The Fungal Side of the Story: Saprotrophic- vs. Symbiotrophic-Predicted Ecological Roles of Fungal Communities in Two Meromictic Hypersaline Lakes from Romania
Contrary to the common perception of Earth as a habitable planet, more than three-quarters of our planet comprises hostile environments, according to an anthropocentric view . Within these challenging conditions, a distinct type of biodiversity has developed, characterized by specific adaptations that enable survival across various physicochemical parameters such as temperature, pH, pressure, radiation, salinity, energy, and nutrient limitation . Exploring life in extreme environments provides an irreplaceable window to peek into the origin and evolution of life on Earth by shedding light on the microorganism’s survival strategies. These microorganisms have also captured great interest due to their potential application in biotechnological and environmental processes . Despite the diverse ecological niches these habitats offer to a wide range of microorganisms from all three domains of life, the spotlight was predominantly on prokaryotes for several years. In a surprising turn, eukaryotes, particularly within the fungal kingdom, have a remarkable capacity to adapt to harsh conditions, exploit novel resources, or establish new associations . As a phylogenetically and functionally diverse group, fungi span multiple scales across terrestrial and aquatic ecosystems and often act as pivotal drivers in food web dynamics and nutrient cycling . However, our knowledge of their diversity, lifestyles, and ecophysiological capabilities is far from being complete . In the realm of extreme environments, permanently stratified saline bodies of water (meromictic lakes) stand out as unique ecosystems of great interest for aquatic biology research. They exhibit consistent physicochemical gradients throughout the water column that prevent deep-water recirculation and thus appear as a natural laboratory to investigate biodiversity distribution based on specific niches . While there is increasing effort in capturing the interplay between microbial communities and biogeochemical cycles in such settings scattered all over the world , the majority of insights have been derived from the study of bacterial and archaeal inhabitants. This scenario also applies to the hypersaline aquatic environments from Romania, except for the Black Sea, where pelagic biodiversity was investigated using the Tree-of-Life metabarcoding approach . The microbial diversity of some saline lakes in the southeastern region of the country (Romanian Plain) has been investigated to uncover microorganisms that may have the potential to produce novel bioactive compounds . Among the 41 salt lakes of the Transylvanian Basin, only a few have had their microbiology explored using either culture-based methods , SSU rDNA plasmid sequencing , or 16S amplicon-based sequencing . These investigations have shed light on the prokaryotic diversity present across the vertical water profiles and their associations with the limnological parameters , leaving mycobiota an uncharted component of these biotopes. Hypersaline waters, along with other salty environments, are known to be populated by fungal taxa, information revealed since initial reports of fungal assemblages inhabiting Slovenian salterns . Since then, fungi have been documented in various hypersaline habitats including microbial mats ecosystems , salterns , deep-sea sediments , endoglacial hypersaline brines in Antarctica , or others reviewed in Gostinčar and Gunde-Cimerman (2023). However, until recent advancements in “omics” techniques, the characterization of fungal communities in such environmental settings was limited, primarily relying on culture-dependent methods, clone libraries, or a combination of these approaches. To withstand the high salt concentrations or to cope with fluctuations in the salinity of their environment, fungi have developed different morphological and osmotic adaptations to tolerate such stressful conditions. These survival strategies include environmental sensing, synthesis of compatible solutes like glycerol, cell wall elasticity and melanization, maintenance of ion homeostasis, and plasma membrane fluidity regulation . These adaptation mechanisms have been mostly studied in black yeast-like Hortaea werneckii and in the basidiomycetous Wallemia ichthyophaga , the most halophilic fungus and an important model organism for the study of halotolerance in Eukarya . Besides other representatives of the meristematic melanized fungi, Phaeotheca triangularis , and Aureobasidium pullulans , hypersaline waters are commonly inhabited by several Aspergillus and Penicillium species or members of the Cladosporium , Alternaria , and Acremonium genera . Despite ongoing efforts to explore the taxonomic profiles of the fungal communities in hypersaline conditions, their potential ecological role or contributions remain insufficiently understood. This study aimed to fill the knowledge gap in fungal biology within hypersaline habitats by examining fungal components of microbial communities in the water column and sediments of two hypersaline meromictic lakes, Ursu and Fără Fund, in the Transylvanian Basin of Romania, using high-throughput multi-marker amplicon sequencing. The spatial variations in fungal communities, their ecological attributes, and the chemical factors influencing their diversity and distribution were investigated. 

