The influence of Dreissena
bugensis (quagga mussel) on
water quality in shallow lakes
Mees Beaumont - University of Amsterdam
Abstract:
Dreissena bugensis (Quagga mussel) (Andrusov 1897) is an invasive species in many freshwater ecosystems around the world. First observation of the quagga mussel in the Netherlands was in 2006. The influence of the quagga mussel on water quality in Dutch shallow lakes is still unknown. As a consequence of the filter capacity of the quagga mussel it may affect the water quality targets set within the European Water Framework Directive, thus is important to know for Waterboard Rijnland. In this research the influence of the quagga mussel on water quality in shallow lakes was investigated. EDNA samples of the quagga mussel were taken in lakes (n = 13) of Waterboard Rijnland. Comparisons between groups of lakes (based on eDNA concentrations) were made between pre- and post-invasion time periods. Differences in phytoplankton, nutrients (P-tot and N-tot), transparency and suspended matter were investigated. Lakes in Rijnland with mean eDNA concentrations above 252.7 ± 73.6 mol/ml decreased significantly in phytoplankton and suspended matter concentrations and transparency significantly increased after the quagga invasion. The increase in transparency and decrease in phytoplankton is an improvement in water quality as set by the standards of the KRW 2027 targets.
Samenvatting:
Sinds 2006 is de quagga mossel een invasieve soort in de Nederlandse wateren. Om een inzicht te krijgen wat de effecten van deze aanwezigheid zijn is in dit onderzoek een antwoord gegeven op de vraag: Wat is de invloed van de quagga mossel op de waterkwaliteit van de meren in Rijnland. Environmental DNA (eDNA) is gebruikt om als detectiemethode van de mosselen. Op basis van de concentratie eDNA in een meer zijn de dertien meren ingedeeld in categorieën. Per categorie is gekeken naar fytoplankton, nutriënten (P-tot en N-tot), zwevende stof en doorzicht. Er is gekeken naar de veranderingen tijdens de quagga invasie in de meren van Rijnland. Er is een significante daling gevonden in fytoplankton en zwevende stof, ook is er een significante stijging gevonden in doorzicht in de meren waar de quagga mossel relatief veel aanwezig is. Deze veranderingen ten gevolge van de filter capaciteit van de quagga mossel kunnen bijdragen aan het verbeteren in de waterkwaliteit in de meren van Rijnland.
Keywords:
Dreissena bugensis, eDNA, water quality, turbidity, nutrients, phytoplankton, quagga mussel
Supervisor:
Drs. Bart Schaub (University of Amsterdam; Hoogheemraadschap van Rijnland)
Examiner:
Dr. Arie Vonk (University of Amsterdam)
Additional support:
Dr. Krijn Trimbos & Martin van der Plas MSc (University of Leiden; CML); Eric Verlaan (Hoogheemraadschap van Rijnland)
Introduction
Dreissena bugensis (Quagga mussel) (Andrusov 1897) is an invasive species in many freshwater ecosystems around the world. The quagga mussel is native to the Black Sea area, but was discovered as an invasive specie in the Great Lakes (between Canada and the United States) in 1992 (May & Marsden, 1992). The first observation of the quagga mussel in western Europe was in the Netherlands in 2006 (Molloy et al., 2007). After this first observation, the quagga mussel distributed fast. The quagga is expected to spread to almost all the waters in the Netherlands within time related to the connectivity of the waters (Matthews et al., 2012). The quagga mussel distributes through connected rivers and channels during their planktonic juvenile stage, and also by attaching to transported boats as adults, even over land (Matthews et al., 2014). The quagga mussel can displace the Zebra mussel (Dreissena Polymorpha) during his expansion (Mills et al., 1999).
The quagga invasion can have an effect on the water quality, which has been investigated during the invasion in the Great Lakes. The quagga mussel can decrease the turbidity because of their great filtering capacity from up to 7,4 L/day and can decrease phytoplankton concentrations (Garton et al., 2014). The increase in transparency facilitates photosynthesis of the phytoplankton and thus has a positive effect on phytoplankton (Zhu et al., 2006). Mida et al. (2010) also linked an increase in nitrogen and phosphor concentrations after the invasion to a decline in nitrogen and phosphor usage by phytoplankton. The filter capacity of the quagga mussel influences thus the primary production and plankton in aquatic systems and can have cascade effects on the food web and indirectly can cause changes at higher trophic levels (Kissman et al., 2010; Mida et al., 2010).
An overview of the quagga mussel presence is needed to investigate their influence on local aquatic systems. Traditional ways of determining quagga mussel presence is done by visual observations, recording methods or taking benthic samples. All three methods are labour intensive and time consuming, thus a more accessible method to determine presence and densities would be favourable. DNA from Dreissena species can be measured in the water column by just taking water samples. This DNA, so called environmental DNA (eDNA), is derived from sources such as faeces, mucus, dead organisms and sloughed-off cells (Klymus et al., 2015). Previous research succeeded in gaining eDNA samples from the quagga mussel out of surface waters (Gingera et al., 2017; Trimbos, 2016). This technique is currently applied to determine the presence of quagga mussels in the lakes of waterboard of Rijnland, which is less labour intensive than the traditionally used techniques.
The influence of the quagga mussel on the water quality (of the lakes of waterboard of Rijnland) will be investigated in this research. Since 2014 the presence of quagga mussels in the area of waterboard of Rijnland is confirmed (Van der Kamp et al., 2014; Schaub et al., 2016). To get insight in how the Dreissena bugensis effects the water quality in the lakes of Rijnland, small scale effects and effects on lower trophic levels need to be known to understand changes on higher scales and trophic levels. This is important to know for the waterboard of Rijnland, because as a consequence it may affect the water quality targets set within the European Water Framework Directive (RIVM, 2014).
This research is divided into four sub questions: i. Can eDNA of the quagga mussel be used as an indicator for quagga biomass? ii. What is the effect of the quagga mussel invasion on biological water quality (phytoplankton)? iii. What is the effect of the quagga mussel invasion on physical water quality characteristics (transparency and suspended matter)? iv. Wat is the effect of the quagga mussel invasion on chemical water quality characteristics (nitrogen and phosphorus)?
Materials and Methods
To examine the influence of quagga mussels on the water systems, data from two organisations was used. Institute of Environmental Sciences of the Faculty of Science of Leiden University (CML) measured concentrations eDNA in several lakes of Rijnland in 2016 and 2017 and made these available. Waterboard Rijnland monitored the biological, physical and chemical water quality characteristics of lakes in Rijnland. For this research, data from 1990 till 2020 was made available. All the analyses in this research were done with these two datasets from the CML and the Waterboard Rijnland.
The analysis is divided into two steps. In the first analysis, the concentration eDNA was measured in thirteen different lakes (n = 13) of Rijnland. Measurements were monthly taken by the CML in 2016 (April August) and 2017 (March -October). The sampling method can be found in the article: ‘Temporal and Spatial variation in eDNA concentration of an invasive mussel species: Dreissena bugensis’ (University of Leiden, 2018). The lakes were subdivided into categories based on the relative abundance of eDNA. Comparisons between these categories were made to investigate if lakes with different eDNA concentrations differed in biological, physical and chemical water quality parameters from each other.
In the second analysis, the influence of the quagga mussel on the water quality of 13 lakes in Waterboard Rijnland was investigated. The analysis was based on a comparison between pre- and post-quagga invasion. A period off five years (2001-2005) before the first observation of quagga in 2006 in the Hollands Diep was taken as pre-invasion period. After the first observation in 2006 the quagga mussel needed to distribute to the waters of Rijnland and establish there. Matthews et al. (2014) estimated the dispersal rate of the quagga mussels at 120 km/year. Heiler et al. (2013) measured a colonisation rate of 26% per year. The time needed for dispersal and establishing was estimated at six years in this research, two years for dispersal and four years for establishing. Thus a period off five years from 2012 (2012-2016) was taken as post-invasion period. The comparison of these periods was divided between lakes with different categories of eDNA concentrations, to gain insight into the differences between lakes with relative small quagga mussel populations and lakes with relative high quagga mussel populations.
