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Study siteThe study was carried out in post-agriculture forests at different growth stages near the forest reserve of Yoko (N00°17′; E25°18′; mean elevation 435 m a.s.l.), situated between 29 and 39 km south east of Kisangani, in the Democratic Republic of the Congo. We set up 15 (40 × 40 m) plots, set out in triplicate along five successional stages (15 plots): agriculture and 5, 12, 20, 60 years old secondary forest (respectively, 5 yrs, 12 yrs, 20 yrs, 60 yrs). Additionally, soils were also characterized in three agricultural plots (Ag). We interviewed owners, farmers, and local experts to determine the time-since-disturbance of all plots. Tree height measurements were recorded at the plot level for 20% of individuals of each diameter class. The climax vegetation in the region is classified as semi-deciduous tropical. Climate falls within the Af-type following the Köppen-Geiger classification33. Annual rainfall ranges from 1418 to 1915 mm with mean monthly temperatures varying from 23.7 to 26.2 °C. Throughout the year, the region is marked by a long and a short rainy season interrupted by two small dry seasons December–January and June–August. Soils in the region are highly weathered Oxisols, being poor in nutrients, with low pH and dominated by sandy texture.Sampling and sample analysisThroughfall and bulk precipitation was collected weekly using polyethylene (PE) funnels supported by a wooden pole of 1.5 m height to which a PE tube was attached and draining into 5 L PE container. A nylon mesh was placed in the neck of the funnel to avoid contamination by large particles. The container was buried in the soil and covered by leaves to avoid the growth of algae and to keep the samples cool. We installed eight throughfall collectors in each plot as two rows of four collectors, with approximately 8 m distance between all collectors. On every sampling occasion, the water volume in each collector was measured in the field, and recipients, funnels and mesh were replaced, rinsed with distilled water. A volume-weighted composite sample of the devices per plot was made. All samples were stored in a freezer immediately and sent in batch to Belgium for chemical analysis. The volume-weighted composite samples were first filtered using a nylon membrane filter of 0.45 µm before freezing. Total phosphorus was measured by inductively coupled plasma atomic emission spectroscopy (ICP AES, IRIS interpid II XSP, Thermo Scientific, USA). Although we acknowledge the potential for microbial activity in the collectors during a 1-week, dark, in situ storage of the samples, the use of total phosphorus concentration and lack of algal growth allow for complete phosphorus recovery.Following analysis, the samples from the replicate field sites per forest stage were pooled into ‘weekly’ forest-type samples, and these were subsequently analyzed for dissolved black carbon (DBC). In short, the pooled water samples were acidified to pH 2 and analyzed for dissolved organic carbon (DOC) concentration via high-temperature catalytic oxidation on Shimadzu TOC-L total organic carbon analyzer following established methodology34. DOC was isolated from the water samples by solid phase extraction (SPE) following Dittmar et al.35. Briefly, SPE cartridges (Varian Bond Elut PPL, 1 g, 6 mL) were conditioned sequentially with methanol, ultrapure water, and ultrapure water acidified to pH 2 using concentrated HCl, then passed through the SPE cartridges by gravity. SPE cartridges were dried under a stream of high-purity N2 gas. DOC was eluted from the SPE cartridge with methanol (SPE-DOC) and stored at −20 °C until further analysis. DBC was quantified using the benzenepolycarboxylic acid (BPCA) method as detailed in Wagner et al.20. The BPCA approach to quantifying DBC involves chemothermal oxidation of condensed aromatic DOC compounds to benzenehexacarboxylic acid (B6CA) and benzenepentacarboxylic acid (B5CA) products. The B6CA and B5CA oxidation products are robustly measured and derive exclusively from pyrogenic sources36. Condensed aromatic DBC, as measured using the BPCA method, is ubiquitous in aquatic environments globally21,37,38,39. DBC has also been quantified in throughfall and stemflow in longleaf pine forests that undergo regular prescribed burning40. Therefore, we use the BPCA method as a proxy for carbon inputs from biomass burning in the current study. To analyze our samples for BPCAs, aliquots of SPE-DOC (~0.5 mg C equivalents) were combined with concentrated HNO3 in flame-sealed glass ampoules and heated to 160 °C for 6 h. The resultant BPCA-containing residue was dried and re-dissolved in mobile phase for subsequent analysis. Individual BPCAs were separated and quantified using an HPLC system (UltiMate 3000, Thermo Fisher, Germany) (CV  More

