WO2019042162A1 - 基于环境因子的西北非海域头足类渔场预报方法 - Google Patents

基于环境因子的西北非海域头足类渔场预报方法 Download PDF

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WO2019042162A1
WO2019042162A1 PCT/CN2018/101111 CN2018101111W WO2019042162A1 WO 2019042162 A1 WO2019042162 A1 WO 2019042162A1 CN 2018101111 W CN2018101111 W CN 2018101111W WO 2019042162 A1 WO2019042162 A1 WO 2019042162A1
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ssha
sst
fishery
depth
index
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PCT/CN2018/101111
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French (fr)
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陈新军
张忠
韦记朋
汪金涛
雷林
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上海海洋大学
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Priority claimed from CN201710757632.6A external-priority patent/CN107609691A/zh
Priority claimed from CN201710756994.3A external-priority patent/CN107578125A/zh
Application filed by 上海海洋大学 filed Critical 上海海洋大学
Priority to AU2018325013A priority Critical patent/AU2018325013A1/en
Priority to US16/334,026 priority patent/US20190272598A1/en
Publication of WO2019042162A1 publication Critical patent/WO2019042162A1/zh
Priority to AU2020100306A priority patent/AU2020100306A4/en

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/02—Agriculture; Fishing; Forestry; Mining
    • 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
    • A01K61/10—Culture of aquatic animals of fish
    • A—HUMAN NECESSITIES
    • A23—FOODS OR FOODSTUFFS; TREATMENT THEREOF, NOT COVERED BY OTHER CLASSES
    • A23B—PRESERVING, e.g. BY CANNING, MEAT, FISH, EGGS, FRUIT, VEGETABLES, EDIBLE SEEDS; CHEMICAL RIPENING OF FRUIT OR VEGETABLES; THE PRESERVED, RIPENED, OR CANNED PRODUCTS
    • A23B4/00—General methods for preserving meat, sausages, fish or fish products
    • A23B4/06—Freezing; Subsequent thawing; Cooling
    • A23B4/08—Freezing; Subsequent thawing; Cooling with addition of chemicals or treatment with chemicals before or during cooling, e.g. in the form of an ice coating or frozen block
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"

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  • the invention relates to a cephalopod fishery forecasting method in the northwestern non-ocean sea area, in particular to a forecasting method for cephalopod fishery in northwest non-sea area based on environmental factors.
  • Mauritania is a country in the western part of the African continent.
  • the coastline is long and the marine environment is unique.
  • the fishery is the main source of income for the national economy.
  • the marine fishery resources have a storage capacity of more than 400 ⁇ l04t, especially the cephalopods are abundant, and it is also the main fishing object of China's ocean fishing vessels.
  • the study of cephalopod resources and fisheries in the waters around Mauritania is of great significance for the efficient production of offshore trawlers in China.
  • Feng Chunlei et al investigated the hydrological situation of the distribution of cephalopod fisheries in Mauritania, and analyzed the spatial structure and changes of various factors such as water mass, temperature, current, hydrological factors (water temperature, dissolved oxygen, salinity, chlorophyll), and discussed the research.
  • the technical problem to be solved by the present invention is to provide an environmental factor-based forecasting method for the northwestern non-cephalopod fishery, and to study the effects of marine environmental factors and habitat indices on the northwestern non-cephalopod fishery, through the cephalopods of the northwestern non-fishing grounds.
  • a method for forecasting cephalopod fishery in northwestern non-ocean based on environmental factors which comprises the following steps:
  • Step 1 Obtain the catch production statistics of the cephalopod fishery in the northwestern non-ocean sea area for many years.
  • the catch production statistics include working time, working water depth, operation network and total catch production;
  • Step 2 Obtain marine environmental data corresponding to the catch production statistics, the marine environment data includes sea surface temperature SST, sea surface height distance average SSHA, and the marine environment data is monthly time resolution, 0.5° ⁇ 0.5° Spatial resolution
  • Step 3 Study the relationship between the operation network and the output of the operation and the average network output in each interval as the index of the central fishery.
  • Step 4 Establish the adaptability index SI of different environmental factors, and use the expert assignment method to calculate the habitat comprehensive index HSI under different weight schemes through different weight schemes, so as to obtain the distribution of the central fishery in the cephalopod fishery in the northwestern non-ocean.
  • the sea area and the best weighting scheme in the distribution sea area is used to predict the center fishery.
  • catch production statistics of the cephalopod fisheries in the northwestern non-sea area are based on data of 4-6 years.
  • the marine environment data corresponding to the catch production statistics are obtained, and the marine environment data includes sea surface temperature SST, sea surface height average SSHA and chlorophyll concentration Chl-a, and SST is separated by 1 °C.
  • For the spacing calculate the job nets, the job output ratio and the average net production in each interval, and then obtain the optimum range of the central fishery SST; calculate the spacing within each pitch by SSHA 10 cm apart.
  • the operating network, the operating yield ratio and the average net output, and the optimal SSHA range of the central fishery is 0.01-1.0, 1.0-2.0, 2.0-5.0, 5.0-20.0 20.0 ⁇ 50.0mg/m 3 is the spacing, calculate the job network, the operation output ratio and the average net output in each interval, and then obtain the optimum range of the central fishery Chl-a; 10m is the distance, and the job network, the operation output ratio and the average net output in each interval are calculated, and the optimum depth of the center fishery is obtained.
  • I SI_SST indicates the adaptability index based on sea surface temperature
  • I SI_SSHA indicates the adaptability index based on the sea surface height distance
  • I SI-CHL-a indicates the adaptability index based on chlorophyll concentration
  • I SI_DEPTH indicates the water depth based adaptation Sex index
  • X SST , X SSHA , X CHL-a , X DEPTH represent the values of sea surface temperature, sea surface height average, chlorophyll concentration, and water depth, respectively.
  • the adaptability index SI is assigned 1; when there is no job network, the adaptability index SI is 0; when the job network is higher than the mean In the sea area, the fitness index SI is assigned a value of 0.5; when the operation network is lower than the mean sea level, the fitness index SI is assigned a value of 0.1.
  • the HSI values and the set thresholds in the above five different weight schemes are analyzed separately, and the optimal weight scheme is compared to predict the central fishery of theixie cephalopod fishery.
  • the SST minimum value of 15 °C, the SSHA minimum value of -45 cm, and the water depth minimum value of 15 m are used as the benchmarks, and the total nets of the catches are counted for each month at intervals of l °C, lOcm, and lOm.
  • HSI X SST *I SI_SST +X SSHA *I SI_SSHA +X DEPTH *I SI_DEPTH ;
  • I SI_SST represents the fitness index based on sea surface temperature
  • I SI_SSHA represents the fitness index based on the sea level height
  • I SI_DEPTH represents the water depth based adaptation index
  • X SST , X SSHA , X DEPTH respectively represent the sea surface The value of the temperature, the sea level height, and the water depth.
  • the fitness index SI of different environmental factors is established, and the value of the fitness index is given by the expert assignment method.
  • the maximum operating network NETmax is set to the highest probability of catch distribution.
  • the fitness index SI is assigned to l; when there is no job network, the fitness index is assigned a value of 0; when the operation network is higher than the mean, the fitness index is assigned a value of 0.5: when the job network is low In the mean sea area, the fitness index SI is assigned a value of 0.1.
  • the HSI values and the set thresholds in the above five different weight schemes are separately analyzed, and the optimal weight scheme is compared to predict the center fishery.
  • the present invention explores the influence of environmental factors with different weights on the cephalopod habitat model in West Africa, and obtains the main environmental factors and the best influence on the distribution of cephalopod habitats.
  • the weighting scheme provides the basis for the prediction of the central fishery of the cephalopods in the northwestern non-ocean.
  • FIG. 1 is a schematic flow chart of a fishery forecasting method of the present invention.
  • FIG. 2 is a schematic flow chart of a method for forecasting a cephalopod fishery in theixie sea area according to the first embodiment of the present invention.
  • Fig. 3 is a flow chart showing a method for predicting a cephalopod fishery in the Mauritania sea area according to the second embodiment of the present invention.
  • cephalopods are a year of species, their fishery conditions and resource abundance are closely related to the marine environment.
  • the differences in climate change and marine environment directly affect the habitat and resource abundance of cephalopods, which in turn affects fishery production and Scientific management. Therefore, it is extremely important to study the main environmental factors affecting the distribution of cephalopod habitats.
  • the use of environmental factors to establish a fishery prediction model can scientifically guide the production of cephalopods in the northwestern non-ocean seas, and also provide efficient fishing production for related enterprises in the sea area. guide.
  • Step 101 Obtain statistics on catch production of theixie fishery in 2012-2015.
  • the catch production statistics include working time, longitude, latitude, working water depth, job network, and operation output.
  • Step 102 Obtain marine environmental data corresponding to the catch production statistical data, where the marine environmental data includes a sea surface temperature SST, a sea surface height average SSHA, and a chlorophyll concentration Chl-a, and the marine environment data has a time resolution of 0.5° ⁇ 0.5° is the spatial resolution, and the statistical time is from January to May and from November to December in 2012-2015.
  • step 103 the relationship between the work network and the output of the work and the average net output in each interval as the index of the central fishery is studied, and the relationship between the SST, the SSHA, the Chl-a and the water depth is studied.
  • Step 104 Establish an adaptability index SI of different environmental factors, and use an expert assignment method to assign the value of the fitness index SI, and set the sea area with the highest job network as the sea area with the highest distribution probability of the central fishery, and the adaptive index SI is assigned. 1; When there is no job network, the adaptability index SI is 0; when the job network is higher than the mean, the adaptability index SI is 0.5; when the job network is lower than the mean, the adaptability index SI Assigned to 0.1.
  • the job net can be regarded as an indicator of the fish, which is used to indicate the adaptability index of the habitat.
  • Step 105 Calculate the habitat comprehensive index HSI under five different weighting schemes by using a formula:
  • HSI Habitat suitability index
  • I SI_SST , I SI_SSHA , I SI-CHL-a , and I SI_DEPTH are respectively adaptive indices based on surface temperature, sea surface height distance average, chlorophyll concentration, and water depth.
  • X SST , X SSHA , X CHL-a , and X DEPTH are the values of the table temperature, the sea level height average, the chlorophyll concentration, and the water depth. A total of five different schemes with different weights are provided, and each scheme is as shown in Table 2 below.
  • the HSI values and the set thresholds of the five different weight schemes are analyzed separately, and the optimal weight scheme is compared to predict the central fishery of theixie cephalopod fishery.
  • the HSI values can be divided into 0-0.2, 0.2-0.4, 0.4-0.6, 0.6-0.8, and 0.8-1.0.
  • the statistical analysis of the different values of HSI value >0.6 and HSI value ⁇ 0.4 in five different weight schemes is carried out to compare the best schemes for predicting the central fishery.
  • the analysis results show that the distribution of cephalopod fishery is closely related to the surface temperature, and there are different suitable SST ranges in different months.
  • the operation was mainly distributed in the SST range of 16 to 19 °C.
  • the suitable SST range for high average net production was 16-17 °C and 18-19 °C, and the average yield was 130-153 kg.
  • the operation was mainly distributed in the SST range of 15 to 19 °C.
  • the suitable SST range for high average net production was 16-17 °C and 18-19 °C, and the average yield was 122-147 kg.
  • the operation was mainly distributed in the SST range of 15 to 17 °C.
  • the suitable SST range for high average net production was 15-16 °C, and the average yield was 89.16 kg.
  • the operation was mainly distributed in the SST range of 18 to 23 °C.
  • the suitable SST range for high average net output was 19-23 °C, and the average yield was 162-185 kg.
  • the operation was mainly distributed in the SST range of 16 to 21 °C.
  • the suitable SST range for high average net production was 20-21 °C, and the average yield was 457 kg.
  • the analysis results show that the distribution of cephalopod fishery is closely related to the sea surface height anomaly, and there are different suitable SSHA ranges in different months.
  • the work was mainly distributed in the SSHA range of -60 to -20 cm.
  • the suitable SSHA range for high average net production was -60 to -30 cm, and the average yield was 124 to 143 kg.
  • the operation was mainly distributed in the SSHA range of -60 to -30 cm.
  • the suitable SSHA range for high average net production was -60 to -40 cm, and the average yield was 123 to 137 kg.
  • the operation was mainly distributed in the SSHA range of -60 to -30 cm.
  • the suitable SSHA range for high average net production was -60 to -40 cm, and the average yield was 96 to 101 kg.
  • the operation was mainly distributed in the SSHA range of -50 to 10 cm.
