Abstract
In many temperate marshes, the surface microtopography is determined by specific growth forms of dominant wetland plants. The formation of long-lasting distinct tussocks by some Carex species represents a growth adaptation, which significantly changes the ecosystem and facilitates the survival of other wetland plants. Therefore, the gradual decline of such an ecosystem engineer may affect both species diversity and the surface microtopography of wetlands in the long term. Using in situ terrestrial laser scanning, we analyzed tussock characteristics in five different stands typical of a temperate sedge-grass marsh to determine potential microtopography changes due to an altered water regime. Tussocks of Carex acuta were different in 2D area, perimeter, height, and roundness. The distances among tussocks were similar and distributed evenly in all vegetation stands. The highest tussocks occurred in stands dominated by Carex acuta and in Carex acuta and Calamagrostis canescens mixture stands. Glyceria maxima and Acorus calamus significantly modified the height and the shape of tussocks in contrast to Calamagrostis canescens, which affects tussocks at least and uses them as habitats. The characteristics of tussocks in mixed stands were influenced by the growth characteristics of all co-occurring dominant plants (ecosystem engineers). Frequent shallow short-term flooding is necessary to maintain the current microtopography in the studied sedge-grass marsh as it promotes the dominance of tussock forming wetland plants and excludes ruderal or invasive terrestrial plant species.
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Introduction
Wetland plants adapt differently to the specific environmental conditions where they grow1,2. Many wetland plant species have specific anatomical and morphological adaptations for growing in the anaerobic waterlogged soils3,4,5,6. These structural adaptations include cellular and tissue level adaptations such as the development of large intercellular spaces and aerenchyma tissues, primarily for supplying oxygen to belowground plant organs (rhizomes and roots)6,7,8,9,10,11.
Specific growth forms represent another level of structural adaptation for wetland plants. For instance, the formation of long-lasting distinct tussocks (hummocks or bog hummocks) is a typical growth adaptation in conditions of fluctuating water levels above the soil surface12,13,14,15. Wetlands with tussock structures are usually dominated by tussock-forming Carex (“true sedges”)16 species and by a mixture of several sedge-like plants (e.g., Eriophorum vaginatum, Juncus effusus). These are widely distributed mainly in temperate freshwater wetlands with semiaquatic conditions and frequent water level fluctuations14,17,18. Many sedge species (e.g., Carex acuta, C. appendiculata, C. meyeriana, C. schmidtii and C. stricta) develop tussock structures due to their ability to form short intravaginal stolons12,14,15,19,20,21. The development of tussock structures is often initiated to some extent by water level fluctuations18,22.
Tussock formations (hummocking) increase species diversity in many wetlands20,23,24. Some plant species can grow in the free space among tussocks referred to as interspaces (hollows) or on the edges and tops of the tussocks25, including rare species, e.g., Lord (1994) found a large population of rare species Lysimachia thyrsiflora and Bidens discoidea inside the Carex stricta tussocks. The diverse surface microtopography enables the co-occurrence of a range of wetland plant species. Moreover, the sedge tussocks may also serve as an important nesting site habitat for several wetland birds26,27. In contrast, non-tussock forming species (e.g., Acorus calamus, Calamagrostis canescens, Glyceria maxima, Iris pseudacorus) can spread when water level is only slightly fluctuating. These species often constitute monodominant stands with low habitat and species diversity22,28.
Different characteristics of the tussocks, such as height, area, or perimeter, determine the microtopography of the tussock wetlands17,19,29. These characteristics (metrics) are essential for future interpretation of different in situ measurements related to carbon or nutrient cycles22,24,30, as they control the sharp redox gradients that determine biogeochemical processes and the complex hydraulics of wetlands31,32,33.
Obtaining detailed information about microtopography is usually very time-consuming and complicated if in situ standard destructive field methods are used34. However, the non-destructive in situ method based on terrestrial laser scanning (TLS) may provide detailed information without the stand’s destruction29,35,36. TLS technology is a sophisticated remote sensing approach that has been used in the last few decades to map 2D or 3D structures of countless different types of objects from man-made structures to processes of the natural environment (e.g., dynamic fluvial processes37, erosion monitoring38). TLS is also an important tool in ecological research including vegetation structure, habitat mapping, biodiversity assessment, ecological succession/restoration, wildlife monitoring and carbon sequestration and ecosystem services (Table 1). Nevertheless, the simplicity of the laser technology during the field in situ measurements contrasts with the difficulty of post-processing, dealing with a vast amount of raw data (very dense point clouds in the 3D space) from in situ measurements39.
The aim of our study was to conduct a detailed analysis of surface tussock structures (microtopography) in a sedge-grass marsh using in situ terrestrial laser scanning (TLS). These analyses provided fundamental characteristics (metrics) of the tussock (polygon) structures during the period of dormancy. The obtained tussock characteristics were subsequently contextualized in relation to potential changes in the water regime, ecology, and biodiversity. Our objective was to verify the following hypothesis: The tussocks in monodominant Carex acuta stands differ from those in mixed Carex acuta stands within the studied sedge-grass marsh. Moreover, we attempt to formulate three scenarios of ecological and biodiversity changes under different water regimes, which may have a significant impact on the microtopography of the sedge-grass marsh. The study’s results were placed in the wider context of the phenomenon of ecosystem engineering, as defined by Jones et al.40,41.
Materials and methods
Site description
The study site was a sedge-grass marsh which is the wettest part of a large wetland complex (450 ha) called “The Wet Meadows” located near the town of Třeboň, South Bohemia, Czech Republic (Central Europe, 49.02465 (North), 14.77035 (East) at an altitude of 426 m a. s. l.). This sedge-grass marsh site belongs to the Czech long-term ecological research network (https://www.lter.cz/index.php/en/en/trebon-wet-meadows)71 and integrated carbon observation system (ICOS, https://www.icos-cp.eu/observations/ecosystem/stations). The mean annual air temperature and mean annual sum of precipitation of the studied site were 7.8 °C and 603 mm in the period 1977 to 2020, respectively (Fig. 1).
(A) Location of the studied sedge-grass marsh in the Czechia (the Czech Republic), Central Europe. (B) Aerial photo of the studied site with research ICOS infrastructure (meteorological station and eddy covariance system). (C) Climadiagram81 of the studied site. (D) Vegetation map of the studied site for the 2017 growth season. (E) Tussocks of Carex acuta during the growing season (beginning of June). (F) Tussocks of Carex acuta during the dormant period (beginning of March) at the time of the terrestrial scanning. A complex figure was created using packages rworldmap, rworldxtra and climatol of the R-computing environment82 (A,C). Abbreviations of plant species: Aco – Acorus calamus, Cal – Calamagrostis canescens, Car – Carex acuta, Pha – Phalaris arundinacea, Gly – Glyceria maxima.
