Moss diatoms show regional structuring, high potential endemism, and an inverse latitudinal diversity gradient in the Arctic

Moss diatoms show regional structuring, high potential endemism, and an inverse latitudinal diversity gradient in the Arctic | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Ecography This is a preprint and has not been peer reviewed. Data may be preliminary. 11 August 2025 V1 Latest version Share on Moss diatoms show regional structuring, high potential endemism, and an inverse latitudinal diversity gradient in the Arctic Authors : Charlotte Goeyers 0000-0002-1573-3923 [email protected] , Elie Verleyen 0000-0003-1426-2960 , Bart Van de Vijver , Tyler Kohler , Petra Klímová , S. Gradstein , and Koen Sabbe Authors Info & Affiliations https://doi.org/10.22541/au.175491397.73446665/v1 Published Ecography Version of record Peer review timeline 362 views 234 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Microorganisms perform essential functions in Arctic terrestrial ecosystems. Yet, their ecology and biogeography are poorly understood, despite being necessary to predict microbial responses to future climate change. Here, we provide the first large-scale floristic and biogeographic study of the moss diatom flora in the tundra regions of the North-Atlantic sector of the Arctic. Diatom communities in 284 terrestrial moss samples from herbarium and recent collections (1962–2023) were analysed by light and scanning electron microscopy. Moss diatoms show clear regionalisation across the Arctic, with strong compositional differences between the three biogeographic regions (High-, Low-, and Subarctic), reflecting contemporary microclimatic conditions and historical processes. We identified an inverse latitudinal diversity gradient with species richness increasing towards the High Arctic, likely driven by a temperature-moisture gradient. Nearly half of all taxa in our study are currently only observed in and known from the Arctic, and 44% were confined to a single biogeographic region, indicating a high degree of potential endemism. Our results serve as a foundation for future studies on polar diatoms and highlight their potential use as bio-indicators for reconstructing and monitoring past, present, and future climate change. Title: Moss diatoms show regional structuring, high potential endemism, and an inverse latitudinal diversity gradient in the Arctic Microorganisms perform essential functions in Arctic terrestrial ecosystems. Yet, their ecology and biogeography are poorly understood, despite being necessary to predict microbial responses to future climate change. Here, we provide the first large-scale floristic and biogeographic study of the moss diatom flora in the tundra regions of the North-Atlantic sector of the Arctic. Diatom communities in 284 terrestrial moss samples from herbarium and recent collections (1962–2023) were analysed by light and scanning electron microscopy. Moss diatoms show clear regionalisation across the Arctic, with strong compositional differences between the three biogeographic regions (High-, Low-, and Subarctic), reflecting contemporary microclimatic conditions and historical processes. We identified an inverse latitudinal diversity gradient with species richness increasing towards the High Arctic, likely driven by a temperature-moisture gradient. Nearly half of all taxa in our study are currently only observed in and known from the Arctic, and 44% were confined to a single biogeographic region, indicating a high degree of potential endemism. Our results serve as a foundation for future studies on polar diatoms and highlight their potential use as bio-indicators for reconstructing and monitoring past, present, and future climate change. Keywords: Arctic, diatoms, biogeography, polar biology, bryophyte Introduction Biogeography concerns the study of the patterns in, and drivers behind, the geographic distributions of biota. Unravelling these patterns and underlying processes is fundamental for biodiversity conservation (Whittaker et al. 2005), and becomes increasingly relevant in the face of climate change. This is especially true in the Arctic, where temperatures are rising four times faster compared to the global average (Rantanen et al. 2022). Here, the most pronounced warming is occurring at the soil surface (Cohen et al. 2020), and given that Arctic terrestrial ecosystems cover >7 million km 2 , any surface-level change will likely not only have local but also global impacts (Callaghan et al. 1999). Despite microorganisms performing essential functions in Arctic terrestrial ecosystems, including primary production, nutrient cycling, and decomposition (Wynn-Williams 1996, Callaghan et al. 2011, Geisen et al. 2018, Malard and Pearce 2018, Rodriguez-Caballero et al. 2018, Oliverio et al. 2020), the patterns and drivers of their biogeography are poorly understood. It thus remains uncertain how Arctic terrestrial microorganisms, and their ecosystem functions, will respond to current and future climate change. Diatoms (siliceous microalgae, SAR group) are key components of terrestrial microbial communities. They form close associations with bryophytes (Smol and Stoermer 2010), which often dominate tundra biomass and productivity (Gornall et al. 2007, Daniëls et al. 2013) . Bryophytes