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Conceptualization of organismal to tumor biogeography through clone phylogeniesWe first map organismal biogeographic concepts and models to the process of migration and colonization of cancer cells during metastasis. Tumors are populations consisting of a diversity of cancer cells with different genetic profiles that may represent different lineages in the clone phylogeny. We use the example in Fig. 1, which contains a phylogeny of 17 clones found in one primary tumor (P) and four metastases (M1–M4). Events occurring along a branch in a phylogeny are anagenetic events, which include diversification, extinction, and expansion12,14. In organismal evolutionary biology, anagenetic events are not directly observed except through the fossil record. However, one can map the collection of genetic variants that likely arose on individual lineage in a phylogeny. In many cancers, sequencing of temporally sampled biopsies’ can directly reveal anagenetic events similar to the sequencing of ancient DNA in paleogenomics.The other types of evolutionary events in the phylogeny are cladogenetic, including genetic divergences and dispersals (Fig. 1). Genetic differences observed among species and populations are the key to detect cladogenetic events reconstructed in molecular phylogenies of living descendants. In cancer, temporal sampling of biopsies can reveal cladogenetic events that produced extinct descendants.In biogeography, genetic divergence results in the diversification of lineages within an area. Sometimes, the term duplication is used, but we avoid its further use because of the confusion it may cause in evolutionary genomics. Divergence events are also observed in a clone phylogeny, particularly when clone lineages diverge from each other within a tumor or across tumors. The exact opposite of genetic diversification can also be observed when lineages partially or fully disappear from the phylogeny. Extinction can occur due to random chance, selection, or environmental pressures. Even though extinction is rarely discussed in tumor clone phylogenetics, it happens frequently.Phylogenies also reveal movements of lineages between locations (geographic areas or body parts) when the locations of individual cells, species, or populations are known5,6,7,8,9,15. When lineages accumulate genetic differences along a branch in the phylogeny, and the evolved lineages migrate to a new area, we observe an expansion event. Expansions differ from dispersals in such that the growth of a population occurs in the same place. This movement of cells of a clone from one location to another, where they would potentially form a metastasis, results in the dispersal of  these cells of that clone to additional areas, which is modeled by a dispersal rate (d) in organismal biogeography. When a clone genetically diverges following its migration, then a distant dispersal event is said to have occurred. Similarly, when a clone diverges from the rest of the clones within a tumor and disperses to another tumor, we have observed an expansion event. Thus, clone phylogenies can give insights into the origin and trajectory of cancer cells between tumors.When a clone is no longer present at a location, it is extinct at that location. Extinctions are modeled by an extinction rate (e) in biogeographic models. As a result of extinction, the range of descendent clones on a phylogeny can be smaller than the ancestors. Biogeography models also have a parameter (J) to consider founder events that establish new populations from a few individuals. In phylogenies, founder events can be detected if only one or a few cells are found to have moved from one location to another to start diversifying in a new area. Both distant dispersal and founder events may result in forming a new colony of cells, i.e., a new metastasis in the case of cancer cell migrations. The primary distinction between dispersal and founder events is the relative number of migrating cells. Founder events are due to one or a few cells, whereas dispersal events involve a larger number of migrating cells. Founder events are expected to be more common in tumor evolution because metastases are thought to be formed by the spread of only one or a few cancer cells. These biogeographic events have been mathematically modeled and implemented in various approaches to infer species migration events12, which are directly applicable in the inference of cancer cell migrations between tumors.Model fitsWe began by analyzing the