Evidence map›Paper›PMID 33972856›Full record

ArticleRoyal Society open science2021

New insights into large tropical tree mass and structure from direct harvest and terrestrial lidar.

Andrew Burt, Matheus Boni Vicari, Antonio C L da Costa, Ingrid Coughlin, Patrick Meir, Lucy Rowland, Mathias Disney

Open access · goldAbstract read
In one paragraph

Article in Royal Society open science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
10.1field-weighted citation impact, top 1% of its field
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

5 citing papers in PubMed, 51 citations in OpenAlex.

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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

7 authors at 5 institutions in 3 countries.

Andrew BurtDepartment of Geography, University College London, London, UK.ORCID 0000-0002-4209-8101
Matheus Boni VicariDepartment of Geography, University College London, London, UK.ORCID 0000-0001-8841-4205
Antonio C L da CostaInstituto de Geosciências, Universidade Federal do Pará, Belém, Brazil.ORCID 0000-0001-8140-4020
Ingrid CoughlinResearch School of Biology, Australian National University, Canberra, Australia.ORCID 0000-0002-8541-2682
Patrick MeirResearch School of Biology, Australian National University, Canberra, Australia.ORCID 0000-0002-2362-0398
Lucy RowlandCollege of Life and Environmental Sciences, University of Exeter, Exeter, UK.ORCID 0000-0002-0774-3216
Mathias DisneyDepartment of Geography, University College London, London, UK.ORCID 0000-0002-2407-4026
Australian National University · AUUniversity College London · GBNational Centre for Earth Observation · GBUniversidade Federal do Pará · BRUniversity of Exeter · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A large portion of the terrestrial vegetation carbon stock is stored in the above-ground biomass (AGB) of tropical forests, but the exact amount remains uncertain, partly owing to the lack of measurements. To date, accessible peer-reviewed data are available for just 10 large tropical trees in the Amazon that have been harvested and directly measured entirely via weighing. Here, we harvested four large tropical rainforest trees (stem diameter: 0.6-1.2 m, height: 30-46 m, AGB: 3960-18 584 kg) in intact old-growth forest in East Amazonia, and measured above-ground green mass, moisture content and woody tissue density. We first present rare ecological insights provided by these data, including unsystematic intra-tree variations in density, with both height and radius. We also found the majority of AGB was usually found in the crown, but varied from 42 to 62%. We then compare non-destructive approaches for estimating the AGB of these trees, using both classical allometry and new lidar-based methods. Terrestrial lidar point clouds were collected pre-harvest, on which we fitted cylinders to model woody structure, enabling retrieval of volume-derived AGB. Estimates from this approach were more accurate than allometric counterparts (mean tree-scale relative error: 3% versus 15%), and error decreased when up-scaling to the cumulative AGB of the four trees (1% versus 15%). Furthermore, while allometric error increased fourfold with tree size over the diameter range, lidar error remained constant. This suggests error in these lidar-derived estimates is random and additive. Were these results transferable across forest scenes, terrestrial lidar methods would reduce uncertainty in stand-scale AGB estimates, and therefore advance our understanding of the role of tropical forests in the global carbon cycle.

Indexed as

above-ground biomassallometrydestructive harvestterrestrial lidartree structuretropical forests

Identifiers

PMID33972856
PMCPMC8074798
OpenAlexW3127141326

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.