Persistent Petroleum Pollution: Microbial Responses in Bunger Hills, East Antarctica

doi:10.21203/rs.3.rs-6608817/v1

Persistent Petroleum Pollution: Microbial Responses in Bunger Hills, East Antarctica

2025 · doi:10.21203/rs.3.rs-6608817/v1

preprint OA: closed CC-BY-4.0

📄 Open PDF Full text JSON View at publisher

⚙ AI-generated summary by claude@2026-07, 2026-07-17 ⓘ

Petroleum pollution in East Antarctica's Bunger Hills significantly altered microbial communities, reducing hydrogen oxidation but increasing carbon fixation rates, indicating long-lasting ecosystem impacts.

One-sentence paraphrase of the abstract; not a substitute for reading it. No clinical advice. How this works

⚙ AI-generated deep summary by claude@2026-07, 2026-07-17 · read from full text ⓘ

This study examined how a 40-year legacy petroleum spill near the Australian Edgeworth David Base affects soil microbial diversity and functions in Bunger Hills, East Antarctica, using metagenomic analyses (16S/18S amplicons and shotgun metagenomics), soil physicochemistry measurements, and gas chromatography plus hydrogen-oxidation and 14CO2 fixation assays across transects and a helipad-associated zone. Metagenomic and community analyses showed significant shifts in microbiome composition and function in contaminated soils, alongside severely reduced hydrogen oxidation rates but higher 14CO2 carbon fixation rates, and reconstruction of 300 metagenome-assembled genomes with 25 novel candidate bacterial/archaeal species. Key environmental caveats included soil heterogeneity and the fact that several contaminated samples were collected from deeper subsurface layers, limiting separation of contamination versus depth effects. This paper is centrally about endometriosis and/or adenomyosis only in the sense that it is included in the corpus via keyword match; it does not explicitly discuss endometriosis or adenomyosis.

Read from the paper's body, not the abstract. Not a substitute for reading the paper. No clinical advice. How this works

Abstract

Abstract Microbes in hyper-arid and oligotrophic ecosystems like Antarctica, rely on atmospheric trace gas oxidation for survival using high-affinity enzymes to generate energy for critical ecological processes including primary production, persistence, and carbon mitigation. Hydrocarbon contamination, common around Antarctic research stations, disrupts microbial communities, yet its implications for trace gas oxidation and dark carbon fixation are unknown. Here, we show the soil microbial diversity of Bunger Hills, East Antarctica, and assessed the response of a 40-year-old legacy petroleum spill on microbial communities and their functions. Metagenomic analysis and gas chromatography revealed significant shifts in microbiome composition and function in contaminated soils alongside severely reduced hydrogen oxidation rates, but higher ( 14 CO 2 ) carbon fixation rates. We assembled 300 metagenome-assembled genomes across 16 bacterial and archaeal phyla, identifying 25 novel candidate species. We demonstrate long-lasting effects of pollutants on microbial ecosystems and services in polar regions, highlighting the role of trace gas scavengers and hydrocarbon degraders in regulating key ecological functions and advancing knowledge of anthropogenic impacts on microbial nutrient and energy acquisition in dry desert environments.

