Comparison of Global and Local Poisson Models for the Number of Recruitment Trees in Natural Forests

Abstract

The recruitment of natural forests is the key to stand growth and regeneration. Constructing theoretical models for recruitment trees is crucial for accurately quantifying stand growth and yield. To this end, the objective was to use relevant Poisson models to study the spatial relationships between the number of recruitment trees (NRTs) and driving factors, such as topography, stand, and remote sensing factors. Taking the Northeast China Liangshui Nature Reserve as the study area and 127 ecological public welfare forest plots based on grid sampling as study data, we constructed global models (Poisson regression (PR) and linear mixed Poisson regression (LMPR)) and local models (geographically weighted Poisson regression (GWPR) and semiparametric GWPR (SGWPR)) to simulate the NRTs. The evaluation indicators were calculated to analyse four model fittings, predictive abilities, and spatial effects of residual analysis. The results show that local (GWPR and SGWPR) models have great advantages in all aspects. Compared with the GWPR model, the SGWPR model exhibited improved performance by considering whether coefficients have geographical variability for all independent variables. Therefore, the SGWPR model more accurately depicts the spatial distributions of NRTs than the other models.