Arctic biocrusts highlight genetic variability in photosynthesis as a key driver of biodiversity

Arctic biocrusts highlight genetic variability in photosynthesis as a key driver of biodiversity | 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 Arctic biocrusts highlight genetic variability in photosynthesis as a key driver of biodiversity Ekaterina Pushkareva, Leonie Keilholz, Sandra Kammann, Karl-Heinz Linne von Berg, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6899352/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 25 You are reading this latest preprint version Abstract Background: Photosynthetic organisms, including cyanobacteria, algae, and bryophytes, are an essential part of biological soil crusts (biocrusts) in Arctic ecosystems. These organisms play key roles in supporting both the biocrusts themselves and associated plant communities by generating energy and nutrients under extreme environmental conditions. However, the genetic mechanisms underlying their adaptation to polar environments remain poorly understood. This study investigated the composition of phototrophic communities and their photosynthetic genetic capacity in Arctic biocrusts located at different elevations. Results: Metagenomic sequencing revealed that cyanobacterial communities exhibited no significant response to elevation, while this factor had a strong effect on the distribution of eukaryotic phototrophs. Increased elevation is typically associated with higher solar radiation, lower temperatures, and reduced water availability, all of which might increase environmental stress and influence the adaptation of photosynthetic organisms. In addition, photosynthetic gene profiling revealed a consistent dominance of photosystem II (PSII) genes across all sites, particularly psbB. In general, genes associated with photosystem I (PSI), chlorophyll biosynthesis, as well as the light-harvesting complexes of PSI and PSII, were significantly influenced by the increased elevation. Conclusions: The results expand our understanding of the functional role of phototrophic organisms in Arctic biocrusts. Furthermore, they highlight the importance of genetic photosynthetic capacity for resilience and adaptability under extreme environmental conditions, which may serve as a key factor in determining the composition of phototrophs in biocrusts. biocrust cyanobacteria eukaryotic phototrophs photosynthesis Arctic Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Biological soil crusts (biocrusts) are complex communities of microorganisms, which form a cohesive layer on top of the soil surface [ 1 ]. Biocrusts play an important role in ecosystem functioning and stability. They contribute significantly to soil fertility by fixing atmospheric nitrogen and carbon, thereby increasing nutrient availability in these nutrient-poor environments [ 2 ]. Furthermore, biocrusts improve soil structure and stability, reduce erosion and aid in water retention. These properties can affect vascular plants in various ways, depending on environmental conditions. In some cases, biocrusts may contribute to improved soil moisture and nutrient availability, potentially facilitating plant establishment [ 3 ]. Additionally, by stabilizing the soil, biocrusts can create conditions that support seed germination and early plant growth. However, in harsh environments where vascular plants struggle to compete, biocrusts may become dominant, forming communities adapted to extreme conditions. The extreme conditions of the Arctic environment, characterized by low temperatures, seasonally fluctuating radiation (polar day and night), and limited nutrient availability, pose significant challenges for biocrust communities [ 4 ]. However, biocrusts exhibit resilience and adaptability to these harsh conditions [ 5 ], as reflected in their generally high abundance in the Arctic tundra with soil coverage up to 90% [ 6 ]. Their ability to survive prolonged periods of desiccation and freeze-thaw cycles allows them to persist and function in environments where other life forms struggle to thrive. Furthermore, despite the severe Arctic environments, biocrusts are able to perform photosynthesis [ 7 ], which contributes to the primary productivity of Arctic ecosystems, forming the foundation for terrestrial food webs. The organic matter produced through photosynthesis supports various forms of life, from microorganisms to larger organisms that depend on biocrusts for nutrients or as a food source [ 4 ]. Photosynthetic organisms including cyanobacteria, eukaryotic algae, bryophytes and lichens are key contributors to the biocrust ecosystem [ 1 ]. These phototrophs are capable of capturing and storing energy from sunlight even under the low light conditions typical for the Arctic. They possess specialized pigments and metabolic pathways that enable them to maximize light absorption and energy conversion, even when sunlight is limited. In the polar biocrusts, these microbial phototrophs adapt to extreme conditions by employing efficient light-harvesting systems and protective mechanisms against high irradiance during the polar summer with 24 h of light [ 8 ]. The genetic potential of individual biocrust organisms has been the focus of extensive research, primarily through genome analyses [ 9 ]. However, not all organisms contribute equally to the ecophysiological performance of the biocrusts [ 10 ] and, to the best of our knowledge, the photosynthetic genetic potential of biocrusts as a whole has not been previously reported. As Arctic regions experience rapid warming and significant environmental changes [ 11 ], the distribution, composition, and activity of phototrophic organisms within biocrusts are likely to shift in response to varying abiotic factors. For example, the differences in elevation introduce environmental gradients, including temperature, moisture and solar radiation, which may influence the phototroph composition and, subsequently, their photosynthetic genetic capacity. Lower elevations might experience greater warming effects, increased moisture retention, and more favorable nutrient availability compared to higher elevations, where colder temperatures and desiccation stress might limit photosynthetic activity [ 12 ]. Here, we hypothesized that the presence of phototrophic organisms in Arctic biocrusts at different elevations with local abiotic conditions might be dependent on their genetic photosynthetic potential. Specifically, we expected that biocrusts at lower elevations will exhibit a higher abundance and diversity of photosynthesis-related genes, corresponding to more favorable conditions for phototrophic growth. Furthermore, we aimed to provide a deeper insight into the molecular mechanisms explaining the observed variations in phototroph composition within biocrusts. In addition to the analysis of the genetic photosynthetic potential of biocrusts, the objective was to document the composition and distribution of vascular plants, bryophytes, and lichens across the studied sites. This approach allowed us to explore how plant community structure may co-exist with the microbial phototrophic community, providing a more comprehensive view of Arctic biocrusts. Materials and Methods Site description and sampling Kongsfjorden is located at the west coast of Svalbard. Ny-Ålesund is a main research city in the area (78°55′26.33 N, 11°55′23.84 E), where continuous meteorological observations are conducted. The coldest and warmest months of the year are March (average air temperature − 12.7°C) and July (average air temperature 4.4°C), respectively. The mean annual precipitation in Ny-Ålesund is around 461 mm. The wind conditions in Kongsfjorden are strongly influenced by a combination of large-scale atmospheric circulation, orographic steering, and katabatic winds [ 13 ]. Three localities were chosen based on their proximity to the open sea to capture the gradient of these atmospheric and climatic effects: Outer Fjord (OF), closest to the open ocean; Mid Fjord (MF), an intermediate location within the fjord; and Inner Fjord (IF), the innermost part of the fjord, furthest from the sea (Table 1 ; Fig. 1 ). Within each locality, biocrusts at a similar developmental stage were selected to ensure consistency across samples. Two different elevations (high: marked with H; low: marked with L) were always included at each locality. The black biocrusts with presence of bryophytes and lichens were collected in summers of 2022 and 2023. Five replicates were taken from each site, brought to the laboratory and kept frozen until further analyses. In addition, the Braun-Blanquet vegetation analysis was conducted in the sampling sites (except Gr samples). Three 1m² plots were analyzed at each site. The area coverage of each species was estimated using the scale described in [ 12 ]. Soil analysis Total carbon and nitrogen (TC and TN, respectively) were determined by dry combustion using an elemental analyzer (UNICUBE® Elementar Analysensysteme GmbH, Langenselbold, Germany). Total phosphorus (TP) was extracted from 500 mg of air-dried material by microwave-assisted digestion with aqua regia [ 14 ]. The concentration was measured by inductively coupled plasma optical emission spectroscopy (ICP-OES Optima 8300, PerkinElmer) at a 214 nm wavelength. The pH was determined electrometrically in a 1:2.5 (w/v) suspension with 0.01 CaCl₂. The METTLER TOLEDO SevenMulti was employed for this purpose (Gießen, Germany). Chlorophyll a was measured using a Shimadzu UV-2401 PC spectrophotometer [ 14 ]. DNA isolation and metagenomic sequencing The uppermost biocrust layer was used for DNA extraction, and visible plant material was avoided. Total DNA was extracted from the collected biocrusts using the DNeasy PowerSoil Pro Kit (QIAGEN, USA) according to a manufacture’s instruction. The DNAs were then sent to the Cologne Center for Genomics (Cologne, Germany) and the quality control was conducted on Agilent 2200 TapeStation System with the help of TapeStation Analysis Software 5.1. Metagenomic sequencing using Illumina NovaSeq6000 platform (PE150) was performed. The raw reads were submitted to the Sequence Read Archive (SRA) under the project PRJNA1124630. Bioinformatic and statistical analyses Bioinformatic analysis was performed in the OmicsBox software (Biobam, Spain). Quality filtering of the reads was conducted in Trimmomatic [ 15 ], and the rRNAs were separated from the dataset using SortMeRNA [ 16 ]. The taxonomic assignment of the extracted 16S and 18S rRNA genes was performed using Silva database (version 138.1). The remaining non-rRNA reads were separated from the dataset and assembled de novo using MEGAHIT (v1.2.8; [ 17 ]). The contigs with lengths < 500 bp were subsequently removed and the remaining non-rRNA reads were quantified as fragments per kilobase of transcript per million fragments sequenced (FPKM). Further, the contigs were annotated using NCBI Blast searches [ 18 ]. The statistical analyses were conducted using the R software (version 4.2.1). To test for differences in environmental parameters, as well as in community composition and gene content among the sampling sites, a one-way and two-way analysis of variance (ANOVA) were performed, followed by Tukey's HSD post hoc test (p-value < 0.05). Normality of variance was assessed using Shapiro–Wilk's test. If necessary, data were SQRT transformed. Furthermore, to illustrate the distribution of contigs obtained by metagenomic sequencing, non-metrical multidimensional scaling (NMDS) was performed using the package vegan [ 19 ]. Statistical difference was tested with the PERMANOVA test and False Discovery Rate (FDR) correction was applied to account for multiple testing. Environmental parameters were fitted into the ordination space using the function envfit and significance of the associations was determined by 9999 random permutations. In addition, an indicator taxa analysis was conducted to identify species that are statistically associated with each locality or elevation. Results Soil characteristics The soil parameters significantly differed among the studied sites (Table 1 ). The highest chlorophyll a content was observed in Blomstrandhalvoya (Bl), while the lowest was reported in the samples from Ossian Sarsfjllet (Os), Knølen (Kn) and Grønlietoppen (Gr). The contents of TN, TC and TP were higher in KnL (Knølen, low elevation) than in the other samples, while C/N ratio prevailed in GrH (Grønlietoppen, high elevation). Two-way ANOVA showed that chlorophyll a content was significantly influenced by the elevation, while the interaction of Locality × Elevation had a significant effect on soil chemistry. Table 1 Soil parameters of the studied sites (a). TC, TN and TP correspond to total carbon, total nitrogen and total phosphorus, respectively. The numbers in the brackets indicate standard error (SE). Two-way ANOVA results (b) show F ratios and significance levels are indicated by superscripts (***p < 0.001, **p < 0.01, *p < 0.05; ns: non-significant). L or H in sample names correspond to low and high elevations, respectively. Locality refers to the position within the fjord: OF = Outer Fjord, MF = Mid Fjord, IF = Inner Fjord. a) Sample Site GPS coordinates Elevation, m a.s.l. Locality pH Chlorophyll a , mg m − ² TC, g kg − 1 TN, g kg − 1 CN ratio TP, mg kg − 1 GeopL northwestern Brøggerhalvøya 78.95213°N, 11.48417°E 49 OF 6.1 (0.1) bc 454 (14) b 99 (7) cde 6.5 (0.4) cd 15.1 (0.3) cd 0.18 (0.02) de KnudL 78.93934°N, 11.80773°E 48 MF 6.9 (0.3) ab 476 (93) b 101 (9) cde 6.7 (0.7) c 15.3 (0.8) cd 0.19 (0.02) de KnauH 78.93582°N, 11.62232°E 263 OF 7.0 (0.1) a 493 (40) b 118 (10) bc 6.2 (0.4) cd 18.9 (0.5) abc 0.30 (0.01) cd GrL Grønlietoppen 78.88518°N, 12.2716°E 34 IF 6.9 (0.2) ab 110 (19) c 108 (11) bcd 5.8 (0.5) cd 18.7 (0.5) abc 0.51 (0.02) ab GrH 78.88266°N, 12.2764°E 142 IF 5.3 (0.1) d 59 (9) c 89 (7) cde 4.2 (0.2) cde 21.3 (0.8) a 0.41 (0.01) bc OsL Ossian Sarsfjellet 78.95044°N, 12.45314°E 46 IF 6.6 (0.1) ab 164 (33) c 76 (9) def 5.0 (0.7) cde 15.5 (0.6) cd 0.38 (0.05) c OsH 78.94403°N, 12.46749°E 354 IF 5.7 (0.3) cd 129 (31) c 67 (7) ef 4.1 (0.4) de 16.9 (2.0) abcd 0.38 (0.02) c BlL Blomstrandhalvøya 78.96775°N, 12.07272°E 52 MF 7.4 (< 0.1) a 811 (60) a 67 (10) ef 4.4 (0.5) cde 15.3 (1.1) cd 0.14 (0.01) e BlH 78.98453°N, 12.0727°E 352 MF 7.1 (0.1) a 636 (30) ab 41 (3) f 3.2 (0.2) e 13.0 (0.3) d 0.15 (0.02) e KnL Knølen 79.03072°N, 11.9577°E 42 OF 7.1 (0.2) a 65 (10) c 256 (10) a 12.9 (0.6) a 19.9 (0.6) ab 0.54 (0.03) a KnH 79.02523°N, 11.93064°E 219 OF 7.0 (0.1) a 86 (9) c 146 (4) b 9.5 (0.7) b 15.7 (1.2) bcd 0.53 (0.04) a b) F-values Locality Elevation Locality × Elevation pH 15.1*** 7.7** 17.4*** Chlorophyll a 29.1*** ns ns TN 20.3*** 7.4** ns TC 15.8*** 6.6* ns CN ratio 5.5*** ns ns TP 16.9*** ns ns Vegetation overview A total of 47 species were observed across the studied sites through the vegetation analysis (Suppl. Table 1). Of these, 23 angiosperms taxa were recorded, with the most commonly occurring being Dryas octopetala , Salix polaris , Saxifraga oppositifolia , Carex sp., Silene acaulis and Bistorta vivipara . In general, angiosperm coverage was significantly higher in Knølen and exhibited a notable decline in the high-elevation sites. The presence of Carex sp. and Silene acaulis was significantly higher in low-elevation sites, whereas Papaver dhalianum was found to be significantly more abundant in a high-elevation sites. Overall, the coverage of lichens and bryophytes did not differ significantly among localities or elevations. However, the abundance of four identified bryophytes ( Racomitrium sp. and Dicranum sp.) and lichens ( Flavocetraria nivalis and Thamnolia vermicularis ) increased with elevation. Metagenomic sequencing overview Metagenomic sequencing generated 9M quality filtered reads in total. Of these, 928K rRNAs reads were separated and taxonomically assigned. Bacterial community composition was dominated by Actinobacteria (15–34% of rRNA reads) and Proteobacteria (18–24% of rRNA reads), while eukaryotes were mainly presented by Chloroplastida (1–5% of rRNA reads) and Fungi (1–8% of rRNA reads; Suppl. Figure 1). The relative abundance of the majority of the taxa was significantly different across studied localities (except Cyanobacteria, Myxococcota, Planctomycetota, Proteobacteria and Metazoa). Furthermore, Actinobacteriota and Gemmatimonadota showed the significant decline in high-elevation sites, while the relative abundance of Amoebozoa and SAR significantly increased with elevation. Phototrophic community composition of the biocrusts assessed by metagenomic sequencing The proportion of reads between cyanobacteria and eukaryotic phototrophs varied across the sites (Suppl. Figure 2). Sites in northwestern Brøggerhalvøya (KnudL, GeopL, KnauH) had a similar distribution of both groups (around 50%). High-elevated sites in Grønlietoppen (GrH) and Ossian Sarsfjellet (OsH) had a higher number of reads assigned to eukaryotic phototrophs (78 and 57%, respectively), while opposite trend was observed at all sites in Blomstrandhalvøya (BlL, BlH) and Knølen (KnL, KnH), where cyanobacteria dominated (67–89%). The majority of cyanobacterial reads were assigned to filamentous cyanobacteria, mainly Leptolyngbyales (27–59% of cyanobacterial rRNA reads), in all samples (except KnL; Fig. 2 ). Nostocales, Gloeobacterales and Chroococcidiopsidales constituted a large fraction depending on the sample (9–31%, 5–25% and 2–30% of cyanobacterial rRNA reads, respectively). Two-way ANOVA showed that locality was the main factor affecting cyanobacterial community composition, while elevation effect was significant only for Chroococcales, Desertifilales and Nodosilineales. On the genus level, only Chalicogloea was found as indicator taxon for low sites and no common taxa were detected as significant at high elevations. However, several genera were significantly associated with specific localities. For example, Nodosilinea was an indicator genus in outer fjord (sites GeopL, KnauH, KnL, KnH), Tychonema , Calothrix , Chalicogloea and Cephalothrix in middle fjord (sites KnudL, BlL and BlH), and Nostoc and Geminocystis in inner fjord (sites GrL, GrH, OsL and OsH). Eukaryotic phototrophs were mainly represented by Streptophyta (42–66% of eukaryotic phototrophs reads), Chlorophyta (24–60% of eukaryotic phototrophs reads) and Stramenopiles (1–13% of eukaryotic phototrophs reads; Fig. 2 ). Bryophyta constituted the major fraction within the Streptophyta and was the dominant taxon in the majority of the sites. Similarly, Trebouxiophyceae had a high relative abundance and dominated the OsL, OsH and GrH. Furthermore, the relative abundance of Anthocerotophyta, Bryophyta, Klebsormidiophyceae, Marchantiophyta and Trebouxiophyceae significantly differed between the localities. A significantly higher relative abundance of Anthocerotophyta and Bryophyta was observed in biocrusts from the outer fjord (OF), whereas Klebsormidiophyceae, Marchantiophyta, and Trebouxiophyceae predominated in the inner fjord (IF). In addition, there was a significant increase in the relative abundance of Anthocerotophyta and Chrysophyceae at high-elevation sites. Besides, 7 indicator genera, including Dictyosphaerium , Hemichloris , Elliptochloris , Fibrocapsa , Nephroselmis , Coccomyxa and Parietochloris , were revealed at the high elevation. Effect of environmental parameters on biocrust phototrophs The NMDS plot and PERMANOVA test indicated that distance from the open sea and elevation significantly influenced the community composition of vegetation (FDR = 0.02) and phototrophic eukaryotes (FDR < 0.001), but not cyanobacteria (FDR = 0.07; Table 2 ; Fig. 3 ). Additionally, the interaction between distance and elevation impacted all three groups, suggesting that these factors are interdependent and jointly shape the structure of these communities. The measured environmental variables that significantly correlated with community composition varied across the different groups of organisms. The C:N ratio was the only significant parameter influencing the structure of the vegetation community. Similarly, the number of cyanobacterial metagenomic reads exhibited a correlation with the C:N ratio, TN, chlorophyll a content, and pH. Finally, for metagenomic reads of phototrophic eukaryotes, TP, chlorophyll a content and pH, were identified as the key environmental factors. Table 2 – PERMANOVA results for the effects of locality, elevation, and their interaction on community composition (* p < 0.05, **p < 0.01, ***p < 0.001, ns: non-significant). Locality Elevation Locality x Elevation FDR-adjusted R 2 F-value p-value R 2 F-value p-value R 2 F-value p-value Vegetation 0.13 2.29 * 0.15 5.34 *** 0.14 2.62 ** 0.02 Cyanobacteria 0.06 1.89 ns 0.01 0.65 ns 0.13 3.93 ** 0.07 Phototropic Eukaryotes 0.14 4.84 *** 0.04 2.44 ** 0.10 3.29 *** < 0.001 Metagenomic profile of genes associated with photosynthesis in phototrophic organisms The photosynthesis-associated genes, reported exclusively in phototrophs through metagenomic analysis, were divided into seven categories (Fig. 4 ): Photosystem I (PSI; 19 genes), Photosystem II (PSII; 29 genes), light-harvesting complexes for PSI and PSII, the electron transport chain (including the Cytochrome b6f complex; 8 genes), chlorophyll biosynthesis (9 genes), and associated or regulatory genes (6 genes). For example, genes associated with the Calvin-Benson cycle were excluded, as this pathway is not restricted to organisms with oxygenic photosynthesis. The abundance of PSII genes consistently exceeded that of PSI genes across all sites. Similarly, genes encoding the light-harvesting complex of PSII (LHCII) were more abundant than those for the light-harvesting complex of PSI (LHCI). Furthermore, genes involved in chlorophyll biosynthesis represented a substantial proportion of the metagenomic reads at each site. Within the PSI-associated genes, psaA and psaB were the most dominant, while psbB predominated among PSII-related genes. The cytochrome b6f complex was dominated by petA or petC depending on the locality. In general, the abundance of the different PSI genes varied across different localities and elevations, whereas the abundance of PSII genes showed significant variation only among the localities (Suppl. Table 2). Several PS genes, including psaJ, psbH, psbK, psbT and psbZ exhibited highly significant differences across localities, elevations as well as their interaction. In contrast, the abundance of psaE, psaF, psaI, ycf4, psa2, psbA, psbB, psbD, psbE, psbL, psbQ, ycf12 and ctpA were significantly different only among the localities. In addition, genes such as psaC, psaD, psaH, psaK, psaM, psaN, btpA, psbF, psbI, psbJ, psbM, psbU, psbV, psbX, psb27, psb28 and ycf48 demonstrated no significant variation under the tested conditions. Furthermore, light-harvesting complex of PSI and PSII exhibited significant differences between localities, elevations and the interaction between these factors. A similar pattern was observed for some other genes, including petG, hcf136 and LhcsR. Discussion Elevation and local environmental factors shape phototrophic communities in Arctic ecosystems Biocrusts are known to facilitate the establishment and maintenance of vascular plants during succession in the High Arctic [ 20 ]. In general, the vegetation in the studied sites were dominated by typical Svalbard vascular plants such as Dryas octopetala , Salix polaris , Saxifraga oppositifolia etc. [ 21 ]. The vegetation analysis revealed significant changes in species distribution and abundance across the studied localities and elevations. Low-elevation sites supported higher angiosperm coverage, with species like Carex sp. and Silene acaulis being particularly abundant. This pattern might indicate more favorable environmental conditions (e.g. higher temperature, reduced solar radiation and wind) at lower elevations, enabling vascular plants to establish and thrive [ 12 , 22 ]. In contrast, Papaver dahlianum abundance increased with elevation. The wider range of plant species at lower elevations likely resulted in greater competition, which Papaver dahlianum might avoid by occupying higher elevations. Furthermore, the high-elevation sites were characterized by increased abundance of bryophytes ( Racomitrium sp., Dicranum sp.) and lichens ( Flavocetraria nivalis , Thamnolia vermicularis ). These organisms generally show an increase in abundance with rising elevation and a reduction in vascular plant cover [ 23 ]. The cyanobacterial community in the studied biocrusts exhibited significant variation primarily based on locality, with elevation playing a minor role. For example, heterocystous cyanobacteria from the order Nostocales were significantly more abundant in the biocrusts of the Inner Fjord (IF). This locality is typically more sheltered from marine influences, experiences less wind exposure, and has more stable, often warmer temperatures. The prevalence of these cyanobacteria might indicate that the biocrusts in the inner fjord are more developed, as Nostocales have previously been observed to dominate the Arctic biocrusts in the later stages of succession [ 24 ]. Furthermore, the physiological performance and adaptive mechanisms of cyanobacteria enable them to colonize ecosystems across a wide range of altitudes [ 25 ] As a result, elevation did not have a significant overall effect on the cyanobacterial community composition. Likewise, chlorophyll a content, which correlated with the cyanobacterial read abundance, remained consistent across elevations. A similar trend was previously observed along the Ossian Sarsfjellet elevation transect (101–314 m a.s.l.) suggesting that the primary productivity of the biocrust cyanobacteria is maintained despite elevation-driven environmental gradients [ 12 ]. Several taxa belonging to eukaryotic phototrophs demonstrated a response to locality and elevation. The abundance of Bryophyta was higher at the outer fjord (OF) sites, where Andreaea was also identified as an indicator species. This moss, commonly found in polar regions [ 26 ], is well-adapted to extreme temperature fluctuations, high UV radiation, and periodic desiccation, which are the characteristic of the ocean-influenced environment of the outer fjord. The elevation had no marked influence on the composition of phototrophic eukaryotes. However, several algal genera were identified as indicator taxa. For example, Elliptochloris and Parietochloris , previously reported as predominant in mountain habitats [ 27 ], were associated with high-elevation sites. Genes associated with photosynthesis reflect adaptive responses of phototrophs to environmental conditions Biocrust organisms at high elevations are often exposed to more extreme conditions, such as enhanced UV radiation, lower temperatures and strong water fluctuations [ 28 ]. To survive such conditions, phototrophs might enhance their photosynthetic machinery to maximize energy capture when conditions allow. Therefore, the increased abundance of several photosynthesis-related genes at higher elevation sites could be a direct result of the need to adapt to more extreme environments [ 29 ]. Furthermore, the PSII was stable between the different elevations but significantly differed between the localities, being higher in the sites of inner fjord (IF). Perhaps the elevation difference of 100–300 meters does not significantly affect the capacity of PSII. However, exposure to the open sea could influence the diversity of organisms and their photosynthetic performance [ 30 ]. LHCII genes showed different patterns and generally increased with elevation, indicating that the two complexes evolved separately to meet specific functional requirements. It has been shown that plants possess multi-gene families of varying sizes for photosynthesis-related processes [ 31 ]. For example, the gene families associated with Photosystem I and II evolve primarily through whole-genome duplications, followed by the retention or loss of duplicated genes. In contrast, genes involved in the Calvin cycle and light-harvesting processes are subject to non-whole-genome duplications, with the latter mechanism likely providing greater potential for functional differentiation and adaptation [ 31 ]. Furthermore, PSI gene content also increased with elevation in most of the studied sites (except Knølen), presumably indicating a larger demand of cyclic photosynthesis there. Recent studies have demonstrated that cyclic photosynthetic electron flow helps alleviate light stress in soybean [ 32 ]. This mechanism is particularly relevant for biocrusts, where light stress is a