SPATIOTEMPORAL ENVIRONMETAL SCREENING AS A BASIS FOR ECOLOGICAL MONITORING OF ENVIRONMENTAL COMPONENTS

Abstract

Spatiotemporal environmental screening has become an essential component of contemporary environmental monitoring under conditions of increasing anthropogenic pressure, climate change, biodiversity loss, and ecosystem degradation. This review synthesizes current conceptual and methodological approaches to integrated spatiotemporal environmental screening and evaluates the role of biomonitoring, biosurveillance, bioindication, geoinformation technologies, remote sensing, molecular methods, and ecological modeling in comprehensive environmental assessment systems. The study examines the hierarchical organization of environmental screening across multiple levels of ecosystem structure, from molecular and physiological responses to landscape-scale ecological processes. Particular attention is given to the integration of bioindicator systems, environmental DNA (eDNA), metagenomics, GIS technologies, remote sensing platforms, sensor networks, and intelligent analytical approaches for ecosystem diagnostics and ecological forecasting. Methodological aspects of data normalization, multi-criteria assessment, fuzzy logic, spatial interpolation, and the development of integral ecological indices are also analyzed. Based on the reviewed methodological approaches, an integrated spatiotemporal environmental screening framework is proposed. The framework combines data acquisition, ecological screening, analytical integration, ecosystem response assessment, and decision support within an adaptive monitoring cycle. Special emphasis is placed on the development of the Source–State–Risk (SSR) analytical model, which integrates environmental pressures, ecosystem condition, and ecological risks within a unified assessment framework. The reviewed literature suggests that integrating biological indicators, geospatial technologies, remote sensing, molecular approaches, and predictive analytics may strengthen the comprehensiveness and spatial representativeness of environmental monitoring while supporting ecosystem resilience assessment, ecological risk analysis, adaptive environmental management, and evidence-based decision-making