Dynamics of heavy metals and microbiota in glacial systems of Zhongar Alatau National Park, Kazakhstan

Dynamics of heavy metals and microbiota in glacial systems of Zhongar Alatau National Park, Kazakhstan | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Dynamics of heavy metals and microbiota in glacial systems of Zhongar Alatau National Park, Kazakhstan Lenka Pániková, Katarína Ondreičková, Patrik Pánik, Marián Janiga, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7176803/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Jan, 2026 Read the published version in Microbial Ecology → Version 1 posted 12 You are reading this latest preprint version Abstract This study presents the results of the relationships between elemental and microbial structure (bacterial fragments) in the glacial region of the Zhongar Alatau Mountains. Water samples were collected from selected sites of the glacial system and subsequently filtered. The highest diversity and species richness of bacteria was found in glacial lakes and the lowest in glaciers. The species evenness indicates that there is a certain dominant bacterial species in glaciers. The species richness of bacteria was positively correlated with the concentration of biotic chemical elements. Community evenness or balance was positively related to chromium concentration but negatively related to mercury concentration. We confirmed synergistic accumulation of manganese, rubidium, potassium, barium and iron in glaciers. Bacteria varied significantly in subglacial moulds. Bacterial communities from glaciers and sediments of subglacial lakes differed significantly. All these factors may be related to anthropogenic pollution; glacier melt and dust storms in the study area. Investigations of bacterial communities and concentrations of chemical elements in the waters of glacial valleys of Zhongar Alatau show that synergistic concentrations of some chemical elements have their analogy in the occurrence of bacterial communities. Finally, we found that the primary chemical environment of glacial valleys correlated with spatial patterns of bacterial community structure. Heavy metals Glacier Glacial water Mountain Lake Bacterial fragments Zhongar Alatau Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Glaciers play a key role in the hydrological cycle of Central Asia [ 1 – 3 ]. With globally increasing temperatures, glaciers around the world are retreating and losing volume [ 4 – 5 ]. They not only act as reservoirs of water but have deposited contaminants from various atmospheric depositions in the past as well as today [ 6 ]. The Zhongar Alatau is a Central Asian ridge of high mountains occurring between the Altai and the Tangshan. Arid deserts and lowlands are found along the border of these mountains. The frequency of dust storms directed into parts of the Zhongar Mountains has increased over the past few decades [ 7 ]. The shrinking of the Zhongar Alatau has the highest rate compared to other glaciated regions of the Central Asian mountains, including the Altai, Pamir, and even the Tien Shan [ 8 ]. Meltwater from these glaciers has distinctive physical, chemical and biological aspects that affect freshwater ecosystems [ 9 ]. Glaciers are important ecosystems [ 10 – 12 ] and research on glacial microbiology has mostly focused to date on understanding the supraglacial ecosystem, in large part because of its importance to the albedo of glaciers and ice sheets [ 13 – 18 ]. Even mountain glaciers, which have a small percentage of catchment area, can affect surrounding aquatic ecosystems [ 19 – 20 ]. Turbidity in glacial meltwater stems from erosive activity of the glacier on the underlying bedrock [ 21 ]. Continued retreat often leads to the exposure of local basal depressions where proglacial lakes form [ 22 ]. Alpine lakes in high mountains and similar lakes in polar regions are inhabited by benthic microorganisms. These microbial communities consist of a unique diversity of bacteria, archaea, and microeukaryotes that drive key biogeochemical processes [ 24 – 25 ]. Nevertheless, their ecology, biodiversity and their linkage to chemical elements contained in sediments a big unknown [ 26 – 27 ]. Supraglacial ecosystems are characterized by low temperature, absence of light, oligotrophic conditions, and high mineral content [ 28 – 30 ] suggesting that chemolithotrophic organisms could play an essential role in subglacial ecosystems [ 31 – 32 ]. Contamination of glaciers is widely discussed through the spectrum of chemical elements present in pollution [ 33 ]. The origin of the elements varies from transboundary flow to local dust particles to various bedrock erosion [ 7 , 34 – 35 ]. Manganese oxides are strong sorbents of heavy metals and nutrients, serving as natural sinks for contaminants [ 36 ], oxidation can be catalysed by a variety of bacteria and fungi [ 37 – 38 ]. Iron (Fe) is an essential nutrient for almost all living organisms [ 39 ]. As a result of long-term exposure to crustal bedrock and physical and chemical weathering processes, glacial basins are rich in soluble bioactive iron. Glaciers can transport soluble iron nutrient elements to downstream aquatic systems such as lakes, rivers, and soil nutrients, thereby affecting the ecological system [ 40 ]. Manganese occurs naturally in water at higher concentrations, especially during periods when high runoff prevails (snowmelt in spring) [ 41 ]. Elements such as manganese, rubidium (Rb), potassium (K) and barium (Ba) are often released during weathering [ 42 ]. Rb is often found in dissolved form by chemical weathering released into the aquatic ecosystem [ 43 ]. It is well known that the complex relationships between glacial hydrology, the microbial community, and geochemistry are affected by climate warming. While we are aware that climate change is altering glaciers and their surroundings, we still have a limited understanding of how these changes will impact the microbes within glaciers [ 44 ]. This study's primary objective was to identify the parallels between the environment of chemical element accumulation in the high mountain valleys of Zhongar Alatau and the environment of bacterial communities. Because glacier melt significantly alters the composition of bacterial communities, this research also aimed to compare the diversity and structure of bacterial communities in habitats directly influenced by glaciers or glacier waters. The study presents novel findings on the biodiversity of bacterial communities in the Central Asian high mountain ecosystem of Zhongar Alatau. Material and methods Study area The Zhongar Alatau is a mountain system located mostly in Kazakhstan, stretching from southwest to northeast along the state border between the Republic of Kazakhstan and the People's Republic of China. The total area of the Zhongar Mountain system is about 40,000 km² [ 45 ]. The climate in Zhongar Alatau is predominantly continental. Average annual precipitation is 600–800 mm, at an altitude of 3200–3600 m, the air temperature during the accumulation period is -8–10◦C [ 46 ]. Because of their westerly orientation, the Zhongar Alatau mountains are also under the influence of warm westerly air masses originating from the deserts located south of Lake Balkhash [ 47 – 48 ]. The mountain system is primarily composed of Precambrian and Palaeozoic rocks, including gneisses, crystalline schists, quartzites, marbles, and limestones. Extensive Palaeozoic volcanic and sedimentary complexes, such as marine terrigenous and continental effusive-sedimentary rocks, are also common. These older rocks are often highly folded, dissected by faults, and intruded by various igneous rocks. Younger Meso-Cenozoic deposits, consisting of Paleogene, Neogene, and Quaternary sediments, are found mainly in the intermountain depressions and foothills [ 82 ]. Sampling Samples were collected in Zhongar Alatau in two valleys. In the first valley, where the large Zhasylkol Lake is located, a total of 11 samples were collected. Six samples were collected from the perimeter of the lake and one sample from the centre of the lake; the samples are labelled as Lake (L1-L7). Subsequently, two samples were collected from the Aganykatta River, which flows out of the lake, at 6 km intervals. The last two samples were collected at the point where the two rivers, the Aganykatta and a local river from an adjacent unglaciated valley, meet. The designation of the samples is River (R1-R4). A total of 7 samples were collected from Lake Zhasylkul in the first valley, along with 3 samples from its catchment area and 1 sample from the adjacent non-glacial valley, making a total of 11 samples. Water surface samples were taken from the shore, including two from the northern side at GPS coordinates 45°23'38.066''N, 80°34'33.429''E and 45°23'39.891''N, 80°34'38.458''E. One sample was collected from the eastern side at 45°23'15.549''N, 80°34'55.989''E, another from the southern side at 45°22'41.079''N, 80°34'51.897''E, and one from the centre of the lake at 45°23'13.477''N, 80°34'40.729''E. Two samples were taken from the western side at 45°23'13.579''N, 80°34'30.043''E and 45°23'22.631''N, 80°34'24.688''E. Additionally, 3 samples were collected from the Aganykatta River at intervals of 7 km, with coordinates 45°25'4.176''N, 80°33'30.432''E and 45°26'54.345''N, 80°32'3.164''E. The third sample, at coordinates 45°28'34.203''N, 80°30'52.808''E, was taken at the point where the river is joined by an adjacent non-glacial valley, from which the fourth sample was collected (Fig. 1 ). The second valley, which was glaciated at the end, was sampled 8 times − 3 from the glacier and 5 from the spring that flows out from under the glacier. The first sample was obtained from the edge of the glacier at 3360 m, the other four samples from the glacier from an area at 3280 m where a crevasse had formed because of the retreating glacier face. Here, ice was collected from a depth of 1 m. The glacier samples are labelled Glacier (G1-G5). Another sample was taken from a glacial spring approximately 5 m from the glacier. A subglacial lake was located at an elevation of 3200 m, from which one sample was collected. Another sample was taken 7 km from the glacier and two more samples were taken 12 km from the glacier at the meeting point with another large, glaciated valley and its source. The designation of the sedimentary lakes (subglacial lakes) is S1-S2, and the glacial flow is GR1-GR3. A total of 5 samples were collected from the glacier in the second valley, along with 2 samples from sedimentary lakes and 3 samples from the glacial river. One surface sample was taken from the glacier at an altitude of 3360m, at GPS coordinates 44°59'8.169''N, 79°23'46.688''E. At an altitude of 3280m, due to glacier retreat and melting, a deep crevasse was formed, from which 4 samples were collected at a depth of 1m, at coordinates 44°59'20.530''N, 79°23'36.227''E. Subsequently, a sample was taken from the stream 5m from the glacier at 44°59'22.841''N, 79°23'35.996''E and another from a sedimentary lake