Next Article in Journal
The bZIP Transcription Factor LkbZIP4 Enhances Drought Tolerance in Hybrid Larch (Larix kaempferi × L. gmelinii)
Previous Article in Journal
Ecological Drivers of Standing Volume and Carbon Stocks in Contrasting Tropical Forests of Mexico and Colombia
Previous Article in Special Issue
Fire Effects on Lichen Biodiversity in Longleaf Pine Habitat
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Forests
Volume 17
Issue 4
10.3390/f17040506
forests-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Forest Type and Environmental Gradients Shape Bryophyte Functional Diversity: Evidence from Epigeic Bryophytes in Beech Forests and Pine Plantations

by
Miloš Ilić
*,
Mirjana Ćuk
and
Dragana Vukov
Department of Biology and Ecology, Faculty of Sciences, University of Novi Sad, Trg Dositeja Obradovića 2, 21000 Novi Sad, Serbia
*
Author to whom correspondence should be addressed.
Forests 2026, 17(4), 506; https://doi.org/10.3390/f17040506
Submission received: 10 March 2026 / Revised: 14 April 2026 / Accepted: 17 April 2026 / Published: 19 April 2026
(This article belongs to the Special Issue The Role of Bryophytes and Lichens in Forest Ecosystem Dynamics)
Browse Figures
Versions Notes

Abstract

We investigated bryophyte communities in mature beech forests (Fagus sylvatica L.) and Austrian pine plantations (Pinus nigra J.F. Arnold) on Fruška Gora Mountain (northern Serbia) to examine how stand structure and edaphic conditions influence trait composition and functional diversity. Environmental predictors included soil pH, soil temperature, herbaceous cover, and shrub density, while collinear structural variables were summarized using principal component analysis into a composite structural–moisture gradient. Community–environment relationships were analyzed using redundancy analysis (RDA) with restricted permutations, trait–environment coupling using RLQ and fourth-corner analysis, and functional diversity using Rao’s quadratic entropy (RaoQ). The RDA indicated significant effects of all predictors. RLQ revealed a structured multivariate coupling between bryophyte traits and environmental gradients. Functional diversity was higher in beech forests than in pine plantations, increasing with shrub density and decreasing along the structural–moisture gradient. Overall, plantation stands supported functionally more homogeneous bryophyte assemblages, highlighting the importance of stand structural complexity for maintaining forest-floor bryophytes’ functional diversity.

1. Introduction

Forest ecosystems, since they are structurally complex and have changing ecological processes, offer a wide range of substrates and microhabitats that allow bryophytes to form distinct micro-communities [1,2]. By colonizing soil, deadwood, and tree bases, forest-floor bryophytes substantially contribute to overall plant diversity [3] and biogeochemical cycling of elements [4,5]. Furthermore, bryophytes influence key ecosystem processes, such as soil stabilization and carbon sequestration [6], nitrogen cycling [7,8], water retention [9,10], habitat provision for a wide range of invertebrates [11], and vegetation dynamics [12].
In forest ecosystems, bryophyte species richness and community composition are shaped by a combination of broad-scale climatic and biogeographic gradients [13,14,15] and fine-scale environmental conditions operating at the stand and microsite level [16,17,18]. Because of their poikilohydric physiology, bryophytes are particularly sensitive to local microclimatic variation, especially near-ground differences in moisture, temperature, light availability, and substrate conditions [1,19,20,21]. Forest management modifies these structural and edaphic conditions, potentially reshaping bryophyte assemblages through changes in canopy closure, soil properties, and resource heterogeneity [22]. Paillet et al. [23] showed that bryophytes are a threatened group due to forest management impact, especially in temperate forests.
In southeastern Europe, large areas of native broadleaved forests have historically been replaced or supplemented by conifer plantations, frequently altering stand structure and soil characteristics. Conifer stands typically differ from mature deciduous forests in canopy homogeneity, litter thickness, soil acidification patterns, and light regimes [24,25,26,27]. Previous studies have reported contrasting patterns regarding bryophyte responses to forest type. While some authors suggest that coniferous stands may promote bryophyte richness due to lower canopy density [16,19], others have shown that terricolous bryophytes often prefer native deciduous forests over pine plantations, indicating that conifer plantations may not consistently provide suitable microhabitat conditions [1,18]. These contrasting results highlight the need to move beyond taxonomic comparisons and examine the functional dimension of bryophyte assemblages. In this context, bryophyte functional diversity refers to the range, distribution, and relative representation of functional traits within a community, that is, the diversity of ecological strategies expressed by co-occurring species. Unlike taxonomic diversity, which describes species identity and number, functional diversity captures how species differ in traits related to water balance, persistence, regeneration, dispersal, and habitat affinity, thereby providing a more mechanistic framework for understanding community assembly and ecosystem functioning [28,29].
Beyond taxonomic composition, functional trait approaches can provide a deeper insight into the mechanisms shaping bryophyte diversity patterns [28]. Bryophyte functional traits can be broadly grouped into autecological, morphological, and reproductive dimensions, each of which may respond differently to environmental variation [29]. Autecological traits, such as indicator values for moisture, light, and substrate reaction, reflect species’ realized ecological niches and are often closely associated with gradients in humidity, canopy openness, and substrate chemistry [30]. Morphological traits, including growth form, life form, and shoot size, are linked to water retention, light interception, and spatial occupation, and may therefore shift along gradients of microclimatic buffering, substrate availability, and forest structural heterogeneity [28,31]. Reproductive traits, such as sexual system, spore size, and the presence of vegetative propagules, are related to dispersal and regeneration strategies and may become particularly important under disturbance or in structurally simplified stands, since reproductive traits can have effects on physiological traits [32,33,34]. Together, these trait dimensions provide a more mechanistic basis for understanding bryophyte community assembly than taxonomic composition alone and can improve comparisons among forest systems exposed to different environmental conditions and management regimes [28,30,35,36]. Functional traits related to life strategy, growth form, reproductive mode, and substrate affinity can reflect adaptive responses to microclimatic stress, disturbance regimes, and resource availability [28,29]. Accordingly, trait-based approaches can move bryophyte ecology beyond local taxonomic description toward more general and transferable ecological interpretation. By describing species in terms of ecological strategies, they enable comparisons of ecological responses across forest types, environmental gradients, and management contexts. Despite growing interest in bryophyte functional ecology [28,29,30,36,37], studies integrating species composition, environmental variables, and trait data within a unified analytical framework remain scarce, particularly in southeastern European forest ecosystems. Moreover, the possibility that forest conversion drives functional homogenization through selective filtering of trait combinations has received limited attention in bryophyte communities. Despite growing interest in bryophyte functional ecology [28,29,30,36,37], studies integrating species composition, environmental variables, and trait data within a unified analytical framework remain scarce, particularly in southeastern European forest ecosystems. Moreover, the extent to which forest conversion promotes functional homogenization through selective filtering of trait combinations has received little attention in bryophyte communities. This perspective is especially relevant because bryophytes are highly sensitive to forest structure, substrate continuity, microclimate, and management intensity [18,22,23], making trait-based analyses particularly valuable for conservation-oriented forest management. The growing availability of standardized bryophyte trait databases, such as BryForTrait [38] and the Bryophytes of Europe Traits (BET) dataset [37], further increases the potential for interregional comparisons and synthesis across forest systems by making trait information more accessible and comparable among studies. In this sense, functional trait composition can help reveal whether forest management or forest conversion reduces the diversity of ecological strategies supported by bryophyte assemblages and may therefore provide ecologically meaningful information for the conservation of bryophyte diversity in managed forests [30].
Conifer plantations typically exhibit more homogeneous canopy structure and thicker litter layers compared to mature deciduous stands. These conditions may reduce microhabitat diversity and favor a narrower subset of functional strategies, potentially leading to lower functional diversity. In contrast, structurally heterogeneous forests with developed shrub layers may provide a wider array of ecological niches, promoting coexistence of multiple functional strategies. In this study, we investigated bryophyte communities across mature beech forests and conifer plantations to assess how environmental gradients and stand structure influence functional patterns of bryophyte diversity. Specifically, we addressed the following questions: (i) Do forest structural and edaphic variables explain variation in bryophyte community composition? (ii) Are shifts in bryophyte functional trait composition associated with structural and edaphic differences between natural beech forests and pine plantations? (iii) Does the replacement of native beech forests with non-native pine plantations lead to reduced functional diversity, as measured by functional dispersion, indicating functional homogenization of bryophyte communities? By integrating environmental predictors and functional trait data, this study aimed to evaluate whether forest conversion alters the functional diversity of bryophyte assemblages.

2. Materials and Methods

2.1. Study Site and Data Collection

This study was conducted within a network of forest research plots established in temperate forest stands of Fruška Gora Mountain (northern Serbia) (Figure 1A,B). The plots are part of a long-term ecological research framework designed to investigate epigeic bryophyte communities under contrasting forest structural conditions and management histories.
The study system comprises mixed broadleaved stands dominated by Fagus sylvatica L. and coniferous plantations dominated by Pinus nigra J.F. Arnold. These forest types represent native deciduous stands and secondary coniferous reforestation, respectively, thereby providing a structural and environmental gradient suitable for examining community assembly processes. Seven localities on Fruška gora Mt. were selected: four mixed broadleaved forests dominated by F. sylvatica, i.e., Stražilovo (ST), Papratski do (PD), Lazin Vir (LV), and Dumbovo (D), and three Austrian pine plantations, i.e., Jazak (J), Kanov breg (KB), and Vrdnik (V). The seven localities were selected from the existing permanent-plot network because they represented the two contrasting forest types of interest within the same regional landscape, namely native broadleaved stands dominated by Fagus sylvatica and secondary Austrian pine plantations dominated by Pinus nigra. This selection allowed comparison of bryophyte assemblages across stands differing in structure and associated environmental conditions while minimizing broad-scale climatic variation because all sites are located within the same mountain system. In addition, only localities with comparable permanent-plot designs and field sampling frameworks were included, ensuring methodological consistency across sites.
The sampling design, geographic locations, and field protocol follow the spatial framework previously described for the same research system [18]. Within each locality, five permanent plots measuring 100 × 100 m were established in interior parts of the stand representative of the target forest type, while avoiding stand edges and obvious local disturbance. Within each permanent plot, five 10 × 10 m subplots were arranged in a standardized design, with one subplot in the center and four in the plot corners (Figure 1C,D). Subplots constituted the basic unit of ecological measurement. The nested design (subplots within plots within localities) was explicitly accounted for in subsequent statistical analyses to avoid pseudoreplication.

2.2. Bryophyte Survey and Taxonomic Standardization

All bryophyte species occurring within each subplot were recorded and their percentage cover visually estimated. Collected bryophytes were identified in the field or laboratory using standard identification keys. All field records and cover estimates were made by the same observer, which reduced inter-observer variation in species recognition and cover attribution. Taxa requiring confirmation were collected in the field and examined microscopically. Percentage cover was visually estimated at the subplot level based on field recognition of taxa, with laboratory verification applied where necessary. Nomenclature follows Hodgetts et al. [40]. A list of species found on all investigated sites is available in Ilić et al. [18]. Species cover values were used for all community-level analyses.

2.3. Environmental Data

Environmental parameters were measured for each subplot to characterize soil conditions, vegetation structure, and local habitat configuration. The 10 × 10 m subplot was treated as the basic analytical unit for environmental measurements. Within each subplot, soil was sampled at five positions (the center and four corners) in the upper 10 cm of the topsoil. These within-subplot measurements represented spatial subsamples rather than independent replicates, and their mean was used as the subplot-level value in subsequent analyses. Across the seven localities, this resulted in 35 subplot-level replicates in total. Soil pH and soil temperature (st) were measured in situ using a direct soil pH/temperature meter (Hanna Instruments Inc., Woonsocket, RI, USA), whereas soil moisture (sm) was determined gravimetrically [41]. Soil moisture and soil temperature were measured three times during the main growing season, specifically in May, July, and September. Mean values across these sampling dates were used in subsequent analyses in order to characterize plot-level environmental conditions during the biologically most relevant part of the year for epigeic bryophyte communities. These variables should therefore be interpreted as growing-season descriptors rather than as year-round environmental averages. All subplot-level environmental variables used in the analyses are provided in Supplementary Table S1. Herbaceous cover (hc) and litter cover (lc) were determined on every subplot and expressed as coverage (%). Stream distance (sd) was measured using field mapping and GIS verification as the distance (m) between the center of the subplot and the closest stream. Tree number (tn) and number of shrubs (bn) were counted on each subplot.

2.4. Bryophyte Functional Traits

Functional traits were compiled for all recorded bryophyte species and included different autecological, morphological, and reproductive traits (Table 1). Trait selection was guided by ecological relevance to moisture, light, substrate chemistry, life-history strategy, and dispersal capacity—factors known to influence bryophyte responses to forest structural gradients. We obtained trait data from the BET dataset [37]. The mean data completeness across all selected traits was 98.4%. Mixed variable types (ordinal, categorical, numerical) were retained in their original form. The functional trait matrix used in the analyses is provided in Supplementary Table S4.

2.5. Statistical Analyses

2.5.1. Community Data Transformation and Gradient Assessment

Continuous environmental variables were standardized (z-transformation) prior to analysis. Count variables (tree and shrub number) were log1p-transformed to improve distributional symmetry before scaling. To reduce multicollinearity among structurally related predictors (soil moisture, litter cover, tree number, distance to stream), we applied principal component analysis (PCA) to standardized variables. The first principal component (grad_PC1) explained 70.2% of the total variance and was retained as a composite structural–moisture gradient, with positive loadings for litter cover (0.573) and tree density (0.503) and negative loadings for soil moisture (−0.551) and distance to stream (−0.339) (Tables S2 and S3). Variance inflation factors (VIFs) were calculated to ensure acceptable levels of collinearity (VIF < 5).
Species cover data were Hellinger-transformed prior to ordination analyses to reduce the influence of double-zero inflation and to allow the application of linear multivariate methods [44]. To evaluate gradient length and justify ordination method selection, detrended correspondence analysis (DCA) was performed. The first-axis length (2.7 SD units) indicated predominantly linear species responses, supporting the use of redundancy analysis (RDA) [45]. Sensitivity analyses excluding rare species (occurring in fewer than two plots) were conducted to assess the robustness of multivariate patterns.

2.5.2. Species–Environment Relationships

Redundancy analysis (RDA) was used to assess the influence of environmental predictors on bryophyte community composition. The constrained model included soil pH, soil temperature, herbaceous cover, number of shrubs, and the composite structural–moisture gradient (grad_PC1).
Because subplots were nested within localities, permutation tests were restricted within sites (stratified permutations) to preserve the hierarchical structure of the sampling design and avoid inflation of Type I error. Model significance was assessed using 999 restricted permutations. Both overall model significance and marginal effects of individual predictors were evaluated using permutation-based ANOVA. Adjusted R2 values were reported as measures of explained variation.