The marsh is situated in the inundation area of a large human-made shallow lake “Rožmberk” a fishpond covering 5 km2 built in the Middle Ages between 1584 and 1590. The water level in the studied site is controlled by a system of ditches which interconnect to the “Rožmberk” fishpond and thus interconnect the system of fishponds in the region. The water level usually fluctuated between 0.1 m below and 0.2 m above the soil surface throughout the year and decreased and fell deeper only during the short fishpond-drawdown periods (0.4 – 0.5 m below the soil surface), when the fish are harvested from the "Rožmberk" fishpond. In some years, spring or summer floods were caused by rapid snow melt or extreme summer rains72. The most extreme floods occurred in summer of 2002 (August 10–14), the spring (April 2) and summer (July 5) of 2006 and summer of 2013 (2–16 June), when the water level reached in the maximum 4.1 m, 2.2 m and 1.3 m above the soil surface for several days respectively72,73(Fig. 7).
Vegetation description
The vegetation is formed mainly of tall “true sedges”16 (Carex acuta [syn. C. gracilis], C. vesicaria) and wetland grasses (mostly Calamagrostis canescens and Phalaris arundinacea)73. The distinct structures of the hummocks are created mainly by the tussocks of C. acuta. The accompanying species colonize the hummock edges and extend down into their interspaces (hollows), especially in dry years. Calamagrostis canescens spreads on the tops of the tussocks in dry years and, thus, is an important dominant species of the plant community. The other dominant species include Glyceria maxima and Acorus calamus, which occur together with C. acuta and establish mixed or monodominant stands. Phalaris arundinacea colonized mainly the banks of drainage ditches and small patches within mixture stands of sedge (Fig. 1D). However, it has spread over a large area of the sedge-grass marsh after the extreme and long-lasting summer flood in 200271,72. The described marsh plant community is classified as the association Caricetum gracilis Almquist 192974.
Vegetation development and changes
The original vegetation pattern corresponded to the management, which consisted of regular hand-made mowing, mostly twice a year75, with occasional manuring and slight drainage by shallow open ditches76. This management was abandoned in the 1950s and was left without any management other water level manipulation by the fishery company. During the subsequent years following the cessation of regular mowing, a distinct vegetation pattern emerged77,78. Additionally, changes in vegetation were recorded in connection with the increase in the occurrence of weather extremes, such as extreme precipitation events or dry summers72,79,80.
Terrestrial mapping of vegetation
Vegetation mapping was carried out using a GPS Ashtech Magellan mobile mapper version 10. The study site was inspected during the growing period in 2017 several times, and individual monodominant stands with a five selected plant species (Acorus calamus, Calamagrostis canescens, Carex acuta, Glyceria maxima and Phalaris arundinacea) were mapped. As a monodominant stand was designated, polygons with homogenous vegetation where plant cover of one plant species was higher than 80%. We also mapped mixture polygons, i.e. homogenous stands with an almost equal cover of 2–4 plant species. The vegetation map was created from raw position data of the borders of individual stands and finalized in the open-source geographic information system QGIS (version 3.22).
Terrestrial laser scanning and point cloud processing
The Riegl VZ-400 terrestrial laser scanner was used for in situ terrestrial scanning (16th March 2017). TLS measurements were done during the period of dormancy to avoid interference with the standing biomass and take advantage of clear visibility of the surface microtopography. We used a period without soil freezing so that the soil surface was not affected by frost heaving.
The scanner was levelled on a tripod in every scanning position. One scan per position was acquired with the following parameters: measurement program = high speed, the vertical angle from 70° to 130°, full circle in the horizontal plane, and angular step in both directions was 0.03°.
The scanned area of the study site was approximately 10,000 m2, as intended for the terrestrial laser scanning (TLS) to characterize surface tussock structures. Five circular vegetation segments (154 m2) with different monodominant and mixture stands were selected for a detailed analysis of tussock structures within the scanned area of the study site (Fig. 1D). The selection of vegetation segments was based on the 2007 vegetation map, aiming to encompass the five dominant plant species (Acorus calamus, Calamagrostis canescens, Carex acuta, Phalaris arundinacea and Glyceria maxima) and their typical mixture stands.
We established a network of scanning positions with a triangular scheme where every position had six neighbors at a distance of 20 m. Predefined points were located in the field with a hand-held GPS, leading to a slightly irregular pattern with a distance between 15 and 25 m for the pairs of neighbouring positions. We made 46 scanning positions in total. The data were registered into a project coordinate system using spherical tie objects (four balls in common for each pair of consecutive scanning positions) (Fig. 2A–C).
(A) Schematic overview of terrestrial in situ scanning (TLS) of the surface tussock structures. (B) In situ photo of the terrestrial scanner Riegl VZ-400 and an accurate ball for connecting the individually scanned positions. (C) Virtual view of the scanned data in the virtual reality environment (VR user Interface toolkit)83. (D) Scheme of the point cloud data processing, including the individual steps of the TLS data processing.
The merged point cloud was translated into a global coordinate system using several artificial objects in the scene. Their horizontal and vertical coordinates were read out from a Google Earth© map. Coordinates “Z” of all points were finally normalized to represent height above the ground (Fig. 2D). A sub-cloud of these vegetation segments was cut out from the overall cloud mosaic. Noisy points were pre-filtered via an intensity threshold (15% quantile). The rest of the 3D point cloud was rescaled in intensity for better visualization. The separation of individual tussocks was done manually using the LidarViewer software built on the Virtual Reality User Interface toolkit (Fig. 3)83. Plant stems and artificial objects exceeding the tussock structure level were removed in the first step. The rest of the point cloud was segmented based on local maxima detection and determination of the entire tussock area following a negative (downhill) gradient from each peak. The TLS data processed in this way were used for obtaining tussocks (polygon) characteristics (metrics) and subsequent detailed statistical analysis.
Three-dimensional (3D) view of the raw point cloud data after manually segmenting the cloud into individual tussocks. Tussocks are distinguished by different false colors. The diameter of the circle segment is 14 m and the total area is 154 m2. The table presents the total number of tussocks for individual vegetation segments. Abbreviations of plant species: Aco – Acorus calamus, Cal – Calamagrostis canescens, Car – Carex acuta, Gly – Glyceria maxima. 3D view were generated using the FugroViewer 3.5. and the base graphics system in the R computing environment82.
Calculation and processing of the tussock characteristics
Surface tussock structures in five main vegetation segments (Car, CarCal, Gly, CarCalGly and CarCalAcoGly; Fig. 1) were analyzed based on unique tussock structure characteristics. For individual tussocks, the following characteristics were calculated: height, 2D area (tussock footprint), perimeter, and position of the 2D area centroid. Other characteristics, such as tussock density and distances among tussock centroids, were calculated for particular vegetation segments using the R statistical software84 version 4.2.1 (“Funny-Looking Kid”) with additional packages.
