The hummocky meadow micro-scale patchiness effect on the Eggleaf twayblade Neottia ovata (L.) Bluff & Fingerh. (Orchidaceae) functional traits

The hummocky meadow micro-scale patchiness effect on the Eggleaf twayblade Neottia ovata (L.) Bluff & Fingerh. 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Bluff & Fingerh. (Orchidaceae) functional traits Igor Paušič, Tomaž Granda, Klavdija Pliberšek, Peter Kozel This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8707471/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Regularly managed hummocky meadows represent a species-rich, distinct type of post-glacial grassland communities, which appear on loose carbonate material. Such micro-reliefs are characterized by numerous humps and pits, i.e. convex and concave features of the surface. Little is known about how plants respond to environmental heterogeneity at such small spatial scales. Neottia ovata (Orchidaceae) is a member of the Eurosiberian, Boreo-temperate element. This species is a well documented habitat generalist, occupying a wide range of habitats ranging from lowlands to mountains and from wet to dry sites, often including anthropogenic habitats. The aim of the present study was to evaluate the effect of hummocky meadow micro-scale habitat patchiness, i.e. small-scale habitat heterogeneity on selected N. ovata functional traits. Across all generalized linear mixed models (GLMMs), plant micro-location on the slope of a hump, expressed as plant height on a hump index was the key predictor of the analysed functional traits. The specific micro-location of N. ovata specimens on the meadow humps plays a vital role in the plant’s functional trait values; it also affects fitness and the percentage of pollinated flowers. Hummocky meadow micro-scale patchiness offers interesting perspectives for further research. Protecting the remaining hummocky meadows contributes to the conservation of biodiversity of the fragile alpine environment. Neottia ovata Orchids Hummocky meadows Habitat micro-scale patchiness Functional trait variability Environmental parameters Figures Figure 1 Figure 2 Figure 3 Introduction The environmental heterogeneity hypothesis holds that spatial heterogeneity in biotic and abiotic environmental conditions increases biodiversity (MacArthur and Wilson 1967 ; Ricklefs 1977 ). This relationship seems to be highly scale- and potentially resource dependent, lending support to the assumption that soil chemistry, texture, and depth, for example, have the greatest impact on community composition at small spatial scales, with climatic variables becoming increasingly important as scale increases (Costanza et al. 2011 ). At micro scales, environmental heterogeneity within communities is assumed to support resource partitioning between competing species (Bolker 2003 ; Costanza et al. 2011 ). In contrast, at the local-patch scale; small-scale soil or resource heterogeneities can provide dissimilar niches, possibly favouring particular individuals of competing species (Olagoke et al. 2023) or providing optimum conditions for a single species. Little is known about how the plants respond to environmental heterogeneity (hump-pit topography) at such small spatial scales (Bell and Lechowicz 1994 ; Kephart and Paladino 1997 ). The treethrow hump-pit microtopography, i.e. hummocky meadows, represents a specific type of grassland surface, that occurs on loose carbonate material. This type of meadows has been studied mainly by German-speaking researchers (Penck 1940/41) and named ‘Buckelwiese’ (German: Buckelwiese, literally ‘hummocky meadow’) with evidence from the Alpine areas including Slovenia. Erosion itself plays a key role, however it still remains unclear which process triggers the initial formation of such micro-geomorphological surfaces. The hillslope topography (hummocky meadow) was also interpreted as an effect of simultaneous congelifluction (Dylikowa 1956 ), meaning a relatively slow and long-term soil flow over permafrost or within a periglacial zone. Later on, a new model of the formation of this topography was introduced, proposing that the hump-pit micro-topography was due to the uprooting of trees caused by a strong wind events (Gerlach 1960 ). The species composition and distribution of alpine meadow plants are influenced by a variety of factors lasting over a long period, including climate, soil, water, topography, and plant biological characteristics (Zhang et al. 2021 ). Among abiotic factors, topographical factors have a strong impact on plant growth (Davies et al. 2007 ), especially when the research scale is small. Topography at the local scale is one the most important factors affecting vegetation; species composition (Swenson et al. 2011), plant growth (Hara et al. 1996 ; Sakai and Ohsawa 1994), and the spatial distribution of plant populations (Palmer and Dixon 1990 ). According to Prezelj ( 2012 ) and regarding hummocky meadow micro-topography; shallower soil, faster drying, and lower amount of the remaining moisture is characteristic at the top of the humps compared to pits. The soil of the larger humps dries faster in comparison to medium and small humps. The position on the hump, size of the hump and insolation affect the vegetation composition and species richness (Prezelj 2012 ). At the top of the humps the vegetation typical for drier and shallower soils is found. Pits are characterized by the vegetation of moist, deeper and fertile soil. Species diversity is greatest in the middle of humps (Prezelj 2012 ). Differences in functional group composition were also observed: moss cover increased towards the top of the hump, and the grass cover, inversely, decreased. The proportion of clay on the hump is smaller than in the depression (pit), which indicates that the particles are washed into the depression. Neottia ovata (L.) Bluff & Fingerh. is a sturdy, robust species: it can thrive without mycorrhiza, the rhizome of adult plants is free of mycorrhizal colonization (Kotilínek et al. 2015 ), and it flowers continuously for over 20 years, pollinated by the numerous insects (Coleoptera, Hymenoptera and Diptera) attracted to the abundant nectar (Delforge 2006 ). N. ovata is self-compatible but has a well-developed rostellum to prevent self-fertilization (Talalaj et al. 2017). It is a rewarding species, with a Eurasian distribution and has also been reported as introduced in Ontario (Canada)- very widespread and common in the temperate zone but rare in the Mediterranean regions of Europe. Preston and Hill ( 1997 ) include N. ovata as a member of the Eurosiberian, Boreo-temperate element. A habitat generalist, it occupies a wide range of habitats from lowlands to mountains and from wet to dry sites, including anthropogenic sites (Kotilínek et al. 2015 ). It is a perennial herb whose populations are maintained predominantly by sexual reproduction, as vegetative spread is limited. N. ovata is a long-lived species, as shown in the long-term observations of Tamm ( 1991 ). Due to its longevity, the species is a suitable indicator of recent land-use changes. Variation of flower structure and nectar chemistry and their weak correlation with reproductive success confirms the generalistic character of N. ovata (Brzosko et al. 2021 ) regarding effective pollinators. The aim of the present study was to evaluate the effect of hummocky meadow micro-scale habitat patchiness, i.e. small-scale habitat heterogeneity on selected N. ovata functional traits. The following two questions were addressed: (i) how does specific hump location ecologically affect measured functional trait values and (ii) is there an ecological optimum hump location for N. ovata considering the observed functional trait values? Materials and Methods Study Area In order to minimize the impact of certain, site-specific environmental factors such as altitude, meadow exposure, shading effects, slope of the terrain, meadow management regime, etc. a single hummocky meadow was sampled in this study. The studied meadow is located in the NE part of Triglav National Park, NW Slovenia (Fig. 1 ), at an average altitude of 1350 m and covers an area of 0.65 ha. This meadow is enclosed on all sides by forest except on its SW side, where it borders a paved road leading towards the Vršič pass (1611 m, centroid point: 46°26'22.84"N, 13°45'22.43"E). Based on the site conditions, the potential forest vegetation of this stand, dominated by Picea abies , Fagus sylvatica and Larix decidua , belongs to the association Polysticho lonchitis - Fagetum var. geogr. Anemone trifolia . In-situ data collecting On site we examined 382 flowering N. ovata specimens from 23 humps. Following functional character values; plant height and inflorescence length were obtained in-situ using a digital beak scale, number of flowers and number of fertilized flowers were counted in the field for each specimen. Total leaf area for each specimen was obtained using a digital camera and ImageJ, open-source software package for processing and analysing scientific images. For leaf chlorophyll content a non-destructive measurement was performed using SPAD-502 Plus device. The SPAD value, determined by SPAD-502 provides an indication of the relative amount of chlorophyll present in plant leaves, which can serve as an indicator of the overall condition of the plant itself. Two measurements (for both leaves) per plant were taken, the average values were used for further analysis. Vegetation height of the surrounding grass (turf) cover at close vicinity of each plant was measured together with substrate depth. For each plant the hump location was observed and measured; vertical distance from the hump base and the plant location. Since humps vary in their height, plant height on a hump index for each plant was calculated: plant height on a hump/total hump height. Value 0 indicates location at the base of the hump while the value 1 means the summit location. Hump slope inclination was calculated: hump total height and horizontal distance: hump summit-hump rim, were measured in the field. For each hump, slope inclination was calculated later on using angular functions. Data Analysis First, to describe the data structure, we calculated means with standard deviations (SD), medians with interquartile ranges (IQR), and the coefficient of variation for all variables. The Kolmogorov–Smirnov test was used to check whether values of variables departed significantly from normality. Redundancy analysis (RDA) was initially conducted to explore the relationships between plant functional traits and environmental variables (micro-relief, substrate depth and turf height) and to evaluate the relative influence of environmental variables on plant functional traits. To account for differences in scale, environmental variables were standardized prior to analysis. To assess multicollinearity among environmental variables, we calculated the Variance Inflation Factor (VIF). Variables with a VIF greater than 3 were removed to reduce multicollinearity and ensure the stability of the RDA results. The statistical significance of the model, axis and environmental variables was evaluated using the Monte Carlo permutation test with 999 permutations. The ordination analysis was conducted in vegan R package (Oksanen et al. 2025 ). To explore the effect of micro-relief, also substrate depth and vegetation (turf) height, on the variability of the functional traits of N. ovata , we applied generalized linear mixed models (GLMMs). Through the mixed procedure, we addressed spatial dependency arising from repeated measurements of plant traits on the same hump by specifying the