DOI: 10.1016/j.foreco.2017.09.018
Scopus记录号: 2-s2.0-85029501209
论文题名: Model selection changes the spatial heterogeneity and total potential carbon in a tropical dry forest
作者: Corona-Núñez R.O. ; Mendoza-Ponce A. ; López-Martínez R.
刊名: Forest Ecology and Management
ISSN: 0378-1127
出版年: 2017
卷: 405 起始页码: 69
结束页码: 80
语种: 英语
英文关键词: Above ground biomass
; Mexico
; Potential carbon stocks
; Reconstruction
; Tropical dry forest
英文摘要: Understanding how aboveground biomass (AGB) is spatially distributed in the landscape and what factors are involved is critical to identify the ecological constraints limiting the magnitude and the allocation of carbon (C) stocks. Yet these factors remain poorly quantified for much of the world. The aim of this study is to identify the factors that influence the reconstruction of potential AGB and its spatial heterogeneity under current climate. A range of statistical approaches is used here to reconstruct the spatial distribution of AGB found in a tropical dry forest in Mexico. This is one of the first studies to directly quantify the predictive performance of various techniques within a common framework applied to AGB estimates from field observations and biophysical variables. The results suggest that general linear model (GLM) and the general additive model (GAM) performed similarly and outperformed other more complex approaches, such as automated neural networks, generalized linear mixed models via penalized quasi-likelihood, MaxEnt and random forest. GLM and GAM approaches also showed good performance in comparison to independent field observations over different spatial resolutions. MaxEnt performed poorly against independent validation data. The GLM, GAM, neural networks and regression tree models returned comparable mean AGB, suggesting that the potential AGB in the studied area is ∼132 Mg ha−1. The biomass spatial distribution is represented differently by the different models. Neural networks and regression tree approaches tend to cluster similar AGB estimates with a large range of the spatial autocorrelation, while the GLM is capable of reproducing the spatial distribution of the biomass. © 2017 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/64068
Appears in Collections: 影响、适应和脆弱性
There are no files associated with this item.
作者单位: Procesos y Sistemas de Información en Geomática, SA de CV (PSIG), Calle 5, Viveros de Petén 18, Col. Viveros del Valle, Tlalnepantla, Mexico; International Institute for Applied Systems Analysis (IIASA) – Schlossplatz, Laxenburg, Austria
Recommended Citation:
Corona-Núñez R.O.,Mendoza-Ponce A.,López-Martínez R.. Model selection changes the spatial heterogeneity and total potential carbon in a tropical dry forest[J]. Forest Ecology and Management,2017-01-01,405