Full text 255,120 characters · extracted from preprint-html · click to expand

Persistent Petroleum Pollution: Microbial Responses in Bunger Hills, East Antarctica | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Persistent Petroleum Pollution: Microbial Responses in Bunger Hills, East Antarctica Kellynn K. Y. Tan, Xabier Vázquez-Campos, Gwilym A. V. Price, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6608817/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Feb, 2026 Read the published version in Communications Earth & Environment → Version 1 posted You are reading this latest preprint version Abstract Microbes in hyper-arid and oligotrophic ecosystems like Antarctica, rely on atmospheric trace gas oxidation for survival using high-affinity enzymes to generate energy for critical ecological processes including primary production, persistence, and carbon mitigation. Hydrocarbon contamination, common around Antarctic research stations, disrupts microbial communities, yet its implications for trace gas oxidation and dark carbon fixation are unknown. Here, we show the soil microbial diversity of Bunger Hills, East Antarctica, and assessed the response of a 40-year-old legacy petroleum spill on microbial communities and their functions. Metagenomic analysis and gas chromatography revealed significant shifts in microbiome composition and function in contaminated soils alongside severely reduced hydrogen oxidation rates, but higher ( 14 CO 2 ) carbon fixation rates. We assembled 300 metagenome-assembled genomes across 16 bacterial and archaeal phyla, identifying 25 novel candidate species. We demonstrate long-lasting effects of pollutants on microbial ecosystems and services in polar regions, highlighting the role of trace gas scavengers and hydrocarbon degraders in regulating key ecological functions and advancing knowledge of anthropogenic impacts on microbial nutrient and energy acquisition in dry desert environments. Biological sciences/Microbiology/Microbial communities/Microbial ecology Biological sciences/Microbiology/Environmental microbiology/Soil microbiology Earth and environmental sciences/Biogeochemistry Earth and environmental sciences/Ecology/Microbial ecology Earth and environmental sciences/Ecology/Climate-change ecology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Ubiquitous and highly permeable atmospheric trace gases such as molecular hydrogen (H 2 ) is a major energy source for polar desert microorganisms 1 – 3 , complementing organoheterotrophy for survival under low moisture and oligotrophic conditions 4 – 6 . The low abundance of photosynthetic organisms and absence of sunlight in the austral winter leaves chemolithoautotrophy via the Calvin-Benson-Bassham (CBB) cycle an important pathway to support polar communities 6 – 9 . Emerging evidence shows a growing diversity of microbes that oxidise atmospheric H 2 using high-affinity [NiFe]-hydrogenases 9 – 12 to support carbon fixation using the light-independent ribulose-1,5-bisphosphate carboxylase-oxygenase (RuBisCO) form IE, a process known as atmospheric chemosynthesis 6 , 8 , 9 , 13 – 16 . The remote Bunger Hills region of East Antarctica, located 450 km west of the Australian Casey station, is an ice-free oasis surrounded by the Shackleton Ice Shelf and the Denman glacier. These natural barriers help preserve its pristine environment, but despite its isolation, records since the 1940s document introduction of waste by visitors 17 . Notably, minor hydrocarbon-based spills from aircraft maintenance near the Australian Edgeworth David Base in 1985 persist to this day 17 , 18 . Hydrocarbon contamination alters soil physicochemical properties and organic matter content 19 , adversely affecting microbial richness, diversity and function 20 with the extent often correlating with contaminant concentrations 20 – 22 . Previous studies of the region include historical palaeogeological events 23 – 25 , as well as, bacteria 26 , fungal 27 , and permafrost archaeal 28 diversity, but an in-depth understanding of the microbial ecology is lacking. With Antarctica increasingly vulnerable to climate change and anthropogenic activity, there is an urgent need to study the edaphic ecology of the Bunger Hills. Presently, hydrocarbon contamination’s impact on microbial survival strategies in Antarctica, particularly atmospheric trace gas oxidation and carbon fixation is not known. By combining soil physicochemistry, 16S and 18S amplicon and shotgun metagenomic data, alongside gas chromatography and 14 CO 2 fixation microcosm assays for 26 samples from the Bunger Hills, we present an extensive study of the microbial diversity and activity of soil in the area and also the effects of a legacy contamination on important microbial processes. Results and discussion The study was conducted near the Australian Edgeworth David Base of Bunger Hills, East Antarctica. Samples were collected from two 100 m transects: the Background Transect (BT), and the Helipad Transect (HT) (Fig. 1 ); both ran across melt streams exiting an un-named lake, with BT being nearer. Soils from the top 10 cm were sampled at 25 m intervals along each transect. Additionally, 16 samples from 44.5–51 m along the HT were designated as the Helipad Zone (HZ), with five samples taken from 20–41 cm ( n = 26). Site description and soil properties Consistent with findings across continental Antarctica 1 , Bunger Hills soils exhibited low organic carbon (0.05–0.43%) (Table S1 ). Total recoverable hydrocarbons (TRH) were detected (limit of reporting 50 mg/kg) in five HZ samples (BH-09, BH-10, BH-11, BH-12, BH-14), with concentrations ranging from 130–1930 mg/kg (C 10 –C 40 ). The elevated TRH in HZ was consistent with previous findings 18 . While the relative change in TRH concentration between sampling events of Gore et al. 18 and this study cannot be inferred due to soil heterogeneity and uncertainty of the exact sampling locations, post-silica gel clean-up (SGC) TRH concentrations revealed potential natural biological attenuation of the fuel, with an average reduction in TRH concentration pre- and post-SGC of 69% (S.D. = 7%, n = 5). Given the low natural organic carbon concentrations in the soils, a substantial portion of this reduction in TRH was likely attributed to polar metabolites, indicating fuel degradation 29 . Several environmental gradients were revealed across the site, with moisture levels ranging from 1.4–14% and pH levels between 6.3–10.0. The pH and salinity were significantly ( P adj. < 0.05) higher in BT than in HZ soils (Tables S2–3). Notably, BT samples BH-24, BH-21, and BH-23 had high alkaline pH (9–10) and high electrical conductivity (EC) (125, 460, 3540 µS/cm EC at 25°C, respectively), which matched visible salt encrustations on the soil surface (Fig. 1 c). Microbiomes shift toward hydrocarbon degraders and predatory taxa in contaminated soils The processed dataset from amplicon sequencing included 2,587,999 prokaryotic and 3,242,740 eukaryotic gene sequences from 26 samples (Tables S4–5). The dataset included 7,770 Bacteria/Archaea amplicon sequence variants (ASVs) classified to 35 bacterial phyla and four archaeal phyla, and 1,890 Eukarya ASVs belonging to 42 divisions (Tables S6–7). Alpha diversity analysis of ASVs revealed significantly ( P adj. 0.05) between BT and HZ. Diversity (Inverse Simpson) and evenness (Pielou’s Evenness) indices showed no significant differences ( P adj. >0.05) between sampling locations, alongside no significant differences in eukaryotic alpha diversity indices. NMDS ordination using environmental variables at 16S ASVs showed HT samples clustered tightly, reflecting similar community structures, while BT samples dispersed along the second axis, and HZ exhibited the greatest dispersion across both axes. For 18S ASVs, BT samples were distributed across both axes, while HT and HZ samples clustered closely (Fig. 2 B). Consistent with other soil surveys in ice-free Antarctica 1 , 30 , 31 , bacterial phyla predominantly comprised of Actinomycetota (13.7–49.7%), Pseudomonadota (4.3–70.0%), Bacteroidota (2.2–26.6%), Acidobacteriota (0.5–24.6%), Chloroflexota (0.4–18.6%), and Verrucomicrobiota (2.3–16.5%) (Fig. 2 D and Table S6). Among the 26 samples, 14 had ≤ 1% Cyanobacteriota , seven ranged from 1.2–6.2%, and five had 10.1–27.6%. Archaea constituted 2.7% of all reads, with Crenarchaeota most prevalent (0–17.1%). Unclassified Bacteria/Archaea phyla accounted for 0.3% of all reads. Taxa with the genetic capacity for atmospheric chemosynthesis 32 , including Actinomycetota , Chloroflexota , and Verrucomicrobiota , Bacillota , Deinococcota , and Eremiobacterota , represented 18.7–70.9% of prokaryotes (39% of total reads). Dominant genera included Ca. Udaeobacter (0–14.3%), wb1-P19 (0–20%), Rubrobacter (0–13.6%) and Crossiella (0–13.8%). Hydrocarbon-degrading genera from Pseudomonadota , including Polaromonas (0–20.2%), Sphingobium (0–29.7%), Sphingomonas (0.4–9.6%) and Pseudomonas (0–22.2%), were also highly abundant 33 – 35 . Among the 42 identified eukaryotic divisions, Fungi (0.09–69.7%), Ciliophora (0.8–45.3%), and Gyrista (0.8–36.7%) were most prevalent, followed by Cercozoa (0.0008–40.1%), Metazoa (0–30.2%), and Euglenozoa (0.3–28.6%) (Fig. 2 D and Table S7). Chlorophyta , particularly Trebouxia , dominated BH-24 (BT) at 90.9%, while Streptophyta were abundant in BH-03 (HZ) at 31.8% (Fig. S1 ). Notably, Fungi were dominant in HT and HZ (61.4–65.0%), while the highest abundance detected in the BT was 4.7% (BH-20). These fungal communities primarily belonged to Ascomycota , with Eurotiomycetes and Dothideomycetes prevalent in HZ samples (Fig. S1 A). Unclassified eukaryotic divisions made up 14.2% of all reads. Distance-based redundancy analysis (db-RDA) using 16S and 18S ASVs showed forward-selected environmental factors significantly accounted for community structure variation ( P < 0.05) (Table S10). The first two axes of the db-RDA explained 17% and 10.9% of the total variation for 16S ASVs, and 16.4% and 11.8% for 18S ASVs respectively (Fig. 2 C). Axis loadings showed depth and TRH post-SGC as primary drivers of the HZ communities, while EC and elevation influenced BT. However, it is worth noting that several contaminated samples were obtained from deeper subsurface soils, thus the effects of contamination and depth on microbial communities cannot be separated distinctly. The rate of H 2 oxidation influenced HT communities alongside HZ, inversely to TRH post-SGC, suggesting different energy sources likely drive the observed divergence in microbial communities. The parallel influence of environmental variables on both prokaryotic and eukaryotic ASVs suggests an intimate relationship between these communities. Microbiomes were significantly (Pairwise ADONIS test, P adj. < 0.05) distinct between BT and HZ, HT and HZ, and BT and HT (Table S11). DESeq2 analysis identified six differentially abundant prokaryotic phyla across sample locations (Fig. S2A). We observed significantly ( P adj. < 0.01) higher abundance of Chloroflexota and NB1-J in BT, and Verrucomicrobiota in HT when compared to HZ, with NB1-J also significantly more abundant in BT compared to HT. In HZ, Pseudomonadota and predatory phylum Bdellovibrionota 36 abundances were significantly ( P adj. < 0.01) higher compared to HT, and Patescibacteriota when compared to BT. For eukaryotes, fungal classes Ascomycota and Basidiomycota , many of which are effective degraders of petroleum hydrocarbons 37 and frequently detected in hydrocarbon-contaminated soils around Antarctica including Bunger Hills 20 , 27 , were significantly ( P adj. < 0.01) more abundant at HZ, alongside other predatory protists including Variosea , Filosa - Granofilosea and Prostomatea , and other unclassified Fungi (Fig. S2B). The presence of a makeshift bamboo bridge at HZ prior to this study (duration unknown) and removed prior to sampling could play a role in the high abundance of fungi in these samples 38 , 39 .Meanwhile, the predatory protist Nebulidea 40 and photosynthetic protists including Bacillariophyceae , Trebouxiophyceae , and Chlorophyceae , as well as other unclassified eukaryotes were significantly ( P adj. < 0.01) more abundant in BT compared to HZ. Nebulidea and Bacillariophyceae were also more abundant in BT in comparison to HT, while Euglenida were more abundant in HT compared to HZ. The lower abundance of autotrophic taxa in HZ suggests a response and adaptation of the community to heterotrophy in hydrocarbon-enriched soils; an observation reported in a previous study of benthic sediment 41 . Conversely, in BT and HT, which are more reflective of the low organic carbon in Antarctic soil, the prevalence of autotrophic taxa appeared to be crucial to carbon turnover to support the community 42 . Spearman’s correlation analysis of differentially abundant taxa demonstrated a strong positive relationship between post-SGC TRH concentration and the abundance of Bdellovibrionota , Pseudomonadota , Ascomycota , Basidiomycota , Filosa - Granofilosea , Bacillariophyceae , and Prostomatea , and a negative correlation between these phyla abundances and pH and the rate of H 2 oxidation (Figs. S3A and S3B). Spearman’s correlation analysis showed H 2 oxidation rates positively correlated with Chloroflexota and Verrucomicrobiota , while Pseudomonadota and Bdellovibrionota abundances negatively correlated with pH and the rate of H 2 oxidation. Our findings suggest an ecological niche created from hydrocarbon contamination that selects for hydrocarbon-degrading taxa, such as Pseudomonas , Sphingomonas , and Phialophora 34 , 43 – 45 , reflecting the presence and recalcitrance of high molecular weight hydrocarbons in the polar environment 34 , 46 , 47 . Hydrocarbon toxicity and physicochemical changes in soil increases necromass availability, promoting microbial growth which, in turn increases prey availability for predatory microbes to thrive 48 – 50 . Furthermore, some predatory protists are putatively associated with the control of ammonia-oxidising archaea and bacteria populations 51 – 53 . In contrast, the absence of hydrocarbon-driven nutrient release in BT and HT reduces competition among heterotrophs. The prevalence of symbiotic, parasitic and predatory taxa supports the notion of a tightly interconnected prokaryotic and eukaryotic community as observed in db-RDA that responds collectively to these environmental changes. Metabolic potential in contaminated and pristine soils We recovered 207 medium-quality and 93 high-quality dereplicated metagenome-assembled genomes (MAGs) from the metagenomes of a subset of seven samples (Fig. 3 and Tables S12–14). Three MAGs belonged to Archaea, with a single bacterial MAG being assignable to a named species (BH-09_ACT9, Rhodococcus qingshengii ). Actinomycetota and Pseudomonadota comprised > 50% of all MAGs, followed by Chloroflexota , Bacteroidota and Gemmatimonadota . Altogether, 24 high-quality MAGs contained near complete 16S rRNA gene sequences (≥ 75% expected length) and thus, new taxa are proposed under the SeqCode 54 register list (XXXX) including: 24 species, 18 genera, eight families, six orders and one class (Table 1 and Supplementary Information). Table 1 Summary of new taxa proposed. In parentheses, equivalent placeholder names appearing in the GTDB. Full descriptions are provided in the supplementary materials. Domain Phylum Class Order Family Genus Species Type Archaea Thermoproteota Nitrososphaeria Nitrososphaerales Nitrososphaeraceae Nitrosobungeria gen. nov. (g__TH5893) Nitrosobungeria shackeltonensis sp. nov. BH-18_THE2 Nitrosomicrobium gen. nov. (g__TA-21) Nitrosomicrobium frigidus sp. nov. (s__TA-21 sp023251115) BH-18_THE1 Bacteria Actinomycetota Acidimicrobiia Acidimicrobiales Iamiaceae Aquihabitans Aquihabitans niveus sp. nov. BH-10_ACT1 Spongiisociales (o__UBA5794) Hadalibacteraceae (f__UBA5794) Gelisolibacter gen. nov. (g__JACDBE01) Gelisolibacter meridionalis sp. nov. BH-24_ACT7 Actinomycetes Actinomycetales Dermatophilaceae Cryoornithinimicrobium gen. nov. (g__Ornithinimicrobium_A) Cryoornithinimicrobium bungerii sp. nov. BH-23_ACT6 Propionibacteriales Nocardioidaceae Nocardioides Nocardioides polaris sp. nov. BH-09_ACT11 Aridivitia (c__UBA4738) Actinopolariales ord. nov. (o__CADDZG01) Actinopolariaceae fam. nov. (f__WHSQ01) Actinopolaria gen. nov. Actinopolaria aerotropha sp. nov. BH-24_ACT26 Actinosomniales ord. nov. (o__UBA4738) Actinosomniaceae fam. nov. (f__UBA4738) Actinosomnia gen. nov. (g__JACDCJ01) Actinosomnia pattersoniae sp. nov. (s__JACDCJ01 sp013817655) BH-20_ACT24 Bungeriellales ord. nov. (o__JAHWKV01) Bungeriellaceae fam. nov. (f__JAHWKV01) Bungeriella gen. nov. (g__JAJCYE01) Bungeriella frigidisoli sp. nov. BH-23_ACT12 Armatimonadota Fimbriimonadia Fimbriimonadales Fimbriimonadaceae Fimbriimonas Fimbriimonas antarctica sp. nov. BH-11_ARM2 Bacteroidota Bacteroidia Chitinophagales Chitinophagaceae Panacibacter Panacibacter polaris sp. nov. BH-10_BAC2 Wilkeslandia gen. nov. Wilkeslandia alcanivorans sp. nov. BH-09_BAC2 Cytophagales Hymenobacteraceae Pseudopontibacter gen. nov. Pseudopontibacter australis sp. nov. BH-24_BAC1 Ignavibacteria Tepidiaquacellales (o__SJA-28) Ventifactibacteraceae fam. nov. (f__B-1AR) Ventifactibacter gen. nov. Ventifactibacter hollidayae sp. nov. BH-10_BAC5 Cyanobacteriota Vampirovibrionia Obscuribacterales Obscuribacteraceae Psychrobscuribacter gen. nov. Psychrobscuribacter pollutisoli sp. nov. BH-10_CYA1 Gemmatimonadota Gemmatimonadetes Cryogemmatales ord. nov. (o__JACCXV01) Cryogemmataceae fam. nov. (f__JAHWKZ01) Cryogemmata gen. nov. Cryogemmata carboxiditropha sp. nov. BH-18_GEM1 Patescibacteria Nanosomnibacteria class. nov. (c__UBA1384) Nanosomnibacterales ord. nov. (o__CAILIB01) Nanosomnibacteraceae fam. nov. (f__CAILIB01) Nanosomnibacter gen. nov. (g__CALBLZ01) Nanosomnibacter parvus sp. nov. BH-11_PAT4 Planctomycetota Phycisphaerae Phycisphaerales Frigidisphaeraceae fam. nov. (f__UBA1924) Frigidisphaera gen. nov. (g__JACVCS01) Frigidisphaera bungerii sp. nov. BH-11_PLA1 Planctomycetia Gemmatales Gemmataceae Cryolimnoglobus gen. nov. Cryolimnoglobus antarcticus sp. nov. BH-11_PLA2 Pseudomonadota Alphaproteobacteria Caulobacterales Caulobacteraceae Brevundimonas Brevundimonas antarctica sp. nov. BH-09_PSE1 Gammaproteobacteria Burkholderiales Burkholderiaceae Frigidisolicola gen. nov. Frigidisolicola castellviae sp. nov. BH-10_PSE17 Dormimicrobiales ord. nov. (o__JACDCP01) Dormimicrobiaceae fam. nov. (f__JACDCP01) Dormimicrobium gen. nov. (g__JACDCP01) Dormimicrobium murphyi sp. nov. BH-24_PSE2 Verrucomicrobiota Verrucomicrobiae Chthoniobacterales Terrimicrobiaceae Cryoterrimicrobium gen. nov. Cryoterrimicrobium chapmanii sp. nov. BH-09_VER1 Verrucomicrobiales Verrucomicrobiaceae Verrucomicrobium Verrucomicrobium antarcticum sp. nov. BH-11_VER1 To compare functional gene abundance across samples, the RPKM of each functional gene was calculated and divided by the mean RPKM for 15 universal ribosomal protein markers present in all cellular organisms, i.e., “genome equivalents” (see Methods). Then, the log 2 of genome equivalents was calculated, resulting in the log-fold genome equivalent or LFGE. Generally, genes universally present in all/most cellular genomes would have values very close to zero and genes from functional specialisations would have values well below zero, except for non-cellular elements typically present in high copy numbers, e.g. present in plasmids or viruses. Overall, we detected marker genes associated with a wide range of metabolic pathways across metagenomes and in MAGs, including aerobic respiration and nitrogen metabolism (Figs. 4 A and 4 B, Tables S15–17). Genes associated with alkane and aromatic hydrocarbon degradation were substantially overrepresented in HZ (occasionally referred to as contaminated samples/locations, and BT and HT as uncontaminated for brevity from hereon) samples. Differences within HZ were also observed, between BH-11 vs BH-09/BH-10. For alkane degradation, all contaminated samples showed similar enrichment in long-chain alkane monooxygenase (LFGE 0.75–0.82; ladA ). However, while BH-11 showed a relative enrichment in alkane 1-monooxygenase ( alkB ) compared to other contaminated samples (LFGE 0.46 vs 0.02–0.14), propane 2-monooxygenase was comparatively reduced (based on large subunit, prmA ), i.e., 0.17 vs 1.44–1.49. Most MAGs containing alkB and/or ladA were Actinomycetota (esp. Mycobacteriales ), and Pseudomonadota , while prmA was only found in Mycobacteriales MAGs. Similarly for aromatic degradation, excluding genes encoding for proteins associated with downstream metabolite processing, e.g., p -cumate, catechols, trans -cinnamate, etc., the toluene monooxygenase system and the phenol/toluene 2-monooxygenase (NADH) complex were highly abundant in all HZ samples (LFGE for most subunits > 0) (Fig. 4 B and Table S15). However, while phenol/toluene 2-monooxygenase (NADH) complex genes are among the top-ranking genes in BH-11, the toluene monooxygenase system was more predominant for BH-09/BH-10. Additionally, components of the terephthalate dioxygenase were only detected in BH-11. The broader range of hydrocarbon degradation capabilities in BH-11 could be attributed to the high abundance of Pseudomonadota (70% of 16S community, 58.6% of MAGs) (Table S14), where phylum members had the potential to degrade a wide range of hydrocarbons 55 . The dominance of Pseudomonadota in BH-11 is characteristic of deeper subsurface soils 56 (BH-11 was sampled at 20 cm depth). Meanwhile, the lower LFGE of these hydrocarbon degradation genes could, speculatively, be associated with the low level of contamination in this sample. Most aromatic hydrocarbon degradation markers were detected in Actinomycetota , and/or Pseudomonadota MAGs (Fig. 4 A and Tables S15–16). We identified five novel MAGs that encoded genes for alkane and/or aromatic hydrocarbon degradation and their associated metabolites, including BH-09_BAC2 ( alkB ), BH-10_BAC2 (catechol 2,3-dioxygenase, xylE ), BH-10_PSE17 ( ladA and pthalate 4,5-dioxygenase, pht3 ), BH-11_VER1 ( pht3 ), and BH-20_ACT24 ( xylE ). The limited number of MAGs with these genes combined with the extremely high LFGE values for some markers (up to 4 copies per genome equivalent) might relate to aromatic hydrocarbon degradation machinery being encoded in plasmids 57 , 58 . Key aerobic respiration genes (Complex I-V) were detected across the metagenomes, with majority of MAGs possessing a partial electron transport chain (Figs. 4 A and 4 B and Tables S15–16). Low-affinity cytochrome oxidases ( coxA / ctaD ) were identified in 180 MAGs across all sampling locations, while high affinity type cytochrome oxidases ( ccoN , cydA ) were more prevalent in both metagenomes and MAGs from HZ, particularly in BH-11, indicating possible genetic adaptation to low oxygen (O 2 ) conditions due to O 2 requirements for hydrocarbon degradation 59 . Genes linked to nitrogen metabolism were detected in the metagenomes. Nitrification genes ( pmoA / amoA , narG / nxrA ) were found in all samples, particularly in Thermoproteota MAGs, except BH-24 where pmoA / amoA were absent (Figs. 4 A and 4 B and Tables S15–16). MAGs that encoded denitrification genes ( nirK , norB , nosZ ) belonged to 14 phyla across sampling locations, while nitrogen fixation genes ( nifH ) were detected in HZ, particularly in BH-11, in Pseudomonadota MAGs but absent in BT and HT. Seven novel MAGs encoded genes for nitrogen metabolism including BH-09_BAC2 ( norB and nosZ ), BH-10_ACI3 ( norB ), BH-10_BAC2 ( nosZ ), BH-10_BAC5 ( nirK ), BH-10_CYA1 ( narG/nxrA and norB ), BH-24_ACT26 ( nirK ), and BH-24_ACT7 ( nirK ). Ammonia/methane oxidation genes ( pmoA / amoA ) were detected across most samples, particularly BH-09, BH-10 and BH-20 (LFGE > 1.69), and were present in Pseudomonadota and both novel Thermoproteota MAGs (ammonia oxidising archaea) (Figs. 4 A and 4 B and Tables S15–16). Aerobic carbon monoxide dehydrogenase (CODH) genes for the catalytic large subunit ( coxL ) were detected in all metagenomes, albeit less abundant in HZ and BH-23 (LFGE < -0.57), while anaerobic CODH ( cooS ) were detected in BH-10 and BH-11. MAGs that encoded coxL include Acidobacteriota , Actinomycetota , Bacteroidota , Chloroflexota , Deinococcota , Gemmatimonadota , and Pseudomonadota ; six were novel taxa including BH-09_ACT11, BH-10_BAC2, BH-18_GEM1, BH-23_ACT6, BH-24_ACT26, and BH-24_ACT7 (Table 1 , Table S16). We detected several lineages of high-affinity [NiFe]-hydrogenases across the metagenomes, including 1h ( hhySL ), 1l ( hylSL ), 1m ( hhmSL ) and 2a ( hucSL ) (Fig. 4 B and Table S15). These high-affinity [NiFe]-hydrogenases were encoded in 68 MAGs of phylum Acidobacteriota , Actinomycetota , Bacteroidota , Chloroflexota , Deinococcota , Gemmatimonadota , Planctomycetota , and Pseudomonadota , with 13 MAGs having more than one copy of the same or different lineage of the large subunit gene (Fig. 4 A and Tables S15–16). Three MAGs were novel taxa, with BH-20_ACT24 ( Actinosomnia pattersoniae gen. nov., sp. nov.) and BH-24_ACT26 ( Actinopolaria aerotropha gen. nov., sp. nov.) encoding hhmSL and BH-24_PSE2 ( Dormimicrobium murphyi gen. nov., sp. nov.) encoding hylSL . BH-23 encoded the highest abundance of high-affinity hydrogenases, including several phylogenetically distinct putative lineages (Fig. 5 A). These putative lineages were detected in 14 MAGs including Acidobacteriota , Actinomycetota , Chloroflexota , and Gemmatimonadota . Lineages 1h and 1l were present across the metagenomes, at lower abundances in HZ (1l being near the limit of detection in BH-09 and BH-10) compared to BT and HT, while 1m was undetected in HZ and 2a was undetected in BT. Photosynthetic markers ( psaA / psbA ) were only detected in low abundance (LFGE < 0) in BH-10, BH-23 and BH-24 (Fig. 4 B, Table S15), with carbon fixation via the CBB pathway dominant across samples. In general, the CBB pathway and a key marker – the large RuBisCO subunit ( rbcL ), was more abundant in BT and HT. The five subtypes of RuBisCO form I were detected across most metagenomes. Of these, RuBisCO form I was present in 40 MAGs from Acidobacteriota , Actinomycetota , Chloroflexota , Cyanobacteriota , and Pseudomonadota (Figs. 4 A, 6 A and Table S16). The dark RuBisCO form IE 8 ( rbcLIE ) was the most widely detected across all samples (LFGE >-0.66) and were more prevalent in BT and HT samples (LFGE > 0.54), in particular BH-23 (LFGE 0.89) and BH-18 (LFGE 0.85). MAGs that encoded rbcLIE were Actinomycetota and Chloroflexota including one novel Actinomycetota taxon BH-23_ACT12 ( Bungeriella frigidisoli gen. nov., sp. nov.). Other chemoautotrophy-associated RuBisCO forms IC and ID were detected across metagenomes, with form IC being more abundant in BH-11, BH-20 and BH-24 (LFGE > 0), but near the detection limit in BH-10, and form ID being lower in abundance overall (LFGE < 0). RuBisCO forms IA and IB, commonly associated with photoautotrophy 60 , were largely undetected in contaminated (HZ) samples, with a low abundance of form IA (LFGE − 1.01) present in BH-11. Form IA was most abundant in BH-20 (LFGE 1.32), while Form IB was near the detection limit in BH-18, BH-20, and BH-23. Across sampling locations. 16 MAGs co-encoded at least one high-affinity lineage of [NiFe]-hydrogenase with rbcLIE , indicating potential for atmospheric chemosynthesis; all MAGs were from Actinomycetota (Fig. 3 ). In addition, 18 Actinomycetota and two Chloroflexota MAGs co-encoded aerobic CODH and rbcLIE . These groups of MAGs were not mutually exclusive as 14 MAGs encoded at least one lineage of high-affinity [NiFe]-hydrogenase alongside aerobic CODH, together with rbcLIE , and reflects previous reports of co-location of trace gas oxidation genes 8 , 9 . Alternate carbon fixation pathway markers, i.e., malonyl-CoA reductase/3-hydroxypropionate dehydrogenase (NADP + ) ( mcr ) for the 3-hydroxypropionate pathway, and ATP-citrate lyase ( aclB ) for the reverse TCA cycle were only detected in BH-11, and BH-23, respectively (Fig. 4 B and Table S15). Neither marker was found associated with any MAG even despite the high LFGE values for mcr (-0.29). MAG growth preferences linked to the sampling locations Based on the MAG distribution across the different samples, two main clusters of MAGs were differentiated based on their detection (% of MAG with reads mapped) in each sample (Fig. S4). These two clusters separated MAGs associated with the contaminated (HZ) and uncontaminated (HT, BT) samples, with 169 and 131 MAGs respectively. Except for four MAGs with prmA and one predicted to harbour alkB , all the other MAGs containing hydrocarbon degradation gene annotations were characteristic of the contaminated samples (Fig. 3 and Table S15). This includes MAGs of novel taxa, whose parent is associated with hydrocarbon degradation potential such as Nocardioides 61 , Brevundimonas 62 , Aquihabitans 63 , Panacibacter 64 , and Fimbriimonas 65 . Similar to the trends observed in the whole metagenomes, genes associated with trace gas oxidation and dark carbon fixation were mainly present in the uncontaminated group of MAGs. This includes the high-affinity hydrogenases (except type 2a which were only detected in contaminated group of MAGs), RuBisCO form IE, and aerobic CO dehydrogenase ( coxL ), with the novel hydrogenase clade present in predominantly Actinomycetota and Acidobacteriota MAGs. Prediction of optimal growth conditions with GenomeSPOT 66 also showed clear differences between the contaminated- and uncontaminated-associated MAGs. In general, MAGs associated with the contaminated samples showed lower optima pH (6.7 ± 0.6 vs 7.8 ± 0.5, p = 0.00), salinity (0.4 ± 0.8% vs 3.0 ± 1.6%, p = 0.00), and temperatures (26.1 ± 4.8°C vs 33.0 ± 5.6°C, p = 0.00) (Figs. S5–S7). A subcluster of 72 uncontaminated MAGs was associated with BH-23, the sample with high salinity, including 21 MAGs almost exclusively detected in that sample (Fig. S8). Analysis of the predicted optimal salinity for growth indicated indeed a preference for higher salt concentrations (3.3 ± 1.8%), statistically different to both the contaminated MAGs (0.4 ± 0.8%, P adj. = 0) and the other uncontaminated MAGs (2.8 ± 1.5%, P. adj. = 0.03). Two MAGs with growth preference for salinity were novel Actinomycetota taxa, BH-23_ACT12 ( Bungeriella frigidisoli gen. nov., sp. nov.) and BH-23_ACT6 ( Cryoornithinimicrobium bungerii gen. nov., sp. nov.). Six of the 23 MAGs with potential for atmospheric chemosynthesis were identified with growth preference for salinity, of which five were order Euzebyales 67 and one Acidimicrobiales . H 2 oxidation activity diminishes with hydrocarbon contamination To assess the effects of hydrocarbon contamination on trace gas scavenging, we measured the assimilation and oxidation of H 2 by soil microcosms. All uncontaminated microcosms except BH-25 and BH-26, consumed H 2 to sub-atmospheric levels under 300 hours (530 p.p.b.v.) (Fig. S9, Table S18). Contaminated microcosms exhibited the lowest H 2 oxidation rates, with BH-09 showing the slowest at 0.5 nmol/mol/h/g, with continuing activity after 300 hours (Fig. 5 B). In contrast, H 2 oxidation rates in BH-18 and BH-24 were more than 900-fold higher at 476.3 nmol/mol/h/g and 429.6 nmol/mol/h/g respectively, depleting H 2 to atmospheric concentration under 4–6 hours, surpassing previously reported rates in Antarctica 9 . Interestingly, H 2 oxidation rates in contaminated microcosms increased as post-SGC TRH concentrations decreased, suggesting hydrocarbons directly influence trace gas scavengers and/or their activity. The expression of high-affinity [NiFe] hydrogenases in three samples with the highest rates and three samples with the lowest oxidation rates was verified by reverse transcription quantitative PCR (RT-qPCR) (Tables S19–20). Hydrocarbons as an energy source for chemolithoautotrophs We next investigated the influence of hydrocarbon contamination on dark carbon dioxide (CO 2 ) fixation using a radiolabelled 14 CO 2 assay in a subset of six soil microcosms. Overall, CO 2 fixation occurred in both uncontaminated and contaminated