The NAO index and the long-term variability of Nephrops norvegicus population and fishery off West of Ireland

Is it possible to differentiate between environmental and fishery effects on abundance‐biomass variation? A case study of blackspot seabream (Pagellus bogaraveo) in the Strait of Gibraltar

“…The selected simulations were assembled by class ages, and their 95% confidence limits were calculated. Subsequently, each biomass time series was used for autoregressive integrated moving average (ARIMA) modelling which has been proven to be an important tool for revealing hidden trends and seasonal patterns in time series with high inter‐annual variability (Farley and Murphy, ; Stergiou et al ., ; Park, ; Lloret et al ., ; Becerra‐Muñoz et al ., ; Koutroumanidis et al ., ; Czerwinski et al ., ; González‐Herraiz et al ., ). A multiplicative seasonal model with the general form of an ARIMA ( p , d , q )( P , D , Q ) S was applied, in which p is the order or the regular (non‐seasonal) autoregressive term (AR), q is the order of the regular moving average term (MA), and d is the order of the regular differentiation, while the respective upper‐case letters ( P , D , Q ) correspond to the seasonal part and S is the seasonal order.…”

Effect of environmental conditions on the seasonal and inter‐annual variability of small pelagic fish abundance off North‐West Africa: The case of both Senegalese sardinella

“…There are sophisticated procedures to carry out seasonal decomposition nevertheless in this study, we used the method of Census II (Makridakis, Wheelwright, & McGee, ) for its flexibility and simplicity. The seasonal decomposition is with a homogeneous variance, with or without long‐term trend, and is applied on log‐transformed and differentiated data (González Herraiz et al., ).…”

“…Off NW Africa, the coastal upwelling intensity during the upwelling season in winter and spring is an important driver of primary production (Auger, Gorgues, Machu, Aumont, & Brehmer, ; Lathuilière, Echevin, & Lévy, ), which may affect the survival of early life stages of Sardinella spp . (for example, Bartolino, Colloca, Sartor, & Ardizonne, ; Faure et al., ; González Herraiz, Torres, Farina, Freire, & Cancelo, ; Gröger, Winkler, & Rountree, ). The mechanisms that determine the spatial and temporal dynamics of sardinella must be elucidated for the sustainable management of these resources.…”

Identification of métiers based on economic and biological data: The Spanish bottom otter trawl fleet operating in non-Iberian European waters

“…These three fishing activities were known in the past, however they have evolved in different ways over the last thirty years: then the vessels fishing hake and Nephrops accounted for 79% of the Spanish trawl fleet in Subarea VII (Fernández et al, 1976). Probably due to the decline in this group of vessels, both métiers OTB3 and OTB4 have usually been combined for the collection of fishery statistics, only eventually split to standardize the catch-per-unit-effort of the Norway lobster on the Porcupine Bank (González-Herráiz et al, 2009). Trawling in Subarea VII is regulated through technical measures derived from the Emergency Plan for the northern stock of hake (CEC, 2001), where a mesh size of 100 mm or more is required for vessels catching more than 20% hake, thus directly affecting OTB3.…”

“…Regarding the species with higher CPUE in the deepest bathymetric range (>350 m; corresponding to métiers OTB3 and OTB4), hake, greater forkbeard and Norway lobster are retained, but greater silver smelt and red crab are completely discarded. Of these species, the two crustaceans species show seasonal patterns in catches, May-June for Norway lobster when catchability increases due to a natural shift in behavioural pattern (González-Herráiz et al, 2009), and August-September for red crab, probably related to its reproductive cycle (Attrill et al, 1991). Finally, from the two species with similar CPUE indices in the three bathymetric ranges, white anglerfish is retained while blue whiting is discarded.…”