Stock Assessment And Manangement For Metapopulation Of Short-lived Species

In recent decades, while the traditional fish species’ catch decreased drastically, the catch of short-lived species like cephalopod increased year by year, and has been become an important fisheries resources in marine fisheries in the world. In order to make sustainable use of fisheries resources, reasonable management strategies must be made to achieve the goal of scientific management for fisheries resources. Evaluating fisheries resources scientifically is the base of making reasonable management strategies, and stock assessment model is an important tool for assessing fisheries resources. Because short-lived species have the characteristics of fast growth, abundant resources, many groups of spawning populations and no surplus population, its resources can easily be affected by marine environment. Therefore, the stock assessment and management research of short-lived species is in the first step in the world.Pelagic squid is an important economic resources. They spawned once in lifetime and died after spawning. So they are one of the short-lived species with a lifespan of one year. How to evaluate the resource of squid precisely and understand its population dynamics is the key problem in the field of squid’s stock assessment and management. A metapopulation is generally considered to consist of several distinct populations together with areas of suitable habitat which are currently unoccupied, but also interrelated. The pelagic squid have the characteristics of metapopulation. In recent years, how to evaluate the resources of metapopulation has become a more and more important problem. Therefore, this study will focus on the stock assessment and management for short-lived species which has metapopulation characteristics, and take red flying squid(Ommastrephes bartramii) as an example.Catch per unit fishing effort(CPUE) of a fishery is often used as an abundance index which is usually assumed to be proportional to the stock abundance. Observed fisheries CPUE data are, however, influenced by many factors, in addition to fish population abundance, including spatial-temporal factors such as area and season and environmental factors such as sea surface temperature(SST) and sea surface salinity. The impacts of these factors on CPUE may shift the assumed proportionality between observed CPUE and stock abundance. Thus, CPUE standardization is needed to remove the impacts of factors other than population abundance. Many statistical models have been developed for CPUE standardization such as General Linear Model(GLM) and General Additive Model(GAM). Generally, statistical methods always assume the independence of the observed CPUEs. However, this assumption is invalid for a ?sh school and distribution because of their spatial autocorrelation. Based on the fishing data in jigging fishery by Chinese fishing fleet and the corresponding Sea Surface Temperature(SST) data and the chlorophylla data in the Northwest Pacific Ocean during June to November in 1999 to 2012, the spatial autocorrelation is incorporated into the standard general linear model(GLM). Four distance models(Gaussian, exponential, linear and spherical) are examined for spatial autocorrelation in the CPUE standardization of red flying squid(Ommastrephes bartramii). Besides, O. bartramii‘s resources was evaluated when they are treated as five different population types(singe population; independent population of west of 170°E; independent population of east of 170°E; metapopulation of west of 170°E; meatpopulation of east of 170°E) using Bayesian Schafer model. The results were as follow:(1) We developed GLM model with incorporated spatial correlation to standardize CPUE. Results showed that ln(N_CPUE+1) is normally distributed with a line in q-q plot. Besides, Spatial GLM’s AIC value is smaller than standard GLM’s, indicating spatial GLM model fit better than standard GLM. In four distance models, exponential model had the smallest AIC value. Therefore, the exponential spatial GLM model is the best CPUE standardization model for O. bartramii in the Northwest Pacific Ocean. There is the same trend for the nominal and standardized CPUE in the plot against year, but nominal CPUE had greater fluctuations among years, while average standardized CPUE had smaller fluctuations among years.(2) The results for single population(r =1.46, K=55.0005, q=0.0391): the biomass of O. bartramii from 1999 to 2012 is greater than BMSY. All F is smaller than Ftar, and biomass increased with F decreased, which indicated that O. bartramii was in good resource status. The value of BMSY, MSY, F0.1, and FMSY is 275 000 tons, 200 000 tons, 0.66 and 0.73, respectively. If we want to get the maximum sustainable yield during 2013 to 2022, we should control the harvest rate at the value of 0.6. And at that harvest rate, the value of B2022/BMSY, B2022/K, P(B2022>B2013), P(B2022<0.5K), and P(B2022<0.125K) is 0.93, 0.46, 0.414, 0.565, and 0, respectively.(3) The results for independent population(r =1.49, K=55.0025, q=0.0228 for west of 170°E population; r =1.07, K=55.0083, q=0.0091 for east of 170°E population): the biomass of O. bartramii from 1999 to 2012 is greater than BMSY. In term of the independent population of west of 170°E, biomass increased with F decreased. In term of the independent population of east of 170°E, biomass increased while F had no big change. All F is smaller than Ftar, which showed that O. bartramii was in good resource status. The value of BMSY, MSY, F0.1, and FMSY for independent population of west of 170°E is 275 000 tons, 200 000 tons, 0.67 and 0.74, respectively. The value of BMSY, MSY, F0.1, and FMSY for independent population of east of 170°E is 275 000 tons, 1500 000 tons, 0.48 and 0.54, respectively. In addition, if we want to get the maximum sustainable yield for independent population of west of 170°E during 2013 to 2022, we should control the harvest rate at the value of 0.6. And at that harvest rate, the value of B2022/BMSY, B2022/K, P(B2022>B2013), P(B2022<0.5K), and P(B2022<0.125K) is 1.10, 0.55, 0.391, 0.884, and 0, respectively. If we want to get the maximum sustainable yield for independent population of east of 170°E during 2013 to 2022, we should control the harvest rate at the value of 0.5. And at that harvest rate, the value of B2022/BMSY, B2022/K, P(B2022>B2013), P(B2022<0.5K), and P(B2022<0.125K) is 1.34, 0.67, 0.388, 1, and 0, respectively.(4) The results for metapopulation(r =1.33, K=55.0035, q=0.0227 for west of 170°E population; r =0.9581, K=55.0041, q=0.0135 for east of 170°E population): the biomass of O. bartramii from 1999 to 2012 is greater than BMSY. In term of the metapopulation of west of 170°E, biomass increased with F decreased; In term of the metapopulation of east of 170°E, biomass increased while F had no big change. All F is smaller than Ftar, which showed that O. bartramii was in good resource status. The value of BMSY, MSY, F0.1, and FMSY for metadependent population of west of 170°E is 275 000 tons, 130 000 tons, 0.43 and 0.48, respectively. The value of BMSY, MSY, F0.1, and FMSY for metapopulation of east of 170°E is 275 000 tons, 1500 000 tons, 0.48 and 0.54, respectively. In addition, if we want to get the maximum sustainable yield for metapopulation of west of 170°E during 2013 to 2022, we should control the harvest rate at the value of 0.6. And at that harvest rate, the value of B2022/BMSY, B2022/K, P(B2022>B2013), P(B2022<0.5K), and P(B2022<0.125K) is 1.15, 0.58, 0.430, 0.987, and 0, respectively. If we want to get the maximum sustainable yield for metapopulation of east of 170°E during 2013 to 2022, we should control the harvest rate at the value of 0.5. And at that harvest rate, the value of B2022/BMSY, B2022/K, P(B2022>B2013), P(B2022<0.5K), and P(B2022<0.125K) is 1.21, 0.61, 0.498, 0.981, and 0, respectively.