Forest floor vegetation contributes to a reduction in nitrogen fluxes in temperate forest understories

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Deilmann, Karin Potthast, Beate Michalzik, Kerstin Näthe, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7356177/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Dec, 2025 Read the published version in Plant and Soil → Version 1 posted 6 You are reading this latest preprint version Abstract Background and Aims: Forest floor vegetation can both respond to and affect the water and nitrogen (N) availability in forest ecosystems. However, their role for influencing the relationship between throughfall and N fluxes has hardly been studied, leaving a knowledge gap in our mechanistic understanding of forest biogeochemical cycling. Here we investigate the impact of the structural and functional role of the herbaceous and moss layer in linking N fluxes and throughfall patterns in beech, spruce, and pine forests in Central Germany. Methods We monitored herbaceous and moss species cover and diversity, as well as throughfall and N fluxes for 93 plots capturing small-scale microclimate variability. For all co-occurring herbaceous species, we measured intraspecific trait variation for specific leaf area (SLA) and plant height (n = 685). Results Multivariate analyses reveal strong differences in the herb and moss layer composition between forest types. The results of analyses of covariance, and of piecewise structural equation models consistently show that N fluxes decreased most under pine and spruce plots where herb and moss cover was high. Species with a high SLA and plant height positively contributed to overall herb cover. Conclusion Our results suggest that plant growth, particularly moss cover, contributed to overall N retention, while acquisitive and fast-growing species with a high SLA contributed to a fast nutrient return to the system, thereby partly increasing N fluxes. We conclude that taxonomic and functional composition of the forest floor vegetation is an important mediator in the link between throughfall and N fluxes. bryophytes functional traits nitrogen cycle plant cover small-scale throughfall water flux Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The forest floor vegetation, including the herbaceous and the moss layer, is an integral link between surface and subsurface processes as it can retain and redistribute both precipitation throughfall and nutrients (Glime, 2022 ; Landuyt et al., 2019 ; Muller, 2014 ). Before reaching the soil layer, precipitation first passes through the canopy of tree, shrub, herbaceous species and moss layers, where it is intercepted and redistributed as throughfall multiple times along the way (Van Stan, II et al., 2020 ) down to the soil. Similarly, nutrients are redistributed several times through spatial and temporal litterfall dynamics, and by decomposers such as shredding soil fauna, fungi and mineralising microbes breaking down litter (Boerner & Koser, 1989; Krishna & Mohan, 2017 ). These nutrients become either plant-available, immobilised in soil or biomass, or are leached into deeper horizons (Attiwill & Adams, 1993 ; Leuschner & Ellenberg, 2017 ). Trees, shrubs, herbaceous and moss species take up available nutrients for biomass production, which subsequently returns to the ground as litter, thus linking surface and subsurface processes (Gilliam et al., 2016 ; Leuschner & Ellenberg, 2017 ). So far, the link of throughfall and nutrient fluxes in the soil has been studied for different forest types (De Schrijver et al., 2007 ; Rijtema & De Vries, 1994 ) or management practices in forests (Wilcke et al., 2009 ), but the influence of forest floor vegetation has rarely been included in such studies despite their functional and structural importance for forests (Gilliam, 2007 ). The herbaceous layer harbours about 80% of vascular plant diversity in temperate forests, thereby significantly impacting their function and structure (Gilliam, 2007 ). It also plays a key role in nutrient cycling, contributing up to 16% of litterfall with foliage that can contain 30% more nitrogen than the overstory trees (Gilliam, 2007 ; Landuyt et al., 2019 ). It accounts for up to 90% of total spring and 50% of total summer nitrate uptake in forests, which reduces nutrient loss during spring runoff when tree uptake is minimal (Mabry et al., 2008 ; Muller & Bormann, 1976 ; Olsson & Falkengren-Grerup, 2003 ). Additionally, a dense herbaceous layer can conserve around 21% of inorganic nitrogen from throughfall by foliar absorption, and adds about 10% organic nitrogen, overall reducing nitrogen reaching the soil by 39% (Muller, 2014 ). The moss layer captures a large proportion of the incoming throughfall owing to its high water storage capacity (Thielen et al., 2021 ). As nutrient uptake mainly happens through their leaf surface, mosses retain N from captured throughfall and strongly impact biogeochemical cycling (Glime, 2022 ; Bates, 2008 ). Mosses significantly increase the overall ecosystem N input through biological bryophyte-diazotroph N 2 -fixation (Hupperts et al., 2021 ; DeLuca et al., 2002 ), and the annual uptake of N per area can even exceed that of trees, hence immobilising nutrients in biomass (Oechel & Van Cleve, 1986 , Gundale et al., 2011 ; Gundale et al., 2014 ). Combined with a high nutrient use efficiency of and nutrient conservation through mosses (Cornelissen et al., 2007 ), this could lead to a significant N retention in the ecosystem, in particular where moss cover is high, such as in pine or spruce forests. Tree morphology and canopy characteristics determine the redistribution of precipitation and dissolved nutrients by e.g., differences in canopy storage capacity or stemflow (Metzger et al., 2019 ; Frischbier et al. 2019 ; Tischer et al., 2020 ; Fischer-Bedtke et al., 2023 ). Thus, different forest types lead to different small-scale availability of resources at the forest floor and thus shape forest floor communities (Wagner et al., 2011 ). Therefore, the main understory plant cover, species, and their specific adaptations to the prevailing conditions differ between forest types (Leuschner & Ellenberg, 2017 ), overall leading to differences in interception and redistribution of incoming nutrients (Metzger et al., 2017 , Landuyt et al., 2019 ; Van Stan, II et al., 2020 ). The functional composition of the forest floor vegetation plays an important role for throughfall and N fluxes. Functional traits reflect plant strategies, and depending on environmental conditions, acquisitive or conservative nutrient strategies dominate (Díaz et al., 2016 ; Wright et al., 2004 ). An acquisitive nutrient strategy is usually linked to species with fast growth rates, high specific leaf area (SLA; leaf area to dry weight), high leaf N content, short leaf lifespans, and high-quality litter leading to fast decomposition (Cornwell et al, 2008 ; Díaz et al., 2016 ). In contrast, a conservative nutrient strategy is linked to species with slower growth rates, low specific leaf area, physically robust leaves with longer leaf lifespans, and low-quality litter leading to a slower decomposition (Cornwell et al, 2008 ; Díaz et al., 2016 ). Intraspecific trait variation further reflects within-species performance differences due to environmental variability (Albert et al., 2010 ; Baughman et al, 2019 ). In forest understories, this variability is directly linked to resource availability (Wagner et al., 2011 ). Thus, functional traits mirror local water and N availability (Lavorel & Garnier, 2002 ; Poorter et al., 2009 ). Traits can either respond to (“response traits”) or affect (“effect traits”) the environment or both (Lavorel & Garnier, 2002 ). For instance, SLA increases with increasing nutrient availability of the environment and positively relates to leaf N, leaf water content, and high rates of nutrient uptake (Díaz et al., 2016 ; Pérez-Harguindeguy et al., 2013 ; Poorter et al., 2009 ; Lavorel & Garnier, 2002 ). At the same time, species with a high SLA have a faster litter decomposition and nutrient turnover, thereby also affecting the nutrient input and biogeochemical cycling (Körner, 2021 ; Cornwell et al., 2008 ). Similarly, plant height is both a response and effect trait (Lienin & Kleyer, 2012 ): it strongly responds to light availability and productivity, thus being closely tied to competition for light (Falster & Westoby, 2003 ). In turn, the shade provided by taller plants mitigates temperature, creating more favourable conditions for ground-dwelling species (De Frenne et al., 2021 ), although lower temperatures generally decrease microbial activity and thus N availability (Muscolo et al., 2014 ). These characteristics of functional traits make them relevant links between above and belowground processes in biogeochemical cycles. However, the role of functional traits of the forest floor vegetation on the relationship between throughfall and N flux is largely understudied. Here we investigate how the herbaceous and moss layer mediate the effect of throughfall on inorganic nitrogen (N) fluxes in forest floor solution in European beech ( Fagus sylvatica L.), Norway spruce [ Picea abies (L.) H.Karst.], and Scots pine ( Pinus sylvestris L.) forests, all representing common forest types in the temperate region. We collected data from 93 plots distributed across a gradient in canopy cover in the three forest types in the Saale-Elster-Sandsteinplatte Observatory (SESO). We focused on herb and moss cover, and the two functional traits, SLA and plant height that act as proxies for water and nutrient fluxes. To investigate the role of the herb and moss species for the relationship of throughfall and N fluxes, we tested three hypotheses, that are also graphically displayed in Fig. 1: We hypothesised that increasing herb and moss cover lead to a reduction in N fluxes below the organic layer. This hypothesis is based on the findings that increasing precipitation promotes plant growth for herbaceous species (Anderson et al., 1969 ; Zhang & Xi, 2021 ), as well as mosses (Deilmann et al. 2025 ; Vitt, 1990 ), leading to potentially higher water and nutrient uptake (Bates, 2008 ; Glime, 2022 ; Muller, 2014 ; Thielen et al., 2021 ). We hypothesised that with increasing SLA and plant height, vegetation will have a stronger effect on the relationship between throughfall and N fluxes. This hypothesis is based on the findings that SLA and plant height positively relate to plant cover (Díaz et al., 2016 ; Pérez-Harguindeguy et al., 2013 ) and mirror plant nutrient strategies (Díaz et al., 2016 ; Wright et al., 2004 ). We hypothesised that the contribution of forest floor vegetation to N fluxes changes with forest type. This hypothesis is based on the findings that different forest types harbour different species in the herb and moss layer, and differ in overall plant cover (Landuyt et al., 2019 ; Leuschner & Ellenberg, 2017 ). With this study, we aim to contribute to a more holistic understanding of the impact of the forest floor vegetation on water and nitrogen fluxes in forests. Materials and Methods Study area and experimental setup We established in total 93 plots to capture the forest floor vegetation in six managed forest stands of three temperate forest types (2 x European beech, 2x Norway spruce, 2x Scots pine) in the Saale-Elster-Sandsteinplatte Observatory (SESO). The SESO is located on acidic weathered sand- and siltstone in Thuringia, Central Germany, with a mean annual temperature of 8.0°C, and an annual precipitation of 525 mm (detailed description of SESO in Stolze et al., 2022 ). To capture the small-scale variation in abiotic conditions and forest floor vegetation, we distributed 15 plots under different canopy cover levels within each forest stand (Fig. 2 ). In one of the pine stands, we established 18 plots due to a comparably high small-scale heterogeneity. A summary of all measured variables is provided in Table 1 , and they are described in detail below. Throughfall and nitrogen flux Canopy throughfall was collected for each plot in biweekly intervals with funnel type samplers (catchment area c. 0.01 m²) from May 2022 to May 2023 (Fig. 2 , Table 1 ). Throughfall fluxes were then aggregated to annual sums for each plot. Missing values resulting from e.g., fallen samplers were filled according to the average spatial deviation from the mean of the respective plot and season. For in total 72 out of the 93 plots, we obtained N flux measurements in soil solution from self-integrating accumulators (SIA; TerrAquat, Germany) being installed from April 2022 to April 2023 below the undisturbed organic