A method for dynamic evaluation of carbon storage amount of bivalve

Abstract

The application provides a kind of dynamic evaluation method for carbon storage of bivalve, which comprises the following steps: monitoring and collecting environmental data of typical aquaculture sea area, calculating a functional function, carrying out on-site flow experiment to determine physiological and biological parameters of bivalve, converting to obtain carbon parameters, based on dynamic energy budget theory, combining multiple environmental factors and carbon parameters to construct a carbon budget model integrating the whole physiological process, and finally simulating the carbon storage flux of bivalve in the growth cycle. The application solves the problems of static evaluation of bivalve carbon sink, insufficient consideration of environmental factor fluctuation, incomplete description of key physiological processes and insufficient precision of carbon budget quantification, and realizes the daily dynamic and accurate evaluation of bivalve carbon storage, quantifies the carbon storage contribution of shell formation and biogenic sedimentation.

Description

Technical Field

[0001] This invention relates to the field of fishery carbon sequestration assessment technology, and in particular to a dynamic assessment method for carbon storage in bivalve mollusks. Background Technology

[0002] With the increasing prominence of global climate change and carbon emissions, the carbon sink function of fisheries has become a key focus of international academic and industrial attention. Bivalves, as important aquaculture species, can participate in carbon fixation through processes such as CaCO3 shell formation and biodeposition. However, there is currently no consensus on whether they function as a carbon source or sink in the marine carbon cycle. Existing technologies mostly rely on static carbon budget assessments on an annual or seasonal scale, making it difficult to systematically characterize the dynamic changes in carbon budget throughout the entire growth process of bivalves. In particular, there is a lack of comprehensive assessment systems that integrate continuous on-site monitoring data with dynamic energy budget growth models. Although dynamic energy budget theory has been applied in the growth simulation of some bivalves, there is still a lack of dynamic carbon budget assessment models that can integrate diverse food sources, key environmental factors, and physiological processes such as feeding, respiration, calcification, and biodeposition for the scientific quantification of carbon storage in bivalves. Furthermore, most existing assessment methods do not fully consider the impact of environmental factor fluctuations on the physiological activities of bivalves, leading to discrepancies between the assessment results and the actual carbon storage under actual aquaculture conditions. Summary of the Invention

[0003] The purpose of this invention is to provide a dynamic assessment method for carbon storage in bivalves, in order to solve the problems of incomplete characterization of key physiological activities, neglect of dynamic changes in environmental conditions, and inaccurate quantification of carbon balance in the prior art.

[0004] To achieve the above objectives, the present invention provides the following solution: A method for dynamic assessment of carbon storage in bivalves includes the following steps: Environmental data monitoring and collection were conducted for typical aquaculture areas, and functional functions were calculated based on the environmental data. A field flow-through experiment was conducted on bivalve mollusks in a typical aquaculture area, and physiological and biological parameters of the bivalve mollusks were measured. Calculate carbon parameters of bivalve mollusks based on physiological parameters; Based on the dynamic energy budget theory, a carbon budget model is constructed using environmental data and carbon parameters. The carbon budget model was used to simulate the carbon storage flux of bivalve mollusks during their growth cycle.

[0005] Optionally, environmental data may include: temperature, salinity, pH, total alkalinity, chlorophyll a concentration, particulate organic matter, particulate organic carbon, and total particulate matter concentration.

[0006] Optionally, physiological parameters include: filtration rate, feeding rate, absorption efficiency, oxygen consumption rate, and ammonia excretion rate; biological parameters include: shell length, shell dry weight, soft tissue dry weight, and soft tissue wet weight.

[0007] Optionally, carbon parameters of bivalves are calculated based on physiological parameters, including: The filtration rate is converted into ingestible carbon through particulate organic carbon; Assimilated carbon is calculated based on ingested carbon and absorption efficiency; Oxygen consumption rate is converted into respiratory carbon through the average respiratory quotient; Convert ammonia excretion rate into carbon excretion rate; Based on the energy budget equation, growth carbon is calculated according to assimilated carbon, respired carbon, and excreted carbon.

