Ecology

Gao, Y., Schofield, O. M. & Leustek, T. Characterization of sulfate assimilation in marine algae focusing on the enzyme 5′-adenylylsulfate reductase. Plant Physiol. 123, 1087–1096 (2000).CAS 
PubMed 
PubMed Central 

Google Scholar 
Huang, C.-W., Walker, M. E., Fedrizzi, B., Gardner, R. C. & Jiranek, V. Hydrogen sulfide and its roles in Saccharomyces cerevisiae in a winemaking context. FEMS Yeast Res. 17, 058 (2017).
Google Scholar 
Kopriva, S., Calderwood, A., Weckopp, S. C. & Koprivova, A. Plant sulfur and big data. Plant Sci. 241, 1–10 (2015).CAS 
PubMed 

Google Scholar 
Shibagaki, N. & Grossman, A. The state of sulfur metabolism in algae: From ecology to genomics. In Sulfur Metabolism in Phototrophic Organisms (eds Hell, C. D. R. et al.) 231–267 (Springer, 2008).
Google Scholar 
Fakhraee, M. & Katsev, S. Organic sulfur was integral to the Archean sulfur cycle. Nat. Commun. 10, 1–8 (2019).CAS 

Google Scholar 
Ho, T. Y. et al. The elemental composition of some marine phytoplankton 1. J. Phycol. 39, 1145–1159 (2003).CAS 

Google Scholar 
Jørgensen, B. B. Unravelling the sulphur cycle of marine sediments. Environ. Microbiol. 21, 3533–3538 (2019).PubMed 

Google Scholar 
El Mahrad, B. et al. Social-environmental analysis for the management of coastal lagoons in North Africa. Front. Environ. Sci. 8, 37 (2020).
Google Scholar 
Srarfi, F. Etude géochimique et état de pollution de la lagune de Bizerte. These de doctorat, Univ. Tunis el Manar 122 (2007).FAO. La Situation Mondiale Des Pêches et de L’aquaculture 2020 (Food & Agriculture Organisation, 2020).
Google Scholar 
Soto, D. & Wurmann, C. The Future of Ocean Governance and Capacity Development 379–384 (Brill Nijhoff, 2019).
Google Scholar 
Ran, W. et al. Storage of starch and lipids in microalgae: Biosynthesis and manipulation by nutrients. Bioresour. Technol. 291, 121894 (2019).CAS 
PubMed 

Google Scholar 
Aikawa, S. et al. Improving polyglucan production in cyanobacteria and microalgae via cultivation design and metabolic engineering. Biotechnol. J. 10, 886–898 (2015).CAS 
PubMed 

Google Scholar 
Klok, A., Lamers, P., Martens, D., Draaisma, R. & Wijffels, R. Edible oils from microalgae: Insights in TAG accumulation. Trends Biotechnol. 32, 521–528 (2014).CAS 
PubMed 

Google Scholar 
Yuan, Y. et al. Enhancing carbohydrate productivity of Chlorella sp. AE10 in semi-continuous cultivation and unraveling the mechanism by flow cytometry. Appl. Biochem. 185, 419–433 (2018).CAS 

Google Scholar 
Rodríguez, M. C., Matulewicz, M. C., Noseda, M., Ducatti, D. & Leonardi, P. I. Agar from Gracilaria gracilis (Gracilariales, Rhodophyta) of the Patagonic coast of Argentina-Content, structure and physical properties. Biores. Technol. 100, 1435–1441 (2009).
Google Scholar 
Lee, W.-K. et al. Factors affecting yield and gelling properties of agar. J. Appl. Phycol. 29, 1527–1540 (2017).
Google Scholar 
Fethi, M. & Ghedifa, A. B. Optimum ranges of combined abiotic factor for Gracilaria gracilis aquaculture. J. Appl. Phycol. 31, 3025–3040 (2019).
Google Scholar 
Friedlander, M. Inorganic nutrition in pond cultivated Gracilaria conferta (Rhodophyta): Nitrogen, phosphate and sulfate. J. Appl. Phycol. 13, 279–286 (2001).CAS 

Google Scholar 
Lee, W.-K., Namasivayam, P. & Ho, C.-L. Effects of sulfate starvation on agar polysaccharides of Gracilaria species (Gracilariaceae, Rhodophyta) from Morib, Malaysia. J. Appl. Phycol. 26, 1791–1799 (2014).CAS 

Google Scholar 
Carfagna, S. et al. Impact of sulfur starvation in autotrophic and heterotrophic cultures of the extremophilic microalga Galdieria phlegrea (Cyanidiophyceae). Plant Cell Physiol. 57, 1890–1898 (2016).CAS 
PubMed 

Google Scholar 
Collén, P. N., Camitz, A., Hancock, R. D., Viola, R. & Pedersén, M. Effect of nutrient deprivation and resupply on metabolites and enzymes related to carbon allocation in gracilaria tenuistipitata (rhodophyta) 1. J. Phycol. 40, 305–314 (2004).
Google Scholar 
Collier, J. L. & Grossman, A. A small polypeptide triggers complete degradation of light-harvesting phycobiliproteins in nutrient-deprived cyanobacteria. EMBO J. 13, 1039–1047 (1994).CAS 
PubMed 
PubMed Central 

Google Scholar 
Richaud, C., Zabulon, G., Joder, A. & Thomas, J.-C. Nitrogen or sulfur starvation differentially affects phycobilisome degradation and expression of the nblA gene in Synechocystis strain PCC 6803. J. Bacteriol. 183, 2989–2994 (2001).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lloyd, A. G., Dodgson, K. S. & Rose, F. A. Infrared studies on sulphate esters I. Polysaccharide sulphates. Biochim. Biophys. Acta 46, 108–115 (1961).CAS 
PubMed 

Google Scholar 
Kawachi, M. & Noël, M.-H. Sterilization and sterile technique. In Algal Culturing Techniques (ed. Anderson, R. A.) 65–81 (Academic Press, 2005).
Google Scholar 
Harrison, P. J. & Berges, J. A. Marine culture media. In Algal Culturing Techniques (ed. Anderson, R. A.) 21–34 (Academic Press, 2005).
Google Scholar 
Guiry, M. & Cunningham, E. Photoperiodic and temperature responses in the reproduction of north-eastern Atlantic Gigartina acicularis (Rhodophyta: Gigartinales). Phycologia 23, 357–367 (1984).
Google Scholar 
Kolmert, Å., Wikström, P. & Hallberg, K. B. A fast and simple turbidimetric method for the determination of sulfate in sulfate-reducing bacterial cultures. J. Microbiol. Methods 41, 179–184 (2000).CAS 
PubMed 

Google Scholar 
Destombe, C., Godin, J., Nocher, M., Richerd, S. & Valero, M. In Fourteenth International Seaweed Symposium (eds Brown, M. T. & Lahaye, M.) 131–137 (Springer, 1993).
Google Scholar 
Rueness, J. & Tananger, T. In Eleventh International Seaweed Symposium (eds Bird, C. J. & Ragan, M. A.) 303–307 (Springer, 1984).
Google Scholar 
Shea, R. & Chopin, T. Effects of germanium dioxide, an inhibitor of diatom growth, on the microscopic laboratory cultivation stage of the kelp, Laminaria saccharina. J. Appl. Phycol. 19, 27–32 (2007).CAS 

Google Scholar 
Dawes, C., Orduna-Rojas, J. & Robledo, D. Response of the tropical red seaweed Gracilaria cornea to temperature, salinity and irradiance. J. Appl. Phycol. 10, 419–425 (1998).
Google Scholar 
Yaphe, W. & Arsenault, G. Improved resorcinol reagent for the determination of fructose, and of 3, 6-anhydrogalactose in polysaccharides. Anal. Biochem. 13, 143–148 (1965).CAS 

Google Scholar 
Mensi, F., Ksouri, J., Seale, E., Romdhane, M. S. & Fleurence, J. A statistical approach for optimization of R-phycoerythrin extraction from the red algae Gracilaria verrucosa by enzymatic hydrolysis using central composite design and desirability function. J. Appl. Phycol. 24, 915–926 (2012).CAS 

Google Scholar 
Dubois, M., Gilles, K. A., Hamilton, J. K., Rebers, P. T. & Smith, F. Colorimetric method for determination of sugars and related substances. Anal. Chem. 28, 350–356 (1956).CAS 

Google Scholar 
Bradford, M. M. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal. Biochem. 72, 248–254 (1976).CAS 
PubMed 

Google Scholar 
Sörbo, B. Sulfate: Turbidimetric and nephelometric methods. Methods Enzymol. 143, 3–6 (1987).PubMed 

Google Scholar 
Redmond, S., Green, L., Yarish, C., Kim, J. & Neefus, C. New England Seaweed Culture Handbook (University of Connecticut Sea Garent, 2014).
Google Scholar 
Kakita, H. & Kamishima, H. Effects of environmental factors and metal ions on growth of the red alga Gracilaria chorda Holmes (Gracilariales, Rhodophyta). J. Appl. Phycol. 18, 469–474 (2006).CAS 

Google Scholar 
Berges, J. A., Franklin, D. J. & Harrison, P. J. Evolution of an artificial seawater medium: Improvements in enriched seawater, artificial water over the last two decades. J. Phycol. 37, 1138–1145 (2001).
Google Scholar 
Shpigun, L. K., Kolotyrkina, I. Y. & Zolotov, Y. A. Experience with flow-injection analysis in marine chemical research. Anal. Chim. Acta 261, 307–314 (1992).CAS 

Google Scholar 
Cosano, J., de Castro, M. & Valcarcel, M. Flow injection analysis of water. Part 1: Automatic preconcentration determination of sulphate, ammonia and iron (II)/iron (III). J. Autom. Chem. 15, 141–146 (1993).CAS 

Google Scholar 
Van Staden, J. & Taljaard, R. Determination of sulphate in natural waters and industrial effluents by sequential injection analysis. Anal. Chim. Acta 331, 271–280 (1996).
Google Scholar 
Petersen, S. P. & Ahring, B. K. Analysis of sulfate in sewage sludge using ion chromatographic techniques. J. Microbiol. Methods 12, 225–230 (1990).CAS 

Google Scholar 
Rand, M., Greenberg, A., Taras, K. & Franson, M. Standard Methods for the Examination of Water and Waste Water (American Public Health Association, 1975).
Google Scholar 
Wanner, G., Henkelmann, G., Schmidt, A. & Köst, H.-P. Nitrogen and sulfur starvation of the cyanobacterium Synechococcus 6301 an ultrastructural, morphometrical, and biochemical comparison. Zeitschrift Naturforschung C 41, 741–750 (1986).CAS 

Google Scholar 
Molloy, F. & Bolton, J. The effect of season and depth on the growth of Gracilaria gracilis at Lüderitz, Namibia. Bot. Mar. 39, 407–414 (1996).
Google Scholar 
Mensi, F., Nasraoui, S., Bouguerra, S., Ben Ghedifa, A. & Chalghaf, M. Effect of lagoon and sea water depth on Gracilaria gracilis growth and biochemical composition in the northeast of Tunisia. Sci. Rep. 10, 1–12 (2020).
Google Scholar 
Mensi, F., Ksouri, J., Hammami, W. & Romdhane, M. État des connaissances et perspectives de recherches sur la culture de Gracilariales (Gracilaria et Gracilariopsis): Application a la lagune de Bizerte. Bull. Inst. Natn. Scien. Tech. Mer Salammbô 41, 101–119 (2014).
Google Scholar 
Sugimoto, K., Sato, N. & Tsuzuki, M. Utilization of a chloroplast membrane sulfolipid as a major internal sulfur source for protein synthesis in the early phase of sulfur starvation in Chlamydomonas reinhardtii. FEBS Lett. 581, 4519–4522 (2007).CAS 
PubMed 

Google Scholar 
Cakmak, T. et al. Nitrogen and sulfur deprivation differentiate lipid accumulation targets of Chlamydomonas reinhardtii. Bioengineered 3, 343–346 (2012).PubMed 
PubMed Central 

Google Scholar 
Ostaszewska-Bugajska, M., Rychter, A. M. & Juszczuk, I. M. Antioxidative and proteolytic systems protect mitochondria from oxidative damage in S-deficient Arabidopsis thaliana. J. Plant Physiol. 186, 25–38 (2015).PubMed 

Google Scholar 
Zhang, L. et al. Sulfur deficiency-induced glucosinolate catabolism attributed to two β-glucosidases, BGLU28 and BGLU30, is required for plant growth maintenance under sulfur deficiency. Plant Cell Physiol. 61, 803–813 (2020).CAS 
PubMed 

Google Scholar 
Takahashi, H., Kopriva, S., Giordano, M., Saito, K. & Hell, R. Sulfur assimilation in photosynthetic organisms: Molecular functions and regulations of transporters and assimilatory enzymes. Annu. Rev. Plant biol. 62, 157–184 (2011).CAS 
PubMed 

Google Scholar 
Butterfield, N. J. Bangiomorpha pubescens n. gen., n. sp.: Implications for the evolution of sex, multicellularity, and the Mesoproterozoic/Neoproterozoic radiation of eukaryotes. Paleobiology 26, 386–404 (2000).
Google Scholar 
Collier, J. L. & Grossman, A. R. Chlorosis induced by nutrient deprivation in Synechococcus sp. strain PCC 7942: Not all bleaching is the same. J. Bacteriol. 174, 4718–4726. https://doi.org/10.1128/jb.174.14.4718-4726.1992 (1992).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kaur, H. et al. Cys-Gly specific dipeptidase Dug1p from S. cerevisiae binds promiscuously to di-, tri-, and tetra-peptides: Peptide-protein interaction, homology modeling, and activity studies reveal a latent promiscuity in substrate recognition. Biochimie 93, 175–186. https://doi.org/10.1016/j.biochi.2010.09.008 (2011).CAS 
Article 
PubMed 

Google Scholar 
Said, R. B. et al. Effects of depth and initial fragment weights of Gracilaria gracilis on the growth, agar yield, quality, and biochemical composition. J. Appl. Phycol. 30, 2499–2512 (2018).
Google Scholar 
Bird, K. T. Agar production and quality from Gracilaria sp. strain G—16: Effects of environmental factors. Bot. Mar. 31, 33–38 (1988).
Google Scholar 
Cote, G. & Hanisak, M. Production and properties of native agars from Gracilaria tikvahiae and other red algae. Bot. Mar. 29, 359–366 (1986).CAS 

Google Scholar 
Lahaye, M. & Yaphe, W. Effects of seasons on the chemical structure and gel strength of Gracilaria pseudoverrucosa agar (Gracilariaceae, Rhodophyta). Carbohydr. Polym. 8, 285–301 (1988).CAS 

Google Scholar 
Yaphe, W. Eleventh International Seaweed Symposium 171–174 (Springer, 1984).
Google Scholar 
Duckworth, M., Hong, K. & Yaphe, W. The agar polysaccharides of Gracilaria species. Carbohydr. Res. 18, 1–9 (1971).CAS 

Google Scholar 
Rotem, A., Roth-Bejerano, N. & Arad, S. Effect of controlled environmental conditions on starch and agar contents of Gracilaria sp. (Rhodophyceae) 1. J. Phycol. 22, 117–121 (1986).CAS 

Google Scholar 
Arad, S. M., Lerental, Y. B. & Dubinsky, O. Effect of nitrate and sulfate starvation on polysaccharide formation in Rhodella reticulata. Bioresour. Technol. 42, 141–148 (1992).CAS 

Google Scholar  More

Carpenter, K. E. et al. One-third of reef-building corals face elevated extinction risk from climate change and local impacts. Science 321, 560–563 (2008).CAS 
PubMed 
Article 

Google Scholar 
Pollock, F. J. et al. Coral-associated bacteria demonstrate phylosymbiosis and cophylogeny. Nat. Commun. 9, 4921–4932 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Vega Thurber, R. et al. Metagenomic analysis of stressed coral holobionts. Environ. Microbiol. 11, 2148–2163 (2009).PubMed 
Article 
CAS 

Google Scholar 
Rosenberg, E. & Zilber-Rosenberg, I. Microbes drive evolution of animals and plants: the hologenome concept. mBio 7, e01395 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
van Oppen, M. J. H. & Blackall, L. L. Coral microbiome dynamics, functions and design in a changing world. Nat. Rev. Microbiol. 17, 557–567 (2019).PubMed 
Article 
CAS 

Google Scholar 
Reshef, L., Koren, O., Loya, Y., Zilber-Rosenberg, I. & Rosenberg, E. The coral probiotic hypothesis. Environ. Microbiol. 8, 2068–2073 (2006).CAS 
PubMed 
Article 

Google Scholar 
Ainsworth, T. D., Thurber, R. V. & Gates, R. D. The future of coral reefs: a microbial perspective. Trends Ecol. Evol. 25, 233–240 (2010).PubMed 
Article 

Google Scholar 
Lee, S. M. et al. Bacterial colonization factors control specificity and stability of the gut microbiota. Nature 501, 426–429 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Freter, R. The fatal enteric cholera infection in the guinea pig, achieved by inhibition of normal enteric flora. J. Infect. Dis. 97, 57–65 (1955).CAS 
PubMed 
Article 

Google Scholar 
Corr, S. C. et al. Bacteriocin production as a mechanism for the antiinfective activity of Lactobacillus salivarius UCC118. Proc. Natl. Acad. Sci. USA 104, 7617–7621 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kamada, N. et al. Regulated virulence controls the ability of a pathogen to compete with the gut microbiota. Science 336, 1325–1329 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Khosravi, A. et al. Gut microbiota promote hematopoiesis to control bacterial infection. Cell Host Microbe 15, 374–381 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Li, J., Kuang, W. Q., Long, L. J. & Zhang, S. Production of quorum-sensing signals by bacteria in the coral mucus layer. Coral Reefs 36, 1235–1241 (2017).Article 

Google Scholar 
Alagely, A., Krediet, C. J., Ritchie, K. B. & Teplitski, M. Signaling-mediated cross-talk modulates swarming and biofilm formation in a coral pathogen Serratia marcescens. ISME J. 5, 1609–1620 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Krediet, C. J., Ritchie, K. B., Alagely, A. & Teplitski, M. Members of native coral microbiota inhibit glycosidases and thwart colonization of coral mucus by an opportunistic pathogen. ISME J. 7, 980–990 (2013).CAS 
PubMed 
Article 

Google Scholar 
Thompson, F. L., Hoste, B., Thompson, C. C., Huys, G. & Swings, G. The coral bleaching Vibrio shiloi Kushmaro et al. 2001 is a later synonym of Vibrio mediterranei Pujalte and Garay 1986. Syst. Appl. Microbiol. 24, 516–519 (2001).CAS 
PubMed 
Article 

Google Scholar 
Santoro, E. P. et al. Coral microbiome manipulation elicits metabolic and genetic restructuring to mitigate heat stress and evade mortality. Sci. Adv. 7, eabg3088 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tang, K. H. et al. Antagonism between coral pathogen Vibrio coralliilyticus and other bacteria in the gastric cavity of scleractinian coral Galaxea fascicularis. Sci. China-Earth Sci. 63, 157–166 (2020).CAS 
Article 

Google Scholar 
Zhou, Y. Q. et al. Identification of bacteria-derived urease in the coral gastric cavity. Sci. China-Earth Sci. 63, 1553–1563 (2020).CAS 
Article 

Google Scholar 
Chen, B. et al. Microbiome community and complexity indicate environmental gradient acclimatisation and potential microbial interaction of endemic coral holobionts in the South China Sea. Sci. Total Environ. 765, 142690 (2021).CAS 
PubMed 
Article 

Google Scholar 
Tout, J. et al. Increased seawater temperature increases the abundance and alters the structure of natural Vibrio populations associated with the coral Pocillopora damicornis. Front. Microbiol. 6, 432 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Savary, R. et al. Fast and pervasive transcriptomic resilience and acclimation of extremely heat-tolerant coral holobionts from the northern Red Sea. Proc. Natl. Acad. Sci. USA 118, e2023298118 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Vezzulli, L. et al. Vibrio infections triggering mass mortality events in a warming Mediterranean Sea. Environ. Microbiol. 12, 2007–2019 (2010).CAS 
PubMed 
Article 

Google Scholar 
Rosenberg, E. & Falkovitz, L. The Vibrio shiloi/Oculina patagonica model system of coral bleaching. Annu. Rev. Microbiol. 58, 143–159 (2004).CAS 
PubMed 
Article 

Google Scholar 
Gibbin, E. et al. Vibrio coralliilyticus infection triggers a behavioural response and perturbs nutritional exchange and tissue integrity in a symbiotic coral. ISME J. 13, 989–1003 (2019).CAS 
PubMed 
Article 

Google Scholar 
Kimes, N. E. et al. Temperature regulation of virulence factors in the pathogen Vibrio coralliilyticus. ISME J. 6, 835–846 (2012).CAS 
PubMed 
Article 

Google Scholar 
Banin, E., Vassilakos, D., Orr, E., Martinez, R. J. & Rosenberg, E. Superoxide dismutase is a virulence factor produced by the coral bleaching pathogen Vibrio shiloi. Curr. Microbiol. 46, 418–422 (2003).CAS 
PubMed 
Article 

Google Scholar 
Meron, D. et al. Role of flagella in virulence of the coral pathogen Vibrio coralliilyticus. Appl. Environ. Microbiol. 75, 5704–5707 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rubio-Portillo, E. et al. Virulence as a side effect of interspecies interaction in Vibrio coral pathogens. mBio 11, e00201-20 (2020).Rubio-Portillo, E., Yarza, P., Penalver, C., Ramos-Espla, A. A. & Anton, J. New insights into Oculina patagonica coral diseases and their associated Vibrio spp. communities. ISME J. 8, 1794–1807 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Bourne, D. G. et al. Microbial disease and the coral holobiont. Trends Microbiol. 17, 554–562 (2009).CAS 
PubMed 
Article 

Google Scholar 
Ben-Haim, Y., Zicherman-Keren, M. & Rosenberg, E. Temperature-regulated bleaching and lysis of the coral Pocillopora damicornis by the novel pathogen Vibrio coralliilyticus. Appl. Environ. Microbiol. 69, 4236–4242 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gavish, A. R., Shapiro, O. H., Kramarsky-Winter, E. & Vardi, A. Microscale tracking of coral–vibrio interactions. ISME Commun. 1, 18 (2021).Shapiro, O. H. et al. Vortical ciliary flows actively enhance mass transport in reef corals. Proc. Natl. Acad. Sci. USA 111, 13391–13396 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Shapiro, O. H., Kramarsky-Winter, E., Gavish, A. R., Stocker, R. & Vardi, A. A coral-on-a-chip microfluidic platform enabling live-imaging microscopy of reef-building corals. Nat. Commun. 7, 10860 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chen, D. D. et al. Identification and characterization of microsatellite markers for scleractinian coral Galaxea fascicularis and its symbiotic zooxanthellae. Conservation. Genet. Resour. 5, 741–743 (2013).Article 

Google Scholar 
Parks, D. H. et al. A complete domain-to-species taxonomy for bacteria and archaea. Nat. Biotechnol. 38, 1079–1086 (2020).CAS 
PubMed 
Article 

Google Scholar 
Liu, X. et al. Symbiosis of a P2-family phage and deep-sea Shewanella putrefaciens. Environ. Microbiol. 21, 4212–4232 (2019).CAS 
PubMed 
Article 

