[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Next Article in Journal
Bacterial Community Structure and Environmental Driving Factors in the Surface Sediments of Six Mangrove Sites from Guangxi, China
Previous Article in Journal
Resistant Rhodococcus for Biodegradation of Diesel Fuel at High Concentration and Low Temperature
Previous Article in Special Issue
Eukaryotic Microalgae Communities from Tropical Karstic Freshwater Lagoons in an Anthropic Disturbance Gradient Microscopic and Metagenomic Analysis
You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Microorganisms
Volume 12
Issue 12
10.3390/microorganisms12122606
microorganisms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Role of Climate and Edaphic Factors on the Community Composition of Biocrusts Along an Elevation Gradient in the High Arctic

by
Isabel Mas Martinez
1,†,
Ekaterina Pushkareva
1,†,
Leonie Agnes Keilholz
1,
Karl-Heinz Linne von Berg
1,
Ulf Karsten
2,
Sandra Kammann
2 and
Burkhard Becker
2,*
1
Department of Biology, Institute for Plant Sciences, University of Cologne, 50674 Cologne, Germany
2
Institute for Biological Sciences, University of Rostock, 18059 Rostock, Germany
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Microorganisms 2024, 12(12), 2606; https://doi.org/10.3390/microorganisms12122606
Submission received: 26 November 2024 / Revised: 10 December 2024 / Accepted: 13 December 2024 / Published: 17 December 2024
(This article belongs to the Special Issue Molecular Ecology of Microalgae and Cyanobacteria)
Browse Figures
Figure 1
<p>Geographical location of sampling sites on Ossian Sarsfjellet, Svalbard. (<b>A</b>)—The red arrow indicates the location of Ossian Sarsfjellet and the red star indicates the location of the weather station in Ny-Ålesund in the Kongsfjorden area. (<b>B</b>)—The red circles indicate the sampling sites. Maps based on TopoSvalbard, courtesy of the Norwegian Polar Institute.</p> "> Figure 2
<p>Temperature data from 13 July 2023 to 4 August 2023 at the three different measurement points across the four sites. T1 = measured 6 cm below the soil surface; T2 = measured at the soil surface; T3 = measured 15 cm above the soil surface; measured every 15 min; measured with TOMST<sup>®</sup> data loggers.</p> "> Figure 3
<p>Soil moisture data from 13 July 2023 to 20 August 2023 across the sites. Measured every 15 min; measured with TOMST<sup>®</sup> data loggers; significant differences observed between all four sites; <span class="html-italic">p</span>-value &lt; 0.05. Data used for statistical analysis are from 13 July 2023 to 4 August 2023 only.</p> "> Figure 4
<p>Non-Metric Multidimensional Scaling (NMDS) plot based on the vegetation analysis conducted at the sites. T1 = temperature measured 6 cm below the soil surface; T2 = temperature measured at the soil surface; T3 = temperature measured 15 cm above the soil surface; C.N = carbon to nitrogen ratio; parameters which did not show significant differences between the sites are not included, <span class="html-italic">p</span>-value &lt; 0.05.</p> "> Figure 5
<p>Overall community composition across sites. Metagenomic 16/18 S rRNA dataset analysed using Silva software; taxonomic groups with total abundance below the 0.5% threshold are grouped as ‘low abundance’; * indicates significant differences between sites based on a one-way ANOVA, <span class="html-italic">p</span>-value * &lt; 0.05, ** &lt; 0.01).</p> "> Figure 6
<p>Relative abundance of fungal taxa and fungal functional guilds across sites. Metagenomic 16/18S rRNA dataset analysed using Silva software. (<b>A</b>)—fungal phyla. (<b>B</b>)—functional guilds; * indicates significant differences between sites based on a one-way ANOVA, <span class="html-italic">p</span>-value &lt; 0.05.</p> "> Figure 7
<p>Relative abundance of photoautotrophic taxa across sites. Metagenomic 16/18S rRNA dataset analysed using Silva software; no significant differences were observed between the taxa, based on a one-way ANOVA, <span class="html-italic">p</span>-value &lt; 0.05.</p> "> Figure 8
<p>Relative abundance of cyanobacterial orders across sites. Metagenomic 16 S rRNA dataset analysed using Silva software; filamentous orders shown in orange/red; heterocystous order shown in green; unicellular orders shown in blue; * indicates significant differences between sites based on a one-way ANOVA, <span class="html-italic">p</span>-value &lt; 0.05.</p> ">
Review Reports Versions Notes

Abstract

:
Biological soil crusts are integral to Arctic ecosystems, playing a crucial role in primary production, nitrogen fixation and nutrient cycling, as well as maintaining soil stability. However, the composition and complex relationships between the diverse organisms within these biocrusts are not well studied. This study investigates how the microbial community composition within Arctic biocrusts is influenced by environmental factors along an altitudinal gradient (101 m to 314 m). Metagenomic analyses were used to provide insights into the community composition, revealing that temperature, pH, and nutrient availability significantly shaped the community. In contrast, altitude did not directly influence the microbial composition significantly. Eukaryotic communities were dominated by Chloroplastida and fungi, while Proteobacteria and Actinobacteria prevailed among prokaryotes. Cyanobacteria, particularly orders such as Pseudoanabaenales, Pleurocapsales, and Nostocales, emerged as the most abundant photoautotrophic organisms. Our findings highlight the impact of environmental gradients on microbial diversity and the functional dynamics of biocrusts, emphasizing their critical role in Arctic tundra ecosystems. Arctic biocrusts are intricate micro-ecosystems, whose structure is strongly shaped by local physicochemical parameters, likely affecting essential ecological functions.

1. Introduction

The Arctic region is characterised by harsh environmental conditions, including low temperatures, short growing seasons and limited water availability, which hinder the growth of numerous organisms. Despite these challenges, biological soil crusts (biocrusts) constitute a substantial proportion of the vegetation in Arctic ecosystems [1]. Biocrusts are communities of autotrophic and heterotrophic organisms, including bacteria, cyanobacteria, microalgae, lichens, and bryophytes that exist in mutual association [2]. They form a thin layer covering the soil surface, typically 5–10 mm thick, and constitute a significant proportion of the total vegetation in Arctic regions. Consequently, biocrusts play a crucial role in primary production and provide a number of benefits including soil surface stabilisation, nitrogen fixation and organic matter accumulation [2,3].
The microbial community composition of biocrusts in Arctic regions encompasses a diverse array of species with distinct ecological functions, shaped by environmental and soil conditions. However, the dominant phyla are often consistent, with typical bacterial phyla including Actinobacteria, Acidobacteria, cyanobacteria and Proteobacteria [4]. Photoautotrophic phyla such as eukaryotic green algae, bryophytes, tracheophytes and cyanobacteria play an important role in such communities, driving primary production within biocrusts [1,3]. The algal classes most commonly found in biocrusts include Klebsormidiophyceae, Zygnematophyceae, Chlorophyceae, Trebouxiophyceae and Ulvophyceae, while the most frequently occurring cyanobacterial orders are Nostocales, Oscillatoriales and Pseudanabaenales [5,6]. In Arctic regions, the combination of low temperatures, low soil moisture due to minimal precipitation, and limited soil nutrients restricts primary production [7]. Lichens, symbiotic associations of fungi and algae (e.g., Trebouxia sp.), are well adapted to these harsh conditions and become more prominent as the climate becomes drier [8]. In addition, nitrogen fixation by bacteria is a critical source of nitrogen in Arctic ecosystems. For example, heterocystous cyanobacteria contribute significantly to soil nitrogen fixation [9].
Svalbard is a Norwegian archipelago located in the Arctic Ocean. Despite its location between continental Norway and the North Pole, the climate is moderated by the North Atlantic Current. The mean temperature in Svalbard remains below 10 °C throughout the summer, with the warmest months occurring between the end of May and the end of August. The winter months are characterised by a decline in temperature to an average of −10 °C. The precipitation level rarely exceeds 50–60 mm per month [10]. Furthermore, Svalbard’s northern geographic location results in extreme variations in daylight throughout the year, with approximately 90 days of polar night in winter, characterised by continuous darkness, and 90 days of midnight sun in summer, during which the archipelago experiences uninterrupted sunlight [11].
The flora of Svalbard is diverse, comprising approximately 160 species of vascular plants, 380 mosses and 600 lichens [12]. The vegetation is predominantly characterised by low-growing species that form dense ground covers, with a variety of plants coexisting in close proximity.
The objective of this study is to enhance our understanding of the microbial community composition of Arctic biocrusts and the climatic and edaphic factors shaping these communities. To achieve this, an altitudinal gradient (101–314 m a.s.l.) was selected as the basis for this study, enabling the investigation of biocrust responses to varying environmental conditions. This gradient reflects the diverse landscape and topography surrounding Kongsfjorden. Kern et al. [13] previously documented significant environmental variation, including soil nutrient levels and water availability, across small spatial scales along the toposequences of Ossian-Sarsfjellet. This study primarily investigates the community composition of photoautotrophic organisms, providing new insights into how microbial communities respond to changing environmental conditions, particularly in extreme Arctic environments. Given the typical changes in environmental conditions with increasing altitude, we hypothesised that harsher environmental conditions at higher altitudes would result in a decline in larger vascular plants, resulting in distinct shifts in microbial community composition. Furthermore, we anticipated that variations in temperature, soil moisture and soil chemistry along the altitudinal gradient would be reflected in the composition of microbial communities.

