Genetic Diversity Maximization as a Strategy for Resilient Forest Ecosystems: A Case Study on Norway Spruce

Abstract

Norway spruce, economically and ecologically one of the most important European forest tree species, rapidly declines due to massive bark beetle outbreaks across many countries. As a prerequisite of ecosystem stability facing climate changes of uncertain predictions, the reforestation management promoting locally adapted resources of broad genetic diversity should be prioritized, especially in nature conservation areas. In our case study carried out in the national park, Krkonoše Mountains (the Giant Mountains, the Czech Republic), we demonstrated a tree breeding strategy aiming at maximizing genetic diversity. More than four hundred unique Norway spruce accessions were genotyped on 15 microsatellite loci (Ne = 5.764, I = 1.713 and He = 0.685). Two core collection selection approaches were proposed to establish a new deployment population providing local gene sources of high genetic diversity. Namely, the Core Hunter selection algorithm, with average entry-to-nearest-entry distance (EN) optimization, was applied to identify the most diverse core collection set with the highest genetic diversity parameters obtained for 57 selected individuals (Ne = 6.507, I = 1.807, and He = 0.731). The latter core collection method proposed is innovative, based on choosing appropriate genotypes from a clustered heatmap. For simplicity, we demonstrated the principle of selection strategy on a reduced dataset. It is vital to promote panmixia of a newly established production population from a core collection to complete the conservation breeding effort. Thus, we demonstrated the utilization of the Optimum Neighborhood Algorithm (ONA) deployment that outperformed other deployment algorithms, especially in the case of balanced clone representation and uneven shapes of planting plots. We believe that the case study presented can be generalized and considered as a guideline for analogical tree breeding intentions.