Data from: Prediction of forest aboveground net primary production from high-resolution vertical leaf-area profiles
Cushman, K. C.; Kellner, James R. (2019), Data from: Prediction of forest aboveground net primary production from high-resolution vertical leaf-area profiles, Dryad, Dataset, https://doi.org/10.5061/dryad.1v72g43
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. Further, 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.
La Selva Biological Station