CN106259059A - A kind of South America sardine Resources Prediction method and application thereof - Google Patents
A kind of South America sardine Resources Prediction method and application thereof Download PDFInfo
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- CN106259059A CN106259059A CN201510314243.7A CN201510314243A CN106259059A CN 106259059 A CN106259059 A CN 106259059A CN 201510314243 A CN201510314243 A CN 201510314243A CN 106259059 A CN106259059 A CN 106259059A
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- 235000019512 sardine Nutrition 0.000 title claims abstract description 53
- 238000000034 method Methods 0.000 title claims abstract description 15
- 241001125046 Sardina pilchardus Species 0.000 title 1
- 241001125048 Sardina Species 0.000 claims abstract description 52
- 230000010355 oscillation Effects 0.000 claims abstract description 12
- 238000004519 manufacturing process Methods 0.000 claims abstract description 8
- 238000013528 artificial neural network Methods 0.000 claims abstract description 5
- 238000004458 analytical method Methods 0.000 claims abstract description 4
- 238000010219 correlation analysis Methods 0.000 claims abstract description 4
- 230000003111 delayed effect Effects 0.000 claims description 25
- 230000017448 oviposition Effects 0.000 claims description 4
- 235000013601 eggs Nutrition 0.000 claims description 3
- 230000000366 juvenile effect Effects 0.000 claims description 2
- 239000007952 growth promoter Substances 0.000 claims 1
- 230000008859 change Effects 0.000 abstract description 3
- 241000251468 Actinopterygii Species 0.000 description 14
- 235000019688 fish Nutrition 0.000 description 14
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 6
- 230000007613 environmental effect Effects 0.000 description 3
- 241001135941 Sardinops sagax Species 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 235000020289 caffè mocha Nutrition 0.000 description 2
- 235000013305 food Nutrition 0.000 description 2
- 230000008520 organization Effects 0.000 description 2
- 230000004083 survival effect Effects 0.000 description 2
- 241000555825 Clupeidae Species 0.000 description 1
- 241001454694 Clupeiformes Species 0.000 description 1
- 241000251464 Coelacanthiformes Species 0.000 description 1
- 241000238424 Crustacea Species 0.000 description 1
- 102000002322 Egg Proteins Human genes 0.000 description 1
- 108010000912 Egg Proteins Proteins 0.000 description 1
- 241001465754 Metazoa Species 0.000 description 1
- 102000003979 Mineralocorticoid Receptors Human genes 0.000 description 1
- 108090000375 Mineralocorticoid Receptors Proteins 0.000 description 1
- 208000028804 PERCHING syndrome Diseases 0.000 description 1
- 241000269799 Perca fluviatilis Species 0.000 description 1
- 241001486861 Sardinops caeruleus Species 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 238000007621 cluster analysis Methods 0.000 description 1
- 238000013527 convolutional neural network Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000005188 flotation Methods 0.000 description 1
- 210000002149 gonad Anatomy 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000007935 neutral effect Effects 0.000 description 1
- 210000004681 ovum Anatomy 0.000 description 1
- 238000001556 precipitation Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 239000004576 sand Substances 0.000 description 1
Classifications
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- 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
-
- 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)
- Environmental Sciences (AREA)
- Marine Sciences & Fisheries (AREA)
- Zoology (AREA)
- Animal Husbandry (AREA)
- Biodiversity & Conservation Biology (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Farming Of Fish And Shellfish (AREA)
Abstract
The present invention relates to South America, southeast Pacific marine site sardine Resources Prediction method, obtain TNI value between EI Nino 1.2 index and EI Nino 4 index, equatorial region Southern oscillation index ESOI, Eastern Pacific equatorial region sea-level pressure EEPSLP, the whole world monthly average comprehensive angular momentum index M GIAMI, Darwin sea-level pressure Dar SLP, Southern oscillation index normal data SOI s, Southern oscillation index anomaly data SOI a by remote sensing satellite；Monthly and annual carries out the lag correlation analysis of 0-6 to 7 marine environment and climatic factor and South America sardine annual production, it is thus achieved that affect the significant factor of stock number；Utilize the significant factor, set up South America based on three hierarchical structures (10-8-1) sardine stock number BP neural network prediction model；By to 1970-2012 South America sardine prognostic resources and real resource component analysis, obtaining prognostic resources (Y) with real resource amount (X) relational expression is: Y=1.0293X-1.1065.This relational expression can explain more than the 90% of South America sardine resource abundance change.
Description
Technical field
The present invention relates to a kind of fishery forescast method, particularly relate to southeast Pacific South America sardine Resources Prediction method and
Application.
Background technology
South America sardine Sardinops sagax belongs to Osteichthyes, Clupeiformes, clupeidae, Sardinops sagax genus.It is mainly distributed on Peru
With the coastal marine site of Chile, the Sechura gulf in north to 5 ° of S, the mocha island (Mocha Island, 38 ° of 30'5) in south to Chile.
South America sardine is the small-sized pelagic fishes of bank property, mainly captures at 40 meters of water layers.The water temperature of perching in summer is 16-23 DEG C,
Perch water temperature winter and be 10-18 DEG C.High density cluster is mainly distributed on peru current.Main predation flotation crustaceans animal.One
Year lays eggs 2 times, the predominantly 7-9 month, is secondly the 2-3 month.Spawning ground is distributed in whole littoral sea.Body length 24 centimetres
Individual gonad is mature on the whole.
Just there is statistics in the fishery output of South America sardine to 1961 FAO (Food and Agriculture Organization of the United Nation), and within 1961, Peru fishes for 2700 tons,
Within 1973, increase to 100,000 tons.Later there is sharp increase in fishery output, within 1985, reaches 6,500,000 tons, but now due to
EI Nino phenomenon Peru fish occurs in that reduces sharply.After 1985, declining occurs in fishery output, and nineteen ninety-five reaches 1,500,000
Ton, essentially from Peru.Fishing operation mode is mainly small-sized purse seine.Within 1999, fishery output is 44.2 ten thousand tons, Qi Zhongzhi
Profit is 24.6 ten thousand tons, and Peru is 18.8 ten thousand tons.Calendar year 2001 fishery output is further lowered into 130,000 tons, after 2007 not
1000 tons of foot.Fishing in country at South America sardine, Chile and Peru are main countries.Its fishery output presents equally
Go out identical variation tendency.
South America sardine and the fish crop fished for according to the production leadtime of FAO (Food and Agriculture Organization of the United Nation), Peru and Chile marine site present phase
Anti-trend.In the sixties (1960-1971) of initial stage of development, Chile's South America, marine site sardine, the average annual output of fish
Amount is respectively 2180 tons and 670,000 tons；Between 1979-1987, South America sardine, the average annual productivity of fish are respectively 221
Ten thousand tons and 290,000 tons；1993-2012, South America sardine, the average annual productivity of fish are respectively 6.8 ten thousand tons and 137.8 ten thousand
Ton.
Fishery forescast is the key link of fish production, the forecast of South America sardine stock number is conducive to South America sardine yield,
Cental fishing ground position and the forecast of fishing season.The survival rate of South America sardine young baby fish except in it physiologic factor relevant
Outward, the most closely related with extraneous Marine Environment Factors.External factor is a lot, the direct or indirect action affecting young baby fish, bag
Include the hydrology (ocean current, water temperature, tide, salinity, water colour, water quality and runoff etc.), meteorological (include wind, air pressure, temperature,
Precipitation etc.), geographical (landform, geology, landforms etc.), biological (swim, dwell in the end, harmful animal etc.).
Study Pacific Ocean sardine stock number according to forefathers to be affected by the marine environment factor, climatic factor etc., utilize these factors to set up
Multiple forecast stock number model.As Manuel (2001) etc. utilizes sea surface temperature (SST), upper up-flow (IS) and adjusts
Looking into fishery data analysis bay, California sardine stock number of sampling and the relation of habitat, result shows: when
When sardine stock number is enriched, its distribution is relatively wide, otherwise, its distribution is less.Between its resource abundance and environmental variable
There is non-linear relation, it is 13-18m that the highest resource abundance occurs in IS3s-1/ 10m, SST are 19-25 DEG C of marine site, utilize this
It is 78.8% that the forecasting model deviation that a little factors are set up explains.Juan (2009) etc. utilizes SST, SL (Sea level), EK
(Ekman transport index in the Chilean coast), N34 (Nino3+4SST), N12 (Nino1+2SST) with
And SOI (south oscillation index) data set up the northern sardine Cluster analysis of Chile (MLRs, GAMs,
CNNs), consider the hysteresis effect of Variable Factors simultaneously.It is 86% that the deviation of the optimal forecasting model selected explains, standard
Difference is 7.66%.It addition, Eleuterio (2010) etc. utilize envirment factor, fish crop and sardine CPUE, set up intelligence
The fish in profit the north and sardine neutral net stock number forecasting model.Optimal forecasting model deviation explanation rate is 82%, in advance
Survey standard deviation less than 45%.Above forecast precision is difficult to meet the requirement of business and production, awaits improving further.
Summary of the invention
The present invention is to solve the problems referred to above, overcome the problem that in prior art, fishery forescast model prediction precision is the highest, the present invention
There is provided one to utilize ocean remote sensing marine environment and weather factor data, analyze difference month difference by relevant statistical software delayed
The dependency with South America sardine stock number, choose 10 factors that dependency is the highest, as BP neural network input layer
The method setting up the forecast model prediction South America sardine stock number size of 10-8-1, to meet fish production and businessization operation
Demand.
To achieve these goals, technical scheme is as follows:
A kind of South America sardine Resources Prediction method, it is characterised in that comprise the following steps:
(1) standard difference index (TNI), equatorial region between EI Nino 1.2 and EI Nino 4 is obtained by remote sensing satellite
Southern oscillation index (ESOI), Eastern Pacific's equatorial region sea-level pressure (EEPSLP), whole world monthly average comprehensive angular momentum index
(MGIAMI), Darwin's sea-level pressure (Dar SLP), Southern oscillation index normal data (SOI s), southern oscillation refer to
Totally 7 marine environment and the climatic factor such as number anomaly data (SOI a)；
(2) monthly and annual carries out the stagnant of 0-6 to 7 marine environment and climatic factor with South America sardine annual production
Rear correlation analysis, it is thus achieved that the statistically evident factor；
(3) analyze 10 factors choosing to be respectively as follows: TNI-8-lag5 and (represent that envirment factor TNI is in August delayed 5
The impact in year)；MGIAMI-10-R (represents the impact in October of envirment factor MGIAMI)；MGIAMI-10-lag1
(representing the impact in delayed 1 year of October of envirment factor MGIAMI)；MGIAMI-9-lag1 (represents envirment factor MGIAMI
In the JIUYUE impact of delayed 1 year)；Dar SLP-7-lag6 (expression envirment factor Dar SLP delayed 6 years in July
Impact)；SOI a-11-lag1 (represents the envirment factor SOI a impact delayed 1 year of November)；EEPSLP-5-lag1
(representing the impact delayed 1 year of May of envirment factor EEPSLP)；SOI s-3-lag 1 (represents envirment factor SOI s
Impact delayed 1 year of March)；EEPSLP-5-R (represents the impact in May of envirment factor EEPSLP)；ESOI-
Annual mean-lag1 (represents the impact of delayed 1 year of envirment factor ESOI annual mean)；
(4) using 10 marine environment chosen and climatic factor as BP neural network input layer, setting up network structure is
The South America sardine Resources Prediction model of 10-8-1.I.e. input layer is 10 factors, and hidden layer is 8 factors, output layer
For 1 factor of South America sardine stock number；
(5) by 1970-2012 South America sardine prognostic resources and real resource component analysis, obtaining prognostic resources
(Y) with real resource amount (X) relational expression it is: Y=1.0293X-1.1065.
According to a kind of South America of the present invention sardine Resources Prediction method, 7-9 month is that South America sardine is when laying eggs suitable
Between, the 2-3 month is secondary egg-laying time.
According to a kind of South America of the present invention sardine Resources Prediction method, 5, October be South America sand fourth larva and juvenile growth
The optimum time grown.
The present invention also provides for the application in fishery forescast of a kind of described South America sardine Resources Prediction method.
The invention has the beneficial effects as follows: this forecast model can explain more than the 90% of South America sardine resource abundance change.South America
The forecast result of sardine stock number is supplied to user and relevant departments in time, for science reference, and from now on can be as a kind of product
Carry out real-time release.
Detailed description of the invention
The present invention is further elaborated below, it will be appreciated by those skilled in the art that described embodiment is merely cited for, and not
The present invention is constituted any restriction.
Southeast Pacific South America sardine be life cycle be the kind of 5-6, for littoral epipelagic fish, its stock number mainly takes
Certainly in the marine environmental conditions in spawning ground.Under suitable marine environmental conditions, ovum survival rate is high, otherwise the lowest.South America sardine
Egg-laying time is usually the 7-9 month, and the secondary egg-laying season is the 2-3 month.
A kind of South America sardine Resources Prediction method, comprises the following steps:
(1) standard difference index (TNI), equatorial region between EI Nino 1.2 and EI Nino 4 is obtained by remote sensing satellite
Southern oscillation index (ESOI), Eastern Pacific's equatorial region sea-level pressure (EEPSLP), whole world monthly average comprehensive angular momentum index
(MGIAMI), Darwin's sea-level pressure (Dar SLP), Southern oscillation index normal data (SOI s), southern oscillation refer to
Totally 7 marine environment and the climatic factor such as number anomaly data (SOI a)；
(2) monthly and annual carries out the stagnant of 0-6 to 7 marine environment and climatic factor with South America sardine annual production
Rear correlation analysis, it is thus achieved that the statistically evident factor；
(3) analyze 10 factors choosing to be respectively as follows: TNI-8-lag5 and (represent that envirment factor TNI is in August delayed 5
The impact in year)；MGIAMI-10-R (represents the impact in October of envirment factor MGIAMI)；MGIAMI-10-lag1
(representing the impact in delayed 1 year of October of envirment factor MGIAMI)；MGIAMI-9-lag1 (represents envirment factor MGIAMI
In the JIUYUE impact of delayed 1 year)；Dar SLP-7-lag6 (expression envirment factor Dar SLP delayed 6 years in July
Impact)；SOI a-11-lag1 (represents the envirment factor SOI a impact delayed 1 year of November)；EEPSLP-5-lag1
(representing the impact delayed 1 year of May of envirment factor EEPSLP)；SOI s-3-lag 1 (represents envirment factor SOI s
Impact delayed 1 year of March)；EEPSLP-5-R (represents the impact in May of envirment factor EEPSLP)；ESOI-
Annual mean-lag1 (represents the impact of delayed 1 year of envirment factor ESOI annual mean)；
(4) using 10 marine environment chosen and climatic factor as BP neural network input layer, setting up network structure is
The South America sardine Resources Prediction model of 10-8-1；
(5) by 1970-2012 South America sardine prognostic resources and real resource component analysis, obtaining prognostic resources
(Y) with real resource amount (X) relational expression it is: Y=1.0293X-1.1065.
The invention has the beneficial effects as follows: this relational expression can explain more than the 90% of South America sardine stock number change, far above front
The forecasting model that people is set up.The forecast result of South America sardine stock number is supplied to user and relevant departments, in time for science
Reference, can carry out real-time release as a kind of product from now on.
The ultimate principle of the present invention, principal character and advantages of the present invention have more than been shown and described.The technical staff of the industry should
This understanding, the present invention is not restricted to the described embodiments, and the simply explanation present invention's described in above-described embodiment and description is former
Reason, the present invention also has various changes and modifications without departing from the spirit and scope of the present invention, and these changes and improvements are all
Fall in scope of the claimed invention.Claimed scope is defined by appending claims and equivalent thereof.