[0008] Optionally, using chlorophyll a concentration and particulate organic matter as food sources, and combining temperature, salinity, pH, and total alkalinity, a carbon budget model is constructed that includes stored energy, structural volume, and reproductive energy. The carbon budget model includes: temperature-dependent function, metabolic rate, energy density, ingestion assimilation rate, functional response, food density, food dependence, inorganic particulate concentration, maintenance rate, reproductive maintenance rate, rate of change in stored energy, rate of change in reproductive stored energy, volumetric growth, soft tissue dry weight, shell dry weight, shell calcification carbon fixation, biogenic sedimentation carbon fixation, respiration-released carbon, and calcification-released carbon.

[0009] Optionally, carbon storage flux in bivalves during their growth cycle can be simulated using a carbon budget model, including: Calculate the carbon reserves of seashells based on their carbon content; Carbon reserves in biogenic sediments are calculated based on temperature dependence function, soft tissue dry weight, and carbon uptake rate at optimal seawater temperature. Carbon storage flux is estimated based on shell carbon storage and biogenic sedimentary carbon storage.

[0010] According to specific embodiments provided by the present invention, the following technical effects are disclosed: The present invention provides a dynamic assessment method for carbon storage in bivalves, which includes: monitoring and collecting environmental data in typical aquaculture areas and calculating a function based on the environmental data; conducting on-site flow-through experiments on bivalves in typical aquaculture areas and measuring the physiological and biological parameters of bivalves; calculating the carbon parameters of bivalves based on the physiological parameters; constructing a carbon budget model based on dynamic energy budget theory, environmental data, and carbon parameters; and simulating the carbon storage flux of bivalves during their growth cycle using the carbon budget model. This method constructs a carbon budget assessment model for bivalves that integrates multiple environmental factors and dynamic energy budget. By combining on-site monitoring with model simulation, it achieves daily dynamic and accurate assessment of carbon storage during the growth cycle of bivalves, quantifies the carbon storage contribution of shell formation and biodeposition, and can be extended to various economically important bivalves. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a flowchart of the dynamic assessment method for carbon storage in bivalve mollusks according to the present invention; Figure 2 This is a dynamic trend diagram of carbon ingestion, assimilation, respiration, excretion, and growth in the Manila clam, according to an embodiment of the present invention. Figure 3 This is a dynamic trend diagram showing the carbon storage in the shell of the Manila clam, the carbon storage in biodeposition, the release of calcified carbon, the release of carbon from respiration, and the carbon storage flux, according to an embodiment of the present invention. Figure 4 This is a comparison chart of measured and simulated values ​​of shell length and soft tissue dry weight of the Manila clam according to an embodiment of the present invention. Detailed Implementation

[0013] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0014] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0015] like Figure 1 As shown, a method for dynamic assessment of carbon storage in bivalves includes the following steps: Step 100: Monitor and collect environmental data for typical aquaculture areas, and calculate the function based on the environmental data; Step 200: Conduct an in-situ flow-through experiment on bivalve mollusks in a typical aquaculture area, and measure the physiological and biological parameters of the bivalve mollusks. Step 300: Calculate the carbon parameters of bivalves based on physiological parameters; Step 400: Based on the dynamic energy budget theory, construct a carbon budget model according to environmental data and carbon parameters; Step 500: Simulate the carbon storage flux of bivalves during their growth cycle using a carbon budget model.

[0016] In the specific implementation process, step 100 sets up monitoring points in the Philippine clam farming area to continuously monitor seawater temperature and chlorophyll a concentration every day, and continuously measure seawater salinity, pH, total alkalinity, particulate organic matter, particulate organic carbon and total particulate matter concentration every month, and calculates the function Ψ based on the in-situ physicochemical environmental parameters.

[0017] In the specific implementation process, step 200 involves conducting on-site flow-through experiments every 1 to 2 months using a "portable particle counter", "multi-channel dissolved oxygen meter" and "shellfish flow-through experimental system" to measure physiological parameters such as the filtration rate, feeding rate, absorption efficiency, oxygen consumption rate and ammonia excretion rate of Manila clams. At the same time, biological parameters such as shell length, shell dry weight, soft tissue dry weight and soft tissue wet weight of 30 to 50 Manila clams are measured.

