Institute at Brown for Environment and Society, Brown University, 85 Waterman Street, Providence, RI, 02912, USA.
Department of Ecology and Evolutionary Biology, Brown University, 80 Waterman Street, Providence, RI, 02912, USA.
Ecol Lett. 2019 Mar;22(3):538-546. doi: 10.1111/ele.13214. Epub 2019 Jan 10.
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.
温度和降水解释了热带森林站点之间地上净初级生产力（ANPP）变化的一半左右，但对其余变化的决定因素了解甚少。在这里，我们检验了这样一个假设，即在其他变量保持不变的情况下，叶片面积的数量及其垂直排列可以预测 ANPP。我们使用低空热带降雨林的机载激光雷达测量值，量化了垂直叶面积分布，并建立了由叶面积和其他森林结构测量值驱动的 ANPP 模型。垂直叶面积分布预测了 38%的地块之间的变异。这个数字是使用总叶面积（忽略垂直排列）的模型的 4.5 倍，是仅使用冠层高度的模型的 2.1 倍。此外，垂直叶面积分布的 ANPP 预测比替代指标的偏差更小。在这个系统中，无法用温度或降水来解释的 ANPP 变化可以通过叶片面积的垂直分布来预测。
