CN109460860A - Argentinian squid Resources Prediction method based on Antarctic Oscillations index - Google Patents
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- 241000238440 Illex argentinus Species 0.000 title claims abstract description 52
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- PEDCQBHIVMGVHV-UHFFFAOYSA-N Glycerine Chemical compound OCC(O)CO PEDCQBHIVMGVHV-UHFFFAOYSA-N 0.000 claims abstract description 12
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- 241000238366 Cephalopoda Species 0.000 claims description 19
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Abstract
The present invention is based on the Argentinian squid Resources Prediction methods of Antarctic Oscillations index, comprising: the Antarctic Oscillations Index A AO value monthly of the south sea area of the Southwest Pacific of the Argentinian squid distribution of N before obtaining；Utilize Time series analysis method, correlation analysis is carried out to the AAO value of Argentinian squid resource abundance ln (CPUE) and preceding N monthly, statistically significant relevant moon AAO value is obtained, those month AAO value is as the climatic factor for influencing Argentinian squid resource abundance；P value on multiple Argentinian squid resource abundance prediction models and counting statistics, formula are as follows: ln (CPUE)=a+b1*x1+b2*x2+b3*x3+ ...+bn*xn are established using multiple linear equation；In above-mentioned multiple Argentinian squid resource abundance prediction models, select statistically the smallest model of P value as optimal models.
Description
Technical field
The present invention relates to squid Resources Prediction technical fields, more particularly to a kind of Ah root based on Antarctic Oscillations index
Court of a feudal ruler squid Resources Prediction method.
Background technique
Argentinian squid is economic Resources of Cephalopods important in the world, and historical high yield is me once more than 1,000,000 tons
The main fished species of the fishery such as the countries and regions squid jigging boat such as state, trawler.Argentinian squid is distributed in South-west Atlantic sea
Domain is influenced by Brazil Current and falkland current, and carries out the migration of North and South direction.Existing research shows that due to Ah
Sliding squid is annual type in the root court of a feudal ruler, and resource magnitude of recruitment is influenced by spawning ground environment, Wang Jintao etc. (2014) basis
Production statistics data and spawning ground marine surface temperature of 2003-2011 years squid jigging boat teams, China in South-west Atlantic
(SST), sea surface temperature anomaly value (SSTA), calculating analyze the sliding squid of Argentina in egg-laying season spawning ground each moon most suitable superficial water
Warm range account for the ratio (being indicated with PS) of the gross area and characterize a variety of environmental variance factors such as SST, SSTA of ocean current intensity with
The correlation of unit amount of fishing catch (CPUE) establishes a variety of resource magnitude of recruitment forecasting models based on the main environment factor.
Wu Yumei etc. (2011) using 2000-2008 years Chinese squid jigging boats the sliding squid of South-west Atlantic Argentina creation data with
Seasat inverting data, analyze the sliding squid fishing ground abundance of nearly 9 years South-west Atlantic Argentina variation and its with it is main
The relationship of ecological factor (sea surface temperature and chlorophyll a) finds the change of 2004-2008 years average annual sea surface temperatures and resource abundance
Dynamic to show as significant negative correlation, 2005-2008 years average annual chlorophyll as and resource abundance show as stronger positive correlation.
Waluda and Rodhouse (2001) think that spawning ground environmental condition influences significant, oviposition on Argentinian squid resource magnitude of recruitment
Water temperature is higher, spawning ground is suitable for generation and growth that table temperature area height is then conducive to Argentinian squid.The studies above shows mesh
Preceding each scholar both at home and abroad influences its resource magnitude of recruitment to Argentinian squid spawning ground environment and has carried out good research, and establishes
Corresponding Resources Prediction model, but be then blank how to carry out look-ahead its stock number with climatic factor.
Summary of the invention
The present invention is in view of the problems of the existing technology and insufficient, provides a kind of novel Ah based on Antarctic Oscillations index
Root court of a feudal ruler squid Resources Prediction method.