significant factor, especially at lower temperatures and higher elevations [ 33 ]. Such findings may reflect selection for organisms with genetic adaptations suited to low-resource environments, photoprotection, or efficient light capture under harsh conditions. Climate and nutritional status might influence genome size and the proportion of polyploid genomes in plants [ 34 ]. Plant species in polar regions tend to have smaller genomes and lower levels of polyploidy, which might result in a higher percentage of photosynthetic reads in the dataset. This could be explained by the increased proportion of the genome dedicated to encoding photosynthesis, even though the relative abundance of these genes would remain unchanged. However, it is unclear whether algae and cyanobacteria exhibit a similar relationship. Furthermore, some Arctic plants, like Draba , exhibit high polyploidy (up to 16x), increasing the expression rates of freezing-tolerance-related genes, such as COR15 [ 35 ]. However, this alone does not explain the observed differences in relative abundances. Shifts in relative abundance require multi-gene families of varying sizes. For example, cyanobacteria possess up to six copies of the psbA gene (encoding D1 protein), which are differentially expressed depending on the environmental conditions (e.g. low or high light; [ 36 ]). In contrast, phototrophic eukaryotes typically have only a single copy gene in the chloroplast genome. Thus, the ratio of cyanobacteria to eukaryotic phototrophs could influence psbA gene abundance across sites. The Cyt b6f coµplex plays a crucial role in photosynthetic electron transport. It µediates electron flow between PSII and PSI, and provides ATP and NADPH for photosynthetic carbon fixation [ 37 ]. The majority of the studied localities exhibited higher petC content within cytochrome b6f complex genes. This could indicate that these biocrusts might harbour communities that are more focused on processes such as carbon fixation and NADPH production. Indeed, PetC1 was identified as the predominant Rieske iron-sulfur protein within the cytochrome b6f complex of Synechocystis sp. [ 38 ]. On contrary, petA, petD, petG, and petN recorded in the chloroplast genome of some plants [ 39 ], could probably indicate a presence of eukaryotic phototrophs in the biocrusts. The samples from Knølen and Grønlietoppen, which exhibited pronounced dominance of cyanobacteria over eukaryotic phototrophs, demonstrated higher petC gene abundance within the ETC genes. Chlorophyll biosynthesis is a fundamental process for the growth and adaptation of phototrophic organisms [ 40 ]. ChlH, the most essential subunit of magnesium chelatase, plays a crucial role in the accumulation of photoassimilate and the promotion of chlorophyll biosythesis under challenging conditions [ 41 ]. In the studied samples, ChlH content prevailed over all other subunits and did not significantly differ between elevations or localities, indicating its importance for all phototrophs in extreme Arctic environments. Furthermore, a significantly different content of the CAO gene (Chlorophyll a Oxygenase) converting chlorophyll a into chlorophyll b was observed between elevations and localities. Some phototrophs (e.g. cyanobacteria, diatoms) lack chlorophyll b . Consequently, increased capacity to produce chlorophyll b could indicate higher presence of other phototrophs (e.g. plants, green algae; [ 42 ]). It is also likely that other pathways exhibit similar expansion in photoautotrophs across different environments. However, presence of many common pathways in diverse organismal groups makes analysis more challenging. As advanced techniques improve the separation of metagenomic datasets by organismal groups, detailed studies would be capable of revealing whether other pathways display similar locally differentiated relative abundances. Conclusion The study revealed that elevation, i.e. differences of 100–300 µ, did not have a pronounced effect on phototrophic biocrust coµµunities as a whole in the Arctic. However, biocrusts at higher elevations exhibited a preference for bryophytes, lichens, and specific taxa of cyanobacteria and algae. In contrast, significant differences between the localities were observed, suggesting that local environmental factors, such as microclimate, soil chemistry, and geology, likely play a crucial role in shaping biocrust community composition and genetic capacity. In addition, the differences observed in the photosynthetic gene content across various localities and elevations suggest that certain phototrophic organisms possess the capacity for specific genetic adaptations that allow them to thrive in extreme environmental conditions. Therefore, a higher genetic capacity for photosynthesis might determine whether organisms can survive in more challenging environments, while in milder conditions, organisms with faster growth but lower genetic capacity might prevail. It is important to note that these findings reflect the potential photosynthetic capacity based on gene presence, rather than actual gene expression or functional activity. While the richness and abundance of genes may indicate variability in underlying functional potential, further analyses, such as metatranscriptomics, are necessary to confirm gene expression and functional relevance under environmental conditions. Declarations Funding This work was supported by the Alfred-Wegner-Institute in Bremerhaven (Grant KOS175). Author Contribution BB and EP designed the study. BB and UK acquired funding for the project. EP, BB, LK and KLB conducted the field sampling, with LK and KLB additionally performing vegetation surveys. EP and LK carried out the molecular laboratory work and SK provided soil chemical measurements. EP performed the bioinformatic analyses and data processing. EP wrote the first draft of the manuscript. All authors revised the manuscript and approved the final version. Acknowledgement We would like to acknowledge members of AWIPEW station in Ny-Ålesund for technical and logistic support during sampling. Furthermore, we are grateful to Isabel Mas Martinez for the laboratory assistance. Data Availability Sequence data used in this study were submitted to the Sequence Read Archive (SRA) under the project PRJNA1124630. References Weber B, Belnap J, Büdel B, Antoninka AJ, Barger NN, Chaudhary VB, et al. What is a biocrust? A refined, contemporary definition for a broadening research community. Biol Rev. 2022;97:1768–85. Weber B, Büdel B, Belnap J. Biological Soil Crusts: An Organizing Principle in Drylands. Springer International Publishing; 2016. Zhang Y, Aradottir AL, Serpe M, Boeken B. Interactions of Biological Soil Crusts with Vascular Plants. 2016. pp. 385–406. Pedersen ÅØ, Convey P, Newsham KK, Mosbacher JB, Fuglei E, Ravolainen V et al. Five decades of terrestrial and freshwater research at Ny-Ålesund, Svalbard. Polar Res. 2022;41. Pushkareva E, Johansen JR, Elster J. A review of the ecology, ecophysiology and biodiversity of microalgae in Arctic soil crusts. Polar Biol. 2016;39:2227–40. Williams L, Borchhardt N, Colesie C, Baum C, Komsic-Buchmann K, Rippin M, et al. Biological soil crusts of Arctic Svalbard and of Livingston Island, Antarctica. Polar Biol. 2017;40:399–411. Pushkareva E, Kvíderová J, Šimek M, Elster J. Nitrogen fixation and diurnal changes of photosynthetic activity in Arctic soil crusts at different development stage. Eur J Soil Biol. 2017;79:21–30. Harel Y, Ohad I, Kaplan A. Activation of photosynthesis and resistance to photoinhibition in cyanobacteria within biological desert crust. Plant Physiol. 2004;136:3070–9. Starkenburg SR, Reitenga KG, Freitas T, Johnson S, Chain PSG, Garcia-Piche F, et al. Genome of the cyanobacterium Microcoleus vaginatus FGP-2, a photosynthetic ecosystem engineer of arid land soil biocrusts worldwide. J Bacteriol. 2011;193:4569–70. Gypser S, Herppich WB, Fischer T, Lange P, Veste M. Photosynthetic characteristics and their spatial variance on biological soil crusts covering initial soils of post-mining sites in Lower Lusatia, NE Germany. Flora - Morphology, Distribution, Functional Ecology of Plants. 2016;220:103–16. Rantanen M, Karpechko AY, Lipponen A, Nordling K, Hyvärinen O, Ruosteenoja K et al. The Arctic has warmed nearly four times faster than the globe since 1979. Commun Earth Environ. 2022;3. Mas Martinez I, Pushkareva E, Keilholz LA, Linne Von Berg K-H, Karsten U, Kammann S et al. Role of Climate and Edaphic Factors on the Community Composition of Biocrusts Along an Elevation Gradient in the High Arctic. 2024. https://doi.org/10.3390/microorganisms12122606 Svendsen H, Besz-Czynska-Møller A, Zajaczkowski M, Zajaczkowski M, Azzolini R, Bruland O, et al. The physical environment of Kongsfjorden-Krossfjorden, an Arctic fjord system in Svalbard. Polar Res. 2002;21:133–66. Kammann S, Schiefelbein U, Dolnik C, Mikhailyuk T, Demchenko E, Karsten U et al. Successional Development of the Phototrophic Community in Biological Soil Crusts on Coastal and Inland Dunes. Biology (Basel). 2023;12. Bolger AM, Lohse M, Usadel B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20. Kopylova E, Noé L, Touzet H, SortMeRNA. Fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics. 2012;28:3211–7. Li D, Liu CM, Luo R, Sadakane K, Lam TW. MEGAHIT: An ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics. 2015;31:1674–6. Götz S, García-Gómez JM, Terol J, Williams TD, Nagaraj SH, Nueda MJ, et al. High-throughput functional annotation and data mining with the Blast2GO suite. Nucleic Acids Res. 2008;36:3420–35. Oksanen J. Multivariate analysis of ecological communities in R:vegan tutorial. R Doc. 2013;:43. Breen K, Lévesque E, Le E. Proglacial succession of biological soil crusts and vascular plants: biotic interactions in the High Arctic. Can J Bot. 2006;84:1714–31. Lee YK. Arctic Plants of Svalbard. Cham: Springer International Publishing; 2020. Wong SK, Cui Y, Chun SJ, Kaneko R, Masumoto S, Kitagawa R et al. Vegetation as a key driver of the distribution of microbial generalists that in turn shapes the overall microbial community structure in the low Arctic tundra. Environ Microbiome. 2023;18. Martins A, Collart F, Sim-Sim M, Patiño J. Ecological drivers of taxonomic, functional, and phylogenetic diversity of bryophytes in an oceanic island. Ecol Evol. 2024;14. Pushkareva E, Pessi IS, Wilmotte A, Elster J. Cyanobacterial community composition in Arctic soil crusts at different stages of development. FEMS Microbiol Ecol. 2015;91:fiv143. Janatková K, Řeháková K, Doležal J, Šimek M, Chlumská Z, Dvorský M, et al. Community structure of soil phototrophs along environmental gradients in arid Himalaya. Environ Microbiol. 2013;15:2505–16. Putzke J, Athanásio CG, De Albuquerque MP, Victoria FC, Pereira AB. Comparative study of moss diversity in South Shetland Islands and in the Antarctic Peninsula. Revista Chil de Historia Nat. 2015;88 March. Mikhailyuk T, Demchenko E, Mikhailyuk TI, Demchenko EM, Sergiy, Kondratyuk Y. Algae of granite outcrops from the left bank of the river Pivdennyi Bug (Ukraine)*. 2003. Körner C. Alpine Plant Life. Springer Berlin Heidelberg; 1999. Zhai B, Hu Z, Sun S, Tang Z, Wang G. Characteristics of photosynthetic rates in different vegetation types at high-altitude in mountainous regions. Sci Total Environ. 2024;907. Pushkareva E, Barrantes I, Leinweber P, Karsten U. Microbial diversity in subarctic biocrusts from west iceland following an elevation gradient. Microorganisms. 2021;9. Coate JE, Schlueter JA, Whaley AM, Doyle JJ. Comparative evolution of photosynthetic genes in response to polyploid and nonpolyploid duplication. Plant Physiol. 2011;155:2081–95. Lei Y, Gao J, Wang Q, Zeng W, Diwakar D, Zhang Y et al. Cyclic Electron Flow Alleviates the Stress of Light Fluctuation on Soybean Photosynthesis. Agric (Switzerland). 2024;14. 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. Bureš P, Elliott TL, Veselý P, Šmarda P, Forest F, Leitch IJ, et al. The global distribution of angiosperm genome size is shaped by climate. New Phytol. 2024;242:744–59. Brochmann CGUSTAFSSON. 1946, 1947a, b; STEBBINS 1950. 1993. Mulo P, Sicora C, Aro EM. Cyanobacterial psbA gene family: Optimization of oxygenic photosynthesis. Cell Mol Life Sci. 2009;66:3697–710. Kramer DM, Evans JR. The importance of energy balance in improving photosynthetic productivity. Plant Physiol. 2011;155:70–8. Schneidere D, Berry S, Volkmer T, Seidler A, Rögner M. PetC1 is the major Rieske iron-sulfur protein in the cytochrome b 6f complex of synechocystis sp. PCC 6803. J Biol Chem. 2004;279:39383–8. Khan N, Choi S-H;, Lee C-H;, Qu M, Jeon J-S, Khan N et al. Citation: Photosynthesis: Genetic Strategies Adopted to Gain Higher Efficiency. 2024. https://doi.org/10.3390/ijms25168933 Janssen PJD, Lambreva MD, Plumeré N, Bartolucci C, Antonacci A, Buonasera K, et al. Photosynthesis at the forefront of a sustainable life. Front Chem. 2014;2:1–22. Sun M, Shen Y. Integrating the multiple functions of CHLH into chloroplast-derived signaling fundamental to plant development and adaptation as well as fruit ripening. Plant Sci. 2024;338. Tanaka A, Ito H, Tanaka R, Tanaka NK, Yoshida K, Okada K. Chlorophyll a oxygenase (CAO) is involved in chlorophyll b formation from chlorophyll a. 1998. Additional Declarations No competing interests reported. Supplementary Files Suppl.Figures.docx SupplementaryTables.