Unlocking flow–habitat relationships in mountain rivers of Epirus, Greece using object detection and hydrodynamic simulation
Year: 2025
Type: article
Abstract: Human activities impact aquatic ecosystems by altering abiotic and biotic factors, which in turn affect habitat structure and biodiversity. Environmental flows, or the necessary water flow levels to sustain ecosystems, influence fish habitats, with flow variations affecting fish distribution, migration, and behavior. This study integrates machine learning (ML) algorithms, specifically the Faster Region-Based Convolutional Neural Network (Faster R-CNN) and the You Only Look Once (YOLO) model, with ecohydrodynamic modeling to assess fish microhabitat suitability under varying flow conditions. Microhabitat characteristics of individual West balkan trout ( Salmo farioides ) were studied in transitional zones between pool and riffles across three representative reaches of the Voidomatis River, with 16 locations sampled at a discharge of 4.1 m 3 /s. Hydrodynamic simulations for 18 discharge scenarios indicated that habitat suitability peaked at a discharge of 14.8 m 3 /s, corresponding to optimal depth (~1.0 m) and velocity (~0.6 m/s) conditions. The resulting maximum Weighted Usable Area (WUA) reflected the best combination of hydraulic parameters for sustaining fish habitat. Both Faster R-CNN and YOLO effectively detect fish in visually noisy, turbid environments, achieving F1-scores above 90 % in their best configurations. Notably, Faster R-CNN outperforms YOLO across the primary performance metric (mAP50–95). While underwater video is limited under high-flow conditions and may miss some fish behaviors, combining it with habitat-hydrodynamic modeling provides valuable insights into microhabitat use. By integrating ML-based detection, hydrodynamic models, and habitat suitability curves, this research offers a robust framework for assessing fish habitats and understanding how flow variability may impact habitat quality. These insights are vital for effective conservation and river management strategies, ensuring the sustainability of aquatic ecosystems. • Machine learning and ecohydraulic modeling were combined to assess fish microhabitats. • In their optimal configurations, Faster R-CNN and YOLOv8 detected trout in underwater videos with over 90% precision and recall. • Habitat suitability peaked at 14.8 m 3 /s based on depth and velocity preferences. • Novel framework integrates video-based fish detection with habitat quality modeling.
Language: English
Subfield: Nature and Landscape Conservation
Field: Environmental Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,250
OpenAlex ID: W4415312303