• Journal of Life Science
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• v.17 no.10
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• pp.1441-1446
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• 2007

This study was conducted to establish the individual identification system and to estimate the genetic characteristic of Jeju native horse (JNH) using 13 microsatellite markers located on different horse autosomes. The markers were genotyped on 191 animals from five horse breeds including Jeju native horse (JNH). In total, 138 alleles were detected from the genotypes of 13 microsatellite markers. The average heterozygosities ranged from 0.317 to 0.902 and the polymorphic information content (PIC) ranged from 0.498 to 0.799 in JNH. We found that there were significant differences in allele frequencies in JNH when compared with other horse breeds. In ATH4 marker, there were specific allele frequence pattern that some of allele only found in JNH, Mongolian horse (MONG) and Jeju racing horse (JRH). The calculated cumulative power of discrimination (CPD) was 99.9% when nine microsatellite loci were used for analysis in the individual identification system. Also, the matching probability that two unrelated animals would show the same genotypes, was estimated to be $0.60{\times}10^{-10}$. Therefore, in the nine markers used in this study can be used for individual identification in the Jeju native horse population.

• Korean Journal of Ecology and Environment
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• v.55 no.1
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• pp.60-75
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• 2022

The Bayesian algorithm model is a model algorithm that calculates probabilities based on input data and is mainly used for complex disasters, water quality management, the ecological structure between living things or living-non-living factors. In this study, we analyzed the main factors affected Korean Estuary Trophic Diatom Index (KETDI) change based on the Bayesian network analysis using the diatom community and physicochemical factors in the domestic estuarine aquatic ecosystem. For Bayesian analysis, estuarine diatom habitat data and estuarine aquatic diatom health (2008~2019) data were used. Data were classified into habitat, physical, chemical, and biological factors. Each data was input to the Bayesian network model (GeNIE model) and performed estuary aquatic network analysis along with the nationwide and each coast. From 2008 to 2019, a total of 625 taxa of diatoms were identified, consisting of 2 orders, 5 suborders, 18 families, 141 genera, 595 species, 29 varieties, and 1 species. Nitzschia inconspicua had the highest cumulative cell density, followed by Nitzschia palea, Pseudostaurosira elliptica and Achnanthidium minutissimum. As a result of analyzing the ecological network of diatom health assessment in the estuary ecosystem using the Bayesian network model, the biological factor was the most sensitive factor influencing the health assessment score was. In contrast, the habitat and physicochemical factors had relatively low sensitivity. The most sensitive taxa of diatoms to the assessment of estuarine aquatic health were Nitzschia inconspicua, N. fonticola, Achnanthes convergens, and Pseudostaurosira elliptica. In addition, the ratio of industrial area and cattle shed near the habitat was sensitively linked to the health assessment. The major taxa sensitive to diatom health evaluation differed according to coast. Bayesian network analysis was useful to identify major variables including diatom taxa affecting aquatic health even in complex ecological structures such as estuary ecosystems. In addition, it is possible to identify the restoration target accurately when restoring the consequently damaged estuary aquatic ecosystem.