Scopus记录号: 2-s2.0-85059862148
论文题名: Prediction of forest aboveground net primary production from high-resolution vertical leaf-area profiles
作者: Cushman K.C. ; Kellner J.R.
刊名: Ecology Letters
ISSN: 1461023X
出版年: 2019
卷: 22, 期: 3 起始页码: 538
结束页码: 546
语种: 英语
英文关键词: Canopy structure
; forest production
; leaf area index
; lidar
; tropical forest
英文摘要: Temperature and precipitation explain about half the variation in aboveground net primary production (ANPP) among tropical forest sites, but determinants of remaining variation are poorly understood. Here, we test the hypothesis that the amount of leaf area, and its vertical arrangement, predicts ANPP when other variables are held constant. Using measurements from airborne lidar in a lowland Neotropical rain forest, we quantify vertical leaf-area profiles and develop models of ANPP driven by leaf area and other measurements of forest structure. Vertical leaf-area profiles predict 38% of the variation among plots. This number is 4.5 times greater than models using total leaf area (disregarding vertical arrangement) and 2.1 times greater than models using canopy height alone. Furthermore, ANPP predictions from vertical leaf-area profiles were less biased than alternate metrics. Variation in ANPP not attributable to temperature or precipitation can be predicted by the vertical distribution of leaf area in this system. © 2019 John Wiley & Sons Ltd/CNRS 资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/122723
Appears in Collections: 气候变化事实与影响
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作者单位: Institute at Brown for Environment and Society, Brown University, 85 Waterman Street, Providence, RI 02912, United States; Department of Ecology and Evolutionary Biology, Brown University, 80 Waterman Street, Providence, RI 02912, United States
Recommended Citation:
Cushman K.C.,Kellner J.R.. Prediction of forest aboveground net primary production from high-resolution vertical leaf-area profiles[J]. Ecology Letters,2019-01-01,22(3)