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Predicting freshwater biological quality using macrophytes: A comparison of empirical modelling approaches

cris.virtual.author-orcid0000-0002-6549-9418
cris.virtual.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtual.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtual.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtualsource.author-orcidd16ffead-1aea-4e76-a330-73e0f4301bfb
cris.virtualsource.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtualsource.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtualsource.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
dc.abstract.enDifficulties have hampered bioassessment in southern European rivers due to limited reference data and the unclear impact of multiple interacting stressors on plant communities. Predictive modelling may help overcome this limitation by aggregating different pressures affecting aquatic organisms and showing the most influential factors. We assembled a dataset of 292 Mediterranean sampling locations on perennial rivers and streams (mainland Portugal) with macrophyte and environmental data. We compared models based on multiple linear regression (MLR), boosted regression trees (BRT) and artificial neural networks (ANNs). Secondarily, we investigated the relationship between two macrophyte indices grounded in distinct conceptual premises (the Riparian Vegetation Index — RVI, and the Macrophyte Biological Index for Rivers — IBMR) and a set of environmental variables, including climatic conditions, geographical characteristics, land use, water chemistry and habitat quality of rivers. The quality of models for the IBMR was superior to those for the RVI in all cases, which indicates a better ecological linkage of IBMR with the stressor and abiotic variables. The IBMR using ANN outperformed the BRT models, for which the r-Pearson correlation coefficients were 0.877 and 0.801, and the normalised root mean square errors were 10.0 and 11.3, respectively. Variable importance analysis revealed that longitude and geology, hydrological/climatic conditions, water body size and land use had the highest impact on the IBMR model predictions. Despite the differences in the quality of the models, all showed similar importance to individual input variables, although in a different order. Despite some difficulties in model training for ANNs, our findings suggest that BRT and ANNs can be used to assess ecological quality, and for decision-making on the environmental management of rivers.
dc.affiliationWydział Inżynierii Środowiska i Inżynierii Mechanicznej
dc.affiliation.instituteKatedra Ekologii i Ochrony Środowiska
dc.contributor.authorGebler, Daniel
dc.contributor.authorSegurado, Pedro
dc.contributor.authorFerreira, Maria Teresa
dc.contributor.authorAguiar, Francisca C.
dc.date.access2025-01-14
dc.date.accessioned2025-01-14T09:52:34Z
dc.date.available2025-01-14T09:52:34Z
dc.date.copyright2024-11-21
dc.date.issued2024
dc.description.abstract<jats:title>Abstract</jats:title><jats:p>Difficulties have hampered bioassessment in southern European rivers due to limited reference data and the unclear impact of multiple interacting stressors on plant communities. Predictive modelling may help overcome this limitation by aggregating different pressures affecting aquatic organisms and showing the most influential factors. We assembled a dataset of 292 Mediterranean sampling locations on perennial rivers and streams (mainland Portugal) with macrophyte and environmental data. We compared models based on multiple linear regression (MLR), boosted regression trees (BRT) and artificial neural networks (ANNs). Secondarily, we investigated the relationship between two macrophyte indices grounded in distinct conceptual premises (the Riparian Vegetation Index — RVI, and the Macrophyte Biological Index for Rivers — IBMR) and a set of environmental variables, including climatic conditions, geographical characteristics, land use, water chemistry and habitat quality of rivers. The quality of models for the IBMR was superior to those for the RVI in all cases, which indicates a better ecological linkage of IBMR with the stressor and abiotic variables. The IBMR using ANN outperformed the BRT models, for which the r-Pearson correlation coefficients were 0.877 and 0.801, and the normalised root mean square errors were 10.0 and 11.3, respectively. Variable importance analysis revealed that longitude and geology, hydrological/climatic conditions, water body size and land use had the highest impact on the IBMR model predictions. Despite the differences in the quality of the models, all showed similar importance to individual input variables, although in a different order. Despite some difficulties in model training for ANNs, our findings suggest that BRT and ANNs can be used to assess ecological quality, and for decision-making on the environmental management of rivers.</jats:p>
dc.description.accesstimeafter_publication
dc.description.bibliographyil., bibliogr.
dc.description.financepublication_nocost
dc.description.financecost0,00
dc.description.number56
dc.description.points100
dc.description.versionfinal_published
dc.description.volume31
dc.identifier.doi10.1007/s11356-024-35497-8
dc.identifier.eissn1614-7499
dc.identifier.issn0944-1344
dc.identifier.urihttps://sciencerep.up.poznan.pl/handle/item/2334
dc.identifier.weblinkhttps://link.springer.com/article/10.1007/s11356-024-35497-8
dc.languageen
dc.pbn.affiliationenvironmental engineering, mining and energy
dc.relation.ispartofEnvironmental Science and Pollution Research
dc.relation.pages65092-65108
dc.rightsCC-BY
dc.sciencecloudsend
dc.subject.enmacrophytes
dc.subject.enbioindication
dc.subject.enriver assessment
dc.subject.enartifcial neural networks
dc.subject.enlinear regression
dc.subject.enboosted regression trees
dc.titlePredicting freshwater biological quality using macrophytes: A comparison of empirical modelling approaches
dc.typeJournalArticle
dspace.entity.typePublication
oaire.citation.issue56
oaire.citation.volume31