Conclusion and Future Directions

Plant response to climate change in the field is complex and can run contrary to empirical expectations (Parmesan & Hanley, 2015). This complexity has made predicting ‘real world’ patterns of climate response challenging and is a significant barrier to successful, climate-resilient revegetation. Our work addresses this gap by presenting a straightforward way of incorporating genetic variation, environmental variation, and their interaction into a single predictive model. Using A. thaliana as an example, we show the capacity for the model to accurately predict non-linear responses to climate change. We demonstrate its potential use in seed provenancing through a worked example involving the selection of relevant traits to quantify climate response, gathering of multi-environment multi-genotype trait data, generation of landscape predictions under different climate change scenarios, and identification of suitable genotypes for revegetation based on available genotypic data. Although the model was developed using a well-characterised species, our framework shows potential for use in non-model species due to its simple data requirements and minimal biological assumptions.
Data Availability
All the experimental data used in the analysis are publicly available at Figshare (https://melbourne.figshare.com/articles/dataset/FIBR_project_data/12824765). All the scripts used to run the analysis are accessible through a Github repository (https://github.com/andhikarp/AraCast). The outcome of the analysis can be further visualised using the AraCast shiny application available at (https://adaptive-evolution.biosciences.unimelb.edu.au/shiny/AraCast2/.
Author Contributions
AFL designed the study; ARP performed modelling work and analysed the results. JDLY and AFL provided feedback and suggestions throughout the project. ARP wrote the initial draft of the manuscript; all three authors provided edits and revisions.
Acknowledgements
The authors would like to thank Mark Taylor for providing the temperature data, Daniel Runcie for helping clarify the maths of the models, Moises Exposito-Alonso for sharing germination time models and Johanna Schmitt for providing feedback on the manuscript.
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Tables and Figures
Table 1. Summary of model performance