Abstract

Habitat fragmentation represents one of the most pervasive threats to global biodiversity, fundamentally altering species distributions, community structure, and ecosystem functioning in terrestrial landscapes. While the negative consequences of habitat loss are well-documented, the responses of insect and animal communities to fragmentation per se remain highly variable and context-dependent, necessitating integrated assessment frameworks that transcend traditional disciplinary boundaries. This review synthesizes current approaches for integrating computational modeling—including GIS-based habitat analysis, species distribution models, machine learning algorithms, and landscape connectivity simulations—with empirical field-based biodiversity assessments in fragmented ecosystems. We examine how this methodological integration enables more robust characterization of species diversity patterns, population dynamics, and community responses to landscape configuration. Major synthesized insights reveal that computational approaches enhance the spatial and temporal scope of biodiversity assessments while field data remain essential for model validation and parameterization, particularly for invertebrate taxa where detection limitations persist. Translational applications include GIS-informed corridor design, prioritization modeling for protected area networks, and decision-support systems for adaptive management. We conclude that integrated computational-field frameworks are essential for evidence-based conservation planning in human-modified landscapes, with emerging technologies offering unprecedented opportunities for near real-time biodiversity monitoring and predictive scenario analysis.