form ideal hosts for diatoms given their ability to retain moisture. Their growth strategy—­forming dense tufts, cushions, or mats—limits evaporation, whereas the space between their leaves, shoots, stems, and rhizoids passively retains water, creating a suitable microhabitat (Ress and Lowe 2014, Glime 2017). Since mosses do not provide the thermal and moisture stability of aquatic habitats, their diatom communities contain taxa able to withstand stresses related to the terrestrial environment, and are often an agglomeration of euterrestrial, epilithic, and typical epibryophytic taxa (Smol and Stoermer 2010). Globally, moss diatom studies report Eunotia, Humidophila, and Pinnularia as dominant, three genera that contain many species that thrive in habitats where intermittent desiccation is common. While biogeographical structuring in Arctic lacustrine diatom communities is well-documented (Weckström and Korhola 2001, Bouchard et al. 2004, Pla-Rabés et al. 2016, Šupraha et al. 2022) , studies on terrestrial diatom biogeography are currently absent. This is problematic, as a solid understanding of biogeographical patterns and drivers is crucial to predict their response to future climate change. In addition, moss diatoms represent a promising yet unexplored tool to track the progression of Arctic terrestrial climate change. In previous work (Goeyers et al. 2024a), we have demonstrated that moss diatom community composition in Greenland exhibits direct, significant responses to temperature and precipitation. However, before their full potential as bio-indicator can be realized, a detailed study of moss diatom biodiversity, ecology, and biogeography is essential. This study aims to: (1) provide the first comprehensive floristic analysis of the moss diatom flora across the Arctic’s North-Atlantic sector spanning a latitudinal gradient of 61°09’–78°55’N, encompassing High-, Low-, and Subarctic regions; and (2) identify drivers governing their diversity, community composition, and distributions. To this end, we generated a diatom species dataset from 284 moss samples from Canada, Greenland, Iceland, and Svalbard. We hypothesize that, similar to other moss diatom studies, (1) these communities harbour high diversity, including many unknown taxa possibly endemic to the Arctic; and (2) environmental and microclimatic factors have the greatest influence on community composition and distribution at local scales, while the influence of spatial factors increases with geographical scale. By establishing baseline biodiversity and ecological benchmarks, this work will advance the use of Arctic terrestrial diatoms as bio-indicators for reconstructing and monitoring past, present, and future climate change. Material and methods Slide preparation and counting We generated a dataset of mosses from herbarium and recent collections (1962–2023), covering the Arctic’s North-Atlantic sector (Table 1). Herbarium materials were retrieved from Meise Botanic Garden (BR, Belgium) and the Natural History Museum Denmark (C, Denmark). Figure 1 provides an overview of the sampling locations. Moss diatoms were prepared for light microscopy (LM) following van der Werff (1955). Mosses were cleaned by adding 37% H 2 O 2 and heating to 80°C for one hour. The reaction was completed with addition of saturated KMnO 4 . After digestion and centrifugation (3 x 10 minutes at 3700 g) with removal of supernatant and rinsing in between, cleaned material was diluted with distilled water to avoid excessive concentrations of diatom valves. Cleaned valves were mounted in Naphrax®. Samples and slides were deposited in the BR collection (Meise, Belgium). Slides were analysed using an Olympus BX53 microscope, equipped with Differential Interference Contrast (Nomarski) and UC30 camera connected to CellSens Standard. For each slide, 400 valves were identified and enumerated on random transects at 1000× magnification using Zeiss Immersol© oil (518N). Scanning electron microscopy (SEM) was used for identifications of smaller taxa. Stubs were prepared by filtering drops of oxidised suspension through 5 μm pore polycarbonate membrane filters (Whatman Cyclopore PC circles, 25 mm diameter). Filters were air-dried and pieces affixed to 12.7 mm aluminium stubs covered with double-sided carbon stickers (Agar Carbon Tabs). Stubs were placed in a high-resolution fine sputter coater for FE-SEM (JFC-2300HR Coating Unit, JEOL) and coated with 10 nm platinum (argon gas, 0.05 mbar). SEM observations were performed using a JEOL JSM-7100FLV Field Emission SEM (1.5 kV, working distance 4.0–6.0 mm). Diatom species identification was based on: Foged (1953, 1955, 1964, 1974, 1977), Crawford and Likhoshway (1999), Lange-Bertalot (1999), Wolfe and Kling (2001), Houk and Klee (2004), Van de Vijver et al. (2004, 2021 a , b , 2022), Werum and Lange-Bertalot (2004), Antoniades et al. (2008, 2009), Kulikovskiy et al. (2010), Zimmermann et al. (2010), Lange-Bertalot et al. (2017), Pinseel et al. (2017), Furey et al. (2020), Heudre et al. (2021), Van de Vijver and Goeyers (2022), Goeyers and Van de Vijver (2023), Goeyers et al. (2024 b , c). Taxa that could not be identified are indicated by ‘sp’ and a numeric identifier (e.g. ‘sp1’, ‘sp2’, etc.). Distributions were assigned to one of the following types: endemic (only occurring in the Arctic), non-endemic (occurring beyond the Arctic), and unknown. Data analysis Vegetation subzones were extracted from Walker et al. (2005). Habitat classification (abrasion, dry, dry heath, fen, glacier, moist tundra, shrub, wet flat, wet heath) was based on high-resolution land cover maps and literature (Zalatan and Gajewski 2006, Johansen et al. 2012, Karami et al. 2018, Rudd et al. 2021), except for Iceland, where assignment was done in the field. Bedrock types (amphibolite/gneiss, basalt, batholith, dolerite, gneiss, granite, limestone/dolomite, sandstone/siltstone/shale) were derived from geological maps and literature (Washburn 1947, Lock et al. 1978, Decaulne and Saemundsson 2006, St-Onge et al. 2009, Kroon et al. 2010, Byun et al. 2014, Piazolo and Jaconelli 2014, Saarela et al. 2020, Hawkings et al. 2021, Verma et al. 2021, Guarnieri et al. 2022, Ellero et al. 2023). Moss identifications were based on the following literature when material was of sufficient quality: Crum and Anderson (1981), Smith and Smith (2004), Hill et al. (2006), and on field notes. F-values were defined on the Jung-scale (F1 = submerged to F8 = dry) (Jung 1936). F-values, pH, and conductivity (µS cm -1 ) were measured by squeezing water out of mosses and using calibrated probes in the field. Annual precipitation and mean annual, summer, and winter air temperatures were calculated with data derived from the Danish Meteorological Institute (DMI) and the European Climate Assessment & Dataset (ECA&D). Data analysis was performed in R version 2022.7.1.554 (R Core Team 2023), with key packages ‘vegan’ 2.6-4 (Oksanen et al. 2019), ‘BiodiversityR’ 2.15-1(Kindt and Coe 2005), ‘dendextend’ 1.17.1 (Galili 2015), and ‘ggOceanMaps’ 1.3.4 (Vihtakari 2022). The basic diversity unit is any subgeneric taxon (i.e., species, subspecies, or varieties), hereafter referred to as “taxon”. Relative abundances were calculated by dividing the number of individuals of a taxon by the total number of taxa in a sample. Frequency of occurrence represents how often a taxon appears across all samples and is calculated by dividing the number of samples in which a taxon is present by the total number of samples. Taxon accumulation curves were constructed with ‘specaccum’, and taxon richness, Shannon-Wiener diversity, and Shannon’s equitability (evenness) were calculated (‘vegan’). We discriminate between α-diversity (mean richness per sample within each (sub)region, i.e. local diversity) and γ-diversity (total number of taxa across all samples in (sub)region, i.e. regional diversity). Habitat diversity measures the variety of habitats found in a (sub)region and was calculated by taking the sum of squares of the standardised environmental variables and standardising the resulting value for the number of samples. β-diversity (species turnover) was calculated as the mean Bray-Curtis value per sample with ‘vegdist’ (‘vegan’). To compare richness, diversity, and evenness between (sub)regions and environmental variables, Kruskal-Wallis test followed by Dunn’s Test (Holm-Bonferroni correction) was used for categorical variables, and Spearman’s rank correlation for continuous variables. Fisher’s exact test was used to compare categorical environmental variables between (sub)regions. To gain insights into community structure, nonmetric multidimensional scaling (NMDS) analysis was performed on a Bray-Curtis dissimilarity matrix using ‘metaMDS’ (‘vegan’). ‘Ordisurf’ was used to fit latitude on the NMDS based on a generalised additive model (GAM). PERMANOVA was used to test differences between communities, using ‘pairwise.adonis’ (‘pairwiseAdonis’) (Martinez 2020). To assess biogeographical structuring, canonical analysis of principal coordinates (CAP) was run (Anderson and Willis 2003). For CAP, principal coordinate analysis (PcoA) was performed on a Bray-Curtis dissimilarity matrix followed by canonical discriminant analysis of a priori defined groups with ‘CAPdiscrim’ in ‘BiodiversityR’. CAP assesses to what extent samples are classified into a priori defined groups using correct classification rates (CCR). For further biogeographical analysis, the dataset was divided into an abundant (species encompassing >90% of the total counts) and rare (<10%) fraction to determine the proportion of endemism per fraction. Variation partitioning (Peres-Neto et al. 2006) (‘varpart’) was applied on two subsets to determine the importance of geographic distance, climate, and habitat characteristics on community structure. A second subset was necessary to include pH and conductivity, since this metadata was not available for all samples. Before variation partitioning, ‘ordistep’ was used for stepwise forward model selection, iteratively selecting the most relevant and significant variables. Four matrices were created: (1) a biotic matrix (species abundance data); (2) a bioclimatic matrix (annual precipitation, mean annual, summer, and winter air temperature, habitat types); (3) a bedrock matrix (bedrock types, pH, conductivity); and (4) a spatial matrix, containing the principal coordinates of neighbour matrices (PCNM) vectors (Borcard and Legendre 2002, Dray et al. 2006), generated by PCoA of a truncated Euclidean distance matrix, based on coordinates. The threshold value corresponds to the