Legacy copper/nickel mine tailings potentially harbor novel iron/sulfur cycling microorganisms within highly variable communities

ABSTRACT The oxidation of sulfide-bearing mine tailings catalyzed by acidophilic iron and sulfur-oxidizing bacteria releases toxic metals and other contaminants into soil and groundwater as acid mine drainage. Understanding the environmental variables that control the community structure and metabolic activity of microbes indigenous to tailings (especially the abiotic stressors of low pH and high dissolved metal content) is crucial to developing sustainable bioremediation strategies. We determined the microbial community composition along two continuous vertical gradients of Cu/Ni mine tailings at each of two tailings impoundments near Sudbury, Ontario. 16S rRNA amplicon data showed high variability in community diversity and composition between locations, as well as at different depths within each location. A temporal comparison for one tailings location showed low fluctuation in microbial communities across 2 years. Differences in community composition correlated most strongly with pore-water pH, Eh, alkalinity, salinity, and the concentration of several dissolved metals (including iron, but not copper or nickel). The relative abundances of individual genera differed in their degrees of correlation with geochemical factors. Several abundant lineages present at these locations have not previously been associated with mine tailings environments, including novel species predicted to be involved in iron and sulfur cycling. IMPORTANCE Mine tailings represent a significant threat to North American freshwater, with legacy tailings areas generating acid mine drainage (AMD) that contaminates rivers, lakes, and aquifers. Microbial activity accelerates AMD formation through oxidative metabolic processes but may also ameliorate acidic tailings by promoting secondary mineral precipitation and immobilizing dissolved metals. Tailings exhibit high geochemical variation within and between mine sites and may harbor many novel extremophiles adapted to high concentrations of toxic metals. Characterizing the unique microbiomes associated with tailing environments is key to identifying consortia that may be used as the foundation for innovative mine-waste bioremediation strategies. We provide an in-depth analysis of microbial diversity at four copper/nickel mine tailings impoundments, describe how communities (and individual lineages) differ based on geochemical gradients, predict organisms involved in AMD transformations, and identify taxonomically novel groups present that have not previously been observed in mine tailings.

ferrous iron (Fe II ) and sulfur generates high amounts of Fe II SO 4 and H + .The Fe II release by sulfide mineral oxidation may be further oxidized to Fe III , which contributes to the oxidation and dissolution of additional pyrite and other metal-bearing minerals.The overall effect of these reactions includes the formation of acidic porewater with high concentrations of heavy metals.Release of AMD to natural environments and freshwater is detrimental to natural ecosystems and human health (1).
Microbial activity can accelerate the rate of AMD formation by hundreds to millions of times compared to abiotic oxidation alone (2).Acidophilic, chemolithotrophic bacteria such as Acidithiobacillus ferrooxidans (3,4) obtain energy through the oxidation of iron and sulfur compounds, releasing additional H + , SO 4 , and regenerating the Fe III ion.While these organisms are detrimental in the context of tailings waste management, they have also been extensively studied for industrial bioleaching applications to extract valuable metals from tailings (4,5).
Microbial consortia isolated from AMD environments also include acidophilic sulfate reducers that precipitate metal ions from solution as metal sulfides (6), and species that immobilize metal ions through bioaccumulation or biosorption (7)(8)(9)(10); all of these processes serve as potential remediation approaches to remove metals from tailings porewater and mitigate the negative environmental and human health impacts of mine wastes.Physical remediation strategies may also be employed to reduce acidity and metal leaching of tailings, such as by the application of cover layers (e.g., fine-grained silt and clay or organic carbon) that limit oxygen and water ingress into the tailings and facilitate the growth of vegetation (11)(12)(13)(14).Vegetation (phytoremediation) and organic matter in organic covers also increase microbial community diversity of the tailings by promoting the establishment of organoheterotrophs and nitrogen-fixing rhizobacteria (increasing plant growth, nutrient turnover, and ecosystem productivity) (15,16).
Many studies have described microbial genera common across varied AMD environ ments, such as the acidophilic iron and/or sulfur oxidizing Acidithiobacillus, Leptospiril lum, and Sulfobacillus spp (17,18).However, mine tailings can be diverse in composition depending on the metals targeted for extraction, resulting in substantial differences in the extent of oxidation and acidification and concentrations of contaminants.As a result, the microbial community compositions found within mine tailings are also highly heterogeneous.pH is an important driving factor in the relative abundance of several lineages, with Proteobacteria generally dominant in slightly to moderately acidic tailings (pH 4-7) and Euryarchaeota (especially Ferroplasma spp.) more abundant in highly acidic tailings (pH <3) (19,20).The concentrations of metal ions (including Fe, Cu, Ni, and trace elements such as Cd, Hg, and U) select for organisms with resistance mechanisms against these contaminants (21)(22)(23)(24).Mine tailings also exhibit vertical stratification, with distinct zones of oxidation, neutralization, and unaltered tailings, heterogeneous physical characteristics (e.g., particle size) and temporal variability in moisture content; all of which influence the microbial guilds dominating each microenvironment (25).
A wide variety of microbial guilds are applicable to AMD bioremediation (e.g., bioreactors with engineered sulfate-reducing consortia, in situ remediation via iron-oxi dizing consortia in aerobic wetlands) (26) but making improvements in bioreactor/in situ remediation efficiency and cost-effectiveness depends on understanding the appropri ate isolates/consortia to leverage given the specific geochemistry of the target mine tailings.Tailings are highly selective environments that may be host to unexplored phylogenetic radiations of bacteria and archaea that have evolved unique adaptations to survive in heavy metal contaminated areas.Therefore, tailings provide an excellent starting point to identify AMD-tolerant microbes that can be used to improve the efficiency of engineered bioreactors and in situ remediation strategies.Recent advan ces in high-throughput sequencing methods such as 16S rRNA amplicon sequencing have enabled the high-resolution examination of the taxonomic diversity of AMD microbiomes (27).
In this study, we compared microbial community diversity across vertical and horizontal transects of tailings associated with two Ni/Cu mining sites in the Sudbury Basin region of Ontario, Canada.16S rRNA amplicon sequencing was used to character ize community composition across 3-5 m depth transects at four tailings locations.A temporal comparison was made for one tailings location to assess community stabil ity over time (2 years).Our objectives were to identify the main geochemical factors influencing microbial diversity and variability between locations, predict the contribu tion of microbial guilds to iron/sulfur cycling within tailings, as well as to explore microbial novelty within the context of mine tailings environments.

