CN107688874B - Method for predicting abundance of cephalopods resources in Haia of Mauritania - Google Patents
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Abstract
The invention discloses a method for predicting cephalopodium resource abundance in a Haimania sea area, which is used for obtaining capture production data of Haitania in 1982-2015, a wave-motion index NAO of the north pole in each month of North Atlantic, 16-22 degrees N and SSTA data of a sea area table of 16-20 degrees W; obtaining NAO corresponding to yeariIndex, calculating the average SSTA of the ith year in the sea area of 16-20 degrees W of each latitude sectioni(ii) a Obtaining 7 marine climate and environmental factors; obtaining the cuttlefish resource abundance index RA1, octopus resource abundance index RA2 and cephalopod resource abundance total index RA3 of each year; performing correlation analysis on 7 marine climate and environmental factors with RA1, RA2 and RA3 respectively, and selecting P<An impact factor of 0.05; and establishing a resource abundance prediction model by utilizing the univariate linear model with RA1, RA2 and RA3 respectively according to the obtained influence factors.
Description
Technical Field
The invention relates to the technical field of cephalopod resource abundance prediction, in particular to a cephalopod resource abundance prediction method in a Haihai area of Curitania virginiana.
Background
The Myya sea area is located in the eastern part of the Atlantic ocean, the coastline ranges from the Branchikura (20 degrees 36 'N) to the Santa Louis (16 degrees 04' N) with a total length of about 754km and a dedicated economic area of 200 nautical miles of 23.4 km2The area of the continental shelf sea area (within 200m equal depth line) is about 3.4 km2The north continental shelf is wide, with the widest of the nuvadbu bay and the alkini shoal being up to 80 nautical miles, while the continental shelf from the tilmiis angle to the south is only 30 nautical miles, the most important ocean current is the canary current that goes from the north to the south, bringing cold water, and the other sub-warmer current is relatively weak, but it flows from the south to the north, mixing with the canary current in the brookfield gorge area, forming a significant upwelling current, which contains a large number of plankton, is well suited for habitat reproduction of fishery resources, forming one of the world famous fisheries. Cephalopods are the most economically valuable fishery resource in the neria maritime area, and are distributed offshore. The cephalopod yield accounts for 75 percent of the economic fishing species,particularly, the quantity of octopuses is the largest, the yield is the largest, and the economic value is the highest.
Since China began to participate in cephalopods fishing operation in the Hainanya sea area from the late stage of the 80 th century, a large amount of production experience is accumulated, fishery resources are continuously declined due to the surplus of offshore fishing capacity, and the coastal fishery management and fishing entering conditions of various countries in the world are increasingly strict, so that the development of cephalopods in the Hainanya sea area has very important significance for promoting the development of the pelagic fishery in China.
As the cephalopods are of one year type, the resource abundance of the cephalopods is closely related to the marine environment, and the resource abundance of the cephalopods is directly influenced by the climate change and the marine environment, so that the fishery production and scientific management are influenced. Therefore, the selection of the environmental factors influencing the resource abundance is very important, and the environmental factors are utilized to establish a statistically significant resource abundance prediction model, so that the production of cephalopods in the Haihai area of Mauritania can be scientifically guided, and the guidance for the efficient fishing production of related enterprises in the Haihai area of Mauritania is also provided.
Disclosure of Invention
Aiming at the problems and the defects in the prior art, the invention provides a method for predicting the abundance of cephalopods resources in the Haichia villitania area.
The invention solves the technical problems through the following technical scheme:
the invention provides a method for predicting cephalopod resource abundance in a Haichia villitania area, which is characterized by comprising the following steps of:
s1, obtaining fishing production data of Maytania in 1982 + 2015, obtaining NAO data of wave indexes of North and Africa in 1982 + 2015, and obtaining SSTA data of surface temperature mean values of sea areas of 16-22 DEG N and 16-20 DEG W;
wherein the fishing production data comprises the fishing yield of cuttlefish, the fishing yield of octopus and the total fishing yield of cephalopod;
s2, averaging NAO data of each year and each month to obtain NAO of corresponding yeariIndices at 16.5 ° N, 17.5 ° N, 18.5 ° N, 19.5 ° N, 20.5 ° N and 2, respectively1.5 degrees N is taken as a reference, and the average SSTA of the ith year of the sea area of 16-20 degrees W of each latitude section is calculatedi；
S3, obtaining 7 marine climate and environmental factors, which are respectively:
climate factor: NAOiIndex, environmental factor: annual average SSTA in sea area of 16.5 degrees N and 16-20 degrees WiIs recorded as SSTA1, 17.5 degrees N and 16 to 20 degrees W average SSTA in sea areaiIs recorded as SSTA2, 18.5 degrees N and 16 to 20 degrees W average SSTA in sea areaiIs recorded as SSTA3, 19.5 degrees N and 16-20 degrees W average SSTA in sea areaiIs recorded as SSTA4, 20.5 degrees N and 16 to 20 degrees W average SSTA in sea areaiIs recorded as SSTA5, 21.5 degrees N and 16-20 degrees W average SSTA in sea areaiAs SSTA 6;
s4, dividing the cuttlefish catching yield of each year by the highest cuttlefish catching yield of 1982 + 2015 years to obtain the cuttlefish resource abundance index RA1 of each year, dividing the cuttlefish catching yield of each year by the highest cuttlefish catching yield of 1982 + 2015 years to obtain the octopus resource abundance index RA2 of each year, and dividing the cephalopod catching total yield of each year by the highest cephalopod catching total yield of 1982 + 2015 years to obtain the cephalopod resource abundance total index RA3 of each year;
s5, respectively carrying out correlation analysis on 7 marine climate and environment factors and a cuttlefish resource abundance index RA1, an octopus abundance index RA2 and a cephalopod resource abundance total index RA3, selecting and obtaining statistically significant (P is less than 0.05) influence factors, and selecting the standard that the absolute value of a correlation coefficient is more than or equal to 0.329;
s6, respectively establishing a resource abundance prediction model by utilizing a unitary linear model together with the corresponding cuttlefish resource abundance index RA1, octopus resource abundance index RA2 and cephalopod resource abundance total index RA3 according to the obtained influence factors.
Preferably, in step S6, the environmental factor that most affects the sepia resource abundance index RA1 is SSTA5, the climate factor is NAO, and a resource abundance prediction model of the sepia resource abundance index RA1 is established based on the environmental factor: RA1 (a 1+ b 1) SSTA5, and carrying out sample training on a cuttlefish resource abundance prediction model by adopting RA1 values and SSTA5 values corresponding to cuttlefish in each year to obtain a1 value and a b1 value;
establishing a resource abundance prediction model of the cuttlefish resource abundance index RA1 based on the climate factors: RA1 (a 2+ b 2) NAO, and carrying out sample training on a cuttlefish resource abundance prediction model by adopting RA1 values and NAO values corresponding to cuttlefish in each year to obtain a2 values and b2 values;
the environmental factor which has the greatest influence on the abundance index RA2 of the octopus resources is SSTA4, the climate factor is NAO, and a resource abundance prediction model of the abundance index RA2 of the octopus resources is established based on the environmental factor: RA2 (a 3+ b 3) SSTA4, and carrying out sample training on an octopus resource abundance prediction model by adopting RA2 values and SSTA4 values corresponding to octopus in each year to obtain a3 and b3 values;
establishing a resource abundance prediction model of octopus resource abundance index RA2 based on climate factors: RA2 (a 4+ b 4) NAO, and carrying out sample training on an octopus resource abundance prediction model by adopting RA2 values and NAO values corresponding to the octopus in each year to obtain a4 values and b4 values;
the environmental factor which has the greatest influence on the total index RA3 of the cephalopod resource abundance is SSTA4, the climate factor is NAO, and a resource abundance prediction model of the total index RA3 of the cephalopod resource abundance is established based on the environmental factor: RA3 (a 5+ b 5) SSTA4, and carrying out sample training on a cephalopod resource abundance prediction model by adopting RA3 values and SSTA4 values corresponding to cephalopods in each year to obtain a5 and b5 values;
establishing a resource abundance prediction model of a cephalopod resource abundance total index RA3 based on climate factors: and RA3 is a6+ b6 NAO, and a model for predicting the resource abundance of the cephalopods is subjected to sample training by adopting RA3 values and NAO values corresponding to the cephalopods in each year to obtain a6 value and a b6 value.
On the basis of the common knowledge in the field, the above preferred conditions can be combined randomly to obtain the preferred embodiments of the invention.
