Error Distribution Model to Standardize LPUE, CPUE and Survey-Derived Catch Rates of Target and Non-Target Species

Régis Santos,Wendell Medeiros-Leal,Mário Pinho,Ana Novoa-Pabon,Osman Crespo

doi:10.3390/modelling3010001

Abstract

Indices of abundance are usually a key input parameter used for fitting a stock assessment model, as they provide abundance estimates representative of the fraction of the stock that is vulnerable to fishing. These indices can be estimated from catches derived from fishery-dependent sources, such as catch per unit effort (CPUE) and landings per unit effort (LPUE), or from scientific survey data (e.g., relative population number—RPN). However, fluctuations in many factors (e.g., vessel size, period, area, gear) may affect the catch rates, bringing the need to evaluate the appropriateness of the statistical models for the standardization process. In this research, we analyzed different generalized linear models to select the best technique to standardize catch rates of target and non-target species from fishery dependent (CPUE and LPUE) and independent (RPN) data. The examined error distribution models were gamma, lognormal, tweedie, and hurdle models. For hurdle, positive observations were analyzed assuming a lognormal (hurdle–lognormal) or gamma (hurdle–gamma) error distribution. Based on deviance table analyses and diagnostic checks, the hurdle–lognormal was the statistical model that best satisfied the underlying characteristics of the different data sets. Finally, catch rates (CPUE, LPUE and RPN) of the thornback ray Raja clavata, blackbelly rosefish Helicolenus dactylopterus, and common mora Mora moro from the NE Atlantic (Azores region) were standardized. The analyses confirmed the spatial and temporal nature of their distribution.

Highlights

Fisheries research is a subject of high interest with both economic and ecological relevance, mainly focused on guaranteeing the sustainability of the resource and the economic performance of the fishery
Indices of relative abundance are usually a key input parameter used for fitting a stock assessment model [1], and they can be estimated from catches derived from fishery-dependent sources, such as catch per unit effort (CPUE) and landings per unit effort (LPUE), or from scientific survey data
The purpose of the present study is to explore different methods for analyzing catch rates (CPUE, LPUE, and relative population number (RPN)) of target and non-target species and define the best suitable statistical generalized linear models (GLMs) technique to remove the influence of potential drivers

Summary

Introduction

Fisheries research is a subject of high interest with both economic and ecological relevance, mainly focused on guaranteeing the sustainability of the resource and the economic performance of the fishery. The integration of information on species’ life history, fisheries monitoring, and resource surveys for assessing the stock size and harvest rate relative to sustainable reference points is known as stock assessment [1]. The most common method for stock assessment is the use of mathematical models that fit the available data to provide simplified representations of population and fishery dynamics [3]. Models used for stock assessment are usually based on several parameters, including mortality rates, reproductive aspects, size composition, and indices of relative abundance, to estimate current population status [1]. The type of model and analysis to be used for stock assessment relies on the species’ available information and the data quality [4,5]

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