Sampling site and tailings core characteristics
The Sudbury Basin is a major geological structure in the Canadian Shield.Formed by a meteorite impact (28), it consists of mainly Fe-Ni-Cu sulfide deposits (e.g., pyrrhotite, chalcopyrite, and pentlandite), which may be enriched in precious metals (29).
Tailings stored at two mines owned and operated by Glencore Sudbury Integra ted Nickel Operations (Sudbury INO) were sampled for characterization of microbial community diversity and function: the Strathcona Waste Water Treatment System (SWWTS), which received tailings from 1968 to 2012 (30,31) and Nickel Rim North tailings area, which received tailings from 1953 to 1958 (32).Operations at Sudbury INO are focused primarily on extracting and processing nickel and copper, with cobalt and precious metals as by-products (33).
At Strathcona, two locations, Moose Lake 25 (ML25) and ML34, were chosen to assess spatial (vertical) variation.The sulfide-rich (9-18 wt.% S) (34), coarse-grained tailings at ML25, which have been exposed to atmospheric weathering since deposition decades ago, are extensively oxidized.The porewater at ML25 is low pH (pH <5) and contains high concentrations of contaminants (35) (Fig. 1b; Data File S1).In contrast, the high sulfide tailings at ML34 are overlain by a 2 m layer of desulfurized tailings (DST) cover of <1.4 wt.% S mainly as pyrrhotite, and a 50 cm organic carbon cover composed of a mixture of a mixed municipal compost amended with biosolid fertilizer.The cover materials are not potentially acid generating, and the fine-grained DST cover maintains a high moisture content, limiting O 2(g) diffusion.The pH is circumneutral and concentrations of dissolved metals are lower than at ML25 (12, 35) (Fig. 1b; Data File S1).
At Nickel Rim North (NR), locations NR18 and NR3 were selected for comparison.The unoxidized tailings contain on the order of 3 wt.% S mainly as pyrrhotite (36).Contaminant profiles are similar at these two locations (Fig. 1b; Data File S1), although NR3 has shallow water-table elevation and the shallow tailings have a high moisture content, leading to a shallower depth of oxidation and lower concentrations of dissolved metals than at NR18 (36).NR18 also has a shallow (< 10 cm) organic soil, deposited in the early 1990s (36).
In 2021, tailings core samples extracted from each location were collected in lengths of aluminum pipe (with inner diameters measuring 5.08 and 7.62 cm) driven into the tailings using the technique of Starr and Ingleton (37).The core samples were subsec tioned in lengths of 10 cm (except for the ends of each core, which were 10-17 cm), generating a total of 112 samples (Table S1).A core collected, frozen, and processed in the same way from NR18 in 2019 was used for comparison to 2021 samples (Table S1).

16S rRNA amplicon sequencing statistics
Out of a total of 7,610,282 raw reads (ranging from 161 to 217,042 per sample), 6,100,701 were kept after filtering, denoising, merging, and detection of chimeric reads.From these, 11,116 amplicon sequence variants (ASVs) were identified.The median ASV frequency per sample was 50,912, though the range varied from 141 to 199,300, reflecting the variability in community composition within samples.The median frequency of each ASV across all samples was 29, with a maximum frequency of 546,045.ASVs with a total frequency of 100 or less were generally confined to one or two samples, while higher abundance ASVs were present across multiple cores and/or multiple depth ranges.

