Genomic forecasting for climate‐resilient fruit trees
Abstract
Fruit trees – long‐lived perennial crops cultivated for their edible fruits or nuts and frequently propagated clonally – are increasingly exposed to climate extremes that threaten their productivity and survival. Yet their capacity to adapt to rapid environmental change remains poorly understood. We argue that fruit trees and their wild relatives are powerful but underused systems for advancing genomic forecasting in perennials, with a focus on genomic offset analyses. Genomic offset estimates the mismatch between current genomic variation and that predicted to be optimal under future climates, offering a promising framework to anticipate maladaptation and guide conservation, breeding, and management strategies. Although its application is expanding rapidly in annual crops and forest trees, its interpretation and predictive value remain actively debated and require stronger empirical validation. Fruit trees are particularly well suited to address these challenges as they combine distinctive biology – including long generation times, clonal propagation and intensive management practices – with expanding genomic resources and common garden networks. Using emblematic Mediterranean and temperate species, we outline a roadmap that combines genomic offset with common‐garden networks, high‐resolution climate data, and trait‐based fitness proxies. Together, these resources position fruit trees as an powerful model to evaluate and refine genomic forecasting into a practical tool for biodiversity‐informed breeding and conservation under global change, and better understand plant adaptation and maladaptation processes.
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BibTeX
@article{criado2026genomic,
title = {{Genomic forecasting for climate‐resilient fruit trees}},
author = {Maxime Criado and Mathieu Brisson and Karine Alix and Yann X.C. Bourgeois and Olivier François and Élodie Marchadier and Amandine Cornille},
journal = {New Phytologist},
year = {2026},
month = jun,
doi = {10.1111/nph.71352},
url = {https://hal.inrae.fr/hal-05780972v1},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Genomic forecasting for climate‐resilient fruit trees
AU - Maxime Criado
AU - Mathieu Brisson
AU - Karine Alix
AU - Yann X.C. Bourgeois
AU - Olivier François
AU - Élodie Marchadier
AU - Amandine Cornille
PY - 2026
DA - 2026/06/30
JO - New Phytologist
DO - 10.1111/nph.71352
UR - https://hal.inrae.fr/hal-05780972v1
AB - Fruit trees – long‐lived perennial crops cultivated for their edible fruits or nuts and frequently propagated clonally – are increasingly exposed to climate extremes that threaten their productivity and survival. Yet their capacity to adapt to rapid environmental change remains poorly understood. We argue that fruit trees and their wild relatives are powerful but underused systems for advancing genomic forecasting in perennials, with a focus on genomic offset analyses. Genomic offset estimates the mismatch between current genomic variation and that predicted to be optimal under future climates, offering a promising framework to anticipate maladaptation and guide conservation, breeding, and management strategies. Although its application is expanding rapidly in annual crops and forest trees, its interpretation and predictive value remain actively debated and require stronger empirical validation. Fruit trees are particularly well suited to address these challenges as they combine distinctive biology – including long generation times, clonal propagation and intensive management practices – with expanding genomic resources and common garden networks. Using emblematic Mediterranean and temperate species, we outline a roadmap that combines genomic offset with common‐garden networks, high‐resolution climate data, and trait‐based fitness proxies. Together, these resources position fruit trees as an powerful model to evaluate and refine genomic forecasting into a practical tool for biodiversity‐informed breeding and conservation under global change, and better understand plant adaptation and maladaptation processes.
SN - 0028-646X
SN - 1469-8137
ER -