scholar 
    Langenkämper, D., van Kevelaer, R., Purser, A. & Nattkemper, T. W. Gear-Induced Concept Drift in Marine Images and Its Effect on Deep Learning Classification. Front. Mar. Sci. 7, 506, https://doi.org/10.3389/fmars.2020.00506 (2020).Article 

    Google Scholar 
    Kloster, M., Langenkämper, D., Zurowietz, M., Beszteri, B. & Nattkemper, T. W. Deep learning-based diatom taxonomy on virtual slides. Scientific Reports 10, 1–13, https://doi.org/10.1038/s41598-020-71165-w (2020).Article 
    CAS 

    Google Scholar 
    Ottaviani, E. et al. Assessing the image concept drift at the OBSEA coastal underwater cabled observatory. Frontiers in Marine Science 9, 1–13, https://doi.org/10.3389/fmars.2022.840088 (2022).Article 

    Google Scholar 
    Katija, K. et al. FathomNet: A global image database for enabling artificial intelligence in the ocean. Scientific reports 12, 1–14, https://doi.org/10.1038/s41598-022-19939-2 (2022).Article 
    ADS 
    CAS 

    Google Scholar 
    Kohavi, R. A study of cross-validation and bootstrap for accuracy estimation and model selection. International Joint Conference on Artificial Intelligence 14, 1137–1145 (1995).
    Google Scholar 
    Tharwat, A. Classification assessment methods. Applied Computing and Informatics 17, 168–192, https://doi.org/10.1016/j.aci.2018.08.003 (2018).Article 

    Google Scholar 
    Qi, C., Diao, J. & Qiu, L. On estimating model in feature selection with cross-validation. IEEE Access 7, 33454–33463, https://doi.org/10.1109/ACCESS.2019.2892062 (2019).Article 

    Google Scholar 
    Lopez-Vazquez, V. et al. Video image enhancement and machine learning pipeline for underwater animal detection and classification at cabled observatories. Sensors 20, 726, https://doi.org/10.3390/s20030726 (2020).Article 
    ADS 

    Google Scholar 
    Francescangeli, M. et al. Underwater camera photos with manual tagging of fish species at OBSEA seafloor observatory from 2013 to 2014. PANGAEA https://doi.pangaea.de/10.1594/PANGAEA.946149 (2022).Marini, S. Source code for: simoneMarinIsmar/Image-Tagging-tool: Image Tagging (v1.0). Zenodo https://doi.org/10.5281/zenodo.6566282 (2022).Froese, R. & Pauly, D. FishBase. www.fishbase.org (2019).Martinez Padro, E. et al. CTD data acquired at the OBSEA seafloor observatory from 2013 to 2014. PANGAEA https://doi.org/10.1594/PANGAEA.946015 (2022).Martinez Padro, E. et al. Meteorological data from a weather station at Vilanova i la Geltrú (Catalonia, Spain) from 2013 to 2014. PANGAEA https://doi.org/10.1594/PANGAEA.945911 (2022).Martinez Padro, E. et al. Meteorological data from a weather station at Sant Pere de Ribes (Catalonia, Spain) from 2013 to 2014. PANGAEA https://doi.org/10.1594/PANGAEA.945906 (2022).Redmon, J., Divvala, S., Girshick, R. & Farhadi, A. You Only Look Once: Unified, Real-Time Object Detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779–788 https://doi.org/10.1109/CVPR.2016.91 (2016).Marrable, D. et al. Accelerating species recognition and labelling of fish from underwater video with machine-assisted deep learning. Frontiers in Marine Science 9, 944582, https://doi.org/10.3389/fmars.2022.944582 (2022).Article 

    Google Scholar 
    Zabala, M., García-Rubies, A., & Corbera, J. Els peixos de les illes Medes i del litoral català: guia per observar-los al seu ambient (Centre d’Estudis Marins de Badalona, 1992).Corbera, J., Sabatés, A., & García-Rubies, A. Peces de mar de la península ibérica (Ed. Planeta, 1996).Mercader, L., Lloris, D., & Rucabado, J. Tots els peixos del mar Català: Diagnosis i claus d’identificació (Institut d’Estudis Catalans, 2001).Aguzzi, J. et al. Daily activity rhythms in temperate coastal fishes: insights from cabled observatory video monitoring. Marine Ecology Progress Series 486, 223–236, https://doi.org/10.3354/meps10399 (2013).Article 
    ADS 

    Google Scholar 
    Campos‐Candela, A. et al. A camera‐based method for estimating absolute density in animals displaying home range behaviour. Journal of Animal Ecology 87, 825–837, https://doi.org/10.1111/1365-2656.12787 (2018).Article 

    Google Scholar 
    Jang, J. & Yoon, S. Feature concentration for supervised and semisupervised learning with unbalanced datasets in visual inspection. IEEE Transactions on Industrial Electronics 68, 7620–7630, https://doi.org/10.1109/TIE.2020.3003622 (2020).Article 

    Google Scholar 
    Zhang, J. et al. Adaptive Vertical Federated Learning on Unbalanced Features. IEEE Transactions on Parallel and Distributed Systems 33, 4006–4018, https://doi.org/10.1109/TPDS.2022.3178443 (2022).Article 

    Google Scholar 
    Lin, C. H., Lin, C. S., Chou, P. Y. & Hsu, C. C. An Efficient Data Augmentation Network for Out-of-Distribution Image Detection. IEEE Access 9, 35313–35323, https://doi.org/10.1109/ACCESS.2021.3062187 (2021).Article 

    Google Scholar 
    Lu, Y., Chen, D., Olaniyi, E. & Huang, Y. Generative adversarial networks (GANs) for image augmentation in agriculture: A systematic review. Computers and Electronics in Agriculture 200, 107208, https://doi.org/10.1016/j.compag.2022.107208 (2022).Article 

    Google Scholar 
    Waqas, N., Safie, S. I., Kadir, K. A., Khan, S. & Khel, M. H. K. DEEPFAKE Image Synthesis for Data Augmentation. IEEE Access 10, 80847–80857, https://doi.org/10.1109/ACCESS.2022.3193668 (2022).Article 

    Google Scholar  More

  • in

    Methane emissions offset atmospheric carbon dioxide uptake in coastal macroalgae, mixed vegetation and sediment ecosystems

    Mcleod, E. et al. A blueprint for blue carbon: toward an improved understanding of the role of vegetated coastal habitats in sequestering CO 2. Front. Ecol. Environ. 9, 552–560 (2011).Article 

    Google Scholar 
    Macreadie, P. I. et al. The future of Blue Carbon science. Nat. Commun. 10, 1–13 (2019).
    Google Scholar 
    Lovelock, C. E. & Duarte, C. M. Dimensions of Blue Carbon and emerging perspectives. Biol. Lett. 15, 20180781 (2019).Article 

    Google Scholar 
    Macreadie, P. I. et al. Blue carbon as a natural climate solution. Nat Rev Earth Environ 2, 826–839 (2021).Al‐Haj, A. N. & Fulweiler, R. W. A synthesis of methane emissions from shallow vegetated coastal ecosystems. Glob. Chang. Biol. 26, 2988–3005 (2020).Article 
    ADS 

    Google Scholar 
    Rosentreter, J. A. et al. Half of global methane emissions come from highly variable aquatic ecosystem sources. Nat. Geosci. https://doi.org/10.1038/s41561-021-00715-2 (2021).Bastviken, D., Tranvik, L. J., Downing, J. A., Crill, P. M. & Enrich-Prast, A. Freshwater methane emissions offset the continental carbon sink. Science (80-) 331, 50–50 (2011).Article 
    ADS 
    CAS 

    Google Scholar 
    Rosentreter, J. A., Maher, D. T., Erler, D. V., Murray, R. H. & Eyre, B. D. Methane emissions partially offset “blue carbon” burial in mangroves. Sci. Adv. 4, eaao4985 (2018).Article 
    ADS 

