Evaluating the accuracy of genomic prediction for the management and conservation of relictual natural tree populations

Studying and understanding the evolution of relictual natural populations is critical for developing conservation initiatives of endangered species, such as management in situ and assisted migration. Recently, genomic and bioinformatics tools have promised a wide avenue for developing more efficient programs. Genomic prediction (GP) models are one of such tools; although, in trees, only some successful examples exit. They have mostly been used to increase predictive ability in commercial traits and reduce breeding cycle length. Thus, it remains to be tested whether GP can be extended for the management and conservation of natural small and secluded populations. Here, we explored such a possibility in a pilot study to predict the performance of introduced saplings in a managed population of sacred fir (Abies religiosa; Pinaceae) in central Mexico. We genotyped over 200 naturally re-generated and introduced individuals with 2286 single nucleotide polymorphisms (SNP), derived from genotyping by sequencing, and used them to develop GP models for growth and physiological traits. After testing different training and validation datasets, and determining predictive ability of "across-groups" models with cross-validation techniques, acceptable predictive abilities (r(y)) were obtained for growth during the previous growing season, water potential, stem diameter, and aboveground biomass (0.36, 0.27, 0.26, and 0.24, respectively). The best models were always those built with natural saplings and used to predict the early performance of introduced individuals in the same environment, although fair predictabilities were also obtained when predicting performance between natural populations. Model fine-tuning resulted in reduced datasets of approximately 700 SNPs that helped optimizing phenotype predictability, particularly for water potential, for which r(y) was up to 0.28. These pilot-scale results are preliminary but encouraging and justify additional research efforts for implementing GP in small and secluded natural populations, particularly for endangered non-model species.