The height of the surface tussock structure was calculated as the average and maximal values of “Z” (height) coordinate above ground (soil surface) of the local coordinate system. For testing the symmetry height distribution (symmetry.test), the “lawstat” package was used (https://cran.r-project.org/web/packages/lawstat/index.html), and standard basic statistics from the core R-package of the R statistical software.
The 2D area of the tussocks defined as a closed polygon (tussock footprint) was calculated using the functions “chull” and “polygon”. Function “chull” determined the border of the polygon (the subset of points which lie on the convex hull of the set of points specified, and https://www.rdocumentation.org/packages/grDevices/versions/3.6.2/topics/chull) for tussock and “polygon” function, which are included in the “sp” package (https://cran.r-project.org/web/packages/sp/index.html) calculate the 2D area of the polygon (tussock footprint).
The perimeter was calculated using the “chull” function to determine the border points of the polygon and finally calculated using the standard distance formula (1).
where, P is the perimeter of the polygon; x and y are the local vegetation cover segment coordinates of points creating the border (from n = 1 to b) of the polygon.
The coordinates of the polygon centroid were determined using the function “chull” which determined the border of the polygon, and coordinates of the polygon centroid were determined by the function “calcCentroid” from the package PBSmapping (https://cran.r-project.org/web/packages/PBSmapping/index.html). Coordinates of centroids are in the local coordinate system for the current vegetation segment.
The tussocks (polygon’s) shape was analyzed as the roundness characteristics of the polygons, characterized by the relationship between the polygon 2D area and the perimeter (Fig. 6). The obtained 2D area/perimeter ratio was statistically tested for possible differences among the vegetation segments.
For analyses of the Euclidean distances among tussocks, the polygon centroids of individual tussock structures were used (Fig. 4). The smallest distances were obtained using the function distance from the package philentropy (https://cran.r-project.org/web/packages/philentropy/index.html). For distribution analyses of the tussock distances, standard base statistic from the core package of the R statistical software and the “lawstat” package (https://cran.r-project.org/web/packages/lawstat/index.html) were used.
Map of individual tussocks (polygons represent surface structure and dots represent polygon centroids) within each selected vegetation segments (14 m in diameter). The bar graph on the right bottom shows relative portions (in percentage) of the polygon (tussocks) 2D area to the total area of the segments (154 m2). Numbers above the bars are the densities of the tussocks (polygons) per square metr. Abbreviations of plant species: Aco – Acorus calamus, Cal – Calamagrostis canescens, Car – Carex acuta, Gly – Glyceria maxima. Maps and graphs were generated using the base graphics system in the R computing environment82.
The tussock structure characteristics and Euclidean distances were tested for their normal distribution using the Shapiro-Wilks test85. Although the data are close to the normal distribution, in several cases they differed significantly from normality. Based on the non-significant tests of normality, the differences between segments with different plant dominants were tested using the non-parametric Kruskal–Wallis test with the possibility of multiple comparisons between groups86. Statistical analyses were done using the R statistical software with the packages: “pgirmess” (http://cran.r-project.org/web/packages/pgirmess/) and “e1071” (https://cran.r-project.org/web/packages/e1071/).
Hierarchical cluster analysis for flood occurrence was made using the “hclust” function from the package stats (https://www.rdocumentation.org/packages/stats/versions/3.6.2/topics/hclust). As agglomeration method for clustering was used the “average method”.
Results
2D area (footprint) of the tussock structures
The tussock structures form close polygons with different shapes and sizes and cover different soil surface areas (Fig. 4). The highest portion of the tussock structure 2D area was in the CarCalAcoGly segment, where the tussock polygons constituted 89.5% of the total segment area. The portion was less than 75% in the vegetation segments dominated by CarCalGly and Gly and about 50% in those dominated by CarCal and Car (Fig. 4).
The 2D area of individual polygons was very different with a range of 0.024–1.62 m2. The distribution of the individual tussock (polygon) 2D areas in the vegetation segments is positively skewed, with some differences between averages and medians (Fig. 5). The medians of the tussock polygon 2D areas were for vegetation segments Car, CarCal, Gly, CarCalGly and the CarCalAcoGly, 0.17 m2, 0.17 m2, 0.30 m2, 0.30 m2, and 0.34 m2 respectively. Statistically significant different tussock polygon 2D areas were found only between Gly, Car segments and the rest of the segments (Chi-squared = 361.2, df = 4, p < 0.001). The largest tussocks were in the mixture vegetation segment CarCalAcoGly and Gly almost monodominant vegetation segment. The smallest tussock 2D areas were found in the CarCal and Car vegetation segments.
The average 2D area (tussock footprint), perimeter (length of polygon border) and average heights of the tussock surface polygon structures for the individually analyzed vegetation segments (Car, CarCal, Gly, CarCalGly, CarCalAcoGly). Histograms show counts of occurrence in individual groups. Boxplots show the means (small open boxes), median values (thick black lines), upper and lower quartile (boxes with 50% of measured data) and error bars with the upper and lower extreme values indicated. The statistical difference among the vegetation segments is indicated by different letters. Abbreviations of plant species: Aco – Acorus calamus, Cal – Calamagrostis canescens, Car – Carex acuta, Gly – Glyceria maxima. Graphs were generated using the base graphics system in the R computing environment82.
The perimeter of the tussock structures
Tussock perimeter is the length of the border of the individual tussock polygon and is related to the shape and 2D area of the individual tussock. The distribution of the tussock perimeters in the different vegetation segments is slightly positively skewed, and the skewness is lower than with the tussock 2D areas (Fig. 5). Due to the symmetry of the perimeter distribution and its low skewness, a minimal difference was found between averages and medians. The medians of the tussock perimeters were for vegetation segments Car, CarCal, Gly, CarCalGly and CarCalAcoGly 1.6 m, 1.6 m, 2.2 m, 2.2 m, and 2.4 m, respectively. Statistically significant different tussock perimeters were only found between similar perimeter of the Gly and Car vegetation segments and the rest of the segments (Gly, CarCalGly and CarCalAcoGly) (Chi-squared = 403.4, df = 4, p < 0.001). The character of the statistical differences is similar to the tussock 2D areas (Fig. 5). The longest perimeter (polygon border) of the tussock structures was found in the Gly almost monodominant vegetation segment and in the mixture vegetation segment CarCalAcoGly. The lowest tussock perimeters were found in the CarCal and Car vegetation segments.
Heights of the surface tussock structures
The distribution of the tussock heights in the individual vegetation segments is symmetric, with a slight positive skewness of the heights in CarCal (skewness = 0.654) and Car (skewness = 0.425) vegetation segments. Due to the symmetry distribution of the height, the difference between averages and medians was minimal. The average tussock height in the vegetation segments (Car, CarCal, Gly, CarCalGly and CarCalAcoGly) includes the most frequent class (0.2–0.3 m) of the heights except the Car vegetation segment, where the average height was in class 0.3–0.4 m. Average heights and standard deviations for vegetation segments Car, CarCal, Gly, CarCalGly and CarCalAcoGly were 0.349 ± 0.06 m, 0.292 ± 0.09 m, 0.229 ± 0.05 m, 0.279 ± 0.06 m and 0.279 ± 0.07 m, respectively. We found almost similar average tussock heights among different vegetation segments (CarCalGly and CarCalAcoGly), and the statistically significant differences were found only in Gly and Car and the rest of the vegetation segments (Chi-squared = 460.6, df = 4, p < 0.001). The average height of the tussocks was the highest in the Car and CarCal segments and the lowest in the Gly segment (Fig. 5).
Roundness of the surface tussock structures
The tussock structures had different individual polygonal characteristics, such as 2D area and perimeter, determining the final shape of the in situ terrestrially scanned tussocks. Based on the 2D area/perimeter ratio, we can show that the tussocks in the Car and CarCal vegetation segments were more round or circular. Their 2D area/perimeter ratios were similar and not statistically different (boxplot, Fig. 6). The other vegetation segments (CarCalGly, CarCalAcoGly and Gly) had apparent differences from the round/circular tussock shape. The difference is more visible if the tussock perimeter is longer than three meters (a threshold based on the perimeter of the Car vegetation segment). These tussocks had an elongated shape different from a precise circle. The elongated shape of the tussocks was typical for the CarCalAcoGly and Gly vegetation segments, where the most statistically significant difference from a round/circular shape was detected (boxplot, Fig. 6). Similar tussocks (polygons) shapes between each other had the CarCalGly and CarCalAcoGly vegetation segments.
The roundness of the tussock structures is calculated as the ratio between the tussock 2D area and tussock perimeter for each individual analyzed vegetation segment (Car, CarCal, Gly, CarCalGly and CarCalAcoGly). The dashed black line is a quadratic relation between the 2D area and perimeter for a precise circle. The full black line with an arrow is the quadratic fit of the data for the analyzed vegetation segment. The adjusted R2 of the quadratic relationship is presented. The vertical dashed line is the perimeter threshold where the relationship between 2D area and perimeter fluctuates. The boxplot of 2D area/perimeter ratio shows the means (small open boxes), median values (thick black lines), upper and lower quartile (boxes with 50% of measured data) and error bars with the upper and lower extreme values indicated. The statistical difference in roundness among the vegetation segments is indicated by different letters. Abbreviations of plant species: Aco – Acorus calamus, Cal – Calamagrostis canescens, Car – Carex acuta, Gly – Glyceria maxima.
Density and distances among the tussock structures
The average tussock density across the sedge-grass marsh is 2.32 ± 0.135 tussocks per square meter (mean ± standard error). The densities for individual vegetation segments were 2.53, 2.73, 1.98, 2.17 and 2.21 tussocks per square meter for the Car, CarCal, Gly, CarCalGly and CarCalAcoGly vegetation segments, respectively (Fig. 4).
The distances between tussocks (tussocks centroids) are similar across different vegetation segments, showing no significant statistical differences. The most frequent tussock distances are between 5 and 6 m and 6 and 7 m. A slight positive skewness was found for the distance distributions. The highest positive skewness was 0.147 and 0.108 in the CarCal and Car vegetation segments, respectively. In Gly, CarCalGly and CarCalAcoGl vegetation segments the skewness was less in the range 0.079 to 0.098.
In summary, tussock density varies across the different vegetation segments of the sedge-grass marsh, while tussock distances (placement) remain consistent with slight positive skewness.
Discussion
Microtopography is determined by growth characteristics of individual species
The microtopography of the studied site is shaped especially by the tussock structures which are the results of the long-term unceasing growth process of the sedge Carex acuta12,15. The tussock formation and its development are enabled by the specific growth of underground rhizomes and short intravaginal stolons, which grow inwards the tussocks instead of forming long rhizomes spreading outwards towards the surrounding soil. Nevertheless, Carex acuta may establish two contrasting surface structures in its almost monodominant stands: (i) conspicuous tussocks with their edges passing down into the spaces between tussocks, and (ii) very rarely also simple flat surface structures. Tussocks formation and development is common especially in wetlands influenced by pronounced water level fluctuation such as alluvial plains18, wet meadows23,94, floating fens90,91, and in wetlands called tussock wetlands or meadows13,19,87 (overview in Table 2).
Jones40,41, defined species like Carex acuta as an autogenic ecosystem engineer which uses its biomass directly to create specific habitats for themselves and also for other species. These habitats created by the tussocks could also be regarded as nursery tussocks87. Co-occurring plant species, such as Lythrum salicaria, Iris pseudacorus, Myosotis palustris, and Calamagrostis canescens, can use tussocks and hollows in between as distinct habitat niches within the wetland ecosystem12,20,73.
Obtained Carex acuta tussock characteristics such as height (22.9–34.8 cm), tussock diameter (23.3–32.0 cm) and density (2.0–2.7 m−2) were in accordance with the values published for other Carex species with the similar character of microtopography (Table 2). Carex acuta tussocks in monodominant Car vegetation segments were rounder shape due to the minimal influence of other plant species.
The microtopography of the studied sedge-grass marsh was not shaped only by Carex acuta. Other tall wetland plant species can participate in the formation of the site microtopography, too. These plant species may be considered as supplementary ecosystem engineers. Nevertheless, the importance of other plant species as ecosystem engineers (Acorus calamus, Calamagrostis canescens, Glyceria maxima and Phalaris arundinacea) is significantly less and relates to the changes of Carex acuta tussock characteristics and thus microtopography of the whole sedge-grass marsh. Glyceria maxima forms long underground (hypogeogenous) rhizomes95 and cannot create significant tussocks like Carex acuta. However, it is a strong competitive species in conditions of steady high water level and high nutrient content in the soil or water96,97. Under such conditions, it can successively replace Carex acuta, as we observed in the Gly vegetation segment (Fig. 4). Glyceria maxima is a significant tussock changer due to an extensively creeping rhizome system and very dynamic growth98,99,100.
Acorus calamus (European neophyte)101 was less important in the modification of the tussock structures than Glyceria maxima due to suboptimal growth in the study site where the open water surface was absent102. Under more suitable conditions it may influence tussock structure more significantly. Under suitable conditions Acorus calamus can create a compact surface network of rhizomes preventing the coexistence of most other species and develops monodominant stands103. Acorus calamus was present mainly in mixture stand with Glyceria maxima and Calamagrostis canescens (CarCalAcoGly vegetation segment). The tussocks of the CarCalAcoGly vegetation segment had the larger tussock 2D area with elongated shapes and significantly less round. Nevertheless, we cannot distinguish in detail the effect of individual plant species presents in mixtures separately. The effect of Acorus calamus in CarCalAcoGly vegetation segments on the tussock characteristics was probably small.
