Understanding the environmental and disturbance determinants of tree species dominance and community composition in an ecosystem, is important for informing management and conservation decisions, through maintaining or improving the existing forest composition and structure. This study was carried out to quantify the relationship between forest tree composition structure and environmental and disturbance gradients, in a tropical sub-montane forest of Eastern Usambara. Vegetation, environmental, and anthropogenic disturbance data for 58 plots across Amani and Nilo nature forest reserves were obtained. Agglomerative hierarchical cluster analysis and canonical correspondence analysis (CCA) were used to identify plant communities and analyze the influence of environmental variables and anthropogenic disturbances on tree species and community composition respectively. Four communities were identified and CCA results showed that the variation was significantly related to elevation, pH, Annual mean temperature, temperature seasonality, phosphorus nutrients and pressures from adjacent villages and roads. Likewise, environmental factors (climate, soil and topography) explained the most variation (14.5%) of tree and community composition in relation to disturbance pressure (2.5%). The large and significant variation in tree species and community patterns explained by environmental factors suggests a need for site-specific assessment of environmental properties for biodiversity conservation plans. Similarly, the intensification of human activities and associated impacts on natural environment should be minimized to maintain forest species composition patterns and communities. The findings are useful in guiding in policy interventions that focus on minimizing human disturbances in the forests and could aid in preserving and restoring the functional organization and tree species composition of the sub-tropical montane forests.
Copyright: © 2023 Lolila et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.