福建灯光围网作业捕捞能力的因子分析

Factor analysis on fishing capacity of light-purse seine along Fujian coastal line area

在渔业生产过程中影响捕捞能力发挥的因素很多,给捕捞能力的估算造成困难。本研究采用因子分析法对影响灯光围网捕捞能力发挥的13个指标变量进行分析,结果表明,可将影响灯光围网捕捞能力发挥的诸多因素归并为渔船作业能力、综合捕捞技术、作业天数、水下灯功率等4个公共因子;它们在围网捕捞作业过程中所发挥的作用由大到小依次为渔船作业能力因子、综合捕捞技术因子、作业天数因子、水下灯功率因子。本研究还计算和讨论了各样品的因子得分,通过各因子的得分和综合得分,可对各样品的捕捞能力发挥情况进行综合评价,为渔业生产和管理提供理论参考。[中国水产科学,2006,13(1):59-64]

The existence of over fishing in the worldwide fisheries is growing deep concern. It is negatively impacting on the sustainable use of many fishery resources. In order to understand and manage the fishing capacity better, it is necessary to study how the various factors in a fishing process affect the fishing capacity. This research addresses the factor analysis on fishing capacity of light-purse seine. There are many factors that affect fishing capacity utilization of light-purse seine in the process of fishing operation. As a resuit, it causes much trouble for estimating the fishing capacity. To investigate this matter, a series of surveys were carried out along Fujian coastal line area, southeast China, where the light-purse seine was a characteristic fishing method. Traditionally, fishing capacity was estimated by single factor such as power of vessel engine or gross tonnage and so on, but the single factor measure could not completely furnish the information of fishing capacity. Although multiple factors measure sometimes was used to estimate fishing capacity, it still had some problems, for example, massive data were always difficult to process. Therefore, a reduction on factor numbers was needed when multifactor was adopted to measure the fishing capacity of gears. The objective of this study was to deal with the data reduction. For this study, we randomly sampied 23 cases from the population of light-purse seine in the Quanzhuo region, southeast Fujian, in 2004, where light-purse seine fishing was one of the most important fishing methods. Then 13 observed variables for each case were selected for the factor analysis. By adopting the factor analysis approach, we first investigated the correlation and interaction among the 13 variables by calculating the correlation matrix R and the rotated factor loading matrix A＇, and then analyzed how each observed variable affected the fishing capacity and how well the hypothesized factors explained the observed data by their eigenvalues ai, the percent of variance and the cumulative percentage. After finishing that, we determined how many common factors m could be extracted according to the principle that the cumulative percentage of variance was more than 85%. The results showed that all the 13 variables could be clustered into 4 common factors which were called as the fishing vessel factor, the general fishing technique factor, the factor of total fishing time in a year and the power factor of underwater lights, respectively. The variance contribution rates and the weights of the four common factors to the fishing capacity utilization were quite different in the fishing process, and they can he ranked orderly as follows： the fishing vessel factor 〉 the general fishing technique factor 〉 the factor of total fishing time in a year 〉 the power factor of underwater lights. The factor scores were also calculated for each case. By interpreting the individual and synthetical factor scores of each case, we could synthetically evaluate the utilization of fishing capacity for each case and assess the economic effi- ciency of each work unit. Thus, the result of this study will give some helpful information for fishery production and fishing capacity management.