2.5.3. Trait–Environment Relationships

Prior to functional analyses, traits were standardized using Gower distance to accommodate mixed data types and ensure comparable scaling across variables.
Trait–environment associations were examined using RLQ analysis [46], which integrates R: environmental matrix (plots × predictors), L: species composition matrix (plots × species), and Q: species trait matrix (species × traits).
Correspondence analysis (CA) was applied to L, PCA to R, and Hill–Smith analysis to Q to accommodate mixed trait types. Global significance of the trait–environment relationship was tested using 9999 permutations under appropriate permutation models.
Complementary fourth-corner analyses were performed to identify pairwise associations between environmental predictors and functional traits. Because the fourth-corner procedure entails multiple simultaneous tests, it increases the risk of inflated Type I error rates; therefore, adjustment for multiple comparisons is required. In this study, significance levels were corrected using the false discovery rate (FDR) approach with α = 0.05 [47,48,49].

2.5.4. Functional Diversity Analysis

Functional diversity at the subplot level was quantified using Rao’s quadratic entropy (RaoQ [50]), calculated from Gower-based functional distance matrices and weighted by species relative cover. RaoQ was selected because it accommodates mixed trait types, does not rely on convex hull geometry, and remains stable in communities with moderate species richness.
Subplots containing fewer than two species were excluded from functional diversity analyses.
Differences in Rao’s quadratic entropy (RaoQ) between forest types were tested using a linear mixed-effects model (LMM), with forest type as a fixed effect and site included as a random effect to account for the non-independence of plots within sites. Environmental drivers of functional diversity were assessed using linear models: RaoQ∼pH+st+hc+bn+grad_PC1. Model assumptions were evaluated through diagnostic plots (residual vs. fitted; Q-Q), the Shapiro–Wilk test for normality, and the Breusch–Pagan test for heteroscedasticity. Independent effects of predictors were evaluated using Type II ANOVA.

2.5.5. Statistical Software

All analyses were conducted in R (version 4.5.0) using the packages vegan [49], ade4 [51], FD [52,53], lme4 [54], and car [55]. Permutation-based tests used 999 or 9999 permutations. Statistical significance was assessed at α = 0.05. Effect sizes and adjusted R2 values are reported alongside p-values.

3. Results

3.1. Species Composition Along Environmental Gradients

The constrained RDA model, including soil pH, soil temperature, herbaceous cover, shrub number, and the stand structural–moisture gradient (grad_PC1), explained 36.2% of the total variance in the Hellinger-transformed species matrix (adjusted R2 = 0.342). The overall model was highly significant based on permutation tests (pseudo-F = 5.9667, p = 0.001, 999 permutations).
All predictors contributed significantly to variation in species composition (permutation test by term, Table 2). Furthermore, variance inflation factors (Table 2) were below 3.1 for all predictors, indicating low multicollinearity.
The first two RDA axes captured the major compositional gradients and separated beech forests from conifer plantations along the structural–moisture gradient and pH axis (Figure 2).

3.2. Trait–Environment Coupling

RLQ analysis (Figure 3) identified a structured association between environmental gradients and species functional traits. The global test under permutation model 2 was highly significant (p < 0.001), indicating a non-random trait–environment coupling. However, under permutation model 4, the global association was not significant (p = 0.17), suggesting that part of the detected signal may be mediated by species composition structure.
Fourth-corner analysis revealed multiple significant trait–environment associations at the nominal level (19 tests with p < 0.05; 4 with p < 0.01). However, after FDR correction for multiple testing, none of the pairwise associations remained significant (adjusted p > 0.05), indicating that trait–environment relationships were generally diffuse rather than driven by a small number of strong associations (Table S5).
Overall, the RLQ ordination showed that structural gradients were aligned with shifts in growth form, life strategy, and ecological indicator values.

3.3. Functional Diversity Patterns

Functional diversity, measured as Rao’s quadratic entropy (RaoQ), differed strongly between forest types (Figure 4). Mean RaoQ in beech forests was 0.294, compared to 0.135 in conifer plantations (LMM: p = 0.004). The 95% confidence interval for the difference ranged from 0.131 to 0.187.

3.4. Environmental Drivers of Functional Diversity

Linear modeling revealed a strong environmental control over bryophyte functional diversity. The full model explained 62.4% of the variance in Rao’s quadratic entropy (R2 = 0.624; adjusted R2 = 0.612; F5,29 = 29.21, p < 0.001; n = 35). Type II ANOVA indicated that two predictors exerted significant independent effects on functional diversity (Table 3). The number of shrubs (bn) had a significant positive effect (F = 8.35, p = 0.007), whereas the stand structural–moisture gradient (grad_PC1) showed a strong negative effect (F = 14.68, p < 0.001). In contrast, soil pH (F = 3.95, p = 0.056), soil temperature (F = 4.16, p = 0.051), and herbaceous cover (F = 0.73, p = 0.400) did not show statistically significant independent effects after accounting for other predictors.
Regression coefficients confirmed these patterns. The number of shrubs was positively associated with functional diversity (β = 0.037, SE = 0.013, p = 0.007), indicating that structurally heterogeneous understories support higher trait dispersion. Conversely, the structural–moisture gradient had a significant negative effect (β = −0.035, SE = 0.009, p < 0.001), suggesting a decline in functional diversity along increasing structural homogenization and litter dominance (Figure 5).
Soil pH (β = 0.023, p = 0.056) and soil temperature (β = 0.019, p = 0.051) showed marginal trends toward positive effects, whereas herbaceous cover had no detectable influence (β = 0.008, p = 0.400). Confidence intervals for pH and temperature overlapped zero, indicating weak or context-dependent effects (Table 4).

4. Discussion

4.1. Environmental Control of Bryophyte Community Composition

Our previous study in the same forest system demonstrated that local environmental conditions, particularly moisture-related and structural variables, are major determinants of forest-floor bryophyte diversity [18]. The present results extend that ecological framework from diversity patterns to community composition and its functional interpretation, showing that soil pH, soil temperature, herbaceous cover, shrub number, and the composite structural–moisture gradient jointly contribute to the organization of bryophyte assemblages. Among these drivers, soil pH is widely recognized as a key ecological filter because it influences substrate chemistry and nutrient availability, thereby constraining species establishment across forest habitats [56,57,58,59]. At the same time, moisture regime, litter accumulation, and stand structure regulate microclimatic buffering, substrate continuity, and microsite heterogeneity, all of which are fundamental for bryophyte occurrence and turnover [18,19,60,61]. Rather than reiterating these environmental relationships in detail, the main implication of our results is that they define the ecological setting in which functional differentiation emerges between mature beech forests and pine plantations. In this context, variation in litter cover, understory structure, and moisture-related conditions is likely to filter coordinated trait syndromes, particularly those associated with growth form, life strategy, forest affinity, and ecological indicator values. Such an interpretation is consistent with previous evidence showing that forest management and stand structural heterogeneity influence bryophyte assemblages not only through compositional change, but also through shifts in the range of ecological strategies supported by the forest floor [1,19,30].

