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Water 2016, 8(5), 188; doi:10.3390/w8050188

Identification of Outlier Loci Responding to Anthropogenic and Natural Selection Pressure in Stream Insects Based on a Self-Organizing Map

1
Department of Integrated Biological Sciences, Pusan National University, Busan 609-735, Korea
2
Department of Civil and Environmental Engineering, Ehime University, Matsuyama 790-8577, Japan
3
Department of Life and Nanopharmaceutical Sciences and Department of Biology, Kyung Hee University, Seoul 130-701, Korea
4
Ecology and Future Research Association, Busan 609-735, Korea
5
Department of Physics, Pusan National University, Busan 609-735, Korea
*
Author to whom correspondence should be addressed.
Academic Editors: Young-Seuk Park and Soon-Jin Hwang
Received: 17 February 2016 / Revised: 26 April 2016 / Accepted: 27 April 2016 / Published: 6 May 2016
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Abstract

Water quality maintenance should be considered from an ecological perspective since water is a substrate ingredient in the biogeochemical cycle and is closely linked with ecosystem functioning and services. Addressing the status of live organisms in aquatic ecosystems is a critical issue for appropriate prediction and water quality management. Recently, genetic changes in biological organisms have garnered more attention due to their in-depth expression of environmental stress on aquatic ecosystems in an integrative manner. We demonstrate that genetic diversity would adaptively respond to environmental constraints in this study. We applied a self-organizing map (SOM) to characterize complex Amplified Fragment Length Polymorphisms (AFLP) of aquatic insects in six streams in Japan with natural and anthropogenic variability. After SOM training, the loci compositions of aquatic insects effectively responded to environmental selection pressure. To measure how important the role of loci compositions was in the population division, we altered the AFLP data by flipping the existence of given loci individual by individual. Subsequently we recognized the cluster change of the individuals with altered data using the trained SOM. Based on SOM recognition of these altered data, we determined the outlier loci (over 90th percentile) that showed drastic changes in their belonging clusters (D). Subsequently environmental responsiveness (Ek) was also calculated to address relationships with outliers in different species. Outlier loci were sensitive to slightly polluted conditions including Chl-a, NH4-N, NOX-N, PO4-P, and SS, and the food material, epilithon. Natural environmental factors such as altitude and sediment additionally showed relationships with outliers in somewhat lower levels. Poly-loci like responsiveness was detected in adapting to environmental constraints. SOM training followed by recognition shed light on developing algorithms de novo to characterize loci information without a priori knowledge of population genetics. View Full-Text
Keywords: AFLP; outlier loci; self-organizing map; aquatic insects; adaptation AFLP; outlier loci; self-organizing map; aquatic insects; adaptation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Li, B.; Watanabe, K.; Kim, D.-H.; Lee, S.-B.; Heo, M.; Kim, H.-S.; Chon, T.-S. Identification of Outlier Loci Responding to Anthropogenic and Natural Selection Pressure in Stream Insects Based on a Self-Organizing Map. Water 2016, 8, 188.

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