    Google Scholar 
    Rosentreter, J. A., Al‐Haj, A. N., Fulweiler, R. W. & Williamson, P. Methane and nitrous oxide emissions complicate coastal blue carbon assessments. Glob. Biogeochem. Cycles 35, e2020GB006858 (2021).Duarte, C. M., Middelburg, J. J. & Caraco, N. Major role of marine vegetation on the oceanic carbon cycle. Biogeosciences 2, 1–8 (2005).Article 
    ADS 
    CAS 

    Google Scholar 
    Snelgrove, P. V. R. et al. Global carbon cycling on a heterogeneous seafloor. Trends Ecol. Evol. 33, 96–105 (2018).Article 

    Google Scholar 
    Ortega, A. et al. Important contribution of macroalgae to oceanic carbon sequestration. Nat. Geosci. 12, 748–754 (2019).Article 
    ADS 
    CAS 

    Google Scholar 
    Barnes, R. O. & Goldberg, E. D. Methane production and consumption in anoxic marine sediments. Geology 4, 297 (1976).Article 
    ADS 
    CAS 

    Google Scholar 
    Reeburgh, W. S. Rates of biogeochemical processes in anoxic sediments. Annu. Rev. Earth Planet. Sci. 11, 269–298 (1983).Article 
    ADS 
    CAS 

    Google Scholar 
    Wallenius, A. J., Dalcin Martins, P., Slomp, C. P. & Jetten, M. S. M. Anthropogenic and environmental constraints on the microbial methane cycle in coastal sediments. Front. Microbiol. 12, 631621 (2021).Tokoro, T. et al. Net uptake of atmospheric CO2 by coastal submerged aquatic vegetation. Glob. Chang. Biol. 20, 1873–1884 (2014).Article 
    ADS 

    Google Scholar 
    Gallagher, J. B., Shelamoff, V. & Layton, C. Seaweed ecosystems may not mitigate CO2 emissions. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsac011 (2022).Oremland, R. S. & Taylor, B. F. Sulfate reduction and methanogenesis in marine sediments. Geochim. Cosmochim. Acta 42, 209–214 (1978).Article 
    ADS 
    CAS 

    Google Scholar 
    Egger, M., Riedinger, N., Mogollón, J. M. & Jørgensen, B. B. Global diffusive fluxes of methane in marine sediments. Nat. Geosci. 11, 421–425 (2018).Article 
    ADS 
    CAS 

    Google Scholar 
    Weber, T., Wiseman, N. A. & Kock, A. Global ocean methane emissions dominated by shallow coastal waters. Nat. Commun. 10, 1–10 (2019).Article 

    Google Scholar 
    Neubauer, S. C. & Megonigal, J. P. Moving beyond global warming potentials to quantify the climatic role of ecosystems. Ecosystems 18, 1000–1013 (2015).Article 

    Google Scholar 
    Neubauer, S. C. Global warming potential is not an ecosystem property. Ecosystems https://doi.org/10.1007/s10021-021-00631-x (2021).Howard, J., Hoyt, S., Isensee, K., Telszewski, M. & Pidgeon, E. Coastal blue carbon: methods for assessing carbon stocks and emissions factors in mangroves, tidal salt marshes, and seagrasses. 1–181 (2014). https://unesdoc.unesco.org/ark:/48223/pf0000372868.Berg, P., Huettel, M., Glud, R. N., Reimers, C. E. & Attard, K. M. Aquatic eddy covariance: the method and its contributions to defining oxygen and carbon fluxes in marine environments. Ann. Rev. Mar. Sci. 14, 431–455 (2022).Article 

    Google Scholar 
    Tokoro, T., Watanabe, K., Tada, K. & Kuwae, T. Air–water CO2 flux in shallow coastal waters: theory, methods, and empirical studies. in Blue Carbon in Shallow Coastal Ecosystems 153–184 (Springer Singapore, 2019).Saintilan, N., Rogers, K., Mazumder, D. & Woodroffe, C. Allochthonous and autochthonous contributions to carbon accumulation and carbon store in southeastern Australian coastal wetlands. Estuar. Coast. Shelf Sci. 128, 84–92 (2013).Article 
    ADS 
    CAS 

    Google Scholar 
    Ollivier, Q. R., Maher, D. T., Pitfield, C. & Macreadie, P. I. Net drawdown of greenhouse gases (CO2, CH4 and N2O) by a temperate australian seagrass meadow. Estuaries Coasts https://doi.org/10.1007/s12237-022-01068-8 (2022).Maher, D. T. et al. Novel use of cavity ring-down spectroscopy to investigate aquatic carbon cycling from microbial to ecosystem scales. Environ. Sci. Technol. 47, 12938–12945 (2013).Article 
    ADS 
    CAS 

    Google Scholar 
    Call, M. et al. Spatial and temporal variability of carbon dioxide and methane fluxes over semi-diurnal and spring–neap–spring timescales in a mangrove creek. Geochim. Cosmochim. Acta 150, 211–225 (2015).Article 
    ADS 
    CAS 

    Google Scholar 
    Maher, D. T., Cowley, K., Santos, I. R., Macklin, P. & Eyre, B. D. Methane and carbon dioxide dynamics in a subtropical estuary over a diel cycle: Insights from automated in situ radioactive and stable isotope measurements. Mar. Chem. 168, 69–79 (2015).Article 
    CAS 

    Google Scholar 
    Attard, K. M. et al. Seasonal metabolism and carbon export potential of a key coastal habitat: The perennial canopy-forming macroalga Fucus vesiculosus. Limnol. Oceanogr. 64, 149–164 (2019).Article 
    ADS 

    Google Scholar 
    Attard, K. M. et al. Seasonal ecosystem metabolism across shallow benthic habitats measured by aquatic eddy covariance. Limnol. Oceanogr. Lett. 4, 79–86 (2019).Article 

    Google Scholar 
    Trevathan-Tackett, S. M. et al. Comparison of marine macrophytes for their contributions to blue carbon sequestration. Ecology 96, 3043–3057 (2015).Article 

    Google Scholar 
    Pessarrodona, A. et al. Global seaweed productivity. Sci. Adv. 8, eabn2465 (2022).Machado, L., Magnusson, M., Paul, N. A., de Nys, R. & Tomkins, N. Effects of marine and freshwater macroalgae on in vitro total gas and methane production. PLoS ONE 9, e85289 (2014).Article 
    ADS 

    Google Scholar 
    Hansson, G. Methane production from marine, green macro-algae. Resour. Conserv. 8, 185–194 (1983).Article 
    CAS 

    Google Scholar 
    Björk, M., Rosenqvist, G., Gröndahl, F. & Bonaglia, S. Methane emissions from macrophyte beach wrack on Baltic seashores. Ambio 52, 171–181 (2023).Article 

    Google Scholar 
    Lundevall-Zara, M., Lundevall-Zara, E. & Brüchert, V. Sea-air exchange of methane in shallow inshore areas of the Baltic sea. Front. Mar. Sci. 8, 1–20 (2021).Article 

    Google Scholar 
    Yvon-Durocher, G. et al. Methane fluxes show consistent temperature dependence across microbial to ecosystem scales. Nature 507, 488–491 (2014).Article 
    ADS 
    CAS 

    Google Scholar 
    Roth, F. et al. High spatiotemporal variability of methane concentrations challenges estimates of emissions across vegetated coastal ecosystems. Glob. Chang. Biol. https://doi.org/10.1111/gcb.16177 (2022).Koweek, D. A. et al. A year in the life of a central California kelp forest: physical and biological insights into biogeochemical variability. Biogeosciences 14, 31–44 (2017).Article 
    ADS 
    CAS 

    Google Scholar 
    Watanabe, K. et al. Macroalgal metabolism and lateral carbon flows can create significant carbon sinks. Biogeosciences 17, 2425–2440 (2020).Article 
    ADS 
    CAS 

    Google Scholar 
    Möller, P., Pihl, L. & Rosenberg, R. Benthic faunal energy flow and biological interaction in some shallow marine soft bottom habitats. Mar. Ecol. Prog. Ser. 27, 109–121 (1985).Article 
    ADS 

    Google Scholar 
    Frigstad, H. et al. Blue Carbon – Climate Adaptation, CO2 Uptake And Sequestration Of Carbon In Nordic Blue Forests – Results From The Nordic Blue Carbon Project. (Nordic Council of Ministers, 2021).Ikawa, H. & Oechel, W. C. Temporal variations in air-sea CO 2 exchange near large kelp beds near San Diego, California. J. Geophys. Res. Ocean. 120, 50–63 (2015).Article 
    ADS 
    CAS 

    Google Scholar 
    Reeburgh, W. S. Oceanic methane biogeochemistry. Chem. Rev. 107, 486–513 (2007).Article 
    CAS 

    Google Scholar 
    Davidson, E. A. & Janssens, I. A. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440, 165–173 (2006).Article 
    ADS 
    CAS 

    Google Scholar 
    Oreska, M. P. J. et al. The greenhouse gas offset potential from seagrass restoration. Sci. Rep. 10, 1–15 (2020).Article 

    Google Scholar 
    Asplund, M. E. et al. Methane emissions from nordic seagrass meadow sediments. Front. Mar. Sci. 8, 811533 (2022).Article 

    Google Scholar 
    Schorn, S., Ahmerkamp, S., Bullock, E., Weber, M. & Lott, C. Diverse methylotrophic methanogenic archaea cause high methane emissions from seagrass meadows. Proc. Natl Acad. Sci. USA. https://doi.org/10.1073/pnas.2106628119/-/DCSupplemental.Published (2022).Koebsch, F., Glatzel, S. & Jurasinski, G. Vegetation controls methane emissions in a coastal brackish fen. Wetl. Ecol. Manag. 21, 323–337 (2013).Article 
    CAS 

    Google Scholar 
    Sansone, F. J. & Martens, C. S. Methane production from acetate and associated methane fluxes from anoxic coastal sediments. Science (80-). 211, 707–709 (1981).Article 
    ADS 
    CAS 

    Google Scholar 
    Egger, M. et al. Rapid sediment accumulation results in high methane effluxes from coastal sediments. PLoS ONE 11, e0161609 (2016).Article 

    Google Scholar 
    Hamdan, L. J. & Wickland, K. P. Methane emissions from oceans, coasts, and freshwater habitats: New perspectives and feedbacks on climate. Limnol. Oceanogr. 61, S3–S12 (2016).Article 
    ADS 

    Google Scholar 
    Cai, M. et al. Metatranscriptomics reveals different features of methanogenic archaea among global vegetated coastal ecosystems. Sci. Total Environ. 802, 149848 (2022).Article 
    ADS 
    CAS 

    Google Scholar 
    Evans, P. N. et al. Methane metabolism in the archaeal phylum Bathyarchaeota revealed by genome-centric metagenomics. Science (80-). 350, 434–438 (2015).Article 
    ADS 
    CAS 

    Google Scholar 
    Zhang, C.-J., Pan, J., Liu, Y., Duan, C.-H. & Li, M. Genomic and transcriptomic insights into methanogenesis potential of novel methanogens from mangrove sediments. Microbiome 8, 94 (2020).Article 
    CAS 

    Google Scholar 
    Hilt, S., Grossart, H., McGinnis, D. F. & Keppler, F. Potential role of submerged macrophytes for oxic methane production in aquatic ecosystems. Limnol. Oceanogr. https://doi.org/10.1002/lno.12095 (2022).Söllinger, A. & Urich, T. Methylotrophic methanogens everywhere — physiology and ecology of novel players in global methane cycling. Biochem. Soc. Trans. 47, 1895–1907 (2019).Article 

    Google Scholar 
    Karl, D. M. et al. Aerobic production of methane in the sea. Nat. Geosci. 1, 473–478 (2008).Article 
    ADS 
    CAS 

    Google Scholar 
    McGenity, T. J. & Sorokin, D. Y. Handbook of Hydrocarbon and Lipid Microbiology. p. 665–680 (Springer, 2010).Murray, B. C., Pendleton, L., Jenkins, W. A. & Sifleet, S. Green Payments for Blue Carbon Economic Incentives for Protecting Threatened Coastal Habitats (Nicholas Institute for Environmental Policy Solutions, 2011).Kuwae, T., Watanabe, A., Yoshihara, S., Suehiro, F. & Sugimura, Y. Implementation of blue carbon offset crediting for seagrass meadows, macroalgal beds, and macroalgae farming in Japan. Mar. Policy 138, 104996 (2022).Article 

    Google Scholar 
    Medvedev, I. P., Rabinovich, A. B. & Kulikov, E. A. Tides in three enclosed basins: the Baltic, Black, and Caspian Seas. Front. Mar. Sci. 3, 46 (2016).Article 

    Google Scholar 
    Haugen, D. A. Workshop on Micrometeorology (American Meteorological Society, 1973).Weiss, R. F. Carbon dioxide in water and seawater: the solubility of a non-ideal gas. Mar. Chem. 2, 203–215 (1974).Article 
    CAS 

    Google Scholar 
    Wiesenburg, D. A. & Guinasso, N. L. Equilibrium solubilities of methane, carbon monoxide, and hydrogen in water and sea water. J. Chem. Eng. Data 24, 356–360 (1979).Article 
    CAS 

    Google Scholar 
    Wanninkhof, R. Relationship between wind speed and gas exchange over the ocean revisited. Limnol. Oceanogr. Methods 12, 351–362 (2014).Article 

    Google Scholar 
    Gülzow, W. et al. One year of continuous measurements constraining methane emissions from the Baltic Sea to the atmosphere using a ship of opportunity. Biogeosciences 10, 81–99 (2013).Article 
    ADS 

    Google Scholar 
    Jähne, B. et al. On the parameters influencing air-water gas exchange. J. Geophys. Res. 92, 1937 (1987).Article 
    ADS 

    Google Scholar 
    Bonaglia, S. et al. Meiofauna improve oxygenation and accelerate sulfide removal in the seasonally hypoxic seabed. Mar. Environ. Res. 159, 104968 (2020).Article 
    CAS 

    Google Scholar 
    Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ. Microbiol. 18, 1403–1414 (2016).Article 
    CAS 

    Google Scholar 
    Apprill, A., McNally, S., Parsons, R. & Weber, L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat. Microb. Ecol. 75, 129–137 (2015).Article 

    Google Scholar 
    St John, J. SeqPrep. https://github.com/jstjohn/SeqPrep (2011).Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).Article 
    CAS 

    Google Scholar 
    R Core Team. R: A Language And Environment For Statistical Computing (R Foundation for Statistical Computing, 2021).Andrews, S. FastQC: A Quality Control Tool For High Throughput Sequence Data (2010).Ewels, P., Magnusson, M., Lundin, S. & Käller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32, 3047–3048 (2016).Article 
    CAS 

    Google Scholar 
    Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 41, D590–D596 (2012).Article 

    Google Scholar 
    Robertson, C. E. et al. Explicet: graphical user interface software for metadata-driven management, analysis and visualization of microbiome data. Bioinformatics 29, 3100–3101 (2013).Article 
    CAS 

    Google Scholar 
    Coolen, M. J. L. et al. Evolution of the methane cycle in Ace Lake (Antarctica) during the Holocene: response of methanogens and methanotrophs to environmental change. Org. Geochem. 35, 1151–1167 (2004).Article 
    CAS 