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A diverse and active DNA virosphereWe first leveraged two existing metagenomes that were constructed from the Konza native prairie soil14,15 to screen for viral sequences at the site. Each of the metagenomes was obtained from a composite of all the replicate soils collected at ambient field moisture conditions. One of the metagenomes was de novo assembled from deep sequence data (1.1 Tb)14 and the second was a hybrid assembly of short and long reads (267.0 Gb)16. The combination of the two metagenomes was used to maximize the coverage of viral sequences from the Konza prairie site. To balance between the detection limits of the viral detection tools and the wide range of viral genome size, the viral contigs > 2.5 kb in length were combined with those obtained from screening of the two largest public viral databases (i.e., IMG/VR17 and NCBI Virus16) to further increase the coverage of DNA viral sequences. We acknowledge that the length cutoff of 2.5 kb would preclude detection of some ssDNA viruses with small segmented genome sizes (e.g., Nanoviridae18). As a result, a DNA viral database for the site was curated that included 726,108 de-replicated viral contigs. The DNA viral database then served as a scaffold for mapping of metatranscriptome and metaproteome datasets to determine the activities of soil DNA viruses and their responses to differences in soil moisture. This approach was also recently applied to detect the transcriptional activity of marine prokaryotic and eukaryotic viruses19,20,21,22 and giant viruses in soil5.The metatranscriptome reads from both wet and dry treatments were mapped to a total of 416 unique DNA viral contigs using stringent criteria (% sequence identity > 95% and % sequence coverage > 80%). The 416 DNA viral contigs with an average sequence length of 19 kb were highly diverse and grouped into 139 clusters, with 111 of the clusters being singletons (Supplementary Data 1).We aimed to assign putative host taxa to the viral clusters by combining several approaches: CRISPR spacer matching, and screening for host and viral sequence similarities to respective databases (details in ‘Methods’). As a result, we assigned putative viral host taxa to 160 out of the 416 transcribed DNA viral contigs. Some of these were assigned to more than one host (Supplementary Data 1), resulting in a total of 181 virus–host pairings (Fig. 1a). Of these, 79 host–virus pairs were detected only in the dry soil treatment, 51 were only in the wet soil treatment, and an additional 51 were found in both dry and wet treatments (Fig. 1a). Consistent with previous reports4, the majority of the transcribed DNA viral contigs were annotated as bacteriophage sequences. Different sets of transcribed DNA viral contigs were unique to wet or dry soils and assigned to specific hosts at the phylum level, whereas others were shared (Fig. 1a). However, the dominant soil taxa, i.e., Proteobacteria and Actinobacteria that were previously identified by 16S rRNA gene sequencing in this soil environment, were predicted as hosts under both wet and dry conditions (Supplementary Fig. 1a). Eukaryotic DNA viruses, such as Bracovirus and Ichnovirus belonging to a family of insect viruses within the Polydnaviridae family, were also transcribed in the soils (Fig. 1a and Supplementary Data 1). Most of these insect viruses were only detected in dry soil conditions. These differences in virus–host pairings suggest that some of the respective hosts were impacted differently by the dry and wet incubation conditions.Fig. 1: Transcribed DNA viral communities and their responses to wet and dry soil conditions.a An alluvium plot that illustrates pairings of the transcribed DNA viral contigs to putative host phyla. The transcribed DNA viral community was comprised of viral contigs from the curated DNA viral databases that were mapped by quality-filtered metatranscriptomic reads. The alluvia are colored by host taxa (first x axis of each sub-panel) assigned to respective transcribed DNA viral contigs (second x axis of each sub-panel). b A Venn diagram showing the number of unique transcribed DNA viral contigs detected in both wet and dry soils and ones exclusively detected in one of the soils. c Number of unique DNA viral contigs detected. A t-Test shows significantly more DNA contigs were transcribed in dry soil (p = 0.044). d Number of transcripts that mapped to the DNA viral contigs. For panels (c) and (d), the two independent field sites of Konza Experimental Field Station are indicated as site A (circles) and site C (triangles), with the wet soil in blue and dry soil in red.Full size imageThere were 21 DNA viral contigs that were assigned to hosts across multiple bacterial phyla suggesting the presence of viral generalists1,23 (Supplementary Data 1). We recognize that host assignment based on CRISPR spacer matching, however, is limited to detection of recent or historical virus–host interactions that were captured at the time of sampling24. As bioinformatics assignment of virus–host linkages only suggests possible pairings based on sequence features, there are also chances of introducing false positives. However, we applied the most stringent criteria possible to provide confident host assignments.Increased activity of a subset of DNA viruses in wet soilSoil moisture has a strong influence on the community structures of transcribed DNA viruses. The majority of the transcriptionally active DNA viral contigs were unique to wet or dry conditions, with only 111 viral contigs (~ 26.7%) detected in both wet and dry soils, suggesting that the different soil moisture conditions may shape the activity of the DNA viral community differently (Fig. 1b). Interestingly, although a significantly higher number of transcribed DNA viral contigs were detected in dry soils (Fig. 1b, c), the levels of transcriptional activity were significantly higher (based on the normalized abundance of RNA reads that mapped to the viral contigs) for DNA viruses in wet soils irrespective of sampling site location (Fig. 1d). DNA viral contigs with mapped transcripts could represent either prophages that are passively replicated along with their host genomes, or (lytic) viruses that are actively regulating early/middle/late expression of viral gene clusters25. In soil, a lysogenic lifestyle is considered to be an adaptive strategy for viruses to cope with long periods of low host activity26,27. Therefore, the 1.5-fold increase in the number of transcribed DNA viral contigs representing transcriptionally active DNA viruses, but with lower levels of overall transcription, in dry soil suggests that the increase was due to a higher prevalence of lysogeny in dry conditions. This hypothesis is strengthened by our finding of a 20-fold increase in transcripts for lysogenic markers (i.e., integrase and excisionase) in one of our replicates (A-2) in dry compared to wet conditions (Supplementary Data 2). High number of lysogenic phages were also previously reported in dry Antarctic soils using a cultivation-independent induction assay28. By contrast, under wet soil conditions we found a 2-fold increase in transcription of fewer viral contigs representing a subset of DNA viruses, suggesting that those viruses were more transcriptionally active in response to higher soil moisture. In addition, there was a higher correlation between prokaryotic abundances, as estimated by 16S rRNA gene sequencing, with DNA viral transcript counts in wet soils (R2 = 0.593, Supplementary Fig. 1d) in comparison to dry soils (R2 = 0.069, Supplementary Fig. 1d), supporting this hypothesis.We then identified which soil DNA viruses were most transcriptionally active and how they responded to the differences in soil moisture. As the majority of the transcribed DNA viral contigs (97%) were environmental viruses with unclassified taxonomy assignment, we were not able to calculate the taxonomic abundance of each and instead compared the differential abundances of the transcribed viral contigs. There were four DNA viral contigs with significantly different levels of transcription under wet and dry conditions (VC_1, VC_19, VC_282, VC_412; Fig. 2a). Contigs VC_1 and VC_19 correspond to unclassified viral contigs deposited in IMG/VR (identifiers of ‘REF:2547132004_2547132004’ and ‘3300010038_Ga0126315_10000854’) that were previously detected in metagenomes from the Rifle site29 and from serpentine soil in the UC McLaughlin Reserve30, respectively. Contigs VC_282 and VC_412 were extracted from our Kansas metagenomes. Contigs VC_1 and VC_19 had significantly higher levels of transcriptional activity in wet soils compared to dry soils (p  More

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Department of Environment and Geography, University of York, York, UKAida Cuni-Sanchez, Philip J. Platts, Rob Marchant & Andrew MarshallDepartment of International Environmental and Development Studies (NORAGRIC), Norwegian University of Life Sciences, Ås, NorwayAida Cuni-SanchezDepartment of Natural Sciences, Manchester Metropolitan University, Manchester, UKMartin J. P. SullivanSchool of Geography, University of Leeds, Leeds, UKMartin J. P. Sullivan, Simon L. Lewis, Serge K. Begne, Amy C. Bennett, Martin Gilpin, Jon Lovett & Oliver L. PhillipsLeverhulme Centre for Anthropocene Biodiversity, University of York, York, UKPhilip J. PlattsClimate Change Specialist Group, Species Survival Commission, International Union for Conservation of Nature, Gland, SwitzerlandPhilip J. PlattsDepartment of Geography, University College London, London, UKSimon L. 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BurgessComputational and Applied Vegetation Ecology (CAVElab), Faculty of Bioscience Engineering, Ghent University, Ghent, BelgiumKim Calders & Hans VerbeeckDepartment of Anthropology, George Washington University, Washington DC, USAColin ChapmanSchool of Life Sciences, University of KwaZulu-Natal, Pietermaritzburg, South AfricaColin ChapmanShaanxi Key Laboratory for Animal Conservation, Northwest University, Xi’an, ChinaColin ChapmanInternational Centre of Biodiversity and Primate Conservation, Dali University, Dali, ChinaColin ChapmanUniversity of Canterbury, Canterbury, New ZealandHazel ChapmanInventory and Monitoring Program, National Park Service, Fredericksburg, VA, USAJames ComiskeyUniversity of Ghent, Ghent, BelgiumThales de HaullevilleWorld Agroforestry (ICRAF), Nairobi, KenyaMathieu DecuyperLaboratory of Geo-Information Science and Remote Sensing, Wageningen University, Wageningen, The NetherlandsMathieu Decuyper & Martin HeroldGeography, Environment and Geomatics, University of Guelph, Guelph, Ontario, CanadaBen DeVriesFaculty of Science, University of South Bohemia, České Budějovice, Czech RepublicJiri DolezalAMAP Lab, Université de Montpellier, IRD, CNRS, INRAE, CIRAD, Montpellier, FranceVincent DroissartFaculté de Gestion de Ressources Naturelles Renouvelables, Université de Kisangani, Kisangani, Democratic Republic of the CongoCorneille Ewango & Janvier LisingoCollege of Development Studies, Addis Ababa University, Addis Ababa, EthiopiaSenbeta FeyeraDendrochronology Laboratory, World Agroforestry Centre (ICRAF), Nairobi, KenyaAster GebrekirstosMissouri Botanical Garden, St Louis, MO, USARoy GereauDepartment of Biology, University of Burundi, Bujumbura, BurundiDismas HakizimanaSmithsonian Institution Forest Global Earth Observatory (ForestGEO), Smithsonian Tropical Research Institute, Washington DC, USAJefferson Hall & David KenfackKunming Institute of Botany, Kunming, ChinaAlan HamiltonUniversité Libre de Bruxelles, Brussels, BelgiumOlivier HardyDivision of Vertebrate Zoology, Yale Peabody Museum of Natural History, New Haven, CT, USATerese HartInstitute for Atmospheric and Earth System Research, Faculty of Science, University of Helsinki, Helsinki, FinlandJanne HeiskanenDepartment of Plant Systematics, University of Bayreuth, Bayreuth, GermanyAndreas HempHelmholtz Center Potsdam GFZ German Research Centre for Geosciences, Section 1.4 Remote Sensing and Geoinformatics, Potsdam, GermanyMartin HeroldHelmholtz-Centre for Environmental Research (UFZ), Leipzig, GermanyUlrike HiltnerDepartment of Ecology, Faculty of Science, Charles University, Prague, Czech RepublicDavid Horak & Ondrej SedlacekInternational Gorilla Conservation Programme, Musanze, RwandaCharles Kayijamahe & Eustrate UzabahoDepartment of Natural Resources, Karatina University, Karatina, KenyaMwangi J. KinyanjuiDepartment of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, CO, USAJulia KleinEco2librium LLC, Boise, ID, USAMark LungDepartment of Ecology, Université de Kisangani, Kisangani, Democratic Republic of the CongoJean-Remy MakanaEnvironmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, UKYadvinder MalhiTropical Forests and People Research Centre, University of the Sunshine Coast, Sippy Downs, Queensland, AustraliaAndrew Marshall & Alain S. K. NguteFlamingo Land Ltd, Malton, UKAndrew MarshallCollege of African Wildlife Management, Mweka, TanzaniaEmanuel H. MartinSchool of GeoSciences, University of Edinburgh, Edinburgh, UKEdward T. A. Mitchard & Charlotte WheelerDepartment of Geography and Environmental Sciences, University of Dundee, Dundee, UKAlexandra MorelIndependent Botanist, Harare, ZimbabweTom MullerDepartment of Horticultural Sciences, Faculty of Applied Sciences, Cape Peninsula University of Technology, Bellville, South AfricaFelix NchuBiology Department, University of Rwanda, Kigali, RwandaBrigitte Nyirambangutse & Etienne ZiberaDepartment of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, SwedenBrigitte Nyirambangutse & Göran WallinMountains of the Moon University, Fort Portal, UgandaJoseph OkelloNational Agricultural Research Organisation, Mbarara Zonal Agricultural Research and Development Institute, Mbarara, UgandaJoseph OkelloSchool of Biological Sciences, University of Southampton, Southampton, UKKelvin S.-H. PehConservation Science Group, Department of Zoology, University of Cambridge, Cambridge, UKKelvin S.-H. PehState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaPetri PellikkaKey Biodiversity Areas Secretariat, BirdLife International, Cambridge, UKAndrew PlumptreSchool of Life Sciences, University of Lincoln, Lincoln, UKLan QieDepartment of Biology, University of Florence, Sesto Fiorentino, ItalyFrancesco RoveroTropical Biodiversity Section, Museo delle Scienze, Trento, ItalyFrancesco RoveroTropical Plant Exploration Group (TroPEG), Mundemba, CameroonMoses N. SaingeCenter for Development Research (ZEF), University of Bonn, Bonn, GermanyChristine B. SchmittConservation and Landscape Ecology, University of Freiburg, Freiburg, GermanyChristine B. SchmittApplied Biology and Ecology Research Unit, University of Dschang, Dschang, CameroonAlain S. K. NguteForest Ecology and Forest Management Group, Wageningen University, Wageningen, The NetherlandsDouglas SheilWater and Land Resources Center, Addis Ababa University, Addis Ababa, EthiopiaDemisse ShelemeAfrican Wildlife Foundation (AWF), Biodiversity Conservation and Landscape Management Program, Simien Mountains National Park, Debark, EthiopiaTibebu Y. SimegnFaculty of Forestry, University of British Columbia, Vancouver, British Columbia, CanadaTerry SunderlandCenter for International Forestry Research (CIFOR), Bogor, IndonesiaTerry SunderlandDepartment of Forest Ecology, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences, Prague, Czech RepublicMiroslav SvobodaDepartment of Plant Biology, Faculty of Sciences, University of Yaoundé I, Yaoundé, CameroonHermann TaedoumgBioversity International, Yaoundé, CameroonHermann TaedoumgUK Research and Innovation, London, UKJames TaplinDepartment of Geography, National University of Singapore, Singapore, SingaporeDavid TaylorInstitute of Forestry and Conservation, University of Toronto, Toronto, Ontario, CanadaSean C. ThomasBiodiversity Foundation for Africa, East Dean, UKJonathan TimberlakeForestry Development Authority of the Government of Liberia (FDA), Monrovia, LiberiaDarlington TuagbenSchool of Forestry and Environmental Studies, Yale University, New Haven, CT, USAPeter UmunayDepartment of Biological Sciences, Florida International University, Miami, FL, USAJason VleminckxSchool of Natural Sciences, University of Bangor, Bangor, UKSimon WillcockRothamsted Research, Harpenden, UKSimon WillcockUniversity of Liberia, Monrovia, LiberiaJohn T. WoodsA.C.-S. conceived the study and assembled the AfriMont dataset. A.C.-S. and M.J.P.S. analysed the plot data (with contributions from S.L.L.) and wrote the manuscript. P.J.P. analysed forest extents and contributed to writing. S.L.L. conceived and managed the AfriTRON forest plot recensus programme. E.T.A.M. and V.A. helped compare plot data with remote sensing carbon maps. All co-authors read and approved the manuscript. More

The results are organized into subsections on in situ temperature profiles, geochemical gradients, and microbial community data. Geochemical data include concentration and isotopic data of dissolved electron acceptors (sulfate, dissolved inorganic carbon (DIC), δ13C-DIC), electron donors (methane, sulfide, SCOAs), and respiration end products (DIC, methane, sulfide), as well as solid-phase organic carbon pools (total organic carbon (TOC), δ13C-TOC, total nitrogen (TN), TOC:TN (C:N)). Microbial community data include bacterial and archaeal 16S rRNA gene copy numbers and bacterial and archaeal community trends. All geochemical and microbiological data are shown in Supplementary Data 1–4.Temperature profilesThe in situ temperatures and temperature gradients differ greatly among sites and hydrothermal areas (Table 1; Fig. 1a, b, 1st column). Certain locations within the SA (Cold Site) and NSA (MUC02, GC13, MUC12) are uniformly cold (~3–5 °C) and thus serve as low-temperature control sites. The fact that Cold Site has no measurable depth-dependent temperature increase suggests