文献类型：期刊文献

英文题名：Modelling tree recruitment in relation to climate and competition in semi-natural Larix-Picea-Abies forests in northeast China

作者：Xiang, Wei[1] Lei, Xiangdong[2] Zhang, Xiongqing[3]

第一作者：Xiang, Wei

通信作者：Lei, XD[1]

机构：[1]Beijing Forestry Univ, Minist Educ, Key Lab Silviculture & Conservat, Sch Forestry, Beijing 100083, Peoples R China;[2]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[3]Chinese Acad Forestry, Res Inst Forestry, Beijing 100091, Peoples R China

年份：2016

卷号：382

起止页码：100-109

外文期刊名：FOREST ECOLOGY AND MANAGEMENT

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000387519700010)】；

基金：This study is supported by the 'Fundamental Research Funds for the Central Universities (No. BLX2014-09)', and 'the National Natural Science Foundation of China (Grant Nos. 31500522, 31270679)'. The authors express their appreciation to Dr. Stephen Hart for editing language and improving the scientific quality of this manuscript. We also sincerely thank two anonymous reviewers for their useful comments on the manuscript.

语种：英文

外文关键词：Tree recruitment; Zero-inflated negative binomial model; Climate; Stand density; Site effect; Natural regeneration

摘要：Tree recruitment models are important tools for predicting forest dynamics, especially for long-term projections of future forest composition. However, as a highly variable, complicated, and largely stochastic process, tree recruitment remains difficult to accurately model. Traditional models neglect climatic variables and are not applicable to forest growth and yield projections under climatic change. In this study, we developed tree recruitment models including site condition, competition, and climate for semi-natural larch-spruce-fir forests under thinning treatments in northeast China. Negative binomial mixture models (zero-inflated and Hurdle models) and Poisson mixture models were compared, with the zero-inflated negative binomial model found to be the best model. Stand density variables (stem density or basal area) were found to be significant for all species categories (larch, conifers, and hardwoods). Additionally, site condition was found to be an important factor affecting recruitment. Four climatic variables, mean annual temperature, annual minimum temperature, growing season minimum temperature, and mean annual temperature divided by mean annual precipitation were found to be directly related to recruitment count. Variance analysis showed significant species-specific thinning effects on tree recruitment. Disentangling different sources of variation in tree recruitment will help further our understanding of the factors driving tree recruitment during climatic change. (C) 2016 Elsevier B.V. All rights reserved.