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Lianas are a quintessential tropical plant growth-form; they are speciose and abundant in tropical forests worldwide. Lianas compete intensely with trees, reducing nearly all aspects of tree performance. However, the negative effects of lianas on trees have never been combined and quantified for multiple tropical forests. Here, we present the first comprehensive standardized quantification of the effect of lianas on trees across tropical forests worldwide. We used data from 50 liana removal experiments and quantified the effect size of lianas on tree growth, biomass accretion, reproduction, mortality, leaf water potential, sap flow velocity, and leaf area index (LAI) across different forest types. Using a three-level mixed-effect meta-analysis, we found unequivocal evidence that lianas significantly reduce tree growth and biomass accretion in ecological, logging, and silvicultural studies. Lianas also significantly reduce tree reproduction, recruitment, and physiological performance. The relative detrimental effect of lianas on trees does not increase in drier forests, where lianas tend to be more abundant. Our results highlight the substantial liana-induced reduction in tree performance and biomass accumulation, and they provide quantitative data on the effects of lianas on trees that are essential for large-scale plant demographic and ecosystem models that predict forest change and carbon dynamics.
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This content will become publicly available on April 1, 2026
When can we detect lianas from space? Toward a mechanistic understanding of liana‐infested forest optics
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.
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- Award ID(s):
- 2021898
- PAR ID:
- 10632284
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Ecology
- Volume:
- 106
- Issue:
- 4
- ISSN:
- 0012-9658
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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