Abundance and distribution of commercial marine resources are influenced by environmental variables, which together with fishery patterns may also influence their catchability. However, Catch Per Unit Effort (CPUE) can be standardized in order to remove most of the variability not directly attributable to fish abundance. In the present study, Generalized Additive Models (GAMs) were used to investigate the effect of some environmental and fishery covariates on the spatial distribution and abundance of the Norway lobsterNephrops norvegicuswithin the Pomo/Jabuka Pits (Central Adriatic Sea) and to include those that resulted significant in a standardization process.N.norvegicusis a commercially important demersal crustacean, altering its catchability over the 24-h cycle and seasons according to its burrowing behavior. A historically exploited fishing ground for this species, since 2015 subject to specific fisheries management measures, is represented by the meso-Adriatic depressions, which are also characterized by particular oceanographic conditions. Both the species behaviour and the features of this study area influence the dynamics of the population offering a challenging case study for a standardization modelling approach. Environmental and catch data were obtained during scientific trawl surveys properly designed to catchN.norvegicus, thus improving the quality of the model input data. Standardization of CPUE from 2 surveys from 2012 to 2019 was conducted building two GAMs for both biomass and density indices. Bathymetry, fishing pressure, dissolved oxygen and salinity proved to be significant drivers influencing catch distribution. After cross validations, the tuned models were then used to predict new indices for the study area and the two survey series by means of informed spatial grids, composed by constant surface cells, to each of which are associated average values of environmental parameters and specific levels of fishing pressure, depending on the management measures in place. The predictions can be used to better describe the structure and the spatio-temporal distribution of the population providing valuable information to evaluate the status of such an important marine resource.

A Before–Intermediate–After Multiple Sites (BIAMS) analysis, namely a modified version of the Before–After–Control–Impact (BACI) approach, was used to evaluate the possible effects of fishery management measures implemented in the Pomo/Jabuka Pits area, a historically highly exploited ground for Italian and Croatian fisheries, whose impact may have contributed over the years to the modification of the ecosystem. Since 2015, the area was subject to fishing regulations changing the type of restrictions over time and space, until the definitive establishment in 2018 of a Fishery Restricted Area. These changes in the regulatory regime result in complex signals to be interpreted. The analysis was carried out on abundance indices (i.e., kg/km² and N/km²) of five commercially or ecologically relevant species, obtained in the period 2012–2019 from two annual trawl surveys. BIAMS was based on the selection of a Closure factor, declined in three levels (i.e., BEFORE/INTERMEDIATE/AFTER) and accounting for regulation changes in time, and on three adjacent strata (i.e., “A”, “B”, and “ext ITA”) a posteriori determined according to the latest regulations. BIAMS allowed us to identify early effects (i.e., changes in abundances), overcoming the unavailability of a proper independent control site; furthermore, the selection of adjacent strata allowed the inference of possible interactions among them.

Stock–recruitment (S–R) relationships are critical for assessing stock productivity and guiding fisheries management. In the Mediterranean Sea, the estimation of accurate S–R relationships is challenging due to limited data. The Adriatic Sea, a highly productive but overfished Mediterranean region, is particularly vulnerable to climate change. Over the past two decades, significant declines in long-lived demersal (e.g., European hake) stocks in the Adriatic and Western Ionian seas have prompted targeted management measures. However, recent positive trends in medium- and short-lived stocks (e.g., shrimps) suggest that reduced coastal fishing pressure and favorable environmental conditions are driving partial stock recovery. The relative influence of these factors, and whether favorable environmental impacts will persist, remains uncertain. This study is the first to estimate environmentally mediated S–R relationships for key stocks in the investigated region. Using the BEMTOOL simulation model, we assess the biological and economic impacts of various management and climate scenarios. Results highlight the importance of integrating climate factors into mixed fisheries management to ensure the robustness of measures preventing stocks from falling below safe biological limits.

Uncertainty in the effects of environmental changes on stock productivity remains a central problem and research priority for fisheries management. Management strategy evaluation (MSE) is increasingly advocated and used to simulation-test harvest strategies against environmental effects on productivity; however, the complexity of the data, models, and decisions entailed remain significant challenges for developers and practitioners. Moreover, practical guidance for conditioning and projecting environment–productivity relationships within MSE is still sparse. Based on the experiences of a diverse group of modelling experts, a generic workflow is devised and presented for developing and subsequently using environment–productivity relationships within single-species, nonspatial operating models (OMs) in MSE. In the first part, key data types, modelling objectives, and approaches for estimating and validating these relationships are described. Importantly, environment–productivity relationships should be based on clear hypotheses (potentially with stakeholder input), have covariates that match temporal and spatial scale of productivity processes, and have unexplained variability (i.e. process error) carefully scrutinized for patterns (e.g. nonstationarity) and projected accordingly. The second part of the paper identifies and describes methods for projecting environmental effects on productivity using climate models and without (i.e. altering productivity dynamics directly). Key tactical considerations are presented for designing OMs (e.g. include an OM with no climate forcing to benchmark results from OMs with forcing, devise edge cases for robustness) and evaluating management performance (e.g. define appropriate projection periods, consider dynamic reference points to calculate metrics). Despite the common use of single-species OMs, it is important to consider multispecies interactions, and fine-scale population and fishery dynamics, to better understand potential environmental effects on a target stock’s productivity. In summarizing key decision points and range of methods applied to date, this work serves to support informed choices and shared vocabulary for developing and conducting these MSEs, rather than a prescriptive checklist.

Understanding how environmental variability shapes growth in marine fish is essential for predicting population dynamics under climate change. This study investigates spatial differences in the growth of red mullet (Mullus barbatus) across three neighbouring regions of the South Adriatic (GSA 18) and North-Western Ionian (GSA 19) Seas (central Mediterranean). Otolith increments widths were analysed to quantify life growth, while environmental drivers were assessed using diverse oceanographic variables, including sea surface temperature, bottom temperature, oxygen concentration, salinity, and chlorophyll concentration. Generalized additive models were fitted to identify the environmental and spatial factors influencing growth under both contemporary and projected climate conditions. Growth exhibited pronounced spatial heterogeneity, with distinct localized hotspots in both the eastern and western portions of GSA 18, reflecting fine-scale hydrographic variability. Temperature emerged as the primary environmental driver, with optimal growth estimated around 18.5°C. Forecasts under representative concentration pathway climate scenarios revealed a non-linear response: a mid-century enhancement in growth followed by a decline by 2099, linked to warming beyond the thermal optimum. These results underscore the significance of local environmental conditions in shaping growth trajectories and highlight the potential vulnerability of red mullet populations to future climate change. Otolith chronologies, combined with spatially explicit modelling, offer an effective framework for detecting environmental impacts on growth. Accounting for spatial and climate-related variability in growth may enhance the accuracy of current assessment models and support the development of climate-ready fisheries management strategies in the Mediterranean.