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Tree mortality is a major control over tropical forest carbon stocks globally but the strength of associations between abiotic drivers and tree mortality within forested landscapes is poorly understood. Here, we used repeat drone photogrammetry across 1500 ha of forest in Central Panama over 5 years to quantify spatial variation in canopy disturbance rates and its predictors. We identified 11,153 canopy disturbances greater than 25 m 2 in area, including treefalls, large branchfalls and standing dead trees, affecting 1.9% of area per year. Soil type, forest age and topography explained up to 46%-67% of disturbance rate variation at spatial grains of 58-64 ha, with higher rates in older forests, steeper slopes and local depressions. Furthermore, disturbance rates predicted the proportion of low canopy area across the landscape, and mean canopy height in old growth forests. Thus abiotic factors drive variation in disturbance rates and thereby forest structure at landscape scales., (© 2022 John Wiley & Sons Ltd. This article has been contributed to by US Government employees and their work is in the public domain in the USA.)

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.)