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베이지안 모델을 이용한 하구수생태계 부착돌말류의 생태 네트워크 KCI 등재

Ecological Network on Benthic Diatom in Estuary Environment by Bayesian Belief Network Modelling

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  • URLhttps://db.koreascholar.com/Article/Detail/413248
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생태와 환경 (Korean Journal of Ecology and Environment)
초록

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.

목차
Abstract
서 론
재료 및 방 법
    1. 대상지역 및 자료수집
    2. Tier 설정
    3. 베이지안 네트워크 (Bayesian Belief Network) 분석
결과 및 고 찰
    1. 하구수생태계 부착돌말 출현 현황
    2. 부착돌말류 건강성 변화
    3. 베이지안 네트워크 분석
결 론
적 요
REFERENCES
저자
  • 김건희((주)시온 E&S 부설연구소) | Keonhee Kim (Zion E&S Co. Ltd., Research Institute) Corresponding author
  • 박채홍(건국대학교 휴먼앤에코케어센터) | Chaehong Park (Human and Eco- Care Center, Sanghuh College of Life Sciences, Konkuk University)
  • 김승희(한양대학교 과학기술융합대학 해양융합과학과) | Seung-hee Kim (Department of Marine Sciences and Convergent Technology, Hanyang University)
  • 원두희((주)생태조사단 부 설 두희생태연구소) | Doo-Hee Won (3Doohee Institute of Ecological Research, Korea Ecosystem Service Inc)
  • 이경락(국립환경과학원 물환경공학연구과) | Kyung-Lak Lee (Water Environmental Engineering Research Division, National Institute of Environmental Research)
  • 전지영(국립환경과학원 물환경공학연구과) | Jiyoung Jeon (Water Environmental Engineering Research Division, National Institute of Environmental Research)