4.2. Trait-Environment Coupling and Functional Filtering

RLQ analysis revealed a structured coupling between environmental gradients and functional traits. While global significance was supported under permutation model 2, the lack of strong pairwise trait–environment associations after FDR correction indicates that filtering operates through multivariate trait syndromes rather than single-trait effects. This pattern aligns with the concept of functional syndromes, where combinations of traits respond jointly to environmental filtering [31,62,63,64,65,66]. In bryophytes, such syndromes often integrate growth form, life strategy, reproductive mode, and ecological indicator values [11]. In our study, the RLQ ordination indicated that the structural-moisture gradient was associated with coordinated shifts in these trait combinations. Communities in structurally complex beech stands were generally associated with stress-tolerant, forest-affiliated taxa adapted to buffered and relatively stable microclimatic conditions. In contrast, communities in structurally simplified pine plantations were more frequently associated with ruderal or disturbance-tolerant species. This pattern reflects altered microclimatic stability and substrate continuity in plantations. Dense litter layers and homogeneous canopy conditions can reduce substrate diversity and limit colonization niches for species requiring microsite heterogeneity. Previous studies have reported higher bryophyte cover in some conifer forests [60], suggesting that conifer stands may locally promote bryophyte abundance. However, our results indicate that such systems may nevertheless support functionally different communities characterized by trait combinations associated with disturbance tolerance and ecological generalism. Thus, rather than strong filtering on a single trait (e.g., spore size [59]), the observed environmental gradients appear to select for coherent functional syndromes combining morphology, life strategy, and habitat affinity.

4.3. Functional Differentiation and Homogenization Between Beech Forests and Pine Plantations

The obtained results can be interpreted through three interacting mechanisms: (i) mature beech forests likely provide more buffered humidity regimes and reduced microclimatic fluctuations, favoring pleurocarpous, perennial, and forest-affiliated bryophyte assemblages; (ii) greater structural heterogeneity increases the availability of microhabitats (e.g., decaying wood, variable litter depth, shaded soil patches), supporting trait divergence and higher RaoQ; and (iii) conifer plantations, often characterized by uniform canopy and litter layers, may favor ruderal and opportunistic species with fast colonization traits (e.g., small size, higher dispersal capacity), reducing overall trait dispersion.
The contrast between forest types was not limited to lower Rao’s quadratic entropy in pine plantations but also involved a broader shift in the functional structure of bryophyte assemblages. Mature beech forests supported functionally richer communities, whereas pine plantations were characterized by a narrower functional space, consistent with functional homogenization under structurally simplified stand conditions. This pattern indicates that forest conversion affects not only species composition but also the diversity of ecological strategies represented in the bryophyte layer. It also suggests that the lower richness and diversity previously reported for forest-floor bryophytes in pine plantations in the same study system [18] are accompanied by a parallel reduction in functional differentiation.
The RLQ results suggest that these differences are best interpreted as coordinated shifts in functional syndromes rather than as strong single-trait responses. Bryophyte assemblages in mature beech forests were associated with trait combinations typical of more buffered and heterogeneous forest-floor environments, including stronger forest affinity, perennial and stress-tolerant life strategies, and growth forms linked to persistent occupation of moist and structurally diverse microsites. In contrast, pine plantations were associated with trait combinations more consistent with ecological generalism and disturbance-tolerant or colonist-type strategies, together with forms better adapted to litter-dominated and structurally more homogeneous conditions. In this sense, the replacement of mature beech stands by pine plantations appears to reduce the range of viable bryophyte functional strategies supported at the forest floor. More specifically, this functional contrast was expressed mainly through shifts in growth form, life strategy, forest affinity, and ecological indicator-related trait combinations, with mature beech forests favoring pleurocarpous, mat- or weft-forming, perennial or stress-tolerant, forest-affiliated assemblages, and pine plantations favoring more acrocarpous, turf-forming, colonist or disturbance-tolerant assemblages.
This interpretation is further supported by the environmental model, in which the structural-moisture gradient had a strong negative effect on functional diversity, while shrub number had a significant positive effect. Together, these results indicate that litter accumulation and structural homogenization constrain trait dispersion, whereas increased understory complexity promotes the coexistence of multiple bryophyte functional strategies through greater microsite heterogeneity. Such patterns are consistent with previous studies showing that structurally simplified or management-modified stands tend to reduce ecological niche diversity and promote compositional or functional homogenization [67,68,69,70]. Likewise, the negative role of litter-dominated conditions agrees with reports that litter accumulation in pine systems can suppress bryophyte diversity [71], whereas greater shrub-layer complexity may increase the availability of microhabitats relevant for cryptogam persistence [72]. Overall, our results indicate that mature beech forests and pine plantations differ not only in the amount of bryophyte functional diversity they support, but also in the dominant functional strategies that characterize their assemblages.

5. Conclusions

Our results show that bryophyte functional diversity in temperate forests is shaped by forest type, stand structural heterogeneity, and local edephic conditions. Native beech forests supported higher functional diversity than pine plantations, indicating that the replacement of structurally complex broadleaved stands by more homogeneous conifer plantations reduces not only species diversity, but also the breadth of ecological strategies maintained at the forest floor.
These findings highlight the importance of retaining structurally heterogeneous native forest stands for the conservation of bryophyte diversity and function. In this context, trait-based approaches provide information beyond taxonomic composition by revealing which ecological strategies persist and which are lost under contrasting forest conditions and management regimes. Because bryophyte functional traits are linked to key ecological roles such as water retention, nutrient cycling, and microclimate regulation, reduced functional diversity may also imply lower ecological resilience of forest-floor communities.
Although trait–environment relationships were more evident at the level of multivariate syndromes than in individual fourth-corner associations after multiple-testing correction, the overall pattern was consistent. Since this study was restricted to one regional forest system, future research should test whether similar patterns occur across other forest types, management regimes, and biogeographic regions, and should further evaluate how bryophyte functional traits relate to directly measured ecosystem processes.
Overall, our study shows that conserving structurally complex native forests is essential not only for preserving bryophyte diversity but also for maintaining the functional breadth and ecological resilience of forest-floor communities.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f17040506/s1. Table S1. Subplot-level environmental data used in the analyses; Table S2. PCA loadings of structurally related predictors used to derive the composite stand structural–moisture gradient (grad_PC1); Table S3. Eigenvalues and variance explained by principal components derived from PCA of stand structural variables; Table S4. Functional trait matrix for bryophyte species included in the analyses; Table S5. Significant trait–environment associations identified by fourth-corner analysis.

Author Contributions

Conceptualization, M.I. and D.V.; methodology, M.I. and D.V.; formal analysis, M.I.; investigation, M.I. and M.Ć.; data curation, M.I.; writing—original draft preparation, M.I. and D.V.; writing—review and editing, M.I., M.Ć., and D.V. All authors have read and agreed to the published version of the manuscript.

Funding

The authors gratefully acknowledge the financial support of the Ministry of Science, Technological Development, and Innovation of the Republic of Serbia (Grants No. 451-03-33/2026-03/200125 & 451-03-34/2026-03/200125).

