Pinus densiflora Siebold & Zucc. is a widely distributed conifer species in the Republic of Korea with economic and ecologic importance. However, P. densiflora is negatively influenced by various factors, such as forest fires, clearing of large numbers of trees infected with Pinus disease, and dieback. We aimed to use microsatellite markers to estimate the genetic diversity, differentiation, and structure of P. densiflora populations in the Republic of Korea. A total of 1844 samples from 60 populations were evaluated using 11 polymorphic microsatellite markers. The observed heterozygosity and expected heterozygosity were 0.652 and 0.673, respectively. The mean genetic differentiation among the populations was 0.013. Moreover, P. densiflora showed high genetic diversity and low genetic differentiation compared with conifer species, including Pinus species with similar life histories. Principal coordinates analysis and Bayesian clustering showed that P. densiflora has a weak geographical structure. The P. densiflora population at Mt. Halla, Jeju Island, showed the lowest genetic diversity and significant genetic differentiation compared with other mainland populations due to genetic drift and restricted gene flow. These findings can be useful for designing new conservation, management, and breeding strategies for P. densiflora populations in response to future environmental changes.

Various problems worldwide are caused by illegal production and distribution of timber, such as deception about timber species and origin and illegal logging. Numerous studies on wood tracking are being conducted around the world to demonstrate the legitimacy of timber. Tree species identification is the most basic element of wood tracking research because the quality of wood varies greatly from species to species and is consistent with the botanical origin of commercially distributed wood. Although many recent studies have combined machine learning-based classification methods with various analytical methods to identify tree species, it is unclear which classification model is most effective. The purpose of this work is to examine and compare the performance of three supervised machine learning classification models, support vector machine (SVM), random forest (RF), and artificial neural network (ANN), in identifying five conifer species and propose an optimal model. Using direct analysis in real-time ionization combined with time-of-flight mass spectrometry (DART-TOF-MS), metabolic fingerprints of 250 individual specimens representing five species were collected three times. When the machine learning models were applied to classify the wood species, ANN outperformed SVM and RF. All three models showed 100% prediction accuracy for genus classification. For species classification, the ANN model had the highest prediction accuracy of 98.22%. The RF model had an accuracy of 94.22%, and the SVM had the lowest accuracy of 92.89%. These findings demonstrate the practicality of authenticating wood species by combining DART-TOF-MS with machine learning, and they indicate that ANN is the best model for wood species identification.

We applied eight primer-restriction enzyme combinations to investigate genetic diversity, genetic differentiation, and genetic structure of Carpinus laxiflora populations with AFLP markers. The average of effective alleles (A_e), the proportion of polymorphic loci (%P), Shannon’s diversity index (I), and the expected heterozygosity (H_e) were 1.4, 82.2%, 0.371, and 0.241, respectively. The expected heterozygosity (Hj) from Bayesian method was 0.270. The level of genetic diversity was high compared to those of Carpinus species and other species with a similar life history. The inbreeding coefficient (F_IS) from approximated Bayesian method was 0.618, which was smaller than that for Acer pseudosieboldianum (F_IS=0.712). Genetic differentiation was 0.060 from AMOVA (Φ_ST) and 0.056 from Bayesian method (θ_II). The level of genetic differentiation was very small compared to that of Carpinus species and other species with a similar life history. According to UPGMA and Bayesian clustering, 10 populations were divided into two genetic groups. Except Mt. Chilgap and Minjuji, most of the populations were detected as weak genetic structures according to the geographical distribution such as mountain ranges. We might consider that demographic disturbance, local specific vegetation change history, and forest succession interrupted the genetic structure of C. laxiflora in South Korea.

We applied seven pairs of primer-restriction enzyme combinations to investigate the genetic diversity, genetic differentiation, and genetic structure of Prunus padus populations with AFLP markers. The values obtained for average of effective alleles (A _e), percentage of polymorphic loci (%P), Shannon’s diversity index (I), and expected heterozygosity (H _e) were 1.38, 81.4, 0.357, and 0.223%, respectively. The expected heterozygosity (Hj) obtained by using a Bayesian method was 0.256. The level of genetic diversity obtained for P. padus was low compared to that of Prunus species and other species with a similar life history. The inbreeding coefficient (F _IS) from the approximated Bayesian method was 0.767. This value was lower than that obtained for Ulmus davidiana, which undergoes both sexual and asexual reproduction. However, the value obtained was larger than that for other species that undergo sexual reproduction, such as, Carpinus laxiflora, Phellodendron amurense, and Acer pseudosieboldianum. The value of genetic differentiation was 0.245 from AMOVA (Φ_ST) and 0.278 from Bayesian method (θ^II). The obtained level of genetic differentiation was large compared to that of other Prunus species plants and other species with a similar life history. According to UPGMA and Bayesian clustering, 11 populations were divided into two genetic groups. However, some populations were detected as weak genetic structures according to the geographical distribution. It was occurred by forest succession, asexual propagation strategies to adapt local environmental change, and gene flow being gradually decreased due to population fragmentation by demographic disturbances.