Assessment of Tree Growth Competition Indices for Biodiversity Conservation in IITA Forest Ibadan, Nigeria

Abstract

Forest growth assessment is a key tool for sustainable forest management. Understanding relative contributions of competition in the growth of the forest is of great important which determines the forest structure and also gives an insight to various influences of tree response to climate. There is dearth of information on forest growth using Competition Indices (CI). Hence, this study assessed CI effects on stand growth in International Institute of Tropical Agriculture (IITA) Forest, Ibadan, Nigeria,Nigeria towards improving the forest health status and biodiversity conservation. Data were collected from the forest using four systematic line transect (270 m each) at 200 m apart for plot demarcation. Sixteen sample plots of 25 m × 25 m were alternately laid to collect data. All trees with diameter at breast height (DBH) ≥ 10 cm were estimated. Characterizing the joint influence of tree size, climate and competition in each plot, overtopped trees were considered subject trees and 10 m search radius was used in identification of competitor’s tree for distance dependent (DD). Measurement of influence of neighbouring trees for distance independent (DI) was based on plot-centred. Eight Competition Indices were assessed (CI1-CI8). Best DD and DI were adapted each into Basal Area Increment model (BAI) before and after adding competition measures. Best model was selected using Root Mean Square Error (RMSE), Coefficient of Determination (R2), Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Data were analysed using descriptive statistic and regression at α0.05. The stand comprises 389 stem ha−1. The diameter at breast height (DBH), tree total height (THt), numbers of tree per hectares (N/ha) and volume (V) ranged from 25.12 ± 1.023 cm, 18.548 ± 0.324 m, 442 and 1.035 ± 0.136m3, respectively. The computational analysis shows that basal area increment (BAI) model is a function of neighbourhood interactions and the best spatial indices were better growth predictors than the best non-spatial indices. The best CI growth model was: BAI = exp (−3.769 + 0.026DBH + 0.012C6) (RMSE = 0.064, AIC = −774.031, BIC = −759.324 and R2 = 0.912). This implied that DD CI predicted the growth predictability well compared to DI indices.