| Chub mackerel (Scomber japonicus) is widely distributed in the Pacific, Atlanticand Indian oceans of temperate and subtropical continental shelf and its adjacentwaters. Among them, the distribution of mackerel in our coastal waters, is currentlyChina (including Taiwan Province), Japan and South Korea and other key fishingobjects, also east of the Yellow Sea ecosystem important species. In recent years, theEast Yellow Sea mackerel resource fluctuations, changes in the larger fishing output,may be suffering from overfishing, or in a state of overfishing. For sustainabledevelopment and effective management of mackerel offshore resources, needs toconsider the biological, economic and social benefits, to optimize the allocation ofmackerel resource development strategy, the scientific development of offshoremackerel management objectives. In this paper, based on1999to2011in Japan,Korea and China, three countries large light purse seine mackerel production statisticsand related economic data, using generalized linear model (GLM) and generalizedadditive models (GAM) for large lighting Wai net fishery CPUE standardization, toenable them to properly reflect the change in status of mackerel resource. ToGordon-Schaefer bio-economic model as the basis, based on the establishment ofmaximum sustainable yield (MSY), maximum economic yield (MEY) and bio-economic balance point (BE) integrated bio-economic model for optimal allocation ofsociety, and the short-term (1to5years), medium-term (10years), long term (20years) of the economic, social and analyze the status of fishery resources. The use ofBayesian methods for the East, yellow mackerel resource assessment, simulationunder different management strategies, mackerel resources and short-term andlong-term changes in fisheries law profits, which quantify the uncertainty and risk.The main findings are as follows:（1）CPUE standardization of mackerel resource. GLM model results showedthat the explanatory variable ln(CPUE+1) follow normal distribution (μ=2.71, σ=0.99), According to the inspection results of sum of squares of mean deviation oftype III, the results indicated that all explanatory variables were the significantvariables. Except for Sea Surface Temperature (P<0.05), the impact of otherexplanatory variables on CPUE are very significant (P<0.01). With the gradualincrease of the explanatory variables, the AIC values were gradually declined, it isconcluded that the GAM model which containing all eight explanatory variables(years, month, longitude, latitude, boat, sea surface temperature, sea surface heightand sea surface chlorophyll concentration) is the best one.GAM model analysisshowed that, year, month, latitude, longitude, boat, sea surface temperature, seasurface height, sea surface chlorophyll concentration eight variables were significantvariables (P <0.01). Best GAM CPUE model to explain the deviation of the total rateof11.69%, the highest of which was explained variable rate of4.52%, followed bythe variable month, explaining1.99%. Variables affecting the ship on CPUE rankedthird in all of the variables in explaining rate of1.56%, the following order of latitude(1.24%), SSC (1.11%), SST (1.02%), Longitude (0.93%) and SSH (0.33%, table2-2).In all of the variables, the impact on CPUE biggest variable in its interpretation of therate of contribution to the CPUE deviations accounted38.67%. This shows that,CPUE of years varies greatly. Research shows that in recent years, the Yellow andEast mackerel resource abundance index (in effect) showed a downward trend in theoverall volatility and its impact factors include environmental factors and fishingintensity.（2）Optimize the allocation and management of biological resources policybased on mackerel comprehensive economic and social factors. After estimating,mackerel maximum sustainable yield (MSY), maximum economic yield (MEY) andbio-economic balance BE corresponding production was380,800tons,288,700tonsand326,200tons, which corresponds to the amount of fishing effort were18606hauls,13396hauls and26792hauls. By short-term comparison of different fishing under theprogram (5years) and long term (20years) and cumulative production of accumulatedprofits, accumulated profits of the size found in relation to: Option2> Option4>program9> Solutions6> Solutions7>8program> Option1> Solutions10>Option5> Option3, but the size of the relationship between cumulative productionwas the opposite. From the mid-profits (before10years of accumulated profits), itseems to Option2(ie MEY as management objectives) maximum cumulative profit ofabout6.272billion yuan; located at the second step is to plan4,9,6, three the profits were approximately5.891billion yuan,5.595billion yuan,5.22billion yuan; locatedin the third step is to program7,8,1(ie MSY as a management goal),10, whoseprofits were approximately4.972billion yuan,4.839billion yuan,4.402billion yuan,4.32billion yuan; ladder is located on the fourth program5,3(ie BE as managementobjectives), and its profits were approximately3.165billion yuan,1.647billion yuan,that points to manage BE the minimum cumulative profit targets. But the results of itscumulative production but contrary to the program’s maximum output3, about3,562,200tons; to plan a minimum yield of2, approximately2,771,800tons.Research shows that fishing Scenario4(to MSY, MEY weight of50%eachmanagement objectives), program9(MSY, MEY, BE each weight were25%,50%,25%) of the overall efficiency for the best, and the difference is not big, neither canmaintain good long-term economic benefits, but also can fully take into accountbiological, social and other aspects of (its fishing effort ranged from1.6to1.8millionnet times).（3）Based on Bayesian model mackerel optimal allocation of resources andmanagement strategy: According to China, Japan and Korea in light seiners fishingmackerel catch statistics and economic data operating costs, catch rates, etc., based onBayesian methods to build the bio-economic model, and Shaefer surplus productionmodel parameters based on a priori probability distribution plan set three kinds ofdistributions: uniform, normal and lognormal distribution, simulation under differentmanagement strategies east Yellow Sea resources and management strategies.Simulation results were the follows. Under the uniform distribution scenario, theestimated MSY was405,200tons and its corresponding stock BMSYwas819,000tons,the estimated MEY and YBEwere235,400and359,700tons respectively, and thecorresponding stock BMEY, BBEwere1,323,600and1,009,200tons. For the normaldistribution scenario, the estimated MSY and its stock size BMSYwere354,700and826,400tons respectively; and the estimated MEY and YBEwere221,100and329,000tons, with the corresponding stock BMEY, BBEwere1,315,400and978,100tons. Forthe log-normal distribution scenario, the estimated MSY and its stock size BMSYwere336,900and805,200tons; the estimated MEY and YBEwere216,000and315,900tons,with the corresponding stock BMEY, BBEwere1,266,700tons and923,000tons. Underthe benchmark scenario, the cumulative catch and profit reached their peaking valuewhen the harvest was0.5, its short, medium and long-term cumulative catch andprofit were2.0197million tons and11.107billion yuan,4.3604million tons and 23.98billion yuan,7.6286million tons and41.952billion yuan respectively. while fornormal program and log-normal program, its maximal cumulative catch and profitwere both occured at the0.4harvest level, under the normal scenario, the short,medium and long term the cumulative catch and profit were1.7796million tons and9.787billion yuan,3.9117million tons and21.512billion yuan,6.8019million tonsand37.406billion yuan respectively;while under the log-normal scenario, the short,medium and long-term cumulative catch and profit were1.6807million tons and9.243billion yuan,3.7114million tons and20.41billion yuan,6.3859million tonsand35.119billion yuan.The study showed that under the three distribution scenarios,by simulating the observation indicators in the different management strategies andanalysising its management effectiveness and risk, we concluded that when theharvest was0.3, P(B2031>BMSY) were all larger than0.85. From a biological point ofview, when the mackerel fishery harvest rate was around0.3, this fishery reached themaximum sustainable yield350,000tons. Likewise, From an economic point of view,when the harvest rate was controlled at0.1, P(B2031>BMEY) equal to1and P(B2031<BBE)equal to0under the above three scenarios. This indicated that management measurescould achieve maximization of economic benefit, and the probability of noeconomic benefit was0. In conclusion, just from the economic point of view, theharvest rate of mackerel fishery should be controlled at about0.1as its optimumfisheries management strategies.Research shows that the three kinds of distributedprograms, a number of observations through simulation under different managementstrategies, and its management effectiveness evaluation and risk analysis, a singlefrom a biological point of view, should mackerel fishery harvest rate controlled atabout0.3to fisheries management as the optimum strategy, but just from theeconomic point of view, it should mackerel fishery harvest rate controlled at about0.1as the optimum fisheries management strategies. |
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