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Indicator macroinvertebrate species in a temporary Mediterranean river: recognition of patterns in binary assemblage data with a Kohonen artificial neural network

2017-02Journal article Restricted
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dc.contributor.authorSroczyƄska, Katarzyna
dc.contributor.authorClaro, M.
dc.contributor.authorKruk, A.
dc.contributor.authorWojtal-Frankiewicz, A.
dc.contributor.authorRange, P.
dc.contributor.authorChicharo, Luis
dc.date.accessioned2019-11-20T15:07:49Z
dc.date.available2019-11-20T15:07:49Z
dc.date.issued2017-02
dc.description.abstractCurrent classifications used in bioassessment programs, as defined by the Water Framework Directive (WFD), do not sufficiently capture the variability present in temporary Mediterranean streams. This may result in inaccurate evaluation of the water quality biological metrics and difficulties in setting reference conditions. The aim of the study was to examine if aquatic invertebrate data of increased taxonomical resolution but expressed on a binary abundance (frequent/rare) scale and referring to good bioindicator species only suffice to indicate clear gradients in water courses with high natural variability such as intermittent Mediterranean streams. Invertebrate samples were collected from 74 sites in the Quarteira River basin, located in southern Portugal. Their classification with the use of a Kohonen artificial neural network (i.e., self-organising map, SOM) resulted in five categories. The variables that drove this categorization were primarily altitude, temperature and conductivity, but also type of substrate, riparian cover and percentage of riffles present. According to the indicator species analysis (ISA), almost all the studied taxa were significantly associated with certain SOM categories except for the category that included sites with disrupted flow regime. The SOM and ISA allowed us to effectively recognize biotic and abiotic patterns. Combined application of both methods may thus greatly enhance the effectiveness and precision of biological surveillance and establish reference sites for specific channel units in streams with high natural variability such as intermittent Mediterranean streams. (C) 2016 Elsevier Ltd. All rights reserved.
dc.description.sponsorshipFoundation for Science and Technology (FCT) of Portugal [ERA-IWRM/0003/2009]
dc.identifier.doi10.1016/j.ecolind.2016.09.010
dc.identifier.issn1470-160X
dc.identifier.issn1872-7034
dc.identifier.urihttp://hdl.handle.net/10400.1/13229
dc.language.isoeng
dc.peerreviewedyes
dc.publisherElsevier Science Bv
dc.relationIMPACT - Developing an integrated model to predict abiotic habitat conditions and biota of rivers for application in climate change research and water management
dc.subjectSelf-organizing map
dc.subjectFish assemblages
dc.subjectFlow permanence
dc.subjectBenthic-macroinvertebrates
dc.subjectIntermittent streams
dc.subjectCommunity structure
dc.subjectBiological traits
dc.subjectClimate streams
dc.subjectLong-term
dc.subjectBasin
dc.titleIndicator macroinvertebrate species in a temporary Mediterranean river: recognition of patterns in binary assemblage data with a Kohonen artificial neural network
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleIMPACT - Developing an integrated model to predict abiotic habitat conditions and biota of rivers for application in climate change research and water management
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/ERA-IWRM%2F0003%2F2009/PT
oaire.citation.endPage330
oaire.citation.startPage319
oaire.citation.titleEcological Indicators
oaire.citation.volume73
oaire.fundingStream3599-PPCDT
person.familyNameSroczynska
person.familyNameChicharo
person.givenNameKasia
person.givenNameLuis
person.identifier.ciencia-idCF1E-A5CE-FD53
person.identifier.ciencia-id6716-EE1C-B995
person.identifier.orcid0000-0003-4104-8185
person.identifier.orcid0000-0002-4933-2300
person.identifier.scopus-author-id57216502155
person.identifier.scopus-author-id6602881426
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a CiĂȘncia e a Tecnologia
rcaap.rightsrestrictedAccess
rcaap.typearticle
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