Genomic ancestry predicts rapid responses to drought across spatiotemporal scales
Bibliographic record
Abstract
Abstract How genetic diversity responds to environmental change across spatiotemporal scales remains poorly understood despite its importance for species persistence in changing landscapes. Agricultural weeds offer ideal models for studying these adaptive dynamics as they rapidly evolve under both the intensive management practices designed to eliminate them and increasingly severe climate challenges such as drought. Here, we combine experimental and herbarium genomic approaches spanning within-generation to century-long timescales to understand how genome-wide variation responds to drought in Amaranthus tuberculatus . In this native species, a history of divergent selection between two ancestral lineages followed by secondary contact is thought to have facilitated its invasion into agriculture. A drought survival experiment on accessions from paired agricultural and natural populations across its range revealed substantial phenotypic variation differentiated by habitat, geography, and ancestry. Ancestry mapping revealed 43 independent regions across nearly all chromosomes that confer protective effects under drought, demonstrate particularly rapid allele frequency changes, and exhibit duration-specific selection over the course of the imposed drought. Observation of allele frequencies across the past century reveal evidence for climate-dependent fluctuating selection governing the evolution of drought-associated loci. Selection favors drought alleles during hot/dry years and selects against them in cool/wet years—a pattern more evident in long-term trends than in shorter temporal intervals, suggestive of adaptive lag in rapidly changing environments. By combining short and long-term spatiotemporal data, we demonstrate that fluctuating selection has preserved the polygenic variation underlying population responses to drought, enabling ongoing adaptive responses to contemporary land-use and climate change. Significance Statement Understanding how species cope with rapid climate and land use change requires studying evolutionary responses across scales. Using Amaranthus tuberculatus , a native species turned major agricultural weed, we bridge timescales by pairing a drought experiment with century-spanning herbarium genomics. We show that ancestry structures fitness under drought and has driven agricultural populations to be better drought-adapted. This involves many genes whose allele frequencies fluctuate with climate: drought-protective alleles increase during hot/dry years and decline in cool/wet years. These fluctuations maintain genetic diversity and enable climate tracking, which is imperfect over short timescales. By linking experimental and historical data, we uncover evolutionary dynamics missed by snapshots, improving predictions of species adaptation to environmental change and informing weed management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".