2. Materials and Methods

2.1. Site Description and Sampling

The sampling sites were located on the slope of the larger mountain on Ossian Sarsfjellet in Svalbard (see Table 2 for GPS coordinates). Four sampling sites, situated at altitudes ranging from 101 m to 314 m a.s.l., were selected for this study at different altitudes (Figure 1). Site 1 was located near Sarsvatnet lake, while Sites 2, 3 and 4 were positioned on the eastern slope of the larger mountain on Ossian Sarsfjellet. The sites were selected in the interior of the island to minimise the influence of the adjacent Kongsfjorden to the west and the glaciers to the east. The soil remained free of snow throughout the project.
Three TOMST® data loggers (TOMST S.R.O., Prag, Czech Republic) were placed in the soil at each site to measure temperature and soil moisture. The temperature measurements were obtained from each sensor at three different positions: 6 cm below the soil surface (T1), the soil surface (T2) and atmospheric temperature 15 cm above the soil surface (T3). Measurements were recorded at 15 min intervals from 13 July 2023 to 21 August 2023. One sensor at Site 1 and all three sensors at Site 2 were removed from the ground due to external factors (most likely animals). Consequently, the data from these covered the period only from 13 July to 4 August 2023. Additionally, data from the meteorological station in Ny-Ålesund (station ID: SN99910) were also used for further analyses.
The minimum fluorescence of the opened reaction centres of photosystem II (PSII) (F0, basal fluorescence of chlorophyll a) and the maximum fluorescence (Fm) were measured with a hand-held field PAM (FluorPen 100 instrument; Photon Systems Instruments, Drásov, Czech Republic) using the OJIP protocol with the following settings: flash pulse 20% and super pulse 80%, after 15 min of dark acclimation. The maximum quantum yield of PSII (QYmax = (Fm − F0)/Fm) was calculated.
Three replicates of the biocrust material were collected at each site on 13 July 2023.

2.2. Vegetation Analysis

The Braun-Blanquet vegetation analysis was conducted on Ossian Sarsfjellet, Svalbard, on 13 July 2023. A total of 12 plots were analysed, with three individual plots of 1 m2 selected at each site. For each plot, individual species were recorded, along with the general layout and appearance of the site. In addition, the coverage of each species was estimated using the scale presented in Table 1. Statistical analyses were conducted to compare the vegetation at different altitudes and identify significant differences in species composition. For statistical purposes, the Braun-Blanquet vegetation scale was converted into numerical values (Table 1).

2.3. Soil Analysis

The chemical analysis of the soil was conducted at the University of Rostock. Samples were dried at 45 °C for 24 h prior to analysis. Total carbon (TC) and total nitrogen (TN) were measured using the Vario EL CNS analyser (Elementar Analysensysteme GmbH, Hanau, Germany). Total phosphorus (TP) was measured following microwave-assisted digestion with aqua regia solution. The concentration was then quantified by inductively coupled plasma (ICP) optical emission spectroscopy using the Optima 8300 ICP-OES Optical System (PerkinElmer, Waltham, MA, USA) at a wavelength of 214 mm.
Soil pH was determined electrometrically in a 0.01 M calcium chloride (CaCl2) solution with a weight/volume ratio (w/v) of 1:2.5, according to DIN ISO 10390 [14]. Measurements were taken after 30 min using a pH meter (METTLER TOLEDO SevenMulti, Giessen, Germany).
The chlorophyll a content was measured by adding 0.1 g of magnesium carbonate (MgCO3) to each sample, and then extracting chlorophyll a in 3 mL of 96% ethanol at 78 °C for 30 min. The concentration was measured using a spectrophotometer (Shimadzu UV-2401 PC, Kyoto, Japan) at wavelengths of 750, 696, 665, 649 and 632 nm. The final chlorophyll a content was calculated according to the given Equation (1).
C h l   a = 0.0604 × A 632 n m − A 750 n m − 4.5224 × A 649 n m − A 750 n m                         + 13.2969 × A 665 n m − A 750 n m − 1.7453 × ( A 696 n m − A 750 n m )  

2.4. DNA Isolation and Sequencing

DNA was extracted from the collected biocrusts using the DNeasy Power Soil Pro Kit (QIAGEN GmbH, Hilden, Germany) according to the manufacturer’s instructions. The DNAs were further sent to the Cologne Center for Genomics (University of Cologne, Cologne, Germany), where the quality control was conducted on an Agilent 2200 TapeStation System (Agilent Technologies, Santa Clara, CA, USA) with the help of TapeStation Analysis Software 5.1. Metagenomic sequencing was conducted using the Illumina NovaSeq6000 sequencing system (PE150) (Illumina, San Diego, CA, USA). The raw reads were deposited in the Sequence Read Archive (SRA) under the project number PRJNA1189472.

2.5. Bioinformatic and Statistical Analyses

The bioinformatic analysis for metagenomics was primarily conducted using the OmicsBox software (Version 3.1.11). FASTQ quality filtering was performed using Trimmomatic (Version 0.38) [15] and rRNA sequences were further extracted using SortMe-RNA [16]. The taxonomic classification of 16S and 18S rRNAs was performed using the Silva database (Version 1.9.10), with default settings and an adjusted cluster sequence identity of 100%. Additionally, the Kraken2 software (Version 2.1.3) was used with a Kraken confidence filter of 0.5 and a minimum hit group of 2 [17].
Statistical analyses were performed in RStudio (Version 4.1.3). Prior to testing, Shapiro–Wilk’s test was performed to assess the normality within the dataset. If necessary, the data were square root transformed. Differences between the individual sampling sites and further parameters were tested with one- and two-way analysis of variance (ANOVA), followed by Tukey’s HSD, with a significance threshold of p < 0.05. To visualise the distribution of reads at the different sites, a Non-Metric Multidimensional Scaling (NMDS) plot was generated, using the vegan package (Version 2.6−6.1) [18]. Additionally, the envfit function was used to fit parameters such as temperature, moisture and soil chemistry. Statistical significance was tested using ANOSIM.
For the vegetation analysis, PERMANOVA (Permutational Multivariate Analysis of Variance) was used to assess differences between the individual sampling sites (p < 0.05). Statistical dispersion was checked to validate the PERMANOVA results, followed by ANOSIM for confirmation.

2.6. Calculation of the Average Dew Point

For the calculation of the average dew point, the following equations were used. Initially, the saturation vapor pressure (es) was calculated using the Magnus–Tetens Formula (2); here, T denotes the daily average temperature [°C], measured 15 cm above the soil surface through TOMST® data loggers.
e s = 6.112 × e ( 17.67 × T T + 243.5 )
Following this, the actual vapor pressure (e) was calculated (3); RH signifies the daily relative humidity [%], measured at Ny-Ålesund station (Station ID: SN99910).
e = R H 100 × e s
Finally, the dew point temperature (Td) was calculated (4), using the previously calculated actual vapor pressure (e).
T d = 243.5 × l n ( e 6.112 )   17.67 − l n ( e 6.112 )  

3. Results

3.1. Environmental Parameters

The data from the weather station in Ny-Ålesund (station ID: SN99910) were used for comparison and to provide an overall picture of the meteorological conditions. Temperature and soil moisture varied between the different sites. Site 2, located at 186 m a.s.l., exhibited the highest temperatures (Figure 2). In contrast, Site 4, situated at 314 m a.s.l., demonstrated the lowest temperatures at all three measurement points (T1, T2, T3). The temperature values recorded above the soil surface exhibited the most pronounced fluctuations throughout the measurement period.
In July and August 2023, winds predominantly originated from the east–southeast direction, with an average speed of approximately 5–6 m/s. Between 15 July and 16 July 2023, weak winds were recorded (3 m/s), which intensified on 17 July 2023, reaching speeds of 9 m/s (Supplementary Figure S1). Considering the geographical location of the four sites, Site 4 experienced the highest exposure, particularly due to its elevated position on the larger Ossian Sarsfjellet mountain.
The highest soil moisture values were observed at Site 4, while the lowest values were recorded at Site 2 (Figure 3). A sudden increase in soil moisture was observed at the beginning of August. A comparison with meteorological data from the weather station in Ny-Ålesund indicates that the observed increase in soil moisture was due to rainfall events during this period. Following the rainfall, the soil moisture levels recorded at Sites 3 and 4 remained elevated throughout August.
The dew point was calculated using the formulas explained in the Section 2 (Formulas (2)–(4)) to explain the highest soil moisture observed at Site 4. It can be assumed that on days when the temperature declines below the calculated dew point, the formation of dew at the location is likely. Consequently, Site 4 had the most days with possible dew formation. Conversely, Site 2 demonstrated the least degree of overlap, in accordance with the measured soil moisture data.
The soil pH of the biocrusts was neutral to slightly acidic at all sites, ranging from 6.13 to 7.23 (Table 2). Significant differences were observed in the pH levels between Sites 1 and 3, which exhibited a pH of approximately 6, and Sites 2 and 4, which demonstrated a pH of approximately 7. Site 3 had the lowest total phosphorus (TP), total carbon (TC) and total nitrogen (TN) contents and the highest carbon to nitrogen ratio (C/N) of 18.6. The chlorophyll a content of the biocrusts did not show any significant changes with increasing elevation (Table 2). Similarly, the quantum yield capacity of photosystem II, (Fv/Fm) measured with a field PAM, remained unchanged (Table 2).

3.2. Vegetation at the Sites

The vegetation at all of the sampling sites was predominantly composed of small vascular plants, mosses, lichens and biocrusts. A total of 26 different species were recorded (Supplementary Table S1). Of these, 12 species could be assigned to eight different families of tracheophytes, ten different species of bryophytes and four different species of lichens. A total of 3 of the 26 species could not be identified. A decline in the total number of species per site was observed, with Site 1 exhibiting the highest number of species (17) and Site 4 the lowest (15). Furthermore, the overall ground cover of the species demonstrated a tendency to decrease with increasing altitude.
The highest ground cover was observed for Dryas octopetala, Sterecaulon alpinum, Salix polaris, Carex sp., Polygonum viviparum and one unidentified lichen species (Supplementary Table S1). Dryas octopetala exhibited the highest ground cover, reaching up to 50% at Site 2. While species such as Dryas octopetala, Sterecaulon alpinum, Salix polaris and Saxifraga oppositifolia were present at all four sites with relatively high cover ratios, Silene acaulis was exclusive to Site 1 and the two bryophytes Thamnolia vermicularis cf. and Dicranella cf. were only recorded at Site 4. The abundance of Tracheophyta exhibited a decline in abundance with increasing altitude (Supplementary Table S1), whereas lichen increased with higher sites. In contrast, the distribution of bryophytes did not show a clear gradient. Instead, similar abundance patterns were observed between Sites 1 and 3, as well as between Sites 2 and 4. NMDS analysis, along with PERMANOVA and ANOSIM significance tests, revealed that the four sites represented different communities. The most important abiotic factors for explaining these differences were temperature, pH, moisture and nutrient contents (Figure 4).

3.3. Sequencing Overview

Metagenomic sequencing generated around 13.5 million initial reads per sample. Following quality filtering, 2.3% were removed (Supplementary Table S2). Approximately 0.2% of the quality-filtered reads consisted of rRNAs, which were used for taxonomic composition analyses.

3.4. Metagenomic Profile of the Biocrusts

Taxonomic classification of the 16S and 18S rRNA reads using the Silva database revealed a diverse range of taxa, with 89.2% of reads belonging to bacteria, 6.6% to eukaryota, 0.1% to archaea and 3.8% of reads unclassified (Figure 5). The highest proportion of bacterial reads was observed in Site 2 (93%), while Site 3 had the lowest number of bacterial reads (87%). The dominant bacterial phyla were Actinobacteria and Proteobacteria. Actinobacteria dominated Site 1 (23.5% of total reads) and Site 2 (29% of total reads), while Proteobacteria were the most prevalent at Site 3 (22.6% of total reads) and Site 4 (26.7% of total reads). Fungi were the dominant eukaryotic phyla at Sites 2, 3 and 4 (1.2, 4 and 3.1% of total reads, respectively), while Chloroplastida dominated Site 1 (3.4% of total reads).
The NMDS analysis and ANOSIM significance test revealed significant similarities between the sites (Supplementary Figure S2), with Sites 1 and 3 displaying the most pronounced similarities. The observed similarities were mainly driven by environmental parameters, particularly temperature, soil moisture, pH, total nitrogen and total phosphorus, and C/N ratio.
The analysis of fungal rRNA reads revealed a clear dominance of Ascomycota, accounting for up to 83% of fungal reads at Site 3 (Figure 6A). Basidiomycota represented an average of 12% of fungal reads, while Mortierellomycota contributed to approximately 9% of fungal reads. Functional guild analysis revealed a prevalence of saprotrophic (30% of fungal reads at Sites 3 and 4) and lichenised fungi (23% of fungal reads at Site 2) (Figure 6B). Additionally, 14 indicator taxa were identified (Supplementary Table S3A). Nine of these taxa were recorded at Site 3, belonging to the Ascomycota (Epiglia, Sporoschismopsis, Rhytisma, Ascosphaera, Leotia, Ochrolechia, Umbilicaria) and the Mucoromycota (Mortierella). Two indicator taxa from the Basidiomycota and two indicator taxa from the Ascomycota were identified at Sites 1, and 4, respectively (Supplementary Table S3A).
The sequences for photoautotrophic taxa, extracted from the 16S and 18S datasets, showed that approximately 6% of all organisms recorded at the sites were classified as primary producers. Cyanobacteria were the most prevalent group at all sites, representing up to 84% of photoautotrophic reads, and exhibited no significant correlation between abundance and altitude (Figure 7). In contrast, the majority of algal groups, bryophytes and tracheophytes demonstrated a pattern of declining abundance at higher elevations. Site 2 exhibited a comparatively low abundance of eukaryotic photoautotrophic species (Figure 7). However, the green algae family Trebouxiophyceae showed a non-significant increase in abundance with increasing altitude from 4% to 8% of photoautotrophic reads, with the highest abundance observed at Site 3. A total of nine indicator species were identified among all photoautotrophic taxa, of which seven were identified as Cyanobacteria (Phormidium, Hormoscilla, Leptolyngbya and Loriellopsis) (Supplementary Table S3B).
The cyanobacterial species were predominantly composed of Pseudoanabaenales, accounting for up to 40% of the total cyanobacterial population, followed by Nostocales and Gloeobacterales (Figure 8). In general, filamentous species constituted the majority of the cyanobacterial population, with approximately 48% of cyanobacterial reads at Sites 1 and 2, and approximately 44% of cyanobacterial reads at Sites 3 and 4. The unicellular orders, Synechococclaes, Gloebacteriales and Chroococcodiopsiales, exhibited a consistent increase in abundance from Site 1 (21% of cyanobacterial reads) to Site 4 (34% of cyanobacterial reads) (Figure 8). The highest abundance of heterocystous cyanobacteria (Nostocales) was recorded at Site 1 (25.5% of cyanobacterial reads), while the lowest abundance was observed at Sites 2 and 4 (14.7% and 15.1% of cyanobacterial reads) (Figure 8). Furthermore, Nostoc sp., Petalonema sp. and Scytonema sp. exhibited the highest abundances, with averages of 5, 4 and 3% of cyanobacterial reads, respectively, across all sites. Overall, Gloeobacter sp., belonging to the order Gloeobacterales, was the most dominant species, with an abundance of up to 23.6% of cyanobacterial reads at Site 3 and approximately 9.7% of cyanobacterial reads at Sites 1, 2 and 4.