Community composition
Multiple changes in the dominant phyla occur along the depth gradients for each sampled core (Fig. 2).In general, upper layers are mainly comprised of Proteobacteria and Acidobacteriota, which is consistent with most studies on mine tailing communities (19,20,24).Desulfobacterota and Firmicutes are abundant in deeper tailings; members of these phyla include anaerobic sulfate-reducing bacteria commonly found in anoxic layers of tailings (38)(39)(40)(41).Actinobacteriota were highly abundant in ML25 tailings below 2 m in depth (Fig. 2), but not at the other locations.While members of the Actinobacter iota phylum display a diverse range of metabolism, the genera that are commonly found in mine tailings (especially metal tolerant Arthrobacter spp.) (42)(43)(44) are usually obligate aerobes associated with the rhizosphere of metal hyper-accumulating plants.Therefore, we would expect to see them in the surface layers of vegetated tailings (e.g., ML34) as opposed to the deeper, ML25 tailings.The Actinobacteriota ASVs in ML25 appear to belong to novel clades not previously found in mine tailings; their classification is discussed in the next section.The WPS-2 phylum is also novel in terms of its presence in mine tailings and seems to be unique to Nickel Rim samples (particularly at NR18 at depths between ~1 and 3 m) (Fig. 2).WPS-2 (or Ca.Eremiobacterota) is a recently described phylum, consisting of a wide range of metabolically diverse bacteria found in many types of terrestrial envi ronments (45).Metagenomes isolated from Antarctic desert soil suggest some WPS-2 members are acidophilic (45).Another study found that the WPS-2 was abundant (with ASV relative frequencies of up to 24%) in the unvegetated soils of extinct iron-sulfur springs in British Columbia (46).Given that sampling occurred during near-freezing temperature periods and the acidic, iron/sulfur contaminated nature of AMD, it is not surprising that the WPS-2 could thrive in tailings environments.However, Ji et al. (45) and Sheremet et al. (46) found no evidence of microaerophilic or anaerobic metabolic capacity from genomic data in their studies, meaning the WPS-2 ASVs present at Nickel Rim likely represent organisms with novel metabolic capacity within this understudied lineage.

