Abstract

Global biodiversity decline necessitates transformative approaches to ecological monitoring that transcend the limitations of conventional field methods. Conservation landscapes face unprecedented pressures from habitat fragmentation, land-use change, and climate stressors, yet traditional monitoring approaches remain constrained by taxonomic expertise shortages, spatial coverage limitations, and temporal resolution gaps. This review synthesizes current advances in artificial intelligence-assisted monitoring and GIS-driven ecological analysis for assessing insect and animal biodiversity within conservation landscapes. We examine how deep learning architectures for species identification—applied to image, acoustic, and sensor data—combined with spatial analytical frameworks for habitat suitability, connectivity modeling, and conservation prioritization, enable integrated biodiversity assessment at previously unattainable scales. Key synthesized insights reveal that AI models trained on multi-source data achieve species identification accuracies exceeding 90% for diverse taxa, while GIS-based prioritization frameworks support systematic conservation planning aligned with global biodiversity targets. Translational applications include automated early warning systems for invasive species, corridor design optimization, and decision-support tools for adaptive ecosystem management. We conclude that AI-GIS integration represents a paradigm shift in biodiversity science, offering scalable, cost-effective solutions for evidence-based conservation in an era of rapid environmental change.