Analysis of genotype-by-environment interaction in a multisite progeny test with Scots pine for supporting selection decisions

Abstract

In multisite forest genetic experiments, the presence of genotype × environment interaction (GEI) is common. GEI may negatively affect the estimates of genetic variance and hamper selection decisions in tree breeding programs. Several measures exist to evaluate the stability of tested genotypes’ performance across environments with a choice of the method likely affecting breeders’ decisions. In this study, we evaluated variation in diameter and height growth performance in the progeny test established at 4 sites with 80 open-pollinated half-sib families of Scots pine. We found significant variation among examined progeny at age 10, reaching up to 31% for diameter and 20% for height depending on site, and significant GEI in both traits. We estimated contribution of each family to GEI using various methods and tools of GEI analysis—AMMI, GGE biplots, heterogeneity of regression coefficients (bi’s), the deviation mean squares from regression (s2di) and Kang’s yield-stability index (YSi). Despite the presence of the cross-over interaction, family ranks did not vary much among sites. The selections based on the phenotype, YSi and restricted bi corresponded well to each other leading to the expected response to selection up to 7.8% on diameter and 4.4% on height, whereas those based on the AMMI stability variance were different and lead to a slight loss in both traits. We discuss our results in the context of the usefulness of those measures of genotype stability for tree breeding programs and propose the procedure to follow for making selection decisions in forest experiments with GEI.

The genetic and environmental variance of radial increment in Scots pine of south-eastern Baltic provenances in response to weather extremes

“…The synergic effect of weather anomalies on the heritability estimates (Figure 3) complied with the multifactorial regulation of the growth plasticity and recovery (efficiency and period) (Pennacchi et al 2021, Vuosku et al 2022. Accordingly, local genetic adaptation can be related to cumulative weather effects and hence the ability to cope with multiple stresses (Ansarifar et al 2020, Chmura et al 2021, which have likely contributed to productivity (Way and Oren 2010) and are increasingly important under the anticipated climatic changes (Li et al 2017). The multifactorial genetic controls under optimal growing conditions (de la Mata et al 2022, Vuosku et al 2022 have likely caused the estimates of H 2 and CVP to be intermediate and low, respectively.…”

“…The local effects (Figure 3, Table 5) implied the limited breeding efficiency to alter tolerance to meteorological anomalies at a wider scale (Loha et al 2006, de la Mata et al 2022. Still, the explicit phenotypic plasticity (G × E interaction) indicated the potential of targeted breeding for local improvements by applying semiconservative (intra-regional) assisted migration (Corcuera et al 2011, Breed et al 2013, Aitken and Bemmels 2016, Chmura et al 2021. However, the selection of the provenances, which generally showed above-average performance, might have introduced some bias in variance partitioning and hence the heritability estimates at the trial level (Falconer andMackay 1996, Leites et al 2012).…”

“…RYT, GUS) to utilise ecological opportunities (Matisons et al 2019) and likely increase competitiveness (Loehle 1998). Furthermore, the presence of the provenance specific changes in growth rate (Figure 3), both positive and negative, implied effects of local adaptation (Loha et al 2006, Moran et al 2017, Chauvin et al 2019, which differed by trial, revealing complex interactions with local conditions (Li et al 2017, Chmura et al 2021, Matisons et al 2021a, especially in the harsher climate (KA trial; Table 1). Still, the consistent responses of the provenances in the PYs in the WS trial (Figure 3) suggested that in a warmer and dryer climate (Table 1), relationships between the sensitivity to anomalies and productivity can intensify (Cavin and Jump 2017, Tei et al 2017, Xu et al 2017).…”

“…However, the selection of the provenances, which generally showed above-average performance, might have introduced some bias in variance partitioning and hence the heritability estimates at the trial level (Falconer andMackay 1996, Leites et al 2012). As the study was based on a limited set of genotypes, the upscaling of the results; however, should be precautious (Loha et al 2006, Arnold et al 2019, Chmura et al 2021.…”

“…Nevertheless, the presence of local genetic adaptation is a precondition for successful forest breeding (Jansson et al 2017, Moran et al 2017, which is crucial for climate-smart management (Aitken andBemmels 2016, Nabuurs et al 2018). Furthermore, the G × E interactions have been revisited as the indicator of adaptability, as they are the measure of the phenotypic and hence the ecological plasticity of tree populations (Li et al 2017, Moran et al 2017, Ansarifar et al 2020, Chmura et al 2021, de la Mata et al 2022. In this regard, quantification of genetic control over the traits relevant for adaptability is necessary , Arnold et al 2019, for which parallel provenance trials testing genotypes across extended environmental gradients, are advantageous (Leites et al 2012, Leites andBenito Garzon 2023).…”