Selecting the Best Growth Model for Fish Using Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC)

應用赤井信息法則(AIC)和貝葉斯信息法則(BIC)對選擇最佳魚類生長模型的研究

克汗龐威(Sher Khan Panhwar) ； 劉群(Qun Liu) ； 福亞汗(Fozia Khan)

《臺灣水產學會刊》 37卷3期 (2010/09) Pp. 183-190

https://doi.org/10.29822/JFST.201009.0003

摘要

魚類的年齡和生長信息對於漁業的評估動力學、規劃和管理具有重要意義。此文從文獻中收集了10種魚類的體長與年齡數據，應用了5個生長模型對數據進行了擬合。生長參數的估計由最大似然法方法完成。最佳模型的選擇使用了赤井信息法則(Akaike information criterion, AIC)和貝葉斯信息法則(Bayesian information criterion, BIC)。多數種類生長的最佳模型是von Bertalanffy和allometric生長模型。結果顯示AIC和BIC方法在比較不同的生長模型時都有其優點。對於底棲魚類（笛鯛類和石斑魚類）在選擇最佳生長模型時BIC優於AIC。

關鍵字

生長模型 ； 體長年齡數據 ； 赤井信息法則AIC ； 貝葉斯信息法則BIC

並列摘要

The age and growth information of fish is important for assessment of the dynamics, planning, and management in fisheries. In this study, the mean size at age data of 10 fish species collected from literature were fitted by the five growth models and the growth parameters were estimated by the maximum likelihood method. The best model was selected using the Akaike information criterion (AIC) and Bayesian information criterion (BIC). The growth of most species can be best described by the von Bertalanffy and allometric growth models. The results showed that both AIC and BIC have their advantages in the testing of significance of the difference between the functions of models. As for the demersal fishes (snappers and groupers), BIC is better than AIC in selecting the best growth model.

並列關鍵字

Growth models ； Length-at-age ； AIC ； and BIC