layer of the plot. A detailed description of a SIA’s functional principle is provided in Bischoff ( 2008 ). In brief, a SIA consists of a cylinder (height and diameter = 10 cm each), filled with a mixture of quartz sand, quartz silt, and an anion-exchange resin. Soil solution passes through, and solutes are immobilised by the adsorber (Bischoff, 2008 ). After one year, all the SIAs were removed from the soil and stored at 4°C. Then, a subsample of 15 g from the top 5 cm layer of water-saturated SIA was extracted using a 100 mL, 1 M NaCl solution by shaking for 45 minutes at 290 rpm. Afterwards, solutions were filtered through a glass fibre filter (GF 6, Whatman, pore size < 1 µm) and analysed for NO 3 − -N and NH 4 + -N concentrations by a continuous flow analyser (SAN + + Classic, Skalar Analytic B. V. Breda, The Netherlands). The respective N flux was calculated by multiplying the concentration of the respective inorganic nitrogen by the extraction volume (100 ml) and the total weight of the sample (top 5 cm of the SIA), divided by the weight of the subsample (15 g) and by the area of the SIA. This way, we yielded an integrated total value of the solutes for a defined area and time to calculate N fluxes. N flux thus represents the overall inorganic nitrogen as the sum of extracted ammonia and nitrate from soil solution below the organic layer. Species cover and functional traits We captured seasonal vegetation dynamics by monitoring vegetation composition on a weekly basis for the entire growing season from April to December 2022 (Fig. 2 , Table 1 ). Plant cover was estimated for all co-occurring herbaceous and moss species following the Schmidt scale (Pfadenhauer, 1997 ) in the following classes with an additional class to capture species with very low coverages: 0, 0.1, 1, 3, 5, 8, 10, 15, 20, 25, 30, 40, 50, 60, 70, 75, 80, 90, 100%. Overall vegetation cover per plot was calculated as species cover per plot and week, averaged over the entire growing season. Note that as a result of species overlap, cover values above 100% are possible. Species-wise covers were used for the calculation of community-weighted means (see below). Taxonomy follows World Flora Online (WFO, 2025 ). For trait measurements, we captured intraspecific trait variation for in total 685 individuals by selecting five healthy individuals per species and plot at peak flowering, following standardised protocols (Pérez-Harguindeguy et al., 2013 ; Fig. 2 , Table 1 ). Plant height (cm) was measured in situ for each individual using a folding rule. For the same individuals, two healthy leaves were collected, scanned and leaf area was determined using an image detection algorithm (Körschens, unpublished ). The leaves were then dried in a compartment drier at 40°C until mass constancy was reached. The specific leaf area (SLA; mm² mg − 1 ) was obtained by dividing the fresh weight by the dry weight. For each trait t and plot p , we calculated community weighted means (CWM) following Eq. (1): CWM tp = \(\:{\sum\:}_{i=1}^{S}{c}_{ip}\times\:\:{t}_{i}\) (1) where c ip represents the relative cover of species i in plot p and t i denominates the trait value of species i , summed up for all species per plot S. On seven plots, no herbaceous species occurred resulting in missing values in CWM SLA and CWM plant height. To minimise statistical leverage, we filled in those missing values as average of comparable canopy positions (here categorised as high, mid, low) within the forest stand. Statistical analysis Prior to the evaluation of the hypotheses, we compared forest types by testing for differences in throughfall, N fluxes, plant cover, and CWM traits using either ANOVA and subsequent t-tests or Kruskal-Wallis and subsequent Mann-Whitney U tests, depending on normal distribution of the data and homogeneity of variances. Furthermore, differences in vegetation composition were assessed in multivariate space by performing a Detrended Correspondence Analysis (DCA) on averaged species cover per year and plot. To test for characteristic species, plant cover, and traits of the different plots, we fitted single species covers, plot-wise plant cover, and traits onto the ordination using the vegan package (Oksanen et al., 2024 ). To test whether increasing herb and moss cover lead to a reduction in N fluxes, we ran linear models with ln( y + 1) transformed N flux as dependent, and herb and moss cover, respectively as independent variables. To further test whether there are forest-type specific differences in N fluxes, we additionally performed analyses of covariances (ANCOVAs) with ln( y + 1) transformed N fluxes as the response variable, and another variable with forest type as interaction term as independent variable. To test if the slopes differed from zero while accounting for forest type, we used the R package emmeans (Lenth, 2023 ). To test whether plant functional traits are linked to a reduction in N fluxes, and whether there are forest-type specific differences, we ran linear models and ANCOVAs as described above, but with CWM SLA and CWM plant height as independent variables. Table 1 Summary of the measured variables with their respective definition, unit, number of plots/measurements within each forest stand, and the used method. variable definition unit number of plots method throughfall annual sum of biweekly throughfall measurements mm 15 plots per forest stand Biweekly throughfall measurements using funnel type samplers with a catchment area of c . 0.01 m² over one year were summed up. inorganic nitrogen flux in soil solution annual sum of extracted NO 3 − -N and NH 4 + -N from seeped soil solution below the organic layer kg ha − 1 a − 1 12 plots per forest stand Self-integrating accumulators (SIA) consisting of a mixture of quartz sand, quartz silt and an anion-exchange resin were installed under the undisturbed organic layer of the plots for one year. NO 3 − -N and NH 4 + -N were extracted from a subsample of 15 g from the top 5 cm layer of the SIA using 100 mL of a 1 M NaCl solution, filtered through a glass fibre filter, and subsequently analysed using a continuous flow analyser. herb cover, moss cover average of weekly observations of species cover over the entire growing season % 15 plots per forest stand Weekly vegetation surveys of each species and plot using the slightly modified Schmidt scale: 0, 0.1, 1, 3, 5, 8, 10, 15, 20, 25, 30, 40, 50, 60, 70, 75, 80, 90, 100%. Plot-wise average was calculated as species cover per plot and week, averaged over the entire growing season (April – December 2022). CWM plant height Community weighted mean (CWM) of plant height cm 15 plots per forest stand Plant height was measured in situ as the shortest distance from the ground to the uppermost part of the main photosynthetic tissues for five individuals using a folding rule. Community-weighted means were calculated using species cover and plant height measurements of five individuals per species and plot. CWM specific leaf area (SLA) Community weighted mean of specific leaf area mm 2 mg − 1 15 plots per forest stand Two fresh leaves per individual were scanned and their area determined by an image detection algorithm. The leaves were then dried in a compartment drier at 40°C until mass constancy. Community-weighted means were calculated using species cover and SLA measurements of five individuals per species and plot. At last, to test all three hypotheses in one overall analysis and to account for interdependent relationships, we used structural equation modelling (SEM) to evaluate the direct effect of throughfall on N fluxes, and the mediating effect of the vegetation, here being included as indirect effects. The SEM was built using the R package piecewiseSEM (Lefcheck, 2016 ), based on prior knowledge as described by the hypotheses stated above and by Fig. 1. To account for forest type-specific differences, we ran a multigroup analysis on the SEM which fits a model-wide interaction with forest type and directly compares group-specific estimates with a global model (Lefcheck, 2021 ). For the global model, we specified each component model as mixed effects model using the R package glmmTMB (Brooks et al., 2017 ) with forest type as random effect. Component models where the random effect variance was estimated close to zero, were specified as linear models to avoid singularity. Global goodness of fit of the SEM was assessed by Fisher’s C statistic (Lefcheck, 2016 ) with a good model fit indicated by a higher p value (≥ 0.05). Compared to running SEMs on subsets of the data, this approach has the advantage of directly detecting group-specific path changes. If the estimates do not differ significantly, the path is constrained to the global model, i.e. estimates and statistics are the same for those paths, but the standardised estimates may still differ due to different within-group variances (Lefcheck, 2021 ). Results Descriptive comparison of forest types Figure 3 show that the three forest types covered a wide range in throughfall (180–450 mm), N fluxes (0-115 kg ha − 1 a − 1 ), herb cover (0–93%), moss cover (0-108%) and CWM traits (SLA: 25–74 mm² mg − 1 , plant height: 3.8–61.8 cm). In the spruce plots, throughfall was on average slightly lower but highly variable (mean ± standard deviation: 305 ± 55 mm), and N fluxes were highest and most variable (15.6 ± 23.5 kg ha − 1 a − 1 ), compared to the beech and pine plots (Table S1). Both, herb and moss cover were highest in the pine (on average c. 70% and 89%, respectively) and lowest in the beech plots (16% and 17%), while the spruce plots showed a low herb (20%) but high moss cover (70%). CWM SLA was highest in the spruce, and lowest in the beech plots (61 and 31 mm² mg − 1 , respectively) while it was the opposite for CWM plant height (13 and 35 cm, respectively; Fig. 3, Table S1). For a subset of plots used in this study, throughfall fluxes and N fluxes in throughfall were measured and showed a strong positive correlation (Spearman rank correlation: ρ = 0.63, p < 0.001; Supplementary Fig. S1), demonstrating that N input from throughfall generally increased with amount of throughfall across all forest types. The forest types also differed in the dominant forest floor species (Fig. 3B; see Table S2 for a complete species list per forest type). The herb cover in the beech plots was mainly dominated by Luzula luzuloides ., in the spruce plots mostly by Vaccinium myrtillus , Oxalis acetosella , and Rubus idaeus , and in the pine plots nearly solely by V. myrtillus , and Deschampsia flexuosa . The moss cover was mainly dominated by Polytrichum formosum in the beech plots, while the spruce plots were dominated by Hypnum cupressiforme, Pseudoscleropodium purum, Brachythecium rutabulum , and Pleurozium schreberi , and the pine plots mostly by P. schreberi and P. purum . The DCA confirms that overall, the forest types differed strongly in vegetation composition along the first axis (36.65% explained variance; Fig. 4A). While the beech forests showed the lowest plant cover in both herb and moss cover, the pine forests were characterised by the highest plant cover, and the spruce forests were in between (Fig. 4A, B). We also found noticeable variation within forest types along the second axis (32.04% expl. var.), with the spruce plots showing the greatest variation in species composition, which was associated with differences in N fluxes and throughfall. Furthermore, differences along DCA2 were linked to CWM plant height, CWM SLA, and the occurrence of the herbs O. acetosella and Mycelis muralis Dumort., and the mosses P. schreberi and P. purum , respectively. Linking throughfall, N fluxes, plant cover, and functional traits The linear models show an overall negative relationship of throughfall, herb cover and CWM plant height with N fluxes, and an overall positive relationship between CWM SLA with N fluxes across all forest types (Fig. 5, grey dashed line). These relationships were forest type-specific differences: N fluxes most strongly changed in spruce plots with throughfall, moss cover, CWM SLA, and CWM plant height, while the other forest types responded less strongly (Fig. 5, coloured solid lines). Details on model statistics are provided in Table S3 (Supplementary Information). The SEMs show that the impact of throughfall on N flux in soil solution was never direct but was affected by forest floor vegetation across all models, generally showing N fluxes decreasing where it was more abundant or denser. We found that higher plant cover and CWM trait values were associated with lower N fluxes (standardized path coefficients are presented in Fig. 6 ; direct and indirect effects in