Do Seedlings Derived from Pinewood Nematode-Resistant Pinus thunbergii Parl. Clones Selected in Southwestern Region Perform Well in Northern Regions in Japan? Inferences from Nursery Inoculation Tests

Abstract

Background and Objectives: To determine whether the progeny of pinewood nematode-resistant Pinus thunbergii Parl. clones selected in the southwestern region of Japan could be successful in reforestation in the northern region, we investigated the magnitude of the genotype–environment interaction effect on the resistance against Bursaphelenchus xylophilus (Steiner and Buhrer) Nickle in P. thunbergii. Materials and Methods: We inoculated P. thunbergii seedlings of six full-sib families, with various resistance levels, with B. xylophilus in nurseries at three experimental sites in the northern and southern regions of Japan. All parental clones of the tested families originated from southwestern Japan, and selection of parental clones for resistance was performed in the same region. Sound rates after nematode inoculation were calculated, and survival analysis, correlation analysis and variance component analysis were performed. Results and Conclusions: Families with high sound rate in the southern region also showed a high sound rate in the northern region. In almost all cases, Spearman’s correlation coefficients for sound rates were more than 0.698 among sites. The variance component of the interaction between site and family was small compared to that of site and family separately. Thus, we conclude that the resistant clones selected in the southern region would retain their genetic resistance in the northern regions.

Assessing Genetic Variation in Resistance to Pinewood Nematode (Bursaphelenchus xylophilus) in Pinus radiata D. Don Half-Sib Families

“…While our results suggested a low level of G × E, we have to take into consideration that we studied G × E under greenhouse conditions where the sources of variation are less pronounced. In a study by Matsunaga et al [55] of six P. thunbergii families at three sites with dissimilar climates, only a low proportion of the total variance was explained by site x family interaction and no changes were observed in the resistance family ranking. Similarly, Suontama [23] did not find a significant G × E interaction that affected P. radiata resistance to Cyclaneusma needle cast.…”

Genetics and Improvement of Forest Trees

“…Carignato et al [7] report genetic variability in drought resistance among Eucalyptus clones and indicate the possibility of early selection in the nursery. Two papers evaluate selected P. thunbergii clones in terms of pine wood nematode resistance [8,9]. Iki et al [8] report that the cumulative temperature after nematode infection does not affect the resistance ranking of progeny seedlings of nematode-resistant pine wood clones.…”

“…Iki et al [8] report that the cumulative temperature after nematode infection does not affect the resistance ranking of progeny seedlings of nematode-resistant pine wood clones. Matsunaga et al [9] examine the stability of traits in progeny seedlings moved to a different area from that in which they were selected. Tsuyama et al [10] report the results of a 10-year provenance trial of A. sachalinensis using multivariate random forests, a machine learning method.…”

Genetics and Improvement of Forest Trees

“…the possibility of early selection in the nursery. Two papers evaluate selected P. thunbergii clones in of pine wood nematode resistance [8,9]. Iki et al [8] report that the cumulative temperature after nematode infection does not the resistance ranking of progeny seedlings of nematode-resistant pine wood clones.…”

“…Iki et al [8] report that the cumulative temperature after nematode infection does not the resistance ranking of progeny seedlings of nematode-resistant pine wood clones. Matsunaga et al [9] examine the stability of traits in progeny seedlings moved to a different area from that in which they were selected. Tsuyama et al [10] report the results of a 10-year provenance trial of A. sachalinensis using multivariate random forests, a machine learning method.…”