The present invention is to solve above-mentioned technical problem by following technical proposals:
The present invention provides a kind of Argentinian squid Resources Prediction method based on Antarctic Oscillations index, it is characterized in that,
Itself the following steps are included:
The Antarctic Oscillations index monthly of the south sea area of the Southwest Pacific of N Argentina squid distribution before S1, acquisition
AAO value；
S2, using Time series analysis method, to Argentinian squid resource abundance ln (CPUE) and the AAO of preceding N monthly
Value carries out correlation analysis, obtains statistically significant relevant moon AAO value, those month AAO value is as the Argentinian squid of influence
The climatic factor of resource abundance；
S3, P value on multiple Argentinian squid resource abundance prediction models and counting statistics is established using multiple linear equation,
Its formula are as follows: ln (CPUE)=a+b1*x1+b2*x2+b3*x3+ ...+bn*xn, in formula, CPUE is single ship annual output, and a is normal
Number, b1, b2, b3 ..., bn be equation coefficient；X1, x2, x3 ..., xn be influence resource abundance moon AAO value；
S4, in above-mentioned multiple Argentinian squid resource abundance prediction models, select statistically the smallest model of P value as
Optimal models.
On the basis of common knowledge of the art, above-mentioned each optimum condition, can any combination to get each preferable reality of the present invention
Example.
The positive effect of the present invention is that:
(1) prediction of Argentinian squid resource abundance is carried out using Antarctic Oscillations AAO；
(2) select first 2 years May AAO value, the first AAO of in August, 1 value is as the climatic prediction factor；
(3) Argentinian squid resource abundance prediction model are as follows: Ln (CPUE)=6.7046+0.3908*AAOt-2-0.3836*
AAOt-1, AAOt-2For preceding in May, 2 AAO value, AAOt-1For preceding in August, 1 AAO value.
Detailed description of the invention
Fig. 1 is the flow chart of the Argentinian squid Resources Prediction method based on Antarctic Oscillations index.
Fig. 2 is 2002-2017 Argentina squid resource abundance ln (CPUE) variation diagram between year.
Fig. 3 and 4 is Argentinian squid resource abundance prediction model distribution map.
Figures 5 and 6 are Argentinian squid resource abundance prediction model distribution map.
Fig. 7 is Argentinian resource abundance ln (CPUE) actual value of 2002-2017 and predicted value change profile figure.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is
A part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art
Every other embodiment obtained without making creative work, shall fall within the protection scope of the present invention.
As shown in Figure 1, the present embodiment provides a kind of Argentinian squid Resources Prediction method based on Antarctic Oscillations index,
Itself the following steps are included:
The Antarctic Oscillations monthly of the south sea area of the Southwest Pacific of the Argentinian squid distribution of N before step 101, acquisition
Index A AO value.
Step 102, using Time series analysis method, monthly to Argentinian squid resource abundance ln (CPUE) and preceding N
AAO value carry out correlation analysis, obtain statistically significant relevant moon AAO value, those month AAO value is as influencing Argentina
The climatic factor of squid resource abundance.
Step 103 establishes multiple Argentinian squid resource abundance prediction models and counting statistics using multiple linear equation
Upper P value, formula are as follows: ln (CPUE)=a+b1*x1+b2*x2+b3*x3+ ...+bn*xn, in formula, CPUE is that single ship is produced per year
Amount, a are constant, b1, b2, b3 ..., the coefficient that bn is equation；X1, x2, x3 ..., xn be influence resource abundance moon AAO
Value.
Step 104, in above-mentioned multiple Argentinian squid resource abundance prediction models, the selection statistically the smallest mould of P value
Type is as optimal models.
A specific example is named to illustrate the present invention, so that those skilled in the art better understood when this
The technical solution of invention.
1, material and method
(1) data source
Argentinian squid is distributed in the south sea area of Southwest Pacific, close to South Pole sea area, spawning ground and feeding ground by
To the influence of Falkland cold current caused by the west wind drift of South Pole sea area.In the sea area, Antarctic Oscillations (Antarctic
Oscillation, AAO) it is an important climatic factor.Antarctic Oscillations are the primary modals of Southern Hemisphere atmospheric circulation, more
The weather system in the Southern Hemisphere and Northern Hemisphere some areas is had an important influence on kind scale.It refers to Southern Hemisphere middle latitude and height
A kind of " seesaw " structure for Global Scale that air quality changes between two atmosphere ring-type active belts of latitude, 40 ° of S and 70 ° of S
On standardization zonal mean sea-level pressure difference can be used as measurement Antarctic Oscillations variation index.Antarctic Oscillations are strong, indicate south
Hemisphere is weak around pole deepening of a depression and middle high latitude west wind reinforcement, and vice versa.
Antarctic Oscillations index comes from U.S.'s marine atmosphere board web (http://www.cpc.ncep.noaa.gov/
products/precip/CWlink/daily_ao_index/aao/monthly.aao.index.b79.current.asc
Ii.table), time period is in January, 2000 in December, 2017 (table 1).
Argentinian squid Resources Richness Rate Index CPUE unit is ton/ship) from Chinese ocean squid jigging boat annual output, the time is
- 2017 years 2002 (table 2).
Table Antarctic Oscillations index monthly returns in December, -2017 in January, 1 2000
| Time | January | 2 months | March | April | May | June | July | August | September | October | November | December |
| 2000 | 1.273 | 0.62 | 0.133 | 0.233 | 1.127 | 0.117 | 0.059 | -0.674 | -1.853 | 0.347 | -1.537 | -1.29 |
| 2001 | -0.471 | -0.265 | -0.555 | 0.515 | -0.262 | 0.386 | -0.928 | 0.91 | 1.161 | 1.277 | 0.996 | 1.474 |
| 2002 | 0.747 | 1.334 | -1.823 | 0.165 | -2.798 | -1.112 | -0.591 | -0.099 | -0.864 | -2.564 | -0.924 | 1.308 |
| 2003 | -0.988 | -0.357 | -0.188 | 0.224 | 0.385 | -0.775 | 0.727 | 0.678 | -0.323 | -0.025 | -0.712 | -1.323 |
| 2004 | 0.807 | -1.182 | 0.432 | 0.151 | 0.46 | 1.195 | 1.474 | -0.071 | 0.254 | -0.042 | -0.242 | -0.973 |
| 2005 | -0.129 | 1.243 | 0.158 | 0.355 | -0.297 | -1.428 | -0.252 | 0.228 | 0.241 | 0.031 | -0.551 | -1.968 |
| 2006 | 0.339 | -0.211 | 0.501 | -0.169 | 1.695 | 0.438 | 0.926 | -1.727 | -0.324 | 0.879 | 0.101 | 0.638 |
| 2007 | -0.083 | 0.075 | -0.57 | -1.035 | -0.612 | -1.198 | -2.631 | -0.108 | 0.031 | -0.434 | -0.984 | 1.929 |
| 2008 | 1.208 | 1.147 | 0.587 | -0.873 | -0.49 | 1.348 | 0.32 | 0.087 | 1.386 | 1.215 | 0.92 | 1.194 |
| 2009 | 0.963 | 0.456 | 0.605 | 0.029 | -0.733 | -0.47 | -1.234 | -0.686 | -0.017 | 0.085 | -1.915 | 0.607 |
| 2010 | -0.757 | -0.775 | 0.108 | 0.377 | 1.021 | 2.071 | 2.424 | 1.51 | 0.402 | 1.335 | 1.516 | 0.205 |
| 2011 | 0.052 | 1.074 | -0.296 | -0.87 | 1.266 | -0.099 | -1.384 | -1.202 | -1.25 | 0.388 | -0.908 | 2.573 |
| 2012 | 1.583 | -0.283 | 0.275 | 0.666 | 0.153 | -0.197 | 1.259 | 0.489 | 0.562 | -0.444 | -1.701 | -0.764 |
| 2013 | 0.071 | 0.716 | 1.375 | 0.611 | 0.36 | -0.271 | 0.945 | -1.561 | -1.658 | -0.458 | 0.189 | 0.061 |
| 2014 | -0.683 | 0.322 | 0.467 | 0.614 | -0.445 | 0.841 | 0.247 | -0.059 | -1.119 | -0.039 | -0.519 | 1.322 |
| 2015 | 0.675 | 1.217 | 0.773 | 1.029 | 0.416 | 0.711 | 1.678 | 1.062 | 0.542 | -0.17 | 0.695 | -0.059 |
| 2016 | 1.392 | 1.093 | 2.038 | 0.097 | 0.012 | 2.565 | 0.407 | -0.739 | 2.333 | -0.177 | -1.508 | -0.711 |
| 2017 | -0.982 | -0.015 | 0.156 | 0.619 | 1.053 | 0.546 | 0.728 | 0.764 | 1.296 | -0.568 | 0.771 | 0.984 |
The Chinese squid jigging boat annual output of 2 2002-2017 of table
(2) research method and step
Since Argentinian squid resource abundance is using squid jigging boat annual yield as index, the index is because of factors such as production statistics
Error can be generated, therefore to its Resources Richness Rate Index by taking natural logrithm ln (CPUE) to be standardized.