that this site, despite being located within the SA, only has minimal hydrothermal fluid seepage. At two sites from the NSA (GC09, GC10), temperatures increase strongly, reaching ~60 °C at 400 cm below the seafloor, with temperature gradients becoming linear below 50 cm. Everest Mound, Orange Mat, and Cathedral Hill in the SA have the steepest temperature gradients ( >165 °C m−1), reaching >80 °C within 25 cm, whereas Yellow Mat from the SA only reaches ~27 °C at 45 cm. Temperature gradients are near-linear at Everest Mound, Cathedral Hill, and Yellow Mat, and in the top ~15 cm of Orange Mat. Below ~15 cm, the temperatures at Orange Mat are nearly constant.Table 1 Overview of all sampling sites.Full size tableFig. 1: Microbial abundance and community structure in relation to temperature and geochemical gradients.Depth profiles of temperature (1st column), porewater dissolved sulfate, methane, and dissolved inorganic carbon (DIC) concentrations (2nd column), bacterial and archaeal 16S rRNA gene abundances (3rd column), bacterial (4th column) and archaeal community structure (5th column) across the 10 study sites. a Sites from the NSA. b Sites from the SA. Bacteria and Archaea community structure is shown at the phylum level, except in Proteobacteria, which are displayed at the class level (see asterisk). To improve visibility, we adjusted the depth axis range for bacterial and archaeal communities at Everest Mound, only showing the top 10 cm, where microbial 16S rRNA genes were above detection. Sulfate and methane data from the NSA, except those from MUC12, were previously published27.Full size imageConcentrations of methane, sulfate, sulfide, and DICPorewater concentration profiles of methane, sulfate and DIC are consistent with higher microbial activity and higher substrate supplies in hydrothermal seep sediments compared to cold control sites or hydrothermal non-seep sediments.Independent of temperature, sediments without fluid seepage, i.e. the hydrothermal NSA sites (GC09, GC10) and low-temperature control sites (MUC02, MUC12, GC13, Cold Site), have similar concentration profiles of sulfate, methane, and DIC (Fig. 1a, b, 2nd column). Methane remains at background concentrations (≤0.02 mM), suggesting minimal methane production. DIC concentrations increase with depth by ~1–2 mM relative to seawater values (~2 mM). Sulfate decreases but remains near seawater values (~28 mM) throughout MUC02, MUC12, and the hydrothermal GC10, but drops more clearly toward the bottom of the hydrothermal GC09 (to 26.4 mM) and the cold GC13 (to 23.8 mM). The only minor deviation is Cold Site from the SA. At this site, sulfate and DIC concentrations change more with depth (sulfate drops to 23.6 mM; DIC increases to 6.2 mM), suggesting higher microbial activity relative to all hydrothermal and control sites within the NSA. Consistent with this interpretation sulfide (HS−) concentrations increase strongly with depth at Cold Site (from 2500 to 6200 µM) but not at the NSA sites, where sulfide concentrations remain much lower (0–52 µM (Supplementary Fig. 1). Furthermore, δ13C-DIC decreases with sediment depth at Cold Site (from −3.3‰ to −10.3‰), suggesting strong input of DIC from organic carbon mineralization (Supplementary Fig. 2). By contrast, δ13C-DIC remains close to seawater values (~0‰) throughout sediments of all NSA sites (−1.7‰ to −0.2‰).Compared to all NSA sites and Cold Site, sulfate, methane, and DIC concentrations are more variable at the seep sites Yellow Mat, Cathedral Hill, Orange Mat, and Everest Mound (Fig. 1b, 2nd column). Methane concentrations at Yellow Mat, Cathedral Hill, and Orange Mat are much higher than at the non-seep sites, reaching 3.3, 5.2, and 2.8 mM, respectively (no data from Everest Mound). These high methane concentrations, which can be mainly attributed to the input of thermogenic methane from below24, almost certainly underestimate in situ concentrations due to outgassing during core retrieval. Sulfate concentrations decrease more strongly with depth than at the NSA sites or Control Site, consistent with previously observed high sulfate-reducing activity6,7 and advection of sulfate-depleted fluid from below29. Nonetheless, sulfate concentrations remain in the millimolar range throughout cores from Yellow and Orange Mat. By contrast, sulfate is below detection (≤0.1 mM) at ≥4.5 cm sediment depth at Everest Mound, and in an intermittent depth interval at Cathedral Hill (~7.5–19.5 cm), below which it increases back to ~6 mM. High, i.e. millimolar, concentrations of sulfide