    Google Scholar 
    Wobbrock, J. O., Findlater, L., Gergle, D. & Higgins, J. J. The aligned rank transform for nonparametric factorial analyses using only anova procedures. in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems 143–146 (ACM, 2011).Hammer, Ø., Harper, D. & Ryan, P. PAST: paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 1–9 (2001). More

  • in

    A watershed moment for healthy watersheds

    Patterson, J. et al. Nat. Sustain. 4, 841–850 (2021).Article 

    Google Scholar 
    Reid, A. J. et al. Biol. Rev. 94, 849–873 (2019).Article 

    Google Scholar 
    Vollmer, D. & Harrison, I. J. Environ. Res. Lett. 16, 011005 (2021).Article 

    Google Scholar 
    Zeitoun, M. et al. Glob. Environ. Change 39, 143–154 (2016).Article 

    Google Scholar 
    Bezerra, M. O. et al. Environ. Manage. 69, 815–834 (2022).Article 

    Google Scholar 
    Souter, N. J. et al. Water 12, 788 (2020).Article 
    CAS 

    Google Scholar 
    Akhmouch, A., Clavreul, D. & Glas, P. Water Int. 43, 5–12 (2018).Article 

    Google Scholar 
    Andersson, E. Ambio 51, 1–8 (2022).Article 

    Google Scholar 
    Huntington, H. P. et al. Nat. Sustain. 4, 672–679 (2021).Article 

    Google Scholar 
    Soames Job, R. F. Am. J. Public Health 78, 163–167 (1988).Article 
    CAS 

    Google Scholar 
    Poff, N. L. et al. Nat. Clim. Change 6, 25–34 (2016).Article 

    Google Scholar 
    Diaz-Kope, L. & Miller-Stevens, K. Public Works Management and Policy 20, 29–48 (2015).Article 

    Google Scholar 
    OECD Financing a Water Secure Future (OECD Publishing, 2022).Cardascia, S. Financing Water Infrastructure and Landscape Approaches in Asia and the Pacific. Background Paper for 5th Roundtable on Financing Water (OECD Publishing, 2019).Schlager, E. & Blomquist, W. Embracing Watershed Politics (University Press of Colorado, 2008).Wehn, U., Collins, K., Anema, K., Basco-Carrera, L. & Lerebours, A. Water Int. 43, 34–59 (2018).Article 

    Google Scholar 
    Shaad, K., Souter, N. J., Vollmer, D., Regan, H. M. & Bezerra, M. O. Environ. Manage. 69, 752–767 (2022).Article 

    Google Scholar  More

  • in

    Bee species perform distinct foraging behaviors that are best described by different movement models

    Plant species and pollinatorsMedicago sativa L. (Fabaceae), also called alfalfa or lucerne, is a perennial legume with flowers arranged in a cluster or raceme. It is a self-compatible plant with fairly high outcrossing rate (5.3–30%)46, and it requires insect visits for seed production47. No plant material was collected for this study. Honey bees, Apis mellifera, and alfalfa leafcutting bees, Megachile rotundata, are used as managed pollinators in alfalfa seed-production fields in the USA while bumble bees are commonly used in alfalfa breeding47.Experimental design and pollinator observationsFive 11 m × 11 m patches of M. sativa plants were set up in an east–west linear arrangement at the West Madison Agricultural Research Station in Madison, Wisconsin, USA. Within each patch, we transplanted 169 young plants grown from seeds in the greenhouse, each placed 90 cm apart. These plants grew and, at flowering, a plant had an average of 30.65 ± 16.4 stems per plant, with 4.93 ± 3.41 racemes per stem, and 7.53 ± 2.44 open flowers per raceme.A honey bee hive was placed approximately 100 m from the patches and a bumble bee hive was set up at the center of the southern edge of the patches. For leafcutting bees, a 60 × 30 × 7.6 cm bee board was set up in each of two boxes placed 1/3 and 2/3 along the southern edge of the patches and a half gallon of bees was released at periodic intervals throughout the alfalfa flowering season.Over two consecutive summers, observers followed bees foraging in the alfalfa patches, marked each raceme visited in succession within a foraging bout with a numbered clip, and recorded the number of flowers visited per raceme. After a bee had left a patch, observers went back to the marked racemes and measured the distance and direction traveled between consecutive racemes. Directions were recorded as one of the cardinal directions: North (N), South (S), East (E) or West (W), or inter-cardinal directions: Northeast (NE), Southeast (SE), Northwest (NW) and Southwest (SW). The frequency distributions of distances and directions traveled between two successive racemes are presented for each bee species each year in Figs. 1 (distances) and 2 (directions). The low pollinator abundance permitted observers to follow individual bees foraging in a patch. Little interference among bee species was observed in the patches.Figure 1Frequency distributions for distances traveled between consecutive racemes (cm) for each bee species each year.Full size imageFigure 2Frequency distributions of directions traveled between consecutive racemes for each bee species each year.Full size imageModel for the distance traveled between consecutive racemesWe first determined whether a statistical model best described the distance traveled between consecutive racemes (Modeled Distance), and examined whether the model differed among bee species. We used mixed effect linear models (proc Mixed in SAS 9.3)48 to identify the model that best described the distance traveled by pollinators between consecutive racemes. The model included loge distance as a linear function of loge flower number and bee species as fixed effects. The distance traveled between consecutive racemes and the number of flowers visited per raceme were log transformed prior to analyses in order to improve the models’ residuals. In addition, we included patch and foraging bout as random effects in the model. A foraging bout includes the racemes visited in succession from the time a bee is spotted in a patch to the time it leaves that patch. We used foraging bout instead of individual bee as the random effect because bees were not individually marked in this study. Moreover, to take into consideration the potential correlation between successive observations within a foraging bout, we added clip to the model. Clip 1 represents the first and second racemes visited in the foraging bout; clip 2, the second and third, and so on. Clip was added to the model either as a random effect or as a repeated measure with an AR(1) structure. The combination of random clip and random foraging bout creates a model that is sometimes called the “compound symmetry” model. The AR(1) structure represents correlations that decline exponentially as the gap between measurements increases such that measurements closer together in time are more strongly correlated than measurements further apart. Because we expected bees to visit flowers at close proximity when resources are abundant, we chose this correlation structure as a good potential descriptor of the way distances might be correlated within foraging bouts. We started with a full model which included loge flower number, bee species, patch, foraging bout, and clip either as a random effect or as a repeated measure with an AR(1) structure. We then removed variables and compared models by inspecting AIC values and the p values for each term in the model. We considered both low AIC and statistically significant (p  More

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    Prediction of tide level based on variable weight combination of LightGBM and CNN-BiGRU model

