| In the last decades, although most demersal fish stocks have collapsed along the coast of China, the Chub mackerel (Scomber japonicus) still supports a large pelagic fishery in the East China Sea and Yellow Sea. The yield of chub mackerel in Chinese offshore fishery is about 0.4 million ton which is about 3 percent of the total output of marine capture fisheries in China. The catch from the East China Sea and Yellow Sea consists of 78 percent of this total yield. However, the future of this important fisheries resource is unclear with high fishing intensity and large changes in the ecosystem. Current research is focused on improved understanding of its population dynamics and spatial dynamics of fishing ground. The improved technology of ocean remote sensing in our country has resulted in increased capacity in monitoring environments of the fishery on a large spatial scale. The two ocean color remote sensing satellites (HY-1A and HY-1B) were launched in May 2002 and July 2007, and the ocean dynamic satellite series (HY-2) and ocean watch & monitor satellite series (HY-3) is scheduled to be launched, respectively, in 2009 and 2012. These satellites greatly enhance our abilities of collecting the information of oceanic environment in China Sea. Chub mackerel, a pelagic fish species, is used as an example to show the potential use of remote sensing in improving modeling the dynamics of marine fisheries stocks.This dissertation consists of four parts. Part one reviews (1) the development of ocean remote sensing in marine fisheries; (2) chub mackerel biology; (3)fishing ground and resource assessment; and (4) the Chub mackerel purse sense fisheries in China and related countries and area. Part two reviews the environments of East China Sea and Yellow Sea, the ocean remote sensing research, and the corresponding remote sensing data. Part three is focused on the methods of estimating relative fishing efficiencies and analyzing the relationship between CPUE and biomass based on the fisheries-dependent data. The last part tries to incorporate the remote sensing data into a production model in assessing fish stock dynamics. The main findings of this study are as follow:(1) Reliable estimation of effective fishing effort, which is proportional to fishing mortality, can provide information critical to the assessment and management of fisheries stocks. To estimate effective fishing effort, we need to understand fishing efficiency and factors that may influence it. In this study fishing efficiency was estimated for mackerel purse seine fisheries using a generalized linear model. This was done for different companies involved in the fisheries. Different choices of error structures were considered in the estimation and their impacts on the estimation were evaluated. The negative binomial distribution, gamma distribution, and log-normal distribution were chosen as error distributions according to log-linear regression of variance versus log-mean of CPUE (catch per unit effort). Zero CPUE values in data were found to have great impacts on the assumed error structure and adding a constant (5) to CPUE was needed for the gamma distribution and log-normal distribution in maximum likelihood estimation. As 5 increased, the contrast of estimated fishing efficiency was reduced greatly. Delta approaches were also chosen as an alternative way to deal with zero CPUE values in this study. Comparing the results of different models, we considered Delta-negative binomial and Delta-gamma as most appropriate error distributions for this study. The results showed that the fishing efficiency differed greatly among fisheries companies and among different areas.(2) CPUE is an abundance index commonly used in fisheries. It is often assumed that CPUE is proportional to fish abundance. However, the relationship between CPUE and fish abundance derived from the fisheries dependent data may be influenced by behaviors of fish and fishermen, making the proportional assumption invalid.The paper presents a cellular automata model which simulates the reproduction and spatial movement of individual fish schools and the corresponding commercial catch and movement of individual fishing boat. The model was applied to evaluate the relationship between commercial CPUE and stock abundance in different distributional patterns of fishing boats and fish schools. The study considers four scenarios such as:â‘ the distribution of fishing boat is random but fish schools may be either random or not,â‘¡the distribution of fish schools is random initially, but become aggregated and the movement of fishing boat is corresponding to such changes in the spatial distribution of fish schools,â‘¢the distribution of fish schools is the same as that inâ‘¡, but spatial distribution of fishing boats is always aggregated; andâ‘£the distribution of fishing boat is random initially, and become aggregated, and fish school is aggregated. I found that when the distribution of fishing vessels was random the commercial CPUE was proportional to abundance otherwise there existed nonproportionality between CPUE and stock abundance. For instance, the abundance is fluctuant in reserve with CPUE as in scenariosâ‘¢and in scenariosâ‘£, I find different relationship between CPUE and abundance in different exploitive stages as fishermen get more and more experience in catching. The result also shows the cellular automata model is useful to explore or analyze some theory relationship in fishery stock assessment and management.We analyze the relationship between the proportion of catch and effort allocated to different locations based on the ideal free distribution theory. The study shows that the prediction of ideal free distribution was approximately supported in north fishing ground, i.e. to a certain extent, the CPUE was equalized and was independent of the fish abundance, and to the contrary, the effort may be in proportion to the abundance. Although this prediction was not supported in south fishing ground, the CPUE decreased with the standardized effort as its value was larger than 26, which implied that the interference competition was present and the relationship between CPUE and fish abundance may be weakened, even break down. Finally, we suggest we need to keep an eye on CPUE equalized when using the fish dependent data to study the dynamic of fish stocks.(3) I laid each position of catch from 1999 to 2003 on maps of SST (sea surface temperature) and chlorophyll-a generated from remote sensing. I find that in north fishing ground, Yellow Sea warm current has an important influence on the location of chub mackerel fishing ground. Although the concentration of chlorophyll-a may not be estimated reliably by remote sensing because of the sediment, the chlorophyll-a doubtlessly limit the distribution of the fish. On the southern fishing ground, the map shows upwelling plays an important role in determining the position of the catch. Generally, the fishing ground is at the warm edge of front and the Taiwan warm current almost controls the range of distribution of chub mackerel. However, the influence of chlorophyll-a on the fish distribution is unclear. A GAM model was used to analyze the quantity connection between CPUE and ocean environmental elements estimated from remote sensing. The result shows that on the northern fishing ground the effects of SST, SST grads, sea wind and mean sea level anomaly are significant. On the southern fishing ground, the effects of SST anomaly, mean sea level anomaly, sea winds and eddies kinetic energy are significant. The effect of chlorophyll-a is not significant on both fishing ground. But the formation of fishing grounds has a tight connection with the fish migrations and the spatial structure of environmental elements. So we should take the necessary cautions in using the model for prediction. In order to explore the connection of the distribution and its corresponding evolvement of fishing ground of chub mackerel with the change of environment, we construct a cell automata model to simulate the distribution of chub mackerel by using SST, chlorophyll-a and sea depth data. The result shows that the model performs reasonably well, but need to be fine-tuned and improved in future.(4) The yield of mackerel is strongly affected by environmental variability. It is important to identify the influence of the ocean environment on these small pelagic fishes for developing a fishery management plan for sustainable development of fisheries. We used offshore catch data in the East China Sea from the large purse sense of China and sea surface temperature derived from TRMM/TMI to explore the relationship between them. The result of principal component analysis of the time-series images showed that the abundance of small pelagic fishes had a short period of fluctuation, positively corresponding to the same period of environmental change and the intensity of the Kuroshio current and Taiwan warm current. The sea surface temperature of spawning ground from March to April also had significant and positive effects on CPUE from the corresponding fishing ground. The spatial and temporal distribution of the fish changed as a part of response to the environment change. Finally, we make suggestions for fisheries stock assessment. First the CPUE should be standardized and adjusted according to different spatial and temporal distributions of fishing effort. The environmental variables must be incorporated in models for fisheries stock assessment and risk evaluation must be made allowing for dynamic resource induced by environmental factors.In this dissertation, I also discussed the framework for expanding the dynamic production model by incorporating remote sensing data in the estimation of fisheries stock dynamics.
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