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Article

Fungal Guilds Reveal Ecological Redundancy in a Post-Mining Environment

by
Geisianny Moreira
1,2,
Jefferson Brendon Almeida dos Reis
2,
Elisa Catão Caldeira Pires
3,
Cristine Chaves Barreto
4 and
Helson Mario Martins do Vale
2,*
1
Department of Civil, Construction & Environmental Engineering and Center for Water and the Environment, University of New Mexico, Albuquerque, NM 87131, USA
2
Department of Plant Pathology, University of Brasilia, Brasilia 70910900, DF, Brazil
3
Université de Toulon, MAPIEM, Toulon 83130, France
4
Institute of Biotechnology, University of Uberlandia, Patos de Minas 38701002, MG, Brazil
*
Author to whom correspondence should be addressed.
Mining 2025, 5(2), 28; https://doi.org/10.3390/mining5020028
Submission received: 28 January 2025 / Revised: 10 April 2025 / Accepted: 17 April 2025 / Published: 23 April 2025
(This article belongs to the Special Issue Post-Mining Management)
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Abstract

Mining significantly impacts terrestrial ecosystems despite its importance to the global economy. As part of soil ecosystems, fungi are highly responsive to environmental and human-induced drivers, shifting community composition and structure. Indeed, fungi play a key role in maintaining ecosystem resilience. Thus, we aim to address the question of whether soil fungal communities maintain similar ecological functions despite changes in community composition due to the impact of mining across ecosystems. To evaluate the ecological role of fungi across four ecosystems with varying iron mining impact levels, we used the FUNGuild database to assign functional guilds at the genus level. Co-occurrence network and ordination analyses were used to infer ecological relationships among fungal taxa and visualize the correlation between edaphic properties and fungal communities. A total of 22 functional guilds were identified, with dung saprotrophs, wood saprotrophs, fungal parasites, plant pathogens, ectomycorrhizal fungi, animal pathogens, and endophytes being the most abundant. Soil properties such as pH, organic matter, texture, and nutrients drive taxonomic and functional shifts. Our findings indicate that while mining activities shift fungal community compositions across ecosystems, the profiles of functional guilds show overlap between highly, moderately, and lowly impacted ecosystems, indicating functional redundancy. Network analysis reveals that highly connected hub taxa contribute to ecological redundancy across ecosystems and might act as a buffer against environmental disturbances. Our findings emphasize the important ecological role of soil fungi and indicate a potential for using fungal communities as bioindicators of ecological recovery in post-mining landscapes. From a mining and restoration perspective, this offers a low-cost, ecologically meaningful tool for monitoring soil recovery and guiding reclamation efforts.

Graphical Abstract

1. Introduction

Iron mining is a critical activity for the global economy. Iron is the fourth most abundant element of several mineral commodities, with iron ore accounting for about 5% of the Earth’s crust [1]. Iron ore is the primary source of iron for the steel industry worldwide, where 98% of iron ore is used for steelmaking [2]. Brazil is ranked as the second top producer worldwide of iron ore in 2023 and holds the second-largest crude ore reserves, estimated at around 29 billion tons [3]. Locally, Pará and Minas Gerais are the largest iron ore states in the country, accounting for 98% of the country’s annual iron ore output [4,5]. Despite its economic relevance, iron mining activity has ecological impacts on terrestrial, atmospheric, and aquatic ecosystems. Topsoil removal during mining and subsequent deposition with excavated subsoil as tailings disrupt pristine vegetation and introduce spatiotemporal heterogeneity into the environment [6]. As a result, together with the aboveground biota, the belowground biota is disrupted, impacting soil health and stability [7].
As part of subsoil ecosystems, fungal communities are overlooked in most environmental studies in post-mining environments despite their key ecological functions for soil health [8], with important implications for ecosystem revegetation and recovery [9,10,11]. Fungi comprise a significant proportion of soil biomass [12], being heterogeneously distributed and hyperdiverse across soil types and horizons with fine-scale niche partitioning [13]. Soil fungi are key mediators of ecological processes, contributing to plant growth (e.g., mycorrhizae and endophytes), organic matter decomposition (e.g., wood-decaying basidiomycetes), nutrient cycling, and soil structure maintenance (e.g., exopolymer production) [14,15]. They also facilitate inorganic transformations, mineral weathering, and metal reduction, oxidation, biosorption, or accumulation, particularly in mining-altered ecosystems [16]. The functional diversity of fungi in terrestrial ecosystems results from adopting versatile metabolic and trophic strategies (e.g., functional fungal guilds), which is of interest for ecological and environmental research, especially for ecosystem resilience and recovery in post-mining environments.
Current omics approaches shed light on the links between functional traits of fungal guilds and ecological processes mediated by fungi in soil [17]. Well-known ecological guilds are classified into the saprotroph, pathotroph, and symbiotroph trophic modes [18]. Ecological guilds of fungi play a crucial role in ecosystem recovery, as their diverse functional traits contribute to ecological processes such as soil regeneration and nutrient cycling (e.g., saprotrophs and symbiotrophs), supporting the re-establishment of plant communities and overall ecosystem resilience. Significant correlations between guilds and the age of rehabilitated post-mining environments have been established [19]. A high abundance of arbuscular mycorrhizal fungi was detected in the initial years of revegetation, shifting to ectomycorrhizas and ericoid mycorrhizas in the later stages of rehabilitation [10].
In comparison, arbuscular mycorrhizal fungi drive plant succession by improving seedling survival and growth [20], and ectomycorrhizas are associated with the establishment of tree species (e.g., forest and woodland tree species) [21]. A similar observation is described after 160 years of natural revegetation along with fungi diversity, where basidiomycetes species and ectomycorrhizal fungi progressively increase, while ascomycetes species and saprotrophic, pathotrophic, arbuscular mycorrhizal, and endophyte fungi gradually decrease [19]. Thus, an association between fungi diversity and functional guilds may indicate ecological restoration after disturbances.
While the diversity of fungal communities has been described in iron post-mining and unmined adjacent ecosystems in central Brazil, associated functional guilds are poorly understood [9,22,23,24,25]. As an example, it has been demonstrated for soil fungal communities that ascomycetes species prevail over basidiomycetes in post-mining environments, shifting to a higher basidiomycetes abundance in unmined environments [23]. These findings indicate that the Ascomycota/Basidiomycota ratio associated with functional guilds may be a potential target indicator of ecological restoration in iron post-mining environments in central Brazil. However, despite the importance of fungi for soil functioning, the diversity and functional role of fungi are not considered in restoration projects, and this information could be helpful in informing soil health and rehabilitation trajectories. Therefore, understanding how functional guilds shift across environments in central Brazil that have been impacted by iron mining to different levels is needed to unravel the fungal contribution to ecosystem resilience.
Here, we investigated the functional guilds of fungal communities across ecosystems with different levels of impact from iron mining. Fungal community diversity was previously described [23], showing a shifting in fungal community composition in response to soil disturbance resulting from mining activity. Based on these findings, we aim to (i) profile functional guilds in four ecosystems, (ii) correlate soil characteristics with fungal taxa and functional guilds, and (iii) investigate the ecological relationships among fungal taxa through co-occurrence network analysis. We hypothesized that despite disruptions in the pristine vegetation changing fungi compositions across ecosystems, it does not affect the profile of fungal communities’ functional guilds due to ecological redundancy. While mining activities lead to significant changes in fungal taxonomies, the profiles of functional guilds show an overlap between highly, moderately, and lowly impacted ecosystems, indicating functional redundancy across ecosystems. Our findings showed that the observed functional redundancy and conservation of fungal guilds across ecosystems indicate a potential for using fungal community profiles as bioindicators of ecological recovery in post-mining landscapes. From a mining and restoration perspective, this offers a low-cost, ecologically meaningful tool for monitoring soil recovery and guiding reclamation efforts.

2. Materials and Methods

2.1. Study Area

Soil samples were collected from four ecosystems located in Sabará, Minas Gerais, Brazil in September 2013 (Figure 1). The study area belongs to the Iron Quadrangle (Quadrilátero Ferrífero), which is well-known as the largest iron ore reserve [26] and for its extensive deposits of gold. The Iron Quadrangle comprises an ecotone ecosystem between the Atlantic Forest and Neotropical Savanna biomes, both important Brazilian hotspots with high endemism rates [27]. The climate in the region is classified as a humid temperate climate with dry winters and hot summers, an average annual temperature of 20 °C, and annual rainfall ranging between 1300 to 2100 mm [28]. Three ecosystems (grass, eucalyptus, and Atlantic forest) are located within the former iron mining site, which has been deactivated since 2006, except for iron outcrops that are located in a surrounding area.
Soil sampling utilized systematic composite sampling with concentric circles [26], with four composite samples collected around georeferenced sampling points for each ecosystem. Each composite sample consisted of 12 individual soil samples collected from two concentric circles around the main georeferenced point: one with 3 m spacing between points and an outer circle with a 6 m radius. The samples were taken from the 0–20 cm topsoil layer and then homogenized to create a composite for each point. The collected topsoil layer includes both mineral soil and organic material such as leaf litter, roots, and decaying plant matter, whose amounts were variable across the different ecosystems (Table S1) but were consistently incorporated into the samples, as this layer is important for microbial activity and community composition. The samples were collected in sterile plastic bags, refrigerated, and transported to the Mycology Laboratory at the University of Brasilia (UNB) for further analysis. Chemical and physical analyses were performed in the Soil Analysis Laboratory of the Department of Soil Science of the Federal University of Lavras, Brazil. The physical and chemical soil properties were previously described [24] and can be found in the Supplementary Information (Table S1).
For the purpose of this manuscript, the four ecosystems are classified based on the mining impact level and ecosystem attributes (Table 1). Two ecosystems are directly impacted by mining activities, where two different types of vegetation were introduced in 2006 to mitigate the impacts of mining activity (grass and eucalyptus), providing nearly a decade of plant–soil–microbe interactions before our sampling in 2013. Thus, both ecosystems have experienced similar post-disturbance timelines, reducing temporal variability in our comparisons. In contrast, two other ecosystems have not been directly impacted by the mining activities: Atlantic forest and iron outcrops. Therefore, ecosystems are classified based on the mining impact level as follows: low impact (Atlantic forest and iron outcrops), moderate impact (eucalyptus), and high impact (grass) (Table 1). The grass ecosystem is considered high impact due to its formation as sterile/tailings piles. It is worth mentioning that eucalyptus and grass originally belonged to the Atlantic forest biome. However, as in many legacy mining areas, there is no comprehensive baseline dataset on the pre-mining vegetation composition, which limited our ability to discuss the plant–soil–microbe interactions before the disturbances.

2.2. Characterization of Fungal Communities

For the characterization of fungal communities, soil samples were used to study yeast diversity using culture-dependent methods [24] and total fungal diversity through amplicon sequencing [23]. An ASV table with taxonomic information generated by the amplicon sequencing was used to access functional guild assignments. Briefly, total environmental DNA was extracted from two replicates of composite samples, followed by sequencing of the ITS2 region of rDNA using barcoded gITS7 and ITS4 primers [27]. Sequencing was performed on Illumina MiSeq with the MiSeq Reagent Kit v2 (300 cycles) and paired-end sequencing libraries (2 × 150 bp). Sequencing delivered 555,769 reads, which were treated with the Dada2 pipeline [28] in R software (v 3.5.3). Taxonomic assignment was performed with the QIIME2 default pipeline classifier using naïve Bayes against the dynamic UNITE [29] database from February 2019. Alpha diversity metrics (Shannon, Simpson, and richness) were calculated using the vegan package in R software (v 4.4.1). More descriptions of fungal diversity can be found in our previous studies [23,24], and the raw sequences are available in the NCBI database under the project number PRJNA629830.

2.3. Functional Guild Analysis

The assignment of the functional guilds of the soil fungal community was performed in R studio version 4.4.1 using “microeco” package version 1.9.1 [30] based on the ASV table from the fungal community study [23]. The assignment of trophic modes and functional guilds was carried out using the FUNGuild database [18] at the genus level (Supplementary Materials, Table S2). To assign fungal functional guilds, the ASV table was filtered to remove ASVs with fewer than 100 abundance counts. Then, the filtered ASV table was combined with environmental variables (soil physical–chemical properties) to create a microeco object. The “cal_spe_func_perc()” method was used to perform the functional assignments, and “abundance_weighted = TRUE” was used to calculate the relative abundance of functional individuals (ASVs).
A nonmetric multidimensional scale (NMDS) was used to visualize the correlation between the soil’s physical–chemical properties (described in our previous study [24] and added to the Supplementary Material, Table S1) and fungal taxa or functional guild distributions across ecosystems. Comparisons between fungal taxa and functional guild distributions were performed to detect ecological redundancies across ecosystems with different mining impact levels, especially similarities between Atlantic forest (low impact) and grass (high impact). Bray–Curtis dissimilarity was used in the construction of the NMDS, and the significant soil variables were chosen using forward selection with a permutation test (p < 0.05). The NMDS was performed with the Vegan package [31] in R software (version 4.4.1).
To explore the relationships between functional guilds and ecosystems, hierarchical clustering was performed on the relative abundance matrix using Euclidean distance. The data were normalized using a Z-score transformation by row, ensuring that each guild had the same mean and standard deviation. The results were visualized as a heatmap created with the pheatmap package (version 1.0.12) [32] in R software (version 4.4.1).

