Philippa Kaur

Marine sediment recordThe Calypso Square gravity core AMD15-CASQ1 (77°15.035′ N, 74°25.500′ W, 692 m water depth) and accompanying box core (BC; same location) were retrieved aboard the CCGS Amundsen during the ArcticNet 2015 Leg 4a expedition in 2015, in accordance with relevant permits and local laws. The CASQ corer recovered a sequence 543 cm long, while the box core was 40 cm long. Sediment material from these cores is stored at the Geological Survey of Denmark and Greenland and available upon reasonable request to the first and corresponding author (SRI).Computed Tomography (CT) scanning of the core was performed using a Siemens SOMATOM Definition AS + 128 at the Institut National de la Recherche Scientifique (INRS), Quebec, Canada. The tomograms were converted into digital DICOM format using a standard Hounsfield scale (HU scale) from −1024 to 3071, where −1024 corresponds to the density of air, 0 to the density of water and 2500 to the density of calcite.The age control on the marine sediment record was provided by 11 accelerator mass spectrometry (AMS) radiocarbon dates on mollusc shells (Supplementary. Table 1) at the Keck Carbon Cycle AMS Facility, University of California, Irvine, US, and 210Pb/137Cs measurements conducted on 20 samples at the Gamma Dating Center, Copenhagen University, Denmark. In the box core, the content of unsupported 210Pb showed a clear exponential decline with depth (Supplementary Fig. 1). A clear 137Cs peak was not detected, but the 210Pb-based chronology dates the earliest sample with 137Cs to 1969 ± 2 years, which is close to the expected date, 1963, for the global 137Cs peak induced by nuclear weapons testing in the atmosphere. This, and the very uniform exponential decline in unsupported 210Pb with depth, gives confidence in the calculated chronology. A mixed age-depth model, using both 210Pb and 14C dates, was constructed using BACON, an open-source package of ‘R’54. This Bayesian accumulation model code allows for greater flexibility in sedimentation rates between dated intervals than traditional linear age-depth models54. The AMS radiocarbon dates were calibrated with the Marine13 IntCal1355, and the regional marine reservoir offset was estimated based on existing 14C data from marine specimens collected before the mid-1950s. Distinct regional offset values have been proposed for Arctic Canada, but do not include the Smith Sound region56. Existing data from NW Greenland show local reservoir correction (ΔR) values ranging from -40 years in the Inglefield Fjord to +320 years in Ellesmere Island (the latter consistent with the proposed 335 ± 85 years for the Canadian Arctic Archipelago56). However, these samples have been retrieved from shallow sites ( More

1.Hobbs, R. J. et al. Restoration ecology: the challenge of social values and expectations. Front. Ecol. Environ. 2, 43–38 (2004).Article 

Google Scholar 
2.Harris, J. A., Hobbs, R. J., Higgs, E. & Aronson, J. C. Ecological restoration and global climate change. Restor. Ecol. 14, 170–176 (2006).3.Aronson, J. C. & Vallejo, R. in Restoration Ecology: The New Frontier (eds. van Andel, J. & Aronson, J. C.) (John Wiley & Sons, 2009).4.Suding, K. et al. Committing to ecological restoration. Science 348, 638–640 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
5.Plaza, C. et al. Soil resources and element stocks in drylands to face global issues. Sci. Rep. 8, 13788 (2018).6.Aronson, J., Goodwin, N., Orlando, L., Eisenberg, C. & Cross, A. T. A world of possibilities: six restoration strategies to support the United Nation’s Decade on Ecosystem Restoration. Restor. Ecol. 28, 730–736 (2020).Article 

Google Scholar 
7.Drylands and Land Degradation (IUCN, 2017).8.Bainbridge, D. A. A Guide for Desert and Dryland Restoration: New Hope for Arid Lands (Island Press, 2012).9.Millennium Ecosystem Assessment Findings (Millennium Ecosystem Assessment, 2005).10.Reynolds, J. F., Maestre, F. T., Kemp, P. R., Stafford-Smith, D. M. & Lambin, E. in Terrestrial Ecosystems in a Changing World (eds. Canadell, J. G., Pataki, D. E. & Pitelka, L. F.) 247–257 (Springer, 2007); https://doi.org/10.1007/978-3-540-32730-1_2011.Hoover, D. L. et al. Traversing the wasteland: a framework for assessing ecological threats to drylands. BioScience 70, 35–47 (2020).Article 

Google Scholar 
12.Hardegree, S. P., Jones, T. A., Roundy, B. A., Shaw, N. L. & Monaco, T. A. in Conservation Benefits of Rangeland Practices 171–213 (United States Department of Agriculture, 2011).13.James, J. J., Svejcar, T. J. & Rinella, M. J. Demographic processes limiting seedling recruitment in arid grassland restoration. J. Appl. Ecol. 48, 961–969 (2011).Article 

Google Scholar 
14.Okin, G. S. et al. Connectivity in dryland landscapes: shifting concepts of spatial interactions. Front. Ecol. Environ. 13, 20–27 (2015).Article 

Google Scholar 
15.Svejcar, L. N. & Kildisheva, O. A. The age of restoration: challenges presented by dryland systems. Plant Ecol. 218, 1–6 (2017).Article 

Google Scholar 
16.Safriel, U. et al. Dryland Systems. Ecosystems and Human Well-being: Current State and Trends.: Findings of the Condition and Trends Working Group 623–662 (Millennium Ecosystem Assessment, 2005).17.Ward, D. The Biology of Deserts (Oxford Univ. Press, 2016).18.Li, Y., Chen, Y. & Li, Z. Dry/wet pattern changes in global dryland areas over the past six decades. Glob. Planet. Change 178, 184–192 (2019).Article 

Google Scholar 
19.Prăvălie, R., Bandoc, G., Patriche, C. & Sternberg, T. Recent changes in global drylands: evidences from two major aridity databases. Catena 178, 209–231 (2019).Article 

Google Scholar 
20.Yao, J. et al. Accelerated dryland expansion regulates future variability in dryland gross primary production. Nat. Commun. 11, 1665 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
21.Aridity Zones and Dryland Populations: An Assessment of Population Levels in the World’s Drylands with Reference to Africa (UNSO/UNDP, 1997); http://digitallibrary.un.org/record/43231222.van den Berg, L. & Kellner, K. Restoring degraded patches in a semi-arid rangeland of South Africa. J. Arid. Environ. 61, 497–511 (2005).Article 

Google Scholar 
23.Valkó, O. et al. Cultural heritage and biodiversity conservation – plant introduction and practical restoration on ancient burial mounds. Nat. Conserv. 24, 65–80 (2018).Article 

Google Scholar 
24.Louhaichi, M., Clifton, K. & Hassan, S. Direct seeding of Salsola vermiculata for rehabilitation of degraded arid and semi-arid rangelands. Range Manag. Agrofor. 35, 182–187 (2014).
Google Scholar 
25.Pérez, D. R., González, F., Ceballos, C., Oneto, M. E. & Aronson, J. Direct seeding and outplantings in drylands of Argentinean Patagonia: estimated costs, and prospects for large-scale restoration and rehabilitation. Restor. Ecol. 27, 1105–1116 (2019).Article 

Google Scholar 
26.Kiehl, K., Kirmer, A., Donath, T. W., Rasran, L. & Hölzel, N. Species introduction in restoration projects: evaluation of different techniques for the establishment of semi-natural grasslands in Central and Northwestern Europe. Basic Appl. Ecol. 11, 285–299 (2010).Article 

Google Scholar 
27.Miguel, M. F., Butterfield, H. S. & Lortie, C. J. A meta-analysis contrasting active versus passive restoration practices in dryland agricultural ecosystems. PeerJ 8, e10428 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
28.Kildisheva, O. A., Erickson, T. E., Merritt, D. J. & Dixon, K. W. Setting the scene for dryland recovery: an overview and key findings from a workshop targeting seed-based restoration. Restor. Ecol. 24, S36–S42 (2016).Article 

Google Scholar 
29.Lewandrowski, W., Erickson, T. E., Dixon, K. W. & Stevens, J. C. Increasing the germination envelope under water stress improves seedling emergence in two dominant grass species across different pulse rainfall events. J. Appl. Ecol. 54, 997–1007 (2017).CAS 
Article 

Google Scholar 
30.Ladouceur, E. & Shackelford, N. The power of data synthesis to shape the future of the restoration community and capacity. Restor. Ecol. 29, e13251 (2020).
Google Scholar 
31.Temperton, V. M., Baasch, A., von Gillhaussen, P. & Kirmer, A. in Foundations of Restoration Ecology (eds. Palmer, M. A., Zedler, J. B. & Falk, D. A.) 245–270 (Island Press/Center for Resource Economics, 2016); https://doi.org/10.5822/978-1-61091-698-1_932.Hulvey, K. B. & Aigner, P. A. Using filter-based community assembly models to improve restoration outcomes. J. Appl. Ecol. 51, 997–1005 (2014).Article 

Google Scholar 
33.van Wilgen, B. W. The evolution of fire and invasive alien plant management practices in fynbos. S. Afr. J. Sci. 105, 335–342 (2009).
Google Scholar 
34.Arianoutsoua, M. & Vilà, M. Fire and invasive plant species in the Mediterranean Basin. Isr. J. Ecol. Evol. 58, 195–203 (2012).
Google Scholar 
35.Leger, E. A. & Baughman, O. W. What seeds to plant in the Great Basin? Comparing traits prioritized in native plant cultivars and releases with those that promote survival in the field. Nat. Areas. J. 35, 54–68 (2015).Article 

Google Scholar 
36.Porensky, L. M., Vaughn, K. J. & Young, T. P. Can initial intraspecific spatial aggregation increase multi-year coexistence by creating temporal priority? Ecol. Appl. 22, 927–936 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
37.FAOSTAT Statistical Database (Food and Agriculture Organization of the United Nations, 1997).38.Balazs, K. R. et al. The right trait in the right place at the right time: matching traits to environment improves restoration outcomes. Ecol. Appl. 30, e02110 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
39.Knutson, K. C. et al. Long-term effects of seeding after wildfire on vegetation in Great Basin shrubland ecosystems. J. Appl. Ecol. 51, 1414–1424 (2014).Article 

Google Scholar 
40.Brown, C. S. & Bugg, R. L. Effects of established perennial grasses on introduction of native forbs in California. Restor. Ecol. 9, 38–48 (2001).Article 

Google Scholar 
41.Porensky, L. M. et al. Arid old-field restoration: native perennial grasses suppress weeds and erosion, but also suppress native shrubs. Agric. Ecosyst. Environ. 184, 135–144 (2014).Article 

Google Scholar 
42.Hardegree, S. P. et al. Hydrothermal assessment of temporal variability in seedbed microclimate. Rangel. Ecol. Manag. 66, 127–135 (2013).Article 

Google Scholar 
43.Copeland, S. M. et al. Long-term trends in restoration and associated land treatments in the southwestern United States. Restor. Ecol. 26, 311–322 (2018).Article 

Google Scholar 
44.Abella, S. R., Craig, D. J., Smith, S. D. & Newton, A. C. Identifying native vegetation for reducing exotic species during the restoration of desert ecosystems. Restor. Ecol. 20, 781–787 (2012).Article 

Google Scholar 
45.Mulroy, T. W. & Rundel, P. W. Annual plants: adaptations to desert environments. BioScience 27, 109–114 (1977).Article 

Google Scholar 
46.Leger, E. A., Goergen, E. M. & Forbis de Queiroz, T. Can native annual forbs reduce Bromus tectorum biomass and indirectly facilitate establishment of a native perennial grass? J. Arid. Environ. 102, 9–16 (2014).Article 

Google Scholar 
47.Gutiérrez, J. R., Arancio, G. & Jaksic, F. M. Variation in vegetation and seed bank in a Chilean semi-arid community affected by ENSO 1997. J. Veg. Sci. 11, 641–648 (2000).Article 

Google Scholar 
48.Venable, D. L. Bet hedging in a guild of desert annuals. Ecology 88, 1086–1090 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
49.Baskin, C. C. Seed ecology: a diverse and vibrant field of study. Seed Sci. Res. 27, 61–64 (2017).Article 

Google Scholar 
50.Padilla, F. M., Ortega, R., Sánchez, J. & Pugnaire, F. I. Rethinking species selection for restoration of arid shrublands. Basic Appl. Ecol. 10, 640–647 (2009).Article 

Google Scholar 
51.SER International Primer on Ecological Restoration (SER, 2004).52.The Plant List (WFO, 2013).53.Seed Information Database (Royal Botanic Gardens, Kew, 2019).54.Kattge, J. et al. TRY plant trait database – enhanced coverage and open access. Glob. Change Biol. 26, 119–188 (2020).Article 

Google Scholar 
55.USDA, NRCS. The PLANTS Database (National Plant Data Team, 2020).56.Western Australian Herbarium. FloraBase—the Western Australian Flora (Department of Biodiversity, Conservation and Attractions, 1998).57.Fick, S. E. & Hijmans, R. J. Worldclim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).58.Trabucco, A. & Zomer, R. J. Global Aridity Index and Potential Evapo-Transpiration (ET0) Climate Database, v3 (CGIAR Consortium for Spatial Information, 2019).59.Barrow, C. J. World atlas of desertification (United Nations Environment Programme). Land Degrad. Dev. 3, 249–249 (1992).Article 

Google Scholar 
60.Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).Article 

Google Scholar 
61.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017).62.Crawley, M. J. in The R Book 569–591 (Wiley, 2007).63.Wortley, L., Hero, J.-M. & Howes, M. Evaluating ecological restoration success: a review of the literature. Restor. Ecol. 21, 537–543 (2013).Article 

Google Scholar  More

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1.Harmon, M. E. et al. Ecology of coarse woody debris in temperate ecosystems. Adv. Ecol. Res. 15, 133–302 (1986).Article 

Google Scholar 
2.Lagomarsino, A. et al. Decomposition of black pine (Pinus nigra J. F. Arnold) deadwood and its impact on forest soil components. Sci. Total Environ. 754, 142039 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
3.Magnússon, R. Í., Tietema, A., Cornelissen, J. H. C., Hefting, M. M. & Kalbitz, K. Tamm review: Sequestration of carbon from coarse woody debris in forest soils. For. Ecol. Manag. 377, 1–15 (2016).Article 

Google Scholar 
4.Vogt, K. Carbon budgets of temperate forest ecosystems. Tree Physiol. 9, 69–86 (1991).PubMed 
Article 

Google Scholar 
5.Stutz, K. P. & Lang, F. Potentials and unknowns in managing coarse woody debris for soil functioning. Forests 8, 37 (2017).Article 

Google Scholar 
6.Ulyshen, M. D. et al. Below- and above-ground effects of deadwood and termites in plantation forests. Ecosphere 8, e01910 (2017).Article 

Google Scholar 
7.Siitonen, J. Ecology of woody debris in boreal forests. Ecol. Bull. 49, 11–41 (2001).
Google Scholar 
8.Pietsch, K. A. et al. Wood decomposition is more strongly controlled by temperature than by tree species and decomposer diversity in highly species rich subtropical forests. Oikos 128, 701–715 (2019).Article 

Google Scholar 
9.Rubenstein, M. A., Crowther, T. W., Maynard, D. S., Schilling, J. S. & Bradford, M. A. Decoupling direct and indirect effects of temperature on decomposition. Soil Biol. Biochem. 112, 110–116 (2017).CAS 
Article 

Google Scholar 
10.Hu, Z. et al. Traits mediate drought effects on wood carbon fluxes. Glob. Chang. Biol. 26, 3429–3442 (2020).ADS 
PubMed 
Article 

Google Scholar 
11.Yoon, T. K., Noh, N. J., Kim, S., Han, S. & Son, Y. Coarse woody debris respiration of Japanese red pine forests in Korea: controlling factors and contribution to the ecosystem carbon cycle. Ecol. Res. 30, 723–734 (2015).Article 

Google Scholar 
12.Wu, D., Pietsch, K. A., Staab, M. & Yu, M. Wood identity alters dominant factors driving fine wood decomposition along a tree diversity gradient in subtropical plantation forests. Biotropica 53, 643–657 (2021).Article 

Google Scholar 
13.Ohtsuka, T. et al. Role of coarse woody debris in the carbon cycle of Takayama forest, central Japan. Ecol. Res. 29, 91–101 (2014).Article 

Google Scholar 
14.Bradford, M. A. et al. Climate fails to predict wood decomposition at regional scales. Nat. Clim. Change 4, 625–630 (2014).ADS 
CAS 
Article 

Google Scholar 
15.Shorohova, E. & Kapitsa, E. Influence of the substrate and ecosystem attributes on the decomposition rates of coarse woody debris in European boreal forests. For. Ecol. Manag. 315, 173–184 (2014).Article 

Google Scholar 
16.Crockatt, M. E. & Bebber, D. P. Edge effects on moisture reduce wood decomposition rate in a temperate forest. Glob. Chang. Biol. 21, 698–707 (2015).ADS 
PubMed 
Article 

Google Scholar 
17.Dossa, G. G. O. et al. Quantifying the factors affecting wood decomposition across a tropical forest disturbance gradient. For. Ecol. Manag. 468, 118166 (2020).Article 

Google Scholar 
18.Eichenberg, D. et al. The effect of microclimate on wood decay is indirectly altered by tree species diversity in a litterbag study. J. Plant Ecol. 10, 170–178 (2017).Article 

Google Scholar 
19.Cornwell, W. K. et al. Plant traits and wood fates across the globe: Rotted, burned, or consumed?. Glob. Chang. Biol. 15, 2431–2449 (2009).ADS 
Article 

Google Scholar 
20.Warren, R. J. & Bradford, M. A. Ant colonization and coarse woody debris decomposition in temperate forests. Insect Soc. 59, 215–221 (2012).Article 

Google Scholar 
21.Acanakwo, E. F., Sheil, D. & Moe, S. R. Wood decomposition is more rapid on than off termite mounds in an African savanna. Ecosphere 10, e02554 (2019).Article 

Google Scholar 
22.Veldhuis, M. P., Laso, F. J., Olff, H. & Berg, M. P. Termites promote resistance of decomposition to spatiotemporal variability in rainfall. Ecology 98, 467–477 (2017).
PubMed 
Article 

Google Scholar 
23.Liu, G. et al. Termites amplify the effects of wood traits on decomposition rates among multiple bamboo and dicot woody species. J. Ecol. 103, 1214–1223 (2015).Article 

Google Scholar 
24.Maynard, D. S., Crowther, T. W., King, J. R., Warren, R. J. & Bradford, M. A. Temperate forest termites: ecology, biogeography, and ecosystem impacts. Ecol. Entomol. 40, 199–210 (2015).Article 

Google Scholar 
25.Jacobsen, R. M., Sverdrup-Thygeson, A., Kauserud, H., Mundra, S. & Birkemoe, T. Exclusion of invertebrates influences saprotrophic fungal community and wood decay rate in an experimental field study. Funct. Ecol. 32, 2571–2582 (2018).Article 

Google Scholar 
26.Ulyshen, M. D., Wagner, T. L. & Mulrooney, J. E. Contrasting effects of insect exclusion on wood loss in a temperate forest. Ecosphere 5, 47 (2014).Article 

Google Scholar 
27.Box, E. O. & Fujiwara, K. A comparative look at bioclimatic zonation, vegetation types, tree taxa and species richness in northeast Asia. Bot. Pac. 1, 5–20 (2012).Article 

Google Scholar 
28.Lee, K.-S. & Jeong, S.-Y. Ecological characteristics of termite (Riticulitermes speratus kyshuensis) for preservation of wooden cultural heritage. Conserv. Stud. 37, 327–348 (2004) ((in Korean with English abstract)).
Google Scholar 
29.Cheesman, A. W., Cernusak, L. A. & Zanne, A. E. Relative roles of termites and saprotrophic microbes as drivers of wood decay: A wood block test. Austral Ecol. 43, 257–267 (2018).Article 

Google Scholar 
30.Stoklosa, A. M. et al. Effects of mesh bag enclosure and termites on fine woody debris decomposition in a subtropical forest. Basic Appl. Ecol. 17, 463–470 (2016).Article 

Google Scholar 
31.Ulyshen, M. D. Interacting effects of insects and flooding on wood decomposition. PLOS ONE 9, e101867 (2014).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
32.Noh, N. J. et al. Carbon and nitrogen accumulation and decomposition from coarse woody debris in a naturally regenerated Korean red pine (Pinus densiflora S. et Z.) forest. Forests 8, 214 (2017).Article 

Google Scholar 
33.Yoon, T. K. et al. Coarse woody debris mass dynamics in temperate natural forests of Mt. Jumbong, Korea. J. Ecol. Field Biol. 34, 115–125 (2011).Article 

Google Scholar 
34.Park, S.-W., Baek, G., Byeon, H.-S., Kim, Y. S. & Kim, C. Carbon and nitrogen dynamics of wood stakes as affected by soil amendment treatments in a post-fire restoration area. Korean J. Agric. For. Meteorol. 20, 357–365 (2018) ((in Korean with English abstract)).
Google Scholar 
35.Ulyshen, M. D. Wood decomposition as influenced by invertebrates. Biol. Rev. 91, 70–85 (2016).PubMed 
Article 

Google Scholar 
36.Gentry, J. B. & Whitford, W. G. The relationship between wood litter infall and relative abundance and feeding activity of subterranean termites Reticulitermes spp. in three southeastern coastal plain habitats. Oecologia 54, 63–67 (1982).ADS 
CAS 
PubMed 
Article 

Google Scholar 
37.Schuurman, G. Decomposition rates and termite assemblage composition in semiarid Africa. Ecology 86, 1236–1249 (2005).Article 

Google Scholar 
38.Weedon, J. T. et al. Global meta-analysis of wood decomposition rates: A role for trait variation among tree species?. Ecol. Lett. 12, 45–56 (2009).PubMed 
Article 

Google Scholar 
39.Yoon, T. K. et al. Effects of sample size and temperature on coarse woody debris respiration from Quercus variabilis logs. J. For. Res. 19, 249–259 (2014).Article 

Google Scholar 
40.Roh, Y. et al. Changes in the contribution of termites to mass loss of dead wood among three tree species during 23 months in a lowland tropical rainforest. Sociobiology 65, 59–66 (2018).Article 

Google Scholar 
41.Vasconcellos, A. & de Moura, F. M. S. Wood litter consumption by three species of Nasutitermes termites in an area of the Atlantic coastal forest in northeastern Brazil. J. Insect Sci. 10, 72 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
42.Kim, S. et al. Differential effects of coarse woody debris on microbial and soil properties in Pinus densiflora Sieb. et Zucc. forests. Forests 8, 292 (2017).Article 

Google Scholar 
43.Kim, R.-H. et al. Coarse woody debris mass and nutrients in forest ecosystems of Korea. Ecol. Res. 21, 819–827 (2006).Article 

Google Scholar 
44.Korea Forest Service. Statistical Yearbook of Forestry. Korea Forest Service, Daejeon (2020) (in Korean)45.Hedges, L. V. & Olkin, I. Statistical methods for meta-analysis 75–106 (Academic Press, New York, 1985).MATH 
Book 

Google Scholar 
46.Nakagawa, S. & Cuthill, I. C. Effect size, confidence interval and statistical significance: A practical guide for biologists. Biol. Rev. 82, 591–605 (2007).PubMed 
Article 

Google Scholar  More

Bioleaching processVariation of pH and ORP during bioleaching processpH and ORP of the sludge are widely known to be the important parameters influencing heavy metal solubilization during bioleaching process, as well as the activity of iron-oxidizing microorganisms10,26,27. The variation of sludge pH and ORP during the single bioleaching process is presented in Fig. 1.Figure 1Variation of pH and ORP during bioleaching process.Full size imageAn appropriate pH could enhance the activities of microbes, affecting the release of metals and the stability of metal ions in the liquid phase5. As shown in Fig. 1, the pH value of sewage sludge quickly decreased from 6.44 to 3.07 in the first 6 days, due to the oxidation of Fe2+ and metal sulfides, the production of sulfuric acid, ferric hydroxide and jarosite from the hydrolysis of Fe3+18. Then the pH gradually decreased to 2.89 on the 10th day. The change of ORP followed an opposite trend. ORP value of the sludge rapidly increased from − 155.6 mV to 480.0 mV in the first 6 days, then to 505.0 mV in the following 4 days, due to the oxidation of Fe2+ to Fe3+ by leaching microorganisms.Heavy metals solubilization and chemical speciation distribution during bioleaching processThe removal of heavy metals during bioleaching process and the distribution of chemical fractions of heavy metals before and after bioleaching are presented in Figs. 2 and 3, respectively. The single bioleaching led to the removal of Zn, Cu, Cd, Cr, Mn, Ni, As and Pb of 67.28%, 50.78%, 64.86%, 6.32%, 56.15%, 49.83%, 20.78% and 10.52% in 10 days, respectively. The solubilization efficiency was highly related to the evolution of pH and ORP, the chemical fraction distributions and the nature of heavy metals.Figure 2Removal of heavy metals during bioleaching process.Full size imageFigure 3Chemical speciation distributions of heavy metals in raw sludge and bioleached sludge, total concentration of heavy metals in the raw sludge was set as 100% (RS raw sludge, BS bioleached sludge).Full size imageFigure 2 illustrated that Zn had the highest solubilization and removal efficiency. It was found that below the threshold pH of 6–6.5, Zn could be dissolved28. Thus, the dissolving out of Zn had started at the beginning of leaching experiment with a removal percentage of 10.15% on the 2nd day. Yet the quick solubilization of Zn was observed from the 4th day (pH 4.01). And until the 6th day (pH 3.00) when the solubilization percentage of Zn was 65.71%, the leaching rate of Zn was slowed down due to the stable pH. In the raw sludge, Zn mainly existed in mobile forms (exchangeable/acid soluble and reducible forms) as shown in Fig. 3. After bioleaching, the solubilization efficiencies of Zn in exchangeable/acid soluble form and reducible form was 58.66% and 87.93%, respectively. Meanwhile, 48.27% of Zn in oxidizable form was also dissolved out due to the oxidation of metal sulfide and loss of sludge organic matter. However, Zn in residual form remained almost unchanged in the bioleached sludge due to its high stability.It has been pointed out that Cu could be rapidly solubilized below pH of 3.7 or under a high ORP condition29. As shown in Fig. 2, in the first 4 days, the solubilization efficiency of Cu was relatively low (11.44%). The removal rate of Cu increased rapidly to 43.54% on the 6th day due to the increase of ORP (480 mV). The proportion of Cu in exchangeable/acid soluble form increased by 55.16% after bioleaching, probably because the solubilized Cu2+ was re-adsorbed on the EPS of sludge cells30,31. Most of Cu was present in reducible and oxidizable forms in the raw sludge as shown in Fig. 3, because the complexation of copper and organic materials was relatively stable30,32,33. The removal percentages of Cu in reducible and oxidizable forms were 71.11% and 61.83% after bioleaching, respectively, which was the main reason for Cu removal.Cd could be solubilized rapidly under acidic conditions as shown in Fig. 2, which is consistent with the previous study34. The solubilization of Cd could be finished in 6 days with the removal rate of 64.36%. Cd was mainly present in mobile forms (91.07%) as shown in Fig. 3, which agreed with the findings of Zeng et al.35 Thus, the acid dissolution was the main removal mechanism of Cd34. Due to the low pH of the bioleached sludge, the content of Cd in mobile forms decreased by 62.77% after bioleaching. Furthermore, Cd in immobile forms (oxidizable and residual forms) also reduced significantly.The previous study found that Cr was relatively stable with the dissolved pH threshold of 2.3–3.028. Although the percentage of Cr present in mobile forms was over 40%, the removal rate of Cr (6.32%) was the lowest among all the heavy metals investigated as shown in Fig. 2, because the lowest pH of the bioleached sludge was about 2.9, which was close to the dissolution threshold limit of Cr.As shown in Fig. 2, Mn and Ni were solubilized quickly in the first 4 days. The solubilization percentage of Mn and Ni were 56.14% and 49.83% after bioleaching, respectively. Mn and Ni mainly existed in the mobile forms (Mn 82.05%, Ni 76.08%). In the early stage of bioleaching, the removal rates of Mn and Ni were closely related to the variation of pH and displayed obvious acid dissolution mechanism. After bioleaching, the concentrations of Mn in exchangeable/acid soluble, reducible and oxidizable forms were reduced by 34.65%, 78.82% and 90.84%, respectively. As for Ni, the removal rates in such forms were 34.66%, 74.58% and 64.99%, respectively. Thus, the higher extraction efficiency of Mn and Ni arose from mixed bioleaching mechanisms, which contain acid dissolution, oxidation and reduction by Fe2+/Fe3+.Relatively low removal efficiency of As (20.78%) was observed in this study. One reason, as shown in Fig. 3, was that As was mainly distributed in residual form with high stability. The other reason was that the dissolved As3+ could be oxidized to As5+ (AsO43-) by Fe3+ generated from the metabolism of iron-oxidizing bacteria, and then insoluble FeAsO4 could be produced through the reaction of AsO43- and Fe3+, which resulted in the reprecipitation of As34.Pb in exchangeable/acid soluble form was not detected in the raw sludge, and mainly existed in reducible (59.20%) and oxidizable (23.19%) forms. The removal rates of Pb in reducible and oxidizable forms were 33.51% and 58.17% after bioleaching, respectively. However, the insoluble compounds such as PbSO4 (Ksp = 1.62 × 10–8) could be generated during the bioleaching process36, which resulted in a significant increase in the concentration of Pb in residual form (from 10.89 to 25.00 mg/kg), and thus led to the low removal ratio of Pb (10.52%).To summarize, the solubilization efficiencies of Zn, Cu, Cd, Mn and Ni, which mainly existed in mobile forms in the raw sludge, were relatively high due to the instability of these metals, while the removal rates of Cr, As and Pb, which mainly existed in immobile forms, were relatively low. However, the contents of most heavy metals in mobile forms decreased obviously after bioleaching and would lead to the corresponding reduction of the environmental risk of the sludge.Combined bioleaching/Fenton-like processEffect of H2O2 dosage on the removal of heavy metals under various pH conditionsPrevious studies have shown that the production ability of hydroxyl radical during the Fenton-like reaction process could be enhanced under pH range of 2.5–4.5, and meanwhile, the amount of H2O2 directly influences the production of hydroxyl radical10,18. Therefore, as shown in Fig. 4, the effects of H2O2 dosage on the solubilization efficiencies of heavy metals were investigated at different stages of the bioleaching process, when the pH values of the bioleached sludge were 4.5 (about 3.5th day), 4.0 (4th day) and 3.0 (6th day).Figure 4Effects of H2O2 dosage on the removal efficiency of heavy metals under various pH conditions.Full size imageWith the increasing concentrations of H2O2 (0.0–8.0 g/L), the solubilization efficiency of Zn increased significantly at pH of 4.5 (Fig. 4) due to the oxidation of metal sulfide and organics by hydroxyl radical10. However, the solubilization percentages of Zn barely changed with further increase of H2O2 dosage (from 8.0 to 15.0 g/L). The solubilization percentage of Zn at the H2O2 dosage of 8.0 g/L (pH of 4.5) was significantly higher than when only using single bioleaching (75.31% vs. 67.64%). The enhancement of solubilization efficiency of Zn at a pH of 4.0 and 3.0 was not very noticeable (Fig. 4), because most of the Zn in immobile forms was dissolved out by bioleaching. The highest solubilization percentages of Zn were 74.96% at a pH of 4.0 and 75.53% at a pH of 3.0, which were 7.32% and 7.89% higher than that of the single bioleaching process.Due to the lower dissolved pH threshold of Cu compared with Zn, the solubilization efficiency of Cu was significantly affected by the dosage of H2O2 at a pH of 4.5 and 4.0 as shown in Fig. 4, while when the reaction pH was 3.0, the subsequent Fenton treatment had a relatively small impact on the removal of Cu. The highest removal rate of Cu (52.17%) was obtained at pH of 3.0 and H2O2 dosage of 13.0 g/L, which was slightly higher than that of the single bioleaching (50.78%). The change in solubilization efficiency of Cd was similar to that of Cu. When the pH values were 4.5 and 4.0, the solubilization percentages of Cd with H2O2 dosage of 15.0 g/L were 4.59% and 1.23% higher than that of the single bioleaching process, respectively. Meanwhile, the highest solubilization percentage of Cd (71.91%) could be reached at a pH of 3.0 and H2O2 dosage of 13.0 g/L, which was higher than that of the single bioleaching process (64.86%).The addition of H2O2 did not increase the removal rate of Cr significantly as shown in Fig. 4. At a reaction pH of 4.5, the solubilization percentage of Cr was 7.59% with H2O2 dosage of 15.0 g/L, which was a little higher than that of the single bioleaching process (6.32%), while the highest solubilization percentages of Cr could reach 11.63% and 9.18% at pH of 4.0 and 3.0, respectively, with H2O2 dosage of 15.0 g/L.The solubilization process of Mn and Ni displayed similar trend as shown in Fig. 4. The solubilization percentage of Mn was not significantly improved when the H2O2 dosage was increased from 5.0 to 11.0 g/L at pH of 4.5 and 4.0, but a much faster increase of the removal rate was observed with the H2O2 dosage over 13.0 g/L. It could be due to the enhanced oxidizing ability of Fenton-like reaction with abundant H2O2. However, the solubilization efficiency of Mn under a pH of 3.0 began to increase with H2O2 concentration of 11.0 g/L, which could be attributed to the high efficiency of Fenton action under lower pH15. The highest removal percentage of Mn was 66.29% at pH of 3.0 and H2O2 dosage of 15.0 g/L, while the removal percentage of Mn in the single bioleaching process was 56.14%. The removal behavior of Ni at various pH was consistent with Mn. The highest removal rate of Ni (65.81%) was found at a pH of 3.0 with H2O2 dosage of 15.0 g/L, which was significantly improved, compared with the single bioleaching process (49.83%).On the contrary, the removal efficiency of As and Pb in the combined process was not promoted compared with the single bioleaching process. Due to the strong oxidizing capacity of Fenton-like process, the yield of SO42− and insoluble FeAsO4 could be improved. Correspondingly, Pb2+ could be transformed into residual form, such as insoluble PbSO410. Therefore, the removal efficiencies of As and Pb decreased in the combined process. The highest removal rates of As and Pb after Fenton-like treatment were 12.46% and 10.20%, respectively.In the combined process, higher solubilization efficiencies of most heavy metals (Zn, Cu, Cd, Mn, Ni, Cr) could be achieved in 6 days. The removal efficiency of heavy metals (except Cr, As and Pb) of combined process (pH of 3.0, H2O2 dosage of 15 g/L) is higher than that of the single bioleaching process. The removal rate of Zn, Cu, Cd, Mn and Ni increased by 7.89%, 0.38%, 5.56%, 10.15% and 15.35%, respectively. Meanwhile, the total concentrations of heavy metals measured in this study after treatment could meet the control standards of pollutants in sludge for agricultural use of China (National Standard GB 4284-2018). The removal of As and Pb was not improved by the combined process, other methods such as chemical leaching, electrokinetic remediation and phytoremediation could be considered as alternatives. However, their transformation into insoluble forms may also reduce the bioavailability of heavy metals and increase the environmental safety of the treated sludge. For that reason, the chemical speciation distributions of heavy metals in the combined process were further analyzed in detail.Chemical fraction distributions of heavy metals in the combined processIt can be seen in Fig. 4 that the solubilization efficiency of most heavy metals did not change significantly with H2O2 dosage below 8.0 g/L. Therefore, the chemical speciation changes of heavy metals after Fenton treatment under H2O2 dosage of 11.0, 13.0 and 15.0 g/L, as shown in Fig. 5, were discussed.Figure 5Change of chemical speciation distributions of heavy metals under different H2O2 dosage at a pH of 4.5, 4.0 and 3.0, total concentration of heavy metal in the raw sludge was set as 100%.Full size imageUnder various pH conditions, the contents of Zn in all of the four forms showed a downward trend along with the increasing H2O2 dosage (Fig. 5). After bioleaching, Zn mainly existed in exchangeable/acid soluble form under the final pH of 4.5 (64.89%), pH of 4.0 (73.33%) and pH of 3.0 (80.82%). The removal of Zn in exchangeable/acid soluble form showed good correlation to the dosage of H2O2, which might be attributed to the destruction of EPS, and the released heavy metals were transferred to the liquid phase. Meanwhile, the improvement of sludge dewaterability could also promote the removal of heavy metals. After Fenton-like reaction at a pH of 4.5, the percentages of Zn in exchangeable/acid soluble forms were reduced by 30.35%, 31.41% and 40.09% at H2O2 dosage of 11.0, 13.0 and 15.0 g/L, respectively, compared with the percentage of Zn in the sludge at the end of the single bioleaching process. However, the percentage of Zn in other forms did not change significantly after Fenton-like treatment. Therefore, the further removal of Zn in exchangeable/acid soluble form and the dewaterability improvement of sludge may be the main reasons for the higher removal efficiency of Zn in the combined process.Cu was still mainly associated with the oxidizable form after bioleaching ended at pH of 4.5, 4.0 and 3.0 (Fig. 5), which might be attributed to the preference of Cu for organic materials22. The addition of H2O2 at pH 4.5 significantly boosted the solubilization efficiency of Cu in exchangeable/acid soluble form. The percentages of Cu in exchangeable/acid soluble form in the sludge after Fenton treatment at pH 4.5 were 24.69% (11.0 g/L), 29.50% (13.0 g/L) and 38.15% (15.0 g/L), which were lower than that at the end of the single bioleaching process. Meanwhile, the content of Cu in reducible form was reduced by nearly 50% with H2O2 dosage of 13.0 and 15.0 g/L, compared with its content after bioleaching ended at pH 4.5. However, the highest removal rate of Cu in oxidizable form was only 33.20% with H2O2 dosage of 15.0 g/L. The removal efficiency of Cu in exchangeable/acid soluble and reducible forms increased with the increasing H2O2 dosage at pH 4.0 and 3.0, similar to the observation at pH 4.5. Under a reaction pH of 4.0, 47.2% of Cu in oxidizable form was removed after Fenton treatment with H2O2 dosage of 13.0 g/L, while only 28.6% was removed at H2O2 dosage of 15.0 g/L. In addition, the removal rates of Cu in oxidizable form were only 4.9–17.7% at various H2O2 dosage at a Fenton reaction pH of 3.0. The removal efficiency of Cu was reduced in despite of the increasing oxidation capacity of Fenton-like reaction. The macro-molecular organic matters could be degraded into small organic molecules during Fenton treatment process, releasing partial Cu. However, the generated small molecule organic matters had more undissociated carboxyl that would combine with released Cu31, which formed Cu in oxidizable form. Thus, it could explain the low removal efficiency of Cu in oxidizable form under stronger oxidizing condition. However, the highest removal rate of Cu (52.17%) was observed at pH 3.0 and H2O2 dosage of 15.0 g/L, due to the high reduction ratio of Cu in mobile forms at that condition.Cd mainly existed in mobile forms in the sludge after bioleaching and Fenton treatment, as shown in Fig. 5. The contents of Cd in mobile and oxidizable forms decreased with the increasing H2O2 dosage at pH 4.5. The content of Cd in exchangeable/acid soluble form after Fenton treatment at pH 4.5 and H2O2 dosage of 15.0 g/L was 29.10% lower than that at the end of the single bioleaching process. Meanwhile, the content of Cd in mobile form was decreased by 27.54% (11.0 g/L), 26.56% (13.0 g/L) and 36.72% (15.0 g/L) after Fenton treatment at pH 4.0. The removal of Cd in exchangeable/acid soluble form after Fenton treatment could be largely due to the improvement of sludge dewaterability. However, the reduction of Cd was not obvious after Fenton treatment at pH 3.0, because the solubilization threshold of most of Cd in various forms were reached after the bioleaching process ended at pH 3.0.The removal efficiency of Cr was not improved obviously by Fenton treatment in this study, as shown in Fig. 5. It was also reported that Cr was difficult to be removed by bioleaching or combined process due to its relatively high stability10. However, the content of Cr in oxidizable form after Fenton treatment at pH 4.5 was 4.76% (11.0 g/L), 9.20% (13.0 g/L) and 9.84% (15.0 g/L) lower than that at the end of the single bioleaching process, due to the strong oxidizing capacity of hydroxyl radical. And the lowest content of Cr in oxidizable form was observed after Fenton treatment at pH 4.0 and H2O2 dosages of 13.0 g/L, which was 39.4% lower than that in the bioleached sludge. Meanwhile, the highest Cr removal rate was also obtained at this condition after Fenton-like treatment. Thus, the improvement of Cr removal in combined process was mainly due to the release of Cr in oxidizable form. Furthermore, the released metals could be absorbed on the surface of oxides31, thus inevitably caused the increase of Cr in reducible form as shown in Fig. 5. The chemical speciation change of Cr after Fenton treatment at pH 3.0 was similar to that at pH 4.0.The removal efficiency and chemical speciation distribution of Mn varied obviously after Fenton treatment with different dosages of H2O2. The removal rate of Mn was improved with the increasing dosage of H2O2 at various pH values. Because most of the Mn in reducible form (over 80%) was removed by bioleaching process, the reduction of Mn in exchangeable/acid soluble form should account for the removal of a substantial part of Mn after Fenton treatment. The highest removal rate of Mn in exchangeable/acid soluble form under different pH conditions was 26.27% (pH 4.5), 25.06% (pH 4.0) and 42.18% (pH 3.0), all with H2O2 dosage of 15.0 g/L. Although nearly 30% of Mn in reducible and oxidizable forms was also removed after Fenton treatment with H2O2 dosage of 15.0 g/L at various pH values, it contributed little to the removal of Mn considering the low concentration of Mn in reducible and oxidizable forms in the raw sludge. Furthermore, the changes of Mn in residual form were not obvious under different pH.The chemical speciation change of Ni was similar to that of Mn after Fenton treatment. The contents of Ni in mobile and oxidizable forms decreased along with the increasing dosage of H2O2, as shown in Fig. 5. Meanwhile, the reduction of Ni in exchangeable/acid soluble form after the addition of H2O2 was the prime reason for the higher removal efficiency of Ni after the combined process than that after the single bioleaching process. The highest removal rate of Ni in exchangeable/acid soluble form was found with H2O2 dosage of 15.0 g/L at pH 4.0, which was 34.47% lower than that in the sludge after the signal bioleaching process. However, the highest removal efficiency of Ni (65.19%) was reached when the reaction pH was 3.0 with H2O2 dosages of 15.0 g/L due to the simultaneous reduction of Ni in reducible and oxidizable forms. The contents of Ni in reducible and oxidizable forms were reduced by 50.30% and 52.83% under this reaction condition, respectively, compared with that at the end of the single bioleaching process.As and Pb were mainly present in residual form before Fenton treatment as shown in Fig. 5. The content of As in exchangeable/acid soluble form decreased significantly due to the degradation of EPS at various pH values with the addition of H2O2. However, the content of As in residual form gradually rose with the increasing dosage of H2O2, probably because As3+ could be oxidized to As5+ by hydroxyl radical and/or Fe3+ with the formation of insoluble FeAsO434. The content of Pb in reducible form showed a trend of increase after Fenton treatment. SO42− was generated due to the oxidation of sulfur elements and/or sulfide in sludge by hydroxyl radicals with the production of insoluble PbSO410, and thus the content of Pb in residual form also increased after further Fenton treatment. Although the Fenton treatment had a negative impact on the removal of As and Pb as shown in Fig. 5, because of the formation of insoluble compounds under strong oxidizing condition, the environmental risk of these two heavy metals decreased to some extent under an appropriate condition, due to the increased proportion of immobile fractions, especially residual form. compared with the bioleached sludge.The content and proportion of most heavy metals (Zn, Cu, Cd, Mn, Ni, As) in mobile forms were lower in the treated sludge after the combined bioleaching and Fenton-like process, compared with the single bioleaching process, which was also the main reason for the high removal efficiency of these metals. Their bioavailability and toxicity were also reduced. However, Fenton treatment was found to have a negative impact on the removal of As, but the increased proportion of As in residual form also lowered its bioavailability and mobility in the environment. The increase in the content of Pb in both mobile forms (mainly in reducible form) and immobile forms (mainly in residual form) was observed under different conditions, so special attention should be paid to the chemical speciation distributions of Pb during sludge treatment process.The effect of H2O2 dosage on sludge dewaterability at different pH valuesThe changes of CST of treated sludge under various conditions are presented in Fig. 6. The CST of the raw sludge (98.7 s) was dramatically reduced by bioleaching and Fenton oxidation treatments. After bioleaching ended on the 10th day (pH 2.89), the 6th day (pH 3.0), the 4th day (4.0) and the 3.5th day (pH 4.5), CST values of 20.3 s, 24.2 s, 30.7 s and 35.0 s were observed. The decreased pH after bioleaching process could destroy the EPS and neutralize the negative charge of the sludge flocs, resulting in the release of bound water37. Moreover, sludge dewatering could also be improved by the coagulation effect of Fe2+ 10. Furthermore, hydroxyl radicals were essential to improve sludge dewatering performance by destroying EPS and porous structure during the Fenton treatment process35. Therefore, the CST value of treated sludge was reduced to 20.6 s after Fenton treatment with H2O2 dosage of 15 g/L at pH 4.5, which was comparable to the CST value at the end of the single bioleaching process. The CST values were further reduced along with the decreasing reaction pH (4.0 and 3.0) and the increasing H2O2 dosage. The lowest CST value of 12.4 s was observed at Fenton reaction pH 3.0 and H2O2 dosage of 15.0 g/L, which meant a reduction from the initial CST of 87.44%. Therefore, the combined process could lead to an obvious improvement of the sludge dewaterability and significantly reduced the treatment period.Figure 6Changes of CST under different H2O2 dosage and pH.Full size image More

1.Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).ADS 
CAS 
PubMed 
Article 

Google Scholar 
2.Hooper, D. U. et al. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecol. Monogr. 75, 3–35 (2005).Article 

Google Scholar 
3.Isbell, F. et al. Linking the influence and dependence of people on biodiversity across scales. Nature 546, 65–72 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
4.Loreau, M. & Hector, A. Partitioning selection and complementarity in biodiversity experiments. Nature 412, 72–76 (2001).ADS 
CAS 
PubMed 
Article 

Google Scholar 
5.Bessler, H. et al. Nitrogen uptake by grassland communities: contribution of N2 fixation, facilitation, complementarity, and species dominance. Plant Soil 358, 301–322 (2012).CAS 
Article 

Google Scholar 
6.Karanika, E. D., Alifragis, D. A., Mamolos, A. P. & Veresoglou, D. S. Differentiation between responses of primary productivity and phosphorus exploitation to species richness. Plant Soil 297, 69–81 (2007).CAS 
Article 

Google Scholar 
7.Lange, M. et al. How plant diversity impacts the coupled water, nutrient and carbon cycles. Adv. Ecol. Res. 61, 185–219 (2019).Article 

Google Scholar 
8.Oelmann, Y. et al. Does plant diversity influence phosphorus cycling in experimental grasslands? Geoderma 167-68, 178–187 (2011).ADS 
Article 
CAS 

Google Scholar 
9.Tilman, D., Wedin, D. & Knops, J. Productivity and sustainability influenced by biodiversity in grassland ecosystems. Nature 379, 718–720 (1996).ADS 
CAS 
Article 

Google Scholar 
10.Leimer, S., Oelmann, Y., Wirth, C. & Wilcke, W. Time matters for plant diversity effects on nitrate leaching from temperate grassland. Agric Ecosyst. Environ. 211, 155–163 (2015).CAS 
Article 

Google Scholar 
11.Scherer-Lorenzen, M., Palmborg, C., Prinz, A. & Schulze, E.-D. The role of plant diversity and composition for nitrate leaching in grasslands. Ecology 84, 1539–1552 (2003).Article 

Google Scholar 
12.Elser, J. & Bennett, E. A broken biogeochemical cycle. Nature 478, 29–31 (2011).ADS 
CAS 
PubMed 
Article 

Google Scholar 
13.Lambers, H., Mougel, C., Jaillard, B. & Hinsinger, P. Plant-microbe-soil interactions in the rhizosphere: an evolutionary perspective. Plant Soil 321, 83–115 (2009).CAS 
Article 

Google Scholar 
14.Wassen, M. J., Olde Venterink, H., Lapshina, E. D. & Tanneberger, F. Endangered plants persist under phosphorus limitation. Nature 437, 547–550 (2005).ADS 
CAS 
PubMed 
Article 

Google Scholar 
15.Cordell, D., Drangert, J.-O. & White, S. The story of phosphorus: Global food security and food for thought. Glob. Environ. Change-Hum. Policy Dimens. 19, 292–305 (2009).Article 

Google Scholar 
16.van der Heijden, M. G. A., Martin, F. M., Selosse, M.-A. & Sanders, I. R. Mycorrhizal ecology and evolution: the past, the present, and the future. N. Phytol. 205, 1406–1423 (2015).Article 
CAS 

Google Scholar 
17.van der Heijden, M. G. A. et al. Mycorrhizal fungal diversity determines plant biodiversity, ecosystem variability and productivity. Nature 396, 69–72 (1998).ADS 
Article 
CAS 

Google Scholar 
18.Richardson, A. E. & Simpson, R. J. Soil microorganisms mediating phosphorus availability. Plant Physiol. 156, 989–996 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
19.Hacker, N. et al. Plant diversity shapes microbe-rhizosphere effects on P mobilisation from organic matter in soil. Ecol. Lett. 18, 1356–1365 (2015).PubMed 
Article 

Google Scholar 
20.Hacker, N., Wilcke, W. & Oelmann, Y. The oxygen isotope composition of bioavailable phosphate in soil reflects the oxygen isotope composition in soil water driven by plant diversity effects on evaporation. Geochim. Cosmochim. Acta 248, 387–399 (2019).ADS 
CAS 
Article 

Google Scholar 
21.Craven, D. et al. Plant diversity effects on grassland productivity are robust to both nutrient enrichment and drought. Philos. Trans. R. Soc. B 371, 8 (2016).Article 

Google Scholar 
22.Fridley, J. D. Resource availability dominates and alters the relationship between species diversity and ecosystem productivity in experimental plant communities. Oecologia 132, 271–277 (2002).ADS 
PubMed 
Article 

Google Scholar 
23.Weigelt, A., Weisser, W. W., Buchmann, N. & Scherer-Lorenzen, M. Biodiversity for multifunctional grasslands: equal productivity in high-diversity low-input and low-diversity high-input systems. Biogeosciences 6, 1695–1706 (2009).ADS 
CAS 
Article 

Google Scholar 
24.Nyfeler, D. et al. Strong mixture effects among four species in fertilized agricultural grassland led to persistent and consistent transgressive overyielding. J. Appl Ecol. 46, 683–691 (2009).Article 

Google Scholar 
25.Oelmann, Y., Vogel, A., Wegener, F., Weigelt, A. & Scherer-Lorenzen, M. Management intensity modifies plant diversity effects on N yield and mineral N in soil. Soil Sci. Soc. Am. J. 79, 559–568 (2015).ADS 
CAS 
Article 

Google Scholar 
26.Manning P., et al. Transferring biodiversity-ecosystem function research to the management of ‘real-world’ ecosystems. In: Mechanisms Underlying the Relationship between Biodiversity and Ecosystem Function (ed^(eds Eisenhauer N., Bohan D. A., Dumbrell A. J.). Academic Press Ltd-Elsevier Science Ltd (2019).27.Kraft, N. J. B. et al. Community assembly, coexistence and the environmental filtering metaphor. Funct. Ecol. 29, 592–599 (2015).Article 

Google Scholar 
28.Allan, E. et al. Land use intensification alters ecosystem multifunctionality via loss of biodiversity and changes to functional composition. Ecol. Lett. 18, 834–843 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
29.Collins, C. D. & Foster, B. L. Community-level consequences of mycorrhizae depend on phosphorus availability. Ecology 90, 2567–2576 (2009).PubMed 
Article 

Google Scholar 
30.Klironomos, J. N., McCune, J., Hart, M. & Neville, J. The influence of arbuscular mycorrhizae on the relationship between plant diversity and productivity. Ecol. Lett. 3, 137–141 (2000).Article 

Google Scholar 
31.Busch, V. et al. Will I stay or will I go? Plant species-specific response and tolerance to high land-use intensity in temperate grassland ecosystems. J. Veg. Sci. 30, 674–686 (2019).Article 

Google Scholar 
32.Sorkau, E. et al. The role of soil chemical properties, land use and plant diversity for microbial phosphorus in forest and grassland soils. J. Plant Nutr. Soil Sci. 181, 185–197 (2018).CAS 
Article 

Google Scholar 
33.Wardle, D. A. A comparative assessment of factors which influence microbial biomass carbon and nitrogen levels in soil. Biol. Rev. Camb. Philos. Soc. 67, 321–358 (1992).Article 

Google Scholar 
34.Lange, M. et al. Plant diversity increases soil microbial activity and soil carbon storage. Nat. Commun. 6, 6707 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
35.Eisenhauer, N. et al. Plant diversity effects on soil microorganisms support the singular hypothesis. Ecology 91, 485–496 (2010).CAS 
PubMed 
Article 

Google Scholar 
36.Cleveland, C. C. & Liptzin, D. C. N: P stoichiometry in soil: is there a “Redfield ratio” for the microbial biomass? Biogeochemistry 85, 235–252 (2007).Article 

Google Scholar 
37.Cardinale, B. J. et al. Impacts of plant diversity on biomass production increase through time because of species complementarity. Proc. Natl Acad. Sci. USA 104, 18123–18128 (2007).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
38.Marquard, E. et al. Plant species richness and functional composition drive overyielding in a 6-year grassland experiment. Ecology 90, 3290–3302 (2009).PubMed 
Article 

Google Scholar 
39.Liebisch, F. et al. Seasonal dynamics and turnover of microbial phosphorus in a permanent grassland. Biol. Fertil. Soils 50, 465–475 (2014).CAS 
Article 

Google Scholar 
40.Boeddinghaus, R. S. et al. Plant functional trait shifts explain concurrent changes in the structure and function of grassland soil microbial communities. J. Ecol. 107, 2197–2210 (2019).CAS 
Article 

Google Scholar 
41.Soussana, J. F. et al. Carbon cycling and sequestration opportunities in temperate grasslands. Soil Use Manag. 20, 219–230 (2004).Article 

Google Scholar 
42.Waldrop, M. P., Zak, D. R., Blackwood, C. B., Curtis, C. D. & Tilman, D. Resource availability controls fungal diversity across a plant diversity gradient. Ecol. Lett. 9, 1127–1135 (2006).PubMed 
Article 

Google Scholar 
43.Kour, D. et al. Biodiversity, current developments and potential biotechnological applications of phosphorus-solubilizing and -mobilizing microbes: a review. Pedosphere 31, 43–75 (2021).Article 

Google Scholar 
44.Dijkstra, F. A., He, M. Z., Johansen, M. P., Harrison, J. J. & Keitel, C. Plant and microbial uptake of nitrogen and phosphorus affected by drought using N-15 and P-32 tracers. Soil Biol. Biochem. 82, 135–142 (2015).CAS 
Article 

Google Scholar 
45.Hiiesalu, I. et al. Species richness of arbuscular mycorrhizal fungi: associations with grassland plant richness and biomass. N. Phytol. 203, 233–244 (2014).CAS 
Article 

Google Scholar 
46.Tilman, D., Cassman, K. G., Matson, P. A., Naylor, R. & Polasky, S. Agricultural sustainability and intensive production practices. Nature 418, 671–677 (2002).ADS 
CAS 
PubMed 
Article 

Google Scholar 
47.Roscher, C. et al. The role of biodiversity for element cycling and trophic interactions: an experimental approach in a grassland community. Bas Appl. Ecol. 5, 107–121 (2004).Article 