    LightGBMBefore explaining LightGBM23, it is necessary to introduce XGBoost24, which is also based on the gradient boosting decision tree (GBDT) algorithm30. XGBoost integrates multiple classification and regression trees (CART) to compensate for the lack of prediction accuracy of a single CART. It is an improved boosting algorithm based on GBDT, which is popular due to its high processing speed, high regression accuracy and ability to process large-scale data31. However, XGBoost uses a presorted algorithm to find data segmentation points, which takes up considerable memory in the calculation and seriously affects cache optimization.LightGBM is improved based on XGBoost. It uses a histogram algorithm to find the best data segmentation point, which occupies less memory and has a lower complexity of data segmentation. The flow of the histogram algorithm to find the optimal segmentation point is shown in Fig. 3.Figure 3Histogram algorithm.Full size imageMoreover, LightGBM abandons the levelwise decision tree growth strategy used by most GBDT tools and uses the leafwise algorithm with depth limitations. This leaf-by-leaf growth strategy can reduce more errors and obtain better accuracy. Decision trees in boosting algorithms may grow too deep while training, leading to model overfitting. Therefore, LightGBM adds a maximum depth limit to the leafwise growth strategy to prevent this from happening and maintains its high computational efficiency. To summarize, LightGBM can be better and faster used in industrial practice and is also very suitable as the base model in our tide level prediction task. The layer-by-layer growth strategy and leaf-by-leaf growth strategy are shown in Fig. 4.Figure 4Two GBDT growth strategies.Full size imageCNN-BiGRUConvolutional neural networkA convolutional neural network (CNN) is a deep feedforward neural network with the characteristics of local connection and weight sharing. It was first used in the field of computer vision and achieved great success32,33. In recent years, CNNs have also been widely used in time series processing. For example, Bai et al.34 proposed a temporal convolutional network (TCN) based on a convolutional neural network and residual connections, which is not worse than recurrent neural networks such as LSTM in some time series analysis tasks. At present, a convolutional neural network is generally composed of convolution layers, pooling layers and a fully connected layer. Its network structure is shown in Fig. 5. The pooling layer is usually added after the convolution layers. The maximum pooling layer can retain the strong features in the data after the convolution operation, eliminate the weak features to reduce the number of parameters in a network and avoid overfitting of the model.Figure 5Schematic diagram of a convolutional neural network.Full size imageBidirectional GRUIn previous attempts at tide level prediction by scholars, bidirectional long short-term memory networks35 have achieved good prediction results. However, in our subsequent experiments, the bidirectional gated recurrent unit achieved higher prediction accuracy than BiLSTM, so we used the BiGRU network for subsequent prediction tasks.The GRU network36 adds a gating mechanism to control information updating in a recurrent neural network. Different from the mechanism in LSTM, GRU consists of only two gates called the update gate ({z}_{t}) and the reset door ({r}_{t}).The recurrent unit structure of the GRU network is shown in Fig. 6.Figure 6Recurrent unit structure of the GRU network.Full size imageEach unit of GRU is calculated as follows:$${z}_{t}= sigma ({W}_{z}{x}_{t}+{U}_{z}{h}_{t-1}+{b}_{z})$$
    (7)
    $${r}_{t}= sigma ({W}_{r}{x}_{t}+{U}_{r}{h}_{t-1}+{b}_{r})$$
    (8)
    $${widetilde{h}}_{t}=tanh({W}_{h}{x}_{t}+{U}_{h}left({r}_{t}odot {h}_{t-1}right)+{b}_{h})$$
    (9)
    $${h}_{t}={z}_{t}odot {h}_{t-1}+left(1-{z}_{t}right)odot {widetilde{h}}_{t}$$
    (10)
    In the above formula, ({z}_{t}) represents the update gate, which controls how much information is retained from the previous state ({h}_{t-1}) (without nonlinear transformation) when calculating the current state ({h}_{t}). Meanwhile, it also controls how much information will be accepted by ({h}_{t}) from the candidate states ({widetilde{h}}_{t}). ({r}_{t}) represents the reset gate, which is used to ensure whether the calculation of the candidate state ({widetilde{h}}_{t}) depends on the previous state ({h}_{t-1}). (upsigma ) is the standard sigmoid activation function; (tanh(cdot )) is the hyperbolic tangent activation function; and (odot ) indicates the Hadamard product. The weight matrices of the update gate, reset gate, and ({widetilde{h}}_{t}) calculation layer are expressed as ({W}_{z},{W}_{r},{W}_{h}); the coefficient matrices of the update gate, reset gate, and ({widetilde{h}}_{t}) calculation layer are expressed as ({U}_{z},{U}_{r},{U}_{h}); and the offset vectors of the update gate, reset gate, and ({widetilde{h}}_{t}) calculation layer are expressed as ({b}_{z},{b}_{r},{b}_{h}).A bidirectional gated recurrent unit network37 is a combination of two GRUs whose information propagating directions are reversed, and it has independent parameters in each, which makes it able to fit both forward and backward data at first and then join up the results from two directions. BiGRU can capture sequence patterns that may be ignored by unidirectional GRU. The structure of BiGRU is shown in Fig. 7.Figure 7The structure of BiGRU.Full size imageTaking the BiGRU’s forward hidden state vector at time (t) as ({h}_{t}^{(1)}) and taking the BiGRU’s backward hidden state vector at time (t) as ({h}_{t}^{(2)}), (upsigma ) indicates the standard sigmoid activation function, and (oplus ) indicates a vector splicing operation. We can calculate the output ({y}_{t}) of a BiGRU network as follows:$${h}_{t}={h}_{t}^{(1)}oplus {h}_{t}^{(2)}$$
    (11)
    $${y}_{t}=sigma ({h}_{t} )$$
    (12)
    CNN-BiGRU prediction modelBecause CNN has significant advantages in extracting useful features from a picture or a sequence and BiGRU is good at processing time series, we combine CNN and BiGRU to build the CNN-BiGRU model. The model can be mainly divided into an input layer, a convolution layer, a BiGRU network layer, a dropout layer, a fully connected layer and an output layer. The CNN layer and BiGRU layer are the core structures of the model. The function of the dropout layer is to avoid model overfitting. The CNN layer consists of two one-dimensional convolution (Conv1D) layers and a one-dimensional maximum pooling (MaxPooling1D) layer. The input of BiGRU is the output sequence of the CNN layer, and the BiGRU network is set as a one-hidden-layer structure. The structure of the CNN-BiGRU combination model is shown in Fig. 8.Figure 8The structure of CNN-BiGRU.Full size imageVariable weight combination modelWhen we analyze and predict relatively stationary tide level time series, LightGBM can perform well. However, due to environmental factors such as air pressure, wind force and terrain in reality, most tide level observation sequences are sometimes not relatively stationary, which requires that our tide level prediction model be reasonably able to “extrapolate” based on the sample observations, that is, be capable of generating values that are not in the sample. LightGBM is a tree-based model, which leads to our prediction results being between the maximum and minimum values of sequences. Therefore, LightGBM will not be able to accurately predict the situation or tidal change trend that did not appear in previous observations. However, the CNN-BiGRU model, which is a kind of neural network, has no such problem in theory and will be able to find the trend information that may be hidden in the tide level series. Therefore, we consider providing an appropriate weight for a single base model to build a combination model to improve the accuracy of the tide level prediction task.Principle of the residual weight combination model and improved variable weight combination modelTo improve the prediction accuracy of the combination model, a simple and effective idea is to determine the base models’ weights in the combination model according to the error between the prediction value and the real value. This method is also called the residual weight method, and its calculation formulas for determining the weights are:$$gleft({x}_{t}right)= sum_{i=1}^{m}{omega }_{i}left(t-1right){f}_{i}({x}_{t})$$
    (13)
    $${omega }_{i}left(t-1right)=frac{frac{1}{overline{{varphi }_{i}}left(t-1right)}}{sum_{i=1}^{m}frac{1}{overline{{varphi }_{i}}left(t-1right)}}$$
    (14)
    $$sum_{i=1}^{m}{omega }_{i}left(t-1right)=1,{omega }_{i}left(t-1right)ge 0$$
    (15)