largest minimum spanning tree value (Legendre and Legendre 2012). Results Patterns in diversity and habitat variability The analysis of 284 Arctic moss samples revealed 555 diatom taxa belonging to 73 genera (see Goeyers 2025 for micrographs). γ-diversity was highest in the Low Arctic (390) and lowest in the Subarctic (257), with 356 in the High Arctic. Many taxa, particularly in the genera Pinnularia, Eunotia, Gomphonema, and Humidophila , could not be identified to species level and are likely new to science. Figure 2 shows the 25 most abundant taxa observed in this study. In general, Pinnularia and Eunotia were the most species-rich in every region (42 and 37 taxa in the High Arctic, 51 and 42 in the Low Arctic, 32 and 23 in the Subarctic, respectively). Other species-rich genera include Navicula (22) and Humidophila (19) in the High Arctic, Luticola (19) , Humidophila (18) , Gomphonema (18) , and Navicula (18) in the Low Arctic, and Gomphonema (16) and Humidophila (14) in the Subarctic. In all subregions, Pinnularia and Eunotia were the most species-rich, followed by Gomphonema (9) in Southwest Greenland, Luticola (19) in West Greenland, Humidophila in East Greenland (15) and Iceland (12), and Navicula (15) and Humidophila (15) in Svalbard. In Canada, Gomphonema (10), Eunotia (9), and Encyonema (8) were the most speciose. In terms of relative abundance (see table S3) and frequency of occurrence, Nitzschia cf. alpina and Tabellaria acidodelicata were dominant in the complete dataset. Nitzschia cf. alpina occurred in 78% of all samples, while Tabellaria acidodelicata was observed in 54%. Other taxa with high occurrences were Rossithidium petersenii (43%) and Pinnularia cf. borealis (39%). Regional differences were present: In the High Arctic, Tabellaria acidodelicata , Nitzschia cf. alpina , and Eunotia zackenbergensis were dominant (in decreasing order of relative abundance), whereas the Low Arctic was dominated by Nitzschia cf. alpina , Tabellaria acidodelicata, Pinnularia cf. borealis , and Meridion circulare . In the Subarctic, Humidophila eldfjallii, Tabellaria acidodelicata, Meridion circulare, Odontidium mesodon , and Aulacoseira sp1 were dominant. The still increasing taxon accumulation curves (Figure 3) suggest that especially the High- and Low Arctic are well-represented, while more work remains to be done in the Subarctic. γ-diversity was highest between 70–75 °N and negatively related to latitude (p<0.001). α-diversity was significantly higher in the High Arctic (29) than in the Low Arctic (24, p<0.01) (Fig. S1), while Shannon’s Equitability (evenness) was significantly higher in the Subarctic (mean = 0.68) compared to the High Arctic (0.62, p<0.05) (Fig. S1). Regions did not differ significantly in habitat diversity and β-diversity. All regions differed significantly in measured environmental variables (pH, mean summer, winter, and annual air temperature, annual precipitation, vegetation subzone, habitat type, bedrock), except for F-value, where only the High- and Subarctic differed significantly, and conductivity, which was significantly lower in the High Arctic than in the other regions (Fig. S2). The High Arctic had the highest pH values and lowest values for conductivity, mean temperatures, and annual precipitation. In the Low Arctic, intermediate values for pH, mean annual and winter temperature, and annual precipitation were found, whereas conductivity and mean summer temperature were highest compared to the other regions. The Subarctic showed the lowest pH values, intermediate values for conductivity and mean summer temperature, and the highest values for mean annual and winter temperature, and annual precipitation. For the subregions, α-diversity was significantly higher in Canada (42) than in Iceland (26, p<0.05), West Greenland (24, p<0.01), Svalbard (21, p<0.001), and Southwest Greenland (16, p<0.001), whereas East Greenland’s α-diversity (34) was significantly higher than in West Greenland (p<0.001), Svalbard (p<0.001), and Southwest Greenland (p<0.001). Canada’s diversity (mean = 2.82) was significantly higher than in East Greenland (2.11, p<0.05), West Greenland (2.07, p<0.01), Svalbard (1.87, p<0.01), and Southwest Greenland (1.35, p<0.001), while Iceland’s diversity (2.34) was significantly higher than in West Greenland (p<0.05), Svalbard (p<0.05), and Southwest Greenland (p<0.01). Evenness was significantly higher in Canada (mean = 0.75) compared to East Greenland (0.60, p<0.05) and Southwest Greenland (0.49, p<0.05), whereas Iceland’s evenness (0.72) was significantly higher than in West Greenland (0.66, p<0.05), Svalbard (0.61, p<0.05), East Greenland (p<0.01), and Southwest Greenland (p<0.05) (Fig. S1). Fig. S1 and S3 show all subregional differences in biodiversity, habitat diversity, and environmental variables. Comparisons of α-diversity, diversity, and evenness revealed significant differences in α-diversity between moss host genera (higher on Aulacomnium than on Bryum, Dicranum, Drepanocladus, and Sphagnum ), vegetation subzones (higher in subzone C than in A, D, and E), and bedrock (lower on granite than on gneiss and limestone/dolomite, lower on batholith than limestone/dolomite and gneiss) (Fig. S4). Significant differences in diversity were found between bedrock types (lower on granite than