Under and above the snow: Drivers of lichen diversity in subarctic birch forests

Under and above the snow: Drivers of lichen diversity in subarctic birch forests | 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 Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 1 September 2025 V1 Latest version Share on Under and above the snow: Drivers of lichen diversity in subarctic birch forests Authors : Lilith Weber , Pekka Niittynen 0000-0002-7290-029X , Leena Myllys , Nick Pepin , Julia Kemppinen 0000-0001-7521-7229 , Juha Aalto , Miska Luoto 0000-0001-6203-5143 , and Annina Kantelinen 0000-0001-8664-7662 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175672446.64811797/v1 376 views 215 downloads Contents Abstract Abstract Introduction Study areas Environmental data Tree selection and lichen sampling Lichen identification Statistical analysis Landscape-level analysis (macro scale) Tree-level analysis (meso scale) Tree section-level analysis (micro scale) Results Tree section-level comparisons under and above snow (microscale) Discussion Future studies References Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Lichens play a vital role in terrestrial and Arctic ecosystems. The subarctic mountain birch forests are among the most threatened habitat types in Fennoscandia and the lichens occupying them are a threatened species group. Snow has an impact on subarctic macrolichens, however, the ecology of lichen communities including the vast diversity of crustose lichens has not been studied. Here, we studied epiphytic lichen species and communities, including macrolichens and crustose lichens, in 74 sites in the subarctic birch forests of northern Finland. Our aim was to understand how ecological parameters, especially snow depth, temperature and humidity affect lichen species. We sampled the birch trees from the ground to a height of 160 cm in sections of 20 cm along the trunk. We used statistical modelling to assess the impact of climatic and environmental variables on the lichen species diversity and community composition. We found that the presence of the species was largely driven by temperature, with snow as a related factor. More specifically, most species were clearly more common and abundant either under or above the annual snow cover showing previously poorly known species-level adaptations to snow conditions. Furthermore, many lichen species only occurred in a single or few sections of the trunk, while few other species were very frequent and abundant. Considering that climate change affects temperature, snow conditions, and alters the composition of subarctic forests from birch to mixed, our results provide valuable insights to the ecology and threat status of lichen species in the region. This is further highlighted by the fact that we found several new species to the area. Most (75 %) of the lichen species that showed strong relationships to ecological parameters were crustose in growth form. This shows the need to include small, overlooked crustose lichens in community ecology studies. Under and above the snow: Drivers of lichen diversity in subarctic birch forests Abstract Lichens play a vital role in terrestrial and Arctic ecosystems. The subarctic mountain birch forests are among the most threatened habitat types in Fennoscandia and the lichens occupying them are a threatened species group. Snow has an impact on subarctic macrolichens, however, the ecology of lichen communities including the vast diversity of crustose lichens has not been studied. Here, we studied epiphytic lichen species and communities, including macrolichens and crustose lichens, in 74 sites in the subarctic birch forests of northern Finland. Our aim was to understand how ecological parameters, especially snow depth, temperature and humidity affect lichen species. We sampled the birch trees from the ground to a height of 160 cm in sections of 20 cm along the trunk. We used statistical modelling to assess the impact of climatic and environmental variables on the lichen species diversity and community composition. We found that the presence of the species was largely driven by temperature, with snow as a related factor. More specifically, most species were clearly more common and abundant either under or above the annual snow cover showing previously poorly known species-level adaptations to snow conditions. Furthermore, many lichen species only occurred in a single or few sections of the trunk, while few other species were very frequent and abundant. Considering that climate change affects temperature, snow conditions, and alters the composition of subarctic forests from birch to mixed, our results provide valuable insights to the ecology and threat status of lichen species in the region. This is further highlighted by the fact that we found several new species to the area. Most (75 %) of the lichen species that showed strong relationships to ecological parameters were crustose in growth form. This shows the need to include small, overlooked crustose lichens in community ecology studies. Keywords: Arctic, Finland, microclimate, new species records, remote sensing, temperature. Introduction Climate change impacts structures and functions of ecosystems. These changes are likely to be especially pronounced in the Arctic, which has warmed two to four times faster than the global average (Rantanen et al., 2022; You et al., 2021). In these areas, annual snow cover is an important aspect of ecosystem properties, and the snow