Contrasting trait-mediated mechanisms shape testate amoeba communities under long- term drying across boreal peatlands

Contrasting trait-mediated mechanisms shape testate amoeba communities under long- term drying across boreal peatlands | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Contrasting trait-mediated mechanisms shape testate amoeba communities under long- term drying across boreal peatlands Brunella Palacios Ganoza¹, Olivia Kuuri-Riutta¹, Anna M. Laine¹, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9404720/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Boreal peatlands store vast amounts of carbon and regulate regional hydrology, making their stability critical under accelerating climate change. This stability depends on community-level recovery and resistance shaped by the functional traits of organisms. Although climate-induced drying is already reshaping peatland communities, the trait-based mechanisms supporting stability in soil biota remain poorly resolved. We assessed stability in testate amoebae (TA) – a key group of the soil food web – by comparing functional trait patterns in ambient control plots with plots subjected to two decades of experimental water level drawdown (WLD) across three peatland types (rich fen, poor fen, and bog). In ambient conditions, null models revealed strengthened environmental filtering along the fertility gradient; nutrient-rich fen showed highest diversity and functional redundancy. Long-term WLD, however, intensified environmental filtering by reshaping the communities across all peatland types. Functional beta diversity revealed contrasting stability mechanisms: rich fen maintained functional stability through resilience, with major species turnover but minimal functional change, whereas bog communities retained function through resistance, relying on drought-adapted traits and showing minimal species turnover. Contrastingly, the poor fen lost functional stability, as low redundancy combined with dominance of wet-adapted traits led to both species and functional turnover. Under moderate drying, bogs are most likely to maintain functional stability, whereas fens, particularly poor fens, are more vulnerable. These trait-mediated differences indicate peatland type-specific functional thresholds with implications for predicting stability and carbon dynamics under future climates. Overall, continued warming increasingly compromises peatland soil biota and the ecosystem functions they mediate. climate change community assembly environmental filtering functional redundancy functional traits protist communities Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Boreal peatlands are major reservoirs of terrestrial carbon [ 1 ] and host specialized biodiversity adapted to persistently wet, acidic, and nutrient-poor conditions [ 2 ]. However, they are increasingly threatened as temperatures in high latitudes rapidly rise [ 3 ], possibly increasing evapotranspiration beyond compensatory precipitation inputs [ 4 , 5 ]. Consequent drying can disrupt ecosystem functions, reduce resilience, and compromise the stability of soil processes. While soil biotic communities play a critical role in sustaining ecosystem stability [ 6 ], the trait-mediated stability mechanisms that buffer the effects of environmental pressures on peatlands’ functioning remain poorly understood. In particular, it is unclear how long-term drying affects the functional stability of soil protists, critical regulators of carbon and nutrient cycling. Addressing this knowledge gap is essential for understanding peatland responses to ongoing climate-driven hydrological shifts. Theoretically, the mechanisms through which environmental changes influence communities’ functional stability are well established [e.g., 7, 8]. Community assemblage is governed by stochasticity (randomness) and deterministic processes: environmental filtering, where species with traits suited to local conditions are selected [ 8 ], and niche differentiation, where species adopt distinct strategies to reduce competitive overlap [ 7 ]. Empirical studies, primarily in aboveground communities, have applied trait-based approaches to infer and disentangle how these mechanisms operate under changing climate [e.g., 9, 10]. However, results vary widely across ecosystems and climate scenarios, which makes it difficult to determine when trait-environment relationships have predictable functional outcomes and when they are shaped by context-dependent factors [ 9 ]. This variability underscores the need for long-term empirical assessments in systems undergoing directional climate forcing. Ecological stability – the tendency of communities to withstand and recover from a disturbance – emerges from both resistance and recovery processes [ 11 , 12 ]. These processes are fundamentally mediated by functional traits. A key example is functional redundancy, where multiple species perform similar ecological roles, thereby buffering ecosystem functioning against species loss or turnover [13, and references therein]. Therefore, communities with high functional redundancy may be more resilient to disturbance, whereas communities with low redundancy may experience disproportionate functional shifts, potentially altering their ecological role. Climate-warming-induced drying of peatlands strongly impacts key soil protists, potentially altering the structure and function of peatland microbial food webs [ 14 ]. Soil protists have a strong effect on ecosystem functions as they represent the main predators of microbes and directly influence photosynthesis and respiration [ 14 , 15 ]. In peatlands, testate amoebae (TA) account for the largest fraction of the total protozoan biomass [ 16 ] and represent the top predators within the microbial food web [ 15 ]. TA are highly sensitive to water table depth, even to the extent that species can be used as valuable palaeohydrological proxies [ 17 ] and their traits respond consistently to environmental changes. For example, TA biovolume typically decreases under drying [ 18 ], which can reduce their trophic status [ 18 ], weaken top-down control on decomposers, and potentially destabilize microbial food webs [ 6 , 20 ]. Because TA traits reflect ecological strategies shaped by hydrology and climate, they are powerful model organisms for investigating the processes governing soil biodiversity-stability relationship under long-term drying. This study aimed to assess whether trait-mediated mechanisms in soil protist communities maintain – or fail to maintain – functional stability under climate-induced drying across boreal peatlands. Using a two-decade water level drawdown (WLD) experiment setup, we compared TA communities and functional traits between WLD treatment and ambient control areas across three peatland types. More specifically, we addressed two hypotheses: H1: TA communities are strongly affected by altered environmental filtering. We therefore expected size-related traits to show a stronger environmental filtering and niche differentiation under WLD than under ambient conditions. H2: TA communities with higher functional redundancy are functionally more stable. Thus, in highly redundant TA communities, we expected WLD-induced species turnover to be decoupled from functional turnover. 2. Materials and Methods 2.1 Study site We used a long-term WLD experiment started in an eccentric raised peatland complex, Lakkasuo (located in Southern Boreal Finland, 61°47′N, 24°18′E), in 2000–2001. It consists of three experimental WLD areas and corresponding control areas, each pair representing a different peatland type (site): a mesotrophic fen, an oligotrophic fen, and an ombrotrophic bog, here referred to as rich fen, poor fen, and bog, respectively. See further details in [ 21 ]. The sampling covers variation representative of boreal peatlands, but as the experiment was conducted within a single peatland complex, we lack true replication across the three sites. Although this limits the generalization of our results, it allows us to reveal the trait-mediated mechanisms driving the biodiversity-stability relationship between control and WLD areas. Pre-experiment, the control and WLD areas had similar vegetation and water table. Since then, significant changes have occurred in the fen WLD areas, where the development of tree stand has altered abiotic conditions for the understory vegetation [ 22 , 23 ]. The contrast between treatments is smaller in the bog, where the vegetation and TA communities have changed slightly [ 22 , 24 ]. 2.2 Data collection Data have previously been used to assess differences in TA community composition [24; for detailed methodological description]. We collected the upper 3 cm of 3 to 10 Sphagnum moss shoots from 8 to 10 samples from each of the six study areas (total = 53) during summer 2022 and stored the samples in 15 ml of 4% formaldehyde. The samples were shaken for two minutes, filtered through a 150 µm sieve, centrifuged, and analyzed by light microscopy at 200x and 400x. We targeted 150 individuals identified to species level [ 25 ] using Siemensma [ 26 ] and McKeown [ 27 ]. For traits, we recorded aperture size, biovolume, trophic status (mixotroph or heterotroph), aperture position, test compression, and test material (Table 1 for definitions and ecological implications). Test length and width and aperture size were microscopically measured, aiming 5 replicates per taxa per sample. Some individuals were unmeasurable (e.g. due to poor placement), but the final dataset represents at least 80% of the community in each sample [ 28 ]. For low-count species, we used the mean value calculated from the same study area. Biovolume was calculated using a different formula for each test shape, as in Fournier [ 29 ]. Taxon-specific values for aperture position, trophic status, test compression, and material derive from Fournier [ 30 ] and supplementary material from Fournier [ 29 ], complemented with Siemensma [ 26 ]. Table 1 Functional traits measured, their response patterns to disturbances, and ecological implications. Trait Unit Description Response to disturbances Ecological impact References Aperture position Factor Position of the aperture within the test: axial (1), acrostomic (2), or plagiostomic (3) Disturbed conditions favor plagiostomic Related to contribution to the food web Jassey [ 31 ], Fournier [ 30 ] Aperture size µm Width of the test aperture Disturbed conditions favor small apertures Related to the prey size and food web functioning Fournier [ 29 ] Biovolume µm³ Volume of the test occupied by the living amoeba (80%) Disturbed conditions favor small size Related to the metabolic rate and the capacity of the food web to process energy Fournier [ 30 ], Reczuga [ 20 ] Mixotrophy Binary Presence (1) or not (0) of photosynthetic endosymbionts Wet and unshaded conditions favor mixotrophy Related to peatland C cycling and bacterial grazing Jassey [ 31 ] Test compression Factor Spherical (1), sub spherical (2), compressed (3), or strongly compressed (4) Disturbed conditions favor the compression of the test Related to the ability to stay active and contribute to the food web in drier conditions Fournier [ 30 ] Test material Factor Test made of protein (1), silica (2), silica + organic (3), calcite (4), recycled idiosomes (5), or xenosomes (6) Protein is favored by wet and unshaded conditions, silica by dry conditions. The knowledge is controversial for recycled idiosomes, silica + organic, calcite and xenosomes Related to the availability of material and/or prey to construct the test or build a self-secreted test Fournier [ 30 ], Geisen [ 15 ] 2.3 Statistical analyzes All statistical analyzes were performed using R version 4.2.2 [ 32 ]. Due to varying total counts across the sampling plots and the correlation between total count and the number of species, we standardized the abundance data. We calculated the relative abundances and applied a threshold based on the minimum relative abundance in the plot with the smallest number of observations (< 0.8%). This resulted in a dataset that covered 52 of the 66 initially identified taxa. We calculated the community weighted mean (CWM) functional traits for each plot by weighing the mean trait value of the taxon in the specific community, by their relative abundances. To examine the variability in TA functional traits between ambient and WLD conditions, we applied a principal component analysis (PCA) using the vegan package [ 33 ]. As environmental variables, we used peat nutrient contents and pH [ 22 ], and Sphagnum water content, water table level, and shading intensity [ 23 ]. Additionally, to further examine trait-variation mechanisms, we constructed linear models to quantify the relative contribution of species turnover and intraspecific trait variability to variation in CWM biovolume and aperture size. Following Laine [ 21 ], we generated two additional CWM datasets: (1) fixed CWM, calculated using average species-level trait values from the full dataset weighted by their relative abundances, and (2) difference CWM, calculated by subtracting the fixed CWM values from the observed CWMs. For each trait, we fitted three linear models (observed CWM, fixed values, or difference), using site fertility and treatment as predictors. Site fertility gradient was represented by the PC1 axis of a previous PCA of environmental variables in the study areas [ 23 ]. From each model, we extracted the sum of squares (SS) to calculate the SS cov. (species turnover and intraspecific trait covariation) by subtracting SS fixed and SS difference from SS observed [ 34 ]. Percentages of variability contributed by each component (species turnover, intraspecific variation, and covariation) per site and treatment were calculated as proportions of total variation. To examine the variability in TA functional traits driven by environmental filtering and niche differentiation in ambient and WLD conditions, we first used violin plots to compare community-weighted trait distributions and assess differences in distribution shape as potential signals of adaptation to WLD. To select the significant traits (p-value < 0.05), we used the results of multivariate analysis of variance (MANOVA) from