Google Scholar 
48.Hoffmann K., Bivour W., Früh B., Koßmann M., Voß P.-H. Climate studies in Jena for adaption to climate change and ist expected consequences. (In German). Selbstverlag des Deutschen Wetterdienstes (2014).49.IUSS Working Group WRB. World Reference Base for Soil Resources 2014, update 2015: International soil classification system for naming soils and creating legends for soil maps. FAO (2015).50.Fischer, M. et al. Implementing large-scale and long-term functional biodiversity research: the biodiversity exploratories. Bas Appl Ecol. 11, 473–485 (2010).Article 

Google Scholar 
51.Alt, F., Oelmann, Y., Herold, N., Schrumpf, M. & Wilcke, W. Phosphorus partitioning in grassland and forest soils of Germany as related to land-use type, management intensity, and land use-related pH. J. Plant Nutr. Soil Sci. 174, 195–209 (2011).CAS 
Article 

Google Scholar 
52.Vogt, J. et al. Eleven years’ data of grassland management in Germany. Biodiver Data J. 7, 38 (2019).Article 

Google Scholar 
53.Alt, F., Oelmann, Y., Schöning, I. & Wilcke, W. Phosphate release kinetics at stable pH in calcareous grassland and forest soils. Soil Sci. Soc. Am. J. 77, 2060–2070 (2013).ADS 
CAS 
Article 

Google Scholar 
54.Jones J. B., Wolf B., Mills H. A. Plant analysis handbook. Micro Macro Publishing (1991).55.Marina, M. A. & Lopez, M. C. B. Determination of phosphorus in raw materials for ceramics: comparison between X-ray fluorescence spectrometry and inductively coupled plasma-atomic emission spectrometry. Anal. Chim. Acta 432, 157–163 (2001).CAS 
Article 

Google Scholar 
56.Hedley, M. J., Stewart, J. W. B. & Chauhan, B. S. Changes in inorganic and organic soil-phosphorus fractions induced by cultivation practices and by laboratory incubations. Soil Sci. Soc. Am. J. 46, 970–976 (1982).ADS 
CAS 
Article 

Google Scholar 
57.Kuo S. Phosphorus. In: Methods of Soil Analysis – Part 3 Chemical Methods (eds Sparks D. L., et al.). SSSA (1996).58.Cross, A. F. & Schlesinger, W. H. A literature review and evaluation of the Hedley fractionation – applications to the biogeochemical cycle of soil phosphorus in natural ecosystems. Geoderma 64, 197–214 (1995).ADS 
CAS 
Article 

Google Scholar 
59.Negassa, W. & Leinweber, P. How does the Hedley sequential phosphorus fractionation reflect impacts of land use and management on soil phosphorus: a review. J. Plant Nutr. Soil Sci. 172, 305–325 (2009).CAS 
Article 

Google Scholar 
60.Murphy, J. & Riley, J. P. A modified single solution method for determination of phosphate in natural waters. Anal. Chim. Acta 26, 31–36 (1962).Article 

Google Scholar 
61.McLaughlin, M. J., Alston, A. M. & Martin, J. K. Measurement of phosphorus in the soil microbial biomass – a modified procedure for field soils. Soil Biol. Biochem. 18, 437–443 (1986).CAS 
Article 

Google Scholar 
62.Kouno, K., Tuchiya, Y. & Ando, T. Measurement of soil microbial biomass phosphorus by an anion exchange membrane method. Soil Biol. Biochem. 27, 1353–1357 (1995).CAS 
Article 

Google Scholar 
63.Bünemann, E. K., Marschner, P., Smernik, R. J., Conyers, M. & McNeill, A. M. Soil organic phosphorus and microbial community composition as affected by 26 years of different management strategies. Biol. Fertil. Soils 44, 717–726 (2008).Article 

Google Scholar 
64.Brookes, P. C., Powlson, D. S. & Jenkinson, D. S. Measurement of microbial biomass phosphorus in soil. Soil Biol. Biochem 14, 319–329 (1982).CAS 
Article 

Google Scholar 
65.Eivazi, F. & Tabatabai, M. A. Phosphatases in soils. Soil Biol. Biochem. 9, 167–172 (1977).CAS 
Article 

Google Scholar 
66.Marx, M. C., Wood, M. & Jarvis, S. C. A microplate fluorimetric assay for the study of enzyme diversity in soils. Soil Biol. Biochem. 33, 1633–1640 (2001).CAS 
Article 

Google Scholar 
67.Berner, D. et al. Land-use intensity modifies spatial distribution and function of soil microorganisms in grasslands. Pedobiologia 54, 341–351 (2011).ADS 
Article 

Google Scholar 
68.White, D. C., Davis, W. M., Nickels, J. S., King, J. D. & Bobbie, R. J. Determination of the sedimentary microbial biomass by extractable lipid phosphate. Oecologia 40, 51–62 (1979).ADS 
CAS 
PubMed 
Article 

Google Scholar 
69.Bligh, E. G. & Dyer, W. J. A rapid method of total lipid extraction and purification. Can. J. Biochem. Physiol. 37, 911–917 (1959).CAS 
PubMed 
Article 

Google Scholar 
70.Kramer, C. & Gleixner, G. Variable use of plant- and soil-derived carbon by microorganisms in agricultural soils. Soil Biol. Biochem. 38, 3267–3278 (2006).CAS 
Article 

Google Scholar 
71.Frostegard, A. & Baath, E. The use of phospholipid fatty acid analysis to estimate bacterial and fungal biomass in soil. Biol. Fertil. Soils 22, 59–65 (1996).Article 

Google Scholar 
72.Zelles, L. Identification of single cultured micro-organisms based on their whole-community fatty acid profiles, using an extended extraction procedure. Chemosphere 39, 665–682 (1999).ADS 
CAS 
PubMed 
Article 

Google Scholar 
73.Dassen, S. et al. Differential responses of soil bacteria, fungi, archaea and protists to plant species richness and plant functional group identity. Mol. Ecol. 26, 4085–4098 (2017).CAS 
PubMed 
Article 

Google Scholar 
74.Kuramae, E. E. et al. Tracking fungal community responses to maize plants by DNA- and RNA-based pyrosequencing. PLoS ONE 8, 8 (2013).Article 
CAS 

Google Scholar 
75.Wubet, T., Weiss, M., Kottke, I. & Oberwinkler, F. Two threatened coexisting indigenous conifer species in the dry Afromontane forests of Ethiopia are associated with distinct arbuscular mycorrhizal fungal communities. Can. J. Bot.-Rev. Canadienne De. Botanique 84, 1617–1627 (2006).CAS 

Google Scholar 
76.Lee, J., Lee, S. & Young, J. P. W. Improved PCR primers for the detection and identification of arbuscular mycorrhizal fungi. FEMS Microbiol. Ecol. 65, 339–349 (2008).CAS 
PubMed 
Article 

Google Scholar 
77.Simon, L., Lalonde, M. & Bruns, T. D. Specific amplification of 18S fungal ribosomal genes from vesicular-arbuscular endomycorrhizal fungi colonizing roots. Appl. Environ. Microbiol. 58, 291–295 (1992).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
78.Lefcheck, J. S. PIECEWISESEM: Piecewise structural equation modelling in R for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).Article 

Google Scholar 
79.van der Heijden, M. G. A. et al. The mycorrhizal contribution to plant productivity, plant nutrition and soil structure in experimental grassland. N. Phytol. 172, 739–752 (2006).Article 

Google Scholar 
80.Frew, A. Arbuscular mycorrhizal fungal diversity increases growth and phosphorus uptake in C-3 and C-4 crop plants. Soil Biol. Biochem. 135, 248–250 (2019).CAS 
Article 

Google Scholar 
81.Hedlund, K. et al. Plant species diversity, plant biomass and responses of the soil community on abandoned land across Europe: idiosyncracy or above-belowground time lags. Oikos 103, 45–58 (2003).Article 

Google Scholar 
82.Treseder, K. K. The extent of mycorrhizal colonization of roots and its influence on plant growth and phosphorus content. Plant Soil 371, 1–13 (2013).CAS 
Article 

Google Scholar 
83.Köhl, L., Oehl, F. & van der Heijden, M. G. A. Agricultural practices indirectly influence plant productivity and ecosystem services through effects on soil biota. Ecol. Appl. 24, 1842–1853 (2014).PubMed 
Article 

Google Scholar 
84.Fornara, D. A. & Tilman, D. Plant functional composition influences rates of soil carbon and nitrogen accumulation. J. Ecol. 96, 314–322 (2008).CAS 
Article 

Google Scholar 
85.Steinbeiss, S. et al. Plant diversity positively affects short-term soil carbon storage in experimental grasslands. Glob. Change Biol. 14, 2937–2949 (2008).ADS 
Article 

Google Scholar 
86.Hacker N. Phosphorus Release Mechanisms in an Experimental Grassland of Varying Biodiversity. Doctoral thesis, University of Tübingen, Germany (2017). More