on basalt and limestone/dolomite, lower on batholith than on limestone/dolomite), and in evenness between bedrock types (higher on basalt than gneiss) (Fig. S4). Spearman’s rank correlation identified a significant negative correlation between α-diversity and conductivity (ρ=-0.26, p<0.01), mean annual temperature (ρ=-0.17, p<0.01), mean summer (ρ=-0.23, p<0.001) and winter temperature (ρ=-0.16, p<0.05), a positive correlation between evenness and annual precipitation (ρ=0.16, p<0.05), conductivity (ρ=0.26, p<0.01), mean annual (ρ=0.15, p<0.05), and winter temperature (ρ=0.15, p<0.05), and a positive correlation between α-diversity and pH (ρ=0.23, p<0.01). Biogeography & community analysis The NMDS (Fig. 4a) highlights (sub)regional differences in species composition, following a latitudinal gradient in community structure. The second axis showed a highly significant negative correlation with latitude (Pearson correlation NMDS 2 : r = -0.5, p<001). Canada, Svalbard, and East Greenland (High Arctic) cluster together, with samples from Canada overlapping with the Svalbard cluster. Iceland (Subarctic) samples mainly cluster together, separate from High Arctic samples. West Greenland (Low Arctic) samples take on an intermediate position between High- and Subarctic samples. Canonical Analysis of Principal Coordinates (CAP) (Fig. 4b) revealed strong biogeographical structuring into a priori defined regions, with a high correct classification rate (CCRs) (83%, p<0.01). Permutational multivariate analyses of variance (PERMANOVA) furthermore showed a significant difference between all regions (r2 = 0.06, adjusted p-value < 0.001) and subregions (r2 = 0.11, adjusted p-value < 0.001). Pairwise PERMANOVA (Table S1) demonstrated that the High- and Subarctic had the strongest regional differentiation (pairwise.adonis: r2 = 0.07, p-value < 0.001), whereas East Greenland and Iceland had the strongest differentiation on subregional scales (pairwise.adonis: r2 = 0.16, p<0.001). Variation partitioning (Fig. 4c) and forward selection on the subset of 211 samples from Canada, East, West and Southwest Greenland, Iceland, and Svalbard identified annual precipitation, mean annual and summer temperature, and habitat types abrasion, dry heath, fen, glacier, moist tundra, and wet flat (bioclimatic matrix), and bedrock types basalt, batholith, gneiss, granite, and limestone/dolomite (bedrock matrix), and PCNM 1–7 (spatial matrix) as significant variables, explaining 21% of the total variation (p<0.001). PCNM 1–2 represent broad spatial scales, PCNM 3–4 intermediate scales, and PCNM 5–7 local-scale patterns. The three matrices had significant, unique contributions of 4% (p<0.001). The overlap between spatial and bioclimatic variables accounted for 3% (p<0.001), between bioclimatic and bedrock variables 4% (p<0.001), and between spatial and bedrock variables 3% (p<0.001). The overlap between all variables explained 1% (p<0.001). A second variation partitioning to include pH and conductivity was conducted on 136 samples from Canada, East and West Greenland, and Iceland and identified annual precipitation, mean annual and summer temperature, and the habitat types fen and dry heath (bioclimatic matrix), conductivity, pH, basalt, and gneiss (bedrock matrix), and PCNM 1–3 (spatial matrix) as significant explanatory variables, together explaining 18% of the total variation (p<0.001). The unique contributions of bioclimatic and bedrock variables accounted for 6% and 1%, respectively (p<0.001), whereas spatial variables had no unique contribution. The overlap between spatial and bioclimatic variables equalled 1% (p<0.001), and the overlap between all variables explained 9% of the variation (p<0.001). Of all taxa, 44% were observed in one region, 34% in two regions, and 22% in all regions. When only unknown taxa were considered, 62% was restricted to one region, 28% to two regions, and 10% to three regions. Based on available literature data, 46% of all taxa was non-endemic to the Arctic, whereas 5% was endemic, and 49% had unknown distributions. Figure 4d shows non-endemic, endemic, and unknown fractions for abundant (>90% of counts) versus rare (<10%) taxa and demonstrates that all fractions are approximately similar across regions. The abundant fraction is predominantly represented by non-endemic taxa, whereas the rare fraction consisted mostly of unknown taxa. The proportion of endemics was 6% in the High and Low Arctic and 4% in the Subarctic. The Subarctic had the highest proportion of non-endemics (54%), compared to 51% in the Low Arctic, and 50% in the High Arctic. The unknown taxa had approximately the same proportions in the Low- (44%), High- (43%), and Subarctic (42%). Discussion Biodiversity patterns Our study provides the first biogeographical analysis of the moss diatom flora in the Arctic’s North-Atlantic sector, significantly advancing our knowledge on boreo-alpine diatoms. We found a highly diverse flora of 555 taxa, exceeding the γ-diversity (549) reported in an Arctic-wide assessment of >1,000 lake and stream samples (Kahlert et al. 2020). However, it is too early to state that the Arctic’s moss diatom flora is richer than its freshwater flora, as Kahlert et al. (2020) applied a more conservative species concept. Most other freshwater studies considered