More stories

  • in

    A path forward for analysing the impacts of marine protected areas

    Sala, E. et al. Protecting the global ocean for biodiversity, food and climate. Nature 592, 397–402 (2021).ADS 
    CAS 
    Article 

    Google Scholar 
    Gillispie, C. C., Gratton-Guinness, I. & Fox, R. Pierre-Simon Laplace, 1749-1827: A Life in Exact Science (Princeton Univ. Press, 1999).Dinmore, T. A., Duplisea, D. E., Rackham, B. D., Maxwell, D. L. & Jennings, S. Impact of a large-scale area closure on patterns of fishing disturbance and the consequences for benthic communities. ICES J. Mar. Sci. 60, 371–380 (2003).Article 

    Google Scholar 
    Hiddink, J. G., Hutton, T., Jennings, S. & Kaiser, M. J. Predicting the effects of area closures and fishing effort restrictions on the production, biomass, and species richness of benthic invertebrate communities. ICES J. Mar. Sci. 63, 822–830 (2006).Article 

    Google Scholar 
    Greenstreet, S. P. R., Fraser, H. M. & Piet, G. J. Using MPAs to address regional-scale ecological objectives in the North Sea: modelling the effects of fishing effort displacement. ICES J. Mar. Sci. 66, 90–100 (2009).Article 

    Google Scholar 
    Suuronen, P. et al. A path to a sustainable trawl fishery in Southeast Asia. Rev. Fish. Sci. Aquac. 28, 499–517 (2020).Article 

    Google Scholar 
    Amoroso, R. O. et al. Bottom trawl fishing footprints on the world’s continental shelves. Proc. Natl Acad. Sci. USA 115, E10275–E10282 (2018).CAS 
    Article 

    Google Scholar 
    Atwood, T. B., Witt, A., Mayorga, J., Hammill, E. & Sala, E. Global patterns in marine sediment carbon stocks. Front. Mar. Sci. 7, 165 (2020).Article 

    Google Scholar 
    Smeaton, C., Hunt, C. A., Turrell, W. R. & Austin, W. E. N. Marine sedimentary carbon stocks of the United Kingdom’s exclusive economic zone. Front. Earth Sci. 9, 593324 (2021).Article 

    Google Scholar 
    Legge, O. et al. Carbon on the northwest European shelf: contemporary budget and future influences. Front. Mar. Sci. 7, 143 (2020).Article 

    Google Scholar 
    Melnychuk, M. C. et al. Identifying management actions that promote sustainable fisheries. Nat. Sustain. 4, 440–449 (2021).Article 

    Google Scholar  More

  • in

    Short-term mercury exposure disrupts muscular and hepatic lipid metabolism in a migrant songbird

    Bowler, D. E. et al. Mapping human pressures on biodiversity across the planet uncovers anthropogenic threat complexes. People Nat. 2, 380–394 (2020).Article 

    Google Scholar 
    Persson, L. et al. Outside the safe operating space of the planetary boundary for novel entities. Environ. Sci. Technol. https://doi.org/10.1021/acs.est.1c04158 (2022).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    United Nations Environment Programme (UNEP). 2019. Global Mercury Assessment 2018. UN Environment Programme, Chemicals and Health Branch Geneva, Switzerland. https://www.unep.org/resources/publication/global-mercury-assessment-2018Rimmer, C. C., Miller, E. K., McFarland, K. P., Taylor, R. J. & Faccio, S. D. Mercury bioaccumulation and trophic transfer in the terrestrial food web of a montane forest. Ecotoxicology 19, 697–709 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    Cristol, D. A. et al. The movement of aquatic mercury through terrestrial food webs. Science 320, 335 (2008).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Evers, D. The effects of methylmercury on wildlife: A comprehensive review and approach for interpretation. Encycl. Anthropocene 5, 181–194 (2018).Article 

    Google Scholar 
    Whitney, M. C. & Cristol, D. A. Impacts of sublethal mercury exposure on birds: a detailed review. Rev. Environ. Contam. Toxicol. 244, 113–163 (2017).
    Google Scholar 
    Seewagen, C. L. Threats of environmental mercury to birds: Knowledge gaps and priorities for future research. Bird Conserv. Int. 20, 112–123 (2010).Article 

    Google Scholar 
    Seewagen, C. L. The threat of global mercury pollution to bird migration: Potential mechanisms and current evidence. Ecotoxicology 29, 1254–1267 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    Ma, Y., Branfireun, B. A., Hobson, K. A. & Guglielmo, C. G. Evidence of negative seasonal carry-over effects of breeding ground mercury exposure on survival of migratory songbirds. J. Avian Biol. 49, jav-01656 (2018).Article 

    Google Scholar 
    Newton, I. Can conditions experienced during migration limit the population levels of birds?. J. Ornithol. 147, 146–166 (2006).Article 

    Google Scholar 
    Klaassen, M., Hoye, B. J., Nolet, B. A. & Buttemer, W. A. Ecophysiology of avian migration in the face of current global hazards. Philos. Trans. R. Soc. B 367, 1719–1732 (2020).Article 

    Google Scholar 
    Zurell, D., Graham, C. H., Gallien, L., Thuiller, W. & Zimmermann, N. E. Long-distance migratory birds threatened by multiple independent risks from global change. Nat. Clim. Chang. 8, 992–996 (2018).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Seewagen, C. L., Ma, Y., Morbey, Y. E. & Guglielmo, C. G. Stopover departure behavior and flight orientation of spring-migrant Yellow-rumped Warblers (Setophaga coronata) experimentally exposed to methylmercury. J. Ornithol. 160, 617–624 (2019).Article 

    Google Scholar 
    Seewagen, C. L. Blood mercury levels and the stopover refueling performance of a long-distance migratory songbird. Can. J. Zool. 91, 41–45 (2013).CAS 
    Article 

    Google Scholar 
    Adams, E. M., Williams, K. A., Olsen, B. J. & Evers, D. C. Mercury exposure in migrating songbirds: Correlations with physical condition. Ecotoxicology 29, 1240–1253 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    Ma, Y., Perez, C. R., Branfireun, B. A. & Guglielmo, C. G. Dietary exposure to methylmercury affects flight endurance in a migratory songbird. Environ. Pollut. 234, 894–901 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Gerson, A. R., Cristol, D. A. & Seewagen, C. L. Environmentally relevant methylmercury exposure reduces the metabolic scope of a model songbird. Environ. Pollut. 246, 790–796 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Jenni, L. & Jenni-Eiermann, S. Fuel supply and metabolic constraints in migrating birds. J. Avian Biol. 29, 521–552 (1998).Article 

    Google Scholar 
    McWilliams, S. R., Guglielmo, C., Pierce, B. & Klaassen, M. Flying, fasting, and feeding in birds during migration: A nutritional and physiological ecology perspective. J. Avian Biol. 35, 377–393 (2004).Article 

    Google Scholar 
    Guglielmo, C. G. Move that fatty acid: Fuel selection and transport in migratory birds and bats. Integr. Comp. Biol. 50, 336–345 (2010).PubMed 
    Article 

    Google Scholar 
    Guglielmo, C. G. Obese super athletes: Fat-fueled migration in birds and bats. J. Exp. Biol. 221(Suppl_1), 165753 (2018).Article 

    Google Scholar 
    Kawakami, T. et al. Differential effects of cobalt and mercury on lipid metabolism in the white adipose tissue of high-fat diet-induced obesity mice. Toxicol. Appl. Pharmacol. 258, 32–42 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Yadetie, F. et al. Global transcriptome analysis of Atlantic cod (Gadus morhua) liver after in vivo methylmercury exposure suggests effects on energy metabolism pathways. Aquat. Toxicol. 126, 314–325 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    Park, K. & Seo, E. Association between toenail mercury and metabolic syndrome is modified by selenium. Nutrients 8, 424 (2016).PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Caito, S. W., Newell-Caito, J., Martell, M., Crawford, N. & Aschner, M. Methylmercury induces metabolic alterations in Caenorhabditis elegans: Role for C/EBP transcription factor. Toxicol. Sci. 174, 112–123 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    Edmonds, S. T., O’Driscoll, N. J., Hillier, N. K., Atwood, J. L. & Evers, D. C. Factors regulating the bioavailability of methylmercury to breeding rusty blackbirds in northeastern wetlands. Environ. Pollut. 171, 148–154 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Rowse, L. M., Rodewald, A. D., Mažeika, S. & Sullivan, P. Pathways and consequences of contaminant flux to Acadian flycatchers (Empidonax virescens) in urbanizing landscapes of Ohio, USA. Sci. Total Environ. 485, 461–467 (2014).ADS 
    PubMed 
    Article 
    CAS 

    Google Scholar 
    Marsh, R. L. Catabolic enzyme activities in relation to premigratory fattening and muscle hypertrophy in the gray catbird (Dumetella carolinensis). J. Comp. Physiol. 141, 417–423 (1981).CAS 
    Article 

    Google Scholar 
    Guglielmo, C. G., Haunerland, N. H., Hochachka, P. W. & Williams, T. D. Seasonal dynamics of flight muscle fatty acid binding protein and catabolic enzymes in a migratory shorebird. Am. J. Physiol.-Regul. Integr. Comp. Physiol. 282(5), R1405–R1413 (2002).CAS 
    PubMed 
    Article 

    Google Scholar 
    Maillet, D. & Weber, J. M. Relationship between n-3 PUFA content and energy metabolism in the flight muscles of a migrating shorebird: Evidence for natural doping. J. Exp. Biol. 210, 413–420 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    Weber, J. M. Metabolic fuels: Regulating fluxes to select mix. J. Exp. Biol. 214, 286–294 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    Feige, J. N., Gelman, L., Michalik, L., Desvergne, B. & Wahli, W. From molecular action to physiological outputs: Peroxisome proliferator-activated receptors are nuclear receptors at the crossroads of key cellular functions. Prog. Lipid. Res. 45, 120–159 (2006).CAS 
    PubMed 
    Article 

    Google Scholar 
    Bensinger, S. J. & Tontonoz, P. Integration of metabolism and inflammation by lipid-activated nuclear receptors. Nature 454, 470–477. https://doi.org/10.1038/nature07202 (2008).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Ynalvez, R., Gutierrez, J. & Gonzalez-Cantu, H. Mini-review: Toxicity of mercury as a consequence of enzyme alteration. Biometals 29, 781–788 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Gerson, A. R. & Guglielmo, C. G. Energetics and metabolite profiles during early flight in American robins (Turdus Migratorius). J. Comp. Physiol. B. 183, 983–991 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    Price, E. R., McFarlan, J. T. & Guglielmo, C. G. Preparing for migration? The effects of photoperiod and exercise on muscle oxidative enzymes, lipid transporters, and phospholipids in white-crowned sparrows. Physiol. Biochem. Zool. 83, 252–262 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    Bradley, S. S., Dick, M. F., Guglielmo, C. G. & Timoshenko, A. V. Seasonal and flight-related variation of galectin expression in heart, liver and flight muscles of yellow-rumped warblers (Setophaga coronata). Glycoconj. J. 34, 603–611 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    McFarlan, J. T., Bonen, A. & Guglielmo, C. G. Seasonal upregulation of fatty acid transporters in flight muscles of migratory white-throated sparrows (Zonotrichia albicollis). J. Exp. Biol. 212, 2934–2940 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    Zhang, Y., King, M. O., Harmon, E., Eyster, K. & Swanson, D. L. Migration-induced variation of fatty acid transporters and cellular metabolic intensity in passerine birds. J. Comp. Physiol. B. 185, 797–810 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Dick, M. F. & Guglielmo, C. G. Dietary polyunsaturated fatty acids influence flight muscle oxidative capacity but not endurance flight performance in a migratory songbird. Am. J. Physiol.-Regul. Integr. Compar. Physiol. 316(4), R362–R375 (2019).CAS 
    Article 

    Google Scholar 
    Schmittgen, T. D. & Livak, K. J. Analyzing real-time PCR data by the comparative CT method. Nat. Protoc. 3, 1101–1108 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    Bittencourt, L. O. et al. Oxidative biochemistry disbalance and changes on proteomic profile in salivary glands of rats induced by chronic exposure to methylmercury. Oxid. Med. Cell. Longev. https://doi.org/10.1155/2017/5653291 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Shi, Q., Sun, N., Kou, H., Wang, H. & Zhao, H. Chronic effects of mercury on Bufo gargarizans larvae: Thyroid disruption, liver damage, oxidative stress and lipid metabolism disorder. Ecotoxicol. Environ. Saf. 164, 500–509 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Nøstbakken, O. J. et al. Dietary methylmercury alters the proteome in Atlantic salmon (Salmo salar) kidney. Aquat. Toxicol. 108, 70–77 (2012).PubMed 
    Article 
    CAS 

    Google Scholar 
    Zink, E. M. Comparison of the mercury induced proteomes of Escherichia coli MG1655 with and without the NR1 plasmid. MSc thesis, Washington State University, Pullman, WA (2009).Lundgren, B. O. & Kiessling, K. H. Seasonal variation in catabolic enzyme activities in breast muscle of some migratory birds. Oecologia 66, 468–471 (1985).ADS 
    PubMed 
    Article 

    Google Scholar 
    Banerjee, S. & Chaturvedi, C. M. Migratory preparation associated alterations in pectoralis muscle biochemistry and proteome in Palearctic-Indian emberizid migratory finch, red-headed bunting, Emberiza bruniceps. Comp. Biochem. Physiol. D Genom. Proteom. 17, 9–25 (2016).CAS 

    Google Scholar 
    Dick, M. F. The long haul: migratory flight preparation and performance in songbirds. Ph.D. dissertation, University of Western Ontario, London, Canada (2017).Driedzic, W. R., Crowe, H. L., Hicklin, P. W. & Sephton, D. H. Adaptations in pectoralis muscle, heart mass, and energy metabolism during premigratory fattening in semipalmated sandpipers (Calidris pusilla). Can. J. Zool. 71, 1602–1608 (1993).Article 

