How the small host the small: Cryptogam trait-mediated structuring of Antarctic microarthropod communities

How the small host the small: Cryptogam trait-mediated structuring of Antarctic microarthropod communities | 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 Search Search ADVANCED SEARCH SCROLL Ecography This is a preprint and has not been peer reviewed.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. 9 May 2025 V1 Latest version Share on How the small host the small: Cryptogam trait-mediated structuring of Antarctic microarthropod communities Authors : Ingeborg J. Klarenberg 0000-0002-9548-9069 [email protected] , Rong Liu , Peter Convey , J. Hans C. Cornelissen , and Stef Bokhorst Authors Info & Affiliations https://doi.org/10.22541/au.174678850.01347224/v1 Published Ecography Version of record Peer review timeline 421 views 208 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Primary producers shape terrestrial biodiversity, yet, most research has focused on vascular plants and the role of cryptogams (mosses, lichens and algae) remains under-explored. Cryptogams dominate Antarctic vegetation and support diverse microarthropod communities. However, how cryptogam traits influence these communities remains poorly understood. We therefore investigates the role of 28 cryptogam species and one vascular plant, via their functional traits, in shaping microarthropod communities across three contrasting sites in the maritime Antarctic. We hypothesized that vegetation traits, major microarthropod taxa, and abiotic drivers interact to influence community patterns The green alga Prasiola crispa hosted the highest microarthropod abundance (737 ind. g -1 ), while mosses supported greater microarthropod diversity and springtail abundance (68.9 ind. g -1 ) than lichens (1.6 ind. g -1 ). In contrast, lichens hosted more mites (38.6 ind. g -1 ) than mosses (13.7 ind. g -1 ). The grass Deschampsia antarctica showed intermediate abundances but the highest species richness and Shannon diversity. As hypothesized, mosses supported twice the richness and 1.4× greater diversity than lichens. Springtails were consistently more abundant in mosses and mites in lichens at the two northern sites, but this pattern disappeared at the climatically harshest southernmost site, suggesting environmental conditions modulate host preferences. Cryptogam nitrogen and moisture contents strongly predicted microarthropod community patterns, although their influence varied with vegetation type and location. Among mosses, moisture increased springtail abundance but reduced diversity due to the dominance of Cryptopygus antarcticus . In lichens, nitrogen had a stronger influence than in mosses, particularly on mite abundance and Shannon diversity. As hypothesized, moisture was more important at the harshest southern site, while nitrogen had stronger effects at more productive northern locations. These findings emphasize the role of cryptogam traits in structuring Antarctic terrestrial biodiversity. With future shifts predicted in vegetation composition, the functional traits of emerging dominant species may restructure microarthropod communities and their ecological functions. Introduction Primary producers form the foundation of terrestrial ecosystems and determine biodiversity patterns by creating and modifying habitats and regulating resource availability. Their composition and traits can shape the abundance, diversity and composition of consumer communities (Rzanny et al., 2013), with cascading effects on ecosystem functioning (Baiser et al., 2013). However, most understanding to date has been derived from studies of vascular plants, while other important groups of primary producers remain understudied. Cryptogams such as mosses, lichens and algae play important roles in structuring ecosystems where vascular plants are scarce or absent, by buffering against abiotic stress and providing food and habitat for microbial organisms and microarthropods (Lindo & Gonzalez, 2010). Plant functional traits, such as water and nutrient content, influence invertebrate diversity by altering habitat conditions and food availability (Eisenhauer & Powell, 2017; Fujii et al., 2020). For vascular plants, traits such as water-holding capacity and leaf nitrogen (N) content are linked to invertebrate abundance and community composition (Levings & Windsor, 1984; Sánchez-Galindo et al., 2021). How these relationships extend to cryptogams remains poorly understood, despite their dominance in several biogeographical regions. Moisture is a key regulator of microarthropod communities (Lindo et al., 2012), but its effects vary among taxa. Springtails (Collembola) are generally more moisture dependent, while some mites (Acari) are more tolerant of drier conditions due to their ability to better regulate water loss (Convey, Block, and Peat 2003; Tsiafouli et al.,2005; Chikoski, Ferguson, and Meyer 2006; Aupic-Samain et al., 2021). Cryptogams vary in their ability to retain and absorb moisture (Cornelissen et al., 2007; Gauslaa & Coxson, 2011; Larson, 1981), with mosses generally holding more water than lichens, but considerable variation exists within these two groups (Gimingham, 1967). Given these physiological differences, cryptogam water content is likely to shape microarthropod community composition, favoring moisture-dependent taxa in wetter microhabitats while allowing more drought-tolerant species to persist under drier cryptogam host conditions. Nutrient availability, particularly N, can further shape microarthropod communities by influencing the nutritional quality and quantity of cryptogamic habitats, which in turn affects growth and reproduction (Wehner et al., 2014). Lichens with N 2 -fixing photobionts typically have higher N contents than lichens without these symbionts (Asplund & Wardle, 2017). Mosses also vary in their N contents (Hájek et al., 2014) as determined by species’ inherent physiology and environmental N availability. In both mosses and lichens, N deposition strongly influences N content (Boltersdorf et al., 2014; Bokhorst et al., 2019). Previous studies indicated that N content positively influences microarthropod abundance and diversity in lichens and mosses (Bokhorst et al., 2015, 2019), but the extent to which these effect are generalizable across cryptogam groups, microarthropod taxa and environments remains unclear. Whether the impacts of cryptogam moisture and N content on microarthropod communities are consistent across broader environmental gradients is currently unknown. Most studies to date have been limited to a single vegetation type or a few species only (Bokhorst et al., 2015, 2019; Gwiazdowicz et al., 2023; Jonsson et al., 2015), and few have addressed whether microarthropods respond similarly to cryptogam traits across vegetation types or bioclimatic contexts. However, trait-based comparative approaches across gradients can reveal how habitat quality and climatic conditions jointly influence biodiversity (Maron et al., 2014). For example, Ball et al., (2022) showed that Antarctic mite and springtail abundances were influenced by vegetation cover and varied with site and latitude, albeit without a clear link to habitat severity. Whether similar patterns occur for microarthropods inhabiting mosses and lichens remains to be determined. The maritime Antarctic presents an ideal system to study how cryptogam functional traits shape microarthropod communities, as cryptogams dominate primary production (Holdgate, 1967) and provide important habitats for taxonomically wide-ranging terrestrial invertebrates in an otherwise harsh environment with low invertebrate diversity. Biodiversity in these ecosystems is closely tied to water availability (Convey et al., 2014), and although often considered predominantly shaped by abiotic conditions (Convey, 1996; Hogg et al., 2006), there is growing recognition of biotic interactions, such as trophic relationships and habitat modification by vegetation, in structuring communities (Caruso et al., 2013, 2019; C. K. Lee et al., 2019). Vegetation can buffer extreme environmental conditions through moisture retention, microclimatic stability and nutrient availability (Convey et al., 2018). This buffering capacity mediates some abiotic stressors and creates more stable microenvironments for diverse invertebrate communities (Caruso et al., 2013; Bokhorst and Convey 2016). Furthermore, although Antarctic terrestrial ecosystems have low nutrient availability and are generally N limited (Davey & Rothery, 1992), localized but intense nutrient inputs from marine vertebrate aggregations influence habitat quality and the invertebrate assemblages present (Bokhorst et al., 2019). Given that desiccation stress intensifies with latitude along the Antarctic Peninsula (Walton, 1984), the relative importance of cryptogam moisture content for microarthropods may increase under harsher southern conditions. Conversely, N effects might become more influential in milder, more productive northern habitats, where resources may be more limiting for microarthropods. This study examines how cryptogam traits influence microarthropod abundance and diversity across 1,323 plant and lichen samples obtained from three locations in the maritime Antarctic, including 29 native cryptogam species (12 lichens, 17 mosses, 1 macroscopic green alga) and the grass Deschampsia antarctica . We test how vegetation type and trait variation in moisture and N content influence microarthropod communities. Specifically, we hypothesized that (1) mosses will support the highest microarthropod abundance/diversity compared to lichens due to their greater water holding capacity; (2) water effects will be stronger for springtails than mites as the latter are typically more drought tolerant; and (3) the role of cryptogam moisture for microarthropods will be more important at southern, drier, locations while N has a stronger effect on microarthropods at milder, lower latitude locations. Finally, we use the data to provide a first estimate of the total abundance of mites and springtails in green vegetation and lichens in the maritime Antarctic. Understanding trait–biodiversity relationships is increasingly urgent given the growing pressures on Antarctic ecosystems from climate change, human activity and invasive species (Convey & Peck, 2019). Terrestrial habitats remain under-represented in Antarctica’s conservation framework (Hughes et al., 2016; Wauchope et al., 2019), partly due to limited detailed knowledge of species distributions