Decreasing Uncertainty in Catch Rate Analyses Using Delta-AdaBoost: An Alternative Approach in Catch and Bycatch Analyses with High Percentage of Zeros

The gillnet data of walleye (Sander vitreus), yellow perch (Perca flavescens), and white perch (Morone americana), collected by a fishery-independent survey (Lake Eire Partnership Index Fishing Survey, PIS) from 1989 to 2008, contained 75-83% of zero observations. AdaBoost algorithm was applied to the model analyses with such fishery data. The 3- and 5-fold cross-validations were conducted to evaluate the performance of each candidate model. The performance of the delta model consisting of one generalized additive model and one AdaBoost model (Delta-AdaBoost) was compared with five candidate models. The five candidate models included the delta model comprising two generalized linear models (Delta-GLM), the delta model comprising two generalized linear models with polynomial terms up to degree 3 (Delta-GLM-Poly), the delta model comprising two generalized additive models (Delta-GAM), the generalized linear model with Tweedie distribution (GLM-Tweedie), and the generalized additive model with Tweedie distribution (GAM-Tweedie). The Delta-AdaBoost model yielded the smallest training error and test error on average, followed by Delta-GLM-Poly model for yellow perch and white perch, and Delta-GAM model for walleye. We suggested AdaBoost algorithm to be an alternative to deal with the high percentage of zero observations in the catch and bycatch analyses in fisheries studies.