The influence of wind wave exposure and clonal integration on plant
performance of clonal salt marsh grass Spartina anglica in The Netherlands
Author: Arjen Schokker
University: University of Amsterdam Study: Future Planet Studies Course: Bachelorproject Biologie Student number: 11318295
Supervisor: Clea van der Ven
Period: 02/02/2020 – 05/07/2020
Data repository:
https://arjenschokker.stackstorage.com/s/VWqMXcqBmMFDqS0 Password data
Abstract
Landscape engineering vegetation on salt marshes enhance sedimentation and thereby decrease environmental stressors. This creates a more favorable environment for vegetation growth causing a positive feedback. Many salt marsh vegetation species overcome the stressors of the tidal flats by spreading clonally. At the seaward edge of the salt marsh, wind wave induced bed shear stress is one of the most important causes for salt marsh deterioration. It is still poorly understood how exposure to wind created waves affect the morphology of individual plants and to what extent landscape-engineering vegetation relies on clonal integration to increase its performance. This study researched both the influence of wind wave exposure and clonal integration on plant performance of landscape engineering species S. anglica in salt marshes. We found that a larger grainsize occurs at salt marshes that have high wind wave exposure, while the percentage organic material in the sediment is lower. In addition, grainsize decreases the shoot length of S. anglica, whereas organic matter increases the shoot length. These findings combined make that high wind wave exposure negatively affects performance of S. anglica. A field experiment revealed that clonal integration in S. anglica improves the recovery of pioneering clusters after clipping all aboveground biomass. Although, the field experiment did not provide evidence that clonal integration affected growth of S. anglica on salt marshes, it can still be concluded that clonal integration improves performance due to the improved recovery.
Introduction
Salt marshes provide various essential ecosystem services, such as flood protection, water storage, carbon storage, nursery functions and biodiversity enhancement (Barbier et al., 2011, 2008; Costanza et al., 1997). These services are supported by specialized vegetation growing in the intertidal flats that modify the landscape with their presence.
Grasses such as Spartina anglica are important ecosystem engineers and when they establish on salt marshes they enhance sedimentation by reducing flow and wave energy in their canopies. Subsequently, environmental stressors decrease which creates a more favorable environment for vegetation growth causing a positive feedback (Balke et al., 2014; Corenblit et al., 2015; Corenblit et al., 2011; Corenblit et al., 2015; Vacchi et al., 2017). These feedbacks occur if plant shoot density and patch size are above a certain threshold. Therefore, salt marshes can deteriorate and disappear when plant shoot density or patch size fall below these critical thresholds, while (re-)establishment is inhibited (Angelini et al., 2016; Christianen et al., 2014; Silliman et al., 2015).
One of the most important causes for salt marsh deterioration are wind created waves (wind waves) (Moeller, Spencer, & French, 1996). The size and energy of the wind waves depend on the fetch (distance wind can blow over a body of water in a constant direction) in combination with windspeed and wind duration (Prahalad, Sharples, Kirkpatrick, & Mount, 2015). Wind waves induce bed shear stress which can eventually lead to scarp erosion (Lawson, Wiberg, McGlathery, & Fugatf, 2007; Prahalad et al., 2015).
Many salt marsh vegetation species overcome the stressors of the tidal flats by spreading clonally (Bouma et al., 2005; Kendrick, Duarte, & Marbà, 2005). Other than increasing the shoot density and patch size, clonal expansion also extends the longevity of an individual (Bricker, Calladine, Virnstein, & Waycott, 2018; De Witte & Stöcklin, 2010; Thomas, 2013). By doing so, clonally spreading vegetation can increase its persistence and is able to form complex biogeomorphic structures (Bouma et al., 2013). Furthermore, spreading through clones can potentially help to overcome the stressors of highly heterogenic landscapes. The mechanism responsible for this is clonal integration (Huang et al., 2018; Hutchings & Wijesinghe, 1997;Stueffer, J. F., De Kroon, H., & During, 1996; Wang et al., 2011). Resources, such as water, carbohydrates and mineral nutrients, can be shared via rhizome connections between the old established patch and the newly formed patch that is situated in a stressful environment despite a local lack of resources (Huang et al., 2018; Hutchings & Wijesinghe, 1997; Pennings & Callaway, 2000; Wang, Li, During, & Li, 2011).
Many previous studies have been dedicated to wind wave exposure and clonal integration. Wind wave exposure has predominantly been researched at a scale of entire salt marsh ecosystems (Lawson et al., 2007; Moeller et al., 1996; Prahalad et al., 2015). However, it is still poorly understood how exposure to wind created waves affect the morphology of individual plants. In addition, clonal integration has been studied extensively. These studies mostly focus on the distribution of resources through division of labor while not examining to what extent landscape-engineering vegetation relies on clonal integration to increase its performance (Huang et al., 2018; Hutchings & Wijesinghe, 1997; Pennings & Callaway, 2000; Stueffer, J. F., De Kroon, H., & During, 1996; Wang et al., 2011). For these reasons, this study aims to research both the influence of wind wave exposure and clonal integration on plant performance of S. anglica in salt marshes. Gained knowledge may contribute to the restoration of salt marshes.
Firstly, we hypothesized that high wind wave exposure affects the morphology of S. anglica on salt marshes. We expected that high wind wave exposure correlates with large grainsize and low organic matter. In addition, we expect that grainsize decreases the shoot length and that organic material increases the shoot length. Therefore, we explored the relationships between wind wave exposure,
previously mentioned sediment characteristics and Spartina anglica morphology. To investigate these relationships, data from an earlier survey on salt marshes across the northwest European coast were used in combination with location specific weather data and measured exposure data.
Secondly, we hypothesized that clonal integration will improve the performance of S. anglica. Therefore, this paper will investigate the effects of clonal integration on the performance using the indicators recovery and growth of Spartina anglica. For this, we set up a field experiment in an Oosterschelde estuary in the Netherlands where multiple S. anglica patches will be tested for the effect of rhizomal connections on the performance of pioneering clusters.
The research will be divided into four subjects. Firstly, the effect of wind wave exposure on sediment characteristics of S. anglica in salt marsh environments was explored. Following this, we researched the influence of these sediment characteristics on the morphology of S. anglica. Hereafter, we investigated the recovery increase of pioneering S. anglica clusters due to clonal integration. Lastly, we researched whether clonal integration had a positive effect on the growth of S. anglica pioneering clusters.
Methods
Wind wave exposure Data collection
In 2019 a survey took place at 15 salt marshes along the European northwest coast from Brittany (France) to the south of Denmark (appendix A). A total of 90 S. anglica patches were selected from the pioneer zones. Shoot lengths and diameters were measured in the field and sediment samples (~ 10 cm depth) were collected per plant and frozen until analysis. Sediment mean and median grain size was measured on freeze dried material with Polarized Intensity Differential Scattering (Beckman Coulter LS 13 320) and the percentage organic matter was estimated as weight loss by ignition at 550 °C.
Fetch is the distance wind can blow over a body of water in a constant direction. The fetch length affects the size of wind waves that are a primary mechanism of change in saltmarshes (Prahalad et al., 2015). Google Earth was used to measure the fetch in 16 wind directions (N, NNE, NE, ENE, E, ESE, SE, SSE, S, SSW, SW, WSW, W, WNW, NW, NNW) at the salt marsh locations. Google Earth contains the tool “ruler” that accurately measures the fetch distance (km) as well as the degrees from North to assign a fetch distance to a certain wind direction. The fetch distance was measured as the distance between the forefront of the salt mars to the first above water obstacle.
Besides fetch, the corresponding wind speed, wind direction and wind duration were needed to investigate the effect of wind wave exposure. Wind wave exposure of the salt marshes was estimated from meteorological data over the year 2018 (KNMI) because the effects lag. We collected hourly wind data from wind stations closest to salt marsh locations (KNMI). From the meteorological data the average hourly windspeed was sorted by wind direction. The hourly windspeed values were averaged per wind direction. Unfortunately, due to time restrictions wind data for locations outside of The Netherlands were not used. Therefore, we will examine the method of estimating the wind wave exposure at salt marsh locations in the Netherlands.
Fetch-index
The fetch-index is created by multiplying the fetch length of a wind direction with the average windspeed in that direction. These values are then weighted by the percentage of the amount of times an hourly average windspeed value is in a certain direction, as some wind directions are more dominant than others. The outcomes per direction are then summed, which results in an aggregate fetch-index. The fetch-index was created for the purpose of comparing different sites on wind wave exposure with the use of one single metric.
