Following an introduction to the nature of fisheries catches and their information content, a new development of CMSY, a data-limited stock assessment method for fishes and invertebrates, is presented. This new version, CMSY++, overcomes several of the deficiencies of CMSY, which itself improved upon the “Catch-MSY” method published by S. Martell and R. Froese in 2013. The catch-only application of CMSY++ uses a Bayesian implementation of a modified Schaefer model, which also allows the fitting of abundance indices should such information be available. In the absence of historical catch time series and abundance indices, CMSY++ depends strongly on the provision of appropriate and informative priors for plausible ranges of initial and final stock depletion. An Artificial Neural Network (ANN) now assists in selecting objective priors for relative stock size based on patterns in 400 catch time series used for training. Regarding the cross-validation of the ANN predictions, of the 400 real stocks used in the training of ANN, 94% of final relative biomass (B/k) Bayesian (BSM) estimates were within the approximate 95% confidence limits of the respective CMSY++ estimate. Also, the equilibrium catch-biomass relations of the modified Schaefer model are compared with those of alternative surplus-production and age-structured models, suggesting that the latter two can be strongly biased towards underestimating the biomass required to sustain catches at low abundance. Numerous independent applications demonstrate how CMSY++ can incorporate, in addition to the required catch time series, both abundance data and a wide variety of ancillary information. We stress, however, the caveats and pitfalls of naively using the built-in prior options, which should instead be evaluated case-by-case and ideally be replaced by independent prior knowledge.

Following an introduction to the nature of fisheries catches and their information content, a new development of CMSY, a data-limited stock assessment method for fishes and invertebrates, is presented. This new version, CMSY++, overcomes several of the deficiencies of CMSY, which itself improved upon the “Catch-MSY” method published by S. Martell and R. Froese in 2013. The catch-only application of CMSY++ uses a Bayesian implementation of a modified Schaefer model, which also allows the fitting of abundance indices should such information be available. In the absence of historical catch time series and abundance indices, CMSY++ depends strongly on the provision of appropriate and informative priors for plausible ranges of initial and final stock depletion. An Artificial Neural Network (ANN) now assists in selecting objective priors for relative stock size based on patterns in 400 catch time series used for training. Regarding the cross-validation of the ANN predictions, of the 400 real stocks used in the training of ANN, 94% of final relative biomass (B/k) Bayesian (BSM) estimates were within the approximate 95% confidence limits of the respective CMSY++ estimate. Also, the equilibrium catch-biomass relations of the modified Schaefer model are compared with those of alternative surplus-production and age-structured models, suggesting that the latter two can be strongly biased towards underestimating the biomass required to sustain catches at low abundance. Numerous independent applications demonstrate how CMSY++ can incorporate, in addition to the required catch time series, both abundance data and a wide variety of ancillary information. We stress, however, the caveats and pitfalls of naively using the built-in prior options, which should instead be evaluated case-by-case and ideally be replaced by independent prior knowledge.

Context: Sharks are frequently caught as bycatch in trawl fisheries in the North Aegean Sea. However, very little information exists on their biology and ecology in the area. Aims: Biological parameters and spatial segregation of sharks in relation to size class, sex, depth and geographic location were examined. Methods: Morphological characteristics and generalised additive models were used to explore biological traits and aggregating behaviour respectively, by using data from the Mediterranean International Trawl Survey between 2014 and 2021. Key results: Overall, 7761 specimens were measured, with Scyliorhinus canicula, Galeus melastomus and Squalus blainville being the most common species. Scyliorhinus canicula was found in shallow coastal waters, whereas the remaining species were detected in deeper waters. Conclusions: Females were larger and heavier than males and the weight–length relationships corroborated size differences between sexes in all species, whereas allometric growth did not share the same patterns as those from the Mediterranean Sea. Generalised additive models showed that the distribution of dominant species is bathymetric and geographically dependent. Additionally, the depth and geographic segregation patterns are highly associated with ontogeny and not sex. Implications: These findings highlighted the importance of incorporating elasmobranch behavioural characteristics in the poorly developed management regulations in the North Aegean Sea.

During 2019, Greece conducted a nationwide telephone survey in order to estimate the number of marine recreational fishers (MRF), to identify the main fishing methods, to estimate their effort, economic aspects of their activity and to evaluate certain perceptions of marine recreational fishing. MRF are estimated to be 7.93% of the country’s resident population. Three main fishing methods were identified, fishing from the shore, fishing by boat and spearfishing. It is estimated that Greek MRF carry out approximately 11,461,765 fishing trips/year, their total catch is 9350 t/year and the total yearly expenditure is 132,186,000 Euros. Marine recreational catches constitute one-third of the reported small scale coastal fisheries’ catches, with their main catches belonging to the Sparidae family. The majority of MRFs are aware of the rules and limits associated with their activity, however a significant percentage seems to ignore or deny their existence. The results of the study indicate the social and economic importance of the activity in Greece and clearly demonstrates the need for regular monitoring and field work in order to properly evaluate marine recreational fisheries and sustainably manage both the activity and fisheries resources in the country.

Aim: We explored the range shifts of alien and native birds, the responses of alien and native β‐diversity to abiotic factors, and the effect of native diversity on alien β‐diversity in two time periods.Location: Great Britain.Time period: 1968–1972, 2007–2011.Taxa studied: Breeding birds.Methods: We estimated range shifts of alien and native species between the periods 1968–1972 and 2007–2011. Following, β‐diversity of alien and native communities was estimated by Jaccard pairwise index (β_tot) and partitioned into richness difference and replacement component for each period. We built abiotic generalized dissimilarity models including abiotic factors for alien and native β_tot and their components and a biotic model for aliens including native taxonomic and functional diversity as predictors.Results: Most alien and half native species expanded into new regions during the 40‐year period. The native species range shifts did not exhibit a clear pattern along the longitudinal or latitudinal gradient, while alien species tended to move north‐westwards. The richness difference was the dominant component of alien β‐diversity, and the replacement component contributed mostly to native β‐diversity. Alien β‐diversity responded similarly but less strongly than native β‐diversity, to the abiotic gradients. Temperature‐related variables, distance and precipitation were the most important abiotic drivers of native and alien β‐diversity. The biotic model of alien β‐diversity explained more deviance than the abiotic model.Main conclusions: Alien species expanded into new regions over the 40 years, with alien β‐diversity driven mostly by species gains. The effect of environmental filtering on alien communities was weaker compared with native communities but was slightly reinforced in the second period compared with the first period, highlighting the role of environmental change in shaping diversity patterns. Native diversity played a key role in driving alien β‐diversity, through biotic interactions or/and by reflecting climatic suitability or niche availability for aliens.