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

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.

Trends in abundance indices derived from commercial fisheries for priority marine stocks in the Azores

“…LPUE and CPUE were estimated for P. phycis, C. conger, S. latus, S. colias, S. cretense, S. atricauda, L. forbesii, P. pagrus, S. scrofa, P. kuhlii, T. picturatus, Seriola spp., P. elephas, B. splendens, B. decadactylus and A. carbo as kg landing À1 vessel À1 and kg day at sea À1 vessel À1 , respectively. To reduce the influence of potential drivers (e.g., targeted species, vessel size, fishing gear) on these catch rates, generalized linear models (GLMs) were utilized to calculate standardized abundance indices (Santos et al, 2022b). The explanatory factors included in the analysis differed across the different datasets and are reported in Table 1.…”

“…To prevent and mitigate the fishing impact on the living marine resources, fishery-dependent (e.g., catch, effort, discard) and surveyderived data have been systematically collected in the Azores for the main commercial species since the 1990s (Santos et al, 2020b). In recent years, efforts have been made to analyse these data and enhance the quality of input information for the assessment of certain stocks identified as priority in the Azores under SDG Indicator 14.4.1 'Proportion of fish stocks within biologically sustainable levels' (Medeiros-Leal et al, 2021;Santos et al, 2020aSantos et al, , 2021aSantos et al, , 2021bSantos et al, , 2022aSantos et al, , 2022b.…”

Life History and Fishing Aspects of the Deep-Sea Silver Scabbardfish Lepidopus caudatus in the Azores

“…As RPN data were obtained directly from the scientific survey with a standardized experimental design, the mean annual abundance index for this index was calculated directly and normalized by the min–max year values. Nevertheless, CPUE and LPUE data came from fishery-dependent sources, so these data can produce biased abundances because of differences in vessel sizes, fishing gear, and even target species [ 39 ]. Therefore, it is necessary to standardize these data by removing the impact of the different factors that affect the catches.…”

“…Therefore, it is necessary to standardize these data by removing the impact of the different factors that affect the catches. A hurdle–lognormal Generalized Linear Model (GLM) was performed to determine which characteristics of the fishing-related catches had impacts on the yearly abundance indices [ 39 ]. The possible factors affecting the LPUE were year, quarter, vessel length, métier , target effect (percentage of the capture of L. caudatus in relation to the total), and for CPUE were year, quarter, vessel length, gear type, depth, target effect (See Table S1 for details in the levels of each factor).…”

“…The possible factors affecting the LPUE were year, quarter, vessel length, métier , target effect (percentage of the capture of L. caudatus in relation to the total), and for CPUE were year, quarter, vessel length, gear type, depth, target effect (See Table S1 for details in the levels of each factor). Statistical details about the standardization procedure are found in Santos et al [ 39 ]. The GLM was run using the lsmeans R package [ 40 ].…”