Table S4; full SEM outputs in Table S5); the strongest indirect effect was mediated by CWM SLA. Contrary to our expectation, the latter decreased with throughfall but positively influenced N fluxes, while the other vegetation variables showed the opposite pattern, as expected. For example, increasing throughfall led to increasing moss cover which then decreased N fluxes. Similarly, there was a trend that increasing plant height led to lower N fluxes. In contrast, herb cover did not mediate the relationship between throughfall and N fluxes itself but was positively influenced by CWM SLA and CWM plant height. Overall, forest floor vegetation enhanced the direct throughfall effect on N fluxes (Table S4). The multigroup analysis shows only few significant differences between forest types: three paths differed, namely throughfall to CWM SLA, CWM plant height, and herb cover, respectively (Fig. 6 B-D, Table S5). The overall pattern was similar but in the spruce plots, the effect of throughfall on CWM SLA was substantially stronger, and we found positive impacts of throughfall on CWM plant height. In the pine plots, throughfall additionally led to increased herb cover. Vegetation-mediated indirect effects were strongest in the spruce and pine plots, and lowest in the beech plots, accounting for c. 70%, 62%, and 11% of the total effects, respectively (Table S4). In the beech plots, vegetation-mediated effects balanced each other out, as CWM SLA and moss cover showed a negative effect, whereas CWM plant height and herb cover showed a positive effect. Overall, the direction of influence of herb cover and CWM plant height was forest type-specific while moss cover and CWM SLA had a consistently negative indirect effect on N fluxes (Table S4). Discussion Our results show that forest floor vegetation affects the relationship of throughfall and N fluxes between canopy inputs and inorganic nitrogen fluxes in soil solution in temperate forests. Generally, we found that forest floor vegetation decreased N fluxes across all forest types. This suggests that this layer contributes to overall N retention in the system, thereby complementing N retention of trees, microbial biomass, and organic matter (Gebauer et al., 2000; Gundale et al., 2014 ; Gurmesa et al., 2016; Tietema et al., 1998 ), and potentially counteracting N input from tree litter, and throughfall (Dise et al., 2009 ; Vesterdal et al., 2008 ; Wälder et al., 2008 ). Particularly, moss cover and CWM SLA are influential mediators irrespective of forest type. Yet, the models suggest that the effect of vegetation is strong where forest floor cover is high, leading to forest type-specific differences such as in temperate pine and spruce forests. N fluxes decrease with increasing plant cover We found that increasing plant cover was generally related to a reduction in N fluxes in soil solution, supporting our hypothesis that forest floor vegetation plays an important role in N retention in temperate forests. This finding is consistent with previous studies reporting a significant proportion of N uptake by understory species across biomes, such as in boreal (Gundale et al., 2011 , 2014 ), temperate (Gebauer et al., 2000), and tropical forests (Gurmesa et al., 2016). Generally, the higher the latitude, the more important is the role of mosses in the forest understory because their abundance increases (Berdugo et al., 2018 ). The substantial role mosses play in N uptake in boreal systems (Gundale et al., 2011 , 2014 ) corroborates our findings that this layer considerably affects N fluxes also in temperate forests with a dense moss cover, such as in pine or spruce forests. Direct absorption of incoming N from throughfall through the leaf surface (Bates, 2008 ; Glime et al., 2022), and subsequent immobilisation in poorly decomposable biomass (Cornelissen et al., 2007 ) could lead to increased N retention where moss cover is high. Similarly, in woody forest floor plants, much of the N taken up is stored in the stem and root system (Gurmesa et al., 2016), further leading to longer-term N immobilisation as it is not seasonally returned to the soil as is the case with deciduous leaf litter. If this pattern results from higher N uptake and storage rates with increasing herb cover or from increased direct absorption of N from throughfall remains unclear and requires further testing. The SEM shows that in the pine plots, which had the most plant cover overall, the indirect effects of vegetation on N fluxes were weaker. This suggests that other factors such as species-specific effects may further contribute to N retention, which has already been shown by previous studies on trees (Andersen et al, 2017 ; Schulz et al., 2011 ), non-woody plants (Freschet et al, 2018 ; Grassein et al., 2015 ; Liu & van Kleunen, 2019 ), and mosses (Hawkins et al., 2018 ; Solga & Frahm, 2013). The pine plots were mainly dominated by P. schreberi which usually forms loose wefts, compared to the dense mat forming H. cupressiforme in the spruce plots, and the turf forming moss P. formosum dominating in the beech plots (Bernhardt-Römermann et al., 2018 ; Mägdefrau, 1982). These different moss life forms and colony structures are linked to water use efficiency and could lead to differences in infiltration of throughfall, potentially reducing the contact time with the moss leaf surface and, thus, the nutrient uptake and immobilisation in biomass (Bates, 1998 ). This hypothesis, however, needs to be tested in future experimental studies. SLA and plant height show overall negative indirect effects on N fluxes The SEM shows that the effects of functional traits on herb cover and N fluxes were more pronounced where the trait values were largest. Species with high SLA values dominating the spruce plots (e.g., O. acetosella , Impatiens parviflora ., Moehringia trinervia . and M. muralis ; Table S2), and large rush species ( L. luzuloides ) in the beech plots explain the strong effects involving CWM SLA and plant height in those forest types. It further demonstrates the importance of species identity and community composition which is linked to a specific suite of traits, representing plant performance and strategies as response to local environmental conditions (Deilmann et al., 2024 ; Díaz et al., 2016 ; Violle et al., 2007). In turn, these traits can affect the ecosystem, where higher CWM SLA values are linked to higher N fluxes. This pattern supports the idea that species with high SLA, being associated to high leaf N content, faster nutrient turnover and mineralisation processes, can lead to a higher N return to the system (Aerts & Chapin III, 1999; Grassein et al., 2015 ; Körner, 2021 ). Forest types differ in strength but not in direction of vegetation-mediated influence on N fluxes Most patterns shown in the global SEM model were also found within each forest type. Yet, we found the strongest indirect effects of forest floor vegetation on N fluxes where the vegetation was abundant, namely in the spruce and pine plots, suggesting a higher N uptake with increasing herb and moss cover. This is in line with previous studies showing that forest floor vegetation can account for large fractions of N uptake, even exceeding uptake of overstory trees in direct comparisons (Gebauer et al., 2000; Gundale et al., 2011 , 2014 ). This constitutes an N sink in the forest ecosystem and a competitor for N uptake with co-occurring trees and their seedlings, as well as soil microbes (Gebauer et al., 2000; Muller, 2014 ). Similarly, a dense herb cover can reduce the amount of N reaching the soil with throughfall by 39% (Muller, 2014 ), explaining the relatively strong indirect effect of herb cover on N fluxes in the pine plots, where the blueberry V. myrtillus covered more than half the area of the understory, compared to the other forest types. Further differences in the forest floor vegetation mediated effects between the spruce and the other forest types may be attributed to species identity as described above, and to species diversity, which, however, needs further investigation in future studies. Preliminary analyses show that both the pine and the beech plots harboured at most only half the number of species found in the spruce plots and had lower Shannon diversity and Evenness (Table S6). Following the niche complementarity hypothesis, higher species diversity leads to higher occupation of the functional trait space, thereby optimising resource utilisation (Loreau & Hector, 2001 ). This effect may be true for bryophytes as well but needs more thorough investigation as this group has received little attention regarding niche complementarity studies (Turetsky et al., 2012 ). However, Michel et al. ( 2012 ) found that when grown in mixtures of different species, individuals tended to be smaller and denser collocated, resulting in improved water retention of the cushions. This in turn may improve nutrient uptake from throughfall and could explain a diversity-mediated effect of the moss layer on N fluxes. Conclusions Our results of an original dataset show that forest floor vegetation decreased N fluxes in soil solution across all forest types, suggesting an important role in the complex relationship of throughfall and N fluxes in temperate forests. Generally, our findings confirm the assertion that the forest floor layer is an important contributor to N retention in temperate forests (Gebauer et al., 2000). We show the importance of incorporating small-scale environmental variability into forest ecosystem studies to reveal patterns between the canopy and the forest floor. Our findings also emphasise the importance of including forest floor vegetation in forest dynamics models, as those often neglect or underestimate the influence of forest floor vegetation on water and N fluxes, and its interaction with the canopy layer (Blanco and Lo, 2023 ). Furthermore, this study suggests that, in forest management, tree species that support abundant forest floor vegetation should be promoted. Given that forest floor vegetation, and in particular the moss layer, has often received little attention in forest studies (Blanco and Lo, 2023 ; Gilliam, 2007 ; Porada et al., 2023 ), our results have implications for larger-scale studies and the general mechanistic understanding of ecological processes in forest ecosystems. We encourage that forest floor vegetation, particularly the moss layer and traits such as SLA, should be more explicitly included in future studies on water and nutrient fluxes and in forest models to fully understand the link between surface and subsurface biogeochemical processes in forest ecosystems. Declarations Funding This study is part of the Collaborative Research Centre AquaDiva of the Friedrich Schiller University Jena, funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project-ID 218627073 – SFB 1076. Competing interests The authors declare no conflict of interest. Author Contributions Study design: Christine Römermann, Beate Michalzik, Anke Hildebrandt; Data collection: Till J. Deilmann, Karin Potthast, Kerstin Näthe, Ruth-Kristina Magh; Data curation: Till J. Deilmann, Ruth-Kristina Magh, Karin Potthast; Data analysis and visualisation: Till J. Deilmann; Writing original draft: Till J. Deilmann; Writing - review and editing: Till J. Deilmann, Karin Potthast, Ruth-Kristina Magh Anke Hildebrandt, Beate Michalzik, Ruth-Kristina Magh, Christine Römermann; Funding acquisition: Christine Römermann, Beate Michalzik, Anke Hildebrandt. Data Availability All data and code used in this study are currently restricted for Reviewers and Editors only and accessible under: https://zenodo.org/records/16361724?token=eyJhbGciOiJIUzUxMiIsImlhdCI6MTc1MzI3MTk3NiwiZXhwIjoxNzY3MTM5MTk5fQ.eyJ pZCI6ImY4NzFjNzBhLTg4MzQtNDg5NS05MTUwLTgwZjgzOTliNTQ4MiIsImRhdGEiOnt9LCJ yYW5kb20iOiJmYzhmNjUyMzgzOGZlOGNmYjIxODFhMDA2YjQ0ZDE3MiJ9._HFK_5e12XI5 FttPvTvL76CzVlkZh-aXh7w75aWzljd40hzeQiM0_OTSz7GpKKEOZOTaQsU0gT4nlc-6j0sHQA After acceptance of the manuscript, all data and code will be made publicly available. Acknowledgments: Tim Kotulla, Christian Gregori, and all people who helped installing and excavating the SIAs, Viktor Schreier, Tiana Hammer, Florian Öchsner, Paul Kempka for support in data collection. This study is part of the Collaborative Research Centre AquaDiva of the Friedrich Schiller University Jena, funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project-ID 218627073 – SFB 1076. References Albert, C. 