CN111626605B

CN111626605B - Dynamic evaluation method and application of aquaculture capacity

Info

Publication number: CN111626605B
Application number: CN202010455299.5A
Authority: CN
Inventors: 蔺凡; 刘慧�; 蒋增杰; 姜娓娓; 蔡碧莹
Original assignee: Yellow Sea Fisheries Research Institute Chinese Academy of Fishery Sciences
Current assignee: Yellow Sea Fisheries Research Institute Chinese Academy of Fishery Sciences
Priority date: 2020-05-26
Filing date: 2020-05-26
Publication date: 2023-01-17
Anticipated expiration: 2040-05-26
Also published as: CN111626605A

Abstract

The invention provides a dynamic evaluation method of aquaculture capacity and application thereof. The method is based on the early-stage research work foundation, a dynamic evaluation method of the culture capacity based on a dynamic model of a culture ecosystem is established, and the adopted ecosystem model comprises modules of nutritive salt, phytoplankton, zooplankton, debris, shellfish and the like and is coupled with a hydrodynamic model in an off-line mode. The weight of the cultured organisms during growth and harvesting under the conditions of different culture densities is predicted, and aquaculture enterprises and management departments can be helped to make reasonable planning on culture scale and culture density according to environmental conditions, market demands and policy requirements. By taking the model evaluation result as guidance, the culture condition can be optimized, the culture yield is improved, and the method has high application value.

Description

Dynamic evaluation method and application of aquaculture capacity

Technical Field

The invention belongs to the field of aquaculture biology and aquaculture management, and particularly relates to a dynamic evaluation method and application of aquaculture capacity.

Technical Field

China is the first major country of aquaculture in the world, and the total aquaculture yield accounts for more than 60% of the global yield. For years, the aquaculture of China is continuously developed, and the types, culture modes, regions and water bodies of the related cultured organisms are more and more diversified; the conflict between the breeding area and other water areas and space utilization modes such as urban development, tourism industry, port and waterway is more and more prominent. Meanwhile, the conditions of ultrahigh-density culture are common, so that the problems of disease transmission, oxygen deficiency, water quality deterioration and the like are caused sometimes, and even death of cultured organisms and reduction of production profit are caused directly. And the aquaculture capacity evaluation is a basic basis for guiding aquaculture environment management and aquaculture production planning, optimizing aquaculture layout and adjusting aquaculture structure and improving aquaculture comprehensive benefits.

With the development of the research on the physiological ecology of the cultured organisms, the update of observation techniques and equipment and the popularization of high-performance computer application, the model of the aquaculture ecosystem now becomes an effective tool for evaluating the aquaculture capacity. Ecosystem models generally have strong regional characteristics, and have strong correlations with the natural environment of the aquaculture area, ecosystem composition, aquaculture methods, and biophysical characteristics of the farmed organisms. Physical, chemical and biological data are obtained through environmental investigation, and the optimum culture density and yield in the culture area can be estimated by inputting individual growth models and ecosystem models of cultured organisms, so that a theoretical basis is provided for reasonable planning and arrangement of culture production.

Disclosure of Invention

The invention aims to solve the technical problem of establishing a dynamic evaluation method and application of aquaculture capacity. The method establishes a dynamic evaluation method of the culture capacity based on a dynamic model of a culture ecosystem based on the early-stage research work basis, the adopted ecosystem model comprises modules of nutritive salt, phytoplankton, zooplankton, debris, shellfish and the like and is coupled with the hydrodynamic model in an off-line manner. The weight of the cultured organisms during growth and harvesting under the conditions of different culture densities is predicted, and aquaculture enterprises and management departments can be helped to make reasonable planning on culture scale and culture density according to environmental conditions, market demands and policy requirements. The model evaluation result is used as guidance, so that the culture conditions can be optimized, the culture yield is improved, and the method has high application value.

The invention is realized by the following technical scheme:

a dynamic evaluation method for aquaculture capacity comprises the following steps:

(1) Research water area partition-division of model box

Partitioning the research sea area according to the similarity of hydrological environment and culture layout, wherein each sea area is a model box, and exchange of main nutrient salts including dissolved inorganic nitrogen, phytoplankton and suspended organic matters exists in the model box and at the boundary of each model box and between the research sea area and adjacent sea areas outside a bay;

(2) Establishing sub-model

Studying main cultured animals and cultured plants in a sea area, and carrying out a general growth process of cultured plant populations through individual growth models of the cultured animals and nutrient salt consumption; constructing a sub-model using a dynamic energy balance theory, simulating the growth of individual farmed organisms, and simultaneously simulating an exponential growth process of a nutrient-consuming farmed plant population based on a daily maximum growth rate, the population dynamics being dependent on the farming activities and the natural mortality, harvesting the farmed organisms and removing them from the ecosystem model at the end of the farming period;

(3) Building ecosystem models

Describing the interaction between each trophic group with inorganic nitrogen as the basic flux in an ecosystem model; establishing a dynamic evaluation method of culture capacity based on a dynamic model of a culture ecosystem, wherein the adopted ecosystem model takes a box-type model as a basic calculation unit, the model comprises modules of nutritive salt, phytoplankton, zooplankton, debris, shellfish and the like and is coupled with a hydrodynamic model in an off-line manner:

(4) And (3) obtaining relevant parameters in the model by sampling on site and carrying out physiological experiments, and substituting the relevant parameters into the model constructed in the steps (2) and (3) to obtain the optimal ecological capacity of cultivation.

The invention also provides a dynamic evaluation method for the culture capacity of the gulf sea area of mulberry by using the method, wherein the main cultured animal of the gulf sea area of Sang Gou is oyster, the cultured plant is kelp, and the method comprises the following specific steps:

(1) Research water area partition-division of model box

According to the similarity of the hydrological environment and the culture layout, the Sang Gou gulf sea area is divided into 4 areas, namely 4 boxes: box 1, box2, box3 and box 4; there is an exchange of primary nutrient salts, phytoplankton and suspended organic matter both within the model boxes and at the boundaries of each model box, and with adjacent waters outside the bay;

5363 aquaculture activities in the bay Sang Gou occur in box2, box3 and box4, but only very limited aquaculture activities can be performed due to the shallow water depth of box 1, so the aquaculture activities in box 1 are ignored in the model;

(2) Sub-model construction

A general growth process of filter-feeding cultured biological population represented by pacific oysters and nutrient-salt-consuming cultured plant population represented by kelp; constructing an individual growth model of oysters and kelp by using a Dynamic Energy Budget (DEB) theory, simulating the growth of a single cultivated oyster, and simultaneously simulating an exponential growth process of cultivated kelp and phytoplankton based on the daily maximum growth rate;

(3) Model equations and parameters

The equations related to the ecosystem model are shown in table 1; table 2 describes the various biological processes in the ecosystem equation.

TABLE 1 model equation

Oyster Individual growth equation

Fang Cheng	Definition of
		dE/dt＝p _A -p _C	Reserve energy (j)
dE _R /dt＝(1-κ)p _C -p _J	Reproductive energy (j)
		dV/dt＝(κp _C -p _M ) ₊ /[E _G ]	Volume growth (cm) ³ )
dN/dt＝-(δ _r +δ _h )·N	Population dynamics (No.)

Sea-tangle individual growth equation

Ecosystem model equation

Note (x) ₊ Is defined as: when x is>At 0, [ x ]] ₊ (= x) when x is 0 or less, [ x ]] ₊ ＝0

TABLE 2 parameterized equations for biological processes

Note that (x) + is defined as: when x >0, [ x ] + = x, when x ≦ 0, [ x ] + =0

(4) Obtaining relevant parameters shown in a table 3 through field sampling and physiological experiments, substituting the relevant parameters into the model constructed in the steps (2) and (3) to obtain the optimal ecological capacity of cultivation;

TABLE 3 parameters used in the ecosystem model

Compared with the prior art, the invention has the beneficial effects that:

the ecological system model takes a box-type model as a basic calculation unit, and the model comprises modules of nutrient salt, phytoplankton, zooplankton, debris, shellfish and the like and is coupled with a hydrodynamic model in an off-line manner. The cultivation capacity of the present invention relates to the dynamic estimation of the maximum cultivation yield of the main aquaculture species from the feed and nutrient supply in the water. The ecological system model evaluates the culture capacity, simulates the individual growth of main culture varieties such as filter-feeding shellfish and kelp by a Dynamic Energy Budget (DEB) model, parameterizes the interaction between culture organisms and the ecological system components, and predicts the individual growth and population condition of a target biological population and main relevant environmental variables. The ecosystem model can accurately reproduce the magnitude and seasonal period of the environmental variables, and the evaluation result of the model provides a reasonable basis for reasonably arranging the aquaculture production according to the aquaculture capacity.