2.4. Co-Occurrence Network Analysis

Co-occurrence network analysis was used to identify and visualize potential ecological relationships or interactions among fungal taxa in the different ecosystems under varying mining impact levels. Networks were generated using Spearman correlation, implemented through the “WGCNA” package (version 1.72-5), with abundance filtering set to “filter_thres = 0.001”. The correlation threshold between pairs of variables was set to at least 0.7, with a significance threshold for p-values of <0.01 and FDR-adjusted p-values. The network properties analyzed included vertices (nodes), edges, average degrees, average path lengths, network diameters, clustering coefficients, density, heterogeneity, and centralization. Gephi software (version 0.10.1) was used to visualize the networks [33]. To assess significant differences between the constructed network and the corresponding random networks, the “random_network()” function was used together with the “erdos.renyi.game” model from the igraph package (version 2.0.3). A total of 1000 random networks were generated, each corresponding to the actual network in size (i.e., the same number of nodes and edges). Topological parameters were calculated for observed and random networks. The significance of differences in topological parameters was assessed using permutation tests. The “cluster_fast_greedy” method was employed to identify modules in the networks. Then, Pearson correlation (correlation > 0.7, p-value < 0.05; and FDR-adjusted p-value) was calculated to identify associations between the fungal ASV co-occurrence network modules and soil parameters of different ecosystems.

3. Results

3.1. Distribution of Fungal Functional Guilds Across Ecosystems

The trophic modes of soil fungi and their functional guilds at the genus level from 3309 ASVs were inferred using the FUNGuild database [18]. As the database infers functional guilds at the genus level, most of the ASVs from the original dataset (a total of 3309) were not classified due to constraints in taxonomic resolution beyond the family level (Supplementary Materials, Table S3). Table 2 shows the relative abundance (%) of the trophic modes and functional guilds inferred with FUNGuild.
Figure 2a shows the distribution of trophic modes across ecosystems. Saprotrophs were the most abundant trophic mode in all ecosystems, while pathotrophs showed a higher relative abundance in grass. The symbiotroph trophic mode was lower in grass, while eucalyptus and Atlantic forest showed similar relative abundances, indicating an association between symbiotrophs and tree-dominated ecosystems.
A total of 22 functional guilds were identified from the symbiotroph, saprotroph, and pathotroph trophic modes (Table 2). The heatmap shows the relative abundance of functional guilds across ecosystems based on Z-score normalization (Figure 2b), where the gradient from red to blue represents higher to lower abundances relative to the group (row). Iron outcrops are associated with saprotrophs, especially dung, plant, and wood saprotrophs. In contrast, grass is associated with pathotroph guilds, such as animal pathogens, plant pathogens, and fungal parasites. Eucalyptus and Atlantic forest are associated with symbiotroph guilds, such as arbuscular mycorrhizal and ectomycorrhizal.
The clustering analysis, which is based on Euclidean distances and considers functional guild profiles, grouped grass close to the Atlantic forest and eucalyptus ecosystems (Figure 2b), while iron outcrops are oriented in a separate clade. Grass and eucalyptus are ecosystems directly impacted by iron mining activity, which was initially part of the Atlantic forest ecosystem. Both impacted ecosystems are in different revegetation stages, with grass being at an early stage and eucalyptus being at a late revegetation stage. The clustering of grass ecosystems and eucalyptus with the underlying ecosystem (Atlantic forest) suggests that they share fungal communities with similar functional guild profiles, which may be indicative of ecological redundancy, showing that they are collaborating to maintain ecosystem functionality despite disturbances. In addition, the clustering of eucalyptus with Atlantic forest demonstrates that the use of trees is more efficient in reclaiming systems disturbed by iron mining in central Brazil.

3.2. Influence of Soil Parameters on the Distribution of Fungal Functional Guilds Across Ecosystems

To check the hypothesis of ecological redundancy between unmined (moderate to low impact) and post-mining (high impact) ecosystems, we used a nonmetric multidimensional scale based on the fungal taxa matrix, functional guild matrix, and soil parameters. Significant soil variables selected with a permutation test (p < 0.05) are shown in Table 3. A full physical–chemical characterization of soil samples can be found in the Supplementary Materials (Table S1).
The NMDS analysis (stress < 0.2) shows that based on the composition of fungal taxa (Figure 3a), the structure of fungal communities in the grass ecosystem differs from Atlantic forest and eucalyptus and is influenced by pH, which is slightly basic in this ecosystem in comparison with moderate- or low-impact ecosystems (Table 3). While Atlantic forest and eucalyptus share similar fungal communities, the iron outcrops ecosystem shows a unique fungal community composition, being influenced by Fe content, total soil acidity, organic matter, and soil texture. These results suggest that the disturbance caused by iron mining significantly affects the fungal taxonomic composition in ecosystems with a high mining impact, shifting fungal taxa and soil properties as a direct result of mining activity.
Regarding fungal functionality, the NMDS analysis (stress < 0.2) shows that Atlantic forest, eucalyptus, and grass share a similar functional guild profile, while iron outcrops show a unique functional guild profile. These results can be linked with the edaphic properties of each ecosystem, especially iron outcrops, due to their higher Fe content and the unique environmental conditions associated with this ecosystem. For Atlantic forest, eucalyptus, and grass, the soil texture influences the functional guilds, suggesting their importance in shaping ecological roles. Furthermore, similarities in the functional guild profiles of the grass, eucalyptus, and Atlantic forest ecosystems may reflect redundancy in the ecological roles of fungal communities after disturbances, corroborating Figure 2b. For both fungal community structure and functional profile, Fe is the predominant residual metal influencing fungal communities. While we acknowledge that other elements may also contribute to shaping fungal communities, the combination of (i) Fe dominance in the soil matrix (Table S1) and (ii) the statistical outcome identifying Fe as the significant explanatory variable (Table 3) indicates that Fe is a key driver in this ecosystem.

