Forest structure and species composition along disturbance gradients in a Philippine natural biotic area

Understanding how forest structure and species composition respond to anthropogenic pressure is essential for sustainable forest management in tropical protected landscapes. This study assessed variations in forest structure and plant species composition along disturbance gradients in the Lagonoy Natural Biotic area (LNBA), a multiple-use natural biotic area characterized by long-term human occupation. Vegetation data were collected from systematically established plots across upland and riparian forest zones, documenting species identity, abundance, and community composition. Anthropogenic pressure gradients were represented using indirect but robust proxies, including spatial accessibility, altitude, proximity to settlements and markets, forest resource dependence, and length of community residency. Results showed pronounced compositional and structural differentiation across pressure gradients. Forest areas characterized by higher accessibility and stronger livelihood dependence exhibited lower species diversity, reduced community heterogeneity, and a greater dominance of disturbance-tolerant and early-successional taxa. Multivariate ordination analyses revealed clear separation of plant communities along anthropogenic pressure gradients, indicating consistent ecosystem responses to cumulative human use. Cluster analysis further identified distinct forest condition classes corresponding to low-, moderate-, and high-pressure environments. Indicator species analysis demonstrated that late-successional and native species were strongly associated with less disturbed forest segments, whereas generalist and pioneer species dominated highly accessible areas. These findings indicate that anthropogenic pressure drives measurable simplification of forest structure and reorganization of species composition within protected landscapes. The study demonstrates the utility of integrating vegetation data with socio-spatial disturbance proxies to detect forest degradation patterns in data-limited tropical contexts. This approach provides a practical and scalable framework for informing adaptive forest management and conservation planning in human-inhabited tropical forests.