  • the suitable SSHA range for high average net production was -40 to -30 and -10 to 0 cm, and the average yield was 189 to 209 kg.
  • the operation was mainly distributed in the SSHA range of -50 to 10 cm.
  • the suitable SSHA range for high average net production was -50 to -40 cm, and the average yield was 558.69 kg.
  • the analysis results show that the distribution of cephalopod fishery is closely related to chlorophyll concentration, and there are different suitable water depth ranges in different months.
  • the operation was mainly distributed in the sea area of 0.01 to 50 mg/m 3 in the case of Chl-a, and the suitable Chl-a range of high average net production was 1.0 to 5.0 mg/m 3 , and the average yield was 96 to 127 kg.
  • the operation was mainly distributed in the sea area of 0.01 to 20 mg/m 3 in the case of Chl-a.
  • the suitable Chl-a range of high average net production was 1.0 to 20.0 mg/m 3 , and the average yield was 119 to 128 kg.
  • the operation was mainly distributed in the sea area of 0.01 to 50 mg/m 3 in the case of Chl-a.
  • the suitable Chl-a range of high average net production was 1.0 to 2.0 and 5.0 to 50 mg/m 3 , and the average yield was 99 to 110 kg.
  • the operation was mainly distributed in the range of 0.01 to 20 mg/m 3 in the range of Chl-a, and the suitable Chl-a range of high average net yield was 0.01 to 5.0 mg/m 3 , and the average yield was 169 to 176 kg.
  • the operation was mainly distributed in the sea area of 0.01 to 50 mg/m 3 in the case of Chl-a, and the suitable Chl-a range of high average net production was 2.0 to 5.0 mg/m 3 , and the average yield was 256.24 kg.
  • the analysis results show that the distribution of cephalopod fishery is closely related to the working water depth, and there are different suitable water depth ranges in different months.
  • the operation was mainly distributed in the water depth range of 20 to 90 m.
  • the suitable water depth range of high average net production was 20-40 m, and the average yield was 131-140 kg.
  • the operation was mainly distributed in the water depth range of 20 to 100 m.
  • the suitable water depth range of high average net output was 20-50 m and 60-70 m, and the average yield was 117-141 kg.
  • the operation was mainly distributed in the water depth range of 20 to 80 m.
  • the suitable water depth range of high average net output was 70-80 m, and the average yield was 169 kg.
  • the operation was mainly distributed in the water depth range of 30-80 m.
  • the suitable water depth range of high average net output was 30-40 m, and the average yield was 246.49 kg.
  • the operation was mainly distributed in the water depth range of 20 to 80 m.
  • the suitable water depth range of high average net production was 20-50 m, and the average yield was 217-283 kg.
  • the adaptability indices based on SST, SSHA, Chl-a and seabed water depth were established for each month (Table 3).
  • the SST, SSHA, Chl-a, and water depth of the highest SI in January are 17-18 °C, -50-40 cm, 2.0-5.0 mg/m, and 30-40 m, respectively;
  • SSHA, Chl-a and water depth are 16-17 °C, -50-40 cm, 2.03 ⁇ 5.0 mg/m, 30-40 m respectively;
  • the highest SI SST, SSHA, Chl-a and water depth in March are 16-17 respectively.
  • the habitat index (Table 3) of the weighted values set by different environmental factors related to the central fishery, and the occupational network and output of the period from January to March and from November to December in 2012-2015 are summarized by different HSI.
  • Table 4 Average of the monthly operating nets, production ratios, and average net production in the five scenarios
  • the habitat distribution of cephalopods in Morocco is closely related to environmental factors such as surface temperature, sea surface height anomaly and water depth.
  • the monthly suitable environmental factors are different.
  • the SST range in the operating fishery distribution area is 15-23 °C, SSHA The range is -60 to 10 cm, the chlorophyll concentration is 0 to 50 mg/m 3 , and the water depth ranges from 20 to 100 m, and the most suitable SST is 16 to 18 and 19 to 20 ° C, and the most suitable SSHA is -50 to -30 cm.
  • the most suitable chlorophyll content is 1.0 to 5.0 mg/m 3 , and the most suitable water depth is 30 to 40 and 60 to 70 m.
  • the weight of scheme 5 is optimal, and the weighting factors of SST, SSHA, CHL-a and water depth are 0.4, 0.4, 0.1 and 0.1, respectively, indicating that SST and SSHA have the greatest influence, water depth in the habitat index model.
  • chlorophyll is again.
  • the present embodiment provides a Mauritania cephalopod fishery prediction method based on habitat index, which includes the following steps:
  • Step 101 Obtain statistics on catch production of the Mauritania fishery in 2010-2015.
  • the catch production statistics include working time, working water depth, operation nets, and total catch production.
  • the catch production statistics are from the Oceanic Fisheries Company. There are more than 10 trawlers with a time span of 2010-2015. Since May and June are often closed fishing seasons, the production statistics are from January to April and July to December.
  • Step 102 Acquire marine environment data corresponding to the catch production statistical data, where the marine environment data includes a sea surface temperature SST and a sea surface height distance average SSHA, and the marine environment data has a time resolution of 0.5. ⁇ 0.5. For spatial resolution, the statistical time is from January to April and July to December of 2010-2015.
  • Step 103 separately calculate the operation network times, the working water depth, and the total catch yield of different time periods in each month of 2010-2015, and filter and collate the statistical data, and use the average net output as the central fishery index, and utilize
  • the expert assignment method establishes the adaptability index, and then designs different weight schemes to calculate and compare the spatial distribution of the Mauritian cephalopod fishery and its relationship with the marine environment and the best weight scheme in the sea area corresponding to the Mauritanian cephalopod fishery. This relationship is the optimal SST, SSHA and operating depth range for the central fishery.
  • the sea level height average interval and the most suitable water depth interval are examples of the sea surface temperature range.
  • the Suitability index SI of different environmental factors is established, and the value of the fitness index is given by the expert assignment method.
  • the maximum job network NETmax is set as the catch distribution. In the sea area with the highest probability, the fitness index is assigned a value of 1; when there is no job network, the fitness index is assigned a value of 0; when the operation network is higher than the mean, the fitness index is assigned a value of 0.5; In the sea below the mean, the fitness index SI is assigned a value of 0.1. See Table 5:
  • HSI Hazard suitability index
  • Xsst represents the weight of the sea surface temperature
  • X SSHA represents the weight of the sea surface height from the average value
  • X DEPTH represents the weight of the water depth.
  • HSI X SST *I SI_SST +X SSHA *I SI_SSHA +X DEPTH *I SI_DEPTH to calculate the habitat comprehensive index HSI under five different weight schemes, where: I SST represents the adaptability index based on sea surface temperature; I SSHA represents the fitness index based on the sea level height mean; I SI_DEPTH represents the water depth based adaptability index.
  • the HSI values can be divided into 0-0.2, 0.2-0.4, 0.4-0.6, 0.6-0.8, and 0.8-1.0.
  • the statistical analysis of the different values of HSI value >0.6 and HSI value ⁇ 0.4 in five different weight schemes is carried out to compare the best schemes for predicting the central fishery.
  • the analysis results show that the distribution of non-cephalopod fish farms in Northwest China is closely related to sea surface temperature, and there are different suitable SST ranges in different months.
  • the main SST of the operating fishery are 16-20 °C, 16-19 °C, 16-19 °C, 17-18 °C; the suitable SST of high average net output is 15-21 °C, 15-19 respectively.
  • °C and 20 to 21 ° C, 15 to 20 ° C, 17 to 20 ° C, the corresponding high average net production is 34 ⁇ 51kg, 30 ⁇ 43kg, 26 ⁇ 37kg, 26 ⁇ 30kg.
  • the main SST of the operating fishery are 20-21 °C, 21-22 °C and 23-26 °C, 25-27 °C, 21-22 °C, 19-21 °C, 20-21 °C and 23 ⁇ 24 ° C;
  • suitable SST of high average net output is 20 ⁇ 22 ° C, 21 ⁇ 24 ° C, 24-27 ° C, 20 ⁇ 22 ° C, 18 - 21 ° C, 20: - 22 ° C and 23: - 24 ° C
  • the output of high average net times is 77-92kg, 54-63kg, 29-34kg, 99-103kg, 36-52kg, 31--47kg.
  • the analysis results show that the distribution of cephalopod fishery is closely related to the sea surface height anomaly, and there are different suitable SSHA ranges in different months.
  • the main SSHA of the operating fishery are -35 ⁇ -25cm and -5 ⁇ 5cm, -45 ⁇ -35cm and -5 ⁇ 5cm, -5 ⁇ 5cm, -5 ⁇ 5cm; high average net output Suitable SSHA are -40 ⁇ -20cm, -50 ⁇ -30cm, -45 ⁇ -35cm, -5 ⁇ 15cm, the corresponding high average net production is 37 ⁇ 47kg, 47 ⁇ 48kg, 59.22kg, 28 ⁇ 35kg.
  • the main SSHA of the operating fishery are -35 to 25 cm, -25 to -15 cm, -35 to -25 cm, and -5 to 5 cm, -35 to -25 cm, -25 to -15 cm, and -5 to 5 cm.
  • the suitable SSHA for high average net production is -45 ⁇ -15cm, -45 ⁇ -15cm, -35 ⁇ -15cm and -5 ⁇ 5cm, -35 ⁇ -15cm, -35 ⁇ -15cm and -5 ⁇ 5cm , -5 ⁇ 5cm, the corresponding high average net production is 67 ⁇ 80kg, 48 ⁇ 56kg, 27 ⁇ 40kg, 81 ⁇ 104kg, 42 ⁇ 50kg, 36.96kg.
  • the analysis results show that the distribution of cephalopod fishery is closely related to the working water depth, and there are different suitable water depth ranges in different months.
  • the main water depths of the operating fishery are 45-65m, 55-75m, 55-85m, 65-75m and 85-95m respectively;
  • the suitable water depths for high-average mesh production are 55-65m, 45-75m, respectively.
  • the corresponding high average net production is 44.32 kg, 30 to 43 kg, 28 to 38 kg, and 31.96 kg, respectively.
  • the main water depths of the operating fishery are 15 to 25 m, 15 to 25 m and 45 to 55 m, 55 to 75 m, 55 to 65 m, 55 to 65 m, 25 to 35 m and 45 to 055 m, respectively.
  • Suitable water depths are 15 to 25 m and 55 to 75 m, 15 to 25 m, 45 to 75 m, 55 to 65 m, 55 to 65 m, 25 to 35 m, and 45 to 55 m, respectively, and the corresponding high average net yields are 58 to 77 kg, respectively. 57.43kg, 27 ⁇ 39kg, 99.62kg, 45.12kg, 36 ⁇ 38kg.
  • the maximum SST, SSHA, and water depth of SI in January are 16 to 17 ° C, -5 to 5 cm, and 55 to 65 m, respectively.
  • the maximum SST, SSHA, and water depth of SI in February are 16 to 17 ° C, respectively.
  • ⁇ 5cm, 65 ⁇ 75m The maximum SST, SSHA and water depth of SI in March are 18 ⁇ 19°C, -5 ⁇ Ocm, 75 ⁇ 85m respectively;
  • the largest SST, SSHA and water depth of SI in July are 20 ⁇ 21°C, respectively. -30 ⁇ -25cm, 20 ⁇ 25m; September's largest SST, SSHA and water depth are 26 ⁇ 27 ° C, -5 ⁇ 5cm, 55 ⁇ 65m.
  • Table 7 Adaptability index based on sea surface temperature, sea surface height anomaly and water depth from January to March, July and September
  • the HSI When the HSI is above 0.6, it is generally the central fishery. At this time, the greater the proportion of the job network and the proportion of the output, the better the corresponding weight scheme model. It can be seen from Table 8 that the scheme l is the best, the HSI value is >0.6, the proportion of the operation network and the proportion of the output are 64.2826 and 67.6196, respectively, and the average net output is 44 to 51 kg; the worst of the scheme 5, the HSI value is >0.6, the operation network The secondary specific gravity and the proportion of production are 57.8826 and 61.92%, respectively, and the average net output is 45 to 48 kg.
  • Table 8 analyzes the number of job nets, the proportion of job output, and the average net production in January-April, July, and September 2010 based on the habitat index model of the five scenarios.
  • scheme l is optimal (weights for SST, SSHA, and water depth are 0.6, 0.3, and 0.1, respectively), and scheme 5 (weights for SST, SSHA, and water depth are both l/3)
  • SST has the greatest impact, followed by SSHA, and the water depth is the last.
  • the present invention explores the influence of environmental factors with different weights on the cephalopod habitat model in West Africa, and obtains the main environmental factors and the best influence on the distribution of cephalopod habitats.
  • the weighting scheme provides the basis for the prediction of the central fishery in the cephalopods of West Africa.