    where ({omega }_{i}left(t-1right)) denotes the weight of the (i) th model at the moment (t-1), ({f}_{i}left({x}_{t}right)) denotes the prediction value of the (i) th model at the moment (t), (gleft({x}_{t}right)) denotes the prediction value of the combination model at the moment (t), and (overline{{varphi }_{i}}left(t-1right)) is the square sum of the predictive errors of the (i) th model at the moment (t-1).Our LightGBM-CNN-BiGRU (combination model) is based on the improved residual weight method. We call it the variable weight combination model. We use the weights calculated by formula (9) and formula (11) to calculate a series of new weights. The new weights from formula (11) will take the residual weight changes in (d) time steps into consideration by averaging the old weights in (d) time steps to improve the stability of the residual weight method.$${omega }_{j}left(tright)=frac{1}{d}sum_{k=1}^{d}{omega }_{i}left(t-kright)left(d=4right)$$
    (16)
    After obtaining a series of weights through formula (9) and formula (11), we take the absolute value of the error between the prediction value and the true value of each combination model at the moment of (t) as ({delta }_{i,t}) and ({delta }_{j,t}), respectively:$${delta }_{i,t}=mid sum_{i=1}^{m}{omega }_{i}left(tright){f}_{i}left({x}_{t}right)-{y}_{t}mid $$
    (17)
    $${delta }_{j,t}=mid sum_{i=1}^{m}{omega }_{j}left(tright){f}_{i}left({x}_{t}right)-{y}_{t}mid $$
    (18)
    Comparing ({delta }_{i,t}) and ({delta }_{j,t}), if ({delta }_{i,t} >{delta }_{j,t}), the combination model uses the new weight ({omega }_{j}left(tright)) in place of the original weight ({omega }_{i}left(tright)). Otherwise, the weight of the combination model remains unchanged.Parameter optimization of the combination modelBecause the LightGBM-CNN-BiGRU (combination model) is a variable weight combination of the prediction results from two base models, the performance of the combination model can be directly improved by separately optimizing the super parameters of the two base models. We mainly use the grid search algorithm and K-fold cross validation method to optimize the parameters. The grid search algorithm is a method to improve the performance of a certain model by iterating over a given set of parameters. With the help of the K-fold cross validation method, we can calculate the performance score of the LightGBM model on the training set and easily optimize its superparameters. The final parameters of the LightGBM model are set to num_leaves = 26, learning_rate = 0.05, and n_estimators = 46.For the CNN-BiGRU network, we mainly improve the prediction accuracy of the model by adjusting the size and number of hidden layers in the BiGRU structure and prevent the model from overfitting by changing the dropout ratio and tracking the validation loss of the network while training.The LightGBM and CNN-BiGRU variable weight combination modelThe workflow of our tide level prediction model is shown in Fig. 9. It mainly includes data preprocessing; training, optimization and prediction of the base models; construction of a variable weight combination prediction model; and evaluation and analysis of the combination model’s performance.

    (1)

    Data preprocessing: The quality of the data directly determines the upper limit of the prediction and generalization ability of a certain machine learning model. Standard, clean and continuous data are conducive to model training. The data used in this study are from the Irish National Tide Gauge Network, and all of them are subject to quality control. We filled in a small number of missing values and normalized the data to speed up the model training.

    (2)

    Construction and optimization of base models: We divide the dataset into a training set, a validation set and a test set according to the proportion of 7:1:2 and train the LightGBM model and CNN-BiGRU model with data on the training set. We optimize the parameters and monitor whether the model has been overfitted by tracking the validation loss of the network while training. Finally, we put the data into two base models for training and then obtain the prediction results of a single base model.

    (3)

    Construction of the variable weight combination model. Based on the prediction results of two single base models obtained in step (2), we calculate the weight of each base model according to the principle of the improved variable weight combination method and then obtain the prediction results of the variable weight combination model.

    (4)

    Model evaluation and analysis: According to the indexes of the model evaluation, the variable weight combination model is compared with other basic models to analyze its prediction performance after being improved.

    Figure 9Prediction flow of the LightGBM-CNN-BiGRU variable weight combination model.Full size image More

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    Sulfoquinovose is a widespread organosulfur substrate for Roseobacter clade bacteria in the ocean

    Snow AJD, Burchill L, Sharma M, Davies GJ, Williams SJ. Sulfoglycolysis: Catabolic pathways for metabolism of sulfoquinovose. Chem Soc Rev. 2021;50:13628–45.Article 
    CAS 

    Google Scholar 
    Van Mooy BAS, Rocap G, Fredricks HF, Evans CT, Devol AH. Sulfolipids dramatically decrease phosphorus demand by picocyanobacteria in oligotrophic marine environments. Proc Natl Acad Sci USA 2006;103:8607–12.Article 

    Google Scholar 
    Wu J, Sunda W, Boyle EA, Karl DM. Phosphate depletion in the western North Atlantic. Ocean Sci 2000;289:759–62.CAS 

    Google Scholar 
    Goddard-Borger ED, Williams SJ. Sulfoquinovose in the biosphere: occurrence, metabolism and functions. Biochem J. 2017;474:827–49.Article 
    CAS 

    Google Scholar 
    Harwood JL, Nicholls RG. The plant sulpholipid- a major component of the sulphur cycle. Biochem Soc Trans. 1979;7:440–7.Article 
    CAS 

    Google Scholar 
    Moran MA, Durham BP. Sulfur metabolites in the pelagic ocean. Nat Rev Microbiol. 2019;17:665–78.Article 
    CAS 

    Google Scholar 
    Tang K. Chemical diversity and biochemical transformation of biogenic organic sulfur in the ocean. Front Mar Sci. 2020;7:68.Article 

    Google Scholar 
    Denger K, Weiss M, Felux AK, Schneider A, Mayer C, Spiteller D, et al. Sulphoglycolysis in Escherichia coli K-12 closes a gap in the biogeochemical sulphur cycle. Nature 2014;507:114–7.Article 
    CAS 

    Google Scholar 
    Hanson BT, Kits KD, Loffler J, Burrichter AG, Fiedler A, Denger K, et al. Sulfoquinovose is a select nutrient of prominent bacteria and a source of hydrogen sulfide in the human gut. ISME J. 2021;15:2779–91.Article 
    CAS 

    Google Scholar 
    Strickland TC, Fitzgerald JW. Mineralization of sulfur in sulfoquinovose by forest soils. Soil Biol Biochem. 1983;15:347–9.Article 
    CAS 

    Google Scholar 
    Felux AK, Spiteller D, Klebensberger J, Schleheck D. Entner-Doudoroff pathway for sulfoquinovose degradation in Pseudomonas putida SQ1. Proc Natl Acad Sci USA 2015;112:E4298–E305.Article 
    CAS 

    Google Scholar 
    Frommeyer B, Fiedler AW, Oehler SR, Hanson BT, Loy A, Franchini P, et al. Environmental and intestinal phylum Firmicutes bacteria metabolize the plant sugar sulfoquinovose via a 6-deoxy-6-sulfofructose transaldolase pathway. Iscience. 2020;23:101510.Article 
    CAS 

    Google Scholar 
    Roy AB, Hewlins MJE, Ellis AJ, Harwood JL, White GF. Glycolytic breakdown of sulfoquinovose in bacteria: A missing link in the sulfur cycle. Appl Environ Microbiol. 2003;69:6434–41.Article 
    CAS 

    Google Scholar 
    Liu J, Wei Y, Ma K, An J, Liu X, Liu Y, et al. Mechanistically diverse pathways for sulfoquinovose degradation in bacteria. ACS Catal. 2021;11:14740–50.Article 
    CAS 

    Google Scholar 
    Zhang S, Li Z, Yan Y, Zhang C, Li J, Zhao B. Bacillus urumqiensis sp. nov., a moderately haloalkaliphilic bacterium isolated from a salt lake. Int J Syst Evol Microbiol. 2016;66:2305–12.Article 
    CAS 

    Google Scholar 
    Durham BP, Sharma S, Luo H, Smith CB, Amin SA, Bender SJ, et al. Cryptic carbon and sulfur cycling between surface ocean plankton. Proc Natl Acad Sci USA 2015;112:453–7.Article 
    CAS 

    Google Scholar 
    Chen X, Liu L, Gao X, Dai X, Han Y, Chen Q, et al. Metabolism of chiral sulfonate compound 2,3-dihydroxypropane-1-sulfo-nate (DHPS) by Roseobacter bacteria in marine environment. Environ Int. 2021;157:106829.Article 
    CAS 

    Google Scholar 
    Liu J, Wei Y, Lin L, Teng L, Yin J, Lu Q, et al. Two Radical-dependent mechanisms for anaerobic degradation of the globally abundant Organosulfur Compound Dihydroxypropanesulfonate. Proc Natl Acad Sci USA 2020;117:15599.Article 
    CAS 

