Evidence map›Paper›PMID 39713342›Full record

ArticlebioRxiv : the preprint server for biology2025

Maladaptation in cereal crop landraces following a soot-producing climate catastrophe.

Chloee M McLaughlin, Yuning Shi, Vishnu Viswanathan, Ruairidh Sawers, Armen R Kemanian, Jesse R Lasky

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for 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

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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

5 · Who and what money

Authors and funding

6 authors.

Chloee M McLaughlinIntercollege Graduate Degree Program in Plant Biology, Pennsylvania State University, University Park, PA 16802.
Yuning ShiDepartment of Plant Science, Pennsylvania State University, University Park, PA 16802.
Vishnu ViswanathanDepartment of Biology, Pennsylvania State University, University Park, PA 16802.
Ruairidh SawersDepartment of Plant Science, Pennsylvania State University, University Park, PA 16802.
Armen R KemanianDepartment of Plant Science, Pennsylvania State University, University Park, PA 16802.
Jesse R LaskyDepartment of Biology, Pennsylvania State University, University Park, PA 16802.

Funding

Evolutionary genetics of genotype-by-environment interactions for complex traitsR35GM138300 · NIGMS · PENNSYLVANIA STATE UNIVERSITY, THE · PI LASKY, JESSE · 2020 to 2024
$1.8M
NIGMS NIH HHS R35 GM138300
6 · The paper itself

Abstract

Aerosol-producing global catastrophes such as nuclear war, super-volcano eruption, or asteroid strike, although rare, pose a serious threat to human survival. Light-absorbing aerosols would sharply reduce temperature and solar radiation reaching the earth's surface, decreasing crop productivity including for locally adapted traditional crop varieties, i.e. landraces. Here, we test post-catastrophic climate impacts on barley, maize, rice, and sorghum, four crops with extensive landrace cultivation, under a range of nuclear war scenarios that differ in the amount of black carbon aerosol (soot) injected into the climate model. We used a crop growth model to estimate gradients of environmental stressors that drive local adaptation. We then fit genotype environment associations using high density genomic markers with gradient forest offset (GF offset) methods and predicted maladaptation through time. As a validation, we found that our GF models successfully predicted local adaptation of maize landraces in multiple common gardens across Mexico. We found strong concordance between GF offset and disruptions in climate, and landraces of all tested crop species were predicted to be the most maladapted across space and time where soot-induced climate change was the greatest. We further used our GF models to identify landrace varieties best matched to specific post-catastrophic conditions, indicating potential substitutions for agricultural resilience. We found the best landrace genotype was often far away or in another nation, though countries with more climatic diversity had better within-country substitutions. Our results highlight that a soot-producing catastrophe would result in the global maladaptation of landraces and suggest that current landrace adaptive diversity is insufficient for agricultural resilience in the case of the soot scenarios with the greatest change to climate.

Indexed as

Biological Sciences (Evolutionary Biology)Crop modelinggenomic offsetgenotype-environment associationsnuclear winter

Identifiers

PMID39713342
PMCPMC11661091

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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.