Calamagrostis canescens usually establishes flat monodominant stands in non-flooded littoral parts of shallow water bodies and does not form its tussocks. In our study site it spreads mainly on the tops of Carex acuta tussocks12. This grass contributes the least to the change in the tussock structure, and it uses tussocks mainly as a suitable growing habitat. The Car and CarCal vegetation segments had very similar characteristics with similar tussock densities (Fig. 4).
Tussock formation is also not typical for Phalaris arundinacea. Werner and Zedler23 reported that Phalaris formed tussocks only exceptionally in the storm water basin near Madison (Wisconsin, North America); nevertheless, they were significantly shorter than Carex tussocks in the same wetland. In our case, the Phalaris arundinacea dominated stands developed along the banks of channels without any marked tussock structure.
High organic matter production of some wetland species (e.g., Phragmites australis, Echinochloa pyramidalis, Phalaris arundinacea) can lead to a significant vertical accretion of the soil (wetland) surface104,105. However, in the studied sedge-grass marsh, no significant accretion of the wetland surface was observed, and the soil level has remained stable. The study site is separated from the free water surface by extensive reed stands, where the material brought by water accumulates. At the same time, there is no significant sedimentation of the material, even during floods. Additionally, the fluctuating water table and the relatively high soil temperature throughout the year allow the decomposition of the organic matter produced, as shown by our long-term carbon balance measurements12,94.
Modulation of ecosystem engineers’ effect on microtopography by water level
In general, the function of Carex acuta as an ecosystem engineer can be modulated by both abiotic and biotic factors. In the case of our study site (i.e., temperate sedge-grass marsh), the most significant abiotic factor is water level fluctuation which directly determines tussock development18,22 by both common and extreme water level fluctuation. Especially long-term droughts and floods can have both positive and negative effects on tussock development and the site’s microtopography (Fig. 7). In general, these extreme conditions shape the wetland microtopography by altering water availability, sometimes promoting growth of tussocks or, conversely, leading to their degradation depending on the severity and duration of extreme events.
Cluster dendrogram of the flood occurrence within 42 years in the periods 1977–2002 and 2005–2022. For cluster analyses (average agglomeration method) 32 floods were selected with a water level of 0.2 m above the soil surface. The individual floods were categorized into seven groups based on their maximum water levels. Dendrogram was generated using the base graphics system in the R computing environment82.
As an optimal flood for the long-term persistence of tussocks, we consider flooding of the Carex acuta tussocks of about one-third (29.6%) of their height as reported by Zhang et al.18. Therefore, tussock heights measured in Car vegetation segments (22.9–34.8 cm) enabled that the meristematic tissues of Carex acuta were usually above the water level during most of common floods (652 times within 45 years) occurring in the studied sedge-grass marsh. In contrast, if we select the floods with water level higher than 0.2 m (this height corresponds to the lowest tussocks in Gly vegetation segment) only 32 individual floods occurred within 45 years in periods 1977–2002 and 2005–2022 (Fig. 7). If the water level reaches or surpasses the height of the tussocks, the plants may die or experience developmental deficiencies.
We expect three possible scenarios (Great wet, Optimum wet and Sufficiently wet) of the vegetation development based on the current water regime and expected in the future (Table 3).
Sufficiently wet – The expected increase in drought frequency106 during the growing period and the cessation of regular spring or summer floods may lower the mean water table significantly below the soil surface. Low water table will increase the cover of Calamagrostis canescens at the expense of Carex acuta and gradually destabilize the tussock structure together with spreading of non-wetland species including ruderal ones (Table 3).
Great wet – Increase the water level above the soil surface in the studied sedge-grass marsh year-round by increasing the water level in the adjacent fishpond. Long-term high-water levels will promote the expansion of Glyceria maxima and Phragmites australis. Permanently flooded Carex acuta tussocks will be disintegrated into parts and degraded overall12. The spatial tussock structure will also change due to the rapid accumulation of litter of expanded highly productive species (vertical accretion).
Optimum wet – Regular (spring or summer) short-term flooding (approx. 0.4 m above the soil surface for 1 week) would help to develop and maintain the sedge tussocks in the long term. Additionally, the characteristic wetland plant species diversity will be preserved by eliminating ruderal and invasive terrestrial plant species due to regular short-term flooding. Nevertheless, the mean water table level during the growing period should be shallow below the soil surface and should not exceed one-third of the height of Carex acuta tussocks for the long term.
Conclusions
Terrestrial in situ scanning revealed that the admixture of some wetland plant species affected the microtopography of the studied sedge-grass marsh, formed by the tussocks of Carex acuta. Tussocks in individual vegetation segments were different in 2D area, perimeter, height, density, and roundness except for distances among tussock (polygon) centroids. The highest tussocks occurred in the vegetation segments dominated by Carex acuta (Car) and by Carex acuta and Calamagrostis canescens (CarCal). Glyceria maxima and Acorus calamus (occurring in CaCalGly and CarCalAcoGly vegetation segments) modified the height and the shape of tussocks to become lower, elongated and less round than in the Car and CarCal segments.
The tussock characteristics correspond to the growth characteristics and functions of individual dominant species. The sedge Carex acuta is the main ecosystem engineer, which creates the specific tussock habitats. Other wetland species (Glyceria maxima, Acorus calamus and Calamagrostis canescens) modify already developed sedge tussocks. The tussock characteristics in mixed stands are therefore influenced by the growth characteristics of all co-occurring dominant plants (main and supplementary ecosystem engineers).
The specific microtopography of the sedge-grass marsh with a typical tussock pattern can occur only in condition of frequent shallow short-time flooding, as we described in the optimum wet scenario (Table 3). Short-time flooding promotes dominance of tussock forming wetland Carex species and leads excluding ruderal and invasive terrestrial plant species from the studied sedge-grass marsh.
Data availability
The datasets from in situ measurements of the current study are available from the corresponding author on request.
References
Larcher, W. Physiological Plant Ecology: Ecophysiology and Stress Physiology of Functional Groups (Springer, 2003).
Crawford, R. M. M. Plants at the Margin Ecological Limits and Climate Change (Cambridge University Press, 2008).
Jackson, M. B. & Armstrong, W. Formation of aerenchyma and the processes of plant ventilation in relation to soil flooding and submergence. Plant Biol. 1, 274–287 (1999).
Dušek, J. Effect of rooting media on the proportion of rhizome cortex and central cylinder of Phalaris arundinacea. Biol. Bratisl. 57, 75–79 (2002).
Kozela, C. & Regan, S. How plants make tubes. Trends Plant Sci. 8, 159–164 (2003).
Evans, D. E. Aerenchyma formation: Tansley review. New Phytol. 161, 35–49 (2003).
