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Dynamic model to calculate the carrying capacity for bivalve growth in a coastal embayment Joana Ibáñez Solé 1, Montserrat Ramón 2,3 and Margarita Fernández-Tejedor.

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Presentation on theme: "Dynamic model to calculate the carrying capacity for bivalve growth in a coastal embayment Joana Ibáñez Solé 1, Montserrat Ramón 2,3 and Margarita Fernández-Tejedor."— Presentation transcript:

1 Dynamic model to calculate the carrying capacity for bivalve growth in a coastal embayment Joana Ibáñez Solé 1, Montserrat Ramón 2,3 and Margarita Fernández-Tejedor 1 1. Institute for Food and Agricultural Research and Technology (IRTA), Sant Carles de la Ràpita, Spain 2. Institut de Ciències del Mar (CSIC), Barcelona, Spain 3. Instituto Español de Oceanografía (IEO), Palma, Spain Symposium on Integrating New Advances in Mediterranean Oceanography and Marine Biology. Barcelona, November 2013

2 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, χ k ). 3.Application of the ecophysiological models SFG and DEB. 4.To calculate the carrying capacity of Alfacs bay for bivalve aquaculture.

3 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.

4 Pycnocline identification Closed channels Density = kg/m 3 T ( º C) Opened channels Density = kg/m 3

5 SITUATION OF SAMPLING STATIONS INSIDE ALFACS BAY Main pattern of water circulation inside Alfacs bay. (Camp et al., 1987). Latitude ( º ) Longitude ( º )

6 Density [kg/m 3 ] Surface (0.5m) Bottom (range 2.5 – 6m)

7 Salinity [PSU] Surface (0.5m) Bottom (range 2.5 – 6m)

8 Chlorophyll [mg/m 3 ] Surface (0.5m) Bottom (range 2.5 – 6m)

9 TRANSECTS SAMPLED IN THE BAY Latitude ( º ) Longitude ( º ) Entrance transectDock transect Central transect

10 ENTRANCE TRANSECT Density, σ t [kg/m 3 ] Salinity [PSU] SerramarFaro Mitad boca Chlorophyll [mg/m 3 ] Stability, E [rad 2 /m]

11 DOCK TRANSECT Density, σ t [kg/m 3 ] Salinity [PSU] ChiringuitoMuelle Chlorophyll [mg/m 3 ]Stability, E [rad 2 /m]

12 CENTRAL TRANSECT Density, σ t [kg/m 3 ] Salinity [PSU] TrabucadorCentral Emisario Chlorophyll [mg/m 3 ] Stability, E [rad 2 /m]

13 ENTRANCEDOCKCENTRAL Salinity [PSU] Chlorophyll [mg/m 3 ]

14 Modelling depletion at the mussel farm Beginning Point A Middle Point B End Point C Latitude ( º ) Longitude ( º ) A BC

15 Area (m 2 ) = Cultured area (m 2 ) = Nº of nurseries = 42 Nº of ropes = Weight of the ropes [kg] At seedtime: 3.23·10 5 At collect: 11.6·10 5 Water transit time [h] Maximum: 2.03 (Camp et al., 1987) Minimum: 1.47 Area (m 2 ) = Cultured area (m 2 ) = Nº of nurseries = 45 Nº of ropes = Weight of the ropes [kg] At seedtime: 3.47·10 5 At collect: 12.4·10 5 Water transit time [h] Maximum: 2.56 (Camp et al., 1987) Minimum: 1.86 Sampling points at the mussel farm m m

16 Depletion [mg/dm 2 ] Seston [mg/dm 2 ] Depletion [mg/dm 2 ] Seston [mg/dm 2 ] Depletion [mg/dm 2 ] End rafts (C) Beginning rafts (A) Middle rafts (B)

17 DEPLETION EQUATION: d( ) = · : concentration of available seston The whole farm Depletion [mg/dm 2 ] Seston [mg/dm 2 ]

18 Winter Spring Summer Autumn From A to B Eastern part [mg/h] From B to C Western part Total: From A to C Average

19 [µg/L] CR [L/h] Datos de campo Datos in situ simulados (Galimany et al., 2009) Winter Spring Summer Autumn Field data In situ simulated data (Galimany et al.) Half-saturation coefficient

20 Models DEB and SFG application DEB equations Rate 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

21 Models DEB and SFG application DEB equations Rate 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

22 Models DEB and SFG application DEB equations Rate 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

23 Models DEB and SFG application DEB equations Rate 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

24 Models DEB and SFG application DEB equations Rate 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

25 DEB Model application Winter SpringAutumnSummerSpringSummerAutumn WinterSpringSummerAutumnWinterSpringSummerAutumn Field data In situ simulated data Chl-a available Chloropyll (mg/m 2 ) P x (J/day)

26 DEB Model application Winter SpringAutumnSummerSpringSummerAutumn WinterSpringSummerAutumnWinterSpringSummerAutumn Field data In situ simulated data Chl-a available Chloropyll (mg/m 2 ) P x (J/day)

27 SFG Model application WinterSpringSummerAutumnWinterSpringSummerAutumn WinterSpringSummerAutumnWinterSpringSummerAutumn Field data In situ simulated data Chl-a available Chloropyll (mg/m 2 ) I/C mi (mg/m 3 )

28 SFG Model application WinterSpringSummerAutumnWinterSpringSummerAutumn WinterSpringSummerAutumnWinterSpringSummerAutumn Field data In situ simulated data Chl-a available Chloropyll (mg/m 2 ) I/C mi (mg/m 3 )

29 Carrying capacity of Alfacs Bay Approximations 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.

30 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 Χ k parameter for Alfacs bay is variable throughout the year due to the wide range of temperatures in the bay water.

31 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

32 Thank you! INIA: RTA 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 característiques ambientals de les badies del Delta de l’Ebre.


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