the same data reported in Kuuri-Riutta [ 24 ]. Skewness and kurtosis were quantified for each of these traits in each study area. As most distributions were non-Gaussian, we calculated the deviations in WLD study areas relative to the mean of all control study areas (% difference) to allow comparisons between treatments. To further quantify the effects of environmental filtering and niche differentiation on size-related traits, we used trait-based null models for TA biovolume and aperture size. We constructed null expectations in two steps, preserving observed species richness and relative abundances while randomizing trait values. First, species were randomly selected from the total species pool (equiprobable selection) that comprised all identified species at the site level. Second, a random plot-wise mean trait value was allocated to each selected species. Finally, 999 null expectations were generated for each trait in the 53 sample plots. For each plot, four functional metrics were calculated using the observed data and compared with the corresponding null expectations. To detect environmental filtering, we compared the mean, range of traits, and mean pairwise trait distance (MTD), and to detect niche differentiation, we compared the coefficient of variation of the nearest-neighbor distance between traits (cv_nnd) [35, and references therein]. We used Wilcoxon signed-rank test to evaluate the deviation of the observed functional metrics from their null expectations across sites. Tests for means were two-tailed in control study areas (allowing shifts in either direction due to environmental filtering) and one-tailed in WLD study areas (expecting downward shifts from treatment). Range, cv_nnd, and MTD tests were one-tailed in control and WLD study areas (with directional shifts expected from environmental filtering and niche differentiation). Secondly, to quantify the strength of the effect of environmental filtering and niche differentiation, we calculated the standardized effect size (SES) as the difference between the observed values and the null expectation mean value divided by the standard deviation of the null expectation. To examine the variability in TA functional structure between ambient and WLD conditions, we used three beta functional diversity indices as proposed by Ricotta & Pavoine [ 36 ]: 'beta redundancy' (different species supporting similar functions between communities), 'functional dissimilarity' (functional divergence among species), and 'taxonomic similarity' (shared species among communities). We used adiv package [ 37 ] for the calculations and a ternary diagram from ade4 package [ 38 ] for illustration. To test functional structure differences between sites and treatments, we used db-MANOVA from PERMANOVA package [ 39 ] and betadisper from vegan package [ 33 ]. Individual indexes were tested using two-way permutational ANOVA from package lmperm [ 40 ]. 3. Results 3.1 Variability in functional compositions of testate amoebae between ambient and WLD conditions TA functional traits differed among sites and between treatments (Fig. 1 ; Table S2). Under ambient conditions, the bog-rich fen gradient was expressed as shifts from smaller to larger biovolume and aperture size, from higher proportion of mixotrophs and proteinaceous tests to assemblages dominated by acrostomic apertures, compressed and recycled idiosomic tests. WLD reshaped TA assemblages across sites by reducing size-related traits (biovolume and aperture) and mixotrophy, shifting apertures positions from axial to plagiostomic, and redistributing test materials from proteinaceous and xenosomic towards siliceous; with greater treatment differences in fens than in bog (Table S2). Consistently, WLD significantly predicted variation in the community-weighted size traits, explaining 22% of biovolume variation and 17% of aperture size variation (Tables S2 - S3). This WLD-induced size reduction was mainly driven by species turnover (15% and 14%, respectively), with minor contribution from intraspecific variation (0.7% and 0.2%), and a positive covariation effect between both mechanisms (Table S3). When the total variation in the dataset (site fertility and WLD) was considered, the dominance of species turnover was further emphasized (85% and 73%), while the contribution of intraspecific variation slightly increased (2% and 5%) (Table S3). 3.2 Variability in functional traits of testate amoebae driven by environmental filtering and niche differentiation in ambient and WLD conditions Skewness and kurtosis of community-weighted functional trait distributions were small (between − 2 and 2, i.e., within the range suggested by Gross [ 9 ]) in both WLD and control study areas (Fig. 2 , Fig. S1 a - b). Deviations showed no notable differences (< 200) between treatments, with exception of mixotrophy in the poor fen WLD area. Comparing size-related trait distributions with null distributions revealed stronger signals of deterministic processes under WLD than under ambient conditions. In control areas, niche differentiation dominated in the rich fen and environmental filtering in the poor fen. In the bog control area, the two processes were equally strong. In the rich fen and in the bog, niche differentiation was detected in biovolume but not in aperture size, whereas weak niche differentiation was detected by both traits in the poor fen. Environmental filtering was detected by biovolume in the poor fen and in the bog (Fig. 3 , Table S4). Under WLD conditions, environmental filtering was the dominant deterministic process, detected by both biovolume and aperture size, with stronger effect sizes than under ambient conditions. The strongest filtering was observed in the rich fen. Niche differentiation was observed in both traits only in the bog WLD area (Fig. 3 , Table S4). 3.3 Variability in functional structure of testate amoebae between ambient and WLD conditions The functional structure of TA communities was measured as beta redundancy, functional dissimilarity, and taxonomic similarity. In control areas, it differed significantly between rich fen and bog and between rich fen and poor fen. TA communities in poor fen and bog shared similar functional structures, both in control and WLD areas (Fig. 4 , Table S5). The test for homogeneity of variance showed no significant pairwise differences between the study areas, therefore differences/similarities in functional structure derived from their average compositions (Fig. 4 , Table S5). Beta redundancy was significantly lowered in both fen WLD areas. Additionally, functional dissimilarity decreased and taxonomic similarity increased in the poor fen WLD. On the contrary, no significant differences were observed between control and WLD in the bog (Fig. 4 ). When considering beta redundancy, taxonomic similarity, and functional dissimilarity together, only poor fen showed significant differences between WLD and control. 4. Discussion We applied functional traits to investigate the mechanisms that have shaped TA communities along a bog-rich fen gradient and assessed how two decades of experimental water level drawdown altered their functionality. The long-term drying acted as an environmental filter that produced drought-adapted functional profiles driven mainly by species turnover. The differences in trait-mediated stability among sites reflected variation in functional thresholds. Together, our results show that functional traits play a central role in stability; however, the strength of the functional buffering effect depends on the disturbance and the local site characteristics that modify the trait-mediated community responses and resilience capacity. 4.1 Differences in abiotic and biotic pressure on TA communities between ambient and WLD conditions Our first hypothesis, that TA communities are more strongly filtered by environment under drying conditions than under ambient conditions, was supported across sites as the strength of environmental filtering increased in WLD areas. The generally small skewness and kurtosis in our dataset indicate that TA traits are not currently exposed to strong pressure even in WLD areas, suggesting that communities have largely adapted to current biotic and abiotic conditions [ 9 ]. This is likely due to tree encroachment – that fundamentally altered local microclimates and resource environments – occurring already for more than a decade in fen WLDs [ 22 ]. Comparatively, the rapid life cycle of TA allows communities to respond to environmental changes within months to a few years [ 18 ]. In line with the dynamic nature of community assembly, where processes vary in effect and strength overtime [ 41 ], WLD communities shifted from stochastic dominance toward more deterministic processes. This shift allowed us to identify the deterministic processes and quantify the strength of their influence. Under ambient conditions, niche differentiation was the primary mechanism structuring TA communities in the rich fen control. This highlights the strong effect of biotic interactions in resource-rich habitats that allow diverse species with contrasting ecological strategies to reach stable competitive differentiation, thus promoting coexistence [ 42 ]. In contrast, in the bog control, where water and nutrient supply depend on precipitation, niche differentiation and environmental filtering were equally strong. Under such resource-limited conditions, species are driven to use the limited resources differently to avoid direct competition (i.e., resource partitioning) [ 43 ]. In the poor fen control, both processes were detected, but environmental filtering had a stronger effect compared to niche differentiation. Because the poor fen represents resource-limited conditions among fen habitats, TA communities there likely have undergone strong selection for traits improving tolerance to nutrient-poor and acidic conditions. This has led to species occupying distinct niches that reduce overlap and enable efficient resource partitioning among coexisting species [ 42 , 43 ]. Under experimental drying (i.e., dry and more shaded conditions), community assembly across all WLD areas was dominated by a strong environmental filtering, which consistently reduced biovolume and aperture size of TA communities. Small individuals have multiple competitive advantages in thin water films around Sphagnum stems and leaves: they have a reduced risk of desiccation [ 29 , 18 ], can reproduce faster, require smaller living space [ 17 ], and recolonize faster from adjacent areas [ 44 ]. In the bog WLD area specifically, environmental filtering intensified niche differentiation for both size traits. This pattern aligns with previous findings that in resource-limited habitats, environmental changes impose additional pressure on community dynamics [ 42 ], leading to stronger trait-based structuring of TA communities. Moreover, decreasing size under drying reflects a shift towards lower trophic levels among predators [ 42 ]. Such size restructuring, along with shifts in vegetation from Sphagnum- dominated peatlands to forest, can alter the diversity and structure of the microbial food web [ 20 , 45 ]. Aperture size and biovolume reflected contrasting roles in mediating community stability and responses to environmental pressure. Biovolume variation was mainly driven by environmental change and species turnover, while aperture size variation was strongly associated with niche differentiation and higher intraspecific variation. Consistent with this, Bobrov & Mazei [ 46 ] reported that aperture size showed the greatest variability among size traits across habitats and can change independently of overall test size. This likely reflects the functional importance of the TA aperture as the main interface with the environment, as it is used in feeding, locomotion, and environmental sensing [17, and references therein]. These contrasting responses highlight how trait-specific strategies underpin community stability, with aperture variation promoting resistance under the drying pressure. 4.2 Functional stability of testate amoeba community mediated by functional redundancy Our second hypothesis - that functionally redundant TA communities are less vulnerable to climate-induced drying - was supported in the fens. In the rich fen, functional redundancy peaked together with functional stability, whereas in the poor fen, where redundancy was low, drying led to functional turnover. However, the bog remained functionally stable despite low redundancy. In the rich fen, despite the strongest WLD-induced species turnover, the overall functional structure of TA did not differ between treatments. Our results suggest that the taxonomically diverse [ 24 ] and functionally redundant TA community in the rich fen was functionally resilient, i.e., the species lost due to drying were replaced by functionally similar species. For example, mixotrophic Amphitrema stenostoma and Amphitrema wrightianum were lost from WLD due to their hydrological sensitivity [ 47 ], but mixotrophy was maintained by the remaining three mixotrophic species. These results reflect the functional insurance effect, according to which the presence of numerous species performing similar functions promotes the functional stability of an ecosystem against disturbances [ 11 , 13 ]. Furthermore, diversity may favour the emergence of novel interactions among species, including complementarity mechanisms [ 48 ]. In the bog, species turnover was the smallest, and functional structure did not differ significantly between treatments despite low functional redundancy. Therefore, functional stability in the bog relied on resistance. In unevenly structured communities, such as in the bog, functional stability depends on the competitive strength and resistance of the dominant species [ 11 , 12 ], whose traits were here adapted to dry and nutrient-poor conditions, e.g., small test sizes [ 30 , 47 ]. Thus, the experimental water level drawdown did not exceed the environmental resistance threshold of the prevailing trait composition. Moreover, the dominance of mixotrophic Archerella flavum suggests that the pressure from WLD was weaker than in the fens, given that mixotrophs are highly sensitive to habitat changes [ 31 , 49 ]. This likely reflects the smaller shifts in vegetation structure and abiotic conditions (e.g., shading intensity). Additionally, Sphagnum mosses may have buffered water table decline through capillarity [ 22 , 23 ] and through decreased height growth, thereby maintaining stable microhabitat conditions. This suggests that a more severe disturbance, such as forestry drainage, would be required to produce detectable functional shifts in bog soil biotic communities [50, and references therein]. The poor fen was the only site where taxonomic and functional differences were coupled, indicating that WLD-induced species loss resulted in functional loss. For instance, carbon-fixation function was almost totally lost along with mixotrophic species. Under ambient conditions in the poor fen, TA communities resembled the functional structure of those of the bog reflecting a limited functional insurance effect. However, the trait composition was adapted to wetter conditions than in the bog (i.e., larger test sizes, more exposed test apertures), making the community inherently more sensitive to drying. Additionally, the poor fen WLD faced more drastic ecosystem changes than the other two WLD areas, including increased nutrient concentrations, higher shading intensity, and substantial replacement of Sphagnum by vascular plants [ 22 , 23 ] – shifts known to affect TA communities [ 49 , 45 ]. This triggered competitive exclusion of moisture and light-demanding TA. Furthermore, species and functional turnover narrowed the functional space, as unique species occupied substantially less niche volume than under ambient conditions, weakening community stability to further disturbances. Thus, as climate warming intensifies drying, the resilience of TA — and the ecosystem functions they mediate — is increasingly at risk of being compromised [ 48 , 50 ]. Altogether, our findings provide mechanistic insights into how trait-mediated soil biodiversity supports ecosystem stability and resilience, offering predictive understanding for peatland responses to global change. In line with “precision ecology” [ 51 ], effective climate adaptation and mitigation of peatlands will require habitat-specific strategies that enhance their functional resilience. Declarations Competing Interests The authors have no relevant financial or non-financial interests to disclose. Funding This work was funded by Maj and Tor Nessling Foundation (3825), Finnish Cultural Foundation (00240717), Olvi Foundation (20230925), the Research Council of Finland (projects BorPeat 30840 and DISPEAT 338631, research infrastructure FIRI 337064, the flagship ACCC 337550, the Centre of Excellence in Peatlands, Climate Change, and Ecological Restoration 374130, and the Biosphere Laboratory of Eastern Finland 353580). This study was conducted at Lakkasuo-Siikaneva peatland experimental site (ID: 10033) that is part of Analysis and Experimentation on Ecosystems Research Infrastructure (AnaEE) Finland. Author Contribution Conceptualization: E.-S. T. and B. P. G. Data curation: B. P. G. and O. K.-R. Formal analysis: B. P. G. Investigation: B. P. G., O. K.-R., A. M. L., M. V., E. A. D. M., and E.-S. T. Methodology: E.-S. T., A. M. L., and B. P. G. Resources: O. K.-R. Supervision: E.-S. T. Visualization: A. M. L. and B. P. G. Writing- original draft: B. P. G. Writing- review & editing: B. P. G., O. K.-R., A. M. L., M. V., E. A. D. M., and E.-S. T. Acknowledgement This study was conducted at Lakkasuo-Siikaneva peatland experimental site (ID: 10033) that is part of Analysis and Experimentation on Ecosystems Research Infrastructure (AnaEE) Finland. Data Availability The microbial data are openly available in Zenodo (https://doi.org/10.5281/zenodo.17940365). The environmental data from Kokkonen et al. (2019) are stored in Pangaea Data Library (https://doi.org/10.1594/PANGAEA.904256), and those from Kuuri-Riutta et al. (2025) are stored in IDA research data storage service (https://doi.org/10.23729/fd-bad048dd-eda3-380f-915a-580ccd150ec2). References Beaulne J, Garneau M, Magnan G, Boucher É (2021) Peat deposits store more carbon than trees in forested peatlands of the boreal biome. Scientific Reports , 11 . https://doi.org/10.1038/s41598-021-82004-x Hyvärinen E, Juslén A, Kemppainen E, Uddström A, Liukko U-M (2019) Suomen lajien uhanalaisuus – Punainen kirja 2019. In helda.helsinki.fi . Ympäristöministeriö & Suomen Ympäristökeskus. http://hdl.handle.net/10138/299501 Rantanen M, Karpechko AY, Lipponen A, Nordling K, Hyvärinen O, Ruosteenoja K, Vihma T, Laaksonen A (2022) The Arctic has warmed nearly four times faster than the globe since 1979. Communications Earth & Environment , 3 , 1–10. https://www.nature.com/articles/s43247-022-00498-3 Gong J, Wang K, Kellomäki S, Zhang C, Martikainen PJ, Shurpali N (2012) Modeling water table changes in boreal peatlands of Finland under changing climate conditions. Ecol Model 244:65–78. https://doi.org/10.1016/j.ecolmodel.2012.06.031 Helbig M, Waddington JM, Alekseychik P, Amiro BD, Aurela M, Barr AG, Black TA, Blanken PD, Carey SK, Chen J, Chi J, Desai AR, Dunn A, Euskirchen ES, Flanagan LB, Forbrich I, Friborg T, Grelle A, Harder S, Heliasz M (2020) Increasing contribution of peatlands to boreal evapotranspiration in a warming climate. Nat Clim Change 10:555–560. https://doi.org/10.1038/s41558-020-0763-7 Qi X, Hu Z, Li X, Wang D, Xu C, Hu F, Chen X, Liu M (2025) Land-use driven changes in elemental stoichiometry decouple the positive soil biodiversity-stability relationship. Soil Biol Biochem 209:109907. https://doi.org/10.1016/j.soilbio.2025.109907 Abrams P (1983) The Theory of Limiting Similarity. Annu Rev Ecol Syst 14:359–376. https://doi.org/10.2307/2096978 Weiher E, Keddy PA (1995) Assembly rules, null models, and trait dispersion: New questions from old patterns. Oikos 74:159–164. https://doi.org/10.2307/3545686 Gross N, Le Bagousse-Pinguet Y, Liancourt P, Saiz H, Violle C, Munoz F (2021) Unveiling ecological assembly rules from commonalities in trait distributions. Ecol Lett 24:1668–1680. https://doi.org/10.1111/ele.13789 Korrensalo A, Kettunen J, Mehtätalo L, Vanhatalo J, Tuittila E-S (2025) Detecting subtle change in species and trait composition and quantifying its uncertainty in a boreal peatland. J Veg Sci 36. https://doi.org/10.1111/jvs.70025 de Bello F, Lavorel S, Hallett LM, Valencia E, Garnier E, Roscher C, Conti L, Galland T, Goberna M, Májeková M, Montesinos-Navarro A, Pausas JG, Verdú M, Götzenberger E-VA, L., Lepš J (2021) Functional trait effects on ecosystem stability: Assembling the jigsaw puzzle. Trends in Ecology & Evolution , 36 . https://doi.org/10.1016/j.tree.2021.05.001 Kunze C, Bahlburg D, Urrutia-Cordero P, Striebel M, Kelpsiene E, Langenheder S, Donohue I, Hillebrand H (2024) Partitioning species contributions to ecological stability in disturbed communities. Ecological Monographs , 95 . https://doi.org/10.1002/ecm.1636 Biggs CR, Yeager LA, Bolser DG, Bonsell C, Dichiera AM, Hou Z, Keyser SR, Khursigara AJ, Lu K, Muth AF, Negrete B, Erisman BE (2020) Does functional redundancy affect ecological stability and resilience? A review and meta-analysis. Ecosphere , 11 . https://doi.org/10.1002/ecs2.3184 Kilner CL, Carrell AA, Wieczynski DJ, Votzke S, DeWitt K, Yammine A, Shaw J, Pelletier DA, Weston DJ, Gibert JP (2024) Temperature and CO2 interactively drive shifts in the compositional and functional structure of peatland protist communities. Glob Change Biol 30(3). https://doi.org/10.1111/gcb.17203 Geisen S, Mitchell EAD, Adl S, Bonkowski M, Dunthorn M, Ekelund F, Fernández LD, Jousset A, Krashevska V, Singer D, Spiegel FW, Walochnik J, Lara E (2018) Soil protists: a fertile frontier in soil biology research. FEMS Microbiol Rev 42(3):293–323. https://doi.org/10.1093/femsre/fuy006 Gilbert D, Amblard C, Bourdier G, Francez A-J (1998) The microbial loop at the surface of a peatland: Structure, function, and impact of nutrient input. Microb Ecol 35(1):83–93. https://doi.org/10.1007/s002489900062 Marcisz K, Jassey VEJ, Kosakyan A, Krashevska V, Lahr DJG, Lara E, Lamentowicz Ł, Lamentowicz M, Macumber A, Mazei Y, Mitchell EA, Nasser NA, Patterson RT, Roe HM, Singer D, Tsyganov AN, Fournier B (2020) Testate amoeba functional traits and their use in paleoecology. Frontiers in Ecology and Evolution , 8 . https://doi.org/10.3389/fevo.2020.575966 Koenig I, Mulot M, Mitchell EA (2018) Taxonomic and functional traits responses of Sphagnum peatland testate amoebae to experimentally manipulated water table. Ecol Ind 85:342–351. https://doi.org/10.1016/j.ecolind.2017.10.017 Perkins DM, Hatton IA, Gauzens B, Barnes AD, Ott D, Rosenbaum B, Vinagre C, Brose U (2022) Consistent predator-prey biomass scaling in complex food webs. Nat Commun 13(1):4990. https://doi.org/10.1038/s41467-022-32578-5 Reczuga MK, Lamentowicz M, Mulot M, Mitchell EA, Buttler A, Chojnicki B, Słowiński M, Binet P, Chiapusio G, Gilbert D, Słowińska S, Jassey VEJ (2018) Predator-prey mass ratio drives microbial activity under dry conditions in Sphagnum peatlands. Ecol Evol 8:5752–5764. https://doi.org/10.1002/ece3.4114 Laine AM, Korrensalo A, Kokkonen NAK, Tuittila E-S (2021) Impact of long‐term water level drawdown on functional plant trait composition of northern peatlands. Funct Ecol 35(10):2342–2357. https://doi.org/10.1111/1365-2435.13883 Kokkonen NAK, Laine AM, Laine J, Vasander H, Kurki K, Gong J, Tuittila E-S (2019) Responses of peatland vegetation to 15‐year water level drawdown as mediated by fertility level. J Veg Sci 30:1206–1216. https://doi.org/10.1111/jvs.12794 Kuuri-Riutta O, Le Geay M, Jassey VEJ, Barel JM, Laine AM, Ylänne H, Tuittila E‐S (2025) Microbial and bryospheric photosynthesis of boreal peatlands have peatland‐type‐specific responses to long‐term drying. New Phytol 248:1336–1350. https://doi.org/10.1111/nph.70519 Kuuri-Riutta O, Palacios Ganoza B, Ylänne H, Mitchell EA, Väliranta MM, Tuittila E-S (2026) Assessing the value of testate amoebae and their functional traits in detecting climate change-induced peatland drying. Microb Ecol 89. https://doi.org/10.1007/s00248-025-02682-2 Payne RJ, Mitchell EA (2008) How many is enough? Determining optimal count totals for ecological and palaeoecological studies of testate amoebae. J Paleolimnol 42:483–495. https://doi.org/10.1007/s10933-008-9299-y Siemensma FJ (2023) Microworld – world of amoeboid organisms . Arcella.nl. https://arcella.nl/ McKeown MM, Wilmshurst JM, Duckert C, Wood JR, Mitchell EA (2019) Assessing the ecological value of small testate amoebae (< 45 µm) in New Zealand peatlands. Eur J Protistol 68:1–16. https://doi.org/10.1016/j.ejop.2018.12.002 Pakeman RJ, Quested HM (2007) Sampling plant functional traits: What proportion of the species need to be measured? Appl Veg Sci 10:91–96. https://doi.org/10.1111/j.1654-109x.2007.tb00507.x Fournier B, Lara E, Jassey VE, Mitchell EA (2015) Functional traits as a new approach for interpreting testate amoeba palaeo-records in peatlands and assessing the causes and consequences of past changes in species composition. Holocene 25:1375–1383. https://doi.org/10.1177/0959683615585842 Fournier B, Malysheva E, Mazei Y, Moretti M, Mitchell EA (2012) Toward the use of testate amoeba functional traits as indicator of floodplain restoration success. Eur J Soil Biol 49:85–91. https://doi.org/10.1016/j.ejsobi.2011.05.008 Jassey VEJ, Signarbieux C, Hättenschwiler S, Bragazza L, Buttler A, Delarue F, Fournier B, Gilbert D, Laggoun-Défarge F, Lara E, Mills TE, Mitchell R, Payne EA, R. J., Robroek BJM (2015) An unexpected role for mixotrophs in the response of peatland carbon cycling to climate warming. Scientific Reports , 5 . https://doi.org/10.1038/srep16931 R Core Team (2022) R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.r-project.org/ Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, Minchin PR, O’Hara RB, Simpson GL, Solymos P, Stevens MHH, Szoecs E, Wagner H (2022) Vegan: Community ecology package . R-Packages. https://cran.r-project.org/package=vegan Lepš J, de Bello F, Šmilauer P, Doležal J (2011) Community trait response to environment: Disentangling species turnover vs intraspecific trait variability effects. Ecography 34:856–863. https://doi.org/10.1111/j.1600-0587.2010.06904.x Perronne R, Munoz F, Borgy B, Reboud X, Gaba S (2017) How to design trait-based analyses of community assembly mechanisms: Insights and guidelines from a literature review. Perspect Plant Ecol Evol Syst 25:29–44. https://doi.org/10.1016/j.ppees.2017.01.004 Ricotta C, Pavoine S (2024) A new look at functional beta diversity. Methods Ecol Evol 12:1062–1069. https://doi.org/10.1101/2024.02.25.580632 Pavoine S (2020) Adiv: An R package to analyse biodiversity in ecology. Methods Ecol Evol 11(9):1106–1112. https://doi.org/10.1111/2041-210x.13430 Dray S, Dufour A-B (2007) The ade4 package: Implementing the duality diagram for ecologists. Journal of Statistical Software , 22 . https://doi.org/10.18637/jss.v022.i04 Vicente-Gonzales L, Vicente-Villardon JL (2022) CRAN: Package PERMANOVA. R-Project.org . https://doi.org/10.32614/CRAN.package.PERMANOVA Wheeler B, Torchiano M (2025) LmPerm: Permutation tests for linear models. R package version 2.1.5 . https://github.com/mtorchiano/lmperm Menéndez-Serra M, Ontiveros VJ, Cáliz J, Alonso D, Casamayor EO (2023) Understanding stochastic and deterministic assembly processes in microbial communities along temporal, spatial and environmental scales. Mol Ecol 32. https://doi.org/10.1111/mec.16842 Pastore AI, Barabás G, Bimler MD, Mayfield MM, Miller TE (2021) The evolution of niche overlap and competitive differences. Nat Ecol Evol 5:330–337. https://doi.org/10.1038/s41559-020-01383-y Hart SP, Turcotte MM, Levine JM (2019) Effects of rapid evolution on species coexistence. Proceedings of the National Academy of Sciences , 116 , 2112–2117. https://doi.org/10.1073/pnas.1816298116 Wilkinson DM, Koumoutsaris S, Mitchell EA, Bey I (2012) Modelling the effect of size on the aerial dispersal of microorganisms. J Biogeogr 39:89–97. https://doi.org/10.1111/j.1365-2699.2011.02569.x Robroek BJM, Martí M, Svensson BH, Dumont MG, Veraart AJ, Jassey VEJ (2021) Rewiring of peatland plant–microbe networks outpaces species turnover. Oikos 130:339–353. https://doi.org/10.1111/oik.07635 Bobrov A, Mazei Y (2004) Morphological variability of Testate Amoebae (Rhizopoda: Testacea lobosea: Testacea filosea) in natural populations. Acta Protozool 43:133–146. https://www.researchgate.net/publication/228889468 Lamentowicz M, Mitchell EAD (2005) The ecology of testate amoebae (protists) in Sphagnum in North-western Poland in relation to peatland ecology. Microb Ecol 50(1):48–63. https://doi.org/10.1007/s00248-004-0105-8 Fetzer I, Johst K, Schäwe R, Banitz T, Harms H, Chatzinotas A (2015) The extent of functional redundancy changes as species’ roles shift in different environments. Proceedings of the National Academy of Sciences , 112 , 14888–14893. https://doi.org/10.1073/pnas.1505587112 Lamentowicz M, Kajukało-Drygalska K, Kołaczek P, Jassey VEJ, Gabka˛ M, Karpinska-Kołaczek M (2020) Testate amoebae taxonomy and trait diversity are coupled along an openness and wetness gradient in pine-dominated Baltic bogs. Eur J Protistol 73:125674. https://doi.org/10.1016/j.ejop.2020.125674 Allison SD, Martiny JBH (2008) Resistance, resilience, and redundancy in microbial communities. Proceedings of the National Academy of Sciences , 105 (Supplement 1), 11512–11519. https://doi.org/10.1073/pnas.0801925105 Spake R, Jackson EE, Bullock JM, Gardner E, Tipton E, Grainger MJ, Doncaster CP (2025) Precision ecology for targeted conservation action. Nat Ecol Evol 9(7):1102–1111. https://doi.org/10.1038/s41559-025-02733-4 Kokkonen NAK (2019) Data from: Responses of peatland vegetation to 15-year water level drawdown as mediated by fertility level. PANGAEA. https://doi.org/10.1594/PANGAEA.904256 Kuuri-Riutta O, Laine A, Tuittila E-S (2025) Data from: Microbial and bryospheric photosynthesis of boreal peatlands have peatland‐type‐specific responses to long‐term drying. IDA. https://doi.org/10.23729/fd-bad048dd-eda3-380f-915a-580ccd150ec2 Kuuri-Riutta O, Tuittila E-S, Palacios Ganoza B (2025) Data from: Testate amoeba data from Lakkasuo peatland water level drawdown experiment. Zenodo. https://doi.org/10.5281/zenodo.17940365 Additional Declarations No competing interests reported. Supplementary Files PalaciosGanozaetalSupplementaryinformation.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 10 May, 2026 Reviews received at journal 09 May, 2026 Reviews received at journal 06 May, 2026 Reviewers agreed at journal 20 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviewers invited by journal 14 Apr, 2026 Editor assigned by journal 14 Apr, 2026 Submission checks completed at journal 14 Apr, 2026 First submitted to journal 13 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9404720","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":626120599,"identity":"f7ddf911-6d48-45e7-9ba4-42f5b318842f","order_by":0,"name":"Brunella Palacios Ganoza¹","email":"data:image/png;base64,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","orcid":"","institution":"University of Eastern Finland","correspondingAuthor":true,"prefix":"","firstName":"Brunella","middleName":"Palacios","lastName":"Ganoza¹","suffix":""},{"id":626120601,"identity":"e120a8a9-46ca-4558-ae45-df0fff227a94","order_by":1,"name":"Olivia Kuuri-Riutta¹","email":"","orcid":"","institution":"University of Eastern Finland","correspondingAuthor":false,"prefix":"","firstName":"Olivia","middleName":"","lastName":"Kuuri-Riutta¹","suffix":""},{"id":626120602,"identity":"20906d20-e739-4771-b4fb-29eb0d2ef957","order_by":2,"name":"Anna M. Laine¹","email":"","orcid":"","institution":"University of Eastern Finland","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"M.","lastName":"Laine¹","suffix":""},{"id":626120603,"identity":"a3932b3b-6aee-4a62-a639-01d1bba31df1","order_by":3,"name":"Minna M. Väliranta²","email":"","orcid":"","institution":"University of Helsinki","correspondingAuthor":false,"prefix":"","firstName":"Minna","middleName":"M.","lastName":"Väliranta²","suffix":""},{"id":626120605,"identity":"33603d5c-320b-4c44-96d8-75639ca4b198","order_by":4,"name":"Edward A. D. Mitchell³","email":"","orcid":"","institution":"University of Neuchâtel","correspondingAuthor":false,"prefix":"","firstName":"Edward","middleName":"A. D.","lastName":"Mitchell³","suffix":""},{"id":626120606,"identity":"5045df4c-602a-485e-b148-e1755b0bb0c1","order_by":5,"name":"Eeva-Stiina Tuittila¹","email":"","orcid":"","institution":"University of Eastern Finland","correspondingAuthor":false,"prefix":"","firstName":"Eeva-Stiina","middleName":"","lastName":"Tuittila¹","suffix":""}],"badges":[],"createdAt":"2026-04-13 13:24:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9404720/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9404720/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107571849,"identity":"a89c5d8e-9cc3-470b-8130-a318fa002663","added_by":"auto","created_at":"2026-04-22 18:37:27","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":192166,"visible":true,"origin":"","legend":"\u003cp\u003ePCA of TA community functional traits. Left biplot shows the importance of different functional traits for PC axes 1 (PCA1) and 2 (PCA2); right biplot shows correlations of environmental variables. Ellipses indicate sample positions by treatment and site within the ordination space.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9404720/v1/300b6c02c2c7f7926e44cce5.jpeg"},{"id":107571848,"identity":"ac1e6ec0-9080-413d-b3e1-c967b6a91e85","added_by":"auto","created_at":"2026-04-22 18:37:27","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":248302,"visible":true,"origin":"","legend":"\u003cp\u003eViolin plots showing community-weighted distributions for biovolume, aperture size, and mixotrophy in TA communities. Distributions differed significantly between treatments and site-treatment interactions (p\u0026lt;0.05; MANOVA tests). Observations are overlaid as semitransparent points with a red dot for the mean point.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9404720/v1/8ade9ceed56e7bc86e815bdf.jpeg"},{"id":107705637,"identity":"6551374b-4293-4509-9c2b-7237aac83e5e","added_by":"auto","created_at":"2026-04-24 09:14:03","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":230454,"visible":true,"origin":"","legend":"\u003cp\u003eNull model comparisons for size traits (biovolume and aperture size) across sites and treatments (control vs. WLD) using four functional metrics: mean, range, mean pairwise trait distance (MTD), and coefficient of variation of nearest-neighbour distance (cv_nnd). Stars denote Wilcoxon signed-rank test significance (*p \u0026lt; 0.05, **p \u0026lt; 0.01) of observed vs. random distributions. Effect sizes use a standardized metric (SES = (observed - null mean)/null SD); see Materials and Methods for details of the tested hypothesis. Darker colors indicate the WLD treatment.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9404720/v1/17636ef43065278ec6bfa49d.jpeg"},{"id":107571851,"identity":"a51b43a9-1035-4b77-94bc-6b50520178f3","added_by":"auto","created_at":"2026-04-22 18:37:27","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":147992,"visible":true,"origin":"","legend":"\u003cp\u003eTriangle plot showing the functional structures and differences in indexes (beta redundancy, functional dissimilarity, and taxonomic similarity) between treatments (control vs. WLD). Differences were tested with permutational two-way ANOVA (*p \u0026lt; 0.05, **p \u0026lt; 0.01).\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9404720/v1/1d15fabadf01f2e83a55e68c.jpeg"},{"id":107711291,"identity":"9783692e-4774-41b1-8d21-1ea2cc399346","added_by":"auto","created_at":"2026-04-24 09:45:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1134428,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9404720/v1/d3b98a3a-06dc-4f15-9123-7e584c60f575.pdf"},{"id":107571847,"identity":"655856f6-3d39-41c5-a467-fa6394553f7d","added_by":"auto","created_at":"2026-04-22 18:37:27","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":910880,"visible":true,"origin":"","legend":"","description":"","filename":"PalaciosGanozaetalSupplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-9404720/v1/763e44cc9ff4b1c37053e04c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Contrasting trait-mediated mechanisms shape testate amoeba communities under long- term drying across boreal peatlands","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBoreal peatlands are major reservoirs of terrestrial carbon [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] and host specialized biodiversity adapted to persistently wet, acidic, and nutrient-poor conditions [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, they are increasingly threatened as temperatures in high latitudes rapidly rise [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], possibly increasing evapotranspiration beyond compensatory precipitation inputs [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Consequent drying can disrupt ecosystem functions, reduce resilience, and compromise the stability of soil processes. While soil biotic communities play a critical role in sustaining ecosystem stability [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], the trait-mediated stability mechanisms that buffer the effects of environmental pressures on peatlands\u0026rsquo; functioning remain poorly understood. In particular, it is unclear how long-term drying affects the functional stability of soil protists, critical regulators of carbon and nutrient cycling. Addressing this knowledge gap is essential for understanding peatland responses to ongoing climate-driven hydrological shifts.\u003c/p\u003e \u003cp\u003eTheoretically, the mechanisms through which environmental changes influence communities\u0026rsquo; functional stability are well established [e.g., 7, 8]. Community assemblage is governed by stochasticity (randomness) and deterministic processes: environmental filtering, where species with traits suited to local conditions are selected [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], and niche differentiation, where species adopt distinct strategies to reduce competitive overlap [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Empirical studies, primarily in aboveground communities, have applied trait-based approaches to infer and disentangle how these mechanisms operate under changing climate [e.g., 9, 10]. However, results vary widely across ecosystems and climate scenarios, which makes it difficult to determine when trait-environment relationships have predictable functional outcomes and when they are shaped by context-dependent factors [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This variability underscores the need for long-term empirical assessments in systems undergoing directional climate forcing.\u003c/p\u003e \u003cp\u003eEcological stability \u0026ndash; the tendency of communities to withstand and recover from a disturbance \u0026ndash; emerges from both resistance and recovery processes [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These processes are fundamentally mediated by functional traits. A key example is functional redundancy, where multiple species perform similar ecological roles, thereby buffering ecosystem functioning against species loss or turnover [13, and references therein]. Therefore, communities with high functional redundancy may be more resilient to disturbance, whereas communities with low redundancy may experience disproportionate functional shifts, potentially altering their ecological role.\u003c/p\u003e \u003cp\u003eClimate-warming-induced drying of peatlands strongly impacts key soil protists, potentially altering the structure and function of peatland microbial food webs [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Soil protists have a strong effect on ecosystem functions as they represent the main predators of microbes and directly influence photosynthesis and respiration [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In peatlands, testate amoebae (TA) account for the largest fraction of the total protozoan biomass [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and represent the top predators within the microbial food web [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. TA are highly sensitive to water table depth, even to the extent that species can be used as valuable palaeohydrological proxies [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and their traits respond consistently to environmental changes. For example, TA biovolume typically decreases under drying [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], which can reduce their trophic status [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], weaken top-down control on decomposers, and potentially destabilize microbial food webs [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Because TA traits reflect ecological strategies shaped by hydrology and climate, they are powerful model organisms for investigating the processes governing soil biodiversity-stability relationship under long-term drying.\u003c/p\u003e \u003cp\u003eThis study aimed to assess whether trait-mediated mechanisms in soil protist communities maintain \u0026ndash; or fail to maintain \u0026ndash; functional stability under climate-induced drying across boreal peatlands. Using a two-decade water level drawdown (WLD) experiment setup, we compared TA communities and functional traits between WLD treatment and ambient control areas across three peatland types. More specifically, we addressed two hypotheses:\u003c/p\u003e \u003cp\u003eH1: TA communities are strongly affected by altered environmental filtering. We therefore expected size-related traits to show a stronger environmental filtering and niche differentiation under WLD than under ambient conditions.\u003c/p\u003e \u003cp\u003eH2: TA communities with higher functional redundancy are functionally more stable. Thus, in highly redundant TA communities, we expected WLD-induced species turnover to be decoupled from functional turnover.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study site\u003c/h2\u003e \u003cp\u003eWe used a long-term WLD experiment started in an eccentric raised peatland complex, Lakkasuo (located in Southern Boreal Finland, 61\u0026deg;47\u0026prime;N, 24\u0026deg;18\u0026prime;E), in 2000\u0026ndash;2001. It consists of three experimental WLD areas and corresponding control areas, each pair representing a different peatland type (site): a mesotrophic fen, an oligotrophic fen, and an ombrotrophic bog, here referred to as rich fen, poor fen, and bog, respectively. See further details in [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The sampling covers variation representative of boreal peatlands, but as the experiment was conducted within a single peatland complex, we lack true replication across the three sites. Although this limits the generalization of our results, it allows us to reveal the trait-mediated mechanisms driving the biodiversity-stability relationship between control and WLD areas.\u003c/p\u003e \u003cp\u003ePre-experiment, the control and WLD areas had similar vegetation and water table. Since then, significant changes have occurred in the fen WLD areas, where the development of tree stand has altered abiotic conditions for the understory vegetation [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The contrast between treatments is smaller in the bog, where the vegetation and TA communities have changed slightly [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data collection\u003c/h2\u003e \u003cp\u003eData have previously been used to assess differences in TA community composition [24; for detailed methodological description].\u003c/p\u003e \u003cp\u003eWe collected the upper 3 cm of 3 to 10 \u003cem\u003eSphagnum\u003c/em\u003e moss shoots from 8 to 10 samples from each of the six study areas (total\u0026thinsp;=\u0026thinsp;53) during summer 2022 and stored the samples in 15 ml of 4% formaldehyde. The samples were shaken for two minutes, filtered through a 150 \u0026micro;m sieve, centrifuged, and analyzed by light microscopy at 200x and 400x. We targeted 150 individuals identified to species level [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] using Siemensma [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and McKeown [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. For traits, we recorded aperture size, biovolume, trophic status (mixotroph or heterotroph), aperture position, test compression, and test material (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for definitions and ecological implications). Test length and width and aperture size were microscopically measured, aiming 5 replicates per taxa per sample. Some individuals were unmeasurable (e.g. due to poor placement), but the final dataset represents at least 80% of the community in each sample [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. For low-count species, we used the mean value calculated from the same study area. Biovolume was calculated using a different formula for each test shape, as in Fournier [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Taxon-specific values for aperture position, trophic status, test compression, and material derive from Fournier [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and supplementary material from Fournier [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], complemented with Siemensma [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFunctional traits measured, their response patterns to disturbances, and ecological implications.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrait\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eResponse to disturbances\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEcological impact\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReferences\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAperture position\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePosition of the aperture within the test: axial (1), acrostomic (2), or plagiostomic (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDisturbed conditions favor plagiostomic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRelated to contribution to the food web\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eJassey [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e],\u003c/p\u003e \u003cp\u003eFournier [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAperture size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026micro;m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWidth of the test aperture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDisturbed conditions favor small apertures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRelated to the prey size and food web functioning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFournier [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiovolume\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026micro;m\u0026sup3;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVolume of the test occupied by the living amoeba (80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDisturbed conditions favor small size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRelated to the metabolic rate and the capacity of the food web to process energy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFournier [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e],\u003c/p\u003e \u003cp\u003eReczuga [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMixotrophy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBinary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePresence (1) or not (0) of photosynthetic endosymbionts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWet and unshaded conditions favor mixotrophy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRelated to peatland C cycling and bacterial grazing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eJassey [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest compression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpherical (1), sub spherical (2), compressed (3), or strongly compressed (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDisturbed conditions favor the compression of the test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRelated to the ability to stay active and contribute to the food web in drier conditions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFournier [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest material\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTest made of protein (1), silica (2), silica\u0026thinsp;+\u0026thinsp;organic (3), calcite (4), recycled idiosomes (5), or xenosomes (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProtein is favored by wet and unshaded conditions, silica by dry conditions. The knowledge is controversial for recycled idiosomes, silica\u0026thinsp;+\u0026thinsp;organic, calcite and xenosomes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRelated to the availability of material and/or prey to construct the test or build a self-secreted test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFournier [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e],\u003c/p\u003e \u003cp\u003eGeisen [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical analyzes\u003c/h2\u003e \u003cp\u003eAll statistical analyzes were performed using R version 4.2.2 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Due to varying total counts across the sampling plots and the correlation between total count and the number of species, we standardized the abundance data. We calculated the relative abundances and applied a threshold based on the minimum relative abundance in the plot with the smallest number of observations (\u0026lt;\u0026thinsp;0.8%). This resulted in a dataset that covered 52 of the 66 initially identified taxa. We calculated the community weighted mean (CWM) functional traits for each plot by weighing the mean trait value of the taxon in the specific community, by their relative abundances.\u003c/p\u003e \u003cp\u003eTo examine the variability in TA functional traits between ambient and WLD conditions, we applied a principal component analysis (PCA) using the \u003cem\u003evegan\u003c/em\u003e package [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. As environmental variables, we used peat nutrient contents and pH [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], and \u003cem\u003eSphagnum\u003c/em\u003e water content, water table level, and shading intensity [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdditionally, to further examine trait-variation mechanisms, we constructed linear models to quantify the relative contribution of species turnover and intraspecific trait variability to variation in CWM biovolume and aperture size. Following Laine [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], we generated two additional CWM datasets: (1) fixed CWM, calculated using average species-level trait values from the full dataset weighted by their relative abundances, and (2) difference CWM, calculated by subtracting the fixed CWM values from the observed CWMs. For each trait, we fitted three linear models (observed CWM, fixed values, or difference), using site fertility and treatment as predictors. Site fertility gradient was represented by the PC1 axis of a previous PCA of environmental variables in the study areas [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. From each model, we extracted the sum of squares (SS) to calculate the SS cov. (species turnover and intraspecific trait covariation) by subtracting SS fixed and SS difference from SS observed [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Percentages of variability contributed by each component (species turnover, intraspecific variation, and covariation) per site and treatment were calculated as proportions of total variation.\u003c/p\u003e \u003cp\u003eTo examine the variability in TA functional traits driven by environmental filtering and niche differentiation in ambient and WLD conditions, we first used violin plots to compare community-weighted trait distributions and assess differences in distribution shape as potential signals of adaptation to WLD. To select the significant traits (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05), we used the results of multivariate analysis of variance (MANOVA) from the same data reported in Kuuri-Riutta [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Skewness and kurtosis were quantified for each of these traits in each study area. As most distributions were non-Gaussian, we calculated the deviations in WLD study areas relative to the mean of all control study areas (% difference) to allow comparisons between treatments.\u003c/p\u003e \u003cp\u003eTo further quantify the effects of environmental filtering and niche differentiation on size-related traits, we used trait-based null models for TA biovolume and aperture size. We constructed null expectations in two steps, preserving observed species richness and relative abundances while randomizing trait values. First, species were randomly selected from the total species pool (equiprobable selection) that comprised all identified species at the site level. Second, a random plot-wise mean trait value was allocated to each selected species. Finally, 999 null expectations were generated for each trait in the 53 sample plots. For each plot, four functional metrics were calculated using the observed data and compared with the corresponding null expectations. To detect environmental filtering, we compared the mean, range of traits, and mean pairwise trait distance (MTD), and to detect niche differentiation, we compared the coefficient of variation of the nearest-neighbor distance between traits (cv_nnd) [35, and references therein]. We used Wilcoxon signed-rank test to evaluate the deviation of the observed functional metrics from their null expectations across sites. Tests for means were two-tailed in control study areas (allowing shifts in either direction due to environmental filtering) and one-tailed in WLD study areas (expecting downward shifts from treatment). Range, cv_nnd, and MTD tests were one-tailed in control and WLD study areas (with directional shifts expected from environmental filtering and niche differentiation). Secondly, to quantify the strength of the effect of environmental filtering and niche differentiation, we calculated the standardized effect size (SES) as the difference between the observed values and the null expectation mean value divided by the standard deviation of the null expectation.\u003c/p\u003e \u003cp\u003eTo examine the variability in TA functional structure between ambient and WLD conditions, we used three beta functional diversity indices as proposed by Ricotta \u0026amp; Pavoine [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]: 'beta redundancy' (different species supporting similar functions between communities), 'functional dissimilarity' (functional divergence among species), and 'taxonomic similarity' (shared species among communities). We used \u003cem\u003eadiv\u003c/em\u003e package [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] for the calculations and a ternary diagram from \u003cem\u003eade4\u003c/em\u003e package [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] for illustration. To test functional structure differences between sites and treatments, we used db-MANOVA from \u003cem\u003ePERMANOVA\u003c/em\u003e package [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] and betadisper from \u003cem\u003evegan\u003c/em\u003e package [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Individual indexes were tested using two-way permutational ANOVA from package \u003cem\u003elmperm\u003c/em\u003e [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Variability in functional compositions of testate amoebae between ambient and WLD conditions\u003c/h2\u003e \u003cp\u003eTA functional traits differed among sites and between treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Table S2). Under ambient conditions, the bog-rich fen gradient was expressed as shifts from smaller to larger biovolume and aperture size, from higher proportion of mixotrophs and proteinaceous tests to assemblages dominated by acrostomic apertures, compressed and recycled idiosomic tests.\u003c/p\u003e \u003cp\u003eWLD reshaped TA assemblages across sites by reducing size-related traits (biovolume and aperture) and mixotrophy, shifting apertures positions from axial to plagiostomic, and redistributing test materials from proteinaceous and xenosomic towards siliceous; with greater treatment differences in fens than in bog (Table S2). Consistently, WLD significantly predicted variation in the community-weighted size traits, explaining 22% of biovolume variation and 17% of aperture size variation (Tables S2 - S3). This WLD-induced size reduction was mainly driven by species turnover (15% and 14%, respectively), with minor contribution from intraspecific variation (0.7% and 0.2%), and a positive covariation effect between both mechanisms (Table S3). When the total variation in the dataset (site fertility and WLD) was considered, the dominance of species turnover was further emphasized (85% and 73%), while the contribution of intraspecific variation slightly increased (2% and 5%) (Table S3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.2 Variability in functional traits of testate amoebae driven by environmental filtering and niche differentiation in ambient and WLD conditions\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSkewness and kurtosis of community-weighted functional trait distributions were small (between \u0026minus;\u0026thinsp;2 and 2, i.e., within the range suggested by Gross [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]) in both WLD and control study areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea - b). Deviations showed no notable differences (\u0026lt;\u0026thinsp;200) between treatments, with exception of mixotrophy in the poor fen WLD area.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eComparing size-related trait distributions with null distributions revealed stronger signals of deterministic processes under WLD than under ambient conditions. In control areas, niche differentiation dominated in the rich fen and environmental filtering in the poor fen. In the bog control area, the two processes were equally strong. In the rich fen and in the bog, niche differentiation was detected in biovolume but not in aperture size, whereas weak niche differentiation was detected by both traits in the poor fen. Environmental filtering was detected by biovolume in the poor fen and in the bog (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table S4).\u003c/p\u003e \u003cp\u003eUnder WLD conditions, environmental filtering was the dominant deterministic process, detected by both biovolume and aperture size, with stronger effect sizes than under ambient conditions. The strongest filtering was observed in the rich fen. Niche differentiation was observed in both traits only in the bog WLD area (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table S4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Variability in functional structure of testate amoebae between ambient and WLD conditions\u003c/h2\u003e \u003cp\u003eThe functional structure of TA communities was measured as beta redundancy, functional dissimilarity, and taxonomic similarity. In control areas, it differed significantly between rich fen and bog and between rich fen and poor fen. TA communities in poor fen and bog shared similar functional structures, both in control and WLD areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table S5). The test for homogeneity of variance showed no significant pairwise differences between the study areas, therefore differences/similarities in functional structure derived from their average compositions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table S5).\u003c/p\u003e \u003cp\u003eBeta redundancy was significantly lowered in both fen WLD areas. Additionally, functional dissimilarity decreased and taxonomic similarity increased in the poor fen WLD. On the contrary, no significant differences were observed between control and WLD in the bog (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). When considering beta redundancy, taxonomic similarity, and functional dissimilarity together, only poor fen showed significant differences between WLD and control.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eWe applied functional traits to investigate the mechanisms that have shaped TA communities along a bog-rich fen gradient and assessed how two decades of experimental water level drawdown altered their functionality. The long-term drying acted as an environmental filter that produced drought-adapted functional profiles driven mainly by species turnover. The differences in trait-mediated stability among sites reflected variation in functional thresholds. Together, our results show that functional traits play a central role in stability; however, the strength of the functional buffering effect depends on the disturbance and the local site characteristics that modify the trait-mediated community responses and resilience capacity.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Differences in abiotic and biotic pressure on TA communities between ambient and WLD conditions\u003c/h2\u003e \u003cp\u003eOur first hypothesis, that TA communities are more strongly filtered by environment under drying conditions than under ambient conditions, was supported across sites as the strength of environmental filtering increased in WLD areas. The generally small skewness and kurtosis in our dataset indicate that TA traits are not currently exposed to strong pressure even in WLD areas, suggesting that communities have largely adapted to current biotic and abiotic conditions [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This is likely due to tree encroachment \u0026ndash; that fundamentally altered local microclimates and resource environments \u0026ndash; occurring already for more than a decade in fen WLDs [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Comparatively, the rapid life cycle of TA allows communities to respond to environmental changes within months to a few years [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In line with the dynamic nature of community assembly, where processes vary in effect and strength overtime [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], WLD communities shifted from stochastic dominance toward more deterministic processes. This shift allowed us to identify the deterministic processes and quantify the strength of their influence.\u003c/p\u003e \u003cp\u003eUnder ambient conditions, niche differentiation was the primary mechanism structuring TA communities in the rich fen control. This highlights the strong effect of biotic interactions in resource-rich habitats that allow diverse species with contrasting ecological strategies to reach stable competitive differentiation, thus promoting coexistence [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In contrast, in the bog control, where water and nutrient supply depend on precipitation, niche differentiation and environmental filtering were equally strong. Under such resource-limited conditions, species are driven to use the limited resources differently to avoid direct competition (i.e., resource partitioning) [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. In the poor fen control, both processes were detected, but environmental filtering had a stronger effect compared to niche differentiation. Because the poor fen represents resource-limited conditions among fen habitats, TA communities there likely have undergone strong selection for traits improving tolerance to nutrient-poor and acidic conditions. This has led to species occupying distinct niches that reduce overlap and enable efficient resource partitioning among coexisting species [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUnder experimental drying (i.e., dry and more shaded conditions), community assembly across all WLD areas was dominated by a strong environmental filtering, which consistently reduced biovolume and aperture size of TA communities. Small individuals have multiple competitive advantages in thin water films around \u003cem\u003eSphagnum\u003c/em\u003e stems and leaves: they have a reduced risk of desiccation [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], can reproduce faster, require smaller living space [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and recolonize faster from adjacent areas [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In the bog WLD area specifically, environmental filtering intensified niche differentiation for both size traits. This pattern aligns with previous findings that in resource-limited habitats, environmental changes impose additional pressure on community dynamics [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], leading to stronger trait-based structuring of TA communities. Moreover, decreasing size under drying reflects a shift towards lower trophic levels among predators [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Such size restructuring, along with shifts in vegetation from \u003cem\u003eSphagnum-\u003c/em\u003edominated peatlands to forest, can alter the diversity and structure of the microbial food web [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAperture size and biovolume reflected contrasting roles in mediating community stability and responses to environmental pressure. Biovolume variation was mainly driven by environmental change and species turnover, while aperture size variation was strongly associated with niche differentiation and higher intraspecific variation. Consistent with this, Bobrov \u0026amp; Mazei [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] reported that aperture size showed the greatest variability among size traits across habitats and can change independently of overall test size. This likely reflects the functional importance of the TA aperture as the main interface with the environment, as it is used in feeding, locomotion, and environmental sensing [17, and references therein]. These contrasting responses highlight how trait-specific strategies underpin community stability, with aperture variation promoting resistance under the drying pressure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Functional stability of testate amoeba community mediated by functional redundancy\u003c/h2\u003e \u003cp\u003eOur second hypothesis - that functionally redundant TA communities are less vulnerable to climate-induced drying - was supported in the fens. In the rich fen, functional redundancy peaked together with functional stability, whereas in the poor fen, where redundancy was low, drying led to functional turnover. However, the bog remained functionally stable despite low redundancy.\u003c/p\u003e \u003cp\u003eIn the rich fen, despite the strongest WLD-induced species turnover, the overall functional structure of TA did not differ between treatments. Our results suggest that the taxonomically diverse [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] and functionally redundant TA community in the rich fen was functionally resilient, i.e., the species lost due to drying were replaced by functionally similar species. For example, mixotrophic \u003cem\u003eAmphitrema stenostoma\u003c/em\u003e and \u003cem\u003eAmphitrema wrightianum\u003c/em\u003e were lost from WLD due to their hydrological sensitivity [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], but mixotrophy was maintained by the remaining three mixotrophic species. These results reflect the functional insurance effect, according to which the presence of numerous species performing similar functions promotes the functional stability of an ecosystem against disturbances [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Furthermore, diversity may favour the emergence of novel interactions among species, including complementarity mechanisms [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the bog, species turnover was the smallest, and functional structure did not differ significantly between treatments despite low functional redundancy. Therefore, functional stability in the bog relied on resistance. In unevenly structured communities, such as in the bog, functional stability depends on the competitive strength and resistance of the dominant species [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], whose traits were here adapted to dry and nutrient-poor conditions, e.g., small test sizes [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Thus, the experimental water level drawdown did not exceed the environmental resistance threshold of the prevailing trait composition. Moreover, the dominance of mixotrophic \u003cem\u003eArcherella flavum\u003c/em\u003e suggests that the pressure from WLD was weaker than in the fens, given that mixotrophs are highly sensitive to habitat changes [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. This likely reflects the smaller shifts in vegetation structure and abiotic conditions (e.g., shading intensity). Additionally, \u003cem\u003eSphagnum\u003c/em\u003e mosses may have buffered water table decline through capillarity [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and through decreased height growth, thereby maintaining stable microhabitat conditions. This suggests that a more severe disturbance, such as forestry drainage, would be required to produce detectable functional shifts in bog soil biotic communities [50, and references therein].\u003c/p\u003e \u003cp\u003eThe poor fen was the only site where taxonomic and functional differences were coupled, indicating that WLD-induced species loss resulted in functional loss. For instance, carbon-fixation function was almost totally lost along with mixotrophic species. Under ambient conditions in the poor fen, TA communities resembled the functional structure of those of the bog reflecting a limited functional insurance effect. However, the trait composition was adapted to wetter conditions than in the bog (i.e., larger test sizes, more exposed test apertures), making the community inherently more sensitive to drying. Additionally, the poor fen WLD faced more drastic ecosystem changes than the other two WLD areas, including increased nutrient concentrations, higher shading intensity, and substantial replacement of \u003cem\u003eSphagnum\u003c/em\u003e by vascular plants [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] \u0026ndash; shifts known to affect TA communities [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. This triggered competitive exclusion of moisture and light-demanding TA. Furthermore, species and functional turnover narrowed the functional space, as unique species occupied substantially less niche volume than under ambient conditions, weakening community stability to further disturbances. Thus, as climate warming intensifies drying, the resilience of TA \u0026mdash; and the ecosystem functions they mediate \u0026mdash; is increasingly at risk of being compromised [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Altogether, our findings provide mechanistic insights into how trait-mediated soil biodiversity supports ecosystem stability and resilience, offering predictive understanding for peatland responses to global change. In line with \u0026ldquo;precision ecology\u0026rdquo; [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], effective climate adaptation and mitigation of peatlands will require habitat-specific strategies that enhance their functional resilience.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was funded by Maj and Tor Nessling Foundation (3825), Finnish Cultural Foundation (00240717), Olvi Foundation (20230925), the Research Council of Finland (projects BorPeat 30840 and DISPEAT 338631, research infrastructure FIRI 337064, the flagship ACCC 337550, the Centre of Excellence in \u003cem\u003ePeatlands, Climate Change, and Ecological Restoration\u003c/em\u003e 374130, and the Biosphere Laboratory of Eastern Finland 353580). This study was conducted at Lakkasuo-Siikaneva peatland experimental site (ID: 10033) that is part of Analysis and Experimentation on Ecosystems Research Infrastructure (AnaEE) Finland.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: E.-S. T. and B. P. G. Data curation: B. P. G. and O. K.-R. Formal analysis: B. P. G. Investigation: B. P. G., O. K.-R., A. M. L., M. V., E. A. D. M., and E.-S. T. Methodology: E.-S. T., A. M. L., and B. P. G. Resources: O. K.-R. Supervision: E.-S. T. Visualization: A. M. L. and B. P. G. Writing- original draft: B. P. G. Writing- review \u0026amp; editing: B. P. G., O. K.-R., A. M. L., M. V., E. A. D. M., and E.-S. T.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis study was conducted at Lakkasuo-Siikaneva peatland experimental site (ID: 10033) that is part of Analysis and Experimentation on Ecosystems Research Infrastructure (AnaEE) Finland.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe microbial data are openly available in Zenodo (https://doi.org/10.5281/zenodo.17940365). The environmental data from Kokkonen et al. (2019) are stored in Pangaea Data Library (https://doi.org/10.1594/PANGAEA.904256), and those from Kuuri-Riutta et al. (2025) are stored in IDA research data storage service (https://doi.org/10.23729/fd-bad048dd-eda3-380f-915a-580ccd150ec2).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBeaulne J, Garneau M, Magnan G, Boucher \u0026Eacute; (2021) Peat deposits store more carbon than trees in forested peatlands of the boreal biome. \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-021-82004-x\u003c/span\u003e\u003cspan address=\"10.1038/s41598-021-82004-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHyv\u0026auml;rinen E, Jusl\u0026eacute;n A, Kemppainen E, Uddstr\u0026ouml;m A, Liukko U-M (2019) Suomen lajien uhanalaisuus \u0026ndash; Punainen kirja 2019. In \u003cem\u003ehelda.helsinki.fi\u003c/em\u003e. Ymp\u0026auml;rist\u0026ouml;ministeri\u0026ouml; \u0026amp; Suomen Ymp\u0026auml;rist\u0026ouml;keskus. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://hdl.handle.net/10138/299501\u003c/span\u003e\u003cspan address=\"http://hdl.handle.net/10138/299501\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRantanen M, Karpechko AY, Lipponen A, Nordling K, Hyv\u0026auml;rinen O, Ruosteenoja K, Vihma T, Laaksonen A (2022) The Arctic has warmed nearly four times faster than the globe since 1979. \u003cem\u003eCommunications Earth \u0026amp; Environment\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e, 1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nature.com/articles/s43247-022-00498-3\u003c/span\u003e\u003cspan address=\"https://www.nature.com/articles/s43247-022-00498-3\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGong J, Wang K, Kellom\u0026auml;ki S, Zhang C, Martikainen PJ, Shurpali N (2012) Modeling water table changes in boreal peatlands of Finland under changing climate conditions. Ecol Model 244:65\u0026ndash;78. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecolmodel.2012.06.031\u003c/span\u003e\u003cspan address=\"10.1016/j.ecolmodel.2012.06.031\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHelbig M, Waddington JM, Alekseychik P, Amiro BD, Aurela M, Barr AG, Black TA, Blanken PD, Carey SK, Chen J, Chi J, Desai AR, Dunn A, Euskirchen ES, Flanagan LB, Forbrich I, Friborg T, Grelle A, Harder S, Heliasz M (2020) Increasing contribution of peatlands to boreal evapotranspiration in a warming climate. Nat Clim Change 10:555\u0026ndash;560. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41558-020-0763-7\u003c/span\u003e\u003cspan address=\"10.1038/s41558-020-0763-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQi X, Hu Z, Li X, Wang D, Xu C, Hu F, Chen X, Liu M (2025) Land-use driven changes in elemental stoichiometry decouple the positive soil biodiversity-stability relationship. Soil Biol Biochem 209:109907. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.soilbio.2025.109907\u003c/span\u003e\u003cspan address=\"10.1016/j.soilbio.2025.109907\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbrams P (1983) The Theory of Limiting Similarity. Annu Rev Ecol Syst 14:359\u0026ndash;376. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/2096978\u003c/span\u003e\u003cspan address=\"10.2307/2096978\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeiher E, Keddy PA (1995) Assembly rules, null models, and trait dispersion: New questions from old patterns. Oikos 74:159\u0026ndash;164. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/3545686\u003c/span\u003e\u003cspan address=\"10.2307/3545686\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGross N, Le Bagousse-Pinguet Y, Liancourt P, Saiz H, Violle C, Munoz F (2021) Unveiling ecological assembly rules from commonalities in trait distributions. Ecol Lett 24:1668\u0026ndash;1680. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/ele.13789\u003c/span\u003e\u003cspan address=\"10.1111/ele.13789\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKorrensalo A, Kettunen J, Meht\u0026auml;talo L, Vanhatalo J, Tuittila E-S (2025) Detecting subtle change in species and trait composition and quantifying its uncertainty in a boreal peatland. J Veg Sci 36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jvs.70025\u003c/span\u003e\u003cspan address=\"10.1111/jvs.70025\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Bello F, Lavorel S, Hallett LM, Valencia E, Garnier E, Roscher C, Conti L, Galland T, Goberna M, M\u0026aacute;jekov\u0026aacute; M, Montesinos-Navarro A, Pausas JG, Verd\u0026uacute; M, G\u0026ouml;tzenberger E-VA, L., Lepš J (2021) Functional trait effects on ecosystem stability: Assembling the jigsaw puzzle. \u003cem\u003eTrends in Ecology \u0026amp; Evolution\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tree.2021.05.001\u003c/span\u003e\u003cspan address=\"10.1016/j.tree.2021.05.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKunze C, Bahlburg D, Urrutia-Cordero P, Striebel M, Kelpsiene E, Langenheder S, Donohue I, Hillebrand H (2024) Partitioning species contributions to ecological stability in disturbed communities. \u003cem\u003eEcological Monographs\u003c/em\u003e, \u003cem\u003e95\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ecm.1636\u003c/span\u003e\u003cspan address=\"10.1002/ecm.1636\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBiggs CR, Yeager LA, Bolser DG, Bonsell C, Dichiera AM, Hou Z, Keyser SR, Khursigara AJ, Lu K, Muth AF, Negrete B, Erisman BE (2020) Does functional redundancy affect ecological stability and resilience? A review and meta-analysis. \u003cem\u003eEcosphere\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ecs2.3184\u003c/span\u003e\u003cspan address=\"10.1002/ecs2.3184\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKilner CL, Carrell AA, Wieczynski DJ, Votzke S, DeWitt K, Yammine A, Shaw J, Pelletier DA, Weston DJ, Gibert JP (2024) Temperature and CO2 interactively drive shifts in the compositional and functional structure of peatland protist communities. Glob Change Biol 30(3). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/gcb.17203\u003c/span\u003e\u003cspan address=\"10.1111/gcb.17203\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeisen S, Mitchell EAD, Adl S, Bonkowski M, Dunthorn M, Ekelund F, Fern\u0026aacute;ndez LD, Jousset A, Krashevska V, Singer D, Spiegel FW, Walochnik J, Lara E (2018) Soil protists: a fertile frontier in soil biology research. FEMS Microbiol Rev 42(3):293\u0026ndash;323. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/femsre/fuy006\u003c/span\u003e\u003cspan address=\"10.1093/femsre/fuy006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGilbert D, Amblard C, Bourdier G, Francez A-J (1998) The microbial loop at the surface of a peatland: Structure, function, and impact of nutrient input. Microb Ecol 35(1):83\u0026ndash;93. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s002489900062\u003c/span\u003e\u003cspan address=\"10.1007/s002489900062\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarcisz K, Jassey VEJ, Kosakyan A, Krashevska V, Lahr DJG, Lara E, Lamentowicz Ł, Lamentowicz M, Macumber A, Mazei Y, Mitchell EA, Nasser NA, Patterson RT, Roe HM, Singer D, Tsyganov AN, Fournier B (2020) Testate amoeba functional traits and their use in paleoecology. \u003cem\u003eFrontiers in Ecology and Evolution\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fevo.2020.575966\u003c/span\u003e\u003cspan address=\"10.3389/fevo.2020.575966\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoenig I, Mulot M, Mitchell EA (2018) Taxonomic and functional traits responses of Sphagnum peatland testate amoebae to experimentally manipulated water table. Ecol Ind 85:342\u0026ndash;351. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecolind.2017.10.017\u003c/span\u003e\u003cspan address=\"10.1016/j.ecolind.2017.10.017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerkins DM, Hatton IA, Gauzens B, Barnes AD, Ott D, Rosenbaum B, Vinagre C, Brose U (2022) Consistent predator-prey biomass scaling in complex food webs. Nat Commun 13(1):4990. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-022-32578-5\u003c/span\u003e\u003cspan address=\"10.1038/s41467-022-32578-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReczuga MK, Lamentowicz M, Mulot M, Mitchell EA, Buttler A, Chojnicki B, Słowiński M, Binet P, Chiapusio G, Gilbert D, Słowińska S, Jassey VEJ (2018) Predator-prey mass ratio drives microbial activity under dry conditions in Sphagnum peatlands. Ecol Evol 8:5752\u0026ndash;5764. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ece3.4114\u003c/span\u003e\u003cspan address=\"10.1002/ece3.4114\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaine AM, Korrensalo A, Kokkonen NAK, Tuittila E-S (2021) Impact of long‐term water level drawdown on functional plant trait composition of northern peatlands. Funct Ecol 35(10):2342\u0026ndash;2357. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1365-2435.13883\u003c/span\u003e\u003cspan address=\"10.1111/1365-2435.13883\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKokkonen NAK, Laine AM, Laine J, Vasander H, Kurki K, Gong J, Tuittila E-S (2019) Responses of peatland vegetation to 15‐year water level drawdown as mediated by fertility level. J Veg Sci 30:1206\u0026ndash;1216. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jvs.12794\u003c/span\u003e\u003cspan address=\"10.1111/jvs.12794\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuuri-Riutta O, Le Geay M, Jassey VEJ, Barel JM, Laine AM, Yl\u0026auml;nne H, Tuittila E‐S (2025) Microbial and bryospheric photosynthesis of boreal peatlands have peatland‐type‐specific responses to long‐term drying. New Phytol 248:1336\u0026ndash;1350. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/nph.70519\u003c/span\u003e\u003cspan address=\"10.1111/nph.70519\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuuri-Riutta O, Palacios Ganoza B, Yl\u0026auml;nne H, Mitchell EA, V\u0026auml;liranta MM, Tuittila E-S (2026) Assessing the value of testate amoebae and their functional traits in detecting climate change-induced peatland drying. Microb Ecol 89. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00248-025-02682-2\u003c/span\u003e\u003cspan address=\"10.1007/s00248-025-02682-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePayne RJ, Mitchell EA (2008) How many is enough? Determining optimal count totals for ecological and palaeoecological studies of testate amoebae. J Paleolimnol 42:483\u0026ndash;495. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10933-008-9299-y\u003c/span\u003e\u003cspan address=\"10.1007/s10933-008-9299-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiemensma FJ (2023) \u003cem\u003eMicroworld \u0026ndash; world of amoeboid organisms\u003c/em\u003e. Arcella.nl. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://arcella.nl/\u003c/span\u003e\u003cspan address=\"https://arcella.nl/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcKeown MM, Wilmshurst JM, Duckert C, Wood JR, Mitchell EA (2019) Assessing the ecological value of small testate amoebae (\u0026lt;\u0026thinsp;45 \u0026micro;m) in New Zealand peatlands. Eur J Protistol 68:1\u0026ndash;16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ejop.2018.12.002\u003c/span\u003e\u003cspan address=\"10.1016/j.ejop.2018.12.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePakeman RJ, Quested HM (2007) Sampling plant functional traits: What proportion of the species need to be measured? Appl Veg Sci 10:91\u0026ndash;96. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1654-109x.2007.tb00507.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1654-109x.2007.tb00507.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFournier B, Lara E, Jassey VE, Mitchell EA (2015) Functional traits as a new approach for interpreting testate amoeba palaeo-records in peatlands and assessing the causes and consequences of past changes in species composition. Holocene 25:1375\u0026ndash;1383. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0959683615585842\u003c/span\u003e\u003cspan address=\"10.1177/0959683615585842\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFournier B, Malysheva E, Mazei Y, Moretti M, Mitchell EA (2012) Toward the use of testate amoeba functional traits as indicator of floodplain restoration success. Eur J Soil Biol 49:85\u0026ndash;91. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ejsobi.2011.05.008\u003c/span\u003e\u003cspan address=\"10.1016/j.ejsobi.2011.05.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJassey VEJ, Signarbieux C, H\u0026auml;ttenschwiler S, Bragazza L, Buttler A, Delarue F, Fournier B, Gilbert D, Laggoun-D\u0026eacute;farge F, Lara E, Mills TE, Mitchell R, Payne EA, R. J., Robroek BJM (2015) An unexpected role for mixotrophs in the response of peatland carbon cycling to climate warming. \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/srep16931\u003c/span\u003e\u003cspan address=\"10.1038/srep16931\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR Core Team (2022) R: A language and environment for statistical computing. R Foundation for Statistical Computing. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, Minchin PR, O\u0026rsquo;Hara RB, Simpson GL, Solymos P, Stevens MHH, Szoecs E, Wagner H (2022) \u003cem\u003eVegan: Community ecology package\u003c/em\u003e. R-Packages. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.r-project.org/package=vegan\u003c/span\u003e\u003cspan address=\"https://cran.r-project.org/package=vegan\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLepš J, de Bello F, Šmilauer P, Doležal J (2011) Community trait response to environment: Disentangling species turnover vs intraspecific trait variability effects. Ecography 34:856\u0026ndash;863. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1600-0587.2010.06904.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1600-0587.2010.06904.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerronne R, Munoz F, Borgy B, Reboud X, Gaba S (2017) How to design trait-based analyses of community assembly mechanisms: Insights and guidelines from a literature review. Perspect Plant Ecol Evol Syst 25:29\u0026ndash;44. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ppees.2017.01.004\u003c/span\u003e\u003cspan address=\"10.1016/j.ppees.2017.01.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRicotta C, Pavoine S (2024) A new look at functional beta diversity. Methods Ecol Evol 12:1062\u0026ndash;1069. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1101/2024.02.25.580632\u003c/span\u003e\u003cspan address=\"10.1101/2024.02.25.580632\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePavoine S (2020) Adiv: An R package to analyse biodiversity in ecology. Methods Ecol Evol 11(9):1106\u0026ndash;1112. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/2041-210x.13430\u003c/span\u003e\u003cspan address=\"10.1111/2041-210x.13430\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDray S, Dufour A-B (2007) The ade4 package: Implementing the duality diagram for ecologists. \u003cem\u003eJournal of Statistical Software\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18637/jss.v022.i04\u003c/span\u003e\u003cspan address=\"10.18637/jss.v022.i04\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVicente-Gonzales L, Vicente-Villardon JL (2022) CRAN: Package PERMANOVA. \u003cem\u003eR-Project.org\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.32614/CRAN.package.PERMANOVA\u003c/span\u003e\u003cspan address=\"10.32614/CRAN.package.PERMANOVA\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWheeler B, Torchiano M (2025) \u003cem\u003eLmPerm: Permutation tests for linear models. R package version 2.1.5\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/mtorchiano/lmperm\u003c/span\u003e\u003cspan address=\"https://github.com/mtorchiano/lmperm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMen\u0026eacute;ndez-Serra M, Ontiveros VJ, C\u0026aacute;liz J, Alonso D, Casamayor EO (2023) Understanding stochastic and deterministic assembly processes in microbial communities along temporal, spatial and environmental scales. Mol Ecol 32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/mec.16842\u003c/span\u003e\u003cspan address=\"10.1111/mec.16842\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePastore AI, Barab\u0026aacute;s G, Bimler MD, Mayfield MM, Miller TE (2021) The evolution of niche overlap and competitive differences. Nat Ecol Evol 5:330\u0026ndash;337. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41559-020-01383-y\u003c/span\u003e\u003cspan address=\"10.1038/s41559-020-01383-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHart SP, Turcotte MM, Levine JM (2019) Effects of rapid evolution on species coexistence. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e, \u003cem\u003e116\u003c/em\u003e, 2112\u0026ndash;2117. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.1816298116\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1816298116\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilkinson DM, Koumoutsaris S, Mitchell EA, Bey I (2012) Modelling the effect of size on the aerial dispersal of microorganisms. J Biogeogr 39:89\u0026ndash;97. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-2699.2011.02569.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-2699.2011.02569.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRobroek BJM, Mart\u0026iacute; M, Svensson BH, Dumont MG, Veraart AJ, Jassey VEJ (2021) Rewiring of peatland plant\u0026ndash;microbe networks outpaces species turnover. Oikos 130:339\u0026ndash;353. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/oik.07635\u003c/span\u003e\u003cspan address=\"10.1111/oik.07635\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBobrov A, Mazei Y (2004) Morphological variability of Testate Amoebae (Rhizopoda: Testacea lobosea: Testacea filosea) in natural populations. Acta Protozool 43:133\u0026ndash;146. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.researchgate.net/publication/228889468\u003c/span\u003e\u003cspan address=\"https://www.researchgate.net/publication/228889468\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLamentowicz M, Mitchell EAD (2005) The ecology of testate amoebae (protists) in Sphagnum in North-western Poland in relation to peatland ecology. Microb Ecol 50(1):48\u0026ndash;63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00248-004-0105-8\u003c/span\u003e\u003cspan address=\"10.1007/s00248-004-0105-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFetzer I, Johst K, Sch\u0026auml;we R, Banitz T, Harms H, Chatzinotas A (2015) The extent of functional redundancy changes as species\u0026rsquo; roles shift in different environments. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e, \u003cem\u003e112\u003c/em\u003e, 14888\u0026ndash;14893. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.1505587112\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1505587112\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLamentowicz M, Kajukało-Drygalska K, Kołaczek P, Jassey VEJ, Gabka˛ M, Karpinska-Kołaczek M (2020) Testate amoebae taxonomy and trait diversity are coupled along an openness and wetness gradient in pine-dominated Baltic bogs. Eur J Protistol 73:125674. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ejop.2020.125674\u003c/span\u003e\u003cspan address=\"10.1016/j.ejop.2020.125674\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAllison SD, Martiny JBH (2008) Resistance, resilience, and redundancy in microbial communities. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e, \u003cem\u003e105\u003c/em\u003e(Supplement 1), 11512\u0026ndash;11519. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.0801925105\u003c/span\u003e\u003cspan address=\"10.1073/pnas.0801925105\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpake R, Jackson EE, Bullock JM, Gardner E, Tipton E, Grainger MJ, Doncaster CP (2025) Precision ecology for targeted conservation action. Nat Ecol Evol 9(7):1102\u0026ndash;1111. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41559-025-02733-4\u003c/span\u003e\u003cspan address=\"10.1038/s41559-025-02733-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKokkonen NAK (2019) Data from: Responses of peatland vegetation to 15-year water level drawdown as mediated by fertility level. PANGAEA. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1594/PANGAEA.904256\u003c/span\u003e\u003cspan address=\"10.1594/PANGAEA.904256\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuuri-Riutta O, Laine A, Tuittila E-S (2025) Data from: Microbial and bryospheric photosynthesis of boreal peatlands have peatland‐type‐specific responses to long‐term drying. IDA. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.23729/fd-bad048dd-eda3-380f-915a-580ccd150ec2\u003c/span\u003e\u003cspan address=\"10.23729/fd-bad048dd-eda3-380f-915a-580ccd150ec2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuuri-Riutta O, Tuittila E-S, Palacios Ganoza B (2025) Data from: Testate amoeba data from Lakkasuo peatland water level drawdown experiment. Zenodo. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5281/zenodo.17940365\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.17940365\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"microbial-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meco","sideBox":"Learn more about [Microbial Ecology](https://www.springer.com/journal/248)","snPcode":"248","submissionUrl":"https://submission.nature.com/new-submission/248/3","title":"Microbial Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"climate change, community assembly, environmental filtering, functional redundancy, functional traits, protist communities","lastPublishedDoi":"10.21203/rs.3.rs-9404720/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9404720/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBoreal peatlands store vast amounts of carbon and regulate regional hydrology, making their stability critical under accelerating climate change. This stability depends on community-level recovery and resistance shaped by the functional traits of organisms. Although climate-induced drying is already reshaping peatland communities, the trait-based mechanisms supporting stability in soil biota remain poorly resolved. We assessed stability in testate amoebae (TA) \u0026ndash; a key group of the soil food web \u0026ndash; by comparing functional trait patterns in ambient control plots with plots subjected to two decades of experimental water level drawdown (WLD) across three peatland types (rich fen, poor fen, and bog). In ambient conditions, null models revealed strengthened environmental filtering along the fertility gradient; nutrient-rich fen showed highest diversity and functional redundancy. Long-term WLD, however, intensified environmental filtering by reshaping the communities across all peatland types. Functional beta diversity revealed contrasting stability mechanisms: rich fen maintained functional stability through resilience, with major species turnover but minimal functional change, whereas bog communities retained function through resistance, relying on drought-adapted traits and showing minimal species turnover. Contrastingly, the poor fen lost functional stability, as low redundancy combined with dominance of wet-adapted traits led to both species and functional turnover. Under moderate drying, bogs are most likely to maintain functional stability, whereas fens, particularly poor fens, are more vulnerable. These trait-mediated differences indicate peatland type-specific functional thresholds with implications for predicting stability and carbon dynamics under future climates. Overall, continued warming increasingly compromises peatland soil biota and the ecosystem functions they mediate.\u003c/p\u003e","manuscriptTitle":"Contrasting trait-mediated mechanisms shape testate amoeba communities under long- term drying across boreal peatlands","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-22 18:37:23","doi":"10.21203/rs.3.rs-9404720/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-10T16:48:38+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-10T00:18:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T13:37:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"203584749169477848460158628594611977337","date":"2026-04-20T08:31:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"217354242178330893469449839265384311945","date":"2026-04-14T16:53:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-14T16:20:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-14T13:54:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-14T13:54:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Microbial Ecology","date":"2026-04-13T13:14:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"microbial-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meco","sideBox":"Learn more about [Microbial Ecology](https://www.springer.com/journal/248)","snPcode":"248","submissionUrl":"https://submission.nature.com/new-submission/248/3","title":"Microbial Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"63767d46-bf8d-471a-8c17-38bdc3922601","owner":[],"postedDate":"April 22nd, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-10T16:48:38+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-10T00:18:44+00:00","index":27,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T13:37:51+00:00","index":26,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T11:54:40+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-22 18:37:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9404720","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9404720","identity":"rs-9404720","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}