hump as a random factor. Plant height, inflorescence length, number of flowers, number of fertilized flowers, percentage of fertilized flowers, leaf area, and leaf chlorophyll concentration were used as dependent (response) variables. All the measured plants were classified into quartiles based on their functional trait values. Since we were interested in the effect of micro-relief, we present in discussion the first quartile (Q1, N = 95), which comprised the highest values of the measured functional traits more in detail. Values of dependent variables were modelled using a negative binomial distribution family, a distribution applied for over-dispersed count data (Zuur et al. 2009 ; Zuur and Ieno 2016 ). The following independent (explanatory) variables were addressed as potential predictors of the dependent variables: hump height (Hump_H), plant height on a hump (PlantH_Hump), plant height on a hump index (PlantH_HumpInd), hump slope inclination (Hump_Inc), substrate depth (Sub_D) and vegetation height (Veg_H). Prior to model fitting, to avoid common statistical problems, a detailed exploratory data analysis was conducted in accordance with the standard procedures for regression-type analyses (Zuur et al. 2010 ; Zuur and Ieno 2016 ). Accordingly, the presence of outliers in response and explanatory variables was visually checked with Cleveland dot plots. Multi-collinearity among variables was assessed using Pearson correlation coefficients and variance inflation factors (VIF), with cutoff values of r ≥ ± 0.7 and VIF > 3 (Zuur et al., 2013 ). Possible non-linear effects of continuous variables in the models were examined using generalized additive models (GAM) with the gam R package (Hastie, 2023 ). Similarly to the RDA analysis, the variables were standardized prior to model fitting to account for differences in their scales. After fitting the initial models, including all noncollinear variables of interest, we conducted model selection. A backward elimination procedure was chosen, whereby variables were progressively deleted according to corrected Akaike information criterion (AICc), with lower values indicating better models (Burnham and Anderson 2002 ). Since in some cases we found high model uncertainty, i.e. similarly high AICc weights in two or more models, we applied model averaging for models with similar AICc weights, since this procedure provides a robust means of obtaining parameter estimates (Burnham and Anderson 2002 ). Both model selection and model averaging were carried out in the MuMIn R package (Bartoń 2025 ). Given the nearly equal Akaike weights of the two models predicting leaf area, model averaging was performed using conditional averaging, which estimates effects only from models that include each variable. This approach highlights the contributions of variables supported by the model set. Model validation was carried out on the final models (Zuur et al. 2009 , 2013 ; Zuur and Ieno 2016 ). All GLMMs were fitted with the glmmTMB R package (Brooks et al. 2024 ). Surface plots were created in the rgl R package (Murdoch and Adler 2023 ). All statistical analyses were carried out in the R version 4.1.2 (R Development Core Team, 2021). Results Descriptive statistics for all variables included in the analysis are shown in Table 1 . Data exploration revealed the presence of two outlying values in the independent variable vegetation (turf) height, so they were removed from the dataset. The independent variable hump height was not included in the initial models because it was collinear (r > 0.7; VIF > 3) with plant height on a hump index and hump slope inclination. Accordingly, the plant height on a hump index, hump slope inclination, substrate depth and vegetation height were included in the initial models. The first two RDA axes explained 79.01% and 14.94% of the plant functional traits variation, respectively (Fig. 2 ). The overall model was found to be significant (p = 0.001), indicating that the environmental variables explained a significant portion of the variation in the plant functional traits. The RDA1 axis was highly significant (p < 0.001), explaining the majority of the variation in the response data. Additionally, the RDA2 axis was also significant (p = 0.045), although it explained a smaller portion of the variation and had a weaker effect compared to RDA1. All environmental variables were found to significantly explain variation in plant functional traits. The variables PlantH_Hump, PlantH_HumpInd, and Veg_H were all highly significant (p = 0.001) predictors of the response variables. The variable Hump_Inc was also significant (p = 0.011), although it had a smaller effect compared to the other variables. Table 1 Descriptive statistics for the independent variables (micro-relief, pedological and vegetational characteristics) and the dependent functional traits of N. ovata . Variables with non-normally distributed data are in bold. Independent variables Hump height (cm) Plant height on a hump (cm) Plant height on a hump index Hump slope inclination (°) Substrate depth (cm) Vegetation height (cm) Mean±standard deviation median, IQR coefficient of variation 185.72 ± 75.64 175.00, 99.00 40.73 120.46 ± 61.59 110.00, 87.00 51.13 0.68 ± 0.24 0.71, 0.42 35.60 27.80 ± 11.21 23.70, 3.50 40.35 12.11 ± 5.43 11.00, 7.00 44.84 23.46 ± 10.99 21.00, 19.25 46.86 Dependent variables Plant height (cm) Inflorescence length (cm) Number of flowers Number of fertilized flowers Percentage of fertilized flowers (%) Leaf area (cm 2 ) Leaf chlorophyll concentration Mean±standard deviation median, IQR coefficient of variation 38.80 ± 8.45 38.50, 12.00 21.78 14.21 ± 5.18 15.00, 7.00 36.42 25.90 ± 10.59 26.00, 13.00 40.88 20.95 ± 10.05 22.00, 14.00 47.99 79.70 ± 18.23 84.00, 18.00 22.87 27.85 ± 11.02 26.00, 17.00 39.56 28.17 ± 10.82 30.00, 14.25 38.41 Given the large number of dependent variables evaluated using GLMMs, Table 2 summarizes effect directions across models. Table 2 Summary of effect directions across GLMMs for all dependent variables. Symbols indicate the direction of effects: + positive effect, – negative effect, and / not included in the final (best) model. Full model estimates and statistical results are provided in the text. Traits↓/Predictors→ Hump height (cm) Plant height on a hump (cm) Plant height on a hump index Hump slope inclination (°) Substrate depth (cm) Vegetation height (cm) Plant height (cm) / / / / + + Inflorescence length (cm) / - + / + + Number of flowers / - + / + + Number of fertilized flowers / - + / + + Percentage of fertilized flowers (%) / - + / / / Leaf area (cm 2 ) / / / / + + Leaf chlorophyll concentration / / / - / + According to model selection based on AICc values (Table 3 ), the plant height was best explained by substrate depth and vegetation height. Plant height increased with both substrate depth (Sub_D Estimated β ± SE: 0.0410 ± 0.0112, p < 0.001) and vegetation height (Veg_H Estimated β ± SE: 0.0504 ± 0.0130, p < 0.001) (Fig. 3 a). Plant height on a hump, plant height on a hump index, substrate depth and vegetation height best explained inflorescence length, number of flowers and number of fertilized flowers (Table 3 ). Inflorescence length decreased with increasing plant height on a hump (PlantH_Hump Estimated β ± SE: -0.0643 ± 0.0240, p = 0.007) and increased with plant height on a hump index (PlantH_HumpInd Estimated β ± SE: 0.0591 ± 0.0225, p = 0.009) (Fig. 3 b). It was also positively associated with substrate depth (Sub_D Estimated β ± SE: 0.0520 ± 0.0195, p = 0.008) and vegetation height (Veg_H Estimated β ± SE: 0.0488 ± 0.0211, p = 0.020) (Fig. 3 c). The number of flowers followed the similar trends, decreasing with increasing plant height on a hump (PlantH_Hump Estimated β ± SE: -0.0718 ± 0.0287, p = 0.012) and increasing with plant height on a hump index (PlantH_HumpInd Estimated β ± SE: 0.0811 ± 0.0266, p = 0.002) (Fig. 3 d), substrate depth (Sub_D Estimated β ± SE: 0.0537 ± 0.0230, p = 0.019) and vegetation height (Veg_H Estimated β ± SE: 0.0710 ± 0.0250, p = 0.004) (Fig. 3 e). Similarly, the number of fertilized flowers decreased with increasing plant height on a hump (PlantH_Hump Estimated β ± SE: -0.1057 ± 0.0343, p = 0.002) and increased with plant height on a hump index (PlantH_HumpInd Estimated β ± SE: 0.1210 ± 0.0317, p < 0.001) (Fig. 3 f), substrate depth (Sub_D Estimated β ± SE: 0.0538 ± 0.0273, p = 0.048) and vegetation height (Veg_H Estimated β ± SE: 0.0710 ± 0.0298, p = 0.017) (Fig. 3 g). Across all models, plant height on a hump and plant height on a hump index were key predictors of the percentage of fertilized flowers (Table 3 ). The percentage decreased with increasing plant height on a hump (PlantH_Hump Estimated β ± SE: -0.0362 ± 0.0183, p = 0.048) and increased with plant height on a hump index (PlantH_HumpInd Estimated β ± SE: 0.0385 ± 0.0174, p = 0.027) (Fig. 3 h). For total leaf area, the parameter estimates from two near-equally weighted models (Table 3 ) were averaged using Akaike weights, yielding the following results: leaf area increased with substrate depth (Sub_D Estimated β ± SE: 0.0326 ± 0.0222, p = 0.144) and vegetation height (Veg_H Estimated β ± SE: 0.0534 ± 0.0255, p = 0.037) (Fig. 3 i). The model including hump inclination and vegetation height best explained leaf chlorophyll concentration (Table 3 ). Chlorophyll concentration decreased with increasing hump inclination (Hump_I Estimated β ± SE: -0.1039 ± 0.0294, p < 0.001) and increased with vegetation height (Veg_H Estimated β ± SE: 0.1008 ± 0.0244, p < 0.001) (Fig. 3 j). Table 3 Model selection according to the corrected Akaike information criterion (AICc) and the Akaike weight (wi). Models are ordered from the most to the least appropriate. df, degrees of freedom. Model structure df AICc w i Plant height y ~ Sub_D + Veg_H + 1|Hump 5 2655.05 0.60 y ~ Hump_Inc + Sub_D + Veg_H + 1|Hump 6 2656.85 0.24 y ~ PlantH_HumpInd + Hump_Inc + Sub_D + Veg_H + 1|Hump 7 2658.28 0.12 Inflorescence length y ~ PlantH_Hump + PlantH_HumpInd+ Sub_D + Veg_H + 1|Hump 7 2351.28 0.53 y ~ PlantH_Hump + PlantH_HumpInd + Hump_Inc + Sub_D + Veg_H + 1|Hump 8 2353.33 0.19 y ~ PlantH_Hump + PlantH_HumpInd + Sub_D + 1|Hump 6 2354.27 0.12 Number of flowers y ~ PlantH_Hump + PlantH_HumpInd + Sub_D + Veg_H + 1|Hump 7 2905.42 0.50 y ~ PlantH_Hump + PlantH_HumpInd + Hump_Inc + Sub_D + Veg_H + 1|Hump 8 2907.02 0.23 y ~ PlantH_HumpInd + Hump_Inc + Sub_D + Veg_H + 1|Hump 7 2908.54 0.11 Number of fertilized flowers y ~ PlantH_Hump + PlantH_HumpInd + Sub_D + Veg_H + 1|Hump 7 2853.82 0.45 y ~ PlantH_Hump + PlantH_HumpInd + Hump_Inc + Sub_D + Veg_H + 1|Hump 8 2855.15 0.23 y ~ PlantH_Hump + PlantH_HumpInd + Veg_H + 1|Hump 9 2855.67 0.18 Percentage of fertilized flowers y ~ PlantH_Hump + PlantH_HumpInd + 1|Hump 5 3417.83 0.60 y ~ PlantH_Hump + PlantH_HumpInd + Veg_H + 1|Hump 6 3419.63 0.25 y ~ PlantH_Hump + PlantH_HumpInd+ Sub_D + Veg_H + 1|Hump 7 3421.70 0.09 Leaf area y ~ Sub_D + Veg_H + 1|Hump 5 2843.44 0.33 y ~ Veg_H + 1|Hump 4 2843.52 0.32 y ~ Hump_Inc + Sub_D + Veg_H + 1|Hump 6 2845.12 0.14 Leaf chlorophyll concentration y ~ Hump_Inc + Veg_H + 1|Hump 5 2938.36 0.54 y ~ Hump_Inc + Sub_D + Veg_H + 1|Hump 6 2940.08 0.23 y ~ PlantH_HumpInd + Hump_Inc + Sub_D + Veg_H + 1|Hump 7 2941.34 0.12 Discussion Hummocky meadow a spectrum of micro-niches Hummocky meadows represent a specific habitat, where every hummock has slightly different biotic and abiotic properties. Hummock micro-topography establishes specific microclimatic conditions, with small-scale variations in soil thermal properties and water regimes, which influence biogeochemical cycles. These properties, coupled with different litter decomposability, may cause variations in soil physical and chemical properties and pedogenesis, as well as a selective distribution of plant species (Pintaldi et al. 2016 ). Many typical taxa of extensive montane grasslands with narrow ecological valence, habitat specialists prefer specific habitat conditions, specific micro sites. On the other hand, generalists are characterized by random spatial distribution within the habitat, regardless of the ecological conditions at a micro scale. However, the heterogeneity of the appropriate habitat provides a spectrum of natural gradients in the ecosystem. Generalists inhabit a wide range of ecological conditions. However, a heterogeneous habitat from a topographic perspective provides niches for generalists where they have optimal conditions as shown in this study. Post-glacial, hummocky type of surface geomorphological profile (Embleton-Hamann 2004 ), has a thinner layer of soil on the hump and a thicker layer of soil in the depression (pit), with the soil layer gradually decreases towards the top of the hump. Such a profile indicates corrosion, which is explained by the fact that more water and nutrients flows into the depression, where the dissolution of the rock forms a thicker soil. The tops of the humps are dominated by washed out rendzines, while macro-elements are washed towards the pits. Pedologically, the height of the hump represents a whole spectrum of potentially favourable ecological niches. Unlike the depressions between the humps (pits), where the soil layer is usually 30 cm thick or more, the tops of the humps are the driest, and the substrate layer is thin and subject of erosion. The cross section of this profile, the hump resembles a sketch of a sinkhole. The humps vary in size, but are similar in shape. They are arranged in no particular order across former alluvial landscape. The upper convex part of the hump descends into a concave depression through the intermediate part (Lukan Klavžer 1997 ). Authors agree that hummocky meadows form on unconsolidated carbonate material. Therefore, hummocky meadows are also associated with areas that are made of carbonate rocks, or were transported to this area from elsewhere, either by glacial or river transport (Cvetek 1971 ; Embleton-Hamann 2004 ). Even if considered as a habitat generalist, the specific micro-location of N. ovata specimens on the meadow humps plays a vital role in the plant’s functional trait values and affects the spatial distribution of N. ovata plants. The effect of hump and pit micro-relief on Neottia ovata functional traits As shown in this study, all the environmental variables were found to significantly explain variation in plant functional traits. The variables PlantH_Hump, PlantH_HumpInd, and Veg H (Table 2 ) were all highly significant predictors of the response variables. According to model selection based on AICc values, the plant height was best explained by substrate depth and vegetation height. N. ovata plant height increases with both, substrate depth and vegetation height. Pits and lower areas of humps are characterized by deeper and more fertile substrate, creating optimal conditions for a whole spectrum of other plant species, especially grasses (fam. Poaceae). N. ovata plant height on a hump index, substrate depth and vegetation height best explained inflorescence length, number of flowers and number of fertilized flowers. Inflorescence length increases with increasing PlantH_HumpInd, i.e. plant height on a hump index. N. ovata specimens with longest inflorescences (Q1, N = 95) are found on hump slopes with mean PlantH_HumpInd value of 0.698- on micro-locations that represent 69% of the total hump height). Specimens of N. ovata growing higher on the humps have in general longer inflorescence compared to plants inhabiting pits or basal parts of the humps. The number of flowers followed the similar trends; increasing with plant height on a hump index, substrate depth and vegetation height. Similarly, the number of fertilized flowers increased with plant height on a hump index, substrate depth and vegetation height. Across all models, PlantH_HumpInd value was key predictor of the percentage of fertilized flowers Per_Fer_Flow. The percentage increased with plant height on a hump index. N. ovata specimens with highest percentage of fertilized flowers (Q1, N = 95) are found oh hump slopes with mean PlantH_HumpInd value of 0.706- on micro-locations that represent 70% of the total hump height. Proportion of the fertilized flowers goes along with plant general viability. In general reproductive success increases with plant height on a hump index. For leaf area, the parameter estimates from two near-equally weighted models were averaged using Akaike weights, yielding the following results: leaf area increased with substrate depth and vegetation height. Plants growing in pits or on the lower parts of the humps experience intense shadowing since vegetation is taller compared to hump tops. In that regard, plants respond by enlarging leaf areas in order to ensure sufficient photosynthesis. N. ovata specimens with the highest total leaf area values (Q1, N = 95) are found on lower parts of the humps with a mean Leaf_A value of 28.279 cm 2 . In contrast, N. ovata specimens with the lowest total leaf area values (Q1, N = 95) are found on upper slopes and bottoms of the humps with mean Leaf_A value of 27.291 cm 2 . The model including hump inclination and vegetation height best explained leaf chlorophyll concentration. Chlorophyll concentration decreased with increasing hump inclination and increased with vegetation height. In conclusion, the micro-location of N. ovata (expressed as plant height on a hump index values) specimens on the humps plays a vital role in plant functional trait values, it also affects plants fitness and the percentage of pollinated flowers. Hummocky meadows, biodiverse but threatened habitat Despite relief heterogeneity and the associated diversity of ecological niches, such meadows are degraded through leveling due to the difficulty of cultivation, mandatory manual mowing. In the winter months, when there was less agricultural activity, farmers leveled them (Cvetek 1971 ). Since such meadows are unsuitable for mechanical cultivation, some meadows were leveled, while others were abandoned and overgrown. However, leveling hummocky meadows is controversial, as it destroys natural, geomorphological and cultural heritage (Ambrožič 2006 ) in the first place. Hummocky topography significantly influences topsoil properties, pedogenesis and vegetation distribution, with large differences between hummocks and interhummocks (Pintaldi et al. 2016 ). A positive relationship has been reported between species richness and odds of hummock occurrence may be related to the heterogeneous microtopography created by the hummocks at those sites (Smith et al. 2012 ). Diversity of plant species is much greater in areas with heterogeneous microtopography (hummocked) compared to flat surfaces (Vivian-Smith 1997 ). In our study, we show that hummocks provide a favourable habitat for N. ovata . We assume there are many other species that prefer different micro habitats, which make them more or less competitive. Moreover, due to these specific properties several plant species are found only where hummocks create favourable habitat because of their heterogeneous microtopography (Smith et al. 2012 ). In addition, hummocky meadows are also much more diverse in terms of biodiversity than leveled meadows, as the growth conditions on humps and in depressions (pits) are very different (Prezelj 2012 ). Also shown in this study, ecological conditions vary greatly on the slopes of the humps. Altering them represents another threat, contributing to the homogenization of the landscapes. In addition, as shown in this study, the micro-patchiness of this particular, valuable habitat provides niches suitable for a large spectrum of different organisms. The leveling of hummocky meadows represents the loss of a diverse range of microclimatic and pedological micro-sites, which are vital for ensuring viable populations of a species, even if being considered as a habitat generalist. These, uneven grasslands in the montane belt of SE Alps represent a rich pool for further research, protecting the remaining hummocky meadows means protecting the biodiversity of the fragile alpine environment. Research limitations Although this study was conducted at a single site, the generalizability of the findings is not affected. The aim of the present study was to explain the micro-scale patchiness effect on the N. ovata functional traits, and not comparing differences in ecological values among humps across multiple meadows, sites. Declarations Author Contribution I.P began the research, led the research and wrote most of the article, T.G. assisted with fieldwork and editing the article, K.P. did the field work, P.K. performed statistical analyses and edited the article. All authors have completed the final review of the final version of the article. References Adewole Olagoke, Florian Jeltsch, Tietjen B, Berger U, Ritter H, Maaß S. 2023. Small‐scale heterogeneity shapes grassland diversity in low‐to‐intermediate resource environments. Journal of Vegetation Science. 34(4). doi:https://doi.org/10.1111/jvs.13196. Ambrožič T. 2006. Hummocky meadows in Zgornja Radovna. Graduation thesis, University of Ljubljana, Biotechnical Faculty, Department of Agronomy, 94 pp. Bartoń, K. 2025. MuMIn: Multi-Model Inference. 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Pintaldi E, D’Amico ME, Siniscalco C, Cremonese E, Celi L, Filippa G, Prati M, Freppaz M. 2016. Hummocks affect soil properties and soil-vegetation relationships in a subalpine grassland (North-Western Italian Alps). CATENA. 145:214–226. doi:https://doi.org/10.1016/j.catena.2016.06.014. Preston CD, Hill MO. 1997. The geographical relationships of British and Irish vascular plants. Botanical Journal of the Linnean Society. 124(1):1–120. doi:https://doi.org/10.1111/j.1095-8339.1997.tb01785.x. Prezelj K. 2012. Vegetation and humidity conditions on the hummocky meadows in Zgornja Radovna and Krma areas. Graduation thesis, University of Ljubljana, Biotechnical Faculty, Department of Agronomy, 67 pp. Ricklefs RE. 1977. Environmental Heterogeneity and Plant Species Diversity: A Hypothesis. The American Naturalist. 111(978):376–381. doi:https://doi.org/10.1086/283169 Solch J. [accessed 2025 Mar 16]. https://www.zobodat.at/pdf/Mitt-Oesterr-Geograph-Ges_89_0088-0122.pdf. R Core Team. 2021. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ Smith ML, Meiman PJ, Brummer JE. 2012. Characteristics of hummocked and non-hummocked Colorado riparian areas and wetlands. Wetlands Ecology and Management. 20(5):409–418. doi:https://doi.org/10.1007/s11273-012-9263-5. Swenson NG, Anglada-Cordero P, Barone JA. 2010. Deterministic tropical tree community turnover: evidence from patterns of functional beta diversity along an elevational gradient. Proceedings of the Royal Society B: Biological Sciences. 278(1707):877–884. doi:https://doi.org/10.1098/rspb.2010.1369. ‌Tałałaj I, Ostrowiecka B, Włostowska E, Rutkowska A, Brzosko E. 2017. The ability of spontaneous autogamy in four orchid Species: Cephalanthera rubra , Neottia ovata , Gymnadenia conopsea , and Platanthera bifolia . Acta Biologica Cracoviensia s Botanica. 59(2):51–61. doi:https://doi.org/10.1515/abcsb-2017-0006. Tamm C.O. 1991. Behavior of some orchid populations in a changing enviroment - observation on permanent plots, 1943-1990. Population Ecology of Terrestrial Orchids (eds T.C.E. Wells & J.H. Willems), pp. 1–13. SPB Academic Publishing, The Hague, the Netherlands. Vivian-Smith G. 1997. Microtopographic Heterogeneity and Floristic Diversity in Experimental Wetland Communities. The Journal of Ecology. 85(1):71. doi:https://doi.org/10.2307/2960628. Zhang Q, Wang J, Wang Q. 2021. Effects of abiotic factors on plant diversity and species distribution of alpine meadow plants. Ecological Informatics. 61(1574-9541):101210. doi:https://doi.org/10.1016/j.ecoinf.2021.101210. https://www.sciencedirect.com/science/article/pii/S1574954121000017. Zuur AF, Ieno EN, Walker N, Saveliev AA, Smith GM. 2009. Mixed effects models and extensions in ecology with R. New York, NY: Springer New York. https://link.springer.com/book/10.1007/978-0-387-87458-6. Zuur AF, Ieno EN. 2016. A protocol for conducting and presenting results of regression-type analyses. Freckleton R, editor. Methods in Ecology and Evolution. 7(6):636–645. doi:https://doi.org/10.1111/2041-210x.12577. Zuur AF, Ieno EN, Elphick CS. 2010. A protocol for data exploration to avoid common statistical problems. Methods in Ecology and Evolution. 1(1):3–14. doi:https://doi.org/10.1111/j.2041-210x.2009.00001.x. Zuur, A.F., Hilbe, J.M. & Ieno, E.N. 2013. A Beginner’s Guide to GLM and GLMM with R . Newburgh : Highland Statistics Ltd.. Murdoch, D., Adler, D. 2023. rgl: 3D Visualization Using OpenGL. R package version 1.0.1. https://CRAN.R-project.org/package=rgl Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8707471","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":584228467,"identity":"2a2a7f47-65bc-4d20-b0ad-916ecb8b7d39","order_by":0,"name":"Igor Paušič","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwUlEQVRIiWNgGAWjYBACNjiLmYHxAaoIEVqYDYjSgqJdgihlfPyLj31g3GGXOL+d+Vk1bw6DPR9hk58lz2A8k5y44TCb2W3ebQyJbYS1nDFmYGxjTtzAzMMG0pJA2PsQLfWJ85t52IqBWuwJa+HvAWk5nNhwmIeNGaiFkQiHsSUzJJ45bgz0i7Hk3G0ShP0i33/4MMPHHdWy8/sPP/zwdpuNvXwDIT0SCQwMiQhVxEQN/wEGBkaCBo+CUTAKRsGIBgDzDzPSFaY1XQAAAABJRU5ErkJggg==","orcid":"","institution":"University of Maribor","correspondingAuthor":true,"prefix":"","firstName":"Igor","middleName":"","lastName":"Paušič","suffix":""},{"id":584228468,"identity":"08848acb-9910-4e64-9fc0-03a73c6c8289","order_by":1,"name":"Tomaž Granda","email":"","orcid":"","institution":"University of Ljubljana","correspondingAuthor":false,"prefix":"","firstName":"Tomaž","middleName":"","lastName":"Granda","suffix":""},{"id":584228469,"identity":"bc444033-cf37-4102-8831-e49713751b11","order_by":2,"name":"Klavdija Pliberšek","email":"","orcid":"","institution":"University of Maribor","correspondingAuthor":false,"prefix":"","firstName":"Klavdija","middleName":"","lastName":"Pliberšek","suffix":""},{"id":584228470,"identity":"87f6a758-6946-40a9-aa32-89fcf55a3f10","order_by":3,"name":"Peter Kozel","email":"","orcid":"","institution":"University of Maribor","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Kozel","suffix":""}],"badges":[],"createdAt":"2026-01-27 07:55:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8707471/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8707471/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101803939,"identity":"7f6602a7-fdf5-4eee-a362-a33669f28054","added_by":"auto","created_at":"2026-02-03 19:07:07","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1143981,"visible":true,"origin":"","legend":"\u003cp\u003eGeographic location of the sampled hummocky meadow (A), sampled hummocky meadow (B), \u003cem\u003eN. ovata \u003c/em\u003eflower close up and (C) hump cross profile (D).\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8707471/v1/82fd348a27fb5b004079a415.jpeg"},{"id":101803940,"identity":"0fd20aec-7295-4f34-8a4f-ed1640c5cd63","added_by":"auto","created_at":"2026-02-03 19:07:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":29741,"visible":true,"origin":"","legend":"\u003cp\u003eRDA triplot of plant functional traits (response variables; red triangles) and environmental variables (explanatory variables; blue arrows). Arrows indicate the direction and magnitude of variables. \u003cstrong\u003ePlant_H\u003c/strong\u003e, plant height; \u003cstrong\u003eInf_L\u003c/strong\u003e, inflorescence length; \u003cstrong\u003eNo_Flow\u003c/strong\u003e, number of flowers; \u003cstrong\u003eNo_Fer_Flow\u003c/strong\u003e, number of fertilized flowers; \u003cstrong\u003ePer_Fer_Flow\u003c/strong\u003e, percentage of fertilized flowers; \u003cstrong\u003eLeaf_A\u003c/strong\u003e, leaf area; \u003cstrong\u003eChl_Con\u003c/strong\u003e, chlorophyll content; \u003cstrong\u003eHump_H\u003c/strong\u003e, hump height; \u003cstrong\u003ePlantH_Hump\u003c/strong\u003e, plant height on a hump; \u003cstrong\u003ePlantH_HumpInd\u003c/strong\u003e, plant height on a hump index; \u003cstrong\u003eHump_Inc\u003c/strong\u003e, hump slope inclination; \u003cstrong\u003eSub_D\u003c/strong\u003e, substrate depth; \u003cstrong\u003eVeg_H\u003c/strong\u003e, vegetation (turf) height.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8707471/v1/208fe549139b991b2990da38.png"},{"id":101803941,"identity":"ccb52d59-ed59-4e6f-8614-d5c22cdc5325","added_by":"auto","created_at":"2026-02-03 19:07:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":491332,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted values from the best GLMM models (Table 2) illustrating the effects of explanatory variables on functional traits of \u003cem\u003eN. ovata\u003c/em\u003e: (\u003cstrong\u003ea) \u003c/strong\u003e– plant height\u003cstrong\u003e, (b, c)\u003c/strong\u003e – inflorescence length, (\u003cstrong\u003ed, e\u003c/strong\u003e) – number of flowers, (\u003cstrong\u003ef, g\u003c/strong\u003e) – number of fertilized flowers, (\u003cstrong\u003eh\u003c/strong\u003e) – percentage of fertilized flowers, (\u003cstrong\u003ei\u003c/strong\u003e) – leaf area, and (\u003cstrong\u003ej\u003c/strong\u003e) – chlorophyll content. In each plot, the unplotted variable is set at the mean value.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8707471/v1/9b9bce9916bbff426f16268b.png"},{"id":101881561,"identity":"a9d68d2f-c4d2-461b-ab02-a36df397c219","added_by":"auto","created_at":"2026-02-04 15:13:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2546820,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8707471/v1/236db48d-bbda-4272-8fd4-8d7a2a83c5a7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The hummocky meadow micro-scale patchiness effect on the Eggleaf twayblade Neottia ovata (L.) Bluff \u0026 Fingerh. (Orchidaceae) functional traits","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe environmental heterogeneity hypothesis holds that spatial heterogeneity in biotic and abiotic environmental conditions increases biodiversity (MacArthur and Wilson \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1967\u003c/span\u003e; Ricklefs \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1977\u003c/span\u003e). This relationship seems to be highly scale- and potentially resource dependent, lending support to the assumption that soil chemistry, texture, and depth, for example, have the greatest impact on community composition at small spatial scales, with climatic variables becoming increasingly important as scale increases (Costanza et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). At micro scales, environmental heterogeneity within communities is assumed to support resource partitioning between competing species (Bolker \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Costanza et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In contrast, at the local-patch scale; small-scale soil or resource heterogeneities can provide dissimilar niches, possibly favouring particular individuals of competing species (Olagoke et al. 2023) or providing optimum conditions for a single species. Little is known about how the plants respond to environmental heterogeneity (hump-pit topography) at such small spatial scales (Bell and Lechowicz \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Kephart and Paladino \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1997\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe treethrow hump-pit microtopography, i.e. hummocky meadows, represents a specific type of grassland surface, that occurs on loose carbonate material. This type of meadows has been studied mainly by German-speaking researchers (Penck 1940/41) and named \u0026lsquo;Buckelwiese\u0026rsquo; (German: Buckelwiese, literally \u0026lsquo;hummocky meadow\u0026rsquo;) with evidence from the Alpine areas including Slovenia. Erosion itself plays a key role, however it still remains unclear which process triggers the initial formation of such micro-geomorphological surfaces. The hillslope topography (hummocky meadow) was also interpreted as an effect of simultaneous congelifluction (Dylikowa \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1956\u003c/span\u003e), meaning a relatively slow and long-term soil flow over permafrost or within a periglacial zone. Later on, a new model of the formation of this topography was introduced, proposing that the hump-pit micro-topography was due to the uprooting of trees caused by a strong wind events (Gerlach \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1960\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe species composition and distribution of alpine meadow plants are influenced by a variety of factors lasting over a long period, including climate, soil, water, topography, and plant biological characteristics (Zhang et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Among abiotic factors, topographical factors have a strong impact on plant growth (Davies et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), especially when the research scale is small. Topography at the local scale is one the most important factors affecting vegetation; species composition (Swenson et al. 2011), plant growth (Hara et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Sakai and Ohsawa 1994), and the spatial distribution of plant populations (Palmer and Dixon \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1990\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccording to Prezelj (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and regarding hummocky meadow micro-topography; shallower soil, faster drying, and lower amount of the remaining moisture is characteristic at the top of the humps compared to pits. The soil of the larger humps dries faster in comparison to medium and small humps. The position on the hump, size of the hump and insolation affect the vegetation composition and species richness (Prezelj \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). At the top of the humps the vegetation typical for drier and shallower soils is found. Pits are characterized by the vegetation of moist, deeper and fertile soil. Species diversity is greatest in the middle of humps (Prezelj \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Differences in functional group composition were also observed: moss cover increased towards the top of the hump, and the grass cover, inversely, decreased. The proportion of clay on the hump is smaller than in the depression (pit), which indicates that the particles are washed into the depression.