Google Scholar 
Wang, P. et al. Eliminating mcr-1-harbouring plasmids in clinical isolates using the CRISPR/Cas9 system. J. Antimicrob. Chemother. 74, 2559–2565 (2019).CAS 
PubMed 
Article 

Google Scholar 
Zeng, Z. et al. Cold adaptation regulated by cryptic prophage excision in Shewanella oneidensis. ISME J. 10, 2787–2800 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Wang, X. et al. Cryptic prophages help bacteria cope with adverse environments. Nat. Commun. 1, 147 (2010).Bardwell, J. C., McGovern, K. & Beckwith, J. Identification of a protein required for disulfide bond formation in vivo. Cell 67, 581–589 (1991).CAS 
PubMed 
Article 

Google Scholar 
Wang, X., Kim, Y. & Wood, T. K. Control and benefits of CP4-57 prophage excision in Escherichia coli biofilms. ISME J. 3, 1164–1179 (2009).CAS 
PubMed 
Article 

Google Scholar 
Wood, T. K., Gonzalez Barrios, A. F., Herzberg, M. & Lee, J. Motility influences biofilm architecture in Escherichia coli. Appl. Microbiol. Biotechnol. 72, 361–367 (2006).CAS 
PubMed 
Article 

Google Scholar 
Song, S., Guo, Y., Kim, J. S., Wang, X. & Wood, T. K. Phages mediate bacterial self-recognition. Cell Rep. 27, 737–749 (2019).CAS 
PubMed 
Article 

Google Scholar 
Krediet, C. J., Carpinone, E. M., Ritchie, K. B. & Teplitski, M. Characterization of the gacA-dependent surface and coral mucus colonization by an opportunistic coral pathogen Serratia marcescens PDL100. FEMS Microbiol. Ecol. 84, 290–301 (2013).CAS 
PubMed 
Article 

Google Scholar 
Guo, Y., Lin, J. & Wang, X. Rapid detection of temperate bacteriophage using a simple motility assay. Environ. Microbiol. Rep. 13, 728–734 (2021).CAS 
PubMed 
Article 

Google Scholar 
Tang, K. et al. Prophage Tracer: precisely tracing prophages in prokaryotic genomes using overlapping split-read alignment. Nucleic Acids Res. 49, e128 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ding, J. Y., Shiu, J. H., Chen, W. M., Chiang, Y. R. & Tang, S. L. Genomic insight into the host–endosymbiont relationship of Endozoicomonas montiporae CL-33(T) with its coral host. Front. Microbiol. 7, 251 (2016).PubMed 
PubMed Central 

Google Scholar 
Yang, C. S. et al. Endozoicomonas montiporae sp. nov., isolated from the encrusting pore coral Montipora aequituberculata. Int. J. Syst. Evol. Microbiol. 60, 1158–1162 (2010).CAS 
PubMed 
Article 

Google Scholar 
Schreiber, L., Kjeldsen, K. U., Obst, M., Funch, P. & Schramm, A. Description of Endozoicomonas ascidiicola sp nov., isolated from Scandinavian ascidians. Syst. Appl. Microbiol. 39, 313–318 (2016).CAS 
PubMed 
Article 

Google Scholar 
Altschul, S. F. et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lu, S. N. et al. CDD/SPARCLE: the conserved domain database in 2020. Nucleic Acids Res. 48, D265–D268 (2020).CAS 
PubMed 
Article 

Google Scholar 
Mai-Prochnow, A. et al. Hydrogen peroxide linked to lysine oxidase activity facilitates biofilm differentiation and dispersal in several Gram-negative bacteria. J. Bacteriol. 190, 5493–5501 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Campillo-Brocal, J. C., Lucas-Elio, P. & Sanchez-Amat, A. Identification in Marinomonas mediterranea of a novel quinoprotein with glycine oxidase activity. MicrobiologyOpen 2, 684–694 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chacon-Verdu, M. D., Gomez, D., Solano, F., Lucas-Elio, P. & Sanchez-Amat, A. LodB is required for the recombinant synthesis of the quinoprotein l-lysine-epsilon-oxidase from Marinomonas mediterranea. Appl. Microbiol. Biotechnol. 98, 2981–2989 (2014).CAS 
PubMed 
Article 

Google Scholar 
Gomez, D., Lucas-Elio, P., Solano, F. & Sanchez-Amat, A. Both genes in the Marinomonas mediterranea lodAB operon are required for the expression of the antimicrobial protein lysine oxidase. Mol. Microbiol. 75, 462–473 (2010).CAS 
PubMed 
Article 

Google Scholar 
Piewngam, P. et al. Pathogen elimination by probiotic Bacillus via signalling interference. Nature 562, 532–537 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zipperer, A. et al. Human commensals producing a novel antibiotic impair pathogen colonization. Nature 535, 511–516 (2016).CAS 
PubMed 
Article 

Google Scholar 
Selva, L. et al. Killing niche competitors by remote-control bacteriophage induction. Proc. Natl. Acad. Sci. USA 106, 1234–1238 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Regev-Yochay, G., Trzcinski, K., Thompson, C. M., Malley, R. & Lipsitch, M. Interference between Streptococcus pneumoniae and Staphylococcus aureus: in vitro hydrogen peroxide-mediated killing by Streptococcus pneumoniae. J. Bacteriol. 188, 4996–5001 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Paul, J. H. Prophages in marine bacteria: dangerous molecular time bombs or the key to survival in the seas? ISME J. 2, 579–589 (2008).CAS 
PubMed 
Article 

Google Scholar 
Frazao, N., Sousa, A., Lassig, M. & Gordo, I. Horizontal gene transfer overrides mutation in Escherichia coli colonizing the mammalian gut. Proc. Natl. Acad. Sci. USA 116, 17906–17915 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Yu, M. et al. Purification and characterization of antibacterial compounds of Pseudoalteromonas flavipulchra JG1. Microbiology-SGM 158, 835–842 (2012).CAS 
Article 

Google Scholar 
James, S. G., Holmstrom, C. & Kjelleberg, S. Purification and characterization of a novel antibacterial protein from the marine bacterium D2. Appl. Environ. Microbiol. 62, 2783–2788 (1996).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lucas-Elio, P., Gomez, D., Solano, F. & Sanchez-Amat, A. The antimicrobial activity of marinocine, synthesized by Marinomonas mediterranea, is due to hydrogen peroxide generated by its lysine oxidase activity. J. Bacteriol. 188, 2493–2501 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Imlay, J. A. & Linn, S. Mutagenesis and stress responses induced in Escherichia coli by hydrogen peroxide. J. Bacteriol. 169, 2967–2976 (1987).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Los, J. M., Los, M., Wegrzyn, G. & Wegrzyn, A. Differential efficiency of induction of various lambdoid prophages responsible for production of Shiga toxins in response to different induction agents. Microb. Pathog. 47, 289–298 (2009).CAS 
PubMed 
Article 

Google Scholar 
Knowles, B. et al. Lytic to temperate switching of viral communities. Nature 531, 466–470 (2016).CAS 
PubMed 
Article 

Google Scholar 
Howard-Varona, C., Hargreaves, K. R., Abedon, S. T. & Sullivan, M. B. Lysogeny in nature: mechanisms, impact and ecology of temperate phages. ISME J. 11, 1511–1520 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Arkin, A. P. et al. KBase: The United States Department of Energy Systems Biology Knowledgebase. Nat. Biotechnol. 36, 566–569 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Luo, P., He, X. Y., Liu, Q. T. & Hu, C. Q. Developing universal genetic tools for rapid and efficient deletion mutation in Vibrio species based on suicide T-vectors carrying a novel counterselectable marker, vmi480. PLoS ONE 10, e0144465 (2015).Wang, P. et al. Development of an efficient conjugation-based genetic manipulation system for Pseudoalteromonas. Microb. Cell Fact. 14, 11 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Bertani, L. E. & Bertani, G. Preparation and characterization of temperate, non-inducible bacteriophage P2 (host: Escherichia coli). J. Gen. Virol. 6, 201–212 (1970).CAS 
PubMed 
Article 

Google Scholar 
Garneau, J. R., Depardieu, F., Fortier, L. C., Bikard, D. & Monot, M. PhageTerm: a tool for fast and accurate determination of phage termini and packaging mechanism using next-generation sequencing data. Sci Rep. 7, 8292 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Pratt, L. A. & Kolter, R. Genetic analysis of Escherichia coli biofilm formation: roles of flagella, motility, chemotaxis and type I pili. Mol. Microbiol. 30, 285–293 (1998).CAS 
PubMed 
Article 

Google Scholar 
Page, A. J. et al. Roary: rapid large-scale prokaryote pan genome analysis. Bioinformatics 31, 3691–3693 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Brynildsrud, O., Bohlin, J., Scheffer, L. & Eldholm, V. Rapid scoring of genes in microbial pan-genome-wide association studies with Scoary. Genome Biol. 17, 238 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Yilmaz, P. et al. The SILVA and ‘All-species Living Tree Project (LTP)’ taxonomic frameworks. Nucleic Acids Res. 42, D643–D648 (2014).CAS 
PubMed 
Article 

Google Scholar 
Chong, J., Liu, P., Zhou, G. & Xia, J. Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data. Nat. Protoc. 15, 799–821 (2020).CAS 
PubMed 
Article 

Google Scholar 
Nagpal, S., Singh, R., Yadav, D. & Mande, S. S. MetagenoNets: comprehensive inference and meta-insights for microbial correlation networks. Nucleic Acids Res. 48, W572–W579 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

Study siteThe study was conducted at the Center for Applied Research on the Environment and Sustainability (CARES) at The American University in Cairo, New Cairo, Egypt (30°01′11.7″N 31°29′59.8″E) from 12/Nov/2019 until 31st/March/2020. The experiment was carried out in a greenhouse-controlled environment with temperatures ranging from 18 to 23 °C and relative humidity between 60 and 70% during the growing period.Experimental designThe proposed design starts by treating brackish water using RO membrane separation technology, powered by an on-grid 10 kW photovoltaic solar panel as shown in Fig. 1. The permeate (freshwater) from the RO facility is directed to the aquaculture units of capacity of 1 m3, where the fish effluents are used as irrigation water and as the sole source of fertilizers for the crops.Figure 1Schematic Integrated model design. T1 Deep water culture system without sand, T2 Sandponics system with sand from October, T3 Sandponics system with sand from Beni suef, T4 Sandponics system with sand from Fayoum.Full size imageThe study followed a completely randomized design with four variants, i.e., an aquaponic deep-water culture system (T1) and three sandponics systems (T2–T4). The three sandponics systems were established with different sand collected from different sand locations in Egypt during the period between September and October 2019.Initially, an exploratory field trip was set to six different locations in Egypt to collect sand samples for lab analysis aimed at sourcing the most suitable sand for the system under study with regards to both the physical and chemical parameters. These areas include Ismailia Governorate; 30°34′55.2″N 31°50′08.1″E, 6th October governorate; 29°54′49.8″N 31°05′51.5″E, Benu Suef governorate; 28°53′18.4″N 30°45′12.9″E, Al-Minya governorate; 28.725799, 30.630305, and two sites from Fayoum governorate; 29°05′07.4″N 30°49′39.9″E.From the six locations in Egypt, preliminary sand analysis was carried out, and sand samples were also collected for both physical and chemical lab analysis at the Soil and Water Lab at the Agricultural Research Center in Dokki, Egypt. Following a thorough technical, field, mechanical, and lab chemical evaluation of the six sand samples from six locations, three sand locations/types were selected for experimentation that seemed fit and suitable for the current study. The criteria parameters for the shortlisting of sand included water retention potential of the sand by the percolation process, testing the carbonates level in the soil, the turbidity of the sand, porosity percentage and drainage potential of the sand. The three locations included 6th October (T2), Benu Suef (T3), and Fayoum site 2 (T4). In the second week of November 2019, ten cubic meter tracks of sand from the three above locations were set to collect sand from these areas to the research facility at CARES where the experiment was carried out.The study was carried out with two systems/setups, i.e., an aquaponic Deep Water Culture (DWC) and SP systems. The DWC model comprises a 1 m3 fish tank, a settlement tank, a mechanical filter, a biological filter, three grow beds, and a drainage tank. This system being the most practiced aquaponics technique was considered as the control. Fish effluent water flowed from the fish tank to the settlement tank to filter big solid wastes through the mechanical filter to remove the smaller solid wastes and the biological filter for the nitrification process. Then filtered water continues to the grow beds, where overflow drains into the drainage tank and back to the fish tank in a closed system.On the other hand, the variable in the three IAVS systems is the sand source. This system comprises three independent set-ups: a 1 m3 fish tank, three grow beds, and a drainage tank. Fish effluents flowed from the fish tank directly to the sand grow beds where water was supplied through irrigation drip lines using diaghram emitters connected with valves to ensure uniformity of water application to each grow bed.All the fish tanks were installed with the same fish stock size of 30 Nile tilapia (Oreochromis niloticus) from an existing fish stock at the research center with an average initial weight of 244 g and the same amount of water, initially 850L per tank. The fish was sourced from an already existing aquaponics system at the research center to avoid any transportation stress effects and related shocks on the small fish, leading to a lot of mortality cases. The fish were fed 3–4 times daily with commercial pellets containing 30% proteins, 5% crude lipid, 6% crude fiber, 13% Ash, and 9% moisture content supplied by Skretting Egypt. The feeding pattern and frequency were according to the fish body biomass percentage of 2–3% depending on the growth stage and upon reaching satiation.DesalinationThe experiment was entirely run with desalinated water produced from a desalination facility at the center. The desalination technology used was Reverse Osmosis (RO); in batch mode; using a Sea Water Pump with Energy Recovery Unit (model Danfoss-APP1.0/APM1.2). The RO membrane used is Hydraunatic SWC5-4040, from Lenntech company with an average salt rejection of 99.7%. Three modules were connected in a series arrangement (3 Pressure Vessels each equipped with a single module). Synthesized brackish water was prepared by dissolving industrial grade sodium chloride (sea salt) from El-Arish Governorate, Egypt. The salt chemical properties are presented in Table 1. Feedwater salinity was 10 mg/L, with an equivalent osmotic pressure equal to 8.61 bars. The osmotic pressure was calculated using Van’t Hoff relation. Permeate Total Dissolved Solids (TDS) was 192 mg/L, and brine TDS was 13.1 g/L as shown in Table 2.Table 1 Chemical properties of the used salt.Full size tableTable 2 Chemical properties of water samples used.Full size tableThe average pure water flux is 9.5 LMH and was calculated by dividing the permeate volume by the product of membrane surface area and time. Each batch run produced around 4 m3 of permeate, which was enough to irrigate the designated plant beds. The estimated average permeate recovery for the RO process is 22% and salt rejection exceeded 98.7%. The differential pressure between membrane inlet and outlet was equal to 1 bar, where membrane inlet pressure was 16 bars, and the outlet was 15 bars. The RO process operated at an average transmembrane pressure equal to 16 bars and an average permeate and brine flow rates equivalent to 3.49 and 12.41 Lpm, respectively. All experiment runs were performed at 25 °C.Plant materials and cultivation practiceSwiss chard bright lights (Beta vulgaris subsp. cicia) seeds were imported from Seed kingdom seed company in the USA. Seeds were sown in ¼ inch holes in a seed starting mix containing perlite and vermiculite and irrigated with a hand mist sprayer daily to keep the growing media always moist. Sowing was done on the 12th of November 2019, and seedlings were transplanted when they were 40 days old. Seedlings were transplanted into raised grow beds made of fiberglass material measuring 1.8 × 1.2 × 0.6 m for each of the four systems. The beds were raised off the ground by 0.5 m to allow drainage water from the bed to be collected and circulated back to the fish tank. Each bed was constructed with a drainage pipe at the bottom covered with a mesh net to prevent water blockage by the sand. Also, a 5 cm layer of small gravel was uniformly laid at the bottom of the beds to facilitate drainage, followed by sand with a height of 50 cm.In the IAVS systems, plants were irrigated using manually punched diaphragm emitters, and the irrigation flow rate was controlled using small plastic valves at the start of every irrigation tube. Emitters were installed in drip tubing at a 30 cm distance as well the tubing lines were also placed 30 cm between each other. Seedlings were transplanted 5 cm away from the emitters at 30 cm between rows and 30 cm within the row. Since the water was pumped with submersible pumps to the grow beds, regulatory pressure valves were installed in between the pump and the main irrigation line, and then water flows through the emitters into the row furrows. Water would then saturate in the sand and eventually drain at the bottom into drainage tanks and pumped back to the fish tanks.To maintain the water quality, two full cycles of water recirculation were run every day. Every irrigation cycle recirculated 25% of the fish tank, and complete drainage was allowed for a maximum of two hours. Plants were harvested upon reaching maturity for three cuts, except with the T1, which could not grow back after the second cut. Plants took 52 days from transplanting to reach the first cut, 20 days from cut 1 to cut 2, and as well 23 days from cut 2 to reach cut 3. Measurable crop parameters included plant height at harvesting/cutting, leaf area, number of leaves per plant, chlorophyll content, fresh weight per plant, and nutrient composition. Since the focus of SP is on the crops, fish were only measured to monitor their relative growth in terms of weight gained at harvesting/cutting time.Measurement of crop parametersPlants were cut 5 cm above the soil surface, and agronomical trait measurements from a representative sample of 12 plants per replicate were taken as follows.Plant heights were taken using a foot ruler and averages determined. Leaf number was obtained as the number of leaves counted per plant and averages determined. Leaf area was calculated according to the equation reported by Yeshitila and Taye16.$${text{Leaf}} , {text{ Area }}left( {{text{cm}}^{{2}} } right) = , – {422}.{973} + { 22}.{752}0{text{L }}left( {{text{cm}}} right) , + { 8}.{text{31W }}left( {{text{cm}}} right)$$where L and W represent the leaf length and Leaf width respectively, − 422.973 is a constant relating to the shape of the leaf of Swiss chard developed by the author under citation.Chlorophyll content was measured using MC-100 chlorophyll meter from Apogee Instruments, Inc, and data was expressed as SPAD averages. Fresh weight was measured using a digital weighing balance and data expressed as g/plant.Sand testSand samples were obtained and sent for analysis at the Soils, Water and Environment Research Institute, Agricultural Research Center, Giza, Egypt. The Electrical conductivity (EC) values were measured from the sand paste extract; pH values were taken from sand suspensions at ratio of 1:2.5 as described by Estefan17. The available nitrogen in the sand sample was extracted using potassium chloride (KCl) as an extractable solution with the ratio of (5gm sand to 50 ml KCl) and determined using the micro- kjeldahl method. Available potassium was determined using a flame photometer, and the other elements in the sand sample were determined by using inductively coupled plasma (ICP) Spectrometry (model Ultima 2 JY Plasma)18,19. The physical and chemical characteristics of the used sand are presented in Table 3.Table 3 (a): Chemical analysis of field sand samples, (b): Available macro, micronutrients, and heavy metals content of the sand samples.Full size tableWater analysisEvery 15 days, a measured amount of desalinated water was added to a standard mark of 850L in the fish tanks to compensate for the consumed amount of water in the system. Fish water quality parameters such as water temperature, pH, and dissolved oxygen (DO) was closely monitored using automated digital Nilebot technologies by Conative labs to fit the ideal required levels as reported by Somerville et al.20. In contrast, ammonia, nitrite, and nitrate were adjusted using an API test kit every week. These parameters’ recorded values were as follows: water temperature ranged between 25 and 28 °C, DO range between 6–7 mg/L, and pH between 6.5 and 7.0. Ammonia levels were kept below 1 mg/L. Elements in water samples were determined according to EPA methods18 using inductively coupled plasma (ICP) Spectrometry (model Ultima 2 JY Plasma) as presented in Table 4.Table 4 Water sample analysis for the different systems’ fish tanks and sump tanks.Full size tableNutritive composition analysisAccording to Official methods of analysis from the association of official analytical chemists (A.O.A.C) (1990), moisture content and Vitamin C were determined. Vitamin A was determined according to the procedures described by Aremu and Nweze21. Briefly, 100 g of the sample were homogenized, from which 1 g was obtained and soaked in 5 mL methanol for two hours at room temperature in the dark for complete extraction of a pro-vitamin A carotenoid, β-carotene. Separation of the β-carotene layer was achieved through the addition of hexane to the sample, and moisture was removed using sodium sulphonate. The absorbance of the layer was measured at 436 nm using hexane as a blank. β-carotene was calculated using the formula:$$beta {text{-carotene }}left( {{mu g}/{1}00{text{ g}}} right) , = {text{ Absorbance }}left( {text{436 nm}} right) , times {text{ V }} times {text{ D }} times { 1}00 , times { 1}00/{text{W }} times {text{ Y}}$$where: V = total volume of the extract; D = Dilution factor; W = Sample weight; Y = Percentage dry matter content of the sample.Vitamin A was then determined according to the concept of Retinol Equivalent (RE) of the β-carotene content of the vegetables using the standard conversion formula. Total hydrolyzable carbohydrates were determined as glucose using phenol–sulfuric acid reagent as described by Michel22.Vitamin C content was determined using dichlorophenol indophenol reagent. As such, 10 g of fresh leaf tissues, were crushed using a motor and pestle in the presence of 10 ml metaphosphoric acid 6% (Merck). This was followed by centrifugation at 4000×g for 5 min at 4 °C. Five mL of the supernatant were transferred into an Erlenmeyer flask, and 20 mL of 3% metaphosphoric acid were added. The extract was titrated by dichlorophenol indophenol (Sigma-Aldrich) until a rose color was observed. Vitamin C (mg/100 g FW) was then calculated and based on the standard curve of l-Ascorbic acid (Merck) concentrations.For the determination of protein and mineral content, 0.5 g of dried samples were digested using sulfuric acid (H2SO4) and hydrogen peroxide (H2O2) as described by Cottenie23. From the extracted sample, the following minerals were determined:Nitrogen was determined according to the procedures described by Plummer24. Briefly, 5 mL of the digestive solution was distilled with 10 mL of sodium hydroxide (NaOH) for 10 min to obtain ammonia. Back titration was then used to determine the amount of nitrogen present in ammonia. Protein content was calculated by multiplying total nitrogen by 6.25 according to methods of AOAC25.Phosphorus content was determined calorimetrically (660 nm) according to the procedures described by Jackson26. Potassium, Calcium, and Sodium were determined against a standard using a flame-photometer (JEN way flame photometer) as described by Piper27. Magnesium (Mg), Copper (Cu), Manganese (Mn), Zinc (Zn), and Iron (Fe) content were determined using Atomic Absorption Spectrophotometer, Pyeunican SP1900, according to methods described by Liu28.The moisture percentage of leaf samples was determined by weighing the fresh weight for each sample (Fw), then dried for 72 h at 80 °C. The dry matter weight was record as Dw. The leaf water content was then calculated as the following:$${text{Moisture}};{text{ content }}left( % right) , = , left( {{text{Fw}} – {text{Dw}}} right) , /{text{ Fw}} * {1}00$$Statistical analysisStatistical comparisons among means of more than two groups were performed with analysis of variance (ANOVA) using SPSS V22, and the difference in means was analyzed by Tukey’s test at α = 0.05. Statistical differences were considered significant at P ≤ 0.05 in triplicates and data expressed as mean ± S.D.Plant materialAll plant materials and related procedures in this study were done in accordance with the guidelines of the Institutional Review Board of the American University in Cairo and the Ministry of Agriculture and Land Reclamation in Egypt.Ethics approvalThis study followed the guidelines and approval of Committee of Animal Welfare and Research Ethics, Faculty of Agriculture, Kafrelsheikh University, Egypt. More

Korup, O. Landslides in the Fluvial System. Treatise on Geomorphology Vol. 9 (Elsevier Ltd., 2013).
Google Scholar 
Kuo, C. W. & Brierley, G. The influence of landscape connectivity and landslide dynamics upon channel adjustments and sediment flux in the Liwu Basin, Taiwan. Earth Surf. Process. Landf. 39, 2038–2055 (2014).ADS 
Article 

Google Scholar 
Tunnicliffe, J. F., Leenman, A. & Reeve, M. The influence of large, chronic landslides on the fluvial system AGU Fall Meeting Abstracts, EP33A-3620 (2014).
Fox, G. A., Purvis, R. A. & Penn, C. J. Streambanks: A net source of sediment and phosphorus to streams and rivers. J. Environ. Manag. 181, 602–614 (2016).CAS 
Article 

Google Scholar 
Biswas, S. P. Restoration of riverine health. Handb. Ecol. Ecosyst. Eng. https://doi.org/10.1002/9781119678595.ch14 (2021).Article 

Google Scholar 
Lutgen, A. et al. Nutrients and heavy metals in legacy sediments: Concentrations, comparisons with upland soils, and implications for water quality. J. Am. Water Resour. Assoc. 56, 669–691 (2020).ADS 
CAS 
Article 

Google Scholar 
Emenike, P. C. et al. An integrated assessment of land-use change impact, seasonal variation of pollution indices and human health risk of selected toxic elements in sediments of River Atuwara, Nigeria. Environ. Pollut. 265, 114795 (2020).CAS 
PubMed 
Article 

Google Scholar 
Fox, G. A. & Wilson, G. V. The role of subsurface flow in hillslope and stream bank erosion: A review. Soil Sci. Soc. Am. J. 74, 717–733 (2010).ADS 
CAS 
Article 

Google Scholar 
Duró, G., Crosato, A., Kleinhans, M. G., Roelvink, D. & Uijttewaal, W. S. J. Bank erosion processes in regulated navigable rivers. J. Geophys. Res. Earth Surf. 125, 1–26 (2020).Article 

Google Scholar 
Keesstra, S. D. et al. Evolution of the morphology of the river Dragonja (SW Slovenia) due to land-use changes. Geomorphology 69, 191–207 (2005).ADS 
Article 

Google Scholar 
Pizzuto, J. & O’Neal, M. Increased mid-twentieth century riverbank erosion rates related to the demise of mill dams, South River, Virginia. Geology 37, 19–22 (2009).ADS 
Article 

Google Scholar 
Abam, T. K. S. Factors affecting distribution of instability of river banks in the Niger delta. Eng. Geol. 35, 123–133 (1993).Article 

Google Scholar 
Jordan, C. et al. Sand mining in the Mekong Delta revisited—current scales of local sediment deficits. Sci. Rep. 9, 1–14 (2019).Article 
CAS 

Google Scholar 
Hackney, C. R. et al. River bank instability from unsustainable sand mining in the lower Mekong River. Nat. Sustain. 3, 217–225 (2020).Article 

Google Scholar 
Yang, S. L., Milliman, J. D., Li, P. & Xu, K. 50,000 dams later: Erosion of the Yangtze River and its delta. Glob. Planet. Change 75, 14–20 (2011).ADS 
Article 

Google Scholar 
Royall, D. Land-use impacts on the hydrogeomorphology of small watersheds. Ref. Modul. Earth Syst. Environ. Sci. https://doi.org/10.1016/B978-0-12-818234-5.00010-9 (2021).Article 

Google Scholar 
Johnson, P. & Royall, D. Evaluating the effects of urbanization age on the morphology of low-order urban streams in the U.S. southern Piedmont. Phys. Geogr. 40, 1–27 (2019).Article 

Google Scholar 
Zaimes, G., Tamparopoulos, A. E., Tufekcioglu, M. & Schultz, R. C. Understanding stream bank erosion and deposition in Iowa, USA: A seven year study along streams in different regions with different riparian land-uses. J. Environ. Manag. 287, 112352 (2021).Article 

Google Scholar 
Zaimes, G. N. & Schultz, R. C. Riparian land-use impacts on bank erosion and deposition of an incised stream in north-central Iowa, USA. CATENA 125, 61–73 (2015).Article 

Google Scholar 
Simon, A., Curini, A., Darby, S. E. & Langendoen, E. J. Bank and near-bank processes in an incised channel. Geomorphology 35, 193–217 (2000).ADS 
Article 

Google Scholar 
Rinaldi, M. & Casagli, N. Stability of streambanks formed in partially saturated soils and effects of negative pore water pressures: The Sieve River (Italy). Geomorphology 26, 253–277 (1999).ADS 
Article 

Google Scholar 
Wynn, T. & Mostaghimi, S. The effects of vegetation and soil type on streambank erosion, Southwestern Virginia, USA. J. Am. Water Resour. Assoc. 42, 69–82 (2006).ADS 
Article 

Google Scholar 
Hecker, G. A., Meehan, M. A. & Norland, J. E. Plant community influences on intermittent stream stability in the great plains. Rangel. Ecol. Manag. 72, 112–119 (2019).Article 

Google Scholar 
Konsoer, K. M. et al. Spatial variability in bank resistance to erosion on a large meandering, mixed bedrock-alluvial river. Geomorphology 252, 80–97 (2016).ADS 
Article 

Google Scholar 
Abernethy, B. & Rutherfurd, I. D. Does the weight of riparian trees destabilize riverbanks?. River Res. Appl. 16, 565–576 (2000).
Google Scholar 
Collison, A. J. C. The distribution and strength of riparian tree roots in relation to riverbank reinforcement. Hydrol. Process. 15, 63–79 (2001).Article 

Google Scholar 
Simon, A. & Collison, A. J. C. Quantifying the mechanical and hydrologic effects of riparian vegetation on streambank stability. Earth Surf. Process. Landf. 27, 527–546 (2002).ADS 
Article 

Google Scholar 
Krzeminska, D., Kerkhof, T., Skaalsveen, K. & Stolte, J. Effect of riparian vegetation on stream bank stability in small agricultural catchments. CATENA 172, 87–96 (2019).Article 

Google Scholar 
Yu, G. A. et al. Effects of riparian plant roots on the unconsolidated bank stability of meandering channels in the Tarim River, China. Geomorphology 351, 106958 (2020).Article 

Google Scholar 
Halder, A. & Mowla Chowdhury, R. Evaluation of the river Padma morphological transition in the central Bangladesh using GIS and remote sensing techniques. Int. J. River Basin Manag. 1–15 (2021).
Bernier, J. F., Chassiot, L. & Lajeunesse, P. Assessing bank erosion hazards along large rivers in the Anthropocene: A geospatial framework from the St. Lawrence fluvial system. Geomat. Nat. Hazards Risk 12, 1584–1615 (2021).Article 

Google Scholar 
Lawler, D. M., Grove, J. R., Couperthwaite, J. S. & Leeks, G. J. L. Downstream change in river bank erosion rates in the Swale-Ouse system, northern England. Hydrol. Process. 13, 977–992 (1999).ADS 
Article 

Google Scholar 
Gholami, V., Sahour, H. & Hadian Amri, M. A. Soil erosion modeling using erosion pins and artificial neural networks. CATENA 196, 104902 (2021).Article 

Google Scholar 
Simon, A., Pollen-Bankhead, N. & Thomas, R. E. Development and application of a deterministic bank stability and toe erosion model for stream restoration. Geophys. Monogr. Ser. 194, 453–474 (2011).ADS 

Google Scholar 
Klavon, K. et al. Evaluating a process-based model for use in streambank stabilization: Insights on the Bank Stability and Toe Erosion Model (BSTEM). Earth Surf. Process. Landf. 42, 191–213 (2017).ADS 
Article 

Google Scholar 
Partheniades, E. Erosion and deposition of cohesive soils. J. Hydraul. Div. 91, 105–139 (1965).Article 

Google Scholar 
Fredlund, D. G., Morgenstern, N. R. & Widger, R. A. Shear strength of unsaturated soils. Can. Geotech. J. 15, 313–321 (1978).Article 

Google Scholar 
Myers, D. T., Rediske, R. R. & McNair, J. N. Measuring streambank erosion: A comparison of erosion pins, total station, and terrestrial laser scanner. Water (Switzerland) 11, 1846 (2019).
Google Scholar 
Casagli, N., Rinaldi, M., Gargini, A. & Curini, A. Pore water pressure and streambank stability: Results from a monitoring site on the Sieve River, Italy. Earth Surf. Process. Landf. 24, 1095–1114 (1999).ADS 
Article 

Google Scholar 
Tufekcioglu, M. et al. Stream bank erosion as a source of sediment and phosphorus in grazed pastures of the Rathbun Lake Watershed in southern Iowa, United States. J. Soil Water Conserv. 67, 545–555 (2012).Article 

Google Scholar 
Palmer, J. A., Schilling, K. E., Isenhart, T. M., Schultz, R. C. & Tomer, M. D. Streambank erosion rates and loads within a single watershed: Bridging the gap between temporal and spatial scales. Geomorphology 209, 66–78 (2014).ADS 
Article 

Google Scholar 
Pollen, N. & Simon, A. Estimating the mechanical effects of riparian vegetation on stream bank stability using a fiber bundle model. Water Resour. Res. 41, 1–11 (2005).Article 

Google Scholar 
Pollen-Bankhead, N. & Simon, A. Sensitivity of post-hurricane beach. Earth Surf. Process. Landf. 34, 471–480 (2009).ADS 
Article 

Google Scholar 
Wasige, J. E. et al. A land use and land cover classification system for use with remote sensor data. Prof. Pap. 100, 753–764 (1976).
Google Scholar 
Al-Doski, J., Mansor, S. B., Ng, H., San, P. & Khuzaimah, Z. Land cover mapping using remote sensing data. Am. J. Geogr. Inf. Syst. 2020, 33–45 (2020).
Google Scholar 
Okeke, C. A. U., Ede, A. N. & Kogure, T. Monitoring of riverbank stability and seepage undercutting mechanisms on the Iju (Atuwara) River, Southwest Nigeria. IOP Conf. Ser. Mater. Sci. Eng. 640, 012105 (2019).Article 

Google Scholar 
Abam, T. K. S. Aspects of alluvial river bank recession: Some examples from the Niger delta. Environ. Geol. 31, 211–220 (1997).Article 

Google Scholar 
Okeke, C. A. U., Azuh, D., Ogbuagu, F. U. & Kogure, T. Assessment of land use impact and seepage erosion contributions to seasonal variations in riverbank stability: The Iju River, SW Nigeria. Groundw. Sustain. Dev. 11, 100448 (2020).Article 

Google Scholar 
Voltz, T. et al. Riparian hydraulic gradient and stream-groundwater exchange dynamics in steep headwater valleys. J. Geophys. Res. Earth Surf. 118, 953–969 (2013).ADS 
Article 

Google Scholar 
Thomas, J., Kumar, S. & Sudheer, K. P. Channel stability assessment in the lower reaches of the Krishna River (India) using multi-temporal satellite data during 1973–2015. Remote Sens. Appl. Soc. Environ. 17, 100274 (2020).
Google Scholar 
Ran, Y. et al. A higher river sinuosity increased riparian soil structural stability on the downstream of a dammed river. Sci. Total Environ. 802, 149886 (2022).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Midgley, T. L., Fox, G. A. & Heeren, D. M. Evaluation of the bank stability and toe erosion model (BSTEM) for predicting lateral retreat on composite streambanks. Geomorphology 145–146, 107–114 (2012).ADS 
Article 

Google Scholar 
Daly, E. R., Miller, R. B. & Fox, G. A. Modeling streambank erosion and failure along protected and unprotected composite streambanks. Adv. Water Resour. 81, 114–127 (2015).ADS 
Article 

Google Scholar 
Saleem, A. et al. Spatial and temporal variations of erosion and accretion: A case of a large tropical river. Earth Syst. Environ. 4, 167–181 (2020).ADS 
Article 

Google Scholar 
Biswas, R. N., Islam, M. N., Islam, M. N. & Shawon, S. S. Modeling on approximation of fluvial landform change impact on morphodynamics at Madhumati River Basin in Bangladesh. Model. Earth Syst. Environ. 7, 71–93 (2021).Article 

Google Scholar 
Li, J., Tooth, S., Zhang, K. & Zhao, Y. Visualisation of flooding along an unvegetated, ephemeral river using Google Earth Engine: Implications for assessment of channel-floodplain dynamics in a time of rapid environmental change. J. Environ. Manag. 278, 111559 (2021).Article 

Google Scholar 
Graziano, M. P., Deguire, A. K. & Surasinghe, T. D. Riparian buffers as a critical landscape feature : Insights for riverscape conservation and policy renovations. Diversity 14, 172 (2022).Article 

Google Scholar 
Rauch, H. P., von der Thannen, M., Raymond, P., Mira, E. & Evette, A. Ecological challenges* for the use of soil and water bioengineering techniques in river and coastal engineering projects. Ecol. Eng. 176, 106539 (2022).Article 

Google Scholar 
East, A. E. et al. Channel-planform evolution in four rivers of Olympic National Park, Washington, USA: The roles of physical drivers and trophic cascades. Earth Surf. Process. Landf. 42, 1011–1032 (2017).ADS 
Article 

Google Scholar 
Kumar, P. et al. Nature-based solutions efficiency evaluation against natural hazards: Modelling methods, advantages and limitations. Sci. Total Environ. 784, 147058 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Laubel, A., Kronvang, B., Hald, A. B. & Jensen, C. Hydromorphological and biological factors influencing sediment and phosphorus loss via bank erosion in small lowland rural streams in Denmark. Hydrol. Process. 17, 3443–3463 (2003).ADS 
Article 

Google Scholar 
Veihe, A., Jensen, N. H., Schiøtz, I. G. & Nielsen, S. L. Magnitude and processes of bank erosion at a small stream in Denmark. Hydrol. Process. 25, 1597–1613 (2011).ADS 
Article 

Google Scholar 
Kronvang, B., Andersen, H. E., Larsen, S. E. & Audet, J. Importance of bank erosion for sediment input, storage and export at the catchment scale. J. Soils Sediments 13, 230–241 (2013).Article 

Google Scholar 
Rajakumari, S., Meenambikai, M., Divya, V., Sarunjith, K. J. & Ramesh, R. Morphological changes in alluvial and coastal plains of Kandaleru river, Andhra Pradesh using RS and GIS, Egypt. J. Remote Sens. Space Sci. 24, 1071–1081 (2021).
Google Scholar 
Zegeye, A. D., Langendoen, E. J., Steenhuis, T. S., Mekuria, W. & Tilahun, S. A. Bank stability and toe erosion model as a decision tool for gully bank stabilization in sub humid Ethiopian highlands. Ecohydrol. Hydrobiol. 20, 301–311 (2020).Article 

Google Scholar 
Shields, F. D. J., Morin, N. & Cooper, C. M. Design of large woody debris structures for channel rehabilitation. In Seventh Federal Interagency Sedimentation Conference, Vol. 8 (2001).C A U, Okeke A N, Ede (2019) Mechanisms of riverbank failure and channel instability on the Nkisi River Southeast Nigeria. IOP Conference Series: Materials Science and Engineering 640(1), 012104. https://doi.org/10.1088/1757-899X/640/1/012104Article 

Google Scholar  More

Chen T, Nomura K, Wang X, Sohrabi R, Xu J, Yao L, et al. A plant genetic network for preventing dysbiosis in the phyllosphere. Nature.2020;580:653–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chaparro JM, Badri DV, Vivanco JM. Rhizosphere microbiome assemblage is affected by plant development. ISME J. 2014;8:790–803.CAS 
PubMed 
Article 

Google Scholar 
Manching HC, Carlson K, Kosowsky S, Smitherman CT, Stapleton AE. Maize phyllosphere microbial community niche development across stages of host leaf growth. F1000Research. 2017;6:1698.PubMed 
Article 

Google Scholar 
Wagner MR, Lundberg DS, Del Rio TG, Tringe SG, Dangl JL, Mitchell-Olds T. Host genotype and age shape the leaf and root microbiomes of a wild perennial plant. Nat Commun. 2016;7:12151.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Agler MT, Ruhe J, Kroll S, Morhenn C, Kim ST, Weigel D, et al. Microbial hub taxa link host and abiotic factors to plant microbiome variation. PLoS Biol. 2016;14:e1002352.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Durán P, Thiergart T, Garrido-Oter R, Agler M, Kemen E, Schulze-Lefert P, et al. Microbial interkingdom interactions in roots promote Arabidopsis survival. Cell. 2018;175:973–83. e14PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Carrión VJ, Perez-Jaramillo J, Cordovez V, Tracanna V, de Hollander M, Ruiz-Buck D, et al. Pathogen-induced activation of disease-suppressive functions in the endophytic root microbiome. Science. 2019;366:606–12.PubMed 
Article 
CAS 

Google Scholar 
Karasov TL, Almario J, Friedemann C, Ding W, Giolai M, Heavens D, et al. Arabidopsis thaliana and Pseudomonas pathogens exhibit stable associations over evolutionary timescales. Cell Host Microbe. 2018;24:168–79.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Coleman-Derr D, Desgarennes D, Fonseca-Garcia C, Gross S, Clingenpeel S, Woyke T, et al. Plant compartment and biogeography affect microbiome composition in cultivated and native Agave species. N. Phytol. 2016;209:798–811.CAS 
Article 

Google Scholar 
Xiong C, Zhu YG, Wang JT, Singh B, Han LL, Shen JP, et al. Host selection shapes crop microbiome assembly and network complexity. N. Phytol. 2021;229:1091–104.CAS 
Article 

Google Scholar 
Lemonnier P, Gaillard C, Veillet F, Verbeke J, Lemoine R, Coutos-Thévenot P, et al. Expression of Arabidopsis sugar transport protein STP13 differentially affects glucose transport activity and basal resistance to Botrytis cinerea. Plant Mol Biol. 2014;85:473–84.CAS 
PubMed 
Article 

Google Scholar 
Nobori T, Cao Y, Entila F, Dahms E, Tsuda Y, Garrido-Oter R, et al. Dissecting the co-transcriptome landscape of plants and microbiota members. bioRxiv; 2022. p. 2021.04.25.440543.Yamada K, Saijo Y, Nakagami H, Takano Y. Regulation of sugar transporter activity for antibacterial defense in Arabidopsis. Science. 2016;354:1427–30.CAS 
PubMed 
Article 

Google Scholar 
Baker RF, Leach KA, Braun DM. SWEET as sugar: new sucrose effluxers in plants. Mol Plant. 2012;5:766–8.PubMed 
Article 

Google Scholar 
Tegeder M, Hammes UZ. The way out and in: phloem loading and unloading of amino acids. Curr Opin Plant Biol. 2018;43:16–21.CAS 
PubMed 
Article 

Google Scholar 
O’Leary BM, Neale HC, Geilfus CM, Jackson RW, Arnold DL, Preston GM. Early changes in apoplast composition associated with defence and disease in interactions between Phaseolus vulgaris and the halo blight pathogen Pseudomonas syringae Pv. phaseolicola. Plant Cell Environ. 2016;39:2172–84.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Rico A, Preston GM. Pseudomonas syringae pv. tomato DC3000 uses constitutive and apoplast-induced nutrient assimilation pathways to catabolize nutrients that are abundant in the tomato apoplast. Mol Plant-Microbe Interact. MPMI. 2008;21:269–82.CAS 
PubMed 
Article 

Google Scholar 
Yu X, Lund SP, Scott RA, Greenwald JW, Records AH, Nettleton D, et al. Transcriptional responses of Pseudomonas syringae to growth in epiphytic versus apoplastic leaf sites. Proc Natl Acad Sci USA. 2013;110:E425.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lohaus G, Winter H, Riens B, Heldt HW. Further studies of the phloem loading process in leaves of barley and spinach. The comparison of metabolite concentrations in the apoplastic compartment with those in the cytosolic compartment and in the sieve tubes. Bot Acta. 1995;108:270–5.CAS 
Article 

Google Scholar 
Chen LQ, Hou BH, Lalonde S, Takanaga H, Hartung ML, Qu XQ, et al. Sugar transporters for intercellular exchange and nutrition of pathogens. Nature. 2010;468:527–32.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Xin XF, Nomura K, Aung K, Velásquez AC, Yao J, Boutrot F, et al. Bacteria establish an aqueous living space in plants crucial for virulence. Nature. 2016;539:524–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Paulsen IT, Press CM, Ravel J, Kobayashi DY, Myers GSA, Mavrodi DV, et al. Complete genome sequence of the plant commensal Pseudomonas fluorescens Pf-5. Nat Biotechnol. 2005;23:873–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
D’Souza G, Shitut S, Preussger D, Yousif G, Waschina S, Kost C. Ecology and evolution of metabolic cross-feeding interactions in bacteria. Nat Prod Rep. 2018;35:455–88.PubMed 
Article 

Google Scholar 
Hoek TA, Axelrod K, Biancalani T, Yurtsev EA, Liu J, Gore J. Resource availability modulates the cooperative and competitive nature of a microbial cross-feeding mutualism. PLOS Biol. 2016;14:e1002540.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Zimmermann J, Obeng N, Yang W, Pees B, Petersen C, Waschina S, et al. The functional repertoire contained within the native microbiota of the model nematode Caenorhabditis elegans. ISME J. 2020;14:26–38.CAS 
PubMed 
Article 

Google Scholar 
Machado D, Maistrenko OM, Andrejev S, Kim Y, Bork P, Patil KR, et al. Polarization of microbial communities between competitive and cooperative metabolism. Nat Ecol Evol. 2021;5:195–203.PubMed 
PubMed Central 
Article 

Google Scholar 
Hassani MA, Durán P, Hacquard S. Microbial interactions within the plant holobiont. Microbiome. 2018;6:1–17.Article 

Google Scholar 
Gerlich SC, Walker BJ, Krueger S, Kopriva S. Sulfate metabolism in C4 Flaveria species is controlled by the root and connected to serine biosynthesis. Plant Physiol. 2018;178:565–82.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gowik U, Bräutigam A, Weber KL, Weber APM, Westhoff P. Evolution of C4 photosynthesis in the genus Flaveria: How many and which genes does it take to make C4? Plant Cell. 2011;23:2087–105.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
McKown AD, Dengler NG. Vein patterning and evolution in C4 plants. Botany. 2010;88:775–86.CAS 
Article 

Google Scholar 
Gentzel I, Giese L, Zhao W, Alonso AP, Mackey D. A simple method for measuring apoplast hydration and collecting apoplast contents. Plant Physiol. 2019;179:1265–72.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mayer T, Mari A, Almario J, Murillo-Roos M, Syed M, Abdullah H, et al. Obtaining deeper insights into microbiome diversity using a simple method to block host and nontargets in amplicon sequencing. Mol Ecol Resour. 2021;21:1952–65.PubMed 
Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2020. Available from: https://www.R-project.org/.Callahan B, McMurdie PJ, Rosen M, Han A, Johnson A, Holmes S. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
McMurdie PJ, Holmes S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLOS ONE. 2013;8:61217.Article 
CAS 

Google Scholar 
Oksanen J, Blanchet GF, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: Community Ecology Package [Internet]. 2020. Available from: https://CRAN.R-project.org/package=vegan.Arkin AP, Cottingham RW, Henry CS, Harris NL, Stevens RL, Maslov S, et al. KBase: The United States Department of Energy Systems Biology Knowledgebase. Nat Biotechnol. 2018;36:566–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Schlechter RO, Jun H, Bernach M, Oso S, Boyd E, Muñoz-Lintz DA, et al. Chromatic bacteria – A broad host-range plasmid and chromosomal insertion toolbox for fluorescent protein expression in bacteria. Front Microbiol. 2018;9:3052.PubMed 
PubMed Central 
Article 

Google Scholar 
Lohaus G, Pennewiss K, Sattelmacher B, Hussmann M, Hermann Muehling K. Is the infiltration-centrifugation technique appropriate for the isolation of apoplastic fluid? A critical evaluation with different plant species. Physiol Plant. 2001;111:457–65.CAS 
PubMed 
Article 

Google Scholar 
Trivedi P, Leach JE, Tringe SG, Sa T, Singh BK. Plant–microbiome interactions: from community assembly to plant health. Nat Rev Microbiol. 2020;18:607–21.CAS 
PubMed 
Article 

Google Scholar 
Goldford JE, Lu N, Bajić D, Estrela S, Tikhonov M, Sanchez-Gorostiaga A, et al. Emergent simplicity in microbial community assembly. Science. 2018;361:469–74.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Dal Bello M, Lee H, Goyal A, Gore J. Resource-diversity relationships in bacterial communities reflect the network structure of microbial metabolism. Nat Ecol Evol. 2021;5:1424–34.PubMed 
Article 

Google Scholar 
Sattelmacher B. The apoplast and its significance for plant mineral nutrition. N. Phytol. 2001;149:167–92.CAS 
Article 

Google Scholar 
Regalado J, Lundberg DS, Deusch O, Kersten S, Karasov T, Poersch K, et al. Combining whole-genome shotgun sequencing and rRNA gene amplicon analyses to improve detection of microbe–microbe interaction networks in plant leaves. ISME J. 2020;14:2116–30.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Morella NM, Weng FCH, Joubert PM, Metcalf CJE, Lindow S, Koskella B. Successive passaging of a plant-associated microbiome reveals robust habitat and host genotype-dependent selection. Proc Natl Acad Sci USA. 2020;117:1148–59.CAS 
PubMed 
Article 

Google Scholar 
Remus-Emsermann MNP, Lücker S, Müller DB, Potthoff E, Daims H, Vorholt JA. Spatial distribution analyses of natural phyllosphere-colonizing bacteria on Arabidopsis thaliana revealed by fluorescence in situ hybridization. Environ Microbiol. 2014;16:2329–40.CAS 
PubMed 
Article 

Google Scholar 
Coyte KZ, Schluter J, Foster KR. The ecology of the microbiome: Networks, competition, and stability. Science. 2015;350:663–6.CAS 
PubMed 
Article 

Google Scholar 
Herren CM. Disruption of cross-feeding interactions by invading taxa can cause invasional meltdown in microbial communities. Proc R Soc B Biol Sci. 2020;287:20192945.Article 

Google Scholar 
Rahme LG, Mindrinos MN, Panopoulos NJ. Plant and environmental sensory signals control the expression of hrp genes in Pseudomonas syringae pv. phaseolicola. J Bacteriol. 1992;174:3499–507.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Morella NM, Zhang X, Koskella B. Tomato seed-associated bacteria confer protection of seedlings against foliar disease caused by Pseudomonas syringae. Phytobiomes J. 2019;3:177–90.Article 

Google Scholar 
Cha JY, Han S, Hong HJ, Cho H, Kim D, Kwon Y, et al. Microbial and biochemical basis of a Fusarium wilt-suppressive soil. ISME J. 2016;10:119–29.CAS 
PubMed 
Article 

Google Scholar 
Lundberg DS, Jové R de P, Ayutthaya PPN, Karasov TL, Shalev O, Poersch K, et al. Contrasting patterns of microbial dominance in the Arabidopsis thaliana phyllosphere. bioRxiv. 2021;2021.04.06.438366.Ikawa Y, Tsuge S. The quantitative regulation of the hrp regulator HrpX is involved in sugar-source-dependent hrp gene expression in Xanthomonas oryzae pv. oryzae. FEMS Microbiol Lett. 2016;363:fnw071.Wei ZM, Sneath BJ, Beer SV. Expression of Erwinia amylovora hrp genes in response to environmental stimuli. J Bacteriol. 1992;174:1875–82.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9:5114.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Akashi H, Gojobori T. Metabolic efficiency and amino acid composition in the proteomes of Escherichia coli and Bacillus subtilis. Proc Natl Acad Sci USA. 2002;99:3695–700.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Oña L, Kost C. Cooperation increases robustness to ecological disturbance in microbial cross-feeding networks. Ecol Lett. 2022;25:1410–20.Cadot S, Guan H, Bigalke M, Walser JC, Jander G, Erb M, et al. Specific and conserved patterns of microbiota-structuring by maize benzoxazinoids in the field. Microbiome. 2021;9:103.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Voges MJEEE, Bai Y, Schulze-Lefert P, Sattely ES. Plant-derived coumarins shape the composition of an Arabidopsis synthetic root microbiome. Proc Natl Acad Sci USA. 2019;116:12558–65.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Aulakh MS, Wassmann R, Bueno C, Kreuzwieser J, Rennenberg H. Characterization of root exudates at different growth stages of ten rice (Oryza sativa L.) cultivars. Plant Biol. 2001;3:139–48.CAS 
Article 

Google Scholar 
Dietz S, Herz K, Gorzolka K, Jandt U, Bruelheide H, Scheel D. Root exudate composition of grass and forb species in natural grasslands. Sci Rep. 2020;10:10691.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

Siderius, C., Biemans, H., Kashaigili, J. & Conway, D. Water conservation can reduce future water-energy-food-environment trade-offs in a medium-sized African river basin. Agric. Water Manag. 266, 107548 (2022).
Google Scholar 
Zhao, G., Liang, R., Li, K., Wang, Y. & Pu, X. Study on the coupling model of urbanization and water environment with basin as a unit: A study on the Hanjiang Basin in China. Ecol. Ind. 131, 108130 (2021).
Google Scholar 
Zhu, Q. et al. Relationship between ecological quality and ecosystem services in a red soil hilly watershed in southern China. Ecol. Ind. 121, 107119 (2021).
Google Scholar 
Fu, A. et al. The effects of ecological rehabilitation projects on the resilience of an extremely drought-prone desert riparian forest ecosystem in the Tarim River Basin, Xinjiang, China. Sci. Rep. 11, 18485 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Dai, D. et al. Comprehensive assessment of the water environment carrying capacity based on the spatial system dynamics model, a case study of Yongding River Basin in North China. J. Clean. Prod. 344, 131137 (2022).
Google Scholar 
Basu, H., Dandele, P. S. & Srivastava, S. K. Sedimentary facies of the Mesoproterozoic Srisailam Formation, Cuddapah basin, India: Implications for depositional environment and basin evolution. Mar. Pet. Geol. 133, 105242 (2021).
Google Scholar 
Capella, W. et al. Sandy contourite drift in the late Miocene Rifian Corridor (Morocco): Reconstruction of depositional environments in a foreland-basin seaway. Sed. Geol. 355, 31–57 (2017).
Google Scholar 
Ilevbare, M. & Omodolor, H. E. Ancient deposition environment, mechanism of deposition and textural attributes of Ajali Formation, western flank of the Anambra Basin, Nigeria. Case Stud. Chem. Environ. Eng. 2, 100022 (2020).
Google Scholar 
Qiao, J. B., Zhu, Y. J., Jia, X. X. & Shao, M. A. Multifractal characteristics of particle size distributions (50–200 m) in soils in the vadose zone on the Loess Plateau, China. Soil Tillage Res. 205, 104786 (2021).
Google Scholar 
Bach, E. M., Baer, S. G., Meyer, C. K. & Six, J. Soil texture affects soil microbial and structural recovery during grassland restoration. Soil Biol. Biochem. 42, 2182–2191 (2010).CAS 

Google Scholar 
Rodríguez-Lado, L. & Lado, M. Relation between soil forming factors and scaling properties of particle size distributions derived from multifractal analysis in topsoils from Galicia (NW Spain). Geoderma 287, 147–156 (2017).ADS 

Google Scholar 
Mozaffari, H., Moosavi, A. A. & Dematte, J. A. M. Estimating particle-size distribution from limited soil texture data: Introducing two new methods. Biosys. Eng. 216, 198–217 (2022).
Google Scholar 
Sudarsan, B., Ji, W., Adamchuk, V. & Biswas, A. Characterizing soil particle sizes using wavelet analysis of microscope images. Comput. Electron. Agric. 148, 217–225 (2018).
Google Scholar 
Pollacco, J. A. P., Fernández-Gálvez, J. & Carrick, S. Improved prediction of water retention curves for fine texture soils using an intergranular mixing particle size distribution model. J. Hydrol. 584, 124597 (2020).
Google Scholar 
Richer-de-Forges, A. C. et al. Hand-feel soil texture and particle-size distribution in central France. Relationships and implications. CATENA 213, 106155 (2022).CAS 

Google Scholar 
Du, W. et al. Insights into vertical differences of particle number size distributions in winter in Beijing, China. Sci. Total Environ. 802, 149695 (2022).ADS 
CAS 
PubMed 

Google Scholar 
Darder, M. L., Paz-González, A., García-Tomillo, A., Lado, M. & Wilson, M. G. Comparing multifractal characteristics of soil particle size distributions calculated by Mie and Fraunhofer models from laser diffraction measurements. Appl. Math. Model. 94, 36–48 (2021).
Google Scholar 
Ke, Z. M. et al. Multifractal parameters of soil particle size as key indicators of the soil moisture distribution. J. Hydrol. 595, 125988 (2021).
Google Scholar 
Qi, F. et al. Soil particle size distribution characteristics of different land-use types in the Funiu mountainous region. Soil Tillage Res. 184, 45–51 (2018).
Google Scholar 
Tyler, S. W. & Wheatcraft, S. W. Fractal scaling of soil particle-size distribution: Analysis and imitations. Soil Sci. Soc. Am. J. 56, 362–369 (1992).ADS 

Google Scholar 
Zhang, Y. et al. Effects of fractal dimension and water content on the shear strength of red soil in the hilly granitic region of southern China. Geomorphology 351, 106956 (2020).
Google Scholar 
Ahmadi, A., Neyshabouri, M.-R., Rouhipour, H. & Asadi, H. Fractal dimension of soil aggregates as an index of soil erodibility. J. Hydrol. 400, 305–311 (2011).ADS 