    Google Scholar 
    Xing M, Wei Y, Zhou Y, Zhang J, Lin L, Hu Y, et al. Radical-mediated C-S bond cleavage in C2 sulfonate degradation by anaerobic bacteria. Nat Commun. 2019;10:1609.Article 

    Google Scholar 
    Sharma M, Lingford JP, Petricevic M, Snow AJD, Zhang Y, Jarva MA, et al. Oxidative desulfurization pathway for complete catabolism of sulfoquinovose by bacteria. Proc Natl Acad Sci USA 2022;119:e2116022119.Article 
    CAS 

    Google Scholar 
    Scholz SS, Serif M, Schleheck D, Sayer MDJ, Cook AM, Kupper FC. Sulfoquinovose metabolism in marine algae. Bot Mar. 2021;64:301–12.Article 
    CAS 

    Google Scholar 
    Abayakoon P, Epa R, Petricevic M, Bengt C, Mui JWY, van der Peet PL, et al. Comprehensive synthesis of substrates, intermediates, and products of the sulfoglycolytic Embden-Meyerhoff-Parnas pathway. J Org Chem. 2019;84:2901–10.Article 
    CAS 

    Google Scholar 
    Denger K, Smits THM, Cook AM. L-Cysteate sulpho-lyase, a widespread pyridoxal 5 ‘-phosphate-coupled desulphonative enzyme purified from Silicibacter pomeroyi DSS-3. Biochem J. 2006;394:657–64.Article 
    CAS 

    Google Scholar 
    Guillard RRL. Culture of Phytoplankton for Feeding Marine Invertebrates. Smith WL, Chanley MH, (eds): Springer US; 1975. Boston, MA. pp 29–60.Moore LR, Coe A, Zinser ER, Saito MA, Sullivan MB, Lindell D, et al. Culturing the marine cyanobacterium Prochlorococcus. Limnol Oceanogr Methods. 2007;5:353–62.Article 
    CAS 

    Google Scholar 
    Waterbury J, Watson S, Valois F, Franks D. Biological and ecological characterization of the marine unicellular cyanobacterium Synechococcus. Platt T, Li WKW, (eds). Department of Fisheries and Oceans, Ottawa 1986. pp 71–120.Olenina I, Hajdu S, Edler L, Andersson A, Wasmund N, Busch S, et al. Biovolumes and size-classes of phytoplankton in the Baltic Sea. HELCOM Balt Sea Environ Proc. 2006;106:144.
    Google Scholar 
    Zheng Q, Wang Y, Lu J, Lin W, Chen F, Jiao N. Metagenomic and metaproteomic insights into photoautotrophic and heterotrophic interactions in a Synechococcus culture. mbio 2020;11:e03261–19.Article 
    CAS 

    Google Scholar 
    Partensky F, Hess WR, Vaulot D. Prochlorococcus, a marine photosynthetic prokaryote of global significance. Microbiol Mol Biol Rev. 1999;63:106–27.Article 
    CAS 

    Google Scholar 
    Han Y, Zhang M, Chen X, Zhai W, Tan E, Tang K. Transcriptomic evidences for microbial carbon and nitrogen cycles in the deoxygenated seawaters of Bohai Sea. Environ Int. 2022;158:106889.Article 
    CAS 

    Google Scholar 
    Li WKW. Primary production of prochlorophytes, cyanobacteria, and eukaryotic ultraphytoplankton – measurements from flow cytometric sorting. Limnol Oceanogr. 1994;39:169–75.Article 
    CAS 

    Google Scholar 
    Denger K, Ruff A, Rein U, Cook AM. Sulphoacetaldehyde sulpho-lyase (EC 4.4.1.12) from Desulfonispora thiosulfatigenes: purification, properties and primary sequence. Biochem J. 2001;357:581–6.Article 
    CAS 

    Google Scholar 
    Ismail R, Lee HY, Mahyudin NA, Abu, Bakar F. Linearity study on detection and quantification limits for the determination of avermectins using linear regression. J Food Drug Anal. 2014;22:407–12.Article 
    CAS 

    Google Scholar 
    Klemetsen T, Raknes IA, Fu J, Agafonov A, Balasundaram SV, Tartari G, et al. The MAR databases: development and implementation of databases specific for marine metagenomics. Nucleic Acids Res. 2018;46:D692–D9.Article 
    CAS 

    Google Scholar 
    Suzek BE, Huang H, McGarvey P, Mazumder R, Wu CH. UniRef: comprehensive and non-redundant UniProt reference clusters. Bioinformatics 2007;23:1282–8.Article 
    CAS 

    Google Scholar 
    Rozewicki J, Li S, Amada KM, Standley DM, Katoh K. MAFFT-DASH: Integrated protein sequence and structural alignment. Nucleic Acids Res. 2019;47:W5–W10.CAS 

    Google Scholar 
    Schuller DJ, Reisch CR, Moran MA, Whitman WB, Lanzilotta WN. Structures of dimethylsulfoniopropionate-dependent demethylase from the marine organism Pelagabacter ubique. Protein Sci. 2012;21:289–98.Article 
    CAS 

    Google Scholar 
    Bharath SR, Bisht S, Harijan RK, Savithri HS, Murthy MR. Structural and mutational studies on substrate specificity and catalysis of Salmonella typhimurium D-cysteine desulfhydrase. PLoS One. 2012;7:e36267.Article 
    CAS 

    Google Scholar 
    Chartron J, Carroll KS, Shiau C, Gao H, Leary JA, Bertozzi CR, et al. Substrate Recognition, Protein Dynamics, and Iron-Sulfur Cluster in Pseudomonas aeruginosa Adenosine 5′-Phosphosulfate Reductase. J Mol Biol. 2006;364:152–69.Article 
    CAS 

    Google Scholar 
    Davis KM, Altmyer M, Martinie RJ, Schaperdoth I, Krebs C, Bollinger JM Jr, et al. Structure of a Ferryl Mimic in the Archetypal Iron(II)- and 2-(Oxo)-glutarate-Dependent Dioxygenase, TauD. Biochemistry 2019;58:4218–23.Article 
    CAS 

    Google Scholar 
    Mirdita M, Schütze K, Moriwaki Y, Heo L, Ovchinnikov S, Steinegger M. ColabFold: Making protein folding accessible to all. Nat Methods. 2022;19:679–82.Article 
    CAS 

    Google Scholar 
    Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature 2021;596:583–9.Article 
    CAS 

    Google Scholar 
    Zhang C, Shine M, Pyle AM, Zhang Y. US-align: universal structure alignments of proteins, nucleic acids, and macromolecular complexes. Nat Methods. 2022;19:1109–15.Article 
    CAS 

    Google Scholar 
    Xu J, Zhang Y. How significant is a protein structure similarity with TM-score = 0.5? Bioinformatics 2010;26:889–95.Article 
    CAS 

    Google Scholar 
    Nguyen L-T, Schmidt HA, von Haeseler A, Minh BQ. IQ-TREE: A fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol. 2014;32:268–74.Article 

    Google Scholar 
    Villar E, Vannier T, Vernette C, Lescot M, Cuenca M, Alexandre A, et al. The Ocean Gene Atlas: exploring the biogeography of plankton genes online. Nucleic Acids Res. 2018;46:W289–W95.Article 
    CAS 

    Google Scholar 
    Vernette C, Henry N, Lecubin J, de Vargas C, Hingamp P, Lescot M. The Ocean barcode atlas: A web service to explore the biodiversity and biogeography of marine organisms. Mol Ecol Resour. 2021;21:1347–58.Article 
    CAS 

    Google Scholar 
    Paoli L, Ruscheweyh H-J, Forneris CC, Hubrich F, Kautsar S, Bhushan A, et al. Biosynthetic potential of the global ocean microbiome. Nature 2022;607:111–8.Article 
    CAS 

