Forest Research: Open Access
Open Access

Abstract

Bayesian Inference Procedure for Structural Characterization of Nigerian Tropical Forest

Sehinde Akinbiola*, Ayobami Salami and Olusegun Awotoye

The complexity of the tropical forest structure remains a challenge in forest physiognomy assessment, which is a key indicator of forest productivity with implications on the carbon cycle, biodiversity, and ecosystem services. The study assessed structural characteristics, described variability within forest stands, and estimated carbon stocks, using simulation tools and tree modeling with a focus on understanding and quantifying ecological relationships. The study discovered a site-specific wood density difference of 0.07 g/cm³ when compared with the generalized wood density for tropical forests by Food and Agricultural Organisation (FAO). Carbon stocks estimated with this site-specific wood density produced; 174 Mg Ca/ha^-1, 155 Mg Ca/ha^-1 and 78 MgCa/ha^-1 respectively from three sampled Forest Reserves. Furthermore, the result showed that the most productive layers (emergent and canopy layers) of the forest clusters were predominantly hardwood species interspersed with softwood species with very large diameters. The height-diameter model indicated that although the height was a better predictor of the forest structural layer than the diameter, there was no clear margin for grouping species into layers in the region because of interspecies variations, temperature, and anthropogenic activities. The Bayesian Inference procedure provided a reliable approach for carbon stock estimate in the tropics with no legacy inventories.

Published Date: 2022-06-08; Received Date: 2022-05-05