DOI: 10.5061/dryad.rxwdbrvpp
title: Data from: Multi-year water level drawdown and wildlife grazing drive wetland vegetation succession

description: In wetlands, multi-annual water level drawdowns and herbivory can induce
 cyclic vegetation succession. While water level drawdowns can be used in
 wetland management to increase the area of reed vegetation, an important
 habitat for wetland birds, herbivory may interfere with this process.
 Here, we studied the combined effects of a human-induced water level
 drawdown, i.e. the intentional temporarily and large scale lowering of the
 water level, and herbivores on wetland vegetation development. In the
 Oostvaardersplassen wetland, we used satellite imagery to assess
 vegetation development with and without water level drawdown and with and
 without red deer presence (introduced in 1992). An herbivore exclosure
 experiment (2022-2024) across an elevational gradient tested the effect of
 grazing on vegetation development during a drawdown. Satellite imagery
 showed an expansion of reed cover by 560 ha in the period without red deer
 (1987-1991) and by 420 ha with red deer (2020-2024), only in the area with
 drawdowns. The exclosure experiment highlighted an interaction between
 herbivory and water depth: The presence of red deer at drier locations had
 minor effects on reed expansion, whereas reed expansion was strongly
 inhibited at wet locations with presence of geese. Our findings provide
 large-scale quantitative evidence of the interaction between a water level
 drawdown and herbivory on the restoration of reed-dominated wetlands. We
 show the effectiveness of a water level drawdown, when dry conditions can
 be maintained for several consecutive years, as a restoration tool to
 promote reed development and the potential to steer the impact of
 herbivores during restoration.
description: <em>Study Site</em> The
 Oostvaardersplassen is a eutrophic wetland in the Netherlands
 (52°27'N 5°19'E) (<strong>Figure 1A</strong>). It is
 in a former part of the Zuiderzee estuary, which was closed off from the
 North Sea in 1932 and gradually turned into a freshwater lake due to input
 from the river IJssel (Van Leeuwen et al., 2021). In 1968, land was
 reclaimed, mainly for agriculture purposes. The lowest and wettest part of
 the polder (around 5600 ha), destined for industrial activities, was left
 unmanaged during the first years after reclamation. This part developed
 into a marsh and attracted many rare and characteristic wetland bird
 species. This natural development prompted a shift in management strategy,
 leading to its designation as a protected nature reserve (Cornelissen et
 al., 2014; Jans and Drost, 1995). The reserve consists of a reed-dominated
 marsh (<em>c.</em> 3600 ha) and a border zone with wet and dry
 grasslands (<em>c.</em> 2000 ha). In this paper, we focussed
 on the reed-dominated marsh of the reserve (<strong>Figure
 1B</strong>). The Oostvaardersplassen is located
 in a polder and isolated from the Dutch river systems. The water level is
 controlled by the water board using a pumping station. The water level is
 kept at a relatively constant level throughout the year for the benefit of
 agriculture, industry, and housing. As the surface level of the marsh is
 higher than its surroundings, there is no inflow from surface water.
 Adjacent to the west side of the polder and the marsh is lake Markermeer,
 which is separated from the marsh by the Oostvaardersdijk. The water level
 of the lake is about 4 m above that of the marsh. Percolation from the
 lake through this dike to the marsh is thought to be minimal due to the
 fine clay particles that limit water infiltration. Because of its higher
 surface level, an embankment was made around the marsh to keep the water
 in. The water level in the marsh is controlled by a weir. Through this
 embankment, water percolates to the lower area surrounding the marsh. The
 amount of seepage through the dike and embankment is unknown. Between 1992
 and 2024, the annual precipitation was about 800 mm/year and the annual
 reference evapotranspiration about 200 mm/year (source KNMI). All
 together, during periods without a water level drawdown, water level
 fluctuations between winter and summer were around 20 cm. To create a
 diverse landscape in the border zone consisting of grasslands, heck cattle
 (<em>Bos taurus</em>) and Konik horses (<em>Equus ferus
 caballus</em>) were introduced in the 1980s (Ejrnæs et al., 2024).
 In 1992, also red deer (<em>Cervus elaphus</em>) were
 introduced, with 42 individuals being released in the grassland area. Up
 to 2018, red deer numbers were controlled by food supply, severity of the
 winter and inter- and intra-specific competition, as they live in a fenced
 area without large predators (Cornelissen, 2014). As a result, the number
 of red deer increased to more than 2500 in 2017. In 2018, the management
 of the large herbivores changed in response to a decrease in bird
 biodiversity in the border zone and animal welfare concerns. The
 population of red deer was gradually reduced to 750 in 2024 through
 population control. During the drawdown, it was estimated that more than
 90% of the red deer population utilized the marsh area (pers. comm. P.
 Cornelissen). Due to the relatively small water level
 fluctuations (Appendix in manuscript for water level data from 1980-2024)
 and high grazing intensity because of tens of thousands of moulting
 greylag geese (Van Eerden et al., 1997; Vulink et al., 2010; Vulink and
 Van Eerden, 1998; Zijlstra et al., 1991), the reed extent and
 heterogeneity of the marsh vegetation decreased in the first decade after
 reclamation (Jans and Drost, 1995; Van Den Wyngaert et al., 2003; Van
 Eerden et al., 1997). In 1987, a water level drawdown was conducted in the
 western section of the wetland area to restore reed vegetation, which
 lasted until 1991, after which the area was inundated again. In the
 eastern section, which is hydrologically separated, no drawdown was
 undertaken, and the water level remained unaltered, to allow waterbirds to
 reside in the eastern part, while the western part underwent a
 multi-annual drawdown. Water level data was monitored on three different
 locations, one in the western drawdown section and two in the eastern
 non-drawdown section (in two separate lakes: Hoekplas and Keersluisplas).
 From 1980 till 2000, water level data was manually monitored twice a month
 using a monitoring well with a known reference height (m NAP). From 2000
 onwards, data loggers were installed in the same monitoring wells and data
 was collected daily. In the period 2005-2015, data loggers in the drawdown
 section and the Hoekplas (non-drawdown section) were malfunctioning. Data
 was still automatically collected in the Keersluisplas in the non-drawdown
 section and there were some additional manual measurements. Keersluisplas
 was still hydrologically connected to the Hoekplas and therefore these
 measurements give a representation of the degree of water level
 fluctuations between summer and winter in this period.
 Other than the difference in water level management, the western
 and the eastern section are similar regarding vegetation composition
 (before drawdown) and the presence of high nutrient concentrations in the
 clay sediment (Bouma et al., 2024). The Oostvaardersplassen organic clay
 consists mostly of microfossils, biological remains, organic
 colloid/tissue clothed silt (quartz mainly), with the presence of less
 than 10% clay minerals, such as illite and chlorite (Cheng et al. 2004).
 Yet, sediment elevation is lower in the western section due to the
 drawdown in the 1990s, which caused subsidence of the sediment. Yearly
 monitoring of sediment height using a dGPS informed us about land
 subsidence during the current drawdown.  30 years after the last water
 level drawdown, the same development was observed, a declined area of reed
 vegetation, and in 2020 another water level drawdown was induced to
 achieve the same goal of restoring the reed beds.
