Multivariate methods and artificial neural networks in the assessment of the response of infaunal assemblages to sediment metal contamination and organic enrichment

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dc.contributor.authorSubida, M. D.
dc.contributor.authorBerihuete, A.
dc.contributor.authorDrake, P.
dc.contributor.authorBlasco, J.
dc.date.accessioned2025-01-24T00:04:24Z
dc.date.available2025-01-24T00:04:24Z
dc.date.issued2013
dc.description.abstractA 4-year annual sediment survey was conducted in an organically enriched tidal channel to compare the performance of univariate community descriptors, traditional multivariate techniques (TM) and artificial neural networks (AANs), in the assessment of infaunal responses to moderate levels of sediment metal contamination. Both TM approaches and the SOM ANN revealed spatiotemporal patterns of environmental and biological variables, suggesting a causal relationship between them and further highlighting subsets of taxa and sediment variables as potential main drivers of those patterns. Namely, high values of non-natural metals and organic content prompted high abundances of opportunists, while high values of natural metals yielded typical tolerant assemblages of organically enriched areas. The two approaches yielded identical final results but ANNs showed the following advantages over TM: ability to generalise results, powerful visualization tools and the ability to account simultaneously for sediment and faunal variables in the same analysis. Therefore, the SOM ANN, combined with the K-means clustering algorithm, is suggested as a promising tool for the assessment of the ecological quality of estuarine infaunal communities, although further work is needed to ensure the accuracy of the method. (c) 2013 Elsevier B.V. All rights reserved.
dc.fuente.origenWOS
dc.identifier.doi10.1016/j.scitotenv.2013.02.009
dc.identifier.eissn1879-1026
dc.identifier.issn0048-9697
dc.identifier.urihttps://doi.org/10.1016/j.scitotenv.2013.02.009
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/101802
dc.identifier.wosidWOS:000317879100033
dc.language.isoen
dc.pagina.final300
dc.pagina.inicio289
dc.revistaScience of the total environment
dc.rightsacceso restringido
dc.subjectMultivariate analysis
dc.subjectArtificial neural networks
dc.subjectSOM
dc.subjectInfauna
dc.subjectSediment contamination
dc.subjectAssessment
dc.subject.ods14 Life Below Water
dc.subject.ods03 Good Health and Well-being
dc.subject.ods06 Clean Water and Sanitation
dc.subject.odspa14 Vida submarina
dc.subject.odspa03 Salud y bienestar
dc.subject.odspa06 Agua limpia y saneamiento
dc.titleMultivariate methods and artificial neural networks in the assessment of the response of infaunal assemblages to sediment metal contamination and organic enrichment
dc.typeartículo
dc.volumen450
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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