3. DISEÑO METODOLÓGICO
3.5 DISCUSIÓN DE RESULTADOS DE TALLERES APLICADOS POR CICLOS
3.5.2 Descripción de los resultados de los talleres correspondientes al ciclo 2
Table 2-2 summarizes some of the available shellfish models for estimating carrying capacity together with their main conclusions and limitations. While these models explain many of the aspects of the studied system, they often fail to simulate the systems dynamics over different scales. Most of the model studies identified the difficulties in dealing with temporal and spatial scales from a simulation point of view and difficulties in obtaining sufficient high frequency field data to calibrate or subsequently validate the model (Raillard and Ménesguen, 1994; Crawford et al., 1996; Gangnery et al., 2001; Bacher et al., 2003; Nunes et al., 2003; Jiang and Gibbs, 2005). Primary productivity is particularly sensitive to the resolution of the simulation as a result of the lack of precision in modeling phytoplankton dynamics (Raillard and Ménesguen, 1994). However, phytoplankton dynamics become less important if the flushing time of a system is small, so that the water exchange is fast, continuously bringing new water in the system. In addition, it was recognised that, in some cases, food limitation was a consequence of the local density-dependence effect, which in most cases was not considered in the simulations (Bacher et al., 1998; Nunes et al., 2003).
A major problem appears to be the lack of empirical data to realistically calibrate models (Crawford et al., 1996; Bacher et al., 2003). Large resources are needed in order to satisfy most numerical models. In addition, the problem expands when considering inter-annual variations in food levels resulting from varying environmental conditions (Bacher et al., 2003). Oyster dynamics can be simulated in models but it is again hard to gather appropriate field information over sufficient timescales, to calibrate or validate the model output, especially for adult size- classes that grow slower than juveniles. Growth is affected by various environmental aspects such as temperature and salinity. Models need to integrate these important environmental effects on oyster behaviour. An example of such a model is shown in Figure 2-1 from a one- dimensional ecosystem model of an Irish oyster cultivating lake (Ferreira et al., 1998). In this case modelled results were well within the real values. Figure 2-1 shows oyster biomass variation through the year, across four cohorts and as a result of harvest after day 265 in each year cycle. Based on a total of forty size classes between 0.65g (FW= fresh weight) and 97.75g (FW) with a class amplitude of 2.5g (FW), the model predicted that in 17 months the oyster would reach market size (above 65g FW) and that a total of 42 tonnes (FW) would be harvested after two summers.
Figure 2-1: Simulation of oyster biomass in box 3 of Ferreira’s model (1998) as a function of time and individual weight for an oyster culture in Carlingford Lough
Source: Ferreira et al. (1998)
In general, most of these models are quite complex coupling both hydrodynamic and ecophysiological processes. Some of the models predicted not only an ecological carrying capacity, which is the level of culture that could be introduced without leading to any significant changes to the food web and to the nutrient flows in the aquatic system, but also a production carrying capacity, which determines the maximum theoretical level of culture. These models included information on the marketable weight and oyster mortality in order to explore the system properties resulting from integrating the environment and the ecophysiology of the cultivated species. An example of this is shown in Figure 2-2, for the model of Bacher et al. (1998). This figure shows the three-dimension relationship between production, annual oyster mortality and standing stocks. Production levels in this system were very sensitive to the marketable weight. In order to sell larger oysters, they need to be kept in the system for longer periods taking up resources and being subject to additional risk of mortality.
Figure 2-2: Model output of production calculated with theoretical models for different values of annual mortality and standing stock
Source: Bacher et al. (1998)
Most of the models summarized in Table 2-2 examined the effect that increasing standing stocks would have on the system in terms of food depletion, growth rates and time to reach harvest weight. Most models showed a clear decline in oyster productivity as standing stock increased. However, growers and other stakeholders need to adapt the level of standing stock that the system could support without negatively affecting production and or the environment, to their management protocols and enterprise goals.
The various models generally gave a representation of the processes taking place in the cultivating systems. However, they showed problems in: a) the inclusion of seasonal and size- related changes in the energy/metabolic demands of the cultured organisms; b) the lack of specification of how bivalves use various particles in the seston and whether some of those particles are ‘available’ or ‘useable’ and; c) the difficulty in predicting the mixing and flow of water through small-scale culture areas. In the following chapters some of these problems will be addressed such as the temporal and spatial growth variation in SRO in the two oyster producing estuaries, the identification of the SRO diet composition and the contribution of each food component towards the overall diet and, a better understanding of the problems encountered when down-scaling the processes that take place at the estuary level to an oyster lease area.
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Table 2-2 (I): Summary of some published carrying capacity (CC) models for different species of oysters and a case study for scallop and polyculture system. Font in italics are suggested improvements. Error!
Reference Purpose / Model characteristics Conclusions Limitations /improvements needed
A. Carvet &
Mallet, 1990 (M. edulis)
Semi-enclosed shallow coastal inlet, food supply driven by tidal currents. Production CC as ratio food
supply:food demand by mussels based on field filtration rates
Low particle exchange due to hydrodynamic of semi-enclosed bay, low POM and depletion through the tide cycle, low food levels to calculate optimum mussel filtration rates. Seasonal variations in food supply and mussel ration affect CC of bay. Importance of dealing with site-specific field data
Simple hydrodynamics as water flows in at high tide, assumes complete mixing between water flow in and remaining & ‘old’ water flows out completely. POM deliver to bay as unique mussel food. Mussels can access 100% of available food
Improvement: include other grazers, consider local prim production from close-by nutrient inputs, use optimum ration instead of observed ration B. Raillard & Menesguen, 1994 (C. gigas)
Ecosystem model of physical & biological dynamics in shellfish culture, computes the sensitivity of oyster growth to oyster abundance (biomass).
Nitrogen cycling between the dissolved phase, phytoplankton, detritus and cultured oysters.
Hydrodynamic regime strongly controls the carrying capacity of the system. Carrying
capacity (CC) is inversely proportional to water
turnover, which controls food renewal
(phytoplankton). Oyster does not exert a strong control on phytoplankton biomass because of low residence time of water. CC is sensitive to spatial distribution and stock levels.
Underestimation of phytoplankton biomass due to a lack of information on dynamics. Limitations = physical transport of suspended matter.
Improvement: include vertical exchange in terms of sedimentation and erosion. C.
Powell et al., 1995
(C. virginica)
Utilises a time-dependent model that simulates the population dynamics of a post-settlement oyster population and examines the characteristics of population as food becomes limiting.
Emphasizes relationship between physical transport and food supply. If food supply becomes limiting, market-size oysters disappear from population size frequency after 3-7 yr. Populations can be controlled: 1) top-down by predation or disease; 2) bottom-up by food supply or space available. System controlled by pelagic productivity in relation to benthic CC. Importance of stock assessment program, monitoring food chain; provides information to fishery management.
Only able to do a rough prediction of future impacts of declining food supplies.
Improvement: More examination on the many factors that might regulate exact timing of events.
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Table 2-2 (II): Summary of some published carrying capacity (CC) models for different species of oysters and a case study for scallop and polyculture system. Font in italics are suggested improvements.
Reference Purpose / Model characteristics Conclusions Limitations /improvements needed D.
Crawford et al., 1996 (C. gigas)
Oyster production in relation to primary production and nutrient cycles in ongrowing and fattening areas. Predict CC of intertidal shellfish areas. Apply model to other shellfish farms and to the day-to-day farming management.
CC models are feasible but require detailed site- specific information of growing area.
Generalized model not achievable because of significant differences in hydrodynamic regimes and physical characteristics of sites.
Insufficient data on primary production collected (theoretical values used).
Need to look at variation over cross-section of each box of hydrological model- more accurate information and use of a 2-D model. More on maximum ingestion rates and environmental factors. Need to add
concentrations of nitrates and phosphates and chl-a. Consider other filter feeders
E.
Bacher et al., 1998
Mathematical models to calculate CC. Highlight and interpret the differences between two systems that were studied in similar ways.
Decrease in production by increasing standing stock. Differences between the 2 systems based on biological and physical flows. Model showed parabolic curve of production/ stock relationship and its sensitivity to ecological parameters and economic constraints. Zooplankton was removed because of a low influence on phytoplankton dynamics in contrast to oyster grazing.
Very simple ecological model (in terms of population dynamics)- no consideration of the import of oysters between and within bays. Comparison was limited as systems have different flow rates.
Food depletion presented for local oyster density consumption but not for overall bay scale. F. Ferreira et al., 1998 (C. gigas)
Simulate oyster growth by means of ecological model performing a mass balance for nitrogen in relation to physical and biological variables. Assess CC for oyster culture and examine different management strategies.
Change in nitrogen loads was not noticeable as oysters depended on other particulate matter than phytoplankton. Use of demographic model to describe biomass dynamics of species. Harvestable and non-harvestable classes and population recovery rate was needed for optimising sustainable yields. Need for coupling physiological & demographic & physical- ecological model for management purposes.
Bias in results due to low number of boxes. Scope for growth was calculated based on constant parameters over a simulation period- weakness: growth changed as physio- and morphological adaptations occurred at different time-scales.
Improve demographic model: 1) use an age- weight only; 2) need more flexibility and utility for management terms.
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Table 2-2 (III): Summary of some published carrying capacity (CC) models for different species of oysters and a case study for scallop and polyculture system. Font in italics are suggested improvements.
Reference Purpose / Model characteristics Conclusions Limitations /improvements needed G. Chapelle et al., 2000 Oyster general
Quantification of the relationships between primary production, zooplankton predation, oyster filtration, sediment exchanges and watershed inputs.
Based on nitrogen cycling and oxygen levels. Exportation of nitrogen by harvesting was considered
Shellfish areas sensitive to oxygen depletion because of benthic sediment demand.
To understand annual primary production it is necessary 1) new primary production (nitrate- diatoms- mesozooplankton) highly controlled by watershed input; 2) regenerated primary
production (ammonia- pico- nano- micro- zooplankton) based on internal nitrogen cycling.
Need to consider production & consumption of oxygen in sediment by algae and fauna in addition to diffusion of oxygen consumed by mineralization and nitrification.
Need to include macrophytes and microphytobenthos.
Improvement on the nutrient input and spatial and temporal variability.
H.
Gangnery et al., 2001 (C. gigas)
Predict changes in standing stock and annual production through a model on population density as a function of mortality rate, individual growth rate & interindividual variability.
Population dynamics represented with a continuous time & weight- dependent equation
Food consumption and biodeposition modify concentration of food and primary production. Growth of oysters strongly related to water renewal time (due to winds), rate of primary production, nutrient recycling, clearance time and biodeposition rate.
The simulated standing stock represented only a fraction of total standing stock as one cultivation technique was assessed only. No temporal and spatial variability of standing stock. Oyster density assumed to be uniform.
Need to address the link between the ecosystem dynamics and feedback on cultivated population dynamics. I. Niquil et al., 2001 Pearl oyster (Pinctada margaritifera & maculata)
Use inverse analysis to obtain a complete food web for the pelagic community including planktonic processes and farmed pearl oysters.
Lack of spatial/temporal variation in water. Detritus constitutes the main food compartment. Low carbon flows from plankton to bivalves. Oysters had little effect on food web because low consumption of primary production. Plankton composition was mainly pico-particles as pearl oysters can not effectively select such size particles (lack of eu-latero-frontal cirri)
Limitation due to methodological choices: simple exchanges between planktonic subsystems and remainder of ecosystem.
Consider coupling between benthic and pelagic process. Calculate consumption of each planktonic compartment. Consider increment of stock or inter-specific competition.
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Table 2-2 (IV): Summary of some published carrying capacity (CC) models for different species of oysters and a case study for scallop and polyculture system. Font in italics are suggested improvements.
Reference Purpose/ Model characteristics Conclusions Limitations /improvements needed J. Bacher et al., 2003 Scallop (Chlamys farreri)
Assess growth and feeding by
combining transport (velocity and food delivery) with ecophysiological model taking into account food depletion at a local scale.
Presence of food enabled a weight increment of 1.5g dry tissue. Growth was correlated to POM and maximum current velocity. Variability of growth due to food limitation.
Model at 1000m scale was optimum for managing growth performance.
Environmental variability masked
measurements of depletion in field. Monthly sampling did not account for short-term variability (e.g. tides). Model sensitive to ‘sink of particles’ based on feeding or ingestion.
Depletion models at large scale involve intensive temporal and spatial sampling. K. Nunes et al., 2003 (C. gigas) Scallop (Chlamys farreri) Kelp (L. japonica)
To integrate a bay-scale ecological simulation with individual-based modelling of scallops and oysters and upscales the individual processes for each species by using multi-cohort population dynamics model
Increasing food limitation caused decreased growth. Changes in phytoplankton had effect on overseeding at ecosystem level. Oysters sensitive to low phytoplankton, taking up most of it and no export to sea from bay.
Reviews to aquaculture practices (i.e. increasing total production will reduce harvest efficiency due to limit resources)
Data gathered did not allow for full validation of model. Human component is a limitation as increase harvesting pressure.
Need other tools for location of farms, food availability and depletion and water flow effects.
Include social component as a state variable more than as a driving force.
L. Jiang &
Gibbs, 2005 Green
Mussel (P.
canaliculus)
CC of suspended bivalve culture was calculated using a linear food web model representing the whole
ecosystem configured for present state, predefining boundary ecological states representing the limits of system. Food web gets perturbed by introducing different densities of mussels until reaching production and ecological CC
Model used to investigate present functioning of system and how this may change if intensive mussel culture is introduced.
Introducing mussels in the system resulted in a decrease in its mean tropic level, increased in total yield and efficiency, shift of zooplankton role by mussels as major grazers.
Estimations for the ecological CC were 5 times lower than production CC
The Ecopath steady state mass balance model used here incorporates all trophic levels of marine systems- it uses too many variables. The present state of the system was best guess leading to potentially large variability
Ecopath cannot be used to simulate changes to flows with time. This model does no consider marketable cohorts so can no be used from a production planning point
Table 2-3: Summary of the parameters used in the models in Table 2-2 Carrying capacity models for oysters
Variables explained in model A B C D E F G H I J K L Physical characteristics Temperature X X X X X X X X Water movement X X X X X X X X X Salinity X X X Turbidity X X Wind X X X Light X X X X X Currents X X X Run-off X Oxygen levels X Sediment surface X Particle sedimentation X X X Water particles DIN X X X TPM X X X X X X POM X X X X POCarbon X X PONitrogen X X X X Phytoplankton X X X X X Chlorophyll-a X X X X X X X X Zooplankton X X Physiological components Respiration X X X X X X Filtration rate X X X X X X X Assimilation efficiency X X X X X X X Reproduction/ spawning X X Somatic growth X X X X X X Other Bivalve biomass X X X X Primary production X X X X Mortality X X X X Stock density X X X X X X X Biodeposits X X X X X X Population level X X X X X Recruitment- larval/seeding X X X X
Sources: A) (Carver and Mallet, 1990); B) (Raillard and Ménesguen, 1994); C) (Powell et al., 1995); D) (Crawford et al., 1996); E) (Bacher et al., 1998); F) (Ferreira et al., 1998); G) (Chapelle et al., 2000); H) (Gangnery et al., 2001); I) (Niquil et al., 2001); J) (Bacher et al., 2003); K) (Nunes et al., 2003); L) (Jiang and Gibbs, 2005)