Using Time series analysis method, correlation is carried out to ln (CPUE) value and the AAO value of the 2000-2017 1-12 month
Analysis obtains statistically relevant moon AAO value (statistically P < 0.05), the AAO value of these months is as the Argentinian squid of influence
The climatic factor of resource abundance.
Multiple Argentinian squid resource abundance prediction models, formula are as follows: ln (CPUE) are established using multiple linear equation
=a+b1*x1+b2*x2+b3*x3+ ...+bn*xn.In formula, CPUE is single ship annual output, and a is constant, b1, b2, b3 ...,
Bn is the coefficient of equation；X1, x2, x3 ..., xn be influence resource abundance moon AAO value.It is predicted in above-mentioned multiple resource abundances
In model, select statistically the smallest model of P value as optimal models.
2, result of study
(1) resource abundance ln (CPUE) changes between year
As shown in Figure 2, Argentinian squid resource abundance ln (CPUE) changes between significant year is presented, 2002-2003,
It is horizontal that 2007-2008,2014-2015 are in high stock number；And 2005-2006,2010-2012 and 2016-
It is horizontal to be then within 2017 low stock number.
(2) the AAO value of resource abundance ln (CPUE) is influenced
Resource abundance ln (CPUE) and the correlation analysis of the AAO value of preceding 2 years each moons think, resource abundance ln (CPUE)
AAO value correlation in May with first 2 years is significant, presents and is positively correlated, and related coefficient is 0.549 (P < 0.05).
Resource abundance ln (CPUE) and the correlation analysis of the AAO value of preceding 1 year each moon think, resource abundance ln (CPUE)
August AAO value correlation with first 1 year is significant, and negative correlation is presented, and related coefficient is -0.497 (P < 0.05).
(3) model of resource abundance prediction is established
1) one of prediction model
In the past AAO value (AAO in May, 2t-2) it is used as predictive factor, establish Argentinian squid resource abundance prediction model
Are as follows:
Ln (CPUE)=6.7305+0.4554*AAOt-2
Its F value is 6.0347 (P=0.0277 < 0.05).
The statistical form of its actual value and predicted value such as table 3, model profile figure is shown in Fig. 3.
The Argentinian resource abundance actual value of table 3 and predicted value and its residual error
2) the two of prediction model
The in the past AAO value of in August, 1 (AAOt-1) it is used as predictive factor, establish Argentinian squid resource abundance prediction model
Are as follows:
Ln (CPUE)=6.7281-0.4694*AAOt-1
Its F value is 4.5480 (P=0.049 < 0.05).
The statistical form of its actual value and predicted value such as table 4, model profile figure is shown in Fig. 4.
The Argentinian resource abundance actual value of table 4 and predicted value and its residual error
3) the three of prediction model
2 years in the past May AAO value, the first AAO of in August, 1 value as predictive factor, establish Argentinian squid resource abundance
Prediction model are as follows:
Ln (CPUE)=6.7046+0.3908*AAOt-2-0.3836*AAOt-1
Its F value is 5.5135 (P=0.018 < 0.05).
The statistical form of its actual value and predicted value such as table 5, model profile figure is shown in Figures 5 and 6.
The Argentinian resource abundance actual value of table 5 and predicted value and its residual error
By the comparative analysis of above three model it can be concluded that, this research select first 2 years May AAO value, preceding in August, 1
AAO value is as the climatic prediction factor, Argentinian squid resource abundance prediction model are as follows: Ln (CPUE)=6.7046+0.3908*
AAOt-2-0.3836*AAOt-1.The resource abundance variation tendency of its actual value and predicted value is as shown in Figure 7.
Although specific embodiments of the present invention have been described above, it will be appreciated by those of skill in the art that these
It is merely illustrative of, protection scope of the present invention is defined by the appended claims.Those skilled in the art is not carrying on the back
Under the premise of from the principle and substance of the present invention, many changes and modifications may be made, but these are changed
Protection scope of the present invention is each fallen with modification.
Claims (4)
1. a kind of Argentinian squid Resources Prediction method based on Antarctic Oscillations index, which is characterized in that it includes following step
It is rapid:
The Antarctic Oscillations Index A AO monthly of the south sea area of the Southwest Pacific of N Argentina squid distribution before S1, acquisition
Value；
S2, using Time series analysis method, to Argentinian squid resource abundance ln (CPUE) and the AAO value of preceding N monthly into
Row correlation analysis obtains statistically significant relevant moon AAO value, those month AAO value is as the Argentinian squid resource of influence
The climatic factor of abundance；
S3, P value on multiple Argentinian squid resource abundance prediction models and counting statistics is established using multiple linear equation, it is public
Formula are as follows: ln (CPUE)=a+b1*x1+b2*x2+b3*x3+ ...+bn*xn, in formula, CPUE is single ship annual output, and a is constant,
B1, b2, b3 ..., bn be equation coefficient；X1, x2, x3 ..., xn be influence resource abundance moon AAO value；
S4, in above-mentioned multiple Argentinian squid resource abundance prediction models, select statistically the smallest model of P value as optimal
Model.
2. the Argentinian squid Resources Prediction method based on Antarctic Oscillations index as described in claim 1, which is characterized in that
In step s 2, according to the correlation analysis of Argentinian squid resource abundance ln (CPUE) and the AAO value of preceding 2 years each moons, A Gen
Court of a feudal ruler squid resource abundance ln (CPUE) is significant (P < 0.05) with first AAO value correlations in May, 2, presents and is positively correlated, related
Coefficient is respectively 0.549；
According to the correlation analysis of Argentinian squid resource abundance ln (CPUE) and the AAO value of preceding 1 year each moon, Argentinian fish resource
Abundance ln (CPUE) is significant (P < 0.05) with the first AAO value of in August, 1 correlation, presents negatively correlated, and related coefficient is-
0.497。
3. the Argentinian squid Resources Prediction method based on Antarctic Oscillations index as claimed in claim 2, which is characterized in that
In step s3,
1) one of prediction model
In the past AAO value (AAO in May, 2t-2) it is used as predictive factor, establish Argentinian squid resource abundance prediction model are as follows:
Ln (CPUE)=6.7305+0.4554*AAOt-2
Its F value is 6.0347, P=0.0277 < 0.05；
2) the two of prediction model
The in the past AAO value of in August, 1 (AAOt-1) it is used as predictive factor, establish Argentinian squid resource abundance prediction model are as follows:
Ln (CPUE)=6.7281-0.4694*AAOt-1
Its F value is 4.5480, P=0.049 < 0.05；
3) the three of prediction model
2 years in the past May AAO value, the first AAO of in August, 1 value as predictive factor, establish Argentinian squid resource abundance prediction
Model are as follows:
Ln (CPUE)=6.7046+0.3908*AAOt-2-0.3836*AAOt-1
Its F value is 5.5135, P=0.018 < 0.05.
4. the Argentinian squid Resources Prediction method based on Antarctic Oscillations index as claimed in claim 3, which is characterized in that
In step s 4, select first 2 years May AAO value, the first AAO of in August, 1 value is as the climatic prediction factor；
Argentinian squid resource abundance prediction model are as follows: Ln (CPUE)=6.7046+0.3908*AAOt-2-0.3836*AAOt-1,
AAOt-2For preceding in May, 2 AAO value, AAOt-1For preceding in August, 1 AAO value.
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| JP2020561696A JP7044417B2 (en) | 2018-10-18 | 2019-10-17 | Prediction method of Argentine pine squid stock based on Antarctic Oscillation Index and its application |
| PCT/CN2019/111757 WO2020078439A1 (en) | 2018-10-18 | 2019-10-17 | Method for predicting illex argentinus resource quantity on basis of antarctic oscillation index, and application thereof |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110533245A (en) * | 2019-08-30 | 2019-12-03 | 上海海洋大学 | A kind of sliding squid fishing ground amount prediction technique of Argentina based on Hai Biaowen |
| CN110555567A (en) * | 2019-09-10 | 2019-12-10 | 上海彩虹鱼海洋科技股份有限公司 | method, system and device for fish flood prediction |
| WO2020078439A1 (en) * | 2018-10-18 | 2020-04-23 | 上海海洋大学 | Method for predicting illex argentinus resource quantity on basis of antarctic oscillation index, and application thereof |
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