at 44°59'47.456''N, 79°23'29.735''E. Another sample was collected from the stream approximately 7 km from the glacier at coordinates 45°0'28.361''N, 79°23'8.627''E. The last two samples, 45°2'10.665''N, 79°21'31.098''E and 45°2'10.665''N, 79°21'31.098''E, were taken approximately 12 km from the glacier. At this location, the studied valley meets the neighbouring glaciated valley, where both an upper and lower sedimentary lake were present. Water samples were collected from the upper lake and its stream (Fig. 2 ). Laboratory analysis Each water sample of 1 litre was filtered through a disposable filter funnel containing 0.22 µm filter membrane. This filtration was accelerated using the vacuum pump N86KN.18, but a water-level of 1 cm was left to avoid drying out the filter membrane. After drying, the trapped sediment on the filter paper was analysed using an ED-XRF DELTA fluorescence spectrometer (Innov-X Technologies, Canada). For this study, the elements detected were K, Ba, Rb, Fe, Mn, Mo, Ni, Hg and Cr. The concentration of the measured elements in the sample is determined by measuring the intensity of its characteristic wave energy. To ensure quality assurance, the instrument was calibrated using a bovine liver standard (NCS ZC 7001 CHNACIS, China). Mercury was analysed separately using a Milestone DMA-80 evo instrument, the echo run is based on sample decomposition by heat, amalgamation and measurement by atomic absorption. Molecular analysis of bacterial communities One litter of water was filtered through 0.2 µm membranes using a vacuum system. Due to varying turbidity among sample types, particularly high in glacial rivers, the actual filtered volume sometimes differed. Glacial rivers or sedimentary lakes, with higher turbidity, yielded more biomass per filtered volume but also caused clogging and reduced total filtered volume. Entire filters were used for DNA extraction using the DNeasy Power Water Kit (Qiagen), regardless of sediment content, using consistent extraction protocols and normalization of elution volumes across all samples (50 µl) helped minimise technical bias. The purity of DNA was measured spectrophotometrically with the NanoDrop One Spectrophotometer (Thermo Scientific Inc., Wilmington, USA). Although sediment mass per sample was not directly measured, dsDNA (double-stranded DNA) concentration was fluorescently quantified using a Qubit™ Flex fluorometer (Invitrogen) with Qubit dsDNA HS (High Sensitivity) Assay Kit, and samples were diluted to the same final concentration (20 ng/µl), and stored at -20°C. Automated ribosomal intergenic spacer analysis (ARISA) was used for detection of genetic diversity of bacterial communities in the water samples. The ITSF/ITSReub [ 49 ] primer set with 6-FAM fluorescent dye on the 5´ end of the reverse primer was used for amplification of the 16S-23S rRNA intergenic transcribed spacer (ITS) region from the bacterial rRNA operon. DNA amplification of bacterial communities was carried out in 50 µL reaction mixture containing 1×PCR buffer (Invitrogen, Thermo Fisher Scientific Inc., Waltham, USA), 1.5 mmol Mg 2+ , 0.25 µmol of both primers, 200 µmol of each dNTP (Invitrogen, Thermo Fisher Scientific Inc., Waltham, USA), 1 U Taq DNA polymerase (Invitrogen, Thermo Fisher Scientific Inc., Waltham, USA), and 1 µL (20 ng) of DNA extracted from the rhizosphere. The PCR was performed in a GeneAmp PCR System 9700 (Applied Biosystems, Thermo Fisher Scientific, Inc., USA) using the following conditions: initial heat denaturation at 94°C for 3 min, followed by 35 cycles each consisting of a denaturation step at 94°C for 45 s, annealing at 60°C for 1 min, extension at 72°C for 2 min and a final extension step at 72°C for 10 min. PCR amplification was confirmed by horizontal electrophoresis on a 1% (w/v) agarose gel in 1×TBE buffer pre-stained with 0.10 µL/mL of ethidium bromide and visualised using UV illumination. PCR products were precipitated with ethanol and dissolved in 10 µL of sterile water. One microliter of purified products was added to 9 µL formamide containing LIZ1200 size standard (Applied Biosystems, Thermo Fisher Scientific, Inc., USA), denatured at 95°C for 3 min and separated by capillary electrophoresis using ABI 3100 Prism Avant (Applied Biosystems, Thermo Fisher Scientific, Inc., USA). Outputs from ARISA in the form of electropherograms were analysed by the Peak Scanner 2 (Applied Biosystems, Thermo Fisher Scientific Inc., Wilmington, USA), and OTUs (Operational Taxonomic Unit) in range 200–1000 bp were used for the evaluation. Only peaks above the threshold of 50 fluorescence units were considered. Statistics Statistical analyses were performed using Statistica Ver. 12 software. We used PCA to determine the relationship between sites with elements and sites with bacteria. Molecular data statistics ARISA profiles were used to define operational taxonomic units (OTUs) based on unique fragment lengths, with each distinct peak representing a putative genetically distinct bacterial population. A bin size of ± 1 bp was applied to account for minor shifts in fragment mobility. To minimise bias due to varying fragment counts, the ARISA dataset was rarefied prior to the statistical analyses. The rarefaction threshold was set according to the sample with the lowest number of detected peaks. Statistically significant differences among groups of samples were tested using ANOVA at the 95% confidence interval for the means and subsequently. The post hoc LSD test using the software Statgraphics x64 (Statpoint Technologies, Inc., Warrenton, USA). The Venn diagram was generated using the online Venn diagram tool provided by the Bioinformatics & Evolutionary Genomics ( https://bioinformatics.psb.ugent.be/webtools/Venn/ ) [ 81 ]. Alpha diversity indices (Chao-1, Simpson, Shannon, and Evenness) were calculated from standardized profiles of individual samples using the number and height of peaks in each profile as representations of the number and relative abundance of phylotypes. It is important to note that ARISA does not provide taxonomic identification; thus, the diversity indices (e.g., Chao-1) reflected fragment-defined OTU richness rather than true species richness. Bacterial communities in different samples were compared from ARISA profiles using the height of fluorescence in individual OTUs. These data were subsequently used for the principal component analysis (PCA) with a correlation matrix. These settings were also combined with alpha diversity indices from molecular data and chemical element values to construct PCA plots. Alpha diversity indices and PCA were evaluated by using the PAST (Palaeontological Statistics) software version 3.19 [ 50 ]. Results Bacterial diversity The alpha diversity indices (Fig. 3 ) indicate that lakes exhibited the highest species richness (Chao-1) and the highest overall diversity (as measured by Shannon, Simpson, and Evenness indices). These indices suggest that bacterial communities within lakes were well-distributed and diverse. Rivers also showed high diversity (Shannon, Simpson, Evenness), comparable to that of lakes, but their species richness (Chao-1) was moderate, suggesting a slightly lower number of species. In contrast, glaciers demonstrated lower species richness (Chao-1), and diversity (Shannon, Simpson) compared to lakes and rivers. The species evenness in glaciers was medium, which implies that certain species could dominate in the community. Glacial rivers had the most variable values across nearly all indices (Chao-1, Shannon, Simpson), indicating low bacterial diversity in some samples and the dominance of a few species. Meanwhile, sedimentary lakes had high species richness (Chao-1) but exhibited low evenness (Evenness), suggesting that a limited number of species dominate these environments. The diversity values measured by Shannon and Simpson were medium in comparison to other aquatic habitats. According to the Venn diagram (Fig. 4 a), the lake habitat contained the highest number of unique taxa, with 82 detected. This suggests a high level of specificity for microbial communities in that environment. The glacier habitat follows, with 43 unique taxa. Sedimentary lakes and rivers showed comparable numbers of unique bacterial taxa, with 28 and 26, respectively. In contrast, the glacial river had only 15 unique taxa, indicating lower species specialization relative to the other ecosystems. In terms of shared bacterial taxa among different habitats, the glacial river shared few taxa with other environments, which could reflect its extreme conditions. Conversely, the most shared taxa were found between lake and river habitats (14 shared taxa) and between lake and glacier habitats (10 shared taxa). Notably, we did not identify any bacterial taxa that were common across all habitats. This variability of bacterial taxa between individual samples is also evident from Fig. 4 b. Bacterial diversity and chemical elements Principal component analysis (PCA) was used to evaluate the relationships between bacterial alpha diversity, DNA concentration and chemical element content in water samples from different types of aquatic environments (lakes, rivers, glaciers, glacial rivers and sedimentary lakes). The results showed that the first four principal components with eigenvalues higher than 1 were significant (Fig. 5 ). PC1 explained 39.0% of the variability and correlated with the increase in the concentrations of elements such as Rb, Fe, Mn, K and Ba, which had high positive loadings on PC1 (Rb: 0.8996, Fe: 0.93156, Mn: 0.90684, K: 0.84095, Ba: 0.89403). DNA concentration also had a high positive loading on PC1 (0.7098), indicating its importance in explaining the variability of the data and the dependence of synergic amount of Rb, Fe, Mn, K and Ba on the amount of extracted DNA concentration in a sample. Conversely, bacterial diversity indices (Chao-1, Shannon, Simpson) had negative loadings on PC1 (e.g. Simpson: -0.42315, Shannon: -0.59845, Chao-1: -0.57411), indicating that higher PC1 values were associated with lower bacterial diversity represented by alpha diversity indices. In other words, samples with high DNA concentrations and high levels of the above chemical elements had low bacterial diversity, and vice versa, samples with lower concentrations of DNA had higher bacterial diversity indices. Thus, the variability on the PC1 axis is significantly influenced by the concentration of DNA in nature and the ability to detect it quantitatively in the laboratory. However, it is significant that the diversity of bacteria decreases in those samples where more Rb, Fe, Mn, K and Ba were present simultaneously. PC2 explained 26.8% of the variability and correlated with element concentrations such as Ni, Cr, Mo, and Hg, which had high positive loadings on PC2 (Cr: 0.87581, Ni: 0.88205, Mo: 0.89487, Hg: 0.705). Thus, PC2 is the main vector to characterize the co-occurrence of heavy metals (Cr, Ni, Mo, Hg), as their concentration is independent of the DNA concentration, but the bacterial diversity in these samples tends to decrease. This is a significant phenomenon indicating the dependence of lower bacterial diversity in samples with higher heavy metal contamination. PC3 explained 15.2% of the variability in the data. Variables with high positive loadings on PC3 were the bacterial diversity indices Shannon (0.61263), Simpson (0.55548) and Chao-1 (0.69064). This suggests that PC3 revealed variability in the data, which was primarily associated with differences in bacterial diversity. Samples with high PC3 values were characterized by high bacterial diversity. Conversely, evenness had a high negative loading on PC3 (-0.75192), indicating that a high PC3 value was associated with low evenness of species representation. PC4 explained 7.7% of the variability in the data. Variables with high positive loadings on PC4 were evenness (0.50763) and Cr (0.34986). This suggests that PC4 revealed variability in the data, which was associated with the evenness of species representation and chromium concentration. Samples with high PC4 values were characterized by high evenness and higher chromium concentrations. DNA concentration had a negative loading on PC4 (-0.33019) as well as mercury (-0.38673), suggesting that a high PC4 value could be associated with lower DNA and mercury concentrations. Samples from which a higher concentration of DNA was extracted had more mercury and less chromium and vice versa. Samples with proportionally more chromium and less mercury were likely to be dominated by some bacterial species (high evenness). The PCA visualization (Fig. 6 ) showed the clustering of samples according to their origin at the mentioned first two components. Lake samples (green dots) were in the lower left quadrant, with their distribution influenced by higher diversity values (Shannon index, Simpson index and Chao-1). River samples (red dots) were close to lake samples, indicating a similarity in bacterial communities in these indices. Glacier samples (blue dots) were clustered on the right side of the graph, indicating different chemical properties, especially higher Fe, Mn, Rb, Ba, and K, as well as being associated with higher DNA concentration. Glacial river samples (orange dots) showed a variable distribution, with their chemical characteristics influenced by the input of elements such as Cr, Ni, Mo and Hg. Sedimentary lakes (brown dots) were distinguished by high values on PC2, indicating an increased content of metals such as Hg, Cr, Mo and Ni. These samples also showed slightly lower values of DNA concentration than those in the glacier, indicating that DNA extraction in environments rich in particulates did not experience significant inhibition. Additionally, the relatively high alpha diversity indices observed in sedimentary lake samples suggest that any potential PCR inhibition present did not significantly affect downstream amplification. Environmental similarity - chemistry and bacterial communities The PCA constructed from chemical element concentration data alone indicates the occurrence of the two most important phenomena in glacial valleys. Statistically, the phenomena could be independent with different evolutionary histories. The most significant phenomenon (PC1, 56.4% of data variance) is the synergistic accumulation of Mn, Ca, Rb, K, Ba and Fe. Especially in glaciers (dark blue dots in Fig. 7 ). The second phenomenon is a rather significant accumulation of manganese (PC2, 27.8%), manifested mainly in high mountain sedimentary lakes just below glaciers (yellow dots). The environment constructed based on the first two axes by chemical elements has its analogy in the occurrence of bacterial communities (Fig. 8 ), (i.e., in the PCA constructed from bacterial fragments). Analogous environments could be detected by phenomena determined by components PC6 (x-axis, Fig. 8 ) of the PCA and PC9 (y-axis). On the x-axis, glaciers were significantly different from sedimentary lakes, and on the y-axis, sedimentary lakes were significantly different from glacial lakes. We observed that the dominant chemical environment of glacial valleys correlated with the spatial patterns of bacterial community structure. Discussion Large amounts of trace elements entering the atmosphere from anthropogenic emissions can have an adverse effect on the environment. In high mountain areas, glaciers play an important role for pollution accumulation. The southeastern mountainous territory of Kazakhstan and its glaciers is influenced by Kazakhstan's economy, based on the extraction of mineral resources such as uranium, chromium, zinc, and manganese. The country's strong industrial development in mining, smelting and energy, leads to relatively large emissions of air pollutants [ 80 ] also affecting the Zhongar Alatau Mountains. Analysis of bacterial diversity in different aquatic habitats showed significant differences in species richness, evenness and overall diversity. Although ARISA provides high-resolution fingerprinting of microbial communities, its fragment-based nature means that inferred OTUs are not taxonomically resolved. Therefore, we considered diversity interpretations in the context of community-level genetic variability, not as precise taxonomic richness. Lakes showed the highest bacterial species richness and are probably home to more stable and complex microbial communities compared to other habitats. Sedimentary lakes just below the glaciers were also characterized by high species richness and diversity of bacteria but had very low community equilibrium. Low evenness may indicate the dominance of certain bacterial species. River samples showed similar diversity values to lakes but with lower species richness. Even though lakes and rivers within the study area may be connected through surface or subsurface flow, the number of shared OTUs between these habitats was limited. This may be attributed to selective environmental pressures, differing sediment loads, glacial influence, and local biotic interactions. It also reflects the fragment-based resolution of ARISA, which may not detect subtle taxonomic overlaps if fragment sizes coalesce. Conversely, glaciers and glacial rivers showed overall lower diversity. The subglacial environment is consistently dark [ 51 – 52 ] making it fundamentally different from the glacial meltwater environment [ 53 ]. Bacteria in meltwater can originate from the glacier or the surrounding environment, being transported by wind or snow from nearby aquatic and terrestrial sources bound to dust or organic particles [ 54 – 56 ]. The diverse bacterial communities in glacial lakes and the pattern of their distribution play a key ecological role in biogeochemical cycling and mineral nutrient cycling. Seasonal changes, including air temperature, nutrient concentrations, and solar radiation, influence microbial community structure by favouring only those species that can cope with environmental stresses [ 57 ]. The study of Bradley et al [ 83 ] highlights that glacier and ice sheet surfaces are home to diverse microbial communities whose activity directly influences biogeochemical cycles and ice melting. A crucial aspect of their survival is dormancy, allowing them to rapidly reactivate upon thawing, showcasing their adaptability to extreme and dynamic conditions. This research emphasizes that these surface environments and their microbial inhabitants are highly vulnerable to climate change that may lead to shifts in those communities. Sedimentary lakes, which had high alpha diversity of bacterial communities (Fig. 3 ), are essential for understanding biodiversity responses to glacial retreat. Distance between glacial lakes and the glacier appears to be key as a driver of microbial diversity in lakes [ 58 ]. Analysis of common and unique bacterial communities revealed that the highest number of unique bacterial groups was found in lake environments, whereas the number of unique bacterial groups was very low in glacial rivers. This low diversity and species specificity may be attributed to significant physicochemical stressors such as low temperatures or high influx of adjacent glacial debris from melting glaciers [ 59 ]. Glacier-derived bacteria can contribute significantly (40–96%) to the stream microbial communities of a glacial lake, but their contribution decreases with increasing distance from the glacier terminus [ 60 ]. Liu et al. [ 61 ] found that the lake closest to the retreating alpine glacier had higher diversity than other lakes. Melted glacial water contains particles that cause high turbidity, reducing the amount of photosynthetically active radiation [ 62 ]. Further, containing high concentrations of nutrients, lakes fed by this water have the potential to be unique ecosystems and hotspots of nutrient cycling [ 63 ]. Lake sediments offer a solid, nutrient-enriched surface that is readily colonized by microbes, while water has the opposite effect, dissolving potentially toxic elements in the water and further reducing microbial diversity [ 64 ]. Guo et al. [84] discus that increased glacier melting due to global warming expands lake areas, and the massive influx of glacial meltwater significantly affects microbial assemblages. This occurs through direct export of microorganisms from glaciers and indirectly by altering abiotic factors like turbidity, which limits light penetration and impacts primary producers. Critically, as glaciers retreat and their meltwater influence diminishes, the microbial sources for these lakes are expected to shift towards non-glacial streams, leading to major changes in the physicochemical characteristics and microbial communities within the lakes. The study also found that bacterial abundance and diversity increased with meltwater during melting seasons, and that factors like pH, conductivity, and temperature significantly influence bacterial distribution. Remarkably, no bacterial taxon was found to be common to all habitats studied, indicating high ecosystem specificity of microbial communities. Ilahi et al. [ 65 ] observed a contrasting phenomenon in their samples collected from the Golen Valley in northern Pakistan. Although there were differences in ecological parameters within the study area, the differences in bacterial diversity were not significant. Their research indicated that bacterial diversity remained relatively stable in lake debris and in meltwater at the glacier margin. Our study documents that concentrations of different chemical elements are important (Fig. 5 , 6 ) environmental factors affecting bacterial communities. Analysis of habitat stratification, like chemistry, (Fig. 7 ) showed that the most comparable environment may be found between concentration of chemical elements and occurrence of specific bacterial communities (Fig. 7 , 8 ). Positive or negative existence of specific bacterial communities on glaciers in relation to glacier chemistry has been observed in other mountain ranges [ 65 ]. Accumulation of heavy metals in high mountain pristine glacial-fluvial environments is the result of natural weathering [ 66 ]. Rock and soil dust is the dominant source of iron and barium and an important source of manganese [ 67 ]. Manganese variations, for which rock and soil contributions are also important, have been found to closely parallel barium variations. Large numbers of bacteria beneath glaciers play an important role in chemical weathering and carbon cycling processes [ 51 , 68 – 71 ]. Abakumov et al. [ 72 ], who investigated a glacier in the Caucasus, found that sediments near the glacier were the main source of contamination. According to Ilahi et al. [ 65 ], iron, zinc, lead and copper are important driving factors in shaping microbial diversity in the glacier ecosystem. In addition, the geochemistry of bedrock and glacial soil directly influences the chemosynthetic properties of microbial communities [ 73 ]. Wu et al. [ 74 ], who investigated glacial catchments in the northeastern region of the Tibetan Plateau, found that, rubidium, molybdenum, barium, chromium, nickel, and other heavy elements were mainly derived from aeolian dust, riverbed sediments, and soil. Moreover, the presence of a select and narrow group of related, but not identical, microorganisms in glaciers is a consequence of the severe constraints that this environment poses to bacteria (desiccation, freezing, high pressure, and low nutrient and oxygen concentrations), which act by selecting for similar phylogenetic groups [ 55 ]. A particular phenomenon in glacial melting today is the increased abundance of mercury [ 75 – 76 ]. Our data confirm that higher mercury concentrations in glacial systems can limit bacterial growth and diversity (Fig. 5 ). Several studies from Tibet have shown that the levels of total mercury (THg) concentrations in glacial snow on were higher in the northern than the southern region [ 76 – 78 ]. Loewen et al. [ 79 ] suggested that particulate matter played an important role in the atmospheric transport and deposition of mercury over western China. Deposition of atmospheric mercury associated with dust particles is reported as a major factor influencing the distribution and concentration levels of mercury in glaciers [ 78 – 79 ]. Conclusion Glacier dynamics associated with elemental concentration and its interaction with the bacterial fragment is a complex topic that remain an understudied area of research. Elements accumulate in glacial systems in multiple ways and have different effects on the microbiota in an already challenging environment. The aim of this study was to investigate this topic in the Zhongar Alatau Mountains. The glaciers proved to be a reservoir of trace elements such as manganese, calcium, rubidium, potassium, barium and iron, but had low species diversity with only a few dominant species. On the other hand, glacial lakes have the highest diversity and less accumulation of elements. However, in the future, the continuous melting of glaciers may lead to an increase in flow and a complete alteration of glacial cycles, leading to a change in bacterial structures and a proliferation of elements. Statements and Declarations Ethical Approval Not applicable Consent to Participate Not applicable Consent to Publish Not applicable Data availability The data are available on request from the corresponding author. References Viviroli, D., Weingartner, R., and Messerli, B. (2003). Assessing the hydrological significance of the world’s mountains. Mountain Research and Development , 23 (1), 32–40. https://doi.org/10.1659/0276-4741(2003)023[0032:ATHSOT]2.0.CO,2 Armstrong, R.L. (2010). The glaciers of the Hindu Kush–Himalayan region: a summary of the science regarding glacier melt/retreat in the Himalayan, Hindu Kush, Karakoram, Pamir and Tien Shan Mountain ranges . International Centre for Integrated Mountain Development, Kathmandu. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7176803","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":490196532,"identity":"403be9ca-e019-4b4c-8914-3b5ecaa0da3d","order_by":0,"name":"Lenka Pániková","email":"data:image/png;base64,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","orcid":"","institution":"University of Žilina","correspondingAuthor":true,"prefix":"","firstName":"Lenka","middleName":"","lastName":"Pániková","suffix":""},{"id":490196533,"identity":"d01e2732-c21e-4308-a910-ac3b41d5cb0c","order_by":1,"name":"Katarína Ondreičková","email":"","orcid":"","institution":"Research Institute of Plant Production","correspondingAuthor":false,"prefix":"","firstName":"Katarína","middleName":"","lastName":"Ondreičková","suffix":""},{"id":490196534,"identity":"5ff2449a-db5f-4b13-b37b-1ff587b3676b","order_by":2,"name":"Patrik Pánik","email":"","orcid":"","institution":"University of Žilina","correspondingAuthor":false,"prefix":"","firstName":"Patrik","middleName":"","lastName":"Pánik","suffix":""},{"id":490196535,"identity":"48a20b7d-4381-4d71-8df1-74d3397c2a06","order_by":3,"name":"Marián Janiga","email":"","orcid":"","institution":"University of Žilina","correspondingAuthor":false,"prefix":"","firstName":"Marián","middleName":"","lastName":"Janiga","suffix":""},{"id":490196536,"identity":"5e5344fd-87e9-4da2-8c83-93e4fc316b90","order_by":4,"name":"Berikzhan Oxikbayev","email":"","orcid":"","institution":"Zhetysu University named after Ilyas Zhansugurov","correspondingAuthor":false,"prefix":"","firstName":"Berikzhan","middleName":"","lastName":"Oxikbayev","suffix":""}],"badges":[],"createdAt":"2025-07-21 11:23:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7176803/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7176803/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00248-025-02674-2","type":"published","date":"2026-01-27T15:58:06+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87594514,"identity":"3d03055e-94c4-40d1-bbe0-40e61fd0f0e1","added_by":"auto","created_at":"2025-07-25 15:36:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":393940,"visible":true,"origin":"","legend":"\u003cp\u003eLake Zhasylkul and the Aganykatta River basin with marked sampling locations – red dots. The sample from the neighbouring basin is marked with a blue dot. Source: Mapy cz, 2023.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7176803/v1/842879cb5f8145962079f475.png"},{"id":87595378,"identity":"5ded9f81-2ee5-44fc-8a84-e98a7d626d51","added_by":"auto","created_at":"2025-07-25 15:44:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":292165,"visible":true,"origin":"","legend":"\u003cp\u003eValey relief of glacial water system in Zhetysu Alatau mountan. Blue dots: Glacier samples, red dots: glacial river samples, yellow dots: sedimentary lake samples. Source: MapTiler, 2025.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7176803/v1/2705189b8216cb603477eb26.png"},{"id":87594372,"identity":"eb330ee0-5f70-4202-a91e-1aa11aee5412","added_by":"auto","created_at":"2025-07-25 15:28:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":66624,"visible":true,"origin":"","legend":"\u003cp\u003eAlpha diversity indices (Chao-1, Simpson, Shannon, and Evenness) of bacterial communities detected in water samples. Different lowercase letters correspond to the statistically significant differences among aquatic habitats (LSD, P ≤ 0.05)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7176803/v1/5890ae47865025e17ca27a6f.png"},{"id":87594520,"identity":"8ab8a6e5-5ff3-4e2e-8ab9-8c31d794495c","added_by":"auto","created_at":"2025-07-25 15:36:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":82504,"visible":true,"origin":"","legend":"\u003cp\u003eVenn diagram displaying the numbers of unique and shared bacterial taxa between individual aquatic habitats. The term ‘unique taxa’ refers to distinct OTUs identified via ARISA fragment lengths. Each OTU represents a unique fragment size, not a taxonomic assignment. Overlap represents shared OTUs across habitat types (a). Relative abundance profiles of bacterial OTUs detected in individual water samples. Values were normalized to 100% per sample to visualize OTU composition independently of fragment count (b). Note: L1-L7 = lake, R1-R4 = river, G1-G5 = glacier, GR1-GR3 = glacial river, SL1 and SL2 = sedimentary lake\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7176803/v1/9d54b3c53c659f0c8198f274.png"},{"id":87594393,"identity":"0b65b4f2-9315-4dac-afcc-9964a58bc3ba","added_by":"auto","created_at":"2025-07-25 15:28:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":55727,"visible":true,"origin":"","legend":"\u003cp\u003eThe component loadings for DNA concentration, indices of bacterial alpha diversity, and amounts of chemical elements from the first four significant principal components in PCA\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7176803/v1/62f7be2e1079b6f28fda20b0.png"},{"id":87595379,"identity":"88ccf0bc-9a9f-47c3-89f7-814becf7b4b6","added_by":"auto","created_at":"2025-07-25 15:44:05","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":62084,"visible":true,"origin":"","legend":"\u003cp\u003ePCA biplot created from DNA concentration, bacterial community alpha diversity indices, and chemical element amounts\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7176803/v1/81eb41610d1e51f12ba9eded.png"},{"id":87594374,"identity":"42706927-9e09-4827-b61a-bbc8b2642c04","added_by":"auto","created_at":"2025-07-25 15:28:05","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":145917,"visible":true,"origin":"","legend":"\u003cp\u003ePCA biplot created from chemical element concentrations. Synergistic accumulation of Mn, Rb, K, Ba and Fe in glaciers (PC1, x-axis). Dark blue dots - glaciers, yellow - sedimentary lakes under glaciers, green - rivers, light blue - lake in forest. The relatively large accumulation of manganese (PC2, y-axis) in subglacial sedimentary lakes is the second most significant phenomenon\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7176803/v1/69049fb2286f18a414880d32.png"},{"id":87594519,"identity":"ce31e2ac-8976-47d0-9b65-97bd0bd25908","added_by":"auto","created_at":"2025-07-25 15:36:05","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":127629,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis constructed from fragments (ARISA profiles) of bacterial communities. Analysis of habitat stratification, like chemistry in Fig.8., showed that the most comparable environments were found in the combination of phenomena in principal components 6 and 9, indicating a habitat link between bacterial communities and water chemistry based on the measured elements Mn, Rb, K, Ba, and Fe (cf. Fig.7). Communities from glaciers (dark blue dots) were different from communities found in other aquatic ecosystems (F6 - x-axis). Sedimentary lakes also differed significantly from the other habitat groups in another phenomenon (F9 - y-axis). Dark blue dots - glaciers, yellow - sedimentary lakes under glaciers, green - rivers, light blue - lake in forest\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7176803/v1/9d03228b77ab6c0b1c6528c2.png"},{"id":101691950,"identity":"942832e2-48c7-4a29-8ff2-bee031b90b80","added_by":"auto","created_at":"2026-02-02 16:16:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1951847,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7176803/v1/b107ccf1-8550-4875-87d0-8acaccb604d8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dynamics of heavy metals and microbiota in glacial systems of Zhongar Alatau National Park, Kazakhstan","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlaciers play a key role in the hydrological cycle of Central Asia [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. With globally increasing temperatures, glaciers around the world are retreating and losing volume [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. They not only act as reservoirs of water but have deposited contaminants from various atmospheric depositions in the past as well as today [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The Zhongar Alatau is a Central Asian ridge of high mountains occurring between the Altai and the Tangshan. Arid deserts and lowlands are found along the border of these mountains. The frequency of dust storms directed into parts of the Zhongar Mountains has increased over the past few decades [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe shrinking of the Zhongar Alatau has the highest rate compared to other glaciated regions of the Central Asian mountains, including the Altai, Pamir, and even the Tien Shan [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Meltwater from these glaciers has distinctive physical, chemical and biological aspects that affect freshwater ecosystems [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Glaciers are important ecosystems [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and research on glacial microbiology has mostly focused to date on understanding the supraglacial ecosystem, in large part because of its importance to the albedo of glaciers and ice sheets [\u003cspan additionalcitationids=\"CR14 CR15 CR16 CR17\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Even mountain glaciers, which have a small percentage of catchment area, can affect surrounding aquatic ecosystems [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Turbidity in glacial meltwater stems from erosive activity of the glacier on the underlying bedrock [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Continued retreat often leads to the exposure of local basal depressions where proglacial lakes form [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Alpine lakes in high mountains and similar lakes in polar regions are inhabited by benthic microorganisms. These microbial communities consist of a unique diversity of bacteria, archaea, and microeukaryotes that drive key biogeochemical processes [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Nevertheless, their ecology, biodiversity and their linkage to chemical elements contained in sediments a big unknown [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Supraglacial ecosystems are characterized by low temperature, absence of light, oligotrophic conditions, and high mineral content [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] suggesting that chemolithotrophic organisms could play an essential role in subglacial ecosystems [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eContamination of glaciers is widely discussed through the spectrum of chemical elements present in pollution [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The origin of the elements varies from transboundary flow to local dust particles to various bedrock erosion [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Manganese oxides are strong sorbents of heavy metals and nutrients, serving as natural sinks for contaminants [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], oxidation can be catalysed by a variety of bacteria and fungi [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Iron (Fe) is an essential nutrient for almost all living organisms [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. As a result of long-term exposure to crustal bedrock and physical and chemical weathering processes, glacial basins are rich in soluble bioactive iron. Glaciers can transport soluble iron nutrient elements to downstream aquatic systems such as lakes, rivers, and soil nutrients, thereby affecting the ecological system [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Manganese occurs naturally in water at higher concentrations, especially during periods when high runoff prevails (snowmelt in spring) [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Elements such as manganese, rubidium (Rb), potassium (K) and barium (Ba) are often released during weathering [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Rb is often found in dissolved form by chemical weathering released into the aquatic ecosystem [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIt is well known that the complex relationships between glacial hydrology, the microbial community, and geochemistry are affected by climate warming. While we are aware that climate change is altering glaciers and their surroundings, we still have a limited understanding of how these changes will impact the microbes within glaciers [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. This study's primary objective was to identify the parallels between the environment of chemical element accumulation in the high mountain valleys of Zhongar Alatau and the environment of bacterial communities. Because glacier melt significantly alters the composition of bacterial communities, this research also aimed to compare the diversity and structure of bacterial communities in habitats directly influenced by glaciers or glacier waters. The study presents novel findings on the biodiversity of bacterial communities in the Central Asian high mountain ecosystem of Zhongar Alatau.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003e\u003cem\u003eStudy area\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe Zhongar Alatau is a mountain system located mostly in Kazakhstan, stretching from southwest to northeast along the state border between the Republic of Kazakhstan and the People's Republic of China. The total area of the Zhongar Mountain system is about 40,000 km\u0026sup2; [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The climate in Zhongar Alatau is predominantly continental. Average annual precipitation is 600\u0026ndash;800 mm, at an altitude of 3200\u0026ndash;3600 m, the air temperature during the accumulation period is -8\u0026ndash;10◦C [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Because of their westerly orientation, the Zhongar Alatau mountains are also under the influence of warm westerly air masses originating from the deserts located south of Lake Balkhash [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. The mountain system is primarily composed of Precambrian and Palaeozoic rocks, including gneisses, crystalline schists, quartzites, marbles, and limestones. Extensive Palaeozoic volcanic and sedimentary complexes, such as marine terrigenous and continental effusive-sedimentary rocks, are also common. These older rocks are often highly folded, dissected by faults, and intruded by various igneous rocks. Younger Meso-Cenozoic deposits, consisting of Paleogene, Neogene, and Quaternary sediments, are found mainly in the intermountain depressions and foothills [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cem\u003eSampling\u003c/em\u003e\u003c/p\u003e\u003cp\u003eSamples were collected in Zhongar Alatau in two valleys. In the first valley, where the large Zhasylkol Lake is located, a total of 11 samples were collected. Six samples were collected from the perimeter of the lake and one sample from the centre of the lake; the samples are labelled as Lake (L1-L7). Subsequently, two samples were collected from the Aganykatta River, which flows out of the lake, at 6 km intervals. The last two samples were collected at the point where the two rivers, the Aganykatta and a local river from an adjacent unglaciated valley, meet. The designation of the samples is River (R1-R4).\u003c/p\u003e\u003cp\u003eA total of 7 samples were collected from Lake Zhasylkul in the first valley, along with 3 samples from its catchment area and 1 sample from the adjacent non-glacial valley, making a total of 11 samples. Water surface samples were taken from the shore, including two from the northern side at GPS coordinates 45\u0026deg;23'38.066''N, 80\u0026deg;34'33.429''E and 45\u0026deg;23'39.891''N, 80\u0026deg;34'38.458''E. One sample was collected from the eastern side at 45\u0026deg;23'15.549''N, 80\u0026deg;34'55.989''E, another from the southern side at 45\u0026deg;22'41.079''N, 80\u0026deg;34'51.897''E, and one from the centre of the lake at 45\u0026deg;23'13.477''N, 80\u0026deg;34'40.729''E. Two samples were taken from the western side at 45\u0026deg;23'13.579''N, 80\u0026deg;34'30.043''E and 45\u0026deg;23'22.631''N, 80\u0026deg;34'24.688''E. Additionally, 3 samples were collected from the Aganykatta River at intervals of 7 km, with coordinates 45\u0026deg;25'4.176''N, 80\u0026deg;33'30.432''E and 45\u0026deg;26'54.345''N, 80\u0026deg;32'3.164''E. The third sample, at coordinates 45\u0026deg;28'34.203''N, 80\u0026deg;30'52.808''E, was taken at the point where the river is joined by an adjacent non-glacial valley, from which the fourth sample was collected (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe second valley, which was glaciated at the end, was sampled 8 times \u0026minus;\u0026thinsp;3 from the glacier and 5 from the spring that flows out from under the glacier. The first sample was obtained from the edge of the glacier at 3360 m, the other four samples from the glacier from an area at 3280 m where a crevasse had formed because of the retreating glacier face. Here, ice was collected from a depth of 1 m. The glacier samples are labelled Glacier (G1-G5). Another sample was taken from a glacial spring approximately 5 m from the glacier. A subglacial lake was located at an elevation of 3200 m, from which one sample was collected. Another sample was taken 7 km from the glacier and two more samples were taken 12 km from the glacier at the meeting point with another large, glaciated valley and its source. The designation of the sedimentary lakes (subglacial lakes) is S1-S2, and the glacial flow is GR1-GR3.