3.3. Co-Occurrence Network Across Ecosystems

A co-occurrence network was constructed to evaluate relationships among taxa from fungal communities in all four ecosystems (Figure 4). The topological network comprised 137 nodes (fungal taxa) and 785 edges with a modularity of 0.64 and a clustering coefficient of 0.98. The higher number of edges indicates a well-connected network, suggesting a high ecological interaction among the fungal taxa inhabiting unmined and post-mining ecosystems. The higher clustering coefficient and modularity indicate that taxa are clustered in interconnected groups that may represent ecological niches influenced by specific functional guilds and/or environmental conditions, corroborating the findings of the NMDS analysis (Figure 3). On the other hand, the co-occurrence network shows a low centralization value of 0.12, indicating a decentralized network where ecological interactions are spread across multiple taxa rather than dominated by a few. Other important topological metrics are heterogeneity (0.90), density (0.084), network diameter (8), average path length (2.35), and average degree (11.47), suggesting that taxa are highly interconnected, reflecting tight ecological networks in the fungi communities from unmined and post-mining ecosystems. However, the high heterogeneity suggests the presence of highly connected nodes (taxa hubs), which may indicate that keystone taxa are critical for maintaining the community structure. Nevertheless, due to the low number of replicates (n = 2), further investigation is necessary to draw robust conclusions.
Ascomycota is the phylum with the highest relative abundance (78.1% of nodes), followed by Basidiomycota (13.9%), Mucoromycota (2.9%), and Mortierellomycota (0.7%) (Figure 4a). A total of 16 distinct modules were identified in the network, with a higher interconnection of nodes in modules 1 to 8 (Figure 4b). A higher diversity of functional guilds was detected in modules M1, M2, M5, and M6, whereas modules M11 to M16 were dominated by no more than two functional guilds (Figure 4c). The observed co-occurrence network showed significantly higher (p < 0.001) topology metrics compared with the simulated random networks (Figure 4d). These results suggest a non-random and more clustered network topology, indicating that the observed network structure is more complex and cohesive than what would be expected in a random network.

4. Discussion

Mining activities have a devastating impact on terrestrial ecosystems around the world. Due to the nature of this activity, soil microorganisms are significantly affected by the shifting of their community composition, structure, and functional traits, especially in open-pit settings [34]. Soil microbes are an essential component of soil ecosystems as they are involved in several biochemical processes and are responsible for regulating, buffering, and purifying polluted soil [35]. As a result, soil microbes are sensitive to environmental stress, where changes in community composition and/or microbial activity are considered a sensitive indicator of soil ecosystems [36].
Our previous study found that iron-mining activity disturbed the composition and structure of fungal communities in a post-mining ecosystem (grass) compared to unmined (Atlantic forest and iron outcrops) and revegetated (eucalyptus) ecosystems [23]. The disturbance introduced by iron mining changed the Ascomycota/Basidiomycota ratio, increasing the proportion of ascomycetes in the post-mining ecosystem. Other factors, such as the soil’s physicochemical properties and vegetation type, significantly affected the fungal compositions and their community structures. Some of the taxa found are well-known for their high stress tolerance and ability to survive severe and hostile conditions.
Herein, we deepen our understanding of the ecological role of fungi in disturbed ecosystems by analyzing the functional guilds associated with the fungal taxa across ecosystems with different levels of impact from iron mining. The concept of guilds refers to a group of taxa that exploit the same environmental resources similarly [18]. Guild assignments to large datasets from high-throughput sequencing can capture the taxonomic richness and composition of microbial communities (OTUs or ASVs) using ecologically meaningful categories [37,38]. The analysis of functional guilds has gained attention in fungal ecology studies and may provide insights into ecological roles in disturbed ecosystems. We assessed the functional profile of fungal taxa in unmined and post-mining ecosystems to address our hypothesis that despite disruptions in the pristine vegetation causing changes in fungal taxonomic compositions, they will not affect fungal communities’ functional guilds due to ecological redundancy.

4.1. Profiling Functional Guilds in Unmined and Post-Mining Ecosystems

Fungal functional guilds reveal ecological redundancy in a post-mining ecosystem. This study found that unmined (Atlantic forest) and post-mining (grass and ecosystems) ecosystems share a similar functional guild profile (Figure 2). A total of 22 functional guilds were identified, with dung saprotroph, ectomycorrhizal, fungal parasite, wood saprotroph, animal pathogen, endophyte, plant pathogen, and soil saprotroph having high relative abundances (>10%). Despite dissimilarities in fungal community compositions between ecosystems with high/moderate to low mining impacts, ecological roles based on functional guilds remain conserved between the grass, eucalyptus, and Atlantic forest ecosystems (Figure 3).
These findings indicate ecological redundancy in fungal communities from ecosystems that share the same underlying biome prior to mining disturbance, such as eucalyptus and grass. Ecological redundancy is an important feature of microbial communities and is key to maintaining ecosystem function in the face of environmental and/or human-induced stressors [39]. The observed functional redundancy and conservation of fungal guilds across the Atlantic forest, eucalyptus, and grass ecosystems indicate a potential for using fungal community profiles as bioindicators of ecological recovery in post-mining landscapes. In addition, spatial connectivity between the three sites (Figure 1) may explain the similarity between the functional guild profiles [9]. Despite the spatial distance, our finding shows the resilience of fungal communities regardless of shifts in taxonomic composition. Therefore, it demonstrates a crucial ecological feature of fungal communities in maintaining ecosystem function and contributing to ecosystem resilience in disrupted ecosystems.
Soil fungi use diverse functional guilds to drive soil nutrient cycling, organic matter decomposition, and the health of aboveground vegetation [40]. Our study reveals distinct patterns in fungal functional guild compositions across the four ecosystems. The Atlantic forest and eucalyptus ecosystems exhibited a higher relative abundance of saprotrophs and symbiotrophs (Figure 2, Table 2), likely due to the complex organic matter inputs and high vegetation diversity that sustain mutualistic and decomposer fungal communities. Therefore, the higher relative abundance of the symbiotroph trophic mode in eucalyptus reflects a more established plant–fungal interaction network, suggesting a more advanced restoration strategy when compared to the grass ecosystem, which showed a lower abundance of symbiotrophs. Although the grass ecosystem retains some features of the original biome, the lack of mycorrhizal guilds suggests that reforestation efforts may have been more effective had tree species been used, as observed for the eucalyptus ecosystem, limiting restoration progress.
Notably, saprotrophs, such as wood and dung saprotrophs, were predominant across ecosystems. Saprotrophs guilds are related to major ecosystem functions such as decomposition and plant and fungi biomass production [14], emphasizing the key role of fungi in the breakdown of organic matter in the soil, which feeds back to primary production and carbon and nutrient cycling [17,41]. In addition, fungal saprotrophs and climate conditions are the main drivers of decomposition in soil [14]. A positive correlation between richness and decomposition rate is reported for soil fungi [42]. This leads to a functional overlap within saprotroph communities [42], corroborating our previous study where grass ecosystems show higher diversity metrics (Table 1). Thus, it may be a critical feature of fungal communities in the grass ecosystem, guaranteeing ecosystem function and recovery by introducing nutrients into the soil system.
Despite the absence of recent mining activity, a higher relative abundance of animal pathogens, plant pathogens, and fungal parasites was found in grass ecosystems (Figure 2 and Table 2), indicating a disturbed or early successional state. Interestingly, animal pathogens, plant pathogens, and fungal parasites progressively increased from ecosystems with a low impact to those with a high impact. It has been demonstrated that pathotrophic fungi show a higher abundance in the early stages and gradually decline in the late stages of revegetation [19,40]. A decrease in pathotrophic fungi in the forested ecosystems may be associated with a reduced risk of fungal disease by the increase in ectomycorrhizal, leading to the establishment of trees. Our results align with other studies describing changes in land cover due to anthropogenic activities and their correlation with shifts in fungal functional guilds [9,10,19,40,43,44,45]. Ectomycorrhizal is documented to have a role in protecting trees from pathogens at the early and final forest stages [46]. Our findings suggest that the functional guilds of ectomycorrhizal fungi and pathotrophic fungi are sensitive to human-induced disturbances, and changes in the patterns of those functional guilds correlate to shifts in aboveground vegetation, corroborating the potential of fungal communities as bioindicators of ecological recovery in post-mining landscapes.
A notable finding is the presence of ericoid and orchid mycorrhizal in the revegetated ecosystems (grass and eucalyptus, Table 2). The presence of ericoid and orchid mycorrhizal guilds reflects the persistence or re-establishment of plant species typically associated with undisturbed forest environments [47,48]. Their presence in reclaimed areas could indicate the beginning stages of biome recovery and the recolonization by more specialized plant–fungi symbioses.
Finally, iron outcrops showed a unique functional guild profile with a predominance of wood saprotrophs, dung saprotrophs, and endophytes (Table 2, Figure 2). Notably, dung saprotrophs showed a higher relative abundance in iron outcrops, which may be related to animal activity, suggesting active faunal colonization and potential contribution to nutrient cycling and fungal dispersal [49]. Due to the nature of the geological formation, the soils in iron outcrops are mainly ferruginous soil, where organic matter, such as wood decay, non-living organic matter, or detritus, will be available. These findings highlighted the ecological importance of fungal communities in iron outcrops for nutrient cycling and their potential to produce a variety of bioactive secondary metabolites [50] and enzymes [51] that can break down complex molecules. Therefore, this ecosystem may serve as a source of resilient fungal taxa that can recolonize disturbed sites, contributing to natural recovery and offering a strategy for passive or assisted restoration.

4.2. Correlations Between Soil Characteristics, Fungal Taxa, and Functional Guilds

The ecological restoration of mining tailings shifts the composition of microbial communities [46]. The observed differences in fungal guild compositions were significantly correlated with variations in soil characteristics, particularly pH, organic matter (OM), and iron (Fe) concentrations (Table 3). It has been demonstrated that edaphic properties and aboveground plant communities are one of the main drivers of the composition and structure of soil fungal communities, especially in topsoil (0–20 cm) [19,52]. Even though grass and eucalyptus were originally part of the Atlantic forest ecosystem, iron mining changed the plant composition aboveground and the edaphic properties, especially by turning the soil more basic (5.95 ± 0.12, p = 0.004, R2 = 0.98) (Table 3). The grass ecosystem is a direct result of mining activity, which is formed by the deposition of subsoil from excavating iron ore as mine tailings. In order to recover this area after the decommissioning of mining, grass species were used as a revegetation strategy due to their rapid growth and ability to incorporate biomass into the system, accelerating the formation of dense vegetation [53]. Changes in the physical and chemical soil environment displace microbial communities and may alter their composition due to the effect of selective pressure on microbial taxa [34].
On the other hand, the eucalyptus ecosystem started with Eucalyptus spp. as a strategy to minimize the effects of mining operations. Currently, this ecosystem shows a more heterogeneous plant community, becoming more similar to its original Atlantic forest ecosystem. The eucalyptus and Atlantic forest ecosystems share similar soil properties, such as pH (4.7 and 4.6) and OM (50.9 and 47.7 g kg−1) content (Table 3). These similarities in plant composition and soil properties between Eucalyptus and Atlantic forest may be reflected in the similarity of the compositions of their fungi communities (Figure 3). It has been documented that soil fungal communities can be restored in terms of abundance and richness during the later stages of revegetation [19], and the fungal community’s composition gradually changes over time after mine reclamation, becoming more taxonomically similar to later or original stages [34].
The iron outcrops ecosystem was shown to have a unique fungal community in terms of both the fungal taxonomic composition and functional guild profile. Iron outcrops are geological sites with extensive iron ore deposits, which harbor rare and endemic plants [54]. Beyond the unique plant diversity, the soil presents a high content of iron (236 ± 199 mg dm−3), which influences both fungal taxonomic compositions (p = 0.02, R2 = 0.74) and the distribution of functional guilds (p = 0.001, R2 = 0.93) (Figure 3). However, due to the rich iron ore deposits, iron outcrops are a threatened Brazilian landscape, and most of this territory in Brazil belongs to mining companies [55]. Iron outcrop ecosystems may harbor important species with a potential for bioremediation strategies due to their ability to tolerate stress, such as higher metal contents, and may act as a natural refugium and reservoir for fungal inoculum.
A correlation between the abundance of fungal taxa and guilds with OM content has been demonstrated [40]. Indeed, the distribution of fungal communities in iron outcrop ecosystems was influenced by organic matter (95.7 ± 34.4 g kg−1) in terms of taxonomic composition (p = 0.004, R2 = 0.90) and functional guilds (p = 0.002, R2 = 0.90), as shown by the NMDS (Figure 3, Table 3). In addition, Atlantic forest (48 ± 10.9 g kg−1), eucalyptus (51 ± 2.3 g kg−1), and iron outcrops (95.7 ± 34.4 g kg−1) showed relatively higher values for organic matter in comparison with grass (13.2 ± 2.5 g kg−1), which correlates with the predominance of trees in the forest-like ecosystems.

4.3. Ecological Relationships Among Fungal Taxa: Co-Occurrence Network Analysis

Co-occurrence patterns indicate cooperative and interconnected ecological relationships in the fungal communities across ecosystems. Most microorganisms thrive in communities and develop close ecological interactions, generating increased benefits for the community [56]. Microorganisms can establish a range of positive (e.g., mutualism and synergism) and negative (e.g., competition and parasitism) ecological relationships. Recent advances in high-throughput sequencing are increasing the capacity of microbial co-occurrence networks as a tool to explore ecological associations (edges) between taxa (nodes) in microbial communities [57]. Thus, the use of co-occurrence networks is increasing in fungal ecology studies, providing insightful ecological inferences about fungal community roles across ecosystems [58,59]. We used co-occurrence network analysis to understand the connections between fungal taxonomic compositions and ecosystem multifunctionality across ecosystems with varying mining impact levels.
In the present study, a co-occurrence network representing four ecosystems under varying mining impact levels was constructed (Figure 4). The network comprises 137 nodes and 785 edges, indicating significant correlations between the ASVs. The higher number of edges than nodes implies a well-connected network, reflecting robust ecological interactions between taxa. This network exhibits a high modularity (0.65) and an average degree of 11.5. Modules M1, M4, and M5 are densely connected, with a degree of centrality higher than the network average (29, 14, and 14, respectively), revealing well-connected and highly interactive modules. Those modules reveal the presence of keystone taxa (hubs) that may play critical ecological roles in maintaining network stability and ecosystem functioning. Additionally, the positive interaction edges of soil fungi networks across ecosystems were higher, indicating the prevalence of ecological interactions through cooperation. This may be an essential feature for ecosystem resilience, where microorganisms cooperate to adapt to a disturbed ecosystem.
Keystone functional guilds were predominant in the densely connected modules. Module 1 (M1) had a higher abundance of soil saprotrophs, wood saprotrophs, dung saprotrophs, plant pathogens, and mycoparasites (Figure 4c). More than 80% of keystone taxa in M1 are from the Ascomycota phylum, including Knufia, Arnium, Penicillium, Coniosporium, and Geoglossum genera. Ascomycota fungi are important in driving carbon and nitrogen cycling in soil [60] and are well-known for their capacity to degrade simple substrates [19]. Thus, the prevalence of saprotrophic fungi in a taxa hub may favor the conversion of soil nutrients, especially during the early revegetation stages after mining disturbance. In contrast, plant pathogens may increase the probability of plant disease outbreaks impacting the aboveground vegetation, which can be detrimental during the establishment of plants during the revegetation process. Finally, mycoparasites were also prevalent in M1, highlighting the cooperative and interconnected ecological relationships since they are known to suppress pathogenic fungi [14], which may benefit plant productivity.
Soil saprotrophs, animal parasites, and ectomycorrhizal were keystone functional guilds in modules M4 and M5. Penicillium, Oidiodendron, Umbelopsis, Laccaria, Talaromyces, Tolypocladium, Aspergillus, and Lactifluus are the keystone taxa in modules M4 and M5, where M4 accounts for almost 50% of Basidiomycota and M5 has 80% of taxa from the Ascomycota phylum. The prevalence of saprotrophic fungi from the Basidiomycota phylum complements the ecological function of saprotrophic ascomycetous since basidiomycetous are inclined to degrade recalcitrant lignin and lignocellulose instead of simple substrates, relying more upon the decomposition of wood components [61]. The presence of ectomycorrhizal fungi plays an important role in the ecosystem under revegetation (grass and eucalyptus) due to their role during the early establishment of trees through the promotion of seedling growth [62] and their ability to mineralize nutrients from organic matter [63].
Other less densely connected modules, albeit still with a relevant degree of centrality, were modules M2, M3, and M6. Soil saprotrophs, animal parasites, plant pathogens, and ectomycorrhizal were the predominant functional guilds, of which Penicillium, Aspergillus, Saitozyma, Fusarium, and Amanita were the abundant taxa. It demonstrates the importance of diverse functional guilds for ecosystem function, with a cooperative and interconnected ecological network across ecosystems with varying mining impact levels. Our finding aligns with the niche complementarity concept [14], where similar functional guilds were distributed across modules despite keystone taxa being present in specific modules. High diversity fosters complex interaction networks, allowing different species to fill similar roles when others are absent or underperforming [13,14]. However, while co-occurrence networks can be valuable for identifying patterns in ecological interactions, caution is necessary when drawing conclusions, as the trophic modes of many fungal species can be highly variable, often shifting between mutualistic, pathogenic, and saprotrophic strategies [64,65].

5. Conclusions

Fungi are highly responsive to environmental and human-induced stressors, such as drought, land use practices, and mining. Our previous study revealed increased species richness in a post-mining environment due to changes in land cover and soil properties. Herein, despite shifting fungal taxonomic compositions, our findings on functional guild profiles show that fungal communities are resilient and maintain essential ecological roles to guarantee ecosystem function in the face of disturbances. This resilience of fungal communities may be crucial for ecological succession, leading to the establishment of plants and the restoration of soil properties in the long term. Our findings emphasize the importance of considering soil fungi in evaluating restoration strategies. Therefore, fungal community analysis offers a practical, ecologically meaningful metric for evaluating post-mining restoration. These insights are not only valuable for scientists but also for land managers, policymakers, and mining companies seeking sustainable reclamation strategies.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/mining5020028/s1, Table S1: Trophic modes and functional guild assignments based on the FUNGuild database for ASVs across ecosystems with varying mining impact levels; Table S2: Soil attributes of four ecosystems under varying mining impact levels in central Brazil. Data are presented as mean ± standard deviation (n = 4); Table S3: Taxonomic assignment for ASVs across ecosystems with varying mining impact levels based on the UNITE dynamic database from February 2019.