at Orange Mat and Cathedral Hill are consistent with high rates of in situ microbial sulfate reduction and advective input of sulfide from the thermochemical reduction of sulfate in hotter, abiotic layers below (Supplementary Fig. 1). DIC concentrations reach values of >10 mM at Orange Mat, Cathedral Hill, and Yellow Mat (no data from Everest Mound). DIC concentrations fluctuate around 20 mM DIC throughout the core from Cathedral Hill, suggesting high DIC input from deeper layers. C-isotopic values of this DIC are close to those of seawater (~−3‰), suggesting an inorganic source. By contrast, surface sedimentary DIC concentrations at Yellow Mat and Orange Mat are close to seawater values but increase with depth to ~20 and ~14 mM, respectively. Lower δ13C-DIC values in surface sediments, which decrease further to values of ~−20‰ to −24‰ at Yellow Mat and −14‰ to −18‰ at Orange Mat within the top 10–20 cm, suggest that most of this DIC comes from the microbial or thermogenic breakdown of organic matter and/or the microbial anaerobic oxidation of methane.Trends in dissolved SCOAs across locationsPorewater concentration profiles of SCOAs are consistent with higher input of reactive organic carbon substrates to hydrothermal seep sediments compared to cold control sites or hydrothermal non-seep sediments.SCOA concentrations at the cold control sites and hot NSA sites are low, showing no clear depth-related trends, consistent with absence of SCOA input from below and/or biological controlled SCOA concentrations. SCOAs are dominated by acetate (cold MUC02, MUC12, and GC13: 1–3 µM; hydrothermal GCs: 3–6 µM; Cold Site: 1–7 µM), which was detected along with formate, propionate, and lactate (Fig. 2).Fig. 2: Depth profiles of short-chain organic acid (SCOA) concentrations across locations.Note the differences in concentration ranges on the x-axis and depth ranges on the y-axis (Cathedral Hill: 0–50 cm; GC13, GC09, and GC10: 0–500 cm; all others: 0–40 cm).Full size imageBy contrast, SCOA concentrations at all hydrothermal seep sites except Orange Mat, increase with depth and temperature, consistent with a thermogenic source below the cored interval. At Yellow Mat, acetate concentrations are already elevated at the seafloor (32 µM) and increase to >100 µM at 20 cm depth. Cathedral Hill has a similar acetate concentration profile, but reaches even higher concentrations (250 µM). At the hottest site, Everest Mound, acetate concentrations increase from ~150 µM at the seafloor to steady concentrations of ~600 µM below 3 cm. Formate concentrations are also (locally) elevated at Yellow Mat (5-8 µM), Cathedral Hill (to 14 µM), and Everest Mound (94-265 µM), and propionate concentrations reach high values at Cathedral Hill (to 21.8 µM) and Everest Mound (to 125 µM). The only exception among the seep sites is Orange Mat, where acetate is only slightly elevated (10–20 µM), and formate and propionate remain at background concentrations. These concentrations suggest that either thermogenic SCOA input from below is low at this site, or SCOA concentrations are biologically controlled throughout the core. Unlike the other three SCOAs, lactate concentrations remain low at all seep sites, apart from one outlier at Cathedral Hill (34.5 cm: 17.3 µM), suggesting that lactate is not a major product of thermogenic organic matter breakdown.Trends in solid-phase organic matter poolsAll sites have similar δ13C-TOC isotopic compositions, with values ranging from −19‰ to −23‰, consistent with a predominant phytoplankton origin of sedimentary organic carbon (Supplementary Fig. 3). Yet, depth profiles of TOC and TN follow different patterns across the locations (Fig. 3). All cold control sites have similar TOC (~2–4 wt%) and TN contents (~0.3–0.6 wt%), with slight decreases in values from the seafloor downward. Compared to cold controls, GC09 and GC10 have lower TOC and TN contents (TOC: ~0.5–3 wt%; TN: ~0.0–0.3 wt%), in particular in deeper horizons with elevated temperatures. Seep sites within the SA have the widest ranges. Seep sites have higher TOC in surface sediment compared to control sites, suggesting net organic carbon assimilation and synthesis by microbial growth. TOC values are 16 wt% at the seafloor of Orange Mat and 6–7 wt% at the seafloor of the other three locations, and then decrease strongly within the top 10 cm, reaching values similar to those of cold sites or hot NSA sites below 10 cm. TN values in surface sediments of seep sites are generally higher than at control sites (~0.7–0.9 wt%), providing additional