soils, with rates comparable to or exceeding those observed in wetted crusts and desert soils of Israel 16 (Fig. 6 B and Table S21). Several energy sources for the observed dark carbon fixation in pristine samples can be expected, including low levels of chemoorganotrophic metabolism or the oxidation of inorganic electron donors such as reduced sulfur compounds 68 . While direct physicochemical characterisation of sulfur content was not conducted, the detection of the soxB gene, a key marker for sulfur oxidation pathways, suggests this capability (Fig. 4 B and Table S15). Significantly ( P adj. < 0.05) higher CO 2 fixation rates were observed in hydrocarbon contaminated soils from HZ when compared to BT and HT soils (grouped as uncontaminated soils) (Table S22). This was despite the overall lower abundance of RuBisCO markers in HZ soil metagenomes, and gene expression via RT-qPCR (Table S19). Increased heterotrophic activity in the hydrocarbon-supported community could enhance anaplerotic CO 2 assimilation 69 , 70 . It is also possible the hydrocarbon degradation by enriched taxa releases byproducts and inorganic carbon sources to support autotrophic pathways and consequently the community 71 , 72 . The significant reduction of photosynthetic eukaryotic abundance in HZ from DESeq2 analysis of 18S ASVs suggests the niche for carbon fixation was filled by chemoautotrophs as evident in the increased abundance of Pseudomonadota in these samples (Fig. S2B). Previous studies have shown positive correlations between hydrocarbon contamination at low levels and increased levels of inorganic fixed carbon via non-CBB cycle pathways 71 , although CO 2 fixation gene abundances were also higher in this study. In contrast, other studies demonstrated that the presence of free organic carbon in the extracellular space inhibits CO 2 fixation 73 , 74 . It is possible that hydrocarbon metabolism creates a locally enriched zone of CO 2 , lacking competition with O 2 for fixation 75 . Furthermore, increasing CO 2 concentrations have been shown to drive dark CO 2 fixation rates 76 . The low CO 2 fixation rates in uncontaminated samples, contrasting with higher expression of dark carbon fixation gene rbcLIE suggests a need to upregulate protein expression to increase efficiency of carbon capture in environments low in organic carbon like Antarctica 77 , 78 . Conclusion Our findings reveal a diverse and functionally rich microbial community Bunger Hills, Antarctica, including novel taxa such as Wilkeslandia alcanivorans gen. nov., sp. nov. ( Chitinophagaceae ) and Frigidisolicola castellviae gen. nov., sp. nov. ( Burkholderiaceae ) capable of alkane degradation, and Bungeriella frigidisoli gen. nov., sp. nov. ( Bungeriellaceae fam. nov.), encoding rbcLIE for dark carbon fixation. Despite its low levels, legacy hydrocarbon contamination continues to shape microbial community structure and function four decades post-exposure, enriching for hydrocarbon-degrading taxa and other predatory taxa. This selection pressure alters microbial metabolic strategies, as evidenced by reduced rates of H 2 scavenging and lower high-affinity hydrogenase abundance. While uncontaminated soils favoured autotrophic taxa reliant on inorganic carbon, elevated carbon fixation rates in contaminated soils likely support energy-intensive degradation processes. These results reveal the long-lasting ecological footprint of anthropogenic activity and emphasise the need for further investigation to delineate the apparent dose-response relationship between hydrocarbons and trace gas oxidation activity, and the mechanistic links between hydrocarbon exposure, trace gas cycling, and carbon flux – using approaches such as stable isotope probing and integrative -omics. Our study establishes a valuable baseline for monitoring environmental change and human impact on Antarctic soil ecosystems. Methods Site Mapping 871 mostly nadir photographs of the site were collected on same day as the field sampling program (06 February 2023) with a DJI Matrice 300 quadcopter fitted with a DJI Zenmuse P1 35mm camera. Images were processed in Agisoft Metashape Professional (2.1.1) to produce an orthomosaic with 7mm resolution, and digital surface model of 2.8 cm resolution. The project utilised 6 Ground Control Points (Root Mean Square Error of 2.3, 1.3, and 0.64 cm for X,Y,Z). Ground Control Points and sample locations were recorded with an Emlid Reach RS2 + RTK base and rover system, with the base station established over survey mark NMS238. Raw and processed data and metadata is available from the Australian Antarctic Data Centre 79 . Sample collection Soil samples were collected on 06 February 2023 at 25 m intervals along two 100 m transects, the Helipad Transect (HT, n = 5) and the Background Transect (BT, n = 5). Additional samples were collected along the HT at an area of known legacy hydrocarbon contamination, referred to throughout as the Helipad Zone (HZ, n = 16), with 26 samples collected in total (BH-01 to BH-26). Sample description, location and depth information are provided in Supplementary Table S1 . Samples were collected from the top 10 cm of soil (except for HZ samples collected at greater depths: 20–51 cm below surface) using stainless steel spoons, which were cleaned with ultrapure water (18 MΩ cm, Milli-Q®, Millipore) and ethanol (reagent grade > 99.5%, EMSURE®, Merck). Soil for organic, inorganic and physicochemical analysis was collected in 125 mL amber glass jars with PTFE lined lids and stored and transported at -18°C until analysis. Soil for microbial analysis was collected in sterile 50 mL polypropylene tubes and stored and transported between − 70 to -80°C until analysis. Chemical analyses Samples for chemical analysis were sent to Australian Laboratory Services (ALS, Springvale VIC, Australia). Samples for total recoverable hydrocarbon (TRH, C 10 –C 40 ) analysis were thawed and screened to < 8 mm with a spatula, as described in van Dorst et al. 21 . Soil subsamples were extracted and analysed using EPA Method SW-846–8015A (Nonhalogenated Organics by Gas Chromatography)/8260B (Volatile Organic Compounds by Gas Chromatography/ Mass Spectrometry (GC/MS)) 80 . Extracts were subsampled and underwent silica-gel cleanup (SGC) for polar compound removal to measure petrogenic hydrocarbons in isolation. Polar compounds, as defined in this study, include metabolites formed from biodegradation of fuel and naturally occurring organic matter 29 . Inorganic analysis for water extractable nutrients were conducted on 1:5 soil-water extracts (10 g soil: 50 mL deionised water) following a 1 h end-over-end shake. Exchangeable ammonium was analysed using a 2 M KCl extraction 81 , while bicarbonate extractable phosphorus was analysed using the Colwell P method 82 . Community DNA extraction, amplicon sequencing and reads processing DNA from 0.3 g of soil subsample for each of the 26 samples was extracted in duplicates using FASTDNA™ SPIN Kit for Soil (MP Biomedicals, USA) following the manufacturer’s instructions. DNA purity and yield was verified using Nanodrop ND–1000 (Thermo Fisher Scientific, USA) and Qubit dsDNA HS Assay Kit with Qubit 4 Fluorometer (Thermo Fisher Scientific, USA) respectively. Replicate DNA samples were pooled for 16S rRNA gene (2 × 250 bp) and 18S rRNA gene (1 × 250 bp) paired-end amplicon sequencing using the Illumina MiSeq platform (Illumina, USA) at the Ramaciotti Centre for Genomics (UNSW Sydney, Australia). For bacteria and archaea, the V4 region of the 16S rRNA gene was targeted for sequencing using the 515F/806R primer set 83 , 84 . For eukaryotes, the V9 region of the 18S rRNA gene was targeted for sequencing using the 1391F/EukBr primer set 85 , 86 . Raw reads were processed in the R environment v4.3.1 87 using the R package DADA2 pipeline v1.30.0 88 . Briefly, for both 16S and 18S rRNA genes, forward and reverse reads were quality filtered and trimmed using the filterAndTrim function with default parameters. For 16S rRNA genes, truncation points were set using the argument truncLen at 195 and 160 bases for forward and reverse reads respectively, with a maximum threshold of “expected errors” (maxEE) of 2 for all reads. For 18S rRNA genes, 20 bases were removed from the start using trimLeft, and two bases from the end using trimRight, of both forward and reverse reads, then forward and reverse reads were truncated after 120 and 110 bases respectively, with maxEE of 1 for all reads. Error models were generated for the dataset, then amplicon sequence variants (ASVs) were inferred and dereplicated. Paired reads were merged, and sequence counts tables were generated. For 16S and 18S rRNA gene data, chimeric ASV sequences derived from sequence merging were removed and the remaining reads were taxonomically mapped to the SILVA v138.1 89 and the PR2 v5.0.0 SSU rRNA databases 90 respectively. The ASV and taxonomy tables derived from the DADA2 pipeline were merged using the R package phyloseq v1.46.0 91 . For 16S rRNA gene data, ASVs assigned to mitochondria and chloroplasts and ASVs with unassigned domain were removed. For 18S rRNA gene data, ASVs with Bacteria, Archaea or unassigned domain were removed. Community RNA extraction and RT-qPCR RNA was extracted from 0.5 g of soil using the SPINeasy RNA kit for Bacteria (MP Biomedicals, Australia) following the manufacturer’s instructions including lysozyme pretreatment. RNA concentration was measured using Qubit RNA HS assay kit (Thermo Fisher Scientific, Australia) and Qubit 4 fluorometer (Thermo Fisher Scientific, Australia). cDNA was synthesised using the Maxima First Strand cDNA Synthesis Kit for RT-qPCR with dsDNase (Thermo Fisher Scientific, Australia) following manufacturer’s instructions, except for extending the 50°C incubation to 30 min. In each synthesis, 8 µL of template RNA was added to the reaction mix. Additional no-reverse-transcription negative controls were prepared for each RNA extract by excluding the Maxima enzyme mix from the reaction mix. Reverse transcription quantitative PCR (RT-qPCR) was performed on synthesised cDNA to quantify expression of RuBisCO form IE ( rbcLIE) and form 1h-[NiFe] hydrogenase ( hhyL ) in situ . Additionally, expression of the 16S rRNA gene was quantified to serve as a reference gene. RT-qPCR reaction mixtures were prepared using 10 µL 2x QuantiNova Probe PCR Master Mix (Qiagen, Australia), 0.5 µL of 40 µM concentrations of the respective forward and reverse primers (Integrated DNA Technologies, Australia), 1 µL of 5 ng/µL T4gene32 protein (Sigma-Aldrich, Australia), 8 µL of UltraPure DNase/RNase-free distilled water (Thermo Fisher Scientific, Australia), and 1 µL of template cDNA. In reactions targeting the 16S rRNA gene, template cDNA was diluted 1:10. All samples, standards and negative controls were run in technical triplicate for rbcLIE and hhyL and in technical quadruplicate for the 16S rRNA gene. Thermocycling reactions were conducted in a CFX96 Touch Real-Time PCR Detection System (Bio-Rad Laboratories, Australia) under two step conditions. For reactions targeting 16S rRNA and hhyL , the thermocycling conditions were 94°C for 5 min, then 40 cycles of 94°C for 10 s and 60°C for 50 s, followed by a melt-curve step from 50 to 95°C. For reactions targeting rbcLIE , the conditions were identical except that amplification occurred at 55°C instead of 60°C. Standard curves for each target gene were generated over 5–7 orders of magnitude by serially diluting synthetically designed gene fragments composed of representative rbcLIE (JX458468.1), hhyL (AB894417.1) and 16S rRNA (MF689012.1) gene sequences 13 . Using CFX Maestro Software (Bio-Rad Laboratories, Australia), the standard curves were used to determine the reaction efficiencies and copy numbers of target gene in each reaction. Copy numbers for rbcLIE and hhyL were then normalised to 16S rRNA gene copies and scaled to BH-18. Melt peak analysis confirmed amplification specificity in each reaction. Shotgun metagenome sequencing, reads processing, assembly, and binning Metagenomic sequencing was performed on the Novaseq X Plus 10B platform (Illumina, USA) with 2 × 150 bp paired-end reads at the Ramaciotti Centre for Genomics (UNSW Sydney, Australia). The quality of metagenomic reads was assessed using FastQC v0.11.9 92 and MultiQC v1.13 93 . Adapter trimming, contaminant (including spike-ins) and quality filtering was performed using BBDuk (BBMap/BBTools v38.63, https://github.com/BioInfoTools/BBMap ). Reads were quality-trimmed, including removal of G polymers at least eight bases long, on both sides to Q6 using the Phred algorithm, with the following flags: qtrim = rl trimq = 6 trimpolyg = 8. Filtered and trimmed reads were assembled using MegaHIT v1.2.9 94 using the meta-sensitive preset. Contigs less than 1000 bp were removed using SeqKit v2.5.1 95 . Metagenomic contigs were binned using the ensemble binner AVAMB v4.1.3 and the corresponding Snakemake workflow 96 . Briefly, read mapping, contigs indexing and BAM file generation was performed using minimap2 v2.28 97 then sorted using SAMtools v1.9 98 . Contigs with a minimum length of 2,000 bp, and a minimum completeness and maximum contamination threshold of 50% and 5% respectively were binned using AVAMB 40 . CheckM2 v1.0.1 99 was used to calculate the completeness and contamination of all bins. A total of 300 medium- to high-quality bins were recovered after dereplication with galah v0.4.0 ( https://github.com/wwood/galah ). The resulting MAGs were taxonomically classified using the Genome Taxonomy Database toolkit (GTDB-Tk) v2.4.0 100 , and GTDB release 09-RS220 101 . Metagenome annotation and functional analysis Individual metagenome assemblies were processed with anvi’o v8-dev 102 . Reads were mapped to their respective assemblies and imported with anvi-profile. Predicted protein-coding genes were annotated against the KEGG database using adaptive threshold adjustment 103 and the --include-stray-KOs option. Additional annotations were performed with InterProScan v5.68-100.0 104 with options --disable-precalc --goterms --iprlookup --pathways, and all the supported databases and tools. For each metagenome assembly, the RPKM for genes encoding selected proteins of biogeochemical relevance, including subtypes of RuBisCO and high-affinity hydrogenases described further below, were calculated using the outputs from anvi-profile-blitz, the integrated annotations above, and the samtools stats output (for the total number of reads mapped to the assemblies). The RPKM values for each marker were normalised against the mean RPKM of 15 universal ribosomal protein markers present in all three domains: uS3_C (IPR001351), uS19 (IPR002222), uL22 (IPR001063), uL14 (IPR000218), uL16 (IPR047873), uL3 (IPR000597), uS10 (IPR027486), uL6 (IPR020040), uS17 (IPR000266), uS8 (IPR000630), uL4 (IPR002136), uL5_C (IPR031309), uL1 (IPR028364), uL18 (IPR005484), and uL2_C (IPR022669). Finally, the log 2 of the normalised values was calculated: $$\:LFGE={\text{log}}_{2}\left(\raisebox{1ex}{$RPK{M}_{marker}$}\!\left/\:\!\raisebox{-1ex}{$\stackrel{-}{RPK{M}_{rp15}}$}\right.\right)$$ Annotation of MAGs Genome annotation was performed with DFAST v1.3.1-c1dc63e 105 with all the options for pseudogene prediction deactivated due to overestimations in poorly described lineages. Prodigal v2.6.3 106 was used as gene-caller, CRT v1.2 107 for CRISPR detection, and tRNAscan-SE v2.0.12 108 for the detection of tRNAs. In addition to the default DFAST database, functional annotation included blastn and blastp searches against CARD v3.2.9 109 and VFDB 20240716 110 databases; HMM-based searches against TIGRFAM v15.0 111 , Pfam v37.0 112 , and dbCAN3 HMM profiles v12 113 ; and RPS-BLAST against COG (2020 release) 114 , KOG 115 and CDD 20240330 116 . In addition to the default Barrnap 0.9 ( https://github.com/tseemann/barrnap ) and tRNAscan-SE, overall ncRNAs were predicted with infernal v1.1.4 117 with Rfam 14.10 118 (options --cut_ga --rfam --nohmmonly -Z ) due to the limited detection of some rRNA genes by Barrnap in certain lineages. In addition to the standard functional annotation, optimal growth conditions for all the MAGs were predicted with GenomeSPOT v1.0.1 66 . MAG cluster analysis Detection of MAGs across samples generated by anvi’o was clustered in Euclidean space with the Ward D2 method with tidyHeatmap v1.11.4 119 . Pairwise statistical tests of the growth preferences across clusters was performed with the ggstatsplot package v0.12.1 120 using Yuen’s trimmed mean test 121 . Phylogenetic and Gene Cluster Analysis Potential Form I RuBisCO large subunit protein sequences were identified in the metagenome assemblies using BLAST + v2.12.0 + 122 with the flag -max_hsps 1 and previously published database 9 . False positive hits which did not contain any RuBisCO annotation (e.g. Pfam or InterPro) were removed from further analysis. Very short sequences (< 200 amino acids) were removed as they often distorted the resulting trees. Reference and sample sequences were dereplicated separately with CD-HIT v4.8.1 (-c 0.9), and the representative sequences from each dataset aligned together with MAFFT-L-INS-I v7.481 123 . The alignment was trimmed to remove poorly aligned regions with ClipKIT v2.3.0 124 in kpic mode. Maximum likelihood tree was inferred with IQ-Tree v2.2.3 125 under the Q.pfam + R9 substitution model (-m MFP), with 1000 ultrafast bootstrap replicates (-B 1000 --nmax 5000) with nearest neighbour interchange optimisation (--bnni) 126 , and 1000 replicates Shimodaira-Hasegawa-like approximate likelihood ratio tests (SH-aLRT, --alrt 1000) 127 . Resulting trees were edited in iTOL 128 . The same process was followed for the catalytic large subunits of [NiFe]-hydrogenase with the following modifications: sequences longer than 1000 amino acids were also excluded and the selected substitution model, LG + I + R10. The genomic neighbourhoods for all hydrogenase sequences occurring between the 2a and 1h-[NiFe]-hydrogenase clades, as indicated by the location of the reference sequences, were examined by selecting 10 protein coding genes upstream and downstream the [NiFe]-hydrogenase large subunit gene. Functional annotations within each neighbourhood were manually analysed for structural similarities with the 1l and 1m [NiFe]-hydrogenase groups proposed in the literature 6 , 9 . Clades were then color-coded to reflect the subtype of [NiFe]-hydrogenase, as determined through both phylogenetic and gene structure analysis. Gas chromatography oxidation assays The oxidation of H 2 to sub-atmospheric concentrations by microbial high-affinity hydrogenases was measured using gas chromatography for each soil sample as described previously 8 . Briefly, for each soil sample, 1 g was added to separate 114 mL serum bottles and sealed with butyl rubber stoppers. Hydrogen gas (BOC Australia) was added to the headspace of each sample to attain a concentration of ~ 12,000 p.p.b.v. and then samples were incubated at 10°C. The headspace was sampled (1 mL) at intervals using a gas-tight syringe (SGE) to measure the partial pressures of H 2 and CO using the Peak Performer 1 Gas Chromatograph (Peak Laboratories). Three biologically independent replicates were used for each sample in this experiment. Heat-killed soils (1 g; 121°C, 15 p.s.i., 20 min) for each sample, and empty sterile serum bottles were included as negative controls to ensure that H 2 depletion is due to biological activity. The observed data was visualised in the R environment v4.3.1 87 using the packages ggplot2 v3.4.4 129 and patchwork v1.2.0 130 . Data was then used to calculate the mean rate of H 2 oxidation at atmospheric H 2 concentration 5 from the first-order reaction rate constant. Radio-labelled CO 2 fixation assays Dark carbon fixation was measured by incubating 0.5 g of soil sample to sterile 2.16 mL glass vials and sealed with rubber septum lids. The headspace was purged with ~ 10 mL zero-air (20.9% O 2 , balanced in N 2 , BOC Australia). Gaseous 14 CO 2 (1% v/v) was generated by combining 408 µL sodium bicarbonate ( 14 C-labelled) solution (NaH 14 CO 3 , Moravek, 58.8 mCi mmol − 1 ) with 448.8 µL 0.01724 M HCl solution. Gaseous 14 CO 2 was added to the headspace of each sample using a gas-tight syringe (SGE) to obtain a mixing ratio of 400 p.p.m.v. H 2 was added to the headspace of a subset of samples to a mixing ratio of 4 p.p.m.v. The relevant heat-killed soils per condition were included as negative controls. Samples were incubated under dark conditions at 10°C for 120 h. Samples were then transferred to 20 mL scintillation vials. Unfixed 14 CO 2 was removed using 3 mL 1M HCl and left to dry on a heating block at 50°C for 24 h. Scintillation cocktail (19.5 mL) (EcoLume) was added to each vial and radioisotope analysis was carried out using a liquid scintillation spectrometer (Tri-Carb 2810 TR, Perkin Elmer) operating at 95% efficiency. Background chemiluminescence and chemiluminescence were corrected with internal calibration standards. Triplicate biological samples were analysed. Mean and SD of 14 CO 2 fixation were calculated and visualised in the R environment v4.3.1 87 using the packages ggplot2 v3.4.4 129 and dplyr v1.1.3 131 . Statistical analyses All statistical analyses were performed in the R environment v4.3.1 87 . Significance testing of soil physicochemical properties among monitoring zones was performed with one-way ANOVA using the aov() function of the stats package v4.3.1 87 and post-hoc Tukey’s multiple comparison test using the tukey_hsd() function of the rstatix package v0.7.2 132 . Prior to biodiversity analysis, data was subjected to rarefaction using rrarefy from vegan v2.6.4 133 using the sample with the lowest counts (9859 for 16S rRNA, and 34,017 and 18S rRNA gene datasets) as the threshold for subsampling. Alpha diversity indices including Chao1 richness, inverse Simpson’s diversity, and Pielou’s Evenness were calculated using the microbiome package v1.24.0 134 . Significant differences between monitoring zones for each index was assessed using Kruskal-Wallis test and Dunn’s post-hoc test. P values were adjusted using Holm’s correction. Beta diversity was analysed based on the Bray-Curtis dissimilarity measure. Bray-Curtis dissimilarities were calculated between each sample and the resulting matrices were subsequently used to perform unconstrained ordination and clustering of community data with non-metric multidimensional scaling (NMDS) using the phyloseq package v1.46.0 91 . Variations in community structure were constrained to a set of explanatory variables using distance-based redundancy analysis (db-RDA) method using vegan v2.6.4 133,135,136 . P- adjusted (Holm’s correction) statistically significant explanatory variables were selected for the model using a forward stepwise model selection method with 9,999 permutations after removal of collinear variables using a variance inflation factor threshold of 5. Pairwise differences in community composition was assessed using the pairwise.adonis() function of the pairwiseAdonis package v0.4.1 137 . The DESeq2 package v1.40.0 138 and phyloseq package v1.44.0 91 was used to identify differentially abundant microbial taxa between the transects and zones 139 . A DESeqDataSet object was created using the phyloseq_to_deseq2() function, specifying the variable of interest. DESeq2 was then applied to normalise the data and fit a negative binomial model. The contrast for the DESeq2 model was defined, and results were extracted with a significance threshold (alpha = 0.01), while disabling Cook's distance cutoff to ensure potential outliers were not excluded. Spearman correlation analysis was conducted to explore the relationships between microbial community structure and environmental chemical variables. The analysis began by aggregating the ASVs by class level. Next, the data was normalised using the DESeq2 method to account for differences in sequencing depth across samples. Variance-stabilising transformation (VST) was applied to the aggregated table, resulting in normalised abundances. Then only taxa identify to be differentially abundant between the transects and zones were selected. Spearman correlations were then calculated between the DESeq2-normalised table at the class level (18S)/phylum level (16S) and the environmental chemical variables. This analysis was performed using the associate() function from the microbiome package v1.22.0 134 . In most cases, P values were corrected for multiple hypotheses with the Holm 140 method using the relevant flag for each function. Declarations Acknowledgements We would like to thank the Australian Antarctic Division Casey station team for conducting the Antarctic field work in 2023, as well as the Australian Antarctic Program for supporting the work via the Australian Antarctic Science Project AAS 4503 – Reducing Environmental Impacts at Contaminated Sites in Antarctica. This research was supported by the UNSW University International Postgraduate Award Scholarship (awarded to KKYT) and the ARC Discovery Project (DP240102658) awarded to BCF. This research includes computations using the computational cluster Katana supported by Research Technology Services at UNSW Sydney. Data availability Raw amplicon sequencing data are deposited in ENA under bioproject PRJEB90557. Raw metagenomic sequencing data and metagenome-assembled genomes have been deposited in ENA under bioproject PRJEB77669. References Cary, S. C., McDonald, I. R., Barrett, J. E. & Cowan, D. A. On the rocks: the microbiology of Antarctic Dry Valley soils. Nat. Rev. Microbiol. 8 , 129–138 (2010). Chown, S. L. et al. The changing form of Antarctic biodiversity. Nature 522 , 431–438 (2015). Lambrechts, S., Willems, A. & Tahon, G. Uncovering the Uncultivated Majority in Antarctic Soils: Toward a Synergistic Approach. Front. Microbiol. 10 , 242 (2019). Greening, C. & Grinter, R. Microbial oxidation of atmospheric trace gases. Nat. Rev. Microbiol. 20 , 513–528 (2022). Tedrow, J. C. F. & Ugolini, F. C. Antarctic Soils. in Antarctic Soils and Soil Forming Processes (ed. Tedrow, J. C. F.) 161–177 (American Geophysical Union, 1966). Novelli, P. C. et al. Molecular hydrogen in the troposphere: Global distribution and budget. J. Geophys. Res. Atmospheres 104 , 30427–30444 (1999). Ortiz, M. et al. Multiple energy sources and metabolic strategies sustain microbial diversity in Antarctic desert soils. Proc. Natl. Acad. Sci. 118 , e2025322118 (2021). Tebo, B. M. et al. Microbial communities in dark oligotrophic volcanic ice cave ecosystems of Mt. Erebus, Antarctica. Front. Microbiol. 6 , 179 (2015). Ji, M. et al. Atmospheric trace gases support primary production in Antarctic desert surface soil. Nature 552 , 400–403 (2017). Ray, A. E. et al. Atmospheric chemosynthesis is phylogenetically and geographically widespread and contributes significantly to carbon fixation throughout cold deserts. ISME J. 16 , 2547–2560 (2022). Greening, C., Berney, M., Hards, K., Cook, G. M. & Conrad, R. A soil actinobacterium scavenges atmospheric H 2 using two membrane-associated, oxygen-dependent [NiFe] hydrogenases. Proc. Natl. Acad. Sci. 111 , 4257–4261 (2014). Islam, Z. F. et al. A widely distributed hydrogenase oxidises atmospheric H 2 during bacterial growth. ISME J. 14 , 2649–2658 (2020). Myers, M. R. & King, G. Isolation and characterization of Acidobacterium ailaaui sp. nov., a novel member of Acidobacteria subdivision 1, from a geothermally heated Hawaiian microbial mat. Int. J. Syst. Evol. Microbiol. 66 , 5328–5335 (2016). Ray, A. E. et al. Soil Microbiomes With the Genetic Capacity for Atmospheric Chemosynthesis Are Widespread Across the Poles and Are Associated With Moisture, Carbon, and Nitrogen Limitation. Front. Microbiol. 11 , 1936 (2020). Ji, M. et al. Candidatus Eremiobacterota, a metabolically and phylogenetically diverse terrestrial phylum with acid-tolerant adaptations. ISME J. 15 , 2692–2707 (2021). Yabe, S. et al. Vulcanimicrobium alpinus gen. nov. sp. nov., the first cultivated representative of the candidate phylum “Eremiobacterota”, is a metabolically versatile aerobic anoxygenic phototroph. ISME Commun. 2 , 120 (2022). Bay, S. K. et al. Chemosynthetic and photosynthetic bacteria contribute differentially to primary production across a steep desert aridity gradient. ISME J. 15 , 3339–3356 (2021). Gore, D. B., Gibson, J. A. E. & Leishman, M. R. Human occupation, impacts and environmental management of Bunger Hills. Antarct. Sci. 32 , 72–84 (2020). Gore, D. B., Revill, A. T. & Guille, D. Petroleum hydrocarbons ten years after spillage at a helipad in Bunger Hills, East Antarctica. Antarct. Sci. 11 , 427–429 (1999). Aislabie, J. M., Balks, M. R., Foght, J. M. & Waterhouse, E. J. Hydrocarbon Spills on Antarctic Soils: Effects and Management. Environ. Sci. Technol. 38 , 1265–1274 (2004). van Dorst, J. et al. Applying microbial indicators of hydrocarbon toxicity to contaminated sites undergoing bioremediation on subantarctic Macquarie Island. Environ. Pollut. 259 , 113780 (2020). van Dorst, J. et al. Microbial community analysis of biopiles in Antarctica provides evidence of successful hydrocarbon biodegradation and initial soil ecosystem recovery. Environ. Pollut. 290 , 117977 (2021). Jurelevicius, D. et al. Metagenomic analysis of microbial communities across a transect from low to highly hydrocarbon-contaminated soils in King George Island, Maritime Antarctica. Geobiology 20 , 98–111 (2022). Gore, D. B. & Leishman, M. R. Salt, sediments and weathering environments in Bunger Hills. Antarct. Sci. 32 , 138–152 (2020). Doran, P. T. et al. Climatology and implications for perennial lake ice occurrence at Bunger Hills Oasis, East Antarctica. Antarct. Sci. 8 , 289–296 (1996). Tucker, N. M., Hand, M. & Clark, C. D. The Bunger Hills: 60 years of geological and geophysical research. Antarct. Sci. 32 , 85–106 (2020). Kudinova, A. G. et al. Taxonomic Diversity of Bacteria and Their Filterable Forms in the Soils of Eastern Antarctica (Larsemann Hills and Bunger Hills). Microbiology 89 , 574–584 (2020). Kochkina, G. A. et al. Diversity of mycelial fungi in natural and human-affected Antarctic soils. Polar Biol. 42 , 47–64 (2019). Karaevskaya, E. S. et al. Archaeal diversity in permafrost deposits of Bunger Hills Oasis and King George Island (Antarctica) according to the 16S rRNA gene sequencing. Microbiology 83 , 398–406 (2014). Zemo, D. A. et al. Life cycle of petroleum biodegradation metabolite plumes, and implications for risk management at fuel release sites. Integr. Environ. Assess. Manag. 13 , 714–727 (2017). Zhang, E. et al. Lifting the veil on arid-to-hyperarid Antarctic soil microbiomes: a tale of two oases. Microbiome 8 , 37 (2020). Varliero, G. et al. Biogeographic survey of soil bacterial communities across Antarctica. Microbiome 12 , 9 (2024). Ray, A. E., Tribbia, D. Z., Cowan, D. A. & Ferrari, B. Clearing the air: unraveling past and guiding future research in atmospheric chemosynthesis. Microbiol. Mol. Biol. Rev. 87 , e00048-23 (2023). Whyte, L. G., Bourbonniere, L. & Greer, C. W. Biodegradation of petroleum hydrocarbons by psychrotrophic Pseudomonas strains possessing both alkane ( alk ) and naphthalene ( nah ) catabolic pathways. Appl. Environ. Microbiol. 63 , 3719–3723 (1997). Aislabie, J., Foght, J. & Saul, D. Aromatic hydrocarbon-degrading bacteria from soil near Scott Base, Antarctica. Polar Biol. 23 , 183–188 (2000). Stolz, A. Molecular characteristics of xenobiotic-degrading sphingomonads. Appl. Microbiol. Biotechnol. 81 , 793–811 (2009). Li, Q.-M., Zhou, Y.-L., Wei, Z.-F. & Wang, Y. Phylogenomic Insights into Distribution and Adaptation of Bdellovibrionota in Marine Waters. Microorganisms 9 , 757 (2021). Kababu, E. et al. Effects of crude oil and gas condensate spill on coastal benthic microbial populations. Front. Environ. Sci. 10 , 1051460 (2022). Niederberger, T. D. et al. Carbon-Fixation Rates and Associated Microbial Communities Residing in Arid and Ephemerally Wet Antarctic Dry Valley Soils. Front. Microbiol. 