The trophic ecology of animals often results from complex interactions between environmental and biological drivers. Yet, studies that explore trophic ecology across multiple fish species while considering a wide range of environmental variables remain limited. In this study, we analysed diet and niche variation in fish communities across five protected areas in the northern Iberian Peninsula, examining their responses along environmental and biological gradients. Our multiple regression analyses showed that both environmental and biological drivers significantly influence fish feeding behaviour and individual specialisation, but these effects are species‐specific. We propose that the primary mechanisms shaping fish feeding strategies, whether benthic foraging or surface‐drift feeding, are determined by intrinsic factors such as species‐specific feeding behaviours and ontogenetic stage, along with benthic macroinvertebrate density and fish density. Specifically, benthic macroinvertebrate density positively affected individual niche specialisation in brown trout (Salmo trutta) and Pyrenean stone loach (Barbatula quignardi), whereas it negatively impacted the Pyrenean minnow (Phoxinus bigerri). An increase in total fish density, measured as the number of fish per square meter, negatively influenced individual niche specialisation of brown trout. However, brown trout density had a positive effect on individual niche specialisation of Pyrenean stone loach and Pyrenean minnow. The findings have direct implications for conservation and restoration efforts, highlighting the importance of increasing habitat heterogeneity to meet species‐specific habitat needs and promoting benthic macroinvertebrate production to support fish feeding requirements.

Land cover is a key determinant of landscape structure and ecological processes across spatial and temporal scales. In freshwater ecosystems, where riparian and catchment land cover regulate hydrology, nutrient inputs, and habitat quality, most ecological assessments rely on systematically produced land cover maps whose reliability varies with scale and context. Despite their widespread use, the extent to which these products accurately represent landscape patterns and long-term dynamics at ecologically relevant scales remains poorly understood. Here, we evaluate the reliability of systematic land cover maps and assess whether Landsat-based supervised classification (SC) provides more accurate and temporally consistent representations of land cover patterns in draining catchments and riparian areas. We analysed five headwater catchments (< 70 km ² ) within Natura 2000 sites in northern Spain and reconstructed land cover dynamics over four decades (1984–2023) using Random Forest classification implemented in Google Earth Engine. SC results were compared with CORINE Land Cover, SIOSE, and the Spanish National Forest Inventory across three spatial scales, relevant for freshwater ecological processes: catchment, riparian corridor, and reach. SC achieved consistently high accuracy across all periods (mean overall accuracy = 87.8%), outperforming CORINE and SIOSE and matching NFI only for forest classes. Discrepancies between SC and systematic maps increased at finer spatial scales and varied with landscape context, particularly in heterogeneous riparian environments. Systematic maps also showed limited temporal consistency, frequently misrepresenting land cover trends relative to SC. These scale-dependent and context-specific biases can alter estimates of riparian vegetation and landscape composition and their temporal dynamics, potentially leading to misleading assessments of freshwater ecosystem condition. Our findings demonstrate that reliance on coarse-scale land cover products can distort ecological inference, particularly for freshwater ecosystems where ecological condition depends strongly on catchment and riparian landscape structure. Integrating reproducible, remote sensing–based classifications offers a practical pathway to improving the accuracy and interpretability of land cover indicators in landscape ecological research.