Subsequent application of self-organizing map and hidden Markov models infer community states of stream benthic macroinvertebrates
Because an ecological community consists of diverse species that vary nonlinearly with environmental variability, its dynamics are complex and difficult to analyze. To investigate temporal variations of benthic macroinvertebrate community, we used the community data that were collected at the samp...
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The Ecological Society of Korea
2015-03-01
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| Series: | Journal of Ecology and Environment |
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| Online Access: | http://dx.doi.org/10.5141/ecoenv.2015.010 |
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| author | Dong-Hwan Kim Tuyen Van Nguyen Muyoung Heo Tae-Soo Chon |
| author_facet | Dong-Hwan Kim Tuyen Van Nguyen Muyoung Heo Tae-Soo Chon |
| author_sort | Dong-Hwan Kim |
| collection | DOAJ |
| description | Because an ecological community consists of diverse species that vary nonlinearly with environmental variability, its
dynamics are complex and difficult to analyze. To investigate temporal variations of benthic macroinvertebrate community,
we used the community data that were collected at the sampling site in Baenae Stream near Busan, Korea, which
is a clean stream with minimum pollution, from July 2006 to July 2013. First, we used a self-organizing map (SOM) to
heuristically derive the states that characterizes the biotic condition of the benthic macroinvertebrate communities in
forms of time series data. Next, we applied the hidden Markov model (HMM) to fine-tune the states objectively and to
obtain the transition probabilities between the states and the emission probabilities that show the connection of the
states with observable events such as the number of species, the diversity measured by Shannon entropy, and the biological
water quality index (BMWP). While the number of species apparently addressed the state of the community, the
diversity reflected the state changes after the HMM training along with seasonal variations in cyclic manners. The BMWP
showed clear characterization of events that correspond to the different states based on the emission probabilities. The
environmental factors such as temperature and precipitation also indicated the seasonal and cyclic changes according
to the HMM. Though the usage of the HMM alone can guarantee the convergence of the training or the precision of the
derived states based on field data in this study, the derivation of the states by the SOM that followed the fine-tuning by
the HMM well elucidated the states of the community and could serve as an alternative reference system to reveal the
ecological structures in stream communities. |
| format | Article |
| id | doaj-art-510563e223284bbf82aff9e354eb80bc |
| institution | OA Journals |
| issn | 2287-8327 2288-1220 |
| language | English |
| publishDate | 2015-03-01 |
| publisher | The Ecological Society of Korea |
| record_format | Article |
| series | Journal of Ecology and Environment |
| spelling | doaj-art-510563e223284bbf82aff9e354eb80bc2025-08-20T02:23:53ZengThe Ecological Society of KoreaJournal of Ecology and Environment2287-83272288-12202015-03-013819510710.5141/ecoenv.2015.010Subsequent application of self-organizing map and hidden Markov models infer community states of stream benthic macroinvertebratesDong-Hwan Kim0Tuyen Van Nguyen1Muyoung Heo2Tae-Soo Chon3Department of Integrated Biological Science, Pusan National University, Busan 609-735, KoreaDepartment of Physics, Pusan National University, Busan 609-735, KoreaDepartment of Physics, Pusan National University, Busan 609-735, KoreaDepartment of Integrated Biological Science, Pusan National University, Busan 609-735, KoreaBecause an ecological community consists of diverse species that vary nonlinearly with environmental variability, its dynamics are complex and difficult to analyze. To investigate temporal variations of benthic macroinvertebrate community, we used the community data that were collected at the sampling site in Baenae Stream near Busan, Korea, which is a clean stream with minimum pollution, from July 2006 to July 2013. First, we used a self-organizing map (SOM) to heuristically derive the states that characterizes the biotic condition of the benthic macroinvertebrate communities in forms of time series data. Next, we applied the hidden Markov model (HMM) to fine-tune the states objectively and to obtain the transition probabilities between the states and the emission probabilities that show the connection of the states with observable events such as the number of species, the diversity measured by Shannon entropy, and the biological water quality index (BMWP). While the number of species apparently addressed the state of the community, the diversity reflected the state changes after the HMM training along with seasonal variations in cyclic manners. The BMWP showed clear characterization of events that correspond to the different states based on the emission probabilities. The environmental factors such as temperature and precipitation also indicated the seasonal and cyclic changes according to the HMM. Though the usage of the HMM alone can guarantee the convergence of the training or the precision of the derived states based on field data in this study, the derivation of the states by the SOM that followed the fine-tuning by the HMM well elucidated the states of the community and could serve as an alternative reference system to reveal the ecological structures in stream communities.http://dx.doi.org/10.5141/ecoenv.2015.010ecological assessmentemission probability matrixevent sequenceMarkov processestemporal dynamics |
| spellingShingle | Dong-Hwan Kim Tuyen Van Nguyen Muyoung Heo Tae-Soo Chon Subsequent application of self-organizing map and hidden Markov models infer community states of stream benthic macroinvertebrates Journal of Ecology and Environment ecological assessment emission probability matrix event sequence Markov processes temporal dynamics |
| title | Subsequent application of self-organizing map and hidden Markov models infer community states of stream benthic macroinvertebrates |
| title_full | Subsequent application of self-organizing map and hidden Markov models infer community states of stream benthic macroinvertebrates |
| title_fullStr | Subsequent application of self-organizing map and hidden Markov models infer community states of stream benthic macroinvertebrates |
| title_full_unstemmed | Subsequent application of self-organizing map and hidden Markov models infer community states of stream benthic macroinvertebrates |
| title_short | Subsequent application of self-organizing map and hidden Markov models infer community states of stream benthic macroinvertebrates |
| title_sort | subsequent application of self organizing map and hidden markov models infer community states of stream benthic macroinvertebrates |
| topic | ecological assessment emission probability matrix event sequence Markov processes temporal dynamics |
| url | http://dx.doi.org/10.5141/ecoenv.2015.010 |
| work_keys_str_mv | AT donghwankim subsequentapplicationofselforganizingmapandhiddenmarkovmodelsinfercommunitystatesofstreambenthicmacroinvertebrates AT tuyenvannguyen subsequentapplicationofselforganizingmapandhiddenmarkovmodelsinfercommunitystatesofstreambenthicmacroinvertebrates AT muyoungheo subsequentapplicationofselforganizingmapandhiddenmarkovmodelsinfercommunitystatesofstreambenthicmacroinvertebrates AT taesoochon subsequentapplicationofselforganizingmapandhiddenmarkovmodelsinfercommunitystatesofstreambenthicmacroinvertebrates |