CN106259059B - A kind of South America sardine Resources Prediction method and its application - Google Patents
A kind of South America sardine Resources Prediction method and its application Download PDFInfo
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- CN106259059B CN106259059B CN201510314243.7A CN201510314243A CN106259059B CN 106259059 B CN106259059 B CN 106259059B CN 201510314243 A CN201510314243 A CN 201510314243A CN 106259059 B CN106259059 B CN 106259059B
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- 235000019512 sardine Nutrition 0.000 title claims abstract description 53
- 238000000034 method Methods 0.000 title claims abstract description 16
- 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 13
- 238000013528 artificial neural network Methods 0.000 claims abstract description 6
- 230000007613 environmental effect Effects 0.000 claims description 31
- 230000017448 oviposition Effects 0.000 claims description 6
- 238000004458 analytical method Methods 0.000 claims description 3
- 238000010219 correlation analysis Methods 0.000 claims description 3
- 230000000366 juvenile effect Effects 0.000 claims description 2
- 241001454694 Clupeiformes Species 0.000 description 8
- 241000251468 Actinopterygii Species 0.000 description 7
- 235000019513 anchovy Nutrition 0.000 description 7
- 235000019688 fish Nutrition 0.000 description 7
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 6
- 238000004519 manufacturing process Methods 0.000 description 4
- 101100074187 Caenorhabditis elegans lag-1 gene Proteins 0.000 description 2
- 241001135941 Sardinops sagax Species 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 235000020289 caffè mocha Nutrition 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 235000013305 food Nutrition 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 230000008520 organization Effects 0.000 description 2
- 239000004576 sand Substances 0.000 description 2
- 230000004083 survival effect Effects 0.000 description 2
- 241000555825 Clupeidae 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
- 102000003979 Mineralocorticoid Receptors Human genes 0.000 description 1
- 108090000375 Mineralocorticoid Receptors Proteins 0.000 description 1
- 244000025271 Umbellularia californica Species 0.000 description 1
- 238000007621 cluster analysis Methods 0.000 description 1
- 238000013527 convolutional neural network Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 235000013601 eggs Nutrition 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000005188 flotation Methods 0.000 description 1
- 210000004907 gland Anatomy 0.000 description 1
- 210000004681 ovum Anatomy 0.000 description 1
- 230000035479 physiological effects, processes and functions Effects 0.000 description 1
- 238000001556 precipitation Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 230000001568 sexual effect Effects 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
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- 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
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- 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 southeast Pacific sea area South America sardine Resources Prediction methods, obtain TNI value between 1.2 index of EI Nino and 4 index of EI Nino, equatorial region Southern oscillation index ESOI, Eastern Pacific equatorial region sea-level pressure EEPSLP, global monthly average comprehensive angular momentum index M GIAMI, Darwin's 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 the lag correlation of 7 marine environment and climatic factor and South America sardine annual output progress 0-6 is analyzed in annual, and obtaining influences the significant factor of stock number；Using the significant factor, the South America sardine stock number BP neural network prediction model for being based on three hierarchical structures (10-8-1) is established；By analyzing the South America 1970-2012 sardine prognostic resources and real resource amount, prognostic resources (Y) and real resource amount (X) relational expression are obtained are as follows: Y=1.0293X-1.1065.This relational expression can explain 90% or more of South America sardine resource abundance variation.
Description
Technical field
The present invention relates to a kind of fishery forescast methods, more particularly to southeast Pacific South America sardine Resources Prediction side
Method and its application.
Background technique
South America sardine Sardinops sagax belongs to Osteichthyes, Clupeiformes, clupeidae, Sardinops sagax category.It is mainly distributed on
The coastal sea area of Peru and Chile, north to the gulf Sechura of 5 ° of S, mocha island (the Mocha Island, 38 ° of 30' in south to Chile
5).South America sardine is the small-sized pelagic fishes of bank property, is mainly captured in 40 meters of water layers.The water temperature of inhabiting of summer is 16-
23 DEG C, it is 10-18 DEG C that winter, which inhabites water temperature,.High density cluster is mainly distributed on peru current.Main predation flotation crustaceans are dynamic
Object.It lays eggs 2 times within 1 year, predominantly the 7-9 month, is secondly the 2-3 month.Spawning ground is distributed in entire littoral sea.Long 24 centimetres of body
Individual sexual gland is mature on the whole.
