Evidence map›Paper›PMID 40400371›Full record

ArticleGlobal change biology2025

Susceptibility to Photosynthesis Suppression From Extreme Storms Is Highly Site-Dependent.

Erica L McCormick, Caroline A Famiglietti, Dapeng Feng, Anna M Michalak, Alexandra G Konings

Abstract read
In one paragraph

Article in Global change biology, 2025. 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

5 authors.

Erica L McCormickDepartment of Earth System Science, Stanford University, Stanford, California, USA.ORCID 0000-0002-7160-398X
Caroline A FamigliettiDepartment of Earth System Science, Stanford University, Stanford, California, USA.ORCID 0000-0002-6073-0457
Dapeng FengDepartment of Earth System Science, Stanford University, Stanford, California, USA.ORCID 0000-0002-5653-6504
Anna M MichalakDepartment of Earth System Science, Stanford University, Stanford, California, USA.ORCID 0000-0002-6152-7979
Alexandra G KoningsDepartment of Earth System Science, Stanford University, Stanford, California, USA.ORCID 0000-0002-2810-1722

Funding

Alfred P. Sloan FoundationCarnegie Institution for ScienceNASA 80NSSC21K1593National Science Foundation Graduate Research Fellowship ProgramStanford Institute for Human-Centered Artificial Intelligence, Stanford UniversityStanford Office of the Vice President for Graduate Education
6 · The paper itself

Abstract

Extreme storms are becoming more intense and frequent under climate change. Although these extreme wet events are smaller in extent and duration than drought events, recent evidence suggests the global impact of both extremes is similar. However, the impact of individual extreme storms on photosynthesis-and therefore on vegetation and the carbon cycle-remains difficult to predict, as photosynthesis may be suppressed via waterlogging or increased by the alleviation of moisture stress. Here, we use random forest models to calculate daily photosynthesis anomalies attributable to extreme soil moisture using data from 54 FLUXNET sites across the globe. We hypothesize that photosynthesis' response to a given extreme event is primarily controlled by storm intensity, and to a lesser degree by site vegetation, climate, soil, and topography. However, we find instead that photosynthesis responses are better explained by site characteristics (soil texture, climate, topography, and vegetation density) than by storm intensity, such that the likelihood of waterlogging from a given storm is heavily site-dependent. Although storms that induce waterlogging are roughly as common as those that induce stress alleviation overall, photosynthesis rarely declines at sites not prone to waterlogging. Instead, photosynthesis anomalies at these sites show a much weaker relationship with storm intensity. Increasingly intense storms are therefore unlikely to impact all locations equally. This highlights the potential to use site characteristics to enhance prediction of storm effects on ecosystems and the land carbon sink.

Indexed as

Climate ChangeExtreme WeatherPhotosynthesisCarbon CycleSoilSoilcarbon uptakeclimate extremesecohydrologyeddy covarianceextreme stormsmachine learningphotosynthesiswaterlogging

Identifiers

PMID40400371
PMCPMC12096146

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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.