Evidence map›Paper›PMID 40289501›Full record

ArticleEcology2025

When can we detect lianas from space? Toward a mechanistic understanding of liana-infested forest optics.

Marco D Visser, Matteo Detto, Félicien Meunier, Jin Wu, Jane R Foster, David C Marvin, Peter M van Bodegom, Boris Bongalov, Matheus Henrique Nunes, David Coomes and 15 more

Abstract read
In one paragraph

Article in Ecology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

25 authors.

Marco D VisserDepartment of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, USA.ORCID https://orcid.org/0000-0003-1200-0852
Matteo DettoDepartment of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, USA.ORCID https://orcid.org/0000-0003-0494-188X
Félicien MeunierComputational and Applied Vegetation Ecology, Department of Environment, Ghent University, Ghent, Belgium.
Jin WuLaboratory for Space Research, University of Hong Kong, Hong Kong.
Jane R FosterRubenstein School of Environment & Natural Resources, University of Vermont, Burlington, Vermont, USA.ORCID https://orcid.org/0000-0002-5997-0269
David C MarvinSalo Sciences, Inc., San Francisco, California, USA.
Peter M van BodegomInstitute of Environmental Sciences, Leiden University, Leiden, the Netherlands.
Boris BongalovDepartment of Plant Sciences, Forest Ecology & Conservation Group, University of Cambridge, Cambridge, UK.
Matheus Henrique NunesDepartment of Plant Sciences, Forest Ecology & Conservation Group, University of Cambridge, Cambridge, UK.
David CoomesDepartment of Plant Sciences, Forest Ecology & Conservation Group, University of Cambridge, Cambridge, UK.
Hans VerbeeckDepartment of Earth & Environment, Boston University, Boston, Massachusetts, USA.
J Antonio Guzmán QCentre for Earth Observation Sciences (CEOS), Earth & Atmospheric Sciences Department, University of Alberta, Edmonton, Alberta, Canada.ORCID https://orcid.org/0000-0002-0721-148X
Arturo Sanchez-AzofeifaCentre for Earth Observation Sciences (CEOS), Earth & Atmospheric Sciences Department, University of Alberta, Edmonton, Alberta, Canada.
Chris J ChandlerSchool of Geography, University of Nottingham, University Park, Nottingham, UK.ORCID https://orcid.org/0000-0002-9280-7244
Geertje M F van der HeijdenSchool of Geography, University of Nottingham, University Park, Nottingham, UK.
Doreen S BoydSchool of Geography, University of Nottingham, University Park, Nottingham, UK.
Giles M FoodySchool of Geography, University of Nottingham, University Park, Nottingham, UK.
Mark E J CutlerSchool of Social Sciences, University of Dundee, Dundee, UK.
Eben N BroadbentSchool of Forest Resources and Conservation, University of Florida, Gainesville, Florida, USA.
Shawn P SerbinTerrestrial Ecosystem Science & Technology Group, Environmental Sciences Department, Brookhaven National Laboratory, Upton, New York, USA.ORCID https://orcid.org/0000-0003-4136-8971
Stefan SchnitzerDepartment of Biological Sciences, Marquette University, Milwaukee, Wisconsin, USA.ORCID https://orcid.org/0000-0002-2715-9455
M Elizabeth Rodríguez-RonderosDepartment of Geography, National University of Singapore, Singapore, Singapore.ORCID https://orcid.org/0000-0001-6107-938X
Frank SterckForest Ecology and Management Group, Wageningen University, Wageningen, the Netherlands.
José A Medina-VegaForest Global Earth Observatory, Smithsonian Tropical Research Institute, Washington, DC, USA.ORCID https://orcid.org/0000-0001-5468-5605
Stephen W PacalaDepartment of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, USA.

Funding

Carnegie Airborne ObservatoryEuropean Research Council 637643FWO 1214720N/1214723NNatural Environment Research Council NE/I528477/1Natural Environment Research Council NE/L002604/1Natural Environment Research Council NE/P004806/1Princeton Universitythe U.S. Department of Agriculture, Forest Service
6 · The paper itself

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.

Indexed as

ForestsTreesPlant LeavesRemote Sensing Technologyalbedobiodiversityleaf anglesleaf opticsleaf traitsradiative transfer models

Identifiers

PMID40289501
PMCPMC12035525

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.