Evidence map›Paper›PMID 38245523›Full record

ArticleNature communications2024

Scattered tree death contributes to substantial forest loss in California.

Yan Cheng, Stefan Oehmcke, Martin Brandt, Lisa Rosenthal, Adrian Das, Anton Vrieling, Sassan Saatchi, Fabien Wagner, Maurice Mugabowindekwe, Wim Verbruggen and 2 more

Open access · goldAbstract read
In one paragraph

Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
13.0field-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

4 citing papers in PubMed, 46 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

12 authors at 4 institutions in 3 countries.

Yan ChengDepartment of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark. yach@ign.ku.dk.ORCID 0000-0001-8658-4673
Stefan OehmckeDepartment of Computer Science, University of Copenhagen, Copenhagen, Denmark.
Martin BrandtDepartment of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark.ORCID 0000-0001-9531-1239
Lisa RosenthalUS Geological Survey, Western Ecological Research Center, Three Rivers, Sequoia and Kings Canyon Field Station, Three Rivers, CA, USA.
Adrian DasUS Geological Survey, Western Ecological Research Center, Three Rivers, Sequoia and Kings Canyon Field Station, Three Rivers, CA, USA.ORCID 0000-0002-3937-2616
Anton VrielingFaculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands.ORCID 0000-0002-7979-1540
Sassan SaatchiUniversity of California, Los Angeles, CA, USA.
Fabien WagnerUniversity of California, Los Angeles, CA, USA.ORCID 0000-0002-9623-1182
Maurice MugabowindekweDepartment of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark.ORCID 0000-0003-0803-7417
Wim VerbruggenDepartment of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark.ORCID 0000-0002-3611-6561
Claus BeierDepartment of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark.
Stéphanie HorionDepartment of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark. smh@ign.ku.dk.ORCID 0000-0001-6716-1333
University of Copenhagen · DKJet Propulsion Laboratory · USUnited States Geological Survey · USUniversity of Twente · NL

Funding

Københavns Universitet (University of Copenhagen) Data+ Strategy 2023 fundsVillum Fonden (Villum Foundation) 37465
6 · The paper itself

Abstract

In recent years, large-scale tree mortality events linked to global change have occurred around the world. Current forest monitoring methods are crucial for identifying mortality hotspots, but systematic assessments of isolated or scattered dead trees over large areas are needed to reduce uncertainty on the actual extent of tree mortality. Here, we mapped individual dead trees in California using sub-meter resolution aerial photographs from 2020 and deep learning-based dead tree detection. We identified 91.4 million dead trees over 27.8 million hectares of vegetated areas (16.7-24.7% underestimation bias when compared to field data). Among these, a total of 19.5 million dead trees appeared isolated, and 60% of all dead trees occurred in small groups ( ≤ 3 dead trees within a 30 × 30 m grid), which is largely undetected by other state-level monitoring methods. The widespread mortality of individual trees impacts the carbon budget and sequestration capacity of California forests and can be considered a threat to forest health and a fuel source for future wildfires.

Indexed as

TreesWildfiresCaliforniaCarbonForestsCarbon

Identifiers

PMID38245523
PMCPMC10799937
OpenAlexW4391047293

What OpenQuestion holds

Textmetadata
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.