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Abstract Lianas, woody vines acting as structural parasites of trees, have profound effects on the composition and structure of tropical forests, impacting tree growth, mortality, and forest succession. Remote sensing could offer a powerful tool for quantifying the scale of liana infestation, provided the availability of robust detection methods. We analyze the consistency and global geographic specificity of spectral signals—reflectance across wavelengths—from liana‐infested tree crowns and forest stands, examining the underlying mechanisms of these signals. We compiled a uniquely comprehensive database, including leaf reflectance spectra from 5424 leaves, fine‐scale airborne reflectance data from 999 liana‐infested canopies, and coarse‐scale satellite reflectance data covering 775 ha of liana‐infested forest stands. To unravel the mechanisms of the liana spectral signal, we applied mechanistic radiative transfer models across scales, establishing a synthesis of the relative importance of different mechanisms, which we corroborate with field data on liana leaf chemistry and canopy structure. We find a consistent liana spectral signal at canopy and stand scales across globally distributed sites. This signature mainly arises at the canopy level due to direct effects of more horizontal leaf angles, resulting in a larger projected leaf area, and indirect effects from increased light scattering in the near and short‐wave infrared regions, linked to lianas' less costly leaf construction compared with trees on average. The existence of a consistent global spectral signal for lianas suggests that large‐scale quantification of liana infestation is feasible. However, because the traits responsible for the liana canopy‐reflectance signal are not exclusive to lianas, accurate large‐scale detection requires rigorously validated remote sensing methods. Our models highlight challenges in automated detection, such as potential misidentification due to leaf phenology, tree life history, topography, and climate, especially where the scale of liana infestation is less than a single remote sensing pixel. The observed cross‐site patterns also prompt ecological questions about lianas' adaptive similarities in optical traits across environments, indicating possible convergent evolution due to shared constraints on leaf biochemical and structural traits. 

Lianas are structural parasites of trees that cause a reduction in tree growth and an increase in tree mortality. Thereby, lianas negatively impact forest carbon storage as evidenced by liana removal experiments. In this proof-of-concept study, we calibrated the Ecosystem Demography model (ED2) using 3 years of observations of net aboveground biomass (AGB) changes in control and removal plots of a liana removal experiment on Gigante Peninsula, Panama. After calibration, the model could accurately reproduce the observations of net biomass changes, the discrepancies between treatments, as well as the observed components of those changes (mortality, productivity, and growth). Simulations revealed that the long-term total (i.e., above- and belowground) carbon storage was enhanced in liana removal plots (+1.2 kg C m –2 after 3 years, +1.8 kg C m –2 after 10 years, as compared to the control plots). This difference was driven by a sharp increase in biomass of early successional trees and the slow decomposition of liana woody tissues in the removal plots. Moreover, liana removal significantly reduced the simulated heterotrophic respiration (−24%), which resulted in an average increase in net ecosystem productivity (NEP) from 0.009 to 0.075 kg C m –2 yr –1 for 10 years after liana removal. Based on the ED2 model outputs, lianas reduced gross and net primary productivity of trees by 40% and 53%, respectively, mainly through competition for light. Finally, model simulations suggested a profound impact of the liana removal on the soil carbon dynamics: the simulated metabolic litter carbon pool was systematically larger in control plots (+51% on average) as a result of higher mortality rates and faster leaf and root turnover rates. By overcoming the challenge of including lianas and depicting their effect on forest ecosystems, the calibrated version of the liana plant functional type (PFT) as incorporated in ED2 can predict the impact of liana removal at large-scale and its potential effect on long-term ecosystem carbon storage.