Drawings

FIG. 1 is a conceptual flow diagram of a model of the ecosystem of the Mulberry gulf;

FIG. 2 is a study of the division of the mold box in a water area;

FIG. 3.2016-2017 chlorophyll a concentration in box2 (red), box3 (blue) and box4 (green) was simulated and chlorophyll a observation at a long term water quality monitoring station (black dots)

The simulated DIN values (straight lines) in the graphs 4.2011-2012 box2 (red), box3 (blue) and box4 (green) are the contemporaneous corresponding observations (scatter). The observed value of DIN is the average of the measured values of all the sampling points (see FIG. 2) in the same box

Detailed Description

The technical solution of the present invention is further explained by the following embodiments with reference to the attached drawings, but the scope of the present invention is not limited in any way by the embodiments.

Example 1

A model evaluation method for aquaculture capacity establishes a dynamic evaluation method for aquaculture capacity based on a dynamic model of an aquaculture ecosystem, the adopted ecosystem model takes a box-type model as a basic calculation unit, and the model comprises modules of nutritive salt, phytoplankton, zooplankton, debris, shellfish and the like and is coupled with a hydrodynamic model in an off-line manner. The main components of the ecological model are shown in fig. 1. The method comprises the following steps:

(1) Division of research water area partition-model box

The hydrodynamic processes in the ecosystem model are derived from hourly flow field conditions in the sanguisorba bay area generated from the fvom 3D hydrodynamic model.

The aquaculture area of Sang Gou gulf was divided into 4 boxes according to similar hydrological conditions and aquaculture activities. As shown in FIG. 2, the water volumes of the boxes 1 to 4 are 0.367km respectively ³ 、0.239km ³ 、0.504km ³ And 0.352km ³ . The average depth of each box was 2.79m,5.46m,8.03m and 6.86m, respectively. We use the results of the hydrodynamic model to estimate the flow on each boundary and at the outer boundary of the open sea area. The hydrodynamic model was constructed by Xuan et al (2019) at the national oceanographic institute second oceanographic institute. Briefly, the hydrodynamic model is based on a 3D finite element hydrodynamic model (fvom) with unstructured mesh and a resolution that can vary from 3 to 10km in the model horizontal resolution in the eastern sea of yellow bohai, up to 200m in the research region of the sang gulf, with the driving forces of the hydrodynamic model including the driving of tidal, ocean current and atmospheric data. The model results included depth-averaged flow field, water temperature and salinity, and the results were interpolated onto a rectangular grid in the gulf sea of sang with a horizontal resolution of 100m. In addition to the hydrodynamic model results, a water quality model was set up by off-line coupling to simulate environmental variables including DIN, granular organic carbon (POC) and phytoplankton (expressed as chlorophyll a concentration). The water quality model is based on the previous mulberryData from studies conducted in gulf (Xuan et al, 2019). In this example, we used the flow field, water characteristics (including temperature and salinity) and simulation results of the water quality model for the period of 7 months in 2010 to 7 months in 2011 as driving force for the ecosystem model.

Step 2, predicting individual growth model of Pacific oyster

Pacific oysters are currently the main bivalve shellfish cultivated in the gulf of morus, and the growth conditions of oyster individuals are simulated according to DEB theory (Kooijman, 2010). Some details of standard DEB models can be found in the literature (e.g., van dermeerer, 2006. According to previous studies, the influence of water temperature on oyster physiological processes was described using the Arrhenius (Arrhenius) relationship (Kooijman, 2010). The Arrenius relationship applied in the model can be referred to the method of Ren et al (2008), as shown in Table 2.

The model equations are mainly referenced to the method of Ren et al (2008), and the three state variables describing the energy of oysters are: an energy storage tank (E), a reproductive energy tank (ER) and a biological volume (V). The oysters absorb the energy in the bait (phytoplankton and debris) and store it in energy tanks (E) first, and the stored energy will be distributed continuously to the corresponding physiological activities, including maintenance, growth and reproduction of the body. The physiological process of oyster feeding is limited by type II functional responses based on bait concentration, described as F = F/(F + F) _H ) Wherein F is the concentration of food, and FH is the half-saturation coefficient of bait intake by oysters. The kappa rule is applied in the energy allocation, and the part of kappa in the fixed energy (E) in the reserve will be used for structural maintenance and growth, resulting in an increase in the volume (V) of the organism, while the remaining 1-kappa will be available for the propagation process. The energy allocated to reproduction will be stored in the reproductive energy sink (ER). When the trigger temperature and gonadal condition (GI) for oviposition is reached>35%), the corresponding sperm egg will be emptied and the energy used for propagation (Pouvreau et al). ,2006). The growth model of oysters is shown in table 1.

The cultured oysters are attached to the culture extension rope and filter water to ingest, and the biological function of the oysters is to filter phytoplankton and particulate organic matter from the system and discharge nitrogen and excrement.

