In order to halt the loss of global biodiversity and considering the United Nations Decade for Ocean Conservation Agenda, increasing efforts to improve biomonitoring programs and assessment of ecosystem health are needed. Aquatic environments are among the most complex to monitor, requiring an array of tools to assess their status and to define conservation targets. Although several parameters need to be considered for a comprehensive ecological status assessment, it is important to identify easy-to-apply high-resolution monitoring methods. Shifts in fish composition and abundance are often good indicators of ecosystem health status in relation to anthropogenic activities. However, traditional monitoring methods are strictly related to the habitat under study and cannot be applied universally. This review summarizes the importance of ecological indicators for aquatic environments subjected to anthropogenic stressors, with a particular focus on fish communities and transitional water ecosystems. We describe the main characteristics of both traditional and novel methods for fish monitoring, highlighting their advantages and shortcomings in an attempt to identify simple and reliable ways for a correct evaluation of the dynamics of aquatic ecosystems.

Body size is a key trait of species, populations, and organisms; it relates to abundance, home range, metabolic rates, and stressors. As reported by Damuth’s law, the relationship between body size and abundance is negative with a slope value of −0.75. In aquatic ecosystems, particularly for benthic macroinvertebrates, the slope value deviates from the expected value of −0.75. This variation may depend on abiotic and biotic factors as well as methods that are used to sample benthic macroinvertebrates. “Leaf-bags” and “Surber-net” are the most used methods and they provide different data about the body size structures and abundance of the sampled benthic macroinvertebrates. Here, we used the slope of the body size–abundance relationships to compare the benthic macroinvertebrate assemblages sampled with two sampling methods (Leaf-bags and Surber-net). We found that Leaf-bags sampled a greater number of smaller taxa than Surber-net, which collected individuals of larger sizes. In our study, differences in the benthic macroinvertebrate body size structures collected with these two sampling methods were evident even if the experiment was performed at a limited spatio–temporal scale. Therefore, Leaf-bags and Surber-net should be used as complementary rather than alternative techniques since they sample integrated information from the benthic macroinvertebrate communities.

Urban air pollution from road traffic remains a major public health concern, with its spatial variability at neighbourhood scales strongly influenced by urban morphology. This study investigates how urban form affects the dispersion of traffic-related PM_2.5 in four Italian cities (Lecce, Bari, Milan and Rome) representing diverse climatic and morphological contexts. Seasonal simulations were conducted using the ADMS-Roads dispersion model, integrating detailed road geometries, standardized traffic emissions, and city-level meteorological data for 2019–2021. Urban morphology was characterized at 100 m resolution using building plan area fraction (λ_p), street-canyon aspect ratio and mean building height derived from GIS analyses. Statistical analysis combined random forest regression with partial dependence plots and quantile regression to explore both average and distributional effects. Results reveal a generally negative association between λ_p and PM_2.5 in Lecce, Milan, and Rome, particularly at higher concentration quantiles, suggesting that denser urban fabrics may mitigate extreme pollution episodes. Bari exhibits a weaker and more heterogeneous response, highlighting the influence of local wind regimes and traffic distribution. Wind speed and temperature consistently reduce PM_2.5 across all cities, while street geometry effects are non-linear and season-dependent. These findings demonstrate the importance of considering urban morphology alongside traffic and meteorology when designing strategies to reduce exposure. Importantly, the methodological framework presented here, combining high-resolution dispersion modelling with interpretable machine-learning analyses, is transferable to other urban contexts, providing a robust approach to assess morphology–pollution interactions beyond the studied cities.

This study applies an artificial neural networks (ANN) to simulate monthly tempera-ture and salinity variations at three stations in the Venice lagoon, which have been se-lected to represent different regimes (marine, riverine and intermediate) in terms of relevance of local processes and exchanges with the open sea. Four key predictors are shown to paly a major role: mean offshore sea level, 2-meter air temperature, precipi-tation for the lagoon water temperature, integrated with offshore surface salinity for the lagoon water salinity. The development of the ANN is based on only 4 years of ob-servations, taken irregularly in time with an approximately monthly frequency. De-spite this, the ANN achieves an accurate reproduction of both variables with large R2 and reasonably small, normalized root-mean-square errors at all stations, except for the salinity at the marine station, where the model presents a spurious variability, which is absent in observations. Sensitivity analysis shows that the 2-meter air tem-perature is the dominant predictor for water temperature while sea-level and sea sur-face salinity are the principal drivers of salinity fluctuations, with precipitation exert-ing a relevant role mainly at the riverine station. The ANN has been used for a set of synthetic climate change analyses considering 1.5, 2 and 3°C global warming levels with respect to preindustrial. It is expected an overall warming of lagoon water with maximum increase in summer (up to 6°C in the 3°C global warming level) resulting in an amplification of the annual cycle amplitude. The expected increases of salinity have a strong gradient across the lagoon, are largest at the riverine station, and (analogously to the changes of temperature) amplify the salinity annual cycle amplitude.

This study applies an artificial neural network (ANN) to simulate monthly temperature and salinity variations at three stations in the Venice lagoon, which have been selected to represent different regimes (marine, riverine and intermediate) in terms of relevance of local processes and exchanges with the open sea. Four key predictors are shown to play a major role: mean offshore sea level, 2 m air temperature, precipitation for the lagoon water temperature, integrated with offshore sea surface salinity for the lagoon water salinity. The development of the ANN is based on only 4 years of observations, taken irregularly over time with an approximately monthly frequency. Despite this, the ANN achieves an accurate reproduction of both variables with large R² and reasonably small, normalized root-mean-square errors at all stations, except for the salinity at the marine station, where the model presents a spurious variability, which is absent in observations. Sensitivity analysis shows that the 2 m air temperature is the dominant predictor for water temperature while sea-level and sea surface salinity are the principal predictor of salinity fluctuations, with precipitation exerting a relevant role mainly at the riverine station. The ANN has been used for a set of synthetic climate change analyses considering 1.5, 2 and 3 °C global warming levels with respect to preindustrial levels. An overall warming of lagoon water with maximum increase in summer is expected (up to 6 °C in the 3 °C global warming level), resulting in an amplification of the annual cycle amplitude. The expected increases in salinity have a strong gradient across the lagoon, are largest at the riverine station, and (analogously to the changes in temperature) amplify the salinity annual cycle amplitude.