l fr e

me

r

Spot Image, 1999, with shellfish leases and polders represented (GIS in development)

Modelling carrying capacity for aquaculture

Cédric BacherIn collaboration with Aline Gangnery, Karine

Grangeré, Stéphane Pouvreau, Joseph Mazurié, Yoann Thomas, Jon Grant

l fr e

me

rSummary

Carrying capacity : definition in the field of aquaculture

System representation : feedback loop and optimum production

2 real cases: Thau lagoon, Bay des Veys

Conclusion: conditions for operational models

l fr e

me

rConcepts of carrying capacity

(Inglis et al., 2002 ; McKindsey et al., 2006)

Physical CCTotal area of rearing structures that can be accommodated

in the available physical space

Production CCStocking density of bivalves at which harvests are

maximized

Ecological CCStocking density which causes unacceptable ecological

impacts

Social CCDevelopment level of the activity that causes unacceptable

social impacts

l fr e

me

rOperational Tools for decision makers

Empirical tool based on geographic information systems To propose scenarios of reorganization of shellfish areas at

a small scale To help selecting new areas for shellfish culture To evaluate growth as a function of density

Non spatialized (0D) biological model To evaluate production To evaluate growth as a function of density, and maximum

production

Spatialized (2/3D) model coupling physical & biological processes To evaluate local & global carrying capacity To propose scenarios of reorganization of shellfish areas at

a large scale

l fr e

me

rHierarchy of models, showing components at each level and

interactions between levels (Beadman, 2002)

l fr e

me

rThe language of systems – Bellinger (2004)

Reinforcing loop

Balancing loop

l fr e

me

rCausal loop in population dynamics – representation

by Bald et al. (2006)

l fr e

me

rFeedbacks (+ or - ) involved in population dynamics –

Beadman (2002)

l fr e

me

rCarrying capacity – system representation

Growth

Food

Time to grow

+

- Survivals

Seeding

Production

Density

+

--

+-

As a result: counter effects and balancing loop

Simulations show that there is an equilibrium and a maximum of production exists

l fr e

me

rCarrying capacity – additional effects

Growth

Food

Time to grow

+

- Survivals

Seeding

Production

Density

+

--

+

Current velocity

Food import+

-

+

l fr e

me

rCarrying capacity – additional effects

Growth

Food

Time to grow

+

- Survivals

Seeding

Production

Density

+

--

+

Current velocity

Food import+

-

+

Food productio

n

l fr e

me

rScales of aquaculture

Lagoon= 58 km²

Bay = 250 km2

Longlines = 1 km 2

Raft: 0.01 km2

Tracadie Bay

122°25' 122°30' 122°35'37°0'

37°5'

37°10'

l fr e

me

rGeneric models and key parameters

Shellfish production and impact depend on key processes/parameters: food availability (primary

production), renewal of sea water (residence

time) food utilisation (ecophysiology)

Combination of these factors allows to assess and compare the

carrying capacity of several ecosystems using mathematical models – see Bacher et al. (1998), Nunes et al. (2003), …

To define relevant indicators of aquaculture impact and potentiality

Factor effects depend on spatial and culture scales: longlines, rafts, ecosystem

Water residence time

Food producti

on

Food utilisation

Nutrient input

l fr e

me

rResidence time

Simulation of a tracer Box model 2D model

Results Marennes Oléron: 10

d Tracadie Bay: 8 d Upper South Cove:

2.5 d Raft: 0.0025 d Baie des Veys: 1d Lagon Grande

Entrée: 30 d Thau lagoon: 200 d

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Grangeré, Bacher, Lefebvre, this Conference

Struski, 2005. Modélisatioin des flux de matières dans la baie de Marennes-Oléron. PhD Univ. La Rochelle

Dowd, 2005. A bio-physical coastal ecosystem model for assessing environmental effects of marine bivalve aquaculture. Ecological Modelling 183, 323–346

Guyondet T., Koutitonski V., Roy S., 2005. Effects of water renewal estimates on the oyster aquaculture potential of an inshore area. Journal of Marine Systems 58, 35– 51

l fr e

me

rHydrodynamics and transport - simulation of a tracer

Transport simulated during 4 days

Conservative tracer

Example with no wind

Initial condition 1 in the

cultivated area

0 elsewhere

l fr e

me

rHydrodynamics and transport - effect of oysters on a

tracer

Simulation of transport and filtration of a tracer (‘theoretical food’) by oysters: 3 l/hr/ind Stock = 10000

tonnes

2 simulations with/without oyster filtration

Map of the tracer after 10 hours

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

without oyster

with oyster

l fr e

me

r

Soma

Assimilation

Shell

Gonade

Gametes

Maintenance Maintenance

Spawning

1-

Storage

phytoplankton

temperature

temperature

Dynamic Energy Budget model - application to shellfish

Forcing : chlorophyll a, temperature

Model: individual growth, allocation of energy (Kooijman, 2000)