    Google Scholar 
    De Moranville, K. J. et al. PPAR expression, muscle size and metabolic rates across the gray catbird’s annual cycle are greatest in preparation for fall migration. J. Exper. Biol. 222, 198028 (2019).Article 

    Google Scholar 
    Zajac, D. M., Cerasale, D. J., Landman, S. & Guglielmo, C. G. Behavioral and physiological effects of photoperiod-induced migratory state and leptin on Zonotrichia albicollis: II. Effects on fatty acid metabolism. Gen. Comp. Endocrinol. 174, 269–275 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    Tinant, G. et al. Methylmercury displays pro-adipogenic properties in rainbow trout preadipocytes. Chemosphere 263, 127917 (2021).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Cambier, S. et al. At environmental doses, dietary methylmercury inhibits mitochondrial energy metabolism in skeletal muscles of the zebra fish (Danio rerio). Int. J. Biochem. Cell Biol. 41, 791–799 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    Ferain, A. et al. Transcriptional effects of phospholipid fatty acid profile on rainbow trout liver cells exposed to methylmercury. Aquat. Toxicol. 199, 174–187 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Börchers, T., Højrup, P., Nielsen, S. U., Roepstorff, P., Spener, F., Knudsen, J. Revision of the amino acid sequence of human heart fatty acid-binding protein. In Cellular Fatty Acid-binding Proteins 127–133 (Springer, Boston, 1990).Dörmann, P. et al. Amino acid exchange and covalent modification by cysteine and glutathione explain isoforms of fatty acid-binding protein occurring in bovine liver. J. Biol. Chem. 268, 16286–16292 (1993).PubMed 
    Article 

    Google Scholar 
    Su, X. & Abumrad, N. A. Cellular fatty acid uptake: A pathway under construction. Trends Endocrinol. Metab. 20(2), 72–77 (2009).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    van Oort, M. M. et al. Each of the four intracellular cysteines of CD36 is essential for insulin-or AMP-activated protein kinase-induced CD36 translocation. Arch. Physiol. Biochem. 120, 40–49 (2014).PubMed 
    Article 
    CAS 

    Google Scholar 
    Wang, G., Bonkovsky, H. L., de Lemos, A. & Burczynski, F. J. Recent insights into the biological functions of liver fatty acid binding protein 1. J. Lipid Res. 56, 2238–2247 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Vallee, B. L. & Ulmer, D. D. Biochemical effects of mercury, cadmium, and lead. Annu. Rev. Biochem. 41, 91–128 (1972).CAS 
    PubMed 
    Article 

    Google Scholar 
    Aschner, M. & Syversen, T. Methylmercury: Recent advances in the understanding of its neurotoxicity. Ther. Drug Monit. 27, 278–283 (2005).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kenow, K. P., Meyer, M. W., Hines, R. K. & Karasov, W. H. Distribution and accumulation of mercury in tissues of captive-reared common loon (Gavia immer) chicks. Environ. Toxicol. Chem. 26, 1047–1055 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    Varian-Ramos, C. W., Whitney, M., Rice, G. W. & Cristol, D. A. Form of dietary methylmercury does not affect total mercury accumulation in the tissues of zebra finch. Bull. Environ. Contam. Toxicol. 99, 1–8 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Rizzetti, D. A. et al. Chronic mercury at low doses impairs white adipose tissue plasticity. Toxicology 418, 41–50 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Richter, C. A. et al. Methylmercury-induced changes in gene transcription associated with neuroendocrine disruption in largemouth bass (Micropterus salmoides). Gen. Comp. Endocrinol. 203, 215–224 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    Barnes, D. M., Hanlon, P. R. & Kircher, E. A. Effects of inorganic HgCl2 on adipogenesis. Toxicol. Sci. 75(2), 368–377 (2003).CAS 
    PubMed 
    Article 

    Google Scholar 
    Corder, K. R., DeMoranville, K. J., Russell, D. E., Huss, J. M. & Schaeffer, P. J. Annual life-stage regulation of lipid metabolism and storage and association with PPARs in a migrant species: the gray catbird (Dumetella carolinensis). J. Exp. Biol. 219, 3391–3398 (2016).PubMed 

    Google Scholar 
    DeMoranville, K. J., Carter, W. A., Pierce, B. J. & McWilliams, S. R. Flight training in a migratory bird drives metabolic gene expression in the flight muscle but not liver, and dietary fat quality influences select genes. Am. J. Physiol.-Regul. Integr. Compar. Physiol. 319(6), R637–R652 (2020).CAS 
    Article 

    Google Scholar 
    Gavrilova, O. et al. Liver peroxisome proliferator-activated receptor γ contributes to hepatic steatosis, triglyceride clearance, and regulation of body fat mass. J. Biol. Chem. 278(36), 34268–34276 (2003).CAS 
    PubMed 
    Article 

    Google Scholar 
    Bedoucha, M., Atzpodien, E. & Boelsterli, U. A. Diabetic KKAy mice exhibit increased hepatic PPARγ1 gene expression and develop hepatic steatosis upon chronic treatment with antidiabetic thiazolidinediones. J. Hepatol. 35, 17–23 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    Egeler, O., Williams, T. D. & Guglielmo, C. G. Modulation of lipogenic enzymes, fatty acid synthase and Δ 9-desaturase, in relation to migration in the western sandpiper (Calidris mauri). J. Comp. Physiol. B 170, 169–174 (2000).CAS 
    PubMed 
    Article 

    Google Scholar 
    Klaper, R. et al. Use of a 15k gene microarray to determine gene expression changes in response to acute and chronic methylmercury exposure in the fathead minnow (Pimephales promelas). J. Fish Biol. 72, 2207–2280 (2008).CAS 
    Article 

    Google Scholar 
    Calow, P. Physiological costs of combating chemical toxicants: Ecological implications. Comp. Biochem. Physiol. C 100, 3–6 (1991).CAS 
    PubMed 
    Article 

    Google Scholar 
    Spalding, M. G. et al. Histologic, neurologic, and immunologic effects of methylmercury in captive great egrets. J. Wildl. Dis. 36, 423–435 (2000).CAS 
    PubMed 
    Article 

    Google Scholar 
    Carlson, J. R., Cristol, D. & Swaddle, J. P. Dietary mercury exposure causes decreased escape takeoff flight performance and increased molt rate in European starlings (Sturnus vulgaris). Ecotoxicology 23, 1464–1473 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    Faaborg, J. et al. Conserving migratory land birds in the New World: Do we know enough?. Ecol. Appl. 20, 398–418 (2010).PubMed 
    Article 

    Google Scholar 
    Duijns, S. et al. Body condition explains migratory performance of a long-distance migrant. Proc. R. Soc. B https://doi.org/10.1098/rspb.2017.1374 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Single-cell stable isotope probing in microbial ecology

    Neufeld JD, Wagner M, Murrell JC. Who eats what, where and when? Isotope-labelling experiments are coming of age. ISME J. 2007;1:103–10.CAS 
    PubMed 
    Article 

    Google Scholar 
    Boschker HTS, Nold SC, Wellsbury P, Bos D, de Graaf W, Pel R, et al. Direct linking of microbial populations to specific biogeochemical processes by 13C-labelling of biomarkers. Nature. 1998;392:801–5Jehmlich N, Schmidt F, von Bergen M, Richnow H-H, Vogt C. Protein-based stable isotope probing (Protein-SIP) reveals active species within anoxic mixed cultures. ISME J. 2008;2:1122–33.CAS 
    PubMed 
    Article 

    Google Scholar 
    Radajewski S, Ineson P, Parekh NR, Colin Murrell J. Stable-isotope probing as a tool in microbial ecology. Nature. 2000;403:646–9.CAS 
    PubMed 
    Article 

    Google Scholar 
    Manefield M, Whiteley AS, Griffiths RI, Bailey MJ. RNA stable isotope probing, a novel means of linking microbial community function to phylogeny. Appl Environ Microbiol. 2002;68:5367–73.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Berry D, Mader E, Lee TK, Woebken D, Wang Y, Zhu D, et al. Tracking heavy water (D2O) incorporation for identifying and sorting active microbial cells. Proc Natl Acad Sci USA. 2015;112:E194–203.CAS 
    PubMed 

    Google Scholar 
    Jehmlich N, Vogt C, Lünsmann V, Richnow HH, von Bergen M. Protein-SIP in environmental studies. Curr Opin Biotechnol. 2016;41:26–33.CAS 
    PubMed 
    Article 

    Google Scholar 
    Haichar, FEZ, Achouak W, Christen R, Heulin T, et al. Identification of cellulolytic bacteria in soil by stable isotope probing. Environ Microbiol. 2007;9:625–34Rangel-Castro JI, Ignacio Rangel-Castro J, Killham K, Ostle N, Nicol GW, Anderson IC, et al. Stable isotope probing analysis of the influence of liming on root exudate utilization by soil microorganisms. Environ Microbiol. 2005;7:828–38.CAS 
    PubMed 
    Article 

    Google Scholar 
    Wang Y, Song Y, Tao Y, Muhamadali H, Goodacre R, Zhou N-Y, et al. Reverse and multiple stable isotope probing to study bacterial metabolism and interactions at the single cell level. Anal Chem. 2016;88:9443–50.CAS 
    PubMed 
    Article 

    Google Scholar 
    Sharma K, Palatinszky M, Nikolov G, Berry D, Shank EA. Transparent soil microcosms for live-cell imaging and non-destructive stable isotope probing of soil microorganisms. Elife. 2020;9:e56275.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lee KS, Landry Z, Pereira FC, Wagner M, Berry D, Huang WE, et al. Raman microspectroscopy for microbiology. Nat. Rev. Methods Primers. 2021;1:80.CAS 
    Article 

    Google Scholar 
    Hatzenpichler R, Krukenberg V, Spietz RL, Jay ZJ. Next-generation physiology approaches to study microbiome function at single cell level. Nat Rev Microbiol. 2020;18:241–56.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Wagner M. Single-cell ecophysiology of microbes as revealed by raman microspectroscopy or secondary ion mass spectrometry imaging. Ann Rev Microbiol. 2009;63:411–29Lennon JT, Jones SE. Microbial seed banks: the ecological and evolutionary implications of dormancy. Nat Rev Microbiol. 2011;9:119–30.CAS 
    PubMed 
    Article 

    Google Scholar 
    Lewis K. Persister cells, dormancy and infectious disease. Nat Rev Microbiol. 2006;5:48–56.PubMed 
    Article 
    CAS 

    Google Scholar 
    Nielsen KM, Johnsen PJ, Bensasson D, Daffonchio D. Release and persistence of extracellular DNA in the environment. Environ Biosafety Res. 2007;6:37–53.CAS 
    PubMed 
    Article 

    Google Scholar 
    Blazewicz SJ, Barnard RL, Daly RA, Firestone MK. Evaluating rRNA as an indicator of microbial activity in environmental communities: limitations and uses. ISME J. 2013;7:2061–8.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Nocker A, Sossa-Fernandez P, Burr MD, Camper AK. Use of propidium monoazide for live/dead distinction in microbial ecology. Appl Environ Microbiol. 2007;73:5111–7.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Tawakoli PN, Al-Ahmad A, Hoth-Hannig W, Hannig M, Hannig C. Comparison of different live/dead stainings for detection and quantification of adherent microorganisms in the initial oral biofilm. Clin Oral Investig. 2013;17:841–50.CAS 
    PubMed 
    Article 

    Google Scholar 
    Netuschil L, Auschill TM, Sculean A, Arweiler NB. Confusion over live/dead stainings for the detection of vital microorganisms in oral biofilms-which stain is suitable? BMC Oral Health. 2014;14:2.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hatzenpichler R, Connon SA, Goudeau D, Malmstrom RR, Woyke T, Orphan VJ. Visualizing in situ translational activity for identifying and sorting slow-growing archaeal−bacterial consortia. Proc Natl Acad Sci USA. 2016;113:E4069–78.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kuru E, Hughes HV, Brown PJ, Hall E, Tekkam S, Cava F, et al. In Situ probing of newly synthesized peptidoglycan in live bacteria with fluorescent D-amino acids. Angew Chem Int Ed Engl. 2012;51:12519–23.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kopf SH, McGlynn SE, Green-Saxena A, Guan Y, Newman DK, Orphan VJ. Heavy water and15N labelling with NanoSIMS analysis reveals growth rate-dependent metabolic heterogeneity in chemostats. Environ Microbiol. 2015;17:2542–56Kopf SH, Sessions AL, Cowley ES, Reyes C, Van Sambeek L, Hu Y, et al. Trace incorporation of heavy water reveals slow and heterogeneous pathogen growth rates in cystic fibrosis sputum. Proc Natl Acad Sci USA. 2016;113:E110–6.CAS 
    PubMed 
    Article 

    Google Scholar 
    Neubauer C, Kasi AS, Grahl N, Sessions AL, Kopf SH, Kato R, et al. Refining the Application of Microbial Lipids as Tracers of Staphylococcus aureus Growth Rates in Cystic Fibrosis Sputum. J Bacteriol. 2018;200:e00365–18.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Haider S, Wagner M, Schmid MC, Sixt BS, Christian JG, Häcker G, et al. Raman microspectroscopy reveals long-term extracellular activity of Chlamydiae. Mol Microbiol. 2010;77:687–700.CAS 
    PubMed 
    Article 

    Google Scholar 
    Kloehn J, Boughton BA, Saunders EC, O’Callaghan S, Binger KJ, McConville MJ. Identification of Metabolically Quiescent Leishmania mexicana Parasites in Peripheral and Cured Dermal Granulomas Using Stable Isotope Tracing Imaging Mass Spectrometry. mBio. 2021;12:e00129–21.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kong L, Setlow P, Li Y-Q. Direct analysis of water content and movement in single dormant bacterial spores using confocal Raman microspectroscopy and Raman imaging. Anal Chem. 2013;85:7094–101.CAS 
    PubMed 
    Article 