cover remains relatively similar between different years (Niittynen et al., 2018, 2020). Reductions in snow cover duration and extent owing to both climate warming and gradual shift from snow to more rain has already been observed (Box et al., 2019; Irannezhad et al., 2016; Rantanen et al. 2023). Also, the properties of snow are changing, e.g. because of rain on snow events that generate ice formation on the snowpack, which in turn affects gas exchange and herbivory (Vikhamar-Schuler et al., 2016). Arctic biodiversity is responding to these climatic changes as plant distributions, diversity, and physiology are changing (Bjo rkman et al., 2018; García Criado et al., 2025; Myers-Smith et al., 2011). However, less studied species groups, such as lichens, and their response to climate change, remain insufficiently understood. Lichens are a key part of biodiversity and important primary producers of Arctic ecosystems. Lichens are composed of a fungus (= mycobiont), one or more photosynthetic partners (= photobionts, typically algae or cyanobacteria) and other microbial species (Hawksworth & Grube, 2020). They are a prominent part of boreal to polar regions and appear to exhibit complex physiological responses to snow cover and winter temperature extremes, which play a crucial role in their survival and ecological functioning (Bjerke, 2011; Niittynen et al., 2018). Snow cover serves a dual role by providing insulation and protection from extreme cold while simultaneously limiting light availability (MacFarlane & Kershaw, 1980). Different lichen species are known to respond to snow cover in contrasting ways (Sonesson et al., 1994, 2011). The species-specific responses and adaptations to snow have so far been investigated for a few selected lichen taxa (Bidussi et al., 2016; Niittynen et al., 2018; Sonesson et al., 1994). The best-known examples are Melanohalea olivacea over the annual snow cover and Parmeliopsis ambigua under the snow (Fig. 1). These two species served as the primary motivation for our study design to explore lichen species and their snow preferences by using vertical occurrence patterns on trees. So far, crustose lichens have not been considered in the studies, even though they comprise the majority of the lichen diversity in Fennoscandian forest habitats (e.g. Pykälä & Lommi, 2021) and at least in the boreal forests, they appear to be especially sensitive to environmental changes (Kantelinen et al., 2022; Selva, 2003), probably because they are so closely associated with their substratum and the microclimate (Tibell, 1992). So far the lack of suitable microclimatic data has hindered further investigations revealing how snow and other key factors shape lichen species and communities in the rapidly changing Arctic. In this study we focused on subarctic birch forests that are among the most threatened habitat types in Fennoscandia (Hyvärinen et al., 2019). To understand the diversity and abundance of epiphytic lichens we collected comprehensive lichen species data by utilizing morphological, chemical and DNA identification. We combined this species data with information from a network of microclimate dataloggers and multidecadal fine-scale data of snow cover. In our study areas in northern Finland, Betula pubescens ssp. czerepanovii is the dominant tree species occasionally mixed with Pinus sylvestris . Betula forms the treeline on mountains, which is not usual in other Arctic biomes (Ashburner & McAllister, 2016; Beck et al., 2016). The goals of this study were to examine: (1) which physical variables are important in structuring lichen communities, (2) which variables are important for the abundance of single species, (3) whether species compositions are different above and under the average snow cover? Material and Methods Study areas The study was conducted in subarctic northern Finland in areas dominated by mountain birch ( Betula pubescens ssp. czerepanovii) with an understory layer dominated by dwarf shrubs (mainly Empetrum nigrum , Vaccinium myrtillus , V. vitis-idaea ). The forests were studied at two areas, Kilpisjärvi and Kevo (described in detail in Supplementary 1), and they included 37 study sites each. Most of the study sites are in nature protection areas (Fig. 1), limiting direct anthropogenic effects. However, grazing of the semi-domesticated reindeer and outbreaks of the larvae of the autumnal and winter moths ( Epirrita autumnata and Operophtera brumata .), can have considerable impact on the forest landscape (Kallio & Lehtonen, 1973; Kopisto et al., 2008; Seppälä & Rastas, 1980). The areas are climatically similar (for details see Supplementary 1), with Kevo characterized by a higher degree of continentality, reaching lower temperatures in the coldest month but having an overall longer vegetation period. In contrast to Kilpisjärvi, where our study sites are located in pure birch stands, several sites in Kevo also had pine and aspen growing nearby. Figure. 