Archaeoglobales as systematic (electro)lithoautotrophs of the communityWe have evidenced the development of microbial electrotrophic communities and metabolic activity supported by current consumption (Fig. 1), product production (Fig. 2), and qPCRs (Fig. 3). These data suggest that growth did occur from energy supplied by the cathode. Our study is the first to show the possibility of growth of biofilm from environments harboring natural electric current in the total absence of soluble electron donors. To further discuss the putative mechanism, it is necessary to have a look at our conditions unfavorable for water electrolysis (see Supplementary Fig. S2). The equilibrium potential for water reduction into hydrogen at 80 °C, pH 7, and 1 atm was calculated at − 0.490 V vs SHE in pure water. The operational reduction potential is expected to be lower than the theoretical value due to internal resistances (from electrical connections, electrolytes, ionic membranes, etc.)16 and overpotentials (electrode material). This was confirmed with the on-set potential of H2 evolution measured at − 0.830 mV vs SHE in both experimental condition and abiotic control, indicating the absence of catalytic effect of putative hydrogenases secreted by the biofilm or metals from inoculum. Also, during preliminar potentials screening, the increase in current consumption and H2 production was observed only below − 0.7 V vs SHE (Supplementary Figs. S1 and S2). In addition, the presence of catalytic waves observed by CV with midpoint potentials between − 0.217 V to − 0.639 V indicate the implication of enzymes directly connected to the surface of the electrode (see Supplementary Fig. S2). Finally, the fixation of 267–1596 Coulombs day−1 into organics (Fig. 1) exceeds the maximum theoretical abiotic generation of hydrogen from abiotic current (~ 3 C day−1) 90- to 530-fold17.Therefore, under our experimental conditions, the biofilm growth should be largely ensured by a significant part of a direct transfer of electrons from the cathode, thereby demonstrating the presence of electrolithoautotroph microorganisms. This is supported by obtaining a similar biodiversity on sulfate with the cathode poised at − 300 mV [compared to − 590 mV vs SHE (Fig. 3)], whose potential is 190 mV more positive than the Equilibrium potential of H2 evolution (− 490 mV vs SHE), with then no electrochemical possibility of H2 production, even at molecular level.Taxonomic analysis of the enriched microbial communities at the end of the experiments showed the systematic presence of Archaeoglobales on cathodes. Moreover, the qPCR and MiSeq data (Fig. 3) highlighted a strong correlation between current consumption and density of Archaeoglobales in the biofilm (Supplementary Fig. S4, R2 = 0.945).The OTUs were related to some Archaeoglobales strains with 95–98% identities. Thus, we assume that under our experimental conditions new specific electrotrophic metabolisms or new electrolithoautotrophic Archaeoglobaceae species were enriched on the cathode. They were retrieved in all conditions and belonged to the only order in our communities exhibiting autotrophic metabolism. Autotrophic growth in the Archaeoglobales order is ensured mainly through using H2 as energy source and requires both branches of the reductive acetyl-CoA/Wood-Ljungdahl pathway for CO2 fixation18. Terminal electron acceptors used by this order include sulfate, nitrate, poorly crystalline Fe (III) oxide, and sulfur oxyanions19. Moreover, Archaeoglobus fulgidus has been recently shown to grow on iron by directly snatching electrons under carbon starvation during the corrosion process20. Furthermore, Ferroglobus and Geoglobus species were shown to be exoelectrogens in pure culture in a microbial electrosynthesis cell12 and have been enriched within a microbial electrolysis cell11,13. Given these elements, the identified Archaeoglobales species could be, under our electrolithoautotrophic conditions, the first colonizers of the electrode during the first days of growth. This hypothesis was confirmed into a more detailed study focusing on the enrichment on nitrate21.The growth of Archaeoglobales species in presence of oxygen is a surprising finding. Archaeoglobales have a strictly anaerobic metabolism, and the reductive acetyl-CoA pathway is very sensitive to the presence of oxygen22. This can be firstly explained by the low solubility of oxygen at 80 °C. Secondly, carbon cloth mesh reduces oxygen in the environment, allowing for anaerobic development of microorganisms into a protective biofilm23. This observation was supported by the near absence of Archaeoglobales in the liquid medium (Fig. 3). One of the hypotheses concerns direct interspecies electron transfer (DIET)24,25, with Archaeoglobales transferring electrons to another microorganism as an electron acceptor. Research into DIET is in its early stages, and further investigations are required to better understand the diversity of microorganisms and the mechanism of carbon and electron flows in anaerobic environments25 such as hydrothermal ecosystems.Electrosynthesis of organic compoundsAccumulation of pyruvate, glycerol and acetate was measured, while another set of compounds that appeared transiently were essentially detectable in the first few days of biofilm growth (Supplementary Table S1). They included amino acids (threonine, alanine) and volatile fatty acids (formate, succinate, lactate, acetoacetate, 3-hydroxyisovalerate) whose concentrations did not exceed 0.1 mM. Despite their thermostability, this transient production suggests they were used by microbial communities developing on the electrode in interaction with the primary producers during enrichment.On the other hand, in presence of nitrate, sulfate and oxygen as electron acceptors, the liquid media accumulated mainly acetate, glycerol, and pyruvate (Fig. 1). Coulombic efficiency calculations (Fig. 2) showed that electron content of the carbon products represented 60–90% of electrons consumed, the rest being potentially used directly for biomass or transferred to an electron acceptor. This concurs with the energy yield from the Wood-Ljungdahl pathway of Archaeoglobales, with only 5% of carbon flux directed to the production of biomass and the other 95% diverted to the production of small organic end-products excreted from the cell26.Pyruvate is a central intermediate of CO2 uptake by the reducing pathway of the acetyl-CoA/WL pathway27. It can be used to drive the anabolic reactions needed for biosynthesis of cellular constituents. Theoretically, the only explanation for improved production and accumulation of pyruvate (up to 5 mM in the liquid media of sulfate experiment) would be that pyruvate-consuming enzymes were inhibited or that pyruvate influx exceeded its conversion rate. Here we could suggest that in-cell electron over-feeding at the cathode leads to significant production of pyruvate when the electron acceptor runs out.In an ecophysiological context, similar pyruvate and glycerol production could occur on hydrothermal chimney walls into which electric current propagates28. The electrotroph biofilms would continually receive electrons, leading to an excess of intracellular reducing power which would be counterbalanced by overproduction of glycerol and pyruvate29,30. Furthermore, these products can serve as carbon and energy sources for heterotrophic microorganisms or for fermentation. In our experiments, pyruvate and glycerol concentrations varied over time, suggesting they were being consumed by heterotrophic microorganisms. Acetate production would thus result from the fermentation of pyruvate or other compounds produced by electrotrophic Archaeoglobales.Enrichment of rich heterotrophic biodiversity from electrotrophic Archaeoglobales communityDuring our enrichment experiments, the development of effective and specific biodiversity was dependent on the electron acceptors used (Fig. 3). Heatmap analyses (Supplementary Fig. S3) showed four distinct communities for the three electron acceptors and the initial inoculum. Thus, at the lower taxonomic level of the biodiversity analysis, most OTUs are not common to multiple enrichments, except for one OTU of Thermococcales that was found in both the nitrate and sulfate experiments. This suggests a real specificity of the communities and a specific evolution or adaptation of the members of the shared phyla to the different electron acceptors available in the environment. However, the various enrichments also showed the presence of Thermococcales regardless of the electron acceptors used, thus demonstrating a strong interaction between Thermococcales, assumed to be heterotrophs, and Archaeoglobales, the only demonstrated autotrophs. Moreover, members of these two groups have frequently been found together in various hydrothermal sites4,5,31,32, where they are considered potential primary colonizers33,34,35,36,37. After Thermococcales, the rest of the heterotrophic biodiversity was specific to each electron acceptor.On nitrate, two additional phylogenetic groups were retrieved: Desulfurococcales and Thermales. OTUs of Desulfurococcales are mainly affiliated to Thermodiscus or Aeropyrum species, which are hyperthermophilic and heterotrophic Crenarchaeota growing by fermentation of complex organic compounds or sulfur/oxygen reduction (Huber and Stetter, 2015). Concerning Thermales, a new taxon was enriched on cathode and only affiliated to Vulcanithermus mediatlanticus with similarity of 90%. This new taxon of Thermales (OTU 15, Supplementary Fig. S3) was also enriched up to 2% on the cathode of sulfate enrichment. Thermales are thermophilic (30–80 °C) and heterotrophic bacteria whose only four genera (Marinithermus, Oceanithermus, Rhabdothermus, and Vulcanithermus) are all retrieved in marine hydrothermal systems. They can grow under aerobic, microaerophilic and some anaerobic conditions with several inorganic electron acceptors such as nitrate, nitrite, Fe (III) and elemental sulfur38. All of the Thermales species can utilize the pyruvate as carbon and energy source with the sulfate or nitrate as electron acceptors.Pseudomonadales and Bacillales were found in the oxygen experiment. Most Pseudomonas are known to be aerobic and mesophilic bacteria, with a few thermophilic species (up to 65 °C)39,40. There have already been some reports of mesophilic Pseudomonas species growing in thermophilic conditions in composting environments41. Moreover, some Pseudomonas sp. are known to be electroactive in microbial fuel cells through long-distance extracellular electron transport42,43,44, and were dominant on the cathodes of a benthic microbial fuel cell on a deep-ocean cold seep45. In Bacillales, the Geobacillus spp. and some Bacillus sp. are known to be mainly (hyper)thermophilic aerobic and heterotrophic Firmicutes46.Hydrothermal electric current: a new energy source for the development of primary producersThe presence of so many heterotrophs in an initially autotrophic condition points to the hypothesis of a trophic relationship inside the electrotrophic community (Fig. 5). This suggests that the only autotrophs retrieved in all communities, the Archaeoglobales, might be the first colonizer of the electrode, using CO2 as carbon source and the cathode as energy source. Models using the REACT module of the Geochemist’s Workbench (GWB) and based on electron donor acceptor availability predicted low abundances of Archaeoglobales ( More