Dynamic model to calculate the carrying capacity for bivalve

growth in a coastal embaymentJoana Ibáñez Solé1, Montserrat Ramón2,3 and Margarita Fernández-Tejedor1

1. Institute for Food and Agricultural Research and Technology (IRTA), Sant Carles de la Ràpita, Spain

2. Institut de Ciències del Mar (CSIC), Barcelona, Spain3. Instituto Español de Oceanografía (IEO), Palma, Spain

Symposium on Integrating New Advances in Mediterranean Oceanography and Marine Biology. Barcelona, 25-29 November 2013

OBJECTIVES

1. To define the different water bodies in Alfacs bay.

2. Modeling the depletion of seston and chlorophyll through a zero-dimensional dynamic model (using the half-saturation coefficient, χk ).

3. Application of the ecophysiological models SFG and DEB.

4. To calculate the carrying capacity of Alfacs bay for bivalve aquaculture.

ALFACS BAY - Characteristics

• Positive estuarine circulation pattern of the water inside the bay.

• Wide range of temperatures.

• Shallow waters.

• Changes in the characteristics of the bay according to whether the irrigation channels are open or closed.

Pycnocline identification

Closed channels

Density = 27.10 kg/m3

T (ºC)

Opened channels

Density = 24.71 kg/m3

SITUATION OF SAMPLING STATIONS INSIDE ALFACS BAY

Main pattern of water circulation inside Alfacs bay. (Camp et al., 1987).

Latitude (º)

Longitude (º)

Density [kg/m3]

Surface (0.5m)

Bottom(range 2.5 – 6m)

0

5

10

15

20

25

30

Canales abiertosdensidad = 27.10 kg/m3

Canales cerradosdensidad = 24.71 kg/m3

T (ºC)

Salinity [PSU]

Surface (0.5m)

Bottom(range 2.5 – 6m)

0

5

10

15

20

25

30

Canales abiertosdensidad = 27.10 kg/m3

Canales cerradosdensidad = 24.71 kg/m3

T (ºC)

Chlorophyll [mg/m3]

Surface (0.5m)

Bottom(range 2.5 – 6m)

0

5

10

15

20

25

30

Canales abiertosdensidad = 27.10 kg/m3

Canales cerradosdensidad = 24.71 kg/m3

T (ºC)

TRANSECTS SAMPLED IN THE BAY

Latitude (º)

Longitude (º)Entrance transect Dock transect Central transect

ENTRANCE TRANSECT Density, σt [kg/m3] Salinity [PSU]

Serramar FaroMitad boca

Chlorophyll [mg/m3] Stability, E [rad2/m]

0

5

10

15

20

25

30

Canales abiertosdensidad = 27.10 kg/m3

Canales cerradosdensidad = 24.71 kg/m3

T (ºC)

DOCK TRANSECTDensity, σt [kg/m3] Salinity [PSU]

Chiringuito Muelle

Chlorophyll [mg/m3] Stability, E [rad2/m]

0

5

10

15

20

25

30

Canales abiertosdensidad = 27.10 kg/m3

Canales cerradosdensidad = 24.71 kg/m3

T (ºC)

CENTRAL TRANSECTDensity, σt [kg/m3] Salinity [PSU]

Trabucador CentralEmisario

Chlorophyll [mg/m3] Stability, E [rad2/m]

0

5

10

15

20

25

30

Canales abiertosdensidad = 27.10 kg/m3

Canales cerradosdensidad = 24.71 kg/m3

T (ºC)

ENTRANCE DOCK CENTRAL

Salinity [PSU]

Chlorophyll [mg/m3]

38.5

36.5

34.5

29.0

38.5

36.5

34.5

29.0

38.5

36.5

34.5

29.0

64

26

12

0

64

26

12

0

64

26

12

0

Modelling depletion at the mussel farm

BeginningPoint A

MiddlePoint B

EndPoint C

Latitude (º)

Longitude (º)

ABC

Area (m2) = 337038.2

Cultured area (m2) = 81274.2

Nº of nurseries = 42

Nº of ropes = 46200

Weight of the ropes [kg] At seedtime: 3.23·105 At collect: 11.6·105

Water transit time [h] Maximum: 2.03 (Camp et al., 1987) Minimum: 1.47

Area (m2) = 469069.0

Cultured area (m2) =86107.2

Nº of nurseries = 45

Nº of ropes = 49500

Weight of the ropes [kg] At seedtime: 3.47·105 At collect: 12.4·105

Water transit time [h] Maximum: 2.56 (Camp et al., 1987) Minimum: 1.86

Sampling points at the mussel farm

2672.5 m

2123.8m

Depletion [mg/dm2]

Seston [mg/dm2]

Depletion [mg/dm2]

Seston [mg/dm2]

Seston [mg/dm2]

Depletion [mg/dm2] End rafts (C)

Beginning rafts (A) Middle rafts (B)

DEPLETION EQUATION: d( ) = 0.7465·

: concentration of available seston

The whole farmDepletion [mg/dm2]

Seston [mg/dm2]

120

150

100

50

0

-50

0 20 40 60 80 100

200

140 160 180 200

0

2

4

6

8

10

12

14

16

18

Invierno Primavera Verano Otoño

Depletion Rate [mg/m3]

De A a B De B a C Total: De A a C

Winter WinterSpring SpringSummer SummerAutumn Autumn

From A to B

Eastern part

[mg/h]

From B to C

Western part

Total: From A to C

Average

[µg/L] CR [L/h]

0

0.5

1

1.5

2

2.5

3

3.5

4

Invierno Primavera Verano Otoño

Datos de campo in situ simuladoDatos de campo

Datos in situ simulados(Galimany et al., 2009)

k

Winter WinterSpring SpringSummer SummerAutumn Autumn

Field data

In situ simulated data

(Galimany et al.)

Half-saturation coefficient

Models DEB and SFG application

• DEB equationsRate of energy ingestion (J/day):

Arrhenius temperature function:

• SFG equations

Ingestion (mg/day):

is the chlorophyll concentration

Standard ingestion function:

k is the half-saturation coefficient

Models DEB and SFG application

• DEB equationsRate of energy ingestion (J/day):

Arrhenius temperature function:

• SFG equations

Ingestion (mg/day):

is the chlorophyll concentration

Standard ingestion function:

k is the half-saturation coefficient

Models DEB and SFG application

• DEB equationsRate of energy ingestion (J/day):

Arrhenius temperature function:

• SFG equations

Ingestion (mg/day):

is the chlorophyll concentration

Standard ingestion function:

k is the half-saturation coefficient

Models DEB and SFG application

• DEB equationsRate of energy ingestion (J/day):

Arrhenius temperature function:

• SFG equations

Ingestion (mg/day):

is the chlorophyll concentration

Standard ingestion function:

k is the half-saturation coefficient

Models DEB and SFG application

• DEB equationsRate of energy ingestion (J/day):

Arrhenius temperature function:

• SFG equations

Ingestion (mg/day):

is the chlorophyll concentration

Standard ingestion function:

k is the half-saturation coefficient

DEB Model application

Winter WinterSpring AutumnSummer Spring Summer Autumn

Winter Spring Summer Autumn Winter Spring Summer Autumn

Field data

In situ simulated data

Chl-a available

Chl-a available

Ch

loro

pyll

(mg

/m2

)C

hlo

rop

yll (m

g/m

2)

Px (J/d

ay)P

x (J/day)

DEB Model application

Winter WinterSpring AutumnSummer Spring Summer Autumn

Winter Spring Summer Autumn Winter Spring Summer Autumn

Field data

In situ simulated data

Chl-a available

Chl-a available

Ch

loro

pyll

(mg

/m2

)C

hlo

rop

yll (m

g/m

2)

Px (J/d

ay)P

x (J/day)

SFG Model application

Winter Spring Summer Autumn Winter Spring Summer Autumn

Winter Spring Summer Autumn Winter Spring Summer Autumn

Field data

In situ simulated dataChl-a available

Chl-a available

Ch

loro

pyll

(mg

/m2

)C

hlo

rop

yll (m

g/m

2)

I/Cm

i (mg

/m3

)I/C

mi (m

g/m

3)

SFG Model application

Winter Spring Summer Autumn Winter Spring Summer Autumn

Winter Spring Summer Autumn Winter Spring Summer Autumn

Field data

In situ simulated dataChl-a available

Chl-a available

Ch

loro

pyll

(mg

/m2

)C

hlo

rop

yll (m

g/m

2)

I/Cm

i (mg

/m3

)I/C

mi (m

g/m

3)

Carrying capacity of Alfacs BayApproximations

• Mussel Mytilus galloprovincialis: The only consumer species.

• Only the food availability (seston/chlorophyll) and temperature are considered as limiting factors. We omitted oxygen concentrations and other characteristics of the bay as limiting factors.

• We considered the circulation of the water inside the bay as unidirectional.

Results

The mussel farm can have 117 rafts similar to the current ones.

The bay, in its whole extension, is able to

accommodate 253 rafts.

Conclusions

• Difficulty for working in shallow depths.

• Chlorophyll spots inside the bay in the central zone, farther from the bay’s mouth.

• Pycnocline variation – opened channels/closed channels.

• DR rate and CR rate are lower during the hottest months of the summer.

• The Χk parameter for Alfacs bay is variable throughout the year due to the wide range of temperatures in the bay water.

• DEB model application in Alfacs bay and to the mussel species Mytilus galloprovincialis has provided satisfying results and also allowed to observe an important dependence between uptake and temperature.

• SFG model is not applicable in Alfacs bay because it does not give a correct dependence between temperature and ingestion. It does not reproduce the observations correctly.

• We were able to calculate a first approximation of the carrying capacity for Alfacs bay. This approximation shows that Alfacs bay is able to accommodate 3 times more rafts than there exist nowadays.

Conclusions

Thank you!

INIA: RTA04-023-Estudio integrado de los factores biológicos y ambientales condicionantes de la producción de mejillón en las bahías del delta del Ebro.

XRAq: Ecofisiologia del musclo en relació a les característiquesambientals de les badies del Delta de l’Ebre.