1: The study areas, explanation of the three different scales used, and vertical distribution patterns of lichens. We studied two areas in northern Finland, comparing the lichen communities between these areas (macroscale), comparing the epiphytic communities on 37 birch trees within each site (mesoscale) and lastly the different sections along the stems (microscale). In the maps, darker green areas indicate nature protection sites. On the bottom row, the photo on the left is landscape in Kevo, with lake Kevojärvi in the background. The central and right photo show the vertical distribution of Melanohalea olivacea (brown) and Parmeliopsis ambigua (pale yellow) on the birch stems. Melanohalea olivacea grows over the annual snow cover and Parmeliopsis ambigua grows under the annual snow cover. The approximate winterly snow cover is visible during summer by looking at the vertical distributions of these two lichen species. Both lichen species are abundant in the Fennoscandian subarctic birch forests, such as Kilpisjärvi. Photos Kantelinen (centre) and Weber (left & right). Environmental data The microclimate data used in our study is based on in situ microclimate and snow depth measurements and high-resolution remote sensing imagery. In Kilpisjärvi, the sites sampled for lichens were part of a larger temperature and humidity logger network that comprises 430 measurement sites (see Niittynen et al., 2024), from which we selected 37 sites for lichen sampling. The datalogger sites were chosen using random stratification to pre-select a suite of candidate measurement sites that would maximally cover the main environmental gradients (i.e., canopy cover, topography) within the Kilpisjärvi study area (Aalto et al. 2022; Kemppinen et al. 2023). For more detailed information on the logger models and data calculation see Supplementary 1. In addition to the loggers, we also measured snow depth (cm) at the approximate time of maximum depth. In Kilpisjärvi we measured snow depth in early April 2021 and 2022. At each study site, we took five measurements: one next to each logger and four in each cardinal compass direction, 5 meters from the logger. Here, we used the average of the five measurements. In Kilpisjärvi, not all locations were measured every year. Thus, we imputed the missing values using a Random Forest-based imputation method, as implemented in the missRanger R package. For details see Supplementary 1. In Kevo the selected 37 dataloggers were part of a larger logger network of 60 temperature data loggers that were installed in September 2007 at a wide range of plots in an area of approximately 20 km 2 surrounding Kevo Subarctic Research Station, primarily distributed along elevational transects on varied aspects, along with a representative sample of topographic features such as ridges and valley bottoms (Pike et al., 2013). Sites were chosen based on stratified sampling to make sure that different land covers, aspects, elevations and exposures (sheltered concavities and exposed ridges) were all included. Similar microclimate variables were calculated for each study site as in Kilpijärvi. Snow depth was measured manually in March 2022 next to each logger. For a detailed description of the logger network see Pike et al. (2013). We calculated the snow disappearance date by utilising information from PlanetScope satellite images (3 × 3 m resolution) from years 2017–2021. See a detailed description of this method in the Supporting information of Kemppinen & Niittynen (2022). Tree selection and lichen sampling Fieldwork in Kilpisjärvi took place July–September 2021 and in Kevo July–August 2022. The selected loggers in Kilpisjärvi are distributed between 479 and 676 m a.s.l., in Kevo between 76 to 291 m a.s.l., a comparison of other variables can be seen in Supplementary 1. Trees were categorized as suitable when they had a circumference of more than 20 cm at the height of 40 cm above the ground, measured with tape. Trees were assessed as living or dead in the field. The tree was marked in eight 20 cm tall sections up to 160 cm, and all lichens including crustose and macrolichens found per section were recorded with the help of an illuminated hand lens (10 x magnification, Lichen candelaris). Species that could not be identified in the field were collected and are stored in the Finnish Museum of Natural History (H). Abundance of each species was scored per sections in five frequency classes: 1) single individual, covering max. 2 x 2 cm; 2) several small individuals or one larger than 2 x 2 cm; 3) many individuals, clustering or spread, covering up to 30 %; 4) individuals spread throughout the section, covering up to 50 %; 5) dominating, covering >50 % to the entirety of the section. The upper limit of Parmeliopsis ambigua on the trunk was measured, as described in Sonesson et al. (1994). Lichen identification Lichen characters were examined using a dissecting microscope (Leica S4E) and a compound microscope (Leica CME9). If needed, hand-cut sections of apothecia, pycnidia and thalli were prepared and mounted in water. Colour reactions were observed using 50 % HNO3, sodium hypochlorite, 10 % KOH, a paraphenyldiamine solution and Lugol’s iodine solution (Orange et al., 2010). When necessary, secondary metabolites were analysed by thin-layer chromatography in solvents A, B and C using the methods of Culberson & Ammann (1979) and Culberson & Johnson (1982). DNA sequences were generated for species that could not be reliably identified based on morphological and chemical characters, all together 88 specimens. The nuclear ribosomal internal transcribed spacer region (ITS) and the mitochondrial small subunit (mtSSU) of ribosomal RNA were sequenced. For more information see Supplementary 1. Statistical analysis All together ten environmental variables, that were calculated per each tree site, were chosen for this study (Tab. 1). The variables represent predictors that are generally known to be ecologically meaningful for lichens. For example, the lowest temperature