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 08 Jan, 2026 Reviews received at journal 16 Dec, 2025 Reviews received at journal 09 Dec, 2025 Reviews received at journal 07 Dec, 2025 Reviews received at journal 27 Nov, 2025 Reviewers agreed at journal 27 Nov, 2025 Reviews received at journal 25 Nov, 2025 Reviews received at journal 24 Nov, 2025 Reviewers agreed at journal 24 Nov, 2025 Reviewers agreed at journal 24 Nov, 2025 Reviewers agreed at journal 24 Nov, 2025 Reviews received at journal 23 Nov, 2025 Reviews received at journal 23 Nov, 2025 Reviewers agreed at journal 23 Nov, 2025 Reviewers agreed at journal 23 Nov, 2025 Reviewers agreed at journal 22 Nov, 2025 Reviews received at journal 09 Nov, 2025 Reviewers agreed at journal 08 Nov, 2025 Reviewers agreed at journal 08 Nov, 2025 Reviewers agreed at journal 08 Nov, 2025 Reviewers agreed at journal 06 Nov, 2025 Reviewers invited by journal 06 Nov, 2025 Editor assigned by journal 20 Jun, 2025 Submission checks completed at journal 19 Jun, 2025 First submitted to journal 15 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6899352","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":545725699,"identity":"4ff04da7-ea3e-4681-9902-91f6f8fab6b5","order_by":0,"name":"Ekaterina Pushkareva","email":"data:image/png;base64,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","orcid":"","institution":"University of Cologne","correspondingAuthor":true,"prefix":"","firstName":"Ekaterina","middleName":"","lastName":"Pushkareva","suffix":""},{"id":545725703,"identity":"286f27f7-b310-4415-a751-28df312c9d29","order_by":1,"name":"Leonie Keilholz","email":"","orcid":"","institution":"University of Cologne","correspondingAuthor":false,"prefix":"","firstName":"Leonie","middleName":"","lastName":"Keilholz","suffix":""},{"id":545725705,"identity":"e89b2431-1b3a-41d5-90f2-16cd8a2e2420","order_by":2,"name":"Sandra Kammann","email":"","orcid":"","institution":"University of Rostock","correspondingAuthor":false,"prefix":"","firstName":"Sandra","middleName":"","lastName":"Kammann","suffix":""},{"id":545725707,"identity":"77e546e6-0d25-4ab4-9064-c32b889454d9","order_by":3,"name":"Karl-Heinz Linne von Berg","email":"","orcid":"","institution":"University of Cologne","correspondingAuthor":false,"prefix":"","firstName":"Karl-Heinz","middleName":"Linne","lastName":"von Berg","suffix":""},{"id":545725710,"identity":"6a53d194-f1f1-40c3-98bb-815bc605d44a","order_by":4,"name":"Ulf Karsten","email":"","orcid":"","institution":"University of Rostock","correspondingAuthor":false,"prefix":"","firstName":"Ulf","middleName":"","lastName":"Karsten","suffix":""},{"id":545725713,"identity":"179d4234-d3c1-4bcc-a0fd-57dd4066cbbe","order_by":5,"name":"Burkhard Becker","email":"","orcid":"","institution":"University of Cologne","correspondingAuthor":false,"prefix":"","firstName":"Burkhard","middleName":"","lastName":"Becker","suffix":""}],"badges":[],"createdAt":"2025-06-15 16:23:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6899352/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6899352/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96249188,"identity":"066107a4-480b-4127-9acc-2afb92dc7c30","added_by":"auto","created_at":"2025-11-19 07:30:36","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3798187,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscriptall.docx","url":"https://assets-eu.researchsquare.com/files/rs-6899352/v1/77482148c5cb62b160a2c09b.docx"},{"id":96113067,"identity":"b319c74f-70cf-4d2f-88da-9bf050836d32","added_by":"auto","created_at":"2025-11-17 17:45:06","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7990,"visible":true,"origin":"","legend":"","description":"","filename":"1f1e334ffb554b298c08265115335183.json","url":"https://assets-eu.researchsquare.com/files/rs-6899352/v1/f59dd75f603e3a144ab33be4.json"},{"id":96247888,"identity":"44bc683a-f3af-4337-b96f-5cb577eadde4","added_by":"auto","created_at":"2025-11-19 07:27:48","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":104591,"visible":true,"origin":"","legend":"","description":"","filename":"Suppl.Figures.docx","url":"https://assets-eu.researchsquare.com/files/rs-6899352/v1/f7478b3926b8d81b96c1be76.docx"},{"id":96113068,"identity":"23487d4c-42b2-46db-a231-7420f8d681b7","added_by":"auto","created_at":"2025-11-17 17:45:06","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":18645,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6899352/v1/a53df50653a49fe9295de8fc.xlsx"},{"id":96113070,"identity":"1d652056-3455-4367-99aa-36975f8b8c7a","added_by":"auto","created_at":"2025-11-17 17:45:06","extension":"xml","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":121148,"visible":true,"origin":"","legend":"","description":"","filename":"1f1e334ffb554b298c082651153351831enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-6899352/v1/91af2eb4a86313b785454ab5.xml"},{"id":96248818,"identity":"8034b106-b39e-4614-b25d-bc7ca6430210","added_by":"auto","created_at":"2025-11-19 07:29:23","extension":"jpeg","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":708084,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6899352/v1/f0f15e7765602978f8192c5e.jpeg"},{"id":96249453,"identity":"70450eb2-8014-4871-a155-ff45f659fcf7","added_by":"auto","created_at":"2025-11-19 07:33:35","extension":"jpeg","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":283371,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6899352/v1/0d94f95fb34de1104c717032.jpeg"},{"id":96113074,"identity":"bfaa00b1-5459-483c-86e1-3952036cca23","added_by":"auto","created_at":"2025-11-17 17:45:06","extension":"jpeg","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2218812,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6899352/v1/479ed3421413e3b5b267d49e.jpeg"},{"id":96113080,"identity":"1f0874fa-960e-4aeb-b15f-1cebb29f5fcb","added_by":"auto","created_at":"2025-11-17 17:45:06","extension":"jpeg","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":256094,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6899352/v1/e2312039d7d7caebc557c1c4.jpeg"},{"id":96250548,"identity":"bd1ddf84-34dd-4de3-a7d6-7504d1f52e83","added_by":"auto","created_at":"2025-11-19 07:38:36","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":583374,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6899352/v1/b100a7d5144d755618f99608.png"},{"id":96113077,"identity":"67b9dd7d-a79e-42f8-aa00-6445b2bbb619","added_by":"auto","created_at":"2025-11-17 17:45:06","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":30816,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6899352/v1/a97054b173d9f791492fa755.png"},{"id":96248624,"identity":"c49ae234-b753-49d0-91fc-34b1213ba7a1","added_by":"auto","created_at":"2025-11-19 07:28:49","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":17254,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6899352/v1/57174548f0386117d6f7f67e.png"},{"id":96113079,"identity":"074143df-bd02-4907-ac06-f323ed52fe27","added_by":"auto","created_at":"2025-11-17 17:45:06","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":27756,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6899352/v1/7aea89ae7e4539e96d0f2de4.png"},{"id":96248286,"identity":"13283b83-2073-42da-b104-8630d635236a","added_by":"auto","created_at":"2025-11-19 07:28:16","extension":"xml","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":120190,"visible":true,"origin":"","legend":"","description":"","filename":"1f1e334ffb554b298c082651153351831structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-6899352/v1/1793cf819b97c12157db1f51.xml"},{"id":96248597,"identity":"b39909f0-5dbc-464a-b1bd-41e954c8a163","added_by":"auto","created_at":"2025-11-19 07:28:47","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":127299,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-6899352/v1/295fa1701989d7d6127062fa.html"},{"id":96113060,"identity":"f6240010-2c9d-466c-839f-42f1da701cbf","added_by":"auto","created_at":"2025-11-17 17:45:05","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":708084,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the sampling sites. Colors represent the fjord zones: Outer Fjord (OF; blue), Mid Fjord (MF; green), and Inner Fjord (IF; yellow). The letters L and H in sample names indicate low and high elevations, respectively.\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6899352/v1/07df9e46f99e8b64516d950e.jpeg"},{"id":96113061,"identity":"72fca8a0-637c-475c-9520-e51e2db6c3f8","added_by":"auto","created_at":"2025-11-17 17:45:06","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":283371,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundances of cyanobacteria (a) and eukaryotic phototrophs (b) based on 16S and 18S rRNA genes, respectively.\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6899352/v1/0ab6774d73fb914de1b8092f.jpeg"},{"id":96250374,"identity":"6e071de3-52a0-4711-ae2f-15d00e580d5a","added_by":"auto","created_at":"2025-11-19 07:38:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":31850,"visible":true,"origin":"","legend":"\u003cp\u003eNon-metric multidimensional scaling (NMDS) plot based on the number of a) vegetation taxa identified by vegetation analysis, b) cyanobacterial 16S rRNA reads, and c) eukaryotic phototroph 18S rRNA reads, obtained through metagenomic sequencing. Arrows indicate significant correlations (p \u0026lt; 0.05) with environmental variables. NMDS stress = 0.2 for all plots.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6899352/v1/546b73d535546b6426c4324c.png"},{"id":96113065,"identity":"73773112-f2d0-4638-96c5-29b61c11157b","added_by":"auto","created_at":"2025-11-17 17:45:06","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":256094,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundance of genes associated with photosynthetic performance of the phototrophic organisms based on number of reads assessed by metagenomic sequencing. Blue color indicates genes belonging to Photosystem I, green to Photosystem II, orange to cytochrome b6f complex, grey to associated and regulatory genes, red to light-harvesting complex for PSI, purple to light-harvesting complex for PSII, and pink to chlorophyll biosynthesis.\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6899352/v1/3ed7f773c1537eeb8641d658.jpeg"},{"id":96257017,"identity":"6ef4d7ce-eb5f-4173-927d-343689f70492","added_by":"auto","created_at":"2025-11-19 07:51:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2271194,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6899352/v1/b722a009-00a4-4d04-a30b-e47152383fd7.pdf"},{"id":96113064,"identity":"fd25fecb-9239-4a4e-ab7c-6720cb9d3f57","added_by":"auto","created_at":"2025-11-17 17:45:06","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":104591,"visible":true,"origin":"","legend":"","description":"","filename":"Suppl.Figures.docx","url":"https://assets-eu.researchsquare.com/files/rs-6899352/v1/e80aad0aeaea97858d66449e.docx"},{"id":96250515,"identity":"9e2a0274-3833-452f-98f2-4ad1f82f07e7","added_by":"auto","created_at":"2025-11-19 07:38:33","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18645,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6899352/v1/6ba9725f85665a834e3f15c1.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Arctic biocrusts highlight genetic variability in photosynthesis as a key driver of biodiversity","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBiological soil crusts (biocrusts) are complex communities of microorganisms, which form a cohesive layer on top of the soil surface [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Biocrusts play an important role in ecosystem functioning and stability. They contribute significantly to soil fertility by fixing atmospheric nitrogen and carbon, thereby increasing nutrient availability in these nutrient-poor environments [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Furthermore, biocrusts improve soil structure and stability, reduce erosion and aid in water retention. These properties can affect vascular plants in various ways, depending on environmental conditions. In some cases, biocrusts may contribute to improved soil moisture and nutrient availability, potentially facilitating plant establishment [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Additionally, by stabilizing the soil, biocrusts can create conditions that support seed germination and early plant growth. However, in harsh environments where vascular plants struggle to compete, biocrusts may become dominant, forming communities adapted to extreme conditions.\u003c/p\u003e\u003cp\u003eThe extreme conditions of the Arctic environment, characterized by low temperatures, seasonally fluctuating radiation (polar day and night), and limited nutrient availability, pose significant challenges for biocrust communities [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, biocrusts exhibit resilience and adaptability to these harsh conditions [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], as reflected in their generally high abundance in the Arctic tundra with soil coverage up to 90% [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Their ability to survive prolonged periods of desiccation and freeze-thaw cycles allows them to persist and function in environments where other life forms struggle to thrive. Furthermore, despite the severe Arctic environments, biocrusts are able to perform photosynthesis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], which contributes to the primary productivity of Arctic ecosystems, forming the foundation for terrestrial food webs. The organic matter produced through photosynthesis supports various forms of life, from microorganisms to larger organisms that depend on biocrusts for nutrients or as a food source [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePhotosynthetic organisms including cyanobacteria, eukaryotic algae, bryophytes and lichens are key contributors to the biocrust ecosystem [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. These phototrophs are capable of capturing and storing energy from sunlight even under the low light conditions typical for the Arctic. They possess specialized pigments and metabolic pathways that enable them to maximize light absorption and energy conversion, even when sunlight is limited. In the polar biocrusts, these microbial phototrophs adapt to extreme conditions by employing efficient light-harvesting systems and protective mechanisms against high irradiance during the polar summer with 24 h of light [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe genetic potential of individual biocrust organisms has been the focus of extensive research, primarily through genome analyses [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, not all organisms contribute equally to the ecophysiological performance of the biocrusts [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and, to the best of our knowledge, the photosynthetic genetic potential of biocrusts as a whole has not been previously reported.\u003c/p\u003e\u003cp\u003eAs Arctic regions experience rapid warming and significant environmental changes [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], the distribution, composition, and activity of phototrophic organisms within biocrusts are likely to shift in response to varying abiotic factors. For example, the differences in elevation introduce environmental gradients, including temperature, moisture and solar radiation, which may influence the phototroph composition and, subsequently, their photosynthetic genetic capacity. Lower elevations might experience greater warming effects, increased moisture retention, and more favorable nutrient availability compared to higher elevations, where colder temperatures and desiccation stress might limit photosynthetic activity [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Here, we hypothesized that the presence of phototrophic organisms in Arctic biocrusts at different elevations with local abiotic conditions might be dependent on their genetic photosynthetic potential. Specifically, we expected that biocrusts at lower elevations will exhibit a higher abundance and diversity of photosynthesis-related genes, corresponding to more favorable conditions for phototrophic growth. Furthermore, we aimed to provide a deeper insight into the molecular mechanisms explaining the observed variations in phototroph composition within biocrusts. In addition to the analysis of the genetic photosynthetic potential of biocrusts, the objective was to document the composition and distribution of vascular plants, bryophytes, and lichens across the studied sites. This approach allowed us to explore how plant community structure may co-exist with the microbial phototrophic community, providing a more comprehensive view of Arctic biocrusts.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eSite description and sampling\u003c/h2\u003e\u003cp\u003eKongsfjorden is located at the west coast of Svalbard. Ny-\u0026Aring;lesund is a main research city in the area (78\u0026deg;55\u0026prime;26.33 N, 11\u0026deg;55\u0026prime;23.84 E), where continuous meteorological observations are conducted. The coldest and warmest months of the year are March (average air temperature \u0026minus;\u0026thinsp;12.7\u0026deg;C) and July (average air temperature 4.4\u0026deg;C), respectively. The mean annual precipitation in Ny-\u0026Aring;lesund is around 461 mm.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe wind conditions in Kongsfjorden are strongly influenced by a combination of large-scale atmospheric circulation, orographic steering, and katabatic winds [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Three localities were chosen based on their proximity to the open sea to capture the gradient of these atmospheric and climatic effects: Outer Fjord (OF), closest to the open ocean; Mid Fjord (MF), an intermediate location within the fjord; and Inner Fjord (IF), the innermost part of the fjord, furthest from the sea (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Within each locality, biocrusts at a similar developmental stage were selected to ensure consistency across samples. Two different elevations (high: marked with H; low: marked with L) were always included at each locality. The black biocrusts with presence of bryophytes and lichens were collected in summers of 2022 and 2023. Five replicates were taken from each site, brought to the laboratory and kept frozen until further analyses. In addition, the Braun-Blanquet vegetation analysis was conducted in the sampling sites (except Gr samples). Three 1m\u0026sup2; plots were analyzed at each site. The area coverage of each species was estimated using the scale described in [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSoil analysis\u003c/h3\u003e\n\u003cp\u003eTotal carbon and nitrogen (TC and TN, respectively) were determined by dry combustion using an elemental analyzer (UNICUBE\u0026reg; Elementar Analysensysteme GmbH, Langenselbold, Germany). Total phosphorus (TP) was extracted from 500 mg of air-dried material by microwave-assisted digestion with aqua regia [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The concentration was measured by inductively coupled plasma optical emission spectroscopy (ICP-OES Optima 8300, PerkinElmer) at a 214 nm wavelength. The pH was determined electrometrically in a 1:2.5 (w/v) suspension with 0.01 CaCl₂. The METTLER TOLEDO SevenMulti was employed for this purpose (Gie\u0026szlig;en, Germany). Chlorophyll \u003cem\u003ea\u003c/em\u003e was measured using a Shimadzu UV-2401 PC spectrophotometer [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eDNA isolation and metagenomic sequencing\u003c/h3\u003e\n\u003cp\u003eThe uppermost biocrust layer was used for DNA extraction, and visible plant material was avoided. Total DNA was extracted from the collected biocrusts using the DNeasy PowerSoil Pro Kit (QIAGEN, USA) according to a manufacture\u0026rsquo;s instruction. The DNAs were then sent to the Cologne Center for Genomics (Cologne, Germany) and the quality control was conducted on Agilent 2200 TapeStation System with the help of TapeStation Analysis Software 5.1. Metagenomic sequencing using Illumina NovaSeq6000 platform (PE150) was performed. The raw reads were submitted to the Sequence Read Archive (SRA) under the project PRJNA1124630.\u003c/p\u003e\n\u003ch3\u003eBioinformatic and statistical analyses\u003c/h3\u003e\n\u003cp\u003eBioinformatic analysis was performed in the OmicsBox software (Biobam, Spain). Quality filtering of the reads was conducted in Trimmomatic [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and the rRNAs were separated from the dataset using SortMeRNA [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The taxonomic assignment of the extracted 16S and 18S rRNA genes was performed using Silva database (version 138.1). The remaining non-rRNA reads were separated from the dataset and assembled \u003cem\u003ede novo\u003c/em\u003e using MEGAHIT (v1.2.8; [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]). The contigs with lengths\u0026thinsp;\u0026lt;\u0026thinsp;500 bp were subsequently removed and the remaining non-rRNA reads were quantified as fragments per kilobase of transcript per million fragments sequenced (FPKM). Further, the contigs were annotated using NCBI Blast searches [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe statistical analyses were conducted using the R software (version 4.2.1). To test for differences in environmental parameters, as well as in community composition and gene content among the sampling sites, a one-way and two-way analysis of variance (ANOVA) were performed, followed by Tukey's HSD post hoc test (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Normality of variance was assessed using Shapiro\u0026ndash;Wilk's test. If necessary, data were SQRT transformed. Furthermore, to illustrate the distribution of contigs obtained by metagenomic sequencing, non-metrical multidimensional scaling (NMDS) was performed using the package \u003cem\u003evegan\u003c/em\u003e [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Statistical difference was tested with the PERMANOVA test and False Discovery Rate (FDR) correction was applied to account for multiple testing. Environmental parameters were fitted into the ordination space using the function \u003cem\u003eenvfit\u003c/em\u003e and significance of the associations was determined by 9999 random permutations. In addition, an indicator taxa analysis was conducted to identify species that are statistically associated with each locality or elevation.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eSoil characteristics\u003c/h2\u003e\u003cp\u003eThe soil parameters significantly differed among the studied sites (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The highest chlorophyll \u003cem\u003ea\u003c/em\u003e content was observed in Blomstrandhalvoya (Bl), while the lowest was reported in the samples from Ossian Sarsfjllet (Os), Kn\u0026oslash;len (Kn) and Gr\u0026oslash;nlietoppen (Gr). The contents of TN, TC and TP were higher in KnL (Kn\u0026oslash;len, low elevation) than in the other samples, while C/N ratio prevailed in GrH (Gr\u0026oslash;nlietoppen, high elevation). Two-way ANOVA showed that chlorophyll \u003cem\u003ea\u003c/em\u003e content was significantly influenced by the elevation, while the interaction of Locality \u0026times; Elevation had a significant effect on soil chemistry.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSoil parameters of the studied sites (a). TC, TN and TP correspond to total carbon, total nitrogen and total phosphorus, respectively. The numbers in the brackets indicate standard error (SE). Two-way ANOVA results (b) show F ratios and significance levels are indicated by superscripts (***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ns: non-significant). L or H in sample names correspond to low and high elevations, respectively. Locality refers to the position within the fjord: OF\u0026thinsp;=\u0026thinsp;Outer Fjord, MF\u0026thinsp;=\u0026thinsp;Mid Fjord, IF\u0026thinsp;=\u0026thinsp;Inner Fjord. a)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSample\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSite\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGPS coordinates\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eElevation, m a.s.l.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLocality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003epH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eChlorophyll \u003cem\u003ea\u003c/em\u003e, mg m\u003csup\u003e\u0026minus;\u003c/sup\u003e\u0026sup2;\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTC, g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eTN, g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eCN ratio\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eTP, mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeopL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003enorthwestern Br\u0026oslash;ggerhalv\u0026oslash;ya\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78.95213\u0026deg;N, 11.48417\u0026deg;E\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.1 (0.1)\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e454 (14)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e99 (7)\u003csup\u003ecde\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6.5 (0.4)\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e15.1 (0.3)\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.18 (0.02)\u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKnudL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78.93934\u0026deg;N, 11.80773\u0026deg;E\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.9 (0.3)\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e476 (93)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e101 (9)\u003csup\u003ecde\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6.7 (0.7)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e15.3 (0.8)\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.19 (0.02)\u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKnauH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78.93582\u0026deg;N, 11.62232\u0026deg;E\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e263\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.0 (0.1)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e493 (40)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e118 (10)\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6.2 (0.4)\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e18.9 (0.5)\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.30 (0.01)\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGr\u0026oslash;nlietoppen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78.88518\u0026deg;N, 12.2716\u0026deg;E\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.9 (0.2)\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e110 (19)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e108 (11)\u003csup\u003ebcd\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.8 (0.5)\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e18.7 (0.5)\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.51 (0.02)\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78.88266\u0026deg;N, 12.2764\u0026deg;E\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.3 (0.1)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e59 (9)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e89 (7)\u003csup\u003ecde\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4.2 (0.2)\u003csup\u003ecde\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e21.3 (0.8)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.41 (0.01)\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOsL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eOssian Sarsfjellet\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78.95044\u0026deg;N, 12.45314\u0026deg;E\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.6 (0.1)\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e164 (33)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e76 (9)\u003csup\u003edef\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.0 (0.7)\u003csup\u003ecde\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e15.5 (0.6)\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.38 (0.05)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOsH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78.94403\u0026deg;N, 12.46749\u0026deg;E\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e354\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.7 (0.3)\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e129 (31)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e67 (7)\u003csup\u003eef\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4.1 (0.4)\u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e16.9 (2.0)\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.38 (0.02)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eBlomstrandhalv\u0026oslash;ya\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78.96775\u0026deg;N, 12.07272\u0026deg;E\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.4 (\u0026lt;\u0026thinsp;0.1)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e811 (60)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e67 (10)\u003csup\u003eef\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4.4 (0.5)\u003csup\u003ecde\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e15.3 (1.1)\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.14 (0.01)\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78.98453\u0026deg;N, 12.0727\u0026deg;E\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.1 (0.1)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e636 (30)\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e41 (3)\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.2 (0.2)\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e13.0 (0.3)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.15 (0.02)\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKnL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eKn\u0026oslash;len\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79.03072\u0026deg;N, 11.9577\u0026deg;E\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.1 (0.2)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e65 (10)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e256 (10)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e12.9 (0.6)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e19.9 (0.6)\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.54 (0.03)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKnH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79.02523\u0026deg;N, 11.93064\u0026deg;E\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.0 (0.1)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e86 (9)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e146 (4)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e9.5 (0.7)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e15.7 (1.2)\u003csup\u003ebcd\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.53 (0.04)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"11\"\u003eb)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eF-values\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eElevation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLocality \u0026times; Elevation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003epH\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15.1***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.7**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.4***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eChlorophyll\u003c/b\u003e \u003cb\u003ea\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29.1***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTN\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.3***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.4**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15.8***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.6*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCN ratio\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.5***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16.9***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eVegetation overview\u003c/h3\u003e\n\u003cp\u003eA total of 47 species were observed across the studied sites through the vegetation analysis (Suppl. Table\u0026nbsp;1). Of these, 23 angiosperms taxa were recorded, with the most commonly occurring being \u003cem\u003eDryas octopetala\u003c/em\u003e, \u003cem\u003eSalix polaris\u003c/em\u003e, \u003cem\u003eSaxifraga oppositifolia\u003c/em\u003e, \u003cem\u003eCarex\u003c/em\u003e sp., \u003cem\u003eSilene acaulis\u003c/em\u003e and \u003cem\u003eBistorta vivipara\u003c/em\u003e. In general, angiosperm coverage was significantly higher in Kn\u0026oslash;len and exhibited a notable decline in the high-elevation sites. The presence of \u003cem\u003eCarex\u003c/em\u003e sp. and \u003cem\u003eSilene acaulis\u003c/em\u003e was significantly higher in low-elevation sites, whereas \u003cem\u003ePapaver dhalianum\u003c/em\u003e was found to be significantly more abundant in a high-elevation sites.\u003c/p\u003e\u003cp\u003eOverall, the coverage of lichens and bryophytes did not differ significantly among localities or elevations. However, the abundance of four identified bryophytes (\u003cem\u003eRacomitrium\u003c/em\u003e sp. and \u003cem\u003eDicranum\u003c/em\u003e sp.) and lichens (\u003cem\u003eFlavocetraria nivalis\u003c/em\u003e and \u003cem\u003eThamnolia vermicularis\u003c/em\u003e) increased with elevation.\u003c/p\u003e\n\u003ch3\u003eMetagenomic sequencing overview\u003c/h3\u003e\n\u003cp\u003eMetagenomic sequencing generated 9M quality filtered reads in total. Of these, 928K rRNAs reads were separated and taxonomically assigned. Bacterial community composition was dominated by Actinobacteria (15\u0026ndash;34% of rRNA reads) and Proteobacteria (18\u0026ndash;24% of rRNA reads), while eukaryotes were mainly presented by Chloroplastida (1\u0026ndash;5% of rRNA reads) and Fungi (1\u0026ndash;8% of rRNA reads; Suppl. Figure\u0026nbsp;1). The relative abundance of the majority of the taxa was significantly different across studied localities (except Cyanobacteria, Myxococcota, Planctomycetota, Proteobacteria and Metazoa). Furthermore, Actinobacteriota and Gemmatimonadota showed the significant decline in high-elevation sites, while the relative abundance of Amoebozoa and SAR significantly increased with elevation.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003ePhototrophic community composition of the biocrusts assessed by metagenomic sequencing\u003c/h2\u003e\u003cp\u003eThe proportion of reads between cyanobacteria and eukaryotic phototrophs varied across the sites (Suppl. Figure\u0026nbsp;2). Sites in northwestern Br\u0026oslash;ggerhalv\u0026oslash;ya (KnudL, GeopL, KnauH) had a similar distribution of both groups (around 50%). High-elevated sites in Gr\u0026oslash;nlietoppen (GrH) and Ossian Sarsfjellet (OsH) had a higher number of reads assigned to eukaryotic phototrophs (78 and 57%, respectively), while opposite trend was observed at all sites in Blomstrandhalv\u0026oslash;ya (BlL, BlH) and Kn\u0026oslash;len (KnL, KnH), where cyanobacteria dominated (67\u0026ndash;89%).\u003c/p\u003e\u003cp\u003eThe majority of cyanobacterial reads were assigned to filamentous cyanobacteria, mainly Leptolyngbyales (27\u0026ndash;59% of cyanobacterial rRNA reads), in all samples (except KnL; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Nostocales, Gloeobacterales and Chroococcidiopsidales constituted a large fraction depending on the sample (9\u0026ndash;31%, 5\u0026ndash;25% and 2\u0026ndash;30% of cyanobacterial rRNA reads, respectively). Two-way ANOVA showed that locality was the main factor affecting cyanobacterial community composition, while elevation effect was significant only for Chroococcales, Desertifilales and Nodosilineales. On the genus level, only \u003cem\u003eChalicogloea\u003c/em\u003e was found as indicator taxon for low sites and no common taxa were detected as significant at high elevations. However, several genera were significantly associated with specific localities. For example, \u003cem\u003eNodosilinea\u003c/em\u003e was an indicator genus in outer fjord (sites GeopL, KnauH, KnL, KnH), \u003cem\u003eTychonema\u003c/em\u003e, \u003cem\u003eCalothrix\u003c/em\u003e, \u003cem\u003eChalicogloea\u003c/em\u003e and \u003cem\u003eCephalothrix\u003c/em\u003e in middle fjord (sites KnudL, BlL and BlH), and \u003cem\u003eNostoc\u003c/em\u003e and \u003cem\u003eGeminocystis\u003c/em\u003e in inner fjord (sites GrL, GrH, OsL and OsH).\u003c/p\u003e\u003cp\u003eEukaryotic phototrophs were mainly represented by Streptophyta (42\u0026ndash;66% of eukaryotic phototrophs reads), Chlorophyta (24\u0026ndash;60% of eukaryotic phototrophs reads) and Stramenopiles (1\u0026ndash;13% of eukaryotic phototrophs reads; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Bryophyta constituted the major fraction within the Streptophyta and was the dominant taxon in the majority of the sites. Similarly, Trebouxiophyceae had a high relative abundance and dominated the OsL, OsH and GrH. Furthermore, the relative abundance of Anthocerotophyta, Bryophyta, Klebsormidiophyceae, Marchantiophyta and Trebouxiophyceae significantly differed between the localities. A significantly higher relative abundance of Anthocerotophyta and Bryophyta was observed in biocrusts from the outer fjord (OF), whereas Klebsormidiophyceae, Marchantiophyta, and Trebouxiophyceae predominated in the inner fjord (IF). In addition, there was a significant increase in the relative abundance of Anthocerotophyta and Chrysophyceae at high-elevation sites. Besides, 7 indicator genera, including \u003cem\u003eDictyosphaerium\u003c/em\u003e, \u003cem\u003eHemichloris\u003c/em\u003e, \u003cem\u003eElliptochloris\u003c/em\u003e, \u003cem\u003eFibrocapsa\u003c/em\u003e, \u003cem\u003eNephroselmis\u003c/em\u003e, \u003cem\u003eCoccomyxa\u003c/em\u003e and \u003cem\u003eParietochloris\u003c/em\u003e, were revealed at the high elevation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eEffect of environmental parameters on biocrust phototrophs\u003c/h2\u003e\u003cp\u003eThe NMDS plot and PERMANOVA test indicated that distance from the open sea and elevation significantly influenced the community composition of vegetation (FDR\u0026thinsp;=\u0026thinsp;0.02) and phototrophic\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eeukaryotes (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but not cyanobacteria (FDR\u0026thinsp;=\u0026thinsp;0.07; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Additionally, the interaction between distance and elevation impacted all three groups, suggesting that these factors are interdependent and jointly shape the structure of these communities.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe measured environmental variables that significantly correlated with community composition varied across the different groups of organisms. The C:N ratio was the only significant parameter influencing the structure of the vegetation community. Similarly, the number of cyanobacterial metagenomic reads exhibited a correlation with the C:N ratio, TN, chlorophyll \u003cem\u003ea\u003c/em\u003e content, and pH. Finally, for metagenomic reads of phototrophic eukaryotes, TP, chlorophyll \u003cem\u003ea\u003c/em\u003e content and pH, were identified as the key environmental factors.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003e\u0026ndash;\u003c/b\u003e PERMANOVA results for the effects of locality, elevation, and their interaction on community composition (* p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ns: non-significant).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eLocality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eElevation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003eLocality x Elevation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFDR-adjusted\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eF-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eF-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVegetation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCyanobacteria\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e3.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhototropic Eukaryotes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e3.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eMetagenomic profile of genes associated with photosynthesis in phototrophic organisms\u003c/h2\u003e\u003cp\u003eThe photosynthesis-associated genes, reported exclusively in phototrophs through metagenomic analysis, were divided into seven categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e): Photosystem I (PSI; 19 genes), Photosystem II (PSII; 29 genes), light-harvesting complexes for PSI and PSII, the electron transport chain (including the Cytochrome b6f complex; 8 genes), chlorophyll biosynthesis (9 genes), and associated or regulatory genes (6 genes). For example, genes associated with the Calvin-Benson cycle were excluded, as this pathway is not restricted to organisms with oxygenic photosynthesis. The abundance of PSII genes consistently exceeded that of PSI genes across all sites. Similarly, genes encoding the light-harvesting complex of PSII (LHCII) were more abundant than those for the light-harvesting complex of PSI (LHCI). Furthermore, genes involved in chlorophyll biosynthesis represented a substantial proportion of the metagenomic reads at each site. Within the PSI-associated genes, psaA and psaB were the most dominant, while psbB predominated among PSII-related genes. The cytochrome b6f complex was dominated by petA or petC depending on the locality.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn general, the abundance of the different PSI genes varied across different localities and elevations, whereas the abundance of PSII genes showed significant variation only among the localities (Suppl. Table\u0026nbsp;2). Several PS genes, including psaJ, psbH, psbK, psbT and psbZ exhibited highly significant differences across localities, elevations as well as their interaction. In contrast, the abundance of psaE, psaF, psaI, ycf4, psa2, psbA, psbB, psbD, psbE, psbL, psbQ, ycf12 and ctpA were significantly different only among the localities. In addition, genes such as psaC, psaD, psaH, psaK, psaM, psaN, btpA, psbF, psbI, psbJ, psbM, psbU, psbV, psbX, psb27, psb28 and ycf48 demonstrated no significant variation under the tested conditions. Furthermore, light-harvesting complex of PSI and PSII exhibited significant differences between localities, elevations and the interaction between these factors. A similar pattern was observed for some other genes, including petG, hcf136 and LhcsR.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eElevation and local environmental factors shape phototrophic communities in Arctic ecosystems\u003c/h2\u003e\u003cp\u003eBiocrusts are known to facilitate the establishment and maintenance of vascular plants during succession in the High Arctic [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In general, the vegetation in the studied sites were dominated by typical Svalbard vascular plants such as \u003cem\u003eDryas octopetala\u003c/em\u003e, \u003cem\u003eSalix polaris\u003c/em\u003e, \u003cem\u003eSaxifraga oppositifolia\u003c/em\u003e etc. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The vegetation analysis revealed significant changes in species distribution and abundance across the studied localities and elevations. Low-elevation sites supported higher angiosperm coverage, with species like \u003cem\u003eCarex\u003c/em\u003e sp. and \u003cem\u003eSilene acaulis\u003c/em\u003e being particularly abundant. This pattern might indicate more favorable environmental conditions (e.g. higher temperature, reduced solar radiation and wind) at lower elevations, enabling vascular plants to establish and thrive [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In contrast, \u003cem\u003ePapaver dahlianum\u003c/em\u003e abundance increased with elevation. The wider range of plant species at lower elevations likely resulted in greater competition, which \u003cem\u003ePapaver dahlianum\u003c/em\u003e might avoid by occupying higher elevations. Furthermore, the high-elevation sites were characterized by increased abundance of bryophytes (\u003cem\u003eRacomitrium\u003c/em\u003e sp., \u003cem\u003eDicranum\u003c/em\u003e sp.) and lichens (\u003cem\u003eFlavocetraria nivalis\u003c/em\u003e, \u003cem\u003eThamnolia vermicularis\u003c/em\u003e). These organisms generally show an increase in abundance with rising elevation and a reduction in vascular plant cover [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe cyanobacterial community in the studied biocrusts exhibited significant variation primarily based on locality, with elevation playing a minor role. For example, heterocystous cyanobacteria from the order Nostocales were significantly more abundant in the biocrusts of the Inner Fjord (IF). This locality is typically more sheltered from marine influences, experiences less wind exposure, and has more stable, often warmer temperatures. The prevalence of these cyanobacteria might indicate that the biocrusts in the inner fjord are more developed, as Nostocales have previously been observed to dominate the Arctic biocrusts in the later stages of succession [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Furthermore, the physiological performance and adaptive mechanisms of cyanobacteria enable them to colonize ecosystems across a wide range of altitudes [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] As a result, elevation did not have a significant overall effect on the cyanobacterial community composition. Likewise, chlorophyll \u003cem\u003ea\u003c/em\u003e content, which correlated with the cyanobacterial read abundance, remained consistent across elevations. A similar trend was previously observed along the Ossian Sarsfjellet elevation transect (101\u0026ndash;314 m a.s.l.) suggesting that the primary productivity of the biocrust cyanobacteria is maintained despite elevation-driven environmental gradients [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSeveral taxa belonging to eukaryotic phototrophs demonstrated a response to locality and elevation. The abundance of Bryophyta was higher at the outer fjord (OF) sites, where \u003cem\u003eAndreaea\u003c/em\u003e was also identified as an indicator species. This moss, commonly found in polar regions [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], is well-adapted to extreme temperature fluctuations, high UV radiation, and periodic desiccation, which are the characteristic of the ocean-influenced environment of the outer fjord. The elevation had no marked influence on the composition of phototrophic eukaryotes. However, several algal genera were identified as indicator taxa. For example, \u003cem\u003eElliptochloris\u003c/em\u003e and \u003cem\u003eParietochloris\u003c/em\u003e, previously reported as predominant in mountain habitats [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], were associated with high-elevation sites.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eGenes associated with photosynthesis reflect adaptive responses of phototrophs to environmental conditions\u003c/h2\u003e\u003cp\u003eBiocrust organisms at high elevations are often exposed to more extreme conditions, such as enhanced UV radiation, lower temperatures and strong water fluctuations [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. To survive such conditions, phototrophs might enhance their photosynthetic machinery to maximize energy capture when conditions allow. Therefore, the increased abundance of several photosynthesis-related genes at higher elevation sites could be a direct result of the need to adapt to more extreme environments [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Furthermore, the PSII was stable between the different elevations but significantly differed between the localities, being higher in the sites of inner fjord (IF). Perhaps the elevation difference of 100\u0026ndash;300 meters does not significantly affect the capacity of PSII. However, exposure to the open sea could influence the diversity of organisms and their photosynthetic performance [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. LHCII genes showed different patterns and generally increased with elevation, indicating that the two complexes evolved separately to meet specific functional requirements. It has been shown that plants possess multi-gene families of varying sizes for photosynthesis-related processes [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. For example, the gene families associated with Photosystem I and II evolve primarily through whole-genome duplications, followed by the retention or loss of duplicated genes. In contrast, genes involved in the Calvin cycle and light-harvesting processes are subject to non-whole-genome duplications, with the latter mechanism likely providing greater potential for functional differentiation and adaptation [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Furthermore, PSI gene content also increased with elevation in most of the studied sites (except Kn\u0026oslash;len), presumably indicating a larger demand of cyclic photosynthesis there. Recent studies have demonstrated that cyclic photosynthetic electron flow helps alleviate light stress in soybean [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. This mechanism is particularly relevant for biocrusts, where light stress is a significant factor, especially at lower temperatures and higher elevations [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Such findings may reflect selection for organisms with genetic adaptations suited to low-resource environments, photoprotection, or efficient light capture under harsh conditions.\u003c/p\u003e\u003cp\u003eClimate and nutritional status might influence genome size and the proportion of polyploid genomes in plants [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Plant species in polar regions tend to have smaller genomes and lower levels of polyploidy, which might result in a higher percentage of photosynthetic reads in the dataset. This could be explained by the increased proportion of the genome dedicated to encoding photosynthesis, even though the relative abundance of these genes would remain unchanged. However, it is unclear whether algae and cyanobacteria exhibit a similar relationship. Furthermore, some Arctic plants, like \u003cem\u003eDraba\u003c/em\u003e, exhibit high polyploidy (up to 16x), increasing the expression rates of freezing-tolerance-related genes, such as \u003cem\u003eCOR15\u003c/em\u003e [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. However, this alone does not explain the observed differences in relative abundances. Shifts in relative abundance require multi-gene families of varying sizes. For example, cyanobacteria possess up to six copies of the psbA gene (encoding D1 protein), which are differentially expressed depending on the environmental conditions (e.g. low or high light; [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]). In contrast, phototrophic eukaryotes typically have only a single copy gene in the chloroplast genome. Thus, the ratio of cyanobacteria to eukaryotic phototrophs could influence psbA gene abundance across sites.\u003c/p\u003e\u003cp\u003eThe Cyt b6f co\u0026micro;plex plays a crucial role in photosynthetic electron transport. It \u0026micro;ediates electron flow between PSII and PSI, and provides ATP and NADPH for photosynthetic carbon fixation [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The majority of the studied localities exhibited higher petC content within cytochrome b6f complex genes. This could indicate that these biocrusts might harbour communities that are more focused on processes such as carbon fixation and NADPH production. Indeed, PetC1 was identified as the predominant Rieske iron-sulfur protein within the cytochrome b6f complex of \u003cem\u003eSynechocystis\u003c/em\u003e sp. [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. On contrary, petA, petD, petG, and petN recorded in the chloroplast genome of some plants [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], could probably indicate a presence of eukaryotic phototrophs in the biocrusts. The samples from Kn\u0026oslash;len and Gr\u0026oslash;nlietoppen, which exhibited pronounced dominance of cyanobacteria over eukaryotic phototrophs, demonstrated higher petC gene abundance within the ETC genes.\u003c/p\u003e\u003cp\u003eChlorophyll biosynthesis is a fundamental process for the growth and adaptation of phototrophic organisms [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. ChlH, the most essential subunit of magnesium chelatase, plays a crucial role in the accumulation of photoassimilate and the promotion of chlorophyll biosythesis under challenging conditions [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. In the studied samples, ChlH content prevailed over all other subunits and did not significantly differ between elevations or localities, indicating its importance for all phototrophs in extreme Arctic environments. Furthermore, a significantly different content of the CAO gene (Chlorophyll \u003cem\u003ea\u003c/em\u003e Oxygenase) converting chlorophyll \u003cem\u003ea\u003c/em\u003e into chlorophyll \u003cem\u003eb\u003c/em\u003e was observed between elevations and localities. Some phototrophs (e.g. cyanobacteria, diatoms) lack chlorophyll \u003cem\u003eb\u003c/em\u003e. Consequently, increased capacity to produce chlorophyll \u003cem\u003eb\u003c/em\u003e could indicate higher presence of other phototrophs (e.g. plants, green algae; [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]).\u003c/p\u003e\u003cp\u003eIt is also likely that other pathways exhibit similar expansion in photoautotrophs across different environments. However, presence of many common pathways in diverse organismal groups makes analysis more challenging. As advanced techniques improve the separation of metagenomic datasets by organismal groups, detailed studies would be capable of revealing whether other pathways display similar locally differentiated relative abundances.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study revealed that elevation, i.e. differences of 100\u0026ndash;300 \u0026micro;, did not have a pronounced effect on phototrophic biocrust co\u0026micro;\u0026micro;unities as a whole in the Arctic. However, biocrusts at higher elevations exhibited a preference for bryophytes, lichens, and specific taxa of cyanobacteria and algae. In contrast, significant differences between the localities were observed, suggesting that local environmental factors, such as microclimate, soil chemistry, and geology, likely play a crucial role in shaping biocrust community composition and genetic capacity.\u003c/p\u003e\u003cp\u003eIn addition, the differences observed in the photosynthetic gene content across various localities and elevations suggest that certain phototrophic organisms possess the capacity for specific genetic adaptations that allow them to thrive in extreme environmental conditions. Therefore, a higher genetic capacity for photosynthesis might determine whether organisms can survive in more challenging environments, while in milder conditions, organisms with faster growth but lower genetic capacity might prevail.\u003c/p\u003e\u003cp\u003eIt is important to note that these findings reflect the potential photosynthetic capacity based on gene presence, rather than actual gene expression or functional activity. While the richness and abundance of genes may indicate variability in underlying functional potential, further analyses, such as metatranscriptomics, are necessary to confirm gene expression and functional relevance under environmental conditions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was supported by the Alfred-Wegner-Institute in Bremerhaven (Grant KOS175).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eBB and EP designed the study. BB and UK acquired funding for the project. EP, BB, LK and KLB conducted the field sampling, with LK and KLB additionally performing vegetation surveys. EP and LK carried out the molecular laboratory work and SK provided soil chemical measurements. EP performed the bioinformatic analyses and data processing. EP wrote the first draft of the manuscript. All authors revised the manuscript and approved the final version.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to acknowledge members of AWIPEW station in Ny-Ålesund for technical and logistic support during sampling. Furthermore, we are grateful to Isabel Mas Martinez for the laboratory assistance.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eSequence data used in this study were submitted to the Sequence Read Archive (SRA) under the project PRJNA1124630.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWeber B, Belnap J, B\u0026uuml;del B, Antoninka AJ, Barger NN, Chaudhary VB, et al. What is a biocrust? A refined, contemporary definition for a broadening research community. Biol Rev. 2022;97:1768\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWeber B, B\u0026uuml;del B, Belnap J. Biological Soil Crusts: An Organizing Principle in Drylands. Springer International Publishing; 2016.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Y, Aradottir AL, Serpe M, Boeken B. Interactions of Biological Soil Crusts with Vascular Plants. 2016. pp. 385\u0026ndash;406.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePedersen \u0026Aring;\u0026Oslash;, Convey P, Newsham KK, Mosbacher JB, Fuglei E, Ravolainen V et al. Five decades of terrestrial and freshwater research at Ny-\u0026Aring;lesund, Svalbard. Polar Res. 2022;41.