\u003c/p\u003e \u003cp\u003e \u003cem\u003eNeottia ovata\u003c/em\u003e (L.) Bluff \u0026amp; Fingerh. is a sturdy, robust species: it can thrive without mycorrhiza, the rhizome of adult plants is free of mycorrhizal colonization (Kotil\u0026iacute;nek et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and it flowers continuously for over 20 years, pollinated by the numerous insects (Coleoptera, Hymenoptera and Diptera) attracted to the abundant nectar (Delforge \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). \u003cem\u003eN. ovata\u003c/em\u003e is self-compatible but has a well-developed rostellum to prevent self-fertilization (Talalaj et al. 2017). It is a rewarding species, with a Eurasian distribution and has also been reported as introduced in Ontario (Canada)- very widespread and common in the temperate zone but rare in the Mediterranean regions of Europe. Preston and Hill (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) include \u003cem\u003eN. ovata\u003c/em\u003e as a member of the Eurosiberian, Boreo-temperate element. A habitat generalist, it occupies a wide range of habitats from lowlands to mountains and from wet to dry sites, including anthropogenic sites (Kotil\u0026iacute;nek et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). It is a perennial herb whose populations are maintained predominantly by sexual reproduction, as vegetative spread is limited. \u003cem\u003eN. ovata\u003c/em\u003e is a long-lived species, as shown in the long-term observations of Tamm (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Due to its longevity, the species is a suitable indicator of recent land-use changes. Variation of flower structure and nectar chemistry and their weak correlation with reproductive success confirms the generalistic character of \u003cem\u003eN. ovata\u003c/em\u003e (Brzosko et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) regarding effective pollinators.\u003c/p\u003e \u003cp\u003eThe aim of the present study was to evaluate the effect of hummocky meadow micro-scale habitat patchiness, i.e. small-scale habitat heterogeneity on selected \u003cem\u003eN. ovata\u003c/em\u003e functional traits. The following two questions were addressed: (i) how does specific hump location ecologically affect measured functional trait values and (ii) is there an ecological optimum hump location for \u003cem\u003eN. ovata\u003c/em\u003e considering the observed functional trait values?\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eStudy Area\u003c/p\u003e \u003cp\u003eIn order to minimize the impact of certain, site-specific environmental factors such as altitude, meadow exposure, shading effects, slope of the terrain, meadow management regime, etc. a single hummocky meadow was sampled in this study. The studied meadow is located in the NE part of Triglav National Park, NW Slovenia (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), at an average altitude of 1350 m and covers an area of 0.65 ha. This meadow is enclosed on all sides by forest except on its SW side, where it borders a paved road leading towards the Vršič pass (1611 m, centroid point: 46\u0026deg;26'22.84\"N, 13\u0026deg;45'22.43\"E). Based on the site conditions, the potential forest vegetation of this stand, dominated by \u003cem\u003ePicea abies\u003c/em\u003e, \u003cem\u003eFagus sylvatica\u003c/em\u003e and \u003cem\u003eLarix decidua\u003c/em\u003e, belongs to the association \u003cem\u003ePolysticho lonchitis\u003c/em\u003e-\u003cem\u003eFagetum\u003c/em\u003e var. geogr. \u003cem\u003eAnemone trifolia\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eIn-situ\u003c/em\u003e data collecting\u003c/p\u003e \u003cp\u003eOn site we examined 382 flowering \u003cem\u003eN. ovata\u003c/em\u003e specimens from 23 humps. Following functional character values; plant height and inflorescence length were obtained \u003cem\u003ein-situ\u003c/em\u003e using a digital beak scale, number of flowers and number of fertilized flowers were counted in the field for each specimen. Total leaf area for each specimen was obtained using a digital camera and ImageJ, open-source software package for processing and analysing scientific images. For leaf chlorophyll content a non-destructive measurement was performed using SPAD-502 Plus device. The SPAD value, determined by SPAD-502 provides an indication of the relative amount of chlorophyll present in plant leaves, which can serve as an indicator of the overall condition of the plant itself. Two measurements (for both leaves) per plant were taken, the average values were used for further analysis. Vegetation height of the surrounding grass (turf) cover at close vicinity of each plant was measured together with substrate depth. For each plant the hump location was observed and measured; vertical distance from the hump base and the plant location. Since humps vary in their height, plant height on a hump index for each plant was calculated: plant height on a hump/total hump height. Value 0 indicates location at the base of the hump while the value 1 means the summit location. Hump slope inclination was calculated: hump total height and horizontal distance: hump summit-hump rim, were measured in the field. For each hump, slope inclination was calculated later on using angular functions.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eFirst, to describe the data structure, we calculated means with standard deviations (SD), medians with interquartile ranges (IQR), and the coefficient of variation for all variables. The Kolmogorov\u0026ndash;Smirnov test was used to check whether values of variables departed significantly from normality. Redundancy analysis (RDA) was initially conducted to explore the relationships between plant functional traits and environmental variables (micro-relief, substrate depth and turf height) and to evaluate the relative influence of environmental variables on plant functional traits. To account for differences in scale, environmental variables were standardized prior to analysis. To assess multicollinearity among environmental variables, we calculated the Variance Inflation Factor (VIF). Variables with a VIF greater than 3 were removed to reduce multicollinearity and ensure the stability of the RDA results. The statistical significance of the model, axis and environmental variables was evaluated using the Monte Carlo permutation test with 999 permutations. The ordination analysis was conducted in vegan R package (Oksanen et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). To explore the effect of micro-relief, also substrate depth and vegetation (turf) height, on the variability of the functional traits of \u003cem\u003eN. ovata\u003c/em\u003e, we applied generalized linear mixed models (GLMMs). Through the mixed procedure, we addressed spatial dependency arising from repeated measurements of plant traits on the same hump by specifying the hump as a random factor. Plant height, inflorescence length, number of flowers, number of fertilized flowers, percentage of fertilized flowers, leaf area, and leaf chlorophyll concentration were used as dependent (response) variables. All the measured plants were classified into quartiles based on their functional trait values. Since we were interested in the effect of micro-relief, we present in discussion the first quartile (Q1, N\u0026thinsp;=\u0026thinsp;95), which comprised the highest values of the measured functional traits more in detail. Values of dependent variables were modelled using a negative binomial distribution family, a distribution applied for over-dispersed count data (Zuur et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Zuur and Ieno \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The following independent (explanatory) variables were addressed as potential predictors of the dependent variables: hump height (Hump_H), plant height on a hump (PlantH_Hump), plant height on a hump index (PlantH_HumpInd), hump slope inclination (Hump_Inc), substrate depth (Sub_D) and vegetation height (Veg_H). Prior to model fitting, to avoid common statistical problems, a detailed exploratory data analysis was conducted in accordance with the standard procedures for regression-type analyses (Zuur et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Zuur and Ieno \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Accordingly, the presence of outliers in response and explanatory variables was visually checked with Cleveland dot plots. Multi-collinearity among variables was assessed using Pearson correlation coefficients and variance inflation factors (VIF), with cutoff values of r\u0026thinsp;\u0026ge;\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7 and VIF\u0026thinsp;\u0026gt;\u0026thinsp;3 (Zuur et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Possible non-linear effects of continuous variables in the models were examined using generalized additive models (GAM) with the gam R package (Hastie, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Similarly to the RDA analysis, the variables were standardized prior to model fitting to account for differences in their scales. After fitting the initial models, including all noncollinear variables of interest, we conducted model selection. A backward elimination procedure was chosen, whereby variables were progressively deleted according to corrected Akaike information criterion (AICc), with lower values indicating better models (Burnham and Anderson \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Since in some cases we found high model uncertainty, i.e. similarly high AICc weights in two or more models, we applied model averaging for models with similar AICc weights, since this procedure provides a robust means of obtaining parameter estimates (Burnham and Anderson \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Both model selection and model averaging were carried out in the MuMIn R package (Bartoń \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Given the nearly equal Akaike weights of the two models predicting leaf area, model averaging was performed using conditional averaging, which estimates effects only from models that include each variable. This approach highlights the contributions of variables supported by the model set. Model validation was carried out on the final models (Zuur et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Zuur and Ieno \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). All GLMMs were fitted with the glmmTMB R package (Brooks et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Surface plots were created in the rgl R package (Murdoch and Adler \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). All statistical analyses were carried out in the R version 4.1.2 (R Development Core Team, 2021).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eDescriptive statistics for all variables included in the analysis are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Data exploration revealed the presence of two outlying values in the independent variable vegetation (turf) height, so they were removed from the dataset. The independent variable hump height was not included in the initial models because it was collinear (r\u0026thinsp;\u0026gt;\u0026thinsp;0.7; VIF\u0026thinsp;\u0026gt;\u0026thinsp;3) with plant height on a hump index and hump slope inclination. Accordingly, the plant height on a hump index, hump slope inclination, substrate depth and vegetation height were included in the initial models.\u003c/p\u003e \u003cp\u003eThe first two RDA axes explained 79.01% and 14.94% of the plant functional traits variation, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The overall model was found to be significant (p\u0026thinsp;=\u0026thinsp;0.001), indicating that the environmental variables explained a significant portion of the variation in the plant functional traits. The RDA1 axis was highly significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), explaining the majority of the variation in the response data. Additionally, the RDA2 axis was also significant (p\u0026thinsp;=\u0026thinsp;0.045), although it explained a smaller portion of the variation and had a weaker effect compared to RDA1. All environmental variables were found to significantly explain variation in plant functional traits. The variables PlantH_Hump, PlantH_HumpInd, and Veg_H were all highly significant (p\u0026thinsp;=\u0026thinsp;0.001) predictors of the response variables. The variable Hump_Inc was also significant (p\u0026thinsp;=\u0026thinsp;0.011), although it had a smaller effect compared to the other 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\u003eDescriptive statistics for the independent variables (micro-relief, pedological and vegetational characteristics) and the dependent functional traits of \u003cem\u003eN. ovata\u003c/em\u003e. Variables with non-normally distributed data are in bold.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHump height (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlant height on a hump (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePlant height on a hump index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHump slope inclination (\u0026deg;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSubstrate depth (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVegetation height (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u0026plusmn;standard deviation\u003c/p\u003e \u003cp\u003emedian, IQR\u003c/p\u003e \u003cp\u003ecoefficient of variation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e185.72\u0026thinsp;\u0026plusmn;\u0026thinsp;75.64\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e175.00, 99.00\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e40.73\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e120.46\u0026thinsp;\u0026plusmn;\u0026thinsp;61.59\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e110.00, 87.00\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e51.13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e0.71, 0.42\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e35.60\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e27.80\u0026thinsp;\u0026plusmn;\u0026thinsp;11.21\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e23.70, 3.50\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e40.35\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e12.11\u0026thinsp;\u0026plusmn;\u0026thinsp;5.43\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e11.00, 7.00\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e44.84\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e23.46\u0026thinsp;\u0026plusmn;\u0026thinsp;10.99\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e21.00, 19.25\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e46.86\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDependent variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlant height (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInflorescence length (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of flowers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNumber of fertilized flowers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePercentage of fertilized flowers (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLeaf area (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLeaf chlorophyll concentration\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u0026plusmn;standard deviation\u003c/p\u003e \u003cp\u003emedian, IQR\u003c/p\u003e \u003cp\u003ecoefficient of variation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.80\u0026thinsp;\u0026plusmn;\u0026thinsp;8.45\u003c/p\u003e \u003cp\u003e38.50, 12.00\u003c/p\u003e \u003cp\u003e21.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e14.21\u0026thinsp;\u0026plusmn;\u0026thinsp;5.18\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e15.00, 7.00\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e36.42\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e25.90\u0026thinsp;\u0026plusmn;\u0026thinsp;10.59\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e26.00, 13.00\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e40.88\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e20.95\u0026thinsp;\u0026plusmn;\u0026thinsp;10.05\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e22.00, 14.00\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e47.99\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e79.70\u0026thinsp;\u0026plusmn;\u0026thinsp;18.23\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e84.00, 18.00\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e22.87\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e27.85\u0026thinsp;\u0026plusmn;\u0026thinsp;11.02\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e26.00, 17.00\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e39.56\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e28.17\u0026thinsp;\u0026plusmn;\u0026thinsp;10.82\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e30.00, 14.25\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e38.41\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGiven the large number of dependent variables evaluated using GLMMs, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes effect directions across models.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of effect directions across GLMMs for all dependent variables. Symbols indicate the direction of effects: + positive effect, \u0026ndash; negative effect, and / not included in the final (best) model. Full model estimates and statistical results are provided in the text.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraits\u0026darr;/Predictors\u0026rarr;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHump height (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlant height on a hump (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePlant height on a hump index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHump slope inclination (\u0026deg;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSubstrate depth (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVegetation height (cm)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlant height (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\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\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflorescence length (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\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\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of flowers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\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\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of fertilized flowers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\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\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercentage of fertilized flowers (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\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\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeaf area (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\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\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeaf chlorophyll concentration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\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\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAccording to model selection based on AICc values (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), the plant height was best explained by substrate depth and vegetation height. Plant height increased with both substrate depth (Sub_D Estimated β\u0026thinsp;\u0026plusmn;\u0026thinsp;SE: 0.0410\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0112, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and vegetation height (Veg_H Estimated β\u0026thinsp;\u0026plusmn;\u0026thinsp;SE: 0.0504\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0130, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003ePlant height on a hump, plant height on a hump index, substrate depth and vegetation height best explained inflorescence length, number of flowers and number of fertilized flowers (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Inflorescence length decreased with increasing plant height on a hump (PlantH_Hump Estimated β\u0026thinsp;\u0026plusmn;\u0026thinsp;SE: -0.0643\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0240, p\u0026thinsp;=\u0026thinsp;0.007) and increased with plant height on a hump index (PlantH_HumpInd Estimated β\u0026thinsp;\u0026plusmn;\u0026thinsp;SE: 0.0591\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0225, p\u0026thinsp;=\u0026thinsp;0.009) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). It was also positively associated with substrate depth (Sub_D Estimated β\u0026thinsp;\u0026plusmn;\u0026thinsp;SE: 0.0520\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0195, p\u0026thinsp;=\u0026thinsp;0.008) and vegetation height (Veg_H Estimated β\u0026thinsp;\u0026plusmn;\u0026thinsp;SE: 0.0488\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0211, p\u0026thinsp;=\u0026thinsp;0.020) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). The number of flowers followed the similar trends, decreasing with increasing plant height on a hump (PlantH_Hump Estimated β\u0026thinsp;\u0026plusmn;\u0026thinsp;SE: -0.0718\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0287, p\u0026thinsp;=\u0026thinsp;0.012) and increasing with plant height on a hump index (PlantH_HumpInd Estimated β\u0026thinsp;\u0026plusmn;\u0026thinsp;SE: 0.0811\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0266, p\u0026thinsp;=\u0026thinsp;0.002) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed), substrate depth (Sub_D Estimated β\u0026thinsp;\u0026plusmn;\u0026thinsp;SE: 0.0537\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0230, p\u0026thinsp;=\u0026thinsp;0.019) and vegetation height (Veg_H Estimated β\u0026thinsp;\u0026plusmn;\u0026thinsp;SE: 0.0710\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0250, p\u0026thinsp;=\u0026thinsp;0.004) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee). Similarly, the number of fertilized flowers decreased with increasing plant height on a hump (PlantH_Hump Estimated β\u0026thinsp;\u0026plusmn;\u0026thinsp;SE: -0.1057\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0343, p\u0026thinsp;=\u0026thinsp;0.002) and increased with plant height on a hump index (PlantH_HumpInd Estimated β\u0026thinsp;\u0026plusmn;\u0026thinsp;SE: 0.1210\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0317, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef), substrate depth (Sub_D Estimated β\u0026thinsp;\u0026plusmn;\u0026thinsp;SE: 0.0538\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0273, p\u0026thinsp;=\u0026thinsp;0.048) and vegetation height (Veg_H Estimated β\u0026thinsp;\u0026plusmn;\u0026thinsp;SE: 0.0710\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0298, p\u0026thinsp;=\u0026thinsp;0.017) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eg).\u003c/p\u003e \u003cp\u003eAcross all models, plant height on a hump and plant height on a hump index were key predictors of the percentage of fertilized flowers (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The percentage decreased with increasing plant height on a hump (PlantH_Hump Estimated β\u0026thinsp;\u0026plusmn;\u0026thinsp;SE: -0.0362\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0183, p\u0026thinsp;=\u0026thinsp;0.048) and increased with plant height on a hump index (PlantH_HumpInd Estimated β\u0026thinsp;\u0026plusmn;\u0026thinsp;SE: 0.0385\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0174, p\u0026thinsp;=\u0026thinsp;0.027) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eh).