Evidence map›Paper›PMID 42596589›Full record

ArticleGlobal change biology2026

Fire-Moisture Interactions Shape Recovery Trajectories Across Temperate Vegetation Communities.

Emily K Simpson, Glenda M Wardle, Christopher R Dickman, Aaron C Greenville

Abstract read
In one paragraph

Article in Global change biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Emily K SimpsonSchool of Life and Environmental Sciences, The University of Sydney, Sydney, New South Wales, Australia.ORCID 0009-0007-9054-1992
Glenda M WardleSchool of Life and Environmental Sciences, The University of Sydney, Sydney, New South Wales, Australia.ORCID 0000-0003-0189-1899
Christopher R DickmanSchool of Life and Environmental Sciences, The University of Sydney, Sydney, New South Wales, Australia.ORCID 0000-0002-1067-3730
Aaron C GreenvilleSchool of Life and Environmental Sciences, The University of Sydney, Sydney, New South Wales, Australia.ORCID 0000-0002-0113-4778

Funding

Ecological Society of Australia
6 · The paper itself

Abstract

The recovery of vegetation following fire is increasingly shaped by concurrent climate-driven hydrological extremes, such as drought and extreme precipitation. Under these multi-disturbance scenarios vegetation communities are likely to show different recovery thresholds and trajectories of change, but this is yet to be fully understood or quantified. We used the Greater Blue Mountains World Heritage Area in south-eastern Australia as a model system to assess how sustained moisture surplus or deficit could differentially influence the recovery of forest, heath and wetland vegetation from low-extreme severity fire. From 2015 to 2024, this region experienced severe drought, extensive bushfires (the 'Black Summer' bushfires) and extreme rainfall events. By using a new remote-sensing index (the Post-fire Stability Index) that measures the rate of change in a disturbed system, we found that most vegetation communities could recover following low-severity fire even in the case of poor soil moisture conditions, but moisture became increasingly limiting as burn severity increased. Certain communities, such as wet sclerophyll forests and freshwater wetlands, experienced disproportionate declines (up to

Indexed as

Climate ChangeDroughtsFiresForestsWildfiresAustraliaRainSouth AustraliaWetlands2019/20 bushfiresdroughtextreme precipitationfire regimesremote‐sensingvegetation recovery

Identifiers

PMID42596589
PMCPMC13473728

What OpenQuestion holds

Textmetadata
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.