1. Introduction
Climate warming is becoming increasingly critical, most notably at high latitudes [
1]. Anthropogenic impacts exacerbate climate change, increase the vulnerability of ecosystems, and have complex implications for various aspects of human life [
2]. Peatlands have a special place in climate change mitigation and adaptation problem solutions [
3], which lead on land in terms of carbon stocks, outstrip all other ecosystems in terms of carbon stocks per unit area, influence greenhouse gas fluxes [
4], and are carriers of specific biodiversity [
5,
6].
The conditions in the north (excess of precipitation over evaporation, presence of permafrost) promote peatland formation. The short vegetation period that is compensated by extended daylight provides sufficient ecosystem productivity. Plant remains that have not fully decayed contribute to peat formation. Thermokarst, frost heaving, and frost cracking form the morphological diversity of peatlands and shallow peat landscapes in the Arctic [
7]. The presence of permafrost and, hence, insignificant summer thawing of the soil limits the biogeochemical processes to the upper soil layers, so, in the Arctic conditions, shallow peatlands are functionally close to oligotrophic mires [
8]. Even though the Conservation of Arctic Flora and Fauna working group classifies watershed peatlands, including shallow peatlands, under the tundra category, peatlands occupy 12.3%, and, together with shallow peatlands (peat less than 30 cm), 34.8% of the Russian Arctic [
9]. They carry critical ecosystem functions, such as greenhouse gas fluxes regulation, protection of the permafrost from thawing, maintenance of the water balance, and diversity of the biota. At the same time, due to the small peat depth, they are the least resistant to anthropogenic mechanical impacts [
3]. Disturbance or loss of vegetation cover can quickly lead to peat soil destruction, water, and wind erosion and further peat decomposition. Shallow tundra peatlands in the Arctic are underestimated in terms of their extent, ecosystem functions, services, and robustness against climate change and human impacts [
8]. Shallow tundra peatlands in the Arctic are underestimated in terms of their extent, ecosystem functions, services, and robustness against climate change and human impacts [
8]. That is one of the reasons that studies insufficiently cover their structural and functional characteristics.
Soil microbiomes play an essential role in soil biochemical processes [
10]. Microorganisms are responsible for both the decomposition and formation of organic matter, as well as ensuring the cycling of nutrients in the soil. These processes are crucial for the regeneration of disturbed ecosystems of the Arctic [
11]. The composition of microbial communities is decisive for ecosystems’ carbon balance and greenhouse gas (GHG) exchange in the rapidly changing environment [
12]. Hence, the microbial composition could be an informative indicator for assessing ecosystem disturbances and regeneration status in relation to the key ecosystem functions.
The connections between vegetation, hydrology, soil properties, and microbial communities have been examined in a number of studies carried out in the boreal [
13,
14] and frozen Arctic peatlands [
15,
16,
17], while, to date, there is practically no research on the interconnections between biophysical features of ecosystems and microbial community structure in the natural shallow peat tundra and in disturbed landscapes in the Arctic zone.
For practical needs of ecological indication, it would be helpful to designate whether the changes in microbial communities’ diversity characteristics follow the biophysical attributes in the course of regeneration changes. The latest publications prove that modern molecular approaches allow for capturing less abundant and uncultured microbial taxa [
18,
19].
We hypothesized that anthropogenic disturbances of the shallow tundra peatland would modulate the composition of the soil resident microbiome and lead to different patterns of bacterial community shifts according to the gradient of environmental conditions and anthropogenic disturbance. Therefore, the present study aimed to determine if the structure of soil microbial communities reflects the level of disturbance/regeneration in the transformed shallow peatland ecosystems in the Arctic and evaluate their indication capacity.