smaller areas, and these demonstrated a relatively high γ-diversity, with for example >300 taxa in 87 samples from Petuniabukta (Svalbard) (Pinseel et al. 2016) and 987 taxa in 258 Alaskan samples (Foged 1981). Nevertheless, it is likely that our analysis still underestimates the true diversity, given that many taxa belong to (semi)cryptic species complexes, particularly Achnanthidium cf. minutissimum , Hantzschia amphioxys , Pinnularia cf. borealis , and the speciose genera Navicula and Nitzschia (Poulíčková et al. 2010, Souffreau et al. 2013, Pinseel et al. 2017, 2020) . Since (semi)cryptic diversity can hardly be detected by morphology, molecular analyses will most likely further increase species numbers. Our samples were largely dominated by Pinnularia, Eunotia, Navicula, Gomphonema, Humidophila, and Luticola . Their importance likely relates to the ability of terrestrial representatives of these genera to resist desiccation (Souffreau et al. 2010, 2013), as they often contain adaptations to reduce moisture loss, including heavily silicified valves, fewer external openings, and siliceous lamina occluding the areolae (Falasco et al. 2014). At species-level, Nitzschia cf. alpina , Pinnularia cf. borealis , Rossithidium petersenii , and Tabellaria acidodelicata were the most abundant, although regional differences were present. Aerial species (e.g. Eunotia zackenbergensis ) generally dominated High Arctic habitats, whereas rheophilic taxa (e.g. Meridion circulare, Aulacoseira ) became more important towards the Subarctic. This contrasts with freshwater studies showing a gradient from pioneer and generalist species (i.e. small, benthic species in Fragilaria, Nitzschia, Pseudostaurosira, Staurosirella, Staurosira ) in the High Arctic towards periphytic, planktonic, and acidophilous assemblages with Cymbella, Cymbopleura, Brachysira, Frustulia, Pantocsekiella , and Discostella in the Subarctic (Michelutti et al. 2003, Bouchard et al. 2004, Weckström et al. 2023) . These observations may nevertheless also relate to differences in sampled habitats (e.g., littoral zones versus deeper lake parts). α-diversity equalled 26 taxa per sample, falling in the higher range of numbers reported globally for moss diatoms (4–36) (Van Kerckvoorde et al. 2000, Buczkó 2006, Gremmen et al. 2007, Van de Vijver et al. 2008, Kopalová et al. 2014, Chattová et al. 2021, 2022, Goeyers et al. 2022, 2024 c , Radhakrishnan et al. 2022) . In addition to East Greenland, which was recently recognised as potential moss diatom ‘hotspot’ (Goeyers et al. 2024 c ) , Cambridge Bay (Victoria Island, Canada) also seems to represent a hotspot. This suggests that moss diatoms may follow the theory of island biogeography (MacArthur and Wilson 1967), which states that islands located closely to large landmasses hold more species than isolated islands. Victoria Island has an insular character, but in contrast to Greenland, Spitsbergen, and Iceland, it is situated closely to the mainland, which may function as species source. Of all subregions, the highest number of non-endemics was also observed here. The high α-diversity may also be associated with the more alkaline conditions, given that pH is positively associated with diatom species richness (DeNicola 2000). This is reflected in Cambridge Bay’s community composition, which was dominated by Encyonema and Gomphonema, which include species preferring circumneutral to alkaline conditions (van Dam et al. 1994, Potapova and Charles 2002, Morales et al. 2007, Vasiljević et al. 2024) , and the alkaline species Nitzschia fossilis and N. amphibia (Owen et al. 2008, Van de Vijver et al. 2003). Community structure While other research consistently showed that freshwater diatoms do not only respond to local environmental factors as previously stated by the ubiquity hypothesis (Baas Becking 1934, Finlay 2002), but also to large-scale historic factors such as paleoclimatic, geological and glaciation history (e.g. Vyverman and Sabbe 1995, Weckstrom et al. 1997, Pajunen et al. 2016, Soininen et al. 2016), it remained unclear if the same is true for moss diatoms. Consistent with our hypotheses, our findings illustrate that moss diatoms also respond to both environmental and historic factors: communities are predominantly structured by (1) moisture availability (captured by habitat type and precipitation); (2) temperature (mean annual and summer air temperatures); and (3) bedrock-related variables (bedrock type, pH, conductivity). Spatial community structuring, likely related to historical factors (Vyverman et al. 2007), was significant at larger geographic scales, and was as important as (micro)climate- and bedrock-related variables in the first variation partitioning analysis. This suggests that dispersal limitation becomes a prominent factor over larger distances, agreeing with freshwater studies (Soininen 2007, Vyverman et al. 2007, Verleyen et al. 2009, Keck et al. 2018). Depending on the dataset, the total explained variation in community structure ranged between 18–21%. While this appears low, it agrees with Arctic freshwater diatom studies, where climate, geology, and habitat explained between 10–36% (Soininen et al. 2016, Keck et al. 2018, Lindholm et al. 2018, Weckström et al. 2023) . The generally low explanatory power can be explained by (1) the inherent complexity of microbial communities, typically containing many rare species with different tolerances; (2) relevant variables not included, e.g., biotic interactions or nutrients; and (3) stochastic factors, including random disturbances, probabilistic dispersal, and ecological drift (Stegen et al. 2012). While Arctic freshwater diatoms exhibit some degree of spatial variation (Bouchard et al. 2004, Mackay et al. 2006, Pla-Rabés et al. 2016, Keck et al. 2018, Kahlert et al. 2020) , we demonstrated that the separation between the three biogeographic regions is more pronounced for moss diatoms. Although freshwater habitats are typically more isolated and fragmented, they share more overall similarities (e.g. waterbodies are uniformly more buffered against fluctuating temperatures), whereas terrestrial habitats have more pronounced differences in microclimate and habitat characteristics. Community-level differentiation was highest between the High- and Subarctic Region. We furthermore found an inverse latitudinal diversity gradient (LDG): α-diversity declined from High- to Subarctic and showed a small, albeit highly significant positive correlation with latitude. This contrasts with freshwater diatoms, who display the opposite pattern (Kahlert et al. 2020, Weckström et al. 2023), which was attributed to lower temperatures and nutrient availability, increased ice and snow cover, shorter growing seasons, and a smaller range of available habitats at higher latitudes (Michelutti et al. 2003, Vyverman et al. 2007, Douglas and Smol 2010, CAFF 2013). We speculate that the inverse LDG observed in moss diatoms is likely related to (1) the higher availability of moss habitats towards the High Arctic and (2) drier microclimatic conditions towards the Subarctic. While moisture is a key factor affecting moss diatoms (Van de Vijver and Beyens 1997, Poulícková et al. 2004, Goeyers et al. 2022, 2024 c ) , the inverse LDG driven by lower moisture in the Subarctic appears to contradict the higher precipitation levels in this region. This can be attributed to the fact that in the High Arctic, soil moisture levels in summer are sustained by the presence of an extensive permafrost layer, which prevents water drainage and promotes saturated soils (Natali et al. 2015). In addition, the extensive moss cover has an insulating effect, lowering thermal conductivity and promoting evaporative cooling, which reduces soil temperatures and prevents permafrost thawing (Soudzilovskaia et al. 2013, Jaroszynska et al. 2023, Schuuring et al. 2024). The same conditions are suboptimal for rheophilic species which are dominant in the Subarctic and require streaming water. In the Subarctic, higher soil temperatures result in increased soil moisture evaporation, despite higher precipitation (Schuuring et al. 2024). Water drainage is also more pronounced due to discontinuous permafrost (Natali et al. 2015), which can also be linked to a lower moss cover due to vascular plant dominance. In summer, both phenomena result in more variable, drier microclimatic conditions at the soil surface, negatively impacting diatom richness. The abundance of rheophilic taxa in the Subarctic can furthermore be explained by the fact that Subarctic waterbodies are less frequently covered by ice, which may result in a continuous ‘spillover’ (due to rainfall, flooding, or biotic vectors) of freshwater species to terrestrial moss habitats. Biogeography and endemism The high number of unknown species in our analysis may include true Arctic endemics, as they have not (yet) been reported from other regions worldwide. While the existence of endemism is difficult to (dis)prove in diatoms (Vyverman et al. 2010), as undersampling of underexplored areas and underreporting of rare taxa can lead to underestimations of geographic ranges (Lee and Patterson 2000, Finlay 2002), the numerous studies describing new species from the Arctic (e.g. Lange-Bertalot and Genkal 1999, Van de Vijver et al. 2004, 2022, Antoniades et al. 2009, Pinseel et al. 2014, Potapova et al. 2014, 2020, Veselá and Potapova 2014, Furey et al. 2020, Goeyers et al. 2024 b , a ) suggest that at least part of the unknown species are endemic. The number of unknown taxa was particularly high (often >50%) in Pinnularia, Eunotia, Gomphonema, and Humidophila . Restricted distribution patterns were already demonstrated in these terrestrial genera (e.g. Kociolek et al. 2004, Van de Vijver et al. 2014, 2022, Kopalová et al. 2015, Pinseel et al. 2020, Goeyers et al. 2024 a ) . Verleyen et al. (2021) argued that in the Antarctic, the high prevalence of endemism in terrestrial diatoms may be due to their higher tolerance to desiccation and freezing, as they possibly survived in terrestrial refugia during glacial periods and subsequently recolonised and radiated in lakes. Similarly, it is possible that during glacials, Arctic terrestrial species within these genera survived in refugia, and subsequently radiated in various terrestrial habitats during glacial and post-glacial periods, resulting in high diversity and endemism. The Low Arctic exhibited the highest putative endemism levels, aligning with Kahlert et al. (2020) who identified more diversity hotspots in this zone than in any other Arctic region, and the highest γ-diversity. The