Evidence map›Paper›PMID 41006451›Full record

ArticleScientific reports2025

Climate vulnerability of Earth's terrestrial biomes.

Katharina Runge, Colin Averill, Thomas Lauber, Tanja N Koch, Daniel S Maynard, Devin Routh, Johan van den Hoogen, Constantin M Zohner, Thomas W Crowther

Abstract read
In one paragraph

Article in Scientific reports, 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

9 authors.

Katharina RungeDepartment of Environmental Systems Science, ETH Zürich, Zürich, Switzerland. katharina.runge@usys.ethz.ch.ORCID http://orcid.org/0000-0001-6959-604X
Colin AverillDepartment of Environmental Systems Science, ETH Zürich, Zürich, Switzerland.
Thomas LauberDepartment of Environmental Systems Science, ETH Zürich, Zürich, Switzerland.ORCID http://orcid.org/0000-0002-3118-432X
Tanja N KochDepartment of Environmental Systems Science, ETH Zürich, Zürich, Switzerland.
Daniel S MaynardDepartment of Genetics, Evolution & Environment, University College London, London, UK.ORCID http://orcid.org/0000-0003-0142-9100
Devin RouthScience IT (ZI), University of Zurich, Zürich, Switzerland.ORCID http://orcid.org/0000-0002-5910-8847
Johan van den HoogenDepartment of Environmental Systems Science, ETH Zürich, Zürich, Switzerland.ORCID http://orcid.org/0000-0001-6624-8461
Constantin M ZohnerDepartment of Environmental Systems Science, ETH Zürich, Zürich, Switzerland.ORCID http://orcid.org/0000-0002-8302-4854
Thomas W CrowtherDepartment of Environmental Systems Science, ETH Zürich, Zürich, Switzerland.ORCID http://orcid.org/0000-0001-5674-8913

Funding

Swiss National Science Foundation Ambizione PZ00P3_17990Swiss National Science Foundation Ambizione PZ00P3_193646
6 · The paper itself

Abstract

Climate-smart restoration and sustainable ecosystem management requires understanding which plant species will survive and thrive under both current and future climate conditions. Changing climate conditions are expected to place new environmental stresses on plants across biomes, potentially shifting the climate envelope, with unexpected consequences for ecological communities. Here, we train machine learning models on the global distribution of Earth's biomes, demonstrating that soil and climate can accurately capture contemporary biome-climate envelopes (BCEs) with 90% accuracy. We then predict how changes in climate might alter BCEs under different climate change scenarios (RCP 4.5 and 8.5). We find that 11-17% of the terrestrial surface is likely to experience a change in BCE by 2080 and 16-19% has a highly uncertain BCE trajectory over the coming decades. BCE boundaries generally shift poleward, with the tundra BCE shrinking by up to 47% under RCP 8.5, and various dryland BCEs overall increasing under RCP 8.5. These models do not necessarily reflect changes in vegetation characteristics under future climate conditions, as uncertainty about ecological feedbacks and migration rates preclude our capacity to predict how climate variation would impact these vegetation dynamics. However, we identify regions where changing climate conditions might make the local environments stressful for the vegetation that presently exists in those regions.

Indexed as

Climate ChangeEarth, PlanetEcosystemClimateMachine LearningPlantsSoilSoilBiome-climate envelopesClimate changeGlobalMachine learning models

Identifiers

PMID41006451
PMCPMC12475491

What OpenQuestion holds

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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.