Končalová, H. Anatomical adaptations to waterlogging in roots of wetland graminoids - limitations and drawbacks. Aquat. Bot. 38, 127–134 (1990).
Armstrong, W., Cousins, D., Armstrong, J., Turner, D. W. & Beckett, P. M. Oxygen distribution in wetland plant roots and permeability barriers to gas-exchange with the rhizosphere: a microelectrode and modelling study with Phragmites australis. Ann. Bot. 86, 687–703 (2000).
Faußer, A. C., Dušek, J., Čížková, H. & Kazda, M. Diurnal dynamics of oxygen and carbon dioxide concentrations in shoots and rhizomes of a perennial in a constructed wetland indicate down-regulation of below ground oxygen consumption. AoB Plants 8, plw025 (2016).
Dušek, J., Dařenová, E., Pavelka, M. & Marek, M. V. Methane and carbon dioxide release from wetland ecosystems. In Climate Change and Soil Interactions 509–553 (Elsevier, 2020).
Doležal, J. et al. Anatomical adaptations in aquatic and wetland dicot plants: Disentangling the environmental, morphological and evolutionary signals. Environ. Exp. Bot. 187, 104495 (2021).
Honissová, M. et al. Seasonal dynamics of biomass partitioning in a tall sedge, Carex acuta L.. Aquat. Bot. 125, 64–71 (2015).
Peach, M. & Zedler, J. B. How tussocks structure sedge meadow vegetation. Wetlands 26, 322–335 (2006).
Qi, Q. et al. Hydrological and microtopographic effects on community ecological characteristics of Carex schmidtii tussock wetland. Sci. Total Environ. 780, 146630 (2021).
Soukupová, L. Life strategy of graminoid populations in the wet meadows. In Freshwater Wetlands and their Sustainable Future. A Case Study of the Třeboň Basin Biosphere Reserve 255–267 (CRC Press, 2002).
Heywood, V. H. (ed.) Flowering Plants of the World (Oxford University Press, 1993).
van de Koppel, J. & Crain, C. M. Scale-dependent inhibition drives regular tussock spacing in a freshwater marsh. Am. Nat. 168, E136–E147 (2006).
Zhang, D. J., Qi, Q. & Tong, S. Z. Growth of carex tussocks as a response of flooding depth and tussock patterning and size in temperate sedge wetland, Northeast China. Russ. J. Ecol. 51, 144–150 (2020).
Costello, D. F. Tussock meadows in Southeastern Wisconsin. Bot. Gaz. 97, 610–648 (1936).
Crain, C. M. & Bertness, M. D. Community impacts of a tussock sedge: Is ecosystem engineering important in benign habitats?. Ecology 86, 2695–2704 (2005).
Lawrence, B. A. & Zedler, J. B. Carbon storage by Carex stricta Tussocks: A restorable ecosystem service?. Wetlands 33, 483–493 (2013).
Lawrence, B. A. & Zedler, J. B. Formation of tussocks by sedges: effects of hydroperiod and nutrients. Ecol. Appl. 21, 1745–1759 (2011).
Werner, K. J. & Zedler, J. B. How sedge meadow soils, microtopography, and vegetation respond to Sedimentation. Wetlands 22, 451–466 (2002).
Lawrence, B. A., Fahey, T. J. & Zedler, J. B. Root dynamics of Carex stricta-dominated tussock meadows. Plant Soil 364, 325–339 (2013).
Yu, F.-H., Krüsi, B., Schütz, M., Schneller, J. & Wildi, O. Is Vegetation inside Carex sempervirens tussocks highly specific or an image of the surrounding vegetation?. J. Veg. Sci. 17, 567–576 (2006).
Musilová, Z., Musil, P., Zouhar, J. & Polakova, S. Nest survival in the Reed Bunting Emberiza schoeniclus in fragmented wetland habitats: The effect of nest-site selection. Ornis Fenn. 91, 138–148 (2014).
Folliot, B., Caizergues, A., Barbotin, A. & Guillemain, M. Environmental and individual correlates of common pochard (Aythya ferina) nesting success. Eur. J. Wildl. Res. 63, 69 (2017).
Frieswyk, C. B., Johnston, C. A. & Zedler, J. B. Identifying and characterizing dominant plants as an indicator of community condition. J. Great Lakes Res. 33, 125–135 (2007).
Stovall, A. E. L., Diamond, J. S., Slesak, R. A., McLaughlin, D. L. & Shugart, H. Quantifying wetland microtopography with terrestrial laser scanning. Remote Sens. Environ. 232, 111271 (2019).
Vítková, J., Dušek, J., Stellner, S., Moulisová, L. & Čížkova, H. Effect of hummock-forming vegetation on methane emissions from a temperate sedge-grass marsh. Wetlands 37, 675–686 (2017).
Courtwright, J. & Findlay, S. E. G. Effects of microtopography on hydrology, physicochemistry, and vegetation in a tidal swamp of the Hudson river. Wetlands 31, 239–249 (2011).
Frei, S. & Fleckenstein, J. H. Representing effects of micro-topography on runoff generation and sub-surface flow patterns by using superficial rill/depression storage height variations. Environ. Model. Softw. 52, 5–18 (2014).
Frei, S. & Peiffer, S. Exposure times rather than residence times control redox transformation efficiencies in riparian wetlands. J. Hydrol. 543, 182–196 (2016).
Anderson, J. T. & Davis, C. A. Wetland Techniques Vol. 1: Foundations 46 (Springer, 2013).
Pirotti, F. Laser scanner applications in forest and environmental sciences. ItJRS https://doi.org/10.5721/ItJRS20124419 (2012).
Pirotti, F., Liang, X. & Chen, Q. Preface of special issue on laser scanning. Appl. Sci. https://doi.org/10.3390/app9132713 (2019).
Jozkow, G., Borkowski, A. & Kasprzak, M. Monitoring of fluvial transport in the mountain river bed using terrestrial laser scanning. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. https://doi.org/10.5194/isprsarchives-XLI-B7-523-2016 (2016).
Yang, Y. et al. Responses of the methanogenic pathway and fraction of CH4 oxidization in a flooded paddy soil to rice planting. Pedosphere 31, 859–871 (2021).
Telling, J., Lyda, A., Hartzell, P. & Glennie, C. Review of earth science research using terrestrial laser scanning. Earth Sci. Rev. 169, 35–68 (2017).
Jones, C. G., Lawton, J. H. & Shachak, M. Organisms as ecosystem engineers. In Ecosystem Management (eds Samson, F. B. & Knopf, F. L.) 130–147 (Springer, 1994).
Jones, C. G., Lawton, J. H. & Shachak, M. Positive and negative effect of organisms as physical ecosystem engineers. Ecology 78, 1946–1957 (1997).
Hannam, M. & Moskal, L. Terrestrial laser scanning reveals seagrass microhabitat structure on a tideflat. Remote Sens. 7, 3037–3055 (2015).
