Predicting zooplankton response to environmental changes in a temperate estuarine ecosystem
Autor(a) principal: | |
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Data de Publicação: | 2008 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10316/7860 https://doi.org/10.1007/s00227-008-1052-6 |
Resumo: | Abstract A novel strategy that allows to predict the responses of zooplanktonic species to environmental conditions in an estuarine temperate ecosystem (Mondego estuary) is presented. It uses 12 indicator species from the zooplanktonic Mondego database (102 species) that are common members of the different habitats, characterized by their specific hydrological conditions. Indicator-species analysis (ISA) was used to define and describe which species were typical of each of the five sampling stations in a 4-year study (2003–2006). First, a canonical correspondence analysis (CCA) was carried out to objectively identify the species-habitat affinity based on the relationship between species, stations and environmental data. Response curves for each of the zooplanktonic species, generated by univariate logistic regression on each of the independent variables temperature and salinity, were generally in agreement with the descriptive statistics concerning the occurrence of those species in this particular estuarine ecosystem. Species-specific models that predict probability of occurrence relative to environmental parameters like salinity, water temperature, turbidity, chlorophyll a, total suspended solids and dissolved oxygen were then developed for the zooplanktonic species. The multiple logistic models used contained between 1 and 3 significant parameters and the percentage correctly predicted was moderate to high, ranging from 62 to 95%. The predictive accuracy of the model was assured by direct comparison of model predictions with the observed occurrence of species obtained in 2006 (validation) and from data collected in the early 2000s in another Portuguese estuary—Ria de Aveiro (Canal de Mira), a complex mesotidal shallow coastal lagoon. The regression logistic model here defined, correctly suggested that the distribution of zooplankton species was mainly dependent on salinity and water temperature. The logistic regression proved to be a useful approach for predicting the occurrence of species under varying environmental conditions at a local scale. Therefore, this model can be considered of reasonable application (and should be tested in other estuarine systems) due to its ability to predict the occurrence of individual zooplanktonic species associated with habitat changes. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Predicting zooplankton response to environmental changes in a temperate estuarine ecosystemAbstract A novel strategy that allows to predict the responses of zooplanktonic species to environmental conditions in an estuarine temperate ecosystem (Mondego estuary) is presented. It uses 12 indicator species from the zooplanktonic Mondego database (102 species) that are common members of the different habitats, characterized by their specific hydrological conditions. Indicator-species analysis (ISA) was used to define and describe which species were typical of each of the five sampling stations in a 4-year study (2003–2006). First, a canonical correspondence analysis (CCA) was carried out to objectively identify the species-habitat affinity based on the relationship between species, stations and environmental data. Response curves for each of the zooplanktonic species, generated by univariate logistic regression on each of the independent variables temperature and salinity, were generally in agreement with the descriptive statistics concerning the occurrence of those species in this particular estuarine ecosystem. Species-specific models that predict probability of occurrence relative to environmental parameters like salinity, water temperature, turbidity, chlorophyll a, total suspended solids and dissolved oxygen were then developed for the zooplanktonic species. The multiple logistic models used contained between 1 and 3 significant parameters and the percentage correctly predicted was moderate to high, ranging from 62 to 95%. The predictive accuracy of the model was assured by direct comparison of model predictions with the observed occurrence of species obtained in 2006 (validation) and from data collected in the early 2000s in another Portuguese estuary—Ria de Aveiro (Canal de Mira), a complex mesotidal shallow coastal lagoon. The regression logistic model here defined, correctly suggested that the distribution of zooplankton species was mainly dependent on salinity and water temperature. The logistic regression proved to be a useful approach for predicting the occurrence of species under varying environmental conditions at a local scale. Therefore, this model can be considered of reasonable application (and should be tested in other estuarine systems) due to its ability to predict the occurrence of individual zooplanktonic species associated with habitat changes.2008info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/7860http://hdl.handle.net/10316/7860https://doi.org/10.1007/s00227-008-1052-6engMarine Biology. 