4. Discussion

4.1. Environmental Conditions

The temperature data recorded at the four sites did not demonstrate a consistent decline with increasing altitude. While Site 4 exhibited significantly lower temperatures, the other three sites showed variability, with Site 2 displaying the highest temperatures and Sites 1 and 3 exhibiting intermediate values. Notably, another study conducted in Svalbard reported a similar temperature pattern across an altitudinal gradient during the summer season, while a clear decline in temperature was observed at higher elevations during the winter months [19]. This variation is likely due to geographical and topographical influences at the selected altitudes, suggesting that the altitude effect between Sites 1 and 4 may have been partly overridden by local spatial or seasonal factors. Meteorological data from the Ny-Ålesund station indicated that local wind patterns and intensities probably influenced the recorded temperatures at the different sites. On 16 July 2023, when wind speeds were relatively low, temperatures were higher at all sites. However, as the wind speeds increased on 17 July 2023, there was a notable decline in temperature, particularly at Site 4, which was the most exposed due to its geographical characteristics. The temperature readings above the soil surface (T3) and at the soil surface (T2) showed pronounced daily fluctuations due to the direct exposure to environmental conditions. T3, which recorded the lowest temperatures and the greatest fluctuations, was significantly influenced by wind and solar radiation. In contrast, soil surface temperatures (T2) exhibited a tendency towards higher temperatures, benefiting from the insulation provided by plant cover, solar absorption and reduced wind exposure [20,21]. Temperatures below the surface (T1) demonstrated greater stability, as the vegetation and soil acted as insulation, reducing sudden temperature changes [21,22].
Although it is generally accepted that temperatures decline with increasing altitude, the influence of soil moisture is more dependent on the specific local conditions [23,24,25]. Warmer temperatures promote soil evaporation, while cooler temperatures reduce it, allowing moisture to accumulate [24]. This pattern was evident in our data, with Site 4, the coldest site, exhibiting the highest soil moisture, and Site 2, the warmest site, displaying the lowest. Furthermore, higher elevations typically experience greater precipitation, which contributes to increased soil moisture. However, no clear pattern of rainfall-related moisture accumulation was observed at the study sites. In regions with low precipitation, alternative sources of water, such as the formation of dew, become significant. Dew forms when temperatures fall below the dew point [26], and although the calculated dew points were based on data from the Ny-Ålesund weather station and may not be entirely accurate for the study sites, they were in good agreement with the measured soil moisture values, potentially reinforcing the existing environmental conditions.
The analysis of soil chemistry revealed no clear altitudinal gradient. Instead, similarities were observed between Sites 1 and 3, as well as between Sites 2 and 4. The pH values at all sites ranged from neutral to slightly acidic, which is consistent with the previous studies conducted in Svalbard [19,27]. The nutrient content generally followed an elevational pattern, with carbon and nitrogen levels increasing at higher elevations and a slight decrease in phosphorus content. However, this trend was less apparent at Site 3, which exhibited different nutrient characteristics compared to the other sites [25,28]. The presence of rocks at these higher elevations may act as nitrogen hotspots by intercepting nutrient-rich precipitation [29], explaining the elevated nitrogen levels observed at the rockier, higher elevation sites. Furthermore, the slower microbial decomposition rates observed at higher elevations are attributed to the lower temperatures, which allow for the accumulation of carbon and nitrogen in the soil [28]. The C/N ratio, a common indicator of organic matter decomposition, provided further insight into nutrient dynamics. A C/N ratio of 18, as recorded at Site 3, may indicate some degree of nitrogen limitation and potentially reduced microbial activity [30]. However, since soils with C/N ratios below 20 are generally considered nutrient-sufficient, it is unlikely that severe competition between plants and microbes for nutrients is occurring at these sites [30]. Site 4, with the highest soil moisture and probably the greatest exposure to precipitation and dew formation, might benefit from nutrient inputs via rainwater, with runoff potentially enriching the lower sites over time. However, the unexpectedly low nutrient content at Site 3 challenges this assumption and suggests more complex, unexplored factors influencing nutrient distribution. This variation suggests that additional variables, such as subtle shifts in community composition, geological conditions, plant–microbe interactions or specific soil characteristics, may play an important role in shaping the nutrient profile at this site [13].

4.2. Vegetation Reacts to Increased Altitude

The vegetation analysis conducted at the four sites revealed a general reduction in ground cover with increasing altitude, accompanied by clear patterns between species groups, as commonly observed and well documented in mountains [13,31].
The abundance of larger vascular plants (Tracheophyta) exhibited a decline with increasing altitude. The most frequently occurring species at all four sites were Dryas octopetala, Saxifraga oppositifolia and Salix polaris, highlighting their adaptability to different habitats. D. octopetala and S. oppositifolia often coexist and dominate a variety of environments in Svalbard [32]. S. polaris, which is also widely distributed in Svalbard, typically grows close to the ground and forms wide, spreading mats [31]. This growth pattern is typical to many tracheophytes growing in Arctic tundra regions, including D. octopetala, S. oppositifolia, Cassipope tetragona, Salix reticulata and Silene acaulis [33]. The mat or cushion structures assist the plants in protecting themselves from harsh environmental conditions by trapping heat during cold periods and cooling the air during the summer, thereby creating microclimates [33,34]. Another common adaptation is the growth of creeping or dwarf shrubs, which keep plants low to the ground, reducing their exposure to strong winds [33,35], as seen in D. octopetala, S. polaris and S. reticulata.
Bryophytes were the most abundant at Sites 2 and 4. A similar pattern was observed in the distribution of total nitrogen and total carbon, pH and carbon/nitrogen ratio, as well as temperature and soil moisture data. Previous studies of bryophytes in the Svalbard region have shown that their abundance tends to increase with altitude [36], which is consistent with our results. Although Site 2 may appear to be contradictory, Prestø’s study [36] also indicated that bryophytes are abundant in dry habitats. This is due to their capacity to absorb water and nutrients over their entire surface area, eliminating the need for a complex root system [36]. Given that Site 2 was the driest surveyed site, this may explain the presence of a large number of bryophytes.
The abundance of lichen species increased in abundance with altitude, accompanied by an increase in ground cover. A total of four lichen species were recorded across all sites, but only two could be identified as Flavocetraria nivalis and Sterecaulon alpinum. In addition to cyanobacteria, eukaryotic algae and bryophytes, lichens represent a significant component of biocrusts [4,5]. However, the identification of lichen species proved to be challenging due to limited field identification at the sites and the limitations of metagenomic taxonomic classification, where lichens are listed under their fungal names, often providing insufficient information at the species-level [37,38].