The positive progress effects of the invention are as follows:
the invention utilizes the marine climate and environmental factors to establish a statistically significant resource abundance prediction model, can scientifically guide the production of cephalopods in the Hardania sea area, and also provides guidance for the high-efficiency fishing production of related enterprises in China in the Hardania sea area.
Drawings
FIG. 1 is a graph showing the distribution of the amount of cuttlefish produced in the Hay region of Mauritania in 1980-2015.
FIG. 2 is a graph showing the distribution of the yield of fishes in the sea area of Maytania in 1980-2015.
FIG. 3 is a graph showing the distribution of cephalopods production in the Haichia of Maytania in 1980-2015.
FIG. 4 is a flowchart illustrating a method for predicting abundance of cephalopod resources in the Haichia of Maytania according to a preferred embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
1. Material collection
(1) Production statistics. The fishing production data of the Muritania in 1982-2015 are downloaded from a global fishery production statistical database of the food and agriculture organization of the United nations, and the fishing production data comprise the fishing yield of cuttlefish, octopus and cephalopod in ton, which is shown in the figures 1-3.
(2) Marine climate and environmental factor data. National weather service, National Centers for Environmental Prediction, under NOAA in the United states, downloaded to obtain the North Atlantic Takayama NAO index, time series 1982-2015. The SSTA data are obtained by downloading marine remote sensing at a website http:// iridl. ldeo. column bia. edu/SOURCES/. IGOSS/. nmc/. Reyn _ SmithOIv2/. monthly/. SSTA/at Columbia university, wherein the range is 16-22 degrees N, and 16-20 degrees W sea area.
2. Data processing
(1) NAO data processing: the NAO data of each month in a certain i year are averaged to obtain the NAO of a certain yeariAnd (4) index.
(2) SSTA data processing: calculating the average SSTA of the ith year in the sea area of 16-20W of a certain latitude section by taking the latitude as a reference, namely 16.5 degrees N, 17.5 degrees N, 18.5 degrees N, 19.5 degrees N, 20.5 degrees N and 21.5 degrees N respectivelyi。
Through the analysis, a total of 7 marine climate and environmental factors are obtained, which are NAO respectivelyiIndex, annual average SSTA in sea area of 16.5 ° N and 16 ° -20 ° Wi(SSTA1), 17.5 ° N and annual average SSTA in the sea at 16 ° -20 ° Wi(SSTA2), annual average SSTA in sea area of 18.5 ° N and 16 ° -20 ° Wi(SSTA3), 19.5 ° N and a yearly average SSTA of 16 ° -20 ° W in the seai(SSTA4), 20.5 ° N and a yearly average SSTA of 16 ° -20 ° W in the seai(SSTA5), 21.5 ° N and annual average SSTA in the sea at 16 ° -20 ° Wi(SSTA6)。
(3) Cephalopod resource abundance index: since cephalopods in the Haichia pilina are mainly cuttlefish and octopus, the abundance index of cephalopods resource includes cuttlefish resource abundance index (RA1), octopus abundance index (RA2) and total abundance index of cephalopods resource (RA 3). The resource abundance index is normalized by dividing the yield of each year by the highest yield of 1982-2015 years, 7873, 38607 and 44237, respectively.
3. Analytical method
Consider that the mauritania sea area is in the upwelling sea area formed by the canary currents. The Canali ocean current belongs to the east boundary current, and the ocean current forms an ascending current under the action of offshore wind in the process of going south, so that the water temperature is low, rich nutritive salt at the bottom layer is brought into the upper layer to form a good fishing ground, and rich baits are provided for resources such as cephalopods and the like. Therefore, the invention selects 6 indexes of average value of surface temperature and 1 index representing climate change.
And (3) respectively carrying out correlation analysis on the 7 marine climate and environmental factors and the cuttlefish resource abundance index (RA1), the octopus abundance index (RA2) and the cephalopod resource abundance total index (RA3), and selecting to obtain statistically significant influence factors (P <0.05) according to the selection standard that the absolute value of the correlation coefficient is more than or equal to 0.329.
And respectively establishing a resource abundance prediction model with the cuttlefish resource abundance index (RA1), the octopus resource abundance index (RA2) and the cephalopod resource abundance total index (RA3) according to the obtained influence factors.