\u003c/p\u003e \u003cp\u003eFor total leaf area, the parameter estimates from two near-equally weighted models (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) were averaged using Akaike weights, yielding the following results: leaf area increased with substrate depth (Sub_D Estimated β\u0026thinsp;\u0026plusmn;\u0026thinsp;SE: 0.0326\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0222, p\u0026thinsp;=\u0026thinsp;0.144) and vegetation height (Veg_H Estimated β\u0026thinsp;\u0026plusmn;\u0026thinsp;SE: 0.0534\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0255, p\u0026thinsp;=\u0026thinsp;0.037) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ei).\u003c/p\u003e \u003cp\u003eThe model including hump inclination and vegetation height best explained leaf chlorophyll concentration (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Chlorophyll concentration decreased with increasing hump inclination (Hump_I Estimated β\u0026thinsp;\u0026plusmn;\u0026thinsp;SE: -0.1039\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0294, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and increased with vegetation height (Veg_H Estimated β\u0026thinsp;\u0026plusmn;\u0026thinsp;SE: 0.1008\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0244, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ej).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel selection according to the corrected Akaike information criterion (AICc) and the Akaike weight (wi). Models are ordered from the most to the least appropriate. df, degrees of freedom.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel structure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAICc\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ew\u003csub\u003ei\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003ePlant height\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ey\u0026thinsp;~\u0026thinsp;Sub_D\u0026thinsp;+\u0026thinsp;Veg_H\u0026thinsp;+\u0026thinsp;1|Hump\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2655.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ey\u0026thinsp;~\u0026thinsp;Hump_Inc\u0026thinsp;+\u0026thinsp;Sub_D\u0026thinsp;+\u0026thinsp;Veg_H\u0026thinsp;+\u0026thinsp;1|Hump\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2656.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ey\u0026thinsp;~\u0026thinsp;PlantH_HumpInd\u0026thinsp;+\u0026thinsp;Hump_Inc\u0026thinsp;+\u0026thinsp;Sub_D\u0026thinsp;+\u0026thinsp;Veg_H\u0026thinsp;+\u0026thinsp;1|Hump\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2658.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInflorescence length\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ey\u0026thinsp;~\u0026thinsp;PlantH_Hump\u0026thinsp;+\u0026thinsp;PlantH_HumpInd+ Sub_D\u0026thinsp;+\u0026thinsp;Veg_H\u0026thinsp;+\u0026thinsp;1|Hump\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2351.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ey\u0026thinsp;~\u0026thinsp;PlantH_Hump\u0026thinsp;+\u0026thinsp;PlantH_HumpInd\u0026thinsp;+\u0026thinsp;Hump_Inc\u0026thinsp;+\u0026thinsp;Sub_D\u0026thinsp;+\u0026thinsp;Veg_H\u0026thinsp;+\u0026thinsp;1|Hump\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2353.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ey\u0026thinsp;~\u0026thinsp;PlantH_Hump\u0026thinsp;+\u0026thinsp;PlantH_HumpInd\u0026thinsp;+\u0026thinsp;Sub_D\u0026thinsp;+\u0026thinsp;1|Hump\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2354.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of flowers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ey\u0026thinsp;~\u0026thinsp;PlantH_Hump\u0026thinsp;+\u0026thinsp;PlantH_HumpInd\u0026thinsp;+\u0026thinsp;Sub_D\u0026thinsp;+\u0026thinsp;Veg_H\u0026thinsp;+\u0026thinsp;1|Hump\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2905.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ey\u0026thinsp;~\u0026thinsp;PlantH_Hump\u0026thinsp;+\u0026thinsp;PlantH_HumpInd\u0026thinsp;+\u0026thinsp;Hump_Inc\u0026thinsp;+\u0026thinsp;Sub_D\u0026thinsp;+\u0026thinsp;Veg_H\u0026thinsp;+\u0026thinsp;1|Hump\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2907.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ey\u0026thinsp;~\u0026thinsp;PlantH_HumpInd\u0026thinsp;+\u0026thinsp;Hump_Inc\u0026thinsp;+\u0026thinsp;Sub_D\u0026thinsp;+\u0026thinsp;Veg_H\u0026thinsp;+\u0026thinsp;1|Hump\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2908.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of fertilized flowers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ey\u0026thinsp;~\u0026thinsp;PlantH_Hump\u0026thinsp;+\u0026thinsp;PlantH_HumpInd\u0026thinsp;+\u0026thinsp;Sub_D\u0026thinsp;+\u0026thinsp;Veg_H\u0026thinsp;+\u0026thinsp;1|Hump\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2853.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ey\u0026thinsp;~\u0026thinsp;PlantH_Hump\u0026thinsp;+\u0026thinsp;PlantH_HumpInd\u0026thinsp;+\u0026thinsp;Hump_Inc\u0026thinsp;+\u0026thinsp;Sub_D\u0026thinsp;+\u0026thinsp;Veg_H\u0026thinsp;+\u0026thinsp;1|Hump\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2855.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ey\u0026thinsp;~\u0026thinsp;PlantH_Hump\u0026thinsp;+\u0026thinsp;PlantH_HumpInd\u0026thinsp;+\u0026thinsp;Veg_H\u0026thinsp;+\u0026thinsp;1|Hump\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2855.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePercentage of fertilized flowers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ey\u0026thinsp;~\u0026thinsp;PlantH_Hump\u0026thinsp;+\u0026thinsp;PlantH_HumpInd\u0026thinsp;+\u0026thinsp;1|Hump\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3417.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ey\u0026thinsp;~\u0026thinsp;PlantH_Hump\u0026thinsp;+\u0026thinsp;PlantH_HumpInd\u0026thinsp;+\u0026thinsp;Veg_H\u0026thinsp;+\u0026thinsp;1|Hump\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3419.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ey\u0026thinsp;~\u0026thinsp;PlantH_Hump\u0026thinsp;+\u0026thinsp;PlantH_HumpInd+ Sub_D\u0026thinsp;+\u0026thinsp;Veg_H\u0026thinsp;+\u0026thinsp;1|Hump\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3421.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLeaf area\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ey\u0026thinsp;~\u0026thinsp;Sub_D\u0026thinsp;+\u0026thinsp;Veg_H\u0026thinsp;+\u0026thinsp;1|Hump\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2843.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ey\u0026thinsp;~\u0026thinsp;Veg_H\u0026thinsp;+\u0026thinsp;1|Hump\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2843.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ey\u0026thinsp;~\u0026thinsp;Hump_Inc\u0026thinsp;+\u0026thinsp;Sub_D\u0026thinsp;+\u0026thinsp;Veg_H\u0026thinsp;+\u0026thinsp;1|Hump\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2845.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLeaf chlorophyll concentration\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ey\u0026thinsp;~\u0026thinsp;Hump_Inc\u0026thinsp;+\u0026thinsp;Veg_H\u0026thinsp;+\u0026thinsp;1|Hump\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2938.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ey\u0026thinsp;~\u0026thinsp;Hump_Inc\u0026thinsp;+\u0026thinsp;Sub_D\u0026thinsp;+\u0026thinsp;Veg_H\u0026thinsp;+\u0026thinsp;1|Hump\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2940.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ey\u0026thinsp;~\u0026thinsp;PlantH_HumpInd\u0026thinsp;+\u0026thinsp;Hump_Inc\u0026thinsp;+\u0026thinsp;Sub_D\u0026thinsp;+\u0026thinsp;Veg_H\u0026thinsp;+\u0026thinsp;1|Hump\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2941.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12\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\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eHummocky meadow a spectrum of micro-niches\u003c/p\u003e \u003cp\u003eHummocky meadows represent a specific habitat, where every hummock has slightly different biotic and abiotic properties. Hummock micro-topography establishes specific microclimatic conditions, with small-scale variations in soil thermal properties and water regimes, which influence biogeochemical cycles. These properties, coupled with different litter decomposability, may cause variations in soil physical and chemical properties and pedogenesis, as well as a selective distribution of plant species (Pintaldi et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Many typical taxa of extensive montane grasslands with narrow ecological valence, habitat specialists prefer specific habitat conditions, specific micro sites. On the other hand, generalists are characterized by random spatial distribution within the habitat, regardless of the ecological conditions at a micro scale. However, the heterogeneity of the appropriate habitat provides a spectrum of natural gradients in the ecosystem. Generalists inhabit a wide range of ecological conditions. However, a heterogeneous habitat from a topographic perspective provides niches for generalists where they have optimal conditions as shown in this study.\u003c/p\u003e \u003cp\u003ePost-glacial, hummocky type of surface geomorphological profile (Embleton-Hamann \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), has a thinner layer of soil on the hump and a thicker layer of soil in the depression (pit), with the soil layer gradually decreases towards the top of the hump. Such a profile indicates corrosion, which is explained by the fact that more water and nutrients flows into the depression, where the dissolution of the rock forms a thicker soil. The tops of the humps are dominated by washed out rendzines, while macro-elements are washed towards the pits. Pedologically, the height of the hump represents a whole spectrum of potentially favourable ecological niches. Unlike the depressions between the humps (pits), where the soil layer is usually 30 cm thick or more, the tops of the humps are the driest, and the substrate layer is thin and subject of erosion. The cross section of this profile, the hump resembles a sketch of a sinkhole. The humps vary in size, but are similar in shape. They are arranged in no particular order across former alluvial landscape. The upper convex part of the hump descends into a concave depression through the intermediate part (Lukan Klavžer \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Authors agree that hummocky meadows form on unconsolidated carbonate material. Therefore, hummocky meadows are also associated with areas that are made of carbonate rocks, or were transported to this area from elsewhere, either by glacial or river transport (Cvetek \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1971\u003c/span\u003e; Embleton-Hamann \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Even if considered as a habitat generalist, the specific micro-location of \u003cem\u003eN. ovata\u003c/em\u003e specimens on the meadow humps plays a vital role in the plant\u0026rsquo;s functional trait values and affects the spatial distribution of \u003cem\u003eN. ovata\u003c/em\u003e plants.