Liang, X. et al. Terrestrial laser scanning in forest inventories. ISPRS J. Photogramm. Remote. Sens. 115, 63–77 (2016).
Calders, K. et al. Terrestrial laser scanning in forest ecology: Expanding the horizon. Remote Sens. Environ. 251, 112102 (2020).
Koma, Z. et al. Quantifying 3D vegetation structure in wetlands using differently measured airborne laser scanning data. Ecol. Ind. 127, 107752 (2021).
Toivonen, J., Kangas, A., Maltamo, M., Kukkonen, M. & Packalen, P. Assessing biodiversity using forest structure indicators based on airborne laser scanning data. For. Ecol. Manag. 546, 121376 (2023).
Penman, S., Lentini, P., Law, B. & York, A. An instructional workflow for using terrestrial laser scanning (TLS) to quantify vegetation structure for wildlife studies. For. Ecol. Manag. 548, 121405 (2023).
Vauhkonen, J. & Imponen, J. Unsupervised classification of airborne laser scanning data to locate potential wildlife habitats for forest management planning. For. Int. J. For. Res. 89, 350–363 (2016).
Koma, Z., Seijmonsbergen, A. C. & Kissling, W. D. Classifying wetland-related land cover types and habitats using fine-scale lidar metrics derived from country-wide airborne laser scanning. Remote Sens. Ecol. Conserv. 7, 80–96 (2021).
Yang, C.-J., Jen, C.-H., Cheng, Y.-C. & Lin, J.-C. Quantification of mudcracks-driven erosion using terrestrial laser scanning in laboratory runoff experiment. Geomorphology 375, 107527 (2021).
Müller, J. & Vierling, K. Assessing biodiversity by airborne laser scanning. In Forestry Applications of Airborne Laser Scanning: Concepts and Case Studies (eds Maltamo, M. et al.) 357–374 (Springer, 2014).
Koarai, M. Landscape ecological mapping for biodiversity evaluation using airborne laser scanning data. In Monitoring and Modeling of Global Changes: A Geomatics Perspective (eds Li, J. & Yang, X.) 137–154 (Springer, 2015).
Guimarães-Steinicke, C. et al. Chapter four—Terrestrial laser scanning reveals temporal changes in biodiversity mechanisms driving grassland productivity. In Advances in Ecological Research (eds Eisenhauer, N. et al.) 133–161 (Academic Press, 2019).
Stereńczak, K. et al. Global airborne laser scanning data providers database (GlobALS)—A new tool for monitoring ecosystems and biodiversity. Remote Sens. 12, 1877 (2020).
Lee, H.-J., Yang, S.-R. & Lee, K.-M. Ecological restoration monitoring of open-pit mines using airborne laser scanning. J. Korean Soc. Geospat. Inf. Sci. 16, 101–107 (2008).
Zlinszky, A., Mücke, W., Lehner, H., Briese, C. & Pfeifer, N. Categorizing wetland vegetation by airborne laser scanning on Lake Balaton and Kis-Balaton, Hungary. Remote Sens. 4, 1617–1650 (2012).
Cordell, S. et al. Remote sensing for restoration planning: how the big picture can inform stakeholders. Restor. Ecol. 25, S147–S154 (2017).
Levick, D. S. R. Monitoring Mangrove Rehabilitation with Terrestrial Laser Scanning (CSIRO, 2021).
Camarretta, N. et al. Handheld laser scanning detects spatiotemporal differences in the development of structural traits among species in restoration plantings. Remote Sens. 13, 1706 (2021).
Valkama, E., Lyytinen, S. & Koricheva, J. The impact of reed management on wildlife: A meta-analytical review of European studies. Biol. Cons. 141, 364–374 (2008).
Gray, M. J., Chamberlain, M. J., Buehler, D. A. & Sutton, W. B. Wetland WILDLIFE MONITORING AND ASSESSMENT. In Wetland Techniques (eds Anderson, J. T. & Davis, C. A.) 265–318 (Springer, 2013).
Helle, P., Ikonen, K. & Kantola, A. Wildlife monitoring in Finland: online information for game administration, hunters, and the wider public. Can. J. For. Res. 46, 1491–1496 (2016).
Shokirov S, Levick SR, Jucker T, Yeoh P, Youngentob K 2020 Comparison of TLS and ULS Data for Wildlife Habitat Assessments in Temperate Woodlands. In IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing, 6097–6100. https://doi.org/10.1109/IGARSS39084.2020.9323451.
Riegel, J. B., Bernhardt, E. & Swenson, J. Estimating above-ground carbon biomass in a newly restored coastal plain wetland using remote sensing. PLoS ONE 8, e68251 (2013).
Olsoy, P. J., Glenn, N. F. & Clark, P. E. Estimating sagebrush biomass using terrestrial laser scanning. Rangel. Ecol. Manag. 67, 224–228 (2014).
Hopkinson, C. et al. Monitoring boreal forest biomass and carbon storage change by integrating airborne laser scanning, biometry and eddy covariance data. Remote Sens. Environ. 181, 82–95 (2016).
Owers, C. J., Rogers, K. & Woodroffe, C. D. Terrestrial laser scanning to quantify above-ground biomass of structurally complex coastal wetland vegetation. Estuar. Coast. Shelf Sci. 204, 164–176 (2018).
Melo, A. M. et al. Monitoring the understory in eucalyptus plantations using airborne laser scanning. Sci. Agric. 78, e20190134 (2020).
Li, S. et al. Harnessing terrestrial laser scanning to predict understory biomass in temperate mixed forests. Ecol. Ind. 121, 107011 (2021).
Brede, B. et al. Non-destructive estimation of individual tree biomass: Allometric models, terrestrial and UAV laser scanning. Remote Sens. Environ. 280, 113180 (2022).
Mirtl, M. Introducing the next generation of ecosystem research in Europe: LTER-Europe’s multi-functional and multi-scale approach. In Long-Term Ecological Research (eds Müller, F. et al.) 75–93 (Springer, 2010).
Dušek, J., Hudecová, Š & Stellner, S. Extreme precipitation and long-term precipitation changes in a Central European sedge-grass marsh in the context of flood occurrence. Hydrol. Sci. J. 62, 1796–1808 (2017).
Mejdová, M., Dušek, J., Foltýnová, L., Macálková, L. & Čížková, H. Photosynthetic parameters of a sedge-grass marsh as a big-leaf: effect of plant species composition. Sci. Rep. 11, 3723 (2021).
Landucci, F. et al. Classification of the European marsh vegetation (Phragmito-Magnocaricetea ) to the association level. Appl. Veg. Sci. 23, 297–316 (2020).
Holubičková, B. Příspěvek ke studiu rašeliništní vegetace. I. Mokré louky u Třeboně (A contribution to the study of peatland vegetation. I. Mokré louky near Třeboň). Proceedings of the University of Agriculture in Prague 1959, 257–285 (1959).