Seasonal plant growth
Because the survey took place over 147 days the plant morphology measurements needed to be corrected for seasonal growth to be able to be analysed. For the plant growth a linear regression model was made (appendix B) which was used to determine the growth rate. With this we created an inverted linear regression model (y = -0.1207X+24.9036) with the intersection at the y-axis at the y-value for the 147th day of the original formula (appendix B). The correction was achieved by adding
the y- values of the inverted linear regression line to the original measurements (appendix B). Note that the absolute values of the corrected shoot length are 6.296 higher than the actual value at day 147, however the relative differences of the response signal are the same and therefore can be used for analysis.
Clonal integration Field experiment
On April 28th 2020, a field experiment was started at the Slikken van Den Dortsman (51°34'28.9"N
4°00'13.2"E) to research the effect of clonal integration on S. anglica growth and recovery after disturbance.
A total of 28 patches of S. anglica were selected, which were approximately 0.5 to 1m in diameter. In each patch, two clusters at the edge were identified and checked for presence of intact rhizomal connection to the bigger mother patch. Of these two clusters, one rhizome was severed while the other was left intact. Each patch was then assigned one of four treatments (seven replicas per treatment) following a randomized block design. The four treatments differ in the applied disturbance, which was applied as cutting aboveground biomass. This resulted in the following treatments: None cut, Cluster cut, Mother cut, All cut (Fig. 1).
To examine plant performance, we used the shoot recovery and growth as indicators. Plant morphology measurements after six weeks since the start of the experiment were compared with the null measurements. Cluster shoots were counted to calculate the recovery, while growth relied on the length of five shoots of every clusters and the mother patch. Difference in recovery was tested by calculating the percentage of shoot regrowth after disturbance. The recovery of the intact and severed clusters of the Cluster cut and All cut groups were compared, for the clusters of these groups were cut. Difference in growth caused by clonal integration was tested by comparing the morphology of intact and severed clusters between the None cut and Mother cut groups, for the clusters of these groups were not cut. The growth is defined as the end length minus the start length, this is averaged per cluster since only five shoots per cluster have been measured.
Fig. 1 Treatments applied to plots at clonal integration field experiment. All plots contain a mother patch and two smaller cluster patches of which one the rhizomal connection is severed. In addition, the treatments differ in disturbance which is applied as cutting biomass above ground. a: no plants cut, b: cluster plants cut, c: mother plant cut, d: all plants cut
Statistical analysis
Statistical analyses were performed using software program R (version 3.61). For every statistic, p-values lower than 0.05 were considered statistically significant.
Wind wave exposure
The correlation between grainsize and the amount of organic material was analyzed using a linear and exponential regression model on log-transformed data. Model selection was done using Akaike Information Criterion (AIC) values, with the lowest AIC value supporting the best model.
The effect of wind wave exposure on the percentage of organic material in the sediment was assessed using a linear regression. Furthermore, we assessed the effect of wind wave exposure on grainsize using a logarithmic regression model.
Correlations between grainsize, organic material and the corrected shoot length have been assessed using linear regression analysis.
Clonal integration
To test the effect of clonal integration on recovery of S. anglica the recovery (percentage of shoot regrowth) was calculated. Normality of the log transformed recovery data and its residuals were visually examined and tested through a Shapiro-Wilk normality test. To meet the normality
assumptions, a boxcox transformation was applied. A mixed effect model was used to investigate the effect of mother plant clipping, cluster plant clipping and clonal integration and their interactions on recovery with block and plot number as random effects. We assessed the mixed effect model using a type-3 F-statistic.
In the same manner, the effects of mother plant clipping, cluster plant clipping, clonal integration and their interactions were examined using a mixed effect model with block and plot number as random effects.
Results
Wind wave exposure
Effect of wind wave exposure on grainsize and organic material
Sediment characteristics differed strongly between locations. Median grainsize has a range of 27.05 μm to 573.00 μm, whereas organic matter in the sediment has a range of 0.46% up to 11.22%. In addition, fetch and wind conditions also varied, resulting in a range of the fetch-index of 5812 to 16498.
We expected that the grainsize and organic material are linked with each other. Therefore, we explored the correlation between them. A negative linear regression line shows that the grainsize correlates with the organic material in a negative manner. The linear model is significant (p-value < 0.001), however it only explains 33.25% of the data. To compare different regression models for the best fit the AIC-values were calculated. The linear regression model has an AIC-value of 398.46, whereas the exponential regression model has a value of 170.60 and thus fits the data better than the linear model. The exponential regression line explains 43.78% of the data and has a p-value below 0.001 (Fig. 2). The correlation between grainsize and organic matter suggests that these characteristics depend on each other or on the same explanatory variable.
To research the effect of exposure on sediment characteristics we examined the correlation between fetch-index and the average median grainsize per location. We found correlation between the fetch index and the average median grainsize per location. The linear model shows a positive relation between fetch-index and the median grainsize (p <0.001, R2 = 0.41). A regression that exceeds the fit
of the linear regression is the exponential regression (p < 0.001, R2 = 0.50; Fig. 3). The exponential
regression line explains the data better and has a lower p-value. The fit was tested by performing AIC on both models. This gives AIC values of 238.02 for the linear model and 233.71 for the exponential model. Conclusively, the exponential model is a better fit for the data.
Another sediment characteristic that could correlate with the fetch-index is the amount of organic material in the sediment. Likewise, there is a correlation between the fetch-index and the percentage organic material in the sediment. Here, a negative correlation is represented by a linear regression line (p-value = 0.011, R2 = 0.25, y = -9.719e-5X + 2.797; Fig. 4).
Effect of sediment characteristics on shoot length of S. anglica
To research the effect of sediment characteristics on S. anglica we performed regression analysis on the relations between the median grainsize and the shoot length of S. anglica and the percentage organic material in the sediment and the shoot length of S. anglica. The locations vary in shoot length (corrected shoot length). The shoot length ranges from 19.02cm to 68.51cm.
We found that the grainsize negatively affects the shoot length. The regression analysis gives that our finding is represented by a linear regression line with the formula y = -0.02307X + 35.73779 (Fig. 5). The line explains the data for 10.53% and has a p-value below 0.001.
In addition, we found that the amount of organic material in the sediment positively correlates with the shoot length. For this data we created a linear regression line (y = 0.7353X + 29.9217; Fig. 6). This regression line explains the data for 7.48% and has a p-value below 0.001.
Fig. 2 log transformed exponential correlation between median grainsize and the organic material percentage of salt marshes across European Westcoast.
Fig. 3 Log-transformed exponential regression between fetch-index and average median grainsize of salt marsh locations in the Netherlands.
Fig. 4 Linear regression between fetch-index and percentage organic material of salt marsh locations in the Netherlands
Fig. 6 Linear regression model of percentage organic material and corrected shoot length of S. anglica at locations along the European Westcoast
Clonal integration
Six weeks (42 days) after the start of the experiment we observed a clear difference relative to the start of the experiment. At the beginning of the experiment the S. anglica patches consisted mostly of small shoots and dead material from the previous year. After six weeks the shoots were greener and seemed overall more vital.
Effect of clonal integration on recovery of S. anglica
Recovery of shoots in clipped clusters was assessed on the treatments Cluster cut and All cut (Figure method). None of the groups made a full recovery after six weeks. Recovery was calculated for the intact and severed groups within the treatments separately. Recovery of the intact Cluster cut was 37.7% ± 11.4% and for the severed Cluster cut 31.0% ± 24.5%. For All cut the recovery of the intact group was 44.5% ± 13.9% and for the severed group 31.5% ± 17.6% (Fig. 7). A mixed effect model was used to investigate the effect of mother plant clipping, cluster plant clipping and clonal integration and their interactions on recovery with block and plot number as random effects. A type-3 F-statistic revealed that the recovery percentages of the intact and severed groups are significantly different (p = 0.032, F-value = 5.17) and are independent of whether the mother had been clipped. Effect of clonal integration on growth S. anglica
Growth between intact and severed clusters of the None cut and All cut groups was compared. The growth is defined as the end length minus the start length (cm), this is averaged per cluster since only a maximum of five shoots per cluster have been measured. According to the data the clusters of the None cut treatment all have shrunken, the intact group decreased -0.10 cm ± 1.62 cm in length and the severed group decreased -2.48 cm ± 1.76 cm in length. In contrast, the clusters of the Mother cut treatment all have grown, the intact group grew 0.20 cm ± 0.54 cm and the severed group with 1.33 cm ± 0.98 cm (Fig. 8). To test for significant differences a type-3 F-statistic was performed on the mixed effect model containing the effects of mother plant clipping, cluster plant clipping, clonal
integration and their interactions with block and plot number as random effects. This did not yield any significant results, meaning that clusters did not differ in growth.
Fig. 7 Recovery of S. anglica clusters of the treatments Cluster cut and All cut with separated clonal integration treatment
Discussion
This paper provides new insights on how the performance of landscape engineering vegetation such as S. anglica is influenced by wind wave exposure and clonal integration.
Wind wave exposure
Firstly, we hypothesized that wind wave exposure affects the morphology of S. anglica. The results show a strong correlation between wind wave exposure and sediment characteristics. More specifically, the grainsize is larger at exposed sites (Fig.3), while the percentage organic material in the sediment is lower (Fig. 4). In addition, grainsize and organic material correlate with the shoot length of S. anglica. Grainsize decreases the shoot length (Fig. 5) whereas organic matter increases the shoot length (Fig.6). These findings combined make that high wind wave exposure negatively affects performance of S. anglica and thus, are in line with our hypothesis.
According to previous studies growth and patch size of sea grasses vary under different hydrodynamic conditions (Fonseca & Bell, 1998; Robbins & Bell, 2000). Although these studies do not examine S. anglica, the findings in these studies show that a higher exposure index resulted in higher grainsize and lower organic matter. As reported by Swales, MacDonald, & Green (2004) higher wind wave exposure leads to low-biomass patches of S. anglica, while high-biomass patches developed at a sheltered location. Therefore, our findings are in line with the statements of these studies.
The results should be interpreted carefully because of the low sample size of wind wave exposure. The low sample size is possibly also responsible for the high R-squared of the exposure regression analysis. The reason for this is that the historic meteorological data from saltmarshes outside of The Netherlands were not used due to time constraints and a strong correlation with low variation is easily obtained with a small sample size. However, previously mentioned studies support our claim that the fetch-index is a suitable metric to estimate the wind wave exposure of salt marshes (Fonseca & Bell, 1998; Robbins & Bell, 2000; Swales, MacDonald, & Green, 2004). The sample size can easily be expanded with locations along the northwest coast of Europe if historic wind data is available. Moreover, the precision of the wind wave exposure model could further improved by including bathymetry and tidal currents (Fonseca & Bell, 1998; Pepper & Puotinen, 2009). The bathymetry can affect the effective fetch as a higher seabed will be an obstacle for wind created waves. In addition, processes such as refraction and diffraction of waves are not accounted for (Pepper & Puotinen, 2009). In addition, tidal currents are responsible for a substantial amount of sediment export to salt marshes and to the ocean and therefore should not be neglected (Fagherazzi & Wiberg, 2009). Insights gained from this study can be used to increase the restoration efficiency. Restorators can assess whether an area will have favorable conditions for salt marsh restoration.
A suggestion for future research is to take tidal currents into account in the wind wave exposure. Wind waves simply cannot occur if the water level is too low due to the tide. Wind data for low tides therefore is obsolete. A next step is then to create a model that predicts the bed development from wind wave exposure data in combination with tidal currents such as Fonseca & Bell (1998) created for sea grass. A prediction model could be integrated in a Generic Relative Exposure Model (GREMO) which can visually display the suitable areas for restoration and account for environmental factors such as nearby rivers to explain deviated grain size and bathymetry (Pepper & Puotinen, 2009). Clonal integration
Our second hypotheses stated that clonal integration increases the performance of S. anglica in terms of recovery after disturbance and growth. The field experiment reveals that clonal integration improves the recovery of pioneering clusters after clipping. Although clonal integration does not
affect growth of S. anglica, it can be concluded that clonal integration improves performance due to the improved recovery. In high wind wave exposure areas where bed shear stress is high, patches can possibly benefit from a high recovery and re-establish itself to continue the positive sediment capture feedback.