Data Availability Statement

Data are available from the authors by reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Müller, J.; Boch, S.; Prati, D.; Socher, S.A.; Pommer, U.; Hessenmöller, D.; Schulze, E.-D.; Fischer, M. Effects of forest management on bryophyte diversity and composition in temperate forests. For. Ecol. Manag. 2019, 432, 850–859. [Google Scholar] [CrossRef]
  2. Stefańska-Krzaczek, E.; Swacha, G.; Żarnowiec, J.; Raduła, M.W.; Kącki, Z.; Staniaszek-Kik, M. Central European Forest floor bryophytes: Richness, species composition, coexistence and diagnostic significance across environmental gradients of forest habitats. Ecol. Indic. 2022, 139, 108954. [Google Scholar] [CrossRef]
  3. Tang, Q.; Liu, Y.; Lou, Y.; Ho, B.-C.; Jin, X.; Wei, Y. Bryophyte diversity, distribution pattern and their environmental drivers in the forest along the vertical gradient of a mature tiankeng, China. Glob. Ecol. Conserv. 2025, 62, e03821. [Google Scholar] [CrossRef]
  4. Rydin, H. Population and Community Ecology of Bryophytes. In Bryophyte Biology; Goffinet, B., Shaw, J.A., Eds.; Cambridge University Press: Cambridge, UK, 2009; pp. 445–486. [Google Scholar]
  5. Vitt, D.H.; Wieder, R.K. The Structure and Function of Bryophyte-Dominated Peatlands. In Bryophyte Biology, 2nd ed.; Goffinet, B., Shaw, A.J., Eds.; Cambridge University Press: Cambridge, UK, 2009; pp. 357–391. [Google Scholar]
  6. Eldridge, D.J.; Guirado, E.; Reich, P.B.; Ochoa-Hueso, R.; Berdugo, M.; Sáez-Sandino, T.; Blanco-Pastor, J.L.; Tedersoo, L.; Plaza, C.; Ding, J.; et al. The global contribution of soil mosses to ecosystem services. Nat. Geosci. 2023, 16, 430–438. [Google Scholar] [CrossRef]
  7. Elbert, W.; Weber, B.; Burrows, S.; Steinkamp, J.; Büdel, B.; Andreae, M.O.; Pöschl, U. Contribution of cryptogamic covers to the global cycles of carbon and nitrogen. Nat. Geosci. 2012, 5, 459–462. [Google Scholar] [CrossRef]
  8. Porada, P.; Weber, B.; Elbert, W.; Pöschl, U.; Kleidon, A. Estimating impacts of lichens and bryophytes on global biogeochemical cycles. Glob. Biogeochem. Cycles 2014, 28, 71–85. [Google Scholar] [CrossRef]
  9. Kolari, P.; Pumpanen, J.; Rannik, Ü.; Ilvesniemi, H.; Hari, P.; Berninger, F. Forest floor vegetation plays an important role in photosynthetic production of boreal forests. For. Ecol. Manag. 2006, 221, 241–248. [Google Scholar] [CrossRef]
  10. Ah-Peng, C.; Cardoso, A.W.; Flores, O.; West, A.; Wilding, N.; Strasberg, D.; Hedderson, T.A.J. The role of epiphytic bryophytes in interception, storage, and regulated release of atmospheric moisture in a tropical montane cloud forest. J. Hydrol. 2017, 548, 665–673. [Google Scholar] [CrossRef]
  11. Glime, J.M. Bryophyte Ecology; Michigan Technological University: Houghton, MI, USA, 2017; Available online: https://digitalcommons.mtu.edu/oabooks/4/ (accessed on 20 February 2026).
  12. Fenton, N.J.; Lecomte, N.; Légaré, S.; Bergeron, Y. Paludification in black spruce (Picea mariana) forests of eastern Canada: Potential factors and management implications. For. Ecol. Manag. 2005, 213, 151–159. [Google Scholar] [CrossRef]
  13. Sun, S.-Q.; Wu, Y.-H.; Wang, G.-X.; Zhou, J.; Yu, D.; Bing, H.-J.; Luo, J. Bryophyte species richness and composition along an altitudinal gradient in Gongga Mountain, China. PLoS ONE 2013, 8, e58131. [Google Scholar] [CrossRef] [PubMed]
  14. Heilmann-Clausen, J.; Aude, E.; van Dort, K.; Christensen, M.; Piltaver, A.; Veerkamp, M.; Walleyn, R.; Siller, I.; Standovár, T.; Ódor, P. Communities of wood-inhabiting bryophytes and fungi on dead beech logs in Europe—Reflecting substrate quality or shaped by climate and forest conditions? J. Biogeogr. 2014, 41, 2269–2282. [Google Scholar] [CrossRef]
  15. Alegro, A.; Šegota, V.; Rimac, A.; Papp, B. Diversity, ecology and phytogeography of bryophytes across temperate forest communities—Insight from Mt. Papuk (Croatia, SE Europe). Plants 2023, 12, 3346. [Google Scholar] [CrossRef] [PubMed]
  16. Tinya, F.; Márialigeti, S.; Király, I.; Németh, B.; Ódor, P. The effect of light conditions on herbs, bryophytes and seedlings of temperate mixed forests in Őrség, Western Hungary. Plant Ecol. 2009, 204, 69–81. [Google Scholar] [CrossRef]
  17. Silva, A.M.; Oliveira, R.R.; Conceição, G.M. Musgos (Bryophyta) do Morro do Alecrim, centro urbano de Caxias, Maranhão, Brasil. Rev. Arq. Cient. 2018, 1, 55–62. [Google Scholar] [CrossRef]
  18. Ilić, M.; Igić, R.; Ćuk, M.; Veljić, M.; Radulović, S.; Orlović, S.; Vukov, D. Environmental drivers of ground-floor bryophytes diversity in temperate forests. Oecologia 2023, 202, 275–285. [Google Scholar] [CrossRef]
  19. Márialigeti, S.; Németh, B.; Tinya, F.; Ódor, P. The effects of stand structure on ground-floor bryophyte assemblages in temperate mixed forests. Biodivers. Conserv. 2009, 18, 2223–2241. [Google Scholar] [CrossRef]
  20. Király, I.; Ódor, P. The effect of stand structure and tree species composition on epiphytic bryophytes in mixed deciduous–coniferous forests of Western Hungary. Biol. Conserv. 2010, 143, 2063–2069. [Google Scholar] [CrossRef]
  21. Erfanzadeh, R. Impact of phanerogam and soil characteristics on bryophyte assemblages with respect to restoration practices (case study: IJzermonding, Belgium). Ecopersia 2013, 1, 41–51. [Google Scholar]
  22. Czerepko, J.; Gawryś, R.; Mańk, K.; Janek, M.; Tabor, J.; Skalski, Ł. The influence of forest management in the Białowieża Forest on the species structure of the forest community. For. Ecol. Manag. 2021, 496, 119363. [Google Scholar] [CrossRef]
  23. Paillet, Y.; Bergès, L.; Hjältén, J.; Ódor, P.; Avon, C.; Bernhardt-Römermann, M.; Bijlsma, R.J.; de Bruyn, L.; Fuhr, M.; Grandin, U.; et al. Biodiversity differences between managed and unmanaged forests: Meta-analysis of species richness in Europe. Conserv. Biol. 2010, 24, 101–112. [Google Scholar] [CrossRef] [PubMed]
  24. Nihlgård, B. Pedological influence of spruce planted on former beech forest soils in Scania, South Sweden. Oikos 1971, 22, 302–314. [Google Scholar] [CrossRef]
  25. Buck, J.R.; Clair, S.B.S. Aspen increase soil moisture, nutrients, organic matter and respiration in Rocky Mountain Forest communities. PLoS ONE 2012, 7, e52369. [Google Scholar] [CrossRef]
  26. Vukov, D.; Galić, Z.; Rućando, M.; Ilić, M.; Ćuk, M.; Igić, D.; Igić, R.; Orlović, S. Effects of Natural Broadleaved Regeneration vs. Conifer Restoration on the Herb Layer and Microclimate. Arch. Biol. Sci. 2016, 68, 483–493. [Google Scholar] [CrossRef]
  27. Rola, K.; Plášek, V.; Rożek, K.; Zubek, S. Effect of tree species identity and related habitat parameters on understorey bryophytes—Interrelationships between bryophyte, soil and tree factors in a 50-year-old experimental forest. Plant Soil 2021, 466, 613–630. [Google Scholar] [CrossRef]
