Forecasting floral futures: leveraging genetic and microenvironmental data to improve seed provenancing under climate change

Abstract

Revegetation projects seeking to restore degraded ecosystems face a major challenge in sourcing appropriate plant material, as identifying plants adapted to future climates requires knowledge of plant performance under novel conditions. In order to support climate-resilient provenancing efforts, we develop a quantitative trait model that integrates genetic and microenvironmental variation. We train our model with multiple natural plantings of Arabidopsis thaliana and predict days-to-bolting and fecundity across the species' European range. Model prediction accuracy was high for days-to-bolting and moderate for fecundity, with the majority of trait variation being explained by temperature variation. Concerningly, fecundity was predicted to decline under future conditions, although this response was heterogeneous across regions, and could be offset through the introduction of specific genotypes. Our study highlights the value of predictive models to aid seed provenancing and improve the success of revegetation projects.

Plant phenotyping relevance

遺伝情報と微環境データからボルティング日数と繁殖成功度という植物形質を予測する定量モデルを開発し、予測精度も評価しているため、計算的な形質推定手法が中心です。

Code and data availability

The article's data accessibility statement only promises future uploads of scripts (GitHub) and data (Figshare) upon acceptance; no public URL, repository, or identifier is provided, and no other paper-specific phenotyping data or code assets appear in the supplied blocks.

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