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Interim Report November 2002 Scoping Study of the carrying capacity for bivalve cultivation in the coastal waters of Great Britain MJ Kaiser and HA Beadman Contents 1. Outline of the project 2 2. Definitions 2 3. Review of approaches used to determine carrying capacity of coastal waters for bivalve cultivation 3 4. Assessment of the present ability to predict: 4.1. Carrying capacity of bivalve production 4.1.1. Dynamic Energy Budget Models 4.1.2. Models relevant to the management of mussel production 4.1.3. Conclusions 4.2. Environmental impacts 4.2.1. 4.2.2. 4.2.3. 4.2.4. 4.2.5. 5. Recommendations of field and laboratory investigations required to provide indicators of carrying capacity of UK coastal waters for bivalve cultivation 5.2. Aim and Rationale 5.3. 6. Seed collection On-growing Harvesting Management Considerations Conclusions References Approach 7 9 14 16 16 17 19 24 24 26 27 29 31 1 1. Outline of the scope of the report The main objectives of the project are: 1. To prepare a review of the approaches used to determine the carrying capacity of coastal waters for bivalve cultivation. 2. To assess the present ability to predict carrying capacity in terms of both shellfish production and ecological capacity (i.e. environmental impacts), and the specific methods that are employed. 3. To produce recommendations of field and laboratory investigations required to provide indicators of the carrying capacity of UK coastal waters for shellfish cultivation. 2. Definitions In accordance with the Scottish Parliament Environment and Transport Committee in its 5th Report 2002 (Report of Phase 1 of the Inquiry into Aquaculture) the following definitions have been adopted: Carrying Capacity of a defined area refers to the potential maximum production a species or population can maintain in relation to available food resources within an area. Assimilative Capacity is the ability of an area to maintain a ‘healthy’ environment and ‘accommodate’ wastes. Environmental Capacity is the ability of the environment to accommodate a particular activity or rate of activity without unacceptable impact. 2 3. Review of approaches used to determine carrying capacity of coastal waters for bivalve cultivation The main driving force behind the development of models to determine the carrying capacity of waters for bivalve cultivation has generally been for commercial interests. As a result the models to date have focused upon determining the stocking density at which production levels are maximised without negatively affecting growth rates (Carver and Mallet 1990). This has therefore entailed study upon the bivalve population and the populations (e.g. phytoplankton) and processes (e.g. tidal currents, flushing) that may effect bivalve productivity rather than the effect that the bivalve population may have on other parts of the ecosystem. The approaches towards determining carrying capacities of bivalve populations can be characterised into three categories: 1) empirical studies, 2) calculation of budgets and 3) simulation modelling (Grant et al. 1993). Empirical studies generally correlate some aspects of shellfish growth to the food supply and/or environmental variables. For example Smaal and Van Stralen (1990) related carrying capacity to primary production, Grizzle and Lutz (1989) related growth to tidal currents, seston and bottom sediments and there are many other examples in the literature (e.g. Wildish and Kristmanson 1979, Officer et al. 1982, Smaal et al. 1986, Grant et al. 1990, Heip et al. 1995). However, these models are limited in use due to their restricted temporal and spatial scales. The spatial variability of both the biological demand and the physical characteristics of the system are not detailed (Raillard and Mengesguen 1994). Feedbacks within the systems of the impact of shellfish culture on food sources and regeneration of food within the system are also not included. Nonetheless, Dame and Prins (1998) used an empirical approach to compare the carrying capacity of 11 ecosystems with a dominant bivalve population, in which carrying capacity was defined in terms of water mass residence time, primary production time and bivalve clearance time. While recognising the limitations of this method it did allow estimations to be made of the requirements of massive and successful bivalve populations in terms of water residence time and primary production time. This method therefore provided a way in which a prospective system could be compared to known and extensively studied systems to determine whether a pilot cultivation study would be worthwhile (Dame and Prims 1998). The determination of carrying capacity through calculation of budgets estimates the balance between production of phytoplankton and ingestion of the bivalve populations (e.g. Carver and Mallet 1990). More complex ecosystem budgets that include more components, such as the benthos and detritus, have been developed providing a better representation of the ecosystem by acknowledging the complex interactions between the various components within the system (e.g. Rosenberg and Loo 1983, Rodhouse and Roden 1987, Grant et al. 1998). Rosenberg and Loo (1983) produced an energy-flow diagram for mussel long-line culture in a Swedish outlet over 571 days of cultivation from seeding to harvest. From this study they were able to predict that mussel culture numbers could be nearly doubled. Rodhousen and Roden (1987) created a more comprehensive carbon budget, for the Killary Harbour System, with 14 carbon components and 5 spatial sectors. This budget was then used to estimate the carrying capacity of the system for mussel cultivation. Grant et al. (1998) employed a similar method to determine a carbon budget for mussel floating raft culture in a bay in South Africa, and were able to identify the 3 potential for further intensification of the present mussel production. However, the extent of the use of these models is limited, as they do not directly allow for feedback mechanisms since the components are averaged over time. Again there is no detailed spatial variability, and no allowance for interaction between sectors where an area has been divided into smaller spatial units. This lack of spatial variability presents a particular problem in predicting where to locate new cultivation if it has been established that the system can tolerate increased bivalve production. Ecosystem budgets do, however, potentially provide a way of estimating the impact of increased mussel production on other components of the ecosystem that are included in the budget such as the benthos. The third approach towards modelling carrying capacity of bivalve cultivation is the use of simulation models. A simulation model is described as a model in which the culture ecosystem is viewed as distinct compartments of state variables (e.g. shellfish, phytoplankton), between which flows of energy or material are quantified based on internal biological fluxes mediated by external forcing functions (Grant et al. 1993). Models have been developed for both oyster (Raillard and Menesguen 1994, Bacher et al. 1998, Ferreira et al. 1998) and mussel cultivation (Grant et al. 1993, Dowd 1997, Campbell and Newell 1998). Oyster cultivation models have been developed for the Marennes-Orleron Bay, the most important shellfish culture site in France (Bacher et al. 1998). Raillard and Menesguen (1994) produced a box model of the site incorporating both physical and biological processes: horizontal transport of suspended matter, feeding and growth of the oyster Crassostrea gigas and primary production. Some of the biological features and physical features of the bay were accurately reproduced and the hydrodynamic regime, due to the short resident time, was found to be the determining factor that controlled carrying capacity. The model was able to predict a density dependent effect of oyster growth, with a reduced maximal dry weight with increased standing stock. The validity of the model was limited mainly by the description of the physical transport of suspended and deposited matter (Raillard and Menesguen 1994). The model of Bacher et al. (1998) has further developed the model of Raillard and Menesguen (1994), and is again based on a box model approach including nutrient inputs, mixing and transport by the currents, the turbidity level and the ecophysiology of the oysters. The model has been modified to include other parts of the bay, and each oyster age class within each box has been given it’s own dynamics. The model still predicts that the system relies on water exchange for phytoplankton and is still sensitive to the standing stock. Further refinement of the model would require greater knowledge on both oyster physiology, particularly gametogenesis, and population effects such as density dependence and mortality (Bacher et al. 1998). An ecological model has also been developed to estimate the carrying capacity for oyster cultivation of Carlingford Lough, Ireland (Ferreira et al. 1998). In this box model Carlingford Lough has been divided into 3 boxes with transport of particulate and dissolved substances between each box. The ecosystem is divided into objects that represent the different functional compartment in the model of: forcing functions (river flow, temperature, light), advection-dispersion, suspended particulate matter, phytoplankton, oysters (at the physiological and population level) and man (different management strategies). The model was able to predict that oyster cultivation in 4 Carlingford Lough is below the level where growth is inhibited by stock density and that a five-fold increase in seeding would maximise oyster production. A limitation of the model is that of the small number of boxes in the model that may cause some bias of the result due to the positioning of the present cultivation close to the boundary of boxes 2 and 3. The methodology of this model and the Bacher et al. (1998) model are very similar as both are based on the coupling between physical and biological processes at the spatial scale of several kilometres in length (Bacher et al. 1998). The major difference between them is in the population dynamics where 40 weight classes are used in the Carlingford Lough model compared to the 10 age classes in the Marennes-Orleron model. The difference in the carrying capacity of the two systems can be explained by the difference in biological and physical flows derived form the model outputs (Bacher et al. 1998). The modelling of carrying capacity for mussel cultivation has been undertaken for both longline culture (Grant et al. 1993, Dowd 1997) and a bottom culture site (Campbell and Newell 1998). Longline culture is examined by Grant et al. (1993) and Dowd (1997) who describe the same model developed for a bay in Nova Scotia, Canada. A box model approach was used to represent the system. Particle exchange was allowed between each section, which contained the state variables: seston, zooplankton, phytoplankton and mussels. The model was reasonably successful and highlighted the relative importance of the internal ecology of the cove versus exchange during different times of the year Dowd (1997). It was also able to estimate the different carrying capacities for different parts of the cove, estimating that the outer cove would have a carrying capacity an order of magnitude greater than the inner cover due to flushing time. Unlike the model of Raillard and Menesguen (1994) in which model uncertainties were placed with the description of particle exchange and mixing processes, sensitivity in this model is as a result of uncertain physiological parameters. This is discussed in more detail in the next section. The model also only includes population interactions as a function of a reduced food supply with increasing standing stock and does not include other density-dependant effects. Campbell and Newell (1998) developed a carrying capacity model for a bottom culture site in Main, USA. This model was developed to be as simple as possible regarding both mussel physiology and physical parameters. Mussel production was simulated using site conditions (water depth, current speed and mussel mortality), forcing functions (temperature and food supply), and initial conditions (seeding date, seed weight and length and seeding density). A sensitivity analysis of the effects of seeding density was conducted and used to determine the carrying capacity of the three test sites. The development of the model also demonstrated the importance of food quality and quantity in explaining mussel growth. However, the model was not able to accurately represent mussel growth over the entire range that mussel are cultured, and this is possibly as a result of the ecophysiological simplifications. The models developed to determine bivalve carrying capacity range from simple correlations between forcing functions and mussel growth, to ecosystem carbon budgets to simulation models. Carbon budgets have provided the only means by which the effect of bivalve cultivation on other components in the ecosystem can presently be modelled. However, through allowing for no inclusion of spatial or temporal variability, or feedbacks within the system they provide limited use towards 5 the comprehensive calculation of bivalve carrying capacities. Simulation models do allow for more temporal and spatial variability and have provided reasonable estimates of carrying capacity for bivalve cultivation. It would be possible to develop these models to address the impacts on other components within the ecosystem, but this would require a large amount of research in order to produce a full ecosystem model for every potential bivalve cultivation site. Therefore, while carrying capacity can be established to a certain degree of accuracy there is no specific method for the determination of assimilative or environmental capacity through modelling. There are still limitations to the prediction of carrying capacities by simulation models concerning spatial variability. The model of Campbell and Newell (1998) does not allow for spatial variability over the mussel bed, and in box models (e.g. Raillard and Menesguen 1994, Bacher et al. 1998, Ferreira et al. 1998) spatial variability is restricted to box size, which can be kilometres in length. Smaller scale variability within the mussel bed, or cultivation area, is not accounted for by any of the present models and this will have repercussions in predicting the variability in flow and hence food supply and resultant growth of bivalve populations. 6 4. Assessment of the present ability to predict: 4.1. Carrying capacity of bivalve production The production of cultivated mussels in the UK far exceeds cultivation of other bivalves (Figure 1). For this reason mussels have been focused upon in this report, although the same approaches are equally applicable to other bivalve production. The previous section has highlighted that simulation models present the most probable approach towards modelling carrying capacity. Minimum requirements for such carrying capacity models can be detailed as transport processes, sediment dynamics and submodels for organism and population level processes (Smaal et al. 1998). These models will permit production (growth and reproduction) to be forecast as a function of food supply and other environmental factors. Complicated interactions at the population level (mortality - self-thinning and predation) requires that models of individual production are integrated (e.g. Dynamic Energy Budget (DEB) models) with models that describe the consequences of these processes on the production of mussels at the population-level. To better describe the levels of model complexity Figure 2 illustrates a hierarchy of modelling, demonstrating how with a need to represent important processes at higher levels in the hierarchy (e.g. population level), the potential complexity of the modelling task increases. As a consequence there is a need to consider the appropriate level of detail required of the physiological DEB model, while meeting the objectives of a useful and ecologically relevant management tool. It has therefore been considered most relevant to assess the current ability to predict mussel production using models based upon a Dynamic Energy Budget approach. 15000 Tonnes 12000 9000 6000 3000 0 Mussels Pacific Oyster Figure 1. Native Oyster Clams Cockles Scallops Queens Farmed shellfish production for the UK in 2000 (Shellfish Association of Great Britain) 7 Ecosystem Level Model Components (Driving variables) Hydrodynamic regime (transport, dispersion) Biological effects e.g. Predator populations Physical environment (Temp, tidal exposure, etc.) Population Level Model Components Recruitment Density Size structure Mortality factors hazard Individual Level Model Spawning Size Mortality risk factors Physiological Component Model Pumping properties Feeding Respiration Growth Gametogenesis Figure 2. Hierarchy of Mussel Modelling, showing components at each level and interactions between levels. The strength of interactions is indicated by the thickness of the arrows. Collectively the physiological components determine the size and reproductive capacity of a mussel. The individual mussels in turn interact at the population level influencing both the size structure and recruitment to the population. The mussel population will have a limited effect on the ecosystem through providing a food source for predator populations and altering the local topography. The ecosystem provides the largest influence from population level to component level as the driving force providing food and the environmental conditions in which the mussel population is situated. 8 4.1.1. Dynamic Energy Budget Models There are a number of DEB models specifically designed to represent mussel growth. Each of these seeks to represent mussel growth as the balance between components of feeding, respiration and reproductive output. Within each model this is achieved with differing levels of complexity of mussel physiology and by the inclusion of various physical and biological factors (Table 1). The differences between each of the models occur as a direct result of the approach taken during the development of the model and are to some extent dependent on the specific aim/s of the model. The most sophisticated model, in terms of physiological complexity, is that of Scholten and Smaal (1998). This model was developed to simulate the growth and reproduction of a subtidal mussel incorporating all the available ecophysiological knowledge. Specifics in the model are detailed from filtration through to ingestion, absorption (incorporating the optimal feeding model of Willows 1992), respiration and excretion. Energy flow is represented by carbon and nitrogen fluxes between the five main compartments in the model: blood, body tissue, storage products, the organic component of the shell, reproductive tissues and activity (gametes and spawning). Growth and reproduction are ascertained from the rates and efficiency of the physiological processes that vary with seasonal variation in temperature, food quality and quantity, and metabolic demands. The incorporation of all the available knowledge on the ecophysiology of mussels resulted in a highly complex and over parameterised model, which is difficult to calibrate (Scholten and Smaal 1998). The complexity has also made the model unidentifiable i.e. there are redundant or ambiguous hypotheses within the model (Scholten and Smaal 1998), and this must be addressed before further meaningful development of the model can occur. However, Scholten and Smaal (1998) state that at present there is insufficient knowledge of mussel ecophysiology to rectify the situation. Nonetheless, the model predicted growth well for the site for which it had been calibrated and moderately well for another site with a high seston level. However, it was not successful in predicting growth at an alternative site that had a low seston and food input. This may be as a result of the adaptation of the mussels to their environment of low total particulate matter (TPM). To overcome this problem would require either a separate calibration of the model with adapted mussels for use in low TPM environments, or further complexity added into the model to account for mussel functions altered by the adaptation to low TPM. The Scholten and Smaal (1998) model has since been developed to examine the ecophysiological response of mussels to differing inorganic nutrient loads. In this investigation the model was simplified. The number of compartments in the model was reduced from 5 to 4 with the removal of the blood compartments. The complexity of the reproductive mechanism was reduced, with no gamete reabsorption mechanism and no link between respiration and spawning, as had been used in the earlier version of the model. The number of input parameters was also reduced from 38 to 30. The resulting model adequately predicted growth in the various inorganic nutrient regimes, although the uncertainty bands (minimum and maximum values of the simulations) remain rather wide. The model also appears to inadequately represent the extent to which mussels can adjust to poor food 9 conditions, even though a specific mechanism had been included within the model to allow for adaptation to these conditions. The models of Scholten and Smaal (1998, 1999) have been designed to be comprehensive, but the approach of including all available mussel ecophysiological information has resulted in models that are complex. The authors recognise the problem identified by Beck (1987) of a comprehensive model that makes correct predictions but with little precision, compared to a simple model that makes incorrect predictions with great precision The