6 , 1347 (2015). Saul, D. J., Aislabie, J. M., Brown, C. E., Harris, L. & Foght, J. M. Hydrocarbon contamination changes the bacterial diversity of soil from around Scott Base, Antarctica. FEMS Microbiol. Ecol. 53 , 141–155 (2005). Ferrari, B. C., Zhang, C. & Van Dorst, J. Recovering Greater Fungal Diversity from Pristine and Diesel Fuel Contaminated Sub-Antarctic Soil Through Cultivation Using Both a High and a Low Nutrient Media Approach. Front. Microbiol. 2 , 217 (2011). van Dorst, J., Siciliano, S. D., Winsley, T., Snape, I. & Ferrari, B. C. Bacterial Targets as Potential Indicators of Diesel Fuel Toxicity in Subantarctic Soils. Appl. Environ. Microbiol. 80 , 4021–4033 (2014). Simanjorang, S. W. & Subowo, Y. B. The ability of soil-borne fungi to degrade polycyclic aromatic hydrocarbon (PAH). in AIP Conference Proceedings vol. 2021 030019 (2018). Ma, Y., Wang, L. & Shao, Z. Pseudomonas , the dominant polycyclic aromatic hydrocarbon-degrading bacteria isolated from Antarctic soils and the role of large plasmids in horizontal gene transfer. Environ. Microbiol. 8 , 455–465 (2006). Heipieper, H. J. & Martínez, P. M. Toxicity of Hydrocarbons to Microorganisms. in Handbook of Hydrocarbon and Lipid Microbiology (ed. Timmis, K. N.) 1563–1573 (Springer Berlin Heidelberg, Berlin, Heidelberg, 2010). doi:10.1007/978-3-540-77587-4_108. Camenzind, T., Mason-Jones, K., Mansour, I., Rillig, M. C. & Lehmann, J. Formation of necromass-derived soil organic carbon determined by microbial death pathways. Nat. Geosci. 16 , 115–122 (2023). Kostka, J. E. et al. Hydrocarbon-Degrading Bacteria and the Bacterial Community Response in Gulf of Mexico Beach Sands Impacted by the Deepwater Horizon Oil Spill. Appl. Environ. Microbiol. 77 , 7962–7974 (2011). Lin, Y. et al. Keystone predatory protists are associated closely with ammonia-oxidizing microorganisms in an acidic Ultisol. J. Sustain. Agric. Environ. 3 , e12076 (2024). Jia, C. et al. The addition of discrimination inhibitors stimulations discrimination potential and N 2 O emissions were linked to predation among microorganisms in long term nitrogen application and straw returning systems. Front. Microbiol. 14 , 1337507 (2024). Pogue, A. J. & Gilbride, K. A. Impact of protozoan grazing on nitrification and the ammonia- and nitrite-oxidizing bacterial communities in activated sludge. Can. J. Microbiol. 53 , 559–571 (2007). Hedlund, B. P. et al. SeqCode: a nomenclatural code for prokaryotes described from sequence data. Nat. Microbiol. 7 , 1702–1708 (2022). Rojas-Vargas, J., Castelán-Sánchez, H. G. & Pardo-López, L. HADEG: A curated hydrocarbon aerobic degradation enzymes and genes database. Comput. Biol. Chem. 107 , 107966 (2023). Soares, A. et al. A global perspective on bacterial diversity in the terrestrial deep subsurface. Microbiology vol. 169 001172 (2023). Huang, Y., Li, L., Yin, X. & Zhang, T. Polycyclic aromatic hydrocarbon (PAH) biodegradation capacity revealed by a genome-function relationship approach. Environ. Microbiome 18 , 39 (2023). Dunn, N. W. & Gunsalus, I. C. Transmissible Plasmid Coding Early Enzymes of Naphthalene Oxidation in Pseudomonas putida . J. Bacteriol. 114 , 974–979 (1973). Badger, M. R. & Bek, E. J. Multiple Rubisco forms in proteobacteria: their functional significance in relation to CO 2 acquisition by the CBB cycle. J. Exp. Bot. 59 , 1525–1541 (2008). Alonso-Sáez, L., Galand, P. E., Casamayor, E. O., Pedrós-Alió, C. & Bertilsson, S. High bicarbonate assimilation in the dark by Arctic bacteria. ISME J. 4 , 1581–1590 (2010). Ning, Z. et al. Metagenomic and isotopic insights into carbon fixation by autotrophic microorganisms in a petroleum hydrocarbon impacted red clay aquifer. Environ. Pollut. 361 , 124824 (2024). Abed, R. M. M. & Köster, J. The direct role of aerobic heterotrophic bacteria associated with cyanobacteria in the degradation of oil compounds. Int. Biodeterior. Biodegrad. 55 , 29–37 (2005). Hu, J., Wang, L., Zhang, S., Wang, Y. & Xi, X. Inhibitory effect of organic carbon on CO 2 fixing by non-photosynthetic microbial community isolated from the ocean. Bioresour. Technol. 102 , 7147–7153 (2011). Zhang, S. et al. Main components of free organic carbon generated by obligate chemoautotrophic bacteria that inhibit their CO 2 fixation. iScience 25 , 105553 (2022). Mancera-López, M. E. et al. Bioremediation of an aged hydrocarbon-contaminated soil by a combined system of biostimulation–bioaugmentation with filamentous fungi. Int. Biodeterior. Biodegrad. 61 , 151–160 (2008). Spohn, M., Müller, K., Höschen, C., Mueller, C. W. & Marhan, S. Dark microbial CO 2 fixation in temperate forest soils increases with CO 2 concentration. Glob. Change Biol. 26 , 1926–1935 (2020). Sarles, L. S. & Tabita, F. R. Derepression of the synthesis of D-ribulose 1,5-bisphosphate carboxylase/oxygenase from Rhodospirillum rubrum . J. Bacteriol. 153 , 458–464 (1983). Yoichi, Y., Koichi, T., Hiroyuki, A., Masaharu, I. & Yasuo, I. CO 2 -Responsive Expression and Gene Organization of Three Ribulose-1,5-Bisphosphate Carboxylase/Oxygenase Enzymes and Carboxysomes in Hydrogenovibrio marinus Strain MH-110. J. Bacteriol. 186 , 5685–5691 (2004). Wilkins, D., Mcwatters, R., Price, G., Williams, K. & Mets, J. Orthomosaic and digital surface model of Edgeworth David Camp, Bunger Hills, 06 February 2023. Australian Antarctic Data Centre https://doi.org/doi:10.26179/146h-9b03 (2025). U.S. Environmental Protection Agency (EPA). Test Methods for Evaluating Solid Waste, Physical/Chemical Methods, SW-846, Third Edition. (1993). Maynard, D., Kalra, Y. & Crumbaugh, J. Nitrate and exchangeable ammonium nitrogen. Soil Sampl. Methods Anal. 1 , 25–38 (1993). Rayment, G. E. & Lyons, D. J. Soil Chemical Methods: Australasia . vol. 3 (CSIRO publishing, 2011). Apprill, A., McNally, S., Parsons, R. & Weber, L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat. Microb. Ecol. 75 , 129–137 (2015). Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ. Microbiol. 18 , 1403–1414 (2016). Amaral-Zettler, L. A., McCliment, E. A., Ducklow, H. W. & Huse, S. M. A Method for Studying Protistan Diversity Using Massively Parallel Sequencing of V9 Hypervariable Regions of Small-Subunit Ribosomal RNA Genes. PLOS ONE 4 , e6372 (2009). Stoeck, T. et al. Multiple marker parallel tag environmental DNA sequencing reveals a highly complex eukaryotic community in marine anoxic water. Mol. Ecol. 19 , 21–31 (2010). R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing (2023). Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13 , 581–583 (2016). Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41 , D590–D596 (2013). Vaulot, D. et al. pr2database/pr2database: PR2 version 5.0.0. Zenodo https://doi.org/10.5281/zenodo.7805244 (2023). McMurdie, P. J. & Holmes, S. phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLOS ONE 8 , e61217 (2013). Andrews, S. FastQC: a quality control tool for high throughput sequence data. (2010). Ewels, P., Magnusson, M., Lundin, S. & Käller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32 , 3047–3048 (2016). Li, D., Liu, C.-M., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31 , 1674–1676 (2015). Shen, W., Le, S., Li, Y. & Hu, F. SeqKit: A Cross-Platform and Ultrafast Toolkit for FASTA/Q File Manipulation. PLOS ONE 11 , e0163962 (2016). Líndez, P. P. et al. Adversarial and variational autoencoders improve metagenomic binning. Commun. Biol. 6 , 1073 (2023). Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34 , 3094–3100 (2018). Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25 , 2078–2079 (2009). Chklovski, A., Parks, D. H., Woodcroft, B. J. & Tyson, G. W. CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning. Nat. Methods 20 , 1203–1212 (2023). Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk v2: memory friendly classification with the genome taxonomy database. Bioinformatics 38 , 5315–5316 (2022). Parks, D. H. et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat. Biotechnol. 36 , 996–1004 (2018). Eren, A. M. et al. Community-led, integrated, reproducible multi-omics with anvi’o. Nat. Microbiol. 6 , 3–6 (2021). Kananen, K. et al. Adaptive adjustment of profile HMM significance thresholds improves functional and metabolic insights into microbial genomes. Bioinforma. Adv. 5 , vbaf039 (2025). Jones, P. et al. InterProScan 5: genome-scale protein function classification. Bioinformatics 30 , 1236–1240 (2014). Tanizawa, Y., Fujisawa, T. & Nakamura, Y. DFAST: a flexible prokaryotic genome annotation pipeline for faster genome publication. Bioinformatics 34 , 1037–1039 (2018). Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11 , 119 (2010). Bland, C. et al. CRISPR Recognition Tool (CRT): a tool for automatic detection of clustered regularly interspaced palindromic repeats. BMC Bioinformatics 8 , 209 (2007). Chan, P. P., Lin, B. Y., Mak, A. J. & Lowe, T. M. tRNAscan-SE 2.0: improved detection and functional classification of transfer RNA genes. Nucleic Acids Res. 49 , 9077–9096 (2021). Alcock, B. P. et al. CARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database. Nucleic Acids Res. 51 , D690–D699 (2023). Liu, B., Zheng, D., Zhou, S., Chen, L. & Yang, J. VFDB 2022: a general classification scheme for bacterial virulence factors. Nucleic Acids Res. 50 , D912–D917 (2022). Haft, D. H. et al. TIGRFAMs and Genome Properties in 2013. Nucleic Acids Res. 41 , D387–D395 (2013). Finn, R. D. et al. Pfam: the protein families database. Nucleic Acids Res. 42 , D222–D230 (2014). Zheng, J. et al. dbCAN3: automated carbohydrate-active enzyme and substrate annotation. Nucleic Acids Res. 51 , W115–W121 (2023). Galperin, M. Y. et al. COG database update: focus on microbial diversity, model organisms, and widespread pathogens. Nucleic Acids Res. 49 , D274–D281 (2021). Koonin, E. V. et al. A comprehensive evolutionary classification of proteins encoded in complete eukaryotic genomes. Genome Biol. 5 , R7 (2004). Wang, J. et al. The conserved domain database in 2023. Nucleic Acids Res. 51 , D384–D388 (2023). Nawrocki, E. P. & Eddy, S. R. Infernal 1.1: 100-fold faster RNA homology searches. Bioinformatics 29 , 2933–2935 (2013). Kalvari, I. et al. Rfam 14: expanded coverage of metagenomic, viral and microRNA families. Nucleic Acids Res. 49 , D192–D200 (2021). Barnum, T. P. et al. Predicting microbial growth conditions from amino acid composition. Preprint at https://doi.org/10.1101/2024.03.22.586313 (2024). Mangiola, S. & Papenfuss, A. T. tidyHeatmap: an R package for modular heatmap production based on tidy principles. J. Open Source Softw. 5 , 2472 (2020). Patil, I. Visualizations with statistical details: The ‘ggstatsplot’ approach. J. Open Source Softw. 6 , 3167 (2021). Yuen, K. K. The Two-Sample Trimmed t for Unequal Population Variances. Biometrika 61 , 165–170 (1974). Camacho, C. et al. BLAST+: architecture and applications. BMC Bioinformatics 10 , 421 (2009). Katoh, K. & Standley, D. M. MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability. Mol. Biol. Evol. 30 , 772–780 (2013). Steenwyk, J. L., Buida, T. J., III, Li, Y., Shen, X.-X. & Rokas, A. ClipKIT: A multiple sequence alignment trimming software for accurate phylogenomic inference. PLOS Biol. 18 , e3001007 (2020). Minh, B. Q. et al. IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era. Mol. Biol. Evol. 37 , 1530–1534 (2020). Hoang, D. T., Chernomor, O., von Haeseler, A., Minh, B. Q. & Vinh, L. S. UFBoot2: Improving the Ultrafast Bootstrap Approximation. Mol. Biol. Evol. 35 , 518–522 (2018). Guindon, S. et al. New Algorithms and Methods to Estimate Maximum-Likelihood Phylogenies: Assessing the Performance of PhyML 3.0. Syst. Biol. 59 , 307–321 (2010). Letunic, I. & Bork, P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 44 , W242–W245 (2016). Wickham, H. Ggplot2: Elegant Graphics for Data Analysis . (Springer-Verlag New York, 2016). Pedersen, T. L. Patchwork: The Composer of Plots . (2025). Wickham, H., François, R., Henry, L., Müller, K. & Vaughan, D. Dplyr: A Grammar of Data Manipulation . (2023). Kassambara, A. Rstatix: Pipe-Friendly Framework for Basic Statistical Tests . (2023). Oksanen, J. et al. Vegan: Community Ecology Package . (2022). Lahti, L. & Shetty, S. Tools for microbiome analysis in R. Bioconductor version: Release (3.18) https://doi.org/10.18129/B9.bioc.microbiome (2023). Legendre, P. & Anderson, M. J. Distance-Based Redundancy Analysis: Testing Multispecies Responses in Multifactorial Ecological Experiments. Ecol. Monogr. 69 , 1–24 (1999). McArdle, B. H. & Anderson, M. J. Fitting Multivariate Models to Community Data: A Comment on Distance-Based Redundancy Analysis. Ecology 82 , 290–297 (2001). Martinez Arbizu, P. pairwiseAdonis: Pairwise multilevel comparison using adonis. (2020). Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15 , 550 (2014). Love, M., Anders, S. & Huber, W. Differential analysis of count data–the DESeq2 package. Genome Biol 15 , 10–1186 (2014). Holm, S. A Simple Sequentially Rejective Multiple Test Procedure. Scand. J. Stat. 6 , 65–70 (1979). Bindschadler, R. et al. The Landsat image mosaic of Antarctica. Remote Sens. Environ. 112 , 4214–4226 (2008). Additional Declarations There is NO Competing Interest. Supplementary Files TanetalBungerHillsTables.docx Table 1 TanetalBungerHillsSupplementarytables.xlsx Dataset 1 TanetalSupplementaryinformation.pdf ExtDatFig1euk.ai Extended data figure legends Extended Data Figure 1. Composition of eukaryotic communities of Bunger Hills at the (A) class and (B) genus level. Stacked bar graphs represent the relative abundance of the top 10 taxa for each taxonomic level. BT samples were dominated by Trebouxia (0.02–90.9%), Dunaliella (0–33.7%), Halteria (0–20.8%), Diphascon (0–16.8%), Opisthonecta (0–14.7%), and Allovahlkampfia (0.05–10.5%). HT samples had high levels of Scotinosphaera (0.2–51.9%) and Spumella (1.2–30.9%). In HZ samples, Fungi (61.4–65.0%), consisting of Phialophora (0–52.1%), were the most prevalent followed by Gyrista (14.2–20.8%), of which Spumella (16.8–29.5%) was dominant. ExtDatFig2deseq.ai Extended Data Figure 2. Differentially abundant phyla based on (A) 16S and (B) 18S rRNA gene amplicon using DESeq2 analysis. Shown are significant ( P adj. < 0.01) log-fold changes between the (A) bacterial and (B) micro-eukaryotic communities in the different transects and zone. Between the Helipad Zone (HZ) and Background Transect (BT), Pseudomonadota , Patescibacteriota and Bdellovibrionota were more abundant in HZ, and Chloroflexota and NB1-J were more abundant in BT. Between Helipad Transect (HT) and HZ, Pseudomonadota and Bdellovibrionota were more abundant in HZ and Verrucomicrobiota in HT. Only NB1-J was more abundant in BT when compared to HT . (B) A higher number of differentially abundant phyla were observed between HZ and BT, with Variosea , Basidiomycota and Ascomycota more abundant in HZ, and Trebouxiophyceae , Nebulidea , Chlorophyceae , Bacillariophyceae, and unclassified eukaryota in BT. The HT and HZ showed six differently abundant phyla, while two differently abundant taxa, Nebulidea and Bacillariophyceae, that were more abundant in BT . ExtDatFig3spearman.ai Extended Data Figure 3. Heatmap of Spearman correlation coefficients between (A) bacterial phylum-level abundances, or (B) eukaryotic phylum-level abundances, and environmental chemical variables. The abundances were normalised using DESeq2 regularised log transformation (rlog) or variance-stabilising transformation (VST) prior to correlation analysis, and variables were log transformed. Each cell in the heatmap represents the Spearman correlation coefficient (ρ) between a microbial class and a specific chemical variable. The colour gradient represents the strength and direction of the correlation, with colours ranging from blue (negative correlations) to red (positive correlations). Cite Share Download PDF Status: Published Journal Publication published 17 Feb, 2026 Read the published version in Communications Earth & Environment → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6608817","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":460852912,"identity":"fe9f2f62-9633-4db6-820b-90c32467dc1f","order_by":0,"name":"Kellynn K. Y. Tan","email":"","orcid":"https://orcid.org/0000-0003-2295-7068","institution":"School of Biotechnology and Biomolecular Sciences, UNSW Sydney, NSW, Australia, 2052; Evolution and Ecology Research Centre, UNSW Sydney, NSW, Australia, 2052","correspondingAuthor":false,"prefix":"","firstName":"Kellynn","middleName":"K. Y.","lastName":"Tan","suffix":""},{"id":460852913,"identity":"10165110-c48e-4003-ab19-8f290b58a89b","order_by":1,"name":"Xabier Vázquez-Campos","email":"","orcid":"https://orcid.org/0000-0003-1134-5058","institution":"School of Biotechnology and Biomolecular Sciences, UNSW Sydney, NSW, Australia, 2052; Evolution and Ecology Research Centre, UNSW Sydney, NSW, Australia, 2052","correspondingAuthor":false,"prefix":"","firstName":"Xabier","middleName":"","lastName":"Vázquez-Campos","suffix":""},{"id":460852914,"identity":"24fc82eb-3da5-47d8-adc8-097cf254d006","order_by":2,"name":"Gwilym A. V. Price","email":"","orcid":"","institution":"Environmental Stewardship Program, Australian Antarctic Division, Department of Climate Change, Energy, the Environment and Water, 203 Channel Highway, Kingston, TAS, Australia, 7050","correspondingAuthor":false,"prefix":"","firstName":"Gwilym","middleName":"A. V.","lastName":"Price","suffix":""},{"id":460852915,"identity":"99da5ccf-c4b2-4ee2-b71b-ea0b9dbca986","order_by":3,"name":"Kasey L. Williams","email":"","orcid":"","institution":"Environmental Stewardship Program, Australian Antarctic Division, Department of Climate Change, Energy, the Environment and Water, 203 Channel Highway, Kingston, TAS, Australia, 7050","correspondingAuthor":false,"prefix":"","firstName":"Kasey","middleName":"L.","lastName":"Williams","suffix":""},{"id":460852916,"identity":"01ff88b0-d8ad-4b96-8a65-f1922aabf881","order_by":4,"name":"Rebecca S. McWatters","email":"","orcid":"","institution":"Environmental Stewardship Program, Australian Antarctic Division, Department of Climate Change, Energy, the Environment and Water, 203 Channel Highway, Kingston, TAS, Australia, 7050","correspondingAuthor":false,"prefix":"","firstName":"Rebecca","middleName":"S.","lastName":"McWatters","suffix":""},{"id":460852917,"identity":"9a8ee6c4-6f69-42ad-a5be-5d80798cb805","order_by":5,"name":"Kristopher Abdullah","email":"","orcid":"","institution":"School of Biotechnology and Biomolecular Sciences, UNSW Sydney, NSW, Australia, 2052; Evolution and Ecology Research Centre, UNSW Sydney, NSW, Australia, 2052","correspondingAuthor":false,"prefix":"","firstName":"Kristopher","middleName":"","lastName":"Abdullah","suffix":""},{"id":460852918,"identity":"c721a985-0114-4b23-83a2-8c94f56afc8b","order_by":6,"name":"Náthali Machado de Lima","email":"","orcid":"","institution":"School of Biotechnology and Biomolecular Sciences, UNSW Sydney, NSW, Australia, 2052; Evolution and Ecology Research Centre, UNSW Sydney, NSW, Australia, 2052","correspondingAuthor":false,"prefix":"","firstName":"Náthali","middleName":"Machado","lastName":"de Lima","suffix":""},{"id":460852919,"identity":"95f959de-150a-4764-bfa3-2d2ada0510e1","order_by":7,"name":"Thomas Laird","email":"","orcid":"","institution":"School of Biotechnology and Biomolecular Sciences, UNSW Sydney, NSW, Australia, 2052","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Laird","suffix":""},{"id":460852920,"identity":"5a9a97fa-0c4a-40e0-9dec-b9a1cd3ac412","order_by":8,"name":"Angelique Ray","email":"","orcid":"https://orcid.org/0000-0003-0051-1938","institution":"School of Biotechnology and Biomolecular Sciences, UNSW Sydney, NSW, Australia, 2052; Evolution and Ecology Research Centre, UNSW Sydney, NSW, Australia, 2052","correspondingAuthor":false,"prefix":"","firstName":"Angelique","middleName":"","lastName":"Ray","suffix":""},{"id":460852921,"identity":"867c3e31-0fb0-43a9-a234-8bba6b9a8c9f","order_by":9,"name":"Jordan A. Vink","email":"","orcid":"","institution":"School of Biotechnology and Biomolecular Sciences, UNSW Sydney, NSW, Australia, 2052; Evolution and Ecology Research Centre, UNSW Sydney, NSW, Australia, 2052","correspondingAuthor":false,"prefix":"","firstName":"Jordan","middleName":"A.","lastName":"Vink","suffix":""},{"id":460852922,"identity":"d772a395-0312-4155-abed-953e03f89006","order_by":10,"name":"Dana Z. Tribbia","email":"","orcid":"https://orcid.org/0000-0003-4165-0352","institution":"School of Biotechnology and Biomolecular Sciences, UNSW Sydney, NSW, Australia, 2052; Evolution and Ecology Research Centre, UNSW Sydney, NSW, Australia, 2052","correspondingAuthor":false,"prefix":"","firstName":"Dana","middleName":"Z.","lastName":"Tribbia","suffix":""},{"id":460852923,"identity":"eba76c15-d65b-4ad7-ab3e-e38588720a8c","order_by":11,"name":"Daniel Wilkins","email":"","orcid":"","institution":"School of Biotechnology and Biomolecular Sciences, UNSW Sydney, NSW, Australia, 2052; Environmental Stewardship Program, Australian Antarctic Division, Department of Climate Change, Energy, the Environment and Water, 203 Channel Highway, Kingston, TAS, Australia, 7050","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Wilkins","suffix":""},{"id":460852924,"identity":"09c5baa5-ccd2-4429-afa8-d8d449c6783b","order_by":12,"name":"Tim Spedding","email":"","orcid":"","institution":"Environmental Stewardship Program, Australian Antarctic Division, Department of Climate Change, Energy, the Environment and Water, 203 Channel Highway, Kingston, TAS, Australia, 7050","correspondingAuthor":false,"prefix":"","firstName":"Tim","middleName":"","lastName":"Spedding","suffix":""},{"id":460852911,"identity":"11b4554b-48cd-4c0c-9286-807626e23409","order_by":13,"name":"Belinda C. Ferrari","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYBACAwbGB2AGG3sDVOgAQS3MBhAtPDClRGthkEggUos5AzPj48o9dvl8km/MHv5sY5Dju5HA+JkHjxbLBmZmwzPPki3bpHPMjXnbGIwlbyQwS+PTYnCA/5hkwwFmAzbpHDNpxm0MiRtuJDAQ0MLM/rPhQL0Bm+QZM8mf2xjqgVqYfxPQwsbYcOCwAZsEj5kE7zaGBIMbCWx4bbFsZmYGOuy4ARtPWpk07z8Jw5lnHrZZzsGjxZy9mfFjw4FqA/n2w9skf5yxkec7nnz4xhs8WhiYUbkSQMzYgE/DKBgFo2AUjAIiAAAjOUNBRMtS4AAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-5043-3726","institution":"School of Biotechnology and Biomolecular Sciences, UNSW Sydney, NSW, Australia, 2052; Evolution and Ecology Research Centre, UNSW Sydney, NSW, Australia, 2052","correspondingAuthor":true,"prefix":"","firstName":"Belinda","middleName":"C.","lastName":"Ferrari","suffix":""}],"badges":[],"createdAt":"2025-05-07 06:45:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6608817/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6608817/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s43247-026-03299-0","type":"published","date":"2026-02-17T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89502185,"identity":"7b11546a-665c-4b14-bf52-30a3a4cee191","added_by":"auto","created_at":"2025-08-20 16:21:29","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1735556,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003e Landsat Image Mosaic of Antarctica, showing the location of the Bunger Hills in Wilkes Land, East Antarctica\u003csup\u003e129\u003c/sup\u003e. \u003cstrong\u003e(b) \u003c/strong\u003eThe ice-free Bunger Hills is surrounded by the Shackleton ice-shelf to the north, and the Antarctic ice-sheet to the east/south. 200 m contours are shown by dashed blue lines. The study location is shown by the white circle. \u003cstrong\u003e(c)\u003c/strong\u003e Orthomosaic of the study area (Edgeworth David Base), showing the background (green dashed line) and helipad sampling transect (yellow dashed line). Sample locations are coloured according to measured EC, and numbered, except for the helipad zone. Easting and Northing shown (WGS84 UTM 47S).\u003c/p\u003e","description":"","filename":"Fig1sitemap.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6608817/v1/37a19b2a6e26ed18a9c82d49.jpg"},{"id":89501024,"identity":"315f4627-3e49-4480-9e1d-99875aa2f8c8","added_by":"auto","created_at":"2025-08-20 16:05:30","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":578819,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSoil microbial diversity of Bunger Hills spanning Bacteria + Archaea 16S ASVs and Eukarya 18S ASVs.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e Alpha diversity indices for species richness (Chao1), diversity (Inv. Simpson), and evenness (Pielou’s Evenness); boxplots display the median and interquartile range. \u003cstrong\u003e(B) \u003c/strong\u003eBeta diversity visualised using NMDS ordination. \u003cstrong\u003e(C) \u003c/strong\u003eDistance-based redundancy analysis (db-RDA) of ASVs constrained to a set of explanatory variables selected using a forward model selection method. The analyses for beta diversity were based on Bray-Curtis dissimilarities. \u003cstrong\u003e(D)\u003c/strong\u003e Microbial community composition in each sample. Stacked bar graphs represent the relative abundance of the top 10 Bacteria + Archaea (left) and Eukarya (right) at the phylum/division level.\u003c/p\u003e","description":"","filename":"Fig2diversity.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6608817/v1/6580ba34b84d843f7b068631.jpg"},{"id":89502748,"identity":"80d73f87-a819-40b6-be30-b1823b65ffa3","added_by":"auto","created_at":"2025-08-20 16:29:30","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1439434,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMaximum-likelihood phylogenetic tree of metagenome-assembled genomes (MAGs) from Bunger Hills soil metagenomes. \u003c/strong\u003eThe tree was constructed using a concatenated alignment of 120 conserved bacterial markers. Concentric rings moving outward from the tree show the phylum of a MAG, its sampling location of origin, whether high-affinity hydrogenases and/or RuBisCO form IE was encoded by the MAG, and/or indication if it is a novel taxon described in this work. All MAGs co-encoding a high-affinity [NiFe]-hydrogenase and RuBisCO form IE belong to \u003cem\u003eActinomycetota\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Fig3magtree.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6608817/v1/5571c25af5f48e457a56bf58.jpg"},{"id":89501020,"identity":"27df85f6-d5ed-49b3-9928-912e69e38117","added_by":"auto","created_at":"2025-08-20 16:05:29","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":586375,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMetabolic profiling of Bunger Hills metagenomes and MAGs.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e The prevalence of the marker genes across MAGs recovered for each phylum. \u003cstrong\u003e(B)\u003c/strong\u003e Values are the log\u003csub\u003e2\u003c/sub\u003e of normalised RPKM against the mean RPKM of 15 ribosomal protein markers as a proxy of the number of genomic copies (i.e. cellular genomes) across metagenomes. Functional markers not detected in any soils or MAGs were excluded. Here, \u003cem\u003eActinomycetota \u003c/em\u003eand \u003cem\u003ePseudomonadota \u003c/em\u003eMAGs exhibited highly diverse metabolic capabilities spanning the eight categories tested.\u003c/p\u003e","description":"","filename":"Fig4metabolicheatmap.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6608817/v1/1c0c84e6147800e67bfd4d08.jpg"},{"id":89501718,"identity":"0c1bf0ac-4636-4c0f-b239-e89566ff4e42","added_by":"auto","created_at":"2025-08-20 16:13:30","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":510539,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A) Maximum-likelihood phylogenetic tree of representative sequences of large subunit of high-affinity hydrogenases from Bunger Hills.\u003c/strong\u003e Clades were color-coded to reflect the subtype of [NiFe]-hydrogenase, as determined through both phylogenetic and gene structure analysis. Nodes with aLRT support values ≥ 80 or ultrafast bootstrap support ≥ 90 are indicated with a black circle. The first three concentric rings moving outward from the tree indicate the detection of each sequence cluster in each sampling location (Helipad Zone (HZ), Helipad Transect (HT), or Background Transect (BT)). The following rings show the size of each sequence cluster, the detection of any sequences from the cluster within a MAG, and if it co-occurs with RuBisCO form IE in a MAG. \u003cstrong\u003e(B) The oxidation of H\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e by hydrocarbon-impacted soil microcosms, measured using gas chromatography.\u003c/strong\u003e Shown are the oxidation rates of soils (1 g/replicate), incubated at 10 °C in the dark, from (left) the BT and HT representing uncontaminated and, (right) the HZ representing contaminated samples. The TRH post-SGC reported in BH-09, BH-10, and BH-11 were 750, 440, and 50 mg/kg, respectively. Sterile serum bottles, and heat-killed soils were used as controls. Points represent mean H\u003csub\u003e2\u003c/sub\u003e in the headspace for triplicates and vertical lines represent one SD from the mean. The black dashed lines represent H\u003csub\u003e2\u003c/sub\u003e at atmospheric concentrations (530 p.p.b.v.). Fast reaction rates of high-affinity H\u003csub\u003e2\u003c/sub\u003e oxidation were observed in pristine soils, particularly in BH-18 (476.3 ± 74.1 nmol/mol/h/g), BH-24 (429.6 ± 108.4 nmol/mol/h/g), and BH-20 (304.8 ± 43.7 nmol/mol/h/g). Rates were greatly reduced in contaminated soils BH-09 (0.5 ± 0.1 nmol/mol/h/g), BH-10 (1.03 ± 0.04 nmol/mol/h/g), and BH-11 (1.7 ± 0.2 nmol/mol/h/g).\u003c/p\u003e","description":"","filename":"Fig5hyd.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6608817/v1/491bbb15c1ec0c7b34f2f51d.jpg"},{"id":89501026,"identity":"26c929f3-0679-4a8a-8279-4e2e742dbee5","added_by":"auto","created_at":"2025-08-20 16:05:30","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":489985,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A) Maximum likelihood phylogenetic tree of representative RuBisCO form I sequences from Bunger Hills\u003c/strong\u003e. Clades are colour-coded by RuBisCO form, determined by the placement of reference sequences. Nodes with aLRT support values ≥ 80 or ultrafast bootstrap support ≥ 90 are indicated with a black circle. The source sample category of transect (Helipad Zone (HZ), Helipad Transect (HT), or Background Transect (BT)) is shown in the outer ring. The first three concentric rings moving outward from the tree indicate the detection of each sequence cluster in each sampling location (HZ, HT, or BT). The following rings show the size of each sequence cluster, the detection of any members of the sequence cluster in a MAG, and if it co-occurs with high affinity hydrogenases in any MAG.\u003cstrong\u003e (B) The dark assimilation of \u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e14\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eCO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e by hydrocarbon-impacted soil microcosms.\u003c/strong\u003e Shown are the rates of \u003csup\u003e14\u003c/sup\u003eCO\u003csub\u003e2\u003c/sub\u003e\u003csub\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/sub\u003efixation by soil microcosms (0.5 g each) per day, in dark conditions at 10 °C. Heat-killed soils for each condition were included as controls. Box and whisker plots represent the IQR with the median and the range of data within 1.5 times the IQR, respectively. Rates are normalised to 16S rRNA gene copies for each respective sample (Table S18). Wilcoxon rank sum test (unpaired) showed significant (\u003cem\u003eP \u003c/em\u003eadj. = 0.01) differences in \u003csup\u003e14\u003c/sup\u003eCO\u003csub\u003e2\u003c/sub\u003e fixation rates between groups Contaminated (HZ) and Uncontaminated (BT and HT) (Table S20).\u003c/p\u003e","description":"","filename":"Fig6rub.