Author Contributions

Conceptualization, G.M. and H.M.M.d.V.; methodology, G.M.; formal analysis, G.M., E.C.C.P. and J.B.A.d.R.; investigation, G.M.; data analysis, G.M., E.C.C.P. and J.B.A.d.R.; writing—original draft preparation, G.M.; writing—review and editing, G.M., J.B.A.d.R., E.C.C.P., C.C.B. and H.M.M.d.V.; visualization, G.M. and J.B.A.d.R.; supervision, H.M.M.d.V. and C.C.B.; funding acquisition, H.M.M.d.V. and C.C.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by FAPEMIG (Fundação de Amparo à Pesquisa do Estado de Minas Gerais), funding grant number CRA-RDP 136-10. GM was funded by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brazil (CAPES)—finance code 001.

Data Availability Statement

Data supporting the reported results can be found in the NCBI database under the project code PRJNA629830.

Conflicts of Interest

The authors declare no conflicts of interest.

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Mining 05 00028 g001
Figure 1. Geographical location of the study area and representative images of ecosystem vegetation. (a) Geographical location of the state of Minas Gerais in Brazil. (b) Geographical location of four ecosystems sampled in Sabara, Minas Gerais: iron outcrops (19°50′2.06″ S, 43°47′7.40″ W), grass (19°51′38.59″ S, 43°47′47.36″ W), eucalyptus (19°51′37.85″ S, 43°48′23.75″ W), and Atlantic forest (19°51′41.41″ S, 43°48′7.90″ W). (c) Representative images of the ecosystem sampled in this study, showing a typical vegetation landscape.
Figure 1. Geographical location of the study area and representative images of ecosystem vegetation. (a) Geographical location of the state of Minas Gerais in Brazil. (b) Geographical location of four ecosystems sampled in Sabara, Minas Gerais: iron outcrops (19°50′2.06″ S, 43°47′7.40″ W), grass (19°51′38.59″ S, 43°47′47.36″ W), eucalyptus (19°51′37.85″ S, 43°48′23.75″ W), and Atlantic forest (19°51′41.41″ S, 43°48′7.90″ W). (c) Representative images of the ecosystem sampled in this study, showing a typical vegetation landscape.
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Figure 2. Fungal trophic modes and functional guild profiles of soil fungal communities (based on the FUNGuild database) from four ecosystems with varying mining impact levels. (a) Relative abundance of the trophic modes across ecosystems. (b) Heatmap of functional guild profiles across ecosystems, with the clustering analysis based on Euclidean distances. The color gradient from red to blue represents higher to lower abundances relative to the group (row) based on Z-score normalization. Grass (high impact, n = 2), Atlantic forest (low impact, n = 2), eucalyptus (moderate impact, n = 2), iron outcrops (low impact, n = 2).
Figure 2. Fungal trophic modes and functional guild profiles of soil fungal communities (based on the FUNGuild database) from four ecosystems with varying mining impact levels. (a) Relative abundance of the trophic modes across ecosystems. (b) Heatmap of functional guild profiles across ecosystems, with the clustering analysis based on Euclidean distances. The color gradient from red to blue represents higher to lower abundances relative to the group (row) based on Z-score normalization. Grass (high impact, n = 2), Atlantic forest (low impact, n = 2), eucalyptus (moderate impact, n = 2), iron outcrops (low impact, n = 2).
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Figure 3. Nonmetric multidimensional scaling (NMDS) plot tracking the correlation between community similarity and soil variables across different ecosystems under varying mining impacts. (a) Distribution of ecosystems based on fungal taxonomic composition and influenced by significant soil variables (p < 0.05). (b) Distribution of ecosystems based on fungal functional guild profiles influenced by significant soil variables (p < 0.05). NMDS stress is near zero for both analyses (stress < 0.2). Cu: copper; Al: aluminum; m: saturation index of aluminum; OM: organic matter; HAl: total acidity; T: cation exchange capacity; Fe: iron; soil texture (sand, silt).
Figure 3. Nonmetric multidimensional scaling (NMDS) plot tracking the correlation between community similarity and soil variables across different ecosystems under varying mining impacts. (a) Distribution of ecosystems based on fungal taxonomic composition and influenced by significant soil variables (p < 0.05). (b) Distribution of ecosystems based on fungal functional guild profiles influenced by significant soil variables (p < 0.05). NMDS stress is near zero for both analyses (stress < 0.2). Cu: copper; Al: aluminum; m: saturation index of aluminum; OM: organic matter; HAl: total acidity; T: cation exchange capacity; Fe: iron; soil texture (sand, silt).
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Figure 4. Co-occurrence networks of soil fungal communities across ecosystems with varying impact levels from iron mining. (a) Co-occurrence network color-coded by fungal phylum; (b) co-occurrence network of modules with the highest number of interactions. The nodes represent the fungal ASVs, with their sizes reflecting the abundance of each ASV. The edges indicate strong and significant correlations between the nodes (Spearman’s correlation coefficient > 0.7 and FDR-adjusted p-value < 0.01). (c) Distribution of trophic modes and functional guilds within modules; and (d) analysis of observed and simulated networks showing network topology metrics (*** = p < 0.001).
Figure 4. Co-occurrence networks of soil fungal communities across ecosystems with varying impact levels from iron mining. (a) Co-occurrence network color-coded by fungal phylum; (b) co-occurrence network of modules with the highest number of interactions. The nodes represent the fungal ASVs, with their sizes reflecting the abundance of each ASV. The edges indicate strong and significant correlations between the nodes (Spearman’s correlation coefficient > 0.7 and FDR-adjusted p-value < 0.01). (c) Distribution of trophic modes and functional guilds within modules; and (d) analysis of observed and simulated networks showing network topology metrics (*** = p < 0.001).