statistical fits of six biogeographic models (Table 1) to 80 computer-simulated tumor evolutionary datasets. Simulations enable us to assess the performance of computational approaches and reveal potential caveats associated with their use because the ground truth is known. These datasets were simulated using four main clone migration schemes defined by the different number of migrating clones (1–3), the small and large number of tumor areas (5–7 tumors, m5 datasets; 8–11 tumors, m8 datasets), and the different types of source areas of migration (primary or metastasis). The following seeding scenarios reflect this complexity of the clone migration schemes: monoclonal single-source seeding (mS), polyclonal single-source seeding (pS), polyclonal multisource seeding (pM), and polyclonal reseeding (pR) (see “Methods” section).Table 1 Phylogenetic and biogeographic events considered in seven biogeographic models used for analysis.Full size tableWe considered biogeographic models that weigh genetic divergence, dispersal/expansion, and extinction events differently (Table 1). We also explored the provision of including founder events in our models on the accuracy of detecting clone migrations. The parameterization of the aforementioned events results in models with two free parameters, i.e., dispersal rate (d) and extinction rate (e), and models with three free parameters by adding the founder-event speciation (J); see “Methods” section for more details.Overall, we tested six biogeographic models for their fit to the tumor data, three models with two free parameters and three others with three free parameters. BAYAREALIKE, DEC, and DIVALIKE models have two parameters each. They are nested within their respective models that add the founder effect, resulting in a model with three free parameters (hereinafter +J models). We used the BioGeoBEARS software for all model fit analyses. In data analysis, we first inferred phylogeny of cancer cell populations (clone phylogeny) using an existing method16, followed by the use of BioGeoBEARS to infer the clone migration history in which the clone phylogeny is used along with the location of tumor sites in which each clone is observed (Fig. 2). BioGeoBEARS estimates the probabilities of annotating internal nodes with tumor locations. These annotations are then used to derive cancer cell migration paths when two adjacent nodes are annotated with different tumor locations. In these analyses, we assumed the correct clone phylogeny because our focus was not assessing the impact of errors in a phylogeny on the accuracy of clone migration inferences. We also compared the accuracy of migration histories reconstructed using biogeographic models in BioGeoBEARS with those obtained from the approaches that do not model biogeographic processes (BBM9, MACHINA5, and PathFinder7).Figure 2Data analysis pipeline using BioGeoBEARS in R14 to infer clonal migration histories.Full size imageWe first conducted Likelihood Ratio Tests (LRTs) to examine the improvement offered by considering founder events in modeling tumor migrations. In this case, the fit of the BAYAREALIKE, DEC, and DIVALIKE models was compared to their +J counterparts, respectively. The null hypothesis was rejected for more than 50% of the datasets (BAYAREALIKE: 71.25%, DEC: 60%, and DIVALIKE: 53.75%; P  More

The Fur-regulated T6SS1 plays an important role in iron acquisition in C. necator
To explore the function of T6SS1 (Reut_A1713 to Reut_A1733) in C. necator (Fig. S1A), we analyzed the T6SS1 promoter and identified a Fur binding site (AGAAATA) upstream of gene reut_A1733. This Fur binding site was highly similar to the Fur-box reported in E. coli [38], with a probability score of 2.25 (out of a maximum score = 2.45) (Fig. S1B), which was calculated by applying the position weight matrix to a sequence [39]. Incubation of the T6SS1 promoter probe with purified Fur protein led to decreased mobility of the probe in the electrophoretic mobility shift assay, suggesting a direct interaction between Fur and the T6SS1 promoter (Fig. 1A). To further determine the function of Fur on the expression of T6SS1, a single-copy PT6SS1::lacZ fusion reporter was introduced into the chromosomes of C. necator wild-type (WT), Δfur deletion mutant, and the Δfur(fur) complementary strain. Compared to WT, the PT6SS1::lacZ promoter activity was significantly increased in the Δfur mutant (about 2.2-fold), and this increase could be restored by introducing the complementary plasmid pBBR1MCS-5-fur (Fig. 1B). Similar results were obtained by analyzing the expression of T6SS1 core component genes (hcp1, clpV1, vgrG1, and tssM1) with qRT-PCR (Fig. S1C). These results demonstrate that the expression of T6SS1 in C. necator is directly repressed by Fur, the master regulator of genes involved in iron homeostasis in many prokaryotes [40, 41].Fig. 1: Regulation of T6SS1 expression by Fur.A The interactions between His6-Fur and the T6SS1 promoter examined by EMSA. Increasing amounts of Fur (0, 0.03, 0.06, 0.13, 0.25, and 1.0 μM) and 10 nM DNA fragments were used in the assay. A 500 bp unrelated DNA fragment (Control A) and 1 µM BSA (Control B) were included in the assay as negative controls. B Fur represses the expression of T6SS1. β-galactosidase activities of T6SS1 promoter from chromosomal lacZ fusions in relevant C. necator strains were measured. C Iron uptake requires T6SS1. Stationary-phase C. necator strains were washed twice with M9 medium. Iron associated with indicated bacterial cells were measured with ICP-MS. The vector corresponds to the plasmid pBBR1MCS-5 (B) and pBBR1MCS-2 (C), respectively. Data are represented as mean values ± SD of three biological replicates, each with three technical replicates. **p  More

AffiliationsAnimal Production Systems group, Wageningen University & Research, Wageningen, The NetherlandsAbigail Muscat, Evelien M. de Olde, Raimon Ripoll-Bosch & Imke J. M. de BoerFarming Systems Ecology group, Wageningen University & Research, Wageningen, The NetherlandsHannah H. E. Van ZantenPublic Administration and Policy group, Wageningen University & Research, Wageningen, The NetherlandsTamara A. P. Metze & Catrien J. A. M. TermeerPlant Production Systems group, Wageningen University & Research, Wageningen, The NetherlandsMartin K. van IttersumAuthorsAbigail MuscatEvelien M. de OldeRaimon Ripoll-BoschHannah H. E. Van ZantenTamara A. P. MetzeCatrien J. A. M. TermeerMartin K. van IttersumImke J. M. de BoerCorresponding authorCorrespondence to
Imke J. M. de Boer. More

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. LewisBiology Department, Université Officielle de Bukavu, Bukavu, Democratic Republic of the CongoGérard Imani & Christian AmaniService of Wood Biology, Royal Museum for Central Africa, Tervuren, BelgiumWannes Hubau, Hans Beeckman & John T. MukendiDepartment of Environment, Laboratory of Wood Technology (Woodlab), Ghent University, Ghent, BelgiumWannes HubauUniversity of Jos, Jos, NigeriaIveren AbiemNigerian Montane Forest Project, Yelwa Village, NigeriaIveren Abiem & Hazel ChapmanDepartment of Geosciences and Geography, University of Helsinki, Helsinki, FinlandHari Adhikari, Janne Heiskanen & Petri PellikkaDepartment of Zoology, Faculty of Science, Charles University, Prague, Czech RepublicTomas AlbrechtInstitute of Vertebrate Biology, Czech Academy of Sciences, Brno, Czech RepublicTomas AlbrechtInstitute of Botany of the Czech Academy of Science, Třeboň, Czech RepublicJan Altman & Jiri DolezalCollege of Natural and Computational Science, Addis Ababa University, Addis Ababa, EthiopiaAbreham B. Aneseyee & Teshome SoromessaDepartment of Natural Resource Management, College of Agriculture and Natural Resource, Wolkite University, Wolkite, EthiopiaAbreham B. AneseyeeEuropean Commission, Joint Research Centre, Ispra, ItalyValerio AvitabileUK Centre for Ecology and Hydrology, Edinburgh, UKLindsay BaninUniversité du Cinquantenaire Lwiro, Département de sciences de l’environnement, Kabare, Democratic Republic of the CongoRodrigue BatumikeIsotope Bioscience Laboratory (ISOFYS), Ghent University, Ghent, BelgiumMarijn Bauters, Pascal Boeckx & Joseph OkelloPlant Systematic and Ecology Laboratory, Higher Teachers’ Training College, University of Yaoundé I, Yaoundé, CameroonSerge K. Begne, Vincent Droissart, Marie-Noel Kamdem, Murielle Simo-Droissart & Bonaventure SonkéInstitute of Tropical Forest Conservation, Mbarara University of Science and Technology, Mbarara, UgandaRobert BitarihoBiodiversity and Landscape Unit, Gembloux Agro-Bio Tech, Université de Liege, Liège, BelgiumJan BogaertInstitute for Geography, Friedrich Alexander University, Erlangen–Nuremberg, GermanyAchim Bräuning & Ulrike HiltnerDépartement de Eaux et Forêts, Institut Supérieur d’Agroforesterie et de Gestion de l’Environnement de Kahuzi-Biega (ISAGE-KB), Kalehe, Democratic Republic of the CongoFranklin BulonvuUN Environment World Conservation Monitoring Center (UNEP-WCMC), Cambridge, UKNeil D. 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

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