Genetic monitoring is increasingly used to inform wildlife management, yet routine genome-wide genotyping can be expensive and often requires invasive tissue sampling. Targeted SNP panels have the potential to overcome these limitations by reducing genotyping costs, providing standardised marker sets that improve repeatability among studies and laboratories, and enabling genetic analyses from low-quality or low-quantity DNA obtained through minimally invasive sources, such as swabs or environmental DNA.
The critically endangered Redfin Blue-eye (Scaturiginichthys vermeilipinnis) provides an ideal case study to evaluate whether optimised SNP panels can deliver the same management-relevant genetic information as genome-wide datasets in a more practical and scalable way. Redfin Blue-eye is restricted to a single remnant wild population, with all other extant populations established through conservation translocations. In 2024, tissue samples were collected from the remnant population and 13 translocated populations. Thousands of genome-wide SNPs were used to evaluate the outcomes of past translocations and inform future recovery actions.
Using a newly developed SNP panel optimisation approach, we identified reduced sets of highly informative markers capable of reproducing the management-relevant genetic inferences obtained from the full genomic dataset. We evaluated the performance of SNP panels of different sizes for estimating population differentiation, genetic diversity and inbreeding, quantifying the trade-offs between marker number, accuracy and cost.
This work demonstrates how optimised SNP panels can translate genomic datasets into practical monitoring tools, enabling routine, cost-effective genetic monitoring to support adaptive management and species recovery. This approach is broadly applicable to threatened species where long-term genetic monitoring is needed but routine genome-wide genotyping is impractical.