2. Materials and Methods
2.1. Study Region
The research object is located in the Nenets Autonomous Okrug in the delta of the Pechora River, Barents Sea basin, on the territory of Nenets State Nature Reserve (
Figure 1). The climate is a moderately cold Arctic climate. The average annual temperature is −2.4 °C, with an average maximum of 18.9 °C and absolute maximum of 33.9 °C in July and an average minimum of −21.1 °C and an absolute minimum of −47.9 °C in January. The mean annual precipitation is 516 mm and ranges from a minimum of 257 mm to a maximum of 706 mm.
The study area is located in the southern tundra belt and from the point of mire zoning in the Arctic seepage and polygon mire region [
20]. This mire region is characterized by the presence of frozen polygon mires in watersheds and patches of lowland fens in river valleys, as well as other watercourses where permafrost is deep or absent. Occasionally, isolated patches of palsa mires, often degraded, appear on the slopes of river valleys and closer to the coast, where drainage is better [
22]. Frozen polygons are usually covered by shallow peat; morphologically and by vegetation, it is often challenging to distinguish polygon mires from polygon shallow peat tundra. The shallow peat tundra covers most of the transgression terraces and lasts up to 50 km from the Barents coast towards the continent. It is found along the coast and in the river deltas on the residual fragments of the terraces. The region’s peatlands are significantly less disturbed by human activities than European peatlands, and the degree of degradation is about 1% [
22]. At the same time, the development of infrastructure and transport in this region leads to violations of vulnerable Arctic ecosystems, which is especially dangerous under current climate conditions [
23]. The presence of sandy bedrocks in the coastal transgression landscapes and deltas makes the landscape degradation almost irreversible. Shallow peatlands get replaced by sandy dunes that carry different ecosystem functions and services [
8].
2.2. Study Sites and Objects
The site under study, the Kumzha site (68°11′ N 53°47′ E, CALM R24A-2), is the part of the slightly elevated (4–10 m asl) remains of the young first inland alluvial terrace (I am) that used to be covered with the relic inland southern tundra vegetation and shallow peat cover. The bedrock is composed of thawed and frozen sand with clay and peat inclusions. An example of the intact peatlands and shallow peat tundra on the residual terrace can be found on Kashin Island (68°14′ N 53°51′ E, CALM R24A—[
19]), which is also a remnant of the first terrace and maintained in its natural status by the Nenets Nature Reserve [
20].
The terraces’ fragments cover not more than 5% of the delta area and are valuable habitats under solid anthropogenic pressure, given that they are handy places used for the development of all types of infrastructure, including traditional use and recreation (hunting and fishing facilities), and use by the oil and gas industry. Specifically, the Kumzha site was used as a natural platform for condensed gas exploration in the late eighties of the 20th century.
The exploration installations included two rows of ridges bounding the drilling pad, depressions from which the ground for artificial ridges was taken, two drilling wells, underground sludge pits for drilling waste water storage, plots cleared for camps and storage facilities, unpaved roads from local ground, and decking roads (
Figure 1).
Since the end of the exploration works, the area has been under natural regeneration and interrupted by several interventions for rehabilitation. In 2008, the technical stage of site rehabilitation aimed at removal of metal trash brought additional disturbances to the ecosystem. In 2014, several experimental sites for ecosystem restoration were set up. In 2016, the drilling pad wellheads were removed, and routine rehabilitation was implemented, such as ploughing and grass sawing. The site was the subject of detailed study in the framework of the Circumpolar Active Layer Monitoring (CALM) program [
24] and ecological restoration experiments supported by the UNDP program [
23,
25].
The typical initial landscape structure of the study site was retrieved from the undisturbed area, e.g., on the Kashin Island. The concept of peatland structure diversity was applied to identify and describe initial spatial landscape units at the level of micro-landscapes [
12]. Further, the disturbed and relevant reference micro-landscapes were paired by predicted successional connections.
The following three classes of micro-landscapes were identified at the natural site: flattened areas parts on the top of the island and flat terraces of the slope are covered by ombrotrophic mires or bogs with peat depth of more than 50 cm (half a meter) and the dense cover of sphagnum mosses, lichens, small sedges, and dwarf shrubs; the drained gentle slopes covered with shallow (less than half a meter) peat and lichen moss dwarf shrub tundra; and the poor minerotrophic fen with mesotrophic mire vegetation, including brown mosses, tall sedges, and willows.
The area under study presents a diverse mosaic of patches with different disturbance character and intensity distributed through three main initial landscape types. The following types of disturbances we used for designation of the testing sites: the areal disturbances, that are the most heavy and where both vegetation and peat cover are destroyed at an area rate of more than the first dozens of square meters; the linear disturbances that include all types of roads, ditches, and artificial ridges where vegetation and peat cover are destroyed at the limited locations along the artificial linear structures; and the scattered disturbances that include the areas with sporadic distribution of patches where vegetation and soil are disturbed at the limited space, and self-regeneration is still visible.
The study design was aimed to cover the diversity of habitats that are formed after the transformation of each of three initial micro-landscapes by each of three types of disturbances and reference sites. For lowland micro-landscapes, the ecological restoration was applied. The newly created habitats were also included in the research. Not all combinations were available for study. Eleven sampling plots were chosen to reflect the diversity of the habitats (
Table 1).
The selected plots represented, at the time of sampling (2020), different types of habitats representing stages of vegetation regeneration succession with a wide range of plant communities (
Figure 2). The vegetation cover of the plots was described by common geobotanical methods, and all vegetation species were classified according to the stratification of the plants [
26]. The spatial structure of the vegetation cover was established using the methods of large-scale geobotanical mapping. The vascular plant, moss, and lichen species were preliminarily identified in the field, sampled, and confirmed by the specialists based on the herbarium; the samples are available for verification.
2.3. Sample Collection and Soil Characterization
Sampling was done in August 2020 at the selected sites. The “envelope” inclusive sampling design was applied. The vegetation relevé was bounded by a circle with the diameter of five meters. Triplicate soil samples were taken by cylinder from the depth 0–4 cm; samples were collected by the sampler at 5 × 5 cm in every corner of every of five 50 × 50 cm squares and further mixed. The vegetation relevé included a list of presented vascular species, bryophytes, and lichens with their phenological status, height, and cover, as well as cover and height of every plant community layer. The active layer depth (distance to permafrost roof) was measured by the probe corer at 1.80 m length. In some cases (sites M9, M39, M30, and M24) the deep coring data were available due to the presence of CALM plot. The soil subsamples were homogenized and stored in the cooling incubator (2–5 °C) before being transferred to the laboratory. Samples for DNA extraction were immediately frozen.
The main soil parameters were determined using standard procedures. Values of pH in soil were measured using a pH150 m (1:5 soil: H2O ratio). Bulk density was determined by the soil core method. The dry matter content of the soil was determined by drying the samples (105 °C, 12 h), and OM content was analyzed by loss upon ignition (475 °C, 4 h). Carbon and nitrogen content were measured using a Vario Max element analyzer (Elementar Analysensystem GmbH, Langenselbold, Germany).
2.4. DNA Extraction, qPCR Assays, and Sequencing
We used the Power Soil DNA Isolation Kit (Qiagen, Carlsbad, CA, USA) to extract total soil DNA from 0.25 g of rhizosphere soil samples, following the manufacturer’s protocol. To evaluate the DNA concentration and purity, a NanoDrop 1000 spectrophotometer (Thermo Scientific, Waltham, MA, USA) was used.
The total abundance of bacterial (16S rRNA genes) and fungal (18S rRNA genes) communities was evaluated by quantitative PCR assays (qPCR) with the primers [
27] for bacteria and [
28] for fungi. Briefly, the synthesis of the first cDNA chain from a single-stranded RNA matrix was carried out using MMLV reverse transcriptase (revertase) according to the manufacturer’s recommendations (CJSC Eurogen, Moscow, Russia). The qPCR reactions were carried out in real-time in a PCR buffer-RV (Syntol LLC, Moscow, Russia), in the presence of SYBR Green I and a passive reference dye ROX using the CFX96 Touch Real-time PCR Detection System (Bio-Rad Laboratories, Hercules, CA, USA). The standard curves were prepared using
Escherichia coli for bacteria and
Penicillium chrysogenum for fungi. Each qPCR mix consisted of 4.2 µL sterilized water, 10 µL SYBR green master mix, 0.4 µL of each primer (0.4 pmoles/ µL) and 5 µL of diluted template DNA for a final reaction volume of 20 µL. The PCR program included a polymerase activation stage for 5 min at 95 °C, and the next 40 cycles were 15 s at 95 °C, 45 s at 62 °C.
High throughput sequencing was applied to evaluate microbial diversity. The variable V4 region of the bacterial 16S rRNA gene was amplified using universal primers 515F and 806R (
https://doi.org/10.17504/protocols.io.nuudeww) (accessed on 26 November 2021), and the fungal ITS1 region was amplified with primers ITS5 and 5.8S_fungi [
29], together with linkers and unique barcodes. PCR was performed on a T100 Thermal Cycler device (Bio-Rad Laboratories, Hercules, CA, USA) in 15 µL containing 0.5 units of Q5
® High-Fidelity DNA Polymerase (New England BioLabs Inc., Ipswich, MA, USA), 1X Q5 reaction buffer, 5 pmol of each of the primers, 3.5 mM dNTP (CJSC Eurogen, Moscow, Russia) and 1–10 ng of the DNA matrix. The PCR program included a denaturation stage at 94 °C for1 min, amplification for 35 cycles (94 °C—30 s, 50 °C—30 s, 72 °C—30 s), and final elongation at 72 °C for3 min.
Further analysis was performed following the Illumina protocol (16S Metagenomic Sequencing Library Preparation) on the Illumina MiSeq instrument (Illumina, Inc., Foster City, CA, USA) using the MiSeq Reagent Kit v3 (600 cycles) (Illumina, San Diego, CA, USA) running 2 × 300 bp paired-end reads.
2.5. Sequence Data Processing and Statistical Analysis
Pre-processing of the Illumina sequencing reads included removal of the adapters and indices using the Cutadapt ver. 1.11 [
30], as well as denoising, combining paired reads, and deleting chimeras using the Dada2 package implemented in the QIIME2 package, ver. 2019.7 [
31].
After filtering, high-quality sequences were obtained and included for subsequent analyses. The OTU abundance of each sample was standardized by using the lowest level of sequence depth as a reference. The OTUs were assigned de novo at 97% identity level. The taxonomic classification of the obtained amplicon sequence variants was also performed using SILVA v.138 database containing data for the SSU rRNA genes [
32]. Features with an abundance of less than 10 or that presented in only one sample were filtered out to remove the possible PCR artifacts or chimeras. The OTUs were assigned de novo at 97% identity level. The taxonomic classification of the obtained amplicon sequence variants was also performed using SILVA database containing data for the SSU rRNA genes [
32].
Further processing, including the construction of a phylogenetic tree using the FastTree algorithm [
33], for the calculation of α and β diversity, was performed within the QIIME2 package [
31] and the plugins implemented in it. The diversity indices reflecting the predicted species richness (Faith’s PD) and the degree of evenness (Pielou’s index) were considered to assess the α- diversity.
All measurements were performed in at least three repetitions. For each sample, the mean and standard deviation were calculated in the Excel software product (Microsoft Corp.). Statistical processing was carried out using the Statistica 8.0 program (StatSoft Inc., Tulsa, OK, USA). For paired comparisons, the Student’s criterion (t-test) was used, and the criteria were considered statistically significant at p < 0.05.
4. Discussion
Arctic terrestrial ecosystems are the largest depositories of organic carbon, and the loss of their stability is becoming increasingly real in the light of ongoing climate change. Even a slight warming can lead to the involvement of a significant part of the organic matter buried in the Arctic in the carbon cycle, which will make these ecosystems the largest source of greenhouse gas methane (CH4). At the same time, the activity and structure of microbial communities of these soils can be used to indicate changes under the influence of global warming and anthropogenic impact, as well as the effectiveness of the restoration of disturbed peat bogs in the permafrost zone. Taking into account the insufficient knowledge of the microbial communities of the cryosphere, the newly obtained data will significantly supplement the knowledge on the diversity of microorganisms and will allow us to assess their role in this ecosystem while taking into account the metabolic potential.
The soil cover and its functional characteristics of the permafrost-affected ecosystems of the Arctic and highlands has been investigated in only a few pedological and geophysical studies, which have mainly been devoted to undisturbed landscapes of palsa and polygon peatlands sites [
36,
37,
38]. From the standpoint of methods of microbiological studies in this region, they have been limited to the application of conventional microbiological methods [
39,
40], and several works were performed based on modern molecular approaches [
41]. In this study, we used a high-throughput next-generation sequencing approach, which allowed us to significantly expand the understanding of microbial diversity. Generally, Arctic bacterial communities seem to be dominated by taxa differently than in other biomes, which likely reflects the impact of polar environmental conditions on microbial communities [
42]. Tundra soils are generally characterized as poorly enriched by organic matter, saturated, and poorly aerated. These conditions result in relatively low levels of microbial diversity [
43].
The integrative analysis of data on characteristics of the primary site is reflected in the matrix (
Table 6).
The initial landscapes were rated along the resilience gradient from lower to higher resilience. Less resilient are slopes. The organic layer is, as a rule, thin and, due to the sandy bedrock, could be easily destroyed. The ombrotrophic mire micro-landscapes were resilient at the medium level. In contrast, the most resilient were peatlands at the lower positions, depressions, riverine habitats, etc. that were represented by poor and transition fens.
The revealed differences in the diversity of the bacterial communities prompted us to study mor deeply the differences in the taxonomic composition and relative abundance of bacterial taxa. Beta diversity was analyzed to elucidate major drivers of microbial community composition using the weighted UniFrac distance metric, which uses phylogenetic information to compare environmental samples. Non-metric multidimensional scaling (NMDS) indicated habitat type as the vector responsible for the greatest variability (
Figure 5). Our results showed that the microbial communities of the studied sites demonstrated pronounced clustering for similar habitats types and were clearly separated from others.
The analysis of the relative representation of bacteria showed that a sharp decrease in the content of acidobacteria of the genus
Granulicella and
Candidatus Solibacter could indicate anthropogenic disturbance (
Figure 6).
Data on the composition of methanotrophs and closely related trophic methylotrophic microorganisms can also be an important indicator of disturbance. Methanotrophic Proteobacteria were not found in the studied samples of undisturbed objects. Methylacidiphilaceae are probably responsible for methane oxidation in these soils (
Figure 7). Their high proportion was also registered on the disturbed slopes, where permafrost thaw is very intensive [
24].
At the same time, methanotrophic Proteobacteria Methylobacterium-Methylorubrum and facultatively methylotrophic psychrotolerant bacterium Methylorosula were detected only in the anthropogenically disturbed objects, and the proportion of methanotrophic verrucomicrobia was significantly lower. The data obtained convincingly indicate changes in microbial communities of the methane cycle under anthropogenic influences, which allows us to consider them as indicators for environmental monitoring.
Numerous natural and anthropogenic factors influence soil microbial communities, and these factors must be considered when interpreting microbiome parameters. In this work, we discuss only some of these factors. It should be noted, however, that, in the current study, climatic parameters (such as temperature and precipitation), which can significantly affect the soil microbial community, probably did not impact microbial diversity. This is because all study sites were chosen in the same bioclimatic region and were, therefore, exposed to similar weather conditions.