With the commercial fishery expansion to deeper waters, some vulnerable deep-sea species have been increasingly captured. To reduce the fishing impacts on these species, exploitation and management must be based on detailed and precise information about their biology. The common mora Mora moro has become the main deep-sea species caught by longliners in the Northeast Atlantic at depths between 600 and 1200 m. In the Azores, landings have more than doubled from the early 2000s to recent years. Despite its growing importance, its life history and population structure are poorly understood, and the current stock status has not been assessed. To better determine its distribution, biology, and long-term changes in abundance and size composition, this study analyzed a fishery-dependent and survey time series from the Azores. M. moro was found on mud and rock bottoms at depths below 300 m. A larger–deeper trend was observed, and females were larger and more abundant than males. The reproductive season took place from August to February. Abundance indices and mean sizes in the catch were marked by changes in fishing fleet operational behavior. M. moro is considered vulnerable to overfishing because it exhibits a long life span, a large size, slow growth, and a low natural mortality.
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.
Elasmobranchs are globally recognized as vulnerable due to their life-history characteristics, fishing pressure, and habitat degradation. Among the skates and rays caught by commercial fisheries, the thornback ray Raja clavata is one of the most economically important in Northwest European seas. However, the scarcity of biological knowledge about this species in Azorean waters has limited the stock assessment types that can be conducted. To improve information on its habitat preferences, spatial distribution and movement pattern, growth, sex ratio, mortality, and reproduction, as well as to investigate long-term changes in abundance and size, this study analyzed approximately 25 years of fishery-dependent and independent data from the Azores. Raja clavata was mainly caught at depths up to 250 m. Most of the tagged fish were recaptured near the release point. A larger–deeper trend was found, and females were larger and more abundant than males. Life-history parameters showed that R. clavata has a long lifespan, large size, slow growth, and low natural mortality. The sustainability of its population is of concern to fisheries management and, while our findings suggested a relatively healthy stock in the Azores, a thorough increase in data quality is required to better understand the stock condition and prevent overexploitation.
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