Parameters estimated from independent studies

Objectives: Prediction of individual

tissue mass, shell length, dry mass, gonad mass

Application to population dynamics

Prediction of density effect through food availability

l fr e

me

rEcosystem model – conceptual scheme (adapted from

Grant et al., 2007)Simile scheme

Phytoplankton

Photosynthesis

Mortality

Grazing

Zooplankton

Grazing (phyto)

Mortality

Respiration

Excretion

Nutrient

Excretion

Photosynthesis

Mussel

Ecophysiology (DEB)

Bou

nd

aries

l fr e

me

rEcosystem model – simulation

Simplified ecosystem

Simulations performed for several key parameters: mussel density, exchange coefficient

Simulations of annual growth

Effect of residence time on mussel growth

Effect of mussel density on growth

0.05 0.1 0.15 0.2 0.25 0.3

0.5

1

1.5

2

2.5

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Final dry weight (g)

Mussel density = 0.29 ind/m3

Residence time = 30 d Mussel density = 0. 9 ind/m3

Residence time = 7 d

Exchange Coefficient (day -1 )

Mu

ssel

Den

sit

y (

ind

.m

)- 3

l fr e

me

rWhat we learned

The role of hydrodynamics has been recently emphasized with the help of hydrodynamical models used to derive residence time at several scales

Individual growth performance is proposed as an indicator of system productivity function of basic parameters, using a generic approach

Practical examples follow

l fr e

me

r

A practical example: assessing growth rate as a

function of oyster density in Baie des Veys (Normandy,

France)Individual growth using DEB model

Constant seeding and schematic population dynamics

Interactions between ecosystem and shellfish

l fr e

me

rOGIVE Project – A. Gangnery

To develop operational tools to sustain shellfish culture in

Normandy

Normandy

• 4 main production sites

• Rearing of Pacific oysters & blue mussels

• Annual production27 000 t oysters16 000 t mussels

C herb o urg

G ra nv ille

Avra nc he s

1 0 k m

O y s te r s

M u s s e l s

Po rt-e n-Be ssin M e uva ines

St Va a st

Baie des Veys

Côte Est Cotentin

Côte OuestCotentin

Meuvaines

l fr e

me

rBaie des Veys

Study site for the development of models and indicators

Intertidal area = 37 km2

Cultivated area = 1.6 km2

Oyster standing stockca. 10 000 t

Influence of nutrient inputs

l fr e

me

rConceptual scheme of the biological model

Inorganic & organic Nitrogen Inorganic & organic Silica

Phytoplankton

Bivalves

NO3 NH4 Norg SiOH Siorg

DIATOMS

OYSTERS

Inputs fromthe Channel

Inputs fromthe watershed

ECOPHYSIOLOGYDEB model

DissolutionMinerali-zation

Nitrifi-cation

Mortality

Gro

wth

Gro

wth

Gro

wth M

ort

ali

ty

IndividualgrowthStanding stock

State variables

Watertemperature

Light

l fr e

me

rCalibration of the DEB model in Baie des Veys

100 200 300 400 500 60060

65

70

75

80

85

90

95

100

105

Indi

vidu

al L

engt

h (m

m)

Days100 200 300 400 500 600

0.5

1

1.5

2

2.5

3

Indi

vidu

al D

ry M

ass

(g)

Days

+ Observations Simulation

Forcing variablesFood = chlorophyll aWater temperature

Calibration of the Xk parameter (Ingestion function)

Field measurements

Dry flesh mass (g) Shell length (mm)

l fr e

me

rParameterization of the biological model

• Baie des VeysResidence time = 4.6 days

• Primary production : measurements/biogeochemical model

• Nutrient inputs, T… Average 2002-2003

0

0.5

1

1.5

2

2.5

3

3.5x 10

12

Input WatershedNO3 moles.j-1

Month

6

8

10

12

14

16

18

20

22

Month

Water temperature°C

l fr e

me

rParameterization & simulations with

the biological model

Simulation conditionsSimulations run for 6 years (to get equilibrium of the model)

OystersCultural practices

• Seeding of 1 cohort per year, each 1st of March• Harvest of each cohort at the end of the year n+1

Standing stock• Number of oysters x Individual total mass• Total mass = 0.0745 x Shell length3