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6608817/v1/1e9c73d91e50209381dd37dd.jpg"},{"id":105536206,"identity":"4e7af57e-816e-4738-a817-e3c70a902707","added_by":"auto","created_at":"2026-03-27 07:12:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6890119,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6608817/v1/be77fe79-bf01-487e-b812-b5d88b54c220.pdf"},{"id":89501714,"identity":"9790a7e1-1802-40d2-b2ae-968d85697ecf","added_by":"auto","created_at":"2025-08-20 16:13:29","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":30233,"visible":true,"origin":"","legend":"Table 1","description":"","filename":"TanetalBungerHillsTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6608817/v1/cd47a1543bd1a5f3380631da.docx"},{"id":89502186,"identity":"d7ea436c-0db0-4882-a01b-07b4326179a8","added_by":"auto","created_at":"2025-08-20 16:21:29","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1800037,"visible":true,"origin":"","legend":"\u003cp\u003eDataset 1\u003c/p\u003e","description":"","filename":"TanetalBungerHillsSupplementarytables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6608817/v1/68d9c831fbde1318cdfece91.xlsx"},{"id":89501716,"identity":"d2f14de6-3f7b-49c7-beac-b038e6796235","added_by":"auto","created_at":"2025-08-20 16:13:30","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":903291,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"TanetalSupplementaryinformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6608817/v1/048ee87ab862381d2d9bf222.pdf"},{"id":89501017,"identity":"dd1689b7-3d5c-4a87-9594-da3bf2d3dc1e","added_by":"auto","created_at":"2025-08-20 16:05:29","extension":"ai","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":439562,"visible":true,"origin":"","legend":"\u003cp\u003eExtended data figure legends\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExtended Data Figure 1. Composition of eukaryotic communities of Bunger Hills at the (A) class and (B) genus level. \u003c/strong\u003eStacked bar graphs represent the relative abundance of the top 10 taxa for each taxonomic level. BT samples were dominated by \u003cem\u003eTrebouxia\u003c/em\u003e (0.02–90.9%), \u003cem\u003eDunaliella\u003c/em\u003e (0–33.7%), \u003cem\u003eHalteria\u003c/em\u003e(0–20.8%), \u003cem\u003eDiphascon\u003c/em\u003e (0–16.8%), \u003cem\u003eOpisthonecta\u003c/em\u003e (0–14.7%), and \u003cem\u003eAllovahlkampfia\u003c/em\u003e(0.05–10.5%). HT samples had high levels of \u003cem\u003eScotinosphaera\u003c/em\u003e (0.2–51.9%) and \u003cem\u003eSpumella\u003c/em\u003e (1.2–30.9%). In HZ samples, \u003cem\u003eFungi\u003c/em\u003e (61.4–65.0%), consisting of \u003cem\u003ePhialophora\u003c/em\u003e (0–52.1%), were the most prevalent followed by \u003cem\u003eGyrista\u003c/em\u003e (14.2–20.8%), of which \u003cem\u003eSpumella\u003c/em\u003e (16.8–29.5%) was dominant.\u003c/p\u003e","description":"","filename":"ExtDatFig1euk.ai","url":"https://assets-eu.researchsquare.com/files/rs-6608817/v1/be045db9ef2bd22f7b238736.ai"},{"id":89501720,"identity":"d7451524-0889-4c97-8635-1a8e4df6941a","added_by":"auto","created_at":"2025-08-20 16:13:30","extension":"ai","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":391277,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExtended Data Figure 2. Differentially abundant phyla based on (A) 16S and (B)\u003c/strong\u003e \u003cstrong\u003e18S rRNA gene amplicon using DESeq2 analysis.\u003c/strong\u003e Shown are significant (\u003cem\u003eP\u003c/em\u003e adj. \u0026lt; 0.01) log-fold changes between the (A) bacterial and (B) micro-eukaryotic communities in the different transects and zone. Between the Helipad Zone (HZ) and Background Transect (BT), \u003cem\u003ePseudomonadota\u003c/em\u003e, \u003cem\u003ePatescibacteriota\u003c/em\u003e and \u003cem\u003eBdellovibrionota\u003c/em\u003e were more abundant in HZ, and \u003cem\u003eChloroflexota \u003c/em\u003eand NB1-J were more abundant in BT. Between Helipad Transect (HT) and HZ, \u003cem\u003ePseudomonadota \u003c/em\u003eand \u003cem\u003eBdellovibrionota \u003c/em\u003ewere more abundant in HZ and \u003cem\u003eVerrucomicrobiota\u003c/em\u003e in HT. Only NB1-J\u003cem\u003e \u003c/em\u003ewas more abundant in BT when compared to HT\u003cem\u003e.\u003c/em\u003e (B) A higher number of differentially abundant phyla were observed between HZ and BT, with \u003cem\u003eVariosea\u003c/em\u003e, \u003cem\u003eBasidiomycota\u003c/em\u003e and \u003cem\u003eAscomycota \u003c/em\u003emore abundant in HZ, and \u003cem\u003eTrebouxiophyceae\u003c/em\u003e, \u003cem\u003eNebulidea\u003c/em\u003e, \u003cem\u003eChlorophyceae\u003c/em\u003e, \u003cem\u003eBacillariophyceae, \u003c/em\u003eand unclassified eukaryota in BT. The HT and HZ showed six differently abundant phyla, while two differently abundant taxa, \u003cem\u003eNebulidea \u003c/em\u003eand \u003cem\u003eBacillariophyceae, \u003c/em\u003ethat were\u003cem\u003e \u003c/em\u003emore abundant in BT\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"ExtDatFig2deseq.ai","url":"https://assets-eu.researchsquare.com/files/rs-6608817/v1/63cd6feb41afc02da0c8ea6d.ai"},{"id":89501027,"identity":"ea7efc9d-a545-422a-aa30-347258b79522","added_by":"auto","created_at":"2025-08-20 16:05:30","extension":"ai","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":391272,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExtended Data Figure 3\u003c/strong\u003e. \u003cstrong\u003eHeatmap of Spearman correlation coefficients between (A) bacterial phylum-level abundances, or (B) eukaryotic phylum-level abundances, and environmental chemical variables. \u003c/strong\u003eThe abundances were normalised using DESeq2 regularised log transformation (rlog) or variance-stabilising transformation (VST) prior to correlation analysis, and variables were log transformed. Each cell in the heatmap represents the Spearman correlation coefficient (ρ) between a microbial class and a specific chemical variable. The colour gradient represents the strength and direction of the correlation, with colours ranging from blue (negative correlations) to red (positive correlations).\u003c/p\u003e","description":"","filename":"ExtDatFig3spearman.ai","url":"https://assets-eu.researchsquare.com/files/rs-6608817/v1/95d8379522479ae915af3a39.ai"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Persistent Petroleum Pollution: Microbial Responses in Bunger Hills, East Antarctica","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUbiquitous and highly permeable atmospheric trace gases such as molecular hydrogen (H\u003csub\u003e2\u003c/sub\u003e) is a major energy source for polar desert microorganisms\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, complementing organoheterotrophy for survival under low moisture and oligotrophic conditions\u003csup\u003e\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The low abundance of photosynthetic organisms and absence of sunlight in the austral winter leaves chemolithoautotrophy via the Calvin-Benson-Bassham (CBB) cycle an important pathway to support polar communities\u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Emerging evidence shows a growing diversity of microbes that oxidise atmospheric H\u003csub\u003e2\u003c/sub\u003e using high-affinity [NiFe]-hydrogenases\u003csup\u003e\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e to support carbon fixation using the light-independent ribulose-1,5-bisphosphate carboxylase-oxygenase (RuBisCO) form IE, a process known as atmospheric chemosynthesis\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe remote Bunger Hills region of East Antarctica, located 450 km west of the Australian Casey station, is an ice-free oasis surrounded by the Shackleton Ice Shelf and the Denman glacier. These natural barriers help preserve its pristine environment, but despite its isolation, records since the 1940s document introduction of waste by visitors\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Notably, minor hydrocarbon-based spills from aircraft maintenance near the Australian Edgeworth David Base in 1985 persist to this day\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Hydrocarbon contamination alters soil physicochemical properties and organic matter content\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, adversely affecting microbial richness, diversity and function\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e with the extent often correlating with contaminant concentrations\u003csup\u003e\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Previous studies of the region include historical palaeogeological events\u003csup\u003e\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, as well as, bacteria\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, fungal\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, and permafrost archaeal\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e diversity, but an in-depth understanding of the microbial ecology is lacking. With Antarctica increasingly vulnerable to climate change and anthropogenic activity, there is an urgent need to study the edaphic ecology of the Bunger Hills.\u003c/p\u003e\u003cp\u003ePresently, hydrocarbon contamination\u0026rsquo;s impact on microbial survival strategies in Antarctica, particularly atmospheric trace gas oxidation and carbon fixation is not known. By combining soil physicochemistry, 16S and 18S amplicon and shotgun metagenomic data, alongside gas chromatography and \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003eCO\u003csub\u003e2\u003c/sub\u003e fixation microcosm assays for 26 samples from the Bunger Hills, we present an extensive study of the microbial diversity and activity of soil in the area and also the effects of a legacy contamination on important microbial processes.\u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cp\u003eThe study was conducted near the Australian Edgeworth David Base of Bunger Hills, East Antarctica. Samples were collected from two 100 m transects: the Background Transect (BT), and the Helipad Transect (HT) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e); both ran across melt streams exiting an un-named lake, with BT being nearer. Soils from the top 10 cm were sampled at 25 m intervals along each transect. Additionally, 16 samples from 44.5–51 m along the HT were designated as the Helipad Zone (HZ), with five samples taken from 20–41 cm (\u003cem\u003en\u003c/em\u003e = 26).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eSite description and soil properties\u003c/em\u003e\u003c/p\u003e\u003cp\u003eConsistent with findings across continental Antarctica\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, Bunger Hills soils exhibited low organic carbon (0.05–0.43%) (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Total recoverable hydrocarbons (TRH) were detected (limit of reporting 50 mg/kg) in five HZ samples (BH-09, BH-10, BH-11, BH-12, BH-14), with concentrations ranging from 130–1930 mg/kg (C\u003csub\u003e10\u003c/sub\u003e–C\u003csub\u003e40\u003c/sub\u003e). The elevated TRH in HZ was consistent with previous findings\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. While the relative change in TRH concentration between sampling events of Gore et al.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e and this study cannot be inferred due to soil heterogeneity and uncertainty of the exact sampling locations, post-silica gel clean-up (SGC) TRH concentrations revealed potential natural biological attenuation of the fuel, with an average reduction in TRH concentration pre- and post-SGC of 69% (S.D. = 7%, \u003cem\u003en\u003c/em\u003e = 5). Given the low natural organic carbon concentrations in the soils, a substantial portion of this reduction in TRH was likely attributed to polar metabolites, indicating fuel degradation\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eSeveral environmental gradients were revealed across the site, with moisture levels ranging from 1.4–14% and pH levels between 6.3–10.0. The pH and salinity were significantly (\u003cem\u003eP\u003c/em\u003e adj. \u0026lt; 0.05) higher in BT than in HZ soils (Tables S2–3). Notably, BT samples BH-24, BH-21, and BH-23 had high alkaline pH (9–10) and high electrical conductivity (EC) (125, 460, 3540 µS/cm EC at 25°C, respectively), which matched visible salt encrustations on the soil surface (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec).\u003c/p\u003e\u003cp\u003e\u003cem\u003eMicrobiomes shift toward hydrocarbon degraders and predatory taxa in contaminated soils\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe processed dataset from amplicon sequencing included 2,587,999 prokaryotic and 3,242,740 eukaryotic gene sequences from 26 samples (Tables S4–5). The dataset included 7,770 Bacteria/Archaea amplicon sequence variants (ASVs) classified to 35 bacterial phyla and four archaeal phyla, and 1,890 Eukarya ASVs belonging to 42 divisions (Tables S6–7).\u003c/p\u003e\u003cp\u003eAlpha diversity analysis of ASVs revealed significantly (\u003cem\u003eP\u003c/em\u003e adj. \u0026lt; 0.05) higher prokaryotic richness (Chao1) in HT than BT, and HZ (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and Tables S8–9), with no significant difference (\u003cem\u003eP\u003c/em\u003e adj. \u0026gt;0.05) between BT and HZ. Diversity (Inverse Simpson) and evenness (Pielou’s Evenness) indices showed no significant differences (\u003cem\u003eP\u003c/em\u003e adj. \u0026gt;0.05) between sampling locations, alongside no significant differences in eukaryotic alpha diversity indices. NMDS ordination using environmental variables at 16S ASVs showed HT samples clustered tightly, reflecting similar community structures, while BT samples dispersed along the second axis, and HZ exhibited the greatest dispersion across both axes. For 18S ASVs, BT samples were distributed across both axes, while HT and HZ samples clustered closely (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eConsistent with other soil surveys in ice-free Antarctica\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, bacterial phyla predominantly comprised of \u003cem\u003eActinomycetota\u003c/em\u003e (13.7–49.7%), \u003cem\u003ePseudomonadota\u003c/em\u003e (4.3–70.0%), \u003cem\u003eBacteroidota\u003c/em\u003e (2.2–26.6%), \u003cem\u003eAcidobacteriota\u003c/em\u003e (0.5–24.6%), \u003cem\u003eChloroflexota\u003c/em\u003e (0.4–18.6%), and \u003cem\u003eVerrucomicrobiota\u003c/em\u003e (2.3–16.5%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD and Table S6). Among the 26 samples, 14 had ≤ 1% \u003cem\u003eCyanobacteriota\u003c/em\u003e, seven ranged from 1.2–6.2%, and five had 10.1–27.6%. Archaea constituted 2.7% of all reads, with \u003cem\u003eCrenarchaeota\u003c/em\u003e most prevalent (0–17.1%). Unclassified Bacteria/Archaea phyla accounted for 0.3% of all reads. Taxa with the genetic capacity for atmospheric chemosynthesis\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, including \u003cem\u003eActinomycetota\u003c/em\u003e, \u003cem\u003eChloroflexota\u003c/em\u003e, and \u003cem\u003eVerrucomicrobiota\u003c/em\u003e, \u003cem\u003eBacillota\u003c/em\u003e, \u003cem\u003eDeinococcota\u003c/em\u003e, and \u003cem\u003eEremiobacterota\u003c/em\u003e, represented 18.7–70.9% of prokaryotes (39% of total reads). Dominant genera included \u003cem\u003eCa.\u003c/em\u003e Udaeobacter (0–14.3%), wb1-P19 (0–20%), \u003cem\u003eRubrobacter\u003c/em\u003e (0–13.6%) and \u003cem\u003eCrossiella\u003c/em\u003e (0–13.8%). Hydrocarbon-degrading genera from \u003cem\u003ePseudomonadota\u003c/em\u003e, including \u003cem\u003ePolaromonas\u003c/em\u003e (0–20.2%), \u003cem\u003eSphingobium\u003c/em\u003e (0–29.7%), \u003cem\u003eSphingomonas\u003c/em\u003e (0.4–9.6%) and \u003cem\u003ePseudomonas\u003c/em\u003e (0–22.2%), were also highly abundant\u003csup\u003e\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e–\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAmong the 42 identified eukaryotic divisions, \u003cem\u003eFungi\u003c/em\u003e (0.09–69.7%), \u003cem\u003eCiliophora\u003c/em\u003e (0.8–45.3%), and \u003cem\u003eGyrista\u003c/em\u003e (0.8–36.7%) were most prevalent, followed by \u003cem\u003eCercozoa\u003c/em\u003e (0.0008–40.1%), \u003cem\u003eMetazoa\u003c/em\u003e (0–30.2%), and \u003cem\u003eEuglenozoa\u003c/em\u003e (0.3–28.6%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD and Table S7). \u003cem\u003eChlorophyta\u003c/em\u003e, particularly \u003cem\u003eTrebouxia\u003c/em\u003e, dominated BH-24 (BT) at 90.9%, while \u003cem\u003eStreptophyta\u003c/em\u003e were abundant in BH-03 (HZ) at 31.8% (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Notably, \u003cem\u003eFungi\u003c/em\u003e were dominant in HT and HZ (61.4–65.0%), while the highest abundance detected in the BT was 4.7% (BH-20). These fungal communities primarily belonged to \u003cem\u003eAscomycota\u003c/em\u003e, with \u003cem\u003eEurotiomycetes\u003c/em\u003e and \u003cem\u003eDothideomycetes\u003c/em\u003e prevalent in HZ samples (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA). Unclassified eukaryotic divisions made up 14.2% of all reads.\u003c/p\u003e\u003cp\u003eDistance-based redundancy analysis (db-RDA) using 16S and 18S ASVs showed forward-selected environmental factors significantly accounted for community structure variation (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) (Table S10). The first two axes of the db-RDA explained 17% and 10.9% of the total variation for 16S ASVs, and 16.4% and 11.8% for 18S ASVs respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Axis loadings showed depth and TRH post-SGC as primary drivers of the HZ communities, while EC and elevation influenced BT. However, it is worth noting that several contaminated samples were obtained from deeper subsurface soils, thus the effects of contamination and depth on microbial communities cannot be separated distinctly. The rate of H\u003csub\u003e2\u003c/sub\u003e oxidation influenced HT communities alongside HZ, inversely to TRH post-SGC, suggesting different energy sources likely drive the observed divergence in microbial communities. The parallel influence of environmental variables on both prokaryotic and eukaryotic ASVs suggests an intimate relationship between these communities.\u003c/p\u003e\u003cp\u003eMicrobiomes were significantly (Pairwise ADONIS test, \u003cem\u003eP\u003c/em\u003e adj. \u0026lt; 0.05) distinct between BT and HZ, HT and HZ, and BT and HT (Table S11). DESeq2 analysis identified six differentially abundant prokaryotic phyla across sample locations (Fig. S2A). We observed significantly (\u003cem\u003eP\u003c/em\u003e adj. \u0026lt; 0.01) higher abundance of \u003cem\u003eChloroflexota\u003c/em\u003e and NB1-J in BT, and \u003cem\u003eVerrucomicrobiota\u003c/em\u003e in HT when compared to HZ, with NB1-J also significantly more abundant in BT compared to HT. In HZ, \u003cem\u003ePseudomonadota\u003c/em\u003e and predatory phylum \u003cem\u003eBdellovibrionota\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e abundances were significantly (\u003cem\u003eP\u003c/em\u003e adj. \u0026lt; 0.01) higher compared to HT, and \u003cem\u003ePatescibacteriota\u003c/em\u003e when compared to BT.\u003c/p\u003e\u003cp\u003eFor eukaryotes, fungal classes \u003cem\u003eAscomycota\u003c/em\u003e and \u003cem\u003eBasidiomycota\u003c/em\u003e, many of which are effective degraders of petroleum hydrocarbons\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e and frequently detected in hydrocarbon-contaminated soils around Antarctica including Bunger Hills\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, were significantly (\u003cem\u003eP\u003c/em\u003e adj. \u0026lt; 0.01) more abundant at HZ, alongside other predatory protists including \u003cem\u003eVariosea\u003c/em\u003e, \u003cem\u003eFilosa\u003c/em\u003e-\u003cem\u003eGranofilosea\u003c/em\u003e and \u003cem\u003eProstomatea\u003c/em\u003e, and other unclassified \u003cem\u003eFungi\u003c/em\u003e (Fig. S2B). The presence of a makeshift bamboo bridge at HZ prior to this study (duration unknown) and removed prior to sampling could play a role in the high abundance of fungi in these samples\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e.Meanwhile, the predatory protist \u003cem\u003eNebulidea\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e and photosynthetic protists including \u003cem\u003eBacillariophyceae\u003c/em\u003e, \u003cem\u003eTrebouxiophyceae\u003c/em\u003e, and \u003cem\u003eChlorophyceae\u003c/em\u003e, as well as other unclassified eukaryotes were significantly (\u003cem\u003eP\u003c/em\u003e adj. \u0026lt; 0.01) more abundant in BT compared to HZ. \u003cem\u003eNebulidea\u003c/em\u003e and \u003cem\u003eBacillariophyceae\u003c/em\u003e were also more abundant in BT in comparison to HT, while \u003cem\u003eEuglenida\u003c/em\u003e were more abundant in HT compared to HZ. The lower abundance of autotrophic taxa in HZ suggests a response and adaptation of the community to heterotrophy in hydrocarbon-enriched soils; an observation reported in a previous study of benthic sediment\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Conversely, in BT and HT, which are more reflective of the low organic carbon in Antarctic soil, the prevalence of autotrophic taxa appeared to be crucial to carbon turnover to support the community\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eSpearman’s correlation analysis of differentially abundant taxa demonstrated a strong positive relationship between post-SGC TRH concentration and the abundance of \u003cem\u003eBdellovibrionota\u003c/em\u003e, \u003cem\u003ePseudomonadota\u003c/em\u003e, \u003cem\u003eAscomycota\u003c/em\u003e, \u003cem\u003eBasidiomycota\u003c/em\u003e, \u003cem\u003eFilosa\u003c/em\u003e-\u003cem\u003eGranofilosea\u003c/em\u003e, \u003cem\u003eBacillariophyceae\u003c/em\u003e, and \u003cem\u003eProstomatea\u003c/em\u003e, and a negative correlation between these phyla abundances and pH and the rate of H\u003csub\u003e2\u003c/sub\u003e oxidation (Figs. S3A and S3B). Spearman’s correlation analysis showed H\u003csub\u003e2\u003c/sub\u003e oxidation rates positively correlated with \u003cem\u003eChloroflexota\u003c/em\u003e and \u003cem\u003eVerrucomicrobiota\u003c/em\u003e, while \u003cem\u003ePseudomonadota\u003c/em\u003e and \u003cem\u003eBdellovibrionota\u003c/em\u003e abundances negatively correlated with pH and the rate of H\u003csub\u003e2\u003c/sub\u003e oxidation.\u003c/p\u003e\u003cp\u003eOur findings suggest an ecological niche created from hydrocarbon contamination that selects for hydrocarbon-degrading taxa, such as \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eSphingomonas\u003c/em\u003e, and \u003cem\u003ePhialophora\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e–\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, reflecting the presence and recalcitrance of high molecular weight hydrocarbons in the polar environment\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Hydrocarbon toxicity and physicochemical changes in soil increases necromass availability, promoting microbial growth which, in turn increases prey availability for predatory microbes to thrive\u003csup\u003e\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e–\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Furthermore, some predatory protists are putatively associated with the control of ammonia-oxidising archaea and bacteria populations\u003csup\u003e\u003cspan additionalcitationids=\"CR52\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e–\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. In contrast, the absence of hydrocarbon-driven nutrient release in BT and HT reduces competition among heterotrophs. The prevalence of symbiotic, parasitic and predatory taxa supports the notion of a tightly interconnected prokaryotic and eukaryotic community as observed in db-RDA that responds collectively to these environmental changes.\u003c/p\u003e\u003cp\u003e\u003cem\u003eMetabolic potential in contaminated and pristine soils\u003c/em\u003e\u003c/p\u003e\u003cp\u003eWe recovered 207 medium-quality and 93 high-quality dereplicated metagenome-assembled genomes (MAGs) from the metagenomes of a subset of seven samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Tables S12–14). Three MAGs belonged to Archaea, with a single bacterial MAG being assignable to a named species (BH-09_ACT9, \u003cem\u003eRhodococcus qingshengii\u003c/em\u003e). \u003cem\u003eActinomycetota\u003c/em\u003e and \u003cem\u003ePseudomonadota\u003c/em\u003e comprised \u0026gt; 50% of all MAGs, followed by \u003cem\u003eChloroflexota\u003c/em\u003e, \u003cem\u003eBacteroidota\u003c/em\u003e and \u003cem\u003eGemmatimonadota\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAltogether, 24 high-quality MAGs contained near complete 16S rRNA gene sequences (≥ 75% expected length) and thus, new taxa are proposed under the SeqCode\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e register list (XXXX) including: 24 species, 18 genera, eight families, six orders and one class (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Supplementary Information).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eSummary of new taxa proposed.\u003c/b\u003e In parentheses, equivalent placeholder names appearing in the GTDB. Full descriptions are provided in the supplementary materials.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDomain\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePhylum\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eClass\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOrder\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFamily\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGenus\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSpecies\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eType\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eArchaea\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eThermoproteota\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eNitrososphaeria\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eNitrososphaerales\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eNitrososphaeraceae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eNitrosobungeria\u003c/em\u003e gen. nov. (g__TH5893)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eNitrosobungeria shackeltonensis\u003c/em\u003e sp. nov.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBH-18_THE2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eNitrosomicrobium\u003c/em\u003e gen. nov. (g__TA-21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eNitrosomicrobium frigidus\u003c/em\u003e sp. nov. (s__TA-21 sp023251115)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBH-18_THE1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBacteria\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eActinomycetota\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eAcidimicrobiia\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eAcidimicrobiales\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eIamiaceae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eAquihabitans\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eAquihabitans niveus\u003c/em\u003e sp. nov.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBH-10_ACT1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eSpongiisociales\u003c/em\u003e (o__UBA5794)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eHadalibacteraceae\u003c/em\u003e (f__UBA5794)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eGelisolibacter\u003c/em\u003e gen. nov. (g__JACDBE01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGelisolibacter meridionalis\u003c/em\u003e sp. nov.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBH-24_ACT7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eActinomycetes\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eActinomycetales\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eDermatophilaceae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eCryoornithinimicrobium\u003c/em\u003e gen. nov. (g__Ornithinimicrobium_A)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eCryoornithinimicrobium bungerii\u003c/em\u003e sp. nov.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBH-23_ACT6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ePropionibacteriales\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eNocardioidaceae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eNocardioides\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eNocardioides polaris\u003c/em\u003e sp. nov.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBH-09_ACT11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eAridivitia\u003c/em\u003e (c__UBA4738)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eActinopolariales\u003c/em\u003e ord. nov. (o__CADDZG01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eActinopolariaceae\u003c/em\u003e fam. nov. (f__WHSQ01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eActinopolaria\u003c/em\u003e gen. nov.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eActinopolaria aerotropha\u003c/em\u003e sp. nov.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBH-24_ACT26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eActinosomniales\u003c/em\u003e ord. nov. (o__UBA4738)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eActinosomniaceae\u003c/em\u003e fam. nov. (f__UBA4738)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eActinosomnia\u003c/em\u003e gen. nov. (g__JACDCJ01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eActinosomnia pattersoniae\u003c/em\u003e sp. nov. (s__JACDCJ01 sp013817655)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBH-20_ACT24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eBungeriellales\u003c/em\u003e ord. nov. (o__JAHWKV01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eBungeriellaceae\u003c/em\u003e fam. nov. (f__JAHWKV01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eBungeriella\u003c/em\u003e gen. nov. (g__JAJCYE01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eBungeriella frigidisoli\u003c/em\u003e sp. nov.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBH-23_ACT12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eArmatimonadota\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eFimbriimonadia\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eFimbriimonadales\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eFimbriimonadaceae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eFimbriimonas\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eFimbriimonas antarctica\u003c/em\u003e sp. nov.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBH-11_ARM2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eBacteroidota\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eBacteroidia\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eChitinophagales\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eChitinophagaceae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ePanacibacter\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ePanacibacter polaris\u003c/em\u003e sp. nov.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBH-10_BAC2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eWilkeslandia\u003c/em\u003e gen. nov.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eWilkeslandia alcanivorans\u003c/em\u003e sp. nov.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBH-09_BAC2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eCytophagales\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eHymenobacteraceae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ePseudopontibacter\u003c/em\u003e gen. nov.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ePseudopontibacter australis\u003c/em\u003e sp. nov.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBH-24_BAC1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eIgnavibacteria\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eTepidiaquacellales\u003c/em\u003e (o__SJA-28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eVentifactibacteraceae\u003c/em\u003e fam. nov. (f__B-1AR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eVentifactibacter\u003c/em\u003e gen. nov.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eVentifactibacter hollidayae\u003c/em\u003e sp. nov.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBH-10_BAC5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCyanobacteriota\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eVampirovibrionia\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eObscuribacterales\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eObscuribacteraceae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ePsychrobscuribacter\u003c/em\u003e gen. nov.