Biological water quality was investigated in this research by analysing phytoplankton in the lakes of Rijnland Chlorophyll a (µg/l) was taken as a standard for phytoplankton in this research. Transparency (m) and suspended matter (mg/l) were used to investigate the physical water quality. Total phosphorus (Ptot) (mg/l) and total nitrogen (Ntot) (mg/l) were used to investigate the chemical water quality. All five parameters were measured as described by European Water Framework Directive (KRW) (RIVM, 2014). Phytoplankton can be limited in their growth, this can be either bottom up limitation by nutrient limitation or top down limitation by grazing. To investigate whether nutrients were occurring as a limiting factor for chlorophyll a, the chlorophyll a / phosphorus ratio (chla/P) and chlorophyll a / nitrogen ratio (chla/N) were studied.
Statistical analysis
The first analysis was executed to show the differences in biological, physic and chemical water quality parameters between the lakes at the time of sampling the eDNA, which was 2016 and 2017. The summer means (April until September) were calculated for each of the parameters. The summer means from 2016 and 2017 formed the values for the analysis. For each parameter it was tested if the parameter values differed from each other between the categories (based on eDNA concentrations). Each group was tested for a normal distribution using a Shapiro-Wilk test. When the data was normally distributed, a one-way ANOVA test was used. To investigate which categories differed from each other a Tukey HSD post hoc test was used. If not normal distributed a Kruskal-Wallis test was used, followed by a Wilcoxon post hoc test.
The second analysis was done to compare the period from before the quagga invasion with the period after the quagga invasion. The summer means from 2001-2005 formed the first period and the summer means from 2006-2012 formed the second period. The analysis was executed per category. The data were tested for a normal distribution with a Shapiro-Wilk test, when normally distributed a t-test was used. If not normally distributed a Wilcoxon test was used. A p-value lower than 0.05 was considered significant for all the statistical analyses, which were all conducted in RStudio (version 1.2.5033).
Results
To analyse the data the mean concentration eDNA per lake was calculated, which together with the maximum concentration formed the distinction for three categories of eDNA concentrations (Table 1). Mean concentrations eDNA (mol/ml) of the quagga mussel per lake in category low ranged from 6.2 - 45.0 (SD = 15.7), category medium ranged from 170.6 - 384.9 (SD = 73.6) and category high ranged from 943.5 - 1111.5 (SD = 66.5). The mean concentrations eDNA per category did significantly differ from each other (ANOVA, F (2, 10) = 233.9, p<0.001).
Table 1. Concentrations eDNA (mol/ml) of the quagga mussel per lake measured 13x during 2016-2017. Where max stands for highest measurement and mean stands for the calculated mean of the measurements. Category low represents the lakes with a relatively low concentration eDNA, category high the relative high concentrations and category medium the eDNA concentrations in between.
Category low Category medium Category high
Lake Max Mean Lake Max Mean Lake Max Mean
Noordeinderplas
(Nieuwkoopse plassen) 80.7 7.0 Westeinder plassen 1275.4 200.2
Haarlemmermeerse
Bosplas 7413.4 1001.5
Noordplas
(Langeraarse plassen) 546.8 45.0 Vlietlanden 987.2 170.6 Braassemmermeer 6730.8 943.5
Reeuwijkse Plassen 53.2 6.2 T'Joppe 950.5 248.2 Zoetermeerse plas 10042.