and their ecological roles. Improving our understanding of vegetation–invertebrate linkages will support future biodiversity monitoring and conservation planning in terrestrial Antarctica. Materials and methods Study sites and data collection We used a combination of previously published data (Bokhorst et al., 2019) and newly collected samples from three locations in the maritime Antarctic: Signy Island (South Orkney Islands), Byers Peninsula (Livingston Island, South Shetland Islands) and islands near Rothera Point (Adelaide Island, western Antarctic Peninsula) (Fig. 1). For simplicity, all samples from the latter are referred to as “Rothera”. Signy Island (~10 km 2 ; -60.717778, -45.627056) lies on the Scotia Arc northeast of the Antarctic Peninsula and supports rich and spatially extensive moss and lichen habitats (Smith, 1990). Byers Peninsula (-62.647222, -61.110278) hosts some of the highest biological diversity in the region, with extensive moss and lichen carpets (Lindsay, 1971). Vegetation near Rothera (-67.569823, -68.122101) is dominated by lichen communities on rocky surfaces, with mosses limited to sheltered, meltwater-fed sites. Due to reduced cloud cover, Rothera receives up to 50% more summer solar radiation than Signy and Byers Peninsula (Bokhorst et al., 2008). The dataset of Bokhorst et al., (2019) included microarthropod counts from cryptogam samples collected at each geographic location. At all three locations, replicate transects (n = 3–6) were established across multiple sites (n = 2–5), both close to and distant from marine vertebrate colonies that influence cryptogam nitrogen (N) content. Our new data comprised 210 moss and lichen samples collected on Signy in the 2022/2023 austral summer, and 90 samples of mosses, lichens and the grass Deschampsia antarctica around Rothera in the austral summer of 2023/2024. On Signy Island (Fig. 1B) two sampling strategies were applied: (1) four lichen species ( Himantormia lugubris , Usnea aurantiaco-atra , Stereocaulon alpinum and Ochrolechia frigida) and three moss species ( Warnstorfia fontinaliopsis , Andreaea depressinervis, A. regularis and Chorisodontium aciphyllum ) were collected at Backslope (unofficial name), North Gneiss and Foca and (2) the moss species Sanionia uncinata was collected in dry and wet habitats close to and distant from penguin colonies. Around Rothera, samples of five lichen species ( Usnea antarctica , S. alpinum , Umbilicaria antarctica , U. decussata and Pseudophebe minuscula ), three moss species ( S. uncinata , A. depressinervis and Polytrichastrum alpinum ) and the grass D. antarctica were collected across Anchorage Island, Lagoon Island and Léonie Island (Fig. 1D). Moss samples were collected using a 5 cm diameter PVC corer and included entire shoots, but excluded underlying soil. Lichen samples were excised from individual rocks. Lichen sample dry mass ranged between 0.5 - 4 g, and moss dry mass between 0.8 and 60 g. Grass samples consisted of whole plants (including roots with small soil particles attached). Moss species were identified following Ochyra et al., (2008) and lichens according to Øvstedal & Smith (2001). Figure 1E provides details of all vegetation taxa sampled. Figure 1 Map of the Antarctic Peninsula (A) showing all sampling locations on Signy Island (B), Byers Peninsula (C) and around Rothera (D), and (E) numbers of samples of moss, lichen, algae and vascular plants species collected at each of the three geographic locations. Dots on the map indicate sampling locations, blue new samples and green previously collected samples. Microarthropod extractions Springtails (Collembola) and mites (Acari) were extracted from vegetation samples into 70% ethanol using Tullgren extractors for 24 h or until the sample was dry. Specimens were identified to species level and counted under a stereomicroscope. We assigned unidentified juvenile mites to a separate group. Initial vegetation fresh mass and post-extraction dry mass were measured, except for Byers Peninsula samples, where field facilities were limited. Plant and lichen physiochemical trait measurements Cryptogam moisture content was calculated as (fresh mass – dry mass) / dry mass * 100. While moisture content reflects recent environmental conditions, moss values were strongly correlated with water-holding capacity (Fig. S1), supporting its use as a trait metric. Lichens showed weaker correlations, likely due to faster desiccation. N content of cryptogam samples was quantified by first oven drying at 70 °C for 24 h, then grinding the samples and using dry combustion in an NC 2500 elemental analyzer (Carlo Erba, Rodana, Italy), coupled with a Deltaplus continuous-flow IRMS (Thermo Finnigan, Bremen, Germany). See Table S1 for mean values and range of sample N and moisture contents. Topographic and bioclimatic variables To calculate mean annual temperature and total annual precipitation for each sampling location, we first downloaded modeled macroclimatic data for mean, minimum and maximum temperature and mean precipitation for the period 1981 to 2010 from CHELSA (Karger et al., 2017) with the R package climenv (Tsakalos et al., 2023). CHELSA modelled data have a resolution of 30 arc sec, equating to ~1 km 2 . Based on these macroclimatic data, bioclimatic variables for each location were calculated in R using dismo (Hijmans, Robert et al., 2023) (Table 1). Table 1 Climatic data for the three geographic locations sampled in this study. We show mean annual temperature and total annual precipitation data from the CHELSA climate model for all our sampling locations across Signy, Byers Peninsula and Rothera. Measured air and soil temperatures originate for Signy from Jane Col (-60.69861, -45.62806) from 2007-2016 (Convey et al., 2021), those for Rothera originate from Anchorage Island (-60.60333, -68.21) from 2001-2009 (Convey et al., 2020). Measured air temperatures from Byers Peninsula originate from -62.647222, -61.110278 between 2002 and 2010 (Bañón et al., 2013), while measured soil temperatures are from adjacent to Limnopolar Lake (-62.6375, -61.108333) between 2010 and 2020 (de Pablo et al., 2024). Mean Annual Temperature (°C) average [min, max] Soil temperature (5 cm) (°C) average [min, max] Total annual precipitation (mm) average [min, max] CHELSA Measured Measured CHELSA Signy -3.4 [-3.6, -2.5] -4.8 [-32.7, 22.4] -2.1 [-15.5, 10.0] 996 [725, 1221] Byers Peninsula -1.7 [-3.8, -1.4] -2.8 -0.42 [-8.2, 14.1] 1488 [1221, 1681] Rothera -6.3 [-7.1, -3.7] -4.6 [-34.0, 7.4] -2.3 [-15.1, 22.9] 1456 [825, 2171] Statistical analyses To determine whether the total microarthropod abundance, springtail abundance, mite abundance, microarthropod species richness and Shannon diversity differed between the four major vegetation types (moss, lichen, green alga, grass), we used generalized linear mixed effect models (GLMM) via the glmmTMB function in the R package glmmTMB (Brooks et al., 2024). We then used separate GLMMs to test for differences in microarthropod abundance, springtail abundance, mite abundance, microarthropod species richness and Shannon diversity between individual moss and lichen species. We focused on mosses and lichens for species-level comparisons. For microarthropod abundance, we applied a negative binomial distribution with sample mass as an offset. For richness, we used a Poisson distribution and for Shannon diversity a Gaussian distribution. Moss and lichen species with fewer than five replicates were excluded from GLMMs comparing differences between species, which applied to A. gainii , Brachythecium austrosalebrosum , Bucklandiella sudetica , Platydictya jungermannioides , S chistidium lewis-smithii and Sphaerophorus globosus (Figure 1E). For all models testing differences between major vegetation taxa and species, geographic location was treated as a random factor. Model convergence was assessed using Q-Q plots. To evaluate the consistency of abundance and diversity patterns between vegetation types across geographic locations, we repeated GLMMs for each geographic location. We tested the overall effects of N content and moisture content on microarthropod abundance and diversity metrics using GLMMs for all samples from Signy Island and Rothera, excluding Byers Peninsula due to the lack of availability of moisture data. These models included N content and moisture content as fixed effects, with species identity and geographic location as random factors to account for interspecific and regional variation. To test if the effects of N and moisture content were consistent for each geographic location, we ran separate GLMMs testing the interaction between N and geographic location (including Byers Peninsula), and moisture content and geographic location separately for mosses and lichens. We also tested how the abundances of the most common microarthropod species were influenced by N and moisture content for mosses and lichens separately. To test whether the different vegetation types hosted distinct microarthropod communities, we conducted a PERMANOVA based on Bray-Curtis dissimilarity matrices using the adonis2 function from the R package vegan (Oksanen et al., 2013). Geographic location was included as a stratification factor to restrict permutations within each location to ensure that differences in community structure were evaluated independently of regional effects. Prior to this analysis, we tested for dispersion homogeneity among vegetation groups, which showed no significant differences. To estimate total regional abundance of mites and springtails, we calculated mean densities (±SD) per m² for green vegetation (algae, moss, grass) and lichens. We assumed lichen samples covered the same area as moss cores. These densities were combined with satellite-based vegetation area estimates from Walshaw et al., (2024). All statistical analyses were conducted in R version 4.4.2 (R Core Team, 2024). Results A total of 247,620 microarthropod individuals were extracted from 1,323 vegetation samples, with an mean abundance 27.4 ± 6.1 individuals g -1 of dry mass, equivalent to a density of 95,780 ± 6,402 individuals m -2 . Microarthropod abundance and community composition across major vegetation types Microarthropod abundance varied significantly between vegetation types and species (Figs. 2A, S2-3). The green alga Prasiola crispa supported the highest abundance, while lichens supported the lowest (Fig. 2A). Mosses harbored twice as many microarthropods per gram as lichens, while the grass Deschampsia antarctica had intermediate abundances, comparable to both mosses and lichens but 12 times lower than the green alga P. crispa (Fig. 2A). Springtail abundance was lowest in lichens, but 18-fold higher in P. crispa . In comparison, mosses and D. antarctica exhibited 1.2 and 1.1 times higher abundance, respectively, relative to lichens (Figs. 2B). Mites were most abundant in lichens, occurring at 2.8 times higher abundances than in mosses, which had the lowest mite abundances (Figs. 2C). These patterns of total microarthropod, springtail and mite abundance between mosses and lichens were consistent across geographic locations, although higher mite abundance in lichens than in mosses at Rothera was not statistically significant (Fig. S4). Microarthropod community composition also differed between vegetation types (PERMANOVA: R 2 = 0.10 P = 0.003) (Fig. 2E), which remained consistent across sampling locations (PERMANOVA: Signy: R 2 = 0.09, P = 0.003; Byers Peninsula R 2 = 0.10, P = 0.003; Rothera R 2 = 0.09, P = 0.003). Microarthropod abundance and community composition across moss and lichen species Mite and springtail abundance varied substantially across moss and lichen species (Fig. 3, S2, S3, S5, S6). For example, no springtails were found in the lichens Ochrolechia frigida , Pseudophebe miniscula , Ramalina terebrata and Usnea spp. , while the highest springtail abundances were recorded in Umbilicaria antarctica (7.77 individuals g -1 of sample dry mass), Stereocaulon alpinum (8.74 g -1 ) and Sphaerophorus globosus (8.75 g -1 ). Likewise, O. frigida did not host any mites, while mite abundances in lichens were highest in Pseudophebe minuscula (71.97 g -1 ), Umbilicaria decussata (89.15 g -1 ), and Lichen sp. (116.93 g -1 ) (Fig. 3, S2). Among moss species, Syntrichia saxicola (18.3 g -1 of sample dry mass) and Chorisodontium aciphyllum (7.36 g -1 of sample dry mass) hosted the lowest numbers of springtails, while Andreaea gainii (138.75 g -1 ), Schistidium antarctici (119.61 g -1 ), Brachythecium austrosalebrosum (341.92 g -1 ) and Ceratodon purpureus (193.73 g -1 ) supported the highest numbers (Fig. 3, S3). For mites, the lowest abundances were observed in Bucklandiella sudetica (0.37 g -1 ) and C. aciphyllum (0.86 g -1 ). The highest mite densities were recorded in Sanionia uncinata (23.44 g -1 ) and S. saxicola (29.62 g -1 ) (Fig 3, S3). Springtail and mite abundance patterns generally aligned with vegetation type preferences, with springtails favoring mosses and mites favoring lichens. The only exception was the predatory mite, Gamasellus racovitzai , which was more abundant in mosses than in lichens (Fig. S6). The mites Halozetes belgicae and Alaskozetes antarcticus showed similar abundance in moss and lichen species (Fig. S6). Richness and diversity across vegetation types and moss and lichen species Microarthropod species richness varied significantly among vegetation types. Vascular plants supported the highest richness, which was nearly 1.3 times higher than in mosses, 2.7 times higher than in lichens, and nearly double that of algae. Mosses had twice the richness of lichens (Fig. 2D). A similar pattern emerged for Shannon diversity. Vascular plants exhibited the highest diversity levels, nearly twice as high as in mosses, almost three times higher than in lichens, and over three times greater than in algae. Mosses supported a 1.4 times higher Shannon diversity than lichens (Fig. 2E). Across all geographic locations, mosses consistently supported higher species richness than lichens. However, Shannon diversity was only significantly higher in mosses than lichens at Signy Island (Fig. S4). At moss and lichen species level, moss species with a high springtail abundance tended to have lower mite abundance and lower Shannon diversity, and vice versa for lichens (Fig. 4). Moss and lichen trait effects on microarthropod abundance and diversity indices Nitrogen (N) content was consistently positively associated with microarthropod abundance and diversity indicators across mosses and lichens (Fig. 5A). However, the influence of N content on microarthropod abundance was 1.64 times stronger in mosses than in lichens. While springtail and mite abundance responded similarly to N across vegetation types, microarthropod richness showed a 1.2 times stronger response in lichens, and Shannon diversity increased 24 times more with N content in lichens than in mosses. N effects were weaker at Rothera than at Byers Peninsula or Signy (Fig. 5B, S8). Moisture content had contrasting effects across vegetation types (Fig. 5A). In mosses, higher moisture increased total abundance and springtail abundance but reduced mite abundance, richness and diversity. In lichens, moisture had no effect on total microarthropod abundance, species richness or Shannon diversity, except for springtails, where the positive effect of moisture was 1.4 times stronger than in mosses. However, effects of moisture on microarthropod abundances and diversity indices were stronger at Rothera than at Signy (Fig. 5C, S9). While N content consistently increased total microarthropod abundance across mosses and lichens, responses differed across individual microarthropod species (Fig. S7). Friesea sp. (springtail) and Gamazellus racovitzai (mite) abundance responded differently depending on vegetation type. Moisture effects were even more variable across microarthropod species and vegetation types. In mosses, springtail abundance increased with moisture content, particularly for Cryptopygus antarcticus , whereas Folsomotoma octooculata and Friesea sp. showed negative responses. In lichens, moisture content increased springtail abundance, except for F. octooculata . Mite abundance responses to moisture content were negative for Alaskozetes antarcticus in lichens and G. racovitzai in mosses, but otherwise non-significant. Estimation of total microarthropod abundance in the maritime Antarctic Based on our microarthropod abundance data, we estimated an average density of 130,380 ± 11,027 springtails m -2 and 21,302 ± 2,275 mites m -2 of green vegetation (green alga, grass and moss). For lichens, average springtail density was 1,598 ± 322 m -2 and mite density 34,984 ± 4,059 per m -2 . Applying these densities to recent satellite-imagery derived area estimations of green vegetation (27.2 km 2 ) and lichen cover (7.9 km 2 ) in the maritime Antarctic taken from Walshaw et al., (2024), we estimate 3.55 × 10 9 springtails and 5.79 × 10 8 mites in green vegetation, and 1.26 x 10 7 springtails and 2.76 x 10 8 mites in lichens across the maritime Antarctic (Table S2). Figure 2 Barplots showing mean (A) total microarthropod abundance, (B) springtail abundance, (C) mite abundance, (D) microarthropod species richness and (E) microarthropod Shannon diversity, associated with the green alga, Prasiola crispa, lichens, mosses and the grass, Deschampsia antarctica. N = 1317, error bars represent standard errors and letters indicate significant differences between groups based on GLMMs. (F) Non-metric multidimensional scaling (NMDS) analysis of microarthropod community composition across multiple transects in three distant sites in the maritime Antarctic. Colors and shapes indicate major vegetation types. NMDS stress: 0.18. Figure 3 Alluvial diagram depicting microarthropod species associated with plant, lichen and green alga species across multiple transects in three distant sites in the maritime Antarctic. The colors represent microarthropod species, with springtail species in green and mite species in brown colors. Figure 4 Barplots of overall abundance and diversity of microarthropods across multiple transects in three distant sites in the maritime Antarctic. A) springtail abundance, B) mite abundance, C) microarthropod Shannon diversity in all sampled moss species, and D) springtail abundance and E) mite abundance and F) microarthropod Shannon diversity in all sampled lichen species. Error bars indicate standard errors. Differences between species are indicated with letters, based on glmms with geographic locations as a random factor (bars without letters were excluded due to limited replication). Blue dots indicate mean moisture content and orange triangles show mean nitrogen contents for each moss and lichen species. Figure 5 A) Standardized effect sizes of terms included in generalized linear mixed models for microarthropod abundance, springtail abundance, mite abundance, richness and Shannon diversity associated with mosses (n = 466) and lichens (n = 369). Samples include both Signy and Rothera. Geographic location and moss and lichen species identity were treated as random factors in these models. B) Standardized effect sizes of nitrogen content in generalized linear mixed models for microarthropod abundance, springtail abundance, mite abundance, richness and Shannon diversity associated with mosses (n = 586) and lichens (n = 636). Moss and lichen species identity were treated as random factors in these models. C) Standardized effect sizes of sample water content in generalized linear mixed models for microarthropod abundance, springtail abundance, mite abundance, richness and Shannon diversity associated with mosses (n = 489) and lichens (n = 486) in Signy and Rothera. Moss and lichen species identity were treated as random factors in these models. Points indicate the estimate and bars represent 95% confidence intervals. Significant effects are shown in black, non-significant effects in grey. Microarthropod communities differ across vegetation types Primary producers shape terrestrial biodiversity through habitat modification and resource provisioning. Our results showed that microarthropod abundance and community structure differ significantly across vegetation types (mosses, lichens, a green alga and grass) and indicated that they each contribute differently in supporting Antarctic terrestrial biodiversity (Ball et al., 2022). These differences were largely driven by springtails being more abundant in mosses and mites more abundant in lichens, at least consistently across the two northern sites (Signy Island and Byers Peninsula). However, this distinction was not evident at the southernmost site, Rothera, where mite abundance did not differ significantly between mosses and lichens. This deviation may reflect a shift in environmental constraints under the coldest, driest conditions, where habitat properties such as moisture retention become less influential, or where local mite communities include taxa less tightly associated with lichens. Notably, moss samples from Rothera were dominated by juvenile or unidentified mites, suggesting that mosses may provide microclimatic buffering particularly valuable for immature life stages under more extreme conditions. Whether similar patterns occur also in cryptogam-dominated ecosystems beyond Antarctica is unclear, although the contrasting patterns in mite and springtail abundances between mosses and lichens partly mirror findings from Arctic ecosystems in Svalbard, where springtails were ten times more abundant than mites in wet moss tundra (Bengtson, Fjellberg and Solhöy 1974). Coulson et al., (2003), in another High Arctic study, showed close connections between plant species and soil fauna. Beyond the clear differences between mosses and lichens, the green alga Prasiola crispa supported exceptionally high microarthropod abundances, likely reflecting its dual role as both food source and habitat (Bokhorst et al., 2007). The grass Deschampsia antarctica , the only vascular plant sampled, supported relatively similar abundance of mites and springtails, and the highest overall richness and diversity of microarthropods. It may offer a more complex habitat with its below- and aboveground parts as well as more stable moisture conditions as regulated by its vascular system as compared to poikylohydric cryptogams. The predicted expansion of this grass, and other vascular plants (Cannone et al., 2022), in the maritime Antarctic under climate warming may therefore lead to an increase of microarthropod diversity. While our study focused on vegetation itself, other habitats, such as bare or sparsely vegetated soils, substrates underlying cryptogams or bird nests, and rock aggregates, provide alternative microhabitats (Block et al., 2009; Block & Convey, 1995; Richard et al., 1994) that may shape invertebrate communities and contribute to population numbers in ways not captured by our vegetation-focused approach. Moss and lichen traits as drivers of microarthropod communities The patterns we found across vegetation types can arise due to shared abiotic preferences between vegetation and microarthropods, or vegetation shaping microarthropod communities through physiochemical traits, or biotic interactions (e.g., trophic relationships). To assess the role of vegetation traits in structuring microarthropod communities, we examined how two key traits (moisture and N content) link to microarthropod abundances and diversity indicators in mosses and lichens. Moisture and N content strongly influence microarthropod abundance and diversity, albeit with differential effects across taxa and vegetation types. Mosses with higher moisture content supported greater springtail abundances, reaffirming the role of water in sustaining moisture-sensitive taxa (Convey et al., 2003) and partly supporting our first hypothesis. However contrary to our first hypothesis, mosses with higher moisture content showed reduced Shannon diversity, due to the dominance of Cryptopygus antarcticus , a springtail species that thrives in moist conditions (Hayward et al., 2004). The positive relationship between moss moisture content and microarthropod abundance aligns with findings from boreal forests (Lindo et al., 2012), but was not consistently observed in studies of wet and dry moss carpets on Signy Island (Block, 1982), potentially due to interactions with other environmental factors or differing moisture optima among microarthropod species (Hayward et al., 2004; Verhoef & van Selm, 1983). In contrast, in lichens, moisture content had no effect on total microarthropod abundance or diversity, likely reflecting the dominance of mites, which are more drought-tolerant and less dependent on high moisture levels (Ball et al., 2022). These findings support our second hypothesis, that mites will react less strongly to moisture than springtails. While our findings underscore the importance of moisture content for springtail distributions, moisture was measured only at the time of sampling. Given the seasonal and microhabitat variability of Antarctic moisture conditions (Bokhorst et al., 2007), future studies should incorporate repeated moisture measurements over time to more accurately describe habitat suitability. Although rarely mechanistically tested (Davey & Rothery, 1992), Antarctic terrestrial ecosystems are considered N limited. N content indeed emerged as a consistent driver of microarthropod abundance and diversity across both mosses and lichens. Higher N levels likely enhance habitat quality, either of the host or of the associated microbial community, or both, supporting greater microarthropod diversity, with mites appearing particularly adept at exploiting N-enriched lichens (Bokhorst et al., 2015). These patterns were reflected in species-level variation. N-rich lichen species such as Umbilicaria antarctica and N-fixing Stereocaulon alpinum supported greater microarthropod diversity than N-poor species like Ochrolechia frigida . Similarly, mosses such as Brachythecium austrosalebrosum and Schistidium antarctici supported high springtail abundances and showed high N content, while species like Andreaea depressinervis hosted fewer microarthropods and had lower levels of N and moisture. In the maritime Antarctic, moss and lichen N contents are strongly influenced by nutrient