at the site is connected to the specific cold resistance of the lichens, especially their photobionts; while lichens are generally considered to have extremely high levels of freeze tolerance, air temperatures as low as -36 ℃ (as can occur in our study sites) are known to depress photosynthetic capabilities (Bjerke, 2011; Nash et al., 1987). Both Thawing Degree Days (TDD) and humidity also influence the metabolic activity, because as desiccation-tolerant organisms, lichens stop metabolising until rehydration. We chose TDD and humidity as representative factors of usable water sources for lichens, although several factors affect whether vapour or liquid water is needed to initiate photosynthetic activity (Gauslaa, 2014). Relative air humidity measurements in July were used to avoid the presence of snow influencing the measurements. The canopy cover is a multifaceted variable that both influences the shade and humidity of the site but was here chosen as a proxy for the stand maturity. In the study areas, a mountain birch of over 3 m height is considered mature (Wahlberg et al., 2005). The incoming solar radiation is important in relation to photosynthesis and potentially also photodamage. This connection is more direct for those lichens growing above the snow, but lichens are also capable of photosynthesising when covered by snow (Pannewitz et al., 2003). Wind is relevant for drying out moist lichens and as a mode of propagule dispersal, and can be a stress factor by damaging lichen thalli (Anstett, 2010; Favero-Longo et al., 2014). Snow cover duration and snow depth are especially relevant for species growing lower on the tree trunks, under the snow. Here, snow acts as an insulator from low temperatures, UV and wind, but also provides moisture (Niittynen et al., 2018). Lastly, it is known that lichen communities differ between living and dead trees (Lõhmus & Lõhmus, 2010; Notov & Zhukova, 2015), and to a smaller degree also between larger and slimmer trees (Thor et al., 2010). To test whether the chosen variables differ significantly (p<0.05) between Kevo and Kilpisjärvi, we used a permutational multivariate analysis of variance (PERMANOVA) as implemented with the function adonis2 in the package vegan (Bray-Curtis dissimilarities). Prior to modeling, possible collinearity between the variables was investigated by examining pairwise Spearman correlations. For all 10 variables the correlation coefficient r s remained < |0.7| and accordingly all were included in the models (Dormann et al., 2013). Table 1. Environmental variables. The data source-box includes new data generated in this study (= primary) and publicly available data (PlanetScope satellite imagery (Planet Team, 2017) & LiDAR data produced and pre-processed by the National Land Survey of Finland). Name Abbr. Definition Unit Data source Minimum Temperature Tmin Minimum air temperature of the coldest month (December) ℃ Primary, data logger network Thawing Degree Days TDD Thermal sum of days with above zero mean air temperature days Primary, data logger network Humidity ARH Mean July air relative humidity RH % Primary, data logger network Solar Radiation pisr Monthly potential incoming solar radiation (psir) assuming clear sky conditions. Sky view factor was calculated with a 1 km search radius. Calculated with psir tool in SAGA-GIS (Böhner & Antonić, 2009) LiDAR Wind Exposure windexp Wind exposition index describing the topographic openness with a 500 m radius search window. Calculated with Wind Exposition Index tool in SAGA-GIS (Böhner & Antonić, 2009) LiDAR Canopy Cover pzabove3 Proportion of area with vegetation higher than 3 metres within a 5-m buffer around the center of the study plot, i.e., canopy cover % LiDAR Snow Duration scd Snow cover duration, the average day of year when the snow melted completely. Constructed from 306 satellite images acquired in 2017–2021 (method and dataset described in Kemppinen & Niittynen (2022)) Day of year PlanetScope satellite images Snow Depth snow Snow depth measured on site at approximate time of maximum snow depth cm Primary Trunk Circumference circumference Trunk circumference at 40 cm above ground cm Primary Tree Status life Status of tree, living or dead Primary All data analyses, when not indicated otherwise, were carried out with R version 4.3.3 (R Core Team, 2021). The code is available at Dryad (10.5061/dryad.866t1g240). For all analysis, the species Lecanora boligera and L. fuscescens were treated as one taxonomic unit because our molecular analysis was not corresponding to morphological characters used to distinguish the species, so no reliable categorization of the samples was possible. In order for statistical analyses, namely the generalised linear mixed model (GLMM) and generalized linear mixed-effects model (GLME), to work reliably, we had to work with a limited list of taxa, setting minima and maxima of occurrence to assure that there is enough variance in the variables to explain species preferences and community composition. Which taxa were used in what analysis can be seen in Supplementary 2. The Cladonia chlorophea - pyxidata group (here represented by C. cryptochlorophaea, C. grayi, C. novochlorophaea and C. pyxidata ) was treated as one taxonomic unit in the statistical analysis, as these species individually were rare and lumping them together allowed us to model their ecological preferences. Landscape-level analysis (macro scale) To link the lichen community composition to the environmental