Evidence map›Paper›PMID 37223308›Full record

ArticleEcology and evolution2023

Spatial predictions of tree density and tree height across Mexico forests using ensemble learning and forest inventory data.

Aylin Barreras, José Armando Alanís de la Rosa, Rafael Mayorga, Rubi Cuenca, César Moreno-G, Carlos Godínez, Carina Delgado, Maria de Los Ángeles Soriano-Luna, Stephanie George, Metzli Ileana Aldrete-Leal and 8 more

Abstract read
In one paragraph

Article in Ecology and evolution, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. 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

18 authors.

Aylin BarrerasDepartment of Forest and Rangeland Stewardship Colorado State University Fort Collins Colorado USA.ORCID https://orcid.org/0000-0002-3987-5563
José Armando Alanís de la RosaComisión Nacional Forestal (CONAFOR) Zapopan Mexico.
Rafael MayorgaComisión Nacional Forestal (CONAFOR) Zapopan Mexico.
Rubi CuencaComisión Nacional Forestal (CONAFOR) Zapopan Mexico.
César Moreno-GComisión Nacional Forestal (CONAFOR) Zapopan Mexico.
Carlos GodínezComisión Nacional Forestal (CONAFOR) Zapopan Mexico.
Carina DelgadoComisión Nacional Forestal (CONAFOR) Zapopan Mexico.
Maria de Los Ángeles Soriano-LunaComisión Nacional Forestal (CONAFOR) Zapopan Mexico.
Stephanie GeorgeComisión Nacional Forestal (CONAFOR) Zapopan Mexico.
Metzli Ileana Aldrete-LealComisión Nacional Forestal (CONAFOR) Zapopan Mexico.ORCID https://orcid.org/0000-0003-3101-0941
Sandra MedinaComisión Nacional Forestal (CONAFOR) Zapopan Mexico.
Johny RomeroComisión Nacional Forestal (CONAFOR) Zapopan Mexico.
Sergio VillelaComisión Nacional Forestal (CONAFOR) Zapopan Mexico.
Andrew ListerUS Forest Service, International Programs Washington District of Columbia USA.
Rachel SheridanUS Forest Service, International Programs Washington District of Columbia USA.
Rafael FloresUS Forest Service, International Programs Washington District of Columbia USA.
Thomas W CrowtherInstitute of Integrative Biology ETH Zurich Zürich Switzerland.
Mario GuevaraCentro de Geociencias Universidad Nacional Autónoma de México Juriquilla Mexico.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The National Forestry Commission of Mexico continuously monitors forest structure within the country's continental territory by the implementation of the National Forest and Soils Inventory (INFyS). Due to the challenges involved in collecting data exclusively from field surveys, there are spatial information gaps for important forest attributes. This can produce bias or increase uncertainty when generating estimates required to support forest management decisions. Our objective is to predict the spatial distribution of tree height and tree density in all Mexican forests. We performed wall-to-wall spatial predictions of both attributes in 1-km grids, using ensemble machine learning across each forest type in Mexico. Predictor variables include remote sensing imagery and other geospatial data (e.g., mean precipitation, surface temperature, canopy cover). Training data is from the 2009 to 2014 cycle (

Indexed as

ensemble machine learningforest inventoryspatial predictiontree densitytree height

Identifiers

PMID37223308
PMCPMC10200803

What OpenQuestion holds

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LicenceCC BY
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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.