Ecology

West, S. A., Diggle, S. P., Buckling, A., Gardner, A. & Griffin, A. S. The social lives of microbes. Annu. Rev. Ecol. Evol. Syst. 38, 53–77 (2007).Article 

Google Scholar 
Diggle, S. P., Griffin, A. S., Campell, G. S. & West, S. A. Cooperation and conflict in quorum-sensing bacterial populations. Nature 450, 411–414 (2007).CAS 
PubMed 
Article 

Google Scholar 
Ebrahimi, A., Schwartzman, J. & Cordero, O. X. Cooperation and spatial self-organization determine rate and efficiency of particulate organic matter degradation in marine bacteria. Proc. Natl Acad. Sci. USA 116, 23309–23316 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Yan, J., Monaco, H. & Xavier, J. B. The ultimate guide to bacterial swarming: An experimental model to study the evolution of cooperative behavior. Annu. Rev. Microbiol. 73, 293–312 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kramer, J., Özkaya, Ö. & Kümmerli, R. Bacterial siderophores in community and host interactions. Nat. Rev. Microbiol. 18, 152–163 (2020).CAS 
PubMed 
Article 

Google Scholar 
Griffin, A., West, S. A. & Buckling, A. Cooperation and competition in pathogenic bacteria. Nature 430, 1024–1027 (2004).CAS 
PubMed 
Article 

Google Scholar 
Sandoz, K. M., Mitzimberg, S. M. & Schuster, M. Social cheating in Pseudomonas aeruginosa quorum sensing. Proc. Natl Acad. Sci. USA 104, 15876–15881 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Xavier, J. B., Kim, W. & Foster, K. R. A molecular mechanism that stabilizes cooperative secretions in Pseudomonas aeruginosa. Mol. Microbiol. 79, 166–179 (2011).CAS 
PubMed 
Article 

Google Scholar 
Drescher, K., Nadell, C. D., Stone, H. A., Wingreen, N. S. & Bassler, B. L. Solutions to the public goods dilemma in bacterial biofilms. Curr. Biol. 24, 50–55 (2014).CAS 
PubMed 
Article 

Google Scholar 
Nadal Jimenez, P. et al. The multiple signaling systems regulating virulence in Pseudomonas aeruginosa. Microbiol. Mol. Biol. Rev. 76, 46–65 (2012).PubMed Central 
Article 
CAS 

Google Scholar 
Schuster, M., Sexton, D. J., Diggle, S. P. & Greenberg, E. P. Acyl-homoserine lactone quorum sensing: from evolution to application. Annu. Rev. Microbiol. 67, 43–63 (2013).CAS 
PubMed 
Article 

Google Scholar 
Papenfort, K. & Bassler, B. L. Quorum sensing signal-response systems in Gram-negative bacteria. Nat. Rev. Microbiol. 14, 576–588 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Darch, S. E., West, S. A., Winzer, K. & Diggle, S. P. Density-dependent fitness benefits in quorum-sensing bacterial populations. Proc. Natl Acad. Sci. USA 109, 8259–8263 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ross-Gillespie, A. & Kümmerli, R. Collective decision-making in microbes. Front. Microbiol. 5, 54 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Whiteley, M., Diggle, S. P. & Greenberg, E. P. Progress in and promise of bacterial quorum sensing research. Nature 551, 313–320 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Avery, A. Microbial cell individuality and the underlying sources of heterogeneity. Nat. Rev. Microbiol. 4, 577–587 (2006).CAS 
PubMed 
Article 

Google Scholar 
Eldar, A. & Elowitz, M. B. Functional roles for noise in genetic circuits. Nature 467, 167–173 (2010).CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
Visca, P., Imperi, F. & Lamont, I. L. Pyoverdine siderophores: From biogenesis to biosignificance. Trends Microbiol. 15, 22–30 (2007).CAS 
PubMed 
Article 

Google Scholar 
Youard, Z. A., Wenner, N. & Reimmann, C. Iron acquisition with the natural siderophore enantiomers pyochelin and enantio-pyochelin in Pseudomonas species. Biometals 24, 513–522 (2011).CAS 
PubMed 
Article 

Google Scholar 
Schalk, I. J. & Cunrath, O. An overview of the biological metal uptake pathways in Pseudomonas aeruginosa. Environ. Microbiol. 18, 3227–3246 (2016).CAS 
PubMed 
Article 

Google Scholar 
Schalk, I. J., Rigouin, C. & Godet, J. An overview of siderophore biosynthesis among fluorescent Pseudomonads and new insights into their complex cellular organization. Environ. Microbiol. 22, 1447–1466 (2020).PubMed 
Article 

Google Scholar 
Ochsner, U. A. & Vasil, M. L. Gene repression by the ferric uptake regulator in Pseudomonas aeruginosa: Cycle selection of iron-regulated genes. Proc. Natl Acad. Sci. USA 93, 4409–4414 (1996).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Leoni, L., Ciervo, A., Orsi, N. & Visca, P. Iron-regulated transcription of the pvdA gene in Pseudomonas aeruginosa: effect of Fur and PvdS on promoter activity. J. Bacteriol. 178, 2299–2313 (1996).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Escolar, L., Pérez-Martín, J. & de Lorenzo, V. Opening the iron box: Transcriptional metalloregulation by the fur protein. J. Bacteriol. 181, 6223–6229 (1999).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Dumas, Z., Ross-Gillespie, A. & Kümmerli, R. Switching between apparently redundant iron-uptake mechanisms benefits bacteria in changeable environments. Proc. R. Soc. B 280, 20131055 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Lamont, I. L., Beare, P., Ochsner, U., Vasil, A. I. & Vasil, M. L. Siderophore-mediated signaling regulates virulence factor production in Pseudomonas aeruginosa. Proc. Natl Acad. Sci. USA 99, 7072–7077 (2002).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Edgar, R. J. et al. Interactions between an anti-sigma protein and two sigma factors that regulate the pyoverdine signaling pathway in Pseudomonas aeruginosa. BMC Microbiol. 14, 287 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Heinrichs, D. E. & Poole, K. Cloning and sequence analysis of a gene (pchR) encoding an AraC family activator of pyochelin and ferripyochelin receptor synthesis in Pseudomonas aeruginosa. J. Bacteriol. 175, 5882–5889 (1993).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Michel, L., Gonzalez, N., Jagdeep, S., Nguyen-Ngoc, T. & Reimmann, C. PchR-box recognition by the AraC-type regulator PchR of Pseudomonas aeruginosa requires the siderophore pyochelin as an effector. Microbiology 58, 495–509 (2005).CAS 

Google Scholar 
Michel, L., Bachelard, A. & Reimmann, C. Ferripyochelin uptake genes are involved in pyochelin-mediated signalling in Pseudomonas aeruginosa. Microbiology 153, 1508–1518 (2007).CAS 
PubMed 
Article 

Google Scholar 
Cornelis, P. & Dingemans, J. Pseudomonas aeruginosa adapts its iron uptake strategies in function of the type of infections. Front. Cell. Infect. Microbiol. 3, 75 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Brandel, J. et al. Pyochelin, a siderophore of Pseudomonas aeruginosa: Physicochemical characterization of the iron(III), copper(II) and zinc(II) complexes. Dalton Trans. 41, 2820–2834 (2012).CAS 
PubMed 
Article 

Google Scholar 
Perraud, Q. et al. Phenotypic adaptation of Pseudomonas aeruginosa in the presence of siderophore-antibiotic conjugates during epithelial cell infection. Microorganisms 8, 1820 (2020).CAS 
PubMed Central 
Article 

Google Scholar 
Mossialos, D. et al. Quinolobactin, a new siderophore of Pseudomonas fluorescens ATCC 17400, the production of which Is repressed by the cognate pyoverdine. Appl. Environ. Microbiol. 66, 487–492 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tyrrell, J. et al. Investigation of the multifaceted iron acquisition strategies of Burkholderia cenocepacia. BioMetals 28, 367–380 (2015).CAS 
PubMed 
Article 

Google Scholar 
Wei, Q. et al. Global regulation of gene expression by OxyR in an important human opportunistic pathogen. Nucleic Acids Res. 40, 4320–4333 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Frangipani, E. et al. The Gac/Rsm and cyclic-di-GMP signalling networks coordinately regulate iron uptake in Pseudomonas aeruginosa. Environ. Microbiol. 16, 676–688 (2014).CAS 
PubMed 
Article 

Google Scholar 
Schulz, S. et al. Elucidation of sigma factor-associated networks in Pseudomonas aeruginosa reveals a modular architecture with limited and function-specific crosstalk. PLoS Pathog. 11, e1004744 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Cunrath, O. et al. The pathogen Pseudomonas aeruginosa optimizes the production of the siderophore pyochelin upon environmental challenges. Metallomics 12, 2108–2120 (2020).CAS 
PubMed 
Article 

Google Scholar 
Wang, T. et al. An atlas of the binding specificities of transcription factors in Pseudomonas aeruginosa directs prediction of novel regulators in virulence. eLife 10, e61885 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tiburzi, F., Imperi, F. & Visca, P. Intracellular levels and activity of PvdS, the major iron starvation sigma factor of Pseudomonas aeruginosa. Mol. Microbiol. 67, 213–227 (2008).CAS 
PubMed 
Article 

Google Scholar 
Kümmerli, R., Jiricny, N., Clarke, L. S., West, S. A. & Griffin, A. S. Phenotypic plasticity of a cooperative behaviour in bacteria. J. Evol. Biol. 22, 589–598 (2009).PubMed 
Article 

Google Scholar 
Harrison, F. Dynamic social behaviour in a bacterium: Pseudomonas aeruginosa partially compensates for siderophore loss to cheats. J. Evol. Biol. 26, 1370–1378 (2013).CAS 
PubMed 
Article 

Google Scholar 
Schiessl, K. T. et al. Individual- versus group-optimality in the production of secreted bacterial compounds. Evolution 73, 675–688 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Cunrath, O. et al. A cell biological view of the siderophore pyochelin iron uptake pathway in Pseudomonas aeruginosa. Environ. Microbiol. 17, 171–185 (2015).CAS 
PubMed 
Article 

Google Scholar 
Leinweber, A., Weigert, M. & Kümmerli, R. The bacterium Pseudomonas aeruginosa senses and gradually responds to interspecific competition for iron. Evolution 72, 1515–1528 (2018).CAS 
PubMed Central 
Article 

Google Scholar 
Julou, T. et al. Cell-cell contacts confine public goods diffusion inside Pseudomonas aeruginosa clonal microcolonies. Proc. Natl Acad. Sci. USA 110, 12577–12582 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Weigert, M. & Kümmerli, R. The physical boundaries of public goods cooperation between surface-attached bacterial cells. Proc. R. Soc. B 284, 20170631 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Jayaraman, P., Sakharkar, M. K., Lim, C. S., Hock Tang, T. & Sakharkar, K. R. Activity and interactions of antibiotic and phytochemical combinations against Pseudomonas aeruginosa in vitro. Int. J. Biol. Sci. 6, 556–568 (2010).Kapoor, G., Saigal, S. & Elongavan, A. Action and resistance mechanisms of antibiotics: A guide for clinicians. J. Anaesthesiol. Clin. Pharmacol. 33, 300–305 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wrobel, A., Arciszewska, K., Maliszewski, D. & Drozdowska, D. Trimethoprim and other nonclassical antifolates an excellent template for searching modifications of dihydrofolate reductase enzyme inhibitors. J. Antibiot. 73, 5–27 (2020).CAS 
Article 

Google Scholar 
van der Veen, D. R. et al. Flexible clock systems: Adjusting the temporal programme. Phil. Trans. R. Soc. B 372, 20160254 (2017).Helm, B. et al. Two sides of a coin: ecological and chronobiological perspectives of timing in the wild. Phil. Trans. R. Soc. B 372, 0246 (2017).Rivera, M. Bacterioferritin: structure, dynamics, and protein–protein interactions at play in iron storage and mobilization. Acc. Chem. Res. 50, 331–340 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Soldano, A., Yao, H., Chandler, J. R. & Rivera, M. Inhibiting iron mobilization from bacterioferritin in Pseudomonas aeruginosa impairs biofilm formation irrespective of environmental iron availability. ACS Infectious Dis. 6, 447–458 (2020).Andrews, S. C., Robinson, A. K. & Rodriguez-Quinones, F. Bacterial iron homeostasis. FEMS Microbiol. Rev. 27, 215–237 (2003).CAS 
PubMed 
Article 

Google Scholar 
Alqarni, B., Colley, B., Klebensberger, J., McDougald, D. & Rice, S. A. Expression stability of 13 housekeeping genes during carbon starvation of Pseudomonas aeruginosa. J. Microbiol. Methods 127, 182–187 (2016).CAS 
PubMed 
Article 

Google Scholar 
Veening, J.-W., Smits, W. K. & Kuipers, O. P. Bistability, epigenetics, and bet-hedging in bacteria. Annu. Rev. Microbiol. 62, 193–210 (2008).CAS 
PubMed 
Article 

Google Scholar 
Ratcliff, W. C. & Denison, R. F. Individual-level bet hedging in the bacterium Sinorhizobium meliloti. Curr. Biol. 20, 1740–1744 (2010).CAS 
PubMed 
Article 

Google Scholar 
Schreiber, F. et al. Phenotypic heterogeneity driven by nutrient limitation promotes growth in fluctuating environments. Nat. Microbiol. 1, 16055 (2016).CAS 
PubMed 
Article 

Google Scholar 
Conradt, L. & Roper, T. J. Consensus decision making in animals. Trends Ecol. Evol. 20, 449–456 (2005).PubMed 
Article 

Google Scholar 
Sumpter, D. J. T. The principles of collective animal behaviour. Philos. Trans. R. Soc. B 361, 5–22 (2006).CAS 
Article 

Google Scholar 
Couzin, I. D. Collective cognition in animal groups. Trends Cogn. Sci. 13, 36–43 (2009).PubMed 
Article 

Google Scholar 
Bose, T., Reina, A. & Marshall, J. A. R. Collective decision-making. Curr. Opin. Behav. Sci. 16, 30–34 (2017).Article 