• Relation between individual growth & standing stock

• Comparison between simulated variables & observations

l fr e

me

rSimulations with the biological model

1 2 3 4 5 60

1

2

3

4

5

6

7

8

9

10

Years

µg

ch

la.l-1

Diatoms

1 2 3 4 5 60

2000

4000

6000

8000

10000

12000

To

ns

Oyster standing stock

ca. 10 000 tYears

1 2 3 4 5 66.5

7

7.5

8

8.5

9

9.5

10

Mean oyster shell length

cm

Equilibrium obtained in year 3

110 millions individuals / year = Standard simulation

l fr e

me

rGrandcamp

Géfosse P North

P South

Grandcamp

Géfosse P North

P South

J F M A M J J A S O N D J0

5

10

15

20

25

30

35

Standard simulationComparison with in situ records (2002 & 2003)

J F M A M J J A S O N D J0

10

20

30

40

50

60

70

simulationGéfosseGrandcampP NorthP South

J F M A M J J A S O N D J0

1

2

3

4

5

6

J F M A M J J A S O N D J0

5

10

15

20

25

30

µm

ole

s N

.l-1

µg

ch

la.l-1

NO3

Diatoms

µm

ole

s N

.l-1

µm

ole

s N

.l-1

NH4

SiOH

l fr e

me

r

0 100 200 300 400 500 600 700 8006.5

7

7.5

8

8.5

9

9.5

10

Ind

ivid

ual

Sh

ell l

eng

th (

cm)

ObservationsCohort 1 simCohort 3 sim

Days

Individual shell length after 22 months of rearing:9.55 cm for cohort 19.23 cm for cohort 3

Standard simulation – Comparison with recordsIndividual growth

l fr e

me

r

N/10 N Nx2 Nx4 Nx1020

30

40

50

60

70

80

90

Relation between individual growth and standing stock at equilibrium

Total individual mass (g)after 22 months of rearing (cohort 3)

Standing stock (t)at the end of September (cohort 3)

Number of oysters

An increase of N oyster by 2 implies:• a decrease > 20% of total individual mass

• an increase ca. 60% of standing stockDepletion effect by oysters is only partially compensated by

primary production & inputs from the Channel

N/10 N Nx2 Nx4 Nx100

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5x 10

4

Standard simulationActual situation

l fr e

me

rWhat we learned

Quality of the model

Good match between observed & simulated nutrients (except NH4 & SiOH underestimated in the fall)

Seasonality of phytoplankton well simulated but underestimation of blooms intensity

Recommendations

To maintain the duration of rearing cycle, the standing stock in the bay should not be increased

But…

Sensitivity of the model to some parametrisation: residence time, boundary condition (relaxation)

Environmental temporal variability will modify the simulated response curves

l fr e

me

r

Modelling variability of oyster production due to

environmental changes in Thau lagoon (France)

Individual growth using DEB model

Population model

Variable forcing using watershed and ecosystem models

l fr e

me

rShellfish culture in Thau lagoon

Lagoon characteristics: 19 km long, 4.5 km wide Mean depth = 5 m Cultivated area = 20 %Standing stock around 20 000

tonnes Dynamics driven by watershed inputs, temperature and wind

Issues : What are the factors limiting/controlling growth, production

and standing stock

Is the production sensitiveto natural variability of

environmental factors

l fr e

me

r

M A M J J A S O N D J F M A40

50

60

70

80

90

100

110

M A M J J A S O N D J F M A0

2

4

6

8

10

12

14

16

A S O N D J F M A M J J A S O0

5

10

15

A S O N D J F M A M J J A S O40

50

60

70

80

90

100

Freshtissue

mass (g)

Shelllength(mm)

Survey 1

Survey 1

Survey 2

Survey 2

Simulation of individual growth DEB model (Bacher and Gangnery, 2006)

Experimental growth data: fresh tissue mass, shell length measured every month at 4 locations in 2000, in 2001 and for 2 rearing techniques

Forcing : chlorophyll a and temperature measured every 2 weeks at 4 sites during 18 months

l fr e

me

r

Ni= number of oysters of cohort iG=individual growth rate (DEB)r=harvest ratew=individual mass at time t

N1

Time

Mass w

G

G

r

N2

Modelling population dynamics

Forcing : chlorophyll a, temperature (measurements or simulations)

Cohort model (individual based model)

Prediction : number of

oyster per cohort

standing stock,

production

Scale: simulation of cohorts over several years

Ni

l fr e

me

r

0.000.040.080.120.160.20

J F M A M J J A S O N D

Seeding rule

Seeding

Enquiry to determine the seeding timetable: Number of oysters

seeded every day Simulation of daily

cohorts Standing stock =

total mass of cohorts

Enquiry to determine the harvesting rule Individual market

weight Production = total

mass of harvested individuals

l fr e

me

rEcosystem modelling

Forcing : air temperature, wind stress, rain

Model from Chapelle et al. (2000), Plus et al. (2003): 3D Nitrogen cycling Watershed model (Plus et

al., 2005) Hydrodynamics Exchange between

sediment/water

Prediction: Phytoplankton Water temperature, Nutrients

Resolution/scale: simulation over one year, timestep= 1hr, spatial grid = 400 x 400 m

l fr e

me

rResults – Environmental and growth variability

Simulation of the ecosystem dynamics from 1998 until 2001

DEB forcing= average of phytoplankton and temperature over the cultivated area

Simulation and comparison of annual oyster growth for 4 years (1998, 1999, 2000, 2001)

0 100 200 300 4000

1

2

3

4

5

6

0 100 200 300 4000

5

10

15

0 100 200 300 4000

5

10

15

20

25

30

Temperature Chlorophyll a

Individual tissue mass (g)

days

days

days

l fr e

me

r

Jan May SeptJan May SeptJan May SeptJan May Sept

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000 Stock (tons)

Cumulated production (tons)

1998 1999 2000 2001

Results. Standing stock and production variability

Simulation of the ecosystem from 1998 until 2001 for DEB forcing

Seeding and harvesting rules = see population model

Simulation of cohorts over 4 years (spin up of 4 years) – one cohort per day

Computation of standing stock = summation of all cohorts

Computation of cumulated annual production: summation of marketable cohorts

l fr e

me

rWhat we learned

Sensitivity of oyster production to environmental factors through individual growth: Food concentration varies by ca. 10%, Annual growth varies by 30 % Ecosystem mainly influenced by meteorological

forcing (temperature, wind) – seasonal/short term Production varies from 3000 to 8000 tons

l fr e

me

rConclusion

Shellfish Production and Ecosystem Carrying capacity are well defined and understood – more and more abundant literature and recommendations

The number of processes to consider depends on the scale (spatial and temporal)

Some processes and feedback are not well known: Risk assessment (GESAMP 2007)

Variability has several sources and is usually difficult to estimate: Environmental : models, monitoring, remote sensing (bay of Mont Saint

Michel) Inter individual : model and measurements

Conditions for operational tools Accurate physical models Robust ecophysiological model Description of farming practices Monitoring of environmental descriptors Understandable system representation Communication tools and strategy

Testing DEB models: mussels (France, Norway, Canada)

l fr e

me

r

Thank you for your attention

l fr e

me

rLetive estuary, Scotland (data from Karayücel et al.,

1998)

100 200 300 400 500 6002

2.5

3

3.5

4

4.5

5

5.5

6

She

ll L

engt

h (c

m)

Days

Shell length

l fr e

me

rSt-Peters’s Bay, Canada (data from Lauzon-Guay et

al. 2005)

300 400 500 600 700 800 9002.5

3

3.5

4

4.5

5

5.5

6

Sh

ell

Le

ng

th (

cm)

Days

Shell length

l fr e

me

r3. Application to rafts

Raft

rope

Water flow

Objective : to determine how mussel density affect food availability and mussel growth

Review of rafts/longlines scales and characteristics

Simulation of mussel growth with various mussel densities and water exchange (current velocity)

l fr e

me

r3. Dynamic Energy Budget model - raft

characteristics

Site Raft Dimensio

ns (L/W/D)

in m

Mussel rope

density (mussel.

m-1)

Mussel raft

density (mussel.

m-3)

Current velocity (cm.s-1)

Reference

Loch Etive

(Scotland)

11/10/8 340-1680

618-3054

1-20 (mean.

5.2)

Karayücel and Karayücel (1998), Karayücel and Karayücel (1999), Okumus and Stirling (1998)

Loch Kishorn

(Scotland)

27/20/10 359-1590

718-3180

1-18 (mean. 5)

Karayücel and Karayücel (1998), Karayücel and Karayücel (1999)

Ria de Arousa (Spain)

27/20/9-12

350-700 324-648 2 Figueiras et al. (2002), Fuentes et al. (2000), Perez-Camacho (1995), Perez-Camacho et al. (1991)

Saldanha Bay

(South Africa)

15-22/11/6

2554 3947-5203

1.25-7.5 Boyd and Heasman (1998) Heasman et al. (1998)

l fr e

me

r3. Dynamic Energy Budget model – simulation of rafts

Application to Loch Etive case: chlorophyll a, temperature, current velocity

Simulation of mussel annual growth with various ; Mussel density Current velocity

Map of mussel final growth vs mussel density and exchange coefficient compared to a standard simulation (%): mass gain or loss

0 200 400 600 8000

100

200

300

400

500

600

700

800

900

1000

-100

-80

-60

-40

-20

0

20

Mu

ssel

Den

sit

y (

ind

.m

)- 3

Exchange Coefficient (day -1 )

Mussel density = 1000 ind/m3

Exchange coefficient = 393 d-1

Dry Weight Increase (%)