Abstract

Global biodiversity decline represents one of the most pressing environmental challenges of the Anthropocene, with insects and animals experiencing unprecedented population losses and extinction rates. Conventional monitoring approaches, while essential, remain constrained by taxonomic expertise shortages, spatial coverage limitations, and temporal resolution gaps that impede evidence-based conservation decision-making. This review synthesizes emerging interdisciplinary approaches that integrate computational modeling, artificial intelligence, GIS-based spatial analysis, and field-based ecological monitoring for quantifying and conserving insect and animal diversity. We examine how species distribution models, machine learning algorithms for automated species identification, landscape connectivity simulations, and ecological network analysis combine with traditional field methods to enable biodiversity assessment at previously unattainable scales. Key synthesized insights reveal that integrated frameworks achieve higher detection probabilities for rare and cryptic species, enable prediction of biodiversity responses to environmental change, and support spatially explicit conservation prioritization aligned with global biodiversity targets. Translational applications include model-informed corridor design, adaptive management decision-support systems, and restoration monitoring frameworks that bridge the gap between biodiversity science and conservation practice. We conclude that interdisciplinary computational-field integration represents a transformative paradigm for biodiversity science, offering scalable solutions for quantifying and conserving insect and animal diversity in an era of rapid environmental transformation.