The aim of this study was to evaluate the population characteristics of spirlin, Alburnoides bipunctatus, in Serbia, since this small fish species is facing a severe decline in its abundance and its natural habitats in Europe. We investigated the spirlin population dynamics, including size, age structure, growth pattern, mortality, and exploitation rate. Additionally, we used the Uniform Manifold Approximation and Projection approach with the Decision Tree algorithm to investigate the influence of different environmental parameters on the population parameters to unveil which factors shape the abundance and distribution of spirlin. The results showed that the highest values of production, abundance, and biomass were estimated in sites with low temperature, optimal pH, and well-oxygenated water, even though we found them in heavily polluted waters with extremely high values of conductivity. Moreover, we observed a pattern of migratory behavior, in which spirlin migrate upstream to sites at a higher altitude in early summer and autumn. Despite the putative vulnerability and high sensitivity of spirlin populations, our results showed that the species was abundant, occurring in altered habitats (due to pollution, climate change, anthropogenic pressure, etc.).

In the inland waters of the Balkans, many brown trout populations have been severely depleted. Therefore, identifying potential threats to their continued survival and ranking populations based on their biological and evolutionary importance enables setting conservation priorities. To assess the sustainability of the brown trout populations in the territory of Serbia (central Balkans), a modification of the ESHIPPO model was performed. The main modification involves incorporating the investigated populations’ genetic structure into the model. Therefore, the new ESHIPPOsalmo model includes an analysis of biological parameters and the impact of multiple factors, including habitat alterations, invasive species, pollution, human population growth, and over-exploitation. In order to investigate individual levels of influence of the model’s analyzed parameters, a combination of supervised and unsupervised machine learning methods was used. The structure of the model is based on general and easily measurable indicators, which enables its application in any salmonid river in the world. By evaluating the parameters of the ESHIPPOsalmo model, we were able to establish that, of the analyzed populations from 46 localities, 37% have a moderate level of sustainability, 43% low, and 20% critically low.