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Table 1. Descriptions of the ecosystems and associated fungal diversity [23].
Table 1. Descriptions of the ecosystems and associated fungal diversity [23].
EcosystemImpact LevelEcosystem AttributesDiversity MetricsDominant Taxa (>1% at Genus level)
ShannonSimpsonRichness
Atlantic ForestLowSemi-deciduous seasonal forest region. Originally belonging to the Atlantic forest biome. Currently defined by secondary vegetation in different stages of natural regeneration.5.14 ±
0.02 		0.98 ±
0.001		199 ±
10.6		Penicillium, Saitozyma, Leohumicola, Talaromyces, Metarhizium, Trichoderma, Calonectria, Amanita
Iron
Outcrops		LowPreserved rock environment. Vegetation on ferruginous substrates where the dominance of Cactaceae and grasses represents the local physiognomy.4.82 ±
0.02		0.97 ±
0.001		194 ±
4.24		Penicillium, Talaromyces, Umbelopsis, Aspergillus, Saitozyma, Tolypocladium, Cladosporium
Eucalyptus ModerateReforested environments since 2006. Originally belonging to the Atlantic forest biome. Homogeneous plantations of Eucalyptus spp. at different ages occupy extensive surfaces to remediate the impacts of the mining activity.4.61 ± 0.0090.96 ±
0.001		196 ±
5.65		Saitozyma, Sagenomella, Penicillium, Oidiodendron, Chloridium, Talaromyces, Leohumicola, Trichoderma, Laccaria
GrassHighSterile/tailings piles and slopes. Originally belonging to the Atlantic forest biome. The ongoing environmental rehabilitation process started in 2006 after the mining activity ended. Currently, the soil is covered with grass, mostly Melinis minutiflora.5.40 ±
0.01		0.99 ±
0.001		232 ±
0.001		Penicillium, Aspergillus, Fusarium, Coniosporium, Mycosymbioces, Trechispora, Periconia, Cladosporium, Knufia
Table 2. Relative abundance of fungal trophic modes and functional guilds across ecosystems.
Table 2. Relative abundance of fungal trophic modes and functional guilds across ecosystems.
FUNGuild ClassificationsRelative Abundance (%)
Iron
Outcrops		GrassEucalyptusAtlantic Forest
Trophic mode
   Saprotroph51.3350.1647.7248.69
   Pathotroph25.56 31.8326.52 26.70
   Symbiotroph23.1118.01 25.76 24.61
Functional guild
   Bryophyte parasite3.272.742.257.48
   Dung saprotroph9.836.647.658.72
   Ectomycorrhizal4.944.119.738.07
   Fungal parasite9.9714.5812.2813.61
   Leaf saprotroph1.881.581.864.16
   Plant parasite1.881.581.633.91
   Wood saprotroph17.1214.4914.8316.06
   Animal pathogen11.0617.2911.0010.60
   Endophyte10.898.219.628.39
   Plant pathogen12.3816.6712.409.05
   Lichen parasite2.572.272.201.95
   Litter saprotroph0.960.611.860.98
   Soil saprotroph4.903.574.052.93
   Plant saprotroph2.991.481.391.30
   Epiphyte3.392.711.971.55
   Lichenized0.450.530.461.06
   Arbuscular mycorrhizal0.561.041.271.14
   Endomycorrhizal0.190.2600
   Ericoid mycorrhizal1.090.354.051.88
   Orchid mycorrhizal00.3500.32
   Clavicipitaceous endophyte0.75000.25
   Animal endosymbiont0.290.090.120.17
Table 3. Explanatory soil variables for nonmetric multidimensional scaling selected with a permutation test for fungal taxa and fungal guild distributions across ecosystems. Data are presented as mean ± standard deviation (n = 4).
Table 3. Explanatory soil variables for nonmetric multidimensional scaling selected with a permutation test for fungal taxa and fungal guild distributions across ecosystems. Data are presented as mean ± standard deviation (n = 4).
Soil Parameters 1Iron
Outcrops		GrassEucalyptusAtlantic
Forest		Fungal TaxaFungal Guild
p-Value 2R2p-Value2R2
pH4.5 ± 0.485.95 ± 0.124.72 ± 0.054.65 ± 0.10.004 **0.980.2030.45
Al3+ (cmolc dm−3)1.55 ± 0.730.1 ± 0.0012.6 ± 0.651.75 ± 0.450.017 *0.840.1680.49
H + Al (cmolc dm−3)22.54 ± 11.431.49 ±0.1312.17 ± 2.669.86 ± 0.890.006 **0.950.001 ***0.91
T (cmolc dm−3)23.61 ± 11.733.15 ± 1.0812.54 ± 2.7711.03 ± 0.980.006 **0.950.002 **0.92
m (%)57.86 ± 18.497.42 ± 4.1887.51 ± 2.4960.69 ± 17.50.037 *0.780.2450.41
OM (g kg−1)95.7 ± 34.413.2 ± 2.5350.9 ± 2.3547.7 ± 10.90.004 **0.900.002 **0.90
Fe (mg dm−3)236.71 ± 199.6243.32 ± 15.56122.66 ± 62.7794.5 ± 26.460.023 *0.730.001 ***0.93
Cu (mg dm−3)0.69 ± 0.510.73 ± 0.622.76 ± 0.583.02 ± 1.350.030 *0.830.2260.45
Sand (g kg−1)62 ± 8.7546.75 ± 6.9930.25 ± 11.5227.25 ± 8.30.006 **0.880.044 *0.71
Silt (g kg−1)17.5 ± 7.0432.75 ± 7.8834 ± 5.4742.25 ± 4.030.0800.680.046 *0.72
1 pH of water at a ratio of 1:2.5; Al3+ extracted using KCl 1 mol L−1; H + Al: total acidity of the soil (extractor SMP); T: cation exchange capacity at pH 7.0; m: Al saturation; OM: organic matter, oxidation Na2Cr2O7 2 mol L−1 + H2SO4 5 mol L−1; Fe, Cu: Mehlich-1 extractor. 2 Significance thresholds for p-values: * p-value < 0.05; ** p-value < 0.01; *** p-value < 0.001.
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Moreira, G.; dos Reis, J.B.A.; Pires, E.C.C.; Barreto, C.C.; do Vale, H.M.M. Fungal Guilds Reveal Ecological Redundancy in a Post-Mining Environment. Mining 2025, 5, 28. https://doi.org/10.3390/mining5020028

AMA Style

Moreira G, dos Reis JBA, Pires ECC, Barreto CC, do Vale HMM. Fungal Guilds Reveal Ecological Redundancy in a Post-Mining Environment. Mining. 2025; 5(2):28. https://doi.org/10.3390/mining5020028

Chicago/Turabian Style

Moreira, Geisianny, Jefferson Brendon Almeida dos Reis, Elisa Catão Caldeira Pires, Cristine Chaves Barreto, and Helson Mario Martins do Vale. 2025. "Fungal Guilds Reveal Ecological Redundancy in a Post-Mining Environment" Mining 5, no. 2: 28. https://doi.org/10.3390/mining5020028

APA Style

Moreira, G., dos Reis, J. B. A., Pires, E. C. C., Barreto, C. C., & do Vale, H. M. M. (2025). Fungal Guilds Reveal Ecological Redundancy in a Post-Mining Environment. Mining, 5(2), 28. https://doi.org/10.3390/mining5020028

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