CN111126768B - Fishery habitat water quality assessment method and system - Google Patents
Fishery habitat water quality assessment method and system Download PDFInfo
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Abstract
The invention discloses a fishery habitat water quality assessment method, which comprises the steps of constructing a water quality prior probability table, a copepod density grade table and a copepod density condition probability table of an assessment object according to historical water quality monitoring data and historical copepod density monitoring data of the assessment object; acquiring the current copepods density of an evaluation object, and constructing a density set; searching a copepod density grade table and a copepod density condition probability table, and acquiring the copepod density condition probability corresponding to each copepod density grade in the density set; and substituting the conditional probability of the density of the copepods and the prior probability of the water quality into a Bayesian inference model, sequentially calculating the posterior probability of the water quality corresponding to the density of each copepod in the density set, and taking the posterior probability of the water quality corresponding to the density of the last copepod as an evaluation result. A corresponding system is also disclosed. According to the invention, only the copepods density is collected, and the water quality evaluation result can be rapidly obtained based on the copepods density without a plurality of physical and chemical indexes.
Description
Technical Field
The invention relates to a fishery habitat water quality assessment method and system, and belongs to the field of fishery habitat water environment quality assessment.
Background
The water quality of the fishery habitat is closely related to fishery production, human life and the health condition of an ecological system. The rapid assessment of the water quality condition of the fishery habitat is a necessary condition for realizing rapid perception and rapid response of fishery water environment management. The traditional water quality assessment method is mainly constructed based on monitoring data of physical and chemical indexes, the collection of the physical and chemical indexes is time-consuming and labor-consuming, information contained in multiple indexes is difficult to integrate, and rapid assessment of water quality is inconvenient.
Disclosure of Invention
The invention provides a fishery habitat water quality assessment method and system, and solves the problems disclosed in the background technology.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
a water quality assessment method for a fishery habitat comprises the following steps,
according to historical water quality monitoring data and historical copepods density monitoring data of an evaluation object, constructing a water quality prior probability table, a copepods density grade table and a copepods density condition probability table of the evaluation object;
acquiring the current copepods density of an evaluation object, and constructing a density set;
searching a copepod density grade table and a copepod density condition probability table to obtain copepod density condition probability corresponding to each copepod density grade in the density set;
substituting the copepods density condition probability and the water quality prior probability into a Bayesian inference model, sequentially calculating the water quality posterior probability corresponding to each copepods density in the density set, and taking the water quality posterior probability corresponding to the last copepods density as an evaluation result; and if not, taking the water quality posterior probability corresponding to the previous copepod density as the water quality prior probability corresponding to the next copepod density.
Responding to the fact that the historical water quality monitoring data meet preset requirements, and constructing a water quality prior probability table;
wherein the preset requirements are as follows:
the quantity of the historical water quality monitoring data exceeds a minimum quantity threshold value;
the quantity of the qualified historical water quality monitoring data reaches or exceeds a minimum quantity threshold value;
the qualified historical water quality monitoring data covers the evaluation range of the evaluation object.
And when the water quality prior probability table is constructed, in response to the fact that the calculated water quality prior probability is smaller than the lowest probability threshold, replacing the water quality prior probability with the lowest probability threshold.
Responding to the fact that historical copepods density monitoring data meet preset requirements, and constructing a copepods density grade table;
wherein the preset requirements are as follows:
the quantity of qualified historical copepod density monitoring data reaches or exceeds a minimum quantity threshold;
qualified historical copepods density monitoring data covers the assessment range of the assessment subjects.
Responding to the fact that historical water quality monitoring data and historical copepods density monitoring data meet preset requirements, and constructing a copepods density condition probability table;
wherein the preset requirements are as follows:
the historical water quality monitoring data and the historical copepods density monitoring data have time-space consistency;
the number of time-space consistent arrays exceeds a minimum number threshold, and one array comprises historical water quality monitoring data and corresponding historical copepods density monitoring data.
