文献类型：期刊文献

英文题名：A climate-sensitive mixed-effects tree recruitment model for oaks (Quercus spp.) in Hunan Province, south-central China

作者：Wang, Wenwen[1] Wang, Jianjun[2] Meng, Jinghui[1]

第一作者：Wang, Wenwen

通信作者：Meng, JH[1]

机构：[1]Beijing Forestry Univ, Res Ctr Forest Management Engn, Natl Forestry Grassland Adm, Beijing 100083, Peoples R China;[2]Chinese Acad Forestry, Res Inst Forestry Policy & Informat, Beijing 100091, Peoples R China

年份：2023

卷号：528

外文期刊名：FOREST ECOLOGY AND MANAGEMENT

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000896909400001)】；

语种：英文

外文关键词：Tree recruitment; Zero-inflated negative binomial model; Mixed-effects modeling; Mean annual temperature; Oaks

摘要：Tree recruitment is an essential component and plays a fundamental role in forest management decision-making. However, the existing tree recruitment models for oak (Quercus spp.) species have been developed using tradi-tional modeling functions, and have neglected the effects of climate, which do not apply to long-term projections of future forest composition under a climatic change scenario. In this study, we developed tree recruitment models based on the data of 548 permanent sample plots distributed in Hunan Province, south-central China. Five commonly used recruitment approaches (negative binomial model, zero-inflated Poisson, zero-inflated negative binomial model, Hurdle-Poisson model, and Hurdle-negative binomial model) were employed to analyze the data. Then the random effects of the regions were included to develop the nonlinear mixed-effects models and account for spatial and temporal correlations. Among the various candidate predictors tested including stand characteristics, site conditions, and climate, stand basal area (BA), quadratic mean diameter (QMD), basal area of the species (Bai), cosine of the slope combined with the natural logarithm of elevation (CIE), and mean annual temperature (MAT) had significant effects on oak recruitment. The zero-inflated negative binomial model was selected as the optimal model compared to the other four basic models. The performance of the models improved significantly when the random effects were added. These results and knowledge of adaptive management will be useful for developing practical forest management plans.