The fishery output of South America sardine just has statistics, Peru's fishing 2700 in 1961 to FAO (Food and Agriculture Organization of the United Nation) in 1961
Ton, increasing within 1973 is 100,000 tons.There is sharp increase in later fishery output, reaches 6,500,000 tons within 1985, but at this time due to
Reducing sharply occurs in EI Nino phenomenon Peru anchovy.After 1985, fishery output declines, and nineteen ninety-five reaches 1,500,000 tons,
Mainly from Peru.Fishing operation mode is mainly small-sized purse seine.Fishery output in 1999 is 44.2 ten thousand tons, and wherein Chile is
24.6 ten thousand tons, Peru is 18.8 ten thousand tons.Fishery output in 2001 is further lowered into after 130,000 tons, 2007 less than 1000
Ton.In the fishing country of South America sardine, Chile and Peru are main countries.Its fishery output equally shows identical
Variation tendency.
According to the production statistics of FAO (Food and Agriculture Organization of the United Nation), the South America sardine and anchovy yield of Peru and Chilean sea area fishing
Opposite trend is presented.In the sixties (1960-1971) of initial stage of development, Chilean sea area South America sardine, anchovy are averaged
Annual output is respectively 2180 tons and 670,000 tons；Between 1979-1987, South America sardine, anchovy average annual productivity be respectively 221
Ten thousand tons and 290,000 tons；1993-2012, South America sardine, anchovy average annual productivity be respectively 6.8 ten thousand tons and 137.8 ten thousand tons.
Fishery forescast is the key link of fish production, is conducive to South America sardine to the forecast of South America sardine stock number
The forecast of yield, cental fishing ground position and fishing season.The survival rate of South America sardine young baby fish in addition to in it physiology because
Plain phase is outside the Pass, also closely related with extraneous Marine Environment Factors.There are many external factor, the row of direct or indirect influence young baby fish
It is dynamic, including the hydrology (ocean current, water temperature, tide, salinity, water colour, water quality and runoff etc.), meteorological (including wind, air pressure, temperature, precipitation
Deng), geographical (landform, geology, landforms etc.), biology (swims, bottom is dwelt, enemy etc.).
Studying Pacific Ocean sardine stock number according to forefathers is influenced by the marine environment factor, climatic factor etc., using these because
Son establishes a variety of forecast stock number models.Such as Manuel (2001) is using sea surface temperature (SST), upper up-flow (IS) and adjusts
The fishery data for looking into sampling analyze the relationship of California bay sardine stock number and habitat, the results showed that
When sardine stock number is abundant, its distribution is wider, conversely, its distribution is smaller.Its resource abundance and environmental variance it
Between there are non-linear relation, highest resource abundance appear in IS be 13-18m3s-1/ 10m, SST is 19-25 DEG C of sea area, utilizes this
It is 78.8% that the forecasting model deviation that a little factors are established, which explains,.Juan (2009) etc. utilizes SST, SL (Sea level), EK
(Ekman transport index in the Chilean coast)、N34(Nino3+4SST)、N12(Nino1+2SST)
And the northern sardine Cluster analysis of SOI (south oscillation index) data foundation Chile (MLRs, GAMs,
CNNs), while considering the hysteresis effects of Variable Factors.It is 86% that the deviation for the best forecasting model selected, which explains, standard deviation
It is 7.66%.In addition, Eleuterio (2010) etc. utilizes environmental factor, anchovy yield and sardine CPUE, it is northern to establish Chile
Anchovy and sardine neural network stock number forecasting model.Optimal forecasting model deviation explain rate as 82%, prediction standard
Difference is lower than 45%.The above forecast precision is difficult to meet the requirement of business and production, needs to be further improved.
Summary of the invention
The present invention to solve the above-mentioned problems, overcomes the problems, such as that fishery forescast model prediction precision is not high in the prior art,
The present invention provides a kind of utilization ocean remote sensing marine environment and weather factor data, analyzes different months by related statistical software
The correlation with South America sardine stock number of difference lag, chooses highest 10 factors of correlation, as BP neural network
The method that input layer establishes the prediction model prediction South America sardine stock number size of 10-8-1, to meet fish production and business
Change the demand of operation.
To achieve the goals above, technical scheme is as follows:
A kind of South America sardine Resources Prediction method, which comprises the following steps:
(1) standard difference index (TNI), equatorial region between EI Nino 1.2 and EI Nino 4 are obtained by remote sensing satellite
Southern oscillation index (ESOI), Eastern Pacific's equatorial region sea-level pressure (EEPSLP), the comprehensive angular motion volume index of global monthly average
(MGIAMI), Darwin's sea-level pressure (Dar SLP), Southern oscillation index normal data (SOI s), Southern oscillation index away from
Totally 7 marine environment and the climatic factor such as flat data (SOI a)；
(2) monthly and annual carries out 0-6's to 7 marine environment and climatic factor and South America sardine annual output
Lag correlation analysis, obtains the statistically significant factor；
(3) 10 factors that analysis is chosen, which are respectively as follows: TNI-8-lag5, (indicates that environmental factor TNI is lagged 5 years in August part
Influence)；MGIAMI-10-R (indicates environmental factor MGIAMI in the influence in October)；MGIAMI-10-lag1 (indicates environment
Factor M GIAMI lags influence in 1 year October)；MGIAMI-9-lag1 (indicates that environmental factor MGIAMI lags 1 in September part
The influence in year)；Dar SLP-7-lag6 (indicates that environmental factor Dar SLP lags influence in 6 years in July)；SOI a-11-
Lag1 (indicates that environmental factor SOI a lags influence in 1 year in November)；EEPSLP-5-lag1 (indicates environmental factor EEPSLP
Influence in 1 year is lagged in May)；SOI s-3-lag 1 (indicates that environmental factor SOI s lags influence in 1 year in March)；
EEPSLP-5-R (indicates environmental factor EEPSLP in the influence in May)；ESOI- annual mean-lag1 (indicates environmental factor
ESOI annual mean lags influence in 1 year)；
(4) using 10 marine environment of selection and climatic factor as BP neural network input layer, establishing 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, and output layer is
1 factor of South America sardine stock number；
(5) by analyzing the South America 1970-2012 sardine prognostic resources and real resource amount, prediction resource is obtained
Measure (Y) and real resource amount (X) relational expression are as follows: Y=1.0293X-1.1065.
A kind of South America sardine Resources Prediction method according to the present invention, 7-9 month are that the oviposition of South America sardine is suitable
Suitable time, the 2-3 month are secondary egg-laying time.
A kind of South America sardine Resources Prediction method according to the present invention, 5, October be that sand fourth larva and juvenile in South America is raw
The optimum time of long development.
The present invention also provides a kind of application of South America sardine Resources Prediction method in fishery forescast.
The beneficial effects of the present invention are: this prediction model can explain South America sardine resource abundance variation 90% with
On.The forecast result of South America sardine stock number is supplied to user and relevant departments in time, for science reference, can be used as from now on
A kind of product progress real-time release.
Specific embodiment
The present invention is further elaborated below, it will be appreciated by those skilled in the art that the embodiment is given for example only,
And it does not form any restrictions to the present invention.
Southeast Pacific South America sardine is the type that life cycle is 5-6, for littoral epipelagic fish, stock number master
To depend on the marine environmental conditions in spawning ground.Ovum high survival rate under suitable marine environmental conditions, it is on the contrary then low.South America sand fourth
The egg-laying time of fish 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, comprising the following steps:
(1) standard difference index (TNI), equatorial region between EI Nino 1.2 and EI Nino 4 are obtained by remote sensing satellite
Southern oscillation index (ESOI), Eastern Pacific's equatorial region sea-level pressure (EEPSLP), the comprehensive angular motion volume index of global monthly average
(MGIAMI), Darwin's sea-level pressure (Dar SLP), Southern oscillation index normal data (SOI s), Southern oscillation index away from
Totally 7 marine environment and the climatic factor such as flat data (SOI a)；
(2) monthly and annual carries out 0-6's to 7 marine environment and climatic factor and South America sardine annual output
Lag correlation analysis, obtains the statistically significant factor；
(3) 10 factors that analysis is chosen, which are respectively as follows: TNI-8-lag5, (indicates that environmental factor TNI is lagged 5 years in August part
Influence)；MGIAMI-10-R (indicates environmental factor MGIAMI in the influence in October)；MGIAMI-10-lag1 (indicates environment
Factor M GIAMI lags influence in 1 year October)；MGIAMI-9-lag1 (indicates that environmental factor MGIAMI lags 1 in September part
The influence in year)；Dar SLP-7-lag6 (indicates that environmental factor Dar SLP lags influence in 6 years in July)；SOI a-11-
Lag1 (indicates that environmental factor SOI a lags influence in 1 year in November)；EEPSLP-5-lag1 (indicates environmental factor EEPSLP
Influence in 1 year is lagged in May)；SOI s-3-lag 1 (indicates that environmental factor SOI s lags influence in 1 year in March)；
EEPSLP-5-R (indicates environmental factor EEPSLP in the influence in May)；ESOI- annual mean-lag1 (indicates environmental factor
ESOI annual mean lags influence in 1 year)；
(4) using 10 marine environment of selection and climatic factor as BP neural network input layer, establishing network structure is
The South America sardine Resources Prediction model of 10-8-1；
(5) by analyzing the South America 1970-2012 sardine prognostic resources and real resource amount, prediction resource is obtained
Measure (Y) and real resource amount (X) relational expression are as follows: Y=1.0293X-1.1065.