inputs from marine bird and seal aggregations, and our analyses of a broader set of cryptogam species confirm the positive correlation between cryptogam N content and microarthropod abundance and species richness reported by Bokhorst, Convey and Aerts (2019). The high abundances of springtails in Prasiola crispa , which is often well developed in an around vertebrate aggregations, is likely also due to high N content. Unlike N, phosphorus (P) is not typically considered a biologically limiting factor in the maritime Antarctic (Chacón et al., 2013). However in more extreme Antarctic environments such as cryoconite holes (Schmidt et al., 2022) or lakes, P can be limiting. While marine vertebrates also strongly influence P input in the maritime Antarctic (Rodrigues et al., 2021; Wasley et al., 2006), their role in shaping microarthropod communities remains unclear. Given that N and P frequently co-limit, further research is warranted to study the potential influence of P availability on microarthropods via vegetation P content. The relative impact of cryptogam N and moisture content on microarthropods varied between locations. N content had a stronger positive effect on microarthropod abundance at the more northern sites (Signy and Byers Peninsula) than at Rothera, the southernmost site. In contrast, the positive influence of cryptogam moisture content on springtail abundance was more pronounced at Rothera, suggesting that desiccation stress may amplify the importance of cryptogam water content and thus water holding capacity under harsher conditions. These patterns support our hypothesis that moisture becomes a more limiting factor in southern, drier environments, while N may play a greater role in more productive northern habitats. Interestingly, N and moisture content did not affect all microarthropod taxa equally. For instance, the springtails Friesea sp. and Folsomotoma octooculata decreased in abundance with increasing moisture in mosses. This aligns with previous suggestions that differential desiccation tolerances and behavioral strategies to drought of F. griesea and C. antarcticus determine their distribution patterns (Hayward et al., 2004). These contrasting responses suggest that cryptogam traits interact with microarthropod species-specific life-history strategies and highlighting the need to consider trait–environment relationships at the species level. Microarthropod population sizes in the maritime Antarctic Our study provides the first quantitative estimates of microarthropod abundance across Antarctic vegetation types. Extrapolating from average densities across our 1,323 samples, we estimated that mosses, P. crispa and D. antarctica collectively support over 3.5 billion springtails and nearly 600 million mites across the maritime Antarctic. Lichens are estimated to host around 13 million springtails and 280 million mites. These numbers highlight the scale of invertebrate biomass associated with Antarctic vegetation. Our estimates are based on average abundances from our sampled sites and do not account for the spatial distribution and total coverage of different vegetation types across the maritime Antarctic, nor habitats that our dataset does not include. Given the unknown extent of specific moss and lichen species in the maritime Antarctic, total abundance estimates remain subject to uncertainty. Future research should integrate high-resolution vegetation mapping with microarthropod sampling to improve Antarctic-wide abundance assessments. Conclusions and future research directions As climate change accelerates, ice-free areas are predicted to expand in the maritime Antarctic (Lee et al., 2017) and consequential shifts in vegetation composition are likely to alter microhabitat availability and associated biodiversity. For example, the increasing abundance of the native vascular plant D. antarctica (Cannone et al., 2016, 2022) may on the one hand enhance biodiversity in the short term given its high richness of microarthropods per unit of plant mass (see above). On the other hand, it may in the long run outcompete cryptogams and lead to homogenization of habitats, thereby reducing the heterogeneity that underpins microarthropod diversity. This study supports the growing recognition that Antarctic ecosystems are not solely abiotically driven but also shaped by biotic interactions, including habitat modification and trophic relationships (Caruso et al., 2013, 2019; Convey et al., 2018; Hogg et al., 2006). Antarctic vegetation supports diverse microbial communities and other invertebrate taxa, such as nematodes and tardigrades (Nielsen et al., 2011; Schwarz et al., 1993). 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Cornelissen VU Amsterdam View all articles by this author Stef Bokhorst VU Amsterdam View all articles by this author Metrics & Citations Metrics Article Usage 421 views 208 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Ingeborg J. Klarenberg, Rong Liu, Peter Convey, et al. How the small host the small: Cryptogam trait-mediated structuring of Antarctic microarthropod communities. Authorea . 09 May 2025. DOI: https://doi.org/10.22541/au.174678850.01347224/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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