Prediction device and prediction method

Abstract

A prediction device 1 includes a model generation unit 21 that generates an ecosystem model for predicting the concentration of phytoplankton. With respect to a photosynthesis term for calculating a change in the concentration of the phytoplankton per unit time, the model generation unit 21 uses a light restriction function that is in accordance with the characteristics of the phytoplankton.

Description

Prediction device and prediction method

[0001] The present disclosure relates to a prediction device and a prediction method.

[0002] Non-Patent Document 1 discloses that the concentration of phytoplankton in the ocean interacts with climate change because it contributes significantly to the amount of CO2 absorbed by the ocean. Non-Patent Document 2 discloses that accurate estimation of phytoplankton is essential when discussing changes in the ecosystem balance because phytoplankton are primary producers in the marine ecosystem.

[0003] Non-Patent Documents 3 and 4 disclose that the market for microalgae such as phytoplankton has been expanding in recent years. Accordingly, in the production of phytoplankton in land-based culture ponds or culture tanks, businesses have been seeking to improve production efficiency by predicting yields according to culture conditions.

[0004] There are ecosystem models that predict the concentration of phytoplankton. Non-Patent Document 5 discloses an NPZD model targeting lower trophic ecosystems. The lower trophic ecosystem consists of nutrients (N), phytoplankton (P), zooplankton (Z), and detritus (D). Non-Patent Document 6 discloses a water quality prediction model for closed water bodies.

[0005] Generally, in ecosystem models, changes in phytoplankton concentration are reproduced by the photosynthesis term, extracellular secretion, death, grazing, and sedimentation. Of these, the photosynthesis term, which contributes to growth, is important in predicting phytoplankton growth. The photosynthesis term is determined by the maximum growth rate, light limitation, temperature limitation, and nutrient limitation. Non-Patent Documents 5 and 6 disclose that the Steele equation can be used for light limitation.

[0006] Non-Patent Document 7 discloses photoinhibition, a phenomenon in which the photosynthetic function of phytoplankton declines under strong light. Non-Patent Document 8 discloses that in actual photoinhibition of phytoplankton growth, when phytoplankton are irradiated with light exceeding the optimal light intensity, the rate at which the photosynthetic function declines varies depending on the state or species of phytoplankton.

[0007] Basu, S.; Mackey, KRM Phytoplankton as Key Mediators of the Biological Carbon Pump: Their Responses to a Changing Climate. Sustainability 2018, 10, 869. https: / / doi.org / 10.3390 / su10030869 Cloern, JE, Foster, SQ, and Kleckner, AE: Phytoplankton primary production in the world's estuarine-coastal ecosystems, Biogeosciences, 11, 2477-2501, https: / / doi.org / 10.5194 / bg-11-2477-2014, 2014. Omori, Masayuki. "The Present and Future of Microalgae Utilization." Journal of the Japanese Society for Microbial Resources = Microbial Resources and Systematics 33.2 (2017): 87-92. Washimi, Yoshihiko "The Future Opened by Microalgae (Microalgae): Their Utility and Use," Science and Technology Trends, 102 (2009): 11-22. "A Practical Guide to Ecological Modeling," Karline Soetaert and Peter MJ Herman, Springer, 2009. Masuda, Shinya, et al. "Construction of a Water Quality Prediction Model Taking into Account Multiple Phytoplankton Species." (2009): 135-145. Uraya, Yoichi, and Takemoto, Shigeyuki. "An Experiment on Algal Growth Characteristics," Civil Engineering Research Institute Monthly Bulletin. (1990): 27-33. Douma, Akifumi, et al. "Dynamic Characteristics of Chlorophyll a and Nutrients in a Eutrophic Reservoir Using a Water Quality Prediction Model Taking into Account Multiple Species Composition." (2010): 73-89.

[0008] However, the problem with the reproduction of light limitation used in conventional models such as the Steele equation is that photoinhibition is not reproduced.When phytoplankton is exposed to light levels exceeding the optimal level for the phytoplankton, the photosynthetic function is uniformly reduced regardless of the state or species of the phytoplankton, and light limitation is not reproduced.

