統計モデルとデータマイニング手法の水産資源解析への応用
1
庄野 宏
2
Application of statistical modeling and data mining method to the fish
stock analyses
Hiroshi SHONO
水研センター研報第22号1-85平成20年
Bull. Fish. Res. Agen. No. 22, 185, 2008
2007年月16日受理 Received on May 16, 2007
1 筑波大学審査学位論文 掲載に際し投稿規定に沿って一部修正した
2 遠洋水産研究所 〒424-8633 静岡市清水区折戸5-7-1 National Research Institute of Far Seas Fisheries, 5-7-1, Orido, Shimizu, Shizuoka 424-8633,
Japan
博士号論文
AbstractIn this thesis, we focused on the various problems in the filed of fish population
analysis, especially regarding the analyses of CPUE catch per unit effort which shows the
relative abundance. We suggested several techniques to solve these issues by the statistical
modeling and approaches for data mining using the actual fishery data on tuna and its re-
lated species, and computer simulation experiments.
Catch per unit effort CPUE is an important concept which is corresponding to the rela-
tive stock size and is proportional to the stock abundance. However, because the nominal
CPUE may include various spatiotemporal and environmental effects except for stock den-
sity such as area, season and fishing gears, we need to remove these effects to grasp the
annual variation of the stock. Therefore, it has been traditionally carried out to estimate the
factorial effect of year using analysis of covariance ANCOVA model e.g. CPUE Log-Nor-
mal model where natural logarithm of CPUE is set to the response variable and assumed
factorial effects are incorporated into the model as explanatory variables under the normal
error, and generalized linear model GLM e.g. Catch Poisson model, Catch Negative-Bino-
mial model in which catch, discrete variable, is set to the response one and Poisson or nega-
tive binomial distribution and so on is assumed. Such work is called CPUE standardization
and approaches for data mining such as tree-regression model and neural networks have
been recently utilized for it in addition to the statistical modeling.
In this study, we dealt with the CPUE standardization, major issue in the fish population
analysis, as main theme of this paper and discuss in detail three problems about CPUE anal-
ysis as follow:
1 Choice of the factorial effects, performance evaluation of the model through the various
information criteria and stepwise test in the ANOVA type model supposing the CPUE
standardization Chapter 3
2 Approach of CPUE prediction and the simple method for attribution analysis i.e. method
for extracting CPUE year trend in the time-space without operation for southern bluefin
tuna by the neural networks Chapter 4
3 Performance evaluation of Tweedie model if it includes many zero-catch and comparison
of Tweedie distribution and the traditional methods ad hoc ANCOVA method, Catch
model Chapter 5
Chapter 1 becomes an introduction and describes the background, purpose of this re-
search and composition of this thesis. In chapter 2, we outlined CPUE standardization from