\u003c/p\u003e\u003cp\u003eA total of 5 samples were collected from the glacier in the second valley, along with 2 samples from sedimentary lakes and 3 samples from the glacial river. One surface sample was taken from the glacier at an altitude of 3360m, at GPS coordinates 44\u0026deg;59'8.169''N, 79\u0026deg;23'46.688''E. At an altitude of 3280m, due to glacier retreat and melting, a deep crevasse was formed, from which 4 samples were collected at a depth of 1m, at coordinates 44\u0026deg;59'20.530''N, 79\u0026deg;23'36.227''E. Subsequently, a sample was taken from the stream 5m from the glacier at 44\u0026deg;59'22.841''N, 79\u0026deg;23'35.996''E and another from a sedimentary lake at 44\u0026deg;59'47.456''N, 79\u0026deg;23'29.735''E. Another sample was collected from the stream approximately 7 km from the glacier at coordinates 45\u0026deg;0'28.361''N, 79\u0026deg;23'8.627''E. The last two samples, 45\u0026deg;2'10.665''N, 79\u0026deg;21'31.098''E and 45\u0026deg;2'10.665''N, 79\u0026deg;21'31.098''E, were taken approximately 12 km from the glacier. At this location, the studied valley meets the neighbouring glaciated valley, where both an upper and lower sedimentary lake were present. Water samples were collected from the upper lake and its stream (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eLaboratory analysis\u003c/em\u003e\u003c/p\u003e\u003cp\u003eEach water sample of 1 litre was filtered through a disposable filter funnel containing 0.22 \u0026micro;m filter membrane. This filtration was accelerated using the vacuum pump N86KN.18, but a water-level of 1 cm was left to avoid drying out the filter membrane. After drying, the trapped sediment on the filter paper was analysed using an ED-XRF DELTA fluorescence spectrometer (Innov-X Technologies, Canada). For this study, the elements detected were K, Ba, Rb, Fe, Mn, Mo, Ni, Hg and Cr. The concentration of the measured elements in the sample is determined by measuring the intensity of its characteristic wave energy. To ensure quality assurance, the instrument was calibrated using a bovine liver standard (NCS ZC 7001 CHNACIS, China). Mercury was analysed separately using a Milestone DMA-80 evo instrument, the echo run is based on sample decomposition by heat, amalgamation and measurement by atomic absorption.\u003c/p\u003e\u003cp\u003e\u003cem\u003eMolecular analysis of bacterial communities\u003c/em\u003e\u003c/p\u003e\u003cp\u003eOne litter of water was filtered through 0.2 \u0026micro;m membranes using a vacuum system. Due to varying turbidity among sample types, particularly high in glacial rivers, the actual filtered volume sometimes differed. Glacial rivers or sedimentary lakes, with higher turbidity, yielded more biomass per filtered volume but also caused clogging and reduced total filtered volume. Entire filters were used for DNA extraction using the DNeasy Power Water Kit (Qiagen), regardless of sediment content, using consistent extraction protocols and normalization of elution volumes across all samples (50 \u0026micro;l) helped minimise technical bias. The purity of DNA was measured spectrophotometrically with the NanoDrop One Spectrophotometer (Thermo Scientific Inc., Wilmington, USA). Although sediment mass per sample was not directly measured, dsDNA (double-stranded DNA) concentration was fluorescently quantified using a Qubit\u0026trade; Flex fluorometer (Invitrogen) with Qubit dsDNA HS (High Sensitivity) Assay Kit, and samples were diluted to the same final concentration (20 ng/\u0026micro;l), and stored at -20\u0026deg;C. Automated ribosomal intergenic spacer analysis (ARISA) was used for detection of genetic diversity of bacterial communities in the water samples. The ITSF/ITSReub [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] primer set with 6-FAM fluorescent dye on the 5\u0026acute; end of the reverse primer was used for amplification of the 16S-23S rRNA intergenic transcribed spacer (ITS) region from the bacterial rRNA operon. DNA amplification of bacterial communities was carried out in 50 \u0026micro;L reaction mixture containing 1\u0026times;PCR buffer (Invitrogen, Thermo Fisher Scientific Inc., Waltham, USA), 1.5 mmol Mg\u003csup\u003e2+\u003c/sup\u003e, 0.25 \u0026micro;mol of both primers, 200 \u0026micro;mol of each dNTP (Invitrogen, Thermo Fisher Scientific Inc., Waltham, USA), 1 U Taq DNA polymerase (Invitrogen, Thermo Fisher Scientific Inc., Waltham, USA), and 1 \u0026micro;L (20 ng) of DNA extracted from the rhizosphere. The PCR was performed in a GeneAmp PCR System 9700 (Applied Biosystems, Thermo Fisher Scientific, Inc., USA) using the following conditions: initial heat denaturation at 94\u0026deg;C for 3 min, followed by 35 cycles each consisting of a denaturation step at 94\u0026deg;C for 45 s, annealing at 60\u0026deg;C for 1 min, extension at 72\u0026deg;C for 2 min and a final extension step at 72\u0026deg;C for 10 min. PCR amplification was confirmed by horizontal electrophoresis on a 1% (w/v) agarose gel in 1\u0026times;TBE buffer pre-stained with 0.10 \u0026micro;L/mL of ethidium bromide and visualised using UV illumination. PCR products were precipitated with ethanol and dissolved in 10 \u0026micro;L of sterile water. One microliter of purified products was added to 9 \u0026micro;L formamide containing LIZ1200 size standard (Applied Biosystems, Thermo Fisher Scientific, Inc., USA), denatured at 95\u0026deg;C for 3 min and separated by capillary electrophoresis using ABI 3100 Prism Avant (Applied Biosystems, Thermo Fisher Scientific, Inc., USA). Outputs from ARISA in the form of electropherograms were analysed by the Peak Scanner 2 (Applied Biosystems, Thermo Fisher Scientific Inc., Wilmington, USA), and OTUs (Operational Taxonomic Unit) in range 200\u0026ndash;1000 bp were used for the evaluation. Only peaks above the threshold of 50 fluorescence units were considered.\u003c/p\u003e\u003cp\u003e\u003cem\u003eStatistics\u003c/em\u003e\u003c/p\u003e\u003cp\u003eStatistical analyses were performed using Statistica Ver. 12 software. We used PCA to determine the relationship between sites with elements and sites with bacteria.\u003c/p\u003e\u003cp\u003e\u003cem\u003eMolecular data statistics\u003c/em\u003e\u003c/p\u003e\u003cp\u003eARISA profiles were used to define operational taxonomic units (OTUs) based on unique fragment lengths, with each distinct peak representing a putative genetically distinct bacterial population. A bin size of \u0026plusmn;\u0026thinsp;1 bp was applied to account for minor shifts in fragment mobility. To minimise bias due to varying fragment counts, the ARISA dataset was rarefied prior to the statistical analyses. The rarefaction threshold was set according to the sample with the lowest number of detected peaks. Statistically significant differences among groups of samples were tested using ANOVA at the 95% confidence interval for the means and subsequently. The post hoc LSD test using the software Statgraphics x64 (Statpoint Technologies, Inc., Warrenton, USA). The Venn diagram was generated using the online Venn diagram tool provided by the Bioinformatics \u0026amp; Evolutionary Genomics (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioinformatics.psb.ugent.be/webtools/Venn/\u003c/span\u003e\u003cspan address=\"https://bioinformatics.psb.ugent.be/webtools/Venn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. Alpha diversity indices (Chao-1, Simpson, Shannon, and Evenness) were calculated from standardized profiles of individual samples using the number and height of peaks in each profile as representations of the number and relative abundance of phylotypes. It is important to note that ARISA does not provide taxonomic identification; thus, the diversity indices (e.g., Chao-1) reflected fragment-defined OTU richness rather than true species richness. Bacterial communities in different samples were compared from ARISA profiles using the height of fluorescence in individual OTUs. These data were subsequently used for the principal component analysis (PCA) with a correlation matrix. These settings were also combined with alpha diversity indices from molecular data and chemical element values to construct PCA plots. Alpha diversity indices and PCA were evaluated by using the PAST (Palaeontological Statistics) software version 3.19 [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eBacterial diversity\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe alpha diversity indices (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) indicate that lakes exhibited the highest species richness (Chao-1) and the highest overall diversity (as measured by Shannon, Simpson, and Evenness indices). These indices suggest that bacterial communities within lakes were well-distributed and diverse. Rivers also showed high diversity (Shannon, Simpson, Evenness), comparable to that of lakes, but their species richness (Chao-1) was moderate, suggesting a slightly lower number of species. In contrast, glaciers demonstrated lower species richness (Chao-1), and diversity (Shannon, Simpson) compared to lakes and rivers. The species evenness in glaciers was medium, which implies that certain species could dominate in the community. Glacial rivers had the most variable values across nearly all indices (Chao-1, Shannon, Simpson), indicating low bacterial diversity in some samples and the dominance of a few species. Meanwhile, sedimentary lakes had high species richness (Chao-1) but exhibited low evenness (Evenness), suggesting that a limited number of species dominate these environments. The diversity values measured by Shannon and Simpson were medium in comparison to other aquatic habitats.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAccording to the Venn diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea), the lake habitat contained the highest number of unique taxa, with 82 detected. This suggests a high level of specificity for microbial communities in that environment. The glacier habitat follows, with 43 unique taxa. Sedimentary lakes and rivers showed comparable numbers of unique bacterial taxa, with 28 and 26, respectively. In contrast, the glacial river had only 15 unique taxa, indicating lower species specialization relative to the other ecosystems. In terms of shared bacterial taxa among different habitats, the glacial river shared few taxa with other environments, which could reflect its extreme conditions. Conversely, the most shared taxa were found between lake and river habitats (14 shared taxa) and between lake and glacier habitats (10 shared taxa). Notably, we did not identify any bacterial taxa that were common across all habitats. This variability of bacterial taxa between individual samples is also evident from Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eBacterial diversity and chemical elements\u003c/em\u003e\u003c/p\u003e\u003cp\u003ePrincipal component analysis (PCA) was used to evaluate the relationships between bacterial alpha diversity, DNA concentration and chemical element content in water samples from different types of aquatic environments (lakes, rivers, glaciers, glacial rivers and sedimentary lakes). The results showed that the first four principal components with eigenvalues higher than 1 were significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). PC1 explained 39.0% of the variability and correlated with the increase in the concentrations of elements such as Rb, Fe, Mn, K and Ba, which had high positive loadings on PC1 (Rb: 0.8996, Fe: 0.93156, Mn: 0.90684, K: 0.84095, Ba: 0.89403). DNA concentration also had a high positive loading on PC1 (0.7098), indicating its importance in explaining the variability of the data and the dependence of synergic amount of Rb, Fe, Mn, K and Ba on the amount of extracted DNA concentration in a sample. Conversely, bacterial diversity indices (Chao-1, Shannon, Simpson) had negative loadings on PC1 (e.g. Simpson: -0.42315, Shannon: -0.59845, Chao-1: -0.57411), indicating that higher PC1 values were associated with lower bacterial diversity represented by alpha diversity indices. In other words, samples with high DNA concentrations and high levels of the above chemical elements had low bacterial diversity, and vice versa, samples with lower concentrations of DNA had higher bacterial diversity indices. Thus, the variability on the PC1 axis is significantly influenced by the concentration of DNA in nature and the ability to detect it quantitatively in the laboratory. However, it is significant that the diversity of bacteria decreases in those samples where more Rb, Fe, Mn, K and Ba were present simultaneously. PC2 explained 26.8% of the variability and correlated with element concentrations such as Ni, Cr, Mo, and Hg, which had high positive loadings on PC2 (Cr: 0.87581, Ni: 0.88205, Mo: 0.89487, Hg: 0.705). Thus, PC2 is the main vector to characterize the co-occurrence of heavy metals (Cr, Ni, Mo, Hg), as their concentration is independent of the DNA concentration, but the bacterial diversity in these samples tends to decrease. This is a significant phenomenon indicating the dependence of lower bacterial diversity in samples with higher heavy metal contamination. PC3 explained 15.2% of the variability in the data. Variables with high positive loadings on PC3 were the bacterial diversity indices Shannon (0.61263), Simpson (0.55548) and Chao-1 (0.69064). This suggests that PC3 revealed variability in the data, which was primarily associated with differences in bacterial diversity. Samples with high PC3 values were characterized by high bacterial diversity. Conversely, evenness had a high negative loading on PC3 (-0.75192), indicating that a high PC3 value was associated with low evenness of species representation. PC4 explained 7.7% of the variability in the data. Variables with high positive loadings on PC4 were evenness (0.50763) and Cr (0.34986). This suggests that PC4 revealed variability in the data, which was associated with the evenness of species representation and chromium concentration. Samples with high PC4 values were characterized by high evenness and higher chromium concentrations. DNA concentration had a negative loading on PC4 (-0.33019) as well as mercury (-0.38673), suggesting that a high PC4 value could be associated with lower DNA and mercury concentrations. Samples from which a higher concentration of DNA was extracted had more mercury and less chromium and vice versa. Samples with proportionally more chromium and less mercury were likely to be dominated by some bacterial species (high evenness).\u003c/p\u003e\u003cp\u003eThe PCA visualization (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) showed the clustering of samples according to their origin at the mentioned first two components. Lake samples (green dots) were in the lower left quadrant, with their distribution influenced by higher diversity values (Shannon index, Simpson index and Chao-1). River samples (red dots) were close to lake samples, indicating a similarity in bacterial communities in these indices. Glacier samples (blue dots) were clustered on the right side of the graph, indicating different chemical properties, especially higher Fe, Mn, Rb, Ba, and K, as well as being associated with higher DNA concentration. Glacial river samples (orange dots) showed a variable distribution, with their chemical characteristics influenced by the input of elements such as Cr, Ni, Mo and Hg. Sedimentary lakes (brown dots) were distinguished by high values on PC2, indicating an increased content of metals such as Hg, Cr, Mo and Ni. These samples also showed slightly lower values of DNA concentration than those in the glacier, indicating that DNA extraction in environments rich in particulates did not experience significant inhibition. Additionally, the relatively high alpha diversity indices observed in sedimentary lake samples suggest that any potential PCR inhibition present did not significantly affect downstream amplification.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eEnvironmental similarity - chemistry and bacterial communities\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe PCA constructed from chemical element concentration data alone indicates the occurrence of the two most important phenomena in glacial valleys. Statistically, the phenomena could be independent with different evolutionary histories. The most significant phenomenon (PC1, 56.4% of data variance) is the synergistic accumulation of Mn, Ca, Rb, K, Ba and Fe. Especially in glaciers (dark blue dots in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The second phenomenon is a rather significant accumulation of manganese (PC2, 27.8%), manifested mainly in high mountain sedimentary lakes just below glaciers (yellow dots). The environment constructed based on the first two axes by chemical elements has its analogy in the occurrence of bacterial communities (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e), (i.e., in the PCA constructed from bacterial fragments). Analogous environments could be detected by phenomena determined by components PC6 (x-axis, Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) of the PCA and PC9 (y-axis). On the x-axis, glaciers were significantly different from sedimentary lakes, and on the y-axis, sedimentary lakes were significantly different from glacial lakes. We observed that the dominant chemical environment of glacial valleys correlated with the spatial patterns of bacterial community structure.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eLarge amounts of trace elements entering the atmosphere from anthropogenic emissions can have an adverse effect on the environment. In high mountain areas, glaciers play an important role for pollution accumulation. The southeastern mountainous territory of Kazakhstan and its glaciers is influenced by Kazakhstan's economy, based on the extraction of mineral resources such as uranium, chromium, zinc, and manganese. The country's strong industrial development in mining, smelting and energy, leads to relatively large emissions of air pollutants [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e] also affecting the Zhongar Alatau Mountains.\u003c/p\u003e\u003cp\u003eAnalysis of bacterial diversity in different aquatic habitats showed significant differences in species richness, evenness and overall diversity. Although ARISA provides high-resolution fingerprinting of microbial communities, its fragment-based nature means that inferred OTUs are not taxonomically resolved. Therefore, we considered diversity interpretations in the context of community-level genetic variability, not as precise taxonomic richness. Lakes showed the highest bacterial species richness and are probably home to more stable and complex microbial communities compared to other habitats. Sedimentary lakes just below the glaciers were also characterized by high species richness and diversity of bacteria but had very low community equilibrium. Low evenness may indicate the dominance of certain bacterial species. River samples showed similar diversity values to lakes but with lower species richness. Even though lakes and rivers within the study area may be connected through surface or subsurface flow, the number of shared OTUs between these habitats was limited. This may be attributed to selective environmental pressures, differing sediment loads, glacial influence, and local biotic interactions. It also reflects the fragment-based resolution of ARISA, which may not detect subtle taxonomic overlaps if fragment sizes coalesce. Conversely, glaciers and glacial rivers showed overall lower diversity. The subglacial environment is consistently dark [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] making it fundamentally different from the glacial meltwater environment [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Bacteria in meltwater can originate from the glacier or the surrounding environment, being transported by wind or snow from nearby aquatic and terrestrial sources bound to dust or organic particles [\u003cspan additionalcitationids=\"CR55\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. The diverse bacterial communities in glacial lakes and the pattern of their distribution play a key ecological role in biogeochemical cycling and mineral nutrient cycling. Seasonal changes, including air temperature, nutrient concentrations, and solar radiation, influence microbial community structure by favouring only those species that can cope with environmental stresses [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. The study of Bradley \u003cem\u003eet al\u003c/em\u003e [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e] highlights that glacier and ice sheet surfaces are home to diverse microbial communities whose activity directly influences biogeochemical cycles and ice melting. A crucial aspect of their survival is dormancy, allowing them to rapidly reactivate upon thawing, showcasing their adaptability to extreme and dynamic conditions. This research emphasizes that these surface environments and their microbial inhabitants are highly vulnerable to climate change that may lead to shifts in those communities.\u003c/p\u003e\u003cp\u003eSedimentary lakes, which had high alpha diversity of bacterial communities (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), are essential for understanding biodiversity responses to glacial retreat. Distance between glacial lakes and the glacier appears to be key as a driver of microbial diversity in lakes [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Analysis of common and unique bacterial communities revealed that the highest number of unique bacterial groups was found in lake environments, whereas the number of unique bacterial groups was very low in glacial rivers. This low diversity and species specificity may be attributed to significant physicochemical stressors such as low temperatures or high influx of adjacent glacial debris from melting glaciers [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Glacier-derived bacteria can contribute significantly (40\u0026ndash;96%) to the stream microbial communities of a glacial lake, but their contribution decreases with increasing distance from the glacier terminus [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Liu \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] found that the lake closest to the retreating alpine glacier had higher diversity than other lakes. Melted glacial water contains particles that cause high turbidity, reducing the amount of photosynthetically active radiation [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Further, containing high concentrations of nutrients, lakes fed by this water have the potential to be unique ecosystems and hotspots of nutrient cycling [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Lake sediments offer a solid, nutrient-enriched surface that is readily colonized by microbes, while water has the opposite effect, dissolving potentially toxic elements in the water and further reducing microbial diversity [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGuo \u003cem\u003eet al.\u003c/em\u003e [84] discus that increased glacier melting due to global warming expands lake areas, and the massive influx of glacial meltwater significantly affects microbial assemblages. This occurs through direct export of microorganisms from glaciers and indirectly by altering abiotic factors like turbidity, which limits light penetration and impacts primary producers. Critically, as glaciers retreat and their meltwater influence diminishes, the microbial sources for these lakes are expected to shift towards non-glacial streams, leading to major changes in the physicochemical characteristics and microbial communities within the lakes. The study also found that bacterial abundance and diversity increased with meltwater during melting seasons, and that factors like pH, conductivity, and temperature significantly influence bacterial distribution.\u003c/p\u003e\u003cp\u003eRemarkably, no bacterial taxon was found to be common to all habitats studied, indicating high ecosystem specificity of microbial communities. Ilahi \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e] observed a contrasting phenomenon in their samples collected from the Golen Valley in northern Pakistan. Although there were differences in ecological parameters within the study area, the differences in bacterial diversity were not significant. Their research indicated that bacterial diversity remained relatively stable in lake debris and in meltwater at the glacier margin.\u003c/p\u003e\u003cp\u003eOur study documents that concentrations of different chemical elements are important (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) environmental factors affecting bacterial communities. Analysis of habitat stratification, like chemistry, (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) showed that the most comparable environment may be found between concentration of chemical elements and occurrence of specific bacterial communities (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Positive or negative existence of specific bacterial communities on glaciers in relation to glacier chemistry has been observed in other mountain ranges [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Accumulation of heavy metals in high mountain pristine glacial-fluvial environments is the result of natural weathering [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Rock and soil dust is the dominant source of iron and barium and an important source of manganese [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Manganese variations, for which rock and soil contributions are also important, have been found to closely parallel barium variations.\u003c/p\u003e\u003cp\u003eLarge numbers of bacteria beneath glaciers play an important role in chemical weathering and carbon cycling processes [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan additionalcitationids=\"CR69 CR70\" citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Abakumov \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e], who investigated a glacier in the Caucasus, found that sediments near the glacier were the main source of contamination. According to Ilahi \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], iron, zinc, lead and copper are important driving factors in shaping microbial diversity in the glacier ecosystem. In addition, the geochemistry of bedrock and glacial soil directly influences the chemosynthetic properties of microbial communities [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. Wu \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e], who investigated glacial catchments in the northeastern region of the Tibetan Plateau, found that, rubidium, molybdenum, barium, chromium, nickel, and other heavy elements were mainly derived from aeolian dust, riverbed sediments, and soil. Moreover, the presence of a select and narrow group of related, but not identical, microorganisms in glaciers is a consequence of the severe constraints that this environment poses to bacteria (desiccation, freezing, high pressure, and low nutrient and oxygen concentrations), which act by selecting for similar phylogenetic groups [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA particular phenomenon in glacial melting today is the increased abundance of mercury [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. Our data confirm that higher mercury concentrations in glacial systems can limit bacterial growth and diversity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Several studies from Tibet have shown that the levels of total mercury (THg) concentrations in glacial snow on were higher in the northern than the southern region [\u003cspan additionalcitationids=\"CR77\" citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. Loewen \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e] suggested that particulate matter played an important role in the atmospheric transport and deposition of mercury over western China. Deposition of atmospheric mercury associated with dust particles is reported as a major factor influencing the distribution and concentration levels of mercury in glaciers [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eGlacier dynamics associated with elemental concentration and its interaction with the bacterial fragment is a complex topic that remain an understudied area of research. Elements accumulate in glacial systems in multiple ways and have different effects on the microbiota in an already challenging environment. The aim of this study was to investigate this topic in the Zhongar Alatau Mountains. The glaciers proved to be a reservoir of trace elements such as manganese, calcium, rubidium, potassium, barium and iron, but had low species diversity with only a few dominant species. On the other hand, glacial lakes have the highest diversity and less accumulation of elements. However, in the future, the continuous melting of glaciers may lead to an increase in flow and a complete alteration of glacial cycles, leading to a change in bacterial structures and a proliferation of elements.\u003c/p\u003e"},{"header":"Statements and Declarations ","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data are available on request from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eViviroli, D., Weingartner, R., and Messerli, B. (2003). 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DOI: 10.1093/femsec/fiae018\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"microbial-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meco","sideBox":"Learn more about [Microbial Ecology](https://www.springer.com/journal/248)","snPcode":"248","submissionUrl":"https://submission.nature.com/new-submission/248/3","title":"Microbial Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Heavy metals, Glacier, Glacial water, Mountain Lake, Bacterial fragments, Zhongar Alatau","lastPublishedDoi":"10.21203/rs.3.rs-7176803/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7176803/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study presents the results of the relationships between elemental and microbial structure (bacterial fragments) in the glacial region of the Zhongar Alatau Mountains. Water samples were collected from selected sites of the glacial system and subsequently filtered. The highest diversity and species richness of bacteria was found in glacial lakes and the lowest in glaciers. The species evenness indicates that there is a certain dominant bacterial species in glaciers. The species richness of bacteria was positively correlated with the concentration of biotic chemical elements. Community evenness or balance was positively related to chromium concentration but negatively related to mercury concentration. We confirmed synergistic accumulation of manganese, rubidium, potassium, barium and iron in glaciers. Bacteria varied significantly in subglacial moulds. Bacterial communities from glaciers and sediments of subglacial lakes differed significantly. All these factors may be related to anthropogenic pollution; glacier melt and dust storms in the study area. Investigations of bacterial communities and concentrations of chemical elements in the waters of glacial valleys of Zhongar Alatau show that synergistic concentrations of some chemical elements have their analogy in the occurrence of bacterial communities. Finally, we found that the primary chemical environment of glacial valleys correlated with spatial patterns of bacterial community structure.\u003c/p\u003e","manuscriptTitle":"Dynamics of heavy metals and microbiota in glacial systems of Zhongar Alatau National Park, Kazakhstan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-25 15:28:00","doi":"10.21203/rs.3.rs-7176803/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-15T23:27:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-14T06:45:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-10T20:12:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-28T08:52:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"123204037902460352262499827292138442286","date":"2025-08-18T07:51:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"318059461157198710344470205835712195880","date":"2025-08-15T07:25:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"222744865910417568739016954182190941023","date":"2025-08-14T22:47:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"132815582359565626631127392427481801715","date":"2025-08-05T08:56:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-23T01:51:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-22T12:31:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-22T12:30:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Microbial Ecology","date":"2025-07-21T11:11:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"microbial-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meco","sideBox":"Learn more about [Microbial Ecology](https://www.springer.com/journal/248)","snPcode":"248","submissionUrl":"https://submission.nature.com/new-submission/248/3","title":"Microbial Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b804e427-fad8-41a5-8177-00075bc2ef71","owner":[],"postedDate":"July 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-02T16:14:47+00:00","versionOfRecord":{"articleIdentity":"rs-7176803","link":"https://doi.org/10.1007/s00248-025-02674-2","journal":{"identity":"microbial-ecology","isVorOnly":false,"title":"Microbial Ecology"},"publishedOn":"2026-01-27 15:58:06","publishedOnDateReadable":"January 27th, 2026"},"versionCreatedAt":"2025-07-25 15:28:00","video":"","vorDoi":"10.1007/s00248-025-02674-2","vorDoiUrl":"https://doi.org/10.1007/s00248-025-02674-2","workflowStages":[]},"version":"v1","identity":"rs-7176803","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7176803","identity":"rs-7176803","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}