Evidence map›Paper›PMID 39969169›Full record

ArticleMolecular ecology2025

Genomic Vulnerability of a Sentinel Mammal Under Climate Change.

Danielle A Schmidt, Michael A Russello

Abstract read
In one paragraph

Article in Molecular ecology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

2 authors.

Danielle A SchmidtDepartment of Biology, The University of British Columbia, Kelowna, British Columbia, Canada.
Michael A RusselloDepartment of Biology, The University of British Columbia, Kelowna, British Columbia, Canada.ORCID 0000-0002-9297-2485

Funding

Natural Sciences and Engineering Research Council of Canada RGPIN-2019-04621
6 · The paper itself

Abstract

Climate change poses a significant threat to biodiversity, particularly in alpine ecosystems where species have already undergone elevational range shifts. Genomics can be used to estimate the adaptive potential of species, as well as the shift in adaptive genomic composition necessary for populations to adjust to climate change (e.g., genomic offset). Here, we investigated patterns of climate-mediated adaptive genetic variation and predicted the degree of genomic offset under multiple climate change scenarios for a sentinel alpine mammal, the American pika (Ochotona princeps). We collected genome-wide data (29,709 SNPs) from 363 individuals spanning the entire range in western North America and employed genotype-environment association analyses to identify 924 robust outlier SNPs, several of which were linked to genes previously associated with high elevation and hypoxia responses in various pika species (Ochotonidae). Adaptive genomic variation was most strongly influenced by mean warmest month temperature, followed by precipitation of the coldest quarter. Spatial patterns of genomic offset were heterogeneous, significantly predicted by levels of adaptive genetic variation, elevation and latitude. Sites within the Northern Rocky Mountains exhibited the highest genomic offset under projected climate change despite possessing high levels of adaptive genetic variation. As such, while our study provides an example of how genomic data can be used to explore the potential consequences of climate change, it further highlights the need for careful consideration of genomic offset values within their proper ecological context.

Indexed as

Adaptation, PhysiologicalClimate ChangeGenetics, PopulationLagomorphaAnimalsEcosystemGene-Environment InteractionGenetic VariationGenomicsGenotypeNorth AmericaPolymorphism, Single NucleotideAmerican pikaclimate changegenomic offsetgradient forestlocal adaptation

Identifiers

PMID39969169
PMCPMC12530299

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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.