\u003c/p\u003e \u003cp\u003eThe effect of hump and pit micro-relief on \u003cem\u003eNeottia ovata\u003c/em\u003e functional traits\u003c/p\u003e \u003cp\u003eAs shown in this study, all the environmental variables were found to significantly explain variation in plant functional traits. The variables PlantH_Hump, PlantH_HumpInd, and Veg H (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) were all highly significant predictors of the response variables. According to model selection based on AICc values, the plant height was best explained by substrate depth and vegetation height. \u003cem\u003eN. ovata\u003c/em\u003e plant height increases with both, substrate depth and vegetation height. Pits and lower areas of humps are characterized by deeper and more fertile substrate, creating optimal conditions for a whole spectrum of other plant species, especially grasses (fam. Poaceae). \u003cem\u003eN. ovata\u003c/em\u003e plant height on a hump index, substrate depth and vegetation height best explained inflorescence length, number of flowers and number of fertilized flowers. Inflorescence length increases with increasing PlantH_HumpInd, i.e. plant height on a hump index. \u003cem\u003eN. ovata\u003c/em\u003e specimens with longest inflorescences (Q1, N\u0026thinsp;=\u0026thinsp;95) are found on hump slopes with mean PlantH_HumpInd value of 0.698- on micro-locations that represent 69% of the total hump height). Specimens of \u003cem\u003eN. ovata\u003c/em\u003e growing higher on the humps have in general longer inflorescence compared to plants inhabiting pits or basal parts of the humps. The number of flowers followed the similar trends; increasing with plant height on a hump index, substrate depth and vegetation height. Similarly, the number of fertilized flowers increased with plant height on a hump index, substrate depth and vegetation height. Across all models, PlantH_HumpInd value was key predictor of the percentage of fertilized flowers Per_Fer_Flow. The percentage increased with plant height on a hump index. \u003cem\u003eN. ovata\u003c/em\u003e specimens with highest percentage of fertilized flowers (Q1, N\u0026thinsp;=\u0026thinsp;95) are found oh hump slopes with mean PlantH_HumpInd value of 0.706- on micro-locations that represent 70% of the total hump height. Proportion of the fertilized flowers goes along with plant general viability. In general reproductive success increases with plant height on a hump index. For leaf area, the parameter estimates from two near-equally weighted models were averaged using Akaike weights, yielding the following results: leaf area increased with substrate depth and vegetation height. Plants growing in pits or on the lower parts of the humps experience intense shadowing since vegetation is taller compared to hump tops. In that regard, plants respond by enlarging leaf areas in order to ensure sufficient photosynthesis. \u003cem\u003eN.\u003c/em\u003e ovata specimens with the highest total leaf area values (Q1, N\u0026thinsp;=\u0026thinsp;95) are found on lower parts of the humps with a mean Leaf_A value of 28.279 cm\u003csup\u003e2\u003c/sup\u003e. In contrast, \u003cem\u003eN. ovata\u003c/em\u003e specimens with the lowest total leaf area values (Q1, N\u0026thinsp;=\u0026thinsp;95) are found on upper slopes and bottoms of the humps with mean Leaf_A value of 27.291 cm\u003csup\u003e2\u003c/sup\u003e. The model including hump inclination and vegetation height best explained leaf chlorophyll concentration. Chlorophyll concentration decreased with increasing hump inclination and increased with vegetation height. In conclusion, the micro-location of \u003cem\u003eN. ovata\u003c/em\u003e (expressed as plant height on a hump index values) specimens on the humps plays a vital role in plant functional trait values, it also affects plants fitness and the percentage of pollinated flowers.\u003c/p\u003e \u003cp\u003eHummocky meadows, biodiverse but threatened habitat\u003c/p\u003e \u003cp\u003eDespite relief heterogeneity and the associated diversity of ecological niches, such meadows are degraded through leveling due to the difficulty of cultivation, mandatory manual mowing. In the winter months, when there was less agricultural activity, farmers leveled them (Cvetek \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1971\u003c/span\u003e). Since such meadows are unsuitable for mechanical cultivation, some meadows were leveled, while others were abandoned and overgrown. However, leveling hummocky meadows is controversial, as it destroys natural, geomorphological and cultural heritage (Ambrožič \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) in the first place. Hummocky topography significantly influences topsoil properties, pedogenesis and vegetation distribution, with large differences between hummocks and interhummocks (Pintaldi et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). A positive relationship has been reported between species richness and odds of hummock occurrence may be related to the heterogeneous microtopography created by the hummocks at those sites (Smith et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Diversity of plant species is much greater in areas with heterogeneous microtopography (hummocked) compared to flat surfaces (Vivian-Smith \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). In our study, we show that hummocks provide a favourable habitat for \u003cem\u003eN. ovata\u003c/em\u003e. We assume there are many other species that prefer different micro habitats, which make them more or less competitive. Moreover, due to these specific properties several plant species are found only where hummocks create favourable habitat because of their heterogeneous microtopography (Smith et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In addition, hummocky meadows are also much more diverse in terms of biodiversity than leveled meadows, as the growth conditions on humps and in depressions (pits) are very different (Prezelj \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Also shown in this study, ecological conditions vary greatly on the slopes of the humps. Altering them represents another threat, contributing to the homogenization of the landscapes. In addition, as shown in this study, the micro-patchiness of this particular, valuable habitat provides niches suitable for a large spectrum of different organisms. The leveling of hummocky meadows represents the loss of a diverse range of microclimatic and pedological micro-sites, which are vital for ensuring viable populations of a species, even if being considered as a habitat generalist. These, uneven grasslands in the montane belt of SE Alps represent a rich pool for further research, protecting the remaining hummocky meadows means protecting the biodiversity of the fragile alpine environment.\u003c/p\u003e \u003cp\u003eResearch limitations\u003c/p\u003e \u003cp\u003eAlthough this study was conducted at a single site, the generalizability of the findings is not affected. The aim of the present study was to explain the micro-scale patchiness effect on the \u003cem\u003eN. ovata\u003c/em\u003e functional traits, and not comparing differences in ecological values among humps across multiple meadows, sites.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eI.P began the research, led the research and wrote most of the article, T.G. assisted with fieldwork and editing the article, K.P. did the field work, P.K. performed statistical analyses and edited the article. All authors have completed the final review of the final version of the article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAdewole Olagoke, Florian Jeltsch, Tietjen B, Berger U, Ritter H, Maa\u0026szlig; S. 2023. Small‐scale heterogeneity shapes grassland diversity in low‐to‐intermediate resource environments. 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R package version 1.0.1. https://CRAN.R-project.org/package=rgl\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-biosystems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Plant Biosystems](https://link.springer.com/journal/44473)","snPcode":"44473","submissionUrl":"https://submission.springernature.com/new-submission/44473/3?","title":"Plant Biosystems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Neottia ovata, Orchids, Hummocky meadows, Habitat micro-scale patchiness, Functional trait variability, Environmental parameters","lastPublishedDoi":"10.21203/rs.3.rs-8707471/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8707471/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRegularly managed hummocky meadows represent a species-rich, distinct type of post-glacial grassland communities, which appear on loose carbonate material. Such micro-reliefs are characterized by numerous humps and pits, i.e. convex and concave features of the surface. Little is known about how plants respond to environmental heterogeneity at such small spatial scales. \u003cem\u003eNeottia ovata\u003c/em\u003e (Orchidaceae) is a member of the Eurosiberian, Boreo-temperate element. This species is a well documented habitat generalist, occupying a wide range of habitats ranging from lowlands to mountains and from wet to dry sites, often including anthropogenic habitats. The aim of the present study was to evaluate the effect of hummocky meadow micro-scale habitat patchiness, i.e. small-scale habitat heterogeneity on selected \u003cem\u003eN. ovata\u003c/em\u003e functional traits. Across all generalized linear mixed models (GLMMs), plant micro-location on the slope of a hump, expressed as plant height on a hump index was the key predictor of the analysed functional traits. The specific micro-location of \u003cem\u003eN. ovata\u003c/em\u003e specimens on the meadow humps plays a vital role in the plant\u0026rsquo;s functional trait values; it also affects fitness and the percentage of pollinated flowers. Hummocky meadow micro-scale patchiness offers interesting perspectives for further research. Protecting the remaining hummocky meadows contributes to the conservation of biodiversity of the fragile alpine environment.\u003c/p\u003e","manuscriptTitle":"The hummocky meadow micro-scale patchiness effect on the Eggleaf twayblade Neottia ovata (L.) Bluff \u0026amp; Fingerh. (Orchidaceae) functional traits","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-03 19:07:03","doi":"10.21203/rs.3.rs-8707471/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-02-06T10:59:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"182782219078014112926230178909201761862","date":"2026-02-06T10:55:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-05T12:46:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"221686252770795658788708724619486281952","date":"2026-02-02T08:13:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-30T14:50:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-29T08:49:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-29T08:48:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Plant Biosystems","date":"2026-01-27T07:32:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"plant-biosystems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Plant Biosystems](https://link.springer.com/journal/44473)","snPcode":"44473","submissionUrl":"https://submission.springernature.com/new-submission/44473/3?","title":"Plant Biosystems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"127bfc26-1f10-492a-b490-d3cd8f5dd8f5","owner":[],"postedDate":"February 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-07T13:55:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-03 19:07:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8707471","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8707471","identity":"rs-8707471","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}