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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
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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. The horizontal
gradient of phytoplankton and SPM concentration should also be observed upstream
and downstream (in the mean flow) of the mussel beds.
30
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