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Abstract

一种基于环境因子的西北非海域头足类渔场预报方法，其特征在于，包括以下步骤：步骤一：获取多年西北非海域头足类渔场的渔获生产统计数据；步骤二：获取该渔获生产统计数据对应的海洋环境数据，该海洋环境数据包括海表温度SST、海面高度距平均值SSHA；步骤三：以作业网次、作业产量所占比率及每个区间内的平均网次产量作为中心渔场的指标，研究其与步骤二所述海洋环境数据之间的关系；步骤四：建立不同环境因子的适应性指数SI，并采用专家赋值法通过不同的权重方案计算得到不同权重方案下的栖息地综合指数HSI，从而获得西北非海域头足类渔场的中心渔场分布海域，用于预测中心渔场。

Description

基于环境因子的西北非海域头足类渔场预报方法 技术领域
本发明涉及西北非海域的头足类渔场预报方法，尤其涉及基于环境因子的西北非海域头足类渔场预报方法。
背景技术
毛里塔尼亚是非洲大陆西部地区的一个国家，海岸线漫长，海洋环境又有其独特性，渔业是国民经济收入的主要来源。海洋渔业资源有400×l04t多的储存量，尤其头足类丰富，它也是我国远洋渔船的主要捕捞对象。开展毛里塔尼亚附近海域头足类资源与渔场的研究，对我国远洋拖网渔船的高效生产具有重要的意义。
徐建国等探究并改善了毛里塔尼亚渔场捕获头足类的作业工具；张进宝研究认为，毛里塔尼亚海域内渔业资源的开发潜力为151.1万t，而在这之中头足类就占有6.5万t。周爱忠等研究显示，近年来在毛塔海域靠岸处已经过分开采利用头足类资源，捕捞上岸量想要增加的地方越来越发局限性。冯春雷等调研了毛里塔尼亚头足类渔场分布的水文情况，简析了各因素如水团、温度、海流、水文要素(水温、溶解氧、盐度、叶绿素)等的空间结构和改变，探讨了研究海区的海洋构造和海洋环境对头足类渔场的影响。结合文献可知，国内外对毛里塔尼亚头足类渔场分有及其栖息地模型的有关研究仍不多。
摩洛哥临北大西洋与地中海，是地中海与大西洋之间相互沟通联系的桥梁。海洋渔业是摩洛哥主要外汇来源，在其国内经济发展中处于关键位置。在摩洛哥的渔业资源中，头足类具有最高经济效益的。在摩洛哥，距离我国 渔船第一次开展底拖网作业，已经过去20多年了，但底拖网主要捕捞对象一直以来还都是章鱼、鱿鱼这些头足类。国内的众多专家们曾对摩洛哥的渔业发展、渔具渔法以及水域的中上层鱼类资源等各方面开展了许多的调查与讨论。但是，国内与摩洛哥的头足类渔场分布有关的文献，这方面的研究仍是不多。
由于西北非海域的头足类资源的富足和渔业的当前状况，需要研究头足类渔场的分布和栖息地模型，从而也利于准确的渔场预报。准确的渔情预报可以指导企业合理安排渔业生产，缩短寻找渔场的时间，减少成本、提高渔获产量。
发明内容
本发明所要解决的技术问题是提供基于环境因子的西北非头足类渔场预报方法，研究了海洋环境因子和栖息地指数对西北非头足类渔场的影响，通过对西北非渔场的头足类资源影响最为显著的海洋环境因子和栖息地指数的研究，建立了用于渔情预报的模型，从而实现准确预报渔场，提高渔获产量。
技术方案
一种基于环境因子的西北非海域头足类渔场预报方法，其特征在于包括以下步骤：
步骤一：获取多年西北非海域头足类渔场的渔获生产统计数据，该渔获生产统计数据包括作业时间、作业水深、作业网次以及渔获物总产量；
步骤二：获取该渔获生产统计数据对应的海洋环境数据，该海洋环境数据包括海表温度SST、海面高度距平均值SSHA，该海洋环境数据以月为时间分辨率、0.5°×0.5°为空间分辨率；
步骤三：以作业网次、作业产量所占比率以及每个区间内的平均网次产 量作为中心渔场的指标，研究其与步骤二所述海洋环境数据之间的关系；
步骤四：建立不同环境因子的适应性指数SI，并采用专家赋值法通过不同的权重方案计算得到不同权重方案下的栖息地综合指数HSI，从而获得西北非海域头足类渔场的中心渔场的分布海域，并得出该分布海域内最佳的权重方案，用于预测中心渔场。
进一步，所述西北非海域头足类渔场的渔获生产统计数据采用4-6年的数据。
进一步，对于西北非的摩洛哥渔场，获取该渔获生产统计数据对应的海洋环境数据，该海洋环境数据包括海表温度SST、海面高度距平均值SSHA和叶绿素浓度Chl-a，以SST相隔1℃为间距，计算出每个间距内的作业网次、作业产量比率以及平均网次产量，并依此得出最适宜的中心渔场SST的范围；以SSHA相隔10cm为间距，计算出每个间距内的作业网次、作业产量比率以及平均网次产量，并依此得出最适宜的中心渔场SSHA的范围；以Chl-a的含量在0.01～1.0、1.0～2.0、2.0～5.0、5.0～20.0、20.0～50.0mg/m 3为间距，计算出每个间距内的作业网次、作业产量比率以及平均网次产量，并依此得出最适宜的中心渔场Chl-a的范围；以水深相隔10m为间距，计算出每个间距内的作业网次、作业产量比率以及平均网次产量，并依此得出最适宜的中心渔场水深的范围。
进一步，对于包括海表温度SST、海面高度距平均值SSHA和叶绿素浓度Chl-a的海洋环境数据建立不同环境因子的适应性指数SI，采用如下公式计算不同权重方案下的栖息地综合指数HSI：
HSI＝X SST*I SI_SST+X SSHA*I SI_SSHA+X CHL-a*I SI-CHL-a+X DEPTH*I SI_DEPTH
其中：I SI_SST表示基于海表温度的适应性指数；I SI_SSHA表示基于海面高度距 平均值的适应性指数；I SI-CHL-a表示基于叶绿素浓度的适应性指数；I SI_DEPTH表示基于水深的适应性指数；X SST、X SSHA、X CHL-a、X DEPTH分别表示海表温度、海面高度距平均值、叶绿素浓度、水深所占权重的值。
进一步，设定作业网次最高的海域为中心渔场的分布概率最高的海域，则适应性指数SI赋值为1；当没有作业网次则适应性指数SI赋值为0；当作业网次高于均值的海域，则适应性指数SI赋值为0.5；当作业网次低于均值的海域，则适应性指数SI赋值为0.1。
进一步，对于海表温度、海面高度距平均值、叶绿素浓度、水深所占权重的值采用以下五种权重方案：
方案1：X SST为0.25，X SSHA为0.25，X CHL-a为0.25，X DEPTH为0.25；
方案2：X SST为0，X SSHA为0.9，X CHL-a为0，X DEPTH为0.1；
方案3：X SST为0.1，X SSHA为0.1，X CHL-a为0，X DEPTH为0.8；
方案4：X SST为0.9，X SSHA为0.1，X CHL-a为0，X DEPTH为0；
方案5：X SST为0.4，X SSHA为0.4，X CHL-a为0.1，X DEPTH为0.1；
分别分析上述五种不同权重方案中HSI值与设定阈值的大小，从而比较得出最佳权重方案，用于预测摩洛哥头足类渔场的中心渔场。
进一步，对于西北非的毛里塔尼亚渔场，分别以SST最小值15℃、SSHA最小值-45cm、水深最低值15m做基准，对应间隔l℃、lOcm、lOm，统计各月的渔获物总网次，总产量和天数，求出每SST l℃、SSHA lOcm、水深lOm间隔内渔获物网次所占的比例、产量所占的比例、平均网次产量，由此得到各月份中心渔场最适宜的海表温度区间、最适宜的距海面高度平均值区间以及最适宜的水深区间。
进一步，对于对应的海洋环境数据，采用如下公式计算不同权重方案下 的栖息地综合指数HSI：
HSI＝X SST*I SI_SST+X SSHA*I SI_SSHA+X DEPTH*I SI_DEPTH；
其中：I SI_SST表示基于海表温度的适应性指数；I SI_SSHA表示基于海面高度距平均值的适应性指数；I SI_DEPTH表示基于水深的适应性指数；X SST、X SSHA、X DEPTH分别表示海表温度、海面高度距平均值、水深所占权重的值。
进一步，以作业网次的频度分布图为基础，建立不同环境因子的适应性指数SI，采用专家赋值法赋予适应性指数SI的值，设定最高作业网次NETmax为渔获物分布概率最高的海域，则适应性指数SI赋值为l；当没有作业网次则适应性指数SI赋值为0；当作业网次高于均值的海域，则适应性指数SI赋值为0.5:当作业网次低于均值的海域，则适应性指数SI赋值为0.1。
进一步，对于海表温度、海面高度距平均值、水深所占权重的值采用以下五种权重方案：
方案l:X SST为0.6，X SSHA为0.3，X DEPTH为0.1；
方案2:X SST为0.5，X SSHA为0.2，X DEPTH为0.3；
方案3:X SST为0.4，X SSHA为0.2，X DEPTH为0.4；
方案4:X SST为0.3，X SSHA为0.4，X DEPTH为0.3；
方案5:X SST为1/3，X SSHA为1/3，X DEPTH为1/3；
分别分析上述五种不同权重方案中HSI值与设定阈值的大小，从而比较得出最佳权重方案，用于预测中心渔场。
有益效果
本发明根据渔业公司的生产统计数据，结合卫星遥感获得的资料，探讨不同权重的环境因子对西非海域头足类栖息地模型的影响，获得影响头足类栖息地分布的主要环境因子和最佳权重方案，为西北非海域头足类的中心渔 场预测提供了基础。
附图说明
图1是本发明的渔场预报方法的流程示意图。
图2是本发明实施例1中针对摩洛哥海域头足类渔场预报方法的流程示意图。
图3是本发明实施例2中针对毛里塔尼亚海域头足类渔场预报方法的流程示意图。
具体实施方式
下面结合具体实施例和附图，进一步阐述本发明。
由于头足类为一年的种类，其渔场情况和资源丰度与海洋环境关系密切，气候变化、海洋环境的差异直接影响到头足类的栖息地情况和资源丰度，进而影响到渔业生产和科学管理。因此，研究影响头足类栖息地分布的主要环境因子极为重要，利用环境因子建立渔场预测模型，能够科学指导在西北非海域头足类的生产，也为相关企业在该海域进行高效捕捞生产提供指导。
实施例1
针对位于大西洋的摩洛哥海域，通过以下步骤研究获得影响头足类栖息地分布的主要环境因子：
步骤101、获取2012-2015年摩洛哥渔场的渔获生产统计数据，该渔获生产统计数据包括作业时间、经度、纬度、作业水深、作业网次以及作业产量。
摩洛哥渔场的渔获生产统计数据来源于上海的远洋渔业公司。
步骤102、获取该渔获生产统计数据对应的海洋环境数据，该海洋环境数据包括海表温度SST、海面高度距平均值SSHA和叶绿素浓度Chl-a，该海洋 环境数据以月为时间分辨率、0.5°×0.5°为空间分辨率，统计时间为2012-2015年的1-3月和11-12月。
步骤103、以作业网次、作业产量所占比率以及每个区间内的平均网次产量作为中心渔场的指标，研究其与SST、SSHA、Chl-a以及水深之间的关系。
1)以SST相隔1℃为间距，计算出每个间距内的作业网次、作业产量比率以及平均网次产量，并依此得出最适宜的中心渔场SST的范围；
2)以SSHA相隔10cm为间距，计算出每个间距内的作业网次、作业产量比率以及平均网次产量，并依此得出最适宜的中心渔场SSHA的范围；
3)以Chl-a的含量在0.01～1.0、1.0～2.0、2.0～5.0、5.0～20.0、20.0～50.0mg/m 3为间距，计算出每个间距内的作业网次、作业产量比率以及平均网次产量，并依此得出最适宜的中心渔场Chl-a的范围；
4)以水深相隔10m为间距，计算出每个间距内的作业网次、作业产量比率以及平均网次产量，并依此得出最适宜的中心渔场水深的范围。
步骤104、建立不同环境因子的适应性指数SI，采用专家赋值法赋予适应性指数SI的值，设定作业网次最高的海域为中心渔场的分布概率最高的海域，则适应性指数SI赋值为1；当没有作业网次则适应性指数SI赋值为0；当作业网次高于均值的海域，则适应性指数SI赋值为0.5；当作业网次低于均值的海域，则适应性指数SI赋值为0.1。
一般渔船在进行作业对渔场进行判别时，都是根据船长的经验以及探鱼仪的影像，所以作业网次可被看作是发现鱼类的指标，用来表示为栖息地的适应性指数。
表1 栖息地适应性指数的确定标准
[根据细则26改正30.09.2018]　
Figure WO-DOC-TABLE-1
步骤105、利用公式计算5种不同权重方案下的栖息地综合指数HSI：
HSI＝X SST*I SI_SST+X SSHA*I SI_SSHA+X CHL-a*I SI-CHL-a+X DEPTH*I SI_DEPTH
计算相关海洋环境因子不同权重下，栖息地综合指数(Habitat suitability index，HSI)，HSI在0到1之间变化。一般认为HSI大于0.6的区域为中心渔场分布的海域。
式中：I SI_SST、I SI_SSHA、I SI-CHL-a、I SI_DEPTH分别是各自基于表温、海面高度距平均值、叶绿素浓度、水深的适应性指数。X SST、X SSHA、X CHL-a、X DEPTH分别是表温、海面高度距平均值、叶绿素浓度、水深所占权重的值。总共设有不同权重的5种方案，各方案如下表2。
表2 基于中心渔场相关的不同环境因子设定权重值
[根据细则26改正30.09.2018]　
Figure WO-DOC-TABLE-2
分别分析5种不同权重方案中HSI值与设定阈值的大小，从而比较得出最佳权重方案，来用于预测摩洛哥头足类渔场的中心渔场。
利用2012年-2015年1-3月、11-12月统计的数据对不同权重方案进行比较，HSI值可分为0-0.2、0.2-0.4、0.4-0.6、0.6-0.8、0.8-1.0，在此基础上分别进行统计分析5种不同权重方案中HSI值>0.6以及HSI值<0.4的大小关系，从而比较得出其中最佳方案，来用于预测中心渔场。
根据上述方法，下面基于具体的统计数据进行分析：
1、生产情况分析
1)渔场分布与表温SST的关系
分析结果表明，头足类渔场分布与表温关系密切，不同月份有不同的适宜SST范围。1月份作业主要分布在SST范围为16～19℃海域，高平均网次产量的适宜SST范围为16～17℃和18～19℃，平均产量为130～153kg。2月份作业主要分布在SST范围为15～19℃海域，高平均网次产量的适宜SST范围为16～17℃和18～19℃，平均产量为122～147kg。3月份作业主要分布在SST范围为15～17℃海域，高平均网次产量的适宜SST范围为15～16℃，平均产量为89.16kg。11月份作业主要分布在SST范围为18～23℃海域，高平均网次产量的适宜SST范围为19～23℃，平均产量为162～185kg。12月份作业主要分布在SST范围为16～21℃海域，高平均网次产量的适宜SST范围为20～21℃，平均产量为457kg。
2)渔场分布与SSHA的关系
分析结果表明，头足类渔场分布与海面高度距平值关系密切，不同月份有不同的适宜SSHA范围。1月份作业主要分布在SSHA范围为-60～-20cm海域，高平均网次产量的适宜SSHA范围为-60～-30cm，平均产量为124～143kg。2月份作业主要分布在SSHA范围为-60～-30cm海域，高平均网次产量的适宜 SSHA范围为-60～-40cm，平均产量为123～137kg。3月份作业主要分布在SSHA范围为-60～-30cm海域，高平均网次产量的适宜SSHA范围为-60～-40cm，平均产量为96～101kg。11月份作业主要分布在SSHA范围为-50～10cm海域，高平均网次产量的适宜SSHA范围为-40～-30和-10～0cm，平均产量为189～209kg。12月份作业主要分布在SSHA范围为-50～10cm海域，高平均网次产量的适宜SSHA范围为-50～-40cm，平均产量为558.69kg。
3)渔场分布与叶绿素浓度的关系
分析结果表明，头足类渔场分布与叶绿素浓度关系密切，不同月份有不同的适宜水深范围。1月份作业主要分布在Chl-a范围为0.01～50mg/m 3海域，高平均网次产量的适宜Chl-a范围为1.0～5.0mg/m 3，平均产量为96～127kg。2月份作业主要分布在Chl-a范围为0.01～20mg/m 3海域，高平均网次产量的适宜Chl-a范围为1.0～20.0mg/m 3，平均产量为119～128kg。3月份作业主要分布在Chl-a范围为0.01～50mg/m 3海域，高平均网次产量的适宜Chl-a范围为1.0～2.0和5.0～50mg/m 3，平均产量为99～110kg。11月份作业主要分布在Chl-a范围为0.01～20mg/m 3海域，高平均网次产量的适宜Chl-a范围为0.01～5.0mg/m 3，平均产量为169～176kg。12月份作业主要分布在Chl-a范围为0.01～50mg/m 3海域，高平均网次产量的适宜Chl-a范围为2.0～5.0mg/m 3，平均产量为256.24kg。
4)渔场分布与水深的关系
分析结果表明，头足类渔场分布与作业水深关系密切，不同月份有不同的适宜水深范围。1月份作业主要分布在水深范围为20～90m海域，高平均网次产量的适宜水深范围为20～40m，平均产量为131～140kg。1月份作业主要分布在水深范围为20～100m海域，高平均网次产量的适宜水深范围为20～50m和60～70m，平均产量为117～141kg。3月份作业主要分布在水深范围为 20～80m海域，高平均网次产量的适宜水深范围为70～80m，平均产量为169kg。11月份作业主要分布在水深范围为30～80m海域，高平均网次产量的适宜水深范围为30～40m，平均产量为246.49kg。12月份作业主要分布在水深范围为20～80m海域，高平均网次产量的适宜水深范围为20～50m，平均产量为217～283kg。
2、适应性指数(SI)建立
依据表1，分别建立各月份间基于SST、SSHA、Chl-a和海底水深的适应性指数(表3)。由表3可知，1月SI最高的SST、SSHA、Chl-a和水深分别为17～18℃、-50～-40cm、2.0～5.0mg/m、30～40m；2月SI最高的SST、SSHA、Chl-a和水深分别为16～17℃、-50～-40cm、2.03～5.0mg/m、30～40m；3月SI最高的SST、SSHA、Chl-a和水深分别为16～17℃、-50～-40cm、2.0～5.0mg/m 3、20～30m；11月SI最高的SST、SSHA、Chl-a和水深分别为19～20℃、0～10cm、0.01～1.0mg/m 3、60～70m；12月SI最高的SST、SSHA、Chl-a和水深分别为18～19℃、-40～-30cm、2.0～5.0mg/m 3、60～70m。其最适的SST、SSHA、Chl-a和水深各月有所不同。
表3 各月基于SST、SSHA、CHL-a和海底水深的适应性指数
[根据细则26改正30.09.2018]　
Figure WO-DOC-TABLE-3
3、基于栖息地综合指数(HSI)相关因子的权重方案比较
通过与中心渔场相关的不同环境因子设定的权重值的栖息地指数(表3)，按不同HSI来汇总2012-2015年间1-3月、11-12月间的作业网次、产量所占比率和平均网次产量，得出5种方案的平均值(表4)。
由表4可知，5种方案中，方案3的网次、产量比重都是最小的，分别为42.97％和38.53％，平均网次产量相较于其他方案，只有130.17kg，因此可知，方案3权重的设定是最差的。方案2与方案4所得数值相近且都低于方案1和方案5(表4)，因此可知，这两种方案也有所不及。方案1与方案5中 HSI大于0.6上的网次、产量比重比较接近，方案1分别为59.69％和60.2％，方案5分别为58.38％和60.96％，但通过表5可以比较发现，平均网次产量以及HSI大于0.8的网次和产量比重相较于方案1更好，因此方案5权重的设定是最佳的。
表4 5种方案中各月作业网次、产量比率以及平均网次产量的平均值
[根据细则26改正30.09.2018]　
Figure WO-DOC-TABLE-4
根据2012-2015年间上海某远洋渔业公司的生产统计数据，结合海表温度(SST)、海面高度距平值(SSHA)、叶绿素质量浓度(CHL-a)以及水深资料，研究不同权重下摩洛哥头足类栖息地模型。
研究认为，摩洛哥头足类栖息地分布与表温、海面高度距平值、水深等环境因子关系密切，每月适宜的环境因子存在差异；作业渔场分布海域中SST范围是15～23℃，SSHA范围是-60～10cm，叶绿素浓度是0～50mg/m 3，水深范围是20～100m，而其中最合适的SST是16～18和19～20℃，最合适的SSHA是-50～-30cm，最合适的叶绿素含量是1.0～5.0mg/m 3，最合适水深是30～40和60～70m。模型分析认为，方案5的权重时最适，其SST、SSHA、CHL-a和水深的权重因子分别为0.4、0.4、0.1和0.1，说明在栖息地指数模型中，SST和SSHA影响最大，水深次之，叶绿素再次之。
实施例2
如附图3所示，本实施例提供基于栖息地指数的毛里塔尼亚头足类渔场预报方法，其包括以下步骤：
步骤101、获取2010-2015年毛里塔尼亚渔场的渔获生产统计数据，该渔获生产统计数据包括作业时间、作业水深、作业网次以及渔获物总产量。
渔获生产统计数据来源于远洋渔业公司，共有10多艘拖网渔船，时间跨度为2010-2015年。由于5、6月份常常是休渔期，因此生产统计时间为每年的1-4月、7-12月。
步骤102、获取该渔获生产统计数据对应的海洋环境数据，该海洋环境数据包括海表温度SST和海面高度距平均值SSHA，该海洋环境数据以月为时间分辨率、0.5。×0.5。为空间分辨率，统计时间为2010-2015年的1-4月和7-12月。
步骤103、分别统计2010-2015年各月不同时间段的作业网次、作业水深、渔获物总产量，将统计出的数据筛选整理合并汇总，以网次平均产量作为中心渔场指标，并利用专家赋值法建立适应性指数，再设计不同权重方案利用图表计算比较，获得毛里塔尼亚头足类渔场的空间分布及其与海洋环境的关系以及毛里塔尼亚头足类渔场对应的海域内最佳的权重方案，该关系为中心渔场最适SST、SSHA和作业水深范围。
具体地，包括以下步骤：
1、分析渔场分布和环境因子的关系
通过绘制频度分布图，了解并掌握产量值、作业网次、平均网次产量与各环境因子的关系，获得渔汛期渔场分布的环境因子整体大小，以及最适区间；找出各环境因子(海表温度、海面高度、水深)最大值与最小值，划分区间。
公式：平均网次产量值＝总产量/作业网次(单位为kg)
渔获物总网次、渔获物总产量、平均网次产量值和海表温度(SST)、海面高度距平均值(SSHA)、水深的关系：
分别以SST最小值15℃、SSHA最小值-45cm、水深最低值15m做基准，对应间隔l℃、lOcm、lOm，统计各月的渔获物总网次，总产量和天数，求出每SST 1℃、SSHA lOcm、水深lOm间隔内渔获物网次所占的比例，产量所占的比例，平均网次产量，由此得到各月份中心渔场最适宜的海表温度区间、最适宜的距海面高度平均值区间以及最适宜的水深区间。
2、建立适应性指数
以作业网次的频度分布图为基础，建立不同环境因子的适应性指数(Suitability index)SI，采用专家赋值法赋予适应性指数SI的值，设定最高作业网次NETmax为渔获物分布概率最高的海域，则适应性指数SI赋值为1；当没有作业网次则适应性指数SI赋值为0；当作业网次高于均值的海域，则适应性指数SI赋值为0.5；当作业网次低于均值的海域，则适应性指数SI赋值为0.1。见表5：
表5 适应性指数的确定标准
[根据细则26改正30.09.2018]　
Figure WO-DOC-TABLE-5
3、建立栖息地综合指数
HSI(Habitat suitability index)即栖息地综合指数，其值范围在0到l间，是以各环境因子的适应性指数为基础求值。
表6 5种不同权重方案
[根据细则26改正30.09.2018]　
Figure WO-DOC-TABLE-6
Xsst表示海表温度的权重，X SSHA表示海面高度距平均值的权重；X DEPTH表示水深的权重。
利用公式HSI＝X SST*I SI_SST+X SSHA*I SI_SSHA+X DEPTH*I SI_DEPTH计算5种不同权重方案下的栖息地综合指数HSI，式中：I SST表示基于海表温度的适应性指数；I SSHA表示基于海面高度距平均值的适应性指数；I SI_DEPTH表示基于水深的适应性指数。
4、比较5种不同权重方案
利用2010年-2015年1-3月、7月和9月统计的数据对不同权重方案进行比较，HSI值可分为0-0.2、0.2-0.4、0.4-0.6、0.6-0.8、0.8-1.0，在此基础上分别进行统计分析5种不同权重方案中HSI值>0.6以及HSI值<0.4的大小关系，从而比较得出其中最佳方案，来用于预测中心渔场。
根据上述方法，下面基于具体的统计数据进行分析：
l、生产情况分析
1)渔场分布与海表温度的关系
分析结果表明，西北非头足类渔场分布与海表温度关系密切，不同月份有不同的适宜SST范围。1～4月份，作业渔场的主要SST分别为16～20℃、16～19℃、16～19℃、17～18℃；高平均网次产量的适宜SST分别为15～21℃、15～19℃和20～21℃、15～20℃、17～20℃，其对应的高平均网次产量分别为34～51kg、30～43kg、26～37kg、26～30kg。7～l2月份，作业渔场的主 要SST分别为20～21℃、21～22℃和23～26℃、25～27℃、21--22℃、19～21℃、20～21℃和23～24℃；高平均网次产量的适宜SST分别为20～22℃、21～24℃、24—27℃、20～22℃、18—21℃、20:—22℃和23:—24℃，其高平均网次的产量分别为77～92kg、54—63kg、29～34kg、99～103kg、36～52kg、31--47kg.
2)渔场分布与海面高度距平值的关系
分析结果表明，头足类渔场分布与海面高度距平值关系密切，不同月份有不同的适宜SSHA范围。l～4月份，作业渔场的主要SSHA分别为-35～-25cm和-5～5cm、-45～-35cm和-5～5cm、-5～5cm、-5～5cm；高平均网次产量的适宜SSHA分别为-40～-20cm、-50～-30cm、-45～-35cm、-5～15cm，其对应的高平均网次产量分别为37～47kg、47～48kg、59.22kg、28～35kg。7～12月份，作业渔场的主要SSHA分别为-35～25cm、-25～-15cm、-35～-25cm和-5～5cm、-35～-25cm、-25～-15cm、-5～5cm；高平均网次产量的适宜SSHA分别为-45～-15cm、-45～-15cm、-35～-15cm和-5～5cm、-35～-15cm、-35～-15cm和-5～5cm、-5～5cm，其对应的高平均网次产量分别为67～80kg、48～56kg、27～40kg、81～104kg、42～50kg、36.96kg。
3)渔场分布与水深的关系
分析结果表明，头足类渔场分布与作业水深关系密切，不同月份有不同的适宜水深范围。1～4月份，作业渔场的主要水深分别为45～65m、55～75m、55～85m、65～75m和85～95m；高平均网次产量的适宜水深分别是55～65m、45～75m、55～85m、85～95m，其对应的高平均网次产量分别是44.32kg、30～43kg、28～38kg、31.96kg。7～12月份，作业渔场的主要水深分别为15～25m、15～25m和45～55m、55～75m、55～65m、55～65m、25～35m和45-055m；高平均网次产量的适宜水深分别是15～25m和55～75m、15～25m、45～75m、55～ 65m、55～65m、25～35m和45～55m，其对应的高平均网次产量分别是58～77kg、57.43kg、27～39kg、99.62kg、45.12kg、36～38kg。
2、适应性指数(SI)
由表7可知：1月份SI最大的SST、SSHA和水深分别是16～l7℃、-5～5cm、55～65m；2月份SI最大的SST，SSHA和水深分别是16～17℃、-5～5cm、65～75m:3月份SI最大的SST、SSHA和水深分别是18～19℃、-5～Ocm、75～85m；7月份SI最大的SST、SSHA和水深分别是20～21℃、-30～-25cm、20～25m；9月份SI最大的SST、SSHA和水深分别是26～27℃、-5～5cm、55～65m。
表7 1-3月、7月和9月以海表温度、海面高度距平值和水深为基础的适应性指数
[根据细则26改正30.09.2018]　
Figure WO-DOC-TABLE-7
3、基于栖息地综合指数(HSI)相关因子的权重方案比较
当HSI在0.6以上，一般是中心渔场，此时作业网次比重和产量比重越大，对应的权重方案模型越好。由表8可知，即方案l最佳，HSI值>0.6，作业网次比重和产量比重分别为64.2826、67.6196，平均网次产量是44～51kg；方案5最差，HSI值>0.6，作业网次比重和产量比重分别为57.8826、61.92％，平均网次产量是45～48kg。
表8基于5种方案的栖息地指数模型分析2010-2015年1-3月、7月和9月的作业网次、作业产量所占比例以及平均网次产量。
[根据细则26改正30.09.2018]　
Figure WO-DOC-TABLE-8
依据2010-2015年在毛里塔尼亚渔场作业搜集获取的生产统计数据，结合卫星遥感获得的海表温度(SST)、海面高度距平均值(SSHA)以及水深数据，分析毛里塔尼亚头足类渔场分布及其不同环境权重下栖息地指数模型，为开展毛里塔尼亚头足类渔场预测提供依据。
研究表明，毛里塔尼亚头足类渔场分布与海洋环境关系十分紧密，1-4月，7-12月间作业渔场的适宜环境范围在一定程度上也有所不同。作业渔场分布在SST为15-28℃、SSHA为-45-15cm、水深为15-85m的海域，最适SST、SSHA、水深分别是16-22℃、-35～-25cm和-5-5cm、15-25m和45-75m。在5种基于不同权重的毛里塔尼亚头足类栖息地模型方案中，方案l最佳(SST、SSHA和水深的权重分别为0.6、0.3、0.1)，方案5(SST、SSHA和水深的权重均为l/3)最差，即模型认为，不同环境因子对头足类渔场形成的影响程度是不一样的，SST影响最大，SSHA次之，水深为最后。
本发明根据渔业公司的生产统计数据，结合卫星遥感获得的资料，探讨不同权重的环境因子对西非海域头足类栖息地模型的影响，获得影响头足类栖息地分布的主要环境因子和最佳权重方案，为西非海域头足类的中心渔场预测提供了基础。
虽然以上描述了本发明的具体实施方式，但是本领域的技术人员应当理解，这些仅是举例说明，本发明的保护范围是由所附权利要求书限定的。本领域的技术人员在不背离本发明的原理和实质的前提下，可以对这些实施方式做出多种变更或修改，但这些变更和修改均落入本发明的保护范围。

Claims (10)

  1. 一种基于环境因子的西北非海域头足类渔场预报方法，其特征在于包括以下步骤：
    步骤一：获取多年西北非海域头足类渔场的渔获生产统计数据，该渔获生产统计数据包括作业时间、作业水深、作业网次以及渔获物总产量；
    步骤二：获取该渔获生产统计数据对应的海洋环境数据，该海洋环境数据包括海表温度SST、海面高度距平均值SSHA，该海洋环境数据以月为时间分辨率、0.5°×0.5°为空间分辨率；
    步骤三：以作业网次、作业产量所占比率以及每个区间内的平均网次产量作为中心渔场的指标，研究其与步骤二所述海洋环境数据之间的关系；
    步骤四：建立不同环境因子的适应性指数SI，并采用专家赋值法通过不同的权重方案计算得到不同权重方案下的栖息地综合指数HSI，从而获得西北非海域头足类渔场的中心渔场的分布海域，并得出该分布海域内最佳的权重方案，用于预测中心渔场。
  2. 如权利要求1所述的基于环境因子的西北非头足类渔场预报方法，其特征在于：所述西北非海域头足类渔场的渔获生产统计数据采用4-6年的数据。
  3. 如权利要求1或2所述的基于环境因子的西北非海域头足类渔场预报方法，其特征在于：对于西北非的摩洛哥渔场，获取该渔获生产统计数据对应的海洋环境数据，该海洋环境数据包括海表温度SST、海面高度距平均值SSHA和叶绿素浓度Chl-a，以SST相隔1℃为间距，计算出每个间距内的作业网次、作业产量比率以及平均网次产量，并依此得出最适宜的中心渔场SST的范围；以SSHA相隔10cm为间距，计算出每个间距内的作业网次、作业产量比率以及平均网次产量，并依此得出最适宜的中心渔场SSHA的范围；以Chl-a的含量在0.01～1.0、1.0～2.0、2.0～5.0、5.0～20.0、20.0～ 50.0mg/m 3为间距，计算出每个间距内的作业网次、作业产量比率以及平均网次产量，并依此得出最适宜的中心渔场Chl-a的范围；以水深相隔10m为间距，计算出每个间距内的作业网次、作业产量比率以及平均网次产量，并依此得出最适宜的中心渔场水深的范围。
  4. 如权利要求3所述的基于环境因子的西北非海域头足类渔场预报方法，其特征在于：对于包括海表温度SST、海面高度距平均值SSHA和叶绿素浓度Chl-a的海洋环境数据建立不同环境因子的适应性指数SI，采用如下公式计算不同权重方案下的栖息地综合指数HSI：
    HSI＝X SST*I SI_SST+X SSHA*I SI_SSHA+X CHL-a*I SI-CHL-a+X DEPTH*I SI_DEPTH；
    其中：I SI_SST表示基于海表温度的适应性指数；I SI_SSHA表示基于海面高度距平均值的适应性指数；I SI-CHL-a表示基于叶绿素浓度的适应性指数；I SI_DEPTH表示基于水深的适应性指数；X SST、X SSHA、X CHL-a、X DEPTH分别表示海表温度、海面高度距平均值、叶绿素浓度、水深所占权重的值。
  5. 如权利要求4所述的基于环境因子的西北非海域头足类渔场预报方法，其特征在于：设定作业网次最高的海域为中心渔场的分布概率最高的海域，则适应性指数SI赋值为1；当没有作业网次则适应性指数SI赋值为0；当作业网次高于均值的海域，则适应性指数SI赋值为0.5；当作业网次低于均值的海域，则适应性指数SI赋值为0.1。
  6. 如权利要求4所述的基于环境因子的西北非海域头足类渔场预报方法，其特征在于：对于海表温度、海面高度距平均值、叶绿素浓度、水深所占权重的值采用以下五种权重方案：
    方案1：X SST为0.25，X SSHA为0.25，X CHL-a为0.25，X DEPTH为0.25；
    方案2：X SST为0，X SSHA为0.9，X CHL-a为0，X DEPTH为0.1；
    方案3：X SST为0.1，X SSHA为0.1，X CHL-a为0，X DEPTH为0.8；
    方案4：X SST为0.9，X SSHA为0.1，X CHL-a为0，X DEPTH为0；
    方案5：X SST为0.4，X SSHA为0.4，X CHL-a为0.1，X DEPTH为0.1；
    分别分析上述五种不同权重方案中HSI值与设定阈值的大小，从而比较得出最佳权重方案，用于预测摩洛哥头足类渔场的中心渔场。
  7. 如权利要求1或2所述的基于环境因子的西北非海域头足类渔场预报方法，其特征在于：对于西北非的毛里塔尼亚渔场，分别以SST最小值15℃、SSHA最小值-45cm、水深最低值15m做基准，对应间隔l℃、lOcm、lOm，统计各月的渔获物总网次，总产量和天数，求出每SST l℃、SSHA lOcm、水深lOm间隔内渔获物网次所占的比例、产量所占的比例、平均网次产量，由此得到各月份中心渔场最适宜的海表温度区间、最适宜的距海面高度平均值区间以及最适宜的水深区间。
  8. 如权利要求7所述的基于环境因子的西北非海域头足类渔场预报方法，其特征在于：对于对应的海洋环境数据，采用如下公式计算不同权重方案下的栖息地综合指数HSI：
    HSI＝X SST*I SI_SST+X SSHA*I SI_SSHA+X DEPTH*I SI_DEPTH；
    其中：I SI_SST表示基于海表温度的适应性指数；I SI_SSHA表示基于海面高度距平均值的适应性指数；I SI_DEPTH表示基于水深的适应性指数；X SST、X SSHA、X DEPTH分别表示海表温度、海面高度距平均值、水深所占权重的值。
  9. 如权利要求8所述的基于环境因子的西北非海域头足类渔场预报方法，其特征在于：以作业网次的频度分布图为基础，建立不同环境因子的适应性指数SI，采用专家赋值法赋予适应性指数SI的值，设定最高作业网次NETmax为渔获物分布概率最高的海域，则适应性指数SI赋值为l；当没有 作业网次则适应性指数SI赋值为0；当作业网次高于均值的海域，则适应性指数SI赋值为0.5:当作业网次低于均值的海域，则适应性指数SI赋值为0.1。
  10. 如权利要求8所述的基于环境因子的西北非海域头足类渔场预报方法，其特征在于：对于海表温度、海面高度距平均值、水深所占权重的值采用以下五种权重方案：
    方案l:X SST为0.6，X SSHA为0.3，X DEPTH为0.1；
    方案2:X SST为0.5，X SSHA为0.2，X DEPTH为0.3；
    方案3:X SST为0.4，X SSHA为0.2，X DEPTH为0.4；
    方案4:X SST为0.3，X SSHA为0.4，X DEPTH为0.3；
    方案5:X SST为1/3，X SSHA为1/3，X DEPTH为1/3；
    分别分析上述五种不同权重方案中HSI值与设定阈值的大小，从而比较得出最佳权重方案，用于预测中心渔场。
PCT/CN2018/101111 2017-08-29 2018-08-17 基于环境因子的西北非海域头足类渔场预报方法 WO2019042162A1 (zh)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110533245A (zh) * 2019-08-30 2019-12-03 上海海洋大学 一种基于海表温的阿根廷滑柔鱼资源量预测方法
CN111581724A (zh) * 2020-05-09 2020-08-25 智慧航海（青岛）科技有限公司 一种基于船舶试验仿真模型的评估方法
CN114528761A (zh) * 2022-02-14 2022-05-24 太湖流域管理局水利发展研究中心 平原水网圩区-圩外系统滞蓄关系优化方法及系统

Families Citing this family (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111680844B (zh) * 2020-06-14 2024-01-05 上海海洋大学 一种基于水温垂直结构的西南大西洋阿根廷滑柔鱼栖息地评估和预测技术方法
CN111784034B (zh) * 2020-06-22 2024-06-11 上海海洋大学 一种影响智利海域美洲赤鱿渔场的关键环境因子的筛选及探测方法
CN112686465A (zh) * 2021-01-08 2021-04-20 中国海洋大学 蓝点马鲛渔场的集合模型预测方法、系统、设备及应用
CN113283193B (zh) * 2021-05-30 2022-04-26 长江水利委员会长江科学院 一种河湖洲滩钉螺适宜分布区确定方法
CN115235431B (zh) * 2022-05-19 2024-05-14 南京大学 一种基于光谱分层的浅海水深反演方法及系统
CN116975787B (zh) * 2023-09-20 2023-11-28 国家海洋环境预报中心 一种enso建模和预测方法及装置
CN117251673B (zh) * 2023-11-17 2024-03-01 中国海洋大学 一种海洋渔业资源动态追踪方法

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2004272890A (ja) * 2003-02-17 2004-09-30 Masamitsu Tonegawa Ｈｓｉモデルの構築方法、生態系定量評価方法及び環境保全措置方法
CN104809479A (zh) * 2015-05-18 2015-07-29 上海海洋大学 基于支持向量机的鱼类栖息地适宜性指数建模方法
CN106157162A (zh) * 2016-07-22 2016-11-23 上海海洋大学 一种北太平洋鱿鱼中心渔场预测方法
CN107578125A (zh) * 2017-08-29 2018-01-12 上海海洋大学 基于不同权重海洋环境因子的摩洛哥头足类渔场预报方法
CN107609691A (zh) * 2017-08-29 2018-01-19 上海海洋大学 基于栖息地指数的毛里塔尼亚头足类渔场预报方法

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2004272890A (ja) * 2003-02-17 2004-09-30 Masamitsu Tonegawa Ｈｓｉモデルの構築方法、生態系定量評価方法及び環境保全措置方法
CN104809479A (zh) * 2015-05-18 2015-07-29 上海海洋大学 基于支持向量机的鱼类栖息地适宜性指数建模方法
CN106157162A (zh) * 2016-07-22 2016-11-23 上海海洋大学 一种北太平洋鱿鱼中心渔场预测方法
CN107578125A (zh) * 2017-08-29 2018-01-12 上海海洋大学 基于不同权重海洋环境因子的摩洛哥头足类渔场预报方法
CN107609691A (zh) * 2017-08-29 2018-01-19 上海海洋大学 基于栖息地指数的毛里塔尼亚头足类渔场预报方法

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
CHEN, CHENG ET AL.: "Study on Fishing Ground of Bottom Trawl Based on the Habitat Suitability Model in the Coastal Waters of Morocco", JOURNAL OF GUANGDONG OCEAN UNIVERSITY, vol. 36, no. 1, 29 February 2016 (2016-02-29), pages 64 - 65; 67 *
GONG, CAIXIA ET AL.: "Research Advances on Habit Suitability Index in Fishery", JOURNAL OF SHANGHAI OCEAN UNIVERSITY, vol. 20, no. 2, 31 March 2011 (2011-03-31), pages 260 - 269 *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110533245A (zh) * 2019-08-30 2019-12-03 上海海洋大学 一种基于海表温的阿根廷滑柔鱼资源量预测方法
CN111581724A (zh) * 2020-05-09 2020-08-25 智慧航海（青岛）科技有限公司 一种基于船舶试验仿真模型的评估方法
CN111581724B (zh) * 2020-05-09 2023-05-02 智慧航海（青岛）科技有限公司 一种基于船舶试验仿真模型的评估方法
CN114528761A (zh) * 2022-02-14 2022-05-24 太湖流域管理局水利发展研究中心 平原水网圩区-圩外系统滞蓄关系优化方法及系统
CN114528761B (zh) * 2022-02-14 2022-09-06 太湖流域管理局水利发展研究中心 平原水网圩区-圩外系统滞蓄关系优化方法及系统

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