Evidence map›Paper›PMID 41800497›Full record

ArticleGlobal change biology2026

Alternative Future Vegetation Pathways Reveal Potential Transformations of Western US Ecosystems.

Tyler J Hoecker, Kimberley T Davis, Caitlin Littlefield, Jeffrey Chandler, Sean Parks, Andrew Maguire, Kerry Kemp, Svetlana Yegorova, Solomon Dobrowski

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

9 authors.

Tyler J HoeckerVibrant Planet, PBC, Truckee, California, USA.
Kimberley T DavisUSDA Forest Service, Rocky Mountain Research Station, Missoula Fire Science Laboratory, Missoula, Montana, USA.ORCID 0000-0001-9727-374X
Caitlin LittlefieldConservation Science Partners, Truckee, California, USA.
Jeffrey ChandlerUniversity of Montana, Missoula, Montana, USA.
Sean ParksUniversity of Montana, Missoula, Montana, USA.
Andrew MaguireConservation Science Partners, Truckee, California, USA.
Kerry KempUSDA Forest Service, Region 6 Ecology Program, Wenatchee, Washington, USA.
Svetlana YegorovaWilfrid Laurier University, Waterloo, Ontario, Canada.
Solomon DobrowskiUniversity of Montana, Missoula, Montana, USA.

Funding

Northwest Climate Adaptation Science Center G23AC00072-00
6 · The paper itself

Abstract

Managing ecosystems in an era of rapid change is inherently challenging not only because of uncertainty in future climate but also due to diverse responses of ecosystems to climate. Projections of ecological transformation alongside information about plausible vegetation trajectories can help land managers explore divergent scenarios and consider how modeled outcomes match their observations. Climate-analog impact models (AIMs) compare environmental information (e.g., vegetation types) between sets of climatically similar locations to infer change and can be used to identify multiple outcomes. We used AIMs to project changes in vegetation across the western United States under a mid-21st century climate scenario, characterize ecological transformation vulnerability based on projection divergence, and demonstrate how AIMs can inform decision-making. We projected high or very high vulnerability to ecological transformation across 29% of the western US, nearly 1 M km

Indexed as

Climate ChangeConservation of Natural ResourcesEcosystemClimate ModelsModels, TheoreticalPlantsUnited Statesclimate adaptationclimate analogsdecision‐supportecological transformationresiliencespecies distribution modeling

Identifiers

PMID41800497
PMCPMC12969547

What OpenQuestion holds

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LicenceCC BY
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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.