Slenderness coefficient models for tree species in Omo biosphere reserve, South-western Nigeria

A. Oladoye, P. Ige, Q. Onilude, Z. T. Animashaun
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引用次数: 5

Abstract

This study was carried out to aid the prediction of tree slenderness coefficient using non-linear regression models for tree species in Omo Biosphere Reserve, Southwestern Nigeria. Systematic line transect design was adopted for the study. Three transects were laid with four plots on each transect at alternate positions which made a total of 12 sample plots (50 m × 50 m) in the study area. Diameter at breast height (DBH), diameter at the top, diameter at the middle and diameter at the base as well as total height and merchantable height of all trees were measured. Descriptive statistics, Pearson’s correlation and regression analysis were adopted for the study. The study showed that about 23.5% of the trees in the study area are susceptible to wind-throw damage. Correlation analysis revealed that DBH is a better predictor of Slenderness coefficient than other tree growth characteristics. Six non-linear models were adopted for the tree slenderness coefficient prediction. The best models were selected based on the highest Adj.R2, lowest AIC and SEE values. Normal logarithmic equation SLC = 30.72 + (-41.21) In(D) was selected as the candidate model for the pooled data. The same candidate model (Natural logarithm) was selected for both the Desplatsia lutea and Strombosia pustulata species with the equation SLC = -0.04 + (-63.82) In(D) and SLC = 22.12 + (-51.40) In(D) respectively while exponential model with equation SLC = 170.94e(-1.93) was selected for Sterculia rhinopetala. These equations were recommended for predicting slenderness coefficient for each of the tree species in Omo Biosphere with apparently valid potentials for enhancing reasonable quantification of the stands’ stability.
尼日利亚西南部Omo生物圈保护区树种长细系数模型
利用非线性回归模型对尼日利亚西南部奥莫生物圈保护区树种的长细细系数进行了预测。采用系统样线设计进行研究。3个样带上,每个样带上交替放置4个样地,共12个样地(50 m × 50 m)。测定各树种胸径、顶径、中径、底径以及总高和可售高。采用描述性统计、Pearson相关和回归分析。研究表明,研究区内约有23.5%的树木易受风投破坏。相关分析表明,胸径比其他生长性状更能预测长细系数。采用6种非线性模型对树木长细系数进行预测。根据最高的Adj.R2、最低的AIC和SEE值选择最佳模型。选择正态对数方程SLC = 30.72 + (-41.21) In(D)作为合并数据的候选模型。对黄斑蝶和褐斑蝶选用相同的候选模型(自然对数),分别为SLC = -0.04 + (-63.82) In(D)和SLC = 22.12 + (-51.40) In(D),对鼻白蝶选用SLC = 170.94e(-1.93)的指数模型。这些方程被推荐用于预测奥莫生物圈各树种的长细系数,对合理量化林分稳定性具有明显的有效潜力。
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