Community change along geochemical gradients
A more detailed analysis of community composition changes (in terms of genus-level relative abundance) across tailings samples is shown in Fig. 1a.The distribution of the most abundant genera (≥ 15% in at least one sample) was highly variable.Most populations did not show consistent abundance across samples and tended to be confined to and/or enriched in specific depth ranges.
To track how variation in geochemistry along cores impacted the distribution of the lineages highlighted in Fig. 1a, pH/Eh and the concentration of the most environmentally relevant contaminants (Fe, Ni, Cu, SO 4 ) were linked to genera abundances (Fig. 1b).The pH of ML25, NR18, and NR3 ranged from moderately to slightly acidic (~3-6) wherein pH increased with depth, while the pH of ML34 was slightly above seven and more stable across all depths.The general trend of peak Fe, Ni, and Cu concentrations was similar between locations: the highest Cu concentrations were close to the surface, followed by nickel, and finally iron.This observation is consistent with previous hydrogeochem ical assessments of Nickel Rim (32).These differences are due to differences in the susceptibility of pyrrhotite (Fe (1-x) S), pentlandite [(Fe,Ni) 9 S 8 ], and chalcopyrite (CuFeS 2 ) to oxidation, and to the formation of secondary Fe oxyhydroxide phases (e.g., goethite; αFeOOH) and the secondary Cu sulfide mineral covellite (CuS) (32).Optical examina tion of mineral thin sections from Nickel Rim and similar tailings impoundments have shown that pyrrhotite is the most susceptible (least resistant) to oxidation, followed by pentlandite, chalcopyrite and pyrite (47).Other factors influencing the localization of dissolved metals include differences in metal sorption capabilities to soil/clay particles (competitive sorption of Cu ions is greater than Ni ions, reducing transport to lower depths) (48,49) as well as pH-dependent precipitation of Fe (oxy)hydroxides (which can sorb Ni) and covellite (CuS) (36).Sulfate concentrations generally followed Fe, as both are released during the dissolution of sulfide minerals (see Fig. S1 for a summary of correlations between geochemical variables).
Correlations between abundance data (Fig. 1a) and geochemistry (variables from Fig. 1, plus Ni, Cu, and SO 4 ) identified several significant connections (Fig. 3).We included Cu and Ni in our analyses despite their concentrations not having a significant impact on overall community diversity because they are the main contaminants released from these tailings, and increased tolerance to these metals would be an asset for bioreme diation-relevant organisms.Dissolved Cu concentrations were moderately correlated with the abundance of almost half of the dominant genera (21 of 44 genera, 48%).Ni showed comparatively fewer significant correlations (Fig. 3).However, it was difficult to determine which specific factors were directly interacting with microorganisms, due to the interconnected nature of environmental variables (Fig. S1).Correlation matrices were also individually calculated for each sampling location (Fig. S2), which differed from the pooled data set (most notably, ML25 had much stronger R values due to a wider range of contaminant concentrations, contributing to stronger selective forces).Diverse consortia associated with iron and sulfur cycling were identified in each core, so their abundance and localization were specifically examined (Fig. 1 and 3).Iron/ sulfur-oxidizing genera common to AMD were typically confined to the upper 2 m of tailings.Acidithiobacillus ASVs (the majority of which were classified as A. ferrooxidans) were the most abundant predicted iron-oxidizer in the subsurface (0.5-1.5 m).The surrounding environment is likely microaerophilic at this depth, which are optimal growth conditions for A. ferrooxidans (50).Pore gas oxygen data were limited to near the tailings surface due to the low gas-filled pore space present in deeper tailings with a high water content and was shown to decrease from atmospheric levels at ground surface to <10% over 1 m in ML25, ML34, and NR18 (Fig. S3).It is worth noting that A. ferrooxidans also exhibits considerable metabolic diversity: not only is this species able to oxidize both ferrous iron and reduced sulfur species in oxic environments, but it can also use ferric iron as an electron acceptor in anoxic conditions.Iron reduction is likely the predominant metabolism supporting A. ferrooxidans populations under anoxic conditions below depths of 2 m.
The abundance of Acidithiobacillus ASVs in ML34 despite pH >7 conditions is unusual, but could be associated with the period of exposure to atmospheric oxygen and sulfide oxidation prior to the installation of the DST and organic carbon cover layers.The persistence of Acidithiobacillus ASVs could be leveraged for bioremediation approaches, such as in aerobic wetlands that use neutrophilic iron oxidizers to precipitate ferric iron minerals (e.g., ferrihydrite and goethite) (51).Existing iron-oxidizers currently applied in aerobic wetlands include Gallionella and Leptothrix spp., which are not native to AMD (26).Acidithiobacillus species/strains that have adapted to survive in neutral or alkaline environments may be more efficient at immobilizing iron by oxidation while also being tolerant to higher concentrations of copper, nickel, and other toxic metals in tailings.
Leptospirillum spp.(mostly L. ferrooxidans), exclusive iron-oxidizers, follow a similar distribution but are much less abundant.L. ferrooxidans prefers lower pH ranges (0.5-0.7) than A. ferrooxidans (1-3) in extreme AMD environments (52), but was co-localized in moderately acidic environments such as ML25. A. ferrooxidans abundance did not significantly correlate with any factor in the combined data set (Fig. 3), but did corre late with a few factors such as pH and Eh within each sampling location (Fig. S2).L. ferrooxidans abundance was moderately-to-strongly correlated with several environmen tal factors in both the combined data set and in ML25.Based on these observations, A. ferrooxidans could be described as more of a "generalist" species that is able to inhabit many types of mine tailings (see earlier discussion on their metabolic diversity), although they will still localize to a niche within each tailings environment based on preferences in pH, oxygen availability, etc.In contrast, L. ferrooxidans is a "specialist" that is adapted to survive at high concentrations of metals, but is outcompeted in mine tailings with low metal contamination.L. ferrooxidans abundance correlates strongest with copper, R = 0.64, although previous studies have shown that their higher affinity for ferrous iron and tolerance to higher concentrations of ferric iron is what drives niche partitioning between A. ferrooxidans and L. ferrooxidans (51).It is also important to note that bioleaching with mixed cultures containing both Acidithiobacillus spp.and Leptospirillium spp. is more effective than pure cultures (53)(54)(55)(56).Therefore, oxidation and AMD formation within ML25 tailings are expected to be higher due to the combined activity of both organisms (as evidenced by the lower pH and higher dissolved metal content) (Fig. 1b), even if their total abundance is similar to single populations of A. ferrooxidans at other locations.