4.3. Microbial Community Composition in Arctic Biocrusts

The eukaryotic community composition at all sites was dominated by Chloroplastida and fungi, which is consistent with the findings of previous research conducted in Svalbard [39]. Chloroplastida play an important role in the formation and function of biocrusts due to their photosynthetic capabilities. Furthermore, the relatively high abundance of bryophytes within the eukaryotic groups is characteristic of Arctic tundra in this region [1]. The bacterial community in biocrusts was dominated by Proteobacteria and Actinobacteria across all study sites. While minor variations in community composition were observed between sites, the overall ratio of bacterial phyla remained relatively stable, aligning with the findings from studies of biocrusts under diverse environmental conditions [2,6].
Photoautotrophic species, including green algae, lichens, bryophytes and cyanobacteria, are frequently observed to be abundant in biocrusts [4]. The biocrusts exhibited a high diversity of green algae from families Trebouxiophyceae, Chlorophyceae and Chrysophyceae being the most prominent among the eukaryotic algae. While the majority of green algal classes demonstrated a decline in abundance with increasing altitude, Trebouxiophyceae showed the opposite trend, becoming more prevalent at higher altitudes (except Site 2). This pattern is likely driven by the well-documented symbiotic associations between these taxa and lichens [2,8], linking their increased abundance to the presence of lichen species. At Site 4 (314 m a.s.l.), where lichen-dominated biocrusts are prominent, this trend was further supported by the high abundance of Proteobacteria and cyanobacteria, which are commonly associated with lichen-rich environments [40]. A further analysis of fungal guilds highlighted the prevalence of lichen-dominated biocrusts at Sites 2 and 4, where lichenised fungi reached their highest abundance. The Ascomycota fungus Verrucaria, known for its symbiotic relationships with green algae from the Trebouxiophyceae and Ulvophyceae taxa [41], was the most abundant overall. Trebouxiophyceae, in particular, exhibited higher abundances at Site 4. Additionally, Umbilicaria, a lichen species frequently found on rocks at higher elevations in Arctic climates [42,43], showed an increased number of reads, particularly at Site 3. The photobiont of this lichen, the green algae Trebouxia sp. [42], exhibited a similar trend, thereby supporting the known ecological association between them.
The photoautotrophic community composition at all sites was dominated by cyanobacteria, which is consistent with the previous research that emphasised their role as early colonisers, soil stabilisers and integral components of biocrusts [2,4]. In contrast, bryophytes and eukaryotic green algae are typically associated with later stages of biocrust development. This indicates that Sites 1 and 3 may represent more advanced stages of biocrust formation, as they exhibited the highest abundance of these later successional groups [2,5].
The cyanobacterial community was dominated by the orders Pseudoanabaenales, Gloeobacterales and Nostocales. Filamentous species such as Leptolyngbya sp., identified as an indicator species at Sites 2 and 4, play a pivotal role in soil stabilisation. By secreting sticky extracellular polymeric substances (EPS), they facilitate soil particle aggregation, forming a foundation for biocrust development [3,44]. These cyanobacteria are often dominant within biocrusts and contribute significantly to their formation and stability [45]. The order Nostocales, which includes heterocystous species such as Nostoc sp., was relatively abundant across all four sites. Heterocystous cyanobacteria are capable of fixing atmospheric nitrogen, thereby supplying essential nutrients to other soil organisms [6,46]. Nostoc sp., frequently observed in Arctic biocrusts, contributes significantly to primary production [1] and thrives in habitats with elevated moisture levels [47]. This was reflected in its high abundance at Site 4, where the highest soil moisture was recorded. Previous studies have demonstrated that while the diversity of cyanobacteria is not affected by altitude, their overall abundance tends to increase at higher altitudes [48,49]. Cyanobacteria are well adapted to harsh conditions due to their rapid metabolic rates, resilience to water shortages, and ability to withstand extreme temperature fluctuations [46,48]. These findings are consistent with our observations, as cyanobacterial community composition showed little variation between sites, but their overall abundance increased with altitude. Under favourable environmental conditions, heterocystous cyanobacteria play a crucial role in fixing atmospheric nitrogen, which not only supports the growth of other soil organisms but may also benefit lichen species [46]. Recent studies have highlighted the mutualistic relationships between microalgae, nitrogen-fixing bacteria, and lichens, forming so-called tripartite lichens [50], suggesting that this interaction may enhance the stability and functionality of Arctic biocrusts [50,51]. Consequently, the high abundance of Nostoc sp. at Site 4 may contribute to primary production while providing essential nutrients for the development of lichen communities, highlighting the complex ecological interactions within biocrust ecosystems.
Overall, the observed changes in photoautotrophic communities appear to offset each other, as no significant changes in total chlorophyll a concentration were observed at the different sites. This suggests that the rate of primary production was consistent across all sites. Similarly, no significant differences were detected in the composition of the photoautotrophic community, indicating a stable functional contribution of these organisms to primary production under the varying environmental conditions studied. In accordance with this, measurements of photosynthetic capacity using a field PAM revealed no significant differences between the sites. These findings are consistent with other studies on biological soil crusts [52], which reported that variations in photoautotroph abundance were not directly correlated with factors such as crust age or the activity of phototrophic microorganisms. Such resilience underscores the adaptive capacity of photoautotrophic organisms in maintaining consistent primary production even under challenging and variable environmental conditions.
However, these findings are subject to certain limitations. As the study focused on a single toposequence, site-specific differences in physicochemical parameters may influence the results. Additionally, seasonal aspects were not considered, limiting the scope of these data to summer conditions. Potential disturbances, such as freeze-thaw cycles or animal activity, which could affect soil and biocrust dynamics, were also not monitored.
While the overall community composition did not show a clear correlation with the altitudinal gradient, the abundance of different phyla reflected the environmental conditions at each site, which were influenced by the altitudes. Previous studies demonstrated that bacterial composition is particularly sensitive to pH, with neutral soils supporting higher bacterial diversity [6,53]. The highest bacterial diversity was observed at Sites 2 and 4, both characterised by pH values close to 7. Furthermore, lower pH values were associated with a higher abundance of Acidobacteria, whereas Proteobacteria were more abundant in near-neutral soils [54]. This pattern aligns with the results observed at Sites 2 and 4, where Proteobacteria were the dominant phylum and pH levels were close to 7. The NMDS analysis further confirmed that environmental factors, including temperature, moisture, pH, nitrogen and phosphorus content, significantly influenced the community composition.

5. Conclusions

Biocrusts are essential to Arctic tundra ecosystems, providing a habitat for a diverse range of organisms and contributing to key ecological processes. Despite their importance, a complete understanding of the interactions among species within biocrusts remains a challenge. This study focused on the community composition of photoautotrophic organisms, with a particular emphasis on cyanobacteria and eukaryotic photoautotrophs. While elevation was not identified as a primary factor influencing microbial communities, environmental conditions played a significant role in shaping community variation. The bacterial community was primarily influenced by shifts in pH, whereas the abundance of eukaryotic organisms, including vascular plants, was more responsive to broader environmental factors. These findings highlight the complexity of Arctic biocrust micro-ecosystems and the significant influence of specific environmental conditions on their structure.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/microorganisms12122606/s1, Figure S1: Wind velocity measured at Ny-Ålesund station; Figure S2: Non-Metric Multidimensional Scaling (NMDS) plot based on relative abundances of DNA sequences; Tabe S1: Overview of the vegetation analysis; Table S2: Metagenomic dataset sequencing overview; Table S3: List of fungal and photoautotrophic indicator taxa.

Author Contributions

I.M.M.: laboratory experiments, formal analysis, investigation, methodology, validation, visualisation, writing—original draft. E.P.: conceptualisation, sampling, supervision of analysis, methodology, writing—review and editing; L.A.K.: vegetation analysis; K.-H.L.v.B.: vegetation analysis; U.K.: soil analysis, writing—review and editing; S.K.: soil analysis; B.B.: conceptualisation, data analysis, funding acquisition, project administration, supervision, writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Alfred Wegener Institute, project number KOS175.

Data Availability Statement

All sequence reads were submitted to the NCBI database project number PRJNA1189472.