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Analysis of OF-layer humus mass variation in a mixed stand of European beech and Norway spruce: An application of structural equation modelling. Ecological Modelling , 213 (3–4), 319–330. https://doi.org/10.1016/j.ecolmodel.2007.12.014 WFO (2025): World Flora Online. Published on the Internet; http://www.worldfloraonline.org. Accessed on: 04 Aug 2025 Wilcke, W., Günter, S., Alt, F., Geißler, C., Boy, J., Knuth, J., Oelmann, Y., Weber, M., Valarezo, C., & Mosandl, R. (2009). Response of water and nutrient fluxes to improvement fellings in a tropical montane forest in Ecuador. Forest Ecology and Management , 257 (4), 1292–1304. https://doi.org/10.1016/j.foreco.2008.11.036 Wright, I. J., Reich, P. B., Westoby, M., Ackerly, D. D., Baruch, Z., Bongers, F., Cavender-Bares, J., Chapin, T., Cornelissen, J. H. C., Diemer, M., Flexas, J., Garnier, E., Groom, P. K., Gulias, J., Hikosaka, K., Lamont, B. B., Lee, T., Lee, W., Lusk, C., … Villar, R. (2004). The worldwide leaf economics spectrum. Nature , 428 (6985), 821–827. https://doi.org/10.1038/nature02403 Zhang, C., & Xi, N. (2021). Precipitation Changes Regulate Plant and Soil Microbial Biomass Via Plasticity in Plant Biomass Allocation in Grasslands: A Meta-Analysis. Frontiers in Plant Science , 12 , 614968. https://doi.org/10.3389/fpls.2021.614968 Supplementary Files SupplementaryInformation.docx Cite Share Download PDF Status: Published Journal Publication published 02 Dec, 2025 Read the published version in Plant and Soil → Version 1 posted Editorial decision: Minor revisions 13 Oct, 2025 Reviewers agreed at journal 20 Aug, 2025 Reviewers invited by journal 20 Aug, 2025 Editor invited by journal 13 Aug, 2025 Editor assigned by journal 13 Aug, 2025 First submitted to journal 12 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7356177","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":502873658,"identity":"8f6e3645-18e6-4511-856f-aa5ede6fce56","order_by":0,"name":"Till J. Deilmann","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-9521-1825","institution":"Friedrich Schiller University Jena: Friedrich-Schiller-Universitat Jena","correspondingAuthor":true,"prefix":"","firstName":"Till","middleName":"J.","lastName":"Deilmann","suffix":""},{"id":502873659,"identity":"e41ca303-e26c-4e21-bd19-d8f5fb9e25c5","order_by":1,"name":"Karin Potthast","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Karin","middleName":"","lastName":"Potthast","suffix":""},{"id":502873660,"identity":"e576696b-31c9-4b0f-a393-e4ff21ab107e","order_by":2,"name":"Beate Michalzik","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Beate","middleName":"","lastName":"Michalzik","suffix":""},{"id":502873661,"identity":"22ba2cac-172d-4034-b4e2-44b6d41f2b2b","order_by":3,"name":"Kerstin Näthe","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Kerstin","middleName":"","lastName":"Näthe","suffix":""},{"id":502873662,"identity":"554c718f-2f68-43d0-a7d7-5b4b47513cf6","order_by":4,"name":"Ruth-Kristina Magh","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ruth-Kristina","middleName":"","lastName":"Magh","suffix":""},{"id":502873663,"identity":"d37de54a-61b3-40dc-bc75-b26fe30128c3","order_by":5,"name":"Anke Hildebrandt","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Anke","middleName":"","lastName":"Hildebrandt","suffix":""},{"id":502873664,"identity":"c6f2c755-c495-4537-895f-fb01b25b2ac5","order_by":6,"name":"Christine Römermann","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Christine","middleName":"","lastName":"Römermann","suffix":""}],"badges":[],"createdAt":"2025-08-12 13:22:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7356177/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7356177/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11104-025-08050-w","type":"published","date":"2025-12-02T15:57:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90077966,"identity":"65e10dd4-08ee-4d4b-bfcf-2f3b77891e86","added_by":"auto","created_at":"2025-08-28 08:21:44","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1478057,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual framework of hypothesised causal relationships described in hypotheses 1 and 2 on \u003cstrong\u003eA) \u003c/strong\u003ehow nitrogen fluxes in soil solution (N fluxes) below the organic layer change with presence of forest floor vegetation, and \u003cstrong\u003eB) \u003c/strong\u003ehow throughfall influences N fluxes through direct and indirect forest floor vegetation-mediated effects. Red and blue arrows indicate a negative and positive influence, respectively. Details on the hypotheses and variables are described in the text.\u003c/p\u003e","description":"","filename":"Figure1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7356177/v1/cf83bb7899d4687dc135b14b.jpg"},{"id":90077768,"identity":"80286dc4-344f-43a3-a027-ed684043bac9","added_by":"auto","created_at":"2025-08-28 08:13:44","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1654233,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the study design to capture small-scale heterogeneity of throughfall, nitrogen (N) fluxes below the organic layer, and vegetation at the forest floor at the Saale-Elster-Sandsteinplatte Observatory in Central Germany. Numbers represent (1) vegetation plots along canopy cover transects with (2) throughfall funnel type samplers, and (3) installed self-integrating accumulators (SIAs) to capture N fluxes below the undisturbed organic layer.\u003c/p\u003e","description":"","filename":"Figure2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7356177/v1/e6ae59089d5d90b15a9fbe6e.jpg"},{"id":90077769,"identity":"9de5c2e3-fa19-4457-8370-57e2c186c002","added_by":"auto","created_at":"2025-08-28 08:13:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":798874,"visible":true,"origin":"","legend":"\u003cp\u003eCharacterization of the forest types regarding \u003cstrong\u003eA) \u003c/strong\u003ethroughfall, nitrogen flux, moss and herb cover, and community-weighted mean (CWM) specific leaf area (SLA) and plant height. Each grey point represents data from one plot, and different small letters indicate statistically significant differences between forest types. \u003cstrong\u003eB)\u003c/strong\u003e shows the species composition of the most dominant species per forest type for herbaceous and moss species, respectively. Values represent the mean species cover, hence, reflecting relative differences between forest types. Species with a mean cover of \u0026lt; 1% were grouped into “other”. Species richness (\u003cem\u003eS\u003c/em\u003e) and Shannon diversity (\u003cem\u003eH\u003c/em\u003e) per forest type are shown on top of each column. Statistical details on the post-hoc tests are provided in Supplementary Table S1. Full species names are provided in Table S2.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7356177/v1/75a4e02eedda3cb2bb252eef.png"},{"id":90077967,"identity":"0fef84b6-6ccd-4103-ba1e-ae664cf82289","added_by":"auto","created_at":"2025-08-28 08:21:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":900816,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences in vegetation composition between the different forest types. \u003cstrong\u003eA)\u003c/strong\u003eDetrended correspondence analysis (DCA) based on averaged weekly vegetation data with superimposed fitted nitrogen (N) flux and throughfall data (brown, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). Small dots represent averaged vegetation data of single plots, larger dots represent forest type centroids with respective standard errors, ellipses show the 95% confidence areas. \u003cstrong\u003eB) \u003c/strong\u003eMost important species (herbs: black, mosses: grey, both \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05), and community-weighted mean (CWM) functional traits, and plant cover (turquoise, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) are fitted onto the DCA showing associations with differences in vegetation composition. SLA = specific leaf area. Details on species abbreviations are provided in the Supplementary Table S2.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7356177/v1/d3f91c5924ecfc211946f5b8.png"},{"id":90077772,"identity":"2b2ee75c-5d58-403f-8bba-8ae00a657f6c","added_by":"auto","created_at":"2025-08-28 08:13:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":729224,"visible":true,"origin":"","legend":"\u003cp\u003eThe overall relationship (grey dashed line; \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05) of nitrogen flux in soil solution (N flux) with throughfall, plant cover, and community-weighted (CWM) functional traits across all forest types, as derived from linear models. Forest type-specific relationships are shown by coloured solid lines in the same panels, derived from ANCOVAs with forest type as interaction term. Each dot shows the averaged value of one plot over the entire period of observation. Goodness of fit\u003cem\u003e \u003c/em\u003e(\u003cem\u003eR\u003c/em\u003e²) and level of significance (*** = \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) of the ANCOVA are shown at the top of each panel. SLA = specific leaf area.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7356177/v1/ca515ab44a914dfdfa2ed7d4.png"},{"id":90077770,"identity":"fc7ccc82-7d89-43ad-accd-0c6e74e56a8e","added_by":"auto","created_at":"2025-08-28 08:13:44","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":979701,"visible":true,"origin":"","legend":"\u003cp\u003eResults from the piecewise structural equation models showing significant direct and indirect relationships of throughfall on nitrogen flux in soil solution, mediated by forest floor vegetation cover and functional traits. \u003cstrong\u003eA)\u003c/strong\u003e shows the global model with goodness of fit measures calculated from d-separation (Lefcheck, 2016) shown in the panel. The other panels show the results from the multigroup analysis for the \u003cstrong\u003eB)\u003c/strong\u003e beech, \u003cstrong\u003eC)\u003c/strong\u003e spruce, and \u003cstrong\u003eD)\u003c/strong\u003e pine forest type. Blue arrows represent positive effects and red arrows negative effects with the according standardised effect sizes as numbers and significance levels as asterisks. Linewidth reflects the relative standardized effect size, dashed arrows reflect marginally significant effects (\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.1), grey arrows represent correlated residual errors in the model, and transparent arrows in B-D) depict constrained paths to the global model. Methodological details are given in the text and full model outputs are provided in Table A4. (*)\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.1, *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7356177/v1/f27e13f79370c2a78d8e4963.png"},{"id":97723792,"identity":"b6d23788-9312-4751-8adc-62ad265d2d43","added_by":"auto","created_at":"2025-12-08 16:06:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6895672,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7356177/v1/93972903-1660-4fca-af12-d157c98ff3ce.pdf"},{"id":90077764,"identity":"7d2e3486-69a9-49cf-9188-a17ea627e76a","added_by":"auto","created_at":"2025-08-28 08:13:44","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":579868,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7356177/v1/e02a0f16dec7b47b5da1c971.docx"}],"financialInterests":"","formattedTitle":"Forest floor vegetation contributes to a reduction in nitrogen fluxes in temperate forest understories","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe forest floor vegetation, including the herbaceous and the moss layer, is an integral link between surface and subsurface processes as it can retain and redistribute both precipitation throughfall and nutrients (Glime, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Landuyt et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Muller, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Before reaching the soil layer, precipitation first passes through the canopy of tree, shrub, herbaceous species and moss layers, where it is intercepted and redistributed as throughfall multiple times along the way (Van Stan, II et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) down to the soil. Similarly, nutrients are redistributed several times through spatial and temporal litterfall dynamics, and by decomposers such as shredding soil fauna, fungi and mineralising microbes breaking down litter (Boerner \u0026amp; Koser, 1989; Krishna \u0026amp; Mohan, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These nutrients become either plant-available, immobilised in soil or biomass, or are leached into deeper horizons (Attiwill \u0026amp; Adams, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Leuschner \u0026amp; Ellenberg, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Trees, shrubs, herbaceous and moss species take up available nutrients for biomass production, which subsequently returns to the ground as litter, thus linking surface and subsurface processes (Gilliam et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Leuschner \u0026amp; Ellenberg, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). So far, the link of throughfall and nutrient fluxes in the soil has been studied for different forest types (De Schrijver et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Rijtema \u0026amp; De Vries, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) or management practices in forests (Wilcke et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), but the influence of forest floor vegetation has rarely been included in such studies despite their functional and structural importance for forests (Gilliam, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe herbaceous layer harbours about 80% of vascular plant diversity in temperate forests, thereby significantly impacting their function and structure (Gilliam, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). It also plays a key role in nutrient cycling, contributing up to 16% of litterfall with foliage that can contain 30% more nitrogen than the overstory trees (Gilliam, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Landuyt et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). It accounts for up to 90% of total spring and 50% of total summer nitrate uptake in forests, which reduces nutrient loss during spring runoff when tree uptake is minimal (Mabry et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Muller \u0026amp; Bormann, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1976\u003c/span\u003e; Olsson \u0026amp; Falkengren-Grerup, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Additionally, a dense herbaceous layer can conserve around 21% of inorganic nitrogen from throughfall by foliar absorption, and adds about 10% organic nitrogen, overall reducing nitrogen reaching the soil by 39% (Muller, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The moss layer captures a large proportion of the incoming throughfall owing to its high water storage capacity (Thielen et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As nutrient uptake mainly happens through their leaf surface, mosses retain N from captured throughfall and strongly impact biogeochemical cycling (Glime, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Bates, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Mosses significantly increase the overall ecosystem N input through biological bryophyte-diazotroph N\u003csub\u003e2\u003c/sub\u003e-fixation (Hupperts et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; DeLuca et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), and the annual uptake of N per area can even exceed that of trees, hence immobilising nutrients in biomass (Oechel \u0026amp; Van Cleve, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1986\u003c/span\u003e, Gundale et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Gundale et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Combined with a high nutrient use efficiency of and nutrient conservation through mosses (Cornelissen et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), this could lead to a significant N retention in the ecosystem, in particular where moss cover is high, such as in pine or spruce forests.\u003c/p\u003e\u003cp\u003eTree morphology and canopy characteristics determine the redistribution of precipitation and dissolved nutrients by e.g., differences in canopy storage capacity or stemflow (Metzger et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Frischbier et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tischer et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Fischer-Bedtke et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Thus, different forest types lead to different small-scale availability of resources at the forest floor and thus shape forest floor communities (Wagner et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Therefore, the main understory plant cover, species, and their specific adaptations to the prevailing conditions differ between forest types (Leuschner \u0026amp; Ellenberg, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), overall leading to differences in interception and redistribution of incoming nutrients (Metzger et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Landuyt et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Van Stan, II et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe functional composition of the forest floor vegetation plays an important role for throughfall and N fluxes. Functional traits reflect plant strategies, and depending on environmental conditions, acquisitive or conservative nutrient strategies dominate (D\u0026iacute;az et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wright et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). An acquisitive nutrient strategy is usually linked to species with fast growth rates, high specific leaf area (SLA; leaf area to dry weight), high leaf N content, short leaf lifespans, and high-quality litter leading to fast decomposition (Cornwell et al, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; D\u0026iacute;az et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In contrast, a conservative nutrient strategy is linked to species with slower growth rates, low specific leaf area, physically robust leaves with longer leaf lifespans, and low-quality litter leading to a slower decomposition (Cornwell et al, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; D\u0026iacute;az et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Intraspecific trait variation further reflects within-species performance differences due to environmental variability (Albert et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Baughman et al, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In forest understories, this variability is directly linked to resource availability (Wagner et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Thus, functional traits mirror local water and N availability (Lavorel \u0026amp; Garnier, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Poorter et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTraits can either respond to (\u0026ldquo;response traits\u0026rdquo;) or affect (\u0026ldquo;effect traits\u0026rdquo;) the environment or both (Lavorel \u0026amp; Garnier, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). For instance, SLA increases with increasing nutrient availability of the environment and positively relates to leaf N, leaf water content, and high rates of nutrient uptake (D\u0026iacute;az et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; P\u0026eacute;rez-Harguindeguy et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Poorter et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Lavorel \u0026amp; Garnier, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). At the same time, species with a high SLA have a faster litter decomposition and nutrient turnover, thereby also affecting the nutrient input and biogeochemical cycling (K\u0026ouml;rner, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Cornwell et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Similarly, plant height is both a response and effect trait (Lienin \u0026amp; Kleyer, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e): it strongly responds to light availability and productivity, thus being closely tied to competition for light (Falster \u0026amp; Westoby, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). In turn, the shade provided by taller plants mitigates temperature, creating more favourable conditions for ground-dwelling species (De Frenne et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), although lower temperatures generally decrease microbial activity and thus N availability (Muscolo et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). These characteristics of functional traits make them relevant links between above and belowground processes in biogeochemical cycles. However, the role of functional traits of the forest floor vegetation on the relationship between throughfall and N flux is largely understudied.\u003c/p\u003e\u003cp\u003eHere we investigate how the herbaceous and moss layer mediate the effect of throughfall on inorganic nitrogen (N) fluxes in forest floor solution in European beech (\u003cem\u003eFagus sylvatica\u003c/em\u003e L.), Norway spruce [\u003cem\u003ePicea abies\u003c/em\u003e (L.) H.Karst.], and Scots pine (\u003cem\u003ePinus sylvestris\u003c/em\u003e L.) forests, all representing common forest types in the temperate region. We collected data from 93 plots distributed across a gradient in canopy cover in the three forest types in the Saale-Elster-Sandsteinplatte Observatory (SESO). We focused on herb and moss cover, and the two functional traits, SLA and plant height that act as proxies for water and nutrient fluxes. To investigate the role of the herb and moss species for the relationship of throughfall and N fluxes, we tested three hypotheses, that are also graphically displayed in Fig.\u0026nbsp;1:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWe hypothesised that increasing herb and moss cover lead to a reduction in N fluxes below the organic layer. This hypothesis is based on the findings that increasing precipitation promotes plant growth for herbaceous species (Anderson et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1969\u003c/span\u003e; Zhang \u0026amp; Xi, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), as well as mosses (Deilmann et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Vitt, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e1990\u003c/span\u003e), leading to potentially higher water and nutrient uptake (Bates, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Glime, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Muller, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Thielen et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWe hypothesised that with increasing SLA and plant height, vegetation will have a stronger effect on the relationship between throughfall and N fluxes. This hypothesis is based on the findings that SLA and plant height positively relate to plant cover (D\u0026iacute;az et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; P\u0026eacute;rez-Harguindeguy et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and mirror plant nutrient strategies (D\u0026iacute;az et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wright et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWe hypothesised that the contribution of forest floor vegetation to N fluxes changes with forest type. This hypothesis is based on the findings that different forest types harbour different species in the herb and moss layer, and differ in overall plant cover (Landuyt et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Leuschner \u0026amp; Ellenberg, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eWith this study, we aim to contribute to a more holistic understanding of the impact of the forest floor vegetation on water and nitrogen fluxes in forests.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy area and experimental setup\u003c/h2\u003e\u003cp\u003eWe established in total 93 plots to capture the forest floor vegetation in six managed forest stands of three temperate forest types (2 x European beech, 2x Norway spruce, 2x Scots pine) in the Saale-Elster-Sandsteinplatte Observatory (SESO). The SESO is located on acidic weathered sand- and siltstone in Thuringia, Central Germany, with a mean annual temperature of 8.0\u0026deg;C, and an annual precipitation of 525 mm (detailed description of SESO in Stolze et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). To capture the small-scale variation in abiotic conditions and forest floor vegetation, we distributed 15 plots under different canopy cover levels within each forest stand (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In one of the pine stands, we established 18 plots due to a comparably high small-scale heterogeneity. A summary of all measured variables is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, and they are described in detail below.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eThroughfall and nitrogen flux\u003c/h3\u003e\n\u003cp\u003eCanopy throughfall was collected for each plot in biweekly intervals with funnel type samplers (catchment area \u003cem\u003ec.