Abstract

Integrating data from different ecosystems is challenging due to the variety of types, unstructured formats, and multiple dimensions involved. These challenges are worsened by differences in schematic, semantic, syntactic, and systemic features. The lack of connections among these systems highlights a knowledge gap, emphasising the need for a framework to explore this overlooked relationship. Gaps in conceptual and contextual understanding, along with new insights into how ecosystems coexist, have prompted us to develop adaptable AI-powered tools and technologies. We examine the integration of AI-driven big data systems, guided by information systems that generate and interpret metadata across various knowledge domains. The spatial features of Big Data influence ecosystems by providing detailed, real-time information on environmental changes, human-environment interactions, and resource management. Big Data and AI-supported multimodal data—including satellite images, sensors, and GPS trackers—enable the analysis of phenomena between human, health, environmental, and economic ecosystems, as well as logistics systems in various locations. As a result, ecosystems can be monitored more effectively, resource use optimised, and ecological governance enhanced. Moreover, understanding various AI-managed data artefacts is crucial for researching the knowledge needed for ecosystem sustainability. AI-enhanced models can leverage complex relationships between ecosystems to illustrate their coexistence and interdependence. Overall, big data offers increased support for conservation and sustainability efforts in ecosystem research.

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