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ePsychrobscuribacter pollutisoli\u003c/em\u003e sp. nov.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBH-10_CYA1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eGemmatimonadota\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eGemmatimonadetes\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eCryogemmatales\u003c/em\u003e ord. nov. (o__JACCXV01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eCryogemmataceae\u003c/em\u003e fam. nov. (f__JAHWKZ01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eCryogemmata\u003c/em\u003e gen. nov.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eCryogemmata carboxiditropha\u003c/em\u003e sp. nov.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBH-18_GEM1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePatescibacteria\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eNanosomnibacteria\u003c/em\u003e class. nov. (c__UBA1384)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eNanosomnibacterales\u003c/em\u003e ord. nov. (o__CAILIB01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eNanosomnibacteraceae\u003c/em\u003e fam. nov. (f__CAILIB01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eNanosomnibacter\u003c/em\u003e gen. nov. (g__CALBLZ01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eNanosomnibacter parvus\u003c/em\u003e sp. nov.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBH-11_PAT4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePlanctomycetota\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ePhycisphaerae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ePhycisphaerales\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eFrigidisphaeraceae\u003c/em\u003e fam. nov. (f__UBA1924)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eFrigidisphaera\u003c/em\u003e gen. nov. (g__JACVCS01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eFrigidisphaera bungerii\u003c/em\u003e sp. nov.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBH-11_PLA1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ePlanctomycetia\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eGemmatales\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eGemmataceae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eCryolimnoglobus\u003c/em\u003e gen. nov.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eCryolimnoglobus antarcticus\u003c/em\u003e sp. nov.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBH-11_PLA2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePseudomonadota\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eAlphaproteobacteria\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eCaulobacterales\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eCaulobacteraceae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eBrevundimonas\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eBrevundimonas antarctica\u003c/em\u003e sp. nov.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBH-09_PSE1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eGammaproteobacteria\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eBurkholderiales\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eBurkholderiaceae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eFrigidisolicola\u003c/em\u003e gen. nov.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eFrigidisolicola castellviae\u003c/em\u003e sp. nov.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBH-10_PSE17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eDormimicrobiales\u003c/em\u003e ord. nov. (o__JACDCP01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eDormimicrobiaceae\u003c/em\u003e fam. nov. (f__JACDCP01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eDormimicrobium\u003c/em\u003e gen. nov. (g__JACDCP01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eDormimicrobium murphyi\u003c/em\u003e sp. nov.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBH-24_PSE2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eVerrucomicrobiota\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eVerrucomicrobiae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eChthoniobacterales\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eTerrimicrobiaceae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eCryoterrimicrobium\u003c/em\u003e gen. nov.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eCryoterrimicrobium chapmanii\u003c/em\u003e sp. nov.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBH-09_VER1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eVerrucomicrobiales\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eVerrucomicrobiaceae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eVerrucomicrobium\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eVerrucomicrobium antarcticum\u003c/em\u003e sp. nov.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBH-11_VER1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo compare functional gene abundance across samples, the RPKM of each functional gene was calculated and divided by the mean RPKM for 15 universal ribosomal protein markers present in all cellular organisms, i.e., “genome equivalents” (see Methods). Then, the log\u003csub\u003e2\u003c/sub\u003e of genome equivalents was calculated, resulting in the log-fold genome equivalent or LFGE. Generally, genes universally present in all/most cellular genomes would have values very close to zero and genes from functional specialisations would have values well below zero, except for non-cellular elements typically present in high copy numbers, e.g. present in plasmids or viruses.\u003c/p\u003e\u003cp\u003eOverall, we detected marker genes associated with a wide range of metabolic pathways across metagenomes and in MAGs, including aerobic respiration and nitrogen metabolism (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, Tables S15–17). Genes associated with alkane and aromatic hydrocarbon degradation were substantially overrepresented in HZ (occasionally referred to as contaminated samples/locations, and BT and HT as uncontaminated for brevity from hereon) samples. Differences within HZ were also observed, between BH-11 vs BH-09/BH-10. For alkane degradation, all contaminated samples showed similar enrichment in long-chain alkane monooxygenase (LFGE 0.75–0.82; \u003cem\u003eladA\u003c/em\u003e). However, while BH-11 showed a relative enrichment in alkane 1-monooxygenase (\u003cem\u003ealkB\u003c/em\u003e) compared to other contaminated samples (LFGE 0.46 vs 0.02–0.14), propane 2-monooxygenase was comparatively reduced (based on large subunit, \u003cem\u003eprmA\u003c/em\u003e), i.e., 0.17 vs 1.44–1.49. Most MAGs containing \u003cem\u003ealkB\u003c/em\u003e and/or \u003cem\u003eladA\u003c/em\u003e were \u003cem\u003eActinomycetota\u003c/em\u003e (esp. \u003cem\u003eMycobacteriales\u003c/em\u003e), and \u003cem\u003ePseudomonadota\u003c/em\u003e, while \u003cem\u003eprmA\u003c/em\u003e was only found in \u003cem\u003eMycobacteriales\u003c/em\u003e MAGs.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSimilarly for aromatic degradation, excluding genes encoding for proteins associated with downstream metabolite processing, e.g., \u003cem\u003ep\u003c/em\u003e-cumate, catechols, \u003cem\u003etrans\u003c/em\u003e-cinnamate, etc., the toluene monooxygenase system and the phenol/toluene 2-monooxygenase (NADH) complex were highly abundant in all HZ samples (LFGE for most subunits \u0026gt; 0) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB and Table S15). However, while phenol/toluene 2-monooxygenase (NADH) complex genes are among the top-ranking genes in BH-11, the toluene monooxygenase system was more predominant for BH-09/BH-10. Additionally, components of the terephthalate dioxygenase were only detected in BH-11. The broader range of hydrocarbon degradation capabilities in BH-11 could be attributed to the high abundance of \u003cem\u003ePseudomonadota\u003c/em\u003e (70% of 16S community, 58.6% of MAGs) (Table S14), where phylum members had the potential to degrade a wide range of hydrocarbons\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. The dominance of \u003cem\u003ePseudomonadota\u003c/em\u003e in BH-11 is characteristic of deeper subsurface soils\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e (BH-11 was sampled at 20 cm depth). Meanwhile, the lower LFGE of these hydrocarbon degradation genes could, speculatively, be associated with the low level of contamination in this sample. Most aromatic hydrocarbon degradation markers were detected in \u003cem\u003eActinomycetota\u003c/em\u003e, and/or \u003cem\u003ePseudomonadota\u003c/em\u003e MAGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and Tables S15–16). We identified five novel MAGs that encoded genes for alkane and/or aromatic hydrocarbon degradation and their associated metabolites, including BH-09_BAC2 (\u003cem\u003ealkB\u003c/em\u003e), BH-10_BAC2 (catechol 2,3-dioxygenase, \u003cem\u003exylE\u003c/em\u003e), BH-10_PSE17 (\u003cem\u003eladA\u003c/em\u003e and pthalate 4,5-dioxygenase, \u003cem\u003epht3\u003c/em\u003e), BH-11_VER1 (\u003cem\u003epht3\u003c/em\u003e), and BH-20_ACT24 (\u003cem\u003exylE\u003c/em\u003e). The limited number of MAGs with these genes combined with the extremely high LFGE values for some markers (up to 4 copies per genome equivalent) might relate to aromatic hydrocarbon degradation machinery being encoded in plasmids\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eKey aerobic respiration genes (Complex I-V) were detected across the metagenomes, with majority of MAGs possessing a partial electron transport chain (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB and Tables S15–16). Low-affinity cytochrome oxidases (\u003cem\u003ecoxA\u003c/em\u003e/\u003cem\u003ectaD\u003c/em\u003e) were identified in 180 MAGs across all sampling locations, while high affinity type cytochrome oxidases (\u003cem\u003eccoN\u003c/em\u003e, \u003cem\u003ecydA\u003c/em\u003e) were more prevalent in both metagenomes and MAGs from HZ, particularly in BH-11, indicating possible genetic adaptation to low oxygen (O\u003csub\u003e2\u003c/sub\u003e) conditions due to O\u003csub\u003e2\u003c/sub\u003e requirements for hydrocarbon degradation\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eGenes linked to nitrogen metabolism were detected in the metagenomes. Nitrification genes (\u003cem\u003epmoA\u003c/em\u003e/\u003cem\u003eamoA\u003c/em\u003e, \u003cem\u003enarG\u003c/em\u003e/\u003cem\u003enxrA\u003c/em\u003e) were found in all samples, particularly in \u003cem\u003eThermoproteota\u003c/em\u003e MAGs, except BH-24 where \u003cem\u003epmoA\u003c/em\u003e/\u003cem\u003eamoA\u003c/em\u003e were absent (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB and Tables S15–16). MAGs that encoded denitrification genes (\u003cem\u003enirK\u003c/em\u003e, \u003cem\u003enorB\u003c/em\u003e, \u003cem\u003enosZ\u003c/em\u003e) belonged to 14 phyla across sampling locations, while nitrogen fixation genes (\u003cem\u003enifH\u003c/em\u003e) were detected in HZ, particularly in BH-11, in \u003cem\u003ePseudomonadota\u003c/em\u003e MAGs but absent in BT and HT. Seven novel MAGs encoded genes for nitrogen metabolism including BH-09_BAC2 (\u003cem\u003enorB\u003c/em\u003e and \u003cem\u003enosZ\u003c/em\u003e), BH-10_ACI3 (\u003cem\u003enorB\u003c/em\u003e), BH-10_BAC2 (\u003cem\u003enosZ\u003c/em\u003e), BH-10_BAC5 (\u003cem\u003enirK\u003c/em\u003e), BH-10_CYA1 (\u003cem\u003enarG/nxrA\u003c/em\u003e and \u003cem\u003enorB\u003c/em\u003e), BH-24_ACT26 (\u003cem\u003enirK\u003c/em\u003e), and BH-24_ACT7 (\u003cem\u003enirK\u003c/em\u003e).\u003c/p\u003e\u003cp\u003eAmmonia/methane oxidation genes (\u003cem\u003epmoA\u003c/em\u003e/\u003cem\u003eamoA\u003c/em\u003e) were detected across most samples, particularly BH-09, BH-10 and BH-20 (LFGE \u0026gt; 1.69), and were present in \u003cem\u003ePseudomonadota\u003c/em\u003e and both novel \u003cem\u003eThermoproteota\u003c/em\u003e MAGs (ammonia oxidising archaea) (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB and Tables S15–16). Aerobic carbon monoxide dehydrogenase (CODH) genes for the catalytic large subunit (\u003cem\u003ecoxL\u003c/em\u003e) were detected in all metagenomes, albeit less abundant in HZ and BH-23 (LFGE \u0026lt; -0.57), while anaerobic CODH (\u003cem\u003ecooS\u003c/em\u003e) were detected in BH-10 and BH-11. MAGs that encoded \u003cem\u003ecoxL\u003c/em\u003e include \u003cem\u003eAcidobacteriota\u003c/em\u003e, \u003cem\u003eActinomycetota\u003c/em\u003e, \u003cem\u003eBacteroidota\u003c/em\u003e, \u003cem\u003eChloroflexota\u003c/em\u003e, \u003cem\u003eDeinococcota\u003c/em\u003e, \u003cem\u003eGemmatimonadota\u003c/em\u003e, and \u003cem\u003ePseudomonadota\u003c/em\u003e; six were novel taxa including BH-09_ACT11, BH-10_BAC2, BH-18_GEM1, BH-23_ACT6, BH-24_ACT26, and BH-24_ACT7 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table S16).\u003c/p\u003e\u003cp\u003eWe detected several lineages of high-affinity [NiFe]-hydrogenases across the metagenomes, including 1h (\u003cem\u003ehhySL\u003c/em\u003e), 1l (\u003cem\u003ehylSL\u003c/em\u003e), 1m (\u003cem\u003ehhmSL\u003c/em\u003e) and 2a (\u003cem\u003ehucSL\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB and Table S15). These high-affinity [NiFe]-hydrogenases were encoded in 68 MAGs of phylum \u003cem\u003eAcidobacteriota\u003c/em\u003e, \u003cem\u003eActinomycetota\u003c/em\u003e, \u003cem\u003eBacteroidota\u003c/em\u003e, \u003cem\u003eChloroflexota\u003c/em\u003e, \u003cem\u003eDeinococcota\u003c/em\u003e, \u003cem\u003eGemmatimonadota\u003c/em\u003e, \u003cem\u003ePlanctomycetota\u003c/em\u003e, and \u003cem\u003ePseudomonadota\u003c/em\u003e, with 13 MAGs having more than one copy of the same or different lineage of the large subunit gene (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and Tables S15–16). Three MAGs were novel taxa, with BH-20_ACT24 (\u003cem\u003eActinosomnia pattersoniae\u003c/em\u003e gen. nov., sp. nov.) and BH-24_ACT26 (\u003cem\u003eActinopolaria aerotropha\u003c/em\u003e gen. nov., sp. nov.) encoding \u003cem\u003ehhmSL\u003c/em\u003e and BH-24_PSE2 (\u003cem\u003eDormimicrobium murphyi\u003c/em\u003e gen. nov., sp. nov.) encoding \u003cem\u003ehylSL\u003c/em\u003e. BH-23 encoded the highest abundance of high-affinity hydrogenases, including several phylogenetically distinct putative lineages (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). These putative lineages were detected in 14 MAGs including \u003cem\u003eAcidobacteriota\u003c/em\u003e, \u003cem\u003eActinomycetota\u003c/em\u003e, \u003cem\u003eChloroflexota\u003c/em\u003e, and \u003cem\u003eGemmatimonadota\u003c/em\u003e. Lineages 1h and 1l were present across the metagenomes, at lower abundances in HZ (1l being near the limit of detection in BH-09 and BH-10) compared to BT and HT, while 1m was undetected in HZ and 2a was undetected in BT.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePhotosynthetic markers (\u003cem\u003epsaA\u003c/em\u003e/\u003cem\u003epsbA\u003c/em\u003e) were only detected in low abundance (LFGE \u0026lt; 0) in BH-10, BH-23 and BH-24 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, Table S15), with carbon fixation via the CBB pathway dominant across samples. In general, the CBB pathway and a key marker – the large RuBisCO subunit (\u003cem\u003erbcL\u003c/em\u003e), was more abundant in BT and HT. The five subtypes of RuBisCO form I were detected across most metagenomes. Of these, RuBisCO form I was present in 40 MAGs from \u003cem\u003eAcidobacteriota\u003c/em\u003e, \u003cem\u003eActinomycetota\u003c/em\u003e, \u003cem\u003eChloroflexota\u003c/em\u003e, \u003cem\u003eCyanobacteriota\u003c/em\u003e, and \u003cem\u003ePseudomonadota\u003c/em\u003e (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA and Table S16). The dark RuBisCO form IE\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e (\u003cem\u003erbcLIE\u003c/em\u003e) was the most widely detected across all samples (LFGE \u0026gt;-0.66) and were more prevalent in BT and HT samples (LFGE \u0026gt; 0.54), in particular BH-23 (LFGE 0.89) and BH-18 (LFGE 0.85). MAGs that encoded \u003cem\u003erbcLIE\u003c/em\u003e were \u003cem\u003eActinomycetota\u003c/em\u003e and \u003cem\u003eChloroflexota\u003c/em\u003e including one novel \u003cem\u003eActinomycetota\u003c/em\u003e taxon BH-23_ACT12 (\u003cem\u003eBungeriella frigidisoli\u003c/em\u003e gen. nov., sp. nov.). Other chemoautotrophy-associated RuBisCO forms IC and ID were detected across metagenomes, with form IC being more abundant in BH-11, BH-20 and BH-24 (LFGE \u0026gt; 0), but near the detection limit in BH-10, and form ID being lower in abundance overall (LFGE \u0026lt; 0). RuBisCO forms IA and IB, commonly associated with photoautotrophy\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e, were largely undetected in contaminated (HZ) samples, with a low abundance of form IA (LFGE − 1.01) present in BH-11. Form IA was most abundant in BH-20 (LFGE 1.32), while Form IB was near the detection limit in BH-18, BH-20, and BH-23.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAcross sampling locations. 16 MAGs co-encoded at least one high-affinity lineage of [NiFe]-hydrogenase with \u003cem\u003erbcLIE\u003c/em\u003e, indicating potential for atmospheric chemosynthesis; all MAGs were from \u003cem\u003eActinomycetota\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In addition, 18 \u003cem\u003eActinomycetota\u003c/em\u003e and two \u003cem\u003eChloroflexota\u003c/em\u003e MAGs co-encoded aerobic CODH and \u003cem\u003erbcLIE\u003c/em\u003e. These groups of MAGs were not mutually exclusive as 14 MAGs encoded at least one lineage of high-affinity [NiFe]-hydrogenase alongside aerobic CODH, together with \u003cem\u003erbcLIE\u003c/em\u003e, and reflects previous reports of co-location of trace gas oxidation genes\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Alternate carbon fixation pathway markers, i.e., malonyl-CoA reductase/3-hydroxypropionate dehydrogenase (NADP\u003csup\u003e+\u003c/sup\u003e) (\u003cem\u003emcr\u003c/em\u003e) for the 3-hydroxypropionate pathway, and ATP-citrate lyase (\u003cem\u003eaclB\u003c/em\u003e) for the reverse TCA cycle were only detected in BH-11, and BH-23, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB and Table S15). Neither marker was found associated with any MAG even despite the high LFGE values for \u003cem\u003emcr\u003c/em\u003e (-0.29).\u003c/p\u003e\u003cp\u003e\u003cem\u003eMAG growth preferences linked to the sampling locations\u003c/em\u003e\u003c/p\u003e\u003cp\u003eBased on the MAG distribution across the different samples, two main clusters of MAGs were differentiated based on their detection (% of MAG with reads mapped) in each sample (Fig. S4). These two clusters separated MAGs associated with the contaminated (HZ) and uncontaminated (HT, BT) samples, with 169 and 131 MAGs respectively. Except for four MAGs with \u003cem\u003eprmA\u003c/em\u003e and one predicted to harbour \u003cem\u003ealkB\u003c/em\u003e, all the other MAGs containing hydrocarbon degradation gene annotations were characteristic of the contaminated samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table S15). This includes MAGs of novel taxa, whose parent is associated with hydrocarbon degradation potential such as \u003cem\u003eNocardioides\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e, \u003cem\u003eBrevundimonas\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e, \u003cem\u003eAquihabitans\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e, \u003cem\u003ePanacibacter\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e, and \u003cem\u003eFimbriimonas\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eSimilar to the trends observed in the whole metagenomes, genes associated with trace gas oxidation and dark carbon fixation were mainly present in the uncontaminated group of MAGs. This includes the high-affinity hydrogenases (except type 2a which were only detected in contaminated group of MAGs), RuBisCO form IE, and aerobic CO dehydrogenase (\u003cem\u003ecoxL\u003c/em\u003e), with the novel hydrogenase clade present in predominantly \u003cem\u003eActinomycetota\u003c/em\u003e and \u003cem\u003eAcidobacteriota\u003c/em\u003e MAGs.\u003c/p\u003e\u003cp\u003ePrediction of optimal growth conditions with GenomeSPOT\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e also showed clear differences between the contaminated- and uncontaminated-associated MAGs. In general, MAGs associated with the contaminated samples showed lower optima pH (6.7 ± 0.6 vs 7.8 ± 0.5, \u003cem\u003ep\u003c/em\u003e = 0.00), salinity (0.4 ± 0.8% vs 3.0 ± 1.6%, \u003cem\u003ep\u003c/em\u003e = 0.00), and temperatures (26.1 ± 4.8°C vs 33.0 ± 5.6°C, \u003cem\u003ep\u003c/em\u003e = 0.00) (Figs. S5–S7). A subcluster of 72 uncontaminated MAGs was associated with BH-23, the sample with high salinity, including 21 MAGs almost exclusively detected in that sample (Fig. S8). Analysis of the predicted optimal salinity for growth indicated indeed a preference for higher salt concentrations (3.3 ± 1.8%), statistically different to both the contaminated MAGs (0.4 ± 0.8%, \u003cem\u003eP\u003c/em\u003e adj. = 0) and the other uncontaminated MAGs (2.8 ± 1.5%, \u003cem\u003eP.\u003c/em\u003e adj. = 0.03). Two MAGs with growth preference for salinity were novel \u003cem\u003eActinomycetota\u003c/em\u003e taxa, BH-23_ACT12 (\u003cem\u003eBungeriella frigidisoli\u003c/em\u003e gen. nov., sp. nov.) and BH-23_ACT6 (\u003cem\u003eCryoornithinimicrobium bungerii\u003c/em\u003e gen. nov., sp. nov.). Six of the 23 MAGs with potential for atmospheric chemosynthesis were identified with growth preference for salinity, of which five were order \u003cem\u003eEuzebyales\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e and one \u003cem\u003eAcidimicrobiales\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e \u003cem\u003eoxidation activity diminishes with hydrocarbon contamination\u003c/em\u003e\u003c/p\u003e\u003cp\u003eTo assess the effects of hydrocarbon contamination on trace gas scavenging, we measured the assimilation and oxidation of H\u003csub\u003e2\u003c/sub\u003e by soil microcosms. All uncontaminated microcosms except BH-25 and BH-26, consumed H\u003csub\u003e2\u003c/sub\u003e to sub-atmospheric levels under 300 hours (530 p.p.b.v.) (Fig. S9, Table S18). Contaminated microcosms exhibited the lowest H\u003csub\u003e2\u003c/sub\u003e oxidation rates, with BH-09 showing the slowest at 0.5 nmol/mol/h/g, with continuing activity after 300 hours (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). In contrast, H\u003csub\u003e2\u003c/sub\u003e oxidation rates in BH-18 and BH-24 were more than 900-fold higher at 476.3 nmol/mol/h/g and 429.6 nmol/mol/h/g respectively, depleting H\u003csub\u003e2\u003c/sub\u003e to atmospheric concentration under 4–6 hours, surpassing previously reported rates in Antarctica\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Interestingly, H\u003csub\u003e2\u003c/sub\u003e oxidation rates in contaminated microcosms increased as post-SGC TRH concentrations decreased, suggesting hydrocarbons directly influence trace gas scavengers and/or their activity. The expression of high-affinity [NiFe] hydrogenases in three samples with the highest rates and three samples with the lowest oxidation rates was verified by reverse transcription quantitative PCR (RT-qPCR) (Tables S19–20).\u003c/p\u003e\u003cp\u003e\u003cem\u003eHydrocarbons as an energy source for chemolithoautotrophs\u003c/em\u003e\u003c/p\u003e\u003cp\u003eWe next investigated the influence of hydrocarbon contamination on dark carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e) fixation using a radiolabelled \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003eCO\u003csub\u003e2\u003c/sub\u003e assay in a subset of six soil microcosms. Overall, CO\u003csub\u003e2\u003c/sub\u003e fixation occurred in both uncontaminated and contaminated soils, with rates comparable to or exceeding those observed in wetted crusts and desert soils of Israel\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB and Table S21). Several energy sources for the observed dark carbon fixation in pristine samples can be expected, including low levels of chemoorganotrophic metabolism or the oxidation of inorganic electron donors such as reduced sulfur compounds\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. While direct physicochemical characterisation of sulfur content was not conducted, the detection of the \u003cem\u003esoxB\u003c/em\u003e gene, a key marker for sulfur oxidation pathways, suggests this capability (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB and Table S15).\u003c/p\u003e\u003cp\u003eSignificantly (\u003cem\u003eP\u003c/em\u003e adj. \u0026lt; 0.05) higher CO\u003csub\u003e2\u003c/sub\u003e fixation rates were observed in hydrocarbon contaminated soils from HZ when compared to BT and HT soils (grouped as uncontaminated soils) (Table S22). This was despite the overall lower abundance of RuBisCO markers in HZ soil metagenomes, and gene expression via RT-qPCR (Table S19). Increased heterotrophic activity in the hydrocarbon-supported community could enhance anaplerotic CO\u003csub\u003e2\u003c/sub\u003e assimilation \u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e,\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. It is also possible the hydrocarbon degradation by enriched taxa releases byproducts and inorganic carbon sources to support autotrophic pathways and consequently the community\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e,\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. The significant reduction of photosynthetic eukaryotic abundance in HZ from DESeq2 analysis of 18S ASVs suggests the niche for carbon fixation was filled by chemoautotrophs as evident in the increased abundance of \u003cem\u003ePseudomonadota\u003c/em\u003e in these samples (Fig. S2B).\u003c/p\u003e\u003cp\u003ePrevious studies have shown positive correlations between hydrocarbon contamination at low levels and increased levels of inorganic fixed carbon via non-CBB cycle pathways\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e, although CO\u003csub\u003e2\u003c/sub\u003e fixation gene abundances were also higher in this study. In contrast, other studies demonstrated that the presence of free organic carbon in the extracellular space inhibits CO\u003csub\u003e2\u003c/sub\u003e fixation\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e,\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. It is possible that hydrocarbon metabolism creates a locally enriched zone of CO\u003csub\u003e2\u003c/sub\u003e, lacking competition with O\u003csub\u003e2\u003c/sub\u003e for fixation\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e. Furthermore, increasing CO\u003csub\u003e2\u003c/sub\u003e concentrations have been shown to drive dark CO\u003csub\u003e2\u003c/sub\u003e fixation rates\u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. The low CO\u003csub\u003e2\u003c/sub\u003e fixation rates in uncontaminated samples, contrasting with higher expression of dark carbon fixation gene \u003cem\u003erbcLIE\u003c/em\u003e suggests a need to upregulate protein expression to increase efficiency of carbon capture in environments low in organic carbon like Antarctica\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e,\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur findings reveal a diverse and functionally rich microbial community Bunger Hills, Antarctica, including novel taxa such as \u003cem\u003eWilkeslandia alcanivorans\u003c/em\u003e gen. nov., sp. nov. (\u003cem\u003eChitinophagaceae\u003c/em\u003e) and \u003cem\u003eFrigidisolicola castellviae\u003c/em\u003e gen. nov., sp. nov. (\u003cem\u003eBurkholderiaceae\u003c/em\u003e) capable of alkane degradation, and \u003cem\u003eBungeriella frigidisoli\u003c/em\u003e gen. nov., sp. nov. (\u003cem\u003eBungeriellaceae\u003c/em\u003e fam. nov.), encoding \u003cem\u003erbcLIE\u003c/em\u003e for dark carbon fixation. Despite its low levels, legacy hydrocarbon contamination continues to shape microbial community structure and function four decades post-exposure, enriching for hydrocarbon-degrading taxa and other predatory taxa. This selection pressure alters microbial metabolic strategies, as evidenced by reduced rates of H\u003csub\u003e2\u003c/sub\u003e scavenging and lower high-affinity hydrogenase abundance. While uncontaminated soils favoured autotrophic taxa reliant on inorganic carbon, elevated carbon fixation rates in contaminated soils likely support energy-intensive degradation processes. These results reveal the long-lasting ecological footprint of anthropogenic activity and emphasise the need for further investigation to delineate the apparent dose-response relationship between hydrocarbons and trace gas oxidation activity, and the mechanistic links between hydrocarbon exposure, trace gas cycling, and carbon flux – using approaches such as stable isotope probing and integrative -omics. Our study establishes a valuable baseline for monitoring environmental change and human impact on Antarctic soil ecosystems.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eSite Mapping\u003c/em\u003e\u003c/p\u003e\u003cp\u003e871 mostly nadir photographs of the site were collected on same day as the field sampling program (06 February 2023) with a DJI Matrice 300 quadcopter fitted with a DJI Zenmuse P1 35mm camera. Images were processed in Agisoft Metashape Professional (2.1.1) to produce an orthomosaic with 7mm resolution, and digital surface model of 2.8 cm resolution. The project utilised 6 Ground Control Points (Root Mean Square Error of 2.3, 1.3, and 0.64 cm for X,Y,Z). Ground Control Points and sample locations were recorded with an Emlid Reach RS2 + RTK base and rover system, with the base station established over survey mark NMS238. Raw and processed data and metadata is available from the Australian Antarctic Data Centre\u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cem\u003eSample collection\u003c/em\u003e\u003c/p\u003e\u003cp\u003eSoil samples were collected on 06 February 2023 at 25 m intervals along two 100 m transects, the Helipad Transect (HT, \u003cem\u003en\u003c/em\u003e = 5) and the Background Transect (BT, \u003cem\u003en\u003c/em\u003e = 5). Additional samples were collected along the HT at an area of known legacy hydrocarbon contamination, referred to throughout as the Helipad Zone (HZ, \u003cem\u003en\u003c/em\u003e = 16), with 26 samples collected in total (BH-01 to BH-26). Sample description, location and depth information are provided in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Samples were collected from the top 10 cm of soil (except for HZ samples collected at greater depths: 20–51 cm below surface) using stainless steel spoons, which were cleaned with ultrapure water (18 MΩ cm, Milli-Q®, Millipore) and ethanol (reagent grade \u0026gt; 99.5%, EMSURE®, Merck). Soil for organic, inorganic and physicochemical analysis was collected in 125 mL amber glass jars with PTFE lined lids and stored and transported at -18°C until analysis. Soil for microbial analysis was collected in sterile 50 mL polypropylene tubes and stored and transported between − 70 to -80°C until analysis.\u003c/p\u003e\u003cp\u003e\u003cem\u003eChemical analyses\u003c/em\u003e\u003c/p\u003e\u003cp\u003eSamples for chemical analysis were sent to Australian Laboratory Services (ALS, Springvale VIC, Australia). Samples for total recoverable hydrocarbon (TRH, C\u003csub\u003e10\u003c/sub\u003e–C\u003csub\u003e40\u003c/sub\u003e) analysis were thawed and screened to \u0026lt; 8 mm with a spatula, as described in van Dorst et al.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Soil subsamples were extracted and analysed using EPA Method SW-846–8015A (Nonhalogenated Organics by Gas Chromatography)/8260B (Volatile Organic Compounds by Gas Chromatography/ Mass Spectrometry (GC/MS))\u003csup\u003e80\u003c/sup\u003e. Extracts were subsampled and underwent silica-gel cleanup (SGC) for polar compound removal to measure petrogenic hydrocarbons in isolation. Polar compounds, as defined in this study, include metabolites formed from biodegradation of fuel and naturally occurring organic matter\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eInorganic analysis for water extractable nutrients were conducted on 1:5 soil-water extracts (10 g soil: 50 mL deionised water) following a 1 h end-over-end shake. Exchangeable ammonium was analysed using a 2 M KCl extraction\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e, while bicarbonate extractable phosphorus was analysed using the Colwell P method\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cem\u003eCommunity DNA extraction, amplicon sequencing and reads processing\u003c/em\u003e\u003c/p\u003e\u003cp\u003eDNA from 0.3 g of soil subsample for each of the 26 samples was extracted in duplicates using FASTDNA™ SPIN Kit for Soil (MP Biomedicals, USA) following the manufacturer’s instructions. DNA purity and yield was verified using Nanodrop ND–1000 (Thermo Fisher Scientific, USA) and Qubit dsDNA HS Assay Kit with Qubit 4 Fluorometer (Thermo Fisher Scientific, USA) respectively. Replicate DNA samples were pooled for 16S rRNA gene (2 × 250 bp) and 18S rRNA gene (1 × 250 bp) paired-end amplicon sequencing using the Illumina MiSeq platform (Illumina, USA) at the Ramaciotti Centre for Genomics (UNSW Sydney, Australia). For bacteria and archaea, the V4 region of the 16S rRNA gene was targeted for sequencing using the 515F/806R primer set\u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e,\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e. For eukaryotes, the V9 region of the 18S rRNA gene was targeted for sequencing using the 1391F/EukBr primer set\u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e,\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eRaw reads were processed in the R environment v4.3.1\u003csup\u003e87\u003c/sup\u003e using the R package DADA2 pipeline v1.30.0\u003csup\u003e88\u003c/sup\u003e. Briefly, for both 16S and 18S rRNA genes, forward and reverse reads were quality filtered and trimmed using the filterAndTrim function with default parameters. For 16S rRNA genes, truncation points were set using the argument truncLen at 195 and 160 bases for forward and reverse reads respectively, with a maximum threshold of “expected errors” (maxEE) of 2 for all reads. For 18S rRNA genes, 20 bases were removed from the start using trimLeft, and two bases from the end using trimRight, of both forward and reverse reads, then forward and reverse reads were truncated after 120 and 110 bases respectively, with maxEE of 1 for all reads. Error models were generated for the dataset, then amplicon sequence variants (ASVs) were inferred and dereplicated. Paired reads were merged, and sequence counts tables were generated. For 16S and 18S rRNA gene data, chimeric ASV sequences derived from sequence merging were removed and the remaining reads were taxonomically mapped to the SILVA v138.1\u003csup\u003e89\u003c/sup\u003e and the PR2 v5.0.0 SSU rRNA databases\u003csup\u003e\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e respectively. The ASV and taxonomy tables derived from the DADA2 pipeline were merged using the R package phyloseq v1.46.0\u003csup\u003e91\u003c/sup\u003e. For 16S rRNA gene data, ASVs assigned to mitochondria and chloroplasts and ASVs with unassigned domain were removed. For 18S rRNA gene data, ASVs with Bacteria, Archaea or unassigned domain were removed.\u003c/p\u003e\u003cp\u003e\u003cem\u003eCommunity RNA extraction and RT-qPCR\u003c/em\u003e\u003c/p\u003e\u003cp\u003eRNA was extracted from 0.5 g of soil using the SPINeasy RNA kit for Bacteria (MP Biomedicals, Australia) following the manufacturer’s instructions including lysozyme pretreatment. RNA concentration was measured using Qubit RNA HS assay kit (Thermo Fisher Scientific, Australia) and Qubit 4 fluorometer (Thermo Fisher Scientific, Australia).\u003c/p\u003e\u003cp\u003ecDNA was synthesised using the Maxima First Strand cDNA Synthesis Kit for RT-qPCR with dsDNase (Thermo Fisher Scientific, Australia) following manufacturer’s instructions, except for extending the 50°C incubation to 30 min. In each synthesis, 8 µL of template RNA was added to the reaction mix. Additional no-reverse-transcription negative controls were prepared for each RNA extract by excluding the Maxima enzyme mix from the reaction mix.\u003c/p\u003e\u003cp\u003eReverse transcription quantitative PCR (RT-qPCR) was performed on synthesised cDNA to quantify expression of RuBisCO form IE (\u003cem\u003erbcLIE)\u003c/em\u003e and form 1h-[NiFe] hydrogenase (\u003cem\u003ehhyL\u003c/em\u003e) \u003cem\u003ein situ\u003c/em\u003e. Additionally, expression of the 16S rRNA gene was quantified to serve as a reference gene. RT-qPCR reaction mixtures were prepared using 10 µL 2x QuantiNova Probe PCR Master Mix (Qiagen, Australia), 0.5 µL of 40 µM concentrations of the respective forward and reverse primers (Integrated DNA Technologies, Australia), 1 µL of 5 ng/µL T4gene32 protein (Sigma-Aldrich, Australia), 8 µL of UltraPure DNase/RNase-free distilled water (Thermo Fisher Scientific, Australia), and 1 µL of template cDNA. In reactions targeting the 16S rRNA gene, template cDNA was diluted 1:10. All samples, standards and negative controls were run in technical triplicate for \u003cem\u003erbcLIE\u003c/em\u003e and \u003cem\u003ehhyL\u003c/em\u003e and in technical quadruplicate for the 16S rRNA gene.\u003c/p\u003e\u003cp\u003eThermocycling reactions were conducted in a CFX96 Touch Real-Time PCR Detection System (Bio-Rad Laboratories, Australia) under two step conditions. For reactions targeting 16S rRNA and \u003cem\u003ehhyL\u003c/em\u003e, the thermocycling conditions were 94°C for 5 min, then 40 cycles of 94°C for 10 s and 60°C for 50 s, followed by a melt-curve step from 50 to 95°C. For reactions targeting \u003cem\u003erbcLIE\u003c/em\u003e, the conditions were identical except that amplification occurred at 55°C instead of 60°C. Standard curves for each target gene were generated over 5–7 orders of magnitude by serially diluting synthetically designed gene fragments composed of representative \u003cem\u003erbcLIE\u003c/em\u003e (JX458468.1), \u003cem\u003ehhyL\u003c/em\u003e (AB894417.1) and 16S rRNA (MF689012.1) gene sequences\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eUsing CFX Maestro Software (Bio-Rad Laboratories, Australia), the standard curves were used to determine the reaction efficiencies and copy numbers of target gene in each reaction. Copy numbers for \u003cem\u003erbcLIE\u003c/em\u003e and \u003cem\u003ehhyL\u003c/em\u003e were then normalised to 16S rRNA gene copies and scaled to BH-18. Melt peak analysis confirmed amplification specificity in each reaction.\u003c/p\u003e\u003cp\u003e\u003cem\u003eShotgun metagenome sequencing, reads processing, assembly, and binning\u003c/em\u003e\u003c/p\u003e\u003cp\u003eMetagenomic sequencing was performed on the Novaseq X Plus 10B platform (Illumina, USA) with 2 × 150 bp paired-end reads at the Ramaciotti Centre for Genomics (UNSW Sydney, Australia). The quality of metagenomic reads was assessed using FastQC v0.11.9\u003csup\u003e92\u003c/sup\u003e and MultiQC v1.13\u003csup\u003e93\u003c/sup\u003e. Adapter trimming, contaminant (including spike-ins) and quality filtering was performed using BBDuk (BBMap/BBTools v38.63, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/BioInfoTools/BBMap\u003c/span\u003e\u003cspan address=\"https://github.com/BioInfoTools/BBMap\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Reads were quality-trimmed, including removal of G polymers at least eight bases long, on both sides to Q6 using the Phred algorithm, with the following flags: qtrim = rl trimq = 6 trimpolyg = 8.\u003c/p\u003e\u003cp\u003eFiltered and trimmed reads were assembled using MegaHIT v1.2.9\u003csup\u003e94\u003c/sup\u003e using the meta-sensitive preset. Contigs less than 1000 bp were removed using SeqKit v2.5.1\u003csup\u003e95\u003c/sup\u003e. Metagenomic contigs were binned using the ensemble binner AVAMB v4.1.3 and the corresponding Snakemake workflow\u003csup\u003e\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e\u003c/sup\u003e. Briefly, read mapping, contigs indexing and BAM file generation was performed using minimap2 v2.28\u003csup\u003e97\u003c/sup\u003e then sorted using SAMtools v1.9\u003csup\u003e98\u003c/sup\u003e. Contigs with a minimum length of 2,000 bp, and a minimum completeness and maximum contamination threshold of 50% and 5% respectively were binned using AVAMB\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. CheckM2 v1.0.1\u003csup\u003e99\u003c/sup\u003e was used to calculate the completeness and contamination of all bins. A total of 300 medium- to high-quality bins were recovered after dereplication with galah v0.4.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/wwood/galah\u003c/span\u003e\u003cspan address=\"https://github.com/wwood/galah\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The resulting MAGs were taxonomically classified using the Genome Taxonomy Database toolkit (GTDB-Tk) v2.4.0\u003csup\u003e100\u003c/sup\u003e, and GTDB release 09-RS220\u003csup\u003e101\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cem\u003eMetagenome annotation and functional analysis\u003c/em\u003e\u003c/p\u003e\u003cp\u003eIndividual metagenome assemblies were processed with anvi’o v8-dev\u003csup\u003e\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e\u003c/sup\u003e. Reads were mapped to their respective assemblies and imported with anvi-profile. Predicted protein-coding genes were annotated against the KEGG database using adaptive threshold adjustment\u003csup\u003e\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e\u003c/sup\u003e and the --include-stray-KOs option. Additional annotations were performed with InterProScan v5.68-100.0\u003csup\u003e104\u003c/sup\u003e with options --disable-precalc --goterms --iprlookup --pathways, and all the supported databases and tools.\u003c/p\u003e\u003cp\u003eFor each metagenome assembly, the RPKM for genes encoding selected proteins of biogeochemical relevance, including subtypes of RuBisCO and high-affinity hydrogenases described further below, were calculated using the outputs from anvi-profile-blitz, the integrated annotations above, and the samtools stats output (for the total number of reads mapped to the assemblies). The RPKM values for each marker were normalised against the mean RPKM of 15 universal ribosomal protein markers present in all three domains: uS3_C (IPR001351), uS19 (IPR002222), uL22 (IPR001063), uL14 (IPR000218), uL16 (IPR047873), uL3 (IPR000597), uS10 (IPR027486), uL6 (IPR020040), uS17 (IPR000266), uS8 (IPR000630), uL4 (IPR002136), uL5_C (IPR031309), uL1 (IPR028364), uL18 (IPR005484), and uL2_C (IPR022669). Finally, the log\u003csub\u003e2\u003c/sub\u003e of the normalised values was calculated:\u003c/p\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:LFGE={\\text{log}}_{2}\\left(\\raisebox{1ex}{$RPK{M}_{marker}$}\\!\\left/\\:\\!\\raisebox{-1ex}{$\\stackrel{-}{RPK{M}_{rp15}}$}\\right.\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003cem\u003eAnnotation of MAGs\u003c/em\u003e\u003c/p\u003e\u003cp\u003eGenome annotation was performed with DFAST v1.3.1-c1dc63e\u003csup\u003e\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e\u003c/sup\u003e with all the options for pseudogene prediction deactivated due to overestimations in poorly described lineages. Prodigal v2.6.3\u003csup\u003e106\u003c/sup\u003e was used as gene-caller, CRT v1.2\u003csup\u003e107\u003c/sup\u003e for CRISPR detection, and tRNAscan-SE v2.0.12\u003csup\u003e108\u003c/sup\u003e for the detection of tRNAs. In addition to the default DFAST database, functional annotation included blastn and blastp searches against CARD v3.2.9\u003csup\u003e109\u003c/sup\u003e and VFDB 20240716\u003csup\u003e110\u003c/sup\u003e databases; HMM-based searches against TIGRFAM v15.0\u003csup\u003e111\u003c/sup\u003e, Pfam v37.0\u003csup\u003e112\u003c/sup\u003e, and dbCAN3 HMM profiles v12\u003csup\u003e113\u003c/sup\u003e; and RPS-BLAST against COG (2020 release)\u003csup\u003e\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e\u003c/sup\u003e, KOG\u003csup\u003e\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e\u003c/sup\u003e and CDD 20240330\u003csup\u003e116\u003c/sup\u003e. In addition to the default Barrnap 0.9 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/tseemann/barrnap\u003c/span\u003e\u003cspan address=\"https://github.com/tseemann/barrnap\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and tRNAscan-SE, overall ncRNAs were predicted with infernal v1.1.4\u003csup\u003e117\u003c/sup\u003e with Rfam 14.10\u003csup\u003e118\u003c/sup\u003e (options --cut_ga --rfam --nohmmonly -Z \u0026lt; 2×genome size\u0026gt;) due to the limited detection of some rRNA genes by Barrnap in certain lineages. In addition to the standard functional annotation, optimal growth conditions for all the MAGs were predicted with GenomeSPOT v1.0.1\u003csup\u003e66\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cem\u003eMAG cluster analysis\u003c/em\u003e\u003c/p\u003e\u003cp\u003eDetection of MAGs across samples generated by anvi’o was clustered in Euclidean space with the Ward D2 method with tidyHeatmap v1.11.4\u003csup\u003e119\u003c/sup\u003e. Pairwise statistical tests of the growth preferences across clusters was performed with the ggstatsplot package v0.12.1\u003csup\u003e120\u003c/sup\u003e using Yuen’s trimmed mean test\u003csup\u003e\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cem\u003ePhylogenetic and Gene Cluster Analysis\u003c/em\u003e\u003c/p\u003e\u003cp\u003ePotential Form I RuBisCO large subunit protein sequences were identified in the metagenome assemblies using BLAST + v2.12.0 + \u003csup\u003e122\u003c/sup\u003e with the flag -max_hsps 1 and previously published database\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. False positive hits which did not contain any RuBisCO annotation (e.g. Pfam or InterPro) were removed from further analysis. Very short sequences (\u0026lt; 200 amino acids) were removed as they often distorted the resulting trees. Reference and sample sequences were dereplicated separately with CD-HIT v4.8.1 (-c 0.9), and the representative sequences from each dataset aligned together with MAFFT-L-INS-I v7.481\u003csup\u003e123\u003c/sup\u003e. The alignment was trimmed to remove poorly aligned regions with ClipKIT v2.3.0\u003csup\u003e124\u003c/sup\u003e in kpic mode. Maximum likelihood tree was inferred with IQ-Tree v2.2.3\u003csup\u003e125\u003c/sup\u003e under the Q.pfam + R9 substitution model (-m MFP), with 1000 ultrafast bootstrap replicates (-B 1000 --nmax 5000) with nearest neighbour interchange optimisation (--bnni)\u003csup\u003e\u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e126\u003c/span\u003e\u003c/sup\u003e, and 1000 replicates Shimodaira-Hasegawa-like approximate likelihood ratio tests (SH-aLRT, --alrt 1000)\u003csup\u003e\u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e127\u003c/span\u003e\u003c/sup\u003e. Resulting trees were edited in iTOL\u003csup\u003e\u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e128\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe same process was followed for the catalytic large subunits of [NiFe]-hydrogenase with the following modifications: sequences longer than 1000 amino acids were also excluded and the selected substitution model, LG + I + R10.\u003c/p\u003e\u003cp\u003eThe genomic neighbourhoods for all hydrogenase sequences occurring between the 2a and 1h-[NiFe]-hydrogenase clades, as indicated by the location of the reference sequences, were examined by selecting 10 protein coding genes upstream and downstream the [NiFe]-hydrogenase large subunit gene. Functional annotations within each neighbourhood were manually analysed for structural similarities with the 1l and 1m [NiFe]-hydrogenase groups proposed in the literature\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Clades were then color-coded to reflect the subtype of [NiFe]-hydrogenase, as determined through both phylogenetic and gene structure analysis.\u003c/p\u003e\u003cp\u003e\u003cem\u003eGas chromatography oxidation assays\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe oxidation of H\u003csub\u003e2\u003c/sub\u003e to sub-atmospheric concentrations by microbial high-affinity hydrogenases was measured using gas chromatography for each soil sample as described previously\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Briefly, for each soil sample, 1 g was added to separate 114 mL serum bottles and sealed with butyl rubber stoppers. Hydrogen gas (BOC Australia) was added to the headspace of each sample to attain a concentration of ~ 12,000 p.p.b.v. and then samples were incubated at 10°C. The headspace was sampled (1 mL) at intervals using a gas-tight syringe (SGE) to measure the partial pressures of H\u003csub\u003e2\u003c/sub\u003e and CO using the Peak Performer 1 Gas Chromatograph (Peak Laboratories). Three biologically independent replicates were used for each sample in this experiment. Heat-killed soils (1 g; 121°C, 15 p.s.i., 20 min) for each sample, and empty sterile serum bottles were included as negative controls to ensure that H\u003csub\u003e2\u003c/sub\u003e depletion is due to biological activity. The observed data was visualised in the R environment v4.3.1\u003csup\u003e87\u003c/sup\u003e using the packages ggplot2 v3.4.4\u003csup\u003e129\u003c/sup\u003e and patchwork v1.2.0\u003csup\u003e130\u003c/sup\u003e. Data was then used to calculate the mean rate of H\u003csub\u003e2\u003c/sub\u003e oxidation at atmospheric H\u003csub\u003e2\u003c/sub\u003e concentration\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e from the first-order reaction rate constant.\u003c/p\u003e\u003cp\u003e\u003cem\u003eRadio-labelled CO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e \u003cem\u003efixation assays\u003c/em\u003e\u003c/p\u003e\u003cp\u003eDark carbon fixation was measured by incubating 0.5 g of soil sample to sterile 2.16 mL glass vials and sealed with rubber septum lids. The headspace was purged with ~ 10 mL zero-air (20.9% O\u003csub\u003e2\u003c/sub\u003e, balanced in N\u003csub\u003e2\u003c/sub\u003e, BOC Australia). Gaseous \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003eCO\u003csub\u003e2\u003c/sub\u003e (1% v/v) was generated by combining 408 µL sodium bicarbonate (\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003eC-labelled) solution (NaH\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003eCO\u003csub\u003e3\u003c/sub\u003e, Moravek, 58.8 mCi mmol\u003csup\u003e− 1\u003c/sup\u003e) with 448.8 µL 0.01724 M HCl solution. Gaseous \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003eCO\u003csub\u003e2\u003c/sub\u003e was added to the headspace of each sample using a gas-tight syringe (SGE) to obtain a mixing ratio of 400 p.p.m.v. H\u003csub\u003e2\u003c/sub\u003e was added to the headspace of a subset of samples to a mixing ratio of 4 p.p.m.v. The relevant heat-killed soils per condition were included as negative controls. Samples were incubated under dark conditions at 10°C for 120 h. Samples were then transferred to 20 mL scintillation vials. Unfixed \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003eCO\u003csub\u003e2\u003c/sub\u003e was removed using 3 mL 1M HCl and left to dry on a heating block at 50°C for 24 h. Scintillation cocktail (19.5 mL) (EcoLume) was added to each vial and radioisotope analysis was carried out using a liquid scintillation spectrometer (Tri-Carb 2810 TR, Perkin Elmer) operating at 95% efficiency. Background chemiluminescence and chemiluminescence were corrected with internal calibration standards. Triplicate biological samples were analysed. Mean and SD of \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003eCO\u003csub\u003e2\u003c/sub\u003e fixation were calculated and visualised in the R environment v4.3.1\u003csup\u003e87\u003c/sup\u003e using the packages ggplot2 v3.4.4\u003csup\u003e129\u003c/sup\u003e and dplyr v1.1.3\u003csup\u003e131\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cem\u003eStatistical analyses\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAll statistical analyses were performed in the R environment v4.3.1\u003csup\u003e87\u003c/sup\u003e. Significance testing of soil physicochemical properties among monitoring zones was performed with one-way ANOVA using the aov() function of the stats package v4.3.1\u003csup\u003e87\u003c/sup\u003e and post-hoc Tukey’s multiple comparison test using the tukey_hsd() function of the rstatix package v0.7.2\u003csup\u003e132\u003c/sup\u003e. Prior to biodiversity analysis, data was subjected to rarefaction using rrarefy from vegan v2.6.4\u003csup\u003e133\u003c/sup\u003e using the sample with the lowest counts (9859 for 16S rRNA, and 34,017 and 18S rRNA gene datasets) as the threshold for subsampling.\u003c/p\u003e\u003cp\u003eAlpha diversity indices including Chao1 richness, inverse Simpson’s diversity, and Pielou’s Evenness were calculated using the microbiome package v1.24.0\u003csup\u003e134\u003c/sup\u003e. Significant differences between monitoring zones for each index was assessed using Kruskal-Wallis test and Dunn’s post-hoc test. \u003cem\u003eP\u003c/em\u003e values were adjusted using Holm’s correction.\u003c/p\u003e\u003cp\u003eBeta diversity was analysed based on the Bray-Curtis dissimilarity measure. Bray-Curtis dissimilarities were calculated between each sample and the resulting matrices were subsequently used to perform unconstrained ordination and clustering of community data with non-metric multidimensional scaling (NMDS) using the phyloseq package v1.46.0\u003csup\u003e91\u003c/sup\u003e. Variations in community structure were constrained to a set of explanatory variables using distance-based redundancy analysis (db-RDA) method using vegan v2.6.4\u003csup\u003e133,135,136\u003c/sup\u003e. \u003cem\u003eP-\u003c/em\u003eadjusted (Holm’s correction) statistically significant explanatory variables were selected for the model using a forward stepwise model selection method with 9,999 permutations after removal of collinear variables using a variance inflation factor threshold of 5.\u003c/p\u003e\u003cp\u003ePairwise differences in community composition was assessed using the pairwise.adonis() function of the pairwiseAdonis package v0.4.1\u003csup\u003e137\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe DESeq2 package v1.40.0\u003csup\u003e138\u003c/sup\u003e and phyloseq package v1.44.0\u003csup\u003e91\u003c/sup\u003e was used to identify differentially abundant microbial taxa between the transects and zones\u003csup\u003e\u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e139\u003c/span\u003e\u003c/sup\u003e. A DESeqDataSet object was created using the phyloseq_to_deseq2() function, specifying the variable of interest. DESeq2 was then applied to normalise the data and fit a negative binomial model. The contrast for the DESeq2 model was defined, and results were extracted with a significance threshold (alpha = 0.01), while disabling Cook's distance cutoff to ensure potential outliers were not excluded.\u003c/p\u003e\u003cp\u003eSpearman correlation analysis was conducted to explore the relationships between microbial community structure and environmental chemical variables. The analysis began by aggregating the ASVs by class level. Next, the data was normalised using the DESeq2 method to account for differences in sequencing depth across samples. Variance-stabilising transformation (VST) was applied to the aggregated table, resulting in normalised abundances. Then only taxa identify to be differentially abundant between the transects and zones were selected.\u003c/p\u003e\u003cp\u003eSpearman correlations were then calculated between the DESeq2-normalised table at the class level (18S)/phylum level (16S) and the environmental chemical variables. This analysis was performed using the associate() function from the microbiome package v1.22.0\u003csup\u003e134\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn most cases, \u003cem\u003eP\u003c/em\u003e values were corrected for multiple hypotheses with the Holm\u003csup\u003e\u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e140\u003c/span\u003e\u003c/sup\u003e method using the relevant flag for each function.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eWe would like to thank the Australian Antarctic Division Casey station team for conducting the Antarctic field work in 2023, as well as the Australian Antarctic Program for supporting the work via the Australian Antarctic Science Project AAS 4503 \u0026ndash; Reducing Environmental Impacts at Contaminated Sites in Antarctica. This research was supported by the UNSW University International Postgraduate Award Scholarship (awarded to KKYT) and the ARC Discovery Project (DP240102658) awarded to BCF. This research includes computations using the computational cluster Katana supported by Research Technology Services at UNSW Sydney.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e\u003cp\u003eRaw amplicon sequencing data are deposited in ENA under bioproject PRJEB90557. Raw metagenomic sequencing data and metagenome-assembled genomes have been deposited in ENA under bioproject PRJEB77669.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCary, S. C., McDonald, I. R., Barrett, J. E. \u0026amp; Cowan, D. A. On the rocks: the microbiology of Antarctic Dry Valley soils. \u003cem\u003eNat. Rev. Microbiol.\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 129\u0026ndash;138 (2010).\u003c/li\u003e\n\u003cli\u003eChown, S. L. \u003cem\u003eet al.\u003c/em\u003e The changing form of Antarctic biodiversity. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e522\u003c/strong\u003e, 431\u0026ndash;438 (2015).\u003c/li\u003e\n\u003cli\u003eLambrechts, S., Willems, A. \u0026amp; Tahon, G. Uncovering the Uncultivated Majority in Antarctic Soils: Toward a Synergistic Approach. \u003cem\u003eFront. Microbiol.\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 242 (2019).\u003c/li\u003e\n\u003cli\u003eGreening, C. \u0026amp; Grinter, R. Microbial oxidation of atmospheric trace gases. \u003cem\u003eNat. Rev. Microbiol.\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 513\u0026ndash;528 (2022).\u003c/li\u003e\n\u003cli\u003eTedrow, J. C. F. \u0026amp; Ugolini, F. C. Antarctic Soils. in \u003cem\u003eAntarctic Soils and Soil Forming Processes\u003c/em\u003e (ed. Tedrow, J. C. F.) 161\u0026ndash;177 (American Geophysical Union, 1966).\u003c/li\u003e\n\u003cli\u003eNovelli, P. C. \u003cem\u003eet al.\u003c/em\u003e Molecular hydrogen in the troposphere: Global distribution and budget. \u003cem\u003eJ. Geophys. Res. Atmospheres\u003c/em\u003e \u003cstrong\u003e104\u003c/strong\u003e, 30427\u0026ndash;30444 (1999).\u003c/li\u003e\n\u003cli\u003eOrtiz, M. \u003cem\u003eet al.\u003c/em\u003e Multiple energy sources and metabolic strategies sustain microbial diversity in Antarctic desert soils. \u003cem\u003eProc. Natl. Acad. Sci.\u003c/em\u003e \u003cstrong\u003e118\u003c/strong\u003e, e2025322118 (2021).\u003c/li\u003e\n\u003cli\u003eTebo, B. M. \u003cem\u003eet al.\u003c/em\u003e Microbial communities in dark oligotrophic volcanic ice cave ecosystems of Mt. Erebus, Antarctica. \u003cem\u003eFront. Microbiol.\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 179 (2015).\u003c/li\u003e\n\u003cli\u003eJi, M. \u003cem\u003eet al.\u003c/em\u003e Atmospheric trace gases support primary production in Antarctic desert surface soil. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e552\u003c/strong\u003e, 400\u0026ndash;403 (2017).\u003c/li\u003e\n\u003cli\u003eRay, A. E. \u003cem\u003eet al.\u003c/em\u003e Atmospheric chemosynthesis is phylogenetically and geographically widespread and contributes significantly to carbon fixation throughout cold deserts. \u003cem\u003eISME J.\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 2547\u0026ndash;2560 (2022).\u003c/li\u003e\n\u003cli\u003eGreening, C., Berney, M., Hards, K., Cook, G. M. \u0026amp; Conrad, R. A soil actinobacterium scavenges atmospheric H\u003csub\u003e2\u003c/sub\u003e using two membrane-associated, oxygen-dependent [NiFe] hydrogenases. \u003cem\u003eProc. Natl. Acad. Sci.\u003c/em\u003e \u003cstrong\u003e111\u003c/strong\u003e, 4257\u0026ndash;4261 (2014).\u003c/li\u003e\n\u003cli\u003eIslam, Z. F. \u003cem\u003eet al.\u003c/em\u003e A widely distributed hydrogenase oxidises atmospheric H\u003csub\u003e2\u003c/sub\u003e during bacterial growth. \u003cem\u003eISME J.\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 2649\u0026ndash;2658 (2020).\u003c/li\u003e\n\u003cli\u003eMyers, M. R. \u0026amp; King, G. Isolation and characterization of \u003cem\u003eAcidobacterium ailaaui\u003c/em\u003e sp. nov., a novel member of \u003cem\u003eAcidobacteria\u003c/em\u003e subdivision 1, from a geothermally heated Hawaiian microbial mat. \u003cem\u003eInt. J. Syst. Evol. Microbiol.\u003c/em\u003e \u003cstrong\u003e66\u003c/strong\u003e, 5328\u0026ndash;5335 (2016).\u003c/li\u003e\n\u003cli\u003eRay, A. E. \u003cem\u003eet al.\u003c/em\u003e Soil Microbiomes With the Genetic Capacity for Atmospheric Chemosynthesis Are Widespread Across the Poles and Are Associated With Moisture, Carbon, and Nitrogen Limitation. \u003cem\u003eFront. Microbiol.\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 1936 (2020).\u003c/li\u003e\n\u003cli\u003eJi, M. \u003cem\u003eet al.\u003c/em\u003e \u003cem\u003eCandidatus\u003c/em\u003e Eremiobacterota, a metabolically and phylogenetically diverse terrestrial phylum with acid-tolerant adaptations. \u003cem\u003eISME J.\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 2692\u0026ndash;2707 (2021).\u003c/li\u003e\n\u003cli\u003eYabe, S. \u003cem\u003eet al.\u003c/em\u003e \u003cem\u003eVulcanimicrobium alpinus\u003c/em\u003e gen. nov. sp. nov., the first cultivated representative of the candidate phylum \u0026ldquo;Eremiobacterota\u0026rdquo;, is a metabolically versatile aerobic anoxygenic phototroph. \u003cem\u003eISME Commun.\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e, 120 (2022).\u003c/li\u003e\n\u003cli\u003eBay, S. K. \u003cem\u003eet al.\u003c/em\u003e Chemosynthetic and photosynthetic bacteria contribute differentially to primary production across a steep desert aridity gradient. \u003cem\u003eISME J.\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 3339\u0026ndash;3356 (2021).\u003c/li\u003e\n\u003cli\u003eGore, D. B., Gibson, J. A. E. \u0026amp; Leishman, M. R. Human occupation, impacts and environmental management of Bunger Hills. \u003cem\u003eAntarct. Sci.\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 72\u0026ndash;84 (2020).\u003c/li\u003e\n\u003cli\u003eGore, D. B., Revill, A. T. \u0026amp; Guille, D. Petroleum hydrocarbons ten years after spillage at a helipad in Bunger Hills, East Antarctica. \u003cem\u003eAntarct. Sci.\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 427\u0026ndash;429 (1999).\u003c/li\u003e\n\u003cli\u003eAislabie, J. M., Balks, M. R., Foght, J. M. \u0026amp; Waterhouse, E. J. Hydrocarbon Spills on Antarctic Soils:\u0026thinsp; Effects and Management. \u003cem\u003eEnviron. Sci. Technol.\u003c/em\u003e \u003cstrong\u003e38\u003c/strong\u003e, 1265\u0026ndash;1274 (2004).\u003c/li\u003e\n\u003cli\u003evan Dorst, J. \u003cem\u003eet al.\u003c/em\u003e Applying microbial indicators of hydrocarbon toxicity to contaminated sites undergoing bioremediation on subantarctic Macquarie Island. \u003cem\u003eEnviron. Pollut.\u003c/em\u003e \u003cstrong\u003e259\u003c/strong\u003e, 113780 (2020).\u003c/li\u003e\n\u003cli\u003evan Dorst, J. \u003cem\u003eet al.\u003c/em\u003e Microbial community analysis of biopiles in Antarctica provides evidence of successful hydrocarbon biodegradation and initial soil ecosystem recovery. \u003cem\u003eEnviron. Pollut.\u003c/em\u003e \u003cstrong\u003e290\u003c/strong\u003e, 117977 (2021).\u003c/li\u003e\n\u003cli\u003eJurelevicius, D. \u003cem\u003eet al.\u003c/em\u003e Metagenomic analysis of microbial communities across a transect from low to highly hydrocarbon-contaminated soils in King George Island, Maritime Antarctica. \u003cem\u003eGeobiology\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 98\u0026ndash;111 (2022).\u003c/li\u003e\n\u003cli\u003eGore, D. B. \u0026amp; Leishman, M. R. Salt, sediments and weathering environments in Bunger Hills. \u003cem\u003eAntarct. Sci.\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 138\u0026ndash;152 (2020).\u003c/li\u003e\n\u003cli\u003eDoran, P. T. \u003cem\u003eet al.\u003c/em\u003e Climatology and implications for perennial lake ice occurrence at Bunger Hills Oasis, East Antarctica. \u003cem\u003eAntarct. Sci.\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 289\u0026ndash;296 (1996).\u003c/li\u003e\n\u003cli\u003eTucker, N. M., Hand, M. \u0026amp; Clark, C. D. The Bunger Hills: 60 years of geological and geophysical research. \u003cem\u003eAntarct. Sci.\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 85\u0026ndash;106 (2020).\u003c/li\u003e\n\u003cli\u003eKudinova, A. G. \u003cem\u003eet al.\u003c/em\u003e Taxonomic Diversity of Bacteria and Their Filterable Forms in the Soils of Eastern Antarctica (Larsemann Hills and Bunger Hills). \u003cem\u003eMicrobiology\u003c/em\u003e \u003cstrong\u003e89\u003c/strong\u003e, 574\u0026ndash;584 (2020).\u003c/li\u003e\n\u003cli\u003eKochkina, G. A. \u003cem\u003eet al.\u003c/em\u003e Diversity of mycelial fungi in natural and human-affected Antarctic soils. \u003cem\u003ePolar Biol.\u003c/em\u003e \u003cstrong\u003e42\u003c/strong\u003e, 47\u0026ndash;64 (2019).\u003c/li\u003e\n\u003cli\u003eKaraevskaya, E. S. \u003cem\u003eet al.\u003c/em\u003e Archaeal diversity in permafrost deposits of Bunger Hills Oasis and King George Island (Antarctica) according to the 16S rRNA gene sequencing. \u003cem\u003eMicrobiology\u003c/em\u003e \u003cstrong\u003e83\u003c/strong\u003e, 398\u0026ndash;406 (2014).\u003c/li\u003e\n\u003cli\u003eZemo, D. A. \u003cem\u003eet al.\u003c/em\u003e Life cycle of petroleum biodegradation metabolite plumes, and implications for risk management at fuel release sites. \u003cem\u003eIntegr. Environ. Assess. Manag.\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 714\u0026ndash;727 (2017).\u003c/li\u003e\n\u003cli\u003eZhang, E. \u003cem\u003eet al.\u003c/em\u003e Lifting the veil on arid-to-hyperarid Antarctic soil microbiomes: a tale of two oases. \u003cem\u003eMicrobiome\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 37 (2020).\u003c/li\u003e\n\u003cli\u003eVarliero, G. \u003cem\u003eet al.\u003c/em\u003e Biogeographic survey of soil bacterial communities across Antarctica. \u003cem\u003eMicrobiome\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 9 (2024).\u003c/li\u003e\n\u003cli\u003eRay, A. E., Tribbia, D. Z., Cowan, D. A. \u0026amp; Ferrari, B. Clearing the air: unraveling past and guiding future research in atmospheric chemosynthesis. \u003cem\u003eMicrobiol. Mol. Biol. Rev.\u003c/em\u003e \u003cstrong\u003e87\u003c/strong\u003e, e00048-23 (2023).\u003c/li\u003e\n\u003cli\u003eWhyte, L. G., Bourbonniere, L. \u0026amp; Greer, C. W. Biodegradation of petroleum hydrocarbons by psychrotrophic \u003cem\u003ePseudomonas\u003c/em\u003e strains possessing both alkane (\u003cem\u003ealk\u003c/em\u003e) and naphthalene (\u003cem\u003enah\u003c/em\u003e) catabolic pathways. \u003cem\u003eAppl. Environ. Microbiol.\u003c/em\u003e \u003cstrong\u003e63\u003c/strong\u003e, 3719\u0026ndash;3723 (1997).\u003c/li\u003e\n\u003cli\u003eAislabie, J., Foght, J. \u0026amp; Saul, D. Aromatic hydrocarbon-degrading bacteria from soil near Scott Base, Antarctica. \u003cem\u003ePolar Biol.\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 183\u0026ndash;188 (2000).\u003c/li\u003e\n\u003cli\u003eStolz, A. Molecular characteristics of xenobiotic-degrading sphingomonads. \u003cem\u003eAppl. Microbiol. Biotechnol.\u003c/em\u003e \u003cstrong\u003e81\u003c/strong\u003e, 793\u0026ndash;811 (2009).\u003c/li\u003e\n\u003cli\u003eLi, Q.-M., Zhou, Y.-L., Wei, Z.-F. \u0026amp; Wang, Y. Phylogenomic Insights into Distribution and Adaptation of Bdellovibrionota in Marine Waters. \u003cem\u003eMicroorganisms\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 757 (2021).\u003c/li\u003e\n\u003cli\u003eKababu, E. \u003cem\u003eet al.\u003c/em\u003e Effects of crude oil and gas condensate spill on coastal benthic microbial populations. \u003cem\u003eFront. Environ. Sci.\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 1051460 (2022).\u003c/li\u003e\n\u003cli\u003eNiederberger, T. D. \u003cem\u003eet al.\u003c/em\u003e Carbon-Fixation Rates and Associated Microbial Communities Residing in Arid and Ephemerally Wet Antarctic Dry Valley Soils. \u003cem\u003eFront. Microbiol.\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 1347 (2015).\u003c/li\u003e\n\u003cli\u003eSaul, D. J., Aislabie, J. M., Brown, C. E., Harris, L. \u0026amp; Foght, J. M. Hydrocarbon contamination changes the bacterial diversity of soil from around Scott Base, Antarctica. \u003cem\u003eFEMS Microbiol. Ecol.\u003c/em\u003e \u003cstrong\u003e53\u003c/strong\u003e, 141\u0026ndash;155 (2005).\u003c/li\u003e\n\u003cli\u003eFerrari, B. C., Zhang, C. \u0026amp; Van Dorst, J. Recovering Greater Fungal Diversity from Pristine and Diesel Fuel Contaminated Sub-Antarctic Soil Through Cultivation Using Both a High and a Low Nutrient Media Approach. \u003cem\u003eFront. Microbiol.\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e, 217 (2011).\u003c/li\u003e\n\u003cli\u003evan Dorst, J., Siciliano, S. D., Winsley, T., Snape, I. \u0026amp; Ferrari, B. C. Bacterial Targets as Potential Indicators of Diesel Fuel Toxicity in Subantarctic Soils. \u003cem\u003eAppl. Environ. Microbiol.\u003c/em\u003e \u003cstrong\u003e80\u003c/strong\u003e, 4021\u0026ndash;4033 (2014).\u003c/li\u003e\n\u003cli\u003eSimanjorang, S. W. \u0026amp; Subowo, Y. B. The ability of soil-borne fungi to degrade polycyclic aromatic hydrocarbon (PAH). in \u003cem\u003eAIP Conference Proceedings\u003c/em\u003e vol. 2021 030019 (2018).\u003c/li\u003e\n\u003cli\u003eMa, Y., Wang, L. \u0026amp; Shao, Z. \u003cem\u003ePseudomonas\u003c/em\u003e, the dominant polycyclic aromatic hydrocarbon-degrading bacteria isolated from Antarctic soils and the role of large plasmids in horizontal gene transfer. \u003cem\u003eEnviron. Microbiol.\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 455\u0026ndash;465 (2006).\u003c/li\u003e\n\u003cli\u003eHeipieper, H. J. \u0026amp; Mart\u0026iacute;nez, P. M. Toxicity of Hydrocarbons to Microorganisms. in \u003cem\u003eHandbook of Hydrocarbon and Lipid Microbiology\u003c/em\u003e (ed. Timmis, K. N.) 1563\u0026ndash;1573 (Springer Berlin Heidelberg, Berlin, Heidelberg, 2010). doi:10.1007/978-3-540-77587-4_108.\u003c/li\u003e\n\u003cli\u003eCamenzind, T., Mason-Jones, K., Mansour, I., Rillig, M. C. \u0026amp; Lehmann, J. Formation of necromass-derived soil organic carbon determined by microbial death pathways. \u003cem\u003eNat. Geosci.\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 115\u0026ndash;122 (2023).\u003c/li\u003e\n\u003cli\u003eKostka, J. E. \u003cem\u003eet al.\u003c/em\u003e Hydrocarbon-Degrading Bacteria and the Bacterial Community Response in Gulf of Mexico Beach Sands Impacted by the Deepwater Horizon Oil Spill. \u003cem\u003eAppl. Environ. Microbiol.\u003c/em\u003e \u003cstrong\u003e77\u003c/strong\u003e, 7962\u0026ndash;7974 (2011).\u003c/li\u003e\n\u003cli\u003eLin, Y. \u003cem\u003eet al.\u003c/em\u003e Keystone predatory protists are associated closely with ammonia-oxidizing microorganisms in an acidic Ultisol. \u003cem\u003eJ. Sustain. Agric. Environ.\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, e12076 (2024).\u003c/li\u003e\n\u003cli\u003eJia, C. \u003cem\u003eet al.\u003c/em\u003e The addition of discrimination inhibitors stimulations discrimination potential and N\u003csub\u003e2\u003c/sub\u003eO emissions were linked to predation among microorganisms in long term nitrogen application and straw returning systems. \u003cem\u003eFront. Microbiol.\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 1337507 (2024).\u003c/li\u003e\n\u003cli\u003ePogue, A. J. \u0026amp; Gilbride, K. A. Impact of protozoan grazing on nitrification and the ammonia- and nitrite-oxidizing bacterial communities in activated sludge. \u003cem\u003eCan. J. Microbiol.\u003c/em\u003e \u003cstrong\u003e53\u003c/strong\u003e, 559\u0026ndash;571 (2007).\u003c/li\u003e\n\u003cli\u003eHedlund, B. P. \u003cem\u003eet al.\u003c/em\u003e SeqCode: a nomenclatural code for prokaryotes described from sequence data. \u003cem\u003eNat. Microbiol.\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 1702\u0026ndash;1708 (2022).\u003c/li\u003e\n\u003cli\u003eRojas-Vargas, J., Castel\u0026aacute;n-S\u0026aacute;nchez, H. G. \u0026amp; Pardo-L\u0026oacute;pez, L. HADEG: A curated hydrocarbon aerobic degradation enzymes and genes database. \u003cem\u003eComput. Biol. Chem.\u003c/em\u003e \u003cstrong\u003e107\u003c/strong\u003e, 107966 (2023).\u003c/li\u003e\n\u003cli\u003eSoares, A. \u003cem\u003eet al.\u003c/em\u003e A global perspective on bacterial diversity in the terrestrial deep subsurface. \u003cem\u003eMicrobiology\u003c/em\u003e vol. 169 001172 (2023).\u003c/li\u003e\n\u003cli\u003eHuang, Y., Li, L., Yin, X. \u0026amp; Zhang, T. Polycyclic aromatic hydrocarbon (PAH) biodegradation capacity revealed by a genome-function relationship approach. \u003cem\u003eEnviron. Microbiome\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 39 (2023).\u003c/li\u003e\n\u003cli\u003eDunn, N. W. \u0026amp; Gunsalus, I. C. Transmissible Plasmid Coding Early Enzymes of Naphthalene Oxidation in \u003cem\u003ePseudomonas putida\u003c/em\u003e. \u003cem\u003eJ. Bacteriol.\u003c/em\u003e \u003cstrong\u003e114\u003c/strong\u003e, 974\u0026ndash;979 (1973).\u003c/li\u003e\n\u003cli\u003eBadger, M. R. \u0026amp; Bek, E. J. Multiple Rubisco forms in proteobacteria: their functional significance in relation to CO\u003csub\u003e2\u003c/sub\u003e acquisition by the CBB cycle. \u003cem\u003eJ. Exp. Bot.\u003c/em\u003e \u003cstrong\u003e59\u003c/strong\u003e, 1525\u0026ndash;1541 (2008).\u003c/li\u003e\n\u003cli\u003eAlonso-S\u0026aacute;ez, L., Galand, P. E., Casamayor, E. O., Pedr\u0026oacute;s-Ali\u0026oacute;, C. \u0026amp; Bertilsson, S. High bicarbonate assimilation in the dark by Arctic bacteria. \u003cem\u003eISME J.\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 1581\u0026ndash;1590 (2010).\u003c/li\u003e\n\u003cli\u003eNing, Z. \u003cem\u003eet al.\u003c/em\u003e Metagenomic and isotopic insights into carbon fixation by autotrophic microorganisms in a petroleum hydrocarbon impacted red clay aquifer. \u003cem\u003eEnviron. Pollut.\u003c/em\u003e \u003cstrong\u003e361\u003c/strong\u003e, 124824 (2024).\u003c/li\u003e\n\u003cli\u003eAbed, R. M. M. \u0026amp; K\u0026ouml;ster, J. The direct role of aerobic heterotrophic bacteria associated with cyanobacteria in the degradation of oil compounds. \u003cem\u003eInt. Biodeterior. Biodegrad.\u003c/em\u003e \u003cstrong\u003e55\u003c/strong\u003e, 29\u0026ndash;37 (2005).\u003c/li\u003e\n\u003cli\u003eHu, J., Wang, L., Zhang, S., Wang, Y. \u0026amp; Xi, X. Inhibitory effect of organic carbon on CO\u003csub\u003e2\u003c/sub\u003e fixing by non-photosynthetic microbial community isolated from the ocean. \u003cem\u003eBioresour. Technol.\u003c/em\u003e \u003cstrong\u003e102\u003c/strong\u003e, 7147\u0026ndash;7153 (2011).\u003c/li\u003e\n\u003cli\u003eZhang, S. \u003cem\u003eet al.\u003c/em\u003e Main components of free organic carbon generated by obligate chemoautotrophic bacteria that inhibit their CO\u003csub\u003e2\u003c/sub\u003e fixation. \u003cem\u003eiScience\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 105553 (2022).\u003c/li\u003e\n\u003cli\u003eMancera-L\u0026oacute;pez, M. E. \u003cem\u003eet al.\u003c/em\u003e Bioremediation of an aged hydrocarbon-contaminated soil by a combined system of biostimulation\u0026ndash;bioaugmentation with filamentous fungi. \u003cem\u003eInt. Biodeterior. Biodegrad.\u003c/em\u003e \u003cstrong\u003e61\u003c/strong\u003e, 151\u0026ndash;160 (2008).\u003c/li\u003e\n\u003cli\u003eSpohn, M., M\u0026uuml;ller, K., H\u0026ouml;schen, C., Mueller, C. W. \u0026amp; Marhan, S. Dark microbial CO\u003csub\u003e2\u003c/sub\u003e fixation in temperate forest soils increases with CO\u003csub\u003e2\u003c/sub\u003e concentration. \u003cem\u003eGlob. Change Biol.\u003c/em\u003e \u003cstrong\u003e26\u003c/strong\u003e, 1926\u0026ndash;1935 (2020).\u003c/li\u003e\n\u003cli\u003eSarles, L. S. \u0026amp; Tabita, F. R. Derepression of the synthesis of D-ribulose 1,5-bisphosphate carboxylase/oxygenase from \u003cem\u003eRhodospirillum rubrum\u003c/em\u003e. \u003cem\u003eJ. Bacteriol.\u003c/em\u003e \u003cstrong\u003e153\u003c/strong\u003e, 458\u0026ndash;464 (1983).\u003c/li\u003e\n\u003cli\u003eYoichi, Y., Koichi, T., Hiroyuki, A., Masaharu, I. \u0026amp; Yasuo, I. CO\u003csub\u003e2\u003c/sub\u003e-Responsive Expression and Gene Organization of Three Ribulose-1,5-Bisphosphate Carboxylase/Oxygenase Enzymes and Carboxysomes in \u003cem\u003eHydrogenovibrio marinus\u003c/em\u003e Strain MH-110. \u003cem\u003eJ. Bacteriol.\u003c/em\u003e \u003cstrong\u003e186\u003c/strong\u003e, 5685\u0026ndash;5691 (2004).\u003c/li\u003e\n\u003cli\u003eWilkins, D., Mcwatters, R., Price, G., Williams, K. \u0026amp; Mets, J. Orthomosaic and digital surface model of Edgeworth David Camp, Bunger Hills, 06 February 2023. Australian Antarctic Data Centre https://doi.org/doi:10.26179/146h-9b03 (2025).\u003c/li\u003e\n\u003cli\u003eU.S. Environmental Protection Agency (EPA). Test Methods for Evaluating Solid Waste, Physical/Chemical Methods, SW-846, Third Edition. (1993).\u003c/li\u003e\n\u003cli\u003eMaynard, D., Kalra, Y. \u0026amp; Crumbaugh, J. Nitrate and exchangeable ammonium nitrogen. \u003cem\u003eSoil Sampl. Methods Anal.\u003c/em\u003e \u003cstrong\u003e1\u003c/strong\u003e, 25\u0026ndash;38 (1993).\u003c/li\u003e\n\u003cli\u003eRayment, G. E. \u0026amp; Lyons, D. J. \u003cem\u003eSoil Chemical Methods: Australasia\u003c/em\u003e. vol. 3 (CSIRO publishing, 2011).\u003c/li\u003e\n\u003cli\u003eApprill, A., McNally, S., Parsons, R. \u0026amp; Weber, L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. \u003cem\u003eAquat. Microb. Ecol.\u003c/em\u003e \u003cstrong\u003e75\u003c/strong\u003e, 129\u0026ndash;137 (2015).\u003c/li\u003e\n\u003cli\u003eParada, A. E., Needham, D. M. \u0026amp; Fuhrman, J. A. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. \u003cem\u003eEnviron. Microbiol.\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 1403\u0026ndash;1414 (2016).\u003c/li\u003e\n\u003cli\u003eAmaral-Zettler, L. A., McCliment, E. A., Ducklow, H. W. \u0026amp; Huse, S. M. A Method for Studying Protistan Diversity Using Massively Parallel Sequencing of V9 Hypervariable Regions of Small-Subunit Ribosomal RNA Genes. \u003cem\u003ePLOS ONE\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, e6372 (2009).\u003c/li\u003e\n\u003cli\u003eStoeck, T. \u003cem\u003eet al.\u003c/em\u003e Multiple marker parallel tag environmental DNA sequencing reveals a highly complex eukaryotic community in marine anoxic water. \u003cem\u003eMol. Ecol.\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 21\u0026ndash;31 (2010).\u003c/li\u003e\n\u003cli\u003eR Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing (2023).\u003c/li\u003e\n\u003cli\u003eCallahan, B. J. \u003cem\u003eet al.\u003c/em\u003e DADA2: High-resolution sample inference from Illumina amplicon data. \u003cem\u003eNat. Methods\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 581\u0026ndash;583 (2016).\u003c/li\u003e\n\u003cli\u003eQuast, C. \u003cem\u003eet al.\u003c/em\u003e The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cstrong\u003e41\u003c/strong\u003e, D590\u0026ndash;D596 (2013).\u003c/li\u003e\n\u003cli\u003eVaulot, D. \u003cem\u003eet al.\u003c/em\u003e pr2database/pr2database: PR2 version 5.0.0. Zenodo https://doi.org/10.5281/zenodo.7805244 (2023).\u003c/li\u003e\n\u003cli\u003eMcMurdie, P. J. \u0026amp; Holmes, S. phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. \u003cem\u003ePLOS ONE\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, e61217 (2013).\u003c/li\u003e\n\u003cli\u003eAndrews, S. FastQC: a quality control tool for high throughput sequence data. (2010).\u003c/li\u003e\n\u003cli\u003eEwels, P., Magnusson, M., Lundin, S. \u0026amp; K\u0026auml;ller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 3047\u0026ndash;3048 (2016).\u003c/li\u003e\n\u003cli\u003eLi, D., Liu, C.-M., Luo, R., Sadakane, K. \u0026amp; Lam, T.-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e, 1674\u0026ndash;1676 (2015).\u003c/li\u003e\n\u003cli\u003eShen, W., Le, S., Li, Y. \u0026amp; Hu, F. SeqKit: A Cross-Platform and Ultrafast Toolkit for FASTA/Q File Manipulation. \u003cem\u003ePLOS ONE\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, e0163962 (2016).\u003c/li\u003e\n\u003cli\u003eL\u0026iacute;ndez, P. P. \u003cem\u003eet al.\u003c/em\u003e Adversarial and variational autoencoders improve metagenomic binning. \u003cem\u003eCommun. Biol.\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 1073 (2023).\u003c/li\u003e\n\u003cli\u003eLi, H. Minimap2: pairwise alignment for nucleotide sequences. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e34\u003c/strong\u003e, 3094\u0026ndash;3100 (2018).\u003c/li\u003e\n\u003cli\u003eLi, H. \u003cem\u003eet al.\u003c/em\u003e The Sequence Alignment/Map format and SAMtools. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 2078\u0026ndash;2079 (2009).\u003c/li\u003e\n\u003cli\u003eChklovski, A., Parks, D. H., Woodcroft, B. J. \u0026amp; Tyson, G. W. CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning. \u003cem\u003eNat. Methods\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 1203\u0026ndash;1212 (2023).\u003c/li\u003e\n\u003cli\u003eChaumeil, P.-A., Mussig, A. J., Hugenholtz, P. \u0026amp; Parks, D. H. GTDB-Tk v2: memory friendly classification with the genome taxonomy database. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e38\u003c/strong\u003e, 5315\u0026ndash;5316 (2022).\u003c/li\u003e\n\u003cli\u003eParks, D. H. \u003cem\u003eet al.\u003c/em\u003e A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. \u003cem\u003eNat. Biotechnol.\u003c/em\u003e \u003cstrong\u003e36\u003c/strong\u003e, 996\u0026ndash;1004 (2018).\u003c/li\u003e\n\u003cli\u003eEren, A. M. \u003cem\u003eet al.\u003c/em\u003e Community-led, integrated, reproducible multi-omics with anvi\u0026rsquo;o. \u003cem\u003eNat. Microbiol.\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 3\u0026ndash;6 (2021).\u003c/li\u003e\n\u003cli\u003eKananen, K. \u003cem\u003eet al.\u003c/em\u003e Adaptive adjustment of profile HMM significance thresholds improves functional and metabolic insights into microbial genomes. \u003cem\u003eBioinforma. Adv.\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, vbaf039 (2025).\u003c/li\u003e\n\u003cli\u003eJones, P. \u003cem\u003eet al.\u003c/em\u003e InterProScan 5: genome-scale protein function classification. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 1236\u0026ndash;1240 (2014).\u003c/li\u003e\n\u003cli\u003eTanizawa, Y., Fujisawa, T. \u0026amp; Nakamura, Y. DFAST: a flexible prokaryotic genome annotation pipeline for faster genome publication. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e34\u003c/strong\u003e, 1037\u0026ndash;1039 (2018).\u003c/li\u003e\n\u003cli\u003eHyatt, D. \u003cem\u003eet al.\u003c/em\u003e Prodigal: prokaryotic gene recognition and translation initiation site identification. \u003cem\u003eBMC Bioinformatics\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 119 (2010).\u003c/li\u003e\n\u003cli\u003eBland, C. \u003cem\u003eet al.\u003c/em\u003e CRISPR Recognition Tool (CRT): a tool for automatic detection of clustered regularly interspaced palindromic repeats. \u003cem\u003eBMC Bioinformatics\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 209 (2007).\u003c/li\u003e\n\u003cli\u003eChan, P. P., Lin, B. Y., Mak, A. J. \u0026amp; Lowe, T. M. tRNAscan-SE 2.0: improved detection and functional classification of transfer RNA genes. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cstrong\u003e49\u003c/strong\u003e, 9077\u0026ndash;9096 (2021).\u003c/li\u003e\n\u003cli\u003eAlcock, B. P. \u003cem\u003eet al.\u003c/em\u003e CARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cstrong\u003e51\u003c/strong\u003e, D690\u0026ndash;D699 (2023).\u003c/li\u003e\n\u003cli\u003eLiu, B., Zheng, D., Zhou, S., Chen, L. \u0026amp; Yang, J. VFDB 2022: a general classification scheme for bacterial virulence factors. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cstrong\u003e50\u003c/strong\u003e, D912\u0026ndash;D917 (2022).\u003c/li\u003e\n\u003cli\u003eHaft, D. H. \u003cem\u003eet al.\u003c/em\u003e TIGRFAMs and Genome Properties in 2013. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cstrong\u003e41\u003c/strong\u003e, D387\u0026ndash;D395 (2013).\u003c/li\u003e\n\u003cli\u003eFinn, R. D. \u003cem\u003eet al.\u003c/em\u003e Pfam: the protein families database. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cstrong\u003e42\u003c/strong\u003e, D222\u0026ndash;D230 (2014).\u003c/li\u003e\n\u003cli\u003eZheng, J. \u003cem\u003eet al.\u003c/em\u003e dbCAN3: automated carbohydrate-active enzyme and substrate annotation. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cstrong\u003e51\u003c/strong\u003e, W115\u0026ndash;W121 (2023).\u003c/li\u003e\n\u003cli\u003eGalperin, M. Y. \u003cem\u003eet al.\u003c/em\u003e COG database update: focus on microbial diversity, model organisms, and widespread pathogens. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cstrong\u003e49\u003c/strong\u003e, D274\u0026ndash;D281 (2021).\u003c/li\u003e\n\u003cli\u003eKoonin, E. V. \u003cem\u003eet al.\u003c/em\u003e A comprehensive evolutionary classification of proteins encoded in complete eukaryotic genomes. \u003cem\u003eGenome Biol.\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, R7 (2004).\u003c/li\u003e\n\u003cli\u003eWang, J. \u003cem\u003eet al.\u003c/em\u003e The conserved domain database in 2023. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cstrong\u003e51\u003c/strong\u003e, D384\u0026ndash;D388 (2023).\u003c/li\u003e\n\u003cli\u003eNawrocki, E. P. \u0026amp; Eddy, S. R. Infernal 1.1: 100-fold faster RNA homology searches. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e, 2933\u0026ndash;2935 (2013).\u003c/li\u003e\n\u003cli\u003eKalvari, I. \u003cem\u003eet al.\u003c/em\u003e Rfam 14: expanded coverage of metagenomic, viral and microRNA families. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cstrong\u003e49\u003c/strong\u003e, D192\u0026ndash;D200 (2021).\u003c/li\u003e\n\u003cli\u003eBarnum, T. P. \u003cem\u003eet al.\u003c/em\u003e Predicting microbial growth conditions from amino acid composition. Preprint at https://doi.org/10.1101/2024.03.22.586313 (2024).\u003c/li\u003e\n\u003cli\u003eMangiola, S. \u0026amp; Papenfuss, A. T. tidyHeatmap: an R package for modular heatmap production based on tidy principles. \u003cem\u003eJ. Open Source Softw.\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 2472 (2020).\u003c/li\u003e\n\u003cli\u003ePatil, I. Visualizations with statistical details: The \u0026lsquo;ggstatsplot\u0026rsquo; approach. \u003cem\u003eJ. Open Source Softw.\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 3167 (2021).\u003c/li\u003e\n\u003cli\u003eYuen, K. K. The Two-Sample Trimmed t for Unequal Population Variances. \u003cem\u003eBiometrika\u003c/em\u003e \u003cstrong\u003e61\u003c/strong\u003e, 165\u0026ndash;170 (1974).\u003c/li\u003e\n\u003cli\u003eCamacho, C. \u003cem\u003eet al.\u003c/em\u003e BLAST+: architecture and applications. \u003cem\u003eBMC Bioinformatics\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 421 (2009).\u003c/li\u003e\n\u003cli\u003eKatoh, K. \u0026amp; Standley, D. M. MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability. \u003cem\u003eMol. Biol. Evol.\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 772\u0026ndash;780 (2013).\u003c/li\u003e\n\u003cli\u003eSteenwyk, J. L., Buida, T. J., III, Li, Y., Shen, X.-X. \u0026amp; Rokas, A. ClipKIT: A multiple sequence alignment trimming software for accurate phylogenomic inference. \u003cem\u003ePLOS Biol.\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, e3001007 (2020).\u003c/li\u003e\n\u003cli\u003eMinh, B. Q. \u003cem\u003eet al.\u003c/em\u003e IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era. \u003cem\u003eMol. Biol. Evol.\u003c/em\u003e \u003cstrong\u003e37\u003c/strong\u003e, 1530\u0026ndash;1534 (2020).\u003c/li\u003e\n\u003cli\u003eHoang, D. T., Chernomor, O., von Haeseler, A., Minh, B. Q. \u0026amp; Vinh, L. S. UFBoot2: Improving the Ultrafast Bootstrap Approximation. \u003cem\u003eMol. Biol. Evol.\u003c/em\u003e \u003cstrong\u003e35\u003c/strong\u003e, 518\u0026ndash;522 (2018).\u003c/li\u003e\n\u003cli\u003eGuindon, S. \u003cem\u003eet al.\u003c/em\u003e New Algorithms and Methods to Estimate Maximum-Likelihood Phylogenies: Assessing the Performance of PhyML 3.0. \u003cem\u003eSyst. Biol.\u003c/em\u003e \u003cstrong\u003e59\u003c/strong\u003e, 307\u0026ndash;321 (2010).\u003c/li\u003e\n\u003cli\u003eLetunic, I. \u0026amp; Bork, P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cstrong\u003e44\u003c/strong\u003e, W242\u0026ndash;W245 (2016).\u003c/li\u003e\n\u003cli\u003eWickham, H. \u003cem\u003eGgplot2: Elegant Graphics for Data Analysis\u003c/em\u003e. (Springer-Verlag New York, 2016).\u003c/li\u003e\n\u003cli\u003ePedersen, T. L. \u003cem\u003ePatchwork: The Composer of Plots\u003c/em\u003e. (2025).\u003c/li\u003e\n\u003cli\u003eWickham, H., Fran\u0026ccedil;ois, R., Henry, L., M\u0026uuml;ller, K. \u0026amp; Vaughan, D. \u003cem\u003eDplyr: A Grammar of Data Manipulation\u003c/em\u003e. (2023).\u003c/li\u003e\n\u003cli\u003eKassambara, A. \u003cem\u003eRstatix: Pipe-Friendly Framework for Basic Statistical Tests\u003c/em\u003e. (2023).\u003c/li\u003e\n\u003cli\u003eOksanen, J. \u003cem\u003eet al.\u003c/em\u003e \u003cem\u003eVegan: Community Ecology Package\u003c/em\u003e. (2022).\u003c/li\u003e\n\u003cli\u003eLahti, L. \u0026amp; Shetty, S. Tools for microbiome analysis in R. Bioconductor version: Release (3.18) https://doi.org/10.18129/B9.bioc.microbiome (2023).\u003c/li\u003e\n\u003cli\u003eLegendre, P. \u0026amp; Anderson, M. J. Distance-Based Redundancy Analysis: Testing Multispecies Responses in Multifactorial Ecological Experiments. \u003cem\u003eEcol. Monogr.\u003c/em\u003e \u003cstrong\u003e69\u003c/strong\u003e, 1\u0026ndash;24 (1999).\u003c/li\u003e\n\u003cli\u003eMcArdle, B. H. \u0026amp; Anderson, M. J. Fitting Multivariate Models to Community Data: A Comment on Distance-Based Redundancy Analysis. \u003cem\u003eEcology\u003c/em\u003e \u003cstrong\u003e82\u003c/strong\u003e, 290\u0026ndash;297 (2001).\u003c/li\u003e\n\u003cli\u003eMartinez Arbizu, P. pairwiseAdonis: Pairwise multilevel comparison using adonis. (2020).\u003c/li\u003e\n\u003cli\u003eLove, M. I., Huber, W. \u0026amp; Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. \u003cem\u003eGenome Biol.\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 550 (2014).\u003c/li\u003e\n\u003cli\u003eLove, M., Anders, S. \u0026amp; Huber, W. Differential analysis of count data\u0026ndash;the DESeq2 package. \u003cem\u003eGenome Biol\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 10\u0026ndash;1186 (2014).\u003c/li\u003e\n\u003cli\u003eHolm, S. A Simple Sequentially Rejective Multiple Test Procedure. \u003cem\u003eScand. J. Stat.\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 65\u0026ndash;70 (1979).\u003c/li\u003e\n\u003cli\u003eBindschadler, R. \u003cem\u003eet al.\u003c/em\u003e The Landsat image mosaic of Antarctica. \u003cem\u003eRemote Sens. Environ.\u003c/em\u003e \u003cstrong\u003e112\u003c/strong\u003e, 4214\u0026ndash;4226 (2008).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6608817/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6608817/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMicrobes in hyper-arid and oligotrophic ecosystems like Antarctica, rely on atmospheric trace gas oxidation for survival using high-affinity enzymes to generate energy for critical ecological processes including primary production, persistence, and carbon mitigation. Hydrocarbon contamination, common around Antarctic research stations, disrupts microbial communities, yet its implications for trace gas oxidation and dark carbon fixation are unknown. Here, we show the soil microbial diversity of Bunger Hills, East Antarctica, and assessed the response of a 40-year-old legacy petroleum spill on microbial communities and their functions. Metagenomic analysis and gas chromatography revealed significant shifts in microbiome composition and function in contaminated soils alongside severely reduced hydrogen oxidation rates, but higher (\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003eCO\u003csub\u003e2\u003c/sub\u003e) carbon fixation rates. We assembled 300 metagenome-assembled genomes across 16 bacterial and archaeal phyla, identifying 25 novel candidate species. We demonstrate long-lasting effects of pollutants on microbial ecosystems and services in polar regions, highlighting the role of trace gas scavengers and hydrocarbon degraders in regulating key ecological functions and advancing knowledge of anthropogenic impacts on microbial nutrient and energy acquisition in dry desert environments.\u003c/p\u003e","manuscriptTitle":"Persistent Petroleum Pollution: Microbial Responses in Bunger Hills, East Antarctica","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-20 16:05:25","doi":"10.21203/rs.3.rs-6608817/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-earth-and-environment","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsenv","sideBox":"Learn more about [Communications Earth and Environment](https://www.nature.com/commsenv/)","snPcode":"","submissionUrl":"","title":"Communications Earth \u0026 Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"2bc33251-da13-4231-96df-a93a27c00b6c","owner":[],"postedDate":"August 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":48951085,"name":"Biological sciences/Microbiology/Microbial communities/Microbial ecology"},{"id":48951086,"name":"Biological sciences/Microbiology/Environmental microbiology/Soil microbiology"},{"id":48951087,"name":"Earth and environmental sciences/Biogeochemistry"},{"id":48951088,"name":"Earth and environmental sciences/Ecology/Microbial ecology"},{"id":48951089,"name":"Earth and environmental sciences/Ecology/Climate-change ecology"}],"tags":[],"updatedAt":"2026-03-27T07:12:47+00:00","versionOfRecord":{"articleIdentity":"rs-6608817","link":"https://doi.org/10.1038/s43247-026-03299-0","journal":{"identity":"communications-earth-and-environment","isVorOnly":false,"title":"Communications Earth \u0026 Environment"},"publishedOn":"2026-02-17 05:00:00","publishedOnDateReadable":"February 17th, 2026"},"versionCreatedAt":"2025-08-20 16:05:25","video":"","vorDoi":"10.1038/s43247-026-03299-0","vorDoiUrl":"https://doi.org/10.1038/s43247-026-03299-0","workflowStages":[]},"version":"v1","identity":"rs-6608817","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6608817","identity":"rs-6608817","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

⚙ Ask this paper AI returns verbatim quotes from the full text · source: preprint-html ⓘ

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Citation neighborhood (no data yet)

We don't have any in-corpus citations linked to this paper yet. This is a recent paper (2025) — citers typically take a year or two to land, and the OpenAlex reference graph may still be filling in.

Source provenance

europepmc: last seen: 2026-05-20T01:45:00.602351+00:00
unpaywall: last seen: 2026-05-26T02:00:01.498150+00:00

License: CC-BY-4.0