When a copepod density condition probability table is constructed; responding to no data under certain water quality, and setting the density conditional probability of copepods under the water quality as a preset value; in response to the radial-foot density conditional probability being less than the minimum probability threshold, replacing the radial-foot density conditional probability with the minimum probability threshold; in response to the radial foot density conditional probability being greater than the highest probability threshold, replacing the radial foot density conditional probability with the highest probability threshold.
The Bayesian inference model has the formula as follows,
wherein, P i(k) The posterior probability of the water quality corresponding to the kth copepod density in the ith class water quality density set is more than or equal to 1 and less than or equal to n, n is the density number of copepods in the ith class water quality set, P is i(k-1) Is the prior probability of water quality corresponding to the density of the kth copepod in the density set under the ith class of water quality, P (j|i) Is the probability of copepod density grade j under the ith water quality.
A water quality assessment system for a fishery habitat, comprising,
a table module: according to historical water quality monitoring data and historical copepods density monitoring data of an evaluation object, constructing a water quality prior probability table, a copepods density grade table and a copepods density condition probability table of the evaluation object;
a density set module: acquiring the current copepods density of an evaluation object, and constructing a density set;
a conditional probability module: searching a copepod density grade table and a copepod density condition probability table, and acquiring the copepod density condition probability corresponding to each copepod density grade in the density set;
an evaluation module: bringing the density conditional probability and the water quality prior probability of the copepods into a Bayesian inference model, sequentially calculating the water quality posterior probability corresponding to the density of each copepod in the density set, and taking the water quality posterior probability corresponding to the density of the last copepod as an evaluation result; and if the water quality posterior probability corresponding to the first copepod density is calculated, the water quality posterior probability is brought into the water quality prior probability table, otherwise, the water quality posterior probability corresponding to the previous copepod density is used as the water quality prior probability corresponding to the next copepod density.
A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform a fisheries habitat water quality assessment method.
A computing device comprising one or more processors, memory, and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing a fishery habitat water quality assessment method.
The invention achieves the following beneficial effects: 1. according to the invention, only the copepods density is collected, and a water quality evaluation result can be rapidly obtained based on the copepods density without a plurality of physical and chemical indexes; 2. the method adopts the probability language to describe the water quality evaluation result of the fishery habitat, can definitely give the hit rate and the false alarm rate of each water quality grade evaluation, and has more real and credible result; 3. according to the invention, historical data information is fully utilized, the historical information is fully mined and used as a basis for evaluating the current situation of water quality, and the accuracy of water quality inference is obviously improved; 4. the invention has memory property, and the prior probability of the previous evaluation result during the next evaluation can be used as the increase of the observation times of the copepod density, and the rapid automatic calculation can be realized through limited circulation.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a flow chart for constructing a water quality prior probability table;
FIG. 3 is a flow chart for constructing a copepod density rank table;
fig. 4 is a flowchart of constructing a probability table of copepods density condition.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
As shown in fig. 1, a water quality assessment method for a fishery habitat comprises the following steps:
Common fishery production water body types comprise reservoirs, lakes, rivers and estuaries, the minimum scale of an evaluation range is hundred meters, and the water quality types of all water bodies comprise I type water, II type water, III type water, IV type water, V type water and inferior V type water.
And 2, constructing a water quality prior probability table, a copepod density grade table and a copepod density condition probability table of the evaluation object according to the historical water quality monitoring data and the historical copepod density monitoring data of the evaluation object.
Responding to the fact that the historical water quality monitoring data meet preset requirements, and constructing a water quality prior probability table; the water quality prior probability table refers to a water quality grade probability distribution in an evaluation range.
The preset requirements are as follows:
the number of the historical water quality monitoring data exceeds a minimum number threshold, and the minimum number threshold is generally 30;
the quantity of qualified historical water quality monitoring data reaches or exceeds a minimum quantity threshold value;
the qualified historical water quality monitoring data covers the evaluation range of the evaluation object.
When a water quality prior probability table is constructed, in response to the fact that the calculated water quality prior probability is smaller than a minimum probability threshold, the water quality prior probability is replaced by the minimum probability threshold; the lowest probability threshold is typically 0.01.
The specific construction process is shown in fig. 2:
a1) examining historical water quality monitoring data, and if the total number is more than 30, rejecting unreliable and repeated data; otherwise, ending;
a2) checking the remaining qualified data, and if the number of qualified data is more than 30, turning to step a 3; otherwise, ending;
a3) judging whether the evaluation range of the evaluation object is covered, if so, calculating the prior probability of the water quality, and turning to the step a 4; otherwise, ending;
a4) and judging whether the water quality prior probability is not less than 0.01, if so, inputting a water quality prior probability table, and otherwise, replacing the water quality prior probability by 0.01 and inputting the water quality prior probability table.
Responding to historical copepod density monitorMeasuring data to meet preset requirements, and constructing a copepod density grade table; the copepods density grade comprises no copepods, low density, medium density and high density, the copepods density is sorted from small to large, and the copepods density is the number (such as one/m) in a unit volume of water 3 ) The critical value of 'low-to-medium density' is 25% quantile of historical copepod density number set, and the critical value of 'medium-to-high density' is 75% quantile of historical copepod density number set.
The preset requirements are as follows:
the number of qualified historical copepods density monitoring data meets or exceeds a minimum number threshold;
qualified historical copepod density monitoring data covers the evaluation range of the evaluation subject.
The specific construction process is shown in fig. 3:
b1) examining historical copepod density monitoring data, and if the total number is greater than 30, step b 2; otherwise, ending;
b2) judging whether the data cover the evaluation range of the evaluation object, if so, rejecting 0 value and unreliable data, and turning to step b 3; otherwise, ending;
b3) if the qualified/available data is more than 30, go to step b 4; otherwise, ending;
b4) and classifying according to a critical value, and inputting into a copepod density grade table.
Responding to the fact that historical water quality monitoring data and historical copepods density monitoring data meet preset requirements, and constructing a copepods density condition probability table; the copepod density conditional probability is the probability distribution of the copepod density grade under a given water quality class in an evaluation range.
The preset requirements are as follows:
the historical water quality monitoring data and the historical copepod density monitoring data have space-time consistency (including space consistency and time consistency, the space consistency means that monitoring sites of the historical monitoring data are consistent, and the time consistency means that monitoring time periods of the historical monitoring data are consistent);
the number of time-space consistent arrays exceeds a minimum number threshold, and one array comprises historical water quality monitoring data and corresponding historical copepods density monitoring data.
When a probability table of the copepods density condition is constructed; responding to no data under certain water quality, wherein the conditional probability of the copepods density under the water quality is a preset value, and the preset value is generally 0.25; in response to the radial-podium density conditional probability being less than the lowest probability threshold, replacing the radial-podium density conditional probability with the lowest probability threshold; in response to the radial foot density conditional probability being greater than the highest probability threshold, the radial foot density conditional probability is replaced with the highest probability threshold, which is typically 0.97.
The specific construction flow is shown in fig. 4:
c1) screening a space-time consistency array, if the array quantity is more than 30, turning to the step c2, otherwise, ending;
c2) grouping according to water quality categories;
c3) calculating the density conditional probability of copepods under a certain type of water quality;
c4) if there is no data (P in the figure) under certain water quality i When the water quality of the ith class is not provided with data and the historical copepod density is not provided with data at the moment), the conditional probability of the copepod density under the water quality of the ith class is 0.25, and a probability table of the copepod density condition is input; otherwise go to step c 5;
c5) if the copepod density conditional probability corresponding to a certain grade under the water quality is less than 0.01, replacing the copepod density conditional probability with 0.01, and inputting a copepod density conditional probability table; if the copepod density conditional probability corresponding to a certain grade under the water quality is greater than 0.97, replacing the copepod density conditional probability with 0.97, and inputting a copepod density conditional probability table; otherwise, the radial foot density conditional probability is directly input into the radial foot density conditional probability table.
If the historical data is not sufficient, the construction is carried out by taking the reference values of the extensive examples and documents as the basis.
And 3, acquiring the current copepods density of the evaluation object, and constructing a density set.
Collecting the copepods density which covers and represents the evaluation range, and collecting the copepods density obtained by n times of observation into a density set, wherein the copepods density comprises the following components: the method comprises the steps of measuring the density of copepods based on unconcentrated undisturbed water samples, measuring the density of copepods based on concentrated samples obtained by treating undisturbed water by a precipitation method, measuring the density of copepods based on concentrated samples quantitatively collected by plankton nets with different specifications, measuring the density of copepods based on in-situ water body observation and measuring the density of copepods based on an effective prediction model.
And 4, searching a copepod density grade table and a copepod density condition probability table, and acquiring the copepod density condition probability corresponding to each copepod density grade in the density set.
Step 5, bringing the copepods density condition probability and the water quality prior probability into a Bayesian inference model, sequentially calculating the water quality posterior probability corresponding to each copepod density in the density set, and taking the water quality posterior probability corresponding to the last copepod density as an evaluation result; and if not, taking the water quality posterior probability corresponding to the previous copepod density as the water quality prior probability corresponding to the next copepod density.
The Bayesian inference model formula is as follows:
wherein, P i(k) The posterior probability of the water quality corresponding to the kth copepod density in the ith class water quality density set is more than or equal to 1 and less than or equal to n, n is the density number of copepods in the ith class water quality set, P is i(k-1) Is the prior probability of water quality corresponding to the density of the kth copepod in the density set under the ith class of water quality, P (j|i) Is a probability distribution table of copepods density grade j under the i-th water quality.
In order to make the evaluation result more intuitive, the evaluation result can be put into an evaluation report with a uniform format, and the content of the evaluation report comprises: basic information (evaluation time, evaluation object, evaluator, water body type, water area range, copepod density data, data source, sampling time), water quality evaluation result (water quality category and posterior probability distribution), water quality prior probability table (water quality category and prior probability distribution, source), copepod density grade table (density grade and limit value, source), copepod density grade condition probability table (copepod density grade, water quality category, condition probability distribution, source).
Taking two reservoirs A and B as an example, the reservoir A and the reservoir B are positioned in the same region, the reservoir A is a large reservoir, the reservoir B is a medium reservoir, each reservoir forms a water quality evaluation unit, the evaluation ranges are all the whole reservoir regions, and a water quality prior probability table, a copepod density grade table and a copepod density condition probability table of an evaluation object built based on historical data are as follows:
TABLE 1 Water quality Prior probability table
TABLE 2 copepods Density Scale Table (units: pieces/m) 3 )
TABLE 3 probability table for copepods density condition
The constructed density set elements are shown in table 4,
TABLE 4 copepods Density (units: pieces/m) 3 )
Based on Bayes inference model, d is obtained 1 The corresponding posterior probability of water quality is shown in table 5,
TABLE 5 d 1 Corresponding posterior probability of water quality
From d 1 Step by step calculation to d 10 The corresponding posterior probability of water quality, as shown in table 6,
TABLE 6 d 10 Corresponding water quality posterior probability (i.e. assessment result)
As can be seen from the above table, the first reservoir has a water quality condition of 70% and a probability of III-IV water, respectively; the water quality condition of the reservoir B is I-III water with the probability of more than 99 percent; the evaluation result shows that the water quality of the reservoir B is superior to that of the reservoir A.
The final evaluation report is shown in tables 7 and 8,
TABLE 7 evaluation report of library A
TABLE 8 evaluation report of reservoir B
The method only collects the copepods density, and can quickly obtain a water quality evaluation result based on the copepods density without a plurality of physical and chemical indexes; the method adopts the probability language to describe the water quality evaluation result of the fishery habitat, can definitely give the hit rate and the false alarm rate of each water quality grade evaluation, and the result is more real and credible; the method fully utilizes the historical data information, fully excavates the historical information and uses the historical information as a basis for evaluating the current situation of the water quality, and the historical data as the prior information can obviously improve the accuracy of water quality inference; the method has memory, and with the increase of the observation times of the copepod density, the prior probability of the previous evaluation result in the next evaluation can be quickly and automatically calculated through limited circulation.
A water quality assessment system for a fishery habitat, comprising,
a table module: according to historical water quality monitoring data and historical copepods density monitoring data of an evaluation object, constructing a water quality prior probability table, a copepods density grade table and a copepods density condition probability table of the evaluation object;
density set module: acquiring the current copepods density of an evaluation object, and constructing a density set;
a conditional probability module: searching a copepod density grade table and a copepod density condition probability table, and acquiring the copepod density condition probability corresponding to each copepod density grade in the density set;
an evaluation module: substituting the copepods density condition probability and the water quality prior probability into a Bayesian inference model, sequentially calculating the water quality posterior probability corresponding to each copepods density in the density set, and taking the water quality posterior probability corresponding to the last copepods density as an evaluation result; and if not, taking the water quality posterior probability corresponding to the previous copepod density as the water quality prior probability corresponding to the next copepod density.
A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform a fisheries habitat water quality assessment method.
A computing device comprising one or more processors, memory, and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing a fisheries habitat water quality assessment method.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The present invention is not limited to the above embodiments, and any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are included in the scope of the claims of the present invention which are filed as the application.
Claims (6)
1. A water quality assessment method for a fishery habitat is characterized by comprising the following steps: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
according to historical water quality monitoring data and historical copepods density monitoring data of an evaluation object, constructing a water quality prior probability table, a copepods density grade table and a copepods density condition probability table of the evaluation object;
the method comprises the following steps of establishing a water quality prior probability table in response to the fact that historical water quality monitoring data meet preset requirements; the preset requirements corresponding to the construction of the water quality prior probability table are as follows: the quantity of the historical water quality monitoring data exceeds a minimum quantity threshold value; the quantity of qualified historical water quality monitoring data reaches or exceeds a minimum quantity threshold value; the qualified historical water quality monitoring data cover the evaluation range of the evaluation object;
responding to the fact that historical copepods density monitoring data meet preset requirements, and constructing a copepods density grade table; the preset requirements corresponding to the radius density grade table are constructed as follows: the quantity of qualified historical copepod density monitoring data reaches or exceeds a minimum quantity threshold; qualified historical copepods density monitoring data covers the evaluation range of the evaluation object;
responding to the fact that the historical water quality monitoring data and the historical copepod density monitoring data meet preset requirements, and constructing a copepod density condition probability table; the preset requirements corresponding to the construction of the radius foot density condition probability table are as follows: the historical water quality monitoring data and the historical copepods density monitoring data have time-space consistency; the number of time-space consistent arrays exceeds a minimum number threshold, and one array comprises historical water quality monitoring data and corresponding historical copepods density monitoring data;
when a probability table of the copepods density condition is constructed; responding to no data under certain water quality, and enabling the density conditional probability of the copepods under the water quality to be a preset value; in response to the radial-foot density conditional probability being less than the minimum probability threshold, replacing the radial-foot density conditional probability with the minimum probability threshold; in response to the radial-foot density conditional probability being greater than the highest probability threshold, replacing the radial-foot density conditional probability with the highest probability threshold;
acquiring the current copepods density of an evaluation object, and constructing a density set;
searching a copepod density grade table and a copepod density condition probability table to obtain copepod density condition probability corresponding to each copepod density grade in the density set;
substituting the copepods density condition probability and the water quality prior probability into a Bayesian inference model, sequentially calculating the water quality posterior probability corresponding to each copepods density in the density set, and taking the water quality posterior probability corresponding to the last copepods density as an evaluation result; and if not, taking the water quality posterior probability corresponding to the previous copepod density as the water quality prior probability corresponding to the next copepod density.
2. The fishery habitat water quality assessment method according to claim 1, characterized in that: and when the water quality prior probability table is constructed, in response to the fact that the calculated water quality prior probability is smaller than the lowest probability threshold, replacing the water quality prior probability with the lowest probability threshold.
3. The fishery habitat water quality assessment method according to claim 1, characterized in that: the Bayesian inference model has the formula as follows,
wherein, P i(k) The posterior probability of the water quality corresponding to the kth copepod density in the ith class water quality density set is more than or equal to 1 and less than or equal to n, n is the density number of copepods in the ith class water quality set, P is i(k-1) Is the prior probability of water quality corresponding to the density of the kth copepod in the density set under the ith class of water quality, P (j|i) Is the probability of copepod density grade j under the ith water quality.
4. A fishery habitat water quality assessment system is characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
a table module: according to historical water quality monitoring data and historical copepods density monitoring data of an evaluation object, constructing a water quality prior probability table, a copepods density grade table and a copepods density condition probability table of the evaluation object;
responding to the fact that historical water quality monitoring data meet preset requirements, and constructing a water quality prior probability table; the preset requirements corresponding to the construction of the water quality prior probability table are as follows: the quantity of the historical water quality monitoring data exceeds a minimum quantity threshold value; the quantity of the qualified historical water quality monitoring data reaches or exceeds a minimum quantity threshold value; the qualified historical water quality monitoring data cover the evaluation range of the evaluation object;
responding to the fact that historical copepods density monitoring data meet preset requirements, and constructing a copepods density grade table; the preset requirements corresponding to the radius density grade table are constructed as follows: the quantity of qualified historical copepod density monitoring data reaches or exceeds a minimum quantity threshold; qualified historical copepods density monitoring data covers the evaluation range of the evaluation object;
responding to the fact that the historical water quality monitoring data and the historical copepod density monitoring data meet preset requirements, and constructing a copepod density condition probability table; the preset requirements corresponding to the construction of the radius foot density condition probability table are as follows: the historical water quality monitoring data and the historical copepods density monitoring data have time-space consistency; the number of time-space consistent arrays exceeds a minimum number threshold, and one array comprises historical water quality monitoring data and corresponding historical copepod density monitoring data;
when a probability table of the copepods density condition is constructed; responding to no data under certain water quality, and setting the density conditional probability of copepods under the water quality as a preset value; in response to the radial-podium density conditional probability being less than the lowest probability threshold, replacing the radial-podium density conditional probability with the lowest probability threshold; in response to the radial-podium density conditional probability being greater than the highest probability threshold, replacing the radial-podium density conditional probability with the highest probability threshold;
a density set module: acquiring the current copepods density of an evaluation object, and constructing a density set;
a conditional probability module: searching a copepod density grade table and a copepod density condition probability table to obtain copepod density condition probability corresponding to each copepod density grade in the density set;
an evaluation module: substituting the copepods density condition probability and the water quality prior probability into a Bayesian inference model, sequentially calculating the water quality posterior probability corresponding to each copepods density in the density set, and taking the water quality posterior probability corresponding to the last copepods density as an evaluation result; and if the water quality posterior probability corresponding to the first copepod density is calculated, the water quality posterior probability is brought into the water quality prior probability table, otherwise, the water quality posterior probability corresponding to the previous copepod density is used as the water quality prior probability corresponding to the next copepod density.
5. A computer readable storage medium storing one or more programs, wherein: the one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the methods of claims 1-3.
6. A computing device, characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
one or more processors, memory, and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods of claims 1-3.
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