evidence of net organic matter synthesis by microbial biomass production, but then decrease steeply to values that are similar to those at hot NSA sites.Fig. 3: Carbon and nitrogen contents of bulk organic matter.Depth profiles of total organic carbon (TOC), total nitrogen (TN), and TOC:TN (C:N) across all sites.Full size imageAs a result of the stable TOC and TN trends, C:N does not change much with depth at the cold locations. Yet, while C:N ranges around 4.4–5.6 at Cold Site, values are considerably higher, around 8.1–10.1, at cold locations within the NSA. By comparison, the hot NSA sites and all seep sites except Orange Mat show increases in C:N with increasing temperature and depth. This increase in C:N is modest, from ~8 to 10 at Yellow Mat, and more pronounced at the hotter GC09 (to 15.9), GC10 (to 13.4), Cathedral Hill (to 14.6), and Everest Mound (to 15.7). Orange Mat has the highest C:N ratios (14.8–26.5), and unlike the other sites does not show an increase in C:N with depth.General trends in bacterial and archaeal 16S rRNA gene copy numbers16S rRNA gene copy numbers indicate distinct trends in bacterial and archaeal abundances that follow temperature increases with sediment depth (Fig. 1a and b, 3rd column).At the four cold locations, bacterial and archaeal gene copy numbers are relatively stable with depth (Bacteria: 108−109 g−1; Archaea: 107−108 g−1). By comparison, gene copy numbers of GC09 and GC10 are in a similar range near the seafloor but decrease strongly with depth. While Archaea are quantifiable throughout both cores to ≤103 gene copies g−1 sediment, bacterial gene copy numbers are not reliably distinguishable from extraction negative controls (~1 × 104 g−1) at temperatures >60 °C. Furthermore, unlike the cold sites, which consistently have higher bacterial gene copy numbers, there is a shift from bacterial to archaeal dominance in gene copy numbers (GC09: at ~50 cm; GC10: at ~150 cm) at both hot NSA sites.Compared to the hot GCs from the NSA, gene copies decrease over much shorter distances at sites with fluid seepage in the SA. This decrease in gene copy numbers appears related to the magnitude of the temperature increase with depth. At Yellow Mat, which only reaches moderately warm temperatures (27 °C), copy numbers of both domains decrease from ~108 g−1 at the seafloor to ~106 g−1 at the bottom of the core. While Orange Mat, Cathedral Hill, and Everest Mound have similar bacterial and archaeal gene copy numbers to Yellow Mat at the seafloor, these values drop off much more steeply with depth, matching the much steeper temperature increases. At Cathedral Hill and Everest Mound, Bacteria could not be reliably detected below 20 and 7.5 cm, respectively. As the only location, the detection limit of archaeal 16S gene sequences was reached at Everest Mound, at a depth of 9.5 cm.Relationships between microbial gene abundances and temperatureWe explored the relationship between 16S rRNA gene copy number and temperature further (Fig. 4a, b). While gene copy numbers of both domains generally decrease with increasing temperature, the shape of this temperature relationship differs between both domains. In bacteria the decrease in gene copy numbers in relation to temperature is nearly linear. By contrast, in Archaea gene copy numbers follow hump-shaped distributions, i.e. they remain stable or only decrease slightly up to a certain temperature threshold, beyond which their copy numbers decrease steeply. This apparent thermal threshold varies between sites, i.e. it is ~85 °C at Orange Mat, ~70 °C at Cathedral Hill, ~50 °C at the NSA sites, and ~20 °C at Everest Mound.Fig. 4: Gene copy trends in relation to temperature.a Bacterial and (b) archaeal 16S rRNA gene copy numbers vs. temperature. c Bacteria-to-Archaea 16S rRNA gene copy ratios vs. temperature (the exponential function and its coefficient of determination (R2), both calculated in Microsoft Excel, are shown in the graph). Symbol sizes indicate the sediment depth of each sample. Cold control sites from both locations are grouped together in the legend for easier viewing.Full size imageThe differences in relationships between bacterial and archaeal gene copy numbers and temperature are reflected in Bacteria-to-Archaea gene copy ratios (Fig. 4c). Bacterial always exceed archaeal gene copies at 45 °C. Between 10 and 45 °C, domain-level gene dominance varies with location. Despite the variability, Bacteria-to-Archaea gene copy ratios follow a highly significant, exponential relationship with temperature (R2 = 0.67, p  More