 <strong>Figure 1.</strong> (A) Overview of the
 Netherlands and (B) Oostvaardersplassen nature reserve with delineated in
 orange the (western) drawdown section (drawdown from 1987 till 1991 and
 from 2020 till 2025). Delineated in blue is the (eastern) non-drawdown
 section. (C) Picture of a reed-dominated exclosure in the summer of 2023,
 surrounded by pioneer vegetation. (D) Locations of the 27 exclosures and
 paired control plots in the drawdown section. Camera icons indicate 9 of
 the 27 exclosures that are equipped with a camera trap. The arrow
 illustrates the sediment height gradient from high to low elevation. The
 yellowish colour illustrates the reed and willow vegetation before
 drawdown. (E) Overview of the monitoring during the first and second water
 level drawdown. The names indicate the methods used for monitoring
 (vegetation monitoring (green), vegetation classification using satellite
 imagery (brown), dGPS measurements (blue)). Additionally, the start of the
 field experiment, building of exclosures, is shown in yellow. The timeline
 with important events in the Oostvaardersplassen wetland is shown at the
 bottom of the line. The pictures illustrate some of the most important
 events (1 = start 1<sup>st</sup> drawdown, 2 = introduction of
 red deer, 3 = start 2<sup>nd</sup> drawdown, 4 = response
 during drawdown). <em>2.2 Vegetation
 classification using satellite imagery</em>
 <em>2.2.1 Vegetation structural types former and present
 drawdown</em> To determine the vegetation
 development during the current drawdown (2020-2024) and compare it to the
 previous drawdown (1987-1991) when red deer where not present, we used
 satellite imagery to classify vegetation structures on a large scale
 (<strong>Figure 1E</strong>). Landsat satellite images (30x30
 m) were obtained from Sentinel Hub EO Browser (Tiff 32-bit, high
 resolution, WGS 84) for the period 2017 till 2024 (June – September). 2019
 is missing due to a lack of suitable images because of cloud cover.
 Clipped images were analysed in R. To quantify the cover of water, bare
 soil, <em>Persicaria lapathifolia</em>, <em>Rumex
 maritimus</em>, pioneer vegetation, new (developed) reed vegetation,
 old reed vegetation and woody vegetation, we informed a model (rpart
 (Therneau et al., 2022)) with ground-truthed vegetation data (2022, 2023
 and 2024). Vegetation recordings (<em>n</em> = 57) were made
 across the entire former lake area which emerged upon drawdown (including
 control plots, see section 2.4). At each location, plant species and their
 cover were recorded (1x1 m). The vegetation recordings were clustered
 (based on kmeans in R) in distinguishable dominant habitat types (pale
 persicaria <em>(Persicaria lapathifolia), golden dock (Rumex
 maritimus), pioneer vegetation and</em> new (developed) reed).
 Additionally, using aerial photographs additional locations (without field
 data) were assigned to the habitat types of “old reed” and “woody
 vegetation”. Clustering was done for 2022 and 2023 combined (k=5) and
 separately for 2024 (k=3), due to inundated conditions that made it not
 possible to combine these into one clustering. These classes and their
 locations were used to inform the model based on the different band
 reflections of the Landsat images that correspond to known habitat types
 (for decision trees and predictions see Figure S3-S6). For the years 2017,
 2018, 2020, 2021 and the entire eastern section (without specific field
 monitoring), knowledge on the field conditions and high-resolution aerial
 photographs (PDOK, https://www.pdok.nl/)
 were used to assign the correct vegetation types to each location and
 inform the model (for decision trees and predictions see Figure S3-S6).
 Total area cover of each habitat type was calculated from the number of
 pixels for each habitat type. <em>2.4 Field
 exclosure experiment</em> To study the effect of
 geese and red deer on vegetation development, 27 ungrazed plots were
 paired with grazed control plots. To exclude red deer and geese,
 <em>we constructed</em> exclosures (2.0 m high to exclude red
 deer from grazing over the wire, 1.5 m in diameter to exclude birds from
 flying in) with PVC rods and steel wire (mesh width of 15×15 cm to exclude
 geese from getting in and red deer from reaching through) in April 2022
 (<strong>Figure 1C</strong>). Within each exclosure we sampled
 a plot of 1x1 meter to exclude edge effects. While red deer and geese
 could technically reach somewhat inside the exclosure through the mesh, no
 signs of grazing on plants inside exclosures were observed during the
 study period. Each paired control plot was located 10 m south-west of the
 exclosure and in parallel to the existing old reed border. To include
 differences in sediment height (elevation measured with a dGPS; Piper,
 TopCon, <strong>Appendix in manuscript</strong>) and
 potentially different vegetation types, the exclosures plus paired control
 plots were spread throughout the area in nine transects of three
 exclosures each at 20-100 (high elevation), c. 300 (intermediate
 elevation) and c. 600 meters (low elevation) from the reed border
 (<strong>Figure 1D</strong>). At the time of the installation
 of the exclosures, vegetation was only present in the exclosures and
 control plots closest to the reed border. Winter inundation of the plots
 occurred in 2022/2023 with 1.39 ± 1.64 cm water on high elevation, 5.83 ±
 4.40 cm on intermediate elevation and 11.74 ± 5.09 cm on low elevation and
 in 2023/2024 with 1.59 ± 3.01 cm on high elevation, 3.83 ± 6.26 cm on
 intermediate elevation and 6.31 ±10.28 cm on low elevation. Although
 differences between locations were small, we did observe locations that
 were dry in winter and locations that were continuously inundated. In
 spring, a standing water level (no matter the depth) will inhibit
 germination (Meredino & Smith 1991) and through that germination
 and species survival. It has been shown that a delay in sediment exposure
 dates of 1 week (Grace 1987) may already impact recruitment from the seed
 bank and eventually vegetation composition. In this study area, the
 vegetation composition along the environmental gradient seems to be driven
 by environmental factors such as timing of drawdown (e.g. a combination of
 water level and microtopography), salinity or nutrient availability (Ter
 Heerdt et al. 2017). A previous study in our study area has shown that the
 seed bank composition itself did not differ along the environmental
 gradient (Bouma et al. 2024).  <em>2.4.1
 Vegetation monitoring</em> At each plot,
 vegetation was monitored during peak standing crop in August 2022, August
 2023 and July 2024. To monitor vegetation development a 1x1 m quadrant was
 placed in the centre of the exclosure and the control plot to visually
 estimate the species cover for each plant species. Total cover could
 exceed 100%, because of undergrowth. In each plot, height of reed was
 recorded for three individual stems.  <em>2.5
 Herbivore species detection and quantification</em>
 Wildlife cameras (Bushnell Core no Glow) were employed
 continuously from August 2022 till August 2024 (<strong>Appendix in
 manuscript</strong>) on 9 out of the 27 exclosures, covering
 elevation gradients (high, intermediate, low) in three rows across the
 area (<strong>Figure 1D</strong>). Cameras were visited every
 2-3 months to replace batteries and SD-cards. The camera traps were facing
 towards the old reed vegetation and were located at a height of
 approximately 150 cm to provide a good view of herbivores visiting the
 area close to the exclosure. The cameras were triggered by movement and
 took one picture each time, after which they became insensitive to
 movement for 10 s. Obtained pictures were loaded into the Agouti
 environment (Casaer et al., 2019). Agouti automatically groups images into
 bursts, which is defined as one event in which occurring (groups of)
 individuals are repeatedly observed (in this case this was defined as
 pictures taken within 120 s). Each burst was annotated manually with the
 present species and the count of individuals per species. The cumulative
 number of red deer or geese (both flying and standing) present at each
 location for a certain period was divided by the total number of days the
 camera was deployed in that period. Overall, we analysed 240.602 images
 that were converted into 32.823 bursts covering the period August 2022 to
 August 2024. Of these bursts, 6.292 showed an animal on the picture, this
 included the observation of red deer (52%) and geese (7%). The lower
 number of pictures with geese compared to red deer may be due to flocking
 of geese which can lead to a sudden peak in observance. Greylag geese
 (<em>Anser anser</em>) were the dominant goose species in the
 wetland area, followed by white-fronted goose (<em>Anser
 albifrons</em>). Other animals seen in the pictures at low
 occurrences were starlings (<em>Sturnus vulgaris</em>), foxes
 (<em>Vulpes vulpes</em>), yellow wagtail (<em>Motacilla
 flava</em>), reed warblers (<em>Acrocephalus</em> spec.)
 and white-tailed eagle (<em>Haliaeetus
 albicilla</em>).  <em>2.5 Data
 analyses</em> <em>2.5.1 Satellite
 imagery</em> Vegetation recordings for
 ground-truthing of satellite imagery of 2022, 2023 and 2024 were clustered
 using kmeans (stats package; (R Core Team, 2023)), resulting in 5 clusters
 (estimated based on the Elbow Method) and nstart set at 25. The clustered
 data was used to train the model for recursive partitioning with the
 function rpart (package rpart; (Therneau et al., 2022)) for the period
 2017 till 2024. The complete Landsat satellite images were predicted using
 the function ”predict” (stats package).
  <em>2.5.2 Field exclosure experiment</em>
 Differences in vegetation composition between exclosure and
 control plots were assessed per year through non-metric multidimensional
 scaling (NMDS) with Bray-Curtis distances (vegan package; Oksanen et al.,
 2022). Subsequently, ADONIS analysis was performed to identify statistical
 differences between groups (exclosure/control). We
 analysed how reed cover (independent variables) was impacted by herbivory
 (presence/absence), sediment elevation (high, intermediate, low), time
 (dependent variables) and their interactions with generalised linear mixed
 models corrected for zero inflation with tweedie family (link = log,
 glmmTMB package; Brooks et al., 2017). Furthermore, we analysed how
 pioneer vegetation and total cover (independent variables) were impacted
 by herbivory (presence/absence), sediment elevation (high, intermediate,
 low), time (dependent variables) and their interactions with generalised
 linear mixed models with tweedie family (link = log) (glmmTMB package;
 Brooks et al., 2017). Cover of cattail and woody vegetation was data-poor
 and therefore the interactions among the dependent variables were removed
 from the model. The model was run with a correction for zero inflation and
 with respectively the negative binomial function and the tweedie
 (link=log) function.    <em>2.5.3 Herbivore
 presence</em> Counts of red deer and geese,
 obtained from the camera images, were tested for dependence on presence of
 water (November 2022 – May 2023, October 2023 – August 2024) and sediment
 elevation (high, intermediate, low) with generalised linear models
 corrected for zero-inflation with Poisson family distribution (package
 glmmTMB; Brooks et al., 2017). Data were analysed in
 RStudio version 4.0.3 (R Core Team, 2023). All data are shown with their
 average ± Standard Deviations (SD), and in all hypotheses testing
 procedures the significance level was pre-set at α = 0.05.
description: # Data from: Multi-year water level drawdown and wildlife grazing drive
 wetland vegetation succession Dataset DOI:
 [10.5061/dryad.rxwdbrvpp](https://doi.org/10.5061/dryad.rxwdbrvpp) ##
 Description of the data and file structure We constructed 27 exclosures
 (paired with a control plot) across the Oostvaardersplassen. The
 exclosures are placed in 9 rows of each 3 exclosures across an elevational
 gradient (high, intermediate, low). Within the exclosures we monitored
 vegetation composition, height, water depth and sediment elevation. \ \
 Additionally, we monitored vegetation across the area without exclosures
 and used this to train a model to classify satellite images (supplemented
 with locations that were assessed by experts to assign a vegetationtype). 
 Lastly, we provide data on red deer and geese presence assessed through
 cameratrap images.  ### Files and variables #### File:
 Data-Bouma_et_al.*2025*-_Ecological_Engineering.zip **Description:** 
 There are three maps within this zip file
 (Data-Bouma_et_al.*2025*-_Ecological_Engineering.zip), below we show the
 different files within each ZIP file. Metadata is included in each of the
 files seperately. Missing values are indicated by blank cells.  Map
 Exclosure experiment: \ \- Sediment elevation
 measurements: `dGPS_measurements_22_23_24.xlsx`\ \- Vegetation cover in
 exclosure and control plot: `Vegetation_monitoring.xlsx`\ \- Reed height
 measurements in exclosure and control plot: `reed_height.xlsx`\ \-
 Coordinates of the paired exclosure-control plot:
 `Locations_Exclosures.xlsx` Map Satellite_classification\ \-
 Classification of satellite imagery based on ground
 data: `Satellite_classification_2017-2024.xlsx`\ \- Trainingdata from East
 (without water level drawdown): `Trainingdata_East.xlsx `\ \- Trainingdata
 from West (with water level drawdown): `Trainingdata_West.xlsx`\ \- Data
 of vegetation composition in the extra locations that were monitored for
 clustering of locations: `Extra_vegetation_monitoring.xlsx` Cameratraps\
 \- Overview of presence of red deer and geese: `cameratrap.xlsx` ##
 Code/software Statistical analysis descriptions can be found in the
 published article.