Surprisingly, after six weeks the recovery of intact clusters were lower than 100% (Fig. 7). This could be due to the counting method of the shoots. The start of the experiment was also the start of the growth season, during that time it was difficult to identify which shoots were vital and therefore all shoots were counted. Later in the growth season, vital shoots were easily identified because of their green colour. At this time only vital shoots were counted. Consequently, it could be that no new shoots emerged from the dead counted remains.
Another unexpected result is the decrease in growth of plants in the Non cut treatments. This could be due to the fact that two severed clusters were washed away because by severing the rhizome they lost their anchoring to the mother patch. Thus, besides sharing resources, clonal integration possibly could be an important mechanism for the anchoring of the plant. Especially in areas with high wind wave exposure and high rates of erosion as a result of bed sheer stress, anchoring could be an essential feature.
Previous studies state that clonal integration does increase the performance of various clonally spreading plants (Huang et al., 2018; Hutchings & Wijesinghe, 1997; Stueffer, J. F., De Kroon, H., & During, 1996; Wang et al., 2011), our results support this claim for the recovery. However, the study of Hutchings & Wijesinghe (1997) states that also growth is increased as a result of clonal integration. Our results show no significant difference in growth between the severed and intact groups of the None cut and Mother cut treatments (Fig. 8). Since our study did not find evidence that clonal integration improves plant growth in salt marsh vegetation we cannot support the claim of Hutchings & Wijesinghe (1997).
Since every treatment has relatively low numbers of replicates (seven per treatment), variation could possibly be reduced by expanding the sample size or by selecting plots with relatively similar wind wave exposure stress.
Gained knowledge can be used to improve salt marsh restoration strategies. Our study and previous studies clearly show that clonal integration improves the performance of S. anglica and therefore the survival of a relocated patch is possibly higher than the survival of seedlings.
Investigating the survival rate of S. anglica seedlings in pioneering areas could be interesting for future research
Acknowledgements
I greatly acknowledge C. van der Ven and V. Reijers for supervising during this research, setting up fieldwork, commenting on earlier versions of this manuscript and assistance with R scripting. I also thank C. Lammers, V. Reijers and C. van der Ven for the 2019 survey were data of European salt marshes was obtained. I also thank The Royal Netherlands Meteorological Institute (KNMI) for providing wind data. I thank Royal Netherlands Institute for Sea Research (NIOZ) for providing an internship.
Appendix
Appendix ASalt mars locations of the survey performed in 2019. Red markers are locations used for wind wave exposure analysis (Google Earth, n.d.).
Salt mars locations of the survey performed in 2019. Red markers are locations used for wind wave exposure analysis, sediment analysis and plant analysis, yellow markers are locations used for sediment analysis (Google Earth, n.d.).
Salt mars locations of the survey performed in 2019. Yellow markers are locations used for sediment analysis (Google Earth, n.d.).
Abbreviation Location full name Latitude Longitude
BDV Baie de Veys 49.364518° -1.171153° BSM Briac-sur-Mer 48.612091° -2.130718° FRI Friedrichskoog 54.039240° 8.846514° GOU Goulven 48.638260° -4.309522° HEL Hellegat 51.366638° 3.948244° HOB Ho Bugt 55.532910° 8.269676° KWH Kwade hoek 51.843155° 3.978949° MAN Mandø 55.277629° 8.527616° MSM Mt st Michel bay 48.679849° -1.498653° PAU Paulinapolder 51.349276° 3.733968°
ROE Roels Hoek /bij Rilland 51.436713° 4.158307°
ROM Romo 55.150159° 8.659651°
ROT Rottumerplaat 53.532389° 6.498927°
SAR Saint Armel/Le passage (onder Vannes,
SCH Schiermonnikoog, NL 53.473977° 6.204960°
SYL Sylt 54.780502° 8.294675°
VAB Vest Abbolingvej / Vester Abolling 55.217274° 8.667593° Appendix B
The days since the start of the survey (x-axis) plotted against the S. anglica shoot length. Regression line of the shoot length of S.
anglica (blue line) and the inverted regression line which was used to correct for seasonal growth (red line).
The days since the start of the survey (x-axis) plotted against the corrected shoot length of S. anglica. Regression line of the corrected shoot length of S. anglica (blue).
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