Josip Ćurko

The stone crayfish, Austropotamobius torrentium, is a native European freshwater species sensitive to habitat alterations such as stream channelization and urban development, putting its populations at risk. This study aims to assess how habitat alteration and subsequent water quality changes affect the stone crayfish population in the Kustošak Stream (Croatia) through three selected sites under different levels of anthropogenic disturbance: (1) undisturbed; (2) recently modified, and (3) long-time modified sites. At each site, crayfish were captured, measured, and marked to estimate population size, structure, and crayfish condition. Additionally, we examined whether water quality (18 measured physicochemical parameters) affects relative crayfish abundance. We also used mitochondrial sequencing and microsatellite genotyping to assess species genetic diversity and population connectivity and to compare results among the sites. The results showed that habitat alteration caused an overall reduction in population abundance and changes in size structure; however, it had not yet resulted in detectable genetic differences. Partial least squares regression showed that crayfish abundance was affected by the physicochemical parameters of water, including, among others, oxygen, calcium ions, nitrates, pH, and water temperature. We discuss our findings in the context of the effects of anthropogenic disturbance on the viability and persistence of this EU priority species.

Bromate formation is a complex process that depends on the properties of water and the ozone used. Due to fluctuations in quality, surface waters require major adjustments to the treatment process. In this work, we investigated how the time of year, ozone dose and duration, and ammonium affect bromides, bromates, absorbance at 254 nm (UV254), near-infrared (NIR) spectra, and fluorescent components (humic-like and tyrosine-like) during surface water ozonation. Linear and non-linear models were used to determine and predict the relationships between input and output variables. Season, ozonation dose and time were correlated with the output variables, while ammonium affected only bromates. All coefficients of determination (R2) for the multiple linear regression models were >0.64, while R2 for the piecewise linear regression models was >0.89. The season had no effect on bromate formation in either model, while ammonium only affected bromides and bromates. Three input variables influenced UV254 in both models. The artificial neural network (ANN) model with the season, ozonation dose and time, ammonium, and NIR spectra was an effective way to describe water ozonation results. The multilayer perception neural network 14-14-5 had the lowest errors and was the best ANN model with R2 values for training, testing, and validation of 0.9916, 0.9826, and 0.9732, respectively.