Genera predicted to exclusively be sulfur oxidizers included Sulfurifustis, Thiobacillus, Halothiobacillus, Sulfuritalea, Sulfuriferula, Sulfuricella, and Desulfovibrio.The Sulfurifustis ASV most abundant in the surface of ML25 and NR18 tailings appears to be a novel species tolerant of acidic environments, as previously isolated Sulfurifustis species are neutrophilic (57).The abundance correlation for ASVs in the Sulfurifustis genus with geochemical factors resembles that of Leptospirillum rather than other sulfur oxidiz ers, suggesting that members of the Sulfurifustis are also specialized for resistance to high metal concentrations (Fig. 3).Thiobacillus and Halothiobacillus ASVs were also not classified to the species level and are predicted to be novel neutrophilic sulfur oxidizers (58) most abundant in ML34; Thiobacillus abundances in particular exhibit a strong positive correlation with pH (R = 0.71) and alkalinity (R = 0.77).The localization of Desulfovibrio (and Sulfuricella within NR18) ASVs to the deeper levels of the tailings is in line with their expected use of sulfur oxidation pathways coupled to NO 3 reduction rather than O 2 (in the case of Desulfurivibrio, it is expected to use the dissimilatory sulfate reduction pathway in the reverse direction), potentially allowing these populations to co-exist with anaerobic sulfate reducers by metabolizing the sulfides generated by the sulfate reducers (59,60).
The distribution of potential iron reducers was more varied than iron oxidizers, and included Geobacter, Acinetobacter, Thermincola, Metallibacterium, and Pseudomonas populations (61)(62)(63).Geobacter and Thermincola generally localize to greater depths in NR18/NR3, where dissolved iron is high.At pH >2.5, ferric iron precipitates as secondary oxyhydroxide minerals (51), and reductive dissolution is problematic in these conditions, as it re-mobilizes ferrous iron.The Pseudomonas and Acinetobacter do not display a consistent localization pattern, but as these genera are highly diverse and ubiquitous in various environments, predictions about iron-reducing capabilities should only be done on the species level, which was not possible as the most abundant ASVs were only classified at the genus level.The Metallibacterium is a recently described genus, with one isolate (M.scheffeleri) from an acidic biofilm demonstrating iron reduction (63), although Bartsch et al. (64) could not identify any iron-reducing genes through genomic, transcriptomic, and proteomic analyses.Unlike the other potential iron reducers, Metallibacterium ASVs co-localized with iron oxidizers (L.ferrooxidans in ML25, and A. ferrooxidans in ML34/NR18); as they are facultative anaerobes and only reduce iron under anoxic conditions (63), it is unlikely that they are actively reducing iron at these depths.However, as Metallibacterium have shown the potential to alkalinize their surroundings through the release of ammonium (64), they are a promising candidate for AMD bioremediation and will be a target for investigation in future multi-omic studies.
Sulfate reducers including members of the Firmicutes (Desulfosporosinus, Ca.Desulforudis, Desulfotomaculum) and Desulfobacterota (unclassified Desulfuromonada ceae) were generally dominant at depths > 2 m, which is below the depth of oxygen ingress.As expected, the sulfate reducers were generally positively correlated with pH (most are inhibited below pH 5.5) (65) and negatively correlated with Eh.They also show moderate negative correlations with dissolved copper concentration, as they are expected to stimulate secondary covellite (CuS) precipitation via the production of sulfide.Relative abundances were not consistent between locations, which could reflect preferences of individual taxa to specific environmental conditions, or legacy impacts from the initial subsurface community present in the ore from which the tailings were generated.The Desulfotomaculum, almost exclusively localized to ML25, is a genus mostly associated with deep subsurface environments (66).Ca.Desulforudis (localized to NR18) is also typically found in very deep environments, the most notable being Ca.Desulforudis audaxviator, which was the sole organism present 2.8 km below the surface in a gold mine (67).Desulfosporosinus are common in natural soil/sediments as well as mine tailings in cold climates (68) and were more evenly distributed in Strathcona Mill tailings compared to Nickel Rim.Desulfuromonadaceae abundance followed a similar trend.Finally, the Sva0485 clade (Ca.Acidulodesulfobacterales) have recently been described as dominant members of sulfate-reducing consortia in AMD and ferruginous lakes (69).While the characterized members of the Sva0485 lineage possess genes for dissimilatory sulfate reduction, they are facultative anaerobes and also encode genes for sulfur oxidation, iron cycling, and methanogenesis (69,70).This varied metabolic potential could explain why their depth range was not consistent between tailings locations here, and why their abundance was not significantly correlated to geochemistry (Fig. 3), although individual locations showed variable correlations between Sva0458 and salinity (Fig. S2).Biological sulfate reduction is leveraged in a variety of AMD bioremediation strategies, including anaerobic (or compost) bioreactors, sulfidogenic bioreactors, and permeable reactive barriers (26).All these approaches rely on biogenic sulfide production, typically in the form of H 2 S, which precipitates various metal-sulfide minerals (26,51).
The archaeon E-plasma (and other unclassified Thermoplasmataceae ASVs) abundant at ~1 m depth at ML25 and NR18 have been identified in various AMD and non-AMD environments (17, 71).E-plasma were found to dominate microbial AMD communities within the Parys Mountain mine (UK), characterized by low-to-moderate temperatures and extremely low pH (72), suggesting that they are adapted to colder temperatures (unlike most members of the order Thermoplasmatales, which are mesophilic to moderately thermophilic) (71).The metabolic potential of E-plasma and other related "alphabet-plasma" lineages belonging to the Thermoplasmataceae is currently unknown, as isolation attempts have not yet been successful.However, genomic data suggests that they are heterotrophic scavengers (72).
Many of the most abundant lineages are taxonomically novel and/or previously not identified within mine tailings (Fig. 1a).The Actinobacteriota (most abundant in ML25 but present at all locations) ASVs mostly belong to the order Gaiellales (common in deep sea sediments) (73), WCHB1-81 (microcystin-contaminated lakes) (74), and OPB41 (subsurface environments) (75).Other ASVs that were unclassified at the genus/species level include the Acidobacteriaceae (associated with mine tailings but highly diverse (76), as shown by the fact that the two subgroups identified in this study exhibit very different localization patterns), as well as the Gemmatimonadaceae, Pirellulaceae, Comamonadaceae, Chitinophagaceae, and Caulobacteraceae, which have not previously been associated with mine tailings.
The high amount of unresolved microbial diversity from 16S rRNA gene sequencing data implicates that the Strathcona Mill and Nickel Rim mine tailings host currently uncharacterized microorganisms that could participate in the biogeochemical processes governing the release, mobility, and/or attenuation of contaminants associated with AMD.Nonetheless, it is important to note that most samples analyzed in this study were low in metal concentration and circumneutral pH, and therefore not representa tive of microbial communities that could be leveraged for the remediation of highly acidic tailings.Future multi-omic and culture-based approaches are required to elucidate the full range of microbial activities present at these sites.The ultimate goal of these combined approaches is to contribute to the development of in situ bioremediation strategies for AMD, or to limit the extent of contaminant release that is accelerated by microbes (77).

Alpha diversity
Plots comparing Faith's phylogenetic distances, Shannon indices, and Observed ASVs of samples (grouped by location) showed consistent trends across the four cores (Fig. 4).Samples from ML25 consistently scored low on α-diversity metrics across all depths, although the specific taxonomic composition of samples was highly variable when comparing upper and lower depths (discussed in the Community Composition section).The α-diversity metrics of NR3 samples were overall similar to ML25, although the deeper fractions of the core tend to have higher diversity (Fig. 4).It is possible that the deposi tion of NR3 tailings on top of native vegetation in the 1950s, the presence of nearby plants, and periodic flooding events (36) account for the higher diversity at greater tailings depths, supported by the increased DOC concentrations in these samples (Data File S1).
ML34 and NR18 showed significantly higher α-diversity metrics compared to ML25/ NR3, which we attribute to the cover layers and vegetation present at these locations (20,78), and is especially evident in the fact that ML34 diversity was highest in the samples closest to the surface.A similar trend was seen for NR18, but due to low read numbers in the deeper NR18 sections, most samples below 2 m were excluded after rarefying to a minimum sampling depth of 12,000, hindering a full comparison.

Beta diversity
Samples did not appear to discretely cluster by location in any of the β-diversity ordinations (Fig. 5a; Fig. S4a and S5a), indicating that the variability between samples at the same location was greater than the overall differences between locations.
The weighted unifrac principal coordinate analysis (Fig. 5a) was the most effective in mapping distances between samples, with the two principal axes explaining 91.7% of variation in the data.ML25 samples showed particularly high separation along the primary axis, which may reflect the much broader range of contaminant concentrations along the core, such as dissolved Fe, Ni, Cu, and SO 4 (Data File S1).Analysis of individual locations showed that weighted unifrac PCoA was also best for mapping distances (Fig. 5b through e), with the principal axes explaining 66.1 -96.4% of the variation.There was some separation of samples along one or both axes based on depth, which was more apparent in the Bray-Curtis and unweighted unifrac PCoA plots (Fig. S4b through e and S5b through e), suggesting the presence of environmental gradients along the depth of the core that influence community composition.
NMDS analysis (Fig. 6) also did not show clustering of samples by location, though there was some separation between ML and NR samples along the secondary axis.Out of all geochemical variables mapped to the samples (Data File S1), the ones significantly correlating with community composition included pH, Eh, alkalinity, and salinity (inferred by electrical conductivity (EC), and dissolved Na, Cl, and K).It was expected that major community shifts occur across these environmental gradients, as microorganisms segregate into niches based on tolerance to selective forces (e.g., stress from low pH or high salinity) (79)(80)(81) and metabolic potential (influenced by redox gradients and oxygen availability) (82)(83)(84).Pore-gas oxygen measurements were taken at ML25, ML34, and NR18 tailings, but were not included in the statistical analyses due to limited data points (Fig. S3).However, the iron-dominated redox potential (Eh) of tailings environments can be used as a proxy to infer the metabolic capacity of samples (Data File S1); with aerobic respiration only supported at highly positive Eh values (above 300-500 mV) (85), although this threshold increases with decreasing pH.
Diversity also correlated with the concentrations of Fe, Mn, B, Ba, Ca, Tl, and V. Ferrous and ferric iron availability is known to correlate with the abundance of iron oxidizing and reducing bacteria, such as members of the Actinobacteriota and Gam maproteobacteria (21).Many iron oxidizing/reducing organisms can also oxidize/reduce manganese (86, 87).As for the other elements, correlation between microbial diversity and concentration is more likely due to the co-variance between ion concentration and another geochemical factor, such as pH, rather than direct interactions between the elements and microorganisms (Fig. S1).For example, calcium (i.e., Ca in Fig. 6) concen tration was strongly correlated with alkalinity (measured in mg/L CaCO 3 ) (R = 0.79).However, it is possible that selection for resistance to Tl and V toxicity may influence community composition (81,88), as these elements are highly toxic at trace concentra tions.In particular, vanadium concentrations in several samples from ML25 and ML34 far exceed the recommended safe limit for drinking water (0.05 mg/L) (89) so selection for vanadium resistance mechanisms such as the reduction of V V to V IV would be relevant in these communities.
Interestingly, β-diversity did not significantly correlate with Ni or Cu concentrations, despite these elements being the major porewater contaminants present in these tailings besides Fe (with upper concentration limits of 1,166 mg/L for Cu and 561.6 mg/L for Ni) (Data File S1).A possible explanation could be that Ni/Cu tolerance is widely distributed across many microbial taxa commonly found in mine tailings, and thus the overall variance in community composition is determined by other selective forces.Ni and Cu are both micronutrients required for the function of metalloenzymes and most prokaryotes encode homeostatic mechanisms to regulate intracellular concentrations (90).Nickel efflux pumps such as RcnA have been identified in organisms isolated from metal contaminated environments (e.g., Cupriavidus metallidurans strain CH34), as well as in non-Ni-resistant E. coli and H. pylori, with homologs found in other proteobacteria, cyanobacteria, and archaea (91).Similarly, extremophiles used in industrial biomining such as A. ferrooxidans and Sulfolobus metallicus share the same ATPases and transporters for copper export (CopA and CusA) with other bacteria, archaea, and eukaryotes (92)(93)(94).The presence of these pathways in Nickel Rim/Strathcona Mill tailings could be confirmed with multi-omic sequencing.

Temporal variation at NR18
Changes in genus-level abundance of ASVs sequenced from NR18 tailings in 2019 compared to 2021 samplings show that the abundance of most genera remained relatively stable over the 2-year period (Fig. 7).Both Acidithiobacillus and Pseudomonas ASVs show an overall trend of decreased abundance at various depths between 0.8 and 2.6 m.We note that prominent increases and decreases in abundance in adjacent core sections is more likely due to imperfect depth mapping between the 2 years' cores rather than large changes in community composition at NR18 as a whole.The WPS-2 relative abundance increased at multiple depth ranges between 1 and 2 m.Many of the NR18 vadose zone pore-water samplers were dry in 2021, and core moisture content is likely to influence WPS-2 abundance, as they are known to be common in dry, bare soil environ ments (46).Drier tailings could also explain the decrease in A. ferrooxidans, which are not desiccation tolerant (95).The taxonomic representation of sulfate-reducing bacteria also changes slightly (Ca.Desulforudis appears to be superseded by Desulfosporosinus between 3 and 3.5 m), which may be a response to changes in an environmental factor such as pH or alkalinity (moderately positively correlated with Desulfosporosinus abundance but not Ca.Desulforudis) (Fig. 3).Water samples were not collected in 2019, so we were unable to further investigate this hypothesis.

Conclusions
Mine tailings present a challenge in waste management practices.AMD environments harbor taxonomically and metabolically diverse microorganisms, which have evolved a wide variety of adaptations to thrive in highly heterogeneous contaminated environ ments.We utilized a 16S rRNA amplicon sequencing approach to characterize microbial communities at narrow (~10 cm) depth intervals at four locations at two tailings sites, providing a high-resolution profile of community changes across tailings gradients of varying geochemical compositions.
Our research shows that microbial communities are highly diverse between and within each sampling location.Our findings suggest that future sampling efforts that capture a wide range of tailings locations and depths could contribute to the isolation of a broad range of bioremediation-relevant species.We identified that overall community composition is correlated with pH, Eh, alkalinity, salinity, and some metal ions, which surprisingly did not include the major constituents Cu and Ni.Other variables that did not correlate with overall diversity were DOC, F, SO 4 , NO 3 , Al, As, Co, Li, Mg, Pb, S, Si, Sr, and Zn.
The abundance of most individual lineages was also closely associated with different geochemical factors.However, due to the covarying nature of many environmental variables, it is important to frame predictions about which factors microorganisms are directly responding to, as well as the trajectory of contaminant cycling within tailings, around what is currently known about the physiology and ecology of each individual organism (or its closest known relatives).
Our results showed that amplicon sequencing data can be used to identify specific microenvironments of tailings harboring novel or currently uncultured species, although this approach is limited in its ability to predict the physiological and metabolic properties of these uncharacterized organisms.Many ASVs present in Strathcona Mill and Nickel Rim tailings could not be classified at the species level, including potential iron/sulfur oxidizers and reducers, though this result is likely influenced by the fact that most samples were circumneutral and low in metal concentration.Functional profiling of tailings known to contain these novel lineages using metagenomics and metaproteo mics will be an important future direction to identify genes associated with metal (Fe, Cu, Ni) resistance and transformation.By informing on geochemical variables required to provide niche-specific conditions for growth in the lab our work also provides an insight into improved approaches for establishing enrichment cultures that target these species.

Tailings core sampling and processing
Sampling of all locations took place in November 2021.NR18 was previously sampled in November 2019, with the 2019 samples included here for an assessment of tempo ral variation.In November 2021, the average monthly temperature was −0.5°C and precipitation was 0.06 mm (96).Sampling in 2019 and 2021 followed the same protocol: Tailings cores of 5.0-7.6 cm (2-3 inches) in diameter were extracted to a depth of approximately 1 m below the water table (approximately 3-5 m per location) using a similar method as described in (37), with a Pionjar 120 gas-powered hammer drill and aluminum pipe.Cores were capped and stored for 1-2 days on-site before being transported back to the lab, where they were stored at −20°C prior to further processing.
Cores were sliced into subsamples at 10 cm intervals along the length of each core using a pipe cutter.The center inch of each subsection was punched out into a sterile plastic bag using a sterile aluminum hollow rod and then stored at −80°C prior to DNA extraction.

Geochemical assessments
Pore-water was collected from model 1900 soil water solution samplers (SWSS, from Soil Moisture Inc.) and piezometers installed approximately 1 week prior to sampling.Measurements of pH, Eh, alkalinity, and electrical conductivity were conducted on-site.Additional water samples were filtered (0.45 µm) and stored at 4°C for laboratory analysis.Inductively coupled plasma-optical emission spectrometry (ICP-OES), induc tively coupled plasma-mass spectrometry (ICP-MS) and ion chromatography (IC) were used to determine cation and anion concentrations respectively.Total dissolved organic carbon (DOC) was measured with a non-dispersive infrared sensor (NDIR).
Field measurements of gaseous O 2 /CO 2 concentrations were taken using the QUANTEK 902P O 2 /CO 2 analyzer, at approximately 10 cm depth intervals (water saturation of tailings beginning between 1 and 2 m in depth prevented gas quantification in deeper samples).

DNA extractions
Approximately 10 g of tailings were measured into a 50 mL falcon tube, washed twice with 35 mL TE buffer (10 mM Tris-HCl, 1 mM disodium EDTA, pH 8.0) to reduce inhibitor concentrations prior to extraction, and transferred to the QIAGEN DNEasy PowerMax Soil kit.DNA was extracted following the manufacturer's instructions, with the following modifications.DNA was initially eluted with 500 µL of elution buffer (10 mM Tris).Due to low concentrations in 23 samples (10 from NR18 and 13 from NR3), the protocol was altered in subsequent samples.DNA was eluted with 5 mL of elution buffer and subsequently concentrated to a volume of 50-100 μL using ethanol/sodium acetate precipitation as follows: 2.5 volumes of ice-cold ethanol was added to eluted DNA, along with 1/10th volume 3 M sodium acetate.Precipitation reactions were incubated at −20°C overnight, following which precipitated DNA was pelleted at 4,000 g and 4°C for 30 minutes, and washed twice with 500 µL of 75% ethanol, spinning for 10 minutes post-wash each time at 21,000 g held at 4°C.The pellets were air dried and resuspended in 50-100 uL of elution buffer.Final DNA concentrations were determined using the Thermo Fisher Qubit dsDNA High Sensitivity Assay Kit.
A total of 112 DNA samples were obtained, which were stored at −20°C prior to sequencing.

Diversity and statistical analyses
α-diversity was evaluated within QIIME2 (using a rarefied sampling depth of 12,000, which excluded 18 samples) using Faith's phylogenetic distance, Shannon diversity (H') index and the observed ASV count.Significant differences between locations were determined using the Kruskal-Wallis test.Plots were generated with Python using the seaborn library (103).
To map each sample to the pore-water chemistry data, the midpoint depth for each core slice was determined (after correcting for the compression factor of the core).Then, the two closest depths (above and below) this value where water chemistry was measured were taken and a weighted average of each geochemical measurement (pH, Eh, dissolved ion concentration, etc.) was calculated based on their respective distance to the midpoint of the core.For dissolved ions, only parameters with ≥50% of measurements above the detection limit of instruments in all cores were included in statistical analyses (with the exception of dissolved organic carbon (DOC) and NO 3 , which were included because they are key metabolic substrates required to support anaerobic respiration and heterotrophy), and values below the detection limit were set to 0.
To determine the geochemical factors affecting β-diversity, a non-metric multidimen sional scaling (NMDS) analysis was performed in R using the vegan package (108).NMDS ordinations were calculated with the metaMDS function using Bray-Curtis distances.The 'envfit' function was used to fit geochemistry factors onto the ordination, using 999 permutations.Variables showing significant correlations (P < 0.05, Bonferroni corrected) with community composition were included in the NMDS plot.
Geochemical variables that significantly correlated with community diversity (as well as Cu, Ni, and SO 4 concentrations due to their importance as environmental contami nants) were also evaluated for their correlation to the abundance of the most dominant genera (present at a relative frequency of ≥15% in at least one sample).Spearman's rank correlation matrices were generated using SciPy (v.1.0)(109) in Python and plotted using the seaborn package.As environmental factors often co-vary, a pairwise correlation matrix between geochemical variables was also calculated.

FIG 1 4 FIG 2
FIG 1 Distribution of bacterial and archaeal genera along tailings depth gradients and pore-water chemistry analyses.The heatmap (a) indicates the percent relative abundance of genera that were present at an abundance of at least 15% in at least one sample, across all depth intervals at each sampling location.The phylum and genus are labeled (if the genus was unknown, the family is shown instead).Relative abundances were log transformed, with 0% abundance values set to −2.999.Genera with predicted iron/sulfur cycling capabilities are indicated in color.The corresponding values for pH, Eh, and contaminant concentrations (Cu, Ni, Fe, SO 4 ) are plotted in (b).For contaminant plots, the coordinates (depth in meters, concentration in mg/L) of the peak concentration value are indicated directly on the plot.

FIG 3
FIG 3 Correlation matrix of genus-level relative abundance and geochemical factors.Spearman's rank correlation coefficients were calculated between the relative abundance of the abundant genera (Fig. 3) and select geochemical factors (Dep = depth, EC = electrical conductivity, Alk = Alkalinity), across all sampling locations.Significant correlations (P < 0.05, Bonferroni corrected) are shown.For location-specific correlations, see Fig. S1.

FIG 4 αFIG 5
FIG 4 α-diversity metrics for all sampling locations and depths.Faith's phylogenetic distances (a), Shannon diversity indices (b), and observed ASVs (c) were calculated with QIIME2 using a rarefied sampling depth of 12,000 reads.Individual samples overlaid on box plots are colored according to the vertical depth below ground level.Significant differences (Kruskal-Wallis, P < 0.05) between locations are designated by different letters above each boxplot.

FIG 6
FIG 6 NMDS biplot of Bray-Curtis distances and environmental variables.Samples are colored by location.Environmental variables that significantly correlated with community dissimilarity (P < 0.05, Bonferroni corrected) were included.For a plot showing all variables, see Fig. S6.

FIG 7
FIG 7 Change in relative abundance of microbial genera across varying depths at the NR18 tailings dump, 2019-2021.The genera listed in Fig. 1 and 3 were compared to NR18 samples taken in 2019, those present in both data sets were included in this figure.The percent relative abundance of the 2019 samples was subtracted from the NR18 2021 samples.Samples were mapped to each other based on the proximity of the midpoint depths of each core section.