variables, we performed nonmetric multidimensional scaling (NMDS) using the metaMDS function and Bray-Curtis distances in the ‘vegan’ package in R to visualize the structure of the lichen communities (Oksanen et al., 2022), using the full taxa list of binary species occurrence at tree-level. We examined the relationship between the ordination axes and environmental variables using vegan’s envfit function, which provides r2 for continuous variables and factors, as well as significance of fitted vectors based on a permutation test (999 replicates). To complement the ordination of lichen communities at site level, we also used PERMANOVA to determine if the study sites contained different lichen assemblages. To examine the similarity of the lichen communities in Kevo and Kilpisjärvi, we calculated the Jaccard Coefficient based on the number of species in each community and the number of common species between the two. Tree-level analysis (meso scale) We used univariate GLMMs with binomial distribution to test the tree-level predictors and their interactions on the presence of lichen species at the tree level. In these models we included variables (see Table 1) which are experienced by single trees and have not been measured separately for the sections, as well as variables exhibited by the trees themselves, and checked for the impact of the area (Kilpisjärvi/Kevo). Taxa that occurred on too few or too many trees (72 out of 74 total) were excluded from the analysis. In total 60 taxa had a suitable prevalence for single species models to be constructed, while 35 taxa were excluded or combined into OTUs. The square-rooted sum of abundances of the full taxa list per site was used for a distance-based multivariate multiple regression (DistLM; step wise, Bray-Curtis, as implemented in PRIMER 7.0.24) to relate the environmental variables to the site level community structure and investigate the relevance of individual factors. This analysis is quite similar to the NMDS, but it complements the set of analysis used. To explain the impact of environmental variables on species occurrence on a site level, we also assembled a table of relevant traits (sexual and asexual reproductive structures, photobiont type, morphology and global distribution pattern), based on information deposited in LIAS (Rambold et al., 2001) and specimens’ presence registered in the Consortium of Lichen Herbaria ( Consortium of Lichen Herbaria , 2025). For further information on the data handling and the species’ corresponding traits see Supplementary 3. Taxa that occurred on too few trees (<2) were excluded from the analysis. The prevalence of different traits under different environmental conditions was also tested with PERMANOVA. Tree section-level analysis (micro scale) We used a GLME with the glmer function in the lme4 package to investigate drivers of the vertical distribution of the species along the 20 cm sections of the tree trunks. More specifically, this was done to test the section-level predictors and their interactions on the number of species and the abundance of the present lichen species. With these models we compared i.) sections under and above the snow cover, ii.) sections at different heights from the ground. The model included the site as a random factor to account for the spatial nestedness of the sampling and the species abundance class as a response variable. In cases where the modelled species did not occur in any sections of the tree, the tree was excluded from the analysis. We used the area under the curve (AUC) score to evaluate the specificity and sensitivity of the model. A perfect model would have an AUC of 1, while a random model would have an AUC of 0.5. An AUC of 0.8 – 0.9 is considered excellent (Hosmer et al., 2013). To study how different the species assemblages between the 20 cm sections on the birch trees are, we used a PERMANOVA test. The study site was used as a controlling factor in the analysis. Results Landscape and tree-level comparison (macro- and mesoscale) Our data included a total of 97 lichen species. Additionally, four taxa could not be identified to species level, even though they were clearly distinguished based on morphological and/or molecular characters. These taxa were treated as taxonomic units (OTU) in the statistical analysis (see Supplementary 2). A full list of species is in the Supplementary 1. Included are two new records for Finland, 12 new for the biogeographical region of Lapponia enontekiensis (Kilpisjärvi) and 13 new for Lapponia inarensis (Kevo) (Westberg et al. 2021). We generated a total of 45 DNA sequences, 57 ITS sequences and 9 mtSSU sequences. Sequences of identified species have been uploaded to GenBank and a full list of available sequences can be found in the Supplementary 1. On average, we recorded 13 species per tree (range 5–22), with an average of 12 species per tree in Kevo and 14 in Kilpisjärvi. Melanohalea olivacea and Parmeliopsis ambigua were found on all trees. Based on one-way PERMANOVA analysis, the Parmeliopsis line and the average snow depth were correlated (r2=0.63) with 64 % of trunks. These trunks showed Parmeliopsis line within 25 cm above or below the average measured snow cover, with snow having a highly significant impact on the height of the Parmeliopsis line (p=3.21e-09). There were 24 species unique to Kevo and 32 species unique to Kilpisjärvi, resulting in a Jaccard Coefficient of 0.434. However, the majority of the unique species are those that occurred only once in the dataset. When subtracting those species, there remain seven unique species for Kevo and 13 for Kilpisjärvi. These include rare species that are likely absent from the other area and widespread species that were probably missed due to the limited number of trees that were inventoried (=74). We used the NMDS approach with 3 dimensions and with a final stress of 0.177, which is considered a fair representation. All environmental variables except potential incoming solar radiation (p=0.519) and canopy cover (p=0.649) demonstrated a highly significant relationship (p equal or less 0.001) or marginally significant (wind exposure (p=0.021)) relationship with the ordination axes. Snow depth was strongly positively related to axis 1 (r2=0.899) and negative correlation with axes 2 and 3 (r2=-0.435 and -0.431, respectively). Snow cover duration was also strongly positively related to axis 1 (r2=0.847) and negatively to axis 2 (r2=0.412), but also positively with axis 3 (r2=0.380). The thawing degree days (TDD) and the minimum temperature had inverse trends: TDD was negatively correlated with axes 1 and 3 (r2=-0.917 and -0.303, respectively) and positively with axis 2 (r2 = 0.258). Minimum temperature was positively correlated with axes 1 and 3 (r2=0.987 and 0.159, respectively) and negatively with axis 2, though with a very low amount of explained variation (r2=-0.023). As can be seen in Fig. 2, the clusters of the two areas are overlapping completely. However, the PERMANOVA showed significant differences between the areas (F=8.76, df=1, p<0.001). Figure 2: NMDS plot based on Bray-Curtis dissimilarities derived from log-transformed species presence/absence data. Sites are colour-coded by area, red being sites in Kilpisjärvi and blue being sites in Kevo. TDD = Thawing Degree Days. In addition to analyzing how the selected environmental variables affect species presence with the NMDS, we also used DistLM analysis that takes species abundances into consideration. The DistLM analysis found that all variables except Solar Radiation (p=0.152) and circumference (p=0.482) had a significant effect, although the effect was rather weak for each factor. The optimal configuration was to include only minimum temperature and snow cover duration, which together explain 15.31 % of individual variation. When forcing only the inclusion of snow variables (duration and depth) this percentage and the AICc drops only minimally to 13 %. The univariate species occurrence GLMM showed that 21 out of 60 modelled species had their tree-level occurrences significantly (p<0.05) affected by at least one of the tested environmental variables. Additionally, six species showed marginally significant (p<0.1) relationships. The variables showing significant effects were mainly related to temperature and snow (Table 2). Table 2 : Significant relationships shown by the univariate species occurrence analyses (GLMM) for all study areas. Species with at least 4–70 site occurrences (n = 60) were modelled, and of those species the ones with significant responses are shown in the table. Significance indicators <0.1 no symbol; <0.05*; 0.01**, 0.001***. + and grey box indicate a positive relationship between predictor and species presence, - and white box indicate a negative relationship between predictor and species presence. Snow Duration = days with snow cover, Snow Depth = snow depth at approximate time of maximum snow depth, Minimum Temperature = lowest temperature in December, Thawing Degree Days = thermal sum of days over 0°C, Canopy Cover = forest cover over 3 m above ground, Wind Exposure = topographical openness, Solar Radiation = potential incoming solar radiation assuming clear sky, Trunk Circumference = circumference of the host tree, Tree Status = living or dead tree, binary factor, positive relationship here means the species is more common on living trees, and vice versa; for more information see Table 1. Tree section-level comparisons under and above snow (microscale) Eight sections were investigated on all study trunks. In the 20 cm trunk sections, there were on average 5 species (range 1 – 15). All eight sections from the base to 160 cm above the ground had on average a similar number of species (4.7 – 5.3), although the species composition varied between them. Only ca. 10 % of species occurred in all height sections. On the lowest section of 0 – 20 cm, 28% of species were unique, and altogether 69 % of the studied species occurred there. All other sections have a lower proportion of unique species, as can be seen in Table 3. Additionally, 9 % of species were unique to the two lowest sections of the trees (0 – 40 cm), and altogether 35 % of all species occurred there. Table 3 : Overlap of lichen species between different 20 cm sections of the studied trees. The top row and far left column (grey) show the distance of the section from the ground in centimeters. The far right column (grey) shows the total number of species per section. The top-right (green) part of the table shows the number of species that are present in both sections and include those present in other sections (e.g. 34 species were present in sections from 0–40 cm above ground, and these particular species might also have been present at higher sections). The lower-left (orange) part shows the number of species that are present exclusively in both sections (e.g. 9 species were present only in sections from 0–40 cm above ground, and these particular species were not