DiscussionTop

Test sites differences: Larger diameter growth at the Dz test site than the Kp test site at all ages could be attributed to environmental differences between test sites. Dz has a first quality site class conditions (index 19,8), whereas Kp is second class (index 13,7) (Usta, 1991). In addition, the Dz site is located on a relatively higher elevation (Table 1) and likely to receive more rainfall than the Kp site (Kantarcı, 1991).

Population variation: The differential growth performances of the populations at the test sites can be explained mainly by two interrelated factors: One is the widely accepted theory that “local populations (or local races) are usually the best adapted populations in an area” (Zobel & Talbert, 2003; White et al., 2007; Vander Mijnsbrugge et al., 2010; Boshier et al., 2015). Aside from the results at early ages, our dbh data at age 35 supports the theory of adaptive superiority of local races, such that low elevation populations (S, D) showed the fastest growth at the low elevation test site (Kp), while mid elevation population (M) performed the best at the mid elevation site (Dz).

The second factor might be the advantages of rich gene pool diversity, which bestows central populations with higher adaptability. Namely, the seed source of population M is located at the central part of both the vertical and horizontal distribution range of the species, and thus has higher gene pool diversity because of gene flow from both the lower and higher elevation populations. On the other hand, the seed source of population H is a peripheral population located at the upper margin of the vertical distribution range of the species, and thus has less gene pool variation and would be expected to be under a higher risk of selective pressures. Indeed, isozyme analyses (Isik & Kaya, 1997; Kaya et al., 1997), RAPD (Kurt et al., 2011) and SSR markers (Kurt et al., 2012) also demonstrated that, when compared with the others, population H has a lower genetic variation.

Variance components: High proportion of Within (Error) variance at early ages is a common pattern in provenance + progeny trials of forest trees (White et al., 2007; Diao et al., 2016). The high Site effect in the study is attributed mainly to ecological differences associated with plantation sites (Li et al., 2017). Statistically significant interactions between sites x populations indicate that genotypes of populations respond differently to different environmental conditions in expressing their quantitative traits. This differential response is not a surprising result for P. brutia which is a relatively complex species with relatively high variation and locally adapted populations in diverse altitudinal and associated climatic environments throughout its distribution range (Isik, 1986; Isik & Kaya, 1997; Kurt et al., 2012). Such adaptations and genotype x environment interactions are common phenomena in P. brutia (Kaya et al., 1997; Isik et al., 2000; Dangasuk & Panetsos, 2004) as well as in many other forest tree species (Li et al., 2017).

Heritability values: Cornelius (1994), in his review on 67 published paper on various forest tree species, about 70% of which are pine species, reported that “heritabilities of height, diameter, volume, branching traits and straightness are generally below 0.4 and frequently in the range of 0.1 – 0.3”. Isik et al. (1999) found that on 17 years old P. brutia at the same test sites as the present study, family heritability for dbh (under bark) was 0.20, which is rather consistent with the results of the present study. The family heritability values in the present study are also in accord with different pine species such as Pinus radiata (0.38; Burdon & Banister, 1992), Pinus sylvestris (0.23; Haapanen, 2001), and Pinus taeda (0.13; Paul et al., 1997). Standard errors of heritabilities for dbh were higher than the corresponding heritability estimates. This might arise primarily due to high variance among families within populations (which is closely related to small sample sizes), and partly due to high Site x Family interactions.

Genetic correlations among characters: As expected, correlation is high and positive between the same traits at different ages. As the age interval between any two age pairs (i.e., J and M) increases, both observed (rGJM-O) and predicted (rGJM-P) genetic correlation coefficients among them decreases, because predicted genetic correlations are found principally by employing observed genetic correlation values. All of these trends are consistent with those found by Lambeth (1980) and other studies on ageage correlations (e.g., Xie & Ying, 1996; Lambeth & Dill, 2001; Jansson et al., 2003; Isik et al., 2010; Ye & Jayawickrama, 2012; Diao et al., 2016).

Isik et al. (1999), when working on the same test sites as the present study when the trees were 13 years old, reported that genetic correlation between dbh and tree height was 0.89 (P < 0.001). In a subsequent study at age 17 years at the same test sites, Isik (1998) also reported high and positive genetic correlations between dbh and tree height (0.84), between dbh and volume (0.99), and between height and volume (0.91). When taking 265 sample plots throughout the natural distribution range in southern Turkey, Erkan (1996) found that P. brutia exhibits strong phenotypic correlations between diameter growth and height growth (r = 0.838; P < 0.001).

Presence of high genetic (and phenotypic) correlation coefficients between any two given characters offers plant breeders great opportunities for concurrent selection. Specifically, if one characteristic is selected for genetic improvement, the other characteristic could simultaneously be selected. For example, high positive genetic correlations between dgl (diameter at ground level) and dbh, and also between tree height and dbh characters at all ages suggest that if dgl and / or height is selected for genetic improvement at early ages dbh characteristics of later ages would also be selected indirectly (Falconer & Mackay, 1996; Lin et al., 2014).

Prediction equation for P. brutia: Estimated “a” (1.034) and “b” (0.241) values in prediction equation in P. brutia in this study are consistent with the corresponding values found by Lambeth (1980) and others [i.e., in Lambeth (1980) a = 1.02 and b = 0.308; in Weng et al. (2007) a = 1.04, b = 0.13; in Isik et al. (2010) a = 1.023, b = 0.384].

Selection efficiency and optimum selection age: In predicting selection efficiencies in this study, we preferred to use genetic correlation coefficients. The preference is because genetic correlations among the characters are more stable and less influenced by environmental factors than that of phenotypic correlations, especially in long-lived organisms such as forest tree species (Libby, 1973; Falconer & Mackay, 1996; Li et al., 2017). In some studies, instead of genetic correlations, phenotypic correlation coefficients have also been used in estimating selection efficiencies (e.g., Burdon, 1989; Xie & Ying, 1996; Lambeth & Dill, 2001).

For any harvest age in P. brutia, as selection age (J) increases starting from early years, selection efficiency per year also increases until it reaches the highest (optimum) value. Then, it declines gradually and, when J becomes equal to M, it reaches the theoretical value 1.0. Selection efficiency curves in most studies also exhibit more or less similar trends as P. brutia (e.g., Lambeth & Dill, 2001; Jansson et al., 2003; Osorio et al., 2003; Xiang et al., 2003; Isik et al., 2010). Obviously, if forest managers decide the harvest age to be a relatively young age, annual genetic gain is maximized at earlier years (i.e., optimum early selection age is reached in earlier years on the test sites). However, there is no any correlation between harvest age and corresponding optimum selection age. For instance, for harvest ages 40 and 60 years, optimum early selection ages are the same.

Fast growing forest tree species, including P. brutia, reach optimum selection ages usually in earlier years compared with the slow growing tree species. For example, on Pinus taeda, which is a relatively fast growing species, Xiang et al. (2003) found that optimum selection age for height growth was 3 or 4 years. On the other hand, in Pinus sylvestris, which is a slow growing pine, optimum selection age (for height) was between 10 to 15 years (Jansson et al., 2003). Isik et al. (2010), by working on a clonal experiment in Picea abies, a slow growing species, reported that for height growth early selection age could be as early as age 13.

Our data for predicting early selection age in P. brutia is based on the dbh characteristic. One can argue that most trees in the species cannot attain enough height to have measurable dbh at early ages. However, it should be emphasized that there are strong genetic correlations between ground level diameter (dgl) and dbh characters, as found in this study. In addition, Kurt et al. (2021) reported that there were very high correlations (r = 0.99) between the mean dgl and mean dbh on data obtained from seven different even-aged P. brutia plantations, ages of which ranged from 10 to 35 years. Isik (1998), based on measurements at age 17 years on the same test sites as the present study, reported high and positive genetic correlations between dbh and tree height (0.84), dbh and volume (0.99), and tree height and volume (0.91). Based on these strong relationships among diameter characters and height, dgl or tree height (or both) characters at early ages can also be measured instead of dbh character to predict optimum selection ages.

Conclusions and management implications 

One of the challenges in breeding of forest trees, which are long-lived species, is the long-time interval between selection age and the harvest age. Determining the early selection age of forest trees instead of waiting until harvest age for direct selection is an effective tool to save both time and money. Development of new DNA sequencing technologies within the past 15 years in molecular biology offers great opportunities in various respects in plant and animal breeding. Genomic selection (GS), which uses large number of DNA markers to cover the whole genome, has been successfully applied in animal breeding, doubling genetic gain in milk yield per unit time (Goddard et al., 2011). Forest geneticists / tree breeders also have embraced GS technology to select superior genotypes and to shorten the breeding cycles. However, Isik (2014) in his extensive review article cautioned that “Forest geneticists should avoid over-promising GS to the community, until all the necessary ingredients are in place”. In this respect, conventional field tests such as progeny trials are very important to characterize the base populations for molecular breeding approaches. Breeding programs for many forest trees, including P. brutia in Turkey, are still in their early stages. Therefore, we consider that both the present and relevant other studies of P. brutia are the pioneering efforts to provide "necessary ingredients” for genetic improvement, and subsequently genomic selection programs for this species. 

There are some limitations regarding Lambeth’s prediction model and the assumptions used in the present study. First, this study includes only six provenances (and 10 mother trees per provenance) planted on only at two test sites located on an elevational transect. However, it is worth noting that the provenances (and mother trees) covered in the present study represent the core and the most productive parts of the vertical and horizontal distribution range of P. brutia (Usta, 1991; Erkan, 1996). Second, this study is based on dbh characteristic, which cannot be directly measured before the trees attain certain height growth as in most forest tree species. Tree breeders thus need to observe other correlated traits, measurable at early ages. Diameter at ground level (dgl) and tree height, both of which are significantly correlated with dbh traits, can be measured at early ages to replace the dbh trait. Third, in estimating selection efficiency, we assumed that broad sense heritability (H2 F) values for the dbh trait beyond the observation ages are the same as those of the nearest measurement ages. Our results also justify this assumption, since H2 F values for dbh remain within a narrow band within the observation ages (i.e., between 0.205 and 0.282). These concerns are also shared by several other researchers in their relevant studies (e.g., Lambeth, 1980; Cotterill & Dean, 1988; Gwaze et al., 2000; Isik et al., 2010). It appears that additional studies are required to fine tune the optimum selection age reported in this study. It remains to be seen how genetic parameters reported here would change by studying larger numbers of populations (and mother trees) covering wider distribution ranges of P. brutia.