title: Biological early warning system based on the responses of aquatic organisms to disturbances: a review.
abstract: Aquatic ecosystems are subject to a number of anthropogenic disturbances, including environmental toxicants. The efficient monitoring of water resources is fundamental for effective management of water quality and aquatic ecosystems. Spot sampling and continuous water quality monitoring based on physicochemical factors are conducted to assess water quality. However, not all contaminants or synergistic and antagonistic toxic effects can be determined by solely analyzing the physicochemical factors. Thus, various biotests have been developed using long-term and automatic observation studies based on the ability of the aquatic organisms to continuously sense a wide range of pollutants. In addition, a biological early warning system (BEWS) has been developed based on the response behaviors of organisms to continuously detect a wide range of pollutants for effective water quality monitoring and management. However, large amounts of data exhibiting non-linearity and individual behavioral variation are continuously accumulated over long-term and continuous behavioral monitoring studies. Thus, appropriate mathematical and computational data analyses are necessary to manage and interpret such large datasets. Here, we review the development and application of BEWS by using various groups of organisms and the computational methods used to process the behavioral monitoring data.
authors: Department of Biology, Kyung Hee University, Dongdaemun, Seoul 130-701, Republic of Korea.
authors: Mi-Jung
authors: MJ
authors: Bae
authors: Young-Seuk
authors: YS
authors: Park
mesh_terms: D059001:Aquatic Organisms / Q000187:drug effects* / Q000502:physiology*; D019295:Computational Biology / Q000379:methods*; D003247:Conservation of Natural Resources; D017753:Ecosystem; D004784:Environmental Monitoring / Q000379:methods; D014873:Water Pollutants / Q000633:toxicity*
chemical_list: D014873:Water Pollutants
keywords: Aquatic ecosystem; Behavioral monitoring; Biological early warning system (BEWS); Computational analysis; Movement behavior; Water management