Forecasting floral futures: leveraging genetic and microenvironmental
data to improve seed provenancing under climate change
- Andhika Putra,
- Jian Yen,
- Alexandre Fournier-Level
Jian Yen
Arthur Rylah Institute for Environmental Research
Author ProfileAbstract
Revegetation projects face the major challenge of sourcing the optimal
plant material. This is often done with limited information about plant
performance and increasingly requires to factor resilience to climate
change. Functional traits can be used as quantitative indices of plant
performance and guide provenancing, but trait values expected under
novel conditions are often unkown. To support climate-resilient
provenancing efforts, we develop a trait prediction model that
integrates the effect of genetic variation with fine-scale temperature
variation. We train our model on multiple field plantings of Arabidopsis
thaliana and predict two relevant fitness traits -- days-to-bolting and
fecundity -- across the species' European range. Prediction accuracies
were high for days-to-bolting and moderate for fecundity, with the
majority of trait variation explained by temperature differences between
plantings. Projection under future climate predicted a decline in
fecundity, although this response was heterogeneous across the range. In
response, we identified novel genotypes that could be introduced to
genetically offset the fitness decay. Our study highlights the value of
predictive models to aid seed provenancing and improve the success of
revegetation projects.20 Jul 2022Submitted to Molecular Ecology Resources 26 Jul 2022Reviewer(s) Assigned
25 Aug 2022Review(s) Completed, Editorial Evaluation Pending
08 Sep 2022Editorial Decision: Revise Minor
24 Oct 2022Review(s) Completed, Editorial Evaluation Pending
24 Oct 20221st Revision Received
27 Oct 2022Editorial Decision: Accept