Sample Collection Water and sediment samples have been collected from both Ursu (46° 36′ 14.0″ N, 25° 05′ 08.1″ E) and Fără Fund (45° 52′ 34.4″ N, 24° 04′ 03.5″ E) lakes in March 2021 following similar practices as described in Andrei et al. (2015). In addition, soil samples were collected from the proximity of each lake to evaluate terrestrial inputs, as well as a single freshwater sample from a rivulet that partially supplies and maintains the freshwater layer at the surface of Ursu Lake. Water samples were collected using a submersible 12-V electric pump from five discrete depths (1.5, 2.5, 3.5, 6, and 9 m). These sampling depths were selected in correspondence with the stratification of chemical parameters. The rivulet water sample was collected at 0.2 m depth using the same procedure as for the water column sample. The collected samples were stored in sterile 2 L polypropylene bottles and transported on ice to the laboratory for processing. A fraction of the collected water samples (both from the lakes and Ursu rivulet) was vacuum filtered, immediately after sampling, using 0.22-µm pore-size sterile mixed cellulose ester (MCE) filter membranes (Merck, Germany) for biomass concentration. The filtered water volumes ranged from 200 to 1200 mL, depending on biomass load. Filtration was stopped when the membranes became clogged, with five biomass-clogged membranes obtained per sample. The total filtered water volume represented the combined amount filtered through all membranes (average volume, 820 mL; freshwater sample, 500 mL). The filter membranes were then stored at − 20 °C until DNA extraction. Lake sediment samples were collected from a depth of 9 m in Ursu Lake and 27 m in Fără Fund Lake using a Petite Ponar dredge (Wildco, USA). Soil samples were taken from the surface (5 cm depth) of the shore after removing vegetation and placed in 50-mL sterile tubes using a sterile spatula. The lake sediment samples were stored in sterile 1-L opaque containers kept on ice, being subsequently processed in the laboratory 48 h after collection. Measurement of Chemical Parameters In situ measurements of temperature, pH, dissolved oxygen (DO), and oxidation/reduction potential (ORP) were performed using a portable water multi-parameter system HI 9828 (Hanna Instruments, USA). Chemical analyses were performed on collected water samples, soil, and lake sediment. For the lake sediment, the pore-water fraction was obtained as the supernatant from centrifuging wet sediment at 8000 × g. Salinity  was determined using a digital portable Kern refractometer ORD 3SM (Kern & Sohn, Germany). Total carbon (TC) and total inorganic carbon (TIC) were measured by high-temperature catalytic combustion and infrared detection of CO 2 using a Multi N/C 2100S Analyser (Analytik Jena, Germany), as described in Baricz et al. (2021). Total organic carbon (TOC) was obtained by subtracting TIC from TC. Total nitrogen (TN) was measured by catalytic combustion followed by oxidation of nitrogen monoxide to nitrogen dioxide and chemiluminescence detection. Ammonium and nitrate ions were analyzed by Lambda 25 UV–VIS spectrophotometer (Perkin Elmer, Beaconsfield, UK) after conversion into indophenol blue complex (ammonium) and yellow complex formed with sulfosalicylic acid (nitrate). Sulfate (SO 4 2− ) was assessed by ion chromatography on 761 Compact IC (Metrohm, Switzerland). Genomic DNA Extraction and Sequencing Total genomic DNA was extracted from biomass-clogged membrane filters (obtained after filtering lake and rivulet water samples), lake sediment, and soil samples using the Quick-DNA Fecal/Soil Microbe Miniprep Kit (Zymo Research, USA), following the manufacturer’s instructions. The total genomic DNA concentration was determined using the NanoDrop spectrophotometer (ThermoFisher Scientific, USA) and the Qubit fluorimeter (ThermoFisher Scientific, USA) alongside its specific Qubit dsDNA HS Assay kit. Extracted DNA was used as a template for generating PCR amplicons using two standard fungal primer sets. The internal transcribed spacer region 2 (ITS2) of the nuclear ribosomal RNA genes was amplified using the ITS3-2024F (5′-GCATCGATGAAGAACGCAGC-3′) and the ITS4-2409R (5′-TCCTCCGCTTATTGATATGC-3′) primers, while a 350 bp fragment of the V4 variable region of the 18S rRNA gene was amplified using the 528F (5′-GCGGTAATTCCAGCTCCAA-3′) and the 706R (5′-AATCCRAGAATTTCACCTCT-3′) primers. All PCR reactions were performed with Phusion High-Fidelity PCR Master Mix (New England Biolabs, UK). Sequencing libraries were constructed using the NEBNext Ultra DNA Library Prep kit and quantified with a Qubit fluorimeter and RT-PCR. ITS2 and 18S amplicon sequencing was performed on an Illumina NovaSeq 6000 platform at Novogene (UK) Co. Ltd. All libraries were sequenced in paired-end mode, generating 250 bp length reads (50k raw tags per sample). Bioinformatics and Statistical Analysis The raw Illumina sequences were assigned to individual samples based on their unique barcodes. The barcodes and primer sequences were trimmed from the reads. The raw tags were generated by merging paired-end reads using the FLASH software v1.2.7  and then quality filtered under specific parameters to obtain the high-quality clean tags , which were further processed using QIIME v1.7.0 . After the effective tags were generated by removing chimera sequences, operational taxonomic units (OTUs) were clustered based on a cut-off value of 97% using QIIME’s Uparse v7.0.1090 . The representative sequence for each OTU was screened for further annotation. Subsequent sequence analysis for taxonomic assignment was performed by blast with blastall (v2.2.25) and Unite fungal ITS database (similarity threshold 97%) , as well as using the QIIME-based wrapper of the Ribosomal Database Project (RDP) classifier against Silva database (similarity threshold 87%)  in case of 18S rRNA gene reads. Both ITS2 and 18S rRNA data sets of the two hypersaline meromictic lakes from Transylvania determined in this study were deposited in the Sequence Read Archive under the PRJNA1000420 BioProject. After the initial bioinformatics analysis of amplicon sequencing data, all downstream statistical analyses were conducted using R v4.1.1 . Venn and Sankey diagrams were used to compare the performance of two DNA markers (ITS2 and 18S) to characterize the diversity and composition of aquatic fungal communities. To eliminate the effect of different sequencing depths across samples on the subsequent analysis, the OTUs abundance information was normalized using a standard sequence number corresponding to the smallest sample size. Rarefaction was carried out at a sequencing depth of 50,350 sequences per sample for the ITS2 dataset and 55,497 sequences for the 18S dataset. Subsequent analyses of alpha and beta diversity were performed based on normalized data using the R microeco package . To check whether the sequencing depth is sufficient to estimate the diversity of the fungal communities, rarefaction curves were generated by plotting the number of OTUs as a function of sequence reads using mecodev , a set of extended classes based on the R microeco package. Six commonly used biodiversity indicators (Observed OTUs, Good’s Coverage index, Chao1, ACE, Shannon, and Simpson) were computed for each sample. The Kruskal–Wallis Rank Sum Test was used to determine whether these diversity estimators significantly vary between the two main sampling sites (Fără Fund and Ursu lakes) or among the water depth profiles. The beta diversity analysis was assessed using the Bray–Curtis distances, visualized using a non-metric multi-dimensional scaling plot (NMDS), and significance was checked with PERMANOVA using default settings of the adonis function from the vegan package . Further, to assess the connections between the fungal community structure and environmental factors, the redundancy analysis (RDA) and Mantel test were performed using the R package microeco . Assigning Ecological Roles of Fungi To categorize the ITS2 dataset into ecologically meaningful groups, FUNGuild  and FungalTraits , two annotation tools integrated into the microeco R package , were used. Fungal OTUs were assigned to specific trophic modes and further subdivided into specific ecological guilds. Subsequently, the relative sequence abundance of fungal trophic modes and the predominant functional groups across the communities were compared. The fungal functional guilds assigned using both databases were visualized in a bubble plot showing the functional individual percentage. The trophic analysis was conducted using the metanetwork R package . The adjacency matrix constructed for the metanetwork usage contained all the fungal OTUs obtained from FungalTraits and the OTUs from the 18S analysis for Plantae and Animalia kingdoms. The OTUs corresponding to the Plantae and Animalia kingdoms were added to the analysis because the identified plants and animals could represent potential feeding sources for the investigated fungi, categorized into plant and animal pathogens and nematophagous. The potential trophic links between the fungal OTUs and the Plantae and Animalia taxa were manually verified based on the existing literature. Four other entries (corresponding to the Poaceae family, litter, dung, and Homo sapiens ) were artificially added into the matrix. Their addition was based on the fact that many fungal OTUs had the trophic functions of plant pathogens, litter saprotrophs, dung saprotrophs, and opportunistic human parasites. Most identified fungal plant pathogens were found as pathogens of the Poaceae family explainable through the fact that the lakes are surrounded by grassland (Fără Fund Lake) or mixed forest-grassland vegetation (Ursu Lake). Litter and dung are also present in or around both lakes. The addition of Homo sapiens was based on the fact that at least Ursu Lake is constantly used for bathing. For each analyzed layer in the two lakes (soil, rivulet, surface layer (1.5–2.5 m), intermediate layer (3.5 m), bottom layer (6–9 m), and sediment), the adjacency-constructed matrices were filtered to obtain directed and connected graphs. No connected graphs were identified, only connected subgraphs. The connected subgraphs were plotted individually, taking into account the most abundant genera for each trophic level within each subgraph (nematophagous as animal parasites, lichenized as symbiotrophs, opportunistic human parasites as animal parasites, saprotrophs as soil/litter/wood/dung, and Poaceae pathogens as plant pathogens). The selected trophic levels were plotted in an unweighted manner. 