4. Analysis results
(1) Selection of influence factors of cuttlefish resource abundance index (RA1)
The analysis shows that the correlation coefficient with the cuttlefish resource abundance index (RA1) is shown in the following table 1. As can be seen from table 1, statistically significant correlation factors include 5 factors, such as NAO, SSTA3, SSTA4, SSTA5, and SSTA6, wherein the factor with the largest correlation coefficient is SSTA 5.
TABLE 1 statistical table of correlation coefficients
| Factor(s) | NAO | SSTA1 | SSTA2 | SSTA3 | SSTA4 | SSTA5 | SSTA6 |
| Correlation coefficient | 0.344 | -0.289 | -0.309 | -0.348 | -0.370 | -0.371 | -0.355 |
| Significance of | P＜0.05 | P＞0.05 | P＞0.05 | P＜0.05 | P＜0.05 | P＜0.05 | P＜0.05 |
(2) Selection of influence factors of octopus resource abundance index (RA2)
The analysis shows that the correlation coefficient with the abundance index (RA2) of octopus resources is shown in the following table 2. As can be seen from table 2, the statistically significant correlation factors are SSTA4 and NAO, where the correlation coefficient is maximized for NAO.
TABLE 2 statistical table of correlation coefficients
| Factor(s) | NAO | SSTA1 | SSTA2 | SSTA3 | SSTA4 | SSTA5 | SSTA6 |
| Correlation coefficient | 0.375 | -0.272 | -0.299 | -0.328 | -0.337 | -0.328 | -0.264 |
| Significance of | P＜0.05 | P＞0.05 | P＞0.05 | P＞0.05 | P＜0.05 | P＞0.05 | P＞0.05 |
(3) Selection of influencing factors for cephalopod resource abundance index (RA3)
The analysis showed that the correlation coefficient with the cephalopod resource abundance index (RA3) is shown in table 3 below. As can be seen from table 3, statistically significant correlation factors include 5 factors, such as NAO, SSTA3, SSTA4, SSTA5, and SSTA6, wherein the factor with the largest correlation coefficient is SSTA 4.
TABLE 3 statistical table of correlation coefficients
| Factor(s) | NAO | SSTA1 | SSTA2 | SSTA3 | SSTA4 | SSTA5 | SSTA6 |
| Correlation coefficient | 0.345 | -0.263 | -0.292 | -0.329 | -0.346 | -0.343 | -0.293 |
| Significance of | P＜0.05 | P＞0.05 | P＞0.05 | P＜0.05 | P＜0.05 | P＜0.05 | P＞0.05 |
(4) Resource abundance prediction model establishment
And respectively establishing a resource abundance prediction model by using the marine environment factor and the climate factor which have the largest influence and a unitary linear model. The models are respectively as follows:
1) cuttlefish resource abundance index (RA1) prediction model
Prediction model based on environmental factors:
RA1＝a+b*SSTA5
wherein, a is 0.57027, b is-0.14133; a 95% confidence interval of [0.19405, 0.64649 ]; b has a 95% confidence interval of [ -0.2686, -0.01406 ]. The correlation coefficient is 0.3713(P ═ 0.0306 < 0.05).
A prediction model based on climate factors:
RA1＝a+b*NAO
wherein, a is 0.51886, b is 0.161244; a 95% confidence interval of [0.45576,0.58197 ]; b has a 95% confidence interval of [0.002821,0.31967 ]. The correlation coefficient was 0.3441(P ═ 0.0463< 0.05).
2) Octopus resource abundance index (RA2) prediction model
Prediction model based on environmental factors:
RA2＝a+b*SSTA4
wherein, a is 0.56359, b is-0.13398; a 95% confidence interval of [0.48009,0.647102 ]; b has a 95% confidence interval of [ -0.26877, -0.00812 ]. The correlation coefficient was 0.3370(P ═ 0.0496< 0.05).
Prediction model based on climate factor
RA2＝a+b*NAO
Wherein, a is 0.49558, b is 0.19803; a 95% confidence interval of [0.42748,0.56367 ]; b has a 95% confidence interval of [0.02785,0.36820 ]. The correlation coefficient is 0.3758(P ═ 0.0238< 0.05).
3) Cephalopod resource abundance index (RA3) prediction model
Prediction model based on environmental factors:
RA3＝a+b*SSTA4
wherein, a is 0.610839, b is-0.1351; a 95% confidence interval of [0.52898,0.69269 ]; b has a 95% confidence interval of [ -0.26723, -0.00297 ]. The correlation coefficient is 0.3455 (P0.0453 < 0.05).
Prediction model based on climate factor
RA3＝a+b*NAO
Wherein, a is 0.558493, b is 0.169413; a 95% confidence interval of [0.492406,0.62458 ]; b has a 95% confidence interval of [0.003506,0.335321 ]. The correlation coefficient is 0.3451 (P0.0456 < 0.05).
In summary, as shown in fig. 4, the method for predicting abundance of cephalopods in the hai territory of lantana provided in this embodiment includes the following steps:
101, obtaining fishing production data of Maytania in 1982 + 2015, obtaining NAO data of wave indexes of North and North of the Atlantic in 1982 + 2015, and obtaining SSTA data of surface temperature range mean values of 16-22 DEG N, 16-20 DEG W sea areas.
Wherein the fishing production data comprises the fishing yield of cuttlefish, the fishing yield of octopus and the total fishing yield of cephalopod.
103, obtaining 7 marine climate and environmental factors, which are respectively as follows:
climate factor: NAOiIndex, environmental factor: annual average SSTA in sea area of 16.5 degrees N and 16-20 degrees Wi(SSTA1), 17.5 ° N and annual average SSTA in the sea at 16 ° -20 ° Wi(SSTA2), annual average SSTA in sea area of 18.5 ° N and 16 ° -20 ° Wi(SSTA3), 19.5 ° N and a yearly average SSTA of 16 ° -20 ° W in the seai(SSTA4), 20.5 ° N and a yearly average SSTA of 16 ° -20 ° W in the seai(SSTA5), 21.5 ° N and annual average SSTA in the sea at 16 ° -20 ° Wi(SSTA6)。
104, dividing the cuttlefish catching yield of each year by the highest cuttlefish catching yield of 1982 + 2015 years to obtain the cuttlefish resource abundance index RA1 of each year, dividing the cuttlefish catching yield of each year by the highest cuttlefish catching yield of 1982 + 2015 years to obtain the octopus resource abundance index RA2 of each year, and dividing the cephalopod catching total yield of each year by the highest cephalopod catching total yield of 1982 + 2015 years to obtain the cephalopod resource abundance total index RA3 of each year.
And 105, respectively carrying out correlation analysis on the 7 marine climate and environment factors and the cuttlefish resource abundance index RA1, the octopus abundance index RA2 and the cephalopod resource abundance total index RA3, and selecting to obtain statistically significant influence factors (P <0.05) according to the selection standard that the absolute value of the correlation coefficient is more than or equal to 0.329.
And 106, respectively establishing a resource abundance prediction model with the corresponding cuttlefish resource abundance index RA1, octopus resource abundance index RA2 and cephalopod resource abundance total index RA3 by using a unitary linear model according to the obtained influence factors.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that these are by way of example only, and that the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the spirit and scope of the invention, and these changes and modifications are within the scope of the invention.
Claims (1)
1. A method for predicting cephalopod resource abundance in a Haichia villitania area is characterized by comprising the following steps:
s1, obtaining fishing production data of Maytania in 1982 + 2015, obtaining NAO data of wave indexes of North and Africa in 1982 + 2015, and obtaining SSTA data of surface temperature mean values of sea areas of 16-22 DEG N and 16-20 DEG W;
wherein the fishing production data comprises the fishing yield of cuttlefish, the fishing yield of octopus and the total fishing yield of cephalopod;
s2, averaging NAO data of each year and each month to obtain NAO of corresponding yeariThe indexes are respectively based on 16.5 degrees N, 17.5 degrees N, 18.5 degrees N, 19.5 degrees N, 20.5 degrees N and 21.5 degrees N, and the average SSTA of the i-th year in the sea area of 16-20 degrees W of each latitudinal section is calculatedi；
S3, obtaining 7 marine climate and environmental factors, which are respectively:
climate factor: NAOi index, environmental factor: 16.5N and 16-20W sea area annual average SSTA Ai of SSTA1, 17.5N and 16-20W sea area annual average SSTA Ai of SSTA2, 18.5N and 16-20W sea area annual average SSTA Ai of SSTA3, 19.5N and 16-20W sea area annual average SSTA of SSTA4, 20.5N and 16-20W sea area annual average SSTA Ai of SSTA5, 21.5N and 16-20W sea area annual average SSTA 6;
s4, dividing the cuttlefish catching yield of each year by the highest cuttlefish catching yield of 1982 + 2015 years to obtain the cuttlefish resource abundance index RA1 of each year, dividing the cuttlefish catching yield of each year by the highest cuttlefish catching yield of 1982 + 2015 years to obtain the octopus resource abundance index RA2 of each year, and dividing the cephalopod catching total yield of each year by the highest cephalopod catching total yield of 1982 + 2015 years to obtain the cephalopod resource abundance total index RA3 of each year;
s5, respectively carrying out correlation analysis on 7 marine climate and environment factors and a cuttlefish resource abundance index RA1, an octopus abundance index RA2 and a cephalopod resource abundance total index RA3, selecting and obtaining statistically significant (P is less than 0.05) influence factors, and selecting the standard that the absolute value of a correlation coefficient is more than or equal to 0.329;
s6, respectively establishing a resource abundance prediction model with corresponding cuttlefish resource abundance index RA1, octopus resource abundance index RA2 and cephalopod resource abundance total index RA3 by using a unitary linear model according to the obtained influence factors;
in step S6, the environmental factor that most affects the sepia resource abundance index RA1 is SSTA5, the climate factor is NAO, and a resource abundance prediction model of the sepia resource abundance index RA1 is established based on the environmental factor: RA1 (a 1+ b 1) SSTA5, and carrying out sample training on a cuttlefish resource abundance prediction model by adopting RA1 values and SSTA5 values corresponding to cuttlefish in each year to obtain a1 value and a b1 value;
establishing a resource abundance prediction model of the cuttlefish resource abundance index RA1 based on the climate factors: RA1 (a 2+ b 2) NAO, and carrying out sample training on a cuttlefish resource abundance prediction model by adopting RA1 values and NAO values corresponding to cuttlefish in each year to obtain a2 values and b2 values;
the environmental factor which has the greatest influence on the abundance index RA2 of the octopus resources is SSTA4, the climate factor is NAO, and a resource abundance prediction model of the abundance index RA2 of the octopus resources is established based on the environmental factor: RA2 (a 3+ b 3) SSTA4, and carrying out sample training on an octopus resource abundance prediction model by adopting RA2 values and SSTA4 values corresponding to octopus in each year to obtain a3 and b3 values;
establishing a resource abundance prediction model of octopus resource abundance index RA2 based on climate factors: RA2 (a 4+ b 4) NAO, and carrying out sample training on an octopus resource abundance prediction model by adopting RA2 values and NAO values corresponding to the octopus in each year to obtain a4 values and b4 values;
the environmental factor which has the greatest influence on the total index RA3 of the cephalopod resource abundance is SSTA4, the climate factor is NAO, and a resource abundance prediction model of the total index RA3 of the cephalopod resource abundance is established based on the environmental factor: RA3 (a 5+ b 5) SSTA4, and carrying out sample training on a cephalopod resource abundance prediction model by adopting RA3 values and SSTA4 values corresponding to cephalopods in each year to obtain a5 and b5 values;
establishing a resource abundance prediction model of a cephalopod resource abundance total index RA3 based on climate factors: and RA3 is a6+ b6 NAO, and a model for predicting the resource abundance of the cephalopods is subjected to sample training by adopting RA3 values and NAO values corresponding to the cephalopods in each year to obtain a6 value and a b6 value.
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| CN104050382A (en) * | 2014-06-26 | 2014-09-17 | 中国环境科学研究院 | Prediction system for fish potential abundance |
| US20160281142A1 (en) * | 2015-03-25 | 2016-09-29 | Nestec Sa | Methods for predicting overweight risk for pets and adult percent body fat |
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| CN104050382A (en) * | 2014-06-26 | 2014-09-17 | 中国环境科学研究院 | Prediction system for fish potential abundance |
| US20160281142A1 (en) * | 2015-03-25 | 2016-09-29 | Nestec Sa | Methods for predicting overweight risk for pets and adult percent body fat |
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| E. G. DAWE等.Ocean climate effects on the relative abundance of short-finned (Illex illecebrosus) and long-finned (Loligo pealeii) squid in the northwest Atlantic Ocean.《FISHERIES OCEANOGRAPHY》.2007,第16卷(第4期),第303-316页. * |
| 汪金涛等.基于产卵场环境因子的阿根廷滑柔鱼资源补充量预报模型研究.《海洋学报》.2014,第36卷(第12期),第119-124页. * |
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