benefits of simpler models have been investigated by Ross and Nisbet (1990), Van Haren and Kooijman (1993) and Grant and Bacher (1998). Ross and Nisbet (1990) developed two models of an intertidal mussel, one a slightly modified version of a model developed by Kooijman (1986) and the other a new model. The two models differed in the partitioning of energy between growth, reproduction and maintenance. In the modified Kooijman model the energy assimilated by the mussel initially goes through a storage compartment and is then split between reproduction, overheads of growth and reproduction and growth, with maintenance as a direct expense of growth. The new model differs in that maintenance is taken out of the assimilated energy first, with extra energy provided from storage when the assimilated energy is insufficient. The remaining energy, termed production, is then divided between growth, overheads and storage. The reproductive allocation is taken from storage, but only when storage is above a predetermined level. However, the analysis showed that neither modelling approach was better in terms of its predictive capabilities. Both models predicted growth acceptably well at three test sites, even with the simplifications to mussel ecophysiology of constant assimilation efficiency and no selection of food particles. Neither model was able to predict observed total reproduction for the site where they had been calibrated, and did not predict the observed timing and number of spawning in another of the test populations. The spawning trigger was related to body tissue weight, and this was accepted as a weak point in the models. Ross and Nisbet’s (1990) main conclusion was that food and seston dynamics are the key factor in growth and reproduction. They identified the interaction between feeding and food/seston concentration as an area of the models that requires further refinement. This is of particular importance since it is also the specific area in the models in which many of their physiological simplifications are apparent. The significance of the relationship between seston/food concentrations and mussel feeding highlights why physiological simplifications are an important factor when examining the potential of a mussel model to predict growth accurately. Van Haren and Kooijman (1993) devised a model to represent the growth and reproduction of a subtidal mussel by modifying a model that had previously been applied to other species. In their model the relationships between seston/food concentration and feeding are simplified by assuming complete retention of particulate organic matter (POM) and no loss of organic material as pseudofaeces. This assumption has the potential to overestimate the level of organic matter that is assimilated by the mussel and hence over predict growth. 10 Table 1 : Selected variables included in Dynamic Energy Budget Mussel Models Feature Identified Physical Characteristic Temperature Water Depth Water Flow Water particles Total particulate matter Particulate organic matter Particulate organic carbon Particulate organic nitrogen Phytoplankton/ Chla Physiological Components Selection efficiency Ingestion rate Absorption efficiency Pseudofaeces production Respiration Basal and active respiration Energy Partitioning Core Storage Shell Reproduction Other Predation Mortality Mussel density Scholten and Smaal 1998 Scholten and Smaal 1999 Ross and Nisbet 1990 Van Haren and Kooijam 1993 Model Grant and Bacher 1998a Grant and Bacher 1998b Grant et al. 1993 Dowd 1999 Campbell and Newell 1998 a = Statistical model b = Mechnaistic model 11 Other models that demonstrate simplifications in physiological functions are those of Grant and Bacher (1998). They developed two models, a statistical and a mechanistic bioenergetic model, to compare just how complex models need to be to accurately predict mussel growth rate. In the statistical model ingestion was related to a single food source component (particulate organic matter), which was converted to particulate organic carbon. Absorption rate was then calculated using a constant absorption efficiency. The statistical model was unsuccessful at predicting growth at sites with high water turbidity, and was very sensitive to the absorption efficiency. The mechanistic model, while simpler than that of Scholten and Smaal (1998), was more complex than, and performed better than the statistical model. Two food components were used, phytoplankton and detrital particulate organic carbon. Clearance, particle rejection and ingestion were then related to turbidity and the availability of these food types. However, the model was sensitive to mussel absorption efficiency, which had two fixed percentage values based on the two food sources. This model would benefit from variable absorption efficiencies related to the quality and quantity of available food. This model was specifically developed with an emphasis on feeding, and for this reason does not include reproduction. Growth is therefore only predicted for juvenile mussels, which means that the application of the model on mussel growth to marketable size is limited since the mussels will have gonads by this stage. The models discussed to this point have aimed to accurately represent growth, and in some cases reproduction, of a single mussel. Population level effects need to be included to model the production of a mussel population effectively. The models that have been produced which include population effects together with DEB physiological models are generally the carrying capacity models. Carrying capacity modelling has been undertaken for both longline commercial cultivation of mussels (Grant et al. 1993; Dowd 1997) and for a bottom culture site (Campbell and Newell 1998). These models are of intermediate physiological complexity (Scholten and Smaal 1999), however they also include transport of food within the system. The investigations of Grant et al. (1993) and Dowd (1997) refer to the same study, but have examined it from different perspectives. They used a box model approach to represent the system, with interactions between seston, zooplankton, phytoplankton and mussels. The carrying capacity of the system is defined as the number of bivalves that can be sustained at a specific growth rate. This is determined by predicting the growth rate of a single mussel and then increasing mussel numbers in the system until the specified growth rate is no longer maintained. However, individual mussel growth was found to be very sensitive to specific physiological parameters, such as seston ingestion rate and assimilation efficiency. This model is therefore constrained by a limited inclusion of mussel physiology. The model does not fully incorporate reproduction, but averages out the effect of weight gain and loss. Dowd (1997) does include density effects of mussel numbers through competition for food, by reducing the concentration of phytoplankton in the water column, an effect that has been demonstrated by Fréchette and Bourget (1985a, b). Others have suggested that direct physical interference between mussels, another example of a population level interaction, can exert a direct effect on individual mussel’s growth performance and survival probability (Okamura 1986; Fréchette et al. 1992). This has not been incorporated within the model of Dowd (1997) although predator induced mortality is included through an overall mortality factor, which varies with time, calculated on a site-specific basis. The model was able to predict 12 the general features of mussel growth in the test areas. However, the adaptation of mussels to their environment is an area that was identified as needing improvement to refine the model. Another model that has been developed to consider carrying capacity is that of Campbell and Newell (1998). This model was developed to be as simple as possible regarding both mussel physiology and physical parameters, with the aim to predict mussel production using food quality and quantity, water flow and depth. The model of Campbell and Newell (1998) was successful in so much as mussel yields were improved by following the seeding density and timing recommendations of the model. Nonetheless, its predictions were not accurate at one of the validation sites and this was attributed to reproduction not having been included in the original model. The model was modified to include spawning but it was neither calibrated nor validated. At present the model, MUSMOD cannot accurately predict mussel growth over the entire range of physical conditions where mussels are cultured. The DEB models discussed previously have been shown to predict mussel growth with moderate success and in many cases have been successful in answering the questions that they have been designed to address. The importance of the relationship between the seston/food concentration and the rate at which carbon or energy is assimilated has been highlighted in many model developments. Much laboratory research has been conducted into this area (e.g. Bayne et al. 1987, 1988; Hawkins et al. 1985, 1996, 1998; Newell 1992 etc.). However, to use this physiological information to manage fisheries, or predict mussel growth in vivo, we need to know more about the characteristics of the available food supply. Another area which has been highlighted by both Scholten and Smaal (1998 and 1999) and Dowd (1997) is the adaptability of mussels to their ambient environmental conditions, which makes modelling the system more challenging. However, many of the models focus solely upon the growth of a single mussel and so in the cultivation of mussels there are still large areas in which these models do not predict. The mussel models developed to date do not generally include population effects e.g. relationships between growth, predation and other sources of mortality which are known to be significant (Goss-Custard and Willows 1996). The lack of population level component within many modelling approaches has precluded the incorporation of feedback mechanisms between the organisms and the environment. The external conditions are mainly given as conditions that the organism reacts to but does not determine or effect, and this is particularly crucial when field situations and model results are to be compared. Therefore, there is much scope for development of models that better predict mussel production. Models have been developed that are concerned with population effects, such as predation, particularly regarding birds, and self-thinning. While some of these models are dynamic others are static, but both provide a greater understanding of the interactions that operate within a system. Therefore, if these models could be coupled to, or the processes assimilated within a dynamic model, such a model may allow us to more accurately forecast mussel production. Models that could be used in this capacity are considered in the next section. 13 4.1.2. Models relevant to the management of mussel production There is another suite of models that are of particular relevance in the prediction of mussel population production. Fundamentally, these models all address mussel stock dependent factors and can be separated into three main groups that deal with self-thinning food/particle depletion, and predation. Self-thinning is potentially a key component in predicting population productivity, especially under conditions of cultivation. Self-thinning describes the negative relationship that is observed between individual mean size and mean population density in a cohort of growing organisms (Westoby 1984). Self-thinning has been most extensively studied by plant ecologists and has been a subject of interest for the past three decades (Yoda et al. 1963; White 1980; Westoby 1984; Weller 1987), where the limiting factor has been identified as space. The concept has been adapted to sedentary animals by Hughes and Griffiths (1988), who describe a geometry of packing leading to observed self-thinning. Food-regulated self-thinning has also been suggested (Begon et al. 1986; Elliott 1993) but generally has focussed on mobile animals. However, Fréchette and Lefaivre (1990) have suggested that in benthic suspension feeders both food and space may regulate self-thinning. The cause of self-thinning in mussels is therefore a question that remains unanswered as is it may be regulated by food or space limitation (the latter resulting in physical interference). Nonetheless, models that predict the effect of self-thinning on a population have been devised. Fréchette et al. (1992) developed a hypothesis to explain the change in absolute growth of a mussel resulting from competition for surface space between neighbouring mussels. The change in absolute growth of the mussels is presumed to be brought about through a size dependant effect of pressure on the mussel shell, resulting in reduced valve gape and hence filtration rate. Guinez and Castilla (1999) proposed a 3-dimensional self-thinning model for multi-layered intertidal mussels. This model suggests that density dependence could be more frequent than has previously been indicated by 2-dimensional models, and is of particular importance to bottom cultivation where layering is more likely to occur. Nonetheless, their model is space-driven and does not consider that competition for food resources may influence self-thinning by reducing growth rate. Self-thinning as a result of food limitation has not been modelled, however, the flow of water over a mussel bed and the corresponding depletion in phytoplankton caused by the filtration of the water has been addressed. Fréchette et al. (1989) developed a two-dimensional model of horizontal advection and vertical diffusion to represent phytoplankton movement within the boundary layer to examine the effect of mussels on phytoplankton distribution. The model has since been modified (Butman et al. 1994) to represent near-bed conditions more accurately. The model allows prediction of phytoplankton depletion where the filtration rate of the population of organisms is known and where the flow is steady and uniform. Unfortunately this is not a condition regularly found in the field, many mussel beds are found in turbulent conditions. Turbulent conditions can result in the resuspension of sea-bed material (Navarro and Inglesias 1993) and this can provide additional organic material, in the form of organic rich detritus and benthic micoalgae, and promote growth where phytoplankton is limiting (Fréchette and Grant 1991). The 14 resuspension of sea-bed material can also promote the growth of phytoplankton and this is an effect that may be particularly important to bivalve communities on a larger spatial scale, such as whole estuaries, embayments etc. Nonetheless the model of Butman et al. (1994) does provide a line of investigation along which to continue further study. Apart from mortality that is intrinsic to the mussel population, external sources of mortality must also be addressed i.e. predation. The most important predators of mussel cultivation are starfish, crabs and shore birds (Seed 1969). The impact of these predators can be very seasonal, for example crabs are generally more active in the spring and summer, and in the winter the impact of birds is greater when large flocks temporarily over-winter in coastal areas (Seed and Suchanek 1992). Predation has been modelled most extensively regarding the effects of birds on mussels. Hilgerloh and Siemoneit (1999) developed a dynamic model of bird predation on mussel beds in the tidal flats of Lower Saxony, Germany. While the quantitative effect was found to be small, it did establish than mussels larger than the mean of the population were more often predated upon. This suggests that where populations suffer significant bird predation the apparent growth of a mussel cohort will be reduced, resulting in a smaller mean mussel size than in a population without predation. Other studies have focused upon single species, for example oystercatchers (Haematopus ostralegus). Oystercatcher feeding can be used to calculate the carrying capacity of mussel beds, and formed the basis of a model developed by Goss-Custard et al. 1995, using an empirical game theory distribution model of oystercatchers feeding on mussels. This involves a description of how a population of oystercatchers, in which individual birds vary in their competitive ability and foraging efficiency, become spatially distributed over the spatially variable mussel food supply. Manipulation of the model output can produce estimates of the mussel biomass removed from the beds, giving an indication of the effect of oystercatcher predation on intertidal mussel beds. There is a lack of specific models relating to invertebrate predation on mussels, although a considerable amount of research has been conducted into this area. Feeding mechanisms by both crabs (Ameyaw-Akumfi and Hughes 1987, Elner 1978, Jubb et al. 1983, Seed 1969) and starfish (Norberg and Tedengren 1995, O’Neill 1983) are well documented. Size selection is also demonstrated with smaller mussels suffering disproportionately high losses from crabs (Seed 1976) and starfish feeding on mussels equal to or larger than the mean size of the mussel population (Dolmer 1998). There is a distinct relationship between the size of mussel taken and type of predator, with crabs responsible for mortality of the smaller mussels in the population and birds and starfish predating on the larger mussels. Therefore, a way of including predation mortality within a mussel population model may be to apply a size specific mortality function dependent on the composition of the predator community. The reduction in mussel population density as a result of predation mortality may also have effects on other density dependent functions operating within the mussel bed e.g. self-thinning and thus may require further interactions within the model. 15 4.1.3. Conclusions The approach of using DEB models has enabled predictions to be made regarding individual mussel growth and production. This method of modelling is of particular value since it has the capacity to represent changes in the mussel populations resulting from variations in the factors operating on the mussel population. Differing levels of complexity of mussel ecophysiology have been used and problems have been encountered in both complex (due to over-parameterization) and simple models (lack of accuracy). Since many of the models were developed to represent the growth and reproduction of a single mussel, population effects have generally been ignored and this reduces their ability to accurately predict carrying capacity. However, some of the models have included varying degrees of population effects and models that specifically address these effects have been identified. The models that have been produced to date are not generic and would therefore require at least the hydrodynamic regime of a site to be established along with quantification of food supply and parameterisation of the ecophysiological submodel. The models also present limitations at the spatial scale, as there is still a lack of knowledge of the small-scale processes occurring at spatial scales smaller than at the level of kilometres. This clearly limits the ability to be able to predict bivalve production over the entire bed or cultivation area. 4.2 Environmental Impacts The determination of bivalve carrying capacity in the past has largely been focused on potential maximum production with the emphasis purely on the bivalve population and immediately associated populations and processes rather than on the ecosystem as a whole. Apart from commercial considerations this has also been rationalised by the view that bivalve populations, whether natural or cultured, often dominate the coastal ecosystem in terms of nutrient flux, seston dynamics, or biodeposition (e.g. Officer et al. 1982; Herman and Scholten 1990; Dame 1991). Hence little work has been done directly on the assimilative capacity of the environment. However, work has been conducted into the impacts of the various stages of bivalve cultivation. Bivalve mariculture is mainly restricted to coastal areas within several km of the shoreline. The location of cultivation sites is based on the availability of suitable conditions for successful collection and cultivation of commercially exploited species. More recently, there has been an increase in the awareness of the environmental effects that may result from the various stages of bivalve cultivation processes. Most notably, adverse effects have been associated with mussel and oyster farms in Spain and France (Tenore et al. 1985, Castel et al. 1989) located at sites where hydrographical conditions were unsuitable for high density cultivation (Castel et al. 1989). To date, the majority of studies that have addressed the environmental impacts of bivalve cultivation have been largely concerned with the on-growing phase of cultivation. However, commercial cultivation of bivalves involves three distinct processes: seed collection, seed nursery and ongrowing, and harvesting. 