Water and sediment samples have been collected from both Ursu (46° 36′ 14.0″ N, 25° 05′ 08.1″ E) and Fără Fund (45° 52′ 34.4″ N, 24° 04′ 03.5″ E) lakes in March 2021 following similar practices as described in Andrei et al. (2015). In addition, soil samples were collected from the proximity of each lake to evaluate terrestrial inputs, as well as a single freshwater sample from a rivulet that partially supplies and maintains the freshwater layer at the surface of Ursu Lake. Water samples were collected using a submersible 12-V electric pump from five discrete depths (1.5, 2.5, 3.5, 6, and 9 m). These sampling depths were selected in correspondence with the stratification of chemical parameters. The rivulet water sample was collected at 0.2 m depth using the same procedure as for the water column sample. The collected samples were stored in sterile 2 L polypropylene bottles and transported on ice to the laboratory for processing. A fraction of the collected water samples (both from the lakes and Ursu rivulet) was vacuum filtered, immediately after sampling, using 0.22-µm pore-size sterile mixed cellulose ester (MCE) filter membranes (Merck, Germany) for biomass concentration. The filtered water volumes ranged from 200 to 1200 mL, depending on biomass load. Filtration was stopped when the membranes became clogged, with five biomass-clogged membranes obtained per sample. The total filtered water volume represented the combined amount filtered through all membranes (average volume, 820 mL; freshwater sample, 500 mL). The filter membranes were then stored at − 20 °C until DNA extraction. Lake sediment samples were collected from a depth of 9 m in Ursu Lake and 27 m in Fără Fund Lake using a Petite Ponar dredge (Wildco, USA). Soil samples were taken from the surface (5 cm depth) of the shore after removing vegetation and placed in 50-mL sterile tubes using a sterile spatula. The lake sediment samples were stored in sterile 1-L opaque containers kept on ice, being subsequently processed in the laboratory 48 h after collection. 

In situ measurements of temperature, pH, dissolved oxygen (DO), and oxidation/reduction potential (ORP) were performed using a portable water multi-parameter system HI 9828 (Hanna Instruments, USA). Chemical analyses were performed on collected water samples, soil, and lake sediment. For the lake sediment, the pore-water fraction was obtained as the supernatant from centrifuging wet sediment at 8000 × g. Salinity  was determined using a digital portable Kern refractometer ORD 3SM (Kern & Sohn, Germany). Total carbon (TC) and total inorganic carbon (TIC) were measured by high-temperature catalytic combustion and infrared detection of CO 2 using a Multi N/C 2100S Analyser (Analytik Jena, Germany), as described in Baricz et al. (2021). Total organic carbon (TOC) was obtained by subtracting TIC from TC. Total nitrogen (TN) was measured by catalytic combustion followed by oxidation of nitrogen monoxide to nitrogen dioxide and chemiluminescence detection. Ammonium and nitrate ions were analyzed by Lambda 25 UV–VIS spectrophotometer (Perkin Elmer, Beaconsfield, UK) after conversion into indophenol blue complex (ammonium) and yellow complex formed with sulfosalicylic acid (nitrate). Sulfate (SO 4 2− ) was assessed by ion chromatography on 761 Compact IC (Metrohm, Switzerland). 

Total genomic DNA was extracted from biomass-clogged membrane filters (obtained after filtering lake and rivulet water samples), lake sediment, and soil samples using the Quick-DNA Fecal/Soil Microbe Miniprep Kit (Zymo Research, USA), following the manufacturer’s instructions. The total genomic DNA concentration was determined using the NanoDrop spectrophotometer (ThermoFisher Scientific, USA) and the Qubit fluorimeter (ThermoFisher Scientific, USA) alongside its specific Qubit dsDNA HS Assay kit. Extracted DNA was used as a template for generating PCR amplicons using two standard fungal primer sets. The internal transcribed spacer region 2 (ITS2) of the nuclear ribosomal RNA genes was amplified using the ITS3-2024F (5′-GCATCGATGAAGAACGCAGC-3′) and the ITS4-2409R (5′-TCCTCCGCTTATTGATATGC-3′) primers, while a 350 bp fragment of the V4 variable region of the 18S rRNA gene was amplified using the 528F (5′-GCGGTAATTCCAGCTCCAA-3′) and the 706R (5′-AATCCRAGAATTTCACCTCT-3′) primers. All PCR reactions were performed with Phusion High-Fidelity PCR Master Mix (New England Biolabs, UK). Sequencing libraries were constructed using the NEBNext Ultra DNA Library Prep kit and quantified with a Qubit fluorimeter and RT-PCR. ITS2 and 18S amplicon sequencing was performed on an Illumina NovaSeq 6000 platform at Novogene (UK) Co. Ltd. All libraries were sequenced in paired-end mode, generating 250 bp length reads (50k raw tags per sample). 

The raw Illumina sequences were assigned to individual samples based on their unique barcodes. The barcodes and primer sequences were trimmed from the reads. The raw tags were generated by merging paired-end reads using the FLASH software v1.2.7  and then quality filtered under specific parameters to obtain the high-quality clean tags , which were further processed using QIIME v1.7.0 . After the effective tags were generated by removing chimera sequences, operational taxonomic units (OTUs) were clustered based on a cut-off value of 97% using QIIME’s Uparse v7.0.1090 . The representative sequence for each OTU was screened for further annotation. Subsequent sequence analysis for taxonomic assignment was performed by blast with blastall (v2.2.25) and Unite fungal ITS database (similarity threshold 97%) , as well as using the QIIME-based wrapper of the Ribosomal Database Project (RDP) classifier against Silva database (similarity threshold 87%)  in case of 18S rRNA gene reads. Both ITS2 and 18S rRNA data sets of the two hypersaline meromictic lakes from Transylvania determined in this study were deposited in the Sequence Read Archive under the PRJNA1000420 BioProject. After the initial bioinformatics analysis of amplicon sequencing data, all downstream statistical analyses were conducted using R v4.1.1 . Venn and Sankey diagrams were used to compare the performance of two DNA markers (ITS2 and 18S) to characterize the diversity and composition of aquatic fungal communities. To eliminate the effect of different sequencing depths across samples on the subsequent analysis, the OTUs abundance information was normalized using a standard sequence number corresponding to the smallest sample size. Rarefaction was carried out at a sequencing depth of 50,350 sequences per sample for the ITS2 dataset and 55,497 sequences for the 18S dataset. Subsequent analyses of alpha and beta diversity were performed based on normalized data using the R microeco package . To check whether the sequencing depth is sufficient to estimate the diversity of the fungal communities, rarefaction curves were generated by plotting the number of OTUs as a function of sequence reads using mecodev , a set of extended classes based on the R microeco package. Six commonly used biodiversity indicators (Observed OTUs, Good’s Coverage index, Chao1, ACE, Shannon, and Simpson) were computed for each sample. The Kruskal–Wallis Rank Sum Test was used to determine whether these diversity estimators significantly vary between the two main sampling sites (Fără Fund and Ursu lakes) or among the water depth profiles. The beta diversity analysis was assessed using the Bray–Curtis distances, visualized using a non-metric multi-dimensional scaling plot (NMDS), and significance was checked with PERMANOVA using default settings of the adonis function from the vegan package . Further, to assess the connections between the fungal community structure and environmental factors, the redundancy analysis (RDA) and Mantel test were performed using the R package microeco . 

Fungi To categorize the ITS2 dataset into ecologically meaningful groups, FUNGuild  and FungalTraits , two annotation tools integrated into the microeco R package , were used. Fungal OTUs were assigned to specific trophic modes and further subdivided into specific ecological guilds. Subsequently, the relative sequence abundance of fungal trophic modes and the predominant functional groups across the communities were compared. The fungal functional guilds assigned using both databases were visualized in a bubble plot showing the functional individual percentage. The trophic analysis was conducted using the metanetwork R package . The adjacency matrix constructed for the metanetwork usage contained all the fungal OTUs obtained from FungalTraits and the OTUs from the 18S analysis for Plantae and Animalia kingdoms. The OTUs corresponding to the Plantae and Animalia kingdoms were added to the analysis because the identified plants and animals could represent potential feeding sources for the investigated fungi, categorized into plant and animal pathogens and nematophagous. The potential trophic links between the fungal OTUs and the Plantae and Animalia taxa were manually verified based on the existing literature. Four other entries (corresponding to the Poaceae family, litter, dung, and Homo sapiens ) were artificially added into the matrix. Their addition was based on the fact that many fungal OTUs had the trophic functions of plant pathogens, litter saprotrophs, dung saprotrophs, and opportunistic human parasites. Most identified fungal plant pathogens were found as pathogens of the Poaceae family explainable through the fact that the lakes are surrounded by grassland (Fără Fund Lake) or mixed forest-grassland vegetation (Ursu Lake). Litter and dung are also present in or around both lakes. The addition of Homo sapiens was based on the fact that at least Ursu Lake is constantly used for bathing. For each analyzed layer in the two lakes (soil, rivulet, surface layer (1.5–2.5 m), intermediate layer (3.5 m), bottom layer (6–9 m), and sediment), the adjacency-constructed matrices were filtered to obtain directed and connected graphs. No connected graphs were identified, only connected subgraphs. The connected subgraphs were plotted individually, taking into account the most abundant genera for each trophic level within each subgraph (nematophagous as animal parasites, lichenized as symbiotrophs, opportunistic human parasites as animal parasites, saprotrophs as soil/litter/wood/dung, and Poaceae pathogens as plant pathogens). The selected trophic levels were plotted in an unweighted manner. 

Environmental Settings of Sampled Lakes Based on the physical and chemical parameters (Table ), the water column strata of Fără Fund and Ursu lakes exhibited contrasting patterns in terms of salinity, dissolved oxygen, TOC, and SO 4 2− . The Fără Fund Lake was hypersaline from the mixolimnion, whereas Ursu Lake’s upper strata had lower salinity due to the freshwater input from a nearby rivulet; it became hypersaline in the anoxic and euphotic deeper layer. Fără Fund Lake appeared to be oxygenated throughout the water column, while Ursu Lake’s water column became anoxic in the monimolimnion. Both water columns were slightly alkaline in the mixolimnion and slightly acidic below 3.5 m in the monimolimnion. The TOC was relatively constant in the water column of Fără Fund Lake and increased with depth in Ursu Lake, maintaining constant values in the deep strata. Sulfate concentration increased with depth in Ursu Lake, while Fără Fund’s water column showed higher sulfate concentrations in the mixolimnion and chemocline and lower in the monimolimnion. Chemical characteristics of the rivulet sample from Ursu Lake and soil samples from both lakes are given in Table . 18S and ITS2 Sequence Data After quality filtration and chimera removal, the paired-end sequencing of the 18S locus for all 15 samples generated a total number of 1,154,152 effective tags with an average length of 309 nt. In addition, 1,201,006 high-quality reads were generated for the ITS2 sequences, with an average length of 369 nt. The number of sequences per sample varied from 50,577 (3.5 m depth water sample from Ursu Lake, ITS2 dataset) to 96,192 (9 m depth sediment sample from Ursu Lake, ITS2 dataset). The total number of OTUs generated at 97% sequence identity for each primer set was slightly above 2000 (2274 for ITS2 and 2093 for 18S). The ITS2 dataset consisted of 6.86% unclassified OTUs and 93.14% fungal OTUs. In the 18S dataset, 9.70% of OTUs were unclassified, 23.46% were classified as Fungi , 58.34% as Eukaryota , and the remaining 8.5% as Metazoa . Most of the detected fungal OTUs in both datasets belonged to the Ascomycota phylum (54.53% for ITS2 and 37.68% for the 18S dataset, respectively), but at lower taxonomic rank, these two genetic markers exhibited different resolutions. There were 19 classes solely found in the ITS2 dataset and only four unique to the 18S dataset (Fig. a). Pezizomycotina_cls_Incertae_sedis was the most abundant class found only in the ITS2 dataset (3.15%), followed by Aphelidiomycetes (0.81%), whereas Blastocladiomycetes (0.06%) was the most abundant fungal class unique to the 18S data. A total of 22 classes were present in both datasets (Fig. b). Besides fungi of unknown class (29.42%), Lecanoromycetes (25.17%) was the most abundant class in the ITS2 dataset, followed by Saccharomycetes (22.43%) and Sordariomycetes (14.19%) (Fig. c). The 18S dataset was clearly dominated by a large proportion of Sordariomycetes (50.03%), followed by fungi of unknown class (14.45%), Dothideomycetes (12.89%), and Agaricomycetes (9.53%) ( Fig. d). A closer examination of the taxonomic classes identified using 18S revealed (Fig. ) that the water column of Fără Fund Lake was dominated by Chlorophyceae . In contrast, Trebouxiophyceae were highly abundant in the water strata of Ursu Lake, except for the surface layer, with their abundance decreasing towards the bottom. This class was represented at the species level by Picochlorum maculatum . At this taxonomic rank, the halophilic microalga Dunaliella parva was well represented at varying abundances across the water column layers of Fără Fund Lake. Meanwhile, the brine shrimp Artemia salina was found in the sediment sample of Fără Fund Lake as well as at a depth of 2.5 m in the water column of Ursu Lake. Due to its higher resolution in fungal community characterization, the fungal diversity in the investigated lakes as reflected by the ITS2 reads was further explored in more detail. In addition, the non-fungal OTUs retrieved by the analysis of the 18S region were used for trophic analysis. ITS2-Based Fungal Diversity and Impact of Environmental Parameters The rarefaction curves (Fig. ) constructed on the observed number of OTUs present in all analyzed samples in the ITS2 dataset tended towards an asymptote indicating that the sequencing depth was sufficient to capture the majority of fugal diversity, with only a few rare species likely remaining to be sampled. This result was confirmed by Good’s coverage index that had a value above 0.99 in all instances (Table ). The highest number of observed OTUs was recorded in the surrounding soils of both lakes as well as in the fresh water sample from the Ursu Lake. Interestingly, of the water column samples, the one showing the utmost was seen in the monimolimnion stratum of the Ursu Lake (9 m depth). The α-diversity indices given in Table . indicated that relatively high fungal diversity with a higher evenness was present in the Ursu Lake. When considering only the water column samples, the α-diversity levels typically decreased from the surface to the hypersaline layer, in contrast to the pattern reflected by the archaeal and bacterial α-diversity in these lakes . The Shannon and Simpson diversity indices for all samples varied from 1.18 to 4.30, and from 0.59 to 0.95, respectively. However, there was no significant difference (Kruskal–Wallis Rank Sum Test, p > 0.05) in any of these indices between the Fără Fund and Ursu lakes or between the three chemically distinctive water masses (mixolimnion, intermediate stratum, and monimolimnion) (Fig. ). The NMDS ordination plot based on Bray–Curtis distance (Fig. ) revealed that the fungal communities from the two sampling sites were relatively distinct, having a small overlap represented by soil samples adjacent to both lakes and Lake Ursu’s tributary. These results were supported by a PERMANOVA analysis that showed significant differences between the two ecosystems ( R -squared, 0.32438; F -value 6.2416; p < 0.001). The samples from Fără Fund Lake seemed to be more tightly clustered in contrast to those from Ursu Lake which were more dispersed. The hierarchical clustering (Fig. ) showed the fungal assemblages were grouped according to depth categories, particularly for the Ursu Lake samples, while in the case of Fără Fund Lake, a less clear transition between water layers was observed. However, the fungal communities derived only from the water column did not reflect a statistically significant difference between depth categories ( R -squared, 0.14323; F -value 0.5851; p = 0.767). The Mantel test performed on the selected chemical parameters (Table ) indicated that salinity, TOC, TN, and NH 4 + held considerable significance in shaping the fungal community structure in the water column of the lakes (Table ). This result was further supported by RDA analysis, with the first RDA axis and the RDA2 axis explaining 56.9% and 27.1% of the variation in overall fungal community composition, respectively (Fig. a). The genera Phoma and Penicillium showed a significant negative relationship with salinity and TOC, but the latter showed a positive association with pH (Fig. b). Saccharomyces exhibited a significant positive correlation with TN, NH 4 + , and SO 4 2− and a negative relationship with DO. Other genera including Dactylonectria , Pseudogymnoascus , Fusarium , Acremonium , Humicola , and Pseudallescheria displayed significantly positive associations with DO or both DO and NO 3 − . Fungal Taxonomy Based on ITS2 Data In contrast to the 18S dataset, all ITS2-generated OTUs were assigned to the Fungi kingdom, except for a small fraction representing 156 OTUs that could not be classified at any taxonomic level and further termed “unidentified taxa” (Table ). A high relative abundance of these unidentified taxa sequences was detected in the upper layer of the water column in Ursu Lake (51% at a 1.5 m depth and 37% at a 2.5 m depth). These samples also had a high percentage of “unknown” fungi—sequences affiliated to the Fungi kingdom but unassignable to a phylum (35% at 1.5 m depth and 30% at 2.5 m depth). Apart from unclassified fungi, all samples seemed to be dominated by Ascomycota , followed by phyla Aphelidiomycota , Chytridiomycota , and Basidiomycota (Fig. a). The class-level taxonomic profiles of the fungal communities across water column revealed contrasting features for the two hypersaline lakes: Fără Fund was dominated by Lecanoromycetes , while a high fraction of Saccharomycetes was observed in Ursu Lake (Fig. b). Taxa distribution and abundances varied across water masses. In the Fără Fund Lake, the relative abundance of Lecanoromycetes decreased from the surface to 3.5 m depth, and then, it increased again with depth. The superficial water layers of Fără Fund Lake also displayed a high percentage of Sordariomycetes , a class that was also predominant in the soil samples and the rivulet that drains into the Ursu Lake (Fig. b) . The prevailing class of the Ursu Lake, Saccharomycetes , had a different trend as its relative abundance increased from 3% at 1.5 m depth, peaked at 3.5 m (95%), and decreased with depth (46% at 9 m). Besides Saccharomycetes , in the mixolimnion of Ursu, the presence of Dothideomycetes was also remarked, while at 6 m, the highest proportion (3.2%) of Aphelidiomycetes was observed. When focusing on the genus level, a comparable pattern became evident (Fig. c). The water column of Fără Fund Lake was primarily governed by Stereocaulon , while Ursu Lake’s water column was distinguished by the significant presence of Saccharomyces . These genera were also identifiable in the sediment samples. In contrast, the surface samples (soil and fresh water) were characterized by genera like Fusarium , Humicola , and Acremonium . Putative Ecological Roles of Fungal Communities Initially, the performance of the two annotation tools on the ITS2 amplicon sequencing dataset appeared quite comparable, with predicted ecological functions assigned to 51.27% of OTUs using FUNGuild and 50.84% using FungalTraits. For the OTUs with taxonomic identification at the genus/species level, 18% had trophic functions assigned with a “Highly probable” confidence ranking score using FUNGuild. In contrast to FungalTraits (Fig. ), the FUNGuild tool appeared to exhibit limited resolution in determining the functions of saprophytic organisms, with 31% of such OTUs in the ITS2 dataset remaining unresolved and categorized as “undefined saprotrophs” (Fig. ). Therefore, functional predictions obtained using the FungalTraits database for the investigated fungi were used to analyze trophic relations. The distributions of each ecological category and their abundance in Fără Fund and Ursu lakes can be observed in Fig. . FungalTraits classified the investigated fungi in 22 ecological categories, with the lichenized fungi as the most frequent (27.7%) and the algal parasites the scarcest (0.03%). Analyzing the most abundant trophic categories, there were differences and similarities between the proportions of trophic modes identified at Fără Fund and Ursu lakes. The proportion of lichenized fungi was 6 times higher for the Fără Fund Lake than for Ursu Lake. The proportion of lichenized fungi in the water column was similar between the strata of Fără Fund Lake (60–70%) and the sediment layer (82%), but their percentage was smaller for the associated soil (7%). Regarding the saprotrophs, wood and soil saprotrophs had similar distributions between the two lakes. They were more abundant in both soils and the associated rivulet (from Ursu Lake) than the water columns and sediments of the lakes. The abundance tended to decrease with the depth, suggesting a possible intake of the fungi from the surrounding soils (and rivulet for Ursu Lake). The same pattern described for the saprotrophs was identified for the most abundant pathogens/parasites (animal parasites, plant pathogens, and lichen parasites). The supplementary image Fig. presents in parallel a few possible trophic interactions among the fungi identified in each of the two lakes in the analyzed strata. The distribution of the fungal genera was very similar for all the investigated niches in both lakes. The similarities might be explained by the overlaps of the general characteristics of fungi being adapted to a hypersaline environment. 

Based on the physical and chemical parameters (Table ), the water column strata of Fără Fund and Ursu lakes exhibited contrasting patterns in terms of salinity, dissolved oxygen, TOC, and SO 4 2− . The Fără Fund Lake was hypersaline from the mixolimnion, whereas Ursu Lake’s upper strata had lower salinity due to the freshwater input from a nearby rivulet; it became hypersaline in the anoxic and euphotic deeper layer. Fără Fund Lake appeared to be oxygenated throughout the water column, while Ursu Lake’s water column became anoxic in the monimolimnion. Both water columns were slightly alkaline in the mixolimnion and slightly acidic below 3.5 m in the monimolimnion. The TOC was relatively constant in the water column of Fără Fund Lake and increased with depth in Ursu Lake, maintaining constant values in the deep strata. Sulfate concentration increased with depth in Ursu Lake, while Fără Fund’s water column showed higher sulfate concentrations in the mixolimnion and chemocline and lower in the monimolimnion. Chemical characteristics of the rivulet sample from Ursu Lake and soil samples from both lakes are given in Table . 

After quality filtration and chimera removal, the paired-end sequencing of the 18S locus for all 15 samples generated a total number of 1,154,152 effective tags with an average length of 309 nt. In addition, 1,201,006 high-quality reads were generated for the ITS2 sequences, with an average length of 369 nt. The number of sequences per sample varied from 50,577 (3.5 m depth water sample from Ursu Lake, ITS2 dataset) to 96,192 (9 m depth sediment sample from Ursu Lake, ITS2 dataset). The total number of OTUs generated at 97% sequence identity for each primer set was slightly above 2000 (2274 for ITS2 and 2093 for 18S). The ITS2 dataset consisted of 6.86% unclassified OTUs and 93.14% fungal OTUs. In the 18S dataset, 9.70% of OTUs were unclassified, 23.46% were classified as Fungi , 58.34% as Eukaryota , and the remaining 8.5% as Metazoa . Most of the detected fungal OTUs in both datasets belonged to the Ascomycota phylum (54.53% for ITS2 and 37.68% for the 18S dataset, respectively), but at lower taxonomic rank, these two genetic markers exhibited different resolutions. There were 19 classes solely found in the ITS2 dataset and only four unique to the 18S dataset (Fig. a). Pezizomycotina_cls_Incertae_sedis was the most abundant class found only in the ITS2 dataset (3.15%), followed by Aphelidiomycetes (0.81%), whereas Blastocladiomycetes (0.06%) was the most abundant fungal class unique to the 18S data. A total of 22 classes were present in both datasets (Fig. b). Besides fungi of unknown class (29.42%), Lecanoromycetes (25.17%) was the most abundant class in the ITS2 dataset, followed by Saccharomycetes (22.43%) and Sordariomycetes (14.19%) (Fig. c). The 18S dataset was clearly dominated by a large proportion of Sordariomycetes (50.03%), followed by fungi of unknown class (14.45%), Dothideomycetes (12.89%), and Agaricomycetes (9.53%) ( Fig. d). A closer examination of the taxonomic classes identified using 18S revealed (Fig. ) that the water column of Fără Fund Lake was dominated by Chlorophyceae . In contrast, Trebouxiophyceae were highly abundant in the water strata of Ursu Lake, except for the surface layer, with their abundance decreasing towards the bottom. This class was represented at the species level by Picochlorum maculatum . At this taxonomic rank, the halophilic microalga Dunaliella parva was well represented at varying abundances across the water column layers of Fără Fund Lake. Meanwhile, the brine shrimp Artemia salina was found in the sediment sample of Fără Fund Lake as well as at a depth of 2.5 m in the water column of Ursu Lake. Due to its higher resolution in fungal community characterization, the fungal diversity in the investigated lakes as reflected by the ITS2 reads was further explored in more detail. In addition, the non-fungal OTUs retrieved by the analysis of the 18S region were used for trophic analysis. 

The rarefaction curves (Fig. ) constructed on the observed number of OTUs present in all analyzed samples in the ITS2 dataset tended towards an asymptote indicating that the sequencing depth was sufficient to capture the majority of fugal diversity, with only a few rare species likely remaining to be sampled. This result was confirmed by Good’s coverage index that had a value above 0.99 in all instances (Table ). The highest number of observed OTUs was recorded in the surrounding soils of both lakes as well as in the fresh water sample from the Ursu Lake. Interestingly, of the water column samples, the one showing the utmost was seen in the monimolimnion stratum of the Ursu Lake (9 m depth). The α-diversity indices given in Table . indicated that relatively high fungal diversity with a higher evenness was present in the Ursu Lake. When considering only the water column samples, the α-diversity levels typically decreased from the surface to the hypersaline layer, in contrast to the pattern reflected by the archaeal and bacterial α-diversity in these lakes . The Shannon and Simpson diversity indices for all samples varied from 1.18 to 4.30, and from 0.59 to 0.95, respectively. However, there was no significant difference (Kruskal–Wallis Rank Sum Test, p > 0.05) in any of these indices between the Fără Fund and Ursu lakes or between the three chemically distinctive water masses (mixolimnion, intermediate stratum, and monimolimnion) (Fig. ). The NMDS ordination plot based on Bray–Curtis distance (Fig. ) revealed that the fungal communities from the two sampling sites were relatively distinct, having a small overlap represented by soil samples adjacent to both lakes and Lake Ursu’s tributary. These results were supported by a PERMANOVA analysis that showed significant differences between the two ecosystems ( R -squared, 0.32438; F -value 6.2416; p < 0.001). The samples from Fără Fund Lake seemed to be more tightly clustered in contrast to those from Ursu Lake which were more dispersed. The hierarchical clustering (Fig. ) showed the fungal assemblages were grouped according to depth categories, particularly for the Ursu Lake samples, while in the case of Fără Fund Lake, a less clear transition between water layers was observed. However, the fungal communities derived only from the water column did not reflect a statistically significant difference between depth categories ( R -squared, 0.14323; F -value 0.5851; p = 0.767). The Mantel test performed on the selected chemical parameters (Table ) indicated that salinity, TOC, TN, and NH 4 + held considerable significance in shaping the fungal community structure in the water column of the lakes (Table ). This result was further supported by RDA analysis, with the first RDA axis and the RDA2 axis explaining 56.9% and 27.1% of the variation in overall fungal community composition, respectively (Fig. a). The genera Phoma and Penicillium showed a significant negative relationship with salinity and TOC, but the latter showed a positive association with pH (Fig. b). Saccharomyces exhibited a significant positive correlation with TN, NH 4 + , and SO 4 2− and a negative relationship with DO. Other genera including Dactylonectria , Pseudogymnoascus , Fusarium , Acremonium , Humicola , and Pseudallescheria displayed significantly positive associations with DO or both DO and NO 3 − . 

In contrast to the 18S dataset, all ITS2-generated OTUs were assigned to the Fungi kingdom, except for a small fraction representing 156 OTUs that could not be classified at any taxonomic level and further termed “unidentified taxa” (Table ). A high relative abundance of these unidentified taxa sequences was detected in the upper layer of the water column in Ursu Lake (51% at a 1.5 m depth and 37% at a 2.5 m depth). These samples also had a high percentage of “unknown” fungi—sequences affiliated to the Fungi kingdom but unassignable to a phylum (35% at 1.5 m depth and 30% at 2.5 m depth). Apart from unclassified fungi, all samples seemed to be dominated by Ascomycota , followed by phyla Aphelidiomycota , Chytridiomycota , and Basidiomycota (Fig. a). The class-level taxonomic profiles of the fungal communities across water column revealed contrasting features for the two hypersaline lakes: Fără Fund was dominated by Lecanoromycetes , while a high fraction of Saccharomycetes was observed in Ursu Lake (Fig. b). Taxa distribution and abundances varied across water masses. In the Fără Fund Lake, the relative abundance of Lecanoromycetes decreased from the surface to 3.5 m depth, and then, it increased again with depth. The superficial water layers of Fără Fund Lake also displayed a high percentage of Sordariomycetes , a class that was also predominant in the soil samples and the rivulet that drains into the Ursu Lake (Fig. b) . The prevailing class of the Ursu Lake, Saccharomycetes , had a different trend as its relative abundance increased from 3% at 1.5 m depth, peaked at 3.5 m (95%), and decreased with depth (46% at 9 m). Besides Saccharomycetes , in the mixolimnion of Ursu, the presence of Dothideomycetes was also remarked, while at 6 m, the highest proportion (3.2%) of Aphelidiomycetes was observed. When focusing on the genus level, a comparable pattern became evident (Fig. c). The water column of Fără Fund Lake was primarily governed by Stereocaulon , while Ursu Lake’s water column was distinguished by the significant presence of Saccharomyces . These genera were also identifiable in the sediment samples. In contrast, the surface samples (soil and fresh water) were characterized by genera like Fusarium , Humicola , and Acremonium . 

Initially, the performance of the two annotation tools on the ITS2 amplicon sequencing dataset appeared quite comparable, with predicted ecological functions assigned to 51.27% of OTUs using FUNGuild and 50.84% using FungalTraits. For the OTUs with taxonomic identification at the genus/species level, 18% had trophic functions assigned with a “Highly probable” confidence ranking score using FUNGuild. In contrast to FungalTraits (Fig. ), the FUNGuild tool appeared to exhibit limited resolution in determining the functions of saprophytic organisms, with 31% of such OTUs in the ITS2 dataset remaining unresolved and categorized as “undefined saprotrophs” (Fig. ). Therefore, functional predictions obtained using the FungalTraits database for the investigated fungi were used to analyze trophic relations. The distributions of each ecological category and their abundance in Fără Fund and Ursu lakes can be observed in Fig. . FungalTraits classified the investigated fungi in 22 ecological categories, with the lichenized fungi as the most frequent (27.7%) and the algal parasites the scarcest (0.03%). Analyzing the most abundant trophic categories, there were differences and similarities between the proportions of trophic modes identified at Fără Fund and Ursu lakes. The proportion of lichenized fungi was 6 times higher for the Fără Fund Lake than for Ursu Lake. The proportion of lichenized fungi in the water column was similar between the strata of Fără Fund Lake (60–70%) and the sediment layer (82%), but their percentage was smaller for the associated soil (7%). Regarding the saprotrophs, wood and soil saprotrophs had similar distributions between the two lakes. They were more abundant in both soils and the associated rivulet (from Ursu Lake) than the water columns and sediments of the lakes. The abundance tended to decrease with the depth, suggesting a possible intake of the fungi from the surrounding soils (and rivulet for Ursu Lake). The same pattern described for the saprotrophs was identified for the most abundant pathogens/parasites (animal parasites, plant pathogens, and lichen parasites). The supplementary image Fig. presents in parallel a few possible trophic interactions among the fungi identified in each of the two lakes in the analyzed strata. The distribution of the fungal genera was very similar for all the investigated niches in both lakes. The similarities might be explained by the overlaps of the general characteristics of fungi being adapted to a hypersaline environment. 

In this study, two standard genetic markers were used to identify key fungal communities in two meromictic hypersaline ecosystems from the Transylvania Basin. The ITS2 subregion, recognized as the universal fungal barcode marker, was used to initially identify phylogenetic lineages, and the 18S gene fragment as a complementary marker. The discrepancy in fungal OTU number between ITS2 and 18S datasets poses challenges for directly comparing shared and unique taxa between the datasets. While ITS2 provided a more comprehensive representation of fungal diversity, the reduced OTU count in the 18S data limits the ability to confidently identify overlapping or distinct taxa. As a result, the Venn diagram (Fig. a) generated from these data should be interpreted with caution. These differences underscore the importance of selecting appropriate and complementary genetic markers for specific microbial community studies and highlight the potential for marker-specific biases in diversity assessments. The high-throughput ITS2-based identification approach could be further improved by using the entire ITS region as a molecular marker and incorporating additional barcode targets, such as the intergenic spacer (IGS), the β-tubulin II (TUB2), or others reviewed by Lücking et al. (2020). Moreover, identification of the investigated fungi could be enhanced by complementing DNA profiling with physiological diagnostic methods, such as VITEK or API systems, high-resolution melting (HRM) assays, and MALDI-TOF proteomics . The high fraction of unclassified fungal OTUs (29.7%) detected by ITS2 analysis in the two selected hypersaline lakes underscores the novelty of this study. This finding suggests that these unique environments harbor a significant proportion of potentially novel or poorly characterized fungal taxa that are not yet represented in current sequence databases. The identification of these unclassified fungi not only contributes to expanding knowledge of fungal diversity but also emphasizes the need for further research and continuous database improvements to capture the full spectrum of fungal diversity in hypersaline ecosystems. The most abundant fungal phyla predicted in the Fără Fund and Ursu lakes, Ascomycota , Aphelidiomycota , Chytridiomycota , Basidiomycota , and Rozellomycota were previously reported in several research papers describing the fungal communities in hypersaline environments . Ascomycota was typically the most abundant phylum identified in saline systems, with its members reported to possess halotolerant adaptive strategies . Halotolerant and halophilic fungi respond to salinity stress by enhanced production of diverse secondary metabolites (e.g., osmolytes, pigments, biosurfactants) and salt-tolerant enzymes (e.g., hydrolytic, ligninolytic, or chitinolytic enzymes), making these underexplored organisms promising candidates for various applications . Differences regarding the identified fungal classes in the Fără Fund and Ursu lakes were easily noticed. Among the classified groups, Lecanoromycetes , Sordariomycetes , and Dothideomycetes were the most abundant classes in the Fără Fund Lake, while Saccharomycetes , Sordariomycetes , and Dothideomycetes predominated in Ursu Lake. Lecanoromycetes was a diverse class containing most lichen-forming fungal species . The high frequency of the Lecanoromycetes could be assured due to the presence of fungal spores and or propagules used by the lichens to reproduce and disperse . Additionally, Sordariomycetes, Dothideomycetes , and Saccharomycetes are known to comprise halophilic and halotolerant genera . In Ursu Lake, fungal genera distribution varied along the water column and, implicitly, salinity gradient. Unclassified fungi-related OTUs predominated in the surficial layers (soil, rivulet, 1.5 m, and 2.5 m water depths), at lower salinity (up to 6.7% salinity) and higher dissolved oxygen levels (40–102%). In the soil and the rivulet samples, Fusarium (16.5%; 24.9%), Humicola (5.8%; 13.7%), and Acremonium (3.0%; 3.8%) also showed high frequencies, likely due to their use of soil and associated plants as habitats . As dissolved oxygen decreased from 40 to 0% below 2.5 m, the Saccharomyces genus-related OTUs prevailed, peaking at 95.19% abundance at 3.5 m, where oxygen is 0% and salinity reached 25%. It was earlier demonstrated that O 2 -depleted conditions favor osmotic adaption in Saccharomyces by enhanced accumulation of glycerol, a well-known compatible solute in halophilic eukaryotes . The high percentage of Saccharomyces -affiliated OTUs persisted at 6 m depth (91.0%) but decreased at 9 m (46.7%) as unclassified fungal OTUs partially regained abundance (37.1%). For the Fără Fund Lake, a particular vertical distribution of the analyzed fungi was observed. In the shore soil, the unclassified fungi were most abundant (27.8%) but followed closely by Fusarium (26.2%), Humicola (8.8%), and Acremonium (13.5%) genera. Stereocaulon was also present in the associated soil (2.1%), but with the lowest abundance. Intriguingly, Stereocaulon -affiliated OTUs gradually decreased along the water column from 58.1% at 1.5 m depth to 17.1% at 3.5 m, then increased, reaching a peak of 84.4% in the lake’s bottom sediment. The abundance of the unclassified fungi varied in an inverse manner to the ones of Stereocaulon . OTUs affiliated with Humicola , Fusarium , and Acremonium were present up to 3.5 m depth, where dissolved oxygen reached 177%, a finding that might be explained by the typical aerobic nature of these fungi . The distribution and abundance of the identified taxa that shaped the fungal communities in the studied lakes could be linked to their specific ecological roles. For example, the high abundance of Saccharomyces -related OTUs in Ursu Lake (from 2.5 to 9 m depth), a saprobic and moderately halotolerant yeast , suggested a significant but still unclear role in nutrient recycling through organic matter decomposition . In contrast, the high abundance of the Stereocaulon genus at Fără Fund Lake (from 1.5 to 27 m depth) was difficult to interpret ecologically, as the genus is primarily described as a terrestrial lichenized fungus. Stereocaulon has been shown to possess internal nitrogen recycling strategies which may enhance its development in harsh, dry environments. Apart from the demonstrated presence of sugar-derived osmolytes like sucrose and trehalose in Stereocaulon sp., very little is known about the mechanisms of halotolerance in lichenized fungi and lichens in general . Additionally, the Stereocaulon genus has been shown to associate with a wide variety of green algal symbionts, broadening its ecological range due to the higher tolerance of the holobiont (i.e., symbiotic association) to environmental stressors . Fusarium sp., abundant in the surficial water strata of both lakes and in soil and rivulets near Ursu Lake, is a halotolerant fungus known to mediate aerobic partial denitrification to nitrous oxide (N₂O), a greenhouse gas of increasing concern in seawater ecosystems . The statistical test performed on the chemical parameters indicated that salinity, TOC, TN, and NH 4 + held considerable significance in shaping the fungal community structure in the water column of the lakes. The effects of chemical parameters on fungal communities’ structures were particular to each investigated lake. Previous studies showed that salinity  or pH  could affect the fungal communities, in contrast to local particular communities that were not significantly shaped by any of the chemical factors . The strong correlation with TOC, TN, and NH 4 + might be explained by the extended presence of the saprotrophs in the fungal communities in both lakes . The genera Phoma and Penicillium showed a significantly negative relationship with salinity and TOC, but the latter showed a positive association with pH. Phoma was characterized to have a high abundance in low-salinity waters . It was also mainly described as plant pathogen fungi, and its presence might not be correlated with TOC. Even though some species from the Penicillium genus were reported to develop hypersaline tolerance, for the present study, Penicillium was negatively correlated to salinity but has more tolerance to acidic pH . The positive correlation of Saccharomyces with TN, NH 4 + , and SO 4 2− could be associated with glycerol production described as a halotolerant strategy for the genus . The glycerol production was also negatively correlated with DO . Other genera Dactylonectria , Pseudogymnoascus , Fusarium, Acremonium , Humicola , and Pseudallescheria are plant pathogens. Their presence might be correlated with microscopic plant fragments in the analyzed lakes , which might explain their significantly positive associations with DO or both DO and NO 3 − , as they might use the plant fragments as a food source. While this study identified relationships between fungal communities and certain environmental factors, further research is necessary to validate these findings and to explore the potential influence of other unexamined variables, such as temperature, nutrient availability, and additional chemical gradients. Such investigations will provide a more comprehensive understanding of the complex interactions that shape fungal community dynamics and their functional roles in permanently stratified hypersaline lakes. The analysis of ecological traits of fungal communities (based on ITS2 data) in the investigated hypersaline habitats was performed using two annotation tools. Although the overall number of assignments was comparable between the two, FungalTraits demonstrated superior performance, particularly in the coverage of saprotrophs. A similar trend was observed in a study by Tanunchai et al. (2023), where FungalTraits provided a higher quantity and quality of functional annotations for fungal guilds on senescing leaves and needles across various temperate tree species. However, it is well known that the performance of such tools is highly influenced by the quality and comprehensiveness of the sequence databases, the accuracy of taxonomic identification, and the detail of functional descriptions available . The FungalTraits functional classification showed that symbiotrophs were the dominant trophic type in Fără Fund Lake, in contrast to saprotrophs that were predominantly present in Ursu Lake. The lichenized fungi were represented by the Stereocaulon genus. The symbiotic coalition was formed with green algae within the genus Trebouxia (identified in the 18S data analysis). The halotolerant adaptation strategy may rely on the production of ribitol by the green alga that could be shared with the mycobiont . The graphical representation of interactions between the identified fungi and other potentially relevant organisms was initially designed as a trophic network. Due to the disconnected nature of the graphs, it was not possible to globally establish potential trophic interactions for each niche. Plotting each connected and directed subgraph individually provided only a fragmented view of the potential trophic connections within each analyzed niche. As the plotting did not account for the abundance of each genus, the depicted trophic relations only illustrated possible interactions of the identified fungi. These inferred interactions were based solely on the DNA sequences retrieved from the analyzed samples, without other complementary analyses. The presence of DNA sequences does not necessarily indicate the viability of the cells containing the DNA or their active participation in trophic interactions; they may reflect an accumulation of DNA molecules from the surrounding environment. The connections between the genera suggested a high similarity between the niches in the two lakes. Salinity likely explains their consistent presence, as many of these genera ( Aspergillus , Talaromyces , Chaetomium , Penicillium , Stachybotrys ) are halotolerant or halophilic fungi . In each studied niche—soil, rivulet (only present for Ursu Lake), surface layer (1.5–2.5 m), intermediate layer (3.5 m), bottom layer (6–9 m), and lake sediment—of the two investigated lakes, saprotrophs including soil, litter, wood, dung, and unspecified types comprised of a variety of genera ( Acremonium , Chaetomium , Cladophialophora , Emericella , Myrothecium , Saccharomyces , Spizellomyces , Stachybotrys , Scedosporum , and Podospora ). Additionally, opportunistic human pathogens, such as Pseudallescheria , Exophiala , Issatchenkia , Mortierella , Cutaneotrichosporon , Talaromyces , Candida , Cryptococcus , Aspergillus , Penicillium , and Fusarium , were also identified. These pathogens have been reported in saltwater bathing locations . Some genera ( Mortierella , Talaromyces , Candida , Cryptococcus , Aspergillus , and Penicillium ) may also function as soil, litter, wood, dung, or unspecified saprotrophs. The lichenized fungi Xanthoria , Evernia , Soliciosporum , and Umbilicaria also form symbiotic relations with the Trebouxiophyceae family . An interesting parasitic interaction might involve Verticillium , Dactylaria , Plectosphaerella , Clonostachys , Haptocillium , and nematodes. The nematophagous fungi produce protease that hydrolyses the host nematode proteins in situ . 

In conclusion, this study provided the first insights into the fungal diversity, putative ecological roles, and their distribution in two meromictic hypersaline lakes from the Transylvanian Basin. Despite the differing taxonomic resolution of the two biomarkers used for amplicon-sequencing of environmental DNA (ITS2 region and 18S rRNA gene), the results indicated that both Fără Fund and Ursu lakes predominantly host Ascomycota fungi, but the taxonomic and functional profiles differed markedly and showed a vertical stratification across the water column. Integrating these findings with previous data on co-occurring and interacting prokaryotes will help unravel the complexity of these unique habitats. Moreover, the results underlined the effectiveness of using the FungalTraits database for evaluating functional guilds in hypersaline waterbodies. While the results suggest that salinity is not the only chemical factor shaping the fungal communities thriving in the investigated ecosystems, the impact of other environmental variables such as seasonal rainfall and winds, proximity of grasslands, and human settlements remains unknown. Future long-term survey examining how multiple physicochemical factors interact to modulate the changes in the fungal community across spatial and temporal scales would definitively deepen the understanding of the fungal ecology and their impact on ecosystem functioning in permanently stratified hypersaline lakes. This fresh perspective on diversity and potential trophic roles of fungi inhabiting meromictic hypersaline lakes can be refined in future studies aiming to determine the extent to which these fungal communities include dead, dormant, or metabolically active members. 

Below is the link to the electronic supplementary material. Supplementary file1 (XLSX 317 KB) Supplementary file2 (DOCX 1142 KB) Supplementary file3 (JPG 2484 KB) 