[0009] The present disclosure has been made in consideration of the above circumstances, and an object of the present disclosure is to provide a technology that can predict the concentration of phytoplankton taking photoinhibition into account.

[0010] A prediction device according to one aspect of the present disclosure includes a model generation unit that generates an ecosystem model for predicting the concentration of phytoplankton, and the model generation unit uses a light-limited function corresponding to the characteristics of the phytoplankton in a photosynthesis term for calculating the change in the concentration of the phytoplankton per unit time.

[0011] In one aspect of the prediction method of the present disclosure, a computer generates an ecosystem model that predicts the concentration of phytoplankton using a light-limited function that corresponds to the characteristics of the phytoplankton in a photosynthesis term for calculating the change in the concentration of the phytoplankton per unit time.

[0012] According to the present disclosure, it is possible to provide a technology that can predict the concentration of phytoplankton taking photoinhibition into account.

[0013] Fig. 1 is a diagram illustrating the system configuration of a prediction system according to the present disclosure and functional blocks of a prediction device. Fig. 2 is a diagram illustrating a light-limiting function for each constant related to photoinhibition. Fig. 3 is a flowchart illustrating an example of prediction processing by the prediction device. Fig. 4 is a diagram illustrating the hardware configuration of a computer used in the prediction device.

[0014] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. In the description of the drawings, the same parts are designated by the same reference numerals and the description thereof will be omitted.

[0015] (Prediction System) A prediction system 5 according to the present disclosure will be described with reference to Fig. 1. The prediction system 5 includes a prediction device 1 and a user terminal 2. The prediction device 1 and the user terminal 2 are connected to each other so as to be able to communicate bidirectionally.

[0016] The prediction device 1 generates an ecosystem model and uses the generated ecosystem model to predict the concentration of phytoplankton, etc. The prediction device 1 makes a prediction according to prediction conditions input from a user terminal 2 and displays the prediction results on the user terminal 2.

[0017] In the present disclosure, the ecosystem model predicts at least the concentration of phytoplankton. In addition to the concentration of phytoplankton, the ecosystem model may also predict the concentrations of other elements such as nutrients, zooplankton, and detritus. The ecosystem model may also express the increase or decrease of phytoplankton in other units that can be converted to the concentration of phytoplankton, such as the amount or number of phytoplankton.

[0018] The user terminal 2 inputs prediction conditions to the prediction device 1. The user terminal 2 displays the prediction results obtained by the prediction device 1.

[0019] (Prediction Device) The prediction device 1 includes model data 11, condition data 12, and result data 13, as well as the functions of a model generation unit 21, a prediction unit 22, an acquisition unit 23, and a display unit 24. Each piece of data is stored in a storage device such as a memory 902 or a storage 903. Each function is implemented in a CPU 901. One or more processors execute the processing of each function.

[0020] The model data 11 is data that identifies the ecosystem model generated by the model generation unit 21. The model data 11 may be in any format as long as it can identify the ecosystem model.

[0021] The condition data 12 is data of prediction conditions input from the user terminal 2. The prediction conditions include initial values ​​such as the concentration of phytoplankton, constants related to photoinhibition, and environmental parameter values ​​such as the amount of light.

[0022] The result data 13 is data output by the prediction unit 22 and is data predicted from the ecosystem model specified by the model data 11 and the condition data 12. The result data 13 includes the concentration of phytoplankton at each time step. In addition to the concentration of phytoplankton, the result data 13 may also include the concentrations of other elements such as nutrients, zooplankton, and detritus at each time step.

[0023] The model generation unit 21 generates an ecosystem model that predicts the amount of phytoplankton. The model generation unit 21 may generate an ecosystem model that predicts the concentrations of other elements such as nutrients, zooplankton, and detritus in addition to the concentration of phytoplankton.

[0024] The prediction unit 22 predicts the concentration of phytoplankton at each time step using the ecosystem model specified by the model data 11 and the condition data 12. In addition to the concentration of phytoplankton, the prediction unit 22 may also predict the concentrations of other elements such as nutrients, zooplankton, and detritus at each time step.

[0025] The acquisition unit 23 acquires the condition data 12 from the user terminal 2 .

[0026] The display unit 24 displays the result data 13 on the user terminal 2 .

[0027] (Ecosystem Model) The ecosystem model generated by the model generation unit 21 will now be described. In the present disclosure, the ecosystem model uses a light-limited function corresponding to the characteristics of each phytoplankton in the photosynthesis term for calculating the change in phytoplankton concentration per unit time. The ecosystem model reproduces the degree of change in photosynthetic function, which varies depending on the state and species of phytoplankton.

[0028] Generally, the change in phytoplankton concentration per unit time is calculated by subtracting the values ​​of the extracellular secretion term, mortality term, grazing term, and sedimentation term from the value specified by the photosynthesis term, as shown in Equation (1). The photosynthesis term is calculated by multiplying the maximum growth rate, the light-limiting function, the temperature-limiting function, and the nutrient-limiting function, as shown in Equation (2).

[0029]

[0030] The light-limited function is calculated according to the characteristics of the phytoplankton, such as the type and state, set in the ecosystem model. The light-limited function calculates the negative gradient when the amount of light is greater than a predetermined value using a formula that can be set according to the characteristics of the phytoplankton.

[0031] More specifically, the light-limiting function is calculated using equation (3). Here, a value corresponding to the characteristics of the phytoplankton is set as the constant a related to photoinhibition. The constant a related to photoinhibition is set to a different value depending on the characteristics of the phytoplankton, such as the type and state. Note that the time t, light intensity I, and optimal light intensity Iopt in equation (3) are specified from the user terminal 2, etc.

[0032] In contrast, Equation (4) is the conventional Steele equation for the light-limiting rate. Equation (4) does not include a constant term that depends on phytoplankton. Equation (4) shows that the amount of photosynthesis decreases at a uniform rate regardless of the characteristics of phytoplankton.

[0033]

[0034] Compared to equation (4), equation (3) has a negative correlation between the argument part of the exponential function and a. As a result, the light-limiting function calculated by equation (3) can control the rate at which photosynthetic function declines when phytoplankton is irradiated with light exceeding the optimal light intensity.

[0035] Referring to FIG. 2, the relationship of the light limiting function to the light amount I when the constant a shown in equation (3) is changed is shown. The thick solid line in FIG. 2 shows the light limiting function when the constant a is 1, i.e., the Steele equation shown in equation (4). In contrast, the other lines show the light limiting function when the constant in equation (3) is a natural number from 2 to 10. In FIG. 2, the optimal light amount Iopt is 20.

[0036] 2 shows that when the light intensity is greater than the optimal light intensity Iopt, the larger the constant a, the larger the negative gradient and the lower the light limiting function. As a result, when light intensity greater than the optimal light intensity is provided, the photosynthetic function is lowered due to light limitation. It is also possible to adjust the gradient at which the photosynthetic function is lowered by changing the setting of a.

[0037] The constant a is determined through a plankton growth experiment. For example, as shown in FIG. 2 , the characteristic is utilized in which, regardless of the value of the constant a, when light above the optimal light level is provided, photoinhibition occurs, photosynthesis stops, and the light-limiting function converges to 0. In an experiment in which the conditions for the light level provided to phytoplankton are changed, the light level at which the light-limiting function becomes 0 (an approximate value) is determined. In the graph shown in FIG. 2 , the light level at which the light-limiting function becomes 0 (an approximate value) for each constant a is compared with the light level determined in the experiment, thereby determining the constant a for the phytoplankton being tested.

[0038] (Prediction Method) A prediction method according to the present disclosure will be described with reference to FIG.

[0039] In step S1, the prediction device 1 generates an ecosystem model. The ecosystem model generated here uses a light-limited function corresponding to the characteristics of phytoplankton in the photosynthesis term for calculating the change in phytoplankton concentration per unit time.

[0040] In step S2, the prediction device 1 acquires prediction conditions from the user terminal 2. These prediction conditions include initial values ​​such as the concentration of phytoplankton, constant parameter values ​​related to photoinhibition, and environmental parameter values. The constant parameter values ​​related to photoinhibition include the optimal light intensity Iopt and the constant a related to photoinhibition in equation (3). The environmental parameter value is the light intensity I.

[0041] In step S3, the prediction device 1 makes a prediction using the model generated in step S1 and the prediction conditions acquired in step S2. In step S4, the prediction device 1 generates result data 13 including the predicted value obtained by the prediction. In step S5, the prediction device 1 displays the result data 13 on the user terminal 2.

[0042] According to the present disclosure, an ecosystem model is generated using a formula that reproduces the photoinhibition effect according to the characteristics of phytoplankton, thereby enabling the photoinhibition effect of phytoplankton to be appropriately reproduced and the concentration of phytoplankton to be predicted.

[0043] Phytoplankton are responsible for ocean CO2 absorption, which is closely related to climate change. The prediction device 1 according to the present disclosure can precisely predict changes in the amount of phytoplankton while reproducing the characteristics of each type of phytoplankton.

[0044] Phytoplankton supports the food chain as a primary producer in the marine ecosystem. Using an ecosystem model that can accurately predict phytoplankton abundance can lead to predictions of other biological species that make up the food chain and material cycle. Refining the prediction of phytoplankton abundance using the prediction device 1 according to the present disclosure will lead to improved prediction accuracy for the entire ecosystem.

[0045] In the microalgae market, phytoplankton is artificially mass-cultured in aquariums and other environments. The microalgae market is expanding into a wide range of fields, including energy, food, cosmetics, medicine, and feed production. The prediction device 1 according to the present disclosure enables accurate yield prediction according to culture conditions in the case of mass culturing phytoplankton. This enables increased profits by reducing material costs and maximizing production volume.

[0046] The prediction device 1 of the present embodiment described above uses, for example, a general-purpose computer system including a CPU (Central Processing Unit, processor) 901, a memory 902, a storage 903 (HDD: Hard Disk Drive, SSD: Solid State Drive), a communication device 904, an input device 905, and an output device 906. In this computer system, the CPU 901 executes a program loaded on the memory 902, thereby realizing each function of the prediction device 1.

[0047] The prediction device 1 may be implemented by one computer or by multiple computers, or may be a virtual machine implemented on a computer.

[0048] The program of the prediction device 1 can be stored in a computer-readable recording medium such as a HDD, SSD, USB (Universal Serial Bus) memory, CD (Compact Disc), or DVD (Digital Versatile Disc), or can be distributed via a network. The computer-readable recording medium is, for example, a non-transitory recording medium.

[0049] The present disclosure is not limited to the above-described embodiments, and various modifications are possible within the scope of the present disclosure.

[0050] REFERENCE SIGNS LIST 1 Prediction device 2 User terminal 5 Prediction system 11 Model data 12 Condition data 13 Result data 21 Model generation unit 22 Prediction unit 23 Acquisition unit 24 Display unit 901 CPU 902 Memory 903 Storage 904 Communication device 905 Input device 906 Output device

Claims

1. A prediction device comprising a model generation unit that generates an ecosystem model that predicts the concentration of phytoplankton, wherein the model generation unit uses a light-limited function corresponding to the characteristics of the phytoplankton in a photosynthesis term for calculating the change in the concentration of the phytoplankton per unit time.

2. The prediction device according to claim 1, wherein the light-limited function calculates a negative gradient when the amount of light is greater than a predetermined value using a formula that can be set according to the characteristics of the phytoplankton.

3. The prediction device according to claim 1, wherein the light-limited function is calculated using equation (1), and a constant related to photoinhibition is set to a value corresponding to the characteristics of the phytoplankton.

4. A prediction method in which a computer generates an ecosystem model that predicts the concentration of phytoplankton by using a light-limited function corresponding to the characteristics of the phytoplankton in a photosynthesis term for calculating changes in the concentration of the phytoplankton per unit time.

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