Search

High-resolution spectroscopy can be used to measure leaf chemical and structural traits. Such leaf traits are often highly correlated to other traits, such as photosynthesis, through the leaf economics spectrum. We measured VNIR (visible-near infrared) leaf reflectance (400–1,075 nm) of sunlit and shaded leaves in ~150 dominant species across ten, 1 ha plots along a 3,300 m elevation gradient in Peru (on 4,284 individual leaves). We used partial least squares (PLS) regression to compare leaf reflectance to chemical traits, such as nitrogen and phosphorus, structural traits, including leaf mass per area (LMA), branch wood density and leaf venation, and “higher-level” traits such as leaf photosynthetic capacity, leaf water repellency, and woody growth rates. Empirical models using leaf reflectance predicted leaf N and LMA (r2 > 30% and %RMSE < 30%), weakly predicted leaf venation, photosynthesis, and branch density (r2 between 10 and 35% and %RMSE between 10% and 65%), and did not predict leaf water repellency or woody growth rates (r2<5%). Prediction of higher-level traits such as photosynthesis and branch density is likely due to these traits correlations with LMA, a trait readily predicted with leaf spectroscopy.

High-resolution spectroscopy can be used to measure leaf chemical and structural traits. Such leaf traits are often highly correlated to other traits, such as photosynthesis, through the leaf economics spectrum. We measured VNIR (visible-near infrared) leaf reflectance (400–1,075 nm) of sunlit and shaded leaves in ~150 dominant species across ten, 1 ha plots along a 3,300 m elevation gradient in Peru (on 4,284 individual leaves). We used partial least squares (PLS) regression to compare leaf reflectance to chemical traits, such as nitrogen and phosphorus, structural traits, including leaf mass per area (LMA), branch wood density and leaf venation, and “higher-level” traits such as leaf photosynthetic capacity, leaf water repellency, and woody growth rates. Empirical models using leaf reflectance predicted leaf N and LMA (r2 > 30% and %RMSE < 30%), weakly predicted leaf venation, photosynthesis, and branch density (r2 between 10 and 35% and %RMSE between 10% and 65%), and did not predict leaf water repellency or woody growth rates (r2<5%). Prediction of higher-level traits such as photosynthesis and branch density is likely due to these traits correlations with LMA, a trait readily predicted with leaf spectroscopy.

We aim to assess net primary productivity (NPP) and carbon cycling in Andean tropical alpine grasslands (puna) and compare it with NPP of tropical montane cloud forests. We ask the following questions: (1) how do NPP and soil respiration of grasslands vary over the seasonal cycle? (2) how do burning and grazing affect puna productivity? (3) if the montane forest expands into the puna, what will be the resulting change in productivity? The study sites are located at the South-eastern Peruvian Andes; one grassland site and the forest sites are in Wayqecha biological station, and another grassland site in Manu National Park. At each grassland site, we selected a burnt and an unburnt area, installed unfenced and fenced transects in each area, and monitored above-ground productivity (NPPAG), below-ground productivity (NPPBG) and soil respiration (Rs) for 2 yr. In the forest, we monitored NPPAG, NPPBG and Rs for 2–4 yr. Grassland NPP varied between 4.6 ± 0.25 (disturbed areas) to 15.3 ± 0.9 Mg C ha-1 yr-1 (undisturbed areas) and cloud forest NPP was between 7.05 ± 0.39 and 8.0 ± 0.47 Mg C ha-1 yr-1, while soil carbon stocks were in the range of 126 ± 22 to 285 ± 31Mg C ha-1. There were no significant differences on NPP between the puna and forest sites. The most undisturbed site had significantly higher NPP than other grassland sites, but no differences were found when relating grazing and fire at other sites. There were lower residence times of above-ground biomass compared to below-ground biomass. There was a strong seasonal signal on grassland NPPAG and NPPBG, with a shift on allocation at the beginning of the austral summer. High elevation tropical grasslands can be as productive as adjacent cloud forests, but have very different carbon cycling and retention properties than cloud forests. S Online supplementary data available from stacks.iop.org/ERL/9/115011/mmedia Keywords: tropical alpine wetlands, above-ground productivity, below-ground productivity, fire, grazing