Analysis of Lake Huron recreational fisheries data using models dealing with excessive zeros

Abstract

Excessive zeros in recreational catch data can cause problems for fish stock assessment and management. We evaluated a range of count regression models for analyzing the recreational catch data of walleye, Chinook salmon, and lake trout in Lake Huron. We also used modern predictive measures of effects to interpret the statistical results and extract year effects from the complex models. We found that models that account for both excessive zeros and overdispersion in recreational data, i.e., the zero-inflated negative binomial (ZINB) and hurdle negative binomial models, performed much better than those that cope with only one or none of the two common count data problems. Using the results from the best ZINB models, we identified important factors affecting catch rate of the three aforementioned species, and constructed standardized CPUE indices for each species.