  28. Stanton, D.E.; Coe, K.K. 500 million years of charted territory: Functional ecological traits in bryophytes. Bryophyt. Divers. Evol. 2021, 43, 234–252. [Google Scholar] [CrossRef]
  29. Coe, K.K.; Carter, B.; Slate, M.; Stanton, D. Moss Functional Trait Ecology: Trends, Gaps, and Biases in the Current Literature. Am. J. Bot. 2024, 111, e16288. [Google Scholar] [CrossRef] [PubMed]
  30. Kutnar, L.; Kermavnar, J.; Sabovljević, M.S. Bryophyte diversity, composition and functional traits in relation to bedrock and tree species composition in close-to-nature managed forests. Eur. J. For. Res. 2023, 142, 865–882. [Google Scholar] [CrossRef]
  31. Chen, Y.; Niu, S.; Li, P.; Jia, H.; Wang, H.; Ye, Y.; Yuan, Z. Stand Structure and Substrate Diversity as Two Major Drivers for Bryophyte Distribution in a Temperate Montane Ecosystem. Front. Plant Sci. 2017, 8, 874. [Google Scholar] [CrossRef]
  32. Slate, M.L.; Rosenstiel, T.N.; Eppley, S.M. Sex-Specific Morphological and Physiological Differences in the Moss Ceratodon purpureus (Dicranales). Ann. Bot. 2017, 120, 845–854. [Google Scholar] [CrossRef] [PubMed]
  33. Moore, J.D.; Kollar, L.M.; McLetchie, D.N. Does Selection for Gamete Dispersal and Capture Lead to a Sex Difference in Clump Water-Holding Capacity? Am. J. Bot. 2016, 103, 1449–1457. [Google Scholar] [CrossRef]
  34. Castetter, R.C.; McLetchie, D.N.; Eppley, S.M.; Stark, L.R. Sex Ratio and Sex Expression in an Urban Population of the Silver Moss, Bryum argenteum Hedw. J. Bryol. 2019, 41, 227–235. [Google Scholar] [CrossRef]
  35. Horvat, V.; Heras, P.; García-Mijangos, I.; Biurrun, I. Intensive Forest Management Affects Bryophyte Diversity in the Western Pyrenean Silver Fir-Beech Forests. Biol. Conserv. 2017, 215, 81–91. [Google Scholar] [CrossRef]
  36. Żarnowiec, J.; Staniaszek-Kik, M.; Chmura, D. Trait-Based Responses of Bryophytes to the Decaying Logs in Central European Mountain Forests. Ecol. Indic. 2021, 126, 107671. [Google Scholar] [CrossRef]
  37. van Zuijlen, K.; Nobis, M.P.; Hedenäs, L.; Hodgetts, N.; Alarcón, J.A.C.; Albertos, B.; Bernhardt-Römermann, M.; Gabriel, R.; Garilleti, R.; Lara, F.; et al. Bryophytes of Europe traits (BET) data set: A fundamental tool for ecological studies. J. Veg. Sci. 2023, 34, e13179. [Google Scholar] [CrossRef]
  38. Bernhardt-Römermann, M.; Poschlod, P.; Hentschel, J. BryForTrait—A Life-History Trait Database of Forest Bryophytes. J. Veg. Sci. 2018, 29, 798–800. [Google Scholar] [CrossRef]
  39. Ilić, M.; Igić, R.; Ćuk, M.; Vukov, D. Field Sampling Methods for Investigating Forest-Floor Bryophytes: Microcoenose vs. Random Sampling. Arch. Biol. Sci. 2018, 70, 589–598. [Google Scholar] [CrossRef]
  40. Hodgetts, N.G.; Söderström, L.; Blockeel, T.L.; Caspari, S.; Ignatov, M.S.; Konstantinova, N.A.; Lockhart, N.; Papp, B.; Schröck, C.; Sim-Sim, M.; et al. An Annotated Checklist of Bryophytes of Europe, Macaronesia and Cyprus. J. Bryol. 2020, 42, 1–116. [Google Scholar] [CrossRef]
  41. ISO 11461:2001; Soil Quality—Determination of Soil Water Content as a Volume Fraction Using Coring Sleeves—Gravimetric Method. International Organization for Standardization: Geneva, Switzerland, 2001.
  42. Mägdefrau, K. Life-forms of bryophytes. In Bryophyte Ecology; Smith, A.J.E., Ed.; Springer: Dordrecht, The Netherlands, 1982; pp. 45–58. [Google Scholar] [CrossRef]
  43. During, H.J. Ecological classifications of bryophytes and lichens. In Bryophytes and Lichens in a Changing Environment; Bates, J.W., Farmer, A.M., Eds.; Clarendon Press: Oxford, UK; New York, NY, USA, 1992; pp. 1–25. [Google Scholar]
  44. Legendre, P.; Gallagher, E.D. Ecologically meaningful transformations for ordination of species data. Oecologia 2001, 129, 271–280. [Google Scholar] [CrossRef]
  45. Šmilauer, P.; Lepš, J. Multivariate Analysis of Ecological Data Using CANOCO 5, 2nd ed.; Cambridge University Press: Cambridge, UK, 2014. [Google Scholar]
  46. Dolédec, S.; Chessel, D.; ter Braak, C.J.F.; Champely, S. Matching species traits to environmental variables: A new three-table ordination method. Environ. Ecol. Stat. 1996, 3, 143–166. [Google Scholar] [CrossRef]
  47. Dray, S.; Legendre, P. Testing the species traits–environment relationships: The fourth-corner problem revisited. Ecology 2008, 89, 3400–3412. [Google Scholar] [CrossRef] [PubMed]
  48. ter Braak, C.J.F.; Cormont, A.; Dray, S. Improved testing of species traits–environment relationships in the fourth-corner problem. Ecology 2012, 93, 1525–1526. [Google Scholar] [CrossRef]
  49. Oksanen, J.; Simpson, G.; Blanchet, F.; Kindt, R.; Legendre, P.; Minchin, P.; O’Hara, R.; Solymos, P.; Stevens, M.; Szoecs, E.; et al. Vegan: Community Ecology Package; R Package Version 2.7-2; R Foundation: Vienna, Austria, 2025; Available online: https://CRAN.R-project.org/package=vegan (accessed on 15 February 2026).
  50. Rao, C.R. Diversity and dissimilarity coefficients: A unified approach. Theor. Popul. Biol. 1982, 21, 24–43. [Google Scholar] [CrossRef]
  51. Dray, S.; Dufour, A.B. The ade4 package: Implementing the duality diagram for ecologists. J. Stat. Softw. 2007, 22, 1–20. [Google Scholar] [CrossRef]
  52. Laliberté, E.; Legendre, P. A distance-based framework for measuring functional diversity from multiple traits. Ecology 2010, 91, 299–305. [Google Scholar] [CrossRef]
  53. Laliberté, E.; Legendre, P.; Shipley, B. FD: Measuring Functional Diversity from Multiple Traits and Other Tools for Functional Ecology; R Package Version 1.0-12.3; R Foundation: Vienna, Austria, 2023. [Google Scholar]
  54. Bates, D.; Maechler, M.; Bolker, B.; Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 2015, 67, 1–48. [Google Scholar] [CrossRef]
  55. Fox, J.; Weisberg, S. An R Companion to Applied Regression, 3rd ed.; Sage: Thousand Oaks, CA, USA, 2019; Available online: https://www.john-fox.ca/Companion/ (accessed on 12 February 2026).
  56. Turetsky, M.R. The role of bryophytes in carbon and nitrogen cycling. Bryologist 2003, 106, 395–409. [Google Scholar] [CrossRef]
  57. Ren, J.; Liu, F.; Luo, Y.; Zhu, J.; Luo, X.Q.; Liu, R. The pioneering role of bryophytes in ecological restoration of manganese waste residue areas, southwestern China. J. Chem. 2021, 2021, 9969253. [Google Scholar] [CrossRef]
  58. Concostrina-Zubiri, L.; Berdugo, M.; Valencia, E.; Mendoza, B.J.; Maestre, F.T. Decomposition of dryland biocrust-forming lichens and mosses contributes to soil nutrient cycling. Plant Soil 2022, 481, 23–34. [Google Scholar] [CrossRef]
  59. Gu, J.; Song, X.; Ye, Y.; Shao, X.; Liao, Y.; Shao, X. Exploring the driving factors of bryophyte assemblage distribution patterns in Tibet. Front. Ecol. Evol. 2024, 12, 1376263. [Google Scholar] [CrossRef]
  60. Bartels, S.F.; Caners, R.T.; Ogilvie, J.; White, B.; Macdonald, S.E. Relating bryophyte assemblages to a remotely sensed depth-to-water index in boreal forests. Front. Plant Sci. 2018, 9, 858. [Google Scholar] [CrossRef]
  61. Startsev, N.; Lieffers, V.J.; Landhäusser, S.M. Effects of leaf litter on the growth of boreal feather mosses: Implications for forest floor development. J. Veg. Sci. 2008, 19, 253–260. [Google Scholar] [CrossRef]
  62. Violle, C.; Navas, M.-L.; Vile, D.; Kazakou, E.; Fortunel, C.; Hummel, I.; Garnier, E. Let the Concept of Trait Be Functional! Oikos 2007, 116, 882–892. [Google Scholar] [CrossRef]
  63. Possen, B.J.H.M.; Oksanen, E.; Rousi, M.; Ruhanen, H.; Heinonen, J.; Heiskanen, J.; Kärenlampi, S.; Vapaavuori, E. Trait syndromes underlying stand-level differences in growth and acclimation in 10 silver birch (Betula pendula Roth) genotypes. For. Ecol. Manag. 2015, 343, 87–98. [Google Scholar] [CrossRef]
  64. Mazziotta, A.; Granath, G.; Rydin, H.; Bengtsson, F.; Norberg, J. Scaling functional traits to ecosystem processes: Towards a mechanistic understanding in peat mosses. J. Ecol. 2019, 107, 843–859. [Google Scholar] [CrossRef]
  65. Alahuhta, J.; Erős, T.; Kärnä, O.-M.; Soininen, J.; Wang, J.; Heino, J. Understanding environmental change through the lens of trait-based, functional, and phylogenetic biodiversity in freshwater ecosystems. Environ. Rev. 2019, 27, 263–273. [Google Scholar] [CrossRef]
  66. Chin, A.R.O.; Guzmán-Delgado, P.; Görlich, A.; HilleRisLambers, J. Towards multivariate functional trait syndromes: Predicting foliar water uptake in trees. Ecology 2023, 104, e4112. [Google Scholar] [CrossRef]
  67. Paritsis, J.; Aizen, M.A. Effects of Exotic Conifer Plantations on the Biodiversity of Understory Plants, Epigeal Beetles and Birds in Nothofagus dombeyi Forests. For. Ecol. Manag. 2008, 255, 1575–1583. [Google Scholar] [CrossRef]
  68. McElhinny, C.; Gibbons, P.; Brack, C.; Bauhus, J. Forest and woodland stand structural complexity: Its definition and measurement. For. Ecol. Manag. 2005, 218, 1–24. [Google Scholar] [CrossRef]
  69. Flynn, D.F.B.; Gogol-Prokurat, M.; Nogeire, T.; Molinari, N.; Richers, B.T.; Lin, B.B.; Simpson, N.; Mayfield, M.M.; DeClerck, F. Loss of functional diversity under land-use intensification across multiple taxa. Ecol. Lett. 2009, 12, 22–33. [Google Scholar] [CrossRef] [PubMed]
  70. Laliberté, E.; Wells, J.A.; DeClerck, F.; Metcalfe, D.J.; Catterall, C.P.; Queiroz, C.; Aubin, I.; Bonser, S.P.; Ding, Y.; Fraterrigo, J.M.; et al. Land-use intensification reduces functional redundancy and response diversity in plant communities. Ecol. Lett. 2010, 13, 76–86. [Google Scholar] [CrossRef]
  71. Zeidler, M.; Šipoš, J.; Banaš, M.; Černohorský, J. Homogenization of bryophyte species after alpine grassland restoration. J. Environ. Manag. 2022, 319, 115628. [Google Scholar] [CrossRef] [PubMed]
  72. Mežaka, A.; Brūmelis, G.; Piterāns, A. Tree and stand-scale factors affecting richness and composition of epiphytic bryophytes and lichens in deciduous woodland key habitats. Biodivers. Conserv. 2012, 21, 3221–3241. [Google Scholar] [CrossRef]
Forests 17 00506 g001
Figure 1. Study area and bryophyte sampling design on Fruška Gora Mountain, northern Serbia. (A) Geographic position of Serbia within Europe. (B) Location of Fruška Gora Mountain within Serbia. (C) Schematic representation of the hierarchical sampling design applied in this study. Within each locality, five permanent plots (100 × 100 m) were established, and within each plot, five 10 × 10 m subplots were arranged in a standardized layout, with one subplot in the center and four in the plot corners. Bryophyte occurrence and cover were recorded in each subplot using the modified microcoenose method [39]. (D) Spatial distribution of the seven sampled localities representing native beech forests and Austrian pine plantations on Fruška Gora Mountain. Blue circles denote beech forest sites (ST-Stražilovo; PD-Papratski do; D-Dumbovo; LV-Lazin Vir), and orange triangles denote pine plantation sites (J-Jazak; KB-Kanov breg; V-Vrdnik). The overall field design follows the sampling framework previously described for this research system [18].
Figure 1. Study area and bryophyte sampling design on Fruška Gora Mountain, northern Serbia. (A) Geographic position of Serbia within Europe. (B) Location of Fruška Gora Mountain within Serbia. (C) Schematic representation of the hierarchical sampling design applied in this study. Within each locality, five permanent plots (100 × 100 m) were established, and within each plot, five 10 × 10 m subplots were arranged in a standardized layout, with one subplot in the center and four in the plot corners. Bryophyte occurrence and cover were recorded in each subplot using the modified microcoenose method [39]. (D) Spatial distribution of the seven sampled localities representing native beech forests and Austrian pine plantations on Fruška Gora Mountain. Blue circles denote beech forest sites (ST-Stražilovo; PD-Papratski do; D-Dumbovo; LV-Lazin Vir), and orange triangles denote pine plantation sites (J-Jazak; KB-Kanov breg; V-Vrdnik). The overall field design follows the sampling framework previously described for this research system [18].
Forests 17 00506 g001
Forests 17 00506 g002
Figure 2. Redundancy analysis (RDA) ordination of bryophyte community composition based on Hellinger-transformed species data and constrained by environmental predictors. RDA1 and RDA2 explain 37.5% and 6.1% of the constrained variance, respectively, while the full model explains 36.2% of the total variance (adjusted R2 = 0.342).
Figure 2. Redundancy analysis (RDA) ordination of bryophyte community composition based on Hellinger-transformed species data and constrained by environmental predictors. RDA1 and RDA2 explain 37.5% and 6.1% of the constrained variance, respectively, while the full model explains 36.2% of the total variance (adjusted R2 = 0.342).
Forests 17 00506 g002
Forests 17 00506 g003
Figure 3. RLQ biplot showing relationships between environmental variables and dominant functional trait axes of bryophyte communities across forest types. Axis 1 and axis 2 explain 84.1% and 8.3% of the total co-inertia, respectively. Points represent sampling plots grouped by forest type (beech and Austrian pine), and ellipses indicate 95% confidence intervals. Solid arrows denote environmental vectors, whereas dashed arrows represent dominant trait loadings. The direction and length of arrows reflect the strength and direction of their association with the RLQ axes.
