4.8 Article

Liana optical traits increase tropical forest albedo and reduce ecosystem productivity

Journal

GLOBAL CHANGE BIOLOGY
Volume 28, Issue 1, Pages 227-244

Publisher

WILEY
DOI: 10.1111/gcb.15928

Keywords

ecosystem demography model (ED2); forest albedo; forest energy balance; PROSPECT-5; radiative transfer models; structural parasitism; tropical lianas

Funding

  1. H2020 European Research Council [637643]
  2. Fonds Wetenschappelijk Onderzoek [1214720N, G002321N]
  3. NSF [1458021]
  4. NASA
  5. USDA
  6. University of Florida
  7. Direct For Biological Sciences
  8. Div Of Biological Infrastructure [1458021] Funding Source: National Science Foundation

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This study used a meta-analysis approach to gather liana leaf optical spectra and canopy spectra data to investigate the impact of liana traits on the energy balance, light competition, and carbon cycle of tropical forests. The results showed that lianas have specific traits that make them more efficient at light interception, leading to modifications in forest energy balance and reductions in tree and ecosystem productivity while increasing liana productivity. This study provides new evidence on the impact of lianas on forest functioning and suggests implications for large-scale forest biogeochemical cycles.
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 degrees 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.

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