Knowledge - Cause - effect chain modelling of sand mining using mussels
Abstract: Dynamic Energy Budget (DEB) model is presented to describe the effect of fine sediment (dredging) on the on filter-feeding bivalve populations (e.g. the blue mussel Mytilus edulis)
Technology Readiness Level: 5 (validated in relevant environment)
Environment: Sandy shores, Estuaries, Lakes and rivers
Keywords: Eco-physiology, Bivalves, Environmental impact, Marine mining, Silts and sediments, Water quality, Dynamic Energy Budget Model
Every year large amounts of sand are extracted from the North Sea to meet the demand for construction and nourishment activities. Potential ecological effects of these activities have to be examined and reported in Environmental Impact Assessments (EIAs). Sand mining cause an increase in fine sediment concentrations. Increased concentrations of fines can increase turbidity, change the relative composition of organic and inorganic particulate matter in the water column and limit the light penetration, thus affecting water quality and reducing primary production. Most of the bivalves are filter feeders and extensive knowledge of how increased sediment concentrations influence their physiological organisation is essential if an impact assessment is to be made. In order to understand the effect of suspended sediment concentrations on the activity of filter-feeding bivalves, deterministic model is presented using the model species, the blue mussel Mytilus edulis.
Project Objective and Approach
The objective of this case study (Wijsman et al. 2012) was to describe the effect of suspended sediment concentrations on the activity of filter-feeding bivalves (i.e. clearance rate, ingestion, pseudofaeces production and growth). The chosen model species was the blue mussel Mytilus edulis, since it is a ubiquitous and well-studied species with commercial importance and is regarded as an indicator species for filter-feeding bivalves.
An extensive literature search was made to identify potential effects of sand mining activities on the blue mussel. Two deterministic models are presented and their behaviour is compared with the data from the literature. This comparison was primarily made to get an impression of the order of magnitude of the rates and the nature of the relationships. The theory presents simple mechanistic rules that describe the uptake and use of energy and nutrients (substrates, food, light) and the consequences for physiological organisation throughout an organism's life cycle.
These models, when properly tuned to the local situation, can be used to investigate or predict the impact of dredging on filter-feeding bivalve populations. The results can be used to decide how much increase in suspended solids is acceptable and what is the best period to carry out dredging and nourishment activities.
In order to calculate the effect of dredging activities on growth and development of a mussel, a Dynamic Energy Budget (DEB) model can be used to simulate the impact of increased suspended sediment concentrations on individual mussels (i.e. community effects are not taken into account). The DEB model includes a functional response that describes the energy uptake of a filter-feeding bivalve as a function of food and silt concentration. The patterns of the functional response are quite comparable with observations reported in the literature.
Prior and during marine construction activities like dredging, it has to be ensured that the activities are performed in an environmentally acceptable manner. For most dredging projects an Environmental Impact Assessment (EIA) is made, often specified and required by law or regulation. An EIA regularly contains an assessment of the potential effects of the project and can be taken into account in the project development and design stage. The DEB-model, as demonstrated in this case study, can be used to assess potential effects of dredging activities on bivalve populations.
Effects of increased suspended solid concentrations
During the dredging process a sand-water mixture is pumped up from the seabed into the hold of the dredging vessel. Although most of the sandy part of this mixture settles in the hold, the excess transport water containing fine sand particles flows back into the sea causing a ‘dredging plume’ near the dredging vessel. As a result of tidal currents and wave action, this dredging plume will be spread over a larger area. A direct effect of increased suspended particle concentrations in the water column is a higher turbidity level and less light penetration. This may affect phytoplankton productivity and consequently food supply to higher trophic levels. The actual impact may be local and restricted in time, although when dredging takes place at the seaward end of an estuary, for instance, a larger water mass may be affected. Potential ecological implications consist of significant decreases in biomass and productivity of phytoplankton, zooplankton and filter-feeding benthos such as Mytilus edulis. For instance, deposition of sediment layers of 1-2 cm thickness can result in increased mortality.
1. Effects of dredging on blue mussel Mytilus edulis
2. Ecology of blue mussel Mytilus edulis
3. Feeding and food selection by filter feeders
Energy Budget Model
Functional responses of filter-feeding bivalves to variations in seston have been discussed by numerous authors and implemented into physiological models (for detailed references see the Case Study Mussels - Modelling the effect of dredging on filter-feeding bivalves). Some of these models are based on the DEB theory, and this will be discussed further.
The Dynamic Energy Budget (DEB) model has been developed by Kooijman 30 years ago (Kooijman 1986; Kooijman 2000; Kooijman 2010). The DEB model describes the energy flow through an organism as a function of its size, its development stage and environmental conditions. An individual organism state is represented by three state variables: structural volume (V, cm3), reserves (E, Joule) and reproduction (R, Joule). The organism acquires energy from food by assimilation. The energy that is not assimilated by the organism is released as faeces. The assimilated energy is first stored into reserves (blood, glycogen). From the reserves, the energy is mobilized into growth, development and maintenance. A fixed fraction (κ) of the energy flow from the reserves is used for growth and somatic maintenance, but the priority is given to the maintenance. The rest of the energy flow from the reserves (1-κ) is spent on maturity maintenance and reproduction (gamete production and spawning). Juveniles spend this energy to maturation, while adults use it for gamete production and spawning. A key process is the assimilation rate of the food. In DEB, this is generally described by a Holling type II functional response. The scaled functional response (f) is the ratio between food uptake rate (in Joules) and the maximum food uptake rate (in Joules) and varies between 0 (no food uptake) and 1 (maximum food uptake rate). At a food concentration X = Xk, the functional response equals 0.5.
DEB model for filter feeding bivalves
Suspension feeding bivalves obtain their energy by pumping water with suspended organic particles over their gills. Edible particles are selected using palps and the non-edible fraction of particles is released in the form of pseudofaeces. The energy that is not assimilated by the bivalve is released via faeces. In DEB models, the effect of inorganic particles can be incorporated in the formulation of a scaled functional response (Kooijman 2006; Wijsman 2011; Wijsman and Smaal 2011).
where X is the food concentration, expressed in Chl a concentration (µg l−1) and Xk is the half saturation constant (µg l−1) when no particulate inorganic matter is present, Y is the concentration of particulate inorganic matter (mg l−1) and Yk is the saturation constant for the particulate inorganic matter (mg l−1).
The scaled functional response increases with food (expressed as Chl a concentration) and decreases with the particulate inorganic matter concentration. The model is in accordance with the Synthesizing Units concept introduced in the DEB theory by Kooijman. This model assumes that handling inorganic particles costs time for the filtering apparatus and that this goes at the expense of time available for food particle handling. With this approach it is possible to model the effect of inorganic particles on the growth of shellfish, although it does not describe the process of filtration and the production of pseudofaeces and faeces explicitly.
Saraiva et al. (2011) have developed a mechanistic approach to model filtration by bivalves and their production of faeces and pseudofaeces based on the Synthesizing Units concept. The model allows for various fractions of the filtered material (e.g. silt, algae, zooplankton, detritus). In this example we only use silt (X0) and algae (X1).
Feeding is split into separate processes:
- Filtration: pumping of water and filtration of the particles by the gills,
- Ingestion and pseudofaeces production: selection the edible particles and disposal of inedible parts,
- Assimilation and faeces production: influx of energy into the reserve pool and production of faeces.
The Case Study Mussels - Modelling the effect of dredging on filter-feeding bivalves focuses on filtration, ingestion and pseudofaeces production.
DEB model: results for varying environmental conditions
A baseline model simulation was run with a standard DEB model for mussels, extended with the formulation of clearance rates (volume of water cleared of particles per unit time) and pseudofaeces production. The model was forced with environmental conditions (temperature, chlorophyll a and particulate inorganic matter) from model calculations for the North Sea (Schellekens, 2012) at location Schouwen 4. The model was run from May 1st, 2007 to December 31st, 2011. The temperature varied more or less sinusoidally between about 6˚C in winter and about 18˚C in summer. The Chl a concentration had a peak in early spring and a second peak in summer. Particulate inorganic matter is highest in winter due to the wind conditions. The resulting scaled functional response fluctuates between about 0.1 in winter and 0.8 in spring and summer.
The results of the DEB model
Mussel growth is mainly influenced by temperature and Chl a. Growth is highest during summer, when temperatures are highest. In winter the mussels do not grow in shell length and their weight even decreases, mainly due to the poor food conditions. Reserves are built-up during spring and summer and decrease during winter. Spawning may occur already in the second year, but is higher in subsequent years. The combined dynamics of length, reserves and gonads are reflected in the weight of the mussels. Highest clearance rates (about 3 l/hr) are achieved by the largest mussels and in winter. Although in general the activity of the mussels is low due to the low temperatures, the low concentrations of Chl a in winter lead to high clearance rates. Since the concentration of Particulate Inorganic Matter is high, the production of pseudofaeces is also high. Scholten and Smaal (1998) compute clearance rates for mussels as a function of seston concentrations in the Oosterschelde, Marennes-Oléron and Upper South Cove using the Emmy model. This model is based on detailed information of food uptake and food processing by mussels. In this study, clearance rates in the Oosterschelde seem to be highest in autumn, when the activity of the mussels is still high and Chla/sestion ratios are already low.
Effect of increased suspended sediment concentration
In order to study the effect of increased particulate inorganic matter on the growth and development of the mussels, three alternative model scenarios are run and compared to the baseline scenario. In all scenario’s the same amount of suspended matter is released, but the scenarios differ in the timing.
- Continuous extra release of particulate inorganic matter of 16.67 g/l
- Pulse increase of 200 g/l in June of each year
- Pulse increase of 200 g/l in January of each year
The other forcing functions, temperature and chl a were kept the same as in the baseline scenario. The resulting clearance rates and pseudofaeces production showed that the pulse inputs cause an increase in particulate inorganic matter, and a decrease of clearance rates. The decrease is more pronounced when the pulse input takes place in January. The pulse inputs have a positive effect on the pseudofaeces production. The continuous release of particulate inorganic matter and the pulse release in June have comparable effects on mussel growth, where the effect on weight is more pronounced than on length. The pulse release in January has almost no effect on mussel growth.
Impact of suspended sediment on filter feeding
The impact of dredging activities on filterfeeding bivalves is studied by a combination of literature research and modeling. The increased suspended sediment concentration in the water decreases the efficiency of the filtration process, since part of the filtered material is rejected and excreted in the form of faeces and pseudofaeces. Laboratory studies show that the clearance rate by filterfeeding bivalves decreases with increasing particle concentration. Laboratory studies also show that the production of pseudofaeces increases with increased silt content. As a result of the decreased feeding efficiency, the growth and development of filterfeeding bivalves will be reduced and this might have a knock-on effect on fish and birds that depend on the bivalves as a food source.
In DEB models, a relatively simple description using a scaled functional response is applied. The scaled functional response describes the energy uptake rate as a function of food concentration. The effect of suspended sediment concentration can be incorporated in the functional response. The low concentrations of particulate inorganic matter have an effect on the food uptake rate. Filter feeding mussels need to invest time and energy processing the inorganic matter. Short-term increases of particulate inorganic matter have less impact on the growth performance of the shellfish compared to a continuous increase. The timing of dredging activities is also important. In the winter period, when the activity of the mussels is low, the impact of increased suspended sediment concentration is much less than during summer time. Because mussels are able to adapt to different silt concentrations in the water column, it is important that impact studies take into account the natural variability of silt concentrations the area, as well as the time spans during which these conditions may remain modified. Short term increases in suspended sediment concentration will have less impact on the food intake of shellfish than a continuous release.
This study is primarily focused on blue mussels as a model species for suspension-feeding lamellibranchiate bivalves. It is assumed that the processes will be comparable for other species and that only the values of the parameters will differ. However, it is good to check this assumption using literature data from other species.The models that were used in this case study were not directly calibrated with field observations and literature information on filtration rates and pseudofaeces production. It would be an improvement to perform an additional calibration with the appropriate data.
Case study conclusions
Under natural conditions, many factors may influence the filtration rate of bivalves. Feeding under laboratory conditions may not always accurately reflect in situ filtration where a wide spectrum of changing environmental factors and species interactions may influence the feeding behavior. The present case study is believed to reflect important basic features of mussels' feeding behavior in nature where phytoplankton is the main source of nutrition. Among the many parameters that may affect the in situ feeding behavior, phytoplankton biomass (expressed as Chl a concentration) seems to be the most important. Yet, high concentrations of silt/seston leading to preingestive rejection/pseudofaeces production, for instance, may also affect the feeding of mussels in estuaries and exposed coastal waters.
Although there is still no general agreement regarding physiological control of water pumping in response to (very) high concentrations of particles in the ambient water, present consensus tends to be that the filtration rate is high and constant, between a lower critical level and an upper seston concentration threshold. It remains to be clarified if the reduced filtration rate at high seston concentrations is caused by physiological regulation (supporting maximum assimilation and growth) or overloading (adversely affecting food uptake and growth). The impact of changes in silt and/or phytoplankton concentrations on the growth of an individual blue mussel (Mytilus edulis) can be modeled in a deterministic way, using the DEB-model. In cases where worst case or conservative assumptions are made to model ecological impacts deterministically, however, the use of a probabilistic instead of a deterministic approach can have several advantages (Van Kruchten 2008). For example: in a probabilistic approach worst-case assumptions can largely be prevented by incorporating the uncertainty itself in the cause-effect chain modeling. In such case, deterministic modeling may lead to an overestimation of the ecological effect, whereas the probabilistic modeling results give information on the probability of occurrence of possible effects.
Although in the DEB model uncertain parameters or variables can be identified, realistic instead of conservative assumptions are made to deal with these uncertainties. In order to quantify the uncertainty, Monte Carlo simulations with varying parameter values can be made. The added value of applying a probabilistic analysis to this case is limited and will not make a difference between a highly conservative and a realistic estimate of the impact on mussels. A probabilistic analysis might be used to quantify the uncertainty margin of the final prediction. Because a probabilistic analysis is quite laborious, however, a sensitivity analysis instead of a probabilistic analysis is recommended to give insight into the uncertainty margins of the results.
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