Akademik Veri Yönetim Sistemi
AACL Bioflux, cilt.19, sa.2, ss.882-898, 2026 (Scopus)
Aquaculture systems exhibit inherently complex, multiscale, and nonlinear dynamics, where biological responses are often non-proportional to environmental drivers and governed by emergent interactions across spatial and temporal scales. In this context, a unified multiscale analytical framework integrating fractal geometry, entropy theory, and fractional dynamics provides a coherent methodological basis for the characterization, modeling, and interpretation of aquaculture systems beyond the limits of classical linear approaches. This integrative review synthesizes and reinterprets recent advances in fractal dimension (FD) analysis, entropy-based metrics, multifractal formalism, and fractional-order modeling within a unified conceptual structure applied to aquaculture. The analyzed domains include fish behaviour and welfare assessment, pollution monitoring, growth dynamics, structural pathology, species identification, and habitat complexity characterization. Within this framework, fractal geometry quantifies scale-invariant structural heterogeneity and morphological irregularity in biological and environmental data, while entropy-based measures capture uncertainty, predictability, and temporal evolution in behavioural and ecological time series. Fractional dynamics introduce non-local memory effects and long-range temporal correlations, enabling a more realistic representation of non-Markovian processes and nonlinear growth behaviour. The coupling of these components reveals subtle system transitions associated with stress responses, behavioural adaptation, and environmental perturbations, thereby providing robust early-warning indicators for precision aquaculture and intelligent monitoring systems. Multifractal analysis further extends this capability by resolving heterogeneity across multiple scaling regimes, while fractional models enhance predictive accuracy and system stability characterization. The reviewed approaches demonstrate that the proposed framework enables automated monitoring, objective diagnostic inference, and detection of emergent behaviours at both individual and collective levels. At the ecosystem scale, fractal-based quantification of habitat complexity provides deeper insight into spatial organization and ecological functionality. Despite these advances, challenges remain related to data quality, cross-scale consistency, parameter identifiability, and the lack of standardized benchmarks for nonlinear biological signals. Overall, the proposed fractal-entropy-fractional framework establishes a scale-aware unified paradigm that bridges biological complexity and computational modeling, advancing precision aquaculture and enhancing the capacity for monitoring, prediction, and system-level management of aquatic environments.