Blažková, D. Pflanzensoziologische studie über die wiesen der südböhmischen becken. Stud. CSAV 73, 1–172 (1973).
Prach, K. Vegetational changes in a wet meadow complex, south-bohemia, Czech Republic. Folia Geobot. Phytotaxon. 28, 1–13 (1993).
Prach, K. Vegetation changes in a wet meadow complex during the past half-century. Folia Geobot. 43, 119–130 (2008).
Dušek, J., Stellner, S. & Komárek, A. Long-term air temperature changes in a Central European sedge-grass marsh. Ecohydrology 6, 182–190 (2013).
Graf, A. et al. Altered energy partitioning across terrestrial ecosystems in the European drought year 2018. Philos. Trans. R. Soc. B https://doi.org/10.1098/rstb.2019.0524 (2020).
Walter, H. & Lieth, H. Klimadiagramm-Weltatlas (Gustav Fischer Verlag, 1960).
R Development Core Team. R: A language and environment for statistical computing. (2022).
Kreylos, O., Bawden, G. W. & Kellogg, L. H. Immersive visualization and analysis of LiDAR data. In Advances in Visual Computing (ed. Bebis, G.) 846–855 (Springer, 2008).
Chambers, J. M. Software for Data Analysis: Programming with R (Springer, 2008).
Zar, J. H. Biostatistical Analysis (Prentice Hall, 2010).
Hollander, M., Wolfe, D. A. & Chicken, E. Nonparametric Statistical Methods (John Wiley & Sons Inc, 2014).
Wang, M., Wang, G., Wang, S. & Jiang, M. Structure and richness of Carex meyeriana tussocks in peatlands of Northeastern China. Wetlands 38, 15–23 (2018).
Qi, Q. et al. The driving mechanisms for community expansion in a restored Carex tussock wetland. Ecol. Ind. 121, 107040 (2021).
Biasi, C. et al. Microtopography and plant-cover controls on nitrogen dynamics in Hummock Tundra ecosystems in Siberia. Arct. Antarct. Alp. Res. 37, 435–443 (2005).
Diamond, J. S. et al. A little relief: Ecological functions and autogenesis of wetland microtopography. WIREs Water 8, e1493 (2021).
van Bergen, T. J. H. M. et al. Self-facilitation and negative species interactions could drive microscale vegetation mosaic in a floating fen. J. Veg. Sci. 31, 343–354 (2020).
Nungesser, M. K. Modelling microtopography in boreal peatlands: hummocks and hollows. Ecol. Model. 165, 175–207 (2003).
Johnson, L. C., Damman, A. W. H. & Malmer, N. Sphagnum macrostructure as an indicator of decay and compaction in peat cores from an ombrotrophic south Swedish peat-bog. J. Ecol. 78, 633 (1990).
Dušek, J. et al. Influence of summer flood on the net ecosystem exchange of CO2 in a temperate sedge-grass marsh. Agric. For. Meteorol. 149, 1524–1530 (2009).
Klimešová, J., Danihelka, J., Chrtek, J., de Bello, F. & Herben, T. CLO-PLA: a database of clonal and bud-bank traits of the Central European flora. Ecology 98, 1179–1179 (2017).
Hejný, S. Dynamic changes in the macrophyte vegetation of South Bohemian fishponds after 35 years. Folia Geobot. Phytotaxon. 25, 245–255 (1990).
Anderson, J. E. & Reznicek, A. A. Glyceria maxima (Poaceae) in New England. Rhodora 96, 97–101 (1994).
Lambert, J. M. Glyceria maxima (Hartm.) Holmb.. J. Ecol. 34, 310 (1947).
Westlake, D. F. The biomass and productivity of glyceria maxima: I. Seasonal changes in biomass. J. Ecol. 54, 745 (1966).
Westlake, D. F. et al. The Production Ecology of Wetlands: The IBP Synthesis (Cambridge University Press, 1998).
Motley, T. J. The ethnobotany of sweet flag, acorus Calamus (Araceae). Econ. Bot. 48, 397–412 (1994).
Soukupová, L. Calamagrostis canescens: Population biology of a clonal grass invading wetlands. Oikos 63, 395–401 (1992).
Dykyjová, D. Production ecology of Acorus calamus. Folia Geobota. Phytotaxon. 15, 29–57 (1980).
Chambers, R. M., Meyerson, L. A. & Saltonstall, K. Expansion of Phragmites australis into tidal wetlands of North America. Aquat. Bot. 64, 261–273 (1999).
López-Rosas, H. & Moreno-Casasola, P. Invader versus natives: Effects of hydroperiod on competition between hydrophytes in a tropical freshwater marsh. Basic Appl. Ecol. 13, 40–49 (2012).
Brázdil, R., Chromá, K., Dobrovolný, P. & Tolasz, R. Climate fluctuations in the Czech Republic during the period 1961–2005. Int. J. Climatol. 29, 223–242 (2009).
Jones, C. G., Lawton, J. H. & Shachak, M. Organisms as ecosystem engineers. Oikos 69, 373–386 (1994).
Acknowledgements
The authors gratefully acknowledge the financial support to their research infrastructure by the projects of the Ministry of Education, Youth and Sports of the Czech Republic LM2023048 (J. Dušek, J. Novotný, B. Navrátilová, M. Mejdová, L. Kokrda) and by a long-term research development project no. RVO 67985939 of the Academy of Sciences of the Czech Republic (A. Kučerová). We thank Steve Ridgill for their helpful comments and linguistics improvements of this article.
Funding
The authors gratefully acknowledge the financial support to their research infrastructure by the projects of the Ministry of Education, Youth and Sports of the Czech Republic LM2023048 (J. Dušek, J. Novotný, B. Navrátilová, M. Mejdová, L. Kokrda) and by a long-term research development project no. RVO 67985939 of the Academy of Sciences of the Czech Republic (A. Kučerová). We thank Steve Ridgill for their helpful comments and linguistics improvements of this article.
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Jiří Dušek: Conceptualization, Writing-Original draft preparation, Reviewing and Editing. Jan Novotný: Methodology, in situ measurements, Data processing, Reviewing and Editing. Barbora Navrátilová: in situ measurements, Data processing. Markéta Mejdová: Vegetation mapping, Data processing. Lukáš Kokrda: Support in statistical analyses. Stanislav Stellner: In situ measurements. Andrea Kučerová: Ecological and biodiversity context, Reviewing and Editing. All authors have reviewed the final version of the paper.
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Dušek, J., Novotný, J., Navrátilová, B. et al. Plant species as ecological engineers of microtopography in a temperate sedge-grass marsh. Sci Rep 15, 13432 (2025). https://doi.org/10.1038/s41598-025-98067-z
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DOI: https://doi.org/10.1038/s41598-025-98067-z