Acknowledgments

The authors would like to express their gratitude to the personnel at the AWIPEV Arctic Research Base for their support during our visits to Ny-Ålesund in 2023. We are also grateful to the soil science group of Peter Leinweber at the University of Rostock for their technical and analytical support.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Pushkareva, E.; Elster, J.; Holzinger, A.; Niedzwiedz, S.; Becker, B. Biocrusts from Iceland and Svalbard: Does microbial community composition differ substantially? Front. Microbiol. 2022, 13, 1048522. [Google Scholar] [CrossRef] [PubMed]
  2. Belnap, J.; Lange, O.L. Biological Soil Crusts: Structure, Function, and Management; Springer: Berlin/Heidelberg, Germany, 2003; Volume 150. [Google Scholar]
  3. Weber, B.; Belnap, J.; Büdel, B.; Antoninka, A.J.; Barger, N.N.; Chaudhary, V.B.; Darrouzet-Nardi, A.; Eldridge, D.J.; Faist, A.M.; Ferrenberg, S.; et al. What is a biocrust? A refined, contemporary definition for a broadening research community. Biol. Rev. 2022, 97, 1768–1785. [Google Scholar] [CrossRef]
  4. Rippin, M.; Lange, S.; Sausen, N.; Becker, B. Biodiversity of biological soil crusts from the Polar Regions revealed by metabarcoding. FEMS Microbiol. Ecol. 2018, 94, fiy036. [Google Scholar] [CrossRef]
  5. Weber, B.; Büdel, B.; Belnap, J. Biological Soil Crusts: An Organizing Principle in Drylands; Springer International Publishing: Cham, Switzerland, 2016; Volume 226. [Google Scholar]
  6. Malard, L.A.; Pearce, D.A. Microbial diversity and biogeography in Arctic soils. Environ. Microbiol. Rep. 2018, 10, 611–625. [Google Scholar] [CrossRef]
  7. Zielke, M.; Ekker, A.S.; Olsen, R.A.; Spjelkavik, S.; Solheim, B. The Influence of Abiotic Factors on Biological Nitrogen Fixation in Different Types of Vegetation in the High Arctic, Svalbard. Arct. Antarct. Alp. Res. 2002, 34, 293–299. [Google Scholar] [CrossRef]
  8. Muggia, L.; Nelsen, M.P.; Kirika, P.M.; Barreno, E.; Beck, A.; Lindgren, H.; Lumbsch, H.T.; Leavitt, S.D. Formally described species woefully underrepresent phylogenetic diversity in the common lichen photobiont genus Trebouxia (Trebouxiophyceae, Chlorophyta): An impetus for developing an integrated taxonomy. Mol. Phylogenetics Evol. 2020, 149, 106821. [Google Scholar] [CrossRef] [PubMed]
  9. Liengen, T.; Olsen, R.A. Seasonal and site-specific variations in nitrogen fixation in a high arctic area, Ny-Ålesund, Spitsbergen. Can. J. Microbiol. 1997, 43, 759–769. [Google Scholar] [CrossRef]
  10. Norwegian Meteorological Institute and NRK. Today’s Weather Forecast for Your Location. Available online: https://www.yr.no/en (accessed on 16 August 2024).
  11. Uni of Lapland. Arctic Region. Available online: https://www.arcticcentre.org/EN/arcticregion (accessed on 16 August 2024).
  12. Norwegian Polar Institute. Norsk Polarinstitutt. Available online: https://www.npolar.no/ (accessed on 16 August 2024).
  13. Kern, R.; Hotter, V.; Frossard, A.; Albrecht, M.; Baum, C.; Tytgat, B.; de Maeyer, L.; Velazquez, D.; Seppey, C.; Frey, B.; et al. Comparative vegetation survey with focus on cryptogamic covers in the high Arctic along two differing catenas. Polar Biol. 2019, 42, 2131–2145. [Google Scholar] [CrossRef]
  14. DIN EN ISO 10390:2022-08; Boden, Behandelter Bioabfall und Schlamm_Bestimmung des pH-Werts. Deutsche Fassung EN_ISO_10390:2022; DIN Media GmbH: Berlin, Germany, 2022.
  15. Bolger, A.M.; Lohse, M.; Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 2014, 30, 2114–2120. [Google Scholar] [CrossRef] [PubMed]
  16. Kopylova, E.; Noé, L.; Touzet, H. SortMeRNA: Fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 2012, 28, 3211–3217. [Google Scholar] [CrossRef] [PubMed]
  17. Wood, D.E.; Salzberg, S.L. Kraken: Ultrafast metagenomic sequence classification using exact alignments. Genome Biol. 2014, 15, R46. [Google Scholar] [CrossRef]
  18. Oksanen, J.; Blanchet, F.G.; Kindt, R.; Legendre, P.; Wagner, H. Vegan: Community Ecology Package; World Agroforestry: Nairobi, Kenya, 2015. [Google Scholar]
  19. Kotas, P.; Šantrůčková, H.; Elster, J.; Kaštovská, E. Soil microbial biomass, activity and community composition along altitudinal gradients in the High Arctic (Billefjorden, Svalbard). Biogeosciences 2018, 15, 1879–1894. [Google Scholar] [CrossRef]
  20. James. Is Soil Temperature Warmer Than Air? SoilThermometer.com. 26 March 2023. Available online: https://soilthermometer.com/is-soil-temperature-warmer-than-air/ (accessed on 12 September 2024).
  21. Molnar, P. Differences between soil and air temperatures: Implications for geological reconstructions of past climate. Geosphere 2022, 18, 800–824. [Google Scholar] [CrossRef]
  22. Cheng, Q.; Zhang, M.; Jin, H.; Ren, Y. Spatiotemporal variation characteristics of hourly soil temperature in different layers in the low-latitude plateau of China. Front. Environ. Sci. 2022, 10, 1091985. [Google Scholar] [CrossRef]
  23. Yan, J.; Tong, M.; Liu, J.; Li, J.; Li, H. Temperature and moisture sensitivities of soil respiration vary along elevation gradients: An analysis from long-term field observations. Sci. Total. Environ. 2024, 912, 169150. [Google Scholar] [CrossRef] [PubMed]
  24. Pellet, C.; Hauck, C. Monitoring soil moisture from middle to high elevation in Switzerland: Set-up and first results from the SOMOMOUNT network. Hydrol. Earth Syst. Sci. 2017, 21, 3199–3220. [Google Scholar] [CrossRef]
  25. Mangral, Z.A.; Islam, S.U.; Tariq, L.; Kaur, S.; Ahmad, R.; Malik, A.H.; Goel, S.; Baishya, R.; Barik, S.K.; Dar, T.U.H. Altitudinal gradient drives significant changes in soil physico-chemical and eco-physiological properties of Rhododendron anthopogon: A case study from Himalaya. Front. For. Glob. Chang. 2023, 6, 1181299. [Google Scholar] [CrossRef]
  26. Agam, N.; Berliner, P.R. Dew formation and water vapor adsorption in semi-arid environments—A review. J. Arid. Environ. 2006, 65, 572–590. [Google Scholar] [CrossRef]
  27. Pushkareva, E.; Pessi, I.S.; Wilmotte, A.; Elster, J. Cyanobacterial community composition in Arctic soil crusts at different stages of development. FEMS Microbiol. Ecol. 2015, 91, fiv143. [Google Scholar] [CrossRef]
  28. Zhang, Y.; Ai, J.; Sun, Q.; Li, Z.; Hou, L.; Song, L.; Tang, G.; Li, L.; Shao, G. Soil organic carbon and total nitrogen stocks as affected by vegetation types and altitude across the mountainous regions in the Yunnan Province, south-western China. CATENA 2021, 196, 104872. [Google Scholar] [CrossRef]
  29. Göransson, H.; Edwards, P.J.; Perreijn, K.; Smittenberg, R.H.; Olde Venterink, H. Rocks create nitrogen hotspots and N:P heterogeneity by funnelling rain. Biogeochemistry 2014, 121, 329–338. [Google Scholar] [CrossRef]
  30. Amelung, W.; Blume, H.-P.; Fleige, H.; Horn, R.; Kandeler, E.; Kögel-Knabner, I.; Kretzschmar, R.; Stahr, K.; Wilke, B.-M. Scheffer/Schachtschabel Lehrbuch der Bodenkunde, 17th ed.; Springer Spektrum: Berlin/Heidelberg, Germany, 2018; ISBN 9783662558713. [Google Scholar]
  31. Lee, Y.K. Arctic Plants of Svalbard: What We Learn from the Green in the Treeless White World; Springer International Publishing AG: Cham, Switzerland, 2020; ISBN 9783030345600. [Google Scholar]
  32. Svalbardflora. Svalbard Flora. Available online: https://svalbardflora.no/ (accessed on 23 November 2024).
  33. Rønning, O.I. The Flora of Svalbard; Norsk Polarinstitutt: Oslo, Norway, 1996; ISBN 8276661009. [Google Scholar]
  34. Plants in Alpine Regions: Cell Physiology of Adaption and Survival Strategies; Lütz, C. (Ed.) Springer: Vienna, Austria, 2012; ISBN 9783709101360. [Google Scholar]
  35. Gehrke, B.; Kandziora, M.; Pirie, M.D. The evolution of dwarf shrubs in alpine environments: A case study of Alchemilla in Africa. Ann. Bot. 2016, 117, 121–131. [Google Scholar] [CrossRef]
  36. Prestø, T.; Lüth, M.; Hassel, K. Bryophytes of the Longyearbyen Area; Svalbards Miljøvernfond: Longyearbyen, Norway, 2014. [Google Scholar]
  37. Diederich, P.; Lawrey, J.D.; Ertz, D. The 2018 classification and checklist of lichenicolous fungi, with 2000 non-lichenized, obligately lichenicolous taxa. Bryologist 2018, 121, 340. [Google Scholar] [CrossRef]
  38. Duran-Nebreda, S.; Valverde, S. Composition, structure and robustness of Lichen guilds. Sci. Rep. 2023, 13, 3295. [Google Scholar] [CrossRef]
  39. Pushkareva, E.; Elster, J.; Kudoh, S.; Imura, S.; Becker, B. Microbial community composition of terrestrial habitats in East Antarctica with a focus on microphototrophs. Front. Microbiol. 2023, 14, 1323148. [Google Scholar] [CrossRef] [PubMed]
  40. Tian, C.; Pang, J.; Bu, C.; Wu, S.; Bai, H.; Li, Y.; Guo, Q.; Siddique, K.H.M. The Microbiomes in Lichen and Moss Biocrust Contribute Differently to Carbon and Nitrogen Cycles in Arid Ecosystems. Microb. Ecol. 2023, 86, 497–508. [Google Scholar] [CrossRef] [PubMed]
  41. Thüs, H.; Muggia, L.; Pérez-Ortega, S.; Favero-Longo, S.E.; Joneson, S.; O’Brien, H.; Nelsen, M.P.; Duque-Thüs, R.; Grube, M.; Friedl, T.; et al. Revisiting photobiont diversity in the lichen family Verrucariaceae (Ascomycota). Eur. J. Phycol. 2011, 46, 399–415. [Google Scholar] [CrossRef]
  42. He, Z.; Naganuma, T.; Faluaburu, M.S.; Nakai, R.; Kanda, H.; Uchida, M.; Imura, S.; Hahn, M.W. Bacterial phylotypes associated with rock-dwelling Umbilicaria Lichens from Arctic/Subarctic areas in North America and Northern Europe. Polar Biol. 2024, 47, 1–15. [Google Scholar] [CrossRef] [PubMed]
  43. Wang, Y.; Li, R.; Wang, D.; Qian, B.; Bian, Z.; Wei, J.; Wei, X.; Xu, J.-R. Regulation of symbiotic interactions and primitive lichen differentiation by UMP1 MAP kinase in Umbilicaria muhlenbergii. Nat. Commun. 2023, 14, 6972. [Google Scholar] [CrossRef] [PubMed]
  44. Mugnai, G.; Rossi, F.; Mascalchi, C.; Ventura, S.; de Philippis, R. High Arctic biocrusts: Characterization of the exopolysaccharidic matrix. Polar Biol. 2020, 43, 1805–1815. [Google Scholar] [CrossRef]
  45. Williams, L.; Loewen-Schneider, K.; Maier, S.; Büdel, B. Cyanobacterial diversity of western European biological soil crusts along a latitudinal gradient. FEMS Microbiol. Ecol. 2016, 92, fiw157. [Google Scholar] [CrossRef] [PubMed]
  46. Pushkareva, E.; Johansen, J.R.; Elster, J. A review of the ecology, ecophysiology and biodiversity of microalgae in Arctic soil crusts. Polar Biol. 2016, 39, 2227–2240. [Google Scholar] [CrossRef]
  47. Pushkareva, E.; Hejduková, E.; Elster, J.; Becker, B. Microbial response to seasonal variation in arctic biocrusts with a focus on fungi and cyanobacteria. Environ. Res. 2024, 263, 120110. [Google Scholar] [CrossRef]
  48. Janatková, K.; Reháková, K.; Doležal, J.; Simek, M.; Chlumská, Z.; Dvorský, M.; Kopecký, M. Community structure of soil phototrophs along environmental gradients in arid Himalaya. Environ. Microbiol. 2013, 15, 2505–2516. [Google Scholar] [CrossRef] [PubMed]
  49. Pushkareva, E.; Elster, J. Biodiversity and ecological classification of cryptogamic soil crusts in the vicinity of Petunia Bay, Svalbard. Czech Polar Rep. 2013, 3, 7–18. [Google Scholar] [CrossRef]
  50. Jung, P.; Brust, K.; Schultz, M.; Büdel, B.; Donner, A.; Lakatos, M. Opening the Gap: Rare Lichens with Rare Cyanobionts—Unexpected Cyanobiont Diversity in Cyanobacterial Lichens of the Order Lichinales. Front. Microbiol. 2021, 12, 728378. [Google Scholar] [CrossRef] [PubMed]
  51. Llamas, A.; Leon-Miranda, E.; Tejada-Jimenez, M. Microalgal and Nitrogen-Fixing Bacterial Consortia: From Interaction to Biotechnological Potential. Plants 2023, 12, 2476. [Google Scholar] [CrossRef] [PubMed]
  52. Colica, G.; Li, H.; Rossi, F.; de Philippis, R.; Liu, Y. Differentiation of the characteristics of excreted extracellular polysaccharides reveals the heterogeneous primary succession of induced biological soil crusts. J. Appl. Phycol. 2015, 27, 1935–1944. [Google Scholar] [CrossRef]
  53. Tian, Q.; Jiang, Y.; Tang, Y.; Wu, Y.; Tang, Z.; Liu, F. Soil pH and Organic Carbon Properties Drive Soil Bacterial Communities in Surface and Deep Layers Along an Elevational Gradient. Front. Microbiol. 2021, 12, 646124. [Google Scholar] [CrossRef]
  54. Männistö, M.K.; Tiirola, M.; Häggblom, M.M. Bacterial communities in Arctic fjelds of Finnish Lapland are stable but highly pH-dependent. FEMS Microbiol. Ecol. 2007, 59, 452–465. [Google Scholar] [CrossRef] [PubMed]
Microorganisms 12 02606 g001
Figure 1. Geographical location of sampling sites on Ossian Sarsfjellet, Svalbard. (A)—The red arrow indicates the location of Ossian Sarsfjellet and the red star indicates the location of the weather station in Ny-Ålesund in the Kongsfjorden area. (B)—The red circles indicate the sampling sites. Maps based on TopoSvalbard, courtesy of the Norwegian Polar Institute.
Figure 1. Geographical location of sampling sites on Ossian Sarsfjellet, Svalbard. (A)—The red arrow indicates the location of Ossian Sarsfjellet and the red star indicates the location of the weather station in Ny-Ålesund in the Kongsfjorden area. (B)—The red circles indicate the sampling sites. Maps based on TopoSvalbard, courtesy of the Norwegian Polar Institute.
Microorganisms 12 02606 g001
Microorganisms 12 02606 g002
Figure 2. Temperature data from 13 July 2023 to 4 August 2023 at the three different measurement points across the four sites. T1 = measured 6 cm below the soil surface; T2 = measured at the soil surface; T3 = measured 15 cm above the soil surface; measured every 15 min; measured with TOMST® data loggers.
Figure 2. Temperature data from 13 July 2023 to 4 August 2023 at the three different measurement points across the four sites. T1 = measured 6 cm below the soil surface; T2 = measured at the soil surface; T3 = measured 15 cm above the soil surface; measured every 15 min; measured with TOMST® data loggers.
Microorganisms 12 02606 g002
Microorganisms 12 02606 g003
Figure 3. Soil moisture data from 13 July 2023 to 20 August 2023 across the sites. Measured every 15 min; measured with TOMST® data loggers; significant differences observed between all four sites; p-value < 0.05. Data used for statistical analysis are from 13 July 2023 to 4 August 2023 only.
