Evaluation of regression methods and competition indices in characterizing height-diameter relationships for temperate and pantropical tree species

Abstract

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Height-diameter relationship models, denoted as H-D models, have important applications in sustainable forest management which include studying the vertical structure of a forest stand, understanding the habitat heterogeneity for wildlife niches, analyzing the growth rate pattern for making decisions regarding silvicultural treatments. Compared to monocultures, characterizing allometric relationships for uneven-aged, mixed-species forests, especially tropical forests, is more challenging and has historically received less attention. Modeling how the competitive interactions between trees of varying sizes and multiple species affects these relationships adds a high degree of complexity. In this study, five regression methods and five distance-independent competition indices were evaluated for temperate and pantropical tree species in different physiographic regions. A total of 163,922 individual tree measurements from the US Department of Agriculture, Forest Inventory and Analysis (FIA) database were used in analyses, which cover Appalachian plateau (AP) and Ridge and Valley (VR) in the southeastern US, as well as Caribbean (CAR) and Pacific (PAC) islands. Results indicated that the generalized additive model (GAM) and the Pearl and Reed model provided more accurate predictions than other regression methods examined. Models with competition indices had a varying level of predictability, while diameter ratio, cumulative distribution function and partitioned stand density index (PSDI) were found to improve the prediction accuracy for AP, VR and CAR. The results of this work provide additional insights on modeling H-D relationships for a variety of species in temperate and pantropical forests.

Keywords

• temperate forest
• Caribbean islands
• Pacific islands
• tree allometry
• generalized additive model
• machine learning