This study presents a new method (LBB) for the analysis of length frequency data from commercial catches. LBB works for species that grow throughout their lives, such as most commercially-important fish and invertebrates, and requires no input in addition to length frequency data. It estimates asymptotic length, length at first capture, relative natural mortality, and relative fishing mortality. Standard fisheries equations can then be used to approximate current exploited biomass relative to unexploited biomass. In addition, these parameters allow the estimation of length at first capture that would maximize catch and biomass for a given fishing effort, and estimation of a proxy for the relative biomass capable of producing maximum sustainable yields. Relative biomass estimates of LBB were not significantly different from the “true” values in simulated data and were similar to independent estimates from full stock assessments. LBB also presents a new indicator for assessing whether an observed size structure is indicative of a healthy stock. LBB results will obviously be misleading if the length frequency data do not represent the size composition of the exploited size range of the stock or if length frequencies resulting from the interplay of growth and mortality are masked by strong recruitment pulses.

Scientific information on the biodiversity of marine caves in the eastern Mediterranean is limited, especially when considering the extensively studied caves of the north-western and central Mediterranean. Aiming to enhance current knowledge regarding cave communities, this study represents a first assessment of the marine cave biota of the eastern Mediterranean, as this has been defined by the European Union’s Marine Strategy Framework Directive (MSFD). Information retrieved from an extensive overview of relevant scientific documents was combined with original data recorded from 23 marine caves of the north-eastern Mediterranean. Our results report a total of 520 taxa recorded in eastern Mediterranean marine caves to date, the majority of which are sponges, polychaetes, rhodophytes, bivalves, fishes, and gastropods. These include several protected, endemic, and alien species. However, not all taxonomic groups have been equally studied among different areas and future studies are expected to raise the number of endemic and alien species. The overall observed trend is that the reported species number is generally related to sampling effort and scientific expertise. The most well-studied marine cave communities of the eastern Mediterranean are those of the Aegean Sea (especially its northern sector), which presented the highest number of species, followed by those of the Levantine. Furthermore, our research in Aegean caves revealed numerous new records for the marine cave fauna of the eastern basin, while several species are reported for the first time in the marine cave habitat. The critical need for further scientific research, monitoring, and conservation of this unique ecosystem was highlighted by (i) the presence of certain species endemic to the eastern Mediterranean coupled with a high proportion of alien species, especially in the Levantine, and (ii) the marine cave habitat availability in isolated insular areas of the eastern Mediterranean.

We estimated the current level of knowledge concerning several biological characteristics of the Mediterranean marine fishes by carrying out a gap analysis based on information extracted from the literature, aiming to identify research trends and future needs in the field of Mediterranean fish biology that can be used in stock assessments, ecosystem modeling and fisheries management. Based on the datasets that emerged from the literature review, there is no information on any biological characteristic for 43% (n = 310) of the Mediterranean fish species, whereas for an additional 15% (n = 109) of them there is information about just one characteristic. The gap between current and desired knowledge (defined here as having information on most biological characteristics for at least half of the Mediterranean marine fishes) is smaller in length-weight relationships, which have been studied for 43% of the species, followed by spawning (39%), diet (29%), growth (25%), maturity (24%), lifespan (19%) and fecundity (17%). The gap is larger in natural mortality for which information is very scarce (8%). European hake (Merluccius merluccius), red mullet (Mullus barbatus), annular seabream (Diplodus annularis), common pandora (Pagellus erythrinus), European anchovy (Engraulis encrasicolus), European pilchard (Sardina pilchardus) and bogue (Boops boops) were the most studied species, while sharks and rays were among the least studied ones. Only 25 species were fully studied, i.e. there was available information on all their biological characteristics. The knowledge gaps per characteristic varied among the western, central and eastern Mediterranean subregions. The number of available records per species was positively related to total landings, while no relationship emerged with its maximum reported length, trophic level and commercial value. Future research priorities that should be focused on less studied species (e.g. sharks and rays) and mortality/fecundity instead of length-weight relationships, as well as the economy of scientific sampling (using the entire catch to acquire data on as many biological characteristics as possible) are discussed.

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.