CN112507533A - Construction method of iris cuttlefish growth model based on marine environment factor - Google Patents
Construction method of iris cuttlefish growth model based on marine environment factor Download PDFInfo
- Publication number
- CN112507533A CN112507533A CN202011333818.7A CN202011333818A CN112507533A CN 112507533 A CN112507533 A CN 112507533A CN 202011333818 A CN202011333818 A CN 202011333818A CN 112507533 A CN112507533 A CN 112507533A
- Authority
- CN
- China
- Prior art keywords
- iris
- sst
- model
- cuttlefish
- growth
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 241000238371 Sepiidae Species 0.000 title claims abstract description 31
- 238000010276 construction Methods 0.000 title claims abstract description 8
- 244000071493 Iris tectorum Species 0.000 claims abstract description 23
- 238000000034 method Methods 0.000 claims abstract description 17
- 230000007613 environmental effect Effects 0.000 claims abstract description 13
- 230000035800 maturation Effects 0.000 claims abstract description 6
- 239000000654 additive Substances 0.000 claims abstract description 5
- 230000000996 additive effect Effects 0.000 claims abstract description 5
- 238000010219 correlation analysis Methods 0.000 claims abstract description 5
- 230000014509 gene expression Effects 0.000 claims abstract description 5
- 241000356847 Otolithes Species 0.000 claims description 16
- 230000002842 otolith Effects 0.000 claims description 16
- 210000001265 otolithic membrane Anatomy 0.000 claims description 16
- 241001663395 Sepiella Species 0.000 claims description 12
- 230000001568 sexual effect Effects 0.000 claims description 11
- 229930002875 chlorophyll Natural products 0.000 claims description 5
- 235000019804 chlorophyll Nutrition 0.000 claims description 5
- ATNHDLDRLWWWCB-AENOIHSZSA-M chlorophyll a Chemical compound C1([C@@H](C(=O)OC)C(=O)C2=C3C)=C2N2C3=CC(C(CC)=C3C)=[N+]4C3=CC3=C(C=C)C(C)=C5N3[Mg-2]42[N+]2=C1[C@@H](CCC(=O)OC\C=C(/C)CCC[C@H](C)CCC[C@H](C)CCCC(C)C)[C@H](C)C2=C5 ATNHDLDRLWWWCB-AENOIHSZSA-M 0.000 claims description 5
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 claims description 4
- 210000003128 head Anatomy 0.000 claims description 4
- 239000012535 impurity Substances 0.000 claims description 4
- 238000012360 testing method Methods 0.000 claims description 4
- 239000002775 capsule Substances 0.000 claims description 3
- 241000238370 Sepia Species 0.000 claims description 2
- 238000001514 detection method Methods 0.000 claims description 2
- 238000006243 chemical reaction Methods 0.000 claims 1
- 238000011161 development Methods 0.000 abstract description 5
- 238000011551 log transformation method Methods 0.000 abstract description 4
- 241000238366 Cephalopoda Species 0.000 description 7
- 238000011160 research Methods 0.000 description 4
- 239000013535 sea water Substances 0.000 description 4
- 210000002149 gonad Anatomy 0.000 description 3
- 238000005070 sampling Methods 0.000 description 3
- 241000251468 Actinopterygii Species 0.000 description 2
- 241000013757 Ranina Species 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 210000003205 muscle Anatomy 0.000 description 2
- 239000002994 raw material Substances 0.000 description 2
- 239000013589 supplement Substances 0.000 description 2
- 241000239250 Copepoda Species 0.000 description 1
- 241000238424 Crustacea Species 0.000 description 1
- 241000238437 Ommastrephidae Species 0.000 description 1
- 241000758713 Piperales Species 0.000 description 1
- 241000269849 Thunnus Species 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 238000000540 analysis of variance Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 235000013601 eggs Nutrition 0.000 description 1
- 230000035558 fertility Effects 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000013508 migration Methods 0.000 description 1
- 230000005012 migration Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000035938 sexual maturation Effects 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01K—ANIMAL HUSBANDRY; AVICULTURE; APICULTURE; PISCICULTURE; FISHING; REARING OR BREEDING ANIMALS, NOT OTHERWISE PROVIDED FOR; NEW BREEDS OF ANIMALS
- A01K61/00—Culture of aquatic animals
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/10—Numerical modelling
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A40/00—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production
- Y02A40/80—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production in fisheries management
- Y02A40/81—Aquaculture, e.g. of fish
Landscapes
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Environmental Sciences (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Computer Hardware Design (AREA)
- Biodiversity & Conservation Biology (AREA)
- Animal Husbandry (AREA)
- Zoology (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Marine Sciences & Fisheries (AREA)
- Farming Of Fish And Shellfish (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to the field of fishery, and discloses a construction method of an iris cuttlefish growth model based on marine environmental factors, which comprises the following steps: 1) calculating the SST of each month operation sea area within a certain time span; 2) calculating the growth speed v = ML/d of the iris cuttlefish at the time of primary maturation; 3) performing typical correlation analysis on the SST of each month operation sea area and the maximum correlation month and the corresponding sea area position of the iris cuttlefish in the corresponding year; 4) selecting SST and v with the maximum correlation months to perform generalized additive model analysis; log transformation is carried out on the growth speed of the iris tectorum, the iris tectorum accords with normal distribution, and GAM results are optimized by AIC; 5) and obtaining a relational expression of the SST and the v, and establishing a model. The model constructed by the method can be used for analyzing and mastering the individual growth rule of the iris tectorum under the influence of different marine environments, and provides reference for subsequent sustainability development of the iris tectorum.
Description
Technical Field
The invention relates to the field of fishery, in particular to a construction method of an iris cuttlefish growth model based on marine environmental factors.
Background
Iris cuttlefish (Septemotis otalianiensis) belongs to Cephalopoda (Cepalopoda), Piperales (Idiosepiida), Carnoea (Ommastrephidae) and Iris (Septemotis), is mainly distributed in east and middle of Pacific ocean and north of American western of India, is a typical ocean warm water Cephalopoda, and has high growth speed exceeding most fishes; short life, typically one year; the sexual maturity is rapid, the fertility is strong, female iris cuttlefish can lay eggs all the year round, the peak period of sexual maturity is in winter, and the peak period of sexual maturity of male iris cuttlefish is mainly in summer; the iris cuttlefish has the characteristic of vertical migration, is positioned in middle and lower sea areas in daytime, floats upwards to prey at night, is predacious, mostly prey objects are small fishes, and crustaceans and copepods have the characteristic of cannibalism, and meanwhile are also preyed by tunas, seabirds, seals and the like, and are positioned at important positions in an ecological system. The iris cuttlefish resource is large in supplement amount and good in development prospect, the status of the iris cuttlefish resource gradually rises in the south China sea commercial fishing along with the decline of offshore resources in recent years, and the iris cuttlefish resource has high economic value.
The ocean is the main carrier for storing energy of a climate system, the environment is complex and changeable, according to the definition of the American national ocean and atmosphere administration, the sea surface temperature in the Nino 3.4 area is an Elnino event when the temperature is higher than 0.5 ℃ for 3 months continuously, a Lanino event is lower than 0.5 ℃ for 3 months continuously, the Annelo event is an uncommon Elnino year in 2015, and the SSTA in the whole year is higher than 0.5 ℃. SSTA is lower than 0.5 ℃ from 7 months to the end of year in 2016, is a Ranina event, is normal months before 10 years in 2017, has no Herniano event or Ranina event, and has severe marine environment change in 2015-2017. The iris cuttlefish is easily influenced by the change of marine environment due to its short life cycle and high growth speed.
At present, researchers have studied how individual biology and resource supplement quantity of iris tectorum are affected by marine environment, but research on how different marine environmental factors affect iris tectorum growth speed has not been reported.
Therefore, there is a need to analyze and master the individual growth rule of iris tectorum under the influence of different marine environments, and provide data for the subsequent sustainable development of iris tectorum.
Disclosure of Invention
In order to solve the technical problems, the invention provides a method for constructing an iris tectorum growth model based on a marine environmental factor, and the model constructed by the method can be used for analyzing and mastering the individual growth rule of iris tectorum under the influence of different marine environments, so as to provide a reference for the subsequent sustainability development of iris tectorum.
The specific technical scheme of the invention is as follows:
in a first aspect, the invention provides a method for constructing an iris sepiella growth model based on a marine environmental factor, which comprises the following steps:
1) and calculating sea surface temperature data, namely SST, of the sea area in each month of operation within a certain time span.
2) Calculating the growth speed of the iris cuttlefish at the initial maturity: v ═ ML/d; in the formula: ML is the first sexual maturity of iris squid carcass length in mm, d is day age.
3) Performing typical correlation analysis on the operation sea area SST and the maximum correlation month and the corresponding sea area position of the iris cuttlefish in the corresponding year in a certain time span.
4) Selecting SST, SSS, Chl-a, LAT, LOG and v with the largest correlation months to perform generalized additive model analysis GAM: log (v) s (SST) + s (SSS) + s (Chl a) + s (LAT) + s (LON).
In the formula: v is the growth rate of iris tectorum at initial maturation, SST is the sea surface temperature of the operating sea area, SSS is the sea surface salinity of the operating sea area, Chl a is chlorophyll, LAT is latitude, and LON is longitude.
Log transformation is carried out on the growth speed of iris tectorum, the growth speed accords with normal distribution, GAM results are optimized through AIC, a GAM model with the minimum value of the AIC value is selected as a best fitting model, and the significance of each factor is evaluated through F test.
5) GAM judges the relation between each environmental factor and v, the most relevant month temperature in the most relevant sea area is used for carrying out relation model fitting, optimization is carried out through AIC, the relational expression of SST and v is finally obtained, and a model is established.
Preferably, the squid iris is male squid iris.
In the research of the group, the variance analysis shows that the growth speed of the female iris inkfish has obvious difference between sexes (F is 11.235, P is 0.002 < 0.05), and the male iris inkfish is more reasonable to be selected as a research object in consideration of that the experimental result may be caused by muscle energy consumed by the female iris inkfish during spawning.
Preferably, in step 1), the certain time span is 2015-2017.
Preferably, in step 1), the working sea area is 15 ° 50 'N to 18 ° 00' N, and 111 ° 50 'E to 114 ° 00' E.
Preferably, in step 1), the resolution at SST is calculated to be 0.5 ° × 0.5 °
Preferably, in step 2), the primary sexual maturation refers to a sexual maturity of III.
Preferably, in the step 2), the carcass length of the iris cuttlefish is accurate to 1 mm.
Preferably, in step 2), the method for detecting the age of day is: taking the otolith out of the iris tectorum head balancing capsule, putting the otolith into a centrifugal container containing alcohol, carrying out centrifugal treatment to remove impurities, embedding, slicing and grinding the otolith, reading the growth lines of the otolith through a microscope, counting by a plurality of experimenters respectively, and only keeping the result with the error of not more than 10 percent as the age in days.
In a second aspect, the invention provides an iris cuttlefish growth model based on a marine environmental factor, wherein the model is that v is 0.0561SST-0.9428, R2＝0.8513。
In a third aspect, the present invention provides a method for predicting the growth rate of iris: and substituting the SST into an iris cuttlefish growth model based on the marine environmental factor to obtain the growth speed of the iris cuttlefish.
Compared with the prior art, the invention has the beneficial effects that: the model constructed by the method can be used for analyzing and mastering the individual growth rule of the iris tectorum under the influence of different marine environments, and provides reference for subsequent sustainability development of the iris tectorum.
Drawings
FIG. 1 is a graph of GAM model analysis of the effect of sea surface temperature (a), latitude (b) and longitude (c) on growth rate of male Iris sepia individuals;
figure 2 is a linear SST relationship of iris cuttlefish growth rate with 112.75 ° E at 5 months, 16.25 ° N.
Detailed Description
The present invention will be further described with reference to the following examples.
General examples
A construction method of an iris cuttlefish growth model based on marine environmental factors comprises the following steps:
1) SST is calculated as sea surface temperature data of sea area in each month of operation within a certain time span, and the resolution is 0.5 degrees multiplied by 0.5 degrees.
2) Calculating the growth speed of the male iris cuttlefish at the time of primary maturity (maturity is III): v ═ ML/d; in the formula: ML is the first sexually mature male squid carcass length in mm (to the nearest 1mm) and d is the day of age. The detection method of the age in days comprises the following steps: taking the otolith out of the iris tectorum head balancing capsule, putting the otolith into a centrifugal container containing alcohol, carrying out centrifugal treatment to remove impurities, embedding, slicing and grinding the otolith, reading the growth lines of the otolith through a microscope, counting by a plurality of experimenters respectively, and only keeping the result with the error of not more than 10 percent as the age in days.
3) And performing typical correlation analysis on the operation sea area SST and the maximum correlation month and the corresponding sea area position of the male iris cuttlefish in the corresponding year in each month within a certain time span.
4) Selecting SST and v with the maximum correlation months to perform generalized additive model analysis GAM: log (v) s (SST) + s (SSS) + s (Chl a) + s (LAT) + s (LON).
In the formula: v is the growth rate of male iris sepiella at initial maturation, SST is the sea surface temperature of the working sea area, SSS is the sea surface salinity of the working sea area, Chl a is chlorophyll, LAT is latitude, and LON is longitude.
Log transformation is carried out on the growth speed of iris tectorum, the growth speed accords with normal distribution, GAM results are optimized through AIC, a GAM model with the minimum value of the AIC value is selected as a best fitting model, and the significance of each factor is evaluated through F test.
5) And obtaining a relational expression of the SST and the v, and establishing a model.
Example 1
(1) The investigation fishing boat is a light cover net fishing boat 'Qiongsan 72057' and 'Qiongsan 72068' belonging to Sanyufeng fishery company in Hainan province, and the two boats are of the same boat type: the total length is 33m, the model width is 6.2m, the total ton is 430t, the main engine power is 237kW, the auxiliary engine power is 163kW, the generator power is 200kW, and the average speed is 9 kn. The sampling time is 2015, 2016 and 2017, month 8, the sea area is 15 degrees 50 'N-18 degrees 00' N, 111 degrees 50 'E-114 degrees 00' E.
(2.1) measuring the carcass length (ML) of the sample, wherein the carcass length is accurate to 1mm, and identifying the sex (sex) and the gonad maturity (maturity stage) of the sample, wherein the gonad maturity is divided into I-V stage, I, II stage is sexual immature stage, III, IV and V stage are sexual maturity stage, and the related data are shown in Table 1.
TABLE 1 Iris Sepiella sex, gonad maturity, carcass length
(2.2) taking the otolith out of the head balance sac of the iris cuttlefish, putting the otolith into a centrifugal tube filled with 95% alcohol to remove impurities, embedding, slicing and grinding the otolith, reading the growth lines of the otolith through a microscope, counting the growth lines respectively by two experimenters, and only keeping the result with the error of not more than 10% as the age in days. The relevant data are shown in table 2.
TABLE 2 day-old iris sepiella
(3) Sea water surface temperature data (SST) of an operating sea area in months of 1-12 in 2015-2017 are calculated, the resolution is 0.5 degrees multiplied by 0.5 degrees, and marine environment data comprise sea water surface temperature (SST), sea level salinity (SSS), chlorophyll concentration (chlophyl-a, Chl a), which are all derived from the U.S. NOAA global marine observation database (http:// oceanwash. pifsc. aa. gov), time span 2015-2017 years and time resolution is month. Because the actual sampling catching point possibly goes in and out with the environmental data, the average value of the four points closest to the actual sampling catching point is selected as the environmental data of the point during actual calculation.
(4) Calculating the seawater surface temperature data (SST) of the sea area in 1-12 months of 2017 with the resolution of 0.5 degree multiplied by 0.5 degree; the relevant data are shown in tables 3-5.
Table 32015 year catching station average temperature
TABLE 42016 year average temperature of fishing site
Table 52017 year fishing station average temperature
(5) Analysis of variance shows that the growth rates of the male and female iris septempunctata have significant difference between sexes (F is 11.235, P is 0.002 < 0.05), and the male iris septempunctata is selected as a research object considering that the male iris septempunctata consumes muscle energy when spawning and possibly causes errors to the experimental result.
Calculating the growth speed of the male iris cuttlefish at the time of primary maturity: v-ML/d
In the formula: ML is the carcass length of the first sexual maturity (sexual maturity is III) of the squid iris, the unit is mm, and d is the age of day; the relevant data are shown in table 6.
TABLE 6 growth Rate of Primary mature Male Iris Sepiella
(6) Typical correlation analysis 2015-2017 operating sea area 0.5 degree multiplied by 0.5 degree SST and corresponding year male iris cuttlefish maximum correlation month and corresponding sea area position, the result shows that the SST and v of 5 months 112.75 degree E and 16.25 degree N have the highest correlation, the non-standardized typical correlation coefficient is 27.051, and therefore, the SST and v of 5 months 112.75 degree E and 16.25 degree N are selected to establish corresponding relational expressions. The relevant data are shown in tables 7-10.
TABLE 7 typical correlation of SST with v in different months
TABLE 8 SST and v non-normalized typical correlation coefficients
TABLE 9 typical correlation of fishing sites to v
TABLE 10 typical correlation coefficient between fishing sites and v non-normalized points
(7) Selecting a maximum correlation value month SST and the growth speed of iris sepiella to carry out generalized additive model analysis (GAM): log (v)(s) (SST) + s (SSS) + s (Chl a) + s (LAT) + s (LON)
In the formula: v is the growth rate of male iris sepiella at initial maturation, SST is the sea surface temperature of the working sea area, SSS is the sea surface salinity of the working sea area, Chl a is chlorophyll, LAT is latitude, and LON is longitude.
And (3) carrying out log transformation on the growth speed of the male individual, then conforming to normal distribution, optimizing the GAM result by using AIC, selecting the GAM model with the minimum value of the AIC value as a best fitting model, and evaluating the significance of each factor by using an F test.
GAM model shows that the growth rate of iris tectorum has significant correlation with surface temperature, latitude and longitude of seawater, the total deviation interpretation rate is 94.7%, the AIC value is-348.988, R20.934; the relevant data are shown in table 11 and fig. 1.
TABLE 11 SST, LAT, LON correlation AIC values
(8) The corresponding relation between SST and v of 112.75 degrees E in 5 months and 16.25 degrees N is established, wherein v is 0.0561SST-0.9428, R20.8513; the relevant data are shown in table 12 and fig. 2.
TABLE 12 different model AIC comparisons
The raw materials and equipment used in the invention are common raw materials and equipment in the field if not specified; the methods used in the present invention are conventional in the art unless otherwise specified.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and all simple modifications, alterations and equivalents of the above embodiments according to the technical spirit of the present invention are still within the protection scope of the technical solution of the present invention.
Claims (10)
1. A construction method of an iris cuttlefish growth model based on marine environmental factors is characterized by comprising the following steps:
1) calculating sea surface temperature data (SST) of each month operation sea area within a certain time span;
2) calculating the growth speed of the iris cuttlefish at the initial maturity: v = ML/d; in the formula: ML is the first sexual maturity of iris cuttlefish trunk length in mm, d is day age;
3) performing typical correlation analysis on the operation sea area SST of each month and the maximum correlation month and the corresponding sea area position of the iris cuttlefish in the corresponding year within a certain time span;
4) selecting SST and v with the maximum correlation months to perform generalized additive model analysis GAM: log (v) s (SST) + s (SSS) + s (Chl a) + s (LAT) + s (LON);
in the formula: v is the growth speed of the iris tectorum in the initial maturation, SST is the sea surface temperature of the operating sea area, SSS is the sea surface salinity of the operating sea area, Chl a is chlorophyll, LAT is latitude, and LON is longitude;
log conversion is carried out on the growth speed of iris tectorum, the iris tectorum accords with normal distribution, GAM results are optimized through AIC, a GAM model with the minimum value of the AIC value is selected as a best fitting model, and the significance of each factor is evaluated through F test;
5) and obtaining a relational expression of the SST and the v, and establishing a model.
2. The method of claim 1, wherein said iris sepiella is a male iris sepiella.
3. The method as claimed in claim 1, wherein the certain time span in step 1) is 2015-2017.
4. The constructing method according to claim 1 or 3, wherein in step 1), the working sea area is 15 ° 50 'N to 18 ° 00' N, 111 ° 50 'E to 114 ° 00' E.
5. The method of construction according to claim 1, wherein in step 1), the resolution at SST is calculated to be 0.5 ° x 0.5 °.
6. The method of claim 1, wherein in step 2), the primary maturation refers to a sexual maturity of iii.
7. The method of claim 1, wherein in step 2), the carcass length of iris sepiella is accurate to 1 mm.
8. The construction method according to claim 1, wherein in the step 2), the detection method of the day age is as follows: taking the otolith out of the iris tectorum head balancing capsule, putting the otolith into a centrifugal container containing alcohol, carrying out centrifugal treatment to remove impurities, embedding, slicing and grinding the otolith, reading the growth lines of the otolith through a microscope, counting by a plurality of experimenters respectively, and only keeping the result with the error of not more than 10 percent as the age in days.
9. A marine environmental factor-based iris sepia growth model constructed by the method of any one of claims 1 to 8, wherein the model is v =0.0561 SST-0.9428, R2=0.8513。
10. A method for predicting the growth rate of iris sepiella by using the model constructed by the method according to any one of claims 1 to 8 or the model according to claim 9, wherein the growth rate of iris sepiella is obtained by substituting SST into the model.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011333818.7A CN112507533B (en) | 2020-11-24 | 2020-11-24 | Method for constructing iris cuttlefish growth model based on marine environmental factors |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011333818.7A CN112507533B (en) | 2020-11-24 | 2020-11-24 | Method for constructing iris cuttlefish growth model based on marine environmental factors |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112507533A true CN112507533A (en) | 2021-03-16 |
| CN112507533B CN112507533B (en) | 2023-03-14 |
Family
ID=74959821
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011333818.7A Active CN112507533B (en) | 2020-11-24 | 2020-11-24 | Method for constructing iris cuttlefish growth model based on marine environmental factors |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112507533B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060015972A1 (en) * | 1999-03-23 | 2006-01-19 | Mendel Biotechnology, Inc. | Plant transcriptional regulators of drought stress |
| CN101539495A (en) * | 2009-03-04 | 2009-09-23 | 上海海洋大学 | Preparation of mackerel otolith cross-sectional slices and method for determining age thereof |
| CN106056240A (en) * | 2016-05-26 | 2016-10-26 | 上海海洋大学 | Method for identifying Dosidicus gigas spatial distribution and marine environment influence |
| CN109165766A (en) * | 2018-06-26 | 2019-01-08 | 中国水产科学研究院南海水产研究所 | A kind of method of South Sea Symeplectoteuthis oualaniensis fishery forescast |
| CN109460867A (en) * | 2018-11-02 | 2019-03-12 | 上海海洋大学 | Chilean scad resource abundance medium- and long-term forecasting method based on ocean Nino index |
-
2020
- 2020-11-24 CN CN202011333818.7A patent/CN112507533B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060015972A1 (en) * | 1999-03-23 | 2006-01-19 | Mendel Biotechnology, Inc. | Plant transcriptional regulators of drought stress |
| CN101539495A (en) * | 2009-03-04 | 2009-09-23 | 上海海洋大学 | Preparation of mackerel otolith cross-sectional slices and method for determining age thereof |
| CN106056240A (en) * | 2016-05-26 | 2016-10-26 | 上海海洋大学 | Method for identifying Dosidicus gigas spatial distribution and marine environment influence |
| CN109165766A (en) * | 2018-06-26 | 2019-01-08 | 中国水产科学研究院南海水产研究所 | A kind of method of South Sea Symeplectoteuthis oualaniensis fishery forescast |
| CN109460867A (en) * | 2018-11-02 | 2019-03-12 | 上海海洋大学 | Chilean scad resource abundance medium- and long-term forecasting method based on ocean Nino index |
Non-Patent Citations (3)
| Title |
|---|
| YAO LU,ET AL.: "Environmental Influence on the Spatiotemporal Variability of Spawning Grounds in the Western Guangdong Waters, South China Sea", 《JOURNAL OF MARINE SCIENCE AND ENGINEERING》 * |
| 刘玉 等: "基于耳石微结构的南海鸢乌贼日龄和生长研究", 《热带海洋学报》 * |
| 范江涛 等: "南沙海域鸢乌贼渔场与海洋环境因子的关系", 《上海海洋大学学报》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112507533B (en) | 2023-03-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Methot et al. | Growth of northern anchovy, Engraulis mordax, larvae in the sea | |
| McKinnell et al. | Oceanic and atmospheric extremes motivate a new hypothesis for variable marine survival of F raser R iver sockeye salmon | |
| Beamesderfer et al. | Size selectivity and bias in estimates of population statistics of smallmouth bass, walleye, and northern squawfish in a Columbia River reservoir | |
| Rogers et al. | Factors affecting marine growth of Bristol Bay sockeye salmon | |
| Matić-Skoko et al. | Mediterranean moray eel Muraena helena (Pisces: Muraenidae): biological indices for life history | |
| Trippel et al. | Age-specific paternal influcnces on reproductive success of Atlantic cod (Gadus morhua L.) of the Grand Banks, Newfoundland | |
| Quinn et al. | Relation of eastern Bering Sea walleye pollock (Theragra chalcogramma) recruitment to environmental and oceanographic variables | |
| CN111680844A (en) | Technical method for evaluating and predicting southwest Atlantic argentina soft fish habitat based on water temperature vertical structure | |
| Buecher et al. | Long-term fluctuations of Liriope tetraphylla in Villefranche bay between 1966 and 1993 compared to Pelagia noctiluca populations | |
| Zhang et al. | Spatial structure and bottom characteristics of the only remaining spawning area of Chinese sturgeon in the Yangtze River | |
| Gordon et al. | Growth and production of the green alga Cladophora montagneana in a eutrophic Australian estuary and its interpretation using a computer program | |
| Altukhov et al. | Straying intensity and genetic differentiation in salmon populations | |
| CN107169679B (en) | Method for evaluating wetland ecological water replenishing implementation effect by using food net stability | |
| Li et al. | Changes in the resource distribution of Acetes chinensis and patterns of species replacement in Haizhou Bay in summer based on BeiDou VMS data | |
| Reeve | Seasonal changes in the zooplankton of south Biscayne Bay and some problems of assessing the effects on the zooplankton of natural and artificial thermal and other fluctuations | |
| CN112507533B (en) | Method for constructing iris cuttlefish growth model based on marine environmental factors | |
| CN108960523A (en) | A method of utilizing the feeding ground habitat of two step Generalized Additive Models prediction squid class | |
| Munk | Cross-frontal variation in growth rate and prey availability of larval North Sea cod Gadus morhua | |
| Willan et al. | A survey of fish populations at Karikari Peninsula, Northland, by scuba diving | |
| BARLOW | Variability, trends, and biases in reproductive rates of spotted dolphins, Stenella attenuata | |
| Yan et al. | Life-history strategies of Acrossocheilus fasciatus (Barbinae, Cyprinidae) in the Huishui Stream of the Qingyi watershed, China | |
| Healey et al. | Precocial maturation of male sockeye salmon in the Fraser River, British Columbia, and its relationship to growth and year-class strength | |
| CN112825797B (en) | Construction method of iris cuttlefish horny jaw growth model based on marine environmental factors | |
| Barlow et al. | An assessment of the 1994 status of harbor porpoise in California | |
| Li et al. | Relationships between daily growth of different groups of sword-tip squid (Uroteuthis edulis) and environmental variables in the East China Sea |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |