Ecological risk assessment based on information network model

Abstract

The natural properties of ecosystems have been widely altered by human activities, which in turn cause the endangerment of a range of species that will eventually affects humanity. Ecological risk assessment (ERA) is capable of modelling and quantifying the potential impact on ecosystems and their components (such as species and communities) initiated by human disturbance. In this study, a conceptual conversion of flow currency in network was accomplished, i.e. from the material/energy flow to the information flow. Based on the introduction of control allocation analysis and the estimation of the components' sensitivities to the stressor, we developed a new type of network analysis for holistic ecological risk assessment, so-called information-based network model. The reservoir ecosystem intercepted by Manwan Dam was used as a case study, The initial environmental risks were calculated based on the changes of three heavy metals (Hg, Pb and Cd), and the propagation of resultant risk between all functional components of the ecosystem was tracked. By incorporating both direct and indirect ecosystem interactions, the risk conditions of the whole ecosystem and its components were quantified and illustrated in the information networks. The results showed that: (1) on both ecosystem level and component level, there were significant differences between integral risk and initial risk after disturbance due to network amplification effect; (2) Hg seemed to cause the most highest integral risk to the ecosystem among the three heavy metals (3) almost all components had multiple sources of risk rather than solely received from the original input source (except the absolute controller, who only gives off risks but never receive one from other components); (4) the number of risk flow pathways notably increased from the input situation to network direct situation and to integral situation, implicating that the dynamics of the ecosystem are better manifested through a network perspective.