Liana optical traits increase tropical forest albedo and reduce ecosystem productivity

• Published in: Global change biology 2022-01, Vol.28 (1), p.227-244
• Main Authors: Meunier, Félicien, Visser, Marco D., Shiklomanov, Alexey, Dietze, Michael C., Q., J. Antonio Guzmán, Sanchez-Azofeifa, G. Arturo, Deurwaerder, Hannes P. T. De, Moorthy, Sruthi M. Krishna, Schnitzer, Stefan A., Marvin, David C., Longo, Marcos, Liu, Chang, Broadbent, Eben N., Zambrano, Angelica M. Almeyda, Muller-Landau, Helene C., Detto, Matteo, Verbeeck, Hans
• Format: Article
• Language: English
• Subjects: Albedo; Bayes Theorem; Bayesian analysis; Biogeochemical cycle; Biogeochemical cycles; Canopies; Canopy; Carbon Cycle; Competitive advantage; Competitors; Data collection; Earth Resources And Remote Sensing; Ecosystem; ecosystem demography model (ED2); Energy balance; forest albedo; Forest ecosystems; forest energy balance; Forests; Infestation; Interception; Leaves; Lianas; Light; Light interception; Mathematical models; Plant cover; Primary; Primary production; Primary s; Probability theory; Productivity; PROSPECT‐5; Radiation; Radiative transfer; radiative transfer models; Short wave radiation; Soil temperature; Spectra; structural parasitism; Trees; Tropical Climate; Tropical forests; tropical lianas; Vertical orientation
• ISSN: 1354-1013; 1365-2486
• DOI: 10.1111/gcb.15928

Lianas are a key growth form in tropical forests. Their lack of self-supporting tissues and their vertical position on top of the canopy make them strong competitors of resources. A few pioneer studies have shown that liana optical traits differ on average from those of colocated trees. Those trait discrepancies were hypothesized to be responsible for the competitive advantage of lianas over trees. Yet, in the absence of reliable modelling tools, it is impossible to unravel their impact on the forest energy balance, light competition, and on the liana success in Neotropical forests. To bridge this gap, we performed a meta-analysis of the literature to gather all published liana leaf optical spectra, as well as all canopy spectra measured over different levels of liana infestation. We then used a Bayesian data assimilation framework applied to two radiative transfer models (RTMs) covering the leaf and canopy scales to derive tropical tree and liana trait distributions, which finally informed a full dynamic vegetation model. According to the RTMs inversion, lianas grew thinner, more horizontal leaves with lower pigment concentrations. Those traits made the lianas very efficient at light interception and significantly modified the forest energy balance and its carbon cycle. While forest albedo increased by 14% in the shortwave, light availability was reduced in the understorey (−30% of the PAR radiation) and soil temperature decreased by 0.5°C. Those liana-specific traits were also responsible for a significant reduction of tree (−19%) and ecosystem (−7%) gross primary productivity (GPP) while lianas benefited from them (their GPP increased by +27%). This study provides a novel mechanistic explanation to the increase in liana abundance, new evidence of the impact of lianas on forest functioning, and paves the way for the evaluation of the large-scale impacts of lianas on forest biogeochemical cycles.