155:5 (2008) 531-541Marques, SóniaAzeiteiro, UlissesLeandro, SérgioQueiroga, HenriquePrimo, AnaMartinho, FilipeViegas, IvanPardal, Miguelinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2021-10-13T09:48:45Zoai:estudogeral.uc.pt:10316/7860Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:55:33.408460Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Predicting zooplankton response to environmental changes in a temperate estuarine ecosystem |
title |
Predicting zooplankton response to environmental changes in a temperate estuarine ecosystem |
spellingShingle |
Predicting zooplankton response to environmental changes in a temperate estuarine ecosystem Marques, Sónia |
title_short |
Predicting zooplankton response to environmental changes in a temperate estuarine ecosystem |
title_full |
Predicting zooplankton response to environmental changes in a temperate estuarine ecosystem |
title_fullStr |
Predicting zooplankton response to environmental changes in a temperate estuarine ecosystem |
title_full_unstemmed |
Predicting zooplankton response to environmental changes in a temperate estuarine ecosystem |
title_sort |
Predicting zooplankton response to environmental changes in a temperate estuarine ecosystem |
author |
Marques, Sónia |
author_facet |
Marques, Sónia Azeiteiro, Ulisses Leandro, Sérgio Queiroga, Henrique Primo, Ana Martinho, Filipe Viegas, Ivan Pardal, Miguel |
author_role |
author |
author2 |
Azeiteiro, Ulisses Leandro, Sérgio Queiroga, Henrique Primo, Ana Martinho, Filipe Viegas, Ivan Pardal, Miguel |
author2_role |
author author author author author author author |
dc.contributor.author.fl_str_mv |
Marques, Sónia Azeiteiro, Ulisses Leandro, Sérgio Queiroga, Henrique Primo, Ana Martinho, Filipe Viegas, Ivan Pardal, Miguel |
description |
Abstract A novel strategy that allows to predict the responses of zooplanktonic species to environmental conditions in an estuarine temperate ecosystem (Mondego estuary) is presented. It uses 12 indicator species from the zooplanktonic Mondego database (102 species) that are common members of the different habitats, characterized by their specific hydrological conditions. Indicator-species analysis (ISA) was used to define and describe which species were typical of each of the five sampling stations in a 4-year study (2003–2006). First, a canonical correspondence analysis (CCA) was carried out to objectively identify the species-habitat affinity based on the relationship between species, stations and environmental data. Response curves for each of the zooplanktonic species, generated by univariate logistic regression on each of the independent variables temperature and salinity, were generally in agreement with the descriptive statistics concerning the occurrence of those species in this particular estuarine ecosystem. Species-specific models that predict probability of occurrence relative to environmental parameters like salinity, water temperature, turbidity, chlorophyll a, total suspended solids and dissolved oxygen were then developed for the zooplanktonic species. The multiple logistic models used contained between 1 and 3 significant parameters and the percentage correctly predicted was moderate to high, ranging from 62 to 95%. The predictive accuracy of the model was assured by direct comparison of model predictions with the observed occurrence of species obtained in 2006 (validation) and from data collected in the early 2000s in another Portuguese estuary—Ria de Aveiro (Canal de Mira), a complex mesotidal shallow coastal lagoon. The regression logistic model here defined, correctly suggested that the distribution of zooplankton species was mainly dependent on salinity and water temperature. The logistic regression proved to be a useful approach for predicting the occurrence of species under varying environmental conditions at a local scale. Therefore, this model can be considered of reasonable application (and should be tested in other estuarine systems) due to its ability to predict the occurrence of individual zooplanktonic species associated with habitat changes. |
publishDate |
2008 |
dc.date.none.fl_str_mv |
2008 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10316/7860 http://hdl.handle.net/10316/7860 https://doi.org/10.1007/s00227-008-1052-6 |
url |
http://hdl.handle.net/10316/7860 https://doi.org/10.1007/s00227-008-1052-6 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Marine Biology. 155:5 (2008) 531-541 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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