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The invasive fish, Amur sleeper, poses a significant and growing threat to Central European freshwater ecosystems. Despite its rapid spread, the ecological implications of its invasion have been poorly explored. Recent findings confirm its presence in various Estonian freshwater systems, raising concerns about its imminent expansion into larger lakes. To better understand its potential ecological impacts, we explored the isotopic niche of the Amur sleeper in comparison with native fish species co-existing in three Estonian freshwater ecosystems. We employed carbon and nitrogen stable isotope analyses alongside gut content analyses. Our findings show that the Amur sleeper's diet in newly-invaded Estonian water bodies predominantly comprises benthic macroinvertebrates, although it may also include fish, confirming its role as a predator in the local food web. Notably, Amur sleeper populations exhibited clear isotopic niche partitioning in three invaded ecosystems. A logistic regression model, based on stomach content analyses, revealed an ontogenetic diet shift from benthivorous to piscivorous feeding habits from small to large specimens. Amur sleeper exhibits voracious, non-selective feeding habits, which can negatively impact native freshwater communities. The ability to occupy a distinct isotopic niche, with minimal overlap with native fish populations, may reduce interspecific competition, facilitating the spread and establishment of Amur sleeper in newly-invaded habitats. Managing the spread of this invasive species thus becomes even more critical to safeguard the integrity of native aquatic ecosystems. [ABSTRACT FROM AUTHOR]

Lakes play a crucial role in the global biogeochemical cycles through the transport, storage, and transformation of different biogeochemical compounds. Their regulatory service appears to be disproportionately important relative to their small areal extent, necessitating continuous monitoring. This study leverages the potential of optical remote sensing sensors, specifically Sentinel-2 Multispectral Imagery (MSI), to monitor and predict water quality parameters in lakes. Optically active parameters, such as chlorophyll a (CHL), total suspended matter (TSM), and colored dissolved matter (CDOM), can be directly detected using optical remote sensing sensors. However, the challenge lies in detecting non-optically active substances, which lack direct spectral characteristics. The capabilities of artificial intelligence applications can be used in the identification of optically non-active compounds from remote sensing data. This study aims to employ a machine learning approach (combining the Genetic Algorithm (GA) and Extreme Gradient Boost (XGBoost)) and in situ and Sentinel-2 Multispectral Imagery data to construct inversion models for 16 physical and biogeochemical water quality parameters including CHL, CDOM, TSM, total nitrogen (TN), total phosphorus (TP), phosphate (PO4), sulphate, ammonium nitrogen, 5-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), and the biomasses of phytoplankton and cyanobacteria, pH, dissolved oxygen (O2), water temperature (WT) and transparency (SD). GA_XGBoost exhibited strong predictive capabilities and it was able to accurately predict 10 biogeochemical and 2 physical water quality parameters. Additionally, this study provides a practical demonstration of the developed inversion models, illustrating their applicability in estimating various water quality parameters simultaneously across multiple lakes on five different dates. The study highlights the need for ongoing research and refinement of machine learning methodologies in environmental monitoring, particularly in remote sensing applications for water quality assessment. Results emphasize the need for broader temporal scopes, longer-term datasets, and enhanced model selection strategies to improve the robustness and generalizability of these models. In general, the outcomes of this study provide the basis for a better understanding of the role of lakes in the biogeochemical cycle and will allow the formulation of reliable recommendations for various applications used in the studies of ecology, water quality, the climate, and the carbon cycle. [ABSTRACT FROM AUTHOR]

This paper uses long memory and fractional integration techniques to analyze the presence of time trends in the water temperatures of three large European rivers (the Rhine at Lobith, the Danube at Wienna, the Meuse at Eijsden) and two lakes (Saimaa in Finland, and Võrtsjäry in Estonia). Long memory is a feature frequently observed in hydrological data, and it is important to consider it to appropriately estimate the potential trends in the data. The results indicate the existence of significant positive trends in all the five series examined, possibly as a consequence of global warming. Interestingly, once the time trends are taken into consideration, the degree of persistence substantially decreases in all cases and the long memory property in the data disappears. [ABSTRACT FROM AUTHOR]