shellfish aquaculture and the environment (shumway/shellfish aquaculture and the environment) || the...

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Chapter 1 The role of shellfish farms in provision of ecosystem goods and services João G. Ferreira, Anthony J.S. Hawkins, and Suzanne B. Bricker Introduction What is a farm? Shellfish farms vary widely in type, situation, and size. The type of culture can vary accord- ing to species, and even within the same species various approaches may be used, depending on factors such as tradition, environmental conditions, and social acceptance. For instance, mussels are cultivated on rafts in Galicia (Spain), and on longlines in the Adriatic Sea (Fabi et al. 2009). But they are also grown on poles in the intertidal area in both France ( bouchot) and China ( muli zhuang), or dredged from the bottom in Carlingford Lough (Ireland) and in the Eastern Scheldt (the Netherlands). It is not unusual to use different culture techniques for the same species at different stages of the growth cycle, or to rear benthic organisms off-bottom, taking advantage of a greater exposure to pelagic primary produc- tion, better oxygenation, and predator exclusion. In a similar way, shellfish can be grown in intertidal areas, competing for space with other uses (e.g., geoduck grown in PVC tubes in Puget Sound, USA; oysters on trestles in Dungarvan Harbour, Ireland), or subtidally (e.g., scallop off Zhangzidao Island, northeast China). Cultivation takes place within estuar- ies, coastal lagoons, and bays (e.g., Figure 1.1), and increasingly in offshore locations, where there are less conflicts with other stake- holders in the coastal zone. In many parts of Shellfish Aquaculture and the Environment, First Edition. Edited by Sandra E. Shumway. © 2011 John Wiley & Sons, Inc. Published 2011 by John Wiley & Sons, Inc. 3

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Page 1: Shellfish Aquaculture and the Environment (Shumway/Shellfish Aquaculture and the Environment) || The Role of Shellfish Farms in Provision of Ecosystem Goods and Services

Chapter 1

The r ole of s hellfi sh f arms in p rovision of e cosystem g oods and s ervices Jo ã o G. Ferreira , Anthony J.S. Hawkins , and Suzanne B. Bricker

Introduction

What i s a f arm?

Shellfi sh farms vary widely in type, situation, and size. The type of culture can vary accord-ing to species, and even within the same species various approaches may be used, depending on factors such as tradition, environmental conditions, and social acceptance. For instance, mussels are cultivated on rafts in Galicia (Spain), and on longlines in the Adriatic Sea (Fabi et al. 2009 ). But they are also grown on poles in the intertidal area in both France ( bouchot ) and China ( muli zhuang ), or dredged from the bottom in Carlingford Lough (Ireland) and in the Eastern Scheldt (the Netherlands).

It is not unusual to use different culture techniques for the same species at different stages of the growth cycle, or to rear benthic organisms off - bottom, taking advantage of a greater exposure to pelagic primary produc-tion, better oxygenation, and predator exclusion.

In a similar way, shellfi sh can be grown in intertidal areas, competing for space with other uses (e.g., geoduck grown in PVC tubes in Puget Sound, USA; oysters on trestles in Dungarvan Harbour, Ireland), or subtidally (e.g., scallop off Zhangzidao Island, northeast China). Cultivation takes place within estuar-ies, coastal lagoons, and bays (e.g., Figure 1.1 ), and increasingly in offshore locations, where there are less confl icts with other stake-holders in the coastal zone. In many parts of

Shellfi sh Aquaculture and the Environment, First Edition. Edited by Sandra E. Shumway.© 2011 John Wiley & Sons, Inc. Published 2011 by John Wiley & Sons, Inc.

3

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4 Shellfi sh Aquaculture and the Environment

For the purposes of this text, a farm is therefore defi ned as an integrated production unit, typically allocated as a lease, subject to specifi c pressures with associated impacts (Fig. 1.2 ). This can be an area of sea bottom where molluscs are grown (e.g., mussel/oyster culture, abalone in pens), off - bottom (but overlying bottom space) such as oyster trestles, or an area of water where rafts or lines are placed (droppers off longlines, Chinese lanterns), or

the world, onshore cultivation is also a reality, as occurs in Guangdong province (China) and elsewhere for razor clams and oysters, fre-quently in multispecies combinations (e.g., Ferreira et al. 2008a ; Zhang et al. 2009 ; Nobre et al. 2010 ).

The size of farms may vary widely, given various constraints imposed by physical space, environmental conditions (which directly infl uence production), ecological effects, and social acceptability. An obvious constraint on the viability of a shellfi sh farm is the natural food supply, which in some areas of the world has a direct relationship to the lease units. For instance, in China, the aquaculture cultivation unit is the Culture Mu (Nunes et al. 2003 ); in a similar way to the medieval bushel, the actual area of this unit varies among different bays, depending on the typical carrying capacity per unit area of each bay, as exemplifi ed in Table 1.1 for Shandong province.

Figure 1.1 Aquaculture in Sanggou Bay, northeast China. Longlines used for shellfi sh culture are clearly visible in satel-lite images.

Table 1.1 Dependency of Chinese lease units on carrying capacity.

Bay or system Unit name Area (m 2 )

All land - based agriculture

Mu 666.66 (1/15 ha)

Sanggou Bay Culture Mu 1600 – 1800

Jiaozhou Bay Culture Mu 3000 – 5000

Laizhou Bay Culture Mu 5000 – 8000

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Role of shellfi sh farms in the ecosystem 5

2004 ). The aim of IMTA is to increase long - term sustainability and profi tability per culti-vation unit (rather than per species in isolation, as is done in monoculture), in which all the cultivation components have economic value, and each has a key role in services and recy-cling processes of the system.

Many of the analyses presented in this chapter are carried out by means of mathemat-ical models, so we begin with a review of methodologies that provide the necessary grounding for the development and applica-tion of such models. We then examine some of the available options in the area of predictive modeling, and the remainder of the chapter reviews two main aspects:

1. Ecosystem goods supplied by shellfi sh farms. The emphasis is on production, and its optimization, including IMTA.

2. Ecosystem services supplied by shellfi sh farms. The environmental role of shellfi sh farms extends well beyond the harvest of shellfi sh per se, and includes interactions such as top - down control of eutrophication symptoms (see Chapters 7 and 8 in this book).

Case studies are used throughout to illustrate the practical application of principles and techniques in real - world situations, drawing from examples worldwide, including the

ponds fringing coastal areas (razor clams). Whether farms are located on the bottom, off - bottom, or as suspended structures, they gen-erally preclude the use of the sea bottom for other human activities, such as fi shing or rec-reation, and raise controversial issues related to multiuser interactions, as discussed in Chapter 9 (in this book).

This chapter examines the role of the shell-fi sh farm as a provider of ecosystem goods and services. The focus is on farms located in open estuarine and marine waters, from the inter-tidal zone to offshore locations. Although this book is aimed at shellfi sh ( sensu bivalve mollusc) aquaculture, it is impossible to address the current state of the art of shellfi sh farming without the inclusion of integrated multitrophic aquaculture ( IMTA ), an approach that has been practiced in Southeast Asia for thousands of years, both in ponds and in open systems (Ferreira et al. 2008a ), and is currently attracting considerable interest (Neori et al. 2004 ; Ridler et al. 2007 ; Paltzata et al. 2008 ).

IMTA combines, in the right proportions, the cultivation of fed aquaculture species (e.g., fi nfi sh) with organic extractive aquaculture species (e.g., molluscan shellfi sh) and inor-ganic extractive aquaculture species (e.g., sea-weeds) for a balanced ecosystem management approach that takes into consideration site specifi city, operational limits, and food safety guidelines and regulations (e.g., Neori et al.