Google Scholar 
Gao, Z., Niu, F., Lin, Z. & Luo, J. Fractal and multifractal analysis of soil particle-size distribution and correlation with soil hydrological properties in active layer of Qinghai-Tibet Plateau, China. CATENA 203, 105373 (2021).
Google Scholar 
Xu, G. et al. New method for the reconstruction of sedimentary systems including lithofacies, environments, and flow paths: A case study of the Xisha Trough Basin, South China Sea. Mar. Pet. Geol. 133, 105268 (2021).
Google Scholar 
Li, Z., Yu, X., Dong, S., Chen, Q. & Zhang, C. Microtextural features on quartz grains from eolian sands in a subaqueous sedimentary environment: A case study in the hinterland of the Badain Jaran Desert, Northwest China. Aeolian Res. 43, 100573 (2020).
Google Scholar 
Chen, T. et al. Modeling the effects of topography and slope gradient of an artificially formed slope on runoff, sediment yield, water and soil loss of sandy soil. CATENA 212, 106060 (2022).
Google Scholar 
George, C. F., Macdonald, D. I. M. & Spagnolo, M. Deltaic sedimentary environments in the Niger Delta, Nigeria. J. Afr. Earth Sci. 160, 103592 (2019).
Google Scholar 
Tian, Y. et al. Petrology, lithofacies, and sedimentary environment of Upper Cretaceous Abu Roash “G” in the AESW Block, Abu Gharadig Basin, Western Desert, Egypt. J. Afr. Earth Sci. 145, 178–189 (2018).ADS 

Google Scholar 
Cheng, Z., Jalon-Rójas, I., Wang, X. H. & Liu, Y. Impacts of land reclamation on sediment transport and sedimentary environment in a macro-tidal estuary. Estuar. Coast. Shelf Sci. 242, 106861 (2020).
Google Scholar 
Wei, X., Li, X. G. & Wei, N. Fractal features of soil particle size distribution in layered sediments behind two check dams: Implications for the Loess Plateau, China. Geomorphology 266, 133–145 (2016).ADS 

Google Scholar 
Wang, S. et al. Grain size characteristics of surface sediment and its response to the dynamic sedimentary environment in Qiantang Estuary, China. Int. J. Sediment Res. 37, 457–467 (2022).
Google Scholar 
Wided, S., Jalila, S. & Kamel, R. Grain size analysis and characterization of sedimentary environment along the Bizerte Coast, N-E of Tunisia. J. Afr. Earth Sc. 184, 104353 (2021).
Google Scholar 
Cai, X., Yang, Y. E., Ringler, C., Zhao, J. & You, L. Agricultural water productivity assessment for the Yellow River Basin. Agric. Water Manag. 98, 1297 (2011).
Google Scholar 
Fu, J., Zang, C. & Zhang, J. Economic and resource and environmental carrying capacity trade-off analysis in the Haihe river basin in China. J. Clean. Prod. 270, 122271 (2020).
Google Scholar 
Zhang, K. et al. Confronting challenges of managing degraded lake ecosystems in the anthropocene, exemplified from the Yangtze River Basin in China. Anthropocene 24, 30–39 (2018).
Google Scholar 
Huybrechts, N., Zhang, Y. F. & Verbanck, M. A. A new closure methodology for 1D fully coupled models of mobile-bed alluvial hydraulics: Application to silt transport in the Lower Yellow River. Int. J. Sedim. Res. 26(1), 36–49 (2011).
Google Scholar 
Cheng, D. Z. Strengthen the financial foundation of ecological protection and development of the Yellow River Basin. People Tribune 27, 76–78 (2021).
Google Scholar 
Yang, W. N., Zhou, L. & Sun, D. Q. Ecological vulnerability assessment of the Yellow River basin based on partition: Integration concept. Remote Sens. Nat. Resourc. 33(03), 211–218 (2021).
Google Scholar 
Sun, H. et al. Exposure of population to droughts in the Haihe river basin under global warming of 1.5 and 2.0 °C Scenarios. Q. Int. 453, 74–84 (2017).ADS 

Google Scholar 
Mandelbrott, B. B. The Fractal Geometry of Nature (W.H. Freeman and Company, 1983).
Google Scholar 
Samiei-Fard, R., Heidari, A., Konyushkova, M. & Mahmoodi, S. Application of particle size distribution throughout the soil profile as a criterion for recognition of newly developed geoforms in the Southeastern Caspian coast. CATANA 203, 105362 (2021).CAS 

Google Scholar 
Guo, J. Y. et al. Grain size characteristics and source analysis of aeolian sediment feed into river in Ulanbuh Desert along bank of Yellow River. J. China Inst. Water Resour. Hydropower Res. 19(01), 15–24 (2021).
Google Scholar 
Ge, T. T., Xue, Y. J., Jiang, X. Y., Zou, L. & Wang, X. C. Sources and radiocarbon ages of organic carbon in different grain size fractions of Yellow River-transported particles and coastal sediments. Chem. Geol. 534, 119452 (2020).ADS 

Google Scholar 
Hou, C. Y., Yi, Y. J., Song, J. & Zhou, Y. Effect of water-sediment regulation operation on sediment grain size and nutrient content in the lower Yellow River. J. Clean. Prod. 279, 123533 (2021).CAS 

Google Scholar 
Ni, S. M., Feng, S. Y., Zhang, D. Q., Wang, J. G. & Cai, C. F. Sediment transport capacity in erodible beds with reconstituted soils of different textures. CATANA 183, 104197 (2019).
Google Scholar 
Li, J. L. et al. Multifractal features of the particle-size distribution of suspended sediment in the Three Gorges Reservoir, China. Int. J. Sedim. Res. 36(4), 489–500 (2021).
Google Scholar 
Wang, W. F., Liu, R. T., Guo, Z. X., Feng, Y. H. & Jiang, J. Y. Physical and chemical properties and fractal dimension distribution of soil under shrubs in the southern area of Tengger Desert. J. Desert Res. 41(01), 209–218 (2021).
Google Scholar 
Wang, K., Pei, Z. Y., Wang, W. M., Hao, S. R. & Pang, G. H. Influence of the flat cycle on the fractal characteristics of soil pore structure in Salix psammophila. Sci. Technol. Eng. 21(07), 2647–2654 (2021).
Google Scholar 
Gao, G. L. et al. Fractal approach to estimating changes in soil properties following the establishment of Caragana korshinskii shelterbelts in Ningxia, NW China. Ecol. Indic. 43, 236–243 (2014).CAS 

Google Scholar 
Liu, X., Zhang, G. C., Heathman, G. C., Wang, Y. Q. & Huang, C. H. Fractal features of soil particle-size distribution as affected by plant communities in the forested region of Mountain Yimeng, China. Geoderma 154(1), 123–130 (2009).ADS 

Google Scholar 
Xu, G. C., Li, Z. B. & Li, P. Fractal features of soil particle-size distribution and total soil nitrogen distribution in a typical watershed in the source area of the middle Dan River, China. CATENA 101, 17–23 (2013).CAS 

Google Scholar 
Zhao, S. Q., Chi, D. Q., Jia, F. C., Deng, Y. P. & Sun, C. T. Fractal characteristics of saline soil particles in different regions. Jiangsu Agric. Sci. 49(06), 203–207 (2021).
Google Scholar  More

High diet overlap is assumed to cause competition between the three dominant pelagic planktivorous mesopredators in the Baltic Sea, sprat, herring, and stickleback11,24,25. Despite this assumption, stickleback populations have increased dramatically over the past decades, which raises the question of whether and how resources are partitioned26. While previous studies of fish diet overlap have mainly relied on microscopic identification of gut content, we implemented a DNA metabarcoding approach targeting two different gene regions, the 18S rRNA gene (18S) and the mitochondrial cytochrome c oxidase I gene (COI) to reveal the taxonomic diversity of prey, and a qPCR step to quantify rotifers that are at times abundant in the Baltic Sea. Our study highlights consistency between methods, with DNA metabarcoding resolving the plankton-fish link at the highest taxonomic resolution. Our results suggest a unique niche of stickleback that may enable high population growth in the open water, despite high competition between mesopredators, although this finding needs to be confirmed at larger scale. More than half of the DNA found in herring and sprat stomach contents was assigned to Pseudocalanus, supporting previous observations of high diet overlap between the two clupeids11,12. On the other hand, the diet of stickleback differed substantially from the two clupeids, with rotifers appearing as main prey DNA in spring. The high rotifer biomass in the environment and lack of competition from other predators indicate that this novel niche utilization may support the drastic increase of pelagic stickleback in the Baltic Sea.We find that copepods dominated the gut content of the two clupeids sprat and herring. Pseudocalanus and Temora occupied most of the sequence reads of the clupeid metabarcoding, two species that are often reported as preferred prey in previous studies11,12. Despite high contributions of these two copepods, Pseudocalanus was more than four times as abundant as Temora in clupeid gut contents. A strong preference for this copepod with marine origin can further confirm the increased competition between the clupeids, as Pseudocalanus has decreased due to decreased salinity12 and shares a similar vertical distribution as clupeid during daytime27. Our study using metabarcoding further reveals a large relative quantity (11%) of the ctenophore Mertensia in the gut samples of both clupeids. Similar, Clarke et al.28 reported an important contribution of gelatinous zooplankton to upper trophic levels in the Southern Ocean. Despite high abundances of ctenophores in the Baltic Sea and their assumed importance in marine food webs19, they are not reported as food for planktivorous fish. A possible explanation is the difficulty observing them microscopically, as their digestion rate is faster than crustaceans29, and no hard parts remain in the digestive system. Further, COI detected the presence of cladocerans, which was confirmed by the microscopic survey, but underrepresented with 18S that strongly amplify copepods20. Interestingly, more than twice annelid COI reads, including the benthic macroinvertebrates Bylgides and Marenzellaria, were associated to stickleback (15%) and herring (8%) than to sprat (4%), highlighting their ability to migrate vertically. These interactions suggest that together stickleback and herring contribute to benthic-pelagic coupling when oxygen is not restricting vertical migration in the southern Baltic Sea30.Sprat and herring share a similar feeding niche, which may explain previously observed declines in body mass and stomach fullness, and supports the theory of competition between the two species31. In contrast, stickleback revealed little diet overlap with the other mesopredators. The low relative abundances of Pseudocalanus (1–8%) in metabarcoding analyses indicates that the density-dependent competition may not limit the population growth of stickleback. The copepods that were shared in the diet of stickleback, sprat, and herring were Temora, Acartia, and Centropages have increased over the last decades, as opposed to Pseudocalanus32. Our results show that stickleback are able to feed on a broader spectrum of prey and highlight that stickleback utilizes the rotifer Synchaeta baltica as prey, which is an important component of the plankton community composition in the Baltic Sea18,20. Due to the difference of prey size, we can expect an overrepresentation of copepod to rotifer sequences compared with microscopic count data. High predation rate on S. baltica is supported by both the qPCR assay as well as microscopic counts, although only the eggshells were visible but not the soft-bodied rotifer. Despite the considerably lower carbon content per S. baltica (ca. 6 µg C ind−1) compared to copepods (ca. 20 µg C ind−1)33, the high number of rotifers likely act as a major food source for stickleback. These results propose that stickleback, due to their opportunistic feeding behaviour34 and smaller size35, have a distinct feeding niche from sprat and herring in the open water, as they feed on a smaller size class of zooplankton compared to the clupeids. Thus, we cannot assume the same process of competition between clupeids and stickleback.Rotifers can at times be very abundant in the Baltic Sea, reaching densities up to 25,000 ind m−3, but their natural predators are poorly studied. An increasing trend in biomass of the two main rotifer genera (Synchaeta and Keratella) was observed since the 1990s36. In a recent study, we showed that rotifers might occupy a unique feeding niche, as direct grazers of dinoflagellate spring bloom, as well as in the recycling of organic matter in summer20. The low level of predation on rotifers by clupeid adults ( More

Ecosystem services are the contributions of nature to human well-being — for example, the provision of raw materials, carbon sequestration and recreation. Although relatively new, the study of these essential services has developed rapidly and is now included in many global policies and assessments. However, mapping and modelling these services is restricted by the availability of data that can account for the multidimensional traits of ecosystem services and model coupled social–ecological systems. Traditional datasets, including surveys, interviews, and focus groups, are often not viable on the scale necessary for many ecosystem service assessments. More