\u003c/em\u003e 0.01 m\u0026sup2;) from May 2022 to May 2023 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Throughfall fluxes were then aggregated to annual sums for each plot. Missing values resulting from e.g., fallen samplers were filled according to the average spatial deviation from the mean of the respective plot and season.\u003c/p\u003e\u003cp\u003eFor in total 72 out of the 93 plots, we obtained N flux measurements in soil solution from self-integrating accumulators (SIA; TerrAquat, Germany) being installed from April 2022 to April 2023 below the undisturbed organic layer of the plot. A detailed description of a SIA\u0026rsquo;s functional principle is provided in Bischoff (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In brief, a SIA consists of a cylinder (height and diameter\u0026thinsp;=\u0026thinsp;10 cm each), filled with a mixture of quartz sand, quartz silt, and an anion-exchange resin. Soil solution passes through, and solutes are immobilised by the adsorber (Bischoff, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). After one year, all the SIAs were removed from the soil and stored at 4\u0026deg;C. Then, a subsample of 15 g from the top 5 cm layer of water-saturated SIA was extracted using a 100 mL, 1 \u003cem\u003eM\u003c/em\u003e NaCl solution by shaking for 45 minutes at 290 rpm. Afterwards, solutions were filtered through a glass fibre filter (GF 6, Whatman, pore size\u0026thinsp;\u0026lt;\u0026thinsp;1 \u0026micro;m) and analysed for NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N and NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N concentrations by a continuous flow analyser (SAN\u0026thinsp;+\u0026thinsp;+\u0026thinsp;Classic, Skalar Analytic B. V. Breda, The Netherlands). The respective N flux was calculated by multiplying the concentration of the respective inorganic nitrogen by the extraction volume (100 ml) and the total weight of the sample (top 5 cm of the SIA), divided by the weight of the subsample (15 g) and by the area of the SIA. This way, we yielded an integrated total value of the solutes for a defined area and time to calculate N fluxes. N flux thus represents the overall inorganic nitrogen as the sum of extracted ammonia and nitrate from soil solution below the organic layer.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eSpecies cover and functional traits\u003c/h3\u003e\n\u003cp\u003eWe captured seasonal vegetation dynamics by monitoring vegetation composition on a weekly basis for the entire growing season from April to December 2022 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Plant cover was estimated for all co-occurring herbaceous and moss species following the Schmidt scale (Pfadenhauer, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) in the following classes with an additional class to capture species with very low coverages: 0, 0.1, 1, 3, 5, 8, 10, 15, 20, 25, 30, 40, 50, 60, 70, 75, 80, 90, 100%. Overall vegetation cover per plot was calculated as species cover per plot and week, averaged over the entire growing season. Note that as a result of species overlap, cover values above 100% are possible. Species-wise covers were used for the calculation of community-weighted means (see below). Taxonomy follows World Flora Online (WFO, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFor trait measurements, we captured intraspecific trait variation for in total 685 individuals by selecting five healthy individuals per species and plot at peak flowering, following standardised protocols (P\u0026eacute;rez-Harguindeguy et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Plant height (cm) was measured \u003cem\u003ein situ\u003c/em\u003e for each individual using a folding rule. For the same individuals, two healthy leaves were collected, scanned and leaf area was determined using an image detection algorithm (K\u0026ouml;rschens, \u003cem\u003eunpublished\u003c/em\u003e). The leaves were then dried in a compartment drier at 40\u0026deg;C until mass constancy was reached. The specific leaf area (SLA; mm\u0026sup2; mg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) was obtained by dividing the fresh weight by the dry weight. For each trait \u003cem\u003et\u003c/em\u003e and plot \u003cem\u003ep\u003c/em\u003e, we calculated community weighted means (CWM) following Eq.\u0026nbsp;(1):\u003c/p\u003e\u003cp\u003eCWM\u003csub\u003e\u003cem\u003etp\u003c/em\u003e\u003c/sub\u003e = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sum\\:}_{i=1}^{S}{c}_{ip}\\times\\:\\:{t}_{i}\\)\u003c/span\u003e\u003c/span\u003e (1)\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003ec\u003c/em\u003e\u003csub\u003e\u003cem\u003eip\u003c/em\u003e\u003c/sub\u003e represents the relative cover of species \u003cem\u003ei\u003c/em\u003e in plot \u003cem\u003ep\u003c/em\u003e and \u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e denominates the trait value of species \u003cem\u003ei\u003c/em\u003e, summed up for all species per plot \u003cem\u003eS.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eOn seven plots, no herbaceous species occurred resulting in missing values in CWM SLA and CWM plant height. To minimise statistical leverage, we filled in those missing values as average of comparable canopy positions (here categorised as high, mid, low) within the forest stand.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003ePrior to the evaluation of the hypotheses, we compared forest types by testing for differences in throughfall, N fluxes, plant cover, and CWM traits using either ANOVA and subsequent t-tests or Kruskal-Wallis and subsequent Mann-Whitney U tests, depending on normal distribution of the data and homogeneity of variances. Furthermore, differences in vegetation composition were assessed in multivariate space by performing a Detrended Correspondence Analysis (DCA) on averaged species cover per year and plot. To test for characteristic species, plant cover, and traits of the different plots, we fitted single species covers, plot-wise plant cover, and traits onto the ordination using the \u003cem\u003evegan\u003c/em\u003e package (Oksanen et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo test whether increasing herb and moss cover lead to a reduction in N fluxes, we ran linear models with ln(\u003cem\u003ey\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1) transformed N flux as dependent, and herb and moss cover, respectively as independent variables. To further test whether there are forest-type specific differences in N fluxes, we additionally performed analyses of covariances (ANCOVAs) with ln(\u003cem\u003ey\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1) transformed N fluxes as the response variable, and another variable with forest type as interaction term as independent variable. To test if the slopes differed from zero while accounting for forest type, we used the R package \u003cem\u003eemmeans\u003c/em\u003e (Lenth, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). To test whether plant functional traits are linked to a reduction in N fluxes, and whether there are forest-type specific differences, we ran linear models and ANCOVAs as described above, but with CWM SLA and CWM plant height as independent variables.\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\u003eSummary of the measured variables with their respective definition, unit, number of plots/measurements within each forest stand, and the used method.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003evariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003edefinition\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eunit\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003enumber of plots\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003emethod\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ethroughfall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eannual sum of biweekly throughfall measurements\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003emm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 plots per forest stand\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBiweekly throughfall measurements using funnel type samplers with a catchment area of \u003cem\u003ec\u003c/em\u003e. 0.01 m\u0026sup2; over one year were summed up.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003einorganic nitrogen flux in soil solution\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eannual sum of extracted NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N and NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N from seeped soil solution below the organic layer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ekg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e a\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 plots per forest stand\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSelf-integrating accumulators (SIA) consisting of a mixture of quartz sand, quartz silt and an anion-exchange resin were installed under the undisturbed organic layer of the plots for one year. NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N and NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N were extracted from a subsample of 15 g from the top 5 cm layer of the SIA using 100 mL of a 1 \u003cem\u003eM\u003c/em\u003e NaCl solution, filtered through a glass fibre filter, and subsequently analysed using a continuous flow analyser.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eherb cover, moss cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eaverage of weekly observations of species cover over the entire growing season\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 plots per forest stand\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWeekly vegetation surveys of each species and plot using the slightly modified Schmidt scale: 0, 0.1, 1, 3, 5, 8, 10, 15, 20, 25, 30, 40, 50, 60, 70, 75, 80, 90, 100%. Plot-wise average was calculated as species cover per plot and week, averaged over the entire growing season (April \u0026ndash; December 2022).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCWM plant height\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCommunity weighted mean (CWM) of plant height\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ecm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 plots per forest stand\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePlant height was measured \u003cem\u003ein situ\u003c/em\u003e as the shortest distance from the ground to the uppermost part of the main photosynthetic tissues for five individuals using a folding rule. Community-weighted means were calculated using species cover and plant height measurements of five individuals per species and plot.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCWM specific leaf area (SLA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCommunity weighted mean of specific leaf area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003emm\u003csup\u003e2\u003c/sup\u003e mg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 plots per forest stand\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTwo fresh leaves per individual were scanned and their area determined by an image detection algorithm. The leaves were then dried in a compartment drier at 40\u0026deg;C until mass constancy. Community-weighted means were calculated using species cover and SLA measurements of five individuals per species and plot.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAt last, to test all three hypotheses in one overall analysis and to account for interdependent relationships, we used structural equation modelling (SEM) to evaluate the direct effect of throughfall on N fluxes, and the mediating effect of the vegetation, here being included as indirect effects. The SEM was built using the R package \u003cem\u003epiecewiseSEM\u003c/em\u003e (Lefcheck, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), based on prior knowledge as described by the hypotheses stated above and by Fig.\u0026nbsp;1. To account for forest type-specific differences, we ran a multigroup analysis on the SEM which fits a model-wide interaction with forest type and directly compares group-specific estimates with a global model (Lefcheck, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For the global model, we specified each component model as mixed effects model using the R package \u003cem\u003eglmmTMB\u003c/em\u003e (Brooks et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) with forest type as random effect. Component models where the random effect variance was estimated close to zero, were specified as linear models to avoid singularity. Global goodness of fit of the SEM was assessed by Fisher\u0026rsquo;s \u003cem\u003eC\u003c/em\u003e statistic (Lefcheck, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) with a good model fit indicated by a higher \u003cem\u003ep\u003c/em\u003e value (\u0026ge;\u0026thinsp;0.05). Compared to running SEMs on subsets of the data, this approach has the advantage of directly detecting group-specific path changes. If the estimates do not differ significantly, the path is constrained to the global model, i.e. estimates and statistics are the same for those paths, but the standardised estimates may still differ due to different within-group variances (Lefcheck, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eDescriptive comparison of forest types\u003c/h2\u003e\u003cp\u003eFigure\u0026nbsp;3 show that the three forest types covered a wide range in throughfall (180\u0026ndash;450 mm), N fluxes (0-115 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e a\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), herb cover (0\u0026ndash;93%), moss cover (0-108%) and CWM traits (SLA: 25\u0026ndash;74 mm\u0026sup2; mg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, plant height: 3.8\u0026ndash;61.8 cm). In the spruce plots, throughfall was on average slightly lower but highly variable (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation: 305\u0026thinsp;\u0026plusmn;\u0026thinsp;55 mm), and N fluxes were highest and most variable (15.6\u0026thinsp;\u0026plusmn;\u0026thinsp;23.5 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e a\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), compared to the beech and pine plots (Table S1). Both, herb and moss cover were highest in the pine (on average \u003cem\u003ec.\u003c/em\u003e 70% and 89%, respectively) and lowest in the beech plots (16% and 17%), while the spruce plots showed a low herb (20%) but high moss cover (70%). CWM SLA was highest in the spruce, and lowest in the beech plots (61 and 31 mm\u0026sup2; mg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively) while it was the opposite for CWM plant height (13 and 35 cm, respectively; Fig.\u0026nbsp;3, Table S1). For a subset of plots used in this study, throughfall fluxes and N fluxes in throughfall were measured and showed a strong positive correlation (Spearman rank correlation: \u003cem\u003eρ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.63, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Supplementary Fig. S1), demonstrating that N input from throughfall generally increased with amount of throughfall across all forest types.\u003c/p\u003e\u003cp\u003eThe forest types also differed in the dominant forest floor species (Fig.\u0026nbsp;3B; see Table S2 for a complete species list per forest type). The herb cover in the beech plots was mainly dominated by \u003cem\u003eLuzula luzuloides\u003c/em\u003e., in the spruce plots mostly by \u003cem\u003eVaccinium myrtillus\u003c/em\u003e, \u003cem\u003eOxalis acetosella\u003c/em\u003e, and \u003cem\u003eRubus idaeus\u003c/em\u003e, and in the pine plots nearly solely by \u003cem\u003eV. myrtillus\u003c/em\u003e, and \u003cem\u003eDeschampsia flexuosa\u003c/em\u003e. The moss cover was mainly dominated by \u003cem\u003ePolytrichum formosum\u003c/em\u003e in the beech plots, while the spruce plots were dominated by \u003cem\u003eHypnum cupressiforme, Pseudoscleropodium purum, Brachythecium rutabulum\u003c/em\u003e, and \u003cem\u003ePleurozium schreberi\u003c/em\u003e, and the pine plots mostly by \u003cem\u003eP. schreberi\u003c/em\u003e and \u003cem\u003eP. purum\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eThe DCA confirms that overall, the forest types differed strongly in vegetation composition along the first axis (36.65% explained variance; Fig.\u0026nbsp;4A). While the beech forests showed the lowest plant cover in both herb and moss cover, the pine forests were characterised by the highest plant cover, and the spruce forests were in between (Fig.\u0026nbsp;4A, B). We also found noticeable variation within forest types along the second axis (32.04% expl. var.), with the spruce plots showing the greatest variation in species composition, which was associated with differences in N fluxes and throughfall. Furthermore, differences along DCA2 were linked to CWM plant height, CWM SLA, and the occurrence of the herbs \u003cem\u003eO. acetosella\u003c/em\u003e and \u003cem\u003eMycelis muralis\u003c/em\u003e Dumort., and the mosses \u003cem\u003eP. schreberi\u003c/em\u003e and \u003cem\u003eP. purum\u003c/em\u003e, respectively.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eLinking throughfall, N fluxes, plant cover, and functional traits\u003c/h3\u003e\n\u003cp\u003eThe linear models show an overall negative relationship of throughfall, herb cover and CWM plant height with N fluxes, and an overall positive relationship between CWM SLA with N fluxes across all forest types (Fig.\u0026nbsp;5, grey dashed line). These relationships were forest type-specific differences: N fluxes most strongly changed in spruce plots with throughfall, moss cover, CWM SLA, and CWM plant height, while the other forest types responded less strongly (Fig.\u0026nbsp;5, coloured solid lines). Details on model statistics are provided in Table S3 \u003c/p\u003e\u003cp\u003e(Supplementary Information).\u003c/p\u003e\u003cp\u003eThe SEMs show that the impact of throughfall on N flux in soil solution was never direct but was affected by forest floor vegetation across all models, generally showing N fluxes decreasing where it was more abundant or denser. We found that higher plant cover and CWM trait values were associated with lower N fluxes (standardized path coefficients are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e6\u003c/span\u003e; direct and indirect effects in Table S4; full SEM outputs in Table S5); the strongest indirect effect was mediated by CWM SLA. Contrary to our expectation, the latter decreased with throughfall but positively influenced N fluxes, while the other vegetation variables showed the opposite pattern, as expected. For example, increasing throughfall led to increasing moss cover which then decreased N fluxes. Similarly, there was a trend that increasing plant height led to lower N fluxes. In contrast, herb cover did not mediate the relationship between throughfall and N fluxes itself but was positively influenced by CWM SLA and CWM plant height. Overall, forest floor vegetation enhanced the direct throughfall effect on N fluxes (Table S4).\u003c/p\u003e\u003cp\u003eThe multigroup analysis shows only few significant differences between forest types: three paths differed, namely throughfall to CWM SLA, CWM plant height, and herb cover, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e6\u003c/span\u003eB-D, Table S5). The overall pattern was similar but in the spruce plots, the effect of throughfall on CWM SLA was substantially stronger, and we found positive impacts of throughfall on CWM plant height. In the pine plots, throughfall additionally led to increased herb cover. Vegetation-mediated indirect effects were strongest in the spruce and pine plots, and lowest in the beech plots, accounting for \u003cem\u003ec.\u003c/em\u003e 70%, 62%, and 11% of the total effects, respectively (Table S4). In the beech plots, vegetation-mediated effects balanced each other out, as CWM SLA and moss cover showed a negative effect, whereas CWM plant height and herb cover showed a positive effect. Overall, the direction of influence of herb cover and CWM plant height was forest type-specific while moss cover and CWM SLA had a consistently negative indirect effect on N fluxes (Table S4).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur results show that forest floor vegetation affects the relationship of throughfall and N fluxes between canopy inputs and inorganic nitrogen fluxes in soil solution in temperate forests. Generally, we found that forest floor vegetation decreased N fluxes across all forest types. This suggests that this layer contributes to overall N retention in the system, thereby complementing N retention of trees, microbial biomass, and organic matter (Gebauer et al., 2000; Gundale et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Gurmesa et al., 2016; Tietema et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), and potentially counteracting N input from tree litter, and throughfall (Dise et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Vesterdal et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; W\u0026auml;lder et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Particularly, moss cover and CWM SLA are influential mediators irrespective of forest type. Yet, the models suggest that the effect of vegetation is strong where forest floor cover is high, leading to forest type-specific differences such as in temperate pine and spruce forests.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eN fluxes decrease with increasing plant cover\u003c/h2\u003e\u003cp\u003eWe found that increasing plant cover was generally related to a reduction in N fluxes in soil solution, supporting our hypothesis that forest floor vegetation plays an important role in N retention in temperate forests. This finding is consistent with previous studies reporting a significant proportion of N uptake by understory species across biomes, such as in boreal (Gundale et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), temperate (Gebauer et al., 2000), and tropical forests (Gurmesa et al., 2016). Generally, the higher the latitude, the more important is the role of mosses in the forest understory because their abundance increases (Berdugo et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The substantial role mosses play in N uptake in boreal systems (Gundale et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) corroborates our findings that this layer considerably affects N fluxes also in temperate forests with a dense moss cover, such as in pine or spruce forests. Direct absorption of incoming N from throughfall through the leaf surface (Bates, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Glime et al., 2022), and subsequent immobilisation in poorly decomposable biomass (Cornelissen et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) could lead to increased N retention where moss cover is high. Similarly, in woody forest floor plants, much of the N taken up is stored in the stem and root system (Gurmesa et al., 2016), further leading to longer-term N immobilisation as it is not seasonally returned to the soil as is the case with deciduous leaf litter. If this pattern results from higher N uptake and storage rates with increasing herb cover or from increased direct absorption of N from throughfall remains unclear and requires further testing.\u003c/p\u003e\u003cp\u003eThe SEM shows that in the pine plots, which had the most plant cover overall, the indirect effects of vegetation on N fluxes were weaker. This suggests that other factors such as species-specific effects may further contribute to N retention, which has already been shown by previous studies on trees (Andersen et al, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Schulz et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), non-woody plants (Freschet et al, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Grassein et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Liu \u0026amp; van Kleunen, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and mosses (Hawkins et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Solga \u0026amp; Frahm, 2013). The pine plots were mainly dominated by \u003cem\u003eP. schreberi\u003c/em\u003e which usually forms loose wefts, compared to the dense mat forming \u003cem\u003eH. cupressiforme\u003c/em\u003e in the spruce plots, and the turf forming moss \u003cem\u003eP. formosum\u003c/em\u003e dominating in the beech plots (Bernhardt-R\u0026ouml;mermann et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; M\u0026auml;gdefrau, 1982). These different moss life forms and colony structures are linked to water use efficiency and could lead to differences in infiltration of throughfall, potentially reducing the contact time with the moss leaf surface and, thus, the nutrient uptake and immobilisation in biomass (Bates, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). This hypothesis, however, needs to be tested in future experimental studies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eSLA and plant height show overall negative indirect effects on N fluxes\u003c/h2\u003e\u003cp\u003eThe SEM shows that the effects of functional traits on herb cover and N fluxes were more pronounced where the trait values were largest. Species with high SLA values dominating the spruce plots (e.g., \u003cem\u003eO. acetosella\u003c/em\u003e, \u003cem\u003eImpatiens parviflora\u003c/em\u003e., \u003cem\u003eMoehringia trinervia\u003c/em\u003e. and \u003cem\u003eM. muralis\u003c/em\u003e; Table S2), and large rush species (\u003cem\u003eL. luzuloides\u003c/em\u003e) in the beech plots explain the strong effects involving CWM SLA and plant height in those forest types. It further demonstrates the importance of species identity and community composition which is linked to a specific suite of traits, representing plant performance and strategies as response to local environmental conditions (Deilmann et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; D\u0026iacute;az et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Violle et al., 2007). In turn, these traits can affect the ecosystem, where higher CWM SLA values are linked to higher N fluxes. This pattern supports the idea that species with high SLA, being associated to high leaf N content, faster nutrient turnover and mineralisation processes, can lead to a higher N return to the system (Aerts \u0026amp; Chapin III, 1999; Grassein et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; K\u0026ouml;rner, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eForest types differ in strength but not in direction of vegetation-mediated influence on N fluxes\u003c/h2\u003e\u003cp\u003eMost patterns shown in the global SEM model were also found within each forest type. Yet, we found the strongest indirect effects of forest floor vegetation on N fluxes where the vegetation was abundant, namely in the spruce and pine plots, suggesting a higher N uptake with increasing herb and moss cover. This is in line with previous studies showing that forest floor vegetation can account for large fractions of N uptake, even exceeding uptake of overstory trees in direct comparisons (Gebauer et al., 2000; Gundale et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This constitutes an N sink in the forest ecosystem and a competitor for N uptake with co-occurring trees and their seedlings, as well as soil microbes (Gebauer et al., 2000; Muller, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Similarly, a dense herb cover can reduce the amount of N reaching the soil with throughfall by 39% (Muller, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), explaining the relatively strong indirect effect of herb cover on N fluxes in the pine plots, where the blueberry \u003cem\u003eV. myrtillus\u003c/em\u003e covered more than half the area of the understory, compared to the other forest types.\u003c/p\u003e\u003cp\u003eFurther differences in the forest floor vegetation mediated effects between the spruce and the other forest types may be attributed to species identity as described above, and to species diversity, which, however, needs further investigation in future studies. Preliminary analyses show that both the pine and the beech plots harboured at most only half the number of species found in the spruce plots and had lower Shannon diversity and Evenness (Table S6). Following the niche complementarity hypothesis, higher species diversity leads to higher occupation of the functional trait space, thereby optimising resource utilisation (Loreau \u0026amp; Hector, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). This effect may be true for bryophytes as well but needs more thorough investigation as this group has received little attention regarding niche complementarity studies (Turetsky et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). However, Michel et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) found that when grown in mixtures of different species, individuals tended to be smaller and denser collocated, resulting in improved water retention of the cushions. This in turn may improve nutrient uptake from throughfall and could explain a diversity-mediated effect of the moss layer on N fluxes.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur results of an original dataset show that forest floor vegetation decreased N fluxes in soil solution across all forest types, suggesting an important role in the complex relationship of throughfall and N fluxes in temperate forests. Generally, our findings confirm the assertion that the forest floor layer is an important contributor to N retention in temperate forests (Gebauer et al., 2000). We show the importance of incorporating small-scale environmental variability into forest ecosystem studies to reveal patterns between the canopy and the forest floor. Our findings also emphasise the importance of including forest floor vegetation in forest dynamics models, as those often neglect or underestimate the influence of forest floor vegetation on water and N fluxes, and its interaction with the canopy layer (Blanco and Lo, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, this study suggests that, in forest management, tree species that support abundant forest floor vegetation should be promoted. Given that forest floor vegetation, and in particular the moss layer, has often received little attention in forest studies (Blanco and Lo, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gilliam, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Porada et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), our results have implications for larger-scale studies and the general mechanistic understanding of ecological processes in forest ecosystems. We encourage that forest floor vegetation, particularly the moss layer and traits such as SLA, should be more explicitly included in future studies on water and nutrient fluxes and in forest models to fully understand the link between surface and subsurface biogeochemical processes in forest ecosystems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is part of the Collaborative Research Centre AquaDiva of the Friedrich Schiller University Jena, funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) \u0026ndash; Project-ID 218627073 \u0026ndash; SFB 1076.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy design: Christine R\u0026ouml;mermann, Beate Michalzik, Anke Hildebrandt; Data collection: Till J. Deilmann, Karin Potthast, Kerstin N\u0026auml;the, Ruth-Kristina Magh; Data curation: Till J. Deilmann, Ruth-Kristina Magh, Karin Potthast; Data analysis and visualisation: Till J. Deilmann; Writing original draft: Till J. Deilmann; Writing - review and editing: Till J. Deilmann, Karin Potthast, Ruth-Kristina Magh Anke Hildebrandt, Beate Michalzik, Ruth-Kristina Magh, Christine R\u0026ouml;mermann; Funding acquisition: Christine R\u0026ouml;mermann, Beate Michalzik, Anke Hildebrandt.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data and code used in this study are currently restricted for Reviewers and Editors only and accessible under: https://zenodo.org/records/16361724?token=eyJhbGciOiJIUzUxMiIsImlhdCI6MTc1MzI3MTk3NiwiZXhwIjoxNzY3MTM5MTk5fQ.eyJ\u003cbr\u003epZCI6ImY4NzFjNzBhLTg4MzQtNDg5NS05MTUwLTgwZjgzOTliNTQ4MiIsImRhdGEiOnt9LCJ\u003cbr\u003eyYW5kb20iOiJmYzhmNjUyMzgzOGZlOGNmYjIxODFhMDA2YjQ0ZDE3MiJ9._HFK_5e12XI5\u003cbr\u003eFttPvTvL76CzVlkZh-aXh7w75aWzljd40hzeQiM0_OTSz7GpKKEOZOTaQsU0gT4nlc-6j0sHQA\u003c/p\u003e\n\u003cp\u003eAfter acceptance of the manuscript, all data and code will be made publicly available.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTim Kotulla, Christian Gregori, and all people who helped installing and excavating the SIAs, Viktor Schreier, Tiana Hammer, Florian \u0026Ouml;chsner, Paul Kempka for support in data collection.\u003c/p\u003e\n\u003cp\u003eThis study is part of the Collaborative Research Centre AquaDiva of the Friedrich Schiller University Jena, funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) \u0026ndash; Project-ID 218627073 \u0026ndash; SFB 1076.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlbert, C. 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Precipitation Changes Regulate Plant and Soil Microbial Biomass Via Plasticity in Plant Biomass Allocation in Grasslands: A Meta-Analysis. \u003cem\u003eFrontiers in Plant Science\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e, 614968. https://doi.org/10.3389/fpls.2021.614968\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"plant-and-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plso","sideBox":"Learn more about [Plant and Soil](https://www.springer.com/journal/11104)","snPcode":"11104","submissionUrl":"https://submission.nature.com/new-submission/11104/3","title":"Plant and Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"bryophytes, functional traits, nitrogen cycle, plant cover, small-scale, throughfall, water flux","lastPublishedDoi":"10.21203/rs.3.rs-7356177/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7356177/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground and Aims:\u003c/h2\u003e\u003cp\u003eForest floor vegetation can both respond to and affect the water and nitrogen (N) availability in forest ecosystems. However, their role for influencing the relationship between throughfall and N fluxes has hardly been studied, leaving a knowledge gap in our mechanistic understanding of forest biogeochemical cycling. Here we investigate the impact of the structural and functional role of the herbaceous and moss layer in linking N fluxes and throughfall patterns in beech, spruce, and pine forests in Central Germany.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe monitored herbaceous and moss species cover and diversity, as well as throughfall and N fluxes for 93 plots capturing small-scale microclimate variability. For all co-occurring herbaceous species, we measured intraspecific trait variation for specific leaf area (SLA) and plant height (n\u0026thinsp;=\u0026thinsp;685).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eMultivariate analyses reveal strong differences in the herb and moss layer composition between forest types. The results of analyses of covariance, and of piecewise structural equation models consistently show that N fluxes decreased most under pine and spruce plots where herb and moss cover was high. Species with a high SLA and plant height positively contributed to overall herb cover.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eOur results suggest that plant growth, particularly moss cover, contributed to overall N retention, while acquisitive and fast-growing species with a high SLA contributed to a fast nutrient return to the system, thereby partly increasing N fluxes. We conclude that taxonomic and functional composition of the forest floor vegetation is an important mediator in the link between throughfall and N fluxes.\u003c/p\u003e","manuscriptTitle":"Forest floor vegetation contributes to a reduction in nitrogen fluxes in temperate forest understories","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-28 08:13:39","doi":"10.21203/rs.3.rs-7356177/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor revisions","date":"2025-10-14T03:37:26+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-08-20T13:06:47+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-20T04:43:21+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Plant and Soil","date":"2025-08-13T04:22:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-13T04:02:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Plant and Soil","date":"2025-08-12T09:21:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"plant-and-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plso","sideBox":"Learn more about [Plant and Soil](https://www.springer.com/journal/11104)","snPcode":"11104","submissionUrl":"https://submission.nature.com/new-submission/11104/3","title":"Plant and Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b1e2c507-d8a9-417e-b428-5abe8694c790","owner":[],"postedDate":"August 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-08T15:59:50+00:00","versionOfRecord":{"articleIdentity":"rs-7356177","link":"https://doi.org/10.1007/s11104-025-08050-w","journal":{"identity":"plant-and-soil","isVorOnly":false,"title":"Plant and Soil"},"publishedOn":"2025-12-02 15:57:11","publishedOnDateReadable":"December 2nd, 2025"},"versionCreatedAt":"2025-08-28 08:13:39","video":"","vorDoi":"10.1007/s11104-025-08050-w","vorDoiUrl":"https://doi.org/10.1007/s11104-025-08050-w","workflowStages":[]},"version":"v1","identity":"rs-7356177","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7356177","identity":"rs-7356177","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}