An ecological-network-analysis based perspective on the biological control of algal blooms in Ulansuhai Lake, China

Xufeng Mao, Xiaoyan Wei, Donghai Yuan, Yanxiang Jin and Xin Jin

Ecological Modelling, 2018, vol. 386, issue C, 11-19

Abstract: Biological control is considered as an environmentally and effective means of reducing or mitigating harmful algal blooms. By biological remediation approach (addition of predators or competitors), it may alter the ecological relationships between the dominant algae and their associated populations in ecosystems. However, it cannot quantify the indirect effects of this approach and the long-term and system-level ecological consequences are hard to assess. Here we focus on the "bloom" areas of the Ulansuhai Lake in Inner Mongolia. Based on historic data, field monitoring and experimental data, we constructed a quantitative phosphorus cycle network model. Using Ecological Network Analysis (ENA), we evaluated how indirect flow influences dominant algal species and alters network control and utility relationships. Results indicate that: (1) Indirect flows have strong influence on the mutual and control relationships between blooming algae and its related functional groups in the Ulansuhai Lake; (2) Some opposite interspecific relationships between the blooming algae and other functional components have been found by ENA; (3) Key populations controlling the blooming algae are observed from the intuitive Zooplankton and Detritus to network-based Microorganisms, Zoobenthos, Zooplankton and Detritus(water). The research results indicate the importance of including indirect relationship into the control of lake algae bloom. The application of ENA was good at revealing indirect effects for ecological restoration of eutrophic lakes.

Keywords: Algal bloom; Control analysis; Utility analysis; Ecological network analysis; Ulansuhai Lake (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:386:y:2018:i:c:p:11-19

DOI: 10.1016/j.ecolmodel.2018.07.020

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