present in any other sections). For both green and orange fields, the shade is darker the higher the number of species is. The central white dotted fields show the number of species that were exclusively present in that section. As can be seen in Table 3, the presence and abundance of species in different sections of the Betula trunks are distributed unevenly. Many species only occurred in a single or few sections while a few occurred very frequently and abundantly. Most species are clearly more common either below or above the maximum annual snow cover height. Of the 42 species for which we modelled their dependence on snow and height on the tree, 13 have an AUC (area under the curve) of higher than 0.8 for snow as the variable (Table 3). For Melanohalea olivacea an AUC >0.8 for snow was shown, but this was not the case for Parmeliopsis ambigua . Selected species and their response to snow cover are presented in Fig. 3. Figure 3: Selected lichen species and their abundance in relation to snow cover on the studied Betula trunks. The 7 species selected represent different growing heights on the tree from the bottom to the top, significant response to temperature and snow, and high AUC (see Supplementary 1). In the species boxes, the average snow cover is represented by the horizontal grey line. Grey dots represent 0 abundance, green represents abundance class 1, yellow abundance class 2, orange abundance class 3, red abundance class 4, and violet abundance class 5 (see Material and Methods: Tree selection and Lichen sampling for the explanation of the classes). Lepraria elobata, Anzina carneonivea and Frutidella furfuracea occur exclusively under the average snow cover. Melanohalea olivacea and Bryoria fuscescens are mostly above the snow cover. Lecanora circumborealis is more common below the snow cover but has the greatest abundance near the average snow line along the trunks. We further studied selected traits (reproduction, photobiont, growth form, global distribution) to understand if they were related to the environmental factors. The PERMANOVA did not show any significant (p<0.05) relationships. Discussion Our study shows that regional and local environmental factors influence epiphytic lichen diversity and abundance in subarctic birch forests. The most relevant factors influencing the community composition and single species are related to temperature and snow. However, it should be noted that the overall variable importance of the tested predictors is quite low (see Supplementary 1), indicating that for the majority of species, other factors are playing a larger role. At the landscape level, the epiphytic lichens that we studied do not show clear patterns. However, on tree and tree-section level, the patterns are clearer. The importance of temperature in explaining geographical patterns of species in lichens at the cold edge is not well known. However, lichen growth and survival are linked to ambient temperature and moisture, which due to the poikilohydric nature of lichens directly influence thallus water saturation and desiccation (Green et al., 2011; Gauslaa et al., 2012; Merinero et al., 2014). The rates of photosynthesis and respiration increase linearly with increases in temperature up to a certain, species-specific optimum after which these processes are increasingly inhibited (Kappen, 1993; Kershaw, 1985). Landscape-level comparison (macroscale) Kilpisjärvi and Kevo had a similar diversity of lichen species, although Kilpisjärvi had a few more unique species and slightly more species per tree than Kevo. This could be explained by the greater variety in soil types in Kilpisjärvi, which support different types of birch forests, with more ‘grove-like’ on acidic soil and very tall and evenly sparse trees on basic soil (pers. obs.). In addition, the forests in Kevo have been heavily impacted by moth-outbreaks in the past (Ammunét et al., 2012; Jepsen et al., 2008). This might have contributed to a more even aged and uniformly open forest type, providing a lower diversity of microhabitats. Drivers of tree-level variation in lichen communities (mesoscale) Predictors that have a significant impact on most of the species are related to temperature and snow. However, when examining the species in Table 2, it appears that some species react to several predictors, while many show no significant relationship. One third of the species that show a strong relationship to any predictor are macrolichens and two-thirds are crustose lichens. Macrolichens are easier to detect in the field and identify than crustose lichens, so they are usually used exclusively in ecological studies. However, as can be seen here, crustose lichens are diverse and show significant impacts on environmental factors. The sample size might affect our results as most species that show a strong response to the predictors are those with a medium number of occurrences in our data set. In other words, these species are not rare or very common. This is likely because statistical models do not have enough information to work effectively if a species is rare or very common, but it could also indicate that with a larger sampling, additional species might show a response to the tested environmental factors. Impacts of snow at tree section-level (microscale) Snow was found to be a statistically significant factor in contributing to the lichen community composition within individual trees. Furthermore, if we view the results simply on species-level, 46 species of the 101 taxa recorded in our study grow exclusively under snow and 7 species grow above it (Supplementary 1). However, many of these species were observed only once or twice in our study (=31 under the snow and 7 over the snow), suggesting that larger sampling would benefit the reliability of the results. The importance of snow in affecting the presence and abundance of epiphytic lichen taxa may be caused by several mechanisms. Snow has an insulating capacity, protecting the lichens from abrasive winds and temperature fluctuations, and to a degree also from predation by large herbivores (Holtan et al. 2023). Most species in our study have distribution in southern areas of Finland, some even in central and southern Europe. These species may benefit from snow cover in an otherwise harsh environment, although our study cannot reliably conclude that. Snow can affect lichens in other ways too, as thick snowpack and long snow cover duration will limit the availability of light below the snow and subsequently shorten the growing season. While some lichens have been shown to be active under the snow (Kappen, 1993; Pannewitz et al., 2003), below ca. 18 cm of continuous snow too little light penetrates to allow for photosynthetic activity (Croxton et al. 1937, Galbavy et al. 2007). Snow conditions are also linked to other environmental factors. Snow impacts the soil moisture, soil forming processes, and nutrient mineralization (Freppaz et al., 2018) which have an impact both on the host tree, and lichen species living close to the ground. Our study shows that many lichen species prefer to grow clearly under or above the snow, but interestingly, we also found species that prefer the transition zone. Several crustose lichens such as Biatora globulosa, Biatora pallens , Lecanora circumborealis , Lecanoropsis subintricata , and Ochrolechia androgyna showed the greatest abundance right at the annual snowcover of the birch trees. We hypothesise that because of their growth form they are more suited to withstand the physical abrasion of snow and the environmental fluctuations in temperature, light, wind and moisture. Crustose lichens have also been connected to stress tolerant and ruderal strategies (Rogers, 1988, 1990). The lichen species in our study have distributions in geographical areas where snow cover is probably not as thick or long lasting as in the subarctic. Regardless, many of these lichen species occur primarily at the base of trees, on dead wood, or soil (Holien, 1997; Muchnik & Blagoveschenskaya, 2022; Stenroos et al., 2016; Wirth et al., 2013). They tend to prefer more humid and shaded habitats, or might live in the crevices of bark, which are also more common at the base (Holien, 1997; Michel et al., 2011). Disentangling the impact of snow and the variety of other factors that shape the microclimate at the base of trees would enhance understanding on lichens and snow. Future studies Earlier research has suggested that biotic interactions can be more important than abiotic environmental variables at fine spatial scales (Reed et al. 1993; Wisz et al. 2013). Species interactions such as competition can be an important factor in describing lichen community variation (Hawksworth & Chater 1979; Pentecost 1980; Woolhouse et al. 1985, Bjelland 2003). It was observed that especially in the canopy of the birches (i.e. above 160 cm), Melanohalea species were dominant, potentially overgrowing almost everything, perhaps with the exception of the fruticose Bryoria species. The role of competition between lichen species will require further studies. Snow and microclimatic factors may also affect species traits such as thallus shape, size, reproductive strategy, and biochemistry and hence affect the lichen communities along the birch stems. However, we did not find a significant relationship between the environmental predictors and the lichen trait data we retrieved from literature. In future studies, it would be important to test this with in situ trait data, as that would potentially reveal more on this interesting relationship (Roos et al. 2019). Our study shows that snow affects more lichen species than previously known. Considering that climate change affects temperature, snow conditions, and may alter the composition of subarctic forests from birch to mixed, future assessments of the ecology and threat status of lichens in arctic environments should acknowledge the role of snow and related factors. 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Keywords arctic finland microclimate new species records remote sensing temperature Authors Affiliations Lilith Weber University of Helsinki View all articles by this author Pekka Niittynen 0000-0002-7290-029X University of Jyväskylä View all articles by this author Leena Myllys University of Helsinki View all articles by this author Nick Pepin University of Portsmouth View all articles by this author Julia Kemppinen 0000-0001-7521-7229 University of Helsinki View all articles by this author Juha Aalto Finnish Meteorological Institute View all articles by this author Miska Luoto 0000-0001-6203-5143 University of Helsinki View all articles by this author Annina Kantelinen 0000-0001-8664-7662 [email protected] Helsingin yliopisto View all articles by this author Metrics & Citations Metrics Article Usage 376 views 215 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Lilith Weber, Pekka Niittynen, Leena Myllys, et al. 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