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

Establishing genetically diverse ex situ collections, particularly seed production areas (SPAs), is essential not only for safeguarding biodiversity but also for generating high-quality and high-quantity germplasm material. However, practical tools for sourcing genetically representative material remain limited, especially for widespread, common species. Here, we present a flexible, data-driven workflow that integrates genomic data, future climate projections and real-world constraints to guide the design of representative SPAs. Using the widespread rainforest tree Neolitsea dealbata as a case study, we identified genetic neighbourhoods (GNs) across its range and used a climate-matching tool to pinpoint an external GN with a future climate analogous to a target restoration area (the Big Scrub). We evaluated how common allelic diversity is captured under three practitioner-defined decisions: (1) whether to minimise individuals or sites sampled, (2) whether to apply sampling constraints and (3) whether to sample randomly or optimally. To support the third decision, we developed a novel optimisation method that identifies combinations of individuals or sites using a down-projected site frequency spectrum (psfs), aiming to maximise allele representation in the final collection. These decisions were then implemented across three provenancing strategies: local, predictive and climate-adjusted. Our results show that multiple sampling approaches can capture over 90% of common alleles (a predefined threshold) for the local GN, even under various logistical and practical constraints. The same is feasible when including future climate-matched sources from an external GN, which nearly doubled allelic representation of the species in the SPA. This workflow is adaptable to practical limitations, such as site inaccessibility or reliance on existing collections. By balancing genetic resolution with practitioner flexibility, our approach supports scalable, evidence-based design of ex situ collections, such as SPAs, to maximise genetic representation under environmental change.