2026-07-27 デラウェア大学（UD）
＜関連情報＞
- https://www.udel.edu/udaily/2026/july/research-artificial-intelligence-plants-yin-bao/
- https://www.cell.com/cell/fulltext/S0092-8674(26)00506-4?rss=yes
市民科学を活用して適応メカニズムの発見を文脈化する Harnessing citizen science to contextualize adaptation mechanism discovery
Laura E. Tibbs-Cortes ∙ Linqian Han ∙ Jeremy B. Jewell ∙ … ∙ Thomas Juenger ∙ Jianming Yu ∙ Xianran Li
Cell Published:May 20, 2026
DOI:https://doi.org/10.1016/j.cell.2026.04.039
Highlights
- Citizen science observations reveal a latitudinal flowering time trend across species
- Designed experiments identify three key regulators of switchgrass flowering time
- Haplotypes of the three regulators, GI-Hd1-FTL1, exhibit varied phenotypic plasticity
- Avoidance of temperature extremes and fitness trade-off shape haplotype geographic range
Summary
Species occupying broad geographic regions have evolved multiple mechanisms to regulate phenological characteristics, enabling adaptations to diverse native habitats. By developing computer vision AI to process citizen science observations across native habitats over North America, we uncovered a consistent latitudinal trend of earlier flowering at higher latitudes in warm-season perennial grasses. To explore the underlying mechanisms of adaptation, we conducted common garden experiments with one species (switchgrass) and discovered the opposite latitudinal flowering-time trend. Integration of differential plasticity of GI-Hd1-FTL1 haplotypes of flowering time regulatory genes, haplotype range, and local environmental profiles found that observations from native habitats capture only part of the genotype-environment-phenotype spectrum established in common garden experiments, therefore reconciling the discrepancy. Two mechanisms emerged as key forces shaping current haplotype ranges and influencing future shifts. Our study highlights the power of combining citizen science observations with designed experiments to uncover mechanisms of adaptation across spatiotemporal scales.