Figure 1.2 Aquaculture farms: illustration of pressures, activities, and impacts on the coastal fringe.

LAND

NitrogenPhosphorus

Pressures

Impacts

PondsFish, shellfish

Shellfish structures,Fish cages

Inshore

Reduced

eutr

ophic

ation

ConflictsLand use, social issues

ConflictsWild species, space, social

SEA

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6 Shellfi sh Aquaculture and the Environment

2. Landings statistics are often inaccurate due to underreporting, and in some cases (Watson and Pauly 2001 ), overreporting.

3. Farming practice, in the sea as on land, is adapted according to variations in growth, environmental conditions, and market requirements. Corresponding data with respect, for example, to the dimensions of target species, or timings of seed and harvest, are by nature fuzzy. Models that use a deterministic approach do not accom-modate this type of information well. In cases where natural spatfall is used (e.g., by means of oyster spat collectors) as opposed to hatchery - purchased seed, there is an additional stochastic component of interan-nual variability.

Because all these data are model forcing func-tions, they severely constrain simulation outputs. For instance, in Belfast Lough, the lease areas for bottom mussel culture are rotated over a 3 - year cycle to allow an annual harvest for animals which grow to maturity over a period of 30 months (Fig. 1.3 ). Clearly, a failure to account for this will overestimate production, irrespective of the accuracy of the underlying growth models.

As a fi nal caveat, the determination of natural mortality ( m ) poses a particular chal-lenge. In general, this is applied as an average

European Union ( EU ), North America, and Southeast Asia.

Methods of s tudy

Defi nition of c ulture p ractice

An accurate description of culture practice is a key factor in the implementation of success-ful aquaculture models. The parameters of interest may be divided into three groups, which will be examined in turn:

1. Spatial parameters : These include the farm dimensions, positioning (e.g., height above tidal datum for intertidal culture such as oyster trestles), orientation, internal parti-tioning, and crop rotation.

2. Temporal parameters : Data such as the periods of seeding and harvest, together with the seeding and harvest effort, are critical for accurate simulations of production.

3. Morphological and physiological parame-ters : The range of weights of seed or spat, the cutoff weight for harvest, mortality rates, and any relevant infl uences on growth (e.g., fouling) are the fi nal elements of culture practice description that must be considered.

Although these data appear, in general terms, easy to acquire and less challenging to inter-pret and simulate than measures of individual growth or biodeposition, experience shows that accurate validation of culture practice poses a major challenge and can be a signifi -cant liability for ecological models of aquacul-ture (Ferreira et al. 2008b ). The main diffi culties in obtaining consistent data are due to the following factors:

1. Commercial interests introduce an element of confi dentiality that is diffi cult to overcome.

Figure 1.3 Crop rotation in northern Irish mussel bottom culture (Ferreira et al. 2007b ) .

HarvestYear 3

HarvestYear 4

HarvestYear 5

SeedingYear 1

SeedingYear 2

SeedingYear 3

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Role of shellfi sh farms in the ecosystem 7

algal blooms, losses of submerged aquatic vegetation [ SAV ]); and (3) Future Outlook (Response) — an evaluation of likely future conditions resulting from changes in nutrient load that are based on projected population and land use change and the success of current or new management measures implemented within the watershed.

The three components are assessed sepa-rately and then combined to provide a single ASSETS rating (Bricker et al. 1999, 2003, 2007, 2008 ; Scavia and Bricker 2006 ; Whitall et al. 2007 ; Ferreira et al. 2007c ; Xiao et al. 2007 ; Borja et al. 2008 ). However, for the local/farm - scale assessment, only the EC com-ponent is relevant.

The assessment of EC includes evaluation of fi ve indicators that are divided into two groups. One group, termed primary symp-toms, consists of indicators of the begin-nings of eutrophication impacts (Chl a , macroalgae).

The other group, secondary symptoms, consists of indicators of more signifi cant nutrient - related degradation (DO, nuisance/toxic bloom occurrences, seagrass losses).

An assessment rating is developed for each indicator based on a combination of charac-teristics: problem concentrations or conditions (e.g., 90th percentile concentration for Chl a and 10th percentile concentration for DO of annual samples), spatial area, and frequency of occurrence of problem conditions. The method applies the assessments by salinity zone which are combined to give an area - weighted average for each indicator at the system level. The average rating for the primary symptoms and the highest (worst) rating for the secondary symptoms, using a precaution-ary approach, are combined in a matrix to determine the EC for the system.

The Farm Aquaculture Resource Manag-ement ( FARM ) model (Ferreira et al. 2007a ) simulates processes at the farm scale (about 100 – 1000 m in length), considering advective water fl ow and the corresponding transport of

for the culture period, neglecting the fact that the mortality rate will depend both on the life stage of the organism (e.g., will be higher for small animals and postspawning) and on environmental factors such as temperature, dissolved oxygen ( DO ), and predation. Furthermore, unless a time step of 1 year is used, an annual mortality of 100% will not reduce the stock to zero if m is simulated by means of a fi rst order decay, since

C C eomt= − , (1.1)

where

C = stock (number of animals); t = time (year); and m = mortality rate (year − 1 ).

A 100% mortality coeffi cient ( m = 1) is equiv-alent to a mortality factor of e − m , reducing a population of 1000 animals to about 370 over 1 year. For an effective 90% mortality, this corresponds to a coeffi cient of m = 2.3.

Eutrophication a ssessment

An important consideration for aquaculture operations, particularly when addressing con-fl icts of use, is the potential environmental impact of shellfi sh farms (see Fig. 1.2 ). Of particular interest is the role of shellfi sh in top - down control of eutrophication symptoms (see Chapter 7 in this book). Assessment of eutro-phication is done using the well - tested Assessment of Estuarine Trophic Status ( ASSETS ) model. The model has three compo-nents: (1) Infl uencing Factors (Pressure) — an evaluation based on the natural processing of a system combined with the level of human related nutrient loads; (2) Eutrophic Condition ( EC ) (State) — an evaluation based on the com-bined condition of fi ve symptoms of nutrient - related water quality problems ( chlorophyll a [ Chl a ] macroalgae, DO, nuisance/toxic

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8 Shellfi sh Aquaculture and the Environment

with certainty and thus, like macroalgae and seagrasses, the results would not necessarily indicate the impact of the aquaculture operation.

Upstream concentrations of Chl a and DO are based on daily values linearly interpolated from monthly FARM model input values. Downstream concentrations are determined from daily FARM model outputs from the most downstream section of the farm. Since the analysis is a comparison of water quality conditions in waters fl owing into and out of the farm, the standard ASSETS salinity zone spatial framework is not used. Water quality is estimated as the 90th percentile Chl a and 10th percentile DO of daily values for the duration of the model run (typically 2 – 3 years for harvestable size, dependent upon species). Those values are assigned a rating based on the standard ASSETS thresholds (Bricker et al. 2003 ). The fi nal Chl a and DO ratings for infl owing and outfl owing waters are compared to examine changes that have occurred as waters travel through the farm, and thus the impacts on these eutrophication indicators that can be attributed to the operation of the shellfi sh farm.

Modeling of i ndividual g rowth

Feeding and metabolism in bivalve shellfi sh are highly responsive to environmental variables that include temperature, aerial exposure, salinity, DO, current speed, food availability, and food composition, all of which differ both spatially and temporally within near - shore waters (Hawkins and Bayne 1992 ; Gosling 2003 ; see also Chapter 4 ). To achieve inte-grated simulation of these interrelations at ever - decreasing scales required to help opti-mize individual growth in space and time, differential equations are normally used to defi ne and integrate physiological responses as component processes in the prediction of

relevant water properties, shellfi sh production, and biodeposition. The water properties include suspended particulate matter (TPM), phytoplankton, organic detritus, and dissolved substances such as ammonia and DO. FARM uses a modifi ed version of ASSETS (Fig. 1.4 ) to determine the impact of shellfi sh farms on eutrophication (Ferreira et al. 2007a, 2009a ). This impact is estimated by examining the dif-ference in Chl a and DO in waters upstream and downstream of the shellfi sh farm, assum-ing primarily a one directional fl ow, knowing that fi lter - feeding bivalves reduce water column phytoplankton, represented by Chl a as a proxy (e.g., Ryther et al. 1972 , Cloern 1982 , Cohen et al. 1984 ; Shumway et al. 1985 ). This in turn reduces the potential for development of hypoxia, despite the shellfi sh respiratory need for DO. The spatial area and frequency of occurrence components of the Chl a and DO indices and the other three ASSETS symptom indicators are not considered in order to keep the method simple and the results clear.

Although macroalgae are often observed growing on aquaculture structures which may promote growth and thus be considered a con-tributor to eutrophication symptoms, mac-roalgal uptake of nutrients excreted by shellfi sh may actually limit the local impacts of eutro-phication. In some places, aquaculture struc-tures may preclude growth of seagrasses, but at the same time, the bivalve fi ltration results in clearer water which may promote seagrass growth in surrounding areas. Because of the equivocal nature of interactions between aqua-culture operations and macroalgae and sea-grasses, as well as the diffi culty in measuring up - and downstream differences at the local scale, these indicators are not included. The nuisance/toxic bloom indicator is also not included in the FARM model assessment of eutrophication because, though bivalves may limit these populations as for Chl a , the trigger for bloom occurrences is not known

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Figure 1.4 Calculation of the eutrophic condition (state) component of ASSETS. The salinity zoning and weighting procedures are simplifi ed in the FARM model (see text).

9

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10 Shellfi sh Aquaculture and the Environment

relations predict rates of energy absorption, energy expenditure, and excretion. By these means, it is possible to successfully simulate short - term adjustments in feeding, fecal pro-duction, excretion, reproduction, net energy balance, and resulting growth across relevant ranges of natural variability (Hawkins et al. 1999, 2002, unpublished ). An alternative approach, based on principles of dynamic energy budget, does not use allometric rela-tions, instead assuming that feeding is propor-tional to surface area, whereas maintenance costs scale to body volume (Ren and Ross 2005 ; Pouvreau et al. 2006 ; Ren and Schiel 2008 ).

An associated advance has been to recog-nize that common relations between those physiological and developmental processes enable generic model structures that may be calibrated to predict responses across the full environmental range experienced by any given species. Pouvreau et al. (2006) described how a single model successfully simulated growth in Crassostrea gigas reared in one natural and two experimental regimes. Other work has shown how a common set of functional dif-ferential equations within the ShellSIM model ( www.shellsim.com ), run with a minimal set of environmental drivers (temperature, salin-ity, total particulate matter, particulate organic matter [ POM ], and Chl a ), using a single standard set of parameters for each different species, those parameters having been opti-mized per species on the basis of calibrations undertaken to date, has effectively ( ± 20%) simulated dynamic responses in growth to natural environmental changes experienced by C. gigas and Mytilus edulis under various culture practices at eight contrasting sites throughout Europe and Asia (Ferreira et al. 2007b, 2008a ; Hawkins et al., unpublished). Both ShellSIM and the model of Pouvreau et al. (ibid) have been calibrated and validated at single sites for other species, pending further validation elsewhere (Cardoso et al. 2006 ) ( www.shellsim.com ).

individual growth, the individual being treated as an input - output system with energy and mass as state variables (e.g., Ross and Nisbet 1990 ; Brylinski and Sephton 1991 ; Powell et al. 1992 ; Raillard et al. 1993 ; van Haren and Kooijman 1993 ; Barill é et al. 1997 ; Campbell and Newell 1998 ; Grant and Bacher 1998 ; Scholten and Smaal 1999 ; Pouvreau et al. 2000, 2006 ; Solidoro et al. 2000 ; Ren and Ross 2001, 2005 ; Hoffmann et al. 2006 ; Ren and Schiel 2008 ; Spillman et al. 2008 ). Stochastic simulations of shellfi sh growth, for instance by modifying von Bertalanffy ’ s model to account for seasonal effects of water temperature alone on growth and develop-ment (e.g., Meli à and Gatto 2005 ; Griebeler and Seitz 2007 ), do not afford such resolution.

Rapid progress is being made in resolving functional relations between those component processes. For example, responsive adjust-ments in differential effi ciencies and resulting rates of particle retention and ingestion are best related to metabolizable components of the available seston, rather than to the total suspended load; whether measured using chlo-rophyll as a marker for living organic matter, and/or the remaining detritus that is increas-ingly recognized as important in the nutrition of bivalve shellfi sh (Hawkins et al. 2002 ; Navarro et al. 2009 ). Perhaps counterintui-tively, those adjustments are especially relevant over the lower end of observed natural ranges in food availability, when responses are higher per unit change in food abundance, and which have the highest proportional impacts on net energy balance (Hawkins et al. 1999 ; Pascoe et al. 2009 ).

When integrated with empirical allometric relations, including interrelated effects of the other main environmental drivers such as tem-perature and salinity, defi ned responses enable simulation of how the organic composition and energy content of ingested matter change with tidal, seasonal, or spatial differences in food availability and composition. Dependent

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Role of shellfi sh farms in the ecosystem 11

1981 ; Cloern 1982 ; Heasman et al. 1998 ), and in the process reduce primary and secondary symptoms of eutrophication (Ferreira et al. 2009a ; see also Chapters 7 and 8 ). Second, sedimentation of shellfi sh feces to the bottom may lead to hypoxic and sulfi dic conditions, with negative consequences for benthic com-munities and processes (Grant et al. 2005 ); pseudofeces, however, may break up too quickly due to water currents to allow deposi-tion on the bottom (Giles and Pilditch 2004 ). Third, nitrogen excreted from shellfi sh or regenerated from fecal deposits may stimulate phytoplankton production, for potential recy-cling and benefi t to shellfi sh (Dame et al. 1991 ; Newell 2004 ).

To simulate these interrelations, farm - scale models (Fig. 1.5 ) typically address hydrody-namics, biogeochemical processes, shellfi sh growth, and population dynamics. Translation to impacts depends in part on advection, dispersion, and/or settlement. Most com-monly, the approach taken at the farm scale is to model relevant water properties such as salinity, temperature, Chl a and detrital organic material to drive shellfi sh growth for assess-ment of production, and disposal of dissolved materials such as ammonia and DO, and

Integration and a nalysis

The system and the f arm

The assessment of the role of aquaculture in coastal ecosystems should be based on a holis-tic defi nition of sustainable carrying capacity, integrating physical, production, ecological and social elements as suggested by Inglis et al. (2000) and McKindsey et al. (2006) . A corollary of this is that carrying capacity should fi rst be determined at the system scale, prior to scaling down. This allows for an appropriate application of marine spatial plan-ning (Buck et al. 2004 ), by establishing which zones are available for shellfi sh cultivation, within the context of multiple water uses of interest to stakeholders within the coastal region.

Farm - s cale m odels

To model growth and environmental interac-tions at the farm scale, a number of key inter-relations need to be addressed (Dowd 1997 ; Prins et al. 1998 ).

First, suspension - feeding shellfi sh may deplete the water of seston, with associated limitation of shellfi sh growth (Incze et al.

Figure 1.5 Schematic diagram of the FARM model.

Farm length

Width

Shellfish

Chl a

POM

Chl a

POM

Biodeposition

Current Current

Depth 1 2 n-1

Sections

n

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12 Shellfi sh Aquaculture and the Environment

user - defi ned seeding and harvesting regimes, plus mortality (e.g., Bacher et al. 2003 ; Aure et al. 2007 ; Ferreira et al. 2007a ; Duarte et al. 2008 ; Spillman et al. 2008 ). Individual - based confi gurations may also be used to resolve effects of genetic variation and/or different stressors on population dynamics (Hoffmann et al. 2006 ; Morales et al. 2006 ).

Just as for models of individual shellfi sh growth, generic approaches to the modeling of culture practice at the farm scale are now established. For example, the FARM model (Ferreira et al. 2007a ) has been validated for four shellfi sh species reared using bottom (blue mussel, Pacifi c oyster, Manila clam), longline (Mediterranean mussel), and pole culture (blue mussel) in fi ve systems throughout the EU

biodeposition of fecal material for evaluation of environmental effects (Fig. 1.6 ). To improve the spatial resolution of different culture layouts, a farm may be defi ned as a series of contiguous sections, each seeded with a pro-portion of fresh cohorts as appropriate. Interactive effects between properties of the water body and cultured shellfi sh can then be simulated as water passes through the farm.

To simulate the biomass production of market - size organisms, each model of shellfi sh growth is integrated in a population dynamics framework based on a standard conservation equation for the number of individuals within weight classes, using growth simulated by each shellfi sh model to calculate transitions of indi-viduals between classes, taking into account

Figure 1.6 Conceptual scheme of the various components of the FARM model. The model core is within the dotted rectangle, the two screening models are external (Ferreira et al. 2007a ) .

Chl a, dissolved oxygen

e.g., Chl a, POM

e.g

., M

PP, V

MP

e.g., Harvestable biomass, APP

e.g

., F

ood d

eple

tion

e.g

., AS

SE

TS

gra

de,

nutrie

nt tra

din

g

Shellfish growth

Shellfishindividual

growth models

Shellfishproduction

screening model

Optimization offarm activities

Morta

lity

Eutrophicationassessment

screening model

Shellfishpopulation

dynamics model

Physics andbiogeochemistry

models

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Role of shellfi sh farms in the ecosystem 13

to production of the marketable cohort ( C m ), are food supply and stocking density ( D ). The fi rst term is conditioned by current velocity ( V ) and food concentration ( F ) (Eq. 1.2 ).

C f V F Dm = ( , , ) (1.2)

An increase in seeding density results in a stan-dard Malthusian curve of diminishing returns as seen in the total physical product ( TPP ), that is, the potential harvestable biomass.

Harmful Algal Bloom s ( HAB s)

Shellfi sh farms can be affected by externalities associated with climatic (see Chapter 17 ), oceanographic, and anthropogenic effects. Perhaps the most important infl uence, due to its regularity and negative consequences for farm profi tability and human health, is the worldwide increase in HABs, (Shumway 1990 ; Hallegraeff 1993 ) as shown in Figure 1.7 for Chinese coastal waters. While this increase may not be as dramatic in the United States or Europe as in Southeast Asia, one of the major concerns is that some HAB events occur natu-rally, are not yet well understood, and cannot be predicted. This is the case in both the north-east and northwest coasts of the United States, the northeast Atlantic, and the Irish Sea, as well as the Benguela upwelling, and South and East China Seas.

(Ferreira et al. 2009a ). A signifi cant benefi t of this modular approach is the capacity to inte-grate further with models that assess farm - related impacts on water quality, nutrient cycling, and benthic processes, as well as mar-ginal analyses of farm production potential and profi t maximization, while assessing potential credits (see Chapter 8 in this book) for carbon and nitrogen trading (Dowd 2005 ; Grant et al. 2005 ; Ferreira et al. 2007a, 2009a ).

Many of the examples discussed in the fol-lowing sections are based on applications of the FARM model, illustrating a means of quantitatively evaluating ecosystem goods and services which result from shellfi sh farming in coastal environments.

Ecosystem g oods: b iomass p roduction

Limits to p roduction

The m arketable c ohort

From a management perspective, the simula-tion of production in shellfi sh aquaculture cannot be addressed solely on the basis of biomass yield, for instance by determining the total carbon produced within a farm. Bacher et al. (1998) defi ned carrying capacity for shellfi sh culture as the standing stock at which the annual production of the marketable cohort is maximized. This defi nition, termed the exploitation carrying capacity by Smaal et al. (1998) , does not include environmental effects (ecological carrying capacity) and social concerns. Nevertheless, it emphasizes the pro-duction of market - sized animals within a spe-cifi c time frame, and encapsulates concepts such as food depletion (which would lead to smaller animals) and negative local environ-mental effects (e.g., oxygen depletion), both of which limit production.

Solely from the viewpoint of production carrying capacity, considering identical salinity and temperature patterns, the main constraints

Figure 1.7 Number of red tide occurrences along the Chinese coast (Xiao et al. 2007 ) .

250

200

150

100

50

0

Num

ber

1950 1960 1970 1980 1990

Bohai Sea

Yellow Sea

East China Sea

South China Sea

Total

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14 Shellfi sh Aquaculture and the Environment

curves, by means of the application of mar-ginal analysis (Jolly and Clonts 1993 ),

Y f x x x xn= ( )1 2 3,| , , , (1.3)

where

Y = output of harvestable shellfi sh; x 1 = initial stocking density of seed, consid-

ered the only variable input; and x 2 – x n = other inputs, considered to be held

constant.

Measured production data or simulation models may be used to calculate the average physical product ( APP ) (Eq. 1.4 ):

APPTPP

xx

11

= (1.4)

The fi rst - order derivative of the production function provides the marginal physical product ( MPP ). For constant input unit cost P x and output unit price P y , profi t will be max-imized when the value of the marginal product ( VMP ) equals P x , VMP may be defi ned as

HABs that co - occur with cyanobacteria in the Baltic have been associated with land - originated nutrient loading, but elsewhere, offshore events (i.e., those associated with upwelling relaxation) are a major issue for farm production. Detection is based on regular phytoplankton monitoring programs and in some cases operational modeling, and pro-vides shellfi sh farmers with early warning only on the scale of days, and in few areas. Due to the serious consequences for human health from ingestion of contaminated shellfi sh, harvest interdiction for prolonged periods (weeks to months) has serious consequences for bivalve production, including direct loss of sales, and lower revenue when harvesting bans are lifted, due to potential changes in the con-dition (tissue weight/total fresh weight) of the animals.

Profi t o ptimization

The Cobb – Douglas production function (TPP curve) shown in Figure 1.8 (e.g., McCausland et al. 2006 ) may be represented by Equation 1.3 , and used to calculate a further two derived

Figure 1.8 Example application of marginal analysis using constant forcing (see text for explanation).

140

120

100

80

60

40

20

0

TP

P (

t T

FW

)A

PP

and M

PP

(no u

nits

)

3.0

2.5

2.0

1.5

1.0

0.5

0.0

–0.5

–1.00 50 100 150

Seed (t TFW)

Optimization

VMP = MPP.Po

VMP = Pi

For Pi = 15% Po, MPP = 0.15

Stage I Stage II Stage III

90 t

MPP

APP

TPP

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Role of shellfi sh farms in the ecosystem 15

seed cost since there is no biomass multiple (Ferreira et al. 2009a ).

Real - w orld a pplications

Table 1.2 presents reported harvest yields and simulation results for fi ve different European shellfi sh farms (Ferreira et al. 2009a ), which cultivate (in monoculture) the four major species commercially produced in Europe: blue and Mediterranean mussels, Pacifi c oyster, and Manila clam. These farms, which range from western Scotland (Loch Creran) to the south-ern coast of Portugal (Ria Formosa), were studied in the ECASA project (Borja et al. 2009 ; www.ecasa.org.uk ) and represent a range of culture types and habitats including pelagic and benthic deployments in intertidal and subtidal locations within coastal bays and offshore sites.

The drivers for the FARM model were obtained from measured data, outputs of system - scale models, or a combination of both. The results (given in total fresh weight) show good agreement with reported annual production, with deviations ranging from − 16% to + 22%.

A profi t maximization scenario was tested for each farm (Table 1.3 ), based on the

Table 1.2 Application of FARM to different species and culture types in Europe.

System Species Landings (t TFW)

Model results (t TFW) Difference (%)

Loch Creran, Scotland Crassostrea gigas (Pacifi c oyster) 155 1 134.4 − 13.3

Pertuis Breton, France Mytilus edulis (blue mussel) 2304 2322 + 0.78

Bay of Piran, Slovenia Mytilus galloprovincialis (Mediterranean mussel)

200 244.6 + 22.3

Chioggia, Italy Mytilus galloprovincialis (Mediterranean mussel)

660 557.1 − 15.6

Ria Formosa, Portugal Ruditapes philippinarum (Manila clam)

104 1 119.3 + 14.7

Source : Adapted from Ferreira et al. (2009a) .

VMP MPP .= ⋅ =P Py x (1.5)

A shellfi sh farmer should clearly aim to culti-vate at a stocking density somewhere between stage 1 (Fig. 1.8 ), where the fi rst derivative of the TPP curve ≥ 1, and stage 3, where further increases in seed density result in lower har-vests, and therefore income reduction. The ideal point on the production function may be determined by means of the VMP, for which both the MPP and fi nancial data with respect to seed and product must be known. Repeated runs of models such as FARM (e.g., Ferreira et al. 2007a, 2009a ) may be used to generate the outputs required for marginal analysis.

Maximum profi t will only occur at the maximum income point if MPP = 0. Under the (reasonable) assumption that P y > 0, MPP will only be zero if P x is zero, that is, if the seed is obtained at no cost. This can occur, as pres-ently observed in the distribution of mussel seed in Ireland, which has the twin effects of (1) reducing the incentive for farmers practic-ing bottom culture to distribute seed evenly and reduce mortality (Ferreira et al. 2007b ) and (2) encouraging overexploitation, result-ing in lower APP. Frequently, the APP value approaches unity, the profi t resulting only from the differential between sale price and

1 Production data for this system were obtained using the EcoWin2000 ecological model.

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16 Shellfi sh Aquaculture and the Environment

Table 1.3 Comparison of standard model and profi t maximization scenarios for the fi ve study sites.

Farm location

Loch Creran Pertuis Breton Bay of Piran Chioggia Ria Formosa

Species Pacifi c oyster Blue mussel Mediterranean Mussel Manila clam Culture type Trestles Longlines Longlines Longlines Bottom Farm area (ha) 16.5 200.0 1.8 200.0 11.4 Cultivation period (days) 730 415 490 308 180 Present setup Seed (t) 41.2 664.0 43.1 660.0 15.3 TPP (t) 134.4 2322.0 244.6 557.1 119.3 TPP (t ha − 1 ) 8.1 11.6 135.9 2.8 10.5 APP 3.3 4.1 5.7 0.8 7.8 Harvest profi t (k 1 ) 630.7 3445.0 184.2 131.1 1,177.0 Harvest income (k 1 year − 1 ) 335.9 3076.7 154.9 429.1 2,418.2 Profi t/income (annualized) 0.94 0.98 0.89 0.36 0.99 Profi t maximization Seed (t) 322.2 1000.0 45.5 396.0 340.8 TPP (t) 440.2 3413.0 247.2 405.8 909.9 TPP (t ha − 1 ) 26.7 17.1 137.3 2.0 79.8 APP 1.4 3.4 5.4 1.0 2.7 Harvest profi t (k 1 ) 1879.0 4356.0 185.1 125.2 8,758.0 Harvest income (k 1 year − 1 ) 1100.5 3902.0 156.5 312.6 18,450.5 Profi t/income (annualized) 0.85 0.98 0.88 0.47 0.96 Profi t ratio (scenario/standard) 3.0 1.3 1.0 1.0 7.4

Source : Adapted from Ferreira et al. (2009a) .

marginal analysis approach described above. Three of the farms can potentially increase production to improve their profi ts, the mussel farm in Slovenia (Piran) appears to be working at optimal capacity, and the Chioggia farm in the Venice area is making less than optimum profi t since it incurs excessive production costs for the seed density applied, with respect to cost - benefi t optimization.

In all the farms except Chioggia, seed is purchased at a very low (in some cases insig-nifi cant) cost. The annualized profi t : income ratio hardly changes, though the profi t itself increases signifi cantly, particularly for Loch Creran and Ria Formosa. In Chioggia, the profi t is practically identical, although the seed tonnage is reduced by 40%.

The only fi nancial variables in this analysis are the cost of seed and price of product. Other marginal costs of shellfi sh farming can be

included in this approach by, for example, increasing the seed cost as a proxy for vari-ables such as labor and fuel (Ferreira et al. 2007a ).

Changes to fi xed costs such as lease charges do not infl uence the decision of a producer on optimal use of the variable input since this is based on the comparison of values of mar-ginal product and marginal input. Multiple input and output variables may also be con-sidered using marginal analysis, or alternative methods may be applied (e.g., Sharma et al. 1999 ).

Production enhancement will be possible through a reduction in food depletion, an inevitable consequence of density increases. One of the ways to achieve this and simultane-ously generate signifi cant positive externalities from an environmental perspective is through the use of IMTA.

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Role of shellfi sh farms in the ecosystem 17

Production of the marketable cohort, in this case individuals with a total fresh weight (TFW) > 50 g, increases by two orders of mag-nitude in IMTA, and the biomass multiple (APP) increases by one order of magnitude. There is an order of magnitude increase in annualized income from shellfi sh alone, to which revenue from the sale of fi nfi sh must be added.

This analysis can be extended to include externalities, discussed in the section below on ecosystem services.

Ecosystem s ervices: e nvironmental q uality

Biodeposition, c onservation, and b iodiversity

The negative externalities of shellfi sh aquacul-ture are usually reported as (1) biodeposition; (2) competition with native (wild) species. Although some of these aspects, particularly those related to conservation of wild species (see Chapters 12 and 14 in this book), are best dealt with at the system scale, some brief con-siderations may be made on farm - scale effects.

Production e nhancement u sing m ultiple s pecies

Zen and the a rt of p olyculture

A number of authors (Neori et al. 2004 ; Reid et al. 2007 ) have reviewed the benefi ts of IMTA. From the point of view of production alone, the use of particulate organic waste from fi nfi sh culture by fi lter - feeders, and of dissolved waste from both fi nfi sh and fi lter - feeders by macroalgae, may provide signifi cant yield improvements.

Table 1.4 shows FARM model results for a 3.2 - ha farm in Sanggou Bay, northeast China (Fig. 1.1 ), for oysters (ShellSIM individual C. gigas growth model) in monoculture and in combined culture with fi nfi sh. Oyster density is 210 ind. m − 2 , a total of about 6.7 × 10 6 animals; for the IMTA scenario 15 fi sh cages are distributed equally throughout the farm, each with 1000 fi sh. The oysters are able to use both the organic waste from the fi sh faeces and surplus fi sh food, and the downstream sections of the farm, which would in monocul-ture be subject to food depletion, show signifi -cantly enhanced production.

Table 1.4 Oyster monoculture and IMTA scenarios in Sanggou Bay.

Scenario A Scenario B

Description Oysters in monoculture all sections

Oyster and fi sh IMTA all sections

People TPP (t TFW) 7.5 219.7 APP 0.22 6.54

Planet Chlorophyll a (P 90 ) 9.4 → 6.2 9.4 → 5.9 N removal (kg year − 1 ) 356 2468 Population equivalents (PEQ) 108 748 Organic detritus removal (kg C year − 1 ) 7816 39,973 ASSETS 44 44

Profi t Income (shellfi sh; k 1 year − 1 ) 22.9 668.4 Gross profi t (aquaculture; k 1 ) 4.0 1065 + 15 1 = 1080

1 Income due to fi nfi sh culture.

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18 Shellfi sh Aquaculture and the Environment

balance that a shellfi sh farm by defi nition reduces POM, converting it into harvestable live biomass. Problems will only occur through the differential settling speed of biodeposits, which form larger aggregates than the source particles.

Table 1.5 shows a modeling analysis of biodeposition for oyster longline monoculture carried out by means of the FARM model. No vertical turbulence factor is applied, which would act to reduce sedimentation, restricting it to periods of low current speed, and no sedi-ment erosion or diagenesis is considered. All sedimented material is considered to remain under the farm area, the worst possible sce-nario both in terms of accretion and organic enrichment.

The left column in Table 1.5 shows results for sedimentation over the culture period for all POM (algae and detritus) transported across an unstocked farm. The middle column adds the biodeposits of cultivated animals to the POM in the water column. The net effect of adding cultivated shellfi sh is to slightly reduce sediment organic enrichment and accre-tion rate, although the settling speed used for the biodeposits is at the low end of the range reported by Weise et al. (2009) , which for the blue mussel M. edulis varies between 0.1 and 1.8 cm s − 1 .

The right column shows biodeposit produc-tion by the cultivated oysters. Even assuming

Biodeposition

Biodeposition of faecal material from shellfi sh farms may lead to changes in bottom sediment composition (Chapter 10 in this book) through the increase of organic material (the equivalent of sediment eutrophication) with secondary symptoms of hypoxia or anoxia, resulting in changes to benthic communities. It is widely recognized that effects are much less extreme than for fi nfi sh aquaculture, due to the absence of artifi cial feed (e.g., Giles et al. 2009 ; Weise et al. 2009 ), and stem from poor regulation (e.g., inappropriate siting with respect to current speed) and/or poor culture practice (e.g., excessive stocking density).

Few effects are reported for bottom culture, where excessive biodeposits would be expected to have a direct effect on the survival of the farm itself, and the ecosystem engineering capabilities of mussels and oysters may enhance epifaunal diversity (Commito et al. 2008 ; see Chapter 9 in this book). Under appropriate conditions for suspended culture, such as the increasing use of offshore sites, few effects on native macrobenthos can be observed (Dumbauld et al. 2009 ; Fabi et al. 2009 ).

Bivalve fi lter feeding is a net removal of particulate organic material that naturally exists in the water column. Therefore, organic enrichment of the environment will at worst be localized since it is clear from a simple mass

Table 1.5 Sedimentation associated with oyster monoculture in Sanggou Bay (sedimentation through an empty farm, fully stocked farm, and shellfi sh biodeposition).

Parameter Empty farm Stocked farm Biodeposit production

Total (t POC) 138.01 136.62 11.88 Annualized (t POC year − 1 ) 83.96 83.11 7.23 Annualized per area (kg POC m − 2 year − 1 ) 2.62 2.6 0.23 Sediment organic enrichment ( Δ % POC year − 1 ) 2.02 2 0.17 Total (t POM) 363.19 359.52 31.26 Annualized (t POM year − 1 ) 220.94 218.71 19.02 Annualized per area (kg POM m − 2 year − 1 ) 6.9 6.83 0.59 Sediment accretion (mm year − 1 ) 2.66 2.63 0.23

POC, particulate organic carbon.

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Role of shellfi sh farms in the ecosystem 19

Wild s pecies

Finally, a brief note should be made about interactions with native species. While resource partitioning effects are best examined at the ecosystem level (Sequeira et al. 2008 ), GIS - based marine spatial planning is appropriate for analysis of habitat preservation. Models such as FARM incorporate algorithms for analysis of biodeposition effects, and for reducing the food supply to cultivated shellfi sh as a function both of the natural distribution of benthic wild species in the farm area and their characteristic fi ltration rates. Simulations for intertidal trestle culture of C. gigas in Dungarvan Harbour (Ferreira et al. 2009b ) showed no signifi cant effects of wild species fi ltration on oyster growth. Nevertheless, modeling of such effects is important for:

1. Improving the accuracy of farm - scale models by partitioning the available resource

2. Determining the baseline food require-ments for natural benthic populations prior to licensing shellfi sh farms

3. Establishing an upper limit to stocking to help ensure adequate food supply and habitat requirements for wild species.

Integrated c atchment m anagement

A shellfi sh farm, like any other assemblage of fi lter - feeders, removes phytoplankton and organic detritus from the water column (Chapter 5 in this book). In doing so, it pro-vides a key ecosystem service by reducing primary symptoms of eutrophication (Bricker et al. 2003 ; Xiao et al. 2007 ; see also Chapter 7 in this book). This reduction has two major consequences:

1. It alters the underwater light climate, enabling autotrophic production to occur at greater depths, and potentially enabling the recovery of SAV (e.g., Zostera sp.,

all such biodeposits actually fall to the bottom within the same farm, with no removal due to horizontal advection and dispersion, the result-ing accretion rate of 0.23 mm year − 1 is very low, corresponding to a POC enrichment factor of only 0.17% year − 1 .

The deposition of shells below suspended culture structures such as mussel droppers as a consequence of natural mortality is often considered a negative environmental effect of shellfi sh aquaculture. Empty shells have a variety of uses, as illustrated in Figure 1.9 for a hatchery in northeast China. CaCO 3 may additionally be used as a source of minerals in agroindustry (see Chapter 8 in this book). Voluntary improvements to culture practice techniques and better regulation are appropri-ate instruments for mitigation of the potential environmental impact of shell debris from farms.

Figure 1.9 Shells of the Chinese scallop Chlamys farreri used as spat collectors. (Photo courtesy of Dr. Q. F. Gao, Ocean University of China.)

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20 Shellfi sh Aquaculture and the Environment

from phytoplankton depletion, practically identical for monoculture and IMTA (Table 1.4 ).

Eutrophication a ssessment

Although the role of shellfi sh farms in reducing eutrophication symptoms is clear, it is helpful to apply a well - tested methodology such as the ASSETS model to translate quantitative con-centrations into qualitative indices. Because this simplifi ed application focuses on the eutrophication status at the infl ow and outfl ow points, and is therefore a differential or spa-tially comparative approach, the role of the farm with respect to the typical water quality at the site becomes clear.

Additionally, the use of a percentile - based approach increases confi dence in the compari-son since the natural variability of many water quality parameters can make it diffi cult to dis-tinguish a trend signal. A meaningful compari-son among farms becomes possible not only at the production level but also with respect to environmental services. In oligotrophic systems, the ASSETS results may suggest that too much POM is being removed, with respect to the supply required to maintain the natural background of wild species.

Table 1.6 (Ferreira et al. 2009a ) represents the ASSETS color grades (corresponding to the EU Water Framework Directive scale:

Posidonia sp.) and long - lived macroalgae (e.g., Laminaria sp.). SAV provides further ecosystem services as a refuge and nursery for juvenile fi sh, as well as increased sedi-ment stability (Yamamuro et al. 2006 ).

2. It shortens the cycling of suspended organic matter by removing the opportunity for bacterial mineralization, and therefore the onset of secondary eutrophication symp-toms such as hypoxia or anoxia.

This top - down control can be an important complement to land - based nutrient removal. Phytoplankton, whether in fringing ponds or coastal and estuarine water, acts as a catch-ment loading fi lter by removing the causative factors of eutrophication, that is, nitrogen and phosphorus. In turn, shellfi sh farms remove the primary eutrophication symptom (Fig. 1.2 ).

The d uality of f ood d epletion

As cultivation density is increased, the law of diminishing returns leads to lower growth of harvestable animals. In the example shown in Table 1.4 , if all oysters above 5 g TFW were collected, the overall harvest would increase from 7.5 t TFW to 95 t TFW. Clearly, from a production perspective, in this example, oyster monoculture is ineffi cient due to food deple-tion. Oysters, however, perform an environ-mental role of bioremediation, as evidenced

Table 1.6 ASSETS results obtained for the fi ve farms in Europe (Ferreira et al. 2009a ).

System Percentile 90 Chl a (mg L−1) Percentile 10 O2 (mg L−1) ASSETS score

Loch Creran

Pertuis Breton

Bay of Piran

Chioggia

Ria Formosa

0.0

−0.1

−0.1

0.0

−0.1

High

High

High

Good

Good

−0.1

−0.1

−0.2

0.5

−4.3

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Role of shellfi sh farms in the ecosystem 21

A fi nal note from these examples is that, even at sites where the cultivation density is high, the effects on DO concentration are neg-ligible, reinforcing the positive value of envi-ronmental externalities of shellfi sh culture with respect to eutrophication.

Trading and v aluation of n utrient c redits

Table 1.7 shows example mass balance outputs during growth of a single animal, using the ShellSIM model for Pacifi c oyster. Models (i.e., FARM) extrapolate such budgets to the farm scale, as illustrated in Figure 1.10 for Ruditapes sp. in southern Portugal (Ferreira et al. 2009a ).

In the Ria Formosa, clam growth is deter-mined mainly by detrital POM, which is a reasonable expectation for a system with a short water residence time (Nobre et al. 2005 ) in which an autochtonous phytoplankton bloom is unable to develop (Ferreira et al. 2005 ). In this particular case, a rather high mortality is involved, given that the nutrient loading to the area results in eutrophication symptoms expressed as overgrowth of oppor-tunistic seaweeds such as Enteromorpha (Fig. 1.11 ), which can smother benthic macrofauna. Nevertheless, about 60% of the nitrogen removed from the system by fi ltration is

blue — high; green — good; yellow — moderate; orange — poor; red — bad). The score for the symptom in the infl owing water is shown on the left, outfl ow on the right. The concentra-tion changes refl ective of eutrophication symp-toms are shown in blue (better or neutral) or red (worse). In these fi ve real - world examples, the effect of shellfi sh farming on the ASSETS eutrophication score only results in a status change in Piran, Slovenia.

In general terms, using the Chl a and DO categories reported in Bricker et al. (2003) , and the synthesis score for EC derived from them (Ferreira et al. 2007a ), a management proposal might be to site shellfi sh farms in areas where the ASSETS score would fall into the moderate or good category, and where the farm might change that score to good (or the low end of high ).

Licensing of farms in areas where the ASSETS score is already high must be carefully considered since an excessive cultivation density might potentially create undesirable food depletion effects. These would, in any case, refl ect on the production success of the farm since the food scarcity would lead to low harvests. This can be seen for Piran and Chioggia, where the APP is 5.7 and 0.8, respectively (Table 1.3 ), which would be expected from the ASSETS scores shown for the two systems in Table 1.6 .

Table 1.7 Mass balance for modeled individual growth of the Pacifi c oyster Crassostrea gigas .

Variable Value (units: see left column) Nitrogen (gN)

Net biomass production (g TFW) 101.44 1.01 1 Clearance (m 3 ) 23.14 — Phytoplankton removal (mg Chl m − 3 ) 83.61 0.2 Detrital POM removal (g POM m − 3 ) 71.98 4.25 POM removal (g POM m − 3 ) 75.28 4.45 Spawning losses (g POM m − 3 ) 0.17 0.01 POM biodeposition (g POM m − 3 ) 43.35 2.56 Ammonia excretion (mM m − 3 ) 3.82 0.05 Total N removal (model) 1.82 Percentage of net biomass production 1.8%

1 Calculated as 1% of biomass production, after Lindahl et al. ( 2005 ).

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22 Shellfi sh Aquaculture and the Environment

Figure 1.11 Clam culture area in the Ria Formosa. (Courtesy of J. Dil ã o.)

Figure 1.10 Mass balance and nutrient emissions trading for clam aquaculture in Ria Formosa, southern Portugal (Ferreira et al. 2009a ) .

Annual income

Population equivalents8748 PEQ year–1

AlgaeDetritusExcretionFecesMortality

N removal (kg year–1)

Detritus removal321,271 kg C year–1

Phytoplankton removal3457 kg C year–1

Mass balance

–538–49975

14221405

100

–28867

Shellf

ish F

iltra

tion

Shellfish farming:Nutrient treatment:

Total income: 2680.6 k€ year–1

2418.2 k€ year–1

262.4 k€ year–1

Assets

Chl aO2

Score

Parameters

Density of 90 clams m–2

180-day cultivation period66% mortality

3.3 kg N year–1 PEQ

retained by the clams; a proportion of these animals is of harvestable size, and therefore is physically taken from the farm.

It is possible to estimate the environmental value of this service by comparing it with the

comparable cost of land - based treatment; in this Ria Formosa farm, shellfi sh fi ltration pro-vides a gross removal of about 325 t C year − 1 , of which about 1% is phytoplankton. This equates to the emissions of 8748 population

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Role of shellfi sh farms in the ecosystem 23

equivalents ( PEQ ) and a net annual nitrogen removal of 29 t year − 1 . Based on substitution costs, this ecosystem service is valued at 0.26 M 1 year − 1 , about 10% of the direct income from shellfi sh culture.

Nitrogen credit trading at the watershed scale (e.g., USEPA 2004 ) is now a reality in parts of the United States. In Connecticut, the Nitrogen Credit Exchange ( NCE ) has been applied since 2002 for improved management of Long Island Sound, with over US$30 million in economic activity in the fi rst 4 years of trading (Stacey, pers. comm.). The dollar value per credit has increased from $1.65 (2002) to $4.36 (2007), an annualized growth of 33%, substantially outperforming both the Dow Jones and the NASDAQ indices.

Nitrogen credit trading has more local appeal than carbon emissions trading since the cause and effect may be observed at the water-shed scale. Shellfi sh farmers have an opportu-nity to participate in the nitrogen trading market through the sale of credits to other stakeholders, such as agriculture.

This may be of particular social relevance in remote areas of Europe, the United States, and Canada, where agriculture is required by law to reduce the application of fertilizer, but may become economically uncompetitive, leading to desertifi cation of rural areas (Ferreira et al. 2007a – d ).

Caveats

The present analysis with respect to environ-mental quality, both in terms of shellfi sh prod-ucts and the water body in which they are farmed, does not address issues such as disease, microbiological contamination, xenobiotics, or bioaccumulation. In general terms, for this type of remediation model to work together with viable market production, a careful control of other types of pollutants, frequently discharged to coastal waters concurrently with nitrogen and phosphorus, must be taken into account.

Benefi ts of m ultitrophic f arming

Apart from production enhancement, the additional environmental benefi ts of IMTA are illustrated in Table 1.4 for Sanggou Bay under the “ Planet ” section. For monoculture, the annualized net nitrogen removal is over 350 kg for one farm alone, corresponding to the emis-sions of 108 PEQ.

Shellfi sh fi ltration in oyster monoculture for this farm in China provides a gross removal of about 11 t C year − 1 , of which about 30% is phytoplankton, corresponding to a net nitro-gen removal valued at 3.2 k 1 year − 1 , about 15% of the direct income from shellfi sh culture.

For IMTA, the total nitrogen removal increases sevenfold, to about 2500 kg N year − 1 , that is, a positive externality valued at 22.4 k 1 year − 1 . However, since the addition of fi sh cages provides a signifi cant source of food to the shellfi sh, derived from uneaten fi sh ration and fecal matter, the direct value of goods (i.e., harvestable shellfi sh) produced is about 30 times greater than the nitrogen removal value. In this example, the shellfi sh are reducing the negative externalities of fi nfi sh aquaculture, which would otherwise represent an environ-mental cost manifested through anoxic sedi-ment conditions and mortality of benthic organisms beneath the cage areas.

A sensitivity analysis performed in FARM is given in Table 1.8 , considering different par-ticle diameters (and thus settling speeds) for biodeposits. The analysis was carried out only for IMTA since the harvest yield in monocul-ture is already very low and does not change much with increasing rates of biodeposition.

As before, this analysis considers a worst - case scenario, with no vertical turbulence (which acts to reduce particle sinking) and no sediment erosion or mineralization. Although the calculation algorithm is precautionary, the trend, as expected, is for an exponential decrease in production as the biodeposit par-ticle size increases. This reduction refl ects an inability to use biodeposits before they sink

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24 Shellfi sh Aquaculture and the Environment

of shellfi sh farming in the marine environment at scales of (1) the system, (2) economic blocks, and (3) the world as a whole. Examples for each of these will be briefl y discussed.

At the system scale, the determination of overall production may be obtained through landings data, which in effect corresponds to integrating the harvest declared by each indi-vidual farm.

This can also be carried out using system scale models (e.g., Ferreira et al. 2008b ), or by the application of models such as FARM to a subset of typical farms. In situations where shellfi sh farming was once an important activ-ity, it may be useful to repopulate those systems with virtual shellfi sh farms, back - calculating densities and areas from historical data.

In Chesapeake Bay, this was done by means of an ecosystem - scale model (Ferreira et al. 2007d ), where production in historical oyster bars (Fig. 1.12 ) was simulated, using C. gigas as a proxy for Crassostrea virginica .

Over the simulation period, a harvestable biomass of 15.8 × 10 6 bushels was obtained, with a combined effect on environmental quality resulting in a reduction in Chl a 90th percentile of about 30%, from 16 to 11 μ g L − 1 , and a net removal of 26,600 t N year − 1 , a popu-lation equivalent of 8 × 10 6 .

The fi ve EU farms simulated in Ferreira et al. (2009a) collectively represent the main species and culture practices in Europe, which allowed an indicative budget calculation for

below the farm area, and leads to an increase of sediment effects. The natural accretion rate for the same drivers without any aquaculture, considering a POM particle diameter of 15.6 μ m, is 2.66 mm year − 1 , and the sediment organic enrichment is 1.66 Δ % POC year − 1 . The inclusion of the effects of biodeposit resus-pension and diagenesis in the simulation may result in a reduction of over 60% in accretion rate (Giles et al. 2009 ), although the erosion component will result in a more widely dis-persed farm biodeposit footprint.

IMTA set out in vertical layers, as occurs in Sanggou Bay, can optimize particle use, taking advantage of oyster droppers which continue well below the fi nfi sh cages, thus profi ting both from a vertical food supply from the fi sh waste and a horizontal one due to advective transport of algae and detrital matter.

Maximization of environmental benefi ts of IMTA must therefore consider a combination of appropriate species (ideally including mac-roalgae for dealing with dissolved waste), den-sities and positioning, in order to progress toward integrated systems with very low nutri-ent emissions.

Scaling

Tools applied to assess the role of shellfi sh farms in the provision of ecosystem goods and services can help to understand the global role

Table 1.8 Sensitivity analysis: IMTA production, nitrogen removal, and biodeposition.

Biodeposit diameter (mm)

Harvestable biomass (t TFW)

Nitrogen removal (kg year − 1 )

Total income (shellfi sh sale + nutrient removal) (k 1 year − 1 )

Sediment accretion (mm year − 1 )

Sediment organic enrichment ( Δ % POC year − 1 )

0.0186 187.8 2188 590.8 5.64 4.29 0.0221 144.7 1851 456.9 6.60 5.02 0.0263 93.0 1473 296.2 7.70 5.85 0.0312 45.6 1091 148.6 8.89 6.75 0.0372 19.4 772 65.9 10.10 7.67 0.0442 10.6 556 37.4 11.25 8.55

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Role of shellfi sh farms in the ecosystem 25

Similar estimates may be carried out based on the worldwide reported shellfi sh aquacul-ture landings (FAO 2009 ), modeling results of nitrogen removal for a typical range of culti-vated species.

Figure 1.13 shows some results from this analysis: the present consumption of shellfi sh corresponds to a per capita equivalent of one mussel per day, and molluscan aquaculture removes the equivalent of 3% of the waste nitrogen produced by the population of the world, a net uptake of slightly over 660,000 t year − 1 .

This uptake, which takes place in the most sensitive area of the world ocean, that is, the

European shellfi sh aquaculture. A total pro-duction of 1051 × 10 3 t year − 1 (FAO 1999 ) was estimated for the major cultivated bivalves. Of these, 70% are mussels (54% blue mussel and 16% Mediterranean mussel), 23% are oysters, and 7% are clams. Production and nutrient removal data were used to calculate the role of EU shellfi sh farms in removing nutrients, which corresponds to a removal of over 55,000 tons of nitrogen per year, that is, a population equivalent of 17 million people, or the population of the Netherlands. The substitution value for land - based nutrient removal is estimated to be 0.4 billion 1 y − 1 .

Figure 1.12 Chesapeake Bay historical oyster bars (only Maryland areas shown).

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26 Shellfi sh Aquaculture and the Environment

models applied herein. As shellfi sh farming develops both in semi - enclosed systems and offshore, and as IMTA becomes a reality for many coastal farmers, the importance of such models to assess sustainability and trade - offs in the context of marine spatial planning will increase. The models themselves will become increasingly realistic as the research that underpins them sheds new light on the physiol-ogy of cultivated species, interactions within the “ managed ” trophic web, and relevant eco-system processes.

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Since the 1950s, world aquaculture (includ-ing that of molluscan shellfi sh) has been expand-ing almost seven times faster than the world population (FAO 2009 ). This suggests that, together with increasing the world food produc-tion and providing jobs in coastal communities, particularly as wild fi sheries contract, the rele-vance of cultured shellfi sh in mitigating the potential consequences of nutrient loading to the coastal zone will increase.

In summary, shellfi sh farms provide a set of valuable ecosystem goods and services, which may be quantifi ed using tools such as the

Figure 1.13 The role of cultivated shellfi sh in the world nitrogen budget.

3.3 kg N year–1

1.8 kg N year –1

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Role of shellfi sh farms in the ecosystem 27

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