Modelling tree volume for a tropical rainforest in Okomu National Park, Edo State, Nigeria

Volume models for the long-term management of Okomu National Park in Nigeria are not available. The main challenge in assessing forest resources is the lack of accurate, species-specific baseline data and updated information on volume models, growth rates, and disturbances. This complicates the development of effective management plans. This study addresses this by modelling tree volume using temporary sample plots laid out using a systematic line transect method Data was collected from 16 40 m x 50 m plots using a Spiegel relascope. DBH, top, middle, and base diameters, and overall height were measured for trees <= 10 cm DBH. Newton's formula calculated volume of each tree, and per hectare estimates generated. The results showed an average of 132 trees per hectare. Population densities of individual species ranged from 1-11/ha, indicating a low density. Strombosia pustulata was the most abundant species. For coefficients that form the basis for species grouping, species-specific volume equations were developed and grouped into three clusters. Regression equations were fitted and selected based on specific statistical metrics. The volume models showed that generalized (Vi=b0+b1(Di2Hi)+epsilon i)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$({V}_{\text{i}}={b}_{0}+{b}_{1}({D}_{\text{i}}<^>{2}{\text{H}}_{\text{i}})+{\varepsilon }_{i})$$\end{document} functions, based on the statistical metrics, performed more effectively. The generalized functions exhibited superior performance, evidenced by the uniform residual plot distribution for DBH2H, implying consistent experimental error and adherence to regression assumptions. A t-test at 95% confidence showed that the discrepancy between predicted and actual values was insignificant. This study indicates that the prediction models provide effective management tools for climate mitigation and determining carbon sequestration by a tropical forest.