Claims (4)
1. a southeast Pacific South America sardine Resources Prediction method, it is characterised in that comprise the following steps:
(1) standard difference index (TNI), equatorial region between EI Nino 1.2 and EI Nino 4 is obtained by remote sensing satellite
Southern oscillation index (ESOI), Eastern Pacific's equatorial region sea-level pressure (EEPSLP), the whole world comprehensive angular momentum of monthly average refer to
Number (MGIAMI), Darwin's sea-level pressure (Dar SLP), Southern oscillation index normal data (SOI s), south great waves
Totally 7 marine environment and the climatic factor such as dynamic index anomaly data (SOI a)；
(2) monthly and annual carries out the stagnant of 0-6 to 7 marine environment and climatic factor with South America sardine annual production
Rear correlation analysis, it is thus achieved that the statistically evident factor；
(3) analyze 10 factors choosing to be respectively as follows: TNI-8-lag5 and (represent that envirment factor TNI is in August delayed 5
The impact in year)；MGIAMI-10-R (represents the impact in October of envirment factor MGIAMI)；MGIAMI-10-lag1
(representing the impact in delayed 1 year of October of envirment factor MGIAMI)；MGIAMI-9-lag1 (represents envirment factor MGIAMI
In the JIUYUE impact of delayed 1 year)；Dar SLP-7-lag6 (represents that envirment factor Dar SLP is in July delayed 6
The impact in year)；SOI a-11-lag1 (represents the envirment factor SOI a impact delayed 1 year of November)；EEPSLP-5-lag1
(representing the impact delayed 1 year of May of envirment factor EEPSLP)；SOI s-3-lag 1 (represents envirment factor SOI
S is in the impact in delayed 1 year of March)；EEPSLP-5-R (represents the impact in May of envirment factor EEPSLP)；ESOI-
Annual mean-lag1 (represents the impact of delayed 1 year of envirment factor ESOI annual mean)；
(4) using 10 marine environment chosen and climatic factor as BP neural network input layer, setting up structure is 10-8-1
South America sardine Resources Prediction model；
(5) by 1970-2012 South America sardine prognostic resources and real resource component analysis, obtaining prognostic resources
(Y) with real resource amount (X) relational expression it is: Y=1.0293X-1.1065.
A kind of southeast Pacific South America sardine Resources Prediction method, it is characterised in that 7-9
Month is that South America sardine is laid eggs suitable time, and the 2-3 month is secondary egg-laying time.
3. state a kind of South America sardine Resources Prediction method according to claim 1, it is characterised in that 5, October be southern
The optimum time of Mei Shading larva and juvenile growth promoter.
4. a South America sardine Resources Prediction method application in fishery forescast according to claim 1.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107609691A (en) * | 2017-08-29 | 2018-01-19 | 上海海洋大学 | Mauritanian siphonopods fishing ground forecasting procedure based on habitat suitability index |
| CN109086823A (en) * | 2018-08-01 | 2018-12-25 | 中国科学院合肥物质科学研究院 | A kind of wheat scab disease tassel yield method for automatically counting |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1575343A (en) * | 2001-08-27 | 2005-02-02 | 新罕布什尔州立大学 | Method for identifying fast-growing fish in different salinity |
| CN103053450A (en) * | 2012-12-24 | 2013-04-24 | 上海海洋大学 | Southwest Atlantic illex argentinus resource supplement quantity forecasting method |
-
2015
- 2015-06-10 CN CN201510314243.7A patent/CN106259059B/en not_active Expired - Fee Related
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1575343A (en) * | 2001-08-27 | 2005-02-02 | 新罕布什尔州立大学 | Method for identifying fast-growing fish in different salinity |
| CN103053450A (en) * | 2012-12-24 | 2013-04-24 | 上海海洋大学 | Southwest Atlantic illex argentinus resource supplement quantity forecasting method |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107609691A (en) * | 2017-08-29 | 2018-01-19 | 上海海洋大学 | Mauritanian siphonopods fishing ground forecasting procedure based on habitat suitability index |
| CN109086823A (en) * | 2018-08-01 | 2018-12-25 | 中国科学院合肥物质科学研究院 | A kind of wheat scab disease tassel yield method for automatically counting |
| CN109086823B (en) * | 2018-08-01 | 2022-02-11 | 中国科学院合肥物质科学研究院 | Automatic statistical method for wheat scab ear disease rate |
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