    Google Scholar 
    Knudsen SM, Cermak N, Delgado FF, Setlow B, Setlow P, Manalis SR. Water and small-molecule permeation of dormant Bacillus subtilis spores. J Bacteriol. 2016;198:168–77.CAS 
    PubMed 
    Article 

    Google Scholar 
    Chen D, Huang S-S, Li Y-Q. Real-time detection of kinetic germination and heterogeneity of single Bacillus spores by laser tweezers Raman spectroscopy. Anal Chem. 2006;78:6936–41.CAS 
    PubMed 
    Article 

    Google Scholar 
    Devictor V, Clavel J, Julliard R, Lavergne S, Mouillot D, Thuiller W, et al. Defining and measuring ecological specialization. J Appl Ecol. 2010;47:15–25.Article 

    Google Scholar 
    Pereira FC, Berry D. Microbial nutrient niches in the gut. Environ Microbiol. 2017;19:1366–78.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Shakya M, Lo C-C, Chain PSG. Advances and challenges in metatranscriptomic analysis. Front Genet. 2019;10:904.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Berry D, Loy A. Stable-Isotope probing of human and animal microbiome function. Trends Microbiol. 2018;26:999–1007.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Terrado R, Pasulka AL, Lie AA-Y, Orphan VJ, Heidelberg KB, Caron DA. Autotrophic and heterotrophic acquisition of carbon and nitrogen by a mixotrophic chrysophyte established through stable isotope analysis. ISME J. 2017;11:2022–34.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Dekas AE, Parada AE, Mayali X, Fuhrman JA, Wollard J, Weber PK, et al. Characterizing Chemoautotrophy and Heterotrophy in Marine Archaea and Bacteria With Single-Cell Multi-isotope NanoSIP. Front Microbiol. 2019;10:2682.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Wegener G, Bausch M, Holler T, Thang NM, Mollar XP, Kellermann MY, et al. Assessing sub-seafloor microbial activity by combined stable isotope probing with deuterated water and 13C-bicarbonate. Environ Microbiol. 2019;14:1517–27Jing X, Gou H, Gong Y, Su X, Xu L, Ji Y, et al. Raman-activated cell sorting and metagenomic sequencing revealing carbon-fixing bacteria in the ocean. Environ Microbiol. 2018;20:2241–55.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Xu J, Zhu D, Ibrahim AD, Allen CCR, Gibson CM, Fowler PW, et al. Raman deuterium isotope probing reveals microbial metabolism at the single-cell level. Anal Chem. 2017;89:13305–12.CAS 
    PubMed 
    Article 

    Google Scholar 
    Zhang M, Hong W, Abutaleb NS, Li J, Dong P-T, Zong C, et al. Rapid determination of antimicrobial susceptibility by stimulated Raman scattering imaging of D2O metabolic incorporation in a single bacterium. Adv Chem Microsc Life Sci Transl Med. 2021.Lima C, Muhamadali H, Xu Y, Kansiz M, Goodacre R. Imaging Isotopically Labeled Bacteria at the Single-Cell Level Using High-Resolution Optical Infrared Photothermal Spectroscopy. Anal Chem. 2021;93:3082–8.CAS 
    PubMed 
    Article 

    Google Scholar 
    Ackermann M. A functional perspective on phenotypic heterogeneity in microorganisms. Nat Rev Microbiol. 2015;13:497–508.CAS 
    PubMed 
    Article 

    Google Scholar 
    Balaban NQ, Merrin J, Chait R, Kowalik L, Leibler S. Bacterial persistence as a phenotypic switch. Science. 2004;305:1622–5.CAS 
    PubMed 
    Article 

    Google Scholar 
    Maamar H, Raj A, Dubnau D. Noise in gene expression determines cell fate in Bacillus subtilis. Science. 2007;317:526–9.CAS 
    PubMed 
    Article 

    Google Scholar 
    Emonet T, Cluzel P. Relationship between cellular response and behavioral variability in bacterial chemotaxis. Proc Natl Acad Sci USA. 2008;105:3304–9.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Ozbudak EM, Thattai M, Lim HN, Shraiman BI, Van Oudenaarden A. Multistability in the lactose utilization network of Escherichia coli. Nature. 2004;427:737–40.CAS 
    PubMed 
    Article 

    Google Scholar 
    Kiviet DJ, Nghe P, Walker N, Boulineau S, Sunderlikova V, Tans SJ. Stochasticity of metabolism and growth at the single-cell level. Nature. 2014;514:376–9.CAS 
    PubMed 
    Article 

    Google Scholar 
    Kotte O, Volkmer B, Radzikowski JL, Heinemann M. Phenotypic bistability in Escherichia coli’s central carbon metabolism. Mol Syst Biol. 2014;10:736.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    New AM, Cerulus B, Govers SK, Perez-Samper G, Zhu B, Boogmans S, et al. Different levels of catabolite repression optimize growth in stable and variable environments. PLoS Biol. 2014;12:e1001764.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Solopova A, van Gestel J, Weissing FJ, Bachmann H, Teusink B, Kok J, et al. Bet-hedging during bacterial diauxic shift. Proc Natl Acad Sci USA. 2014;111:7427–32.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Schreiber F, Littmann S, Lavik G, Escrig S, Meibom A, Kuypers MMM, et al. Phenotypic heterogeneity driven by nutrient limitation promotes growth in fluctuating environments. Nat Microbiol. 2016;1:16055.CAS 
    PubMed 
    Article 

    Google Scholar 
    Nikolic N, Schreiber F, Dal Co A, Kiviet DJ, Bergmiller T, Littmann S, et al. Cell-to-cell variation and specialization in sugar metabolism in clonal bacterial populations. PLoS Genet. 2017;13:e1007122.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Takhaveev V, Heinemann M. Metabolic heterogeneity in clonal microbial populations. Curr Opin Microbiol. 2018;45:30–8.CAS 
    PubMed 
    Article 

    Google Scholar 
    Altschuler SJ, Wu LF. Cellular heterogeneity: do differences make a difference? Cell. 2010;141:559–63.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Beaumont HJE, Gallie J, Kost C, Ferguson GC, Rainey PB. Experimental evolution of bet hedging. Nature. 2009;462:90–3.CAS 
    PubMed 
    Article 

    Google Scholar 
    Calabrese F, Voloshynovska I, Musat F, Thullner M, Schlömann M, Richnow HH, et al. Quantitation and comparison of phenotypic heterogeneity among single cells of monoclonal microbial populations. Front Microbiol. 2019;10:2814.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Zimmermann M, Escrig S, Hübschmann T, Kirf MK, Brand A, Inglis RF, et al. Phenotypic heterogeneity in metabolic traits among single cells of a rare bacterial species in its natural environment quantified with a combination of flow cell sorting and NanoSIMS. Front Microbiol. 2015;6:243.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Zimmermann M, Escrig S, Lavik G, Kuypers MMM, Meibom A, Ackermann M, et al. Substrate and electron donor limitation induce phenotypic heterogeneity in different metabolic activities in a green sulphur bacterium. Environ Microbiol Rep. 2018;10:179–83.CAS 
    PubMed 
    Article 

    Google Scholar 
    Sheik AR, Muller EE, Audinot J-N, Lebrun LA, Grysan P, Guignard C, et al. In situ phenotypic heterogeneity among single cells of the filamentous bacterium Candidatus Microthrix parvicella. ISME J. 2016;10:1274–9.CAS 
    PubMed 
    Article 

    Google Scholar 
    Seyedsayamdost MR, Case RJ, Kolter R, Clardy J. The Jekyll-and-Hyde chemistry of Phaeobacter gallaeciensis. Nat Chem. 2011;3:331–5.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Ferrier-Pagès C, Leal MC. Stable isotopes as tracers of trophic interactions in marine mutualistic symbioses. Ecol Evol. 2019;9:723–40.PubMed 
    Article 

    Google Scholar 
    Pasulka AL, Thamatrakoln K, Kopf SH, Guan Y, Poulos B, Moradian A, et al. Interrogating marine virus-host interactions and elemental transfer with BONCAT and nanoSIMS-based methods. Environ Microbiol. 2018;20:671–92.CAS 
    PubMed 
    Article 

    Google Scholar 
    Kopp C, Domart-Coulon I, Escrig S, Humbel BM, Hignette M, Meibom A. Subcellular investigation of photosynthesis-driven carbon assimilation in the symbiotic reef coral Pocillopora damicornis. mBio. 2015;6:e02299–14.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Rädecker N, Pogoreutz C, Gegner HM, Cárdenas A, Roth F, Bougoure J, et al. Heat stress destabilizes symbiotic nutrient cycling in corals. Proc Natl Acad Sci U S A. 2021;118:e2022653118.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Krueger T, Bodin J, Horwitz N, Loussert-Fonta C, Sakr A, Escrig S, et al. Temperature and feeding induce tissue level changes in autotrophic and heterotrophic nutrient allocation in the coral symbiosis – a NanoSIMS study. Sci Rep. 2018;8:12710.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Gibbin E, Gavish A, Krueger T, Kramarsky-Winter E, Shapiro O, Guiet R, et al. Vibrio coralliilyticus infection triggers a behavioural response and perturbs nutritional exchange and tissue integrity in a symbiotic coral. ISME J. 2019;13:989–1003.CAS 
    PubMed 
    Article 

    Google Scholar 
    Rix L, Ribes M, Coma R, Jahn MT, de Goeij JM, van Oevelen D, et al. Heterotrophy in the earliest gut: a single-cell view of heterotrophic carbon and nitrogen assimilation in sponge-microbe symbioses. ISME J. 2020;14:2554–67.CAS 
    PubMed 
    PubMed Central 
    Article 

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

    Google Scholar 
    Mills MM, Turk-Kubo KA, van Dijken GL, Henke BA, Harding K, Wilson ST, et al. Unusual marine cyanobacteria/haptophyte symbiosis relies on N2 fixation even in N-rich environments. ISME J. 2020;14:2395–406.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Turk-Kubo KA, Mills MM, Arrigo KR, van Dijken G, Henke BA, Stewart B, et al. UCYN-A/haptophyte symbioses dominate N2 fixation in the Southern California Current System. ISME Commun. 2021;1:1–13.Article 

    Google Scholar 
    Moore CM, Mills MM, Arrigo KR, Berman-Frank I, Bopp L, Boyd PW, et al. Processes and patterns of oceanic nutrient limitation. Nat Geosci. 2013;6:701–10.CAS 
    Article 

    Google Scholar 
    Scheller S, Yu H, Chadwick GL, McGlynn SE, Orphan VJ. Artificial electron acceptors decouple archaeal methane oxidation from sulfate reduction. Science. 2016;351:703–7.CAS 
    PubMed 
    Article 

    Google Scholar 
    Pereira FC, Wasmund K, Cobankovic I, Jehmlich N, Herbold CW, Lee KS, et al. Rational design of a microbial consortium of mucosal sugar utilizers reduces Clostridiodes difficile colonization. Nat Commun. 2020;11:5104.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Mooshammer M, Kitzinger K, Schintlmeister A, Ahmerkamp S, Nielsen JL, Nielsen PH, et al. Flow-through stable isotope probing (Flow-SIP) minimizes cross-feeding in complex microbial communities. ISME J. 2021;15:348–53.CAS 
    PubMed 
    Article 

    Google Scholar 
    Drescher K, Nadell CD, Stone HA, Wingreen NS, Bassler BL. Solutions to the public goods dilemma in bacterial biofilms. Curr Biol. 2014;24:50–5.CAS 
    PubMed 
    Article 

    Google Scholar 
    Słomka J, Alcolombri U, Secchi E, Stocker R, Fernandez VI. Encounter rates between bacteria and small sinking particles. New J Phys. 2020;22:043016.Article 

    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;14:775–80.CAS 
    Article 

    Google Scholar 
    University of Massachusetts Amherst Massachusetts Lynn Margulis, Margulis L, Fester R. Symbiosis as a source of evolutionary innovation: speciation and morphogenesis. MIT Press; 1991. 454 p.Legin AA, Schintlmeister A, Sommerfeld NS, Eckhard M, Theiner S, Reipert S, et al. Nano-scale imaging of dual stable isotope labeled oxaliplatin in human colon cancer cells reveals the nucleolus as a putative node for therapeutic effect. Nanoscale Adv. 2021;3:249–62.CAS 
    Article 

    Google Scholar 
    Schaible GA, et al. Correlative SIP-FISH-Raman-SEM-NanoSIMS links identity, morphology, biochemistry, and physiology of environmental microbes. ISME COMMUN. 2022;2:52.Article 

    Google Scholar 
    Yu G-H, Chi Z-L, Kappler A, Sun F-S, Liu C-Q, Teng HH, et al. Fungal nanophase particles catalyze iron transformation for oxidative stress removal and iron acquisition. Curr Biol. 2020;30:2943–50.e4.CAS 
    PubMed 
    Article 

    Google Scholar 
    Subirana MA, Riemschneider S, Hause G, Dobritzsch D, Schaumlöffel D, Herzberg M. High spatial resolution imaging of subcellular macro and trace element distribution during phagocytosis. Metallomics. 2022;14:mfac011.PubMed 
    Article 

    Google Scholar 
    Bonnin EA, Fornasiero EF, Lange F, Turck CW, Rizzoli SO. NanoSIMS observations of mouse retinal cells reveal strict metabolic controls on nitrogen turnover. BMC Mol Cell Biol. 2021;22:5.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Jo MC, Liu W, Gu L, Dang W, Qin L. High-throughput analysis of yeast replicative aging using a microfluidic system. Proc Natl Acad Sci U S A. 2015;112:9364–9.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Anggraini D, Ota N, Shen Y, Tang T, Tanaka Y, Hosokawa Y, et al. Recent advances in microfluidic devices for single-cell cultivation: methods and applications. Lab Chip. 2022;22:1438–68.CAS 
    PubMed 
    Article 