Figure 3. RLQ biplot showing relationships between environmental variables and dominant functional trait axes of bryophyte communities across forest types. Axis 1 and axis 2 explain 84.1% and 8.3% of the total co-inertia, respectively. Points represent sampling plots grouped by forest type (beech and Austrian pine), and ellipses indicate 95% confidence intervals. Solid arrows denote environmental vectors, whereas dashed arrows represent dominant trait loadings. The direction and length of arrows reflect the strength and direction of their association with the RLQ axes.
Forests 17 00506 g003
Forests 17 00506 g004
Figure 4. Differences in bryophyte functional diversity (Rao’s quadratic entropy, RaoQ) between beech and conifer forest types. Dots represent individual subplots. Functional diversity was significantly higher in beech stands compared to conifer plantations (LMM: p = 0.004).
Figure 4. Differences in bryophyte functional diversity (Rao’s quadratic entropy, RaoQ) between beech and conifer forest types. Dots represent individual subplots. Functional diversity was significantly higher in beech stands compared to conifer plantations (LMM: p = 0.004).
Forests 17 00506 g004
Forests 17 00506 g005
Figure 5. Scatterplot of RaoQ vs. stand structural–moisture gradient (grad_PC1), with regression line.
Figure 5. Scatterplot of RaoQ vs. stand structural–moisture gradient (grad_PC1), with regression line.
Forests 17 00506 g005
Table 1. List of selected traits [37].
Table 1. List of selected traits [37].
Functional TraitVariable TypeDescription
Autecological traits
indFordinalindicator value F (moisture): 1 (extreme dryness) to 9 (wet-site indicator), x (indifferent)
indLordinalindicator value L (light): 1 (deep shade) to 9 (full light), x (indifferent)
indRordinalindicator value R (reaction/acidity): 1 (extreme acidity) to 9 (high pH soils), x (indifferent)
forestordinalhow strong species are bound to forest habitats; 1 (largely restricted to closed forest), 2 (prefers forest edges and clearings), 3 (occurs in forest as well as in open land), 4 (may occur in forest, but prefers open land)
hemerobyordinaloccurrence in the gradient of background human impact on the ecosystem; 1 (absent), 2 (absent to weak), 3 (weak), 4 (weak to moderate), 5 (moderate), 6 (moderate to strong), 7 (strong), 8 (strong to very strong), 9 (very strong)
Morphological traits
gformcategoricalgrowth form: acr (acrocarpous), fol (foliose), ple (pleurocarpous), sph (Sphagnum), tha (thalloid)
lformcategoricallife form, simplified system [42]: annual, cushion, dendroid, mat, rosette, turf, weft
lstrat_ecategoricallife strategy, extended system [43]: a (annual shuttle), c (colonist), ce (ephemeral colonists), cp (pioneer colonists), f (fugitive), l (long-lived shuttle), p (perennial), pc (competitive perennial), ps (stress tolerant perennials), s (short-lived shuttle)
sizenumericalmean size of shoot/gametophyte (mm)
Reproductive traits
smeandnumericalmean spore diameter
vpcategoricalwhether the species has vegetative propagules;
1 (present), 0 (absent)
sexcategoricalmating type/sexual condition (M (monoicous), D (dioicous), M/D (can be monoicous or dioicous))
Table 2. Sequential (by-term) permutation tests of environmental predictors in redundancy analysis (RDA) of bryophyte community composition, including explained variance, pseudo-F values, and variance inflation factors (VIFs).
Table 2. Sequential (by-term) permutation tests of environmental predictors in redundancy analysis (RDA) of bryophyte community composition, including explained variance, pseudo-F values, and variance inflation factors (VIFs).
DfVariancePseudo-FPr (>F)VIF
pH10.1099.0420.0011.723
Soil temperature10.0423.4580.0081.158
Herbaceous cover10.0594.9220.0011.206
Shrub number10.0776.4190.0012.108
grad_PC110.0725.9910.0033.003
total50.362
Table 3. Type II ANOVA results for the linear model testing the effects of environmental predictors on bryophyte functional diversity (RaoQ).
Table 3. Type II ANOVA results for the linear model testing the effects of environmental predictors on bryophyte functional diversity (RaoQ).
PredictorSumSqDfFp
pH0.01113.9540.056
Soil temperature0.01114.1590.051
Herbaceous cover0.00210.7290.4
Number of shrubs0.02218.3470.007
grad_PC10.039114.6830.001
Residuals0.07729
Table 4. Linear model coefficients explaining variation in bryophyte functional diversity (RaoQ). Regression coefficients (β), standard errors (SE), t-statistics, p-values, and 95% confidence intervals are shown. Predictors were standardized before analysis. Significant effects (p < 0.05) are indicated in bold. The model explained 62.4% of the variance in RaoQ (R2 = 0.624; adjusted R2 = 0.612; F5,29 = 29.21, p < 0.001).
Table 4. Linear model coefficients explaining variation in bryophyte functional diversity (RaoQ). Regression coefficients (β), standard errors (SE), t-statistics, p-values, and 95% confidence intervals are shown. Predictors were standardized before analysis. Significant effects (p < 0.05) are indicated in bold. The model explained 62.4% of the variance in RaoQ (R2 = 0.624; adjusted R2 = 0.612; F5,29 = 29.21, p < 0.001).
PredictorβSEtpCI_lowCI_high
(Intercept)0.2070.00923.68500.1890.224
pH0.0230.0121.9890.056−0.0010.047
Soil temperature0.0190.0092.0390.051−0.00010.039
Herbaceous cover0.0080.0090.8540.4−0.0120.028
Number of shrubs0.0370.0132.8890.0070.0110.063
grad_PC1−0.0350.009−3.8320.001−0.054−0.016
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Ilić, M.; Ćuk, M.; Vukov, D. Forest Type and Environmental Gradients Shape Bryophyte Functional Diversity: Evidence from Epigeic Bryophytes in Beech Forests and Pine Plantations. Forests 2026, 17, 506. https://doi.org/10.3390/f17040506

AMA Style

Ilić M, Ćuk M, Vukov D. Forest Type and Environmental Gradients Shape Bryophyte Functional Diversity: Evidence from Epigeic Bryophytes in Beech Forests and Pine Plantations. Forests. 2026; 17(4):506. https://doi.org/10.3390/f17040506

Chicago/Turabian Style

Ilić, Miloš, Mirjana Ćuk, and Dragana Vukov. 2026. "Forest Type and Environmental Gradients Shape Bryophyte Functional Diversity: Evidence from Epigeic Bryophytes in Beech Forests and Pine Plantations" Forests 17, no. 4: 506. https://doi.org/10.3390/f17040506

APA Style

Ilić, M., Ćuk, M., & Vukov, D. (2026). Forest Type and Environmental Gradients Shape Bryophyte Functional Diversity: Evidence from Epigeic Bryophytes in Beech Forests and Pine Plantations. Forests, 17(4), 506. https://doi.org/10.3390/f17040506

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop