Machine Learning‐Based Bioindicator Assessment: Driver Forces of Chironomidae Community Structure in the Eastern Black Sea Basin

ABSTRACT Freshwater ecosystems are under increasing anthropogenic pressure, impacting aquatic biodiversity and ecosystem services. This study investigates the intricate relationships between environmental parameters and the diversity of Chironomidae (Diptera) species in 17 streams across the Eastern Black Sea Basin, Türkiye. Utilizing a comprehensive approach involving physicochemical analysis, macroinvertebrate sampling and advanced statistical and machine learning techniques, we identified 28 Chironomidae species from 17 streams seasonally and elucidated the primary drivers of their community structure. Our findings reveal that species identity is the most significant predictor of abundance patterns, underscoring the importance of species‐specific ecological niches. Biological oxygen demand (BOD) and ammonium nitrogen emerged as key environmental factors influencing the distribution of pollution‐tolerant taxa such as Prodiamesa olivacea and Eukiefferiella clypeata , respectively, highlighting their utility as bioindicators. Furthermore, the analysis revealed significant spatial and seasonal variability in physicochemical parameters across the basin, contributing to observed differences in species assemblages and diversity. A novel thermal classification of Chironomidae into eurythermic, stenothermic, and thermophilic guilds provides crucial insights into their differential responses to temperature fluctuations, emphasizing the vulnerability of cold‐water stenotherm species to climate change. This research provides a scientific basis for robust water quality assessment, informing targeted conservation strategies, and emphasizing the need for adaptive management to safeguard freshwater biodiversity in the face of environmental change.