1 1111.5
Amstelveense Poel 231.2 19.3 Molenplas 1546.6 284.9 Kagerplassen 9615.4 954.4
Zeegerplas 1079.3 384.9
The measured water quality parameters in 2016 and 2017 differed significantly between categories of lakes (Fig. 1). Mean values and standard deviation will be given as (mean ± SD). Mean summer mean chlorophyll a concentration from 2016 and 2017 differed significantly between categories (ANOVA, F(2, 10) = 19.25, p<0.001). Lakes in category low (111.1 ± 56.5 µg/l) differed significantly from the lakes in categories medium (10.4 ± 1.8 µg/l) (Tukey post-hoc, p=0.001) and high (15.1 ± 6.6 µg/l) (Tukey post-hoc, p=0.001). However, the two nutrient parameters did not differ significantly: Ptot (Kruskal-Wallis, H(2) = 0.86, p=0.650) and Ntot (ANOVA, F(2, 10) = 2.96, p=0.098). Mean summer mean transparency values did differ significantly differ between categories (Kruskal-Wallis, H(2) = 7.74, p=0.021). Lakes in category low (0.4 ± 0.9 m) did significantly differ from the lakes in categories medium (1.8 ± 0.2 m) (Wilcoxon post-hoc, p=0.016) and high (2.2 ± 0.7 m) (Wilcoxon post-hoc, p=0.029). Suspended matter mean summer mean concentrations differed significantly (ANOVA, F(2, 9) = 19.78, p<0.001). Lakes in category low (31.6 ± 14.5 mg/l) differed significantly from lakes in categories medium (4.2 ± 0.3 mg/l) (Tukey post-hoc, p<0.001) and high (4.9 ± 1.0 mg/l) (Tukey post-hoc, p=0.002). Ratio chla/P (ANOVA, F(2, 10) = 14.77, p=0.001) and ratio chla/N (ANOVA, F(2, 10) = 52.05, p<0.001) differed both significantly between categories. Ratio chla/P from lakes in category low (895.5 ± 495.2) did significantly differ from lakes in categories medium (64.2 ± 30.6) (Tukey post-hoc, p=0.002) and high (97.5 ± 38.6) (Tukey post-hoc, p=0.003). Ratio chla/N from lakes in category low (37.4 ± 15.5) did significantly differ from lakes in categories medium (4.2 ± 1.2) (Tukey post-hoc, p<0.001) and high (9.2 ± 3.6) (Tukey post-hoc, p<0.001).
Figure 1. Relative values of mean summer mean water quality parameter values from 2016 and 2017. Lakes in the category low (categories are based on eDNA concentrations from quagga mussels) are indicated by a dark bar. Lakes in category medium by a light grey bar and lakes in category high by a dark grey bar. Categories that differed significantly (p<0.05) from each other are indicated with different letters. Standard deviation is represented by the error bars. Absolut values at a relative value of 1 per parameter: chlorophyll a 1 = 136,6 µg/l ; transparency 1 = 4.4 m ; Ptot 1 = 0.6 mg/l; Ntot 1 = 7.2 mg/l; suspended matter 1 = 40.7 mg/l; chla/P 1 = 1057.3; chla/N 1 = 50.8.
The categories based on eDNA concentrations of the quagga mussel were used to investigate change in water quality before and after the quagga mussel invasion. Significant differences between pre- and post-invasion time periods of five years were found in several categories of lakes (Fig. 2B-H). However, no significant changes during the invasion were found for the lakes with low concentrations of eDNA. Significant differences between the pre- and post-invasion period were found for chlorophyll a (Fig. 2B). A significant decrease was found for lakes in categories medium (-60.8%) (Wilcoxon, W=595, p<0.001) and high (-64.3%) (Wilcoxon, W=320, p<0.001). Mean summer mean transparency increased for lakes in with medium concentrations eDNA (+40.5%) (t-test, t(46.15) = -5.55, p<0.001) as well for lakes with high concentrations eDNA (+73.8) (Wilcoxon, W=39, p<0.001) (Fig 2C). Suspended matter mean summer mean concentrations decreased for medium (-37.0%) (Wilcoxon, W=498, p<0.001) and high (-43.2%) (Wilcoxon, W=228, p<0.001) concentrations of eDNA (Fig 2D). Mean summer mean concentration of Ptot changed only significant for high concentrations of eDNA (-28.5%) (t-test, t(21.73) = 2.36, p=0.019) (Fig 2E). Mean summer mean concentrations Ntot decreased for every category, but not significantly (Fig 2F). Both chlorophyll a nutrient ratios declined for all categories. The decline in chla/P was significant for categories medium (-63.1%) (Wilcoxon, W=526, p<0.001) and high 57.0%) (Wilcoxon, W=275, p<0.001) (Fig 2G), as well for chla/N in categories medium (-58.2%) (Wilcoxon, W=576, p<0.001) and high (-57.0%) (t-test, t(27.46) = 4.54, p<0.001) (Fig 2H).
Figure 1. A) Mean concentrations eDNA of the quagga mussel of the lakes per category. B) Mean summer mean concentrations of chlorophyll a, C) transparency, D) suspended matter content, E) total phosphorus concentrations, F) total nitrogen concentrations, G) ratio of chlorophyll a to phosphorus concentrations and H) ratio chlorophyll a to nitrogen concentrations. The dark grey bars indicates for the pre-invasion period (2001-2005), the light grey bars indicate for the post-invasion period (2012-2016). Significant differences (p<0.05) between categories are indicated by different letters. Significant differences (p<0.05) between pre- and post-invasion are indicated by *. Standard deviation is indicated by the error bars.
Discussion
Quagga mussels did not become highly abundant in the lakes in category low. All of the lakes in category low are isolated peat lakes (Trimbos, 2016), this could have prevented the establishment of the quagga mussel in these lakes since distribution of quagga mussels slows down when waters are more isolated (Matthews et al., 2012). Lacking habitat requirements could be a second reason that prevented the quagga mussels from colonizing the lakes in category low. Quagga mussels prefer hard substrate for establishing, which is absent in Dutch peat lakes (Wilson et al., 2006; Matthews et al., 2014). Thereby, biological and physical water quality parameters from category low are different from categories medium and high in the pre-invasion time period. Transparency was low in lakes with low concentrations eDNA, which can negatively impact metabolic functions of the quagga mussel (Garton et al., 2014) and thus could have prevented the quagga mussel from colonizing lakes in category low.
In the lakes with medium and high eDNA concentrations, where the quagga mussel succeeded to colonize, did suspended matter decrease after the quagga invasion. Transparency increased in the lakes with medium and high concentrations eDNA after the quagga invasion, which was expected since transparency and suspended matter are correlated (Swift et al., 2006). Research in the Great Lakes and lake IJsselmeer showed that the filter capacity of the quagga mussels also decreased suspended matter and increased transparency (Noordhuis et al., 2014; Zhu et al., 2006).
Chlorophyll a concentrations, used as a proxy for phytoplankton densities, were significantly higher in lakes with low concentrations quagga eDNA (Fig 1). The concentrations chlorophyll a decreased after the quagga invasion, which was significant for the lakes with medium and higher concentrations eDNA. This is in line with previous research which described that the filter capacity of the quagga mussel is able to decline the spring phytoplankton production (Fahnenstiel et al., 2010; Mida et al., 2010). These differences in chlorophyll a may be the effect of two phenomena: grazing (top down limitation) or nutrient limitation (bottom up limitation) (Kivi et al., 1993).
The concentrations of phosphorus and nitrogen did not significantly differ between lakes with different eDNA concentrations (Fig. 1). The concentrations of nitrogen did not significantly change during the quagga invasion for all three categories of eDNA, although a little decrease was observed for each category. Concentrations Ptot significantly decreased after the quagga invasion in the lakes with the highest concentrations of eDNA. The decline in phosphorus concentrations in lakes with medium and higher concentrations of eDNA can be redirected to the filter capacity of the quagga mussel, which has been described in research in the Great Lakes as well (Heckey et al., 2004). The quagga mussel increases the water clarity which affects the benthic primary production to increase because more light reaches the benthic environment. Benthic primary production uses phosphorus from the water column which tends phosphorus to decrease (Auer et al., 2010; Garton et al., 2014). This process, called benthification, could explain the decline in phosphorus in lakes with medium and high concentrations of eDNA. Although the nutrients decreased for the lakes with medium and high concentrations, the concentrations remained in eutrophic states (Puijenbroek et al., 2010). Thus nutrients were not likely to limit phytoplankton (bottom up limitation).
Chla/P and chla/N ratios can give insight in top down limitation. Chla/P and chla/N ratios declined in lakes with medium and high concentrations eDNA. Since the nutrient concentrations decreased in lakes in categories medium and high, the decrease in chla/P and chla/N ratios can be explained by predation from a grazer on phytoplankton (top down limitation). Without a grazer, chla/P and chla/N ratios were expected to increase or stay stable if nutrients were decreasing (Higgins et al., 2011). The decline of chla/P and chla/N ratios under decreasing nutrient concentrations, together with the fact that nutrients stayed in eutrophic states, and thus did not limit phytoplankton, strongly indicates for top down limitation.
Trimbos (2016) successfully used eDNA as a detection method for quagga mussels. However, using eDNA as a measurement tool for determining relative quagga mussel presence had many implications. Notable is that research probably succeeded in observing the effects of relative abundances of quagga mussels. Significant differences in water quality were measured after the quagga invasion in lakes with medium and high concentrations eDNA. These differences were higher for the lakes in category high than in category medium, what implicates that the effects of the quagga mussel increases with higher abundances of quagga mussels. This probably approves the assumption that concentrations eDNA can be used as a tool to measure relative biomass of the quagga mussel. When average concentrations of quagga eDNA (if measured similar as in this research) are below 19.4 ± 15.7 mol/ml it is likely that no changes in water quality will be found.
Research on other invasive benthic filter feeders showed that the filter capacity can have effect on aquatic ecosystems (Sousa et al., 2009). The zebra mussel can control phytoplankton growth and can be used as a biomanipulation tool to increase water quality, which gives the zebra mussel a potential to be used in water quality management (Reeders et al., 1993; Bastviken et al., 1998). The filter capacity of the Asiatic clam (Corbicula fluminea) can increase clarity which has influenced the reappearance of submerged aquatic vegetation in the Potomac River estuary (Phelps, 1994). To investigate whether the quagga mussel can be used in water quality management in Dutch shallow lakes, more research at the effects at higher trophic levels is needed.
Conclusion of this research is that it is likely that the quagga mussel invasion in Rijnland had an effect on the water quality. Lakes in Rijnland with mean eDNA concentrations above 252.7 ± 73.6 mol/ml decreased significantly in chlorophyll a and suspended matter concentrations and increased significantly in transparency after the quagga invasion. The increase in transparency and decrease in phytoplankton is an improvement in water quality as set by the standards of the KRW 2027 targets.
Acknowledgments
This research is commissioned and assisted by Arie Vonk from University of Amsterdam department IBED-FAME. Further assistance was performed by Bart Schaub and Eric Verlaan from the Rijnland Waterboard and Dr. Krijn Trimbos and Martin van der Plas MSc, Institute for Environmental Studies (CML), University of Leiden.
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Data:
Data used in research can be found at:
https://surfdrive.surf.nl/files/index.php/s/NucA63cJuAb2wPX.
This data is authorized with a password which can retrieved by mailing for permission at meesbeaumont@gmail.com.Addendum:
In dit onderzoek is aan de hand van data analyse inzicht verkregen over de invloed van de quagga mossel op de waterkwaliteit. Hiervoor is eerst geprobeerd om met een mixed effect model de data te analyseren. Voor mij, met een maar beperkte RStudio kennis, bleek dit echter te lastig om uit te voeren. Om eerst een beeld te krijgen over de eDNA concentraties heb ik geprobeerd om te onderzoeken welke fysisch/chemische factoren van invloed zijn op het eDNA, denk hierbij aan bijvoorbeeld temperatuur van het water en pH. Hierin zouden dit soort effecten als fixed effect in het model meegegeven worden en het meer en de maand van samplen als random effect. Mijn onderzoek bestond echter niet uit het onderzoeken van welke invloeden er van invloed zijn op eDNA concentraties water, maar naar wat de invloed van eDNA (en dus quagga mossel aanwezigheid) op de waterkwaliteit is. Naast dat het niet lukte om een goed beeld te krijgen welke factoren van invloed waren op de eDNA concentraties had dit in mijn onderzoek ook minder prioriteit. Vandaar dat in dit onderzoek niet verder gegaan is met het gebruik van mixed effect models als statistische analyse methode.
