Evidence map›Paper›PMID 42668802›Full record

ArticleNational science review2026

Facing a harsh climate: terrestrial plant functional strategies in a changing world.

Jing Wang, Simon Pierce, Yuanzhi Li, Donghao Wu, Heng Huang, Josep Peñuelas, Bill Shipley, Chengjin Chu

Abstract read
In one paragraph

Article in National science review, 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

8 authors.

Jing WangState Key Laboratory of Biocontrol, School of Ecology, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.ORCID https://orcid.org/0000-0003-0116-6179
Simon PierceDepartment of Agricultural and Environmental Sciences (DiSAA), University of Milan, Milan 20122, Italy.ORCID https://orcid.org/0000-0003-1182-987X
Yuanzhi LiState Key Laboratory of Biocontrol, School of Ecology, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
Donghao WuState Key Laboratory of Biocontrol, School of Ecology, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
Heng HuangState Key Laboratory of Biocontrol, School of Ecology, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
Josep PeñuelasCREAF, Cerdanyola del Vallès, Barcelona 08193 Catalonia, Spain.
Bill ShipleyDépartement de Biologie, Université de Sherbrooke, Sherbrooke, Québec J1K 2R1, Canada.
Chengjin ChuState Key Laboratory of Biocontrol, School of Ecology, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.ORCID https://orcid.org/0000-0002-0606-449X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding how terrestrial plant functional strategies (competitive, stress-tolerant, ruderal; CSR) respond to environmental conditions is crucial for predicting ecosystem dynamics under global climate change, yet remains unexplored at the global community level. Leveraging a machine learning approach, and utilizing multi-source satellite remote-sensing and field-collected sPlotOpen measurements data, we generated the first global community-level map of CSR functional strategy variations. Results show that S-selected strategies are globally dominant (C:S:R = 23.66:62.40:13.94%), with substantial spatial variations across biomes. This variability is strongly influenced by climatic variables (e.g. mean annual precipitation, diurnal temperature range) and soil properties (e.g. cation exchange capacity, total nitrogen). Future projections show that climate change favours S- (+0.33%) and R- (+0.31%) at the expense of C-selected strategy (-0.64%), alongside marked biome-specific shifts. Despite potential underestimation of localized climate uncertainties, these findings provide critical insights into global plant community dynamics under challenging abiotic conditions.

Indexed as

ecosystem dynamicsfuture projectionglobal mappingmachine learningplant functional traitsplant strategy

Identifiers

PMID42668802
PMCPMC13525714

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.