Aboveground biomass allocation and additive allometric models of fifteen tree species in northeast China based on improved investigation methods
文献类型：期刊论文
| 作者 | Xu, Zhenzhao2; Du, Wenxian3; Zhou, Guang2,4; Qin, Lihou5; Meng, Shengwang6; Yu, Jian1; Sun, Zhen2; SiQing, Bilige2,7; Liu, Qijing2 |
| 刊名 | FOREST ECOLOGY AND MANAGEMENT
 |
| 出版日期 | 2022-02-01 |
| 卷号 | 505页码:19 |
| 关键词 | Additive allometric equations Aboveground biomass Biomass allocation Temperate forest Non-destructive Wind fallen trees |
| ISSN号 | 0378-1127 |
| DOI | 10.1016/j.foreco.2021.119918 |
| 通讯作者 | Liu, Qijing(wdmtg666@sina.com) |
| 英文摘要 | Tree allometric models are crucial to accurate forest biomass assessment, but the acquisition of biomass data is one of the most important limitations in establishing biomass models. This study aims to propose an improved investigation method to quickly and practically obtain biomass data, establish species-specific allometric equations and evaluate the biomass allocation pattern for various tree species in a broad-leaved Korean pine forest in northeast China. Based on the non-destructive measurement and sampling of 390 trees from 15 tree species, partial and aggregate biomass models with DBH and DBH2 x H as predictors were established. Results indicated that all allometric models could effectively estimate the dependent variables, and almost no antilog correction was required. Specifically, for the stem and bark components of most tree species, using the DBH2 x H predictor improved the prediction performance of the model. However, DBH was the best predictor for branch and leaf biomass models. The biomass allocation of trees was affected by tree diameter. Small-diameter trees (DBH < 8) had more leaf investment than other size trees, medium-diameter trees (8 < DBH 20) increased the allocation of resources to stems, and large-diameter trees (DBH 20) paid more attention to the growth and accumulation of branches. Our allometric model and biomass allocation research based on the non-destructive measurement and sampling will help improve the accuracy of biomass assessment in broad-leaved Korean pine forests and advance our understanding of woody plant development and survival strategies. |
| WOS关键词 | CARBON STOCKS ; FOREST BIOMASS ; LIVE BIOMASS ; EQUATIONS ; GROWTH ; PRODUCTIVITY ; METAANALYSIS ; EMISSIONS ; NITROGEN ; LIGHT |
| 资助项目 | National Natural Science Foundation of China (NSFC)[31670436] |
| WOS研究方向 | Forestry |
| 语种 | 英语 |
| WOS记录号 | WOS:000745188800010 |
| 出版者 | ELSEVIER |
| 资助机构 | National Natural Science Foundation of China (NSFC) |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/169887]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Liu, Qijing |
| 作者单位 | 1.Jiangsu Vocat Coll Agr & Forestry, Sch Landscape Architecture, Jurong 212400, Peoples R China 2.Beijing Forestry Univ, Coll Forestry, Beijing 100083, Peoples R China 3.Zunyi Acad Forestry, Zunyi 563001, Guizhou, Peoples R China 4.Jiangxi Acad Forestry, Nanchang 330032, Jiangxi, Peoples R China 5.Natl Forestry & Grassland Adm, Acad Inventory & Planning, Beijing 100714, Peoples R China 6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Qianyanzhou Ecol Res Stn, Beijing 100101, Peoples R China 7.Ordos Forestry & Grassland Dev Ctr, Ordos 017000, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xu, Zhenzhao,Du, Wenxian,Zhou, Guang,et al. Aboveground biomass allocation and additive allometric models of fifteen tree species in northeast China based on improved investigation methods[J]. FOREST ECOLOGY AND MANAGEMENT,2022,505:19. |
| APA | Xu, Zhenzhao.,Du, Wenxian.,Zhou, Guang.,Qin, Lihou.,Meng, Shengwang.,...&Liu, Qijing.(2022).Aboveground biomass allocation and additive allometric models of fifteen tree species in northeast China based on improved investigation methods.FOREST ECOLOGY AND MANAGEMENT,505,19. |
| MLA | Xu, Zhenzhao,et al."Aboveground biomass allocation and additive allometric models of fifteen tree species in northeast China based on improved investigation methods".FOREST ECOLOGY AND MANAGEMENT 505(2022):19. |
入库方式： OAI收割
来源：地理科学与资源研究所
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