    Google Scholar 
    Eriksen R, Daria V, Gluckstad J. Fully dynamic multiple-beam optical tweezers. Opt Express. 2002;10:597–602.PubMed 
    Article 

    Google Scholar 
    Dai X, Fu W, Chi H, Mesias VSD, Zhu H, Leung CW, et al. Optical tweezers-controlled hotspot for sensitive and reproducible surface-enhanced Raman spectroscopy characterization of native protein structures. Nat Commun. 2021;12:1292.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Collins DJ, Morahan B, Garcia-Bustos J, Doerig C, Plebanski M, Neild A. Two-dimensional single-cell patterning with one cell per well driven by surface acoustic waves. Nat Commun. 2015;6:8686.CAS 
    PubMed 
    Article 

    Google Scholar 
    Hu F, Shi L, Min W. Biological imaging of chemical bonds by stimulated Raman scattering microscopy. Nat Methods. 2019;16:830–42.CAS 
    PubMed 
    Article 

    Google Scholar 
    Ge X, Pereira FC, Mitteregger M, Berry D, Zhang M, Hausmann B, et al. SRS-FISH: A high-throughput platform linking microbiome metabolism to identity at the single-cell level. Proc Natl Acad Sci U S A. 2022;119:e2203519119.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Vandergrift GW, Kew W, Lukowski JK, Bhattacharjee A, Liyu AV, Shank EA, et al. Imaging and direct sampling capabilities of nanospray desorption electrospray ionization with absorption-mode 21 Tesla Fourier transform ion cyclotron resonance mass spectrometry. Anal Chem. 2022;94:3629–36.CAS 
    PubMed 
    Article 

    Google Scholar 
    Harrison JP, Berry D. Vibrational spectroscopy for imaging single microbial cells in complex biological samples. Front Microbiol. 2017;8:675.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Mayali X. NanoSIMS: microscale quantification of biogeochemical activity with large-scale impacts. Ann Rev Mar Sci. 2020;12:449–67.PubMed 
    Article 

    Google Scholar 
    Alexandrov T. Spatial metabolomics and imaging mass spectrometry in the age of artificial intelligence. Annu Rev Biomed Data Sci. 2020;3:61–87.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Boschker HTS, Middelburg JJ. Stable isotopes and biomarkers in microbial ecology. FEMS Microbiol Ecol. 2002;40:85–95.CAS 
    PubMed 
    Article 

    Google Scholar 
    Mayali X, Weber PK, Nuccio E, Lietard J, Somoza M, Blazewicz SJ, et al. Chip-SIP: Stable Isotope Probing analyzed with rRNA-targeted microarrays and nanoSIMS. Methods Mol Biol. 2019;2046:71–87.PubMed 
    Article 

    Google Scholar 
    Chokkathukalam A, Kim D-H, Barrett MP, Breitling R, Creek DJ. Stable isotope-labeling studies in metabolomics: new insights into structure and dynamics of metabolic networks. Bioanalysis. 2014;6:511–24.CAS 
    PubMed 
    Article 

    Google Scholar 
    Hiller K, Metallo CM, Kelleher JK, Stephanopoulos G. Nontargeted elucidation of metabolic pathways using stable-isotope tracers and mass spectrometry. Anal Chem. 2010;82:6621–8.CAS 
    PubMed 
    Article 

    Google Scholar 
    Rusconi R, Garren M, Stocker R. Microfluidics expanding the frontiers of microbial ecology. Annu Rev Biophys. 2014;43:65–91.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lee KS, Pereira FC, Palatinszky M, Behrendt L, Alcolombri U, Berry D, et al. Optofluidic Raman-activated cell sorting for targeted genome retrieval or cultivation of microbial cells with specific functions. Nat Protoc. 2021;16:634–76.CAS 
    PubMed 
    Article 

    Google Scholar 
    Wagner M, Haider S. New trends in fluorescence in situ hybridization for identification and functional analyses of microbes. Curr Opin Biotechnol. 2012;23:96–102.CAS 
    PubMed 
    Article 

    Google Scholar  More

  • in

    Potential impacts of climate change on agriculture and fisheries production in 72 tropical coastal communities

    Sampling of coastal communitiesHere, we integrated data from five different projects that had surveyed coastal communities across five countries47,48,49,50. Between 2009 and 2015, we conducted socioeconomic surveys in 72 sites from Indonesia (n = 25), Madagascar (n = 6), Papua New Guinea (n = 10), the Philippines (n = 25), and Tanzania (Zanzibar) (n = 6). Site selection was for broadly similar purposes- to evaluate the effects of various coastal resource management initiatives (collaborative management, integrated conservation and development projects, recreational fishing projects) on people’s livelihoods in rural and peri-urban villages. Within each project, sites were purposively selected to be representative of the broad range of socioeconomic conditions (e.g., population size, levels of development, integration to markets) experienced within the region. We did not survey strictly urban locations (i.e., major cities). Because our sampling was not strictly random, care should be taken when attempting to make inferences beyond our specific study sites.We surveyed between 13 and 150 households per site, depending on the population of the communities and the available time to conduct interviews per site. All projects employed a comparable sampling design: households were either systematically (e.g., every third house), randomly sampled, or in the case of three villages, every household was surveyed (a census) (see Supplementary Data file). Respondents were generally the household head, but could have been other household members if the household head was not available during the study period (i.e. was away). In the Philippines, sampling protocol meant that each village had an even number of male and female respondents. Respondents gave verbal consent to be interviewed.The following standard methodology was employed to assess material style of life, a metric of material assets-based wealth48,51. Interviewers recorded the presence or absence of 16 material items in the household (e.g., electricity, type of walls, type of ceiling, type of floor). We used a Principal Component Analysis on these items and kept the first axis (which explained 34.2% of the variance) as a material wealth score. Thus, each community received a mean material style of life score, based on the degree to which surveyed households had these material items, which we then scaled from 0 to 1. We also conducted an exploratory analysis of how material style of life has changed in two sites in Papua New Guinea (Muluk and Ahus villages) over fifteen and sixteen-year time span across four and five-time periods (2001, 2009, 2012, 2016, and 2002, 2009, 2012, 2016, 2018), respectively, that have been surveyed since 2001/200252. These surveys were semi-panel data (i.e. the community was surveyed repeatedly, but we did not track individuals over each sampling interval) and sometimes occurred in different seasons. For illustrative purposes, we plotted how these villages changed over time along the first two principal components.SensitivityWe asked each respondent to list all livelihood activities that bring in food or income to the household and rank them in order of importance. Occupations were grouped into the following categories: farming, cash crop, fishing, mariculture, gleaning, fish trading, salaried employment, informal, tourism, and other. We considered fishing, mariculture, gleaning, fish trading together as the ‘fisheries’ sector, farming and cash crop as the ‘agriculture’ sector and all other categories into an ‘off-sector’.We then developed three distinct metrics of sensitivity based on the level of dependence on agriculture, fisheries, and both sectors together. Each metric incorporates the proportion of households engaged in a given sector (e.g., fisheries), whether these households also engage in occupations outside of this sector (agriculture and salaried/formal employment; referred to as ‘linkages’ between sectors), and the directionality of these linkages (e.g., whether respondents ranked fisheries as more important than other agriculture and salaried/formal employment) (Eqs. 1–3)$${{{{{{rm{S}}}}}}}_{{{{{{rm{A}}}}}}}=,frac{{{{{{rm{A}}}}}}}{{{{{{rm{A}}}}}}+{{{{{rm{NA}}}}}}},times ,frac{{{{{{rm{N}}}}}}}{{{{{{rm{A}}}}}}+{{{{{rm{NA}}}}}}},times ,frac{left(frac{{{{{{{rm{r}}}}}}}_{{{{{{rm{a}}}}}}}}{2}right),+,1}{{{{{{{rm{r}}}}}}}_{{{{{{rm{a}}}}}}}+,{{{{{{rm{r}}}}}}}_{{{{{{rm{na}}}}}}}+1}$$
    (1)
    $${{{{{{rm{S}}}}}}}_{{{{{{rm{F}}}}}}}=,frac{{{{{{rm{F}}}}}}}{{{{{{rm{F}}}}}}+{{{{{rm{NF}}}}}}},times ,frac{{{{{{rm{N}}}}}}}{{{{{{rm{F}}}}}}+{{{{{rm{NF}}}}}}},times ,frac{left(frac{{{{{{{rm{r}}}}}}}_{{{{{{rm{f}}}}}}}}{2}right),+,1}{{{{{{{rm{r}}}}}}}_{{{{{{rm{f}}}}}}}+,{{{{{{rm{r}}}}}}}_{{{{{{rm{nf}}}}}}}+1}$$
    (2)
    $${{{{{{rm{S}}}}}}}_{{{{{{rm{AF}}}}}}}=,frac{{{{{{rm{AF}}}}}}}{{{{{{rm{AF}}}}}}+{{{{{rm{NAF}}}}}}},times ,frac{{{{{{rm{N}}}}}}}{{{{{{rm{AF}}}}}}+{{{{{rm{NAF}}}}}}},times ,frac{left(frac{{{{{{{rm{r}}}}}}}_{{{{{{rm{af}}}}}}}}{2}right),+,1}{{{{{{{rm{r}}}}}}}_{{{{{{rm{af}}}}}}}+,{{{{{{rm{r}}}}}}}_{{{{{{rm{naf}}}}}}}+1}$$
    (3)
    where ({{{{{{rm{S}}}}}}}_{{{{{{rm{A}}}}}}}), ({{{{{{rm{S}}}}}}}_{{{{{{rm{F}}}}}}}) and ({{{{{{rm{S}}}}}}}_{{{{{{rm{AF}}}}}}}) are a community’s sensitivity in the context of agriculture, fisheries and both sectors, respectively. A, F and AF are the number of households relying on agriculture-related occupations within that community, fishery-related and agriculture- and fisheries-related occupations within the community, respectively. NA, NF and NAF are the number of households relying on non-agriculture-related, non-fisheries-related, and non-agriculture-or-fisheries-related occupations within the community, respectively. N is the number of households within the community. ({{{{{{rm{r}}}}}}}_{{{{{{rm{a}}}}}}}), ({{{{{{rm{r}}}}}}}_{{{{{{rm{f}}}}}}}) and ({{{{{{rm{r}}}}}}}_{{{{{{rm{af}}}}}}}) are the number of times agriculture-related, fisheries-related and agriculture-and-fisheries-related occupations were ranked higher than their counterpart, respectively. ({{{{{{rm{r}}}}}}}_{{{{{{rm{na}}}}}}}), ({{{{{{rm{r}}}}}}}_{{{{{{rm{nf}}}}}}}) and ({{{{{{rm{r}}}}}}}_{{{{{{rm{naf}}}}}}}) are the number of times non-agriculture, non-fisheries, and non-agriculture-and-fisheries-related occupations were ranked higher than their counterparts. As with the material style of life, we also conducted an exploratory analysis of how joint agriculture-fisheries sensitivity has changed over time in a subset of sites (Muluk and Ahus villages in Papua New Guinea) that have been sampled since 2001/200252. Although our survey methodology has the potential for bias (e.g. people might provide different rankings based on the season, or there might be gendered differences in how people rank the importance of different occupations53), our time-series analysis suggest that seasonal and potential respondent variation do not dramatically alter our community-scale sensitivity metric.ExposureTo evaluate the exposure of communities to the impact of future climates on their agriculture and fisheries sectors, we used projections of production potential from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) Fast Track phase 3 experiment dataset of global simulations. Production potential of agriculture and fisheries for each of the 72 community sites and 4746 randomly selected sites from our study countries with coastal populations >25 people/km2 were projected to the mid-century (2046–2056) under two emission scenarios (SSP1-2.6, and SSP5-8.5) and compared with values from a reference historical period (1983–2013).For fisheries exposure (EF), we considered relative change in simulated total consumer biomass (all modelled vertebrates and invertebrates with a trophic level >1). For each site, the twenty nearest ocean grid cells were determined using the Haversine formula (Supplementary Fig. 5). We selected twenty grid cells after a sensitivity analysis to determine changes in model agreement based on different numbers of cells used (1, 3, 5, 10, 20, 50, 100; Supplementary Figs. 6–7), which we balanced off with the degree to which larger numbers of cells would reduce the inter-site variability (Supplementary Fig. 8). We also report 25th and 75th percentiles for the change in marine animal biomass across the model ensemble. Projections of the change in total consumer biomass for the 72 sites were extracted from simulations conducted by the Fisheries and marine ecosystem Model Intercomparison Project (FishMIP3,54). FishMIP simulations were conducted under historical, SSP1-2.6 (low emissions) and SSP5-8.5 (high emissions) scenarios forced by two Earth System Models from the most recent generation of the Coupled Model Intercomparison project (CMIP6);55 GFDL-ESM456 and IPSL-CM6A-LR57. The historical scenario spanned 1950–2014, and the SSP scenarios spanned 2015–2100. Nine FishMIP models provided simulations: APECOSM58,59, BOATS60,61, DBEM2,62, DBPM63, EcoOcean64,65, EcoTroph66,67, FEISTY68, Macroecological69, and ZooMSS11. Simulations using only IPSL-CM6A-LR were available for APECOSM and DBPM, while the remaining 7 FishMIP models used both Earth System Model forcings. This resulted in 16 potential model runs for our examination of model agreement, albeit with some of these runs being the same model forced with two different ESMs. Thus, the range of model agreement could range from 8 (half model runs indicating one direction of change, and half indicating the other) to 16 (all models agree in direction of change). Model outputs were saved with a standardised 1° spatial grid, at either a monthly or annual temporal resolution.For agriculture exposure (EA), we used crop model projections from the Global Gridded Crop model Intercomparison Project (GGCMI) Phase 314, which also represents the agriculture sector in ISIMIP. We used a window of 11×11 cells centred on the site and removed non-land cells (Supplementary Fig. 5). The crop models use climate inputs from 5 CMIP6 ESMs (GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL), downscaled and bias-adjusted by ISIMIP and use the same simulation time periods. We considered relative yield change in three rain-fed and locally relevant crops: rice, maize, and cassava, using outputs from 4 global crop models (EPIC-IIASA, LPJmL, pDSSAT, and PEPIC), run at 0.5° resolution. These 4 models with 5 forcings generate 20 potential model runs for our examination of model agreement. Yield simulations for cassava were only available from the LPJmL crop model. All crop model simulations assumed no adaptation in growing season and fertilizer input remained at current levels. Details on model inputs, climate data, and simulation protocol are provided in ref. 14. At each site, and for each crop, we calculated the average change (%) between projected vs. historical yield within 11×11 cell window. We then averaged changes in rice, maize and cassava to obtain a single metric of agriculture exposure (EA).We also obtained a composite metric of exposure (EAF) by calculating each community’s average change in both agriculture and fisheries:$${{{{{{rm{E}}}}}}}_{{{{{{rm{AF}}}}}}}=,frac{{{{{{{rm{E}}}}}}}_{{{{{{rm{A}}}}}}}+,{{{{{{rm{E}}}}}}}_{{{{{{rm{F}}}}}}}}{2}$$
    (4)
    Potential ImpactWe calculated relative potential impact as the Euclidian distance from the origin (0) of sensitivity and exposure.Sensitivity testTo determine whether our sites displayed a particular exposure bias, we compared the distributions of our sites and 4746 sites that were randomly selected from 47,460 grid cells within 1 km of the coast of the 5 countries we studied which had population densities >25 people/km2, based on the SEDAC gridded populating density of the world dataset (https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density-rev11/data-download).We used Cohen’s D to determine the size of the difference between our sites and the randomly selected sites.Validating ensemble modelsWe attempted a two-stage validation of the ensemble model projections. First, we reviewed the literature on downscaling of ensemble models to examine whether downscaling validation had been done for the ecoregions containing our study sites.While no fisheries ensemble model downscaling had been done specific to our study regions, most of the models of the ensemble have been independently evaluated against separate datasets aggregated at scales down to Large Marine Ecosystems (LMEs) or Exclusive Economic Zones (EEZs) (see11). For example, the DBEM was created with the objective of understanding the effects of climate change on exploited marine fish and invertebrate species2,70. This model roughly predicts species’ habitat suitability; and simulates spatial population dynamics of fish stocks to output biomass and maximum catch potential (MCP), a proxy of maximum sustainable yield2,62,71. Compared with spatially-explicit catch data from the Sea Around Us Project (SAUP; www.seaaroundus.org)70 there were strong similarities in the responses to warming extremes for several EEZs in our current paper (Indonesia and Philippines) and weaker for the EEZs of Madagascar, Papua New Guinea, and Tanzania. At the LME level, DBEM MCP simulations explained about 79% of the variation in the SAUP catch data across LMEs72. The four LMEs analyzed in this paper (Agulhas Current; Bay of Bengal; Indonesian Sea; and Sulu-Celebes Sea) fall within the 95% confidence interval of the linear regression relationship62. Another example, BOATS, is a dynamic biomass size-spectrum model parameterised to reproduce historical peak catch at the LME scale and observed catch to biomass ratios estimated from the RAM legacy stock assessment database (in 8 LMEs with sufficient data). It explained about 59% of the variability of SAUP peak catch observation at the LME level with the Agulhas Current, Bay of Bengal, and Indonesian Sea catches reproduced within +/-50% of observations61. The EcoOcean model validation found that all four LMEs included in this study fit very close to the 1:1 line for overserved and predicted catches in 200064,65. DBPM, FEISTY, and APECOSM have also been independently validated by comparing observed and predicted catches. While the models of this ensemble have used different climate forcings when evaluated independently, when taken together the ensemble multi-model mean reproduces global historical trends in relative biomass, that are consistent with the long term trends and year-on-year variation in relative biomass change (R2 of 0.96) and maximum yield estimated from stock assessment models (R2 of 0.44) with and without fishing respectively11.Crop yield estimates simulated by GGCMI crop models have been evaluated against FAOSTAT national yield statistics14,73,74. These studies show that the models, and especially the multi-model mean, capture large parts of the observed inter-annual yield variability across most main producer countries, even though some important management factors that affect observed yield variability (e.g., changes in planting dates, harvest dates, cultivar choices, etc.) are not considered in the models. While GCM-based crop model results are difficult to validate against observations, Jägermeyr et al14. show that the CMIP6-based crop model ensemble reproduces the variability of observed yield anomalies much better than CMIP5-based GGCMI simulations. In an earlier crop model ensemble of GGCMI, Müller et al.74 show that most crop models and the ensemble mean are capable of reproducing the weather-induced yield variability in countries with intensely managed agriculture. In countries where management introduces strong variability to observed data, which cannot be considered by models for lack of management data time series, the weather-induced signal is often low75, but crop models can reproduce large shares of the weather-induced variability, building trust in their capacity to project climate change impacts74.We then attempted to validate the models in our study regions. For the crop models, we examined production-weighted agricultural projections weighted by current yields/production area (Supplementary Fig. 1). We used an observational yield map (SPAM2005) and multiplied it with fractional yield time series simulated by the models to calculate changes in crop production over time, which integrates results in line with observational spatial patterns. The weighted estimates were not significantly different to the unweighted ones (t = 0.17, df = 5, p = 0.87). For the fisheries models, our study regions were data-poor and lacked adequate stock assessment data to extend the observed global agreement of the sensitivity of fish biomass to climate during our reference period (1983-2013). Instead, we provide the degree of model run agreement about the direction of change in the ensemble models to ensure transparency about the uncertainty in this downscaled application.AnalysesTo account for the fact that communities were from five different countries we used linear mixed-effects models (with country as a random effect) for all analyses. All averages reported (i.e. exposure, sensitivity, and model agreement) are estimates from these models. In both our comparison of fisheries and agriculture exposure and test of differences between production-weighted and unweighted agriculture exposure we wanted to maintain the paired nature of the data while also accounting for country. To accomplish this we used the differences between the exposure metrics as the response variable (e.g. fisheries exposure minus agriculture exposure), testing whether these differences are different from zero. We also used linear mixed-effects models to quantify relationships between the material style of life and potential impacts under different mitigation scenarios (SSP1-2.6 and 8.5), estimating standard errors from 1000 bootstrap replications. To further explore whether these relationships between the material style of life and potential impacts were driven by exposure or sensitivity, we conducted an additional analysis to quantify relationships between the material style of life and: 1) joint fisheries and agricultural sensitivity; 2) joint fisheries and agricultural exposure under different mitigation scenarios. We present both the conditional R2 (i.e., variance explained by both fixed and random effects) and the marginal R2 (i.e., variance explained by only the fixed effects) to help readers compare among the material style of life relationships.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

  • in

    Cysteine mitigates the effect of NaCl salt toxicity in flax (Linum usitatissimum L) plants by modulating antioxidant systems

    Kaya, C., Murillo-Amador, B. & Ashraf, M. Involvement of L-cysteine desulfhydrase and hydrogen sulfide in glutathione-induced tolerance to salinity by accelerating ascorbate-glutathione cycle and glyoxalase system in capsicum. Antioxidants (Basel, Switzerland) 9, 1–29 (2020).
    Google Scholar 
    Darwesh, O. M., Shalaby, M. G., Abo-Zeid, A. M. & Mahmoud, Y. A. G. Nano-bioremediation of municipal wastewater using myco-synthesized iron nanoparticles. Egypt. J. Chem. 64, 2499–2507 (2021).
    Google Scholar 
    Bimurzayev, N., Sari, H., Kurunc, A., Doganay, K. H. & Asmamaw, M. Effects of different salt sources and salinity levels on emergence and seedling growth of faba bean genotypes. Sci. Rep. 11, 1–17 (2021).Article 
    CAS 

    Google Scholar 
    Li, W. et al. A salt tolerance evaluation method for sunflower (Helianthus annuus L.) at the seed germination stage. Sci. Rep. 10, 1–9 (2020).ADS 
    Article 
    CAS 

    Google Scholar 
    Hussien, H. A., Salem, H. & Mekki, B. E. D. Ascorbate-glutathione-α-tocopherol triad enhances antioxidant systems in cotton plants grown under drought Stress. Int. J. ChemTech Res. 8, 1463–1472 (2015).CAS 

    Google Scholar 
    Hussein, H. A. A., Mekki, B. B., El-Sadek, M. E. A. & El Lateef, E. E. Effect of L-ornithine application on improving drought tolerance in sugar beet plants. Heliyon 5, e02631 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Guo, H., Huang, Z., Li, M. & Hou, Z. Growth, ionic homeostasis, and physiological responses of cotton under different salt and alkali stresses. Sci. Rep. 10, 2 (2020).Article 
    CAS 

    Google Scholar 
    Khataar, M., Mohammadi, M. H., Shabani, F., Mohhamadi, M. H. & Shabani, F. Soil salinity and matric potential interaction on water use, water use efficiency and yield response factor of bean and wheat. Sci. Rep. 8, 1–13 (2018).
    Google Scholar 
    Hernández, J. A. Salinity tolerance in plants: Trends and perspectives. Int. J. Mol. Sci. 20, 2408 (2019).PubMed Central 
    Article 

    Google Scholar 
    Dubey, S., Bhargava, A., Fuentes, F., Shukla, S. & Srivastava, S. Effect of salinity stress on yield and quality parameters in flax (Linum usitatissimum L.). Not. Bot. Horti Agrobot. Cluj-Napoca 48, 954–966 (2020).CAS 
    Article 

    Google Scholar 
    Devarshi, P., Grant, R., Ikonte, C. & Hazels Mitmesser, S. Maternal omega-3 nutrition, placental transfer and fetal brain development in gestational diabetes and preeclampsia. Nutrients 11, 2 (2019).Article 
    CAS 

    Google Scholar 
    Takahashi, H. Sulfur assimilation in photosynthetic organisms: Molecular functions and regulations of transporters and assimilatory enzymes. Annu. Rev. Plant Biol. 62, 157–184 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    Bakhoum, G. S. et al. Improving growth, some biochemical aspects and yield of three cultivars of soybean plant by methionine treatment under sandy soil condition. Int. J. Environ. Res. 13, 35–43 (2018).Article 
    CAS 

    Google Scholar 
    Adams, E. et al. A novel role for methyl cysteinate, a cysteine derivative, in cesium accumulation in Arabidopsis thaliana. Sci. Rep. 7, 1–12 (2017).Article 
    CAS 

    Google Scholar 
    Sadak, M. S., Abd El-Hameid, A. R., Zaki, F. S. A., Dawood, M. G. & El-Awadi, M. E. Physiological and biochemical responses of soybean (Glycine max L.) to cysteine application under sea salt stress. Bull. Natl. Res. Cent. 44, 1–10 (2020).Article 

    Google Scholar 
    Wani, S. H. et al. Engineering salinity tolerance in plants: Progress and prospects. Planta 251, 1–29 (2020).Article 
    CAS 

    Google Scholar 
    Genisel, M., Erdal, S. & Kizilkaya, M. The mitigating effect of cysteine on growth inhibition in salt-stressed barley seeds is related to its own reducing capacity rather than its effects on antioxidant system. Plant Growth Regul. 75, 187–197 (2015).CAS 
    Article 

    Google Scholar 
    Salem, H., Abo-Setta, Y., Aiad, M., Hussein, H.-A. & El-Awady, R. Effect of potassium humate on some metabolic products of wheat plants grown under saline conditions. J. Soil Sci. Agric. Eng. 8, 565–569 (2017).
    Google Scholar 
    El-Awadi, M. E., Ibrahim, S. K., Sadak, M. S., Abd Elhamid, E. M. & Gamal El-Din, K. M. Impact of cysteine or proline on growth, some biochemical attributes and yield of faba bean. Int. J. PharmTech Res. 9, 100–106 (2016).CAS 

    Google Scholar 
    Nasibi, F., Kalantari, K. M., Zanganeh, R., Mohammadinejad, G. & Oloumi, H. Seed priming with cysteine modulates the growth and metabolic activity of wheat plants under salinity and osmotic stresses at early stages of growth. Indian J. Plant Physiol. 21, 279–286 (2016).Article 

    Google Scholar 
    Romero, I. et al. Transsulfuration is an active pathway for cysteine biosynthesis in Trypanosoma rangeli. Parasit. Vectors 7, 1–11 (2014).Article 
    CAS 

    Google Scholar 
    Guo, H. et al. l-cysteine desulfhydrase-related H2S production is involved in OsSE5-promoted ammonium tolerance in roots of Oryza sativa. Plant Cell Environ. 40, 1777–1790 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Colak, N., Tarkowski, P. & Ayaz, F. A. Effect of N-acetyl-L-cysteine (NAC) on soluble sugar and polyamine content in wheat seedlings exposed to heavy metal stress (Cd, Hg and Pb). Bot. Serbica 44, 191–201 (2020).Article 

    Google Scholar 
    Teixeira, W. F. et al. Foliar and seed application of amino acids affects the antioxidant metabolism of the soybean crop. Front. Plant Sci. 8, 2 (2017).Article 

    Google Scholar 
    Perveen, S. et al. Cysteine-induced alterations in physicochemical parameters of oat (Avena sativa L var Scott and F-411) under drought stress. Biol. Futur. 70, 16–24 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Marrez, D. A., Abdelhamid, A. E. & Darwesh, O. M. Eco-friendly cellulose acetate green synthesized silver nano-composite as antibacterial packaging system for food safety. Food Packag. Shelf Life 20, 100302 (2019).Article 

    Google Scholar 
    Acharya, B. R. et al. Morphological, physiological, biochemical, and transcriptome studies reveal the importance of transporters and stress signaling pathways during salinity stress in Prunus. Sci. Rep. 12, 1274 (2022).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hayat, S. et al. Role of proline under changing environments: A review. Plant Signal. Behav. 7, 2 (2012).
    Google Scholar 
    Thomas, J., Mandal, A. K. A., Kumar, R. R. & Chordia, A. Role of biologically active amino acid formulations on quality and crop productivity of tea (Camellia sp.). Int. J. Agric. Res. 4, 228–236 (2009).CAS 
    Article 