Google Scholar 
Dussutour, A., Ma, Q. & Sumpter, D. Phenotypic variability predicts decision accuracy in unicellular organisms. Proc. R. Soc. B 286, 20182825 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Ross-Gillespie, A., Dumas, Z. & Kümmerli, R. Evolutionary dynamics of interlinked public goods traits: an experimental study of siderophore production in Pseudomonas aeruginosa. J. Evol. Biol. 28, 29–39 (2015).CAS 
PubMed 
Article 

Google Scholar 
Choi, K.-H. & Schweizer, H. P. mini-Tn7 insertion in bacteria with single attTn7 sites: Example Pseudomonas aeruginosa. Nat. Protoc. 1, 153–161 (2006).CAS 
PubMed 
Article 

Google Scholar 
Rezzoagli, C., Granato, E. T. & Kümmerli, R. In-vivo microscopy reveals the impact of Pseudomonas aeruginosa social interactions on host colonization. ISME J. 13, 2403–2414 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Minoia, M. et al. Stochasticity and bistability in horizontal transfer control of a genomic island in Pseudomonas. Proc. Natl Acad. Sci. USA 105, 20792–20797 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mellini, M. et al. Generation of genetic tools for gauging multiple-gene expression at the single-cell level. Appl. Environ. Microbiol. 87, e02956–02920 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Li, S., Crooks, P. A., Wei, X. & de Leon, J. Toxicity of dipyridyl compounds and related compounds. Crit. Rev. Toxicol. 34, 447–460 (2004).CAS 
PubMed 
Article 

Google Scholar 
Liu, Y., Yang, L. & Molin, S. Synergistic activities of an efflux pump inhibitor and iron chelators against Pseudomonas aeruginosa growth and biofilm formation. Antimicrob. Agents Chemother. 54, 3960–3963 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Henriquez, T., Stein, N. V. & Jung, H. Resistance to bipyridyls mediated by the TtgABC efflux system in Pseudomonas putida KT2440. Front. Microbiol. 11, 1974 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Meyer, J.-M., Neely, A., Stintzi, A., Georges, C. & Holder, I. A. Pyoverdin is essential for viruence of Pseudomonas aeruginosa. Infect. Immun. 64, 518–523 (1996).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
de Jong, I. G., Beilharz, K., Kuipers, O. P. & Veening, J. W. Live cell imaging of Bacillus subtilis and Streptococcus pneumoniae using automated time-lapse microscopy. J. Vis. Exp. 53, e3145 (2011).
Google Scholar 
Berg, S. et al. ilastik: Interactive machine learning for (bio)image analysis. Nat. Methods 16, 1226–1232 (2019).CAS 
PubMed 
Article 

Google Scholar 
Schindelin, J. et al. Fiji: An open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).Mridha, S. and Kuemmerli, R. Mridha_Kummerli_2022_CommsBiol_raw_data_figshare.xlsx. figshare. Dataset. https://doi.org/10.6084/m9.figshare.19681962.v1 (2022) More

Field, C. B., Behrenfeld, M. J., Randerson, J. T. & Falkowski, P. Primary production of the biosphere: integrating terrestrial and oceanic components. Science 281, 237–240 (1998).Article 

Google Scholar 
Guidi, L. et al. Plankton networks driving carbon export in the oligotrophic ocean. Nature 532, 465–470 (2016).Article 

Google Scholar 
Ptacnik, R. et al. Diversity predicts stability and resource use efficiency in natural phytoplankton communities. Proc. Natl.Acad. Sci. USA 105, 5134–5138 (2008).Article 

Google Scholar 
Corcoran, A. A. & Boeing, W. J. Biodiversity increases the productivity and stability of phytoplankton communities. PLoS ONE 7, e49397 (2012).Article 

Google Scholar 
Arteaga, L., Pahlow, M. & Oschlies, A. Global patterns of phytoplankton nutrient and light colimitation inferred from an optimality-based model. Glob. Biogeochem. Cycles 28, 648–661 (2014).Article 

Google Scholar 
Lewis, M., Hebert, D., Harrison, W. G., Platt, T. & Oakey, N. S. Vertical nitrate fluxes in the oligotrophic ocean. Science 234, 870–873 (1986).Article 

Google Scholar 
McGillicuddy, D. J. J. et al. Eddy/wind interactions stimulate extraordinary mid-ocean plankton blooms. Science 316, 1021–1026 (2007).Article 

Google Scholar 
Duce, R. A. et al. Impacts of atmospheric anthropogenic nitrogen on the open ocean. Science 320, 893–897 (2008).Article 

Google Scholar 
Tang, W. et al. Revisiting the distribution of oceanic N2 fixation and estimating diazotrophic contribution to marine production. Nat. Commun. 10, 831 (2019).Article 

Google Scholar 
Letscher, R. T., Primeau, F. & Moore, J. K. Nutrient budgets in the subtropical ocean gyres dominated by lateral transport. Nat. Geosci. 9, 815–819 (2016).Article 

Google Scholar 
Righetti, D., Vogt, M., Gruber, N., Psomas, A. & Zimmermann, N. E. Global pattern of phytoplankton diversity driven by temperature and environmental variability. Sci. Adv. 5, eaau6253 (2019).Article 

Google Scholar 
Ibarbalz, F. M. et al. Global trends in marine plankton diversity across kingdoms of life. Cell 179, 1084–1097 (2019).Article 

Google Scholar 
Lévy, M., Franks, P. J. S. & Smith, K. S. The role of submesoscale currents in structuring marine ecosystems. Nat. Commun. 9, 4758 (2018).Article 

Google Scholar 
Dutkiewicz, S. et al. Dimensions of marine phytoplankton diversity. Biogeosciences 17, 609–634 (2020).Article 

Google Scholar 
Gove, J. M. et al. Near-island biological hotspots in barren ocean basins. Nat. Commun. 7, 10581 (2016).Article 

Google Scholar 
Doty, M. S. & Oguri, M. The island mass effect. ICES J. Mar. Sci. 22, 33–37 (1956).Article 

Google Scholar 
Bell, J. D. et al. Planning the use of fish for food security in the Pacific. Mar. Policy 33, 64–76 (2009).Article 

Google Scholar 
Bakker, D. C., Nielsdóttir, M. C., Morris, P. J., Venables, H. J. & Watson, A. J. The island mass effect and biological carbon uptake for the subantarctic Crozet Archipelago. Deep Sea Res. Pt II 54, 2174–2190 (2007).Article 

Google Scholar 
Heywood, K. J., Stevens, D. P. & Bigg, G. R. Eddy formation behind the tropical island of Aldabra. Deep Sea Res. Pt I 43, 555–578 (1996).Article 

Google Scholar 
Palacios, D. M. Factors influencing the island-mass effect of the Galapagos archipelago. Geophys. Res. Lett. 29, 2134 (2002).Article 

Google Scholar 
Gilmartin, M. & Revelante, N. The ‘island mass’ effect on the phytoplankton and primary production of the Hawaiian Islands. J. Exp. Mar. Biol. Ecol. 16, 181–204 (1974).Article 

Google Scholar 
Signorini, S. C., McClain, C. R. & Dandonneau, Y. Mixing and phytoplankton bloom in the wake of the Marquesas Islands. Geophys. Res. Lett. 26, 3121–3124 (1999).Article 

Google Scholar 
Messié, M., Radenac, M.-H., Lefèvre, J. & Marchesiello, P. Chlorophyll bloom in the western Pacific at the end of the 1997-98 El Niño: the role of the Kiribati Islands. Geophys. Res. Lett. 33, L14601 (2006).Article 

Google Scholar 
Messié, M. & Radenac, M.-H. Seasonal variability of the surface chlorophyll in the western tropical Pacific from SeaWiFS data. Deep Sea Res. Pt I 53, 1581–1600 (2006).Article 

Google Scholar 
Le Borgne, R., Dandonneau, Y. & Lemasson, L. The problem of the island mass effect on chlorophyll and zooplankton standing crops around Mare (Loyalty Islands) and New Caledonia. Bull. Mar. Sci. 37, 450–459 (1985).
Google Scholar 
Messié, M. et al. The delayed island mass effect: how islands can remotely trigger blooms in the oligotrophic ocean. Geophys. Res. Lett. 47, e2019GL085282 (2020).Article 

Google Scholar 
Dandonneau, Y. & Charpy, L. An empirical approach to the island mass effect in the south tropical Pacific based on sea surface chlorophyll concentrations. Deep Sea Res. Pt A 32, 707–721 (1985).Article 

Google Scholar 
Shiozaki, T., Kodama, T. & Furuya, K. Large-scale impact of the island mass effect through nitrogen fixation in the western South Pacific Ocean. Geophys. Res. Lett. 41, 2907–2913 (2014).Article 

Google Scholar 
Caputi, L. et al. Community-level responses to iron availability in open ocean plankton ecosystems. Glob. Biogeochem. Cycles 33, 391–419 (2019).Article 

Google Scholar 
Martinez, E., Rodier, M., Pagano, M. & Sauzède, R. Plankton spatial variability within the Marquesas archipelago, South Pacific. J. Mar. Syst. 212, 103432 (2020).Article 

Google Scholar 
Behrenfeld, M. J. & Falkowski, P. G. Photosynthetic rates derived from satellite-based chlorophyll concentration. Limnol. Oceanogr. 42, 1–20 (1997).Article 

Google Scholar 
Laws, E. A., Ducklow, H. & McCarthy, J. J. Temperature effects on export production in the open ocean. Glob. Biogeochem. Cycles 14, 1231–1246 (2000).Article 

Google Scholar 
Messié, M. & Chavez, F. P. A global analysis of ENSO synchrony: the oceans’ biological response to physical forcing. J. Geophys. Res. 117, C09001 (2012).
Google Scholar 
Luo, Y.-W., Lima, I. D., Karl, D. M., Deutsch, C. A. & Doney, S. C. Data-based assessment of environmental controls on global marine nitrogen fixation. Biogeosciences 11, 691–708 (2014).Article 

Google Scholar 
Messié, M. & Chavez, F. P. Seasonal regulation of primary production in eastern boundary upwelling systems. Prog. Oceanogr. 134, 1–18 (2015).Article 

Google Scholar 
Mouw, C. B. et al. A consumer’s guide to satellite remote sensing of multiple phytoplankton groups in the global ocean. Front. Mar. Sci. 4, 41 (2017).Article 

Google Scholar 
Alvain, S., Moulin, C., Dandonneau, Y. & Bréon, F. M. Remote sensing of phytoplankton groups in case 1 waters from global SeaWiFS imagery. Deep Sea Res. Pt I 52, 1989–2004 (2005).Article 

Google Scholar 
Rêve-Lamarche, A.-H. et al. Ocean color radiance anomalies in the North Sea. Front. Mar. Sci. https://doi.org/10.3389/fmars.2017.00408 (2017).Alvain, S., Loisel, H. & Dessailly, D. Theoretical analysis of ocean color radiances anomalies and implications for phytoplankton groups detection in case 1 waters. Opt. Express 20, 1070–1083 (2012).Article 

Google Scholar 
Mackey, D. J., Blanchot, J., Higgins, H. W. & Neveux, J. Phytoplankton abundances and community structure in the equatorial Pacific. Deep Sea Res. Pt II 49, 2561–2582 (2002).Article 

Google Scholar 
Johnson, Z. I. Niche partitioning among Prochlorococcus ecotypes along ocean-scale environmental gradients. Science 311, 1737–1740 (2006).Article 

Google Scholar 
Martiny, A. C., Kathuria, S. & Berube, P. M. Widespread metabolic potential for nitrite and nitrate assimilation among Prochlorococcus ecotypes. Proc. Natl. Acad. Sci. USA 106, 10787–10792 (2009).Article 

Google Scholar 
Vallina, S. M. et al. Global relationship between phytoplankton diversity and productivity in the ocean. Nat. Commun. 5, 4299 (2014).Article 

Google Scholar 
Dai, S. et al. The seamount effect on phytoplankton in the tropical western Pacific. Mar. Environ. Res. 162, 105094 (2020).Article 

Google Scholar 
Leitner, A. B., Neuheimer, A. B. & Drazen, J. C. Evidence for long-term seamount-induced chlorophyll enhancements. Sci. Rep. 10, 12729 (2020).Article 

Google Scholar 
Bowen, B. W., Rocha, L. A., Toonen, R. J. & Karl, S. A. The origins of tropical marine biodiversity. Trends Ecol. Evol. 28, 359–366 (2013).Article 

Google Scholar 
Worm, B., Lotze, H. K. & Myers, R. A. Predator diversity hotspots in the blue ocean. Proc. Natl. Acad. Sci. USA 100, 9884–9888 (2003).Article 

Google Scholar 
Block, B. A. et al. Tracking apex marine predator movements in a dynamic ocean. Nature 475, 86–90 (2011).Article 

Google Scholar 
Harrison, A.-L. et al. The political biogeography of migratory marine predators. Nat. Ecol. Evol. 2, 1571–1578 (2018).Article 

Google Scholar 
Pompa, S., Ehrlich, P. R. & Ceballos, G. Global distribution and conservation of marine mammals. Proc. Natl. Acad. Sci. USA 108, 13600–13605 (2011).Article 

Google Scholar 
Wessel, P. & Smith, W. H. F. A global, self-consistent, hierarchical, high-resolution shoreline database. J. Geophys. Res. 101, 8741––8743 (1996).Article 

Google Scholar 
Nunn, P. D., Kumar, L., Eliot, I. & McLean, R. F. Classifying Pacific islands. Geosci. Lett 3, 7 (2016).Article 

Google Scholar 
Hasegawa, D., Lewis, M. R. & Gangopadhyay, A. How islands cause phytoplankton to bloom in their wakes. Geophys. Res. Lett. 36, L20605 (2009).Article 

Google Scholar 
Platt, T. & Sathyendranath, S. Oceanic primary production: estimation by remote sensing at local and regional scales. Science 241, 1613–1620 (1988).Article 

Google Scholar 
Hasegawa, D., Yamazaki, H., Ishimaru, T., Nagashima, H. & Koike, Y. Apparent phytoplankton bloom due to island mass effect. J. Mar. Syst. 69, 238–246 (2008).Article 

Google Scholar 
Silsbe, G. M., Behrenfeld, M. J., Halsey, K. H., Milligan, A. J. & Westberry, T. K. The CAFE model: a net production model for global ocean phytoplankton. Glob. Biogeochem. Cycles 30, 1756–1777 (2016).Article 

Google Scholar 
Ben Mustapha, Z., Alvain, S., Jamet, C., Loisel, H. & Dessailly, D. Automatic classification of water-leaving radiance anomalies from global SeaWiFS imagery: application to the detection of phytoplankton groups in open ocean waters. Remote Sens. Environ. 146, 97–112 (2014).Article 

Google Scholar 
Alvain, S., Moulin, C., Dandonneau, Y. & Loisel, H. Seasonal distribution and succession of dominant phytoplankton groups in the global ocean: a satellite view. Glob. Biogeochem. Cycles 22, GB3001 (2008).Article 

Google Scholar 
Bray, J. R. & Curtis, J. T. An ordination of the upland forest communities of Southern Wisconsin. Ecol. Monogr. 27, 325–349 (1957).Article 

Google Scholar 
Pielou, E. The measurement of diversity in different types of biological collections. J. Theor. Biol. 13, 131–144 (1966).Article 

Google Scholar 
Shannon, C. E. A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948).Article 

Google Scholar 
Colwell, R. K., Mao, C. X. & Chang, J. Interpolating, extrapolating, and comparing incidence-based species accumulation curves. Ecology 85, 2717–2727 (2004).Article 

Google Scholar 
De Monte, S., Soccodato, A., Alvain, S. & d’Ovidio, F. Can we detect oceanic biodiversity hotspots from space? ISME J. 7, 2054–2056 (2013).Article 

Google Scholar 
Soccodato, A. et al. Estimating planktonic diversity through spatial dominance patterns in a model ocean. Mar. Geonom. 29, 9–17 (2016).Article 

Google Scholar 
Messié, M., Petrenko, A., Doglioli, A., Martinez, E. & Alvain, S. Data from: Basin-scale biogeochemical and ecological impacts of islands in the tropical Pacific Ocean (v1.0.0). Zenodo https://doi.org/10.5281/zenodo.6416130 (2022).Messié, M. Code for: Basin-scale biogeochemical and ecological impacts of islands in the tropical Pacific Ocean (v1.0.0). Zenodo https://doi.org/10.5281/zenodo.6494328 (2022). More

Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Classifying drivers of global forest loss. Science 361, 1108–1111 (2018).CAS 
Article 

Google Scholar 
Gibbs, H. K. et al. Tropical forests were the primary sources of new agricultural land in the 1980s and 1990s. Proc. Natl Acad. Sci. USA 107, 16732–16737 (2010).CAS 
Article 

Google Scholar 
Payn, T. et al. Changes in planted forests and future global implications. Ecol. Manag. 352, 57–67 (2015).Article 

Google Scholar 
Pendrill, F., Persson, U. M., Godar, J. & Kastner, T. Deforestation displaced: trade in forest-risk commodities and the prospects for a global forest transition. Environ. Res. Lett. 14, 055003 (2019).Article 

Google Scholar 
Hurni, K. & Fox, J. The expansion of tree-based boom crops in mainland Southeast Asia: 2001 to 2014. J. Land Use Sci. 13, 198–219 (2018).Article 

Google Scholar 
Vijay, V. et al. The impacts of oil palm on recent deforestation and biodiversity loss. PLoS ONE 11, e0159668 (2016).Heilmayr, R., Echeverría, C. & Lambin, E. F. Impacts of Chilean forest subsidies on forest cover, carbon and biodiversity. Nat. Sustain. 3, 701–709 (2020).Article 

Google Scholar 
le Maire, G., Dupuy, S., Nouvellon, Y., Loos, R. A. & Hakamada, R. Mapping short-rotation plantations at regional scale using MODIS time series: case of eucalypt plantations in Brazil. Remote Sens. Environ. 152, 136–149 (2014).Article 

Google Scholar 
Wang, M. M. H., Carrasco, L. R. & Edwards, D. P. Reconciling rubber expansion with biodiversity conservation. Curr. Biol. 30, 3825–3832 (2020).CAS 
Article 

Google Scholar 
Lewis, S. L., Wheeler, C. E., Mitchard, E. T. A. & Koch, A. Restoring natural forests is the best way to remove atmospheric carbon. Nature 568, 25–28 (2019).CAS 
Article 

Google Scholar 
Dave, R. et al. Second Bonn Challenge Progress Report: Application of the Barometer in 2018 (IUCN, 2019).Sloan, S., Meyfroidt, P., Rudel, T. K., Bongers, F. & Chazdon, R. The forest transformation: planted tree cover and regional dynamics of tree gains and losses. Glob. Environ. Change 59, 101988 (2019).Article 

Google Scholar 
Petersen, R. et al. Mapping Tree Plantations with Multispectral Imagery: Preliminary Results for Seven Tropical Countries (WRI, 2016).Erb, K.-H. et al. Land management: data availability and process understanding for global change studies. Glob. Change Biol. 23, 512–533 (2017).Article 

Google Scholar 
Souza, C. M. et al. Reconstructing three decades of land use and land cover changes in Brazilian biomes with Landsat Archive and Earth Engine. Remote Sens. 12, 2735 (2020).Article 

Google Scholar 
Miettinen, J. et al. Extent of industrial plantations on Southeast Asian peatlands in 2010 with analysis of historical expansion and future projections. GCB Bioenergy 4, 908–918 (2012).Article 

Google Scholar 
Vancutsem, C. et al. Long-term (1990–2019) monitoring of forest cover changes in the humid tropics. Sci. Adv. 7, eabe1603 (2021).Puyravaud, J.-P., Davidar, P. & Laurance, W. F. Cryptic destruction of India’s native forests. Conserv. Lett. 3, 390–394 (2010).Article 

Google Scholar 
Fagan, M. E. et al. Mapping pine plantations in the southeastern U.S. using structural, spectral, and temporal remote sensing data. Remote Sens. Environ. 216, 415–426 (2018).Article 

Google Scholar 
Tropek, R. et al. Comment on “High-resolution global maps of 21st-century forest cover change”. Science 344, 981 (2014).CAS 
Article 

Google Scholar 
Global Forest Resources Assessment 2020 (FAO, 2020).FAOSTAT Agricultural Statistics Database (FAO, 2019); http://faostat.fao.org/site/291/default.aspxCook-Patton, S. C. et al. Mapping carbon accumulation potential from global natural forest regrowth. Nature 585, 545–550 (2020).CAS 
Article 

Google Scholar 
Hurni, K., Schneider, A., Heinimann, A., Nong, D. H. & Fox, J. Mapping the expansion of boom crops in mainland Southeast Asia using dense time stacks of Landsat data. Remote Sens. 9, 320 (2017).Article 

Google Scholar 
Miettinen, J., Shi, C. & Liew, S. C. 2015 Land cover map of Southeast Asia at 250 m spatial resolution. Remote Sens. Lett. 7, 701–710 (2016).Article 

Google Scholar 
Torbick, N., Ledoux, L., Salas, W. & M. Zhao, M. Regional mapping of plantation extent using multisensor imagery. Remote Sens. 8, 236 (2016).Azizan, F. A., Kiloes, A. M., Astuti, I. S. & Abdul Aziz, A. Application of optical remote sensing in rubber plantations: a systematic review. Remote Sens. 13, 429 (2021).Article 

Google Scholar 
Bégué, A. et al. Remote sensing and cropping practices: a review. Remote Sens. 10, 99 (2018).Article 

Google Scholar 
Bey, A. & Meyfroidt, P. Improved land monitoring to assess large-scale tree plantation expansion and trajectories in Northern Mozambique. Environ. Res. Commun. 3, 115009 (2021).Jucker, T. et al. Topography shapes the structure, composition and function of tropical forest landscapes. Ecol. Lett. 21, 989–1000 (2018).Article 

Google Scholar 
Féret, J.-B. & Asner, G. P. Spectroscopic classification of tropical forest species using radiative transfer modeling. Remote Sens. Environ. 115, 2415–2422 (2011).Article 

Google Scholar 
Poortinga, A. et al. Mapping plantations in Myanmar by fusing Landsat-8, Sentinel-2 and Sentinel-1 data along with systematic error quantification. Remote Sens. 11, 831 (2019).Article 

Google Scholar 
Gutiérrez-Vélez, V. H. et al. High-yield oil palm expansion spares land at the expense of forests in the Peruvian Amazon. Environ. Res. Lett. 6, 044029 (2011).Article 

Google Scholar 
Descals, A. et al. High-resolution global map of smallholder and industrial closed-canopy oil palm plantations. Earth Syst. Sci. Data. 13, 1211–1231 (2021).Article 

Google Scholar 
Ordway, E. M., Naylor, R. L., Nkongho, R. N. & Lambin, E. F. Oil palm expansion and deforestation in Southwest Cameroon associated with proliferation of informal mills. Nat. Commun. 10, 114 (2019).CAS 
Article 

Google Scholar 
Heilmayr, R., Echeverría, C., Fuentes, R. & Lambin, E. F. A plantation-dominated forest transition in Chile. Appl. Geogr. 75, 71–82 (2016).Article 

Google Scholar 
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).CAS 
Article 

Google Scholar 
Bond, W. J., Stevens, N., Midgley, G. F. & Lehmann, C. E. R. The trouble with trees: afforestation plans for Africa. Trends Ecol. Evol. 34, 963–965 (2019).Article 

Google Scholar 
Veldman, J. W. et al. Where tree planting and forest expansion are bad for biodiversity and ecosystem services. Bioscience 65, 1011–1018 (2015).Article 

Google Scholar 
Venter, O. et al. Global terrestrial Human Footprint maps for 1993 and 2009. Sci. Data 3, 160067 (2016).Article 

Google Scholar 
Fagan, M. E. A lesson unlearned? Underestimating tree cover in drylands biases global restoration maps. Glob. Change Biol. 26, 4679–4690 (2020).Bastin, J. F. et al. The extent of forest in dryland biomes. Science 356, 635–638 (2017).CAS 
Article 

Google Scholar 
Fagan, M. E., Reid, J. L., Holland, M. B., Drew, J. G. & Zahawi, R. A. How feasible are global forest restoration commitments? Conserv. Lett. 13, e12700 (2020).Article 

Google Scholar 
Malkamäki, A. et al. A systematic review of the socio-economic impacts of large-scale tree plantations, worldwide. Glob. Environ. Change 53, 90–103 (2018).Article 

Google Scholar 
Schwartz, N. B., Aide, T. M., Graesser, J., Grau, H. R. & Uriarte, M. Reversals of reforestation across Latin America limit climate mitigation potential of tropical forests. Front. For. Glob. Change 3, 85 (2020).Article 

Google Scholar 
Noojipady, P. et al. Managing fire risk during drought: the influence of certification and El Niño on fire-driven forest conversion for oil palm in Southeast Asia. Earth Syst. Dynam. 8, 749–771 (2017).Bullock, E. L., Woodcock, C. E., Souza, C. Jr. & Olofsson, P. Satellite-based estimates reveal widespread forest degradation in the Amazon. Glob. Change Biol. 26, 2956–2969 (2020).Article 

Google Scholar 
Sloan, S. & Sayer, J. A. Forest Ecology and Management Forest Resources Assessment of 2015 shows positive global trends but forest loss and degradation persist in poor tropical countries. Ecol. Manag. 352, 134–145 (2015).Article 

Google Scholar 
Heinrich, V. H. A. et al. Large carbon sink potential of secondary forests in the Brazilian Amazon to mitigate climate change. Nat. Commun. 12, 1785 (2021).CAS 
Article 

Google Scholar 
Potapov, P. et al. Mapping global forest canopy height through integration of GEDI and Landsat data. Remote Sens. Environ. 253, 112165 (2021).Article 

Google Scholar 
Bernal, B., Murray, L. T. & Pearson, T. R. H. Global carbon dioxide removal rates from forest landscape restoration activities. Carbon Balance Manag. 13, 22 (2018).CAS 
Article 

Google Scholar 
Li, W., Goodchild, M. F. & Church, R. An efficient measure of compactness for two-dimensional shapes and its application in regionalization problems. Int. J. Geogr. Inf. Sci. 27, 1227–1250 (2013).Article 

Google Scholar 
Asner, G. P. Cloud cover in Landsat observations of the Brazilian Amazon. Int. J. Remote Sens. 22, 3855–3862 (2001).Article 

Google Scholar 
Wilson, A. M. & Jetz, W. Remotely sensed high-resolution global cloud dynamics for predicting ecosystem and biodiversity distributions. PLoS Biol. 14, e1002415 (2016).Article 
CAS 

Google Scholar 
Gutiérrez-Vélez, V. H. & DeFries, R. Annual multi-resolution detection of land cover conversion to oil palm in the Peruvian Amazon. Remote Sens. Environ. 129, 154–167 (2013).Article 

Google Scholar 
Reiche, J. et al. Combining satellite data for better tropical forest monitoring. Nat. Clim. Change 6, 120–122 (2016).Article 

Google Scholar 
Erinjery, J. J., Singh, M. & Kent, R. Mapping and assessment of vegetation types in the tropical rainforests of the Western Ghats using multispectral Sentinel-2 and SAR Sentinel-1 satellite imagery. Remote Sens. Environ. 216, 345–354 (2018).Article 

Google Scholar 
Shimada, M. et al. New global forest/non-forest maps from ALOS PALSAR data (2007–2010). Remote Sens. Environ. 155, 13–31 (2014).Article 

Google Scholar 
Torres, R. et al. GMES Sentinel-1 mission. Remote Sens. Environ. 120, 9–24 (2012).Article 

Google Scholar 
Potapov, P. et al. The last frontiers of wilderness: tracking loss of intact forest landscapes from 2000 to 2013. Sci. Adv. 3, e1600821 (2017).Article 

Google Scholar 
World Database on Protected Areas User Manual 1.4 (UNEP-WCMC, 2016).AutoML: Automatic Machine Learning (H2O.ai, 2020); https://h2o-release.s3.amazonaws.com/h2o/rel-yau/5/docs-website/h2o-docs/automl.htmlHealey, S. P. et al. Mapping forest change using stacked generalization: an ensemble approach. Remote Sens. Environ. 204, 717–728 (2018).Article 

Google Scholar 
Lagomasino, D. et al. Measuring mangrove carbon loss and gain in deltas. Environ. Res. Lett. 14, 25002 (2019).Article 

Google Scholar 
Bunting, P. et al. The global mangrove watch—a new 2010 global baseline of mangrove extent. Remote Sens. 10, 1669 (2018).Article 

Google Scholar 
Pickens, A. H. et al. Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series. Remote Sens. Environ. 243, 111792 (2020).Article 

Google Scholar 
Olofsson, P. et al. Good practices for estimating area and assessing accuracy of land change. Remote Sens. Environ. 148, 42–57 (2014).Article 

Google Scholar 
Stehman, S. V. Estimating area and map accuracy for stratified random sampling when the strata are different from the map classes. Int. J. Remote Sens. 35, 4923–4939 (2014).Article 

Google Scholar 
Olofsson, P. et al. Mitigating the effects of omission errors on area and area change estimates. Remote Sens. Environ. 236, 111492 (2020).Article 

Google Scholar 
Database of Global Administrative Areas (GADM) v.3.6 (GADM, 2018); https://gadm.org/download_country_v3.htmlHijmans, R. J., Williams, E., Vennes, C. M. & Hijmans, M. R. J. Package ‘geosphere’ version 1.5-10. Spherical trigonometry (2017).Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth: a new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. Bioscience 51, 933–938 (2001).Article 

Google Scholar 
Mittermeier, R. A., Turner, W. R., Larsen, F. W., Brooks, T. M. & Gascon, C. in Biodiversity Hotspots: Distribution and Protection of Conservation Priority Areas (eds Zachos, F. E. & Habel, J. C.) 3–22 (Springer, 2011).Potapov, P. et al. The last frontiers of wilderness: tracking loss of intact forest landscapes from 2000 to 2013. Sci. Adv. 3, e1600821 (2017). More

Considering the actual and predicted values, the model generated through the different inputted parameters should be diagnosed satisfactorily. It is pretty understanding that agreement between the actual and predicted values given the effectiveness and accuracy of the generated model, as shown in Fig. 2. The following polynomial regression model equations were obtained:$$begin{aligned} COD;removal , % , & = 76.63 – 0.019*A , + , 0.064*B , – 0.511*C , – 0.405*AB , – 0.153*AC , \ &quad – 0.099*BC , + , 0.263*A^{2} + , 0.479*B^{2} – 0.303*C^{2} \ end{aligned}$$
(1)
$$begin{aligned} Nitrate;Removal , % , & = 72.04 , – 1.881*A – 0.142* , B , + , 2.384*C , + , 2.623*AB , + , 8.579*AC , \ &quad – 2.626*BC , – 10.783*A^{2} + , 0.223*B^{2} + , 0.963*C^{2 } hfill \ end{aligned}$$
(2)
$$begin{aligned} & Phosphate , Removal , % , = \ & 67.179 – 1.215*A , + , 3.539*B , – 1.068*C , + , 1.610*AB , – 2.559*AC , + , 0.392*BC , + , 0.788*A^{2} – 2.943*B^{2} + , 0.564*C^{2} \ end{aligned}$$
(3)
where A is initial pH, B is current time (min), C is MLSS concentration (mg L−1) at which the study was carried out.Figure 2Normal probability versus studentized residuals and predicted versus actual plots for (i) COD removal, (ii) nitrate removal, and (iii) phosphate removal.Full size imageIt has been observed that statistics for the model having low values represent well for the system and its predictions.Statistical analysis of COD, nitrate and phosphate removalIt was seen that 3D surface plots could provide a better understanding of the interactive effects of the parameters. The 3D surface plots are illustrated in Figs. 3, 4, and 5, respectively. It was observed that the maximum removal efficiency for COD, nitrate, and phosphate is in the range of 59% to 74%.Figure 3Model generated surface plot of % COD removal (i) pH versus current time (ii) pH vs. MLSS (iii) MLSS vs. current time.Full size imageFigure 4Model generated surface plot of %nitrate removal (i) pH versus current time (ii) pH vs. MLSS (iii) MLSS vs. current time.Full size imageFigure 5Model generated surface plot of %phosphate removal (i) pH versus current time (ii) pH versus MLSS (iii) MLSS versus current time.Full size imageTable 4 (i) shows the statistics for COD removal. Adeq Precision is desirable, which measures the signal-to-noise ratio and a ratio greater than 4. For the COD removal, Adeq Precision was 19.255, indicating an adequate signal. It was also observed that the adjusted R2 is 0.9118 (difference less than 0.2), and the predicted R2 of 0.8601 was significant, implying that the predictions are in good agreement with experimental values.Table 4 Fit statistics for (i) COD removal, (ii) Nitrate removal, (iii) Phosphate removal.Full size tableFigure 3 illustrates the effect of current flow time and pH concerning the percentage removal of COD. The model predicted values observed were seen to lie in the range of 73.1% at MLSS values of 2500 mg L−1, keeping initial COD values as 200 mg L−1. As the COD load increases, it seems to be predicted that the overloading of bacteria occurs, thereby slowing down the consumption of organics. In Fig. 4, the expected removal efficacy shows upward trends with an increase in the values of MLSS, which also coincided with previous studies. As the value of MLSS increases, the contact time of biomass in the system increases, hence producing more effective results than others.Table 4 (ii) shows the statistics for nitrate removal. The predicted R2 of 0.9164 was in reasonable agreement with the adjusted R2 of 0.9730. For the nitrate removal, Adeq Precision was 29.608, indicating an adequate signal. This model can be used to navigate the design space.Table 4 (iii) shows the statistics for phosphate removal. The predicted R2 of 0.9165 was in reasonable agreement with the adjusted R2 of 0.9720. For the phosphate removal, Adeq Precision was 34.945, indicating an adequate signal. This model can be used to navigate the design space.Figure 5 illustrates that as we reduce the cycle time from 24 to 18 h, the system efficacy, i.e., COD removal effectiveness shows a downward trend due to less contact time with biomass. Meanwhile, if we increase the cycle time, we observe higher efficacy in the system. The model generated surface plot in Fig. 5 illustrated that increasing MLSS values by 3000 mg L−1 will enhance the COD removal by 73.1%, keeping the initial pH constant. This may be due to many microbes that can break down organic matter. In aerobic reactors, pH is an essential factor in the growth of the microbial population. To create granules, the pH of the reactor has a direct impact. Studies have shown that granule formation occurs when bacteria grow at the ideal pH level, whereas mass proliferation of fungus occurs in an acidic environment.COD removal in EBR and tubesettlerThe Influence, effluent, and removal of COD in EBR & tubesettler are illustrated in Fig. 6a,b. Results demonstrate that the COD concentration is consistent and better COD removal efficacy rate. The average removal rate values observed in the EBR were between 74 and 79%, with the initial COD concentration kept around 360–396 mg L−1. It was also observed that tubesettler resulted in approximately 25–36% efficacy when the initial concentration was between 75 and 97 mg L−1. The results of EBR are promising and can be attributed to the fact that electrocoagulation takes place along with the oxidation and biodegradation process. It was also observed that the percentage removal of COD shows downward trends due to electrochemical oxidation and adsorption, thereby resulting in physical entrapment and electrostatic attraction30. It has also been reported in many other studies that COD removal of around 85–90% was observed using composite cathode membrane using MRB/MFC system19 for the specialized treatment of landfill leachate. It was seen with the electrooxidation process having COD removal of around 80–84% and 84–96% with submerged membrane bioreactors, using Iron electrode6. For the Coal industry, it was found to be around 85% using membrane electro bioreactors31.Figure 6(a) Influent, effluent and removal of COD in EBR (IEBR = Influent Electrobioreactor, EEBR = Effluent Electrobioreactor, STD = Standard, REBR = Removal Electrobioreactor), (b) Influent, effluent, and removal of COD in tubesettler (IT = Influent tubesettler, ET = Effluent tubesettler, STD = Standard, RT = Removal tubesettler).Full size imageIn the current study, results seemed to be lower than the values reported in the previous studies. The main reason might be the employment of a modified EBR system and the production of biomass species. When the overall COD removal with tubesettler is considered, up to 83.58% removal efficiency is observed. The overall COD removal efficiency is significant and is at par with other studies3,4,5. This signifies that EBR performed better than tubesettler in COD removal. The tubesettler’s lower removal efficiency can be attributed to lower influent concentration from already reduced wastewater from EBR.Nitrate removal in EBR and tubesettlerIt was observed in many studies that nitrifying is the leading cause of nitrification, i.e., conversion of NH3-N to nitrate NO3-N10. The indirect method of system nitrification process claudication was to be ascertained using measurements concerning ammonia values32,33. In the current study, the nitrification process was considered using the nitrate concentration measurement from the influent and effluent in both systems, i.e., EBR and tubesettler34,35,36. The nitrate concentration of influent and effluent was observed and illustrated in Fig. 7a,b. The system stabilized and produced enhanced results up to 70% of nitrate removal, and it was seen to be in the range of 40–45% for the tubesettler. It has been observed that EBR produced better results than the tubesettler. The results variation in both the systems were reasonably attributed mainly to two primary reasons (1) low influent concentration in the influent compared to the EBR system and (2) inhibition effect due to the applied DC field, which was absent in tubesettlers.Figure 7(a) Influent, effluent, and removal of nitrate in EBR (IEBR = Influent Electrobioreactor, EEBR = Effluent Electrobioreactor, STD = Standard, REBR = Removal Electrobioreactor), (b) Influent, effluent, and removal of nitrate in tubesettler (IT = Influent tubesettler, ET = Effluent tubesettler, STD = Standard, RT = Removal tubesettler).Full size imageThe removal efficiency of around 70% was achieved, lower than the values in submerged membrane bioreactors, i.e., 82%6. However, including a membrane would have enhanced the removal efficiency and considered a hybrid EBR system. The results of the current study are close enough to many other studies with a similar system and different operating parameters. Hence, a combined approach can be used for better efficacy. During the weekly analysis, the nitrate concentration during the 1st to 3rd week is lower than in the following weeks. As the concentration of nitrifying bacteria decreased, they had less to work with. Thus, the substrate concentration grew, and so did the removal rate. Nitrate concentrations rose by more than twice the previous week during Week 7. They slowed the bacterial activity, resulting in an efficiency decline to 47% from 70% during the last week’s study period and weeks 6 and 8. A similar pattern emerged for the seventh week in a row in tubesettler. On the other hand, microorganisms overcame differences in engagement because the nitrate content was low in other weeks.Phosphate removal in EBR and tubesettlerMany researchers have looked at nitrate content, but none have looked at phosphate concentration. Eutrophication in receiving water bodies, on the other hand, is predominantly caused by phosphate and nitrate. Additionally, there is a lack of information available on hospital wastewater. The influent and effluent phosphate concentrations in the Electro bioreactor and the tubesettler is shown in Fig. 8a,b. A 75% reduction in the effluent phosphate content in EBR was achieved tubesettler had a 67% effectiveness in phosphate removal but a lower efficiency in nitrate reduction. A previous similar study that used a Submerged Membrane Electro bioreactor claimed a clearance rate of 76% to 95%, which is lower than this study’s results6. Phosphate removal was reported at 50–70% using the electrocoagulation process for different Ph and current6.Figure 8(a) Influent, effluent, and removal of phosphate in EBR (IEBR = Influent Electrobioreactor, EEBR = Effluent Electrobioreactor, STD = Standard, REBR = Removal Electrobioreactor), (b) Influent, effluent, and removal of phosphate in tubesettler (IT = Influent tubesettler, ET = Effluent tubesettler, STD = Standard, RT = Removal tubesettler).Full size imageIn week 6 and week 8, the EBR’s phosphate removal efficiency fluctuated dependent on the weekly average concentration in EBR. This volatility can be linked to a shift in the composition of hospital wastewater. tubesettler had a modest variation ranging from 5 to 6%. Although phosphate concentrations rose in week two, tubesettler removal efficiency improved. As demonstrated in Fig. 8a,b, the arriving wastewater ingredient exhibited a strong affinity in terms of phosphate reduction.Excess effluent concentration and standard deviation from EBR and tubesettler are shown in Table 5. EBR performed better than tubesettler in COD reduction when nitrate and phosphate were compared. Because tubesettler solely employs a physical process to remove contaminants, this is to be anticipated. Effluent from the secondary treatment facility is sent to a tubesettler, which acts as a polishing unit. EBR eliminated COD by 91%, nitrate by 85%, and Phosphate reduction by 81% compared to tubesettler’ s total efficiency. At the same time, tubesettler reduced COD by 37%, nitrate by 51%, and phosphate by 53%. Hence, EBR primarily removed pollutants from wastewater while tubesettler acted as a polishing unit. Table 5 illustrates the effluent wastewater characteristics of EBR and tubesettler.Table 5 Effluent wastewater characteristics of EBR and tubesettler.Full size tableKinetic models post optimizationFirst-order modelA first-order linear model was analyzed on the experimental data by plotting (So − Se)/Se against hydraulic retention time (HRT), providing K1 and R2. For COD, R2 values were 0.761 with a constant value of 1.213, as shown in Table 6. Henceforth based on the results, the obtained model did not seem to fit well for either of the cases.Table 6 Analyzed kinetic models.Full size tableGrau second-order modelA Grau second-order model was analyzed on the experimental data by plotting HRT/((So − Se)/So) versus HRT. The COD constant obtained was Ks = 10–5, as shown in Table 6. The R2 value of 0.99 suggests a good correlation coefficient. Therefore, the obtained results fit well for AOX and COD.Modified Stover–Kincannon modelSubstrate utilization rate expressed as organic loading in this model is widely used in biological reactor kinetic modelling of wastewater. The developed model can evaluate the performance of the biological system and estimate its efficiency based on the input parameters. The kinetic constant KB and Umax for COD were 0.35 and 1.73 g L−1 d−1, respectively. The R2 was 0.98 for the substrate removal, as presented in Table 6.Monod modelCOD utilization rate was obtained by plotting VX/Q (So − Se) against 1/Se. The value of 1/K (0.421) was obtained from the intercept, while the Ks/K value (1.235) was the slope of the line. COD removal half-saturation values were 0.045 and 0.056 g L−1. These values infer a high affinity of bacteria for the substrate. The R2 value of 0.95 depicted an excellent correlation coefficient in the case of COD. The Monod model fits well for COD, resulting in R2 = 0.98, as shown in Table 6. More

Laity JJ. Deserts and desert environments. John Wiley & Sons; UK, 2009.Huang J, Yu H, Guan X, Wang G, Guo R. Accelerated dryland expansion under climate change. Nat Clim Chang. 2015;6:166–71.Article 

Google Scholar 
Berdugo M, Delgado-Baquerizo M, Soliveres S, Hernández-Clemente R, Zhao Y, Gaitán JJ, et al. Global ecosystem thresholds driven by aridity. Science. 2020;367:787–90.CAS 
PubMed 
Article 

Google Scholar 
Danin A. Plant adaptations to environmental stresses in desert dunes. In: Cloudsley-Thompson J, Punzo F, editors. Adaptations of desert organisms. Plant of desert dunes. Springer; Verlag Berlin Heidelberg, 1996.Makhalanyane TP, Valverde A, Gunnigle E, Frossard A, Ramond J-B, Cowan DA. Microbial ecology of hot desert edaphic systems. FEMS Microbiol Rev. 2015;39:203–21.CAS 
PubMed 
Article 

Google Scholar 
Fierer N, Leff JWJ, Adams BJ, Nielsen UN, Bates ST, Lauber CL, et al. Cross-biome metagenomic analyses of soil microbial communities and their functional attributes. Proc Natl Acad Sci USA. 2012;109:21390–5.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ronca S, Ramond J-BB, Jones BE, Seely M, Cowan DA. Namib Desert dune/interdune transects exhibit habitat-specific edaphic bacterial communities. Front Microbiol. 2015;6:1–12.Article 

Google Scholar 
Pointing SB, Belnap J. Microbial colonization and controls in dryland systems. Nat Rev Microbiol. 2012;10:551–62.CAS 
PubMed 
Article 

Google Scholar 
Noy-Meir I. Desert ecosystems: higher trophic levels. Annu Rev Ecol Syst. 1974;5:195–214.Article 

Google Scholar 
Danin A. Plants of desert dunes. In: Cloudsley-Thompson J, editor. Adaptations of desert organisms. Springer; Verlag Berlin Heidelberg, 2000.Roth-Nebelsick A, Ebner M, Miranda T, Gottschalk V, Voigt D, Gorb S, et al. Leaf surface structures enable the endemic Namib Desert grass Stipagrostis sabulicola to irrigate itself with fog water. J R Soc Interface. 2012;9:1965–74.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ebner M, Miranda T, Roth-Nebelsick A. Efficient fog harvesting by Stipagrostis sabulicola (Namib dune bushman grass). J Arid Environ. 2011;75:524–31.Article 

Google Scholar 
Cartwright J. Ecological islands: conserving biodiversity hotspots in a changing climate. Front Ecol Environ. 2019;17:fee.2058.Article 

Google Scholar 
André HM, Noti MI, Jacobson KM. The soil microarthropods of the Namib Desert: a patchy mosaic. J African Zool. 1997;111:499–517.
Google Scholar 
Marasco R, Mosqueira MJ, Fusi M, Ramond J, Merlino G, Booth JM, et al. Rhizosheath microbial community assembly of sympatric desert speargrasses is independent of the plant host. Microbiome. 2018;6:215.PubMed 
PubMed Central 
Article 

Google Scholar 
Brown LK, George TS, Neugebauer K, White PJ. The rhizosheath—a potential trait for future agricultural sustainability occurs in orders throughout the angiosperms. Plant Soil. 2017;418:115–28.CAS 
Article 

Google Scholar 
Pang J, Ryan MH, Siddique KHMM, Simpson RJ. Unwrapping the rhizosheath. Plant Soil. 2017;418:129–39.CAS 
Article 

Google Scholar 
Marasco R, Fusi M, Mosqueira M, Booth JM, Rossi F, Cardinale M, et al. Rhizosheath–root system changes exopolysaccharide content but stabilizes bacterial community across contrasting seasons in a desert environment. Environ Microbiome. 2022;17:14.PubMed 
PubMed Central 
Article 

Google Scholar 
Moreno-Espíndola IP, Rivera-Becerril F, de Jesús Ferrara-Guerrero M, De León-González F. Role of root-hairs and hyphae in adhesion of sand particles. Soil Biol Biochem. 2007;39:2520–6.Article 
CAS 

Google Scholar 
Wullstein LHH, Pratt SAA. Scanning electron microscopy of rhizosheaths of Oryzopsis hymenoides. Am J Bot. 1981;68:408–19.Article 

Google Scholar 
Young IM. Variation in moisture contents between bulk soil and the rhizosheath of wheat (Triticum aestivum L. cv. Wembley). New Phytol. 1995;130:135–9.Article 

Google Scholar 
Ashraf M, Hasnain S, Berge O, Campus Q. Effect of exo-polysaccharides producing bacterial inoculation on growth of roots of wheat (Triticum aestivum L.) plants grown in a salt-affected soil. Int J Environ Sci Technol. 2006;3:45–53.Article 

Google Scholar 
George TS, Brown LK, Ramsay L, White PJ, Newton AC, Bengough AG, et al. Understanding the genetic control and physiological traits associated with rhizosheath production by barley (Hordeum vulgare). New Phytol. 2014;203:195–205.CAS 
PubMed 
Article 

Google Scholar 
Ndour PMS, Heulin T, Achouak W, Laplaze L, Cournac L. The rhizosheath: from desert plants adaptation to crop breeding. Plant Soil. 2020;456:1–13.CAS 
Article 

Google Scholar 
Othman AA, Amer WM, Fayez M, Monib M, Hegazi NA. Biodiversity of diazotrophs associated to the plant cover of north sinai deserts. Arch Agron Soil Sci. 2003;49:683–705.Article 

Google Scholar 
Bergmann D, Zehfus M, Zierer L, Smith B, Gabel M. Grass rhizosheaths: associated bacterial communities and potential for nitrogen fixation. West North Am Nat. 2009;69:105–14.Article 

Google Scholar 
Marasco R, Rolli E, Ettoumi B, Vigani G, Mapelli F, Borin S, et al. A drought resistance-promoting microbiome is selected by root system under desert farming. PLoS ONE. 2012;7:e48479.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Marasco R, Mapelli F, Rolli E, Mosqueira MJ, Fusi M, Bariselli P, et al. Salicornia strobilacea (synonym of Halocnemum strobilaceum) grown under different tidal regimes selects rhizosphere bacteria capable of promoting plant growth. Front Microbiol. 2016;7:1–11.Article 

Google Scholar 
Rolli E, Marasco R, Vigani G, Ettoumi B, Mapelli F, Deangelis ML, et al. Improved plant resistance to drought is promoted by the root-associated microbiome as a water stress-dependent trait. Environ Microbiol. 2015;17:316–31.PubMed 
Article 

Google Scholar 
Alsharif W, Saad MM, Hirt H. Desert microbes for boosting sustainable agriculture in extreme environments. Front Microbiol. 2020;11:1666.Zhang Y, Du H, Xu F, Ding Y, Gui Y, Zhang J, et al. Root-bacteria associations boost rhizosheath formation in moderately dry soil through ethylene responses. Plant Physiol. 2020;183:780–92.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Soussi A, Ferjani R, Marasco R, Guesmi A, Cherif H, Rolli E, et al. Plant-associated microbiomes in arid lands: diversity, ecology and biotechnological potential. Plant Soil. 2016;405:357–70.CAS 
Article 

Google Scholar 
Livingston G, Matias M, Calcagno V, Barbera C, Combe M, Leibold MA, et al. Competition-colonization dynamics in experimental bacterial metacommunities. Nat Commun. 2012;3:1–8.Article 
CAS 

Google Scholar 
Smith GR, Steidinger BS, Bruns TD, Peay KG. Competition–colonization tradeoffs structure fungal diversity. ISME J. 2018;12:1758–67.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Seely MK. The Namib dune desert: an unusual ecosystem. J Arid Environ. 1978;1:117–28.Article 

Google Scholar 
Klaassen E, Craven P. Checklist of grasses in Namibia. SABONET; Pretoria & Windhoek, 2014. (Produced by National Botanical Research Institute Private Bag 13184).Neilson JW, Califf K, Cardona C, Copeland A, van Treuren W, Josephson KL, et al. Significant impacts of increasing aridity on the arid soil microbiome. mSystems. 2017;2:1–15.Article 

Google Scholar 
Darwin C. On the origin of species. London: Routledge; 1859.Gunnigle E, Frossard A, Ramond J-B, Guerrero L, Seely M, Cowan DA. Diel-scale temporal dynamics recorded for bacterial groups in Namib Desert soil. Sci Rep. 2017;7:40189.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wickham H. ggplot2: Elegant graphics for data analysis. Media. Springer; New York, NY 2016.RC-Team. R: A language and environment for statistical computing (Version 3.5. 2, R foundation for statistical computing, Vienna, Austria, 2018). R Foundation for Statistical Computing; 2019.Anderson MMJJ, Gorley RNRN, Clarke KRR. PERMANOVA + for PRIMER: guide to software and statistical methods; PRIMER-E. Plymouth, UK: PRIMER-E Ltd.; 2008.Cherif H, Marasco R, Rolli E, Ferjani R, Fusi M, Soussi A, et al. Oasis desert farming selects environment-specific date palm root endophytic communities and cultivable bacteria that promote resistance to drought. Environ Microbiol Rep. 2015;7:668–78.CAS 
PubMed 
Article 

Google Scholar 
Lee KC, Caruso T, Archer SDJ, Gillman LN, Lau MCY, Craig Cary S, et al. Stochastic and deterministic effects of a moisture gradient on soil microbial communities in the McMurdo dry valleys of Antarctica. Front Microbiol. 2018;9:1–12.Article 

Google Scholar 
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2012;41:D590–6.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Koljalg U, Nilsson RH, Abarenkov K, Tedersoo L, Taylor AFS, Bahram M. Towards a unified paradigm for sequence-based identification of fungi. Mol Ecol. 2014;22:5271–7.Article 
CAS 

Google Scholar 
Ramette A. Multivariate analyses in microbial ecology. FEMS Microbiol Ecol. 2007;62:142–60.CAS 
PubMed 
Article 

Google Scholar 
Clarke KR, Gorley RN. PRIMER v7: user manual/tutorial. Plymouth, UK: PRIMER-E; 2015.Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, O’Hara B, et al. The vegan R package: community ecology. 2013:0–291Wang Y, Naumann U, Wright ST, Warton DI. mvabund—an R package for model-based analysis of multivariate abundance data. Methods Ecol Evol. 2012;3:471–4.Article 

Google Scholar 
Legendre P. Interpreting the replacement and richness difference components of beta diversity. Glob Ecol Biogeogr. 2014;23:1324–34.Article 

Google Scholar 
Dray S, Blanchet G, Borcard D, Guenard G, Jombart T, Larocque G, et al. Package ‘adespatial’. R package version. 2018.Hammer Ø, Harper DAT, Ryan PD. PAST: paleontological statistics software package for education and data analysis. Palaeontol Electron. 2001;4:1–9.
Google Scholar 
Weiss S, Van Treuren W, Lozupone C, Faust K, Friedman J, Deng Y, et al. Correlation detection strategies in microbial data sets vary widely in sensitivity and precision. ISME J. 2016;10:1669–81.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Faust K, Raes J. Microbial interactions: from networks to models. Nat Rev Microbiol. 2012;10:538–50.CAS 
PubMed 
Article 

Google Scholar 
Bastian M, Heymann S, Jacomy M. Gephi: an open source software for exploring and manipulating networks. Third International AAAI Conference on Weblogs and Social Media. 2009;8:361–2.Barberán A, Bates ST, Casamayor EO, Fierer N. Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J. 2012;6:343–51.PubMed 
Article 
CAS 

Google Scholar 
Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E. Fast unfolding of communities in large networks. J Stat Mech Theory Exp. 2008;2008:P10008.Article 

Google Scholar 
de Vries FT, Griffiths RI, Bailey M, Craig H, Girlanda M, Gweon HS, et al. Soil bacterial networks are less stable under drought than fungal networks. Nat Commun. 2018;9:3033.PubMed 
PubMed Central 
Article 
CAS 

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

Google Scholar 
Andrews S. FastQC: a quality control tool for high throughput sequence data. Cambridge, United Kingdom: Babraham Bioinformatics, Babraham Institute; 2010.Schmieder R, Edwards R. Quality control and preprocessing of metagenomic datasets. Bioinformatics. 2011;27:863–4.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rodriguez-R LM, Gunturu S, Tiedje JM, Cole JR, Konstantinidis KT. Nonpareil 3: fast estimation of metagenomic coverage and sequence diversity. mSystems. 2018;3:1–9.Article 

Google Scholar 
Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol. 2019;20:257.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017;27:824–34.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mikheenko A, Saveliev V, Gurevich A. MetaQUAST: evaluation of metagenome assemblies. Bioinformatics. 2016;32:1088–90.CAS 
PubMed 
Article 

Google Scholar 
Hyatt D, Chen G-L, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics. 2010;11:119.PubMed 
PubMed Central 
Article 
CAS 

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

Google Scholar 
Blin K, Shaw S, Steinke K, Villebro R, Ziemert N, Lee SY, et al. antiSMASH 5.0: updates to the secondary metabolite genome mining pipeline. Nucleic Acids Res. 2019;47:W81–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhang H, Yohe T, Huang L, Entwistle S, Wu P, Yang Z, et al. dbCAN2: a meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 2018;46:W95–101.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2014;12:59–60.PubMed 
Article 
CAS 

Google Scholar 
Vigani G, Rolli E, Marasco R, Dell’Orto M, Michoud G, Soussi A, et al. Root bacterial endophytes confer drought resistance and enhance expression and activity of a vacuolar H+-pumping pyrophosphatase in pepper plants. Environ Microbiol. 2019;21:3212–28.CAS 
Article 

Google Scholar 
Al-Hosni K, Shahzad R, Khan AL, Muhammad Imran Q, Al Harrasi A, Al Rawahi A, et al. Preussia sp. BSL-10 producing nitric oxide, gibberellins, and indole acetic acid and improving rice plant growth. J Plant Interact. 2018;13:112–8.CAS 
Article 

Google Scholar 
Sen D, Paul K, Saha C, Mukherjee G, Nag M, Ghosh S, et al. A unique life-strategy of an endophytic yeast Rhodotorula mucilaginosa JGTA-S1—a comparative genomics viewpoint. DNA Res. 2019;26:131–46.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Johnson JM, Ludwig A, Furch ACU, Mithöfer A, Scholz S, Reichelt M, et al. The beneficial root-colonizing fungus Mortierella hyalina promotes the aerial growth of Arabidopsis and activates calcium-dependent responses that restrict Alternaria brassicae–induced disease development in roots. Mol Plant-Microbe Interact. 2019;32:351–63.CAS 
PubMed 
Article 

Google Scholar 
van Dam NM, Bouwmeester HJ. Metabolomics in the rhizosphere: tapping into belowground chemical communication. Trends Plant Sci. 2016;21:256–65.PubMed 
Article 
CAS 

Google Scholar 
Zeng Y, Charkowski AO. The role of ATP-binding cassette transporters in bacterial phytopathogenesis. Phytopathology®. 2021;111:600–10.Article 

Google Scholar 
Louca S, Polz MF, Mazel F, Albright MBN, Huber JA, O’Connor MI, et al. Function and functional redundancy in microbial systems. Nat Ecol Evol. 2018;2:936–43.PubMed 
Article 

Google Scholar 
Balskus EP, Walsh CT. The genetic and molecular basis for sunscreen biosynthesis in cyanobacteria. Science. 2010;329:1653–6.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Makarova KS, Wolf YI, Iranzo J, Shmakov SA, Alkhnbashi OS, Brouns SJJ, et al. Evolutionary classification of CRISPR–Cas systems: a burst of class 2 and derived variants. Nat Rev Microbiol. 2020;18:67–83.CAS 
PubMed 
Article 

Google Scholar 
Smith VH. Effects of resource supplies on the structure and function of microbial communities. Antonie Van Leeuwenhoek. 2002;81:99–106.CAS 
PubMed 
Article 

Google Scholar 
Albalasmeh AA, Ghezzehei TA. Interplay between soil drying and root exudation in rhizosheath development. Plant Soil. 2014;374:739–51.CAS 
Article 

Google Scholar 
Devitt DA, Smith SD. Root channel macropores enhance downward movement of water in a Mojave Desert ecosystem. J Arid Environ. 2002;50:99–108.Article 

Google Scholar 
Othman AA, Amer WM, Fayez M, Hegazi NA. Rhizosheath of sinai desert plants is a potential repository for associative diazotrophs. Microbiol Res. 2004;159:285–93.PubMed 
Article 

Google Scholar 
Naseem H, Ahsan M, Shahid MA, Khan N. Exopolysaccharides producing rhizobacteria and their role in plant growth and drought tolerance. J Basic Microbiol. 2018;58:1009–22.CAS 
PubMed 
Article 

Google Scholar 
Toju H, Peay KG, Yamamichi M, Narisawa K, Hiruma K, Naito K, et al. Core microbiomes for sustainable agroecosystems. Nat Plants. 2018;4:247–57.PubMed 
Article 

Google Scholar 
Banerjee S, Schlaeppi K, van der Heijden MGAA. Keystone taxa as drivers of microbiome structure and functioning. Nat Rev Microbiol. 2018;16:567–76.CAS 
PubMed 
Article 

Google Scholar 
Agler MT, Ruhe J, Kroll S, Morhenn C, Kim S-TT, Weigel D, et al. Microbial hub taxa link host and abiotic factors to plant microbiome variation. PLoS Biol. 2016;14:1–31.Article 
CAS 

Google Scholar 
Delgado-Baquerizo M, Reich PB, Trivedi C, Eldridge DJ, Abades S, Alfaro FD, et al. Multiple elements of soil biodiversity drive ecosystem functions across biomes. Nat Ecol Evol. 2020;4:210–20.PubMed 
Article 

Google Scholar 
Hassani MA, Durán P, Hacquard S. Microbial interactions within the plant holobiont. Microbiome. 2018;6:58.PubMed 
PubMed Central 
Article 

Google Scholar 
Lopez BR, Bacilio M. Weathering and soil formation in hot, dry environments mediated by plant–microbe interactions. Biol Fertil Soils. 2020;56:447–59.CAS 
Article 

Google Scholar 
Hernandez DJ, David AS, Menges ES, Searcy CA, Afkhami ME. Environmental stress destabilizes microbial networks. ISME J. 2021;15:1722–34.PubMed 
PubMed Central 
Article 

Google Scholar 
Yuan MM, Guo X, Wu L, Zhang Y, Xiao N, Ning D, et al. Climate warming enhances microbial network complexity and stability. Nat Clim Chang. 2021;11:343–8.Article 

Google Scholar 
Safronova VI, Kuznetsova IG, Sazanova AL, Belimov AA, Andronov EE, Chirak ER, et al. Microvirga ossetica sp. nov., a species of rhizobia isolated from root nodules of the legume species Vicia alpestris Steven. Int J Syst Evol Microbiol. 2017;67:94–100.CAS 
PubMed 
Article 

Google Scholar 
Jiménez-Gómez A, Saati-Santamaría Z, Igual J, Rivas R, Mateos P, García-Fraile P. Genome insights into the novel species Microvirga brassicacearum, a rapeseed endophyte with biotechnological potential. Microorganisms. 2019;7:354.PubMed Central 
Article 
CAS 

Google Scholar 
Liu T, Ye N, Wang X, Das D, Tan Y, You X, et al. Drought stress and plant ecotype drive microbiome recruitment in switchgrass rhizosheath. J Integr Plant Biol. 2021;63:1753–74.Blouin M. Chemical communication: an evidence for co-evolution between plants and soil organisms. Appl Soil Ecol. 2018;123:409–15.Article 

Google Scholar 
Sarrocco S, Diquattro S, Baroncelli R, Cimmino A, Evidente A, Vannacci G, et al. A polyphasic contribution to the knowledge of Auxarthron (Onygenaceae). Mycol Prog. 2015;14:112.Macías-Rubalcava ML, Sánchez-Fernández RE. Secondary metabolites of endophytic Xylaria species with potential applications in medicine and agriculture. World J Microbiol Biotechnol. 2017;33:15.Zhang K, Bonito G, Hsu C, Hameed K, Vilgalys R, Liao H-L. Mortierella elongata increases plant biomass among non-leguminous crop species. Agronomy. 2020;10:754.Article 

Google Scholar 
Kobayashi DY, Crouch JA. Bacterial/fungal interactions: from pathogens to mutualistic endosymbionts. Annu Rev Phytopathol. 2009;47:63–82.CAS 
PubMed 
Article 

Google Scholar 
Asmelash F, Bekele T, Birhane E. The potential role of arbuscular mycorrhizal fungi in the restoration of degraded lands. Front Microbiol. 2016;7:1–15.Article 

Google Scholar 
Kohlmeier S, Smits THM, Ford RM, Keel C, Harms H, Wick LY. Taking the fungal highway: mobilization of pollutant-degrading bacteria by fungi. Environ Sci Technol. 2005;39:4640–6.CAS 
PubMed 
Article 

Google Scholar 
Warmink JA, Nazir R, Corten B, van Elsas JD. Hitchhikers on the fungal highway: the helper effect for bacterial migration via fungal hyphae. Soil Biol Biochem. 2011;43:760–5.CAS 
Article 

Google Scholar 
Booth JM, Fusi M, Marasco R, Michoud G, Fodelianakis S, Merlino G, et al. The role of fungi in heterogeneous sediment microbial networks. Sci Rep. 2019;9:7537.Article 
CAS 

Google Scholar 
Deveau A, Bonito G, Uehling J, Paoletti M, Becker M, Bindschedler S, et al. Bacterial-fungal interactions: ecology, mechanisms and challenges. FEMS Microbiol Rev. 2018;42:335–52.CAS 
PubMed 
Article 

Google Scholar 
Simon A, Hervé V, Al-Dourobi A, Verrecchia E, Junier P. An in situ inventory of fungi and their associated migrating bacteria in forest soils using fungal highway columns. FEMS Microbiol Ecol. 2017;93:fiw217.PubMed 
Article 
CAS 

Google Scholar 
Faust K, Raes J. CoNet app: inference of biological association networks using Cytoscape. F1000Research. 2016;5:1519.PubMed 
PubMed Central 
Article 

Google Scholar 
Fierer N, Lauber CL, Ramirez KS, Zaneveld J, Bradford MA, Knight R. Comparative metagenomic, phylogenetic and physiological analyses of soil microbial communities across nitrogen gradients. ISME J. 2012;6:1007–17.CAS 
PubMed 
Article 

Google Scholar 
Zablocki O, Adriaenssens EM, Cowan D. Diversity and ecology of viruses in hyperarid desert soils. Appl Environ Microbiol. 2016;82:770–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Van Goethem MW, Swenson TL, Trubl G, Roux S, Northen TR. Characteristics of wetting-induced bacteriophage blooms in biological soil crust. MBio. 2019;10:e02287–19.Lambers H, Mougel C, Jaillard B, Hinsinger P. Plant-microbe-soil interactions in the rhizosphere: an evolutionary perspective. Plant Soil. 2009;321:83–115.CAS 
Article 

Google Scholar 
Ghoul M, Mitri S. The ecology and evolution of microbial competition. Trends Microbiol. 2016;24:833–45.CAS 
PubMed 
Article 

Google Scholar 
Schlatter DC, Kinkel LL. Antibiotics: conflict and communication in microbial communities. Microbe Mag. 2014;9:282–8.Article 

Google Scholar  More

Santana, S. E., Alfaro, J. L. & Alfaro, M. E. Adaptive evolution of facial colour patterns in Neotropical primates. Proc. R. Soc. B Biol. Sci. 279, 2204–2211 (2012).
Google Scholar 
Santana, S. E., Alfaro, J. L., Noonan, A. & Alfaro, M. E. Adaptive response to sociality and ecology drives the diversification of facial colour patterns in catarrhines. Nat. Commun. 4, 25 (2013).
Google Scholar 
Kobayashi, H. & Kohshima, S. Unique morphology of the human eye and its adaptive meaning: Comparative studies on external morphology of the primate eye. J. Hum. Evol. 40, 419–435 (2001).CAS 
PubMed 

Google Scholar 
Tomasello, M., Hare, B., Lehmann, H. & Call, J. Reliance on head versus eyes in the gaze following of great apes and human infants: The cooperative eye hypothesis. J. Hum. Evol. 52, 314–320 (2007).PubMed 

Google Scholar 
Farroni, T. et al. Newborns’ preference for face-relevant stimuli: Effects of contrast polarity. Proc. Natl. Acad. Sci. USA 102, 17245–17250 (2005).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Farroni, T., Massaccesi, S., Pividori, D. & Johnson, M. H. Gaze following in newborns. Infancy 5, 39–60 (2004).
Google Scholar 
Itakura, S. & Tanaka, M. Use of experimenter-given cues during object-choice tasks by chimpanzees (Pan troglodytes), an orangutan (Pongo pygmaeus), and human infants (Homo sapiens). J. Comp. Psychol. 112, 119–126 (1998).CAS 
PubMed 

Google Scholar 
Yorzinski, J. L., Thorstenson, C. A. & Nguyen, T. P. Sclera and iris color interact to influence gaze perception. Front. Psychol. 12, 1–11 (2021).
Google Scholar 
Yorzinski, J. L., Harbourne, A. & Thompson, W. Sclera color in humans facilitates gaze perception during daytime and nighttime. PLoS One 16, 1–15 (2021).
Google Scholar 
Yorzinski, J. L. & Miller, J. Sclera color enhances gaze perception in humans. PLoS One 15, 1–14 (2020).
Google Scholar 
Tomasello, M., Call, J. & Hare, B. Five primate species follow the visual gaze of conspecifics. Anim. Behav. 55, 1063–1069 (1998).CAS 
PubMed 

Google Scholar 
Kano, F. & Call, J. Cross-species variation in gaze following and conspecific preference among great apes, human infants and adults. Anim. Behav. 91, 137–150 (2014).
Google Scholar 
Kano, F., Kawaguchi, Y. & Yeow, H. Experimental evidence for the gaze-signaling hypothesis: White sclera enhances the visibility of eye gaze direction in humans and chimpanzees. bioRxiv 2021.09.21.461201 (2021).Perea-García, J. O., Kret, M. E., Monteiro, A. & Hobaiter, C. Scleral pigmentation leads to conspicuous, not cryptic, eye morphology in chimpanzees. Proc. Natl. Acad. Sci. USA 116, 19248–19250 (2019).PubMed 
PubMed Central 

Google Scholar 
Mearing, A. S. & Koops, K. Quantifying gaze conspicuousness: Are humans distinct from chimpanzees and bonobos ?. J. Hum. Evol. 157, 103043 (2021).PubMed 

Google Scholar 
Mearing, A. S., Burkart, J. M., Dunn, J., Street, S. E. & Koops, K. The evolutionary origins of primate scleral coloration. bioRxiv 40, 2021.07.25.453695 (2021).Mayhew, J. A. & Gómez, J. C. Gorillas with white sclera: A naturally occurring variation in a morphological trait linked to social cognitive functions. Am. J. Primatol. 77, 869–877 (2015).PubMed 

Google Scholar 
Caspar, K. R., Biggemann, M., Geissmann, T. & Begall, S. Ocular pigmentation in humans, great apes, and gibbons is not suggestive of communicative functions. Sci. Rep. 11, 1–14 (2021).
Google Scholar 
Kano, F. et al. What is unique about the human eye? Comparative image analysis on the external eye morphology of human and nonhuman great apes. Evol. Hum. Behav. https://doi.org/10.1016/j.evolhumbehav.2021.12.004 (2021).
Google Scholar 
Caves, E. M. & Johnsen, S. AcuityView: An r package for portraying the effects of visual acuity on scenes observed by an animal. Methods Ecol. Evol. 9, 793–797 (2018).
Google Scholar 
Osorio, D. & Vorobyev, M. Photoreceptor spectral sensitivities in terrestrial animals: Adaptations for luminance and colour vision. Proc. R. Soc. B Biol. Sci. 272, 1745–1752 (2005).CAS 

Google Scholar 
Troscianko, J. & Stevens, M. Image calibration and analysis toolbox—a free software suite for objectively measuring reflectance, colour and pattern. Methods Ecol. Evol. 6, 1320–1331 (2015).PubMed 
PubMed Central 

Google Scholar 
Stevens, M., Párraga, C. A., Cuthill, I. C., Partridge, J. C. & Troscianko, T. S. Using digital photography to study animal coloration. Biol. J. Linn. Soc. 90, 211–237 (2007).
Google Scholar 
Whitham, W., Schapiro, S. J., Troscianko, J. & Yorzinski, J. L. The gaze of a social monkey is perceptible to conspecifics and predators but not prey. Proc. R. Soc. B Biol. Sci. 20, 10 (2002).
Google Scholar 
Bethell, E. J., Vick, S. & Bard, K. A. Measurement of eye-gaze in chimpanzees (Pan troglodytes). Am. J. Primatol. 69, 562–575 (2007).PubMed 

Google Scholar 
Sreekar, R. & Quader, S. Influence of gaze and directness of approach on the escape responses of the Indian rock lizard, Psammophilus dorsalis (Gray, 1831). J. Biosci. 38, 829–833 (2013).CAS 
PubMed 

Google Scholar 
Lee, S. et al. Direct look from a predator shortens the risk-assessment time by prey. PLoS One 8, 1–7 (2013).
Google Scholar 
Carter, J., Lyons, N. J., Cole, H. L. & Goldsmith, A. R. Subtle cues of predation risk: Starlings respond to a predator’s direction of eye-gaze. Proc. R. Soc. B Biol. Sci. 275, 1709–1715 (2008).
Google Scholar 
Newton-Fisher, N. E. Chimpanzee hunting. Behav. Handb. Paleoanthropol. https://doi.org/10.1007/978-3-540-33761-4_42. (2007).
Google Scholar 
Caro, T. et al. The evolution of primate coloration revisited. Behav. Ecol. 32, 555–567 (2021).
Google Scholar 
Kilkenny, C., Browne, W., Cuthill, I. C., Emerson, M. & Altman, D. G. Animal research: Reporting in vivo experiments: The ARRIVE guidelines. Br. J. Pharmacol. 160, 1577–1579 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Bergman, T. J. & Beehner, J. C. A simple method for measuring colour in wild animals: Validation and use on chest patch colour in geladas (Theropithecus gelada). Biol. J. Linn. Soc. 94, 231–240 (2008).
Google Scholar 
Stevens, M., Stoddard, M. C. & Higham, J. P. Studying primate color: Towards visual system-dependent methods. Int. J. Primatol. 30, 893–917 (2009).
Google Scholar 
van den Berg, C. P., Troscianko, J., Endler, J. A., Marshall, N. J. & Cheney, K. L. Quantitative Colour Pattern Analysis (QCPA): A comprehensive framework for the analysis of colour patterns in nature. Methods Ecol. Evol. 11, 316–332 (2020).
Google Scholar 
Deeb, S. S., Jorgensen, A. L., Battisti, L., Iwasaki, L. & Motulsky, A. G. Sequence divergence of the red and green visual pigments in great apes and humans. Proc. Natl. Acad. Sci. USA 91, 7262–7266 (1994).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Matsuzawa, T. Form perception and visual acuity. Folia Primatol. Int. J. Primatol. 55, 24–32 (1990).CAS 

Google Scholar 
Jacobs, G. H., Deegan, J. F. & Moran, J. L. ERG measurements of the spectral sensitivity of common chimpanzee (Pan troglodytes). Vis. Res. 36, 2587–2594 (1996).CAS 
PubMed 

Google Scholar 
Jacobs, G. H. & Deegan, J. F. Uniformity of colour vision in Old World monkeys. Proc. R. Soc. B Biol. Sci. 266, 2023–2028 (1999).CAS 

Google Scholar 
Kemp, A. D. & Christopher Kirk, E. Eye size and visual acuity influence vestibular anatomy in mammals. Anat. Rec. 297, 781–790 (2014).
Google Scholar 
Osorio, D., Smith, A. C., Vorobyev, M. & Buchanan-Smith, H. M. Detection of fruit and the selection of primate visual pigments for color vision. Am. Nat. 164, 696–708 (2004).CAS 
PubMed 

Google Scholar 
Vorobyev, M. & Osorio, D. Receptor noise as a determinant of colour threshoIds. Proc. R. Soc. B Biol. Sci. 265, 351–358 (1998).CAS 

Google Scholar 
Siddiqi, A., Cronin, T. W., Loew, E. R., Vorobyev, M. & Summers, K. Interspecific and intraspecific views of color signals in the strawberry poison frog Dendrobates pumilio. J. Exp. Biol. 207, 2471–2485 (2004).PubMed 

Google Scholar  More

Fox MD, Williams GJ, Johnson MD, Radice VZ, Zgliczynski BJ, Kelly ELA, et al. Gradients in primary production predict trophic strategies of mixotrophic corals across spatial scales. Curr Biol. 2018;28:3355–63.CAS 
PubMed 
Article 

Google Scholar 
Selosse MA, Charpin M, Not F. Mixotrophy everywhere on land and in water: the grand écart hypothesis. Ecol Lett. 2017;20:246–63.PubMed 
Article 

Google Scholar 
Ferrier-Pagès C, Hoogenboom M, Houlbreque F. The role of plankton in coral trophodynamics. In: Dubinsky Z, Stambler N (eds). Coral Reefs: An Ecosystem in Transition. 2011. Springer, pp 215–29.Hartmann M, Grob C, Tarran GA, Martin AP, Burkill PH, Scanlan DJ, et al. Mixotrophic basis of Atlantic oligotrophic ecosystems. Proc Natl Acad Sci USA. 2012;109:5756–60.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Stoecker DK, Hansen PJ, Caron DA, Mitra A. Mixotrophy in the marine plankton. Ann Rev Mar Sci. 2017;9:311–35.PubMed 
Article 

Google Scholar 
Fabricius KE, Klumpp DW. Widespread mixotrophy in reef-inhabiting soft corals: the influence of depth, and colony expansion and contraction on photosynthesis. Mar Ecol Prog Ser. 1995;125:195–204.Article 

Google Scholar 
Bell JJ, McGrath E, Kandler NM, Marlow J, Beepat SS, Bachtiar R, et al. Interocean patterns in shallow water sponge assemblage structure and function. Biol Rev. 2020;95:1720–58.PubMed 
Article 

Google Scholar 
Freeman CJ, Easson CG, Fiore CL, Thacker RW. Sponge–microbe interactions on coral reefs: multiple evolutionary solutions to a complex environment. Front Mar Sci. 2021;8:1–24.Article 

Google Scholar 
Yin Z, Zhu M, Davidson EH, Bottjer DJ, Zhao F, Tafforeau P. Sponge grade body fossil with cellular resolution dating 60 Myr before the Cambrian. Proc Natl Acad Sci USA. 2015;112:E1453–60.
Google Scholar 
Taylor MW, Radax R, Steger D, Wagner M. Sponge-associated microorganisms: evolution, ecology, and biotechnological potential. Microbiol Mol Biol Rev. 2007;71:295–347.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;7:1–12.CAS 

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

Google Scholar 
Weisz JB, Massaro AJ, Ramsby BD, Hill MS. Zooxanthellar symbionts shape host sponge trophic status through translocation of carbon. Biol Bull. 2010;219:189–97.Zhang F, Blasiak LC, Karolin JO, Powell RJ, Geddes CD, Hill RT, et al. Phosphorus sequestration in the form of polyphosphate by microbial symbionts in marine sponges. Proc Natl Acad Sci USA. 2015;112:4381–6.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rützler K. Associations between Caribbean sponges and photosynthetic organisms. In: New Perspectives in Sponge Biology: 3d International Sponge Conference, 1985. 1990. Smithsonian Institution Press.Trautman DA, Hinde R, Borowitzka MA. Population dynamics of an association between a coral reef sponge and a red macroalga. J Exp Mar Bio Ecol. 2000;244:87–105.Article 

Google Scholar 
Sarà M. Ultrastructural aspects of the symbiosis between two species of the genus Aphanocapsa (Cyanophyceae) and Ircinia variabilis (Demospongiae). Mar Biol. 1971;11:214–21.Article 

Google Scholar 
Erwin PM, Thacker RW. Incidence and identity of photosynthetic symbionts in Caribbean coral reef sponge assemblages. J Mar Biol Assoc U Kingd. 2007;87:1683–92.CAS 
Article 

Google Scholar 
Arillo A, Bavestrello G, Burlando B, Sarà M. Metabolic integration between symbiotic cyanobacteria and sponges: a possible mechanism. Mar Biol. 1993;117:159–62.CAS 
Article 

Google Scholar 
Wilkinson CR, Fay P. Nitrogen fixation in coral reef sponges with symbiotic cyanobacteria. Nature. 1979;279:527–9.CAS 
Article 

Google Scholar 
Regoli F, Cerrano C, Chierici E, Bompadre S, Bavestrello G. Susceptibility to oxidative stress of the Mediterranean demosponge Petrosia ficiformis: role of endosymbionts and solar irradiance. Mar Biol. 2000;137:453–61.CAS 
Article 

Google Scholar 
Unson MD, Faulkner DJ. Cyanobacterial symbiont biosynthesis of chlorinated metabolites from Dysidea herbacea (Porifera). Experientia. 1993;49:349–53.CAS 
Article 

Google Scholar 
Freeman CJ, Thacker RW, Baker DM, Fogel ML. Quality or quantity: is nutrient transfer driven more by symbiont identity and productivity than by symbiont abundance? ISME J. 2013;7:1116–25.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wilkinson CR. Net primary productivity in coral reef sponges. Science. 1983;219:410–2.CAS 
PubMed 
Article 

Google Scholar 
Wilkinson CR. Productivity and abundance of large sponge populations on Flinders Reef flats, Coral Sea. Coral Reefs. 1987;5:183–8.Article 

Google Scholar 
Cheshire AC, Wilkinson CR, Seddon S, Westphalen G. Bathymetric and seasonal changes in photosynthesis and respiration of the phototrophic sponge Phyllospongia lamellosa in comparison with respiration by the heterotrophic sponge Ianthella basta on Davies Reef, Great Barrier Reef. Mar Freshw Res. 1997;48:589–99.Article 

Google Scholar 
Thacker RW, Diaz MC, Rützler K, Erwin PM, Kimble SJ, Pierce MJ, et al. Phylogenetic relationships among the filamentous cyanobacterial symbionts of Caribbean sponges and a comparison of photosynthetic production between sponges hosting filamentous and unicellular cyanobacteria. In: Hajdu E, Muricy G (eds). Porifera Research: Biodiversity, Innovation and Sustainability. 2007. Museu Nacional: Rio de Janeiro, pp 621–6.Erwin PM, Thacker RW. Phototrophic nutrition and symbiont diversity of two Caribbean sponge-cyanobacteria symbioses. Mar Ecol Prog Ser. 2008;362:139–47.CAS 
Article 

Google Scholar 
Wilkinson CR, Trott L. Light as a factor determining the distribution of sponges across the central Great Barrier Reef. Proc. 5th Int. Coral Reef Congr. 1985. pp 125–30.Richter C, Wunsch M, Rasheed M, Kötter I, Badran MI. Endoscopic exploration of Red Sea coral reefs reveals dense populations of cavity-dwelling sponges. Nature. 2001;413:726–30.CAS 
PubMed 
Article 

Google Scholar 
Gerovasileiou V, Voultsiadou E. Marine caves of the mediterranean sea: a sponge biodiversity reservoir within a biodiversity hotspot. PLoS One. 2012;7:1–17.Article 
CAS 

Google Scholar 
Kornder NA, Cappelletto J, Mueller B, Zalm MJL, Martinez SJ, Vermeij MJA, et al. Implications of 2D versus 3D surveys to measure the abundance and composition of benthic coral reef communities. Coral Reefs. 2021;40:1137–53.PubMed 
PubMed Central 
Article 

Google Scholar 
Vicente J, Webb MK, Paulay G, Rakchai W, Timmers MA, Jury CP, et al. Unveiling hidden sponge biodiversity within the Hawaiian reef cryptofauna. Coral Reefs 2021; https://doi.org/10.1007/s00338-021-02109-7.Beer S, Ilan M. In situ measurements of photosynthetic irradiance responses of two Red Sea sponges growing under dim light conditions. Mar Biol. 1998;131:613–7.Article 

Google Scholar 
Erwin PM, López-Legentil S, Turon X. Ultrastructure, molecular phylogenetics, and chlorophyll a content of novel cyanobacterial symbionts in temperate sponges. Micro Ecol. 2012;64:771–83.CAS 
Article 

Google Scholar 
Thacker RW. Impacts of shading on sponge-cyanobacteria symbioses: a comparison between host-specific and generalist associations. Integr Comp Biol. 2005;45:369–76.PubMed 
Article 

Google Scholar 
Biggerstaff A, Smith DJ, Jompa J, Bell JJ. Photoacclimation supports environmental tolerance of a sponge to turbid low-light conditions. Coral Reefs. 2015;34:1049–61.Article 

Google Scholar 
Freeman CJ, Baker DM, Easson CG, Thacker RW. Shifts in sponge-microbe mutualisms across an experimental irradiance gradient. Mar Ecol Prog Ser. 2015;526:41–53.Article 

Google Scholar 
Burgsdorf I, Sizikov S, Squatrito V, Britstein M, Slaby BM, Cerrano C, et al. Lineage-specific energy and carbon metabolism of sponge symbionts and contributions to the host carbon pool. ISME J. 2021;16:1163–75.Achlatis M, Pernice M, Green K, de Goeij JM, Guagliardo P, Kilburn MR, et al. Single-cell visualization indicates direct role of sponge host in uptake of dissolved organic matter. Proc R Soc B Biol Sci 2019;286:20192153.Hentschel U, Usher KM, Taylor MW. Marine sponges as microbial fermenters. FEMS Microbiol Ecol. 2006;55:167–77.CAS 
PubMed 
Article 

Google Scholar 
Rützler K, Duran S, Piantoni C. Adaptation of reef and mangrove sponges to stress: evidence for ecological speciation exemplified by Chondrilla caribensis new species (Demospongiae, Chondrosida). Mar Ecol. 2007;28:95–111.Article 

Google Scholar 
de Goeij JM, van Oevelen D, Vermeij MJA, Osinga R, Middelburg JJ, de Goeij AFPM, et al. Surviving in a marine desert: the sponge loop retains resources within coral reefs. Science. 2013;342:108–10.PubMed 
Article 
CAS 

Google Scholar 
Chalker BE. Simulating light-saturation curves for photosynthesis and calcification by reef-building corals. Mar Biol. 1981;63:135–41.Article 

Google Scholar 
Cheshire AC, Wilkinson CR. Modelling the photosynthetic production by sponges on Davies Reef, Great Barrier Reef. Mar Biol. 1991;109:13–18.Article 

Google Scholar 
Muscatine L, McCloskey LR, Marian R. Estimating the daily contribution of carbon from zooxanthellae to coral animal respiration. Limnol Oceanogr. 1981;26:601–611.CAS 
Article 

Google Scholar 
Koopmans M, Martens D, Wijffels RH. Growth efficiency and carbon balance for the sponge Haliclona oculata. Mar Biotechnol. 2010;12:340–349.CAS 
Article 

Google Scholar 
Leys SP, Kahn AS, Fang JKH, Kutti T, Bannister RJ. Phagocytosis of microbial symbionts balances the carbon and nitrogen budget for the deep-water boreal sponge Geodia barretti. Limnol Oceanogr. 2018;63:187–202.CAS 
Article 

Google Scholar 
de Kluijver A, Bart MC, van Oevelen D, de Goeij JM, Leys SP, Maier SR, et al. An integrative model of carbon and nitrogen metabolism in a common deep-sea sponge (Geodia barretti). Front Mar Sci. 2021;7:1–18.Article 

Google Scholar 
de Goeij JM, van den Berg H, van Oostveen MM, Epping EHG, van Duyl FC. Major bulk dissolved organic carbon (DOC) removal by encrusting coral reef cavity sponges. Mar Ecol Prog Ser. 2008;357:139–51.Article 
CAS 

Google Scholar 
Bart MC, Mueller B, Rombouts T, van de Ven C, Tompkins G, Osinga R, et al. Dissolved organic carbon (DOC) is essential to balance the metabolic demands of four dominant North-Atlantic deep-sea sponges. Limnol Oceanogr. 2021;66:925–38.CAS 
Article 

Google Scholar 
Scheffers SR, Nieuwland G, Bak RPM, Van Duyl FC. Removal of bacteria and nutrient dynamics within the coral reef framework of Curaçao (Netherlands Antilles). Coral Reefs. 2004;23:413–22.Article 

Google Scholar 
Pernice M, Dunn SR, Tonk L, Dove S, Domart-Coulon I, Hoppe P, et al. A nanoscale secondary ion mass spectrometry study of dinoflagellate functional diversity in reef-building corals. Environ Microbiol. 2015;17:3570–80.CAS 
PubMed 
Article 

Google Scholar 
Hudspith M, Rix L, Achlatis M, Bougoure J, Guagliardo P, Clode P, et al. Subcellular view of host–microbiome nutrient exchange in sponges: insights into the ecological success of an early metazoan–microbe symbiosis. Microbiome. 2021;9:1–15.Article 
CAS 

Google Scholar 
Clarke KR, Gorley RN. PRIMER v7: User Manual/Tutorial. Plymouth, UK. 2015. pp 1–296.Anderson MJ, Gorley RN, Clarke KR. PERMANOVA+ for PRIMER: Guide to software and statistical methods. Plymouth, UK. 2008. pp 1–214.Muscatine L, Falkowski PG, Porter JW, Dubinsky Z. Fate of photosynthetically fixed carbon in light- and shade-adapted colonies of the symbiotic coral Stylophora pistillata. Proc R Soc B Biol Sci. 1984;222:181–202.CAS 

Google Scholar 
Grottoli AG, Rodrigues LJ, Palardy JE. Heterotrophic plasticity and resilience in bleached corals. Nature. 2006;440:1186–9.CAS 
PubMed 
Article 

Google Scholar 
Fang JKH, Schönberg CHL, Mello-Athayde MA, Hoegh-Guldberg O, Dove S. Effects of ocean warming and acidification on the energy budget of an excavating sponge. Glob Chang Biol. 2014;20:1043–54.PubMed 
Article 

Google Scholar 
Li G, Cheng L, Zhu J, Trenberth KE, Mann ME, Abraham JP. Increasing ocean stratification over the past half-century. Nat Clim Chang. 2020;10:1116–23.Article 

Google Scholar 
Coma R, Ribes M, Serrano E, Jiménez E, Salat J, Pascual J. Global warming-enhanced stratification and mass mortality events in the Mediterranean. Proc Natl Acad Sci USA. 2009;106:6176–81.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Stoecker DK. Conceptual models of mixotrophy in planktonic protists and some ecological and evolutionary implications. Eur J Protistol. 1998;34:281–90.Article 

Google Scholar 
de Goeij JM, Lesser MP, Pawlik JR. Nutrient fluxes and ecological functions of coral reef sponges in a changing ocean. In: Carballo JL, Bell JJ (eds). Climate Change, Ocean Acidification and Sponges. 2017. Springer, Cham, pp 373–410.Hoer DR, Gibson PJ, Tommerdahl JP, Lindquist NL, Martens CS. Consumption of dissolved organic carbon by Caribbean reef sponges. Limnol Oceanogr. 2018;63:337–51.CAS 
Article 

Google Scholar 
McMurray SE, Stubler AD, Erwin PM, Finelli CM, Pawlik JR. A test of the sponge-loop hypothesis for emergent Caribbean reef sponges. Mar Ecol Prog Ser. 2018;588:1–14.CAS 
Article 

Google Scholar 
Morganti T, Coma R, Yahel G, Ribes M. Trophic niche separation that facilitates co-existence of high and low microbial abundance sponges is revealed by in situ study of carbon and nitrogen fluxes. Limnol Oceanogr. 2017;62:1963–83.CAS 
Article 

Google Scholar 
Fang JKH, Schönberg CHL, Hoegh-Guldberg O, Dove S. Day–night ecophysiology of the photosymbiotic bioeroding sponge Cliona orientalis, Thiele, 1900. Mar Biol. 2016;163:100.Article 

Google Scholar 
Pineda MC, Strehlow B, Duckworth A, Doyle J, Jones R, Webster NS. Effects of light attenuation on the sponge holobiont-implications for dredging management. Sci Rep. 2016;6:39038.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mews LK. The green hydra symbiosis. III. The biotrophic transport of carbohydrate from alga to animal. Proc R Soc Lond Ser B Biol Sci. 1980;209:377–401.CAS 

Google Scholar 
Titlyanov EA, Titlyanova TV, Leletkin VA, Tsukahara J, van Woesik R, Yamazato K. Degradation of zooxanthellae and regulation of their density in hermatypic corals. Mar Ecol Prog Ser. 1996;139:167–178.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;6:1–9.CAS 
Article 

Google Scholar 
Wilkinson CR. Nutrient translocation from symbiotic cyanobacteria to coral reef sponges. In: Levi C, Boury-Esnault N (eds). Biologie des Spongiaires. 1979. Coli. Int. C.N.R.S., Paris, p No. 291.Wilkinson CR. Microbial associations in sponges. III. Ultrastructure of the in situ associations in coral reef sponges. Mar Biol. 1978;49:177–85.Article 

Google Scholar 
Berthold RJ, Borowitzka MA, Mackay MA. The ultrastructure of Oscillatoria spongeliae, the blue-green algal endosymbiont of the sponge Dysidea herbacea. Phycologia. 1982;21:327–35.Article 

Google Scholar 
Burgsdorf I, Slaby BM, Handley KM, Haber M, Blom J, Marshall CW, et al. Lifestyle evolution in cyanobacterial symbionts of sponges. mBio. 2015;6:1–14.Article 
CAS 

Google Scholar 
Nguyen MTHD, Liu M, Thomas T. Ankyrin-repeat proteins from sponge symbionts modulate amoebal phagocytosis. Mol Ecol. 2014;23:1635–45.CAS 
PubMed 
Article 

Google Scholar 
Gao ZM, Zhou GW, Huang H, Wang Y. The cyanobacteria-dominated sponge Dactylospongia elegans in the South China Sea: prokaryotic community and metagenomic insights. Front Microbiol. 2017;8:1–12.
Google Scholar 
Reynolds D, Thomas T. Evolution and function of eukaryotic-like proteins from sponge symbionts. Mol Ecol. 2016;25:5242–53.CAS 
PubMed 
Article 

Google Scholar 
Trautman DA, Hinde R. Sponge/algal symbioses: a diversity of associations. In: Seckback J (ed). Symbiosis. Springer, Dordrecht; 2006, pp 521–37.Pile AJ, Grant A, Hinde R, Borowitzka MA. Heterotrophy on ultraplankton communities is an important source of nitrogen for a sponge-rhodophyte symbiosis. J Exp Biol. 2003;206:4533–8.PubMed 
Article 

Google Scholar 
Davy SK, Lucas IAN, Turner JR. Carbon budgets in temperate anthozoan-dinoflagellate symbioses. Mar Biol. 1996;126:773–83.Article 

Google Scholar 
Pupier CA, Fine M, Bednarz VN, Rottier C, Grover R, Ferrier-Pagès C. Productivity and carbon fluxes depend on species and symbiont density in soft coral symbioses. Sci Rep. 2019;9:1–10.CAS 
Article 

Google Scholar 
Podell S, Blanton JM, Oliver A, Schorn MA, Agarwal V, Biggs JS, et al. A genomic view of trophic and metabolic diversity in clade-specific Lamellodysidea sponge microbiomes. Microbiome. 2020;8:1–17.Article 
CAS 

Google Scholar 
Koch H, Lücker S, Albertsen M, Kitzinger K, Herbold C, Spieck E, et al. Expanded metabolic versatility of ubiquitous nitrite-oxidizing bacteria from the genus Nitrospira. Proc Natl Acad Sci USA. 2015;112:11371–6.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Engelberts JP, Robbins SJ, de Goeij JM, Aranda M, Bell SC, Webster NS. Characterization of a sponge microbiome using an integrative genome-centric approach. ISME J. 2020;14:1100–10.CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
Wilkinson CR. Interocean differences in size and nutrition of coral reef sponge populations. Science. 1987;236:1654–1657.CAS 
PubMed 
Article 

Google Scholar 
Wilken S, Huisman J, Naus-Wiezer S, Van Donk E. Mixotrophic organisms become more heterotrophic with rising temperature. Ecol Lett. 2013;16:225–233.PubMed 
Article 

Google Scholar 
Steindler L, Beer S, Ilan M. Photosymbiosis in intertidal and subtidal tropical sponges. Symbiosis. 2002;33:263–73.
Google Scholar 
Lemloh M-L, Fromont J, Brümmer F, Usher KM. Diversity and abundance of photosynthetic sponges in temperate Western Australia. BMC Ecol. 2009;9:4.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar  More

Parmesan, C. et al. in Climate Change 2022: Impacts, Adaptation, and Vulnerability (eds Pörtner, H.-O. et al.) Ch. 2 (IPCC, Cambridge Univ. Press, 2022).Lenoir, J. et al. Nat. Ecol. Evol. 4, 1044–1059 (2020).Article 

Google Scholar 
Antão, L. et al. Nat. Clim. Change https://doi.org/10.1038/s41558-022-01381-x (2022).Article 

Google Scholar 
Trisos, C. H. et al. Nature 580, 496–501 (2020).CAS 
Article 

Google Scholar 
Outhwaite, C. L. et al. Nat. Ecol. Evol. 4, 384–392 (2020).Article 

Google Scholar 
Pilotto, F. et al. Nat. Commun. 11, 3486 (2020).CAS 
Article 

Google Scholar 
Marta, S. et al. Nat. Ecol. Evol. 5, 1291–1300 (2021).Article 

Google Scholar 
Outhwaite, C. L. et al. Nature https://doi.org/10.1038/s41586-022-04644-x (2022).Article 

Google Scholar 
Sonne, J. et al. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-022-01693-3 (2022).Article 

Google Scholar 
Stefanescu, C. et al. J. Anim. Ecol. https://doi.org/10.1111/1365-2656.13689 (2022).Article 

Google Scholar 
Rumpf, S. et al. Nat. Commun. 10, 4293 (2019).Article 

Google Scholar 
Fourcade, Y. et al. Ecol. Lett. 24, 950–957 (2021).Article 

Google Scholar 
Mingarro, M. et al. Insect Conserv. Diversity 14, 647–660 (2021).Article 

Google Scholar 
Hodgson, J. et al. Glob. Change Biol. https://doi.org/10.1111/gcb.16220 (2022). More