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePushkareva E, Johansen JR, Elster J. A review of the ecology, ecophysiology and biodiversity of microalgae in Arctic soil crusts. Polar Biol. 2016;39:2227\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWilliams L, Borchhardt N, Colesie C, Baum C, Komsic-Buchmann K, Rippin M, et al. Biological soil crusts of Arctic Svalbard and of Livingston Island, Antarctica. Polar Biol. 2017;40:399\u0026ndash;411.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePushkareva E, Kv\u0026iacute;derov\u0026aacute; J, Šimek M, Elster J. Nitrogen fixation and diurnal changes of photosynthetic activity in Arctic soil crusts at different development stage. Eur J Soil Biol. 2017;79:21\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHarel Y, Ohad I, Kaplan A. Activation of photosynthesis and resistance to photoinhibition in cyanobacteria within biological desert crust. Plant Physiol. 2004;136:3070\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStarkenburg SR, Reitenga KG, Freitas T, Johnson S, Chain PSG, Garcia-Piche F, et al. Genome of the cyanobacterium Microcoleus vaginatus FGP-2, a photosynthetic ecosystem engineer of arid land soil biocrusts worldwide. J Bacteriol. 2011;193:4569\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGypser S, Herppich WB, Fischer T, Lange P, Veste M. Photosynthetic characteristics and their spatial variance on biological soil crusts covering initial soils of post-mining sites in Lower Lusatia, NE Germany. Flora - Morphology, Distribution, Functional Ecology of Plants. 2016;220:103\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRantanen M, Karpechko AY, Lipponen A, Nordling K, Hyv\u0026auml;rinen O, Ruosteenoja K et al. The Arctic has warmed nearly four times faster than the globe since 1979. Commun Earth Environ. 2022;3.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMas Martinez I, Pushkareva E, Keilholz LA, Linne Von Berg K-H, Karsten U, Kammann S et al. Role of Climate and Edaphic Factors on the Community Composition of Biocrusts Along an Elevation Gradient in the High Arctic. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/microorganisms12122606\u003c/span\u003e\u003cspan address=\"10.3390/microorganisms12122606\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSvendsen H, Besz-Czynska-M\u0026oslash;ller A, Zajaczkowski M, Zajaczkowski M, Azzolini R, Bruland O, et al. The physical environment of Kongsfjorden-Krossfjorden, an Arctic fjord system in Svalbard. Polar Res. 2002;21:133\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKammann S, Schiefelbein U, Dolnik C, Mikhailyuk T, Demchenko E, Karsten U et al. Successional Development of the Phototrophic Community in Biological Soil Crusts on Coastal and Inland Dunes. Biology (Basel). 2023;12.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBolger AM, Lohse M, Usadel B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKopylova E, No\u0026eacute; L, Touzet H, SortMeRNA. Fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics. 2012;28:3211\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi D, Liu CM, Luo R, Sadakane K, Lam TW. MEGAHIT: An ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics. 2015;31:1674\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eG\u0026ouml;tz S, Garc\u0026iacute;a-G\u0026oacute;mez JM, Terol J, Williams TD, Nagaraj SH, Nueda MJ, et al. High-throughput functional annotation and data mining with the Blast2GO suite. Nucleic Acids Res. 2008;36:3420\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOksanen J. Multivariate analysis of ecological communities in R:vegan tutorial. R Doc. 2013;:43.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBreen K, L\u0026eacute;vesque E, Le E. Proglacial succession of biological soil crusts and vascular plants: biotic interactions in the High Arctic. Can J Bot. 2006;84:1714\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee YK. Arctic Plants of Svalbard. Cham: Springer International Publishing; 2020.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWong SK, Cui Y, Chun SJ, Kaneko R, Masumoto S, Kitagawa R et al. Vegetation as a key driver of the distribution of microbial generalists that in turn shapes the overall microbial community structure in the low Arctic tundra. Environ Microbiome. 2023;18.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMartins A, Collart F, Sim-Sim M, Pati\u0026ntilde;o J. Ecological drivers of taxonomic, functional, and phylogenetic diversity of bryophytes in an oceanic island. Ecol Evol. 2024;14.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePushkareva E, Pessi IS, Wilmotte A, Elster J. Cyanobacterial community composition in Arctic soil crusts at different stages of development. FEMS Microbiol Ecol. 2015;91:fiv143.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJanatkov\u0026aacute; K, Řeh\u0026aacute;kov\u0026aacute; K, Doležal J, Šimek M, Chlumsk\u0026aacute; Z, Dvorsk\u0026yacute; M, et al. Community structure of soil phototrophs along environmental gradients in arid Himalaya. Environ Microbiol. 2013;15:2505\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePutzke J, Athan\u0026aacute;sio CG, De Albuquerque MP, Victoria FC, Pereira AB. Comparative study of moss diversity in South Shetland Islands and in the Antarctic Peninsula. Revista Chil de Historia Nat. 2015;88 March.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMikhailyuk T, Demchenko E, Mikhailyuk TI, Demchenko EM, Sergiy, Kondratyuk Y. Algae of granite outcrops from the left bank of the river Pivdennyi Bug (Ukraine)*. 2003.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eK\u0026ouml;rner C. Alpine Plant Life. Springer Berlin Heidelberg; 1999.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhai B, Hu Z, Sun S, Tang Z, Wang G. Characteristics of photosynthetic rates in different vegetation types at high-altitude in mountainous regions. Sci Total Environ. 2024;907.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePushkareva E, Barrantes I, Leinweber P, Karsten U. Microbial diversity in subarctic biocrusts from west iceland following an elevation gradient. Microorganisms. 2021;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCoate JE, Schlueter JA, Whaley AM, Doyle JJ. Comparative evolution of photosynthetic genes in response to polyploid and nonpolyploid duplication. Plant Physiol. 2011;155:2081\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLei Y, Gao J, Wang Q, Zeng W, Diwakar D, Zhang Y et al. Cyclic Electron Flow Alleviates the Stress of Light Fluctuation on Soybean Photosynthesis. Agric (Switzerland). 2024;14.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePushkareva E, Hejdukov\u0026aacute; 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.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBureš P, Elliott TL, Vesel\u0026yacute; P, Šmarda P, Forest F, Leitch IJ, et al. The global distribution of angiosperm genome size is shaped by climate. New Phytol. 2024;242:744\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrochmann CGUSTAFSSON. 1946, 1947a, b; STEBBINS 1950. 1993.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMulo P, Sicora C, Aro EM. Cyanobacterial psbA gene family: Optimization of oxygenic photosynthesis. Cell Mol Life Sci. 2009;66:3697\u0026ndash;710.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKramer DM, Evans JR. The importance of energy balance in improving photosynthetic productivity. Plant Physiol. 2011;155:70\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchneidere D, Berry S, Volkmer T, Seidler A, R\u0026ouml;gner M. PetC1 is the major Rieske iron-sulfur protein in the cytochrome b 6f complex of synechocystis sp. PCC 6803. J Biol Chem. 2004;279:39383\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKhan N, Choi S-H;, Lee C-H;, Qu M, Jeon J-S, Khan N et al. Citation: Photosynthesis: Genetic Strategies Adopted to Gain Higher Efficiency. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms25168933\u003c/span\u003e\u003cspan address=\"10.3390/ijms25168933\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJanssen PJD, Lambreva MD, Plumer\u0026eacute; N, Bartolucci C, Antonacci A, Buonasera K, et al. Photosynthesis at the forefront of a sustainable life. Front Chem. 2014;2:1\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSun M, Shen Y. Integrating the multiple functions of CHLH into chloroplast-derived signaling fundamental to plant development and adaptation as well as fruit ripening. Plant Sci. 2024;338.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTanaka A, Ito H, Tanaka R, Tanaka NK, Yoshida K, Okada K. Chlorophyll a oxygenase (CAO) is involved in chlorophyll b formation from chlorophyll a. 1998.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-microbiome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sigs","sideBox":"Learn more about [Environmental Microbiome](https://environmentalmicrobiome.biomedcentral.com)","snPcode":"40793","submissionUrl":"https://submission.nature.com/new-submission/40793/3","title":"Environmental Microbiome","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"biocrust, cyanobacteria, eukaryotic phototrophs, photosynthesis, Arctic","lastPublishedDoi":"10.21203/rs.3.rs-6899352/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6899352/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e\u003cp\u003ePhotosynthetic organisms, including cyanobacteria, algae, and bryophytes, are an essential part of biological soil crusts (biocrusts) in Arctic ecosystems. These organisms play key roles in supporting both the biocrusts themselves and associated plant communities by generating energy and nutrients under extreme environmental conditions. However, the genetic mechanisms underlying their adaptation to polar environments remain poorly understood. This study investigated the composition of phototrophic communities and their photosynthetic genetic capacity in Arctic biocrusts located at different elevations.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eMetagenomic sequencing revealed that cyanobacterial communities exhibited no significant response to elevation, while this factor had a strong effect on the distribution of eukaryotic phototrophs. Increased elevation is typically associated with higher solar radiation, lower temperatures, and reduced water availability, all of which might increase environmental stress and influence the adaptation of photosynthetic organisms. In addition, photosynthetic gene profiling revealed a consistent dominance of photosystem II (PSII) genes across all sites, particularly psbB. In general, genes associated with photosystem I (PSI), chlorophyll biosynthesis, as well as the light-harvesting complexes of PSI and PSII, were significantly influenced by the increased elevation.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e\u003cp\u003eThe results expand our understanding of the functional role of phototrophic organisms in Arctic biocrusts. Furthermore, they highlight the importance of genetic photosynthetic capacity for resilience and adaptability under extreme environmental conditions, which may serve as a key factor in determining the composition of phototrophs in biocrusts.\u003c/p\u003e","manuscriptTitle":"Arctic biocrusts highlight genetic variability in photosynthesis as a key driver of biodiversity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-17 17:45:01","doi":"10.21203/rs.3.rs-6899352/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-08T08:26:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-17T03:06:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-09T10:40:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-07T21:37:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-27T15:58:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"29421571269149013272307993694462592898","date":"2025-11-27T12:37:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-26T03:15:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-24T15:26:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"110282920764855045626767990018406032589","date":"2025-11-24T10:38:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"143643339284850423498490932668786816980","date":"2025-11-24T08:01:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"336638438177478983290070125885116365059","date":"2025-11-24T07:43:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-24T03:46:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-24T03:12:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"66538133514353837689391186480249923295","date":"2025-11-23T14:06:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"168237969185584999924728135433466240367","date":"2025-11-23T12:48:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"176985017049239054203104837201924812005","date":"2025-11-22T10:19:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-09T06:22:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"61147435314892801000307373494397488025","date":"2025-11-09T03:53:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"117119993706766840442204068633757268627","date":"2025-11-08T21:31:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"205882756688955820172724608839262007519","date":"2025-11-08T09:33:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"242871720249795228383180175454561647593","date":"2025-11-06T08:50:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-06T08:29:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-20T12:49:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-19T13:59:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Microbiome","date":"2025-06-15T16:07:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-microbiome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sigs","sideBox":"Learn more about [Environmental Microbiome](https://environmentalmicrobiome.biomedcentral.com)","snPcode":"40793","submissionUrl":"https://submission.nature.com/new-submission/40793/3","title":"Environmental Microbiome","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a7d6a95b-5440-4123-b46b-f59f621504b8","owner":[],"postedDate":"November 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T16:38:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-17 17:45:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6899352","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6899352","identity":"rs-6899352","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}