Step 3, predicting kelp individual growth and phytoplankton biomass models

Kelp, as the major farmed species in the gulf of morus, covers most of the gulf, so the growth of kelp was simulated using individual growth models of Zhang Jigong et al (2016) and Cai Biying et al (2018). Detailed description specific related information can be found in the original literature, where we summarize the main model overview.

The biomass of kelp (B, unit: gram dry weight/ind) was modeled as the difference between 3 dynamic processes: growth, respiration and decay. These three processes are described as the effect on daily growth rate (%). The growth of the kelp is defined as the maximum growth rate (. Mu.max, unit: day 1) multiplied by a limiting factor (from 0 to 1) due to water temperature, light intensity and nutrients. Details of the correlation equation can be found in table 2. The temperature limiting function f (T) is defined as an exponential function that produces a local normal distribution according to the optimal temperature for kelp growth (Radach et al, 1993). For the nutrient limiting function f (N), only nitrogen was considered in our model and the limiting effect of phosphorus on kelp growth was stabilized around 0.8-0.9 according to the simulation results of Cai et al (2018). f (N) is calculated by the Michaelis-Menten equation (Caperon and Meyer, 1972). According to Steele et al. (1962) there is an optimum light intensity Iopt (μmol. M-2. S-1) and the light attenuation at the cultivation depth (Z, unit: m) is calculated according to Lambert-beer's law. According to the method of Cai et al (2018), the respiration of kelp is calculated by an exponential function, with the maximum respiration (Rmax 20, unit: day 1) set at 20 ℃. The decay of kelp is quantified in a form similar to respiration, and the maximum erosion rate (Emax) is set as the optimum growth temperature of kelp. Another variable describing kelp growth is the concentration of internal nitrogen (Nint, unit: μmol/(g DW)), the absorption of Dissolved Inorganic Nitrogen (DIN) also following the Michaelis-Menten equation. The natural mortality of kelp was included in the model to calculate the biomass loss during the simulation. The aquaculture of kelp usually starts from 11 months and the harvest starts at 5 months, and the biomass of kelp after harvest will be removed from the system. The growth pattern of kelp is shown in table 1.

Phytoplankton biomass (CP, mgC/m 3) was simulated as a physiological process based on maximum daily growth rate (Gpm, unit: day 1) and is a major producer in ecosystems. In addition to growth and respiration, phytoplankton changes also include exchange processes between adjacent boxes. The model describes the carbon uptake by phytoplankton, whose function is limited by light, temperature and nitrogen content, which is the second largest nitrogen sink in the system. The temperature limit of phytoplankton is similar to oysters with different boundary values. According to N: c-ratio, we set a switch function in the phytoplankton DIN uptake equation to prevent the phytoplankton from infinite DIN uptake (Ren et al, 2012).

Step 4, model quantitative prediction of suspended organic matters and soluble nutrient salts

The main particulate organic matter considered in the model consisted of particulate organic carbon in crumb (POC, unit: mgC/m) ³ ) And particulate organic nitrogen (PON, unit: mgN/m ³ ) Description (see table 1). Also in table 1 are equations describing the change in organics. The main source of POC and PON is oyster feces, which, as planktonic matter, can be exchanged with matter between adjacent tanks, and with the outer boundary of the sulcus of morus. Oysters are also ingested POC by filter feeding. For simplicity, PON concentration is calculated as part of the POC in the ecosystem model.

Dissolved inorganic nitrogen (DIN, unit: mgN/m) ³ ) Is the main nutrient salt variable in the ecosystem model. Phytoplankton and kelp absorb DIN for growth, while oyster excrement is the source of DIN. Exchange between the boxes and the outer boundary is another process that affects the change in DIN.

Example 2

Taking the sang-gulf as an example, parameters of the ecosystem model for evaluating aquaculture capacity are set, and model results are verified.

Step 1. Parameterization and setting

For model application, based on the aforementioned studies, we parameterized growth models of the major cultivated species pacific oysters and kelp, and specific values of each parameter are shown in table 3.

The parameters of the oyster dynamic energy budget model are referenced from articles of Pouvreau et al (2006) and Ren et al (2008). Parameters of the individual growth model of kelp reference previous studies in the gulf of morus sulcus (Wu et al, 2009, zhang et al, 2016 cai et al, 2018. The framework of the ecosystem model is mainly referred to the study of Ren et al (2012), which includes most parameters of the phytoplankton, phytoplankton and nutrient salt models. However, due to the differences in geographical conditions, certain parameters employed in the model (e.g., oyster half-saturation food intake concentration, maximum phytoplankton/seaweed daily growth rate, etc.) have been tested and corrected by previous studies conducted in the gulf of sang, and have been validated by observations. We apply the verified parameter set to the ecosystem model to maintain consistency of the results.

The initial environmental variables in the model (including phytoplankton, POC and DIN concentration variables) were set to the values in the water quality model in the corresponding season and averaged to each box. Oyster farming begins in june, usually in the gulf of morus for one year, and is then transferred to other waters for fattening, according to the actual aquaculture cycle. In consideration of the existing aquaculture layout situation, oyster cultivation is set in the box3 only, and the initial density of the oyster cultivation is the current actual cultivation situation. Because the culture conditions of the mulberry gulf sea area are complex, the culture entity is various, reasonable average density is necessary for maintaining reasonable total culture amount, and the current density of the culture farm is about 50 oysters cultured per square meter of water surface. The initial energy trough of the oysters was set to 40J, the reproductive reserve was set to 10J, the initial biological volume was 0.6cm3, and the initial oyster soft Tissue Wet Weight (TWW) was about 0.2g. The kelp is cultivated in box2, box3 and box4, usually with seedlings beginning at 11 months and the harvest continues from 5 months of the following year to 8 months. The initial kelp biomass and physiological characteristic parameters were taken from Cai et al (2018). For all the panel, the initial Tissue Dry Weight (TDW) of kelp biomass was set to 0.5g, while the initial nitrogen (Nint) in the body tissue was set to 1071. Mu. Mol/(g DW) (see Zhang et al, 2016). Raft culture is the main method for culturing Sang Gou bay kelp, and the existing culture density is estimated to be 4-5 kelp per square meter (Mao et al, 2018).

Step 2, verification of ecosystem model

In model validation, we set the oyster culture density of box3 to 50/m ² Water surface, setting the density of the kelp culture of box2, box3 and box4 to be 4 pieces/m ² The surface of the water. Culturing oysters in 7 months, wherein the culture period is 12 months; the kelp is cultivated in 11 months and harvested in 6 middle-aged days of the next year. The ecosystem model from 7 months 2010 to 6 months 2011 was simulated using hydrodynamic information and water quality model results. The model time step was 1 hour (Δ t =1/24 days). At each time step, the growth of the cultivated kelp and oyster, DIN, phytoplankton and inflow/outflow per box will be calculated. Comparison of magnitude and trend of results was performed using previous extensive surveys and fixed-point environmental observations.

FIG. 3 is a comparison of simulated phytoplankton biomass (converted to chlorophyll a at a phytoplankton C: chl a =40 ratio) per box with chlorophyll a observed in long-term stand 2016-2017. In the vigorous growing season of 5 to 10 months, the ecosystem model reproduces the chlorophyll a content and seasonal variation to some extent. However, in winter when kelp is cultivated, the ecosystem model underestimates the change of chlorophyll a. One explanation for this is that phytoplankton growth is strongly constrained by the temperature in the ecosystem model, which results in very low phytoplankton reproduction in winter. And the observed location is at the outer boundary of the bay, advection with the open sea may cause higher chlorophyll a in winter. Although the seasonal period of phytoplankton in this model is not perfectly reproduced, the magnitude of the general chlorophyll a content and the short-term peak-to-valley variations caused by water exchange can be seen approximately in the model results.

FIG. 4 shows the simulated DIN for each box compared to the DIN averagely observed from the sample site in 2011-2012, in units of mgN/m ³ The conversion ratio applied to μmol/L was 14. Although the simulation period is different from the observed value, the simulated seasonal variation roughly conforms to the observed trend. Since there were no kelp in 7 months, DIN in each box was above 10. Mu. Mol/L. In winter, simulatedDIN remained high at around 12.5. Mu. Mol/L, but the DIN observed in January had dropped to around 7.5. Mu. Mol/L, indicating that kelp growth in the model was underestimated. When kelp begins to grow rapidly around march, the model simulated DIN drops sharply. When the harvest was started at 5 months, the growth of the

boxes

3 and 4 kelp was slowed down and DIN stabilized at a lower level around 2.5. Mu. Mol/L. DIN in the ecosystem model quickly returned to the outer boundary forced value in each aquaculture after 6 months of kelp harvest. Since the observed DIN for each bin was averaged from the water samples taken during the broad survey of 2011-2012 (fig. 2), the standard deviation for each bin was large when calculating the average.

The differences in DIN concentration and phytoplankton in the model may be due to constraints of the outer boundary conditions and spatial data averaging operations, and the assumptions in the model may not be sufficient to account for complex natural variability. Within these limits, the model can correctly reproduce the magnitude and seasonal period of the environmental variables, and we consider that the results of the ecosystem model provide a reasonable basis for evaluating aquaculture scenarios.

Step 3, scene simulation

Using the ecosystem model, simulations were performed for 10 scenes representing different seeding combinations during the simulated breeding cycle (see table 4). Where scenario 3 is a validation simulation close to existing aquaculture activities. Scenes 1-6 were studies of the response of different oyster seeding densities to phytoplankton and yield in the case of a fixed number of cultivated algae.

Scenes

3, 7, 8, 9 and 10 are used for researching the growth conditions and the environmental feedback of different kelp seeding densities under the fixed oyster quantity.

TABLE 4 oyster and kelp culture Density scenarios simulated by ecosystem models

Density scenes	Oyster (ind/hm) ² ；box3)	Kelp (ind/hm) ² ；box2,box3,box4)
			I	0	40,000
II	300,000	40,000
			III	500,000	40,000
IV	700,000	40,000
			V	1,000,000	40,000
VI	1,500,000	40,000
			VII	500,000	50,000
VIII	500,000	60,000
			IX	500,000	70,000
X	500,000	80,000

The scene simulation result shows that:

oysters are mainly cultivated in box 3. When the culture period of scenes 2-6 is finished, the wet weights of the soft tissues of the single oysters are 7.71, 5.86, 4.77, 3.78 and 2.83g/ind respectively; namely, the weight of the oyster individual shows nonlinear reduction along with the increase of the culture density. The lower the density of oyster cultivation, the higher the production efficiency during simulation, since each individual can occupy more resources.

In scenes 7-10, the maximum kelp tissue dry weight became smaller and the average DIN content level decreased as the kelp seeding density increased. For box2, when 80,000 kelp are cultivated per hectare, the yield increases to 6.82 tons/hm ² When the cultivation density is 40,000/hm ² The hourly production was 5.84 ton/hm ² The growth rate was 16.8%. The same operation in box3 resulted in a production of from 7.73 tons/hm ² Increased to 9.80 tons/hm ² Increase by 27.3%; in box4, the yield was from 8.76 tons/hm ² Increased to 12.00 tons/hm ² And the growth is 37.0 percent. Through environmental changes and physiological processes set by the model, it appears that the production of kelp production in box2 has approached the breeding capacity in box2, and DIN supplementation does not support a significant increase in biomass. In the box3 and the box4, the DIN supplement is more sufficient, and the kelp culture density can be doubled without considering other limiting conditions.

References mentioned in the examples:

Bleck R.2002An oceanic general circulation model framed in hybrid isopycnic-Cartesian coordinatesOcean.Model.455–88.https://doi.org/10.1016/ S1463-5003(01)00012-9

Cai,B.,Zhu,C.,Liu,H.,Chang,L.,Xiao,L.,Sun,Q.,Lin,F.(2018).Model Simulated growth ofkelp Saccharina japonica in Sanggou Bay.Progress in Fishery Science,40(3).https://dx.doi.org/10.19663/j.issn2095-9869.20180419001(in Chinesewith English abstract)

Cao,L.,Wang,W.,Yang,Y.,Yang,C.,Yuan,Z.,Xiong,S.,&Diana,J.(2007).Environmental impact of aquaculture and countermeasures to aquaculture pollution in China.EnvironmentalScience andPollutionResearch-International,14(7),452-462.https://doi.org/10.1065/espr2007.05.426

Caperon,J.,Meyer,J.(1972).Nitrogen-limited growth of marine phytoplankton—I.changes in population characteristicswith steady-state growth rate Deep Sea Research and Oceanographic Abstracts 19(9),601-618.https://dx.doi.org/10.1016/0011-7471(72)90089-7

Chen,C.,R.C.Beardsley,and G.Cowles.2006.An unstructured grid,finite-volume coastal ocean model(FVCOM)system.Oceanography 19(1):78–89,https:// doi.org/10.5670/oceanog.2006.92.

Chopin,T.,Buschmann,A.,Halling,C.,Troell,M.,Kautsky,N.,Neori,A.,Kraemer,G.,Zertuche-González,J.,Yarish,C.,Neefus,C.(2001).INTEGRATING SEAWEEDS INTO MARINE AQUACULTURE SYSTEMS:A KEY TOWARD SUSTAINABILITY Journal of Phycology 37(6),975-986.https://dx.doi.org/10.1046/j.1529- 8817.2001.01137.x

Duarte,P.,Meneses,R.,Hawkins,A.,Zhu,M.,Fang,J.,Grant,J.(2003).Mathematical modelling to assess the carrying capacity for multi-species culture within coastal waters Ecological Modelling 168(1-2),109143.https:// dx.doi.org/10.1016/s0304-3800(03)00205-9

Egbert G.D.and Erofeeva S.Y.2002 Efficient inverse modeling of barotropic ocean tides J.Atmos.Ocean.Tech.19183–204.https://doi.org/10.1175/ 1520-0426(2002)019％3C0183:EIMOBO％3E2.0.CO；2

EPA(Environmental Protection Agency,USA).Rates,constants,and kinetics.Formulations in surface water quality modeling:2nd Edition,1985:455

FAO(2018).Fishery and Aquaculture Statistics 2016/FAO annuaire.Statistiques des

et de l′aquaculture 2016/FAO anuario.Estadísticas de pesca y acuicultura 2016.Rome/Roma.104pp.

Fang,J.-G&Sun,H.-L&Kuang,S.-H.(1996).Assessing the carrying capacity of Sanggou Bay for culture of kelp Laminaria japonica.Marine Fisheries Research.17.7-17.(in Chinese with English abstract)

Ferreira,J.G.,Hawkins,A.J.S.,&Bricker,S.B.(2007).Management of productivity,environmental effects and profitability of shellfish aquaculture—the Farm Aquaculture Resource Management(FARM)model.Aquaculture,264(1-4),160-174.https://doi.org/10.1016/j.aquaculture.2006.12.017Getrightsa ndcontent

Filgueira,R.,Grant,J.,Strand,

(2014).Implementation of marine spatial planning in shellfish aquaculture management:modeling studies in a Norwegian fjord Ecological Applications 24(4),832843.https://dx.doi.org/ 10.1890/13-0479.1

Filgueira,R.,Guyondet,T.,Bacher,C.,Comeau,L.(2015).Informing Marine Spatial Planning(MSP)with numerical modelling:A case-study on shellfish aquaculture in Malpeque Bay(Eastern Canada)Marine Pollution Bulletin 100(1),200-216.https://dx.doi.org/10.1016/j.marpolbul.2015.08.048

Galparsoro,I.,Murillas,A.,Pinarbasi,K.,Sequeira,A.M.,Stelzenmüller,V.,Borja,

...&Gimpel,A.Global stakeholder vision for ecosystem-based marine aquaculture expansion from coastal to offshore areas.Reviews in Aquaculture.https://doi.org/10.1111/raq.12422

Grant,J.,Bacher,C.(2001).A numerical model of flow modification induced by suspended aquaculture in a Chinese bay Canadian Journal of Fisheries and Aquatic Sciences 58(5),10031011.https://dx.doi.org/10.1139/f01- 027

Grant,J.,Curran,K.,Guyondet,T.,Tita,G.,Bacher,C.,Koutitonsky,V.,Dowd,M.(2007).A box model of carrying capacity for suspended mussel aquaculture in Lagune de la Grande-Entrée,Iles-de-la-Madeleine,Québec Ecological Modelling 200(1-2),193 206.https://dx.doi.org/10.1016/j.ecolmodel.2006.07.026

Guyondet,T.,Roy,S.,Koutitonsky,V.,Grant,J.,Tita,G.(2010).Integrating multiple spatial scales in the carrying capacity assessment of a coastal ecosystem for bivalve aquaculture Journal of Sea Research 64(3),341359.https://dx.doi.org/10.1016/j.seares.2010.05.003

Holling,C.S.(July 1959)."Some characteristics of simple types of predation and parasitism".The Canadian Entomologist.91(7).pp.385–98.doi:10.4039/Ent91385-7

Milewski,I.(2001).Impacts of salmon aquaculture on the coastal environment:a review.In MF Tlusty,DA Bengston,HO Halvorson,SD Oktay,JB,Pearce and RB Rheault Jr.,(eds).Marine aquaculture and the environment:A meeting for stakeholders in the Northeast.Cape Cod Press,Falmouth,MA(pp.166-197).

NACA.1989.Integrated Fish Farming in China.NACA Technical Manual 7.A World Food Day Publication of the Network of Aquaculture Centres in Asia and the Pacific,Bangkok,Thailand.278pp.

Pouvreau,S.,Bourles,Y.,Lefebvre,S.,Gangnery,A.,Alunno-Bruscia,M.(2006).Application of a dynamic energy budget model to the Pacific oyster,Crassostrea gigas,reared under various environmental conditions Journal of Sea Research 56(2),156-167.https://dx.doi.org/10.1016/j.seares.2006.03.007

Radach G,and Moll A.Estimation of the variability of production by simulating annual cycles of phytoplankton in the central North Sea.Prog.Oceanog.1993.31:339-419.https://doi.org/10.1016/0079-6611(93)90001- T.

Reid,G.,Lefebvre,S.,Filgueira,R.,Robinson,S.,Broch,O.,Dumas,A.,Chopin,T.(2018).Performance measures and models for open-water integrated multi-trophic aquaculture Reviews in Aquaculture 293(357),21130.https:// dx.doi.org/10.1111/raq.12304

Ren,J.,Stenton-Dozey,J.,Plew,D.,Fang,J.,Gall,M.(2012).An ecosystem model for optimising production in integrated multitrophic aquaculture systems Ecological Modelling 246(),3446.https://dx.doi.org/10.1016/ j.ecolmodel.2012.07.020

Ren,J.,Schiel,D.(2008).A dynamic energy budget model:parameterisation and application to the Pacific oyster Crassostrea gigas in New Zealand waters Journal of Experimental Marine Biology and Ecology 361(1),4248.https:// dx.doi.org/10.1016/j.jembe.2008.04.012

Rongjun,W.U.,Xuelei,Z.,Mingyuan,Z.,&Youfei,Z..(2009).A model for the growth of haidai(laminaria japonica)in aquaculture.Marine Science Bulletin.28(02):34-40.(in Chinese with English abstract)

S.A.L.M.Kooijman.Dynamic Energy Budget theory for metabolic organisation.Cambridge University Press,2010.https://doi.org/10.1017/ CBO9780511805400

Shi,J.,Wei,H.,Zhao,L.,Yuan,Y.,Fang,J.,Zhang,J.(2011).A physical–biological coupled aquaculture model for a suspended aquaculture area of China Aquaculture 318(3-4),412424.https://dx.doi.org/10.1016/ j.aquaculture.2011.05.048

Steele,J.H.,(2003)ENVIRONMENTAL CONTROL OF PHOTOSYNTHESIS IN THE SEA.Limnology and Oceanography,7,doi:https://doi.org/10.4319/ lo.1962.7.2.0137.

Whitmarsh D.,Palmieri M.G.(2008)Aquaculture in the Coastal Zone:Pressures,Interactions and Externalities.In:Holmer M.,Black K.,Duarte C.M.,MarbàN.,Karakassis I.(eds)Aquaculture in the Ecosystem.Springer,Dordrechthttps://doi.org/10.1007/978-1-4020-6810-2_8

Xuan,J.,He,Y.,Zhou,F.,Tang,C.,Zheng,X.,Liu,H.,Yu,L.,Chen,J.(2019).Aquaculture-induced boundary circulation and its impact on coastal frontal circulation Environmental Research Communications 1(5),051001.https:// dx.doi.org/10.1088/2515-7620/ab22cd

Yuze,Mao.,Jiaqi,Li.,Suyan,Xue.,Fan,Lin.,Zengjie,Jiang.,Jianguang,Fang.,Qisheng,Tang.(2018).Ecological functions of the kelp Saccharina japonicain integrated multi-trophic aquaculture,Sanggou Bay,China Acta Ecologica Sinica 38(9),1 8.https://dx.doi.org/10.5846/stxb201703160444

Zhang,J.,Wu,W.,Ren,J.,Lin,F.(2016).A model for the growth of mariculture kelp Saccharina japonica in Sanggou Bay,China Aquaculture Environment Interactions 8(),273 283.https://dx.doi.org/10.3354/aei00171

Zhao,J.&S.L.,Zhou&J.G.,Fang.(1996).Research on Sanggou Bay aquaculture hydro-environment.Mar.Fish.Res.17.68-79.(in Chinese with English abstract)

Zhao,Y.,Zhang,J.,Lin,F.,Ren,J.,Sun,K.,Liu,Y.,Wu,W.,Wang,W.(2019).An ecosystem model for estimating shellfish production carrying capacity in bottom culture systems Ecological Modelling 393(Aquat.Living Resour.16 1 2003),1-11.https://dx.doi.org/10.1016/j.ecolmodel.2018.12.005

JihongZhang,Pia Kupka Hansen,Jianguang Fang,Wei Wang,ZengjieJiang.(2009)

Assessment of the local environmental impact of intensive marine shellfish and seaweed farming—Application of the MOM system in the Sungo Bay,China Aquaculture,Volume 287,Issues 3–4,https://doi.org/10.1016/ j.aquaculture.2008.10.008。