[0018] In the specific implementation process, step 300 uses a body mass index of 0.67 for the physiological rate function, and sets the filtration rate ( CR ), oxygen consumption rate ( OR ) and ammonia excretion rate ( NR The data were standardized, and each physiological rate was converted into carbon equivalent to assess the carbon budget of Manila clams. Specifically, the filtration rate was converted into feeding carbon using the concentration of particulate organic carbon in seawater. IR C The assimilated carbon () is calculated by multiplying the ingested carbon by the absorption efficiency. AR C Based on the average respiratory quotient (1 μmol O2 = 0.85 μmol CO2), the oxygen consumption rate is converted into respiratory carbon (…). OR C The calculation formula is: OR C =0.85×12 / 32× OR Carbon excretion ( NR C ) through formula NR C =12 / 28× NR Confirmed. Growth carbon ( SfG C The energy budget equation is used for calculation: SfG C = AR C – OR C - NR C ,in AR C (mgCg) -1 h -1 ) is assimilated carbon, OR CRespiratory carbon (mgCg) -1 h -1 ), NR C To excrete carbon (mgCg) -1 h -1 ).

[0019] In the specific implementation process, step 400 constructs a carbon budget model for individual Manila clams based on dynamic energy budget theory. The model includes three state variables: stored energy, structural volume, and reproductive energy. Chlorophyll a and particulate organic matter are used as food sources, and the influence of various environmental factors such as temperature, salinity, pH, and total alkalinity is considered. The specific equations and parameters of the dynamic carbon model for Manila clams are shown in Tables 1 and 2. Shape coefficient ( δ M ), the ratio of soft tissue dry weight to shell dry weight ( δ S ), shell carbon content ( S c Carbon uptake rate at optimum temperature ( IR co ) and respiratory carbon release rate ( OR co It is calculated using shell length, shell dry weight, soft tissue wet and dry weight, and related physiological parameters.

[0020] Table 1. Main Equations of the Carbon Budget Model

[0021] Table 2. Parameter values ​​for the carbon budget model

[0022] In the specific implementation process, step 500 compares the shell length, tissue dry weight, shell carbon storage, biogenic sedimentary carbon storage, and carbon released by respiration and calcification obtained from the model simulation with actual observed data, and verifies the accuracy of the model using indicators such as correlation coefficient, mean deviation, and root mean square error. Next, the carbon storage flux of the Manila clam throughout its entire growth cycle is calculated using model simulation. Specifically, the carbon release from respiration (… C resp Due to the influence of the size of the Manila clams and the seawater temperature, the calculation method is as follows: C resp =k(T)·OR co ·W f ,in, k(T) It is a temperature-dependent function. OR co The respiratory carbon release rate (mgCg) at the optimum temperature-1 d -1 ), W f Dry weight of soft tissue of Philippine clam (gind) -1 Carbon release during calcification ( C calc The formula for calculating the value depends on the shell growth rate and the functional function Ψ: C calc = dW s / dt∙Ψ∙prCaCO 3 ∙0.12, where, dWs / dt The shell growth rate (mgind) -1 d -1 ); Ψ represents the ratio of released carbon to precipitated CaCO3 during the calcification process; prCaCO 3 The CaCO3 content in the shell of the Manila clam (0.97 in some examples) is given, and 0.12 represents the molecular weight ratio of CaCO3 to carbon. The carbon storage flux (Cstor) of the Manila clam is estimated by the following equation: C stor =C shel +C biod ,in, C shel Shell carbon storage (mgCind) -1 d -1 The calculation formula is: C shel =S c ∙dW s / dt , S c Carbon content per unit mass of seashells (gg) -1 ); C biod Biogenic carbon storage (mgCind) -1 d -1 The calculation formula is: C biod =k(T)∙IR co ∙W f ∙( 1 -AE) ,in IR co Carbon uptake rate (mgCg) at optimal seawater temperature -1 d -1 ), AEFor absorption efficiency. It should be noted that the carbon storage quantified by the model output is the carbon storage achieved by the Manila clam through shell formation and biodeposition, and does not simulate the subsequent decomposition process of organic carbon in the sediment.

[0023] This invention uses the actual cultivation of Manila clams as a specific embodiment. From June 2022 to May 2023, monitoring equipment was deployed in a Manila clam farming area in a bay. Daily water temperature and chlorophyll a concentration were recorded, and monthly salinity, pH, total alkalinity, particulate organic matter, particulate organic carbon, and total particulate matter concentration were measured. Simultaneously, nine Manila clams were randomly collected monthly for on-site flow-through physiological experiments involving filtration, feeding, respiration, and excretion. Another 30 Manila clams were collected for measuring biological parameters such as shell length, shell dry weight, soft tissue dry weight, and soft tissue wet weight. The body mass index of the physiological rate function was 0.67, and the filtration rate (… CR ), oxygen consumption rate ( OR ) and ammonia excretion rate ( NR The data were standardized, and each physiological rate was converted into carbon equivalent to assess the carbon budget of the Manila clam. For example... Figure 2 As shown, the carbon intake of Manila clams exhibited a fluctuating downward trend, with the lowest value occurring in November at 1.50 ± 0.66 mg Cg. -1 h -1 The highest value was observed in August, reaching 2.70 ± 0.37 mg Cg. -1 h -1 ( Figure 2 A). Assimilated carbon showed a similar trend, but rose significantly in October, reaching 0.70 ± 0.22 mg Cg. -1 h -1 ( Figure 2 B). Respiratory carbon showed a significant seasonal variation, with the lowest value occurring in March at 0.07 ± 0.04 mg Cg. -1 h -1 The highest value was observed in August, reaching 0.23 ± 0.04 mg Cg. -1 h -1 ( Figure 2 C). Carbon excretion gradually increased from June, peaking in August at 0.013 ± 0.004 mg Cg. -1 h -1 It then gradually declined from August to October, finally reaching its lowest level of the year in March. Figure 2 D). On an annual scale, the average percentage of growth carbon in Manila clams relative to their total dietary carbon intake is 80.93 ± 9.90%. Figure 2 E).

[0024] Next, a carbon budget model for individual Manila clams was constructed based on dynamic energy budget theory. The model includes three state variables: stored energy, structural volume, and reproductive energy. Chlorophyll a and particulate organic matter are used as food sources, and the influence of various environmental factors such as temperature, salinity, pH, and total alkalinity is considered. Among these, the shape coefficient (… δ M ), the ratio of soft tissue dry weight to shell dry weight ( δ S ), shell carbon content ( S c Carbon uptake rate at optimum temperature ( IR co ) and respiratory carbon release rate ( OR co It is calculated using shell length, shell dry weight, soft tissue wet and dry weight, and related physiological parameters.

[0025] Then, carbon storage is assessed based on the simulation results of the dynamic carbon budget model. For example... Figure 3 As shown, the carbon storage of Manila clams' shells is correlated with shell growth, with the lowest value occurring in December at 0.40 ± 0.12 mg Cind. -1 The highest value was observed in June, reaching 1.65 ± 0.17 mg Cind. -1 ( Figure 3 A). The carbon release trend during calcification is similar to that of shell carbon storage, with an annual average of 0.54 ± 0.23 mg Cind. -1 ( Figure 3 C). Biodeposited carbon storage and respiration carbon release are related to tissue weight and water temperature: the average biodeposited carbon storage is 1.87 ± 1.08 mg Cind. -1 The highest value was observed in May, at 4.09 ± 0.24 mg Cind. -1 ( Figure 3 B). Respiratory carbon release increased from July, then decreased between November and February of the following year, before gradually recovering and reaching a peak of 1.70 ± 0.10 mg Cind in May. -1 ( Figure 3 D). Carbon storage flux fluctuated, with the highest value occurring in May at 4.86 ± 0.23 mg Cind. -1 The lowest value was observed in January, at 1.56 ± 0.43 mg Cind. -1 ( Figure 3 E). The total carbon storage of each Manila clam in one year is 973 mg, which is equivalent to 3561 mg CO2.

[0026] At the same time, the model is validated, such as Figure 4As shown, the shell length of the Manila clam exhibited two distinct growth stages throughout the assessment period: the first stage was from June to November 2022, and the second stage was from March to May 2023. Figure 4 A). Meanwhile, its soft tissue dry weight continued to increase throughout the study period ( Figure 4 B). The simulated results for shell length and soft tissue dry weight predicted by the model are in high agreement with the measured data. Specifically, the simulated shell length is between 2.32 and 3.21 cm, and shows a high correlation with the measured values ​​(correlation coefficient of 0.961). Figure 4 C). The simulated soft tissue dry weight ranged from 0.10 to 0.33 g, and showed a high correlation with the measured data (correlation coefficient of 0.912). Comparing the simulated shell length and tissue dry weight of Manila clams with the actual observed data, the average deviations of shell length and tissue dry weight were 0.040 and 0.012, respectively, with root mean square errors of 0.084 and 0.035, respectively.

[0027] It should be noted that this invention is not only applicable to Manila clams, but can also be applied to the carbon storage assessment of other economically important bivalve mollusks (such as oysters and scallops) by adjusting the model parameters, thereby providing a general tool for marine aquaculture carbon sink management.

[0028] The beneficial effects of this invention are as follows: 1) A dynamic carbon budget assessment model for Manila clams, integrating multiple environmental factors and energy budget, was constructed, improving the systematicness and accuracy of the assessment method; 2) By combining continuous on-site monitoring with model simulation, dynamic assessment of carbon storage in bivalves was achieved daily; 3) The contributions of two carbon storage pathways, shell formation and biodeposition, were quantified, providing specific data support for assessing the carbon sink function of shellfish aquaculture; 4) It can be extended to the assessment of carbon storage in other bivalve mollusks and provide a scientific basis for optimizing aquaculture density and carbon trading.

[0029] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0030] Specific examples have been used to illustrate the principles and implementation methods of this invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this invention. Furthermore, those skilled in the art will recognize that, based on the ideas of this invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A method for dynamic assessment of carbon storage in bivalves, characterized in that, Includes the following steps: Environmental data is monitored and collected for typical aquaculture areas, and functional functions are calculated based on the environmental data. A field flow-through experiment was conducted on bivalve mollusks in the typical aquaculture area, and the physiological and biological parameters of the bivalve mollusks were measured. The carbon parameters of the bivalve mollusks were calculated based on the physiological parameters. Based on the dynamic energy budget theory, a carbon budget model is constructed according to the environmental data and the carbon parameters. The carbon budget model is used to simulate the carbon storage flux of the bivalve mollusks during their growth cycle.

2. The method for dynamic assessment of carbon storage in bivalves according to claim 1, characterized in that, The environmental data include: temperature, salinity, pH, total alkalinity, chlorophyll a concentration, particulate organic matter, particulate organic carbon, and total particulate matter concentration.

3. The method for dynamic assessment of carbon storage in bivalves according to claim 2, characterized in that, The physiological parameters include: filtration rate, feeding rate, absorption efficiency, oxygen consumption rate, and ammonia excretion rate; the biological parameters include: shell length, shell dry weight, soft tissue dry weight, and soft tissue wet weight.

4. The method for dynamic assessment of carbon storage in bivalves according to claim 3, characterized in that, The carbon parameters of the bivalve mollusks are calculated based on the physiological parameters, including: The filtration rate is converted into ingestible carbon through the particulate organic carbon; Assimilated carbon is calculated based on the ingested carbon and the absorption efficiency; The oxygen consumption rate is converted into respiratory carbon by the average respiratory quotient; The ammonia excretion rate is converted into carbon excretion. Based on the energy budget equation, the growth carbon is calculated according to the assimilated carbon, the respired carbon, and the excreted carbon.

5. The method for dynamic assessment of carbon storage in bivalves according to claim 2, characterized in that, Using the chlorophyll a concentration and the particulate organic matter as food sources, and combining the temperature, salinity, pH and total alkalinity, a carbon budget model is constructed that includes stored energy, structural volume and reproductive energy. The carbon budget model includes: temperature dependence function, metabolic rate, energy density, food assimilation rate, functional response, food density, food dependence, inorganic particle size concentration, maintenance rate, reproductive maintenance rate, energy storage change rate, reproductive energy storage change rate, volumetric growth, soft tissue dry weight, shell dry weight, shell calcification carbon fixation, biogenic sedimentation carbon fixation, respiration carbon release, and calcification carbon release.

6. The method for dynamic assessment of carbon storage in bivalves according to claim 5, characterized in that, The carbon budget model simulates the carbon storage flux of the bivalve mollusks during their growth cycle, including: Calculate the carbon reserves of seashells based on their carbon content; The carbon storage of biogenic sediments is calculated based on the temperature dependence function, the dry weight of the soft tissue, and the carbon uptake rate at the optimal seawater temperature. The carbon storage flux is estimated based on the carbon storage in the shells and the carbon storage in the biogenic sediments.

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