The beneficial effects of the present invention are: this relational expression can explain 90% or more of South America sardine stock number variation, far
The forecasting model established higher than forefathers.The forecast result of South America sardine stock number is supplied to user and relevant departments in time,
For science reference, a kind of product progress real-time release can be used as from now on.
The basic principles, main features and advantages of the present invention have been shown and described above.The technology of the industry
Personnel are it should be appreciated that the present invention is not limited to the above embodiments, and the above embodiments and description only describe this
The principle of invention, various changes and improvements may be made to the invention without departing from the spirit and scope of the present invention, these changes
Change and improvement all fall within the protetion scope of the claimed invention.The claimed scope of the invention by appended claims and its
Equivalent defines.
Claims (4)
1. a kind of southeast Pacific South America sardine Resources Prediction method, which comprises the following steps:
(1) standard difference index (TNI), equatorial region south between EI Nino 1.2 and EI Nino 4 are obtained by remote sensing satellite
Oscillation Index (ESOI), Eastern Pacific's equatorial region sea-level pressure (EEPSLP), the comprehensive angular motion volume index of global monthly average
(MGIAMI), Darwin's sea-level pressure (DarSLP), Southern oscillation index normal data (SOIs), Southern oscillation index anomaly
Totally 7 marine environment and the climatic factor such as data (SOIa)；
(2) monthly and annual carries out the lag of 0-6 to 7 marine environment and climatic factor and South America sardine annual output
Correlation analysis obtains the statistically significant factor；
(3) 10 factors that analysis is chosen, which are respectively as follows: TNI-8-lag5, indicates that environmental factor TNI lags 5 years shadows in August part
It rings；MGIAMI-10-R indicates environmental factor MGIAMI in the influence in October；MGIAMI-10-lag1 indicates environmental factor
MGIAMI10 month lags influence in 1 year；MGIAMI-9-lag1 indicates that environmental factor MGIAMI lags 1 year shadow in September part
It rings；DarSLP-7-lag6 indicates that environmental factor DarSLP lags influence in 6 years in July；SOIa-11-lag1 indicate environment because
Sub- SOIa lags influence in 1 year in November；EEPSLP-5-lag1 indicates that environmental factor EEPSLP lags 1 year shadow in May
It rings；SOIs-3-lag1 indicates that environmental factor SOIs lags influence in 1 year in March；EEPSLP-5-R indicates environmental factor
Influence of the EEPSLP in May；ESOI- annual mean-lag1 indicates that environmental factor ESOI annual mean lags influence in 1 year；
(4) using 10 marine environment of selection and climatic factor as BP neural network input layer, establishing structure is 10-8-1's
South America sardine Resources Prediction model；
(5) by analyzing the South America 1970-2012 sardine prognostic resources and real resource amount, prognostic resources Y is obtained
With real resource amount X relational expression are as follows: Y=1.0293X-1.1065.
2. a kind of southeast Pacific South America sardine Resources Prediction method according to claim 1, which is characterized in that 7-9
Month is South America sardine oviposition suitable time, and the 2-3 month is secondary egg-laying time.
3. stating a kind of South America sardine Resources Prediction method according to claim 1, which is characterized in that 5, October be that South America is husky
The optimum time of fourth larva and juvenile growth and development.
4. a kind of application of the South America sardine Resources Prediction method in fishery forescast according to claim 1.
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| 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 |
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| 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 |
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