    Google Scholar 
    Mekki, B. E. D. B. & Hussein, H. A. A. Influence of L-ascorbate on yield components, biochemical constituents and fatty acids composition in seeds of some groundnut (Arachis hypogaea L.) cultivars grown in sandy soil. Biosci. Res. 14, 75–83 (2017).
    Google Scholar 
    Cuin, T. A. & Shabala, S. Amino acids regulate salinity-induced potassium efflux in barley root epidermis. Planta 225, 753–761 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    Hussein, H.-A.A. et al. Grain-priming with L-arginine improves the growth performance of wheat (Triticum aestivum L.) plants under drought stress. Plants 11, 1219 (2022).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Azarakhsh, M. R., Asrar, Z. & Mansouri, H. Effects of seed and vegetative stage cysteine treatments on oxidative stress response molecules and enzymes in Ocimum basilicum L. under cobalt stress. J. Soil Sci. Plant Nutr. 15, 651–662 (2015).
    Google Scholar 
    Mekki, B. E. D., Hussien, H. A. & Salem, H. Role of glutathione, ascorbic acid and α-tocopherol in alleviation of drought stress in cotton plants. Int. J. ChemTech Res. 8, 1573–1581 (2015).
    Google Scholar 
    Zhao, Y. S. et al. Fermentation affects the antioxidant activity of plant-based food material through the release and production of bioactive components. Antioxidants 10, 2004 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Elsayed, A. A., Ibrahim, A. A. & Dakroury, M. Z. Effect of salinity on growth and genetic diversity of broad bean (Vicia faba L.) cultivars. Alexandria Sci. Exch. J. An Int Q. J. Sci. Agric. Environ. 37, 467–479 (2016).
    Google Scholar 
    Darwesh, O. M. & Elshahawy, I. E. Silver nanoparticles inactivate sclerotial formation in controlling white rot disease in onion and garlic caused by the soil borne fungus Stromatinia cepivora. Eur. J. Plant Pathol. 160, 917–934 (2021).CAS 
    Article 

    Google Scholar 
    Metzner, H., Rau, H. & Senger, H. Untersuchungen zur Synchronisierbarkeit einzelner Pigmentmangel-Mutanten von Chlorella. Planta 65, 186–194 (1965).CAS 
    Article 

    Google Scholar 
    Cerning, B. J. A note on sugar determination by the anthrone method. Cereal Chem. 52, 857–860 (1975).
    Google Scholar 
    Pourmorad, F., Hosseinimehr, S. J. & Shahabimajd, N. Antioxidant activity, phenol and flavonoid contents of some selected Iranian medicinal plants. Afr. J. Biotechnol. 5, 1142–1145 (2006).CAS 

    Google Scholar 
    Bates, L. S., Waldren, R. P. & Teare, I. D. Rapid determination of free proline for water-stress studies. Plant Soil 39, 205–207 (1973).CAS 
    Article 

    Google Scholar 
    Rosen, H. A modified ninhydrin colorimetric analysis for amino acids. Arch. Biochem. Biophys. 67, 10–15 (1957).CAS 
    PubMed 
    Article 

    Google Scholar 
    Darwesh, O. M., Ali, S. S., Matter, I. A., Elsamahy, T. & Mahmoud, Y. A. Enzymes immobilization onto magnetic nanoparticles to improve industrial and environmental applications. In Methods in Enzymology Vol. 630 481–502 (Academic Press, 2020).
    Google Scholar 
    Kong, F. X., Hu, W., Chao, S. Y., Sang, W. L. & Wang, L. S. Physiological responses of the lichen Xanthoparmelia mexicana to oxidative stress of SO2. Environ. Exp. Bot. 42, 201–209 (1999).CAS 
    Article 

    Google Scholar 
    Asada, K. Ascorbate peroxidase—a hydrogen peroxide-scavenging enzyme in plants. Physiol. Plant. 85, 235–241 (1992).CAS 
    Article 

    Google Scholar 
    Hodges, D. M., DeLong, J. M., Forney, C. F. & Prange, R. K. Improving the thiobarbituric acid-reactive-substances assay for estimating lipid peroxidation in plant tissues containing anthocyanin and other interfering compounds. Planta 207, 604–611 (1999).CAS 
    Article 

    Google Scholar 
    Laemmli, U. K. Cleavage of structural proteins during the assembly of the head of bacteriophage T4. Nature 227, 680–685 (1970).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Snedecor, G. W. & Cochran, W. G. Statistical Methods (The Iowa State University Press, 1989).MATH 

    Google Scholar  More

  • in

    Complex extracellular biology drives surface competition during colony expansion in Bacillus subtilis

    Riley M, Gordon D. The ecological role of bacteriocins in bacterial competition. Trends Microbiol. 1999;7:129–33.CAS 
    PubMed 
    Article 

    Google Scholar 
    Griffin A, West S, Buckling A. Cooperation and competition in pathogenic bacteria. Nature. 2004;430:1024–7.CAS 
    PubMed 
    Article 

    Google Scholar 
    Velicer G, Vos M. Sociobiology of the myxobacteria. Annu Rev Microbiol. 2009;63:599–623.CAS 
    PubMed 
    Article 

    Google Scholar 
    Brockhurst M, Habets M, Libberton B, Buckling A, Gardner A. Ecological drivers of the evolution of public-goods cooperation in bacteria. Ecology. 2010;91:334–40.PubMed 
    Article 

    Google Scholar 
    Drescher K, Nadell CD, Stone HA, Wingreen NS, Bassler BL. Solutions to the public goods dilemma in bacterial biofilms. Curr Biol. 2014;24:50–55.CAS 
    PubMed 
    Article 

    Google Scholar 
    van Gestel J, Weissing FJ, Kuipers OP, Kovács ÁT. Density of founder cells affects spatial pattern formation and cooperation in Bacillus subtilis biofilms. ISME J. 2014;8:2069–79.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Henrichsen J. Bacterial surface translocation: a survey and a classification. Bacteriol Rev. 1972;36:478–503.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    van Gestel J, Vlamakis H, Kolter R. From cell differentiation to cell collectives: Bacillus subtilis uses division of labor to migrate. PLoS Biol. 2015;13:e1002141.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Hölscher T, Kovács ÁT. Sliding on the surface: bacterial spreading without an active motor. Environ Microbiol. 2017;19:2537–45.PubMed 
    Article 

    Google Scholar 
    Kearns D. A field guide to bacterial swarming motility. Nat Rev Microbiol. 2010;8:634–44.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Nogales J, Bernabéu-Roda L, Cuéllar V, Soto M. ExpR is not required for swarming but promotes sliding in Sinorhizobium meliloti. J Bacteriol. 2012;194:2027–35.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Murray T, Kazmierczak B. Pseudomonas aeruginosa exhibits sliding motility in the absence of type IV pili and flagella. J Bacteriol. 2008;190:2700–8.CAS 
    PubMed 
    Article 

    Google Scholar 
    Kinsinger R, Shirk M, Fall R. Rapid surface motility in Bacillus subtilis is dependent on extracellular surfactin and potassium ion. J Bacteriol. 2003;185:5627–31.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Grau RR, De Oña P, Kunert M, Leñini C, Gallegos-Monterrosa R, Mhatre E, et al. A duo of potassium-responsive histidine kinases govern the multicellular destiny of Bacillus subtilis. MBio. 2015;6:e00581–15.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kobayashi K, Iwano M. BslA(YuaB) forms a hydrophobic layer on the surface of Bacillus subtilis biofilms. Mol Microbiol. 2012;85:51–66.CAS 
    PubMed 
    Article 

    Google Scholar 
    Hobley L, Ostrowski A, Rao FV, Bromley KM, Porter M, Prescott AR, et al. BslA is a self-assembling bacterial hydrophobin that coats the Bacillus subtilis biofilm. Proc Natl Acad Sci USA. 2013;110:13600–5.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Seminara A, Angelini T, Wilking J, Vlamakis H, Ebrahim S, Kolter R, et al. Osmotic spreading of Bacillus subtilis biofilms driven by an extracellular matrix. Proc Natl Acad Sci USA. 2012;109:1116–21.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kafri M, Metzl-Raz E, Jona G, Barkai N. The cost of protein production. Cell Rep. 2016;14:22–31.CAS 
    PubMed 
    Article 

    Google Scholar 
    Sexton D, Schuster M. Nutrient limitation determines the fitness of cheaters in bacterial siderophore cooperation. Nat Commun. 2017;8:230.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Xavier J, Kim W, Foster K. A molecular mechanism that stabilizes cooperative secretions in Pseudomonas aeruginosa. Mol Microbiol. 2011;79:166–79.CAS 
    PubMed 
    Article 

    Google Scholar 
    Tai JSB, Mukherjee S, Nero T, Olson R, Tithof J, Nadell CD, et al. Social evolution of shared biofilm matrix components. Proc Natl Acad Sci USA. 2022;119:e2123469119.PubMed 
    Article 

    Google Scholar 
    Branda SS, Chu F, Kearns DB, Losick R, Kolter R. A major protein component of the Bacillus subtilis biofilm matrix. Mol Microbiol. 2006;59:1229–38.CAS 
    PubMed 
    Article 

    Google Scholar 
    Martin M, Dragoš A, Hölscher T, Maróti G, Bálint B, Westermann M, et al. De novo evolved interference competition promotes the spread of biofilm defectors. Nat Commun. 2017;8:15127.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Dragoš A, Kiesewalter H, Martin M, Hsu C-Y, Hartmann R, Wechsler T, et al. Division of labor during biofilm matrix production. Curr Biol. 2018;28:1903–13.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Martin M, Dragoš A, Schäfer D, Maróti G, Kovács ÁT. Cheaters shape the evolution of phenotypic heterogeneity in Bacillus subtilis biofilms. ISME J. 2020;14:2302–12.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Otto SB, Martin M, Schäfer D, Hartmann R, Drescher K, Brix S, et al. Privatization of biofilm matrix in structurally heterogeneous biofilms. mSystems. 2020;5:e00425–20.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Arnaouteli S, Bamford NC, Stanley-Wall NR, Kovács ÁT. Bacillus subtilis biofilm formation and social interactions. Nat Rev Microbiol. 2021;19:600–14.CAS 
    PubMed 
    Article 

    Google Scholar 
    Kovács ÁT, Dragoš A. Evolved Biofilm: review on the experimental evolution studies of Bacillus subtilis pellicles. J Mol Biol. 2019;431:4749–59.Dragos A, Lakshmanan N, Martin M, Horvath B, Maroti G, Falcon Garcia C, et al. Evolution of exploitative interactions during diversification in Bacillus subtilis biofilms. FEMS Microbiol Ecol. 2018;94:fix155.Article 
    CAS 

    Google Scholar 
    Dragoš A, Martin M, Garcia CF, Kricks L, Pausch P, Heimerl T, et al. Collapse of genetic division of labour and evolution of autonomy in pellicle biofilms. Nat Microbiol. 2018;3:1451–60.PubMed 
    Article 
    CAS 

    Google Scholar 
    van Gestel J, Bareia T, Tenennbaum B, Dal Co A, Guler P, Aframian N, et al. Short-range quorum sensing controls horizontal gene transfer at micron scale in bacterial communities. Nat Commun. 2021;12:2324.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Gore J, Youk H, Van Oudenaarden A. Snowdrift game dynamics and facultative cheating in yeast. Nature. 2009;459:253–6.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Konkol MA, Blair KM, Kearns DB. Plasmid-encoded comI inhibits competence in the ancestral 3610 strain of Bacillus subtilis. J Bacteriol. 2013;195:4085–93.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hölscher T, Dragoš A, Gallegos-Monterrosa R, Martin M, Mhatre E, Richter A, et al. Monitoring spatial segregation in surface colonizing microbial populations. J Vis Exp. 2016;2016:e54752.
    Google Scholar 
    Morris R, Schor M, Gillespie R, Ferreira A, Baldauf L, Earl C, et al. Natural variations in the biofilm-associated protein BslA from the genus Bacillus. Sci Rep. 2017;7:6730.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Dogsa I, Brloznik M, Stopar D, Mandic-Mulec I. Exopolymer diversity and the role of levan in Bacillus subtilis biofilms. PLoS One. 2013;8:e62044.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Branda SS, González-Pastor JE, Ben-Yehuda S, Losick R, Kolter R. Fruiting body formation by Bacillus subtilis. Proc Natl Acad Sci USA. 2001;98:11621–6.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lenski RE, Rose M, Simpson S, Tadler S. Long-term experimental evolution in Escherichia coli. I Adaptation and divergence during 2,000 generations. Am Nat. 1991;138:1315–41.Article 

    Google Scholar 
    Hallatschek O, Hersen P, Ramanathan S, Nelson DR. Genetic drift at expanding frontiers promotes gene segregation. Proc Natl Acad Sci USA. 2007;104:19926–30.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Slatkin M, Excoffier L. Serial founder effects during range expansion: a spatial analog of genetic drift. Genetics. 2012;191:171–81.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    MacLean R, Fuentes-Hernandez A, Greig D, Hurst L, Gudelj I. A mixture of ‘cheats’ and ‘co-operators’ can enable maximal group benefit. PLoS Biol. 2010;8:e1000486.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Kearns DB. Division of labour during Bacillus subtilis biofilm formation. Mol Microbiol. 2008;67:229–31.CAS 
    PubMed 
    Article 

    Google Scholar 
    Kiesewalter HT, Lozano-Andrade CN, Wibowo M, Strube ML, Maróti G, Snyder D, et al. Genomic and chemical diversity of Bacillus subtilis secondary metabolites against plant pathogenic fungi. mSystems. 2021;6:e00770–20.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Stefanic P, Mandic-Mulec I. Social interactions and distribution of Bacillus subtilis pherotypes at microscale. J Bacteriol. 2009;191:1756–64.CAS 
    PubMed 
    Article 

    Google Scholar 
    Even-Tov E, Omer Bendori S, Valastyan J, Ke X, Pollak S, Bareia T, et al. Social evolution selects for redundancy in bacterial quorum sensing. PLoS Biol. 2016;14:e1002386.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Kalamara M, Spacapan M, Mandic-Mulec I, Stanley-Wall N. Social behaviours by Bacillus subtilis: quorum sensing, kin discrimination and beyond. Mol Microbiol. 2018;110:863–78.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Aframian N, Eldar A. A bacterial tower of Babel: Quorum-Sensing signaling diversity and its evolution. Annu Rev Microbiol. 2020;74:587–606.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kiesewalter HT, Lozano-Andrade CN, Strube ML, Kovács ÁT. Secondary metabolites of Bacillus subtilis impact the assembly of soil-derived semisynthetic bacterial communities. Beilstein J Org Chem. 2020;16:2983–98.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Dragoš A, Kovács ÁT. The peculiar functions of the bacterial extracellular matrix. Trends Microbiol. 2017;25:257–66.PubMed 
    Article 
    CAS 

    Google Scholar 
    Kovács ÁT. Impact of spatial distribution on the development of mutualism in microbes. Front Microbiol. 2014;5:649.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Zhang F, Kwan A, Xu A, Süel G. A synthetic quorum sensing system reveals a potential private benefit for public good production in a biofilm. PLoS One. 2015;10:e0132948.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Bruce J, West S, Griffin A. Functional amyloids promote retention of public goods in bacteria. Proc Biol Sci. 2019;286:20190709.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ma L, Conover M, Lu H, Parsek M, Bayles K, Wozniak D. Assembly and development of the Pseudomonas aeruginosa biofilm matrix. PLoS Pathog. 2009;5:e1000354.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Hartmann R, Jeckel H, Jelli E, Singh PK, Vaidya S, Bayer M, et al. Quantitative image analysis of microbial communities with BiofilmQ. Nat Microbiol. 2021;6:151–6.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Dar D, Dar N, Cai L, Newman DK. Spatial transcriptomics of planktonic and sessile bacterial populations at single-cell resolution. Science. 2021;373:eabi4882.CAS 
    PubMed 
    Article 

    Google Scholar 
    Lozano-Andrade CN, Nogueira CG, Wibowo M, Kovács ÁT. Establishment of a transparent soil system to study Bacillus subtilis chemical ecology. bioRxiv. 2022. https://doi.org/10.1101/2022.01.10.475645.Article 

    Google Scholar  More

  • in

    Comprehensive climatic suitability evaluation of peanut in Huang-Huai-Hai region under the background of climate change

    Overview of the study areaBased on the actual cultivation of peanuts, the Huang-Huai-Hai region is selected as the study area (Fig. 1). The main body of the study area is the Huang-Huai-Hai Plain (North China Plain), which is a typical alluvial plain resulting from extensive sediment deposition carried by the Yellow River, the Huaihe River and the Haihe River and their tributaries, and the hills in central and southern Shandong Peninsula adjacent to it. Administrative zones include 5 provinces, 2 cities, 53 cities and 376 counties (districts). In China, The Huang-Huai-Hai region is an important production and processing centre for agricultural products, with a total land area of 4.10 × 105 square kilometers and cultivated fields of 2.15 × 107 hm2, accounting for 4.3% and 16.3% of the total amount of the country, respectively. It belongs to temperate continental monsoon climate with distinct seasons, accumulated temperature of 3600–4800 degrees above 10 °C, frost-free period of 170–200 days and annual precipitation of 500–950 mm27. The Huang-huai-hai region is the largest peanut growing area, accounting for more than 50% of the country’s peanut production and area28.Figure 1Location of the study areas. The figure was made in the ArcGIS 10.2 platform (https://www.esri.com/en-us/home).Full size imageData sourcesThe data used in the study mainly include meteorological data, geographic information data and crop data. The meteorological data comes from China Meteorological Information Center (http://data.cma.cn), including the daily maximum temperature (℃), daily minimum temperature (℃), daily average temperature (℃), daily precipitation (mm) and daily average wind speed (M/s) observed by 186 ground observation meteorological stations in the Huang-Huai-Hai region from 1960 to 2019 (Fig. 1). Geographic information data include elevation DEM data (resolution of 1 km × 1 km) and land use data in the study area, which are from the resource and environmental science and data center of Chinese Academy of Sciences (http://www.resdc.cn). Crop data, including peanut sowing area and yield data, are derived from the statistical yearbooks of provinces and cities in the study area and China Agricultural Technology Network (http://www.cast.net.cn).Data processingMeteorological data processingAnusplin software is a tool to interpolate multivariate data based on ordinary thin disks and local thin disk spline functions, enabling the introduction of covariates for simultaneous spatial interpolation of multiple surfaces, suitable for meteorological data time series29. First, the Anusplin software is used to spatially interpolate the meteorological data and suitability data of the peanut growing season (April to September) from 1960 to 2019 based on the elevation data with a resolution of 1 km × 1 km. The Inverse Distance Weight (IDW) interpolation can make the meteorological data after Anusplin interpolation maintain consistency with the original data, and is able to improve the interpolation accuracy. Finally, the meteorological and suitability data set with a resolution of 1 km × 1 km is obtained. ArcGIS and MATLAB software were used to count the median of regional meteorological factors in agricultural fields of different cities (counties), and the meteorological factors and suitability of different periods of peanut growth season in each city (county) were obtained.Yield data processingMany factors affect crop yield formation, which can be generally divided into three main categories: meteorological conditions, agronomic and technological measures, and stochastic factors. Agricultural technical measures reflect the development level of social production in a certain historical period and become time technology trend output, which is referred to as trend output for short, and meteorological production reflects short period yield components that are affected by meteorological elements. Stochastic factors account for a small proportion and are often ignored in actual calculations30. The specific calculation is as follows:$$Y={Y}_{t}+{Y}_{w}$$
    (1)

    where Y is the actual yield (single production) of the crop, Yt is the trend yield, and Yw is the meteorological yield.In this paper, a straight-line sliding average method is used to simulate the trend yield. The straight-line sliding average method is a very commonly used method to model yield, and it considers the change in the time series of yield within a certain stage as a linear function, showing a straight line, as the stage continuously slides, the straight line continuously changes the position, and the backward slip reflects the continuous change in the evolution trend of the yield history31. The regression models in each stage are obtained in turn, and the mean value of each linear sliding regression simulation value at each time point is taken as its trend yield value. The linear trend equation at some stage is:$${Y}_{i}left(tright)={a}_{i}+{b}_{i}t$$
    (2)
    where i = n-k + 1, is the number of equations; k is the sliding step; n is the number of sample sequences; t is the time serial number. Yi(t) is the function value of each equation at point t. there are q function values at point t. the number of q is related to n and k. Calculate the average value of each function value at each point:$$overline{{Y }_{i}(t)}=frac{1}{q}sum_{j=1}^{q}{Y}_{i}left(tright)$$
    (3)
    Connecting the (overline{{Y }_{i}(t)}) value of each point can represent the historical evolution trend of production. Its characteristics depend on the value of k. Only when k is large enough, the trend yield can eliminate the influence of short cycle fluctuation. After comparison and considering the length of yield series, k is taken as 5 in this paper.After the trend yield is obtained, the meteorological yield is calculated using Eq. (1), then the relative meteorological production is$${Y}_{r}=frac{{Y}_{w}}{{Y}_{t}}$$
    (4)
    The relative meteorological yield shows that the relative variability of yield fluctuation deviating from the trend, that is, the amplitude of yield fluctuation, is not affected by time and space, and is comparable. However, when the value is negative, it indicates that the meteorological conditions are unfavorable to the overall crop production, and the crop yield reduction, that is, the yield reduction rate32.Characteristics of spatial and temporal distribution of climatic resources in the Huang-Huai-Hai regionCollect meteorological resource data from 1960 to 2019. Taking 1960–1989 as the first three decades of the study and 1990–2019 as the last three decades, the climatic resource changes of peanut growth in the Huang-Huai-Hai region are analyzed by interpolation of heat resources (average temperature), water resources (precipitation) and light resources (sunshine hours) in the study area in two periods combined with topographic factors.Establishment of suitability modelAccording to the definition of phenological time and growth period of peanut planting practice in the Huang-Huai-Hai region, the growth season of peanut is divided into three growth periods and five growth stages (Table 1). Temperature, precipitation and sunshine hours are the necessary meteorological factors to determine the normal development of peanut. Therefore, combined with climatic resources in the study area, temperature, precipitation and sunshine suitability model was introduced to quantitatively analyze the suitability of peanut planting.Table 1 Division of peanut growth periods.Full size tableTemperature suitability modelTemperature is a very important factor in the growth period of peanut, and the change of temperature in different growth periods will have a great influence on the yield and quality of peanut. As a warm-loving crop, accumulated temperature plays a decisive role in the budding condition and nutrient growth stage of peanut. Temperature determines the quality of fruit and the final yield of peanut. Beta function33 is used to calculate temperature suitability, which is universal for crop-temperature relationship. The specific calculation is as follows:$${F}_{i}left(tright)=frac{(t-{t}_{1}){({t}_{h}-t)}^{B}}{({t}_{0}-{t}_{1}){({t}_{h}-{t}_{0})}^{B}}$$
    (5)
    where the value of B is shown in$$B=frac{{t}_{h}-{t}_{0}}{{t}_{0}-{t}_{1}}$$
    (6)
    where Fi(t) is the temperature suitability of a certain growth period; t is the daily average temperature of peanut at a certain development stage; t1, th and t0 are the lower limit temperature, upper limit temperature and appropriate temperature required for each growth period of peanut. Refer to the corresponding index system and combined with the peanut production practice in Huang-Huai-Hai region34,35,36, determine the three base point temperature of peanut in each growth period, as shown in the Table 2.Table 2 Three fundamental points temperature and crop coefficient of peanut at each growth stage in the study area.Full size tablePrecipitation suitability modelPeanut has a long growth period, which is nearly half a year. Insufficient or excessive water during the growth period has a great impact on the growth and development, pod yield and quality of peanut. Combined with the actual situation of Huang-Huai-Hai region and peanut precipitation / water demand index, the water suitability function is determined and calculated as follows:$${text{F}}_{{text{i}}} left( {text{r}} right) = left{ {begin{array}{*{20}l} {frac{{text{r}}}{{0.9{text{ET}}_{{text{c}}} }}} hfill & {r < 0.9E{text{T}}_{{text{c}}} } hfill \ 1 hfill & {0.9E{text{T}}_{{text{c}}} le r le 1.2E{text{T}}_{{text{c}}} } hfill \ {frac{{1.2{text{ET}}_{{text{c}}} }}{{text{r}}}} hfill & {r > 1.2E{text{T}}_{{text{c}}} } hfill \ end{array} } right.$$
    (7)
    where Fi(r) is the water suitability of a certain growth period; r is the accumulated precipitation of peanut in a certain development period; ETc is the water demand of peanut in each growth period.$${mathrm{ET}}_{mathrm{c}}={mathrm{K}}_{mathrm{c}}cdot {mathrm{ET}}_{0}$$
    (8)
    where Kc is the peanut crop coefficient (Table 2) and ET0 is the crop reference evapotranspiration, which is calculated by the Penman Monteith method recommended by the international food and Agriculture Organization (FAO).Sunshine suitability modelSunshine hours are an important condition for photosynthesis. The “light compensation point” and “light saturation point” of peanut are relatively high, and more sunshine hours are required for photosynthesis. Under certain conditions of water, temperature and carbon dioxide, photosynthesis increases or decreases with the increase or decrease of light. Relevant studies show that when the sunshine hours reach more than 55% of the available sunshine hours, the crops reach the appropriate state to reflect the light37. The following formula is used to calculate the sunshine suitability of peanut in each growth period.$${mathrm{F}}_{mathrm{i}}left(mathrm{s}right)=left{begin{array}{l}frac{mathrm{S}}{{mathrm{S}}_{0}} quad S{mathrm{S}}_{0}end{array}right.$$
    (9)
    where Fi(s) is the sunshine suitability of peanut in a certain development period, S is the actual sunshine hours in a certain growth period, S0 is 55% of the sunshine hours (L0), and the calculation method of L0 refers to the following formula.$${mathrm{L}}_{0}=frac{2mathrm{t}}{15}$$
    (10)
    $$mathrm{sin}frac{mathrm{t}}{2}=sqrt{frac{mathrm{sin}(45^circ -frac{mathrm{varnothing }-updelta -upgamma }{2})times mathrm{sin}(45^circ +frac{mathrm{varnothing }-updelta -upgamma }{2})}{mathrm{cosvarnothing }times mathrm{cosdelta }}}$$
    (11)
    where Φ is the geographic latitude, δ is the declination, γ is the astronomical refraction, t is the angle.Comprehensive suitability modelPeanut has different needs for meteorological elements such as temperature, sunshine and precipitation in different growth periods. In order to analyze the impact of meteorological factors in different growth periods on yield, correlation analysis was conducted between the suitability of temperature, precipitation and sunshine in each growth period and the relative meteorological yield of peanut, and the correlation coefficient of each growth period divided by the sum of the correlation coefficients of the whole growth period was used as the weight coefficient of the suitability of temperature, precipitation and sunshine in each growth period (Table 3). The climatic suitability of each single element in peanut growing season is calculated by using formulas (12) and (13):Table 3 The weight coefficients of climatic suitability at each growth stage.Full size table$$left{begin{array}{c}{mathrm{b}}_{mathrm{ti}}=frac{{mathrm{a}}_{mathrm{ti}}}{sum_{mathrm{i}=1}^{mathrm{n}}{mathrm{a}}_{mathrm{ti}}}\ {mathrm{b}}_{mathrm{ri}}=frac{{mathrm{a}}_{mathrm{ri}}}{{sum }_{mathrm{i}=1}^{mathrm{n}}{mathrm{a}}_{mathrm{ri}}}\ {mathrm{b}}_{mathrm{si}}=frac{{mathrm{a}}_{mathrm{si}}}{{sum }_{mathrm{i}=1}^{mathrm{n}}{mathrm{a}}_{mathrm{si}}}end{array}right.$$
    (12)
    $$left{begin{array}{c}F(t)={sum }_{mathrm{i}=1}^{mathrm{n}}left[{mathrm{b}}_{mathrm{ti}}{mathrm{F}}_{mathrm{i}}(mathrm{t})right]\ F(r)={sum }_{mathrm{i}=1}^{mathrm{n}}left[{mathrm{b}}_{mathrm{ri}}{mathrm{F}}_{mathrm{i}}(mathrm{r})right]\ F(s)={sum }_{mathrm{i}=1}^{mathrm{n}}left[{mathrm{b}}_{mathrm{si}}{mathrm{F}}_{mathrm{i}}(mathrm{s})right]end{array}right.$$
    (13)
    where bti, bri and bsi are the weight coefficients of temperature, precipitation and sunshine suitability in the i growth period respectively, ati, ari and asi are the correlation coefficients between temperature, precipitation and sunshine suitability and meteorological impact index of peanut yield in the i growth period respectively, and F(t), F(r) and F(s) are the temperature, precipitation and sunshine suitability in peanut growth season respectively.Then, the geometric average method is used to obtain the comprehensive suitability of peanut growth season, as shown in formula (14).$$F(S)=sqrt[3]{F(t)times F(r)times F(s)}$$
    (14)
    Verification of climatic zoning resultsDrought and flood disaster indexOn the basis of previous studies, in view of the different water demand of peanut in different development stages, this paper adds the water demand of peanut in different development stages as an important index to calculate, and constructs a standardized precipitation crop water demand index (SPRI) that can comprehensively characterize the drought and flood situation of peanut, so as to judge and analyze the occurrence of drought and flood disasters of peanut.Step 1: calculate the difference D between precipitation and crop water demand at each development stage$${D}_{i}={P}_{i}-{ET}_{ci}$$
    (15)
    where Pi is the precipitation in the i development period (mm), and ETci is the crop water demand in the i development period (mm).Step 2: normalize the data sequence.Since there are negative values in the original sequence, it is necessary to normalize the data when calculating the standardized precipitation crop water demand index. The normalized value is the SPRI value. The normalization method and drought and flood classification are consistent with SPEI index38,39,40.Chilling injury indexBased on the results of previous studies41, the abnormal percentage of caloric index was selected as the index of low-temperature chilling injury of peanut to judge and analyze the occurrence of chilling injury in different growth stages. The specific calculation process and formula are as follows:Step 1: calculate the caloric index of different development stages.Combined with the growth and development characteristics of peanut and considering the appropriate temperature, lower limit temperature and upper limit temperature at different growth stages of peanut, the caloric index can reflect the response of crops to environmental heat conditions. The average value of daily heat index is taken as the heat index of growth stage to reflect the influence of heat conditions in different growth stages on crop growth and development. Refer to formulas (5) and (6) to calculate the heat index Fi(t) at different development stages.Step 2: calculate the percentage of heat index anomaly$${I}_{ci}=frac{{F}_{i}(t)-overline{{F }_{i}(t)}}{overline{{F }_{i}(t)}}times 100%$$
    (16)
    where Ici is the Chilling injury index of stage i, Fi(t) is the heat index of stage i, and (overline{{F }_{i}(t)}) is the average value of the heat index of stage i over the years.Heat injury indexBased on the results of previous studies42, taking the average temperature of 26 °C, 30 °C and 28 °C and the daily maximum temperature of 35 °C, 35 °C and 37 °C as the critical temperature index to identify the heat damage of peanut in three growth stages, if this condition is met and lasts for more than 3 days, it will be recorded as a high temperature event.Disaster frequencyDisaster frequency (Pi) is defined as the ratio of the number of years of disaster at a certain station to the total number of years in the study period43, which is calculated by formula (17).$${P}_{i}=frac{n}{N}times 100%$$
    (17)
    where n is the number of years of disaster events to some extent at a certain growth period at a certain station, and N is the total number of years. More

  • in

    Co-occurrence networks reveal more complexity than community composition in resistance and resilience of microbial communities

    Testing H1 and H2 at community composition levelAs noted above, the simple fact that fungi grow more slowly than bacteria is the basis of the hypotheses that (H1) fungal communities should be more resistant than bacterial communities to drought stress, and (H2) that fungal communities should be less resilient than bacterial communities when the stress is relieved by rewetting18. In addition to growth rate, these two hypotheses may be related to differences in the form of growth between fungi and bacteria. For example, multicellular hyphal growth versus unicellular division or the greater thickness of fungal cell walls as compared to those of bacteria47,48. We tested H1 and H2 at the community composition level by blending the fungal and bacterial datasets generated from the same leaf, root, rhizosphere and soil samples collected from field-grown sorghum that had been either irrigated as a control, or subjected to preflowering drought followed by regular wetting beginning at flowering10,11.We followed the approach of Shade et al.17 to detect resistance and resilience, which had been developed for univariate variables, e.g., richness. For multivariate data, e.g., community composition, we modified it by calculating pairwise community dissimilarity for two groups: within-group (control-control pairs, drought-drought pairs, or rewetting-rewetting pairs), and between-group (control-drought pairs, or control-rewetting pairs). Ecological resistance to drought stress is detected by comparing compositional dissimilarity of between-group pairs (control-drought pairs) against within-group pairs (control-control pairs and drought-drought pairs) for each of the droughted weeks (weeks 3–8). Ecological resilience to rewetting is detected by assessing, from before to after rewetting, the change in the difference of compositional dissimilarity between within-group pairs and between-group pairs. Here, the point just before rewetting was week 8 and the points after rewetting were weeks 9–17. A t-test was used to assess the statistical significance of the differences in resistance or resilience between bacterial and fungal communities at each time point for each compartment.To account for the different resolutions of ITS and 16 S, we compared bacterial 16 S OTUs against both fungal ITS, species-level OTUs as well the fungal family level (Supplementary Fig. 1). The results of analyses using either fungal families or OTUs are consistent. Out of 36 comparisons (15 roots, 15 rhizospheres and 6 soils), different family and OTUs results were detected in four instances. In two of these, significances detected by OTUs were not detected by family (root, weeks 4 and 17) and, in the other two cases, significances detected by family were not detected by OTUs (rhizosphere, weeks 7 and 8). (Fig. 1). We report only results that are consistent at both the species and family levels (Fig. 1).In line with our first hypothesis, H1, we found that the resistance to drought stress for fungal mycobiomes was consistently stronger than that for bacterial microbiomes for weeks 5 in root, weeks 4–6 in rhizosphere, and weeks 4 and 6–8 in soil (Fig. 1, Supplementary Fig. 1 and Supplementary Table 2). In support of our second hypothesis, H2, when the stress of pre-flowering drought was relieved by rewetting, we found that the resilience of the bacterial communities was consistently higher than that for the fungi in weeks 9–16 in root, and weeks 11–17 in rhizosphere (Fig. 1, Supplementary Fig. 1 and Supplementary Table 2).Surprisingly, we found that resilience was stronger for fungal than bacterial communities in the first week (week 9) of rewetting in the rhizosphere (Fig. 1, Supplementary Fig. 1 and Supplementary Table 2). This high resilience of fungi may be associated with the quick growth of sorghum roots when rewetted. The rhizosphere zone around these newly formed roots may be quickly colonized by soil fungi, a community that was weakly affected by drought. This result suggests that re-assembly of the rhizosphere microbial community is more complex than previously expected.The finding that fungal community composition in the soil is not shaped by drought prevented us from further detecting resilience (Fig. 1). Note fungal community in early leaves was excluded from analysis due to the high proportion of non-fungal reads in sequencing11.Testing H1 and H2 at all-correlation levelNext, we moved from the comparison of whole communities to correlation among individual bacterial and fungal taxa to test the hypotheses about resistance, H1, and resilience, H2. As noted above, previous research provided the foundation for the stress gradient hypothesis, which predicts an increase in positive associations in stress32,33,34,35,36,37. Further, ecological modeling predicts that negative associations promote stability40. Concerning specific associations, studies of Arabidopsis and associated microbes reported that positive associations are favored within kingdoms, i.e., within bacteria or within fungi, while negative associations predominate between kingdoms38,39. Given these foundations, concerning H1, we expected an increase in the proportion of positive correlation by drought stress that would be strongest for B-B, followed by F-F, and lastly by B-F; for H2 we expected rewetting to cause a decrease in the proportion of positive correlation, again most strongly for B-B, followed by F-F, and lastly by B-F.Overall, at the all-correlation level, we found no consistent support for the differences postulated for bacterial and fungal responses in H1. For example, strong increases in the proportion of positive correlations under drought could be found in all microbial pairings for some compartments (B-B in leaf and root, F-F in rhizosphere and soil, and B-F in root and rhizosphere) (Fig. 2a, Supplementary Figs. 2, 3). Neither did we find consistent support for the differences ascribed to bacteria and fungi in H2 as the strongest decreases in the proportion of positive correlations during rewetting occurred at F-F in rhizosphere and soil, and B-B in leaf and root (Fig. 2b, Supplementary Figs. 2, 3).Fig. 2: Correlations of microbes in drought stress and drought relief.Estimates of combined correlations (row a) show an increase in positive correlations under drought stress across the four compartments (root, black; rhizosphere, blue; soil, red; leaf, green). Data points underlying the lines in the figure are provided in the alternative version in Supplementary Fig. 2. This result is in line with the stress gradient hypothesis which posits that stressful environments favor positive associations because competition will be less intense than in benign environments32,33,36,37. Note that positive trends in combined correlations can arise in two ways. First, from an increase of positive correlations (row b) that exceeds the rise in negative correlations (row c), e.g., Leaf bacterial-bacterial (Bac-Bac) correlations or rhizosphere fungal-fungal (Fun-Fun) correlations in the drought period (Negative correlations in row C values are multiplied by −1 to facilitate comparison). Second, from a decrease in negative correlations that exceeds a decrease in positive correlations, e.g., root bacterial-bacterial correlations or root bacterial-fungal (Bac-Fun) correlations in drought. Combined (a), positive (b) and negative (c) estimates of correlation (Spearman’s rho, ρ) are given for four compartments (root, rhizosphere, soil and leaf), and three types of correlations (Bacterium-Bacterium, Fungus-Fungus, Bacterium-Fungus). T-tests (two sided) were carried out for linear mixed effect modelling that incorporates link type and compartments as random factors. Detailed distribution densities of correlations are presented in Supplementary Fig. 3. Source data are provided as a Source Data file.Full size imageWe found support for the stress gradient hypothesis because drought increased the relative frequency of positive correlations among microbial taxa (Fig. 2a, Supplementary Figs. 2, 3). The increases were due, largely, to B-B correlations in leaf and F-F correlations in the rhizosphere during drought, when the relative frequency of positive correlations was increased (Fig. 2b, Supplementary Figs. 2, 3) and the frequencies of negative correlations were decreased or weakly increased (Fig. 2c, Supplementary Figs. 2, 3). Less obvious increases in the relative frequency of positive correlations (such as B-B in root, F-F in soil, and B-F in root and rhizosphere) occurred where drought reduced both positive and negative correlations, but the losses of negative correlations exceeded those of positive correlations (Fig. 2, Supplementary Figs. 2, 3).In support of the expectation that correlations would be more negative between taxonomic groups than within taxonomic groups, we found that the relative frequency of positive correlations was generally lower for B-F than B-B and F-F correlations (Fig. 2, Supplementary Figs. 2, 3). Moreover, as ecological modeling has indicated that negative associations should promote stability of communities40, we hypothesize that B-F correlations would be more stable than B-B and F-F networks in response to drought stress. However, we found no support for this hypothesis, as B-F correlations (for example in root) did not always show the least response to drought stress (Fig. 2, Supplementary Figs. 2, 3).Testing H1 and H2 at species co-occurrence levelFor our final test of H1 (resistance) and H2 (resilience) we focused on co-occurrence networks based on significant, positive correlations. These networks have been reported to be destabilized for bacteria but not for fungi in mesocosms subject to drought stress19, and shown to be disrupted for bacteria in natural vegetation studied over gradients of increasing aridity41,42. Using these results as guides, for H1 we expected that drought stress should disrupt co-occurrence networks most strongly for B-B, followed by F-F, and lastly by B-F. For H2 we expected that relief of stress by rewetting should strengthen microbial co-occurrence networks most strongly for B-B, followed by F-F, and lastly by B-F.For this test we constructed microbial co-occurrence networks using significant positive pairwise correlations between microbial taxa, B-B, F-F and B-F, and compared the network complexity between fully irrigated control and drought, and between control and rewetting following drought. In general, we found no consistent support for the difference between bacteria and fungi inherent in H1. Rhizosphere was the one compartment where B-B vertices dropped and F-F vertices rose in response to drought, as expected, but this result was offset in root and soil, where vertices dropped in all networks, B-B, F-F and B-F (Figs. 3, 4; Supplementary Figs. 4, 5). Analysis by co-occurrence networks highlighted the differences between plant compartments. In root drought strongly disrupted networks of B-B, B-F and F-F, but in the other three compartments, network disruption was weaker, and networks were even enhanced by drought for F-F in rhizosphere and B-B in leaf (Figs. 3, 4).Fig. 3: Networks of significant positive cross-taxonomic group correlations (bacteria and fungi).a Fungal operational taxonomic units (OTUs) (blue) and bacterial OTUs (black) are graphed as nodes. Significant positive Spearman correlations are graphed as edges (ρ  > 0.6, false discovery rate adjusted P  More