Predicting zooplankton response to environmental changes in a temperate estuarine ecosystem
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 di...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Marques, Sónia Cotrim [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2008 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag 2008 |
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| Übergeordnetes Werk: |
Enthalten in: Marine biology - Berlin : Springer, 1967, 155(2008), 5 vom: 10. Sept., Seite 531-541 |
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| Übergeordnetes Werk: |
volume:155 ; year:2008 ; number:5 ; day:10 ; month:09 ; pages:531-541 |
| Links: |
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| DOI / URN: |
10.1007/s00227-008-1052-6 |
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SPR002534215 |
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| 245 | 1 | 0 | |a Predicting zooplankton response to environmental changes in a temperate estuarine ecosystem |
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| 520 | |a 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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| 700 | 1 | |a Azeiteiro, Ulisses Miranda |4 aut | |
| 700 | 1 | |a Leandro, Sérgio Miguel |4 aut | |
| 700 | 1 | |a Queiroga, Henrique |4 aut | |
| 700 | 1 | |a Primo, Ana Lígia |4 aut | |
| 700 | 1 | |a Martinho, Filipe |4 aut | |
| 700 | 1 | |a Viegas, Ivan |4 aut | |
| 700 | 1 | |a Pardal, Miguel Ângelo |4 aut | |
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10.1007/s00227-008-1052-6 doi (DE-627)SPR002534215 (SPR)s00227-008-1052-6-e DE-627 ger DE-627 rakwb eng Marques, Sónia Cotrim verfasserin aut Predicting zooplankton response to environmental changes in a temperate estuarine ecosystem 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2008 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. Total Suspended Solid (dpeaa)DE-He213 Indicator Species (dpeaa)DE-He213 Zooplanktonic Community (dpeaa)DE-He213 Zooplanktonic Species (dpeaa)DE-He213 Estuarine Ecosystem (dpeaa)DE-He213 Azeiteiro, Ulisses Miranda aut Leandro, Sérgio Miguel aut Queiroga, Henrique aut Primo, Ana Lígia aut Martinho, Filipe aut Viegas, Ivan aut Pardal, Miguel Ângelo aut Enthalten in Marine biology Berlin : Springer, 1967 155(2008), 5 vom: 10. Sept., Seite 531-541 (DE-627)25377067X (DE-600)1459413-4 1432-1793 nnns volume:155 year:2008 number:5 day:10 month:09 pages:531-541 https://dx.doi.org/10.1007/s00227-008-1052-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 155 2008 5 10 09 531-541 |
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10.1007/s00227-008-1052-6 doi (DE-627)SPR002534215 (SPR)s00227-008-1052-6-e DE-627 ger DE-627 rakwb eng Marques, Sónia Cotrim verfasserin aut Predicting zooplankton response to environmental changes in a temperate estuarine ecosystem 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2008 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. Total Suspended Solid (dpeaa)DE-He213 Indicator Species (dpeaa)DE-He213 Zooplanktonic Community (dpeaa)DE-He213 Zooplanktonic Species (dpeaa)DE-He213 Estuarine Ecosystem (dpeaa)DE-He213 Azeiteiro, Ulisses Miranda aut Leandro, Sérgio Miguel aut Queiroga, Henrique aut Primo, Ana Lígia aut Martinho, Filipe aut Viegas, Ivan aut Pardal, Miguel Ângelo aut Enthalten in Marine biology Berlin : Springer, 1967 155(2008), 5 vom: 10. Sept., Seite 531-541 (DE-627)25377067X (DE-600)1459413-4 1432-1793 nnns volume:155 year:2008 number:5 day:10 month:09 pages:531-541 https://dx.doi.org/10.1007/s00227-008-1052-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 155 2008 5 10 09 531-541 |
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10.1007/s00227-008-1052-6 doi (DE-627)SPR002534215 (SPR)s00227-008-1052-6-e DE-627 ger DE-627 rakwb eng Marques, Sónia Cotrim verfasserin aut Predicting zooplankton response to environmental changes in a temperate estuarine ecosystem 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2008 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. Total Suspended Solid (dpeaa)DE-He213 Indicator Species (dpeaa)DE-He213 Zooplanktonic Community (dpeaa)DE-He213 Zooplanktonic Species (dpeaa)DE-He213 Estuarine Ecosystem (dpeaa)DE-He213 Azeiteiro, Ulisses Miranda aut Leandro, Sérgio Miguel aut Queiroga, Henrique aut Primo, Ana Lígia aut Martinho, Filipe aut Viegas, Ivan aut Pardal, Miguel Ângelo aut Enthalten in Marine biology Berlin : Springer, 1967 155(2008), 5 vom: 10. Sept., Seite 531-541 (DE-627)25377067X (DE-600)1459413-4 1432-1793 nnns volume:155 year:2008 number:5 day:10 month:09 pages:531-541 https://dx.doi.org/10.1007/s00227-008-1052-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 155 2008 5 10 09 531-541 |
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10.1007/s00227-008-1052-6 doi (DE-627)SPR002534215 (SPR)s00227-008-1052-6-e DE-627 ger DE-627 rakwb eng Marques, Sónia Cotrim verfasserin aut Predicting zooplankton response to environmental changes in a temperate estuarine ecosystem 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2008 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. Total Suspended Solid (dpeaa)DE-He213 Indicator Species (dpeaa)DE-He213 Zooplanktonic Community (dpeaa)DE-He213 Zooplanktonic Species (dpeaa)DE-He213 Estuarine Ecosystem (dpeaa)DE-He213 Azeiteiro, Ulisses Miranda aut Leandro, Sérgio Miguel aut Queiroga, Henrique aut Primo, Ana Lígia aut Martinho, Filipe aut Viegas, Ivan aut Pardal, Miguel Ângelo aut Enthalten in Marine biology Berlin : Springer, 1967 155(2008), 5 vom: 10. Sept., Seite 531-541 (DE-627)25377067X (DE-600)1459413-4 1432-1793 nnns volume:155 year:2008 number:5 day:10 month:09 pