Philippa Kaur

Anae, J. et al. Recent advances in biochar engineering for soil contaminated with complex chemical mixtures: Remediation strategies and future perspectives. Sci. Total Environ. 767, 144351. https://doi.org/10.1016/j.scitotenv.2020.144351 (2021).Article 
ADS 
CAS 

Google Scholar 
Kiran, B. R. & Prasad, M. N. V. Biochar and rice husk ash assisted phytoremediation potentials of Ricinus communis L. for lead-spiked soils. Ecotoxicol Environ Saf 183, 109574. https://doi.org/10.1016/j.ecoenv.2019.109574 (2019).Article 
CAS 

Google Scholar 
Bolan, N. et al. Remediation of heavy metal(loid)s contaminated soils – To mobilize or to immobilize?. J. Hazard. Mater. 266, 141–166. https://doi.org/10.1016/j.jhazmat.2013.12.018 (2014).Article 
CAS 

Google Scholar 
Burachevskaya, M. et al. The effect of granular activated carbon and biochar on the availability of Cu and Zn to Hordeum sativum distichum in contaminated soil. Plants https://doi.org/10.3390/plants10050841 (2021).Article 

Google Scholar 
Cao, P. et al. Mercapto propyltrimethoxysilane- and ferrous sulfate-modified nano-silica for immobilization of lead and cadmium as well as arsenic in heavy metal-contaminated soil. Environ. Pollut. 266, 115152. https://doi.org/10.1016/j.envpol.2020.115152 (2020).Article 
CAS 

Google Scholar 
Ok, Y. S. et al. Ameliorants to immobilize Cd in rice paddy soils contaminated by abandoned metal mines in Korea. Environ. Geochem. Health 33(Suppl 1), 23–30. https://doi.org/10.1007/s10653-010-9364-0 (2011).Article 
CAS 

Google Scholar 
Qin, Y. et al. Dual-wastes derived biochar with tailored surface features for highly efficient p-nitrophenol adsorption. J. Clean. Prod. 353, 131571. https://doi.org/10.1016/j.jclepro.2022.131571 (2022).Article 
CAS 

Google Scholar 
Rajput, V. D. et al. Nano-biochar: A novel solution for sustainable agriculture and environmental remediation. Environ. Res. 210, 112891. https://doi.org/10.1016/j.envres.2022.112891 (2022).Article 
CAS 

Google Scholar 
Ding, Y. et al. Biochar to improve soil fertility. A review. Agron. Sustain. Dev. 36, 36. https://doi.org/10.1007/s13593-016-0372-z (2016).Article 
CAS 

Google Scholar 
Oni, B. A., Oziegbe, O. & Olawole, O. O. Significance of biochar application to the environment and economy. Ann. Agric. Sci. 64, 222–236. https://doi.org/10.1016/j.aoas.2019.12.006 (2019).Article 

Google Scholar 
He, E. et al. Two years of aging influences the distribution and lability of metal(loid)s in a contaminated soil amended with different biochars. Sci. Total Environ. 673, 245–253. https://doi.org/10.1016/j.scitotenv.2019.04.037 (2019).Article 
ADS 
CAS 

Google Scholar 
Netherway, P. et al. Phosphorus-rich biochars can transform lead in an urban contaminated soil. J. Environ. Qual. 48, 1091–1099. https://doi.org/10.2134/jeq2018.09.0324 (2019).Article 
CAS 

Google Scholar 
O’Connor, D. et al. Biochar application for the remediation of heavy metal polluted land: A review of in situ field trials. Sci. Total Environ. 619–620, 815–826. https://doi.org/10.1016/j.scitotenv.2017.11.132 (2018).Article 
ADS 
CAS 

Google Scholar 
Xu, X. et al. Effect of physicochemical properties of biochar from different feedstock on remediation of heavy metal contaminated soil in mining area. Surf. Interfaces 32, 102058. https://doi.org/10.1016/j.surfin.2022.102058 (2022).Article 
CAS 

Google Scholar 
Melo, L. C. A. et al. Sorption and desorption of cadmium and zinc in two tropical soils amended with sugarcane-straw-derived biochar. J. Soils Sediments 16, 226–234. https://doi.org/10.1007/s11368-015-1199-y (2016).Article 

Google Scholar 
Uchimiya, M., Chang, S. & Klasson, K. T. Screening biochars for heavy metal retention in soil: Role of oxygen functional groups. J. Hazard. Mater. 190, 432–441. https://doi.org/10.1016/j.jhazmat.2011.03.063 (2011).Article 
CAS 

Google Scholar 
Jatav, H. S. et al. Sustainable approach and safe use of biochar and its possible consequences. Sustainability https://doi.org/10.3390/su131810362 (2021).Article 

Google Scholar 
Varalta, F. & Sorvari, J. In Organic Waste Composting through Nexus Thinking: Practices, Policies, and Trends (eds Hettiarachchi, H. et al.) 213–232 (Springer International Publishing, 2020).Chapter 

Google Scholar 
Pinotti, L. et al. Recycling food leftovers in feed as opportunity to increase the sustainability of livestock production. J. Clean. Prod. 294, 126290. https://doi.org/10.1016/j.jclepro.2021.126290 (2021).Article 

Google Scholar 
Jafri, N., Wong, W. Y., Doshi, V., Yoon, L. W. & Cheah, K. H. A review on production and characterization of biochars for application in direct carbon fuel cells. Process Saf. Environ. Prot. 118, 152–166. https://doi.org/10.1016/j.psep.2018.06.036 (2018).Article 
CAS 

Google Scholar 
Jin, Y. et al. Characterization of biochars derived from various spent mushroom substrates and evaluation of their adsorption performance of Cu(II) ions from aqueous solution. Environ. Res. 196, 110323. https://doi.org/10.1016/j.envres.2020.110323 (2021).Article 
CAS 

Google Scholar 
Tomczyk, A., Sokołowska, Z. & Boguta, P. Biomass type effect on biochar surface characteristic and adsorption capacity relative to silver and copper. Fuel 278, 118168. https://doi.org/10.1016/j.fuel.2020.118168 (2020).Article 
CAS 

Google Scholar 
FAO. Food Outlook – Biannual Report on Global Food Markets: November 2020. Rome. Phytoremediation of copper-contaminated soil by Artemisia absinthium: comparative effect of chelating agents. Environmental Geochemistry and Health. (2020). https://doi.org/10.4060/cb1993enRussian-Statistical-Year-Book. Statistical handbook. P76 M., 2020 – 700 p. ISBN 978-5-89476-497-9 (2020).Cheng, C.-H., Lehmann, J., Thies, J. E. & Burton, S. D. Stability of black carbon in soils across a climatic gradient. J. Geophys. Res. Biogeosci. 113, 55. https://doi.org/10.1029/2007JG000642 (2008).Article 
CAS 

Google Scholar 
Singh, B. P., Cowie, A. L. & Smernik, R. J. Biochar carbon stability in a clayey soil as a function of feedstock and pyrolysis temperature. Environ. Sci. Technol. 46, 11770–11778. https://doi.org/10.1021/es302545b (2012).Article 
ADS 
CAS 

Google Scholar 
He, Y. et al. Effects of biochar application on soil greenhouse gas fluxes: A meta-analysis. GCB Bioenergy 9, 743–755. https://doi.org/10.1111/gcbb.12376 (2017).Article 
CAS 

Google Scholar 
Janu, R. et al. Biochar surface functional groups as affected by biomass feedstock, biochar composition and pyrolysis temperature. Carbon Resour. Convers. 4, 36–46. https://doi.org/10.1016/j.crcon.2021.01.003 (2021).Article 
CAS 

Google Scholar 
Tan, X. et al. Application of biochar for the removal of pollutants from aqueous solutions. Chemosphere 125, 70–85. https://doi.org/10.1016/j.chemosphere.2014.12.058 (2015).Article 
ADS 
CAS 

Google Scholar 
Ni, B.-J. et al. Competitive adsorption of heavy metals in aqueous solution onto biochar derived from anaerobically digested sludge. Chemosphere 219, 351–357. https://doi.org/10.1016/j.chemosphere.2018.12.053 (2019).Article 
ADS 
CAS 

Google Scholar 
Park, J.-H. et al. Competitive adsorption of heavy metals onto sesame straw biochar in aqueous solutions. Chemosphere 142, 77–83. https://doi.org/10.1016/j.chemosphere.2015.05.093 (2016).Article 
ADS 
CAS 

Google Scholar 
Methodological-Guidelines. Methodological guidelines for the determination of heavy metals in the soils of agricultural land and crop production – M., TSINAO, 61 (1992)Zhang, A., Li, X., Xing, J. & Xu, G. Adsorption of potentially toxic elements in water by modified biochar: A review. J. Environ. Chem. Eng. 8, 104196. https://doi.org/10.1016/j.jece.2020.104196 (2020).Article 
CAS 

Google Scholar 
Avramiotis, E., Frontistis, Z., Manariotis, I. D., Vakros, J. & Mantzavinos, D. On the performance of a sustainable rice husk biochar for the activation of persulfate and the degradation of antibiotics. Catalysts 11, 1303 (2021).Article 
CAS 

Google Scholar 
Maiti, S., Dey, S., Purakayastha, S. & Ghosh, B. Physical and thermochemical characterization of rice husk char as a potential biomass energy source. Biores. Technol. 97, 2065–2070. https://doi.org/10.1016/j.biortech.2005.10.005 (2006).Article 
CAS 

Google Scholar 
Herrera, K., Morales, L. F., Tarazona, N. A., Aguado, R. & Saldarriaga, J. F. Use of biochar from rice husk pyrolysis: Part A: Recovery as an adsorbent in the removal of emerging compounds. ACS Omega 7, 7625–7637. https://doi.org/10.1021/acsomega.1c06147 (2022).Article 
CAS 

Google Scholar 
Szewczuk-Karpisz, K., Tomczyk, A., Grygorczuk-Płaneta, K. & Naveed, S. Rhizobium leguminosarum bv. trifolii exopolysaccharide and sunflower husk biochar as factors affecting immobilization of both tetracycline and Cd2+ ions on soil solid phase. J. Soils Sediments 22, 2620–2639. https://doi.org/10.1007/s11368-022-03255-3 (2022).Article 
CAS 

Google Scholar 
Hubetska, T. S., Kobylinska, N. G. & García, J. R. Sunflower biomass power plant by-products: Properties and its potential for water purification of organic pollutants. J. Anal. Appl. Pyrolysis 157, 105237. https://doi.org/10.1016/j.jaap.2021.105237 (2021).Article 
CAS 

Google Scholar 
Braghiroli, F. L. et al. The influence of pilot-scale pyro-gasification and activation conditions on porosity development in activated biochars. Biomass Bioenerg. 118, 105–114. https://doi.org/10.1016/j.biombioe.2018.08.016 (2018).Article 
CAS 

Google Scholar 
Braghiroli, F. L. et al. The conversion of wood residues, using pilot-scale technologies, into porous activated biochars for supercapacitors. J. Porous Mater. 27, 537–548. https://doi.org/10.1007/s10934-019-00823-w (2020).Article 
CAS 

Google Scholar 
Boraah, N., Chakma, S. & Kaushal, P. Attributes of wood biochar as an efficient adsorbent for remediating heavy metals and emerging contaminants from water: A critical review and bibliometric analysis. J. Environ. Chem. Eng. 10, 107825. https://doi.org/10.1016/j.jece.2022.107825 (2022).Article 
CAS 

Google Scholar 
Phillips, C. L. et al. Towards predicting biochar impacts on plant-available soil nitrogen content. Biochar 4, 9. https://doi.org/10.1007/s42773-022-00137-2 (2022).Article 
CAS 

Google Scholar 
Sun, L. & Gong, K. Silicon-based materials from rice husks and their applications. Ind. Eng. Chem. Res. 40, 5861–5877. https://doi.org/10.1021/ie010284b (2001).Article 
CAS 

Google Scholar 
Islam, T. et al. Synthesis of rice husk-derived magnetic biochar through liquefaction to adsorb anionic and cationic dyes from aqueous solutions. Arab. J. Sci. Eng. 46, 233–246. https://doi.org/10.1007/s13369-020-04537-z (2021).Article 
CAS 

Google Scholar 
Mohan, D. et al. Biochar production and applications in soil fertility and carbon sequestration – a sustainable solution to crop-residue burning in India. RSC Adv. 8, 508–520. https://doi.org/10.1039/C7RA10353K (2018).Article 
ADS 
CAS 

Google Scholar 
Li, F. et al. Preparation and characterization of biochars from Eichornia crassipes for cadmium removal in aqueous solutions. PLoS ONE 11, e0148132. https://doi.org/10.1371/journal.pone.0148132 (2016).Article 
CAS 

Google Scholar 
Song, H. et al. Potential of novel biochars produced from invasive aquatic species outside food chain in removing ammonium nitrogen: Comparison with conventional biochars and clinoptilolite. Sustainability https://doi.org/10.3390/su11247136 (2019).Article 

Google Scholar 
Yang, G. et al. Effects of pyrolysis temperature on the physicochemical properties of biochar derived from vermicompost and its potential use as an environmental amendment. RSC Adv. 5, 40117–40125. https://doi.org/10.1039/C5RA02836A (2015).Article 
ADS 
CAS 

Google Scholar 
Enders, A., Hanley, K., Whitman, T., Joseph, S. & Lehmann, J. Characterization of biochars to evaluate recalcitrance and agronomic performance. Bioresour. Technol. 114, 644–653. https://doi.org/10.1016/j.biortech.2012.03.022 (2012).Article 
CAS 

Google Scholar 
Zhang, Y., Wang, J. & Feng, Y. The effects of biochar addition on soil physicochemical properties: A review. CATENA 202, 105284. https://doi.org/10.1016/j.catena.2021.105284 (2021).Article 
CAS 

Google Scholar 
Özçimen, D. & Ersoy-Meriçboyu, A. Characterization of biochar and bio-oil samples obtained from carbonization of various biomass materials. Renew. Energy 35, 1319–1324. https://doi.org/10.1016/j.renene.2009.11.042 (2010).Article 
CAS 

Google Scholar 
Lin, Q. et al. Effects of biochar-based materials on the bioavailability of soil organic pollutants and their biological impacts. Sci. Total Environ. 826, 153956. https://doi.org/10.1016/j.scitotenv.2022.153956 (2022).Article 
ADS 
CAS 

Google Scholar 
Yang, H. et al. Thermogravimetric analysis−fourier transform infrared analysis of palm oil waste pyrolysis. Energy Fuels 18, 1814–1821. https://doi.org/10.1021/ef030193m (2004).Article 
CAS 

Google Scholar 
Pasangulapati, V. et al. Effects of cellulose, hemicellulose and lignin on thermochemical conversion characteristics of the selected biomass. Biores. Technol. 114, 663–669. https://doi.org/10.1016/j.biortech.2012.03.036 (2012).Article 
CAS 

Google Scholar 
Kim, P. et al. Surface functionality and carbon structures in lignocellulosic-derived biochars produced by fast pyrolysis. Energy Fuels 25, 4693–4703. https://doi.org/10.1021/ef200915s (2011).Article 
CAS 

Google Scholar 
Keiluweit, M., Nico, P. S., Johnson, M. G. & Kleber, M. dynamic molecular structure of plant biomass-derived black carbon (biochar). Environ. Sci. Technol. 44, 1247–1253. https://doi.org/10.1021/es9031419 (2010).Article 
ADS 
CAS 

Google Scholar 
Wijeyawardana, P. et al. Removal of Cu, Pb and Zn from stormwater using an industrially manufactured sawdust and paddy husk derived biochar. Environ. Technol. Innov. 28, 102640. https://doi.org/10.1016/j.eti.2022.102640 (2022).Article 
CAS 

Google Scholar 
Kołodyńska, D., Krukowska, J. & Thomas, P. Comparison of sorption and desorption studies of heavy metal ions from biochar and commercial active carbon. Chem. Eng. J. 307, 353–363. https://doi.org/10.1016/j.cej.2016.08.088 (2017).Article 
CAS 

Google Scholar 
Uchimiya, M. et al. Immobilization of heavy metal ions (CuII, CdII, NiII, and PbII) by broiler litter-derived biochars in water and soil. J. Agric. Food Chem. 58, 5538–5544. https://doi.org/10.1021/jf9044217 (2010).Article 
CAS 

Google Scholar 
Misono, M., Ochiai, E. I., Saito, Y. & Yoneda, Y. A new dual parameter scale for the strength of lewis acids and bases with the evaluation of their softness. J. Inorg. Nucl. Chem. 29, 2685–2691. https://doi.org/10.1016/0022-1902(67)80006-X (1967).Article 
CAS 

Google Scholar 
McBride, M. B. Environmental Chemistry of Soils (Oxford University Press, 1994).
Google Scholar 
Basta, N. T. & Tabatabai, M. A. Effect of cropping systems on adsorption of metals by soils: III. Competitive adsorption1. Soil Sci. 153, 331–337 (1992).Article 
ADS 
CAS 

Google Scholar 
Sposito, G. The Chemistry of Soils (Oxford University Press, 2016).Bauer, T. V. et al. Application of XAFS and XRD methods for describing the copper and zinc adsorption characteristics in hydromorphic soils. Environ. Geochem. Health 44, 335–347. https://doi.org/10.1007/s10653-020-00773-2 (2022).Article 
CAS 

Google Scholar 
Abd-Elfattah, A. L. Y. & Wada, K. Adsorption of lead, copper, zinc, cobalt, and cadmium by soils that differ in cation-exchange materials. J. Soil Sci. 32, 271–283. https://doi.org/10.1111/j.1365-2389.1981.tb01706.x (1981).Article 
CAS 

Google Scholar 
Etesami, H., Fatemi, H. & Rizwan, M. Interactions of nanoparticles and salinity stress at physiological, biochemical and molecular levels in plants: A review. Ecotoxicol. Environ. Saf. 225, 112769. https://doi.org/10.1016/j.ecoenv.2021.112769 (2021).Article 
CAS 

Google Scholar 
Soria, R. I., Rolfe, S. A., Betancourth, M. P. & Thornton, S. F. The relationship between properties of plant-based biochars and sorption of Cd(II), Pb(II) and Zn(II) in soil model systems. Heliyon 6, e05388. https://doi.org/10.1016/j.heliyon.2020.e05388 (2020).Article 

Google Scholar 
Alfarra, A., Frackowiak, E. & Béguin, F. The HSAB concept as a means to interpret the adsorption of metal ions onto activated carbons. Appl. Surf. Sci. 228, 84–92. https://doi.org/10.1016/j.apsusc.2003.12.033 (2004).Article 
ADS 
CAS 

Google Scholar 
Hu, J., Zhou, X., Shi, Y., Wang, X. & Li, H. Enhancing biochar sorption properties through self-templating strategy and ultrasonic fore-modified pre-treatment: Characteristic, kinetic and mechanism studies. Sci. Total Environ. 769, 144574. https://doi.org/10.1016/j.scitotenv.2020.144574 (2021).Article 
ADS 
CAS 

Google Scholar 
Ward, J., Rasul, M. G. & Bhuiya, M. M. K. Energy recovery from biomass by fast pyrolysis. Proced. Eng. 90, 669–674. https://doi.org/10.1016/j.proeng.2014.11.791 (2014).Article 
CAS 

Google Scholar 
Al-Wabel, M. I., Al-Omran, A., El-Naggar, A. H., Nadeem, M. & Usman, A. R. A. Pyrolysis temperature induced changes in characteristics and chemical composition of biochar produced from conocarpus wastes. Biores. Technol. 131, 374–379. https://doi.org/10.1016/j.biortech.2012.12.165 (2013).Article 
CAS 

Google Scholar 
Calvelo Pereira, R. et al. Contribution to characterisation of biochar to estimate the labile fraction of carbon. Org. Geochem. 42, 1331–1342. https://doi.org/10.1016/j.orggeochem.2011.09.002 (2011).Article 
CAS 

Google Scholar 
Vorob’eva, L. A. Theory and Practice Chemical Analysis of Soils (GEOS Press, Moscow, 2006).
Google Scholar 
Pinskii, D. L. et al. Copper adsorption by chernozem soils and parent rocks in Southern Russia. Geochem. Int. 56, 266–275. https://doi.org/10.1134/S0016702918030072 (2018).Article 
CAS 

Google Scholar 
Wang, Q., Wang, B., Lee, X., Lehmann, J. & Gao, B. Sorption and desorption of Pb(II) to biochar as affected by oxidation and pH. Sci. Total Environ. 634, 188–194. https://doi.org/10.1016/j.scitotenv.2018.03.189 (2018).Article 
ADS 
CAS 

Google Scholar 
Pourret, O. & Houben, D. Characterization of metal binding sites onto biochar using rare earth elements as a fingerprint. Heliyon 4, e00543. https://doi.org/10.1016/j.heliyon.2018.e00543 (2018).Article 

Google Scholar 
Huang, L. et al. High-resolution insight into the competitive adsorption of heavy metals on natural sediment by site energy distribution. Chemosphere 197, 411–419. https://doi.org/10.1016/j.chemosphere.2018.01.056 (2018).Article 
ADS 
MathSciNet 
CAS 

Google Scholar 
Ming, H. et al. Competitive sorption of cadmium and zinc in contrasting soils. Geoderma 268, 60–68. https://doi.org/10.1016/j.geoderma.2016.01.021 (2016).Article 
ADS 
CAS 

Google Scholar 
Musso, T. B., Parolo, M. E., Pettinari, G. & Francisca, F. M. Cu(II) and Zn(II) adsorption capacity of three different clay liner materials. J. Environ. Manag. 146, 50–58. https://doi.org/10.1016/j.jenvman.2014.07.026 (2014).Article 
CAS 

Google Scholar 
Cui, H. et al. Immobilization of Cu and Cd in a contaminated soil: One- and four-year field effects. J. Soils Sediments 14, 1397–1406. https://doi.org/10.1007/s11368-014-0882-8 (2014).Article 
CAS 

Google Scholar 
Elbana, T. A. et al. Freundlich sorption parameters for cadmium, copper, nickel, lead, and zinc for different soils: Influence of kinetics. Geoderma 324, 80–88. https://doi.org/10.1016/j.geoderma.2018.03.019 (2018).Article 
ADS 
CAS 

Google Scholar  More

Analysis of FTIRUnderstanding the functional groups involved in the biosorption of toxic metals is essential to elucidate the mechanism of this process. Groups such as carboxylic, hydroxyl and amine are among the main responsible for the absorption of metals by cellulose34 In the Fig. 1, show the FTIR of ECx.Figure 1FTIR of ECx before and after of adsorptions of Cr (VI).Full size imageAccording to13 the bandwidth at 3000–3600 cm−1 corresponds to bonds related to the -OH group. These hydrogen bonds are useful tools for cation exchange with heavy metals. This evidenced in the color spectrum (dark green) that represents an ECx sample with attached Cr (VI) after the adsorption process, where the stretching of the (OH) group lost part of its extension. The change observed in the peak from 3420 cm−1 of ECx to 3440 cm−1 in ECx-Cr indicates that these groups have a participation in the bond with the Cr (VI) ions. The variation of bands in the peak of the amines after adsorption confirms the participation of these groups in the adsorption process. This result confirmed by the ion exchange evaluation experiment discussed later in section SEM–EDX.The change in peak 3280, after Cr (VI) adsorption, indicates that EC removed Cr (VI) based on interaction with (OH), part of (OH) lost due to formation of vibrations of ascension O–Cr. Also, after Cr (VI) biosorption on ECx, the peak of the EC-S group is shifted to 590. This can be explained by surface complexation or ion exchange35.In general, comparable results reported in the literature for cellulose in the absorption of other toxic metals, as for other cellulose-derived biosorbentes in the removal of Cr (VI) ions36.One way to corroborate the information presented in the FTIR measurements is through SEM images since with these images it is possible to observe the distribution of the reagents in the ECx biomass treatment and subsequently the Cr (VI) adsorption process.SEM–EDXFigure 2 shows the micrographs obtained for the biomass before (a) the adsorption of Cr (VI), in addition to showing the distribution of the different biomass chemical modifications in (b) and in (c) it shows the distribution of chromium around all biomasses.Figure 2Biomass before (a) Cr (VI) adsorption, biomass chemical modifications in (b) and shows the distribution of chromium around the whole biomass (c).Full size imageFrom Fig. 2a, it can see that the biomass has a very irregular rough surface, with macropores and cracks. Many of these irregularities may associated with damage caused by the delignification process of E. crassipes cellulose with NaOH14. In Fig. 2b it is possible to visualize the components of the cellulose xanthogenate, coming from sodium, distributed throughout the biomass, a result like that reported in other studies35 The colored dots represent the elements in the samples, green dots represent carbon, red dots represent oxygen, and yellow dots represent the places where sodium lodged.Table 2 shows that, in addition to carbon and oxygen, the element with the greatest presence in the composition of pure waste is sodium and sulfur from the xanthogenate cellulose transformation process. Table 2 shows the physicochemical characterization of the ECx sample, through EDS.Table 2 Features of sample of ECx.Full size tableCellulose xanthogenate, is one of the cellulose transformations to improve the adsorption performance of heavy metals, this compound produced from dry and ground biomass, mixing with sodium hydroxide (NaOH) to remove lignin, creating alkaline biomass, then disulfide (CS2) added13,14. (CS2) reacts with hydratable hydroxycellulose, forming C-SNa complexes; these are responsible for the cation exchange with heavy metals. Metal ions enter the interior of E. crassipes with (CS2), exchanging with Na36,37.The SEM morphology of ECx and coupled with the high content of sulfides (7.3%) determined by the spectrum in Table 2, it further confirms that xanthate groups are successfully grafted onto the biomass of E. crassipes, and Fig. 3 represents this information based on13,36,37,38.Figure 3Prototype.Full size imageExchange biochemistry is usually identified as the main mechanism for the adsorption of metals in cellulose and its derivatives35 and through the evaluation of EDS this process could verify. Similar observations were made by36 where the adhesion of Cr (VI) in this biomass was observed. Also, in xanthogenate cellulose processes, the adhesion of Pb (II) to this type of biomass verified, concluding that this cellulose is important in the removal of heavy metals from water13.The SEM morphology of ECx with Cr (VI) coupled with the high content of sulfides determined by the spectrum in Table 3, was the determinate for the chemisorption’s of Cr (VI). The mechanism of Cr (VI) sorption by cellulose xanthate is:$$left[ {{4}left( {{text{C}}_{{6}} {text{H}}_{{{12}}} {text{O}}_{{6}} } right)} right]*{text{2CS}}_{{2}} {text{Na }} + {text{ Cr}}_{{2}} {text{O}}_{7}^{ – 2} to left{ {left[ {{4}left( {{text{C}}_{{6}} {text{H}}_{{5}} {text{O}}_{{6}} } right)} right] , *{text{2CS}}_{{2}} } right}*{mathbf{Cr}}_{{mathbf{2}}} + {text{Na}} + {text{7H}}_{{2}} {text{O}}$$where [4(C6H12O6)] *2CS2Na represents the xanthogenate biomass, and Cr2O7–2 represents the Cr (VI), that 4 parts of glucose xanthate react with the dichromate. In the Tables 3 and 4, the relationship between cellulose xanthogenate and Cr (VI), with related weights of 10.4 for Cr (VI).Table 3 Features of sample of ECx with Cr (VI).Full size tableTable 4 Researcher of process of the desorption.Full size tableMass balance in treatmentAdsorption is the phenomenon through which the removal of Cr (VI) achieved in the treatment systems; this quantified by means of the general balance equation of the treatment system as shown in Fig. 3.Adsorption is the phenomenon through which the removal of Cr (VI) achieved in treatment systems, this quantified by mass balance. Equation (1) shows the general balance of matter in the treatment system, together with the accumulation, inputs, and outputs of the system and the chemical process of adsorption.$${text{Acumulation }}upvarepsilon *frac{{partial {text{Cr}}left( {{text{VI}}} right)}}{{partial {text{t}}}} = {text{In}} frac{{partial {text{Cr}}left( {{text{VI}}} right)_{0} }}{{partial {text{t}}}} – {text{Out}}frac{{partial {text{Cr}}left( {{text{VI}}} right)}}{{partial {text{t}}}} – {text{Adsortion}},{rho b}frac{{partial {text{q}}}}{{partial {text{t}}}}$$
(1)
Accumulation represents by Eq. (1), where ∂C(VI) is the contaminant input to the treatment system, (ε) is the porosity of the bed, which calculated as the ratio between the density of the bed of treatment and the density of the microparticle of this biomass. This parameter must be above 0.548 achieved using particle diameters less than 0.212 mm, which favors contact between the contaminant and the particle49. The contaminant input to the treatment system represents by the design speed and the amount of contaminant that the system could treat. The output in the treatment system represents by the same input speed and the amount of contaminant that comes out. With these equations, the general material balance will be complete, summarized in Eq. (2), where it can see that the accumulation is equal to the input to the system, minus the output, and minus the adsorption.$$upvarepsilon *frac{{partial {text{Cr}}left( {{text{VI}}} right)}}{{partial {text{t}}}} = frac{{partial {text{Cr}} left( {{text{VI}}} right)}}{{partial {text{t}}}} – frac{{partial {text{Cr}} left( {{text{VI}}} right)}}{{partial {text{t}}}} – frac{{text{M}}}{{text{V}}}*frac{{partial {text{q}}}}{{partial {text{t}}}}$$
(2)
where V = System volume (ml), ε = Porosity, Co = Initial concentration of Cr (VI) (mg/ml), C = Final concentration Cr (VI) in the treated solution (mg/ml), Q = design flow (ml/min), Tb = Breaking time (Min), M = amount of biomass used (g), q = Adsorption capacity of the biomass used (mg/g).$${text{V}}*upvarepsilon *{text{Co}} = {text{Q}}*{text{Tb}}*{text{Co}} – {text{Q}}*{text{Tb}}*{text{C}} – {text{M}}*{text{q}}$$
(3)
Depending on the most important parameters when building a treatment system, Eq. (3) could use to model and validate the best form of treatment, for example, the necessary amount of biomass to use to treat a certain amount of contaminant, in the present investigation it used to establish the adsorption capacity in these initial treatment conditions. The remaining Eq. (4) determines the adsorption capacity.$${text{q}} = frac{{{text{QTbCo}}}}{{text{M}}} – frac{{{text{QTbCf}}}}{{text{M}}} – frac{{upvarepsilon {text{VCo}}}}{{text{M}}}$$
(4)
Adsorption capacity is generally taken through Eq. (5) for both batch and continuous experiments20,21But unlike Eqs. (5), (4) takes into account design variables such as flow rate (Q), rupture time (Tb), particle bed porosity ε, and vessel design volume (v).$${text{q}} = frac{{{text{v}}left( {{text{Co}} – {text{C}}} right)}}{{text{m }}}$$
(5)
where m: Mass used in the treatment, V: Volume, Co: Initial concentration, C: Final Concentration, Q: adsorption capacity.However, unlike Eqs. (5),  (4) considers the design variables such as flow rate (Q), rupture time (Tb), particle bed porosity ε and vessel design volume (v).When a desorption-elution process is involved for the reuse of biomass, Eq. (4) would be:$${text{q}}_{{text{T}}} = mathop sum limits_{j = 1}^{n} left[ {frac{{{text{QTbjCo}}}}{{text{M}}} – frac{{{text{QTbjCj}}}}{{text{M}}} – frac{{upvarepsilon {text{VCo}}}}{{text{M}}}} right]$$
(6)
where Q = design flow (ml/min), Tbj = Break time of use number j (Min), Co = Initial concentration of Cr (VI) (mg/ml), C = Final concentration Cr (VI) in the treated solution (mg/ml), V = System volume (ml), ε = Porosity, M = amount of biomass used (g), q_T = Total adsorption capacity of the biomass used (mg/g).This model (6) is design to determine the adsorption capacity when different elution processes have conducted, it will used to determine the new adsorption capacity and is one of the contributions of the present investigation.Result process of adsorptionsIn Fig. 4 shows the Cr (VI) adsorption process of the system.Figure 4Percentages of Cr (VI) removal the system for ECx.Full size imageVarious researchers have extensively studied the influence of factors such as bed height, flow rate and metal inlet concentration on rupture (Tb) curves. For example, the influence and similarity of the initial contaminant concentrations should be reflected as in the case of a tannery, with initial concentrations of 600 mg/l. Figure 4 shows the progress curves obtained for the study of Cr (VI) removal by the studied biomasses, reflecting the percentage of Cr (VI) removal in contrast to the treated volume, which is a very important parameter to time to scale the process.Regarding the effect of the input concentration, it can see in Fig. 5 that the breakpoint had a better performance in all the initial concentrations in the ECx biomass. comparing it with the EC-Na biomass (see Fig. 5), always obtaining breakpoints with more treated volume.Figure 5Percentages of Cr (VI) removal the system for EC-Na.Full size imageThe difference between the rupture curves between ECx and EC-Na indicates that the cellulose xanthate modification scheme should completed, although it can also elucidate that the EC-Na biomass has high yields compared to other biomass studied. for example, in Ref.34 investigate the biomass of E. crassipes without modifying, having removals below this alkaline cellulose.Adsorption capacitiesThrough Eq. (3), the adsorption capacity of ECx, using the initial concentration of 600 mg/l, since it was the maximum concentration used.The break point was around 1200 ml according to Fig. 6 and together with the flow rate of 15 ml/min; the break time obtained in 80 min.$${text{q}} = frac{{80{*}15{*}0.6}}{40} – frac{{80{*}15{*}0.04}}{40} – frac{{0.66{*}78{*}0.6}}{40}$$q: Adsorption capacity, Co: 0.6 mg/ml, C: 0.06 mg/ml, M: 40 g, Tb: rupture time 80 min, Q: 15 Flow rate ml/min, ε: 0.6649, V: Occupied volume: 70 ml.Figure 6Adsorption capacities in the different adsorption processes in the biomass ECx.Full size imageA result of 16 mg/g obtained in this continuous study for the biomass ECx. With this same equation it gives the capacity of the biomass EC-Na, with 11 mg/g.Desorption-Elution and reuseThrough Eq. (6), the sum of the Cr (VI) adsorption capacities established, after different biomass reuses due to EDTA elution. In the second treatment process, it yielded the following results under concentrations of 6 g/l of EDTA.$${text{q}}left( {text{T}} right) = frac{{60{*}15{*}0.6}}{40} – frac{{50{*}15{*}0.06}}{40} – frac{{0.66{*}68{*}0.6}}{40}$$Co: 0.6 mg/ml, C: 0.06 mg/ml, M: 45 g Biomass eluted with EDTA, Tb: rupture time: 60 min, Q: 15 Flow ml/min, ε: 0.6649, V: Occupied volume: 68 ml, q: 10 mg/g.Five Cr (VI) adsorption cycles performed using ECx and EC-Na cellulose in a continuous system to evaluate the regeneration and reuse potential. Between each biosorption cycle, a desorption cycle performed using three different concentrations of EDTA eluent.According to Figs. 6 and 7, although the adsorption capacity gradually decreases from the first adsorption process, it could consider that it is a satisfactory biomass recycling process and a design parameter for later stages of this treatment system.Figure 7Adsorption capacities in the different adsorption processes in the biomass EC-Na.Full size imageIn the experiments with concentrations of 6 g/l, five reuse processes obtained, obtaining a final sum of 52 mg/g. In concentrations of 3 g/l of EDTA, final capacities of 51 mg/g obtained lower than concentrations of 6 g/l but with half of this reagent. With concentrations of 1 g/l, final capacities of 33 mg/g obtained.The desorption processes of the EC-Na biomass with initial capacities of 11 mg/g were also evaluated and through desorption processes with EDTA of 3 g/l this biomass recycled on 5 occasions, reaching 32 mg/l in capacities of adsorption and like the EC-Na biomass, the ideal concentration in the process for desorption processes is 3 g/l, due to the considerable increase in reuse processes and low concentration compared to 6 g/l, which, although higher, does not this value is significant in the absorption capacity.Through Eq. (6) and with different bibliographic references, representative data obtained to feed this equation, determining the capacities of each of these biomasses together with the new capacities determining the desorption power of the different eluents shown and summarized in Table 4.For the EDTA eluent and with Eq. (6), satisfactory results evidenced by removing Al (II), reaching almost 150% of its adsorption capacity, corroborating what presented in the present investigation, also the EDTA reagent obtained interesting yields to recycle the cassava biomass increasing up to 40 mg/g. In Ref.39 used the biomass of Phanera vahlii to remove Cr (VI) obtaining results of 30 mg/g and with NaOH they reached capacities in the reuse process of this biomass up to 62 mg/g, reaching almost double of its total capacity41, also used NaOH for desorption processes with green synthesized nanocrystalline chlorapatite biomass, achieving results of 75% more. The eluent HCl is also a good chemical agent to use in desorption processes since it reached more than 100% in the reuse of biochar alginate for Cr (VI) but not so significant with biomass A. barbadensis Miller to remove Ni (II) and in40 significant results were also obtained to remove Pb (II) with pine cone Shell biomass. With the chemical agent HNO3, interesting contaminant recycling processes obtained, since more than 100% of the adsorption capacity of the biomasses used in this process used1,45.Mathematical models of adsorptionIn general, the models presented R2 greater than 0.95 for the adjustment of all the advance curves, which indicates a good adherence to the data, the model that best describes the behavior of the ECx system was the phenomenological model Thomas, which presented all the R2 values above 0.99.This model could use for the extension of the Cr (VI) ion biosorption system using cellulose xanthogenate, in the literature it is possible to observe that this model often tends to better adapt to the experimental data of the adsorption systems that use cellulose for the absorption of toxic metals28,30,31.With qt values remarkably close to the experimental values of Eq. (4) designed and presented in this investigation, indicating the validity of this equation where it reflects the maximum capacity obtained. Table 5 shows the adsorption constant of the Thomas model (Kt), which corresponds to the adsorption rate of Cr (VI) in the biomass49 This value was 0.048 (ml/mg*min) reflecting the speed with which Cr (VI) is chemisorbed in the biomass of ECx, in the EC-Na cellulose there was a Thomas model speed of 0.039 (ml/ mg*min) evidencing a lower adsorption rate than ECx. In the adsorption of Cr (VI) by rice biomass, the Thomas constant is 0.1 (ml/mg*min)47,50 also in the adsorption of Cr (VI) by biomass. Nanocrystalline chlorapatite biomass obtained at the Thomas constant 0.013 (ml/mg*min)49.Table 5 Summary of the experiments obtained with material ECx.Full size tableIn the Table 6, it presents summary of the experiments obtained with material EC-Na.Table 6 Summary of the experiments obtained with material EC-Na.Full size tableThe Cr (VI) adsorption process in the EC-Na biomass represented through the Bohart equation, since the sorption rate is proportional to the biomass capacity, obtaining an adsorption rate of 0.85(ml/mg*min). Having an alkalized biomass represents this model due to the homogeneity of this adsorbent.Mathematical models in desorption processesThe continuous desorption process with its fit to the Thomas model for biomass ECx always shows the fit of this model with significance, because this type of model fits representatively to desorption processes with good performance32,51 It can also verify that with values of qt it is close to the experimental values of Eq. (6) designed and presented in this research, indicating the validity of this equation again, where it reflects the maximum capacity obtained.In the Table 7. Show Summary of the experiments obtained with material ECx in process of desorption’s.Table 7 Summary of the experiments obtained with material ECx in process of desorption’s.Full size tableIn the Table 8 the EC-Na biomass had a different behavior and in its second and third cycle it adjusted to the Yoon model and later to the Bohart model.Table 8 Summary of the experiments obtained with material EC-Na in process of desorption’s.Full size tableThis behavior is due to the alkalinization of the biomass and this process makes the biomass a little more unstable. The values of qt, although a resemblance evidenced, were not so representative due to the little adjustment that there was with respect to the Thomas model. More

No plants were collected or harmed during this study, and all research involving plants followed relevant national, and international guidelines and legislation.Study areaThe study site encloses a wetland area bordering Ramalhete Channel, in the western part of the Ria Formosa lagoon, a mesotidal system located in southern Portugal (Fig. 1). Lunar tides are semi-diurnal, with a mean tidal range of about 2 m that can reach up to 3.5 m during spring tides. Offshore waves have no major propagation inside the lagoon33,34. Water circulation inside the lagoon is mostly driven by tides. The lagoon extends over 55 km along the coast and is connected to the ocean through six tidal inlets35. The three westmost inlets of the system (Ancão, Faro-Olhão, and Armona), which together capture ca. 90% of the total prism, are highly interconnected, with a strong residual circulation from Faro-Olhão Inlet directed towards Ancão and Armona inlets (located in Fig. 1), during both spring and neap tides36. The tidal currents in Ramalhete Channel, connecting the Faro-Olhão and Ancão Inlet, have high tidal asymmetry and shifts in tidal dominance, from flood to ebb. There are no significant fluvial inputs into the lagoon, with a yearly average terrestrial sediment influx of around 2 × 105 m3/yr37, reaching the system through small streams. The main sediment delivery to the system is through the inlets, though there are few studies assessing related fluxes. The net sediment entry through the stabilized Faro-Olhão Inlet is estimated at 1.4 × 105 m3/year38. Recent sedimentation rates in the marsh of the westmost edge of the lagoon were estimated at 1.1 ± 0.1 mm/yr39.The lagoon system is composed of large salt marsh patches, tidal flats and a complex net of natural, and partially dredged tidal channels. The tidal flats (vegetated and non-vegetated) and salt marshes represent more than 2/3 of the total lagoon area. The salt marshes comprise silt and fine sand40, while coarser (sand to shingle) shell-rich sediment, of marine provenance, is found on tidal channels and the lower domain of intertidal flats41. The dominant intertidal species are Spartina maritima and the seagrass Zostera noltei, the latter occupying an estimated area of 1304 ha, which represent 45% of the total intertidal area42.Figure 1Location of the field site in the Ria Formosa lagoon western sector over a satellite image collected in 2019 (South Portugal; upper panel); zoom to monitoring stations S1 to S4 (left lower panel); and field view of the studied site (right lower panel). Map generated with ArcGIS 10.8 (http://www.esri.com) and Adobe Illustrator 2022. Map data: Google Earth 7.3, image Landsat / Copernicus.Full size imageExperimental setup and data analysisAn experimental setup was deployed in the study area to assess dominant local topography, hydrodynamics (water levels and current velocities), Suspended Sediment Concentrations (SSCs), Deposition Rates (DRs), vegetation characteristics, and bed sediment grain size and organic matter content. Measurements were made during a full tide cycle, on a spring tide (tidal range = 3.2 m), and on a neap tide (tidal range = 1.8 m). Sampling was conducted in four wetland stations: S1 and S2 in a vegetated tidal flat comprising Zostera noltei; S3 in the low marsh comprising Spartina maritima; and S4 in the mid-upper marsh with the most abundant species of Sarcocornia perennis and Atriplex portucaloides (see S1 to S4, Fig. 1); the tidal flat is interrupted by a small oblique secondary tidal creek that flows near S2 station.Stations of sediment sampling and equipment deployment along the transect are illustrated in Fig. 2. During neap tide there was no data collection in S4, since the inundation time of the station was very short. The profile elevation was measured using Real Time Kinematic Differential Global Positioning System (RTK-DGPS, Trimble R6; vertical error in the order of few centimetres), and the slope of each habitat within a transect was calculated and expressed in percentage (%). Vegetation at each point was characterized by the canopy height, calculated as the average shoot length.Suspended Sediment Samplers (SSSs) were installed during low tide in the monitored stations using siphon samplers (Fig. 2) and recovered in the next low tide. These samplers consist of 0.5 L bottles with two holes on the cap, one for water intake and the other for air exhaust, according to the method described in13. Each intake tube is adjusted to form a siphon (i.e., inverse U), allowing to control the water level at which intake starts. Siphons were aligned at the same elevation along the transect for spring and neap tides, which means that all SSSs were collecting at the same time within the tidal cycle. During spring tide, in S1 and S2 at the tidal flat, SSSs were sampling at 0.1, 0.9, and 1.2 m from the bed, while at S3 SSSs were sampling at 0.7 and 1.0 m from the bed, and at S4 the SSS was sampling at 0.1 m from the bed (Fig. 2). During neap tide, in S1 and S2, SSSs were sampling at 0.1 and 0.9 m from the bed, while at S3 the SSS was sampling at 0.7 m from the bed.Surficial sediment samples were collected in each habitat to characterize the sediment grain size (d50) and content of organic matter (% OM). Sediment traps were installed in 3 replicates, during low tide, at each sampling point to measure the short-term sediment deposition rate (i.e., deposition over a tidal cycle, following procedures of43). Traps consisted of 3 cm diameter pre-labeled cylindrical tubes (Falcon® tubes, 50 ml). Traps and sediment samples were transported to the laboratory and maintained in a fridge. The sediment content was washed, and both the inorganic and organic weights were determined.The measured inorganic DR (g/m2/hr) was calculated as:$${text{DR}} = {raise0.7exhbox{${{text{W}}_{{{text{DS}}}} }$} !mathord{left/ {vphantom {{{text{W}}_{{{text{DS}}}} } {{text{A}} cdot {text{T}}}}}right.kern-0pt} !lower0.7exhbox{${{text{A}} cdot {text{T}}}$}}$$
(1)
where WDS is the weight of deposited sediment (in grams), A is the area of the sediment trap opening (m2), and T is in hours. Two different tide durations were considered to compute DRs, one assuming T equal to the hydroperiod in each station, and one assuming T equal to the entire tide duration (~ 12.4 h). These measured DRs are hereon mentioned as flood and tide DRs (DRflood and DRtide, respectively). The former is an expression of the actual deposition rate within the flood phase, during the period in which each station is inundated (and therefore active deposition can take place). The latter is the value used to compare with DRs in literature, which typically corresponds to values averaged over multiple tidal cycles (thus accounting for the entire tide duration).Tide levels were measured in the field using pressure sensors (PT, InSitu Inc. Level TROLL; ~ 2 cm from the bed), deployed from S2 towards S4 (Fig. 2). Velocity currents were measured at 20 cm from the bed, using an electromagnetic current meter (EMCM; Infinity Series JFE Advantech Co., Ltd; in S2 to S4; Fig. 2), and raw data (recording interval: 30 s) were filtered using a 10 min moving average for cross-shore and longshore components. To identify tidal asymmetry and assess the related phase dominance, tidal current skewness was calculated through the formula described in44 by which:
$$Sk_{U} = frac{{frac{1}{N – 1}mathop sum nolimits_{t = 1}^{N} left( {U_{t} – overline{U}} right)^{3} }}{{left( {frac{1}{N – 1}mathop sum nolimits_{t = 1}^{N} left( {U_{t} – overline{U}} right)^{2} } right)^{{{raise0.7exhbox{$3$} !mathord{left/ {vphantom {3 2}}right.kern-0pt} !lower0.7exhbox{$2$}}}} }}$$
(2)
where N is the number of recordings, Ut is the input velocity signal and (overline{U}) is the mean velocity. Positive/negative skewness indicates flood/ebb dominance (assuming that flood currents are positive).Figure 2Deployment of the sediment traps, SSSs and devices (electromagnetic current meter EMCM; pressure transducer PT) in the stations (S1 to S4) during spring tide (sketch is exaggerated in the vertical).Full size imageComplementary to the measured DRs, theoretical DRs were also determined from the data, allowing us to link the sediment and flow data collected, and validate the deposition patterns from the traps. The theoretical deposition rate was determined based on45 formula:$${text{DR}} = left{ {begin{array}{*{20}c} {{text{C}}_{{text{b}}} cdot {text{w}}_{{text{s}}} cdot left( {1 – frac{{{uptau }_{{text{b}}} }}{{{uptau }_{{{text{cd}}}} }}} right)} & {{uptau }_{{text{b}}} < {uptau }_{{{text{cd}}}} } \ 0 & {{uptau }_{{text{b}}} ge {uptau }_{{{text{cd}}}} } \ end{array} } right.$$ (3) where Cb is the SSC at the bed, ws is the flock settling velocity, τb is the bed shear stress and τcd is the corresponding critical value for deposition.To determine the settling rate of the flocculates, the modified Stokes’ velocity for cohesive sediment was used, taking shape factors α and β (α = β = 1 for perfectly spherical particles):$${text{w}}_{{text{s}}} = frac{{upalpha }}{{upbeta }} cdot frac{{left( {{uprho }_{{text{s}}} - {uprho }_{{text{w}}} } right) cdot {text{g}} cdot {text{D}}_{50}^{2} }}{{{uprho }_{{text{w}}} cdot 18 cdot {upnu }}}$$ (4) where ρw and ρs are the densities of the water and sediment, respectively and ν is the kinematic viscosity of water (~ 106 m2/s).The bed shear stress τb was calculated from the measured current magnitude, |U| using the law of the wall:$$begin{array}{*{20}c} \ {{uptau }_{{text{b}}} = {uprho }_{{text{w}}} cdot {text{u}}_{*}^{2} , {text{u}}_{*} = frac{left| U right| cdot kappa }{{ln left( {{raise0.7exhbox{$z$} !mathord{left/ {vphantom {z {z_{0} }}}right.kern-0pt} !lower0.7exhbox{${z_{0} }$}}} right)}} } \ end{array} { }$$ (5) where κ is the von Kármán constant (~ 0.4) and z0 is the roughness length. For Zostera noltei, the roughness length was estimated at 5 mm46, value that was also used in the other stations, in lack of related estimate for marsh plants.The critical shear for deposition, τcd, was calculated using the formula47:$$sqrt {frac{{{uptau }_{{{text{cd}}}} }}{{{uprho }_{{text{w}}} }}} = left{ {begin{array}{*{20}c} {0.008} & {{text{w}}_{{text{s}}} le 5 cdot 10^{ - 5} {text{m}}/{text{s}}} \ {0.094 + 0.02 cdot {text{log}}_{10} left( {{text{w}}_{{text{s}}} } right)} & {3 cdot 10^{ - 4} le {text{w}}_{{text{s}}} le 5 cdot 10^{ - 5} {text{m}}/{text{s}}} \ {0.023} & {{text{w}}_{{text{s}}} ge 3 cdot 10^{ - 4} {text{m}}/{text{s}}} \ end{array} } right.$$ (6) Theoretical values of minimum SSCs needed for these DRs were also calculated, assuming that there is constant deposition (i.e., setting τb = 0), and compared with the field results. More

Data were analyzed from samples collected, processed, and published previously [21, 25, 29] and have been summarized here. The present analysis, which consisted of sequence data processing, the calculation of taxon-specific isotopic signatures, and subsequent analyses, reflects original work.Sample collection and isotope incubationTo generate experimental data, three replicate soil samples were collected from the top 10 cm of plant-free patches in four ecosystems along the C. Hart Merriam elevation gradient in Northern Arizona. From low to high elevation, these sites are located in the following environments: desert grassland (GL; 1760 m), piñon-pine juniper woodland (PJ; 2020 m), ponderosa pine forest (PP; 2344 m), and mixed conifer forest (MC; 2620 m). Soil samples were air-dried for 24 h at room temperature, homogenized, and passed through a 2 mm sieve before being stored at 4 °C for another 24 h. This produced three distinct but homogenous soil samples from each of the four ecosystems that were subject to experimental treatments. Three treatments were applied to bring soils to 70% water-holding capacity: water alone (control), water with glucose (C treatment; 1000 µg C g−1 dry soil), or water with glucose and a nitrogen source (CN treatment; [NH4]2SO4 at 100 µg N g−1 dry soil). To track growth through isotope assimilation, both 18O-enriched water (97 atom %) and 13C-enriched glucose (99 atom %) were used. In all treatments isotopically heavy samples were paired with matching “light” samples that received water with a natural abundance isotope signatures. For 18O incubations, this design resulted in three soil samples per ecosystem per treatment (across four ecosystems and three treatments, n = 36) while 13C incubations were limited to only C and CN treatments (n = 24). Previous analyses suggest that three replicates is sufficient to detect growth of 10 atom % 18O in microbial DNA with a power of 0.6 and a growth of 5 atom % 18O with a power of 0.3 (12 and 6 atom % respectively for 13C) [30]. All soils were incubated in the dark for one week. Following incubation, soils were frozen at −80 °C for one week prior to DNA extraction.Quantitative stable isotope probingThe procedure of qSIP (quantitative stable isotope probing) is described here but has been applied to these samples as previously published [17, 21, 25]. DNA extraction was performed on soils using a DNeasy PowerSoil HTP 96 Kit (MoBio Laboratories, Carlsbad, CA, USA) and following manufacturer’s protocol. Briefly, 0.25 g of soils from each sample were carefully added to deep, 96-well plates containing zirconium dioxide beads and a cell lysis solution with sodium dodecyl sulfate (SDS) and shaken for 20 min. Following cell lysis, supernatant was collected and centrifuged three times in fresh 96-well plates with reagents separating DNA from non-DNA organic and inorganic materials. Lastly, DNA samples were collected on silica filter plates, rinsed with ethanol and eluted into 100 µL of a 10 mM Tris buffer in clean 96-well plates. To quantify the degree of 18O or 13C isotope incorporation into bacterial DNA (excess atom fraction or EAF), the qSIP protocol [31] was used, though modified slightly as reported previously [21, 24, 32]. Briefly, microbial growth was quantified as the change in DNA buoyant density due to incorporation of the 18O or 13C isotopes through the method of density fractionation by adding 1 µg of DNA to 2.6 mL of saturated CsCl solution in combination with a gradient buffer (200 mM Tris, 200 mM KCL, 2 mM EDTA) in a 3.3 mL OptiSeal ultracentrifuge tube (Beckman Coulter, Fullerton, CA, USA). The solution was centrifuged to produce a gradient of increasingly labeled (heavier) DNA in an Optima Max bench top ultracentrifuge (Beckman Coulter, Brea, CA, USA) with a Beckman TLN-100 rotor (127,000 × g for 72 h) at 18 °C. Each post-incubation sample was thus converted from a continuous gradient into approximately 20 fractions (150 µL) using a modified fraction recovery system (Beckman Coulter). The density of each fraction was measured with a Reichart AR200 digital refractometer (Reichert Analytical Instruments, Depew, NY, USA). Fractions with densities between 1.640 and 1.735 g cm−3 were retained as densities outside this range generally did not contain DNA. In all retained fractions, DNA was cleaned and purified using isopropanol precipitation and the abundance of bacterial 16S rRNA gene copies was quantified with qPCR using primers specific to bacterial 16S rRNA genes (Eub 515F: AAT GAT ACG GCG ACC ACC GAG TGC CAG CMG CCG CGG TAA, 806R: CAA GCA GAA GAC GGC ATA CGA GGA CTA CVS GGG TAT CTA AT). Triplicate reactions were 8 µL consisting of 0.2 mM of each primer, 0.01 U µL−1 Phusion HotStart II Polymerase (Thermo Fisher Scientific, Waltham, MA), 1× Phusion HF buffer (Thermo Fisher Scientific), 3.0 mM MgCl2, 6% glycerol, and 200 µL of dNTPs. Reactions were performed on a CFX384 Touch Real-Time PCR Detection System (Bio-Rad, Hercules, CA, USA) under the following cycling conditions: 95 °C at 1 min and 44 cycles at 95 °C (30 s), 64.5 °C (30 s), and 72 °C (1 min). Separate from qPCR, retained sample-fractions were subject to a similar amplification step of the 16S rRNA gene V4 region (515F: GTG YCA GCM GCC GCG GTA A, 806R: GGA CTA CNV GGG TWT CTA AT) in preparation for sequencing with the same reaction mix but differing cycle conditions – 95 °C for 2 min followed by 15 cycles at 95 °C (30 s), 55 °C (30 s), and 60 °C (4 min). The resulting 16S rRNA gene V4 amplicons were sequenced on a MiSeq sequencing platform (Illumina, Inc., San Diego, CA, USA). DNA sequence data and sample metadata have been deposited in the NCBI Sequence Read Archive under the project ID PRJNA521534.Sequence processing and qSIP analysisIndependently from previous publications, we processed raw sequence data of forward and reverse reads (FASTQ) within the QIIME2 environment [33] (release 2018.6) and denoised sequences within QIIME2 using the DADA2 pipeline [34]. We clustered the remaining sequences into amplicon sequence variants (ASVs, at 100% sequence identity) against the SILVA 138 database [35] using a pre-trained open-reference Naïve Bayes feature classifier [36]. We removed samples with less than 3000 sequence reads, non-bacterial lineages, and global singletons and doubletons. We converted ASV sequencing abundances in each fraction to the number of 16S rRNA gene copies per gram dry soil based on qPCR abundances and the known amount of dry soil equivalent added to the initial extraction. This allowed us to express absolute population densities, rather than relative abundances. Across all replicates, we identified 114 543 unique bacterial ASVs.We calculated the 18O and 13C excess atom fraction (EAF) for each bacterial ASV using R version 4.0.3 [37] and data.table [38] with custom scripts available at https://www.github.com/bramstone/. Negative enrichment values were corrected using previously published methods [17]. ASVs that appeared in less than two of the three replicates of an ecosystem-treatment combination (n = 3) and less than three density fractions within those two replicates were removed to avoid assigning spurious estimates of isotope enrichment to infrequent taxa. Any ASVs filtered out of one ecosystem-treatment group were allowed to be present in another if they met the frequency threshold. Applying these filtering criteria, we limited our analysis towards 3759 unique bacterial ASVs which accounted for a small proportion of the total diversity but represented 68.0% of all sequence reads, and encompassed most major bacterial groups (Supplementary Fig. 1).Analysis of life history strategies and nutrient responseAll statistical tests were conducted in R version 4.0.3 [37]. We assessed the ability of phylum-level assignment of life history strategy to predict growth in response to C and N addition, as proxied by the incorporation of heavy isotope during DNA replication [39, 40]. Phylum-level assignments (Table 1) were based on the most frequently observed behavior of lineages with a representative phylum (or subphylum) as compiled previously [23]. We averaged 18O EAF values of bacterial taxa for each treatment and ecosystem and then subtracted the values in control soils from values in C-amended soils to determine C response (∆18O EAFC) and from the 18O EAF of bacteria in CN-amended soils to determine C and N response (Δ18O EAFCN). Because an ASV must have a measurable EAF in both the control and treatment for a valid Δ18O EAF to be calculated, we were only able to resolve the nutrient response for 2044 bacterial ASVs – 1906 in response to C addition and 1427 in response to CN addition.We used Gaussian finite mixture modeling, as implemented by the mclust R package [41], to demarcate plausible multi-isotopic signatures for oligotrophs and copiotrophs. For each treatment, we calculated average per-taxon 13C and 18O EAF values. To compare both isotopes directly, we divided 18O EAF values by 0.6 based on the estimate that this value (designated as µ) represents the fraction of oxygen atoms in DNA derived from the 18O-water, rather than from 16O within available C sources [42]. Two mixture components, corresponding to oligotrophic and copiotrophic growth modes, were defined using the Mclust function using ellipsoids of equal volume and shape. We observed several microorganisms with high 18O enrichment but comparatively low 13C enrichment, potentially indicating growth following the depletion of the added glucose, and that were reasonably clustered as oligotrophs in our mixture model.We tested how frequently mixture model clustering of each microorganism’s growth (based on average 18O–13C EAF in a treatment) could predict its growth across replicates (n = 12 in each treatment—although individual). We applied the treatment-level mixture models defined above to the per-taxon isotope values in each replicate, recording when a microorganism’s life history strategy in a replicate agreed with the treatment-level cluster, and when it didn’t. We used exact binomial tests to test whether the number of “successes” (defined as a microorganism being grouped in the same life history category as its treatment-level cluster) was statistically significant. To account for type I error across all individual tests (one per ASV per treatment), we adjusted P values in each treatment using the false-discovery rate (FDR) method [43].To determine the extent that life history categorizations may be appropriately applied at finer levels of taxonomic resolution, we constructed several hierarchical linear models using the lmer function in the nlme package version 3.1-149 [44]. To condense growth information from both isotopes into a single analysis, 18O and 13C EAF values were combined into a single variable using principal components analysis separately for each treatment. Across the C and CN treatments, the first principal component (PC1) was able to explain – respectively – 86% and 91% of joint variation of 18O and 13C EAF values. In all cases, we applied PC1 as the response variable and treated taxonomy and ecosystem as random model terms to limit the potential of pseudo-replication to bias significance values. We used likelihood ratio analysis and Akaike information criterion (AIC) values to compare models where life history strategy was determined based on observed nutrient responses at different taxonomic levels (Eq. 1) against a model with the same random terms but without any life history strategy data (Eq. 2). Separate models were applied to each treatment. To reduce model overfitting, we removed families represented by fewer than three bacterial ASVs as well as phyla represented by only one order. In addition, we removed bacterial ASVs with unknown taxonomic assignments (following Morrissey et al. [21]). This limited our analysis to 1 049 ASVs in the C amendment and 984 in the CN amendment.$${{{{{rm{PC}}}}}}{1}_{{18{{{{{rm{O}}}}}} – 13{{{{{rm{C}}}}}}}}sim {{{{{rm{strategy}}}}}} + 1|{{{{{rm{phylum}}}}}}/{{{{{rm{class}}}}}}/{{{{{rm{order}}}}}}/{{{{{rm{family}}}}}}/{{{{{rm{genus}}}}}}/{{{{{rm{eco}}}}}}$$
(1)
$${{{{{rm{PC}}}}}}{1}_{{18{{{{{rm{O}}}}}} – 13{{{{{rm{C}}}}}}}}sim 1 + 1|{{{{{rm{phylum}}}}}}/{{{{{rm{class}}}}}}/{{{{{rm{order}}}}}}/{{{{{rm{family}}}}}}/{{{{{rm{genus}}}}}}/{{{{{rm{eco}}}}}}$$
(2)
Here, life history strategy was defined at each taxonomic level using the mixture models above and based on the mean 18O and 13C EAF values of each bacterial lineage (Supplemental Fig. 2). We compared these models with the no-strategy model (Eq. 2) directly using likelihood ratio testing. More

Saponari, M. et al. Infectivity and transmission of Xylella fastidiosa by Philaenus spumarius (Hemiptera: Aphrophoridae) in Apulia, Italy. J. Econ. Entomol. 107, 1316–1319. https://doi.org/10.1603/EC14142 (2014).Article 

Google Scholar 
Cornara, D., Bosco, D. & Fereres, A. Philaenus spumarius: When an old acquaintance becomes a new threat to European agriculture. J. Pest Sci. 91, 957–972. https://doi.org/10.1007/s10340-018-0966-0 (2018).Article 

Google Scholar 
Weaver, C. R. & King, D. Meadow spittlebug, Philaenus leucophthalmus (L.). Ohio Agric. Exp. Stn. Res. Bull. 741, 258 (1954).
Google Scholar 
Halkka, A., Halkka, L., Halkka, O., Roukka, K. & Pokki, J. Lagged effects of North Atlantic Oscillation on spittlebug Philaenus spumarius (Homoptera) abundance and survival. Glob. Change Biol. 12, 2250–2262. https://doi.org/10.1111/j.1365-2486.2006.01266.x (2006).Article 

Google Scholar 
Cruaud, A. et al. Using insects to detect, monitor and predict the distribution of Xylella fastidiosa: A case study in Corsica. Sci. Rep. 8, 15628. https://doi.org/10.1038/s41598-018-33957-z (2018).Article 
CAS 

Google Scholar 
Godefroid, M. et al. Climate tolerances of Philaenus spumarius should be considered in risk assessment of disease outbreaks related to Xylella fastidiosa. J. Pest Sci. 2021, 1–14. https://doi.org/10.1007/s10340-021-01413-z (2021).Article 

Google Scholar 
Farigoule, P. et al. Vectors as sentinels: Rising temperatures increase the risk of Xylella fastidiosa outbreaks. Biology 11, 1299. https://doi.org/10.3390/biology11091299 (2022).Article 

Google Scholar 
Drosopoulos, S. & Asche, M. Biosystematic studies on the spittlebug genus Philaenus with the description of a new species. Zool. J. Linn. Soc. 101, 169–177. https://doi.org/10.1111/j.1096-3642.1991.tb00891.x (1991).Article 

Google Scholar 
Godefroid, M. & Durán, J. M. Composition of landscape impacts the distribution of the main vectors of Xylella fastidiosa in southern Spain. J. Appl. Entomol. 146, 666–675. https://doi.org/10.1111/jen.13003 (2022).Article 

Google Scholar 
Karban, R. & Strauss, S. Y. Physiological tolerance, climate change, and a northward range shift in the spittlebug, Philaenus spumarius. Ecol. Entomol. 29, 251–254. https://doi.org/10.1111/j.1365-2311.2004.00576.x (2004).Article 

Google Scholar 
Chmiel, S. M. & Wilson, M. C. Estimation of the lower and upper developmental threshold temperatures and duration of the nymphal stages of the meadow Spittlebug, Philaenus spumarius. Environ. Entomol. 8, 682–685. https://doi.org/10.1093/ee/8.4.682 (1979).Article 

Google Scholar 
Yurtsever, S. On the polymorphic meadow spittlebug, Philaenus spumarius (L.) (Homoptera: Cercopidae). Turk. J. Zool. 24, 447–460 (2000).
Google Scholar 
Ahmed, D. D. & Davidson, R. H. Life history of the meadow spittlebug in Ohio. J. Econ. Entomol. 43, 905–908. https://doi.org/10.1093/jee/43.6.905 (1950).Article 

Google Scholar 
Whittaker, J. B. Cercopid spittle as a microhabitat. Oikos 21, 59–64. https://doi.org/10.2307/3543839 (1970).Article 

Google Scholar 
Drosopoulos, S. New data on the nature and origin of colour polymorphism in the spittlebug genus Philaenus (Hemiptera: Aphorophoridae). Ann. Soc. Entomol. Fr. NS 39, 31–42. https://doi.org/10.1080/00379271.2003.10697360 (2003).Article 

Google Scholar 
Bodino, N. et al. Phenology, seasonal abundance, and host-plant association of spittlebugs (Hemiptera: Aphrophoridae) in vineyards of Northwestern Italy. Insects 12, 1012. https://doi.org/10.3390/insects12111012 (2021).Article 

Google Scholar 
Cornara, D. et al. Natural areas as reservoir of candidate vectors of Xylella fastidiosa. Bull. Insectol. 74, 173–180 (2021).
Google Scholar 
Gargani, E. et al. A five-year survey in Tuscany (Italy) and detection of Xylella fastidiosa subspecies multiplex in potential insect vectors, collected in Monte Argentario. Redia 104, 75–88. https://doi.org/10.19263/REDIA-104.21.09 (2021).Article 

Google Scholar 
Morente, M. et al. Distribution and relative abundance of insect vectors of Xylella fastidiosa in olive groves of the iberian peninsula. Insects 9, 175. https://doi.org/10.3390/insects9040175 (2018).Article 

Google Scholar 
Delong, D. et al. Spittle-insect vectors of Pierce’s disease virus. I. Characters, distribution, and food plants. Hilgardia 19, 339–356 (1950).Article 

Google Scholar 
Bodino, N. et al. Phenology, seasonal abundance and stage-structure of spittlebug (Hemiptera: Aphrophoridae) populations in olive groves in Italy. Sci. Rep. 9, 1–17. https://doi.org/10.1038/s41598-019-54279-8 (2019).Article 
CAS 

Google Scholar 
Wiegert, R. G. Population energetics of meadow spittlebugs (Philaenus spumarius L.) as affected by migration and habitat. Ecol. Monogr. 34, 217–241. https://doi.org/10.2307/1948501 (1964).Article 

Google Scholar 
Dongiovanni, C. et al. Plant selection and population trend of spittlebug immatures (Hemiptera: Aphrophoridae) in olive groves of the Apulia region of Italy. J. Econ. Entomol. 112, 67–74. https://doi.org/10.1093/jee/toy289 (2019).Article 

Google Scholar 
Bodino, N. et al. Spittlebugs of mediterranean olive groves: Host-plant exploitation throughout the year. Insects 11, 130. https://doi.org/10.3390/insects11020130 (2020).Article 

Google Scholar 
Villa, M., Rodrigues, I., Baptista, P., Fereres, A. & Pereira, J. A. Populations and host/non-host plants of spittlebugs nymphs in olive orchards from northeastern Portugal. Insects 11, 720. https://doi.org/10.3390/insects11100720 (2020).Article 

Google Scholar 
Antonatos, S. et al. Seasonal appearance, abundance, and host preference of Philaenus spumarius and Neophilaenus campestris (Hemiptera: Aphrophoridae) in olive groves in Greece. Environ. Entomol. 50, 1474–1482. https://doi.org/10.1093/ee/nvab093 (2021).Article 

Google Scholar 
Hasbroucq, S., Casarin, N., Ewelina, C., Bragard, C. & Grégoire, J.-C. Distribution, adult phenology and life history traits of potential insect vectors of Xylella fastidiosa in Belgium. Belg. J. Entomol. 92, 2569 (2020).
Google Scholar 
Mesmin, X. et al. Interaction networks between spittlebugs and vegetation types in and around olive and clementine groves of Corsica; implications for the spread of Xylella fastidiosa. Agric. Ecosyst. Environ. 334, 107979. https://doi.org/10.1016/j.agee.2022.107979 (2022).Article 

Google Scholar 
Albre, J., García-Carrasco, J. M. & Gibernau, M. Ecology of the meadow spittlebug Philaenus spumarius in the Ajaccio region (Corsica)—I: Spring. Bull. Entomol. Res. 111, 246–256. https://doi.org/10.1017/S0007485320000711 (2021).Article 

Google Scholar 
Andersson, P., Löfstedt, C. & Hambäck, P. A. Insect density–plant density relationships: A modified view of insect responses to resource concentrations. Oecologia 173, 1333–1344. https://doi.org/10.1007/s00442-013-2737-1 (2013).Article 

Google Scholar 
Hambäck, P. A., Inouye, B. D., Andersson, P. & Underwood, N. Effects of plant neighborhoods on plant–herbivore interactions: Resource dilution and associational effects. Ecology 95, 1370–1383. https://doi.org/10.1890/13-0793.1 (2014).Article 

Google Scholar 
Otway, S. J., Hector, A. & Lawton, J. H. Resource dilution effects on specialist insect herbivores in a grassland biodiversity experiment. J. Anim. Ecol. 74, 234–240 (2005).Article 

Google Scholar 
Lago, C. et al. Flight performance and the factors affecting the flight behaviour of Philaenus spumarius the main vector of Xylella fastidiosa in Europe. Sci. Rep. 11, 17608. https://doi.org/10.1038/s41598-021-96904-5 (2021).Article 
CAS 

Google Scholar 
Casarin, N. et al. Investigating dispersal abilities of Aphrophoridae in European temperate regions to assess the threat of potential Xylella fastidiosa-based pathosystems. J. Pest Sci. https://doi.org/10.1007/s10340-022-01562-9 (2022).Article 

Google Scholar 
Bodino, N. et al. Dispersal of Philaenus spumarius (Hemiptera: Aphrophoridae), a vector of Xylella fastidiosa, in olive grove and meadow agroecosystems. Environ. Entomol. 50, 267–279. https://doi.org/10.1093/ee/nvaa140 (2020).Article 
CAS 

Google Scholar 
Santoiemma, G., Tamburini, G., Sanna, F., Mori, N. & Marini, L. Landscape composition predicts the distribution of Philaenus spumarius, vector of Xylella fastidiosa, in olive groves. J. Pest. Sci. 92, 1101–1109. https://doi.org/10.1007/s10340-019-01095-8 (2019).Article 

Google Scholar 
Cappellari, A. et al. Spatio-temporal dynamics of vectors of Xylella fastidiosa subsp. pauca across heterogeneous landscapes. Entomol. Gen. 42, 515–521. https://doi.org/10.1127/entomologia/2022/1427 (2022).Article 

Google Scholar 
Avosani, S., Tattoni, C., Mazzoni, V. & Ciolli, M. Occupancy and detection of agricultural threats: The case of Philaenus spumarius, European vector of Xylella fastidiosa. Agric. Ecosyst. Environ. 324, 107707. https://doi.org/10.1016/j.agee.2021.107707 (2022).Article 

Google Scholar 
Allier, C. & Lacoste, A. Processus dynamiques de reconstitution dans la série du Quercus ilex en Corse. In Vegetation Dynamics in Grasslans, Healthlands and Mediterranean Ligneous Formations 83–91 (Springer, 1981).Delbosc, P., Bioret, F. & Panaïotis, C. Plant landscape of Corsica: Typology and mapping plant landscape of Cap Corse region and Biguglia Pond (Springer Nature, 2020).Book 

Google Scholar 
Chessel, D., Dufour, A.-B. & Thioulouse, J. The ade4 package—I: One-table methods. R. News 4, 5–10 (2004).
Google Scholar 
Biedermann, R. & Niedringhaus, R. The Plant-and Leafhoppers of Germany: Identification Key to All Species (Wabv Fründ, 2009).
Google Scholar 
Stöckmann, M., Biedermann, R., Nickel, H. & Niedringhaus, R. The Nymphs of the Planthoppers and Leafhoppers of Germany (WABV, 2013).
Google Scholar 
INRAE-CBGP. Arthemis DB@se – ARTHropod Ecology, Molecular Identification and Systematics. https://arthemisdb.supagro.inrae.frhttps://doi.org/10.15454/TBGRIB. Accessed 2021.Xu, T. & Hutchinson, M. ANUCLIM version 6.1 user guide. Aust. Natl. Univ. Fenner Sch. Environ. Soc. Canberra 2011, 256 (2011).
Google Scholar 
Quintana-Seguí, P. et al. Analysis of near-surface atmospheric variables: Validation of the SAFRAN analysis over France. J. Appl. Meteorol. Climatol. 47, 92–107. https://doi.org/10.1175/2007JAMC1636.1 (2008).Article 

Google Scholar 
Hijmans, R. J., Phillips, S., Leathwick, J. & Elith, J. Dismo: Species Distribution Modeling https://CRAN.R-project.org/package=dismo (2017).Faraway, J. J. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models (Chapman and Hall/CRC, 2006).MATH 

Google Scholar 
Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R. J. 9, 378–400. https://doi.org/10.3929/ethz-b-000240890 (2017).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing https://www.R-project.org/ (2019).Hardin, J. W. & Hilbe, J. M. Generalized Linear Models and Extensions 4th edn. (Stata Press, 2018).MATH 

Google Scholar 
Harrison, X. A. Using observation-level random effects to model overdispersion in count data in ecology and evolution. PeerJ 2, e616. https://doi.org/10.7717/peerj.616 (2014).Article 

Google Scholar 
Bolker, B. M. et al. Generalized linear mixed models: A practical guide for ecology and evolution. Trends Ecol. Evol. 24, 127–135. https://doi.org/10.1016/j.tree.2008.10.008 (2009).Article 

Google Scholar 
Hartig, F. DHARMa: Residual Diagnostics for Hierarchical (Multi-Level/Mixed) Regression Models https://CRAN.R-project.org/package=DHARMa (2020).Lüdecke, D., Ben-Shachar, M. S., Patil, I., Waggoner, P. & Makowski, D. performance: An R package for assessment, comparison and testing of statistical models. J. Open Sourc. Softw. 6, 3139. https://doi.org/10.21105/joss.03139 (2021).Article 

Google Scholar 
Fox, J. & Weisberg, S. An {R} Companion to Applied Regression Third edn, https://socialsciences.mcmaster.ca/jfox/Books/Companion/ (Sage, Thousand Oaks CA, 2019).Lenth, R. V. Emmeans: Estimated Marginal Means, Aka Least-Squares Means https://CRAN.R-project.org/package=emmeans (2021).Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biom. J. 50, 346–363. https://doi.org/10.1002/bimj.200810425 (2008).Article 
MATH 

Google Scholar 
Fernández-Mazuecos, M. & Vargas, P. Ecological rather than geographical isolation dominates Quaternary formation of Mediterranean Cistus species. Mol. Ecol. 19, 1381–1395. https://doi.org/10.1111/j.1365-294X.2010.04549.x (2010).Article 
CAS 

Google Scholar 
Berenbaum, M. R. & Feeny, P. P. 1. Chemical mediation of host-plant specialization: The papilionid paradigm. In Specialization, Speciation, and Radiation (ed. Tilmon, K.) 3–19 (University of California Press, 2008). https://doi.org/10.1525/california/9780520251328.003.0001.Kapantaidaki, D. E., Antonatos, S., Evangelou, V., Papachristos, D. P. & Milonas, P. Genetic and endosymbiotic diversity of Greek populations of Philaenus spumarius, Philaenus signatus and Neophilaenus campestris, vectors of Xylella fastidiosa. Sci. Rep. 11, 3752. https://doi.org/10.1038/s41598-021-83109-z (2021).Article 
CAS 

Google Scholar 
Mesmin, X. et al. Ooctonus vulgatus (Hymenoptera, Mymaridae), a potential biocontrol agent to reduce populations of Philaenus spumarius (Hemiptera, Aphrophoridae) the main vector of Xylella fastidiosa in Europe. PeerJ 8, e8591. https://doi.org/10.7717/peerj.8591 (2020).Article 

Google Scholar 
Denancé, N. et al. Several subspecies and sequence types are associated with the emergence of Xylella fastidiosa in natural settings in France. Plant Pathol. 66, 1054–1064. https://doi.org/10.1111/ppa.12695 (2017).Article 
CAS 

Google Scholar 
EFSA, Delbianco, A., Gibin, D., Pasinato, L. & Morelli, M. Update of the Xylella spp host plant database—systematic literature search up to 31 December 2020. EFSA J. 19, 6. https://doi.org/10.2903/j.efsa.2021.6674 (2021).Article 

Google Scholar 
Soubeyrand, S. et al. Inferring pathogen dynamics from temporal count data: The emergence of Xylella fastidiosa in France is probably not recent. New Phytol. 219, 824–836. https://doi.org/10.1111/nph.15177 (2018).Article 

Google Scholar 
Roy, J. & Sonié, L. Germination and population dynamics of Cistus species in relation to fire. J. Appl. Ecol. 29, 647–655. https://doi.org/10.2307/2404472 (1992).Article 

Google Scholar 
Whittaker, J. B. Density regulation in a population of Philaenus spumarius (L.) (Homoptera: Cercopidae). J. Anim. Ecol. 42, 163–172. https://doi.org/10.2307/3410 (1973).Article 

Google Scholar 
Chapman, D. et al. Improving knowledge of Xylella fastidiosa vector ecology: modelling vector occurrence and abundance in the wider landscape in Scotland. Project Final Report. PHC2020/04, Scotland’s Centre of Expertise for Plant Health (PHC) https://doi.org/10.5281/zenodo.6523478 (2022).Saponari, M., Giampetruzzi, A., Loconsole, G., Boscia, D. & Saldarelli, P. Xylella fastidiosa in olive in Apulia: Where we stand. Phytopathology 109, 175–186. https://doi.org/10.1094/PHYTO-08-18-0319-FI (2019).Article 
CAS 

Google Scholar 
López-Mercadal, J. et al. Collection of data and information in Balearic Islands on biology of vectors and potential vectors of Xylella fastidiosa (GP/EFSA/ALPHA/017/01). EFSA Supp. Publ. 18, 10. https://doi.org/10.2903/sp.efsa.2021.EN-6925 (2021).Article 

Google Scholar  More

Ren, W. et al. Changes of periphyton abundance and biomass driven by factors specific to flooding inflow in a river inlet area in Erhai Lake, China. Front. Environ. Sci. 9, 680718. https://doi.org/10.3389/fenvs.2021.680718 (2021).Article 

Google Scholar 
Woodruff, S. L. et al. The effects of a developing biofilm on chemical changes across the sediment-water interface in a freshwater environment. Int. Rev. Hydrobiol. 84(5), 509–532 (1999).CAS 

Google Scholar 
Muñoz, I., Real, M., Guasch, H., Navarro, E. & Sabater, S. Effects of atrazine on periphyton under grazing pressure. Aquat. Toxicol. 55(3–4), 239–249 (2001).
Google Scholar 
Hoagland, K. D., Roemer, S. C. & Rosowski, J. R. Colonization and community structure of two periphyton assemblages, with emphasis on the diatoms (Bacillariophyceae). Am. J. Bot. 69, 188–213. https://doi.org/10.2307/2443006 (1982).Article 

Google Scholar 
Steinman, A. D. & McIntire, C. D. Effects of current velocity and light energy on the structure of periphyton assemblages in laboratory streams. J. Phycol. 22, 352–361. https://doi.org/10.1111/J.1529-8817.1986.TB00035.X (1986).Article 

Google Scholar 
Tonkin, J. D., Death, R. G. & Barquín, J. Periphyton control on stream invertebrate diversity: Is periphyton architecture more important than biomass?. Mar. Freshw. Res. 65(9), 818–829 (2014).
Google Scholar 
Beck, W. S., Markman, D. W., Oleksy, I. A., Lafferty, M. H. & Poff, N. L. Seasonal shifts in the importance of bottom-up and top-down factors on stream periphyton community structure. Oikos 128, 680–691. https://doi.org/10.1111/oik.05844 (2018).Article 
CAS 

Google Scholar 
Hogsden, K. L. & Harding, J. S. Consequences of acid mine drainage for the structure and function of benthic stream communities: A review. Freshw. Sci. 31, 108–120. https://doi.org/10.1899/11-091.1 (2012).Article 

Google Scholar 
Sofi, M. S., Bhat, S. U., Rashid, I. & Kuniyal, J. C. The natural flow regime: A master variable for maintaining river ecosystem health. Ecohydrology 13(8), e2247. https://doi.org/10.1002/eco.2247 (2020).Article 

Google Scholar 
Biggs, B. J. F. Eutrophication of streams and rivers: Dissolved nutrient-chlorophyllrelationship for benthic algae. J. N. Am. Benthol. Soc. 19, 17–31 (2000).
Google Scholar 
Ormerod, S. J., Dobson, M., Hildrew, A. G. & Townsend, C. Multiple stressors in freshwater ecosystems. Freshw. Biol. 55, 1–4 (2010).
Google Scholar 
Poff, et al. The natural flow regime: A paradigm for river conservation and restoration. Bioscience 47, 769–784 (1997).
Google Scholar 
Naiman, R. J., Décamps, H., & McClain, M. E. Riparia: Ecology, Conservation and Management of Streamside Communities, (Elsevier/Academic Press, 2005).Gleick, P. H. Water use. Annu. Rev. Environ. Resour. 28, 275–314 (2003).
Google Scholar 
Jenkins, K. M. & Boulton, A. J. Connectivity in a dryland river: Short-term aquatic macroinvertebrate recruitment following floodplain inundation. Ecology 84(10), 2708–2723 (2003).
Google Scholar 
Biggs, B. J. F. Patterns in benthic algae of streams. In Algal Ecology in Freshwater Benthic Ecosystems (eds. Stevenson, R. J., Bothwell, M. L., & Lowe, R. L.) 31–56 (Academic Press, 1996).Smolar-Žvanut, N. & Mikoš, M. The impact of flow regulation by hydropower dams on the periphyton community in the Soča River, Slovenia. Hydrol. Sci. J. 59(5), 1032–1045. https://doi.org/10.1080/02626667.2013.834339 (2014).Article 
CAS 

Google Scholar 
Curry, C. J. & Baird, D. J. Habitat type and dispersal ability influence spatial structuring of larval Odonata and Trichoptera assemblages. Freshw. Biol. 60, 2142–2152 (2015).
Google Scholar 
Wu, N., Cai, Q. & Fohrer, N. Contribution of microspatial factors to benthic diatom communities. Hydrobiologia 732, 49–60. https://doi.org/10.1007/s10750-014-1843-3 (2014).Article 
CAS 

Google Scholar 
Mueller, M., Pander, J. & Geist, J. The effects of weirs on structural stream habitat and biological communities. J. Appl. Ecol 48(6), 1450–1461. https://doi.org/10.1111/j.1365-2664.2011.02035.x (2011).Article 

Google Scholar 
Davies, P. M. et al. Flow–ecology relationships: closing the loop on effective environmental flows. Mar. Freshw. Res. 65(2), 133–141 (2013).
Google Scholar 
Jun, Y. C. et al. Spatial distribution of benthic macroinvertebrate assemblages in relation to environmental variables in Korean nationwide streams. Water 8(1), 27. https://doi.org/10.3390/w8010027 (2016).Article 

Google Scholar 
Biggs, B. J. F. & Close, M. E. Periphyton biomass dynamics in gravel bed rivers: The relative effects of flows and nutrients. Freshw. Biol. 22, 209–231 (1989).CAS 

Google Scholar 
Jowett, I. & Biggs, B. J. F. Flood and velocity effects on periphyton and silt accumulation in two New Zealand rivers. N. Zeal. J. Mar. Freshw. Res. 31, 287–300 (1997).
Google Scholar 
Biggs, B. J. F., Goring, D. G. & Nikora, V. I. Subsidy and stress responses of stream periphyton to gradients in water velocity as a function of community growth form. J. Phycol. 34, 598–607 (1998).
Google Scholar 
Malmqvist, B. & Englund, G. Effects of hydropower-induced flow perturbations on mayfly (Ephemeroptera) richness and abundance in north Swedish river rapids. Hydrobiologia 341(2), 145–158 (1996).
Google Scholar 
Poff, N. L. & Ward, J. V. Herbivory under different flow regimes: A field experiment and test of a model with a benthic stream insect. Oikos 72, 179–188 (1995).
Google Scholar 
Poff, L. N., Wellnitz, T. & Monroe, J. B. Redundancy among three herbivorous insects across an experimental current velocity gradient. Oecologia 134, 262–269. https://doi.org/10.1007/s00442-002-1086-2 (2003).Article 

Google Scholar 
Vaughn, C. C. The role of periphyton abundance and quality in the microdistribution of a stream grazer, Helicopsyche borealis (Trichoptera: Helicopsychidae). Freshw. Biol. 16, 485–493 (1986).
Google Scholar 
Francoeur, S. N. Meta-analysis of lotic nutrient amendment experiments: Detecting and quantifying subtle responses. J. N. Am. Benthol. Soc. 20, 358–368 (2001).
Google Scholar 
Elser, J. J. et al. Global analysis of nitrogen and phosphorus limitation of primary producers in freshwater, marine and terrestrial ecosystems. Ecol. Lett. 10, 1135–1142 (2007).
Google Scholar 
Hillebrand, H. Meta-analysis of grazer control of periphyton biomass across aquatic ecosystems. J. Phycol. 45, 798–806 (2009).
Google Scholar 
Lamberti, G. A. The role of periphyton in benthic food webs. In Algal Ecology—Freshwater Benthic Ecosystems, 533–572 (eds. Stevenson, R. J., Bothwell, M. L. & Lowe, R. L.) (Academic Press, 1996).Lamberti, G. A. et al. Influence of grazer type and abundance on plant–herbivore interactions in streams. Hydrobiologia 306, 179–188 (1995).
Google Scholar 
Gregory, S. V. Plant–herbivore interactions in stream systems. In Stream Ecology (eds. Barnes, J. R. & Minshall, G. W.) 157–189 (Plenum, 1983).Lamberti, G. A. & Moore, J. W. Aquatic insects as primary consumers. In The Ecology of Aquatic Insects (eds Resh, V. H. & Rosenberg, D. M.) 164–195 (Praeger, 1984).
Google Scholar 
Sterner, R. W., Elser, J. J. & Hessen, D. O. Stoichiometric relationships among producers, consumers and nutrient cycling in pelagic ecosystems. Biogeochemistry 17, 49–67 (1992).CAS 

Google Scholar 
Kahlert, M. & Baunsgaard, M. T. Nutrient recycling—A strategy of a grazer community to overcome nutrient limitation. J. N. Am. Benthol. Soc. 18, 363–369 (1999).
Google Scholar 
Burkholder, J. M., Wetzel, R. G. & Klomparens, K. L. Direct comparison of phosphate uptake by adnate and loosely attached microalgae within and intact biofilm matrix. Appl. Environ. Microbiol. 56, 2882–2890 (1990).CAS 

Google Scholar 
Steinman, A. D. Effects of grazers on freshwater benthic algae. In Algal Ecology: Freshwater Benthic Ecosystems (eds. Stevenson, R. J., Bothwell & Lowe, R. L.) 341–366 (Academic Press, 1996).Smucker, N. J. & Vis, M. L. Spatial factors contribute to benthic diatom structure in streams across spatial scales: Considerations for biomonitoring. Ecol. Indic. 11, 1191–1203 (2011).
Google Scholar 
Myers, et al. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).Article 
CAS 

Google Scholar 
Wang, J., Pan, F., Soininen, J., Heino, J. & Shen, J. Nutrient enrichment modifies temperature-biodiversity relationship in large scale field experiments. Nat. Commun. 7, 13 (2016).
Google Scholar 
Wu, et al. Flow regimes filter species traits of benthic diatom communities and modify the functional features of lowland streams-a nationwide scale study. Sci. Total Environ. 651, 357–366 (2019).CAS 

Google Scholar 
Nisar, M. A. Geospatial approach to study environmental characterization of a Kashmir wetland (Anchar) catchment with special reference to land use/land cover and changing climate. Ph.D Thesis, Sher-e-Kashmir University of Agricultural Sciences and Technology, Kashmir. Weblink. http://krishikosh.egranth.ac.in/handle/1/91309 (2012).Bhat, S. U., Sofi, A. H., Yaseen, T., Pandit, A. K. & Yousuf, A. R. Macro invertebrate community from Sonamarg streams of Kashmir Himalaya. Pak. J. Biol. Sci. 14(3), 182–194. https://doi.org/10.3923/pjbs.2011.182.194 (2011).Article 
CAS 

Google Scholar 
Baba, A. I., Sofi, A. H., Bhat, S. U., & Pandit, A. K. Periphytic algae of river Sindh in the Sonamarg area of Kashmir valley. J. Phytol. 3(6) (2011).Sofi, M. S., Rautela, K. S., Bhat, S. U., Rashid, I. & Kuniyal, J. C. Application of geomorphometric approach for the estimation of hydro-sedimentological flows and cation weathering rate: Towards understanding the sustainable land use policy for the Sindh Basin, Kashmir Himalaya. Water Air Soil Pollut. 232(7), 1–11. https://doi.org/10.1007/s11270-021-05217-w (2021).Article 
CAS 

Google Scholar 
Romshoo, S. A., & Fayaz, M. Use of high resolution remote sensing for improving environmental Friendly tourism master planning in the Alpine Himalaya: A case study of Sonamarg tourist resort, Kashmir. J. Himalayan Ecol. Sustain. Dev. 14 (2019).Biggs, B. J. F. & Kilroy, C. Stream periphyton monitoring manual. Published by NIWA for Ministry for the Environment, 226 Christchurch, New Zealand: NIWA (2000).APHA. Standard Methods for Examination of Water and Wastewater, 22nd edn. (American Public Health Association, 2012).Cox, E. J. Identification of Freshwater Diatoms from Live Material. (Chapman and Hall, 1996). https://doi.org/10.1017/S0025315400041023.Krammer, K., & Lange-Bertalot, H. Bacillariophyceae, Part 5. English and French Translation of the Keys. (VEB Gustav Fisher Verlag, 2000).Reichardt, E. A remarkable association of diatoms in a spring habitat in the Grazer Bergland, Austria. In Iconographia Diatomologica (ed. Lange-Bertalot, H.) 419–479 (2004).Żelazna-Wieczorek, J. Diatom flora in springs of Lódz Hills (Central Poland). Biodiversity, taxonomy and temporal changes of epipsammic diatom assemblages in springs affected by human impact, 419. Volume 13 of Diatom monographs. Gantner. https://books.google.co.in/books?id=bdxeewAACAAJ (2011).Stark, J. D., Boothroyd, I. K. G., Harding, J. S., Maxted, J. R. & Scarsbrook, M. R. Protocols for sampling macroinvertebrates in wadeable streams. In New Zealand Macroinvertebrate Working Group Report no. 1. Prepared for the Ministry for the Environment. Sustainable Management Fund Project, 5103 (2001).Winterbourn, M. J. Sampling stream invertebrates. In Biological Monitoring of Freshwaters. Proceedings of the Seminar. Water and Soil Miscellaneous Publication No. 83 (eds. Pridmore, R. D., Cooper, A. B.) 241–258. (National Water and Soil Conservation Authority, 1985).Barbour, M. T., Gerritsen, J., Snyder, B. D., Stribling, J. B. Rapid bioassessment protocols for use in streams and wadeable rivers: periphyton, benthic macroinvertebrates and fish, 339. (United States Environmental Protection Agency, Office of Water, 1999).Malmqvist, B. & Hoffsten, P. O. Macroinvertebrate taxonomic richness, community structure and nestedness in Swedish streams. Fundam. Appl. Limnol. 150(1), 29–54. https://doi.org/10.1127/archiv-hydrobiol/150/2000/29 (2000).Article 

Google Scholar 
Ilmonen, J. & Paasivirta, L. Benthic macrocrustacean and insect assemblages in relation to spring habitat characteristics: Patterns in abundance and diversity. Hydrobiologia 533(1–3), 99–113. https://doi.org/10.1007/s10750-004-2399-4 (2005).Article 

Google Scholar 
Munasinghe, D. S. N., Najim, M. M. M., Quadroni, S. & Musthafa, M. M. Impacts of streamflow alteration on benthic macroinvertebrates by mini-hydro diversion in Sri Lanka. Sci. Rep. 11(1), 546. https://doi.org/10.1038/s41598-020-79576-5 (2021).Article 
CAS 

Google Scholar 
Edmondson, W. T. Fresh-Water Biology, 2nd ed. 1050–1056 (Wiley, 1959).Pennak, R. W. Freshwater Invertebrates of United States. (Wiley, 1978).McCafferty, W. P., Provonsha, A. V. Aquatic entomology: The fishermen’s and ecologists’ Illustrated Guide to Insects and their Relatives. (Jones and Bartlett Publishers, 1983).Borror, D., Triplehorn, C., Johnson, N. An Introduction to the Study of Insects, 6th ed. (Saunders College Publishing, 1989).Ward, J. V. Aquatic Insect Ecology, Biology and Habitat. (Wiley, 1992).Engblom, E. & Lingdell, P.E. Analyses of Benthic Invertebrates (ed. Nyman, L.) (1999).Bouchard, R. W. Guide to Aquatic Invertebrates of the Upper Midwest: Identification Manual for Students (University of Minnesota, 2004).
Google Scholar 
Subramanian, K. A. & Sivaramakrishnan, K. G. Aquatic Insects for Biomonitoring Freshwater Ecosystems—A Methodology Manual. (Ashoka Trust for Ecology and Environment (ATREE), 2007).Thorp, J. H., & Covich, A. P. (eds.) Ecology and Classification of North American Freshwater Invertebrates. (Academic Press, 2009).Allan, J. D. & Castillo, M.M. An introduction to fluvial ecosystems. In Stream Ecology: Structure and Function of Running Waters, 1–12 (2007).Oksanen, et al. Vegan: Community ecology package. In: R package version 2.4-3.McCune, B. & Grace, B. Analysis of Ecological Communities (MjM Software Design, 2016).Clarke, K. R. & Gorley, R. N. Primer v6 Permanova+ (Primer-E Ltd., 2006).
Google Scholar 
Salazar, G. EcolUtils: Utilities for Community Ecology Analysis. R package version 0.1 software (2018).Anderson, M. J., Ellingsen, K. E. & McArdle, B. H. Multivariate dispersion as a measure of beta diversity. Ecol. Lett. 9(6), 683–693 (2006).
Google Scholar 
Gardener, M. Community Ecology: Analytical Methods in Using R and Excel. (Pelagic Publishing, 2014).Chao, A. & Bunge, J. Estimating the number of species in a stochastic abundance model. Biometrics 58, 531–539. https://doi.org/10.1111/j.0006-341X.2002.00531.x (2002).Article 
MATH 

Google Scholar 
Peres-Neto, P. R., Legendre, P., Dray, S. & Borcard, D. Variation partitioning of species data matrices: estimation and comparison of fractions. Ecology 87, 2614–2625 (2006).
Google Scholar 
Meng, X. L. et al. Responses of macroinvertebrates and local environment to short-term commercial sand dredging practices in a flood-plain lake. Sci. Total Environ. 631, 1350–1359 (2018).
Google Scholar 
Core Team, R. R: A Language and Environmental for Statistical Computing. (R Foundation for Statistical Computing, 2017).Wood, P. J. & Armitage, P. D. Biological effects of fine sediment in the lotic environment. Environ. Manag. 21, 203–217 (1997).CAS 

Google Scholar 
Marchant, R. Changes in the benthic invertebrate communities of the Thomson River, southeastern Australia, after dam construction. Regul. Rivers Res. Manag. 4, 71–89 (1989).
Google Scholar 
Gray, L. J. & Ward, J. V. Effects of sediment releases from a reservoir on stream macroinvertebrates. Hydrobiologia 96, 177–184 (1982).
Google Scholar 
Sand-Jensen, K., Moller, J. & Olesen, B. H. Biomass regulation of microbenthic algae in Danish lowland streams. Oikos 53, 332–340 (1988).
Google Scholar 
Lewis, M. A., Weber, D. E., Stanley, R. S. & Moore, J. C. Dredging impact on an urbanized Florida bayou: Effects on benthos and algal-periphyton. Environ. Pollut. 115(2), 161–171 (2001).CAS 

Google Scholar 
Biggs, B. J. Algal ecology in freshwater benthic ecosystems geology and landuse to the habitat template of periphyton in stream ecosystems. Freshw. Biol. 33, 419–438 (1995).
Google Scholar 
Taylor, et al. Can diatom-based pollution indices be used for biomonitoring in South Africa? A case study of the Crocodile West and Marico water management area. Hydrobiologia 592, 455–464 (2007).
Google Scholar 
Porter, et al. Efficacy of algal metrics for assessing nutrient and organic enrichment in flowing waters. Freshw. Biol. 53, 1036–1054 (2008).
Google Scholar 
Wetzel, R. G. & Likens, G. E. Limnological analyses, 3rd ed. In Nitrogen, Phosphorus, and Other Nutrients, 85–113. (Springer, 2000). https://doi.org/10.1007/978-1-4757-3250-4.Wetzel, R. G. Attached algal-substrata interactions: Fact or myth, and when and how? vol. 17. In Periphyton of Freshwater Ecosystems (ed. Wetzel, R.) 207–215 (Springer, 1983). https://doi.org/10.1007/978-94-009-7293-3_28.Krajenbrink, H. J. et al. Diatoms as indicators of the effects of river impoundment at multiple spatial scales. PeerJ 7, e8092. https://doi.org/10.7717/peerj.8092 (2019).Article 

Google Scholar 
Poff, N. L., Voelz, N. J., Ward, J. V. & Lee, R. E. Algal colonization under four experimentally-controlled current regimes in a high mountain stream. J. N. Am. Benthol. Soc. 9, 303–318 (1990).
Google Scholar 
Dodds, W. K. & Marra, J. L. Behaviors of the midge, Cricotopus (Diptera; Chironomidae) related to mutualism with Nostoc parmeloides (Cyanobacteria). Aquat. Insects 11, 201–208 (1989).
Google Scholar 
Tang, T., Niu, S. Q. & Dudgeon, D. Responses of epibenthic algal assemblages to water abstraction in Hong Kong streams. Hydrobiologia 703(1), 225–237. https://doi.org/10.1007/s10750-012-1362-z (2013).Article 
CAS 

Google Scholar 
Maheshwari, K., Vashistha, J., Paulose, P. V. & Agarwal, T. Seasonal changes in phytoplankton community of lake Ramgarh, India. Int. J. Curr. Microbiol. Appl. Sci. 4(11), 318–330 (2015).CAS 

Google Scholar 
Luttenton, M. R., & Baisden, C. The relationships among disturbance, substratum size and periphyton community structure. In Advances in Algal Biology: A Commemoration of the Work of Rex Lowe 111–117. (Springer, 2006).Uehlinger, U. Spatial and temporal variability of periphyton biomass in a prealpine river (Necker, Switzerland). Arch. Fur. Hydrobiol. 123, 219–237 (1991).
Google Scholar 
Hill, W. R. Effects of light. In Algal Ecology in Freshwater Benthic Ecosystems. 121–148 (eds. Stevenson, R. J., Bothwell, M. L., Lowe, R. L.) (Academic Press, 1996).DeNichola, D. M. Periphyton responses to temperature at different ecological levels. In Algal Ecology in Freshwater Benthic Ecosystems. (eds. Stevenson, R. J., Bothwell, M. L., Lowe, R. L.) 149–181 (Academic Press, 1996).O’Reilly, C. M. Seasonal dynamics of periphyton in a large tropical lake. Hydrobiologia 553, 293–301. https://doi.org/10.1007/s10750-005-0878-x (2006).Article 

Google Scholar 
Borduqui, M. & Ferragut, C. Factors determining periphytic algae succession in a tropical hypereutrophic reservoir. Hydrobiologia 683, 109–122. https://doi.org/10.1007/s10750-011-0943-6 (2012).Article 
CAS 

Google Scholar 
De Souza, M. L., Pellegrini, B. G. & Ferragut, C. Periphytic algal community structure in relation to seasonal variation and macrophyte richness in a shallow tropical reservoir. Hydrobiologia 755, 183–196. https://doi.org/10.1007/s10750-015-2232-2 (2015).Article 

Google Scholar 
Prowse, T. D. River-ice hydrology. In Encyclopedia of Hydrological Sciences, vol. 4 (ed. Anderson, M. G.). (Wiley, 2005).Rusanov, A. G., Stanislavskaya, E. V. & Ács, É. Periphytic algal assemblages along environmental gradients in the rivers of the Lake Ladoga basin, Northwestern Russia: Implication for the water quality assessment. Hydrobiologia 695(1), 305–327 (2012).CAS 

Google Scholar 
Sofi, M. S., Hamid, A., Bhat, S. U., Rashid, I. & Kuniyal, J. C. Impact evaluation of the run-of-river hydropower projects on the water quality dynamics of the Sindh River in the Northwestern Himalayas. Environ. Monit. Assess. 194(9), 1–6 (2022).
Google Scholar 
MCCormick, P. V. Resource competition and species coexistence in freshwater algal assemblages. In Algal ecology—Freshwater Benthic Ecosystems (eds. Stevenson, R. J., Bothwell, M. L., Lowe, R. L.) 229–252 (Academic, 1996).Hillebrand, H., Worm, B. & Lotze, H. K. Marine microbenthic community structure regulated by nitrogen loading and grazing pressure. Mar. Ecol. Prog. Ser. 204, 27–38 (2000).CAS 

Google Scholar  More

Pitois, S. G., Lynam, C. P., Jansen, T., Halliday, N. & Edwards, M. Bottom-up effects of climate on fish populations: data from the Continuous Plankton Recorder. Mar. Ecol. Prog. Ser. 456, 169–186 (2012).ADS 

Google Scholar 
Ruzicka, J. J. et al. Interannual variability in the Northern California Current food web structure: changes in energy flow pathways and the role of forage fish, euphausiids, and jellyfish. Prog. Oceanogr. 102, 19–41 (2012).ADS 

Google Scholar 
Lauria, V., Attrill, M. J., Brown, A., Edwards, M. & Votier, S. C. Regional variation in the impact of climate change: evidence that bottom-up regulation from plankton to seabirds is weak in parts of the Northeast Atlantic. Mar. Ecol. Prog. Ser. 488, 11–22 (2013).ADS 

Google Scholar 
Heneghan, R. F., Everett, J. D., Blanchard, J. L. & Richardson, A. J. Zooplankton are not fish: improving zooplankton realism in size-spectrum models mediates energy transfer in food webs. Front. Mar. Sci. https://doi.org/10.3389/fmars.2016.00201 (2016).Lehette, P., Tovar-Sánchez, A., Duarte, C. M. & Hernández-León, S. Krill excretion and its effect on primary production. Mar. Ecol. Prog. Ser. 459, 29–38 (2012).ADS 
CAS 

Google Scholar 
Arístegui, J., Duarte, C. M., Reche, I. & Gómez-Pinchetti, J. L. Krill excretion boosts microbial activity in the Southern Ocean. PLoS ONE 9, e89391 (2014).ADS 

Google Scholar 
Tovar-Sánchez, A., Duarte, C. M., Hernández-León, S. & Sañudo-Wilhelmy, S. A. Krill as a central node for iron cycling in the Southern Ocean. Geophys. Res. Lett. 34, 1–4 (2007).Schmidt, K. et al. Seabed foraging by Antarctic krill: Implications for stock assessment, bentho-pelagic coupling, and the vertical transfer of iron. Limnol. Oceanogr. 56, 1411–1428 (2011).ADS 
CAS 

Google Scholar 
Cavan, E. L. et al. The importance of Antarctic krill in biogeochemical cycles. Nat. Commun. 10, 4742 (2019). This Review demonstrates how the dominant grazer in Antarctica plays a critical role in biogeochemical cycles.ADS 
CAS 

Google Scholar 
Ratnarajah, L., Nicol, S. & Bowie, A. R. Pelagic iron recycling in the southern ocean: exploring the contribution of marine animals. Front. Mar. Sci. https://doi.org/10.3389/fmars.2018.00109 (2018).Halfter, S., Cavan, E. L., Swadling, K. M., Eriksen, R. S. & Boyd, P. W. The role of zooplankton in establishing carbon export regimes in the southern ocean – a comparison of two representative case studies in the subantarctic region. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.567917 (2020).Schmidt, K. et al. Zooplankton gut passage mobilizes lithogenic iron for ocean productivity. Curr. Biol. 26, 2667–2673 (2016).CAS 

Google Scholar 
Brun, P. et al. Climate change has altered zooplankton-fuelled carbon export in the North Atlantic. Nat. Ecol. Evol. 3, 416–423 (2019).
Google Scholar 
Chust, G. et al. Are Calanus spp. shifting poleward in the North Atlantic? A habitat modelling approach. ICES J. Mar. Sci. 71, 241–253 (2014).
Google Scholar 
Batten, S. D. & Walne, A. W. Variability in northwards extension of warm water copepods in the NE Pacific. J. Plankton Res. 33, 1643–1653 (2011).
Google Scholar 
Fu, W., Randerson, J. T. & Moore, J. K. Climate change impacts on net primary production (NPP) and export production (EP) regulated by increasing stratification and phytoplankton community structure in the CMIP5 models. Biogeosciences 13, 5151–5170 (2016).ADS 

Google Scholar 
Tagliabue, A. et al. Persistent uncertainties in ocean net primary production climate change projections at regional scales raise challenges for assessing impacts on ecosystem services. Front. Clim. https://doi.org/10.3389/fclim.2021.738224 (2021).Edwards, M. & Richardson, A. J. Impact of climate change on marine pelagic phenology and trophic mismatch. Nature 430, 881–884 (2004).ADS 
CAS 

Google Scholar 
Mackas, D. L. et al. Changing zooplankton seasonality in a changing ocean: comparing time series of zooplankton phenology. Prog. Oceanogr. 97-100, 31–62 (2012).ADS 

Google Scholar 
Freer, J. J., Daase, M. & Tarling, G. A. Modelling the biogeographic boundary shift of Calanus finmarchicus reveals drivers of Arctic Atlantification by subarctic zooplankton. Glob. Change Biol. 28, 429–440 (2021).
Google Scholar 
Daufresne, M., Lengfellner, K. & Sommer, U. Global warming benefits the small in aquatic ecosystems. Proc. Natl Acad. Sci. USA 106, 12788–12793 (2009).ADS 
CAS 

Google Scholar 
Brandão, M. C. et al. Macroscale patterns of oceanic zooplankton composition and size structure. Sci. Rep. 11, 15714 (2021). This study showed that zooplankton abundance and median size decreased towards warmer and less productive environments due to changes in copepod composition, but some groups displayed the opposite relationships potentially due to alternative feeding strategies.ADS 

Google Scholar 
Campbell, M. D. et al. Testing Bermann’s rule in marine copepods. Ecography 44, 1283–1295 (2021). This global study found that temperature better predicted copepod size than did latitude or oxygen, with body size decreasing by 43.9% across the temperature range (−1.7 to 30 °C).
Google Scholar 
Barange, M. et al. Impacts of Climate Change on Fisheries and Aquaculture. Synthesis of Current Knowledge, Adaptation, and Mitigation Options. (FAO, 2018).Atkinson, A. et al. Questioning the role of phenology shifts and trophic mismatching in a planktonic food web. Prog. Oceanogr. 137, 498–512 (2015).ADS 

Google Scholar 
Thackeray, S. J. et al. Phenological sensitivity to climate across taxa and trophic levels. Nature 535, 241–245 (2016).ADS 
CAS 

Google Scholar 
Sasaki, M. & Dam, H. G. Global patterns in copepod thermal tolerance. J. Plankton Res. 43, 598–609 (2021).
Google Scholar 
Dam, H. G. et al. Rapid, but limited, zooplankton adaptation to simultaneous warming and acidification. Nat. Clim. Change 11, 780–786 (2021).ADS 

Google Scholar 
Cooley, S. et al. Ocean and Coastal Ecosystems and their Services. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. (Cambridge University Press, 2022). This IPCC report synthesizes changes in zooplankton phenology compared to other marine life.Mackas, D. L., Goldblatt, R. & Lewis, A. G. Interdecadal variation in developmental timing of Neocalanus plumchrus populations at Ocean Station P in the subarctic North Pacific. Can. J. Fish. Aquat. Sci. 55, 1878–1893 (1998).
Google Scholar 
Edwards, M. et al. Ecological Status Report: results from the CPR survey 2007/2008. 1-12 (2009).Richardson, A. J. In hot water: zooplankton and climate change. ICES J. Mar. Sci. 65, 279–295 (2008).
Google Scholar 
Costello, J. H., Sullivan, B. K. & Gifford, D. J. A physical–biological interaction underlying variable phenological responses to climate change by coastal zooplankton. J. Plankton Res. 28, 1099–1105 (2006).
Google Scholar 
Chevillot, X. et al. Toward a phenological mismatch in estuarine pelagic food web? PLoS ONE 12, e0173752 (2017).
Google Scholar 
Ji, R., Edwards, M., Mackas, D. L., Runge, J. A. & Thomas, A. C. Marine plankton phenology and life history in a changing climate: current research and future directions. J. Plankton Res. 32, 1355–1368 (2010).
Google Scholar 
Thibodeau, P. S. et al. Long-term observations of pteropod phenology along the Western Antarctic Peninsula. Deep Sea Res. Part I: Oceanogr. Res. Pap. 166, 103363 (2020).
Google Scholar 
Beaugrand, G., Reid Philip, C., Ibañez, F., Lindley, J. A. & Edwards, M. Reorganization of North Atlantic marine copepod biodiversity and climate. Science 296, 1692–1694 (2002).ADS 
CAS 

Google Scholar 
Edwards, M. et al. North Atlantic warming over six decades drives decreases in krill abundance with no associated range shift. Commun. Biol. 4, 644 (2021). This regional study showed that ocean warming is causing a decrease in krill abundance but no poleward movement in range.
Google Scholar 
Chivers, W. J., Walne, A. W. & Hays, G. C. Mismatch between marine plankton range movements and the velocity of climate change. Nat. Commun. 8, 14434 (2017).ADS 
CAS 

Google Scholar 
Lindley, J. A. & Daykin, S. Variations in the distributions of Centropages chierchiae and Temora stylifera (Copepoda: Calanoida) in the north-eastern Atlantic Ocean and western European shelf waters. ICES J. Mar. Sci. 62, 869–877 (2005).
Google Scholar 
Atkinson, A. et al. Krill (Euphausia superba) distribution contracts southward during rapid regional warming. Nat. Clim. Change 9, 142–147 (2019). This regional study shows that the dominant grazer in Antarctic waters, Antarctic krill is moving southward due to regional warming.ADS 

Google Scholar 
Atkinson, A., Siegel, V., Pakhomov, E. & Rothery, P. Long-term decline in krill stock and increase in salps within the Southern Ocean. Nature 432, 100–103 (2004).ADS 
CAS 

Google Scholar 
Pakhomov, E. A., Froneman, P. W., Wassmann, P., Ratkova, T. & Arashkevich, E. Contribution of algal sinking and zooplankton grazing to downward flux in the Lazarev Sea (Southern Ocean) during the onset of phytoplankton bloom: a lagrangian study. Mar. Ecol. Prog. Ser. 233, 73–88 (2002).ADS 

Google Scholar 
Tarling, G. A., Ward, P. & Thorpe, S. E. Spatial distributions of Southern Ocean mesozooplankton communities have been resilient to long-term surface warming. Glob. Change Biol. 24, 132–142 (2017). This study shows that 16 mesozooplankton taxa in the in the southwest Atlantic sector of the Southern Ocean are resilient to ocean warming.ADS 

Google Scholar 
Atkinson, A. et al. Stepping stones towards Antarctica: switch to southern spawning grounds explains an abrupt range shift in krill. Glob. Change Biol. 28, 1359–1375 (2021).
Google Scholar 
Jonkers, L., Hillebrand, H. & Kucera, M. Global change drives modern plankton communities away from the pre-industrial state. Nature 570, 372–377 (2019).ADS 
CAS 

Google Scholar 
Yebra, L. et al. Spatio-temporal variability of the zooplankton community in the SW Mediterranean 1992–2020: Linkages with environmental drivers. Prog. Oceanogr. 209, 1–10 (2022).Cowen, T. et al. Report on the status and trends of the Southern Ocean zooplankton based on the SCAR Southern Ocean Continuous Plankton Recorder (SO-CPR) survey. (2020).Corona, S., Hirst, A., Atkinson, D. & Atkinson, A. Density-dependent modulation of copepod body size and temperature–size responses in a shelf sea. Limnol. Oceanogr. 66, 3916–3927 (2021).ADS 

Google Scholar 
Horne, C. R., Hirst, A. G., Atkinson, D., Neves, A. & Kiørboe, T. A global synthesis of seasonal temperature–size responses in copepods. Glob. Ecol. Biogeogr. 25, 988–999 (2016).
Google Scholar 
Hobday, A. J. et al. A hierarchical approach to defining marine heatwaves. Prog. Oceanogr. 141, 227–238 (2016).ADS 

Google Scholar 
Brodeur, R. D., Auth, T. D. & Phillips, A. J. Major shifts in pelagic micronekton and macrozooplankton community structure in an upwelling ecosystem related to an unprecedented marine heatwave. Front. Mar. Sci. https://doi.org/10.3389/fmars.2019.00212 (2019).Lavaniegos, B. E., Jiménez-Herrera, M. & Ambriz-Arreola, I. Unusually low euphausiid biomass during the warm years of 2014–2016 in the transition zone of the California Current. Deep Sea Res. Part II: Top. Stud. Oceanogr. 169-170, 104638 (2019).
Google Scholar 
Peterson, W. T. et al. The pelagic ecosystem in the Northern California Current off Oregon during the 2014–2016 warm anomalies within the context of the past 20 years. J. Geophys. Res.: Oceans 122, 7267–7290 (2017).ADS 

Google Scholar 
O’ Loughlin, J. H. O. et al. Implications of Pyrosoma atlanticum range expansion on phytoplankton standing stocks in the Northern California Current. Prog. Oceanogr. 188, 1–9 (2020).Robertson, R. R. & Bjorkstedt, E. P. Climate-driven variability in Euphausia pacifica size distributions off northern California. Prog. Oceanogr. 188, 102412 (2020).
Google Scholar 
Stephens, J. A., Jordan, M. B., Taylor, A. H. & Proctor, R. The effects of fluctuations in North Sea flows on zooplankton abundance. J. Plankton Res. 20, 943–956 (1998).
Google Scholar 
Greene, C. H. & Pershing, A. J. The response of Calanus finmarchicus populations to climate variability in the Northwest Atlantic: basin-scale forcing associated with the North Atlantic Oscillation. ICES J. Mar. Sci. 57, 1536–1544 (2000).
Google Scholar 
Saba, G. K. et al. Winter and spring controls on the summer food web of the coastal West Antarctic Peninsula. Nat. Commun. 5, 4318 (2014).ADS 
CAS 

Google Scholar 
Steinberg, D. K. et al. Long-term (1993–2013) changes in macrozooplankton off the Western Antarctic Peninsula. Deep Sea Res. Part I: Oceanogr. Res. Pap. 101, 54–70 (2015).ADS 

Google Scholar 
Steinke, K. B., Bernard, K. S., Ross, R. M. & B, Q. L. Environmental drivers of the physiological condition of mature female Antarctic krill during the spawning season: implications for krill recruitment. Mar. Ecol. Prog. Ser. 669, 65–82 (2021).ADS 

Google Scholar 
Brodeur, R. D. et al. Rise and fall of jellyfish in the eastern Bering Sea in relation to climate regime shifts. Prog. Oceanogr. 77, 103–111 (2008).ADS 

Google Scholar 
Quiñones, J. et al. Climate-driven population size fluctuations of jellyfish (Chrysaora plocamia) off Peru. Mar. Biol. 162, 2339–2350 (2015).
Google Scholar 
Lynam, C. P., Attrill, M. J. & Skogen, M. D. Climatic and oceanic influences on the abundance of gelatinous zooplankton in the North Sea. J. Mar. Biol. Assoc. UK 90, 1153–1159 (2009).
Google Scholar 
Schmidt, K. et al. Increasing picocyanobacteria success in shelf waters contributes to long-term food web degradation. Glob. Change Biol. 26, 5574–5587 (2020).ADS 

Google Scholar 
Laglera, L. M. et al. Iron partitioning during LOHAFEX: Copepod grazing as a major driver for iron recycling in the Southern Ocean. Mar. Chem. 196, 148–161 (2017).CAS 

Google Scholar 
Cavan, E. L., Henson, S. A., Belcher, A. & Sanders, R. Role of zooplankton in determining the efficiency of the biological carbon pump. Biogeosciences 14, 177–186 (2017).ADS 
CAS 

Google Scholar 
Valdés, V. et al. Nitrogen and phosphorus recycling mediated by copepods and response of bacterioplankton community from three contrasting areas in the western tropical South Pacific (20° S). Biogeosciences 15, 6019–6032 (2018).ADS 

Google Scholar 
Steinberg, D. K. & Landry, M. R. Zooplankton and the Ocean Carbon Cycle. Annu. Rev. Mar. Sci. 9, 413–444 (2017). This Review synthesizes the role of zooplankton within the ocean carbon cycle.ADS 

Google Scholar 
Ratnarajah, L. et al. Understanding the variability in the iron concentration of Antarctic krill. Limnol. Oceanogr. 61, 1651–1660 (2016).ADS 

Google Scholar 
Bernard, K. S., Steinberg, D. K. & Schofield, O. M. Summertime grazing impact of the dominant macrozooplankton off the Western Antarctic Peninsula. Deep Sea Res. Part I: Oceanogr. Res. Pap. 62, 111–122 (2012).ADS 

Google Scholar 
Böckmann, S. et al. Salp fecal pellets release more bioavailable iron to Southern Ocean phytoplankton than krill fecal pellets. Curr. Biol. 31, 2737–2746.e2733 (2021).
Google Scholar 
Cabanes, D. J. E. et al. First Evaluation of the Role of Salp Fecal Pellets on Iron Biogeochemistry. Front. Mar. Sci. https://doi.org/10.3389/fmars.2016.00289 (2017).Ratnarajah, L. Regenerated iron: how important are different zooplankton groups to oceanic productivity. Curr. Biol. 31, R848–R850 (2021).CAS 

Google Scholar 
Giering, S. L., Steigenberger, S., Achterberg, E. P., Sanders, R. & Mayor, D. J. Elevated iron to nitrogen recycling by mesozooplankton in the Northeast Atlantic Ocean. Geophys. Res. Lett. 39, 1–5 (2012).Svensen, C. et al. Zooplankton communities associated with new and regenerated primary production in the Atlantic inflow North of Svalbard. Front. Mar. Sci. https://doi.org/10.3389/fmars.2019.00293 (2019).Darnis, G. & Fortier, L. Zooplankton respiration and the export of carbon at depth in the Amundsen Gulf (Arctic Ocean). J. Geophys. Res. Oceans 117, 1–12 (2012).Miquel, J.-C. et al. Downward particle flux and carbon export in the Beaufort Sea, Arctic Ocean; the role of zooplankton. Biogeosciences 12, 5103–5117 (2015).ADS 

Google Scholar 
Hernández-León, S. et al. Carbon export through zooplankton active flux in the Canary Current. J. Mar. Syst. 189, 12–21 (2019).
Google Scholar 
Gorgues, T., Aumont, O. & Memery, L. Simulated changes in the particulate carbon export efficiency due to diel vertical migration of zooplankton in the North Atlantic. Geophys. Res. Lett. 46, 5387–5395 (2019).ADS 
CAS 

Google Scholar 
Steinberg, D. K. et al. Zooplankton vertical migration and the active transport of dissolved organic and inorganic carbon in the Sargasso Sea. Deep Sea Res. Part I: Oceanogr. Res. Pap. 47, 137–158 (2000).ADS 
CAS 

Google Scholar 
Lebrato, M., Molinero, J.-C., Mychek-Londer, J. G., Gonzalez, E. M. & Jones, D. O. B. Gelatinous carbon impacts benthic megafaunal communities in a continental margin. Front. Mar. Sci. https://doi.org/10.3389/fmars.2022.902674 (2022).Lebrato, M. & Jones, D. O. B. Mass deposition event of Pyrosoma atlanticum carcasses off Ivory Coast (West Africa). Limnol. Oceanogr. 54, 1197–1209 (2009).ADS 
CAS 

Google Scholar 
Kobari, T. et al. Impacts of ontogenetically migrating copepods on downward carbon flux in the western subarctic Pacific Ocean. Deep Sea Res. Part II: Top. Stud. Oceanogr. 55, 1648–1660 (2008).ADS 

Google Scholar 
Wilson, S. E., Steinberg, D. K. & Buesseler, K. O. Changes in fecal pellet characteristics with depth as indicators of zooplankton repackaging of particles in the mesopelagic zone of the subtropical and subarctic North Pacific Ocean. Deep Sea Res. Part II: Top. Stud. Oceanogr. 55, 1636–1647 (2008).ADS 

Google Scholar 
Laurenceau-Cornec, E. et al. The relative importance of phytoplankton aggregates and zooplankton fecal pellets to carbon export: insights from free-drifting sediment trap deployments in naturally iron-fertilised waters near the Kerguelen Plateau. Biogeosciences 12, 1007–1027 (2015).ADS 

Google Scholar 
Manno, C., Stowasser, G., Enderlein, P., Fielding, S. & Tarling, G. The contribution of zooplankton faecal pellets to deep-carbon transport in the Scotia Sea (Southern Ocean). Biogeosciences 12, 1955–1965 (2015).ADS 

Google Scholar 
Cavan, E. et al. Attenuation of particulate organic carbon flux in the Scotia Sea, Southern Ocean, is controlled by zooplankton fecal pellets. Geophys. Res. Lett. 42, 821–830 (2015).ADS 
CAS 

Google Scholar 
Lebrato, M. et al. Jelly biomass sinking speed reveals a fast carbon export mechanism. Limnol. Oceanogr. 58, 1113–1122 (2013).ADS 

Google Scholar 
Ducklow, H. W., Steinberg, D. K. & Buesseler, K. O. Upper ocean carbon export and the biological pump. Oceanography 14, 50–58 (2001).
Google Scholar 
Yebra, L. et al. Zooplankton production and carbon export flux in the western Alboran Sea gyre (SW Mediterranean). Prog. Oceanogr. 167, 64–77 (2018).ADS 

Google Scholar 
Yebra, L. et al. Mesoscale physical variability affects zooplankton production in the Labrador Sea. Deep Sea Res. Part I: Oceanogr. Res. Pap. 56, 703–715 (2009).ADS 
CAS 

Google Scholar 
Beaugrand, G., Edwards, M. & Legendre, L. Marine biodiversity, ecosystem functioning, and carbon cycles. Proc. Natl Acad. Sci. USA 107, 10120–10124 (2010).ADS 
CAS 

Google Scholar 
Benson, A. J. & Trites, A. W. Ecological effects of regime shifts in the Bering Sea and eastern North Pacific Ocean. Fish. Fish. 3, 95–113 (2002).
Google Scholar 
Coyle, K. O. & Pinchuk, A. I. Climate-related differences in zooplankton density and growth on the inner shelf of the southeastern Bering Sea. Prog. Oceanogr. 55, 177–194 (2002).ADS 

Google Scholar 
Duffy-Anderson, J. T. et al. Return of warm conditions in the southeastern Bering Sea: Phytoplankton – Fish. PLoS ONE 12, e0178955 (2017).
Google Scholar 
Odebrecht, C., Secchi, E. R., Abreu, P. C., Muelbert, J. H. & Uiblein, F. Biota of the Patos Lagoon estuary and adjacent marine coast: long-term changes induced by natural and human-related factors. Mar. Biol. Res. 13, 3–8 (2017).
Google Scholar 
Eisner, L. B. et al. Seasonal, interannual, and spatial patterns of community composition over the eastern Bering Sea shelf in cold years. Part I: zooplankton. ICES J. Mar. Sci. 75, 72–86 (2018).
Google Scholar 
Trueblood, L. A. Salp metabolism: temperature and oxygen partial pressure effect on the physiology of Salpa fusiformis from the California Current. J. Plankton Res. 41, 281–291 (2019).CAS 

Google Scholar 
Hernández-León, S. & Ikeda, T. in Respiration in aquatic ecosystems. p. 57-82 (Oxford University Press, 2005).Lewandowska, A. M. et al. Effects of sea surface warming on marine plankton. Ecol. Lett. 17, 614–623 (2014).
Google Scholar 
O’Connor, M. I., Piehler, M. F., Leech, D. M., Anton, A. & Bruno, J. F. Warming and resource availability shift food web structure and metabolism. PLoS Biol. 7, e1000178 (2009).
Google Scholar 
Chen, B., Landry, M. R., Huang, B. & Liu, H. Does warming enhance the effect of microzooplankton grazing on marine phytoplankton in the ocean? Limnol. Oceanogr. 57, 519–526 (2012).ADS 
CAS 

Google Scholar 
Paul, C., Matthiessen, B. & Sommer, U. Warming, but not enhanced CO2 concentration, quantitatively and qualitatively affects phytoplankton biomass. Mar. Ecol. Prog. Ser. 528, 39–51 (2015).ADS 
CAS 

Google Scholar 
Sommer, U. & Lewandowska, A. Climate change and the phytoplankton spring bloom: warming and overwintering zooplankton have similar effects on phytoplankton. Glob. Change Biol. 17, 154–162 (2010).ADS 

Google Scholar 
Beaugrand, G. et al. Prediction of unprecedented biological shifts in the global ocean. Nat. Clim. Change 9, 237–243 (2019).ADS 

Google Scholar 
Sterner, R. W. & Elser, J. J. Ecological Stoichiometry: The Biology of Elements from Molecules to the Biosphere (Princeton University Press, 2002).Matsumoto, K., Tanioka, T. & Rickaby, R. Linkages between dynamic phytoplankton C:N:P and the ocean carbon cycle under climate change. Oceanography 33, 44–52 (2020).
Google Scholar 
Finkel, Z. V. et al. Phytoplankton in a changing world: cell size and elemental stoichiometry. J. Plankton Res. 32, 119–137 (2010).CAS 

Google Scholar 
Bank, T. W. Blue Economy. https://www.worldbank.org/en/topic/oceans-fisheries-and-coastal-economies#1 (2021).Burthe, S. et al. Phenological trends and trophic mismatch across multiple levels of a North Sea pelagic food web. Mar. Ecol. Prog. Ser. 454, 119–133 (2012).ADS 

Google Scholar 
Durant, J. M. et al. Contrasting effects of rising temperatures on trophic interactions in marine ecosystems. Sci. Rep. 9, 15213 (2019).ADS 

Google Scholar 
Otero, J. et al. Basin-scale phenology and effects of climate variability on global timing of initial seaward migration of Atlantic salmon (Salmo salar). Glob. Change Biol. 20, 61–75 (2014).ADS 

Google Scholar 
Kovach, R. P., Ellison, S. C., Pyare, S. & Tallmon, D. A. Temporal patterns in adult salmon migration timing across southeast Alaska. Glob. Change Biol. 21, 1821–1833 (2014).ADS 

Google Scholar 
Chust, G. et al. Earlier migration and distribution changes of albacore in the Northeast Atlantic. Fish. Oceanogr. 28, 505–516 (2019).
Google Scholar 
McQueen, K. & Marshall, C. T. Shifts in spawning phenology of cod linked to rising sea temperatures. ICES J. Mar. Sci. 74, 1561–1573 (2017).
Google Scholar 
Kanamori, Y., Takasuka, A., Nishijima, S. & Okamura, H. Climate change shifts the spawning ground northward and extends the spawning period of chub mackerel in the western North Pacific. Mar. Ecol. Prog. Ser. 624, 155–166 (2019).ADS 

Google Scholar 
Henderson, M. E., Mills, K. E., Thomas, A. C., Pershing, A. J. & Nye, J. A. Effects of spring onset and summer duration on fish species distribution and biomass along the Northeast United States continental shelf. Rev. Fish. Biol. Fish. 27, 411–424 (2017).
Google Scholar 
Beaugrand, G., Brander, K. M., Alistair Lindley, J., Souissi, S. & Reid, P. C. Plankton effect on cod recruitment in the North Sea. Nature 426, 661–664 (2003).ADS 
CAS 

Google Scholar 
Kang, Y. S., Kim, J. Y., Kim, H. G. & Park, J. H. Long-term changes in zooplankton and its relationship with squid, Todarodes pacificus, catch in Japan/East Sea. Fish. Oceanogr. 11, 337–346 (2002).
Google Scholar 
Mackas, D. et al. Zooplankton time series from the Strait of Georgia: results from year-round sampling at deep water locations, 1990–2010. Prog. Oceanogr. 115, 129–159 (2013).ADS 

Google Scholar 
Daly, E. A., Brodeur, R. D. & Auth, T. D. Anomalous ocean conditions in 2015: impacts on spring Chinook salmon and their prey field. Mar. Ecol. Prog. Ser. 566, 169–182 (2017).ADS 

Google Scholar 
Feuilloley, G. et al. Concomitant changes in the environment and small pelagic fish community of the Gulf of Lions. Prog. Oceanogr. 186, 102375 (2020).
Google Scholar 
Yebra, L. et al. Molecular identification of the diet of Sardina pilchardus larvae in the SW Mediterranean Sea. Mar. Ecol. Prog. Ser. 617-618, 41–52 (2019).ADS 
CAS 

Google Scholar 
Record, N. et al. Copepod diapause and the biogeography of the marine lipidscape. J. Biogeogr. 45, 2238–2251 (2018).
Google Scholar 
Yebra, L. et al. Zooplankton biomass depletion event reveals the importance of small pelagic fish top-down control in the Western Mediterranean Coastal Waters. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.608690 (2020).Friedland, K. D. et al. Pathways between primary production and fisheries yields of large marine ecosystems. PLoS ONE 7, e28945 (2012).Santora, J. A. et al. Habitat compression and ecosystem shifts as potential links between marine heatwave and record whale entanglements. Nat. Commun. 11, 536 (2020).ADS 
CAS 

Google Scholar 
Piatt, J. et al. Extreme mortality and reproductive failure of common murres resulting from the northeast Pacific marine heatwave of 2014-2016. PLOS ONE 15, e0226087 (2020).Meyer-Gutbrod, E., Greene, C., Davies, K. & Johns, D. G. Ocean regime shift is driving collapse of the North Atlantic Right Whale Population. Oceanography 34, 22–31 (2021).
Google Scholar 
Beltran, R. S. et al. Seasonal resource pulses and the foraging depth of a Southern Ocean top predator. Proc. R. Soc. B 288, 1–9 (2021).Everett, J. D. et al. Modeling what we sample and sampling what we model: challenges for zooplankton model assessment. Front. Mar. Sci. https://doi.org/10.3389/fmars.2017.00077 (2017). This article synthesizes key information required for better parameterize zooplankton in various models.Gibbs Samantha, J. et al. Algal plankton turn to hunting to survive and recover from end-Cretaceous impact darkness. Sci. Adv. 6, eabc9123 (2020).Kwiatkowski, L. et al. Twenty-first century ocean warming, acidification, deoxygenation, and upper-ocean nutrient and primary production decline from CMIP6 model projections. Biogeosciences 17, 3439–3470 (2020).ADS 
CAS 

Google Scholar 
Mitra, A. et al. Bridging the gap between marine biogeochemical and fisheries sciences; configuring the zooplankton link. Prog. Oceanogr. 129, 176–199 (2014).ADS 

Google Scholar 
Gentleman, W., Leising, A., Frost, B., Strom, S. & Murray, J. Functional responses for zooplankton feeding on multiple resources: a review of assumptions and biological dynamics. Deep Sea Res. Part II: Top. Stud. Oceanogr. 50, 2847–2875 (2003).ADS 
CAS 

Google Scholar 
Chenillat, F., Rivière, P. & Ohman, M. D. On the sensitivity of plankton ecosystem models to the formulation of zooplankton grazing. PLOS ONE 16, e0252033 (2021).CAS 

Google Scholar 
Stemmann, L. & Boss, E. Plankton and particle size and packaging: from determining optical properties to driving the biological pump. Annu. Rev. Mar. Sci. 4, 263–290 (2012).ADS 
CAS 

Google Scholar 
Kiørboe, T., Saiz, E., Tiselius, P. & Andersen, K. H. Adaptive feeding behavior and functional responses in zooplankton. Limnol. Oceanogr. 63, 308–321 (2017).ADS 

Google Scholar 
Grigor, J. J. et al. Non-carnivorous feeding in Arctic chaetognaths. Prog. Oceanogr. 186, 102388 (2020).
Google Scholar 
Yeh, H. D., Questel, J. M., Maas, K. R. & Bucklin, A. Metabarcoding analysis of regional variation in gut contents of the copepod Calanus finmarchicus in the North Atlantic Ocean. Deep Sea Res. Part II: Top. Stud. Oceanogr. 180, 104738 (2020).
Google Scholar 
Novotny, A., Zamora-Terol, S. & Winder, M. DNA metabarcoding reveals trophic niche diversity of micro and mesozooplankton species. Proc. R. Soc. B 288, 1–10 (2021).Käse, L. et al. Metabarcoding analysis suggests that flexible food web interactions in the eukaryotic plankton community are more common than specific predator–prey relationships at Helgoland Roads, North Sea. ICES J. Mar. Sci. 78, 3372–3386 (2021).
Google Scholar 
Greco, M., Morard, R. & Kucera, M. Single-cell metabarcoding reveals biotic interactions of the Arctic calcifier Neogloboquadrina pachyderma with the eukaryotic pelagic community. J. Plankton Res. 43, 113–125 (2021).CAS 

Google Scholar 
Serra-Pompei, C., Soudijn, F., Visser, A. W., Kiørboe, T. & Andersen, K. H. A general size- and trait-based model of plankton communities. Prog. Oceanogr. 189, 102473 (2020).
Google Scholar 
Heneghan, R. F. et al. A functional size-spectrum model of the global marine ecosystem that resolves zooplankton composition. Ecol. Model. 435, 109265 (2020).CAS 

Google Scholar 
Ward, B. A. et al. EcoGEnIE 1.0: plankton ecology in the cGEnIE Earth system model. Geosci. Model Dev. 11, 4241–4267 (2018).ADS 
CAS 

Google Scholar 
Sosik, H. M. & Olson, R. J. Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry. Limnol. Oceanogr. Methods 5, 204–216 (2007).
Google Scholar 
Lombard, F. et al. Globally consistent quantitative observations of planktonic ecosystems. Front. Mar. Sci. https://doi.org/10.3389/fmars.2019.00196 (2019).Pitois, S. G. et al. A first approach to build and test the Copepod Mean Size and Total Abundance (CMSTA) ecological indicator using in-situ size measurements from the Plankton Imager (PI). Ecol. Indic. 123, 107307 (2021).Irisson, J.-O., Ayata, S.-D., Lindsay, D. J., Karp-Boss, L. & Stemmann, L. Machine learning for the study of plankton and marine snow from images. Annu. Rev. Mar. Sci. 14, 277–301 (2022).ADS 

Google Scholar 
Cornils, A. et al. Testing the usefulness of optical data for zooplankton long-term monitoring: Taxonomic composition, abundance, biomass and size spectra from ZooScan image analysis. Limnol. Oceanogr. Methods 20, 428–450 (2022).Henson, S. A., C, B. & R, L. Observing climate change trends in ocean biogeochemistry: when and where. Glob. Change Biol. 22, 1561–1571 (2016).ADS 

Google Scholar 
García-Comas, C. et al. Zooplankton long-term changes in the NW Mediterranean Sea: Decadal periodicity forced by winter hydrographic conditions related to large-scale atmospheric changes? J. Mar. Syst. 87, 216–226 (2011).
Google Scholar 
Vucetich, J. A., Nelson, M. P. & Bruskotter, J. T. What drives declining support for long-term ecological research? BioScience 70, 168–173 (2020).
Google Scholar 
Lindenmayer, D. B. et al. Value of long-term ecological studies. Austral Ecol. 37, 745–757 (2012).
Google Scholar 
Giron-Nava, A. et al. Quantitative argument for long-term ecological monitoring. Mar. Ecol. Prog. Ser. 572, 269–274 (2017).ADS 

Google Scholar 
Hughes, B. B. et al. Long-term studies contribute disproportionately to ecology and policy. BioScience 67, 271–281 (2017).
Google Scholar 
Berline, L., Siokou-Frangou, I. & Marasovic, I. Intercomparison of six Mediterranean zooplankton time series. Prog. Oceanogr. 97-100, 76–91 (2012).ADS 

Google Scholar 
Beaugrand, G. et al. Synchronous marine pelagic regime shifts in the Northern Hemisphere. Philos. Trans. R. Soc. B: Biol. Sci. 370, 20130272 (2015).
Google Scholar 
Mackas, D. L. & Beaugrand, G. Comparisons of zooplankton time series. J. Mar. Syst. 79, 286–304 (2010).
Google Scholar 
O’Brien, T. D., Lorenzoni, L., Isensee, K. & Valdés, L. What are Marine Ecological Time Series Telling Us About The Ocean? A Status Report. (2017).Ratnarajah, L. Map of BioEco Observing networks/capability (https://eurosea.eu/download/eurosea-d1-2-bioeco-observing-networks/?wpdmdl=3580&refresh=637b1a59bb2011669012057, 2021).Wright, R. M., Le Quéré, C., Buitenhuis, E. T., Pitois, S. & Gibbons, M. J. Role of jellyfish in the plankton ecosystem revealed using a global ocean biogeochemical model. Biogeosciences 18, 1291–1320 (2021).ADS 
CAS 

Google Scholar 
Buitenhuis, E. T. et al. MAREDAT: towards a world atlas of MARine Ecosystem DATa. Earth Syst. Sci. Data 5, 227–239 (2013).ADS 

Google Scholar 
O’Brien, T. D. COPEPOD: The Global Plankton Database. An overview of the 2014 database contents, processing methods, and access interface. U.S. Dep. Commerce, NOAA Tech. Memo. NMFS-F/ST-37, 29p. (2014).Pitois, S. G., Bouch, P., Creach, V. & van der Kooij, J. Comparison of zooplankton data collected by a continuous semi-automatic sampler (CALPS) and a traditional vertical ring net. J. Plankton Res. 38, 931–943 (2016).
Google Scholar 
Wiebe, P. H. & Benfield, M. C. From the Hensen net toward four-dimensional biological oceanography. Prog. Oceanogr. 56, 7–136 (2003).ADS 

Google Scholar 
Boss, E. et al. Recommendations for plankton measurements on oceansites moorings with relevance to other observing sites. Front. Mar. Sci. https://doi.org/10.3389/fmars.2022.929436 (2022).Pollina, T. et al. PlanktoScope: affordable modular quantitative imaging platform for citizen oceanography. Front. Mar. Sci. https://doi.org/10.3389/fmars.2022.949428 (2022).Pitois, S. G. et al. Comparison of a cost-effective integrated plankton sampling and imaging instrument with traditional systems for mesozooplankton sampling in the Celtic Sea. Front. Mar. Sci. https://doi.org/10.3389/fmars.2018.00005 (2018).Ohman, M. D. et al. Zooglider: an autonomous vehicle for optical and acoustic sensing of zooplankton. Limnol. Oceanogr.: Methods 17, 69–86 (2018).
Google Scholar 
Picheral, M. et al. The Underwater Vision Profiler 6: an imaging sensor of particle size spectra and plankton, for autonomous and cabled platforms. Limnol. Oceanogr. Methods 20, 115–129 (2021).
Google Scholar 
Picheral, M. et al. The Underwater Vision Profiler 5: an advanced instrument for high spatial resolution studies of particle size spectra and zooplankton. Limnol. Oceanogr. Methods 8, 462–473 (2010).
Google Scholar 
Richardson, A. et al. in Guidelines for the study of climate change effects on HABs Vol. 88 23 (UNESCO-IOC/SCOR, 2022).Drago, L. et al. Global distribution of zooplankton biomass estimated by in situ imaging and machine learning. Front. Mar. Sci. https://doi.org/10.3389/fmars.2022.894372 (2022).Forest, A. et al. Ecosystem function and particle flux dynamics across the Mackenzie Shelf (Beaufort Sea, Arctic Ocean): an integrative analysis of spatial variability and biophysical forcings. Biogeosciences 10, 2833–2866 (2013).ADS 

Google Scholar 
Haëntjens, N. et al. Detecting mesopelagic organisms using biogeochemical-argo floats. Geophys. Res. Lett. 47, 1–10 (2020).Clayton, S. et al. Bio-GO-SHIP: the time is right to establish global repeat sections of ocean biology. Front. Mar. Sci. https://doi.org/10.3389/fmars.2021.767443 (2022).Miloslavich, P. et al. Essential ocean variables for global sustained observations of biodiversity and ecosystem changes. Glob. Change Biol. 24, 2416–2433 (2018).ADS 

Google Scholar 
McPhaden, M. J., Santoso, A. & Cai, W. El Niño Southern Oscillation in a Changing Climate: Glossary (John Wiley & Sons, Inc, 2021). More

Now consider a generalized model in which the species interactions are heterogeneous. A natural way of introducing heterogeneity in the system is by having a species diversify into subpopulations with different interaction strengths12,13,14,15. This way of modeling heterogeneity is useful as it can describe different kinds of heterogeneity. For example, the subpopulations could represent polymorphic traits that are genetically determined or result from plastic response to heterogeneous environments. A population could also be divided into local subpopulations in different spatial patches, which can migrate between patches and may face different local predators. We can also model different behavioral modes as subpopulations that, for instance, spend more time foraging for food or hiding from predators. We study several kinds of heterogeneity after we introduce a common mathematical framework. By studying these different scenarios using variants of the model, we show that our main results are not sensitive to the details of the model.We focus on the simple case where only the prey species splits into two types, (C_1) and (C_2), as illustrated in Fig. 1b. The situation is interesting when predator A consumes (C_1) more readily than predator B and B consumes (C_2) more readily than A (i.e., (a_1 / a_0 > b_1 / b_0) and (b_2 / b_0 > a_2 / a_0), which is equivalent to the condition that the nullclines of A and B cross, see section “Resources competition and nullcline analysis”). The arrows between (C_1) and (C_2) in Fig. 1b represent the exchange of individuals between the two subpopulations, which can happen by various mechanisms considered below. Such exchange as well as intraspecific competition between (C_1) and (C_2) result from the fact that the two prey types remain a single species.The system is now described by an enlarged Lotka-Volterra system with four variables, A, B, (C_1), and (C_2): $$begin{aligned} dot{A}&= varepsilon _A ,alpha _{A1} , A , C_1 + alpha _{A2} , A , C_2 – beta _A , A end{aligned}$$
(3a)
$$begin{aligned} dot{B}&= varepsilon _B , alpha _{B1} , B , C_1 + alpha _{B2} , B , C_2 – beta _B , B end{aligned}$$
(3b)
$$begin{aligned} dot{C_1}&= C_1 , (beta _C – alpha _{CC} , C)-alpha _{A1} , C_1 A-alpha _{B1} , C_1 B – sigma _1 , C_1 + sigma _2 , C_2 end{aligned}$$
(3c)
$$begin{aligned} dot{C_2}&= C_2 , (beta _C – alpha _{CC} , C) -alpha _{A2} , C_2 A -alpha _{B2} , C_2 B + sigma _1 , C_1 – sigma _2 , C_2 end{aligned}$$
(3d)
The parameters in these equations and their meanings are listed in Table 1. Here we assume that the prey types (C_1) and (C_2) have the same birth rate and intraspecific competition strength, but different interaction strengths with A and B. Note that (C_1) and (C_2) are connected by the (sigma _i) terms, which represent the exchange of individuals between these subpopulations through mechanisms studied below; these terms indicate a major difference between our model of a prey with intraspecific heterogeneity and other models of two prey species. For the convenience of analysis, we transform the variables (C_1) and (C_2) to another pair of variables C and (lambda), where (C equiv C_1 + C_2) is the total population of C as before, and (lambda equiv C_2 / (C_1 + C_2)) represents the composition of the population (Fig. 1c). After this transformation and rescaling of variables (described in “Methods”), the new dynamical system can be written as: $$begin{aligned} dot{A}&= A , big ( C , (a_1 (1-lambda ) + a_2 lambda ) – a_0 big ) end{aligned}$$
(4a)
$$begin{aligned} dot{B}&= B , big ( C , (b_1 (1-lambda ) + b_2 lambda ) – b_0 big ) end{aligned}$$
(4b)
$$begin{aligned} dot{C}&= C , big ( 1 – C – A (a_1 (1-lambda ) + a_2 lambda ) – B (b_1 (1-lambda ) + b_2 lambda ) big ) end{aligned}$$
(4c)
$$begin{aligned} dot{lambda }&= lambda (1-lambda ) , big ( A (a_1 – a_2) + B (b_1 – b_2) big ) + eta _1 (1-lambda ) – eta _2 lambda end{aligned}$$
(4d)
Here, (a_i) and (b_i) are the (rescaled) feeding rates of the predators on the prey type (C_i); (a_0) and (b_0) are the death rates of the predators as before; (eta _1) and (eta _2) are the exchange rates of the prey types (Table 1). The latter can be functions of other variables, representing different kinds of heterogeneous interactions that we study below. Notice that Eqs. (4a–4c) are equivalent to the homogeneous Eqs. (2a–2c) but with effective interaction strengths (a_text {eff} = (1-lambda ) , a_1 + lambda , a_2) and (b_text {eff} = (1-lambda ) , b_1 + lambda , b_2) that both depend on the prey composition (lambda) (Fig. 1c).Table 1 Model parameters (before/after rescaling) and their meanings.Full size tableThe variable (lambda) can be considered an internal degree of freedom within the C population. In all of the models we study below, (lambda) dynamically stabilizes to a special value (lambda ^*) (a bifurcation point), as shown in Fig. 3a. Accordingly, a new equilibrium point (P_N) appears (on the line (mathscr {L}) in Fig. 2), at which all three species coexist. For comparison, Fig. 3b shows the equilibrium points if (lambda) is held fixed at any other values, which all result in the exclusion of one of the predators. Thus, heterogeneous interactions give rise to a new coexistence phase (see Fig. 4 below) by bringing the prey composition (lambda) to the value (lambda ^*), instead of having to fine-tune the interaction strengths. The exact conditions for this new equilibrium to be stable are detailed in “Methods”.Figure 3(a) Time series of (lambda) for systems with each kind of heterogeneity. All three systems stabilize at the same (lambda ^*) value, which is the bifurcation point in panel (b). (b) Equilibrium population of each species (X = A), B, or C, with (lambda) held fixed at different values. Solid curves represent stable equilibria and dashed curves represent unstable equilibria (see Eq. (9) in “Methods”). The vertical dashed line is where (lambda = lambda ^*), which is also the bifurcation point. Notice that the equilibrium population of C is maximized at this point (for (a_1 > a_2) and (b_2 > b_1)). Parameters used here are ((a_0, a_1, a_2, b_0, b_1, b_2, rho , eta _1, eta _2, kappa ) = (0.25, 0.5, 0.2, 0.4, 0.2, 0.6, 0.5, 0.05, 0.05, 50)).Full size imageInherent heterogeneityWe first consider a scenario where individuals of the prey species are born as one of two types with a fixed ratio, such that a fraction (rho) of the newborns are (C_2) and ((1-rho )) are (C_1). This could describe dimorphic traits, such as the winged and wingless morphs in aphids12 or the horned and hornless morphs in beetles13. We call this “inherent” heterogeneity, because individuals are born with a certain type and cannot change in later stages of life. The prey type given at birth determines the individual’s interaction strength with the predators. This kind of heterogeneity can be described by Eq. (4d) with (eta _1 = rho (1-C)) and (eta _2 = (1-rho ) (1-C)) (see “Methods”).Figure 4Phase diagrams showing regions of parameter space identified by the stable equilibrium points. Yellow region represents (P_C) (predators A, B both extinct), red represents (P_A) (A excludes B), blue represents (P_B) (B excludes A), and green represents (P_N) (A, B coexist). The middle point (black dot) is where the preferences of the two predators are identical, (a_2/a_0=b_2/b_0) and (b_1/b_0=a_1/a_0). The coexistence phase appears in all three kinds of heterogeneity modeled here. (a–d) Inherent heterogeneity: Individuals of the prey population are born in two types with a fixed composition (rho). In the extreme cases of (rho = 0) and 1, the prey is homogeneous and there is no coexistence of the predators. (e–h) Reversible heterogeneity: Individual prey can switch types with fixed switching rates (eta _1) and (eta _2). As the switching rates increase, the coexistence region shrinks because the prey population becomes effectively homogeneous (the occasional green spots are numerical artifacts because the time to reach the equilibrium becomes long in this limit). (i–l) Adaptive heterogeneity: The switching rates (eta _i) dynamically adapt to the predator densities, so as to maximize the growth rate of the prey. As the sharpness (kappa) of the sigmoidal decision function is increased, the prey adapts more optimally and the region of coexistence expands. Parameters used here are ((a_0, a_1, b_0, b_2) = (0.3, 0.5, 0.4, 0.6)).Full size imageThe stable equilibrium of the system can be represented by phase diagrams that show the identities of the species at equilibrium. We plot these phase diagrams by varying the parameters (a_2) and (b_1) while keeping (a_1) and (b_2) constant. As shown in Fig. 4a–d, the equilibrium state depends on the parameter (rho). In the limit (rho = 0) or 1, we recover the homogeneous case because only one type of C is produced. The corresponding phase diagrams (Fig. 4a, d) contain only two phases where either of the predators is excluded, illustrating the competitive exclusion principle. For intermediate values of (rho), however, there is a new phase of coexistence that separates the two exclusion phases (Fig. 4b, c). There are two such regions of coexistence, which touch at a middle point and open toward the bottom left and upper right, respectively. The middle point is at ((a_2/a_0 = b_2/b_0, b_1/b_0 = a_1/a_0)), where the feeding preferences of the two predators are identical (hence their niches fully overlap). Towards the origin and the far upper right, the predators consume one type of C each (hence their niches separate). The coexistence region in the bottom left is where the feeding rates of the predators are the lowest overall. There can be a region (yellow) where both predators go extinct, if their primary prey type alone is not enough to sustain each predator. Increasing the productivity of the system by increasing the birth rate ((beta _C)) of the prey eliminates this extinction region, whereas lowering productivity causes the extinction region to take over the lower coexistence region. Because the existence and identity of the phases is determined by the configuration of the equilibrium points (Fig. 2, see also section “Mathematical methods”), the qualitative shape of the phase diagram is not sensitive to changes of parameter values.The new equilibrium is not only where the predators A and B can coexist, but also where the prey species C grows to a larger density than what is possible for a homogeneous population. This is illustrated in Fig. 3b, which shows the equilibrium population of C if we hold (lambda) fixed at different values. The point (lambda = lambda ^*) is where the system with a dynamic (lambda) is stable, and also where the population of C is maximized (when A and B prefer different prey types). That means the population automatically stabilizes at the optimal composition of prey types. Moreover, the value of (C^*) at this coexistence point can even be larger than the equilibrium population of C when there is only one predator A or B. This is discussed further in section “Multiple-predator effects and emergent promotion of prey”. These results suggest that heterogeneity in interaction strengths can potentially be a strategy for the prey population to leverage the effects of multiple predators against each other to improve survival.Reversible heterogeneityWe next consider a scenario where individual prey can switch their types. This kind of heterogeneity can model reversible changes of phenotypes, i.e., trait changes that affect the prey’s interaction with predators but are not permanent. For example, changes in coat color or camouflage14,16,17, physiological changes such as defense15, and biomass allocation among tissues18,19. One could also think of the prey types as subpopulations within different spatial patches, if each predator hunts at a preferred patch and the prey migrate between the patches20,21. With some generalization, one could even consider heterogeneity in resources, such as nutrients located in different places, that can be reached by primary consumers, such as swimming phytoplankton22. We can model this “reversible” kind of heterogeneity by introducing switching rates from one prey type to the other. In Eq. (4d), (eta _1) and (eta _2) now represent the switching rates per capita from (C_1) to (C_2) and from (C_2) to (C_1), respectively. Here we study the simplest case where both rates are fixed.In the absence of the predators, the natural composition of the prey species given by the switching rates would be (rho equiv eta _1 / (eta _1 + eta _2)), and the rate at which (lambda) relaxes to this natural composition is (gamma equiv eta _1 + eta _2). Compared to the previous scenario where we had only one parameter (rho), here we have an additional parameter (gamma) that modifies the behavior of the system. Fig. 4e–h shows phase diagrams for the system as (rho) is fixed and (gamma) varies. We again find the new equilibrium (P_N) where all three species coexist. When (gamma) is small, the system has a large region of coexistence. As (gamma) is increased, this region is squeezed into a border between the two regions of exclusion, where the slope of the border is given by (eta _1/eta _2) as determined by the parameter (rho). However, this is different from the exclusion we see in the case of inherent heterogeneity, which happens only for (rho rightarrow 0) or 1, where the borders are horizontal or vertical (Fig. 4a,d). Here the predators exclude each other despite having a mixture of prey types in the population.This special limit can be understood as follows. For a large (gamma), (lambda) is effectively set to a constant value equal to (rho), because it has a very fast relaxation rate. In other words, the prey types exchange so often that the population always maintains a constant composition. In this limit, the system behaves as if it were a homogeneous system with effective interaction strengths (a_text {eff} = (1-rho ) , a_1 + rho , a_2) and (b_text {eff} = (1-rho ) , b_1 + rho , b_2). As in a homogeneous system, there is competitive exclusion between the predators instead of coexistence. This demonstrates that having a constant level of heterogeneity is not sufficient to cause coexistence. The overall composition of the population must be able to change dynamically as a result of population growth and consumption by predators.An interesting behavior is seen when we examine a point inside the shrinking coexistence region as (gamma) is increased. Typical trajectories of the system for such parameter values are shown in Fig. 5. As (gamma) increases, the system relaxes to the line (mathscr {L}) quickly, then slowly crawls along it towards the final equilibrium point (P_N). This is because increasing (gamma) increases the speed that (lambda) relaxes to (lambda ^*), and when (lambda rightarrow lambda ^*), (mathscr {L}) becomes marginally stable. Therefore, the attraction to (mathscr {L}) in the perpendicular direction is strong, but the attraction towards the equilibrium point along (mathscr {L}) is weak. This leads to a long transient behavior that makes the system appear to reach no equilibrium in a limited time23,24. It is especially true when there is noise in the dynamics, which causes the system to diffuse along (mathscr {L}) with only a weak drift towards the final equilibrium (Fig. 5). Thus, the introduction of a fast timescale (quick relaxation of (lambda) due to a large (gamma)) actually results in a long transient.Figure 5Trajectories of the system projected in the A-B plane, with parameters inside the coexistence region (by holding the position of (P_N) fixed). As (gamma) increases, the system tends to approach the line (mathscr {L}) quickly and then crawl along it. The grey trajectory is with independent Gaussian white noise ((sim mathscr {N}(0,0.5))) added to each variable’s dynamics. Noise causes the system to diffuse along (mathscr {L}) for a long transient period before coming to the equilibrium point (P_N). Parameters used here are ((a_0, a_1, a_2, b_0, b_1, b_2) = (0.2, 0.8, 0.5, 0.2, 0.6, 0.9)), chosen to place (P_N) away from the middle of (mathscr {L}) to show the trajectory drifting toward the equilibrium.Full size imageAdaptive heterogeneityA third kind of heterogeneity we consider is the change of interactions in time. By this we mean an individual can actively change its interaction strength with others in response to certain conditions. This kind of response is often invoked in models of adaptive foraging behavior, where individuals choose appropriate actions to maximize some form of fitness25,26. For example, we may consider two behaviors, resting and foraging, as our prey types. Different predators may prefer to strike when the prey is doing different things. In response, the prey may choose to do one thing or the other depending on the current abundances of different predators. Such behavioral modulation is seen, for example, in systems of predatory spiders and grasshoppers27. Phenotypic plasticity is also seen in plant tissues in response to consumers28,29,30.This kind of “adaptive” heterogeneity can be modeled by having switching rates (eta _1) and (eta _2) that are time-dependent. Let us assume that the prey species tries to maximize its population growth rate by switching to the more favorable type. From Eq. (4c), we see that the growth rate of C depends linearly on the composition (lambda) with a coefficient (u(A,B) equiv (a_1 – a_2) A + (b_1 – b_2) B). Therefore, when this coefficient is positive, it is favorable for C to increase (lambda) by switching to type (C_2). This can be achieved by having a positive switching rate (eta _2) whenever (u(A,B) > 0). Similarly, whenever (u(A,B) < 0), it is favorable for C to switch to type (C_1) by having a positive (eta _1). In this way, the heterogeneity of the prey population constantly adapts to the predator densities. We model such adaptive switching by making (eta _1) and (eta _2) functions of the coefficient u(A, B), e.g., (eta _1(u) = 1/(1+mathrm {e}^{kappa u})) and (eta _2(u) = 1/(1+mathrm {e}^{-kappa u})). The sigmoidal form of the functions means that the switching rate in the favorable direction for C is turned on quickly, while the other direction is turned off. The parameter (kappa) controls the sharpness of this transition.Phase diagrams for the system with different values of (kappa) are shown in Fig. 4i–l. A larger (kappa) means the prey adapts its composition faster and more optimally, which causes the coexistence region to expand. In the extreme limit, the system changes its dynamics instantaneously whenever it crosses the boundary where (u(A,B) = 0), like in a hybrid system31. Such a system can still reach a stable equilibrium that lies on the boundary, if the flow on each side of the boundary points towards the other side32. This is what happens in our system and, interestingly, the equilibrium is the same three-species coexistence point (P_N) as in the previous scenarios. The region of coexistence turns out to be largest in this limit (Fig. 4l).Our results suggest that the coexistence of the predators can be viewed as a by-product of the prey’s strategy to maximize its own benefit. The time-dependent case studied here represents a strategy that involves the prey evaluating the risk posed by different predators. This is in contrast to the scenarios studied above, where the prey population passively creates phenotypic heterogeneity regardless of the presence of the predators. These two types of behavior are analogous to the two strategies studied for adaptation in varying environments, i.e., sensing and bet-hedging33,34. The former requires accessing information about the current environment to make optimal decisions, whereas the latter relies on maintaining a diverse population to reduce detrimental effects caused by environmental changes. Here the varying abundances of the predators play a similar role as the varying environment. From this point of view, the heterogeneous interactions studied here can be a strategy of the prey species that is evolutionarily favorable. More