    Google Scholar 
    Sunagawa S, Acinas SG, Bork P, Bowler C, Acinas SG, Babin M, et al. Tara Oceans: towards global ocean ecosystems biology. Nat Rev Microbiol. 2020;18:428–45.Article 
    CAS 

    Google Scholar 
    Acinas SG, Sánchez P, Salazar G, Cornejo-Castillo FM, Sebastián M, Logares R, et al. Deep ocean metagenomes provide insight into the metabolic architecture of bathypelagic microbial communities. Commun Biol. 2021;4:604.Article 
    CAS 

    Google Scholar 
    Biller SJ, Berube PM, Dooley K, Williams M, Satinsky BM, Hackl T, et al. Marine microbial metagenomes sampled across space and time. Sci Data. 2018;5:180176.Article 
    CAS 

    Google Scholar 
    Pachiadaki MG, Brown JM, Brown J, Bezuidt O, Berube PM, Biller SJ, et al. Charting the Complexity of the Marine Microbiome through Single-Cell Genomics. Cell 2019;179:1623–35.Article 
    CAS 

    Google Scholar 
    Delmont TO, Quince C, Shaiber A, Esen ÖC, Lee STM, Rappé MS, et al. Nitrogen-fixing populations of Planctomycetes and Proteobacteria are abundant in surface ocean metagenomes. Nat Microbiol. 2018;3:804–13.Article 
    CAS 

    Google Scholar 
    Tamura K, Stecher G, Peterson D, Filipski A, Kumar S. MEGA6: Molecular evolutionary genetics analysis version 6.0. Mol Biol Evol. 2013;30:2725–9.Article 
    CAS 

    Google Scholar 
    Subramanian B, Gao S, Lercher MJ, Hu S, Chen W-H. Evolview v3: A webserver for visualization, annotation, and management of phylogenetic trees. Nucleic Acids Res. 2019;47:W270–W5.Article 
    CAS 

    Google Scholar 
    Xing M, Wei Y, Zhou Y, Zhang J, Lin L, Hu Y, et al. Radical-mediated C-S bond cleavage in C2 sulfonate degradation by anaerobic bacteria. Nat Commun. 2019;10:1609.Article 

    Google Scholar 
    Biebl H, Allgaier M, Tindall BJ, Koblizek M, Lunsdorf H, Pukall R, et al. Dinoroseobacter shibae gen. nov., sp nov., a new aerobic phototrophic bacterium isolated from dinoflagellates. Int J Syst Evol Microbiol. 2005;55:1089–96.Article 
    CAS 

    Google Scholar 
    Fu H, Uchimiya M, Gore J, Moran MA. Ecological drivers of bacterial community assembly in synthetic phycospheres. Proc Natl Acad Sci USA 2020;117:3656–62.Article 
    CAS 

    Google Scholar 
    Chen I-MA, Chu K, Palaniappan K, Ratner A, Huang J, Huntemann M, et al. The IMG/M data management and analysis system v.7: content updates and new features. Nucleic Acids Res. 2022. https://doi.org/10.1093/nar/gkac976.Shiba T. Roseobacter litoralis gen. nov., sp. nov., and Roseobacter denitrificans sp. nov., aerobic pink-pigmented bacteria which contain bacteriochlorophyll a. Syst Appl Microbiol. 1991;14:140–5.Article 

    Google Scholar 
    Kopriva S, Calderwood A, Weckopp SC, Koprivova A. Plant sulfur and big data. Plant Sci. 2015;241:1–10.Article 
    CAS 

    Google Scholar 
    Simon J, Kroneck PMH. Microbial sulfite respiration. Adv Micro Physiol. 2013;62:45–117.Article 
    CAS 

    Google Scholar 
    Gonzalez JM, Covert JS, Whitman WB, Henriksen JR, Mayer F, Scharf B, et al. Silicibacter pomeroyi sp nov and Roseovarius nubinhibens sp nov., dimethylsulfoniopropionate-demethylating bacteria from marine environments. Int J Syst Evol Microbiol. 2003;53:1261–9.Article 
    CAS 

    Google Scholar 
    Liang KYH, Orata FD, Boucher YF, Case RJ. Roseobacters in a sea of poly- and paraphyly: whole genome-based taxonomy of the family Rhodobacteraceae and the proposal for the split of the “Roseobacter clade” into a novel family, Roseobacteraceae fam. nov. Front Microbiol. 2021;12:683109.Article 

    Google Scholar 
    Howard EC, Sun S, Biers EJ, Moran MA. Abundant and diverse bacteria involved in DMSP degradation in marine surface waters. Environ Microbiol. 2008;10:2397–410.Article 
    CAS 

    Google Scholar 
    Howard EC, Henriksen JR, Buchan A, Reisch CR, Buergmann H, Welsh R, et al. Bacterial taxa that limit sulfur flux from the ocean. Science. 2006;314:649–52.Article 
    CAS 

    Google Scholar 
    Durham BP, Boysen AK, Carlson LT, Groussman RD, Heal KR, Cain KR, et al. Sulfonate-based networks between eukaryotic phytoplankton and heterotrophic bacteria in the surface ocean. Nat Microbiol. 2019;4:1706–15.Article 
    CAS 

    Google Scholar 
    Smetacek V. Diatoms and the ocean carbon cycle. Protist 1999;150:25–32.Article 
    CAS 

    Google Scholar 
    Stoecker DK, Lavrentyev PJ. Mixotrophic plankton in the polar seas: A pan-Arctic review. Front Mar Sci. 2018;5:292.Article 

    Google Scholar 
    Turner SM, Malin G, Liss PS, Harbour DS, Holligan PM. The seasonal-variation of dimethyl sulfide and dimethylsulfoniopropionate concentrations in nearshore waters. Limnol Oceanogr. 1988;33:364–75.Article 
    CAS 

    Google Scholar 
    Belviso S, Kim S-K, Rassoulzadegan F, Krajka B, Nguyen BC, Mihalopoulos N, et al. Production of dimethylsulfonium propionate (DMSP) and dimethylsulfide (DMS) by a microbial food web. Limnol Oceanogr. 1990;35:1810–21.Article 
    CAS 

    Google Scholar 
    Simo R, Pedros-Alio C, Malin G, Grimalt JO. Biological turnover of DMS, DMSP and DMSO in contrasting open-sea waters. Mar Ecol Prog Ser. 2000;203:1–11.Article 
    CAS 

    Google Scholar 
    Flombaum P, Gallegos JL, Gordillo RA, Rincon J, Zabala LL, Jiao N, et al. Present and future global distributions of the marine Cyanobacteria Prochlorococcus and Synechococcus. Proc Natl Acad Sci USA 2013;110:9824–9.Article 
    CAS 

    Google Scholar 
    Gasparovic B, Penezic A, Frka S, Kazazic S, Lampitt RS, Holguin FO, et al. Particulate sulfur-containing lipids: Production and cycling from the epipelagic to the abyssopelagic zone. Deep Sea Res Part I Oceanogr Res Pap. 2018;134:12–22.Article 
    CAS 

    Google Scholar 
    Zhan P, Tang K, Chen X, Yu L. Complete genome sequence of Maribacter sp T28, a polysaccharide-degrading marine flavobacteria. J Biotechnol. 2017;259:1–5.Article 
    CAS 

    Google Scholar 
    Van Mooy BAS, Fredricks HF. Bacterial and eukaryotic intact polar lipids in the eastern subtropical South Pacific: Water-column distribution, planktonic sources, and fatty acid composition. Geochim Cosmochim Acta. 2010;74:6499–516.Article 

    Google Scholar 
    Popendorf KJ, Tanaka T, Pujo-Pay M, Lagaria A, Courties C, Conan P, et al. Gradients in intact polar diacylglycerolipids across the Mediterranean Sea are related to phosphate availability. Biogeosciences 2011;8:3733–45.Article 
    CAS 

    Google Scholar  More