Figure 3. Soil moisture data from 13 July 2023 to 20 August 2023 across the sites. Measured every 15 min; measured with TOMST® data loggers; significant differences observed between all four sites; p-value < 0.05. Data used for statistical analysis are from 13 July 2023 to 4 August 2023 only.
Microorganisms 12 02606 g003
Microorganisms 12 02606 g004
Figure 4. Non-Metric Multidimensional Scaling (NMDS) plot based on the vegetation analysis conducted at the sites. T1 = temperature measured 6 cm below the soil surface; T2 = temperature measured at the soil surface; T3 = temperature measured 15 cm above the soil surface; C.N = carbon to nitrogen ratio; parameters which did not show significant differences between the sites are not included, p-value < 0.05.
Figure 4. Non-Metric Multidimensional Scaling (NMDS) plot based on the vegetation analysis conducted at the sites. T1 = temperature measured 6 cm below the soil surface; T2 = temperature measured at the soil surface; T3 = temperature measured 15 cm above the soil surface; C.N = carbon to nitrogen ratio; parameters which did not show significant differences between the sites are not included, p-value < 0.05.
Microorganisms 12 02606 g004
Microorganisms 12 02606 g005
Figure 5. Overall community composition across sites. Metagenomic 16/18 S rRNA dataset analysed using Silva software; taxonomic groups with total abundance below the 0.5% threshold are grouped as ‘low abundance’; * indicates significant differences between sites based on a one-way ANOVA, p-value * < 0.05, ** < 0.01).
Figure 5. Overall community composition across sites. Metagenomic 16/18 S rRNA dataset analysed using Silva software; taxonomic groups with total abundance below the 0.5% threshold are grouped as ‘low abundance’; * indicates significant differences between sites based on a one-way ANOVA, p-value * < 0.05, ** < 0.01).
Microorganisms 12 02606 g005
Microorganisms 12 02606 g006
Figure 6. Relative abundance of fungal taxa and fungal functional guilds across sites. Metagenomic 16/18S rRNA dataset analysed using Silva software. (A)—fungal phyla. (B)—functional guilds; * indicates significant differences between sites based on a one-way ANOVA, p-value < 0.05.
Figure 6. Relative abundance of fungal taxa and fungal functional guilds across sites. Metagenomic 16/18S rRNA dataset analysed using Silva software. (A)—fungal phyla. (B)—functional guilds; * indicates significant differences between sites based on a one-way ANOVA, p-value < 0.05.
Microorganisms 12 02606 g006
Microorganisms 12 02606 g007
Figure 7. Relative abundance of photoautotrophic taxa across sites. Metagenomic 16/18S rRNA dataset analysed using Silva software; no significant differences were observed between the taxa, based on a one-way ANOVA, p-value < 0.05.
Figure 7. Relative abundance of photoautotrophic taxa across sites. Metagenomic 16/18S rRNA dataset analysed using Silva software; no significant differences were observed between the taxa, based on a one-way ANOVA, p-value < 0.05.
Microorganisms 12 02606 g007
Microorganisms 12 02606 g008
Figure 8. Relative abundance of cyanobacterial orders across sites. Metagenomic 16 S rRNA dataset analysed using Silva software; filamentous orders shown in orange/red; heterocystous order shown in green; unicellular orders shown in blue; * indicates significant differences between sites based on a one-way ANOVA, p-value < 0.05.
Figure 8. Relative abundance of cyanobacterial orders across sites. Metagenomic 16 S rRNA dataset analysed using Silva software; filamentous orders shown in orange/red; heterocystous order shown in green; unicellular orders shown in blue; * indicates significant differences between sites based on a one-way ANOVA, p-value < 0.05.
Microorganisms 12 02606 g008
Table 1. Scale used for the determination of soil coverage ratio of organisms on the sampling sites. Categorisation according to visual assessment.
Table 1. Scale used for the determination of soil coverage ratio of organisms on the sampling sites. Categorisation according to visual assessment.
ScaleCoverageNumeric Conversion for Statistical Purposes
5>75% of surfaceDid not occur
451–75% of surfaceDid not occur
326–50% of surface50
25–25% of surface25
1<5% of surface, but many individuals10
+<5% of surface, but few individuals 5
rrare1
Table 2. Description of the studied sites, including soil chemistry. The same letters indicate no statistical difference between the samples according to a one-way ANOVA, followed by the Tukey HSD post hoc test (p < 0.05); TP = total phosphorus; TN = total nitrogen; TC = total carbon; C/N = carbon to nitrogen ratio; Chl a = chlorophyll a content; Fv/Fm = quantum yield capacity of photosystem II.
Table 2. Description of the studied sites, including soil chemistry. The same letters indicate no statistical difference between the samples according to a one-way ANOVA, followed by the Tukey HSD post hoc test (p < 0.05); TP = total phosphorus; TN = total nitrogen; TC = total carbon; C/N = carbon to nitrogen ratio; Chl a = chlorophyll a content; Fv/Fm = quantum yield capacity of photosystem II.
SiteGPSElevation a.s.l. [m]pHTP, [g/kg]TN, [g/kg]TC, [g/kg]C/NChl a, [mg/m2]Fv/Fm
178.94935° N 12.48538° E1016.26 a0.42 a4.67 ab73.30 a15.77 ab183.88 a0.343 a
278.94562° N 12.48588° E1867.28 b0.39 a5.53 a76.33 a13.79 a151.39 a0.305 a
378.94352° N 12.48400° E2386.13 a0.27 a3.40 b64.80 a18.66 b137.33 a0.353 a
478.94226° N 12.47245° E3147.13 b0.37 a5.97 a99.60 a16.61 ab215.57 a0.344 a
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Mas Martinez, I.; Pushkareva, E.; Keilholz, L.A.; Linne von Berg, K.-H.; Karsten, U.; Kammann, S.; Becker, B. Role of Climate and Edaphic Factors on the Community Composition of Biocrusts Along an Elevation Gradient in the High Arctic. Microorganisms 2024, 12, 2606. https://doi.org/10.3390/microorganisms12122606

AMA Style

Mas Martinez I, Pushkareva E, Keilholz LA, Linne von Berg K-H, Karsten U, Kammann S, Becker B. Role of Climate and Edaphic Factors on the Community Composition of Biocrusts Along an Elevation Gradient in the High Arctic. Microorganisms. 2024; 12(12):2606. https://doi.org/10.3390/microorganisms12122606

Chicago/Turabian Style

Mas Martinez, Isabel, Ekaterina Pushkareva, Leonie Agnes Keilholz, Karl-Heinz Linne von Berg, Ulf Karsten, Sandra Kammann, and Burkhard Becker. 2024. "Role of Climate and Edaphic Factors on the Community Composition of Biocrusts Along an Elevation Gradient in the High Arctic" Microorganisms 12, no. 12: 2606. https://doi.org/10.3390/microorganisms12122606

APA Style

Mas Martinez, I., Pushkareva, E., Keilholz, L. A., Linne von Berg, K. -H., Karsten, U., Kammann, S., & Becker, B. (2024). Role of Climate and Edaphic Factors on the Community Composition of Biocrusts Along an Elevation Gradient in the High Arctic. Microorganisms, 12(12), 2606. https://doi.org/10.3390/microorganisms12122606

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop