Determining temporal pattern of community dynamics by using unsupervised learning algorithms

Chon, Tae‐Soo; Park, Young‐Seuk; Park, June Ho

doi:10.1016/s0304-3800(00)00312-4

Cited by 55 publications

(29 citation statements)

References 21 publications

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“…The sample sites (Y1 (Imgok: YIG), Y2 (Changki; YCK) and Y3 (Shinchon; YSC)) in the Suyong Stream were located in the intermediately polluted agricultural areas (BOD 2.8 -3.7 mg/L, conductivity 134.3 -324.1 µS/ cm) , Park et al 2004). The Biological Monitoring Working Party (BMWP) scores were accordingly low around 3 (median) at the polluted sites, while the values were correspondingly higher in the range of 17 -46 (median) at less polluted sites in the Suyong River basin (Table 1) (Kwon & Chon 1993, Youn & Chon 1996, Chon et al 2000.…”

Section: Study Areamentioning

confidence: 99%

Species abundance patterns of benthic macroinvertebrate communities in polluted streams

Song²,

Park

et al. 2008

Ann. Limnol. - Int. J. Lim.

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Benthic macroinvertebrates collected at seven different streams displaying different pollution levels were used to investigate species abundance patterns in polluted streams. Community response to disturbances in streams was analysed using species abundance distribution (SAD) for benthic macroinvertebrates across different levels of pollution. The slopes of rank abundance were characteristically steeper with decreasing species richness at the polluted sample sites, while the slopes were less steep with higher species richness at the clean or slightly disturbed sample sites. The SADs were broadly fitted to the log normal distribution in benthic macroinvertebrate communities across different levels of pollution. A geometric series was partly accepted for the communities at the severely polluted sites where new species could not be readily introduced. A power law was applied to the SADs, and the parameters reflected the states pertaining to the sample sites. The SADs were also efficient in revealing ecological state of communities where physico-chemical indicators could not be easily differentiated for stressful conditions in streams.

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Section: Study Areamentioning

confidence: 99%

Species abundance patterns of benthic macroinvertebrate communities in polluted streams

Song²,

Park

et al. 2008

Ann. Limnol. - Int. J. Lim.

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“…Other researchers have reduced the number of taxa before analysis. Chon et al (2000), for example, summed the species density into 7 selected taxa on high taxonomic level to avoid noise, caused by species with low densities. They assumed that this might have contributed to stabilising the process.…”

Section: Cluster Characteristicsmentioning

confidence: 99%

“…Among ANN techniques, the self-organising map (SOM), which is based on an unsupervised learning algorithm, is often used to analyse the community structure. In several applications, e.g., Chon et al (1996Chon et al ( , 2000 and Park et al (2001Park et al ( , 2003, SOM was successfully used to pattern benthic macroinvertebrate communities.…”

Section: Introductionmentioning

confidence: 99%

“…However, analysing community patterns is difficult because the data sets are non-linear and composed of many species varying over different locations and time (Chon et al, 2000) and with a different distribution and density. At a site, only part of the present community is collected at a certain moment.…”

Section: Introductionmentioning

confidence: 99%

“…Many researchers reduced the species data before using ANN to pattern the community. Chon et al (2000) for example, summed the densities of the taxa to seven selected taxa. Park et al (2003) expressed the biotic data as EPTC (Ephemeroptera, Plecoptera, Trichoptera, and Coleoptera) richness.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The myth of communities: Determining ecological quality of surface waters using macroinvertebrate community patterns

Nijboer¹

2006

SIL Proceedings, 1922-2010

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Assessment of an artificial intelligence technique in investigating habitat partitioning by coexisting benthic invertebrates in gravel-bed rivers

et al. 2005

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Artificial neural networks were used to pattern the use and partition of riverbed mesohabitats by four coexisting mayflies (Ephemera danica, Rhithrogena semicolorata, Caenis sp., and Serratella ignita), in a fast-flowing stream. Sixty-six samples were taken from the various mesohabitat types throughout a one-year period. Water depth, current velocity, substratum composition, and particulate organic matter were used to describe the mesohabitats within each sample unit. The species and abiotic data were computed separately with the self-organizing map (SOM) algorithm. Then, using the k-means algorithm, clusters were detected on the maps and the sampling units were classified separately for each variable and for species densities. Four clusters could be identified on the SOM according to nine environmental variables, and this classification was related to the morphodynamic conditions, chiefly with respect to substrate composition and current velocity. Similarly, three subsets were derived from the SOM according to gradients of species densities. The coincidence between 'abiotic' and 'mayfly' clusters showed that: (1) E. danica, Caenis sp. and S. ignita used similar mesohabitats, but E. danica was temporally segregated; (2) competition for space was likely to occur between Caenis sp. and S. ignita; and (3) R. semicolorata was spatially segregated from the three other mayflies. The method is discussed with reference to two alternative techniques: habitat preference curves, and multivariate analyses. The former implies a greater computation effort and is rather convenient under applied perspectives. The second approach faces the basic assumption that non-linear modelling methods should be preferred for dealing with ecological data which vary and covary in non-linear fashions. By combining ordering and clustering abilities (and other advantages such as gradient analysis and detection of outliers), SOM provides a visual and efficient way to bring out structures in the distribution of co-occurring species within multivariate microenvironments.

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Determining temporal pattern of community dynamics by using unsupervised learning algorithms

Cited by 55 publications

References 21 publications

Species abundance patterns of benthic macroinvertebrate communities in polluted streams

Species abundance patterns of benthic macroinvertebrate communities in polluted streams

The myth of communities: Determining ecological quality of surface waters using macroinvertebrate community patterns

Assessment of an artificial intelligence technique in investigating habitat partitioning by coexisting benthic invertebrates in gravel-bed rivers

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