Genomic forecasting for climate-resilient fruit trees.

Area of Science:

• * Plant sciences
• * Genomics
• * Climate change adaptation

Background:

• * Fruit trees, long-lived perennial crops, are vital for global food security but threatened by climate extremes.
• * Their adaptive capacity to rapid environmental changes is poorly understood, hindering effective conservation and breeding.
• * Genomic forecasting, particularly genomic offset analysis, offers a framework to predict maladaptation in perennial species.

Purpose of the Study:

• * To highlight fruit trees as powerful, underutilized models for advancing genomic forecasting in perennial crops.
• * To propose a roadmap integrating genomic offset analysis with common garden networks and climate data for fruit trees.
• * To refine genomic forecasting as a practical tool for biodiversity-informed breeding and conservation under global change.

Main Methods:

• * Utilizing genomic offset analysis to estimate the mismatch between current and future optimal genomic variation.
• * Leveraging common garden networks and high-resolution climate data for empirical validation.
• * Employing trait-based fitness proxies to assess adaptation and maladaptation in fruit tree populations.

Main Results:

• * Fruit trees possess unique biological traits (long generation times, clonal propagation) that make them suitable for studying adaptation.
• * Integrating genomic offset with empirical data provides a robust framework for evaluating predictive models.
• * The proposed roadmap facilitates the application of genomic forecasting in fruit trees.

Conclusions:

• * Fruit trees are excellent models for evaluating and refining genomic forecasting for perennial crops.
• * Genomic offset analysis, combined with empirical data, can guide effective conservation and breeding strategies.
• * This approach enhances our understanding of plant adaptation and maladaptation processes under global change.