Integrating Genomic and Climate Data to Design Representative Seed Production Areas: A Pragmatic Workflow for...

Area of Science:

• Conservation Biology
• Genetics
• Climate Change Ecology

Background:

• Ex situ collections, especially seed production areas (SPAs), are vital for biodiversity conservation and germplasm supply.
• Limited practical tools exist for sourcing genetically representative material, particularly for widespread species.

Purpose of the Study:

• To develop a flexible, data-driven workflow for designing representative SPAs by integrating genomic data, climate projections, and real-world constraints.
• To evaluate different sampling strategies and their effectiveness in capturing allelic diversity for ex situ collections.

Main Methods:

• Utilized genomic data and climate-matching tools to identify genetic neighbourhoods (GNs) and suitable external GNs for future climate analogues.
• Developed a novel optimization method using a down-projected site frequency spectrum (psfs) to maximize allele representation.
• Implemented and evaluated three provenancing strategies (local, predictive, climate-adjusted) under various sampling decisions and constraints.

Main Results:

• Multiple sampling approaches successfully captured over 90% of common alleles within local GNs, even with practical constraints.
• Incorporating future climate-matched sources from external GNs nearly doubled the allelic representation in SPAs.
• The workflow demonstrated adaptability to practical limitations like site inaccessibility and reliance on existing collections.

Conclusions:

• The developed workflow supports the scalable, evidence-based design of ex situ collections like SPAs.
• It effectively balances genetic resolution with practitioner flexibility to maximize genetic representation under environmental change.
• This approach enhances the utility of SPAs for both biodiversity safeguarding and providing high-quality germplasm.