Patterning zooplankton community pattern in an agricultural reservoir using computational methods
Young-Seuk Park
Last modified: 2008-09-13
Abstract
Yong-Su Kwon1, Ku-SungPark2, Mi-Jeong Bae1, Sun-JinHwang2, Young-SeukPark1
1 Department of Biology, Kyung Hee University, Seoul 130-701, Korea.
2 Department of Environmental Science, Kon Kuk University, Seoul 143-701, Korea
Zooplankton community was sampled bimonthly from November, 2002 to February, 2004 in an agricultural reservoir, Shingu, in Korea. Additionally, 14 environmental variables (temperature, electric conductivity, chemical oxygen demand (COD), total phosphorus (TP), total nitrogen (TN), etc.) were measured in order to characterize relationships with zooplankton community dynamics. In the dataset, 28 species were identified, belonging to 3 taxonomic groups. Keratella cochlearis in Rotifer was the most abundant species. Changes of zooplankton community were compared with their environmental factors as well as phytoplankton communities. To characterize patterns of zooplankton community dynamics and the relationships with their environmental variables, computational methods such as self-organizing map (SOM) and Non-metric multidimensional scaling (NMS) were used. In the results, they showed strong seasonality of the community dynamics which was well explained with their environment variables. They displayed also interactions of dynamics between phytoplankton communities and zooplankton communities. The capability of SOM and NMS for patterning community dynamics were also compared.
1 Department of Biology, Kyung Hee University, Seoul 130-701, Korea.
2 Department of Environmental Science, Kon Kuk University, Seoul 143-701, Korea
Zooplankton community was sampled bimonthly from November, 2002 to February, 2004 in an agricultural reservoir, Shingu, in Korea. Additionally, 14 environmental variables (temperature, electric conductivity, chemical oxygen demand (COD), total phosphorus (TP), total nitrogen (TN), etc.) were measured in order to characterize relationships with zooplankton community dynamics. In the dataset, 28 species were identified, belonging to 3 taxonomic groups. Keratella cochlearis in Rotifer was the most abundant species. Changes of zooplankton community were compared with their environmental factors as well as phytoplankton communities. To characterize patterns of zooplankton community dynamics and the relationships with their environmental variables, computational methods such as self-organizing map (SOM) and Non-metric multidimensional scaling (NMS) were used. In the results, they showed strong seasonality of the community dynamics which was well explained with their environment variables. They displayed also interactions of dynamics between phytoplankton communities and zooplankton communities. The capability of SOM and NMS for patterning community dynamics were also compared.