16 4.2.1. Seed Collection Subtidal dredging While the adults of many species are harvested subtidally with dredges (e.g. clams, Arctica islandica, Mercenaria mercenaria, Ensis spp. and scallops, Pecten maximus, Aequipecten opercularis), only Mytilus edulis is dredged as seed for relaying and ongrowing (W Cook, North West and North Wales Sea Fisheries Committee, UK). An increasing number of experimental studies have examined the ecological effects of disturbance created by bivalve dredging activities. In the majority of cases the target species considered have been scallops or clams (Caddy 1973; Peterson et al. 1987; Eleftheriou and Robertson 1992; Thrush et al. 1995; Currie and Parry 1996; Hill et al. 1996) which live as separate individuals either just below the surface of or within the seabed. Studies of the ecological impact of heavy commercial scallop dredges on benthic communities have demonstrated that large reductions in species numbers and abundance occur immediately after dredging and that in some cases, recovery of the fauna had not occurred after 3 months (Thrush et al. 1995; Currie and Parry 1996). Whereas scallops and clams are found in relatively low densities (< 1 m-2) seed mussels form dense aggregations in discrete areas of the seabed (Dankers and Zuidema 1995). As a result, the extent of physical disturbance caused while dredging for mussel seed will be confined to relatively small areas of the seabed. In the United Kingdom, licences are issued to collect seed once a layer of ‘mussel mud’ has built up beneath the mussel seed bed. ‘Mussel mud’ is the accumulated dead shells, silt and pseudofaeces that build up beneath the mussel bed (Davies et al. 1980). At this point the mussels detach their byssus from the substratum and the whole bed becomes unstable. This enables dredgers to efficiently skim off the mussel seed while leaving the substratum relatively unaffected. It is important to note that if the mussel seed was not exploited, winter storms would destroy the seed beds which are then lost from the fishery or are eaten by starfish predators (Straaten 1965; Seed 1993; W. Cook North Wales and North West Sea Fisheries Committee and D.B. Edwards CEFAS Conwy Laboratory personal communication). The non-target benthic communities found in this habitat are probably adapted to large-scale natural disturbances and are likely to recolonise harvested areas rapidly (Hall and Harding 1997). The Baird and Dutch dredges used to collect the seed mussels are used singly and are relatively light compared with gangs of up to 20 Newhaven dredges which are fished in pairs by commercial scallop boats working the English Channel and Irish Sea (Dare 1974; Kaiser et al. 1996). As a result, we postulate that environmental degradation that may occur as a result of mechanical mussel seed harvesting are unlikely to cause major environmental changes to the substratum and are restricted to the areas in which the seed beds have formed. In addition, dredging activities for mussel seed are seasonal which allows a period of recovery from one year to the next. The benefits in harvesting a resource which is regularly lost to natural perturbations probably outweigh the limited negative effects. In extensive fisheries, such as the Wadden Sea, the depletion of the seed mussel stocks has the greatest effect on trophic interactions within this system. Dankers and Vlas (1992) estimated that from 1984 to 1990 between 30 - 130 x 106 kg of mussel seed were harvested from an estimated subtidal stock of 165 x 10 6 kg. One consequence of the heavy exploitation of these seed beds is that they are not 17 permitted to develop into mature adult mussel beds. In 1990 and 1991 the entire intertidal mussel stock was removed by mussel seed dredging in the Wadden Sea. This resulted in increased mortalities of eider duck and reduced breeding success for oystercatchers, which depend on mussels as a food source. Dankers and Zuidema (1995) suggested that these ecological effects would persist for many years until new mussel beds develop and mature. Intertidal collection Juvenile mussels are gathered from intertidal rocky shores by scraping them from the surface of the rock. This is the main source of seed for long-line mussel cultivation in southwestern Ireland. An estimated, 1,000 tonnes per annum is removed to support farming activities in Bantry Bay and the Kenmare estuary. Although there is no published information on the direct impact of this harvest, there may be some effects associated with trampling across the shore (Brosnan and Crumrine 1994) and the disturbance of foraging birds and the removal of an important winter food source (Cross-Custard and Verboven 1993). In addition the removal of large patches of mussels from the middle to lower shore will presumably modify local community structure by altering small-scale hydrography, trophic interactions and the exchange of organic matter. Use of collectors and cultch Spat collectors are made from a wide variety of natural and man-made materials and are generally laid out on the shore or suspended in the water from ropes. There are few environmental effects of these collectors outwith removal of the spat of both target and non-target species. The use of cultch to encourage spatfalls often requires modification of the substratum, e.g. laying broken shell onto mudflats. The implications of using cultch are discussed later in the report. Hatchery seed As in finfish hatcheries, antibiotics are used in some bivalve hatcheries. To date there is little information on the environmental effects of using antimicrobial products in bivalve hatcheries. Drug resistance in fish pathogens is now well established as are the effects of antibiotics on microfaunal communities beneath fish cages (Anonymous 1996). However, only a few bivalve hatcheries supply a much larger on-growing industry (2 hatcheries supply the United Kingdom seed requirements), hence any effects are likely to be highly localised. Introductions of alien species Several alien marine organisms have become established after having been unintentionally introduced with imports of bivalve mollusc seed (Utting and Spencer 1994; Eno 1996; Minchin 1996; Eno et al. 1997). These include competitors of bivalves, such as the slipper limpet (Crepidula fornicata). Slipper limpets can reach a high level of biomass and compete for food and space and, in silty waters, can change the benthic environment through their feeding activities and excretions. However, the presence of slipper limpets can also provide a suitable surface on which oyster spat and other organisms can settle. Other introductions include bivalve 18 pests, such as the American whelk tingle (Urosalpinx cinerea) and diseases, such as Bonamia, which infects the blood cells of flat oysters, causing high mortalities under conditions of intensive cultivation. The spread of this and other serious diseases of bivalves is restricted through EU regulations on movements of shellfish (EU Directive 95/70/EC). This legislation controls the movements of shellfish to prevent the introduction and spread of disease agents to EU countries while encouraging free trade between member states. Many countries also have additional national legislation to control the introduction of exotic bivalve species for cultivation. In the UK release of exotic species into the wild is only permissible by licence under the Wildlife and Countryside Act (1981). The International Council for the Exploration of the Sea (ICES) has produced a Code of Practice entitled “The Introductions and Transfers of Marine Organisms 1994”. This most recent version of the Code addresses three challenges that face aquaculture today. Firstly, inadvertent co-introductions of harmful organisms associated with the target species, as occurred recently in Pacific oyster shipments from France to Ireland (Holmes and Minchin 1995); secondly, the ecological and environmental impacts of introduced and transferred species; thirdly, the genetic impact of introduced and transferred species on indigenous stocks. Although there is concern in the salmon industry that if farmed fish escape they may affect the genetic diversity of native stocks, the genetic impacts of transferring bivalve stocks from one area to another have not been addressed. Introductions of algae, including toxic dinoflagellates, blooms of which can have a significant impact on commercial bivalve mollusc culture, have generally been attributed to the transportation of resting cysts in ships’ ballast water (Hallegraeff and Bolch 1991). However, normal trading, involving transport of shellfish stocks from one area to another followed by relaying or storage in open basins, can provide another mechanism of transfer. The faeces and digestive tracts of bivalves can be packed with viable dinoflagellate cells or can contain resting cysts (Scarratt et al. 1993). Viable cysts may also be found in the mud and sand retained with dredged mussels. These cysts may then be released into coastal waters at a new location. In the Netherlands, recirculating storage systems are used to quarantine mussels and oysters as a precaution against such introductions (Dijkema 1995). Invasive alien seaweeds, including Sargassum muticum, Undaria pinnatifida and Laminaria japonica are also thought to have been introduced into European waters through transport of the sporophyte stage in oyster juveniles, or as small plants attached to bivalve shells (Rueness 1989). 4.2.2 On-Growing One of the main attractions of bivalve species for aquaculture, is that they are ongrown to market size in the natural environment making use of an unsupplemented natural food supply. Nevertheless, this can require the introduction of structures into the marine environment on or from which the bivalves are either supported or suspended. The introduction of such structures has an immediate effect on local hydrography and provides a new substratum upon which epibiota can settle and grow. In addition, the introduction of high densities of cultivated organisms increases local oxygen demand and elevates the input of organic matter into the immediate 19 environment. Where there is a high density of bivalve stock the larval settlement of other benthic species may be reduced. It has been shown that larvae are filtered and digested by adult bivalves. Most of these larvae either pass through the digestive system alive or are rejected by the adults, but they then become bound in the faeces or pseudofaeces (Baldwin et al. 1995). Notwithstanding the above documented effects on the ecosystem through commercial cultivation of stocks of bivalve molluscs it should be remembered that natural beds of these animals were once extensive and played a vital part in the functioning of the local ecosystem, processing excess phytoplankton and cycling organic material. Overharvesting, disease and to some extent pollution have often almost destroyed these native populations and the introduction of commercial cultivation may be no more than re-establishment of the status quo. The literature on the role of bivalve molluscs in estuarine ecosystems shows that they are an essential part of healthy estuaries around the world in which they fulfil an important role in the retention of phosphorus and nitrogen (Dame et al. 1989; Gottlieb and Schweighofer 1996). Intertidal cultivation In Arcachon Bay, France, 10 km² of the lower intertidal zone is occupied by oyster parks, which constitutes ca. 7% of the total intertidal area within the bay (Castel et al. 1989). Oysters are ongrown by either relaying them directly onto the substratum, or growing them in net bags (poches) suspended above the substratum on trestles. The trestles and poches often become fouled with green algae (personal observations), which will further increase levels of organic enrichment when it dies back in autumn and winter. Castel et al. (1989) found that the presence of densely stocked oyster parks elevated organic carbon levels in the local sediments which elevated oxygen demand and produced anoxic conditions. As a result meiofauna increased in abundance by a factor of 3 - 4, while macrofaunal abundance decreased by nearly a half. Nugues et al. (1996) examined environmental changes at a relatively small oyster farm in the River Exe, England, and also found that the abundance of macrofauna beneath trestles decreased by a half. They found that water currents were significantly reduced in close proximity to oyster trestles, which doubled sedimentation rate and increased the organic content of the underlying sediments and led to a reduction in the depth of the oxygenated layer of sediment (Nugues et al. 1996). Nevertheless, the changes observed in the benthic fauna were restricted to the area immediately beneath the trestles. Hence, at low stocking densities, the effects of oyster cultivation are relatively benign and highly localised. It is not surprising, however, that environmental effects are exacerbated as the carrying capacity of enclosed systems is exceded and the extent of cultivated areas is increased (Castel et al. 1989). In some areas of Europe and North Amercia, oysters are cultivated directly on the ground on a variety of substrata including mud, sand and gravel (Simenstad and Fresh 1995). In North America, preparation of ground cultivation plots can involve severe levels of disturbance. In some areas, such as Willapa Bay, Washington, the insecticide carbaryl is sprayed on intertidal areas to kill populations of burrowing shrimp (Neotrypaea californiensis and Upogebia pugettensis) which destabilise the sediment and smother oysters by their burrowing activities. Spraying is carried out 20 every 6 years and is strictly regulated due to its controversial nature (Simenstad and Fresh 1995). In addition to the application of chemicals, oyster grounds are harrowed to level the ground prior to cultivation, and raked and dredged to distribute and thin oysters during on-growing (Simenstad and Fresh 1995). Removal of burrowing fauna and harrowing are likely to induce habitat and community changes similar to those attributed to dredge harvesting techniques discussed earlier in this paper. In North America, many native clam species are cultivated which include: Manila clam, Tapes philippinarum; hard-shelled clam, Mercenaria mercenaria. Cultivation usually involves some form of habitat modification in the form of adding gravel or gravel and crushed shell to the substratum, placing protective plastic netting over seed clams, or laying the clams directly onto the sediment in poches. Not surprisingly, such habitat modifications lead to alterations in the local environment and consequently faunal composition. Simenstad and Fresh (1995) reported that the application of gravel to intertidal sediments resulted in a shift from a polychaete to a bivalve and nemertean dominated community, but emphasised that changes are likely to be site-specific. Such shifts in community composition could have repercussions at other trophic levels e.g. changes in the abundance of certain harpacticoid copepod populations which are important prey for juvenile salmon and flatfish species (Simenstad and Fresh 1995). In the United Kingdom, Parliamentary law necessitates the use of protective netting in Manila clam cultivation to prevent escape of this introduced species (Spencer et al. 1997). Spencer et al. (1996, 1997) found that the application of plastic netting to an estuarine silty sand substratum led to an immediate increase in sedimentation rate over cultivated plots which elevated the organic content of the sediment. Within 6 months the cultivated plots were dominated by opportunistic spionid worms. During the following 24 months, the spionids were replaced by high abundances of larger deposit feeding worm species. The plastic netting also became fouled with Enteromorpha spp. which in turn attracted grazing littorinid snails. Hard-shelled clams, on-grown in plastic bags placed directly on the sediment, had no detectable effects on the benthic invertebrate population found in the sediment between the bags (Mojica and Nelson 1993). Although Mojica and Nelson (1993) also found no differences in the infauna sampled directly beneath clam poches they expressed caution about this result due to their low number of samples. Relaid mussels lead to the development of ‘mussel mud’ beneath the mussel bed as the filtration and feeding activities of the mussels increase sedimentation rate. These deposits are composed of dead shells, silt and pseudofaeces, which persist in excess of 18 months after the mussels have been removed. The cohesive nature of the ‘mussel mud’ is degraded by a combination of bacterial activity and wave erosion (Davies et al. 1980). The relaying of cultivated mussels onto the seabed also causes a change in the infaunal community (Beadman et al. in press, Ragnarsson & Raffaelli 1999, Dittman 1990, Committo 1987). This is demonstrated by a change in the composition of species of the infaunal community, and also the number of individuals and number of species present. At all but the lowest mussel densities, the 21 infaunal communities of areas cultivated with mussels were found to be less abundant, in terms of both individuals and numbers of species, than the surrounding areas (Beadman et al. in press, Dittman 1990). However, the impact was localised with a reduced effect with increasing distance from the mussel bed. In summary, both the addition of gravel or shell substrata, the formation of ‘mussel mud’ and the use of protective netting induces localised change in benthic community composition. However, while netting is easily removed and accumulated sediment is rapidly reworked by tidal currents, waves and bioturbatory activity (Spencer et al. 1998), the addition of gravel and shell material effectively creates a new habitat leading to more persistent changes in local community composition. Suspended raft cultivation Superficially, suspended rope culture of bivalves has little visual impact on the landscape. However, the large biomass of cultivated and fouling organisms suspended beneath rafts and buoys has a major effect on phytoplanktonic, benthic and hydrographic processes in close proximity to the cultivation site. Mussels provide a complex surface area on which dense epifaunal communities consisting of over 100 species can develop (Tenore and Gonzalez 1976). Small portunid crabs, Pisidia longicornis, were found to be abundant among fallen mussels beneath rafts in the Spanish rias. These, in turn, were fed upon opportunisitically by several fish species which normally consume polychaete worms (Lopez-Jamar et al. 1984). P. longicornis are so abundant in areas of mussel cultivation that their larvae dominate (90% of the biomass) the zooplankton community normally characterised by copepods (Alvarez-Ossorio 1977). Mussels excrete high levels of ammonia (Tenore and Gonzalez 1976) which promotes high levels of productivity in algae attached to mussel lines which is equivalent to algal production in local intertidal systems (Lapointe et al. 1981). So great is the productivity associated with mussel lines in the Spanish rias, that Tenore et al. (1982) speculated that inshore fisheries were potentially enhanced by the bedload transport of organic rich sediment into coastal areas. Cultivation sites that are well flushed by tidal currents, as in the Spanish Rias, do not encourage the accumulation of pseudofaeces beneath mussel rafts which results in a favourable increase in macrofaunal biomass (Rodhouse and Roden 1987). The relatively beneficial effects that occur in the Spanish rias contrast sharply with the effects observed by Dahlbäck and Gunnarsson (1981) in Sweden. They demonstrated organic sedimentation rates of 2.4-3.1 g organic C m-2 d-1 beneath mussel longlines which was twice as much as found in adjacent uncultivated areas. This excessive organic enrichment was associated with anoxic sediment and bacterial mats of bacteria, Beggiatoa spp., developing beneath the longlines. In this situation, the benthic infauna had low diversity and biomass which is a well documented response to polluted sites (Pearson and Rosenberg 1978). Similarly, the productivity of densely stocked Japanese oyster grounds was detrimentally affected by the generation of large quantities of pseudofaeces and high filtration rates (Ito and Imai 1955; Kusuki 1977). Pseudofaeces production was so great beneath oyster cultivation rafts that it was at least equivalent to natural sources of sedimentation (Mariojouls and Kusuki 1987). 22 4.2.3 Harvesting Harvesting of subtidally grown species occurs using towed dredges or suction pumps, the effects of which were discussed earlier and are well reviewed elsewhere (Messieh et al. 1991; Jones 1992; Dayton et al. 1995; Jennings and Kaiser 1998). The harvesting of trestle-grown and suspended bivalves has little, if any effect as they are removed without interfering directly with the environment. In contrast, the harvesting of intertidal species that are cultivated directly on or in the substratum requires various means of mechanical extraction. Physical disturbance of intertidal sediments by invasive commercial bivalve harvesting activities is of concern to fisheries managers because of direct effects on populations of target species, causing non-catch mortality, and to nature conservationists because of the interference with the feeding behaviour of wading birds (Goss-Custard and Verboven 1993; Shepherd and Clark 1994), habitat degradation or the alteration of infaunal invertebrate community structure. The environmental effects of harvesting natural populations of intertidal and shallow sublittoral bivalves has received considerable attention in the United Kingdom because of the scale and intensity of operation, particularly with respect to tractor and suction dredging intertidally for cockles (Franklin and Pickett 1978; Allen 1995; Cotter et al. 1997; Hall and Harding 1997). It is surprising that the environmental effects of cultivated bivalve harvesting have been little studied to date, especially as clam beds can occupy large areas of the intertidal zone with individual commercial plots that usually measure 40 m² or greater. The greatest visible effect of suction dredging or mechanical raking on the sediment is the creation of depressions or trenches which may take days to months to restore depending on sediment type and location (Dyrynda and Lewis 1995; Hall and Harding 1997). These trenches may encourage larval settlement by providing an environment subject to lower current velocities (Snelgrove and Butman 1994). However, Thrush et al. (1996) report that defaunated sediments become destabilized leading to faunal emigration which greatly delayed recolonization. Recolonisation rate is likely to differ between habitats-types depending on a combination of factors including sediment stability and exposure to wave action and currents. In addition, the scale of disturbance will have important implications for recolonisation rate depending whether this occurs through active/passive movement of adults or through larval recruitment (Hall and Harding 1997). Most studies of recolonisation rate have been performed on scales of < 1 m² which do not equate to the scale of commercial harvesting practices. However, more recent studies have been designed at a scale appropriate to examine the effects of disturbance associated with commercial harvesting activities. In Hall and Harding’s (1997) study of tractor dredging in the Solway Firth, they found that the benthic community within dredged plots was indistinguishable only 3 months after harvesting regardless of the scale of disturbance which ranged from 225 m² to 2025 m². They emphasised that the sum total of disturbed areas on a sandflat subjected to commercial harvesting would exceed that examined in their study, but that it would be patchily distributed and unlikely to extend the recovery trajectory much further. The rapid recovery in the Solway Firth was attributed to large-scale sediment movements that obliterated their treatment effects (Hall and Harding 1997). In another, similar study undertaken 23 at a commercial Manila clam farm at Whitstable on the southeast coast of England, Kaiser et al. (1996) found that the infaunal community was restored within 7 months after suction harvesting clam parks (each park, 40 m²). This site had an underlying sediment of cohensive mud/clay overlayed with a veneer of coarser sediments. Suction dredging removed this veneer leaving the mud/clay exposed. Tube dwelling polychaetes, such as Lanice conchilega and Euclymene lumbricoides, burrowed down into the mud/clay fraction and were less adversely affected than more mobile species such as Macoma balthica and Scoloplos armiger which were found in the coarser overlying sediment. It is likely that these fauna would only recolonise harvested areas once this veneer of coarse sediment had been restored. Wave action is probably the main agent of sediment restoration at his site, which is exposed to prevailing northeasterly and easterly winds. Spencer et al. (1998) conducted a similar experiment in the River Exe, Devon, England. This site was much more sheltered than those studied by Hall and Harding (1997) and Kaiser et al. (1996) and characterised by fine muddy sand. Although sediment structure and profile was restored 3 months after suction harvesting, the benthic community was not fully restored until 9 to 12 months after harvesting occurred (Spencer et al. 1998). The immediate effects of suction dredging are, not surprisingly, quite severe, as the entire upper layers of the substratum and fauna are removed. In some fisheries, bivalves are collected by hand or mechanised raking. As yet unpublished data (Kaiser, Broad and Hall) suggests that the composition of benthic fauna within handraked plots recovers within 54 days of initial disturbance. Unlike suction-dredging techniques, hand-raking leaves the sediment in situ and does not affect all the animals within the path of the rake. Soft sediments recover relatively quickly after physical disturbance (but see Thrush et al. 1996 for a good example of an exception). However, disturbance of key habitat structures, such as hard substrata or plants, particularly seagrasses, are likely to have much more severe effects. Seagrass beds are highly specialised habitats, acting as nursery grounds for juveniles of many species and they are important for the productivity of coastal areas. The effects of anthorpogenic disturbance to these habitats is excellently reviewed by Short and Wyllie-Echeverria (1996). Fishers gathering molluscs in the intertidal zone may cause disturbance as they walk across substrata. Recent studies by Chandrasekara and Frid (1996) and Fletcher and Frid (1996) emphasise how repeated trampling can induce localised changes in invertebrate and plant communities on tideflats and rocky shores. As a result, they suggested the use of restricted walkways to minimise this disturbance. 4.2.4 Management Considerations Seed collection Mechanical methods of harvesting bivalve seed have the potential to modify nontarget communities associated with seedbeds. In the United Kingdom, mussel seed collection is managed by issuing licences to dredge defined areas of the seabed which will constrain the limits of habitat modification that may occur as a result of harvesting. Over-exploitation of seedbeds in The Netherlands has caused declines 24 in bird populations that depend upon this food resource and has also caused a decline in the sustainable fishery, such that mussel seed are presently imported for relaying. The consequences of the decline of mussel stocks for other key predators of mussels such as green crabs, Carcinus maenas, and starfish, Asterias rubens, is unknown. These species have presumably switched to feeding on alternative prey species. The use of spat collectors in midwater has little, if any secondary effects on the environment. The continuous relaying of cultch, however, leads to habitat modification. This may result in an increase in local habitat and species diversity which is more likely to be perceived as a beneficial rather than negative effect. However, any negative effects that might arise from the use of cultch in the coastal zone might be constrained by restricting its use to areas specially designated for bivalve cultivation. The introduction of alien species is generally regarded as undesirable and their potential ecological effects are now well known (Ludyanskiy et al. 1993; Eno 1996; Minchin 1996). The risk of inadvertantly introducing alien species with shipments of bivalves and via other vectors such as ballast water can be significantly reduced if Codes of Practice are adopted as part of a management plan, such as that produced by ICES (Anon. 1994; Minchin 1996). Some of these introduced species are harmful to cultivated bivalves (e.g. starfish, green crabs) or create management problems (e.g. fouling organisms). On-growing The environmental effects of mariculture during the on-growing stage will vary according to the nature of the habitat and the scale of cultivation. Problems associated with eutrophication beneath mussel lines are ameliorated in situations where tidal flow is sufficient to disperse particulate matter generated by the mussels and other epibiota (Tenore et al. 1982; Rodhouse and Roden 1987). Most of the documented environmental problems seem to occur in systems where water exchange is restricted (Castel et al. 1989; Bacher et al. 1991). While small-scale culture seems to have only very limited effects on local benthic communities (Nugues et al. 1996), areas of intense cultivation, may require the development of energetic models to help manage these ecosystems e.g. oyster cultivation in Marennes-Oléron (Bacher et al. 1991). Cultivation sites occupy areas of the intertidal zone that may conflict with bird feeding or roosting sites. However, this remains an understudied area, and is only likely to be problematic when cultivation areas occupy significant areas of the available feeding grounds. Goss-Custard and Verboven (1993) reported that husbandry activities associated with clam cultivation were less intrusive than recreational activities in the River Exe. 25 Harvesting In northern Europe, clam harvesting normally occurs in early winter when the clams are in peak condition for the shellfish market. This coincides with decreasing abundances of infauna, and avoids peak recruitment times for benthic larvae. Restricting harvesting to early winter could ameliorate site restoration if the main mechanism for recolonisation is by larval settlement. However, harvesting during the summer peak in abundance would be best for species that are redistributed through active or passive movement (Hall and Harding 1997). Staggering the use of different areas of sediment in a cycle of cultivation, havesting and restoration could provide an effective means of limiting environmental disturbance associated with intertidal bivalve havesting. The length of time that harvested areas would require for restoration will be a function of the amount of natural disturbance experienced in that environment and the timing of harvesting in relation to larval recruitment of target and non-target species. Beneficial Effects of mariculture The cultivation of bivalves can be a method of alleviating adverse environmental impacts arising from other activities in the coastal zone. For example, intensive fish farming has undesirable environmental impacts, particularly as the effluents are highly nutrient enriched, promoting the development of sometimes high microalgal populations, some of which are toxic. It has been proposed that integrated fish/bivalve mariculture systems can ameliorate this effect, as the bivalves reduce algal densities and nutrients which are effectively removed when the bivalve product is harvested (Folke and Kautsky 1989; Shpigel et al. 1993). It has also been suggested that mussel culture can be a means of removing excess particulate-bound nutrients from eutrophic systems (Haamer 1996), or for the restoration of enclosed water masses which are polluted (Russell et al. 1983). While these mussels are unfit for human consumption, they represent a cost-effective and self-perpetuating means of maintaining water quality. 4.2.5 Conclusions The impacts of bivalve cultivation have been assessed by a case-study approach concerning the effects of various cultivation activities, from seed collection to harvest, in specific coastal areas. Neither the assimilative or environmental capacity of a system for bivalve cultivation has been specifically addressed. This is probably due to the enormous amount of work that would be required to produce a model to reasonably estimate the full ecological system of a particular area. The impacts of activities such as on-growing, on the seabed or suspended on rafts and longlines, have been found to be very localised. The capacity of a system to withstand such practices could therefore be determined by an estimation of the proportion of the site that would be effected. The determination of other effects, such as depletion of food sources (e.g. phytoplankton) on other filter feeding populations would be more difficult to determine. However, if the system is close to reaching it’s carrying capacity then the cultivated bivalves would also display effects such as reduced growth rate, which could be used as an indicator of the state of the system as a whole. 26 5. Recommendations of field and laboratory investigations to provide indicators of carrying capacity of UK coastal waters for bivalve cultivation At present bivalve carrying capacity can be predicted with a reasonable amount of accuracy. The major area where there is scope for development is at smaller spatial scales than have been considered previously. A better understanding of processes at smaller spatial scales would allow for more accurate predictions of bivalve growth and hence carrying capacity. Monitoring of observed bivalve growth against expected growth should also provide clear indicators of the state of the ecological system as a whole since bivalves within a population are likely to be affected to the same extent by degradation (if any occurs) of the system. If the carrying capacity of the system is reached or surpassed bivalve growth rate will be reduced, and this will be rapidly and easily detected as bivalve cultivators monitor closely bivalve growth and condition. 5.1. Aim and Rationale Future approaches should aim to elucidate the mechanisms underlying the spatial variability in mussel density and bedform within mussel beds. Such an approach could yield important new data on the turbulent flow over mussel beds of different densities, the associated vertical mixing in the water column and its impact on the phytoplankton concentration and mussel feeding. As well as leading directly to new insights into the processes involved in the supply of phytoplankton to filter feeders, such data would facilitate the development and testing of new/existing models of the musselbed/phytoplankton system. The testing and validation of such models would enhance our understanding of the processes involved and identify the need for improved information on key parameters. The intensive filtration activities of mussels in high-density beds constitutes a major sink for phytoplankton and particulate matter and can result in a significant depletion in a “concentration boundary layer” above the bed (Peterson and Black 1991; Asmus and Asmus 1991; Koseff et al. 1993). Indeed, in beds of cultivated mussels, significant depletion of food can occur within metres of the leading edge of the bed (Newell et al. 1989). The depletion of food in the boundary layer has implications for the growth and reproductive output of mussels sited within the bed that lie behind the leading edge (Newell 1990). The food supply within the boundary layer behind the leading edge of mussel beds is replenished to some degree by vertical diffusion from above. Hence, in order to understand the processes that affect food supply, it is important to understand how turbulence in the water column affects the rate at which plankton diffuse downwards. This is a key control on the food uptake of the mussels and is hypothesised to explain the observed changes in growth rate with flow speed, observed for example in response to the springs/neaps cycle in the tides (Ramsay et al. 2001). The level of turbulence is determined by the flow speed over the bed and the effective roughness of the bed. The latter is increased, relative to flat beds by the presence of the mussels, which act as large roughness elements. The frictional drag of the bed and the production of turbulent flow may be further enhanced by the tendency of the mussels to form ridges. Where mussels that are elevated above the food-depleted zone they may grow at a faster rate and this may explain the phenomenon of ridge formation (Frechette and Bourget 1985). 27 The intensification of boundary stresses may lead to an improved efficiency of feeding by the mussels through the increase in vertical mixing which will improve replenishment of phytoplankton in the benthic boundary layer. On the other hand, increased turbulence will tend to bring more inorganic sediments into suspension that could impede feeding mechanisms. Increased turbulence also leads to enhanced erosion and subsequent deposition of sediments (Widdows et al. 1998a) which may result in temporary burial of mussels, while the “packaging” of fine sediments in the production of pseudofaeces and mucous-bound aggregates acts to substantially modify sediment properties. Aggregates are bound by polysaccharides secreted by microorganisms associated with plankton (Jago et al. 2001) and mussels. Biologically-mediated sediments may be more susceptible to erosion (because of increased roughness elements) or less susceptible to erosion (because of increased microbial cohesion). Furthermore, such low-density particles in suspension have enhanced settling rates because of their size, they are more susceptible to aggregation because of their stickiness, but they are readily broken up by turbulence because of their low strength (Jago et al. 2001). It follows that the properties and fluxes of suspended particulate matter over mussel beds probably change on small time and spatial scales. Mussel beds show a high level of variability in terms of structure at a variety of spatial scales (Lawrie and McQuaid 2001) that may be explained in part by population processes such as self-thinning. Existing models of self-thinning in animals (e.g.Frechette et al. 1992; Frechette and LeFaivre 1990) utilise allometric relationships between size and other physiological components to 'predict' the selfthinning relationship between biomass and density. Such models are generally inadequate since self-thinning is the product of a combination of compensatory processes that include levels of density-independent mortality, quality of the feeding environment, and density-dependent effects on growth and mortality rates. Within mussel beds high growth rates due to good feeding conditions may exacerbate mortality losses. Conversely, high rates of predation within high-density beds may remove mussels, allowing increased rates of growth to be achieved by the surviving mussels (Beadman et al. 2002). Hence, predator-dependent mortality is believed to substitute for the self-thinning mortality imposed by the growth of the cohort (Willows 1996). To date, attempts to measure food supply have been restricted to general background measurements of phytoplankton levels or the depletion that occurs over an entire mussel bed. However, such measurements do not provide information at an appropriate scale that might explain the mechanism of population density variation that occurs within a mussel bed. The water column, sediment bed and mussel community, therefore constitute a complex system that interacts through a variety of processes, most of which are imperfectly understood and unquantified despite a considerable effort to model these systems over the last 20 years (e.g. Frechette et al. 1989; Asmus and Asmus 1991; Butman et al. 1994). 28 5.2. Approach 5.2.1 Growth rate and variability with mussel beds A large scale field experiment should be set up consisting of replicated mussel lays. Differences in growth rate within each replicate bed should be measured. The advantage of creating mussel beds is that the industry can control (within limits) the size-class of mussels laid over the area. At the start of the experiment, each bed should be sampled randomly according to a grid reference system that is predetermined. Sampling should be sufficiently frequent and intensive so as to ensure that variability in growth rate within each bed can be determined at a number of scales. A number of individual mussels should also be marked for the determination of tidal growth rates to elucidate patterns and variation in growth rate at different positions in the mussel bed. Such patterns should be used to corroborate the predictions of the physical-biological models with respect to variation in the food supply across each mussel bed. The allocation of energy resources into shell and flesh production should be ascertained from the ash free dry weight of mussels collected from different sites (e.g. ridge crest, trough etc) from each of the mussel beds. This information should then be used to ascertain the energetic consequences of a variable supply of food across the mussel bed. 5.2.2 Physical model development The foundation of an appropriate model would be a representation of water column dynamics using a turbulent closure. Vertical mixing parameters from the dynamical model should form the basis of sub-models of (i) phytoplankton biomass redistribution and consumption and (ii) particle re-suspension and deposition. These sub-models will solve the 2-d advection-diffusion equation for phytoplankton and SPM concentrations in a channel flow. The advection diffusion equations can then be integrated in parallel with the dynamical equation to provide predictions/hindcasts of the mussel-plankton ecosystem. Testing and development of the biomass submodel should be facilitated by the measurements of vertical and horizontal gradients of chlorophyll fluorescence over the beds. 5.2.3 a) Turbulence in the water column Recent developments in the processing of data from high frequency Acoustic Doppler Current Profilers (ADCP) allow the determination of the turbulent shear stress through the water column (Stacey et al. 1999; Rippeth et al., 2002 ). Combined with the mean velocity shear, the stress profile can be used to estimate the rate of production of turbulent kinetic energy (the product of stress and shear) and the eddy viscosity (the quotient of stress over shear) which is closely related to the eddy mixing coefficient. The technique may be readily applied to measure the turbulence over natural and artificially laid shellfish beds. b) Near-bed vertical profiles and processes Fine-scale studies should compliment the large-scale effects observed by the seabed deployed ADCP. Detailed vertical profiles of near bed currents, turbulence and suspended particulate matter should be analysed above different parts of the bed and in relation to increasing current velocities. This can then be compared with 29 similar field measurements of near bed processes over spring and neap tidal cycles such that predictable temporal patterns in variation in near-bed turbulence can be quantified. 5.2.4 Phytoplankton and SPM in the water column Vertical profile measurements should be made of phytoplankton concentration to reveal directly the extent, timing and vertical structure of depletion of phytoplankton due to mussel filtering. At the same time the concentration of SPM should be observed, and also the size spectrum of suspended particles. Measurement of particle size and its variability are important for modelling aggregation and vertical fluxes of SPM, and have significant implications for mussel feeding. Settling velocities of SPM, and their temporal and spatial variability over the mussel bed, should be measured along with settling velocity spectra of chlorophyll; this is an important parameter for modelling plankton biomass distribution. 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