latter may be explained by the Low Arctic being an ‘overlap’ zone­—both environmentally and biogeographically—between High- and Subarctic. Species numbers may be elevated due to spillover from these adjacent regions, as species from both find suitable niches in the Low Arctic. The high endemism may relate to the observations of Perren et al. (2009) who suggested that West Greenland (where most Low Arctic samples were collected) has not yet been significantly affected by climate change and anthropogenic forcing. If true, this may indicate that communities have remained more stable over time, reducing extinctions and replacements from lower latitudes. In addition, this stability might indicate that parts of the Low Arctic acted as refugia. Conversely, the Subarctic exhibited the highest proportion of non-endemics and the most similar communities across different habitats. It also shared the lowest number of species with other regions. This pattern likely reflects greater connectivity among Subarctic moss habitats and with lower latitudes. 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Eunotia sp7, B. Eunotia zackenbergensis , C. Eunotia cf. praerupta , D. Eunotia curtagrunowii , E. Tabellaria acidodelicata , F. Pinnularia sp37, G. Nitzschia cf. alpina , H. Humidophila perpusilla , I. Psammothidium helveticum , J. Rossithidium petersenii , K. Caloneis sp1, L. Pinnularia sinistra , M. Meridion circula re, N. Meridion constrictum , O. Odontidium mesodon , P. Aulacoseira sp1, Q. Pinnularia intermedia , R. Diatomella balfouriana , S. Achnanthidium cf. lineare , T. Hygropetra balfouriana , U. Staurosirella sp7, V. Staurosirella sp6, W. Pinnularia cf. borealis , X. Planothidium lanceolatum , Y. Pinnularia sp7. Scale bar = 10 µm. Figure 3: Taxon accumulation curves of the (a) three Arctic regions and (b) individual subregions with 95% confidence intervals. Figure 4: (a) NMDS on Bray-Curtis dissimilarity matrix based on Hellinger transformed abundance data. Curves represent the generalised additive model (GAM) latitude fitted by ‘ordisurf’ (‘vegan’ R package); (b) biplot of canonical analysis of principal coordinates (CAP) of the High Arctic (green), Low Arctic (yellow), and Subarctic (red). CCR = correct classification rate (i.e., % samples grouped in their a priori defined biogeographic entities); (c) variation partitioning on (left) 211 samples showing that spatial, bedrock, and bioclimatic variables explain equal variation in community structure; (right) 136 samples showing bioclimatic variables explain the most variation. Significant values are indicated by asterisks; (d) distribution of non-endemic, endemic, and unknown taxa in the abundant (>90% of all counts) versus rare fraction (<10% of all counts) in the regions (in %). Table 1: Overview of the sampling locations and years, subregion and region of origin, number of samples used, preservation methods, coordinates, and vegetation subzones. Herbarium material is indicated with an asterisk. Edgeoya (1984*) Svalbard, High Arctic 10 Dry, ethanol 77°49’N, 22°22’E Subzone A Cambridge Bay (1992*) Canada, High Arctic 9 Dry, ethanol 69°06’N, 105°06’W Subzone D Zackenberg (1998*,1999*,2000*) East Greenland, High Arctic 53 Dry, ethanol, 3% formaldehyde 74°28’N, 20°34’W Subzone C Disko (2002*, 2004*) West Greenland, Low Arctic 55 Dry & ethanol 70°N, 54°W Subzone D Kangerlussuaq (1988*,2017, 2018,2022) West Greenland, Low Arctic 42 Ethanol 67°01’N, 50°40’W Subzone E Barentsoya (2015) Svalbard, High Arctic 8 Ethanol 78°21’N, 21°05’E Subzone A Barentsburg (1985*, 1986*) Svalbard, High Arctic 5 Dry 78°04’N, 14°13’E Subzone A Hornsund (1985*, 1986*) Svalbard, High Arctic 5 Dry 76°55’N, 16°06’E Subzone A Ny-Alesund (2016) Svalbard, High Arctic 10 Ethanol 78°55’N, 11°54’E Subzone A Nuuk (1963*, 2017,2019) West Greenland, Low Arctic 10 Ethanol 64 °07’N, 51°21’W Subzone D Kobbefjord (2021) West Greenland, Low Arctic 13 Ethanol 64°09’N, 51°34’W Subzone E Narsarquaq (1962*, 1970*, 1974*, 2022) South-West Greenland, Subarctic 12 Ethanol 61°09’N, 45°25’W / Westfjords (2023) Iceland, Subarctic 52 Ethanol 65°41’N, 23°11’ W Subzone E Information & Authors Information Version history V1 Version 1 11 August 2025 Peer review timeline Published Ecography Version of Record 11 Feb 2026 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection Ecography Keywords arctic biogeography bryophyte diatoms polar biology Authors Affiliations Charlotte Goeyers 0000-0002-1573-3923 [email protected] Ghent University View all articles by this author Elie Verleyen 0000-0003-1426-2960 Ghent University View all articles by this author Bart Van de Vijver Botanic Garden Meise View all articles by this author Tyler Kohler Charles University View all articles by this author Petra Klímová Charles University View all articles by this author S. Gradstein Botanic Garden Meise View all articles by this author Koen Sabbe Ghent University View all articles by this author Metrics & Citations Metrics Article Usage 362 views 234 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Charlotte Goeyers, Elie Verleyen, Bart Van de Vijver, et al. Moss diatoms show regional structuring, high potential endemism, and an inverse latitudinal diversity gradient in the Arctic. Authorea . 11 August 2025. 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