생태계 네트워크 분석을 이용한 생물 군집의 구조와 기능에 대한 연구: 청계천을 사례로Exploring Community Structure and Function with Network Analysis: a Case Study of Cheonggye Stream
- Other Titles
- Exploring Community Structure and Function with Network Analysis: a Case Study of Cheonggye Stream
- Authors
- 이민영; 김용은; 조기종
- Issue Date
- 2018
- Publisher
- 한국환경생물학회
- Keywords
- species interaction; food web ecology; biodiversity; network theory; species diversity
- Citation
- 환경생물, v.36, no.3, pp.370 - 376
- Indexed
- KCI
- Journal Title
- 환경생물
- Volume
- 36
- Number
- 3
- Start Page
- 370
- End Page
- 376
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/79053
- ISSN
- 1226-9999
- Abstract
- It is important to consider interaction between species in understanding structure and function of the biological community. Current ecological issues such as climate change and habitat loss emphasize the significance of the concept of species interaction in that varying species’ interaction across environmental gradients may lead to altered ecological function and services. However, most community studies have focused on species diversity through analysis of quantitative indices based on species composition and abundance data without considering species interactions in the community. ‘Ecological network analysis’ based on network theory enables exploration of structural and functional properties of ecosystems composed of various species and their interactions. In this paper, network analysis of Cheonggye stream as a case study was presented to promote uses of network analysis on ecological studies in Korea. Cheonggye stream has a simple biological structure with link density of 1.48, connectance 0.07, generality 4.43, and vulnerability 1.94. The ecological network analysis can be used to provide ecological interpretations of domestic long-term monitoring data and can contribute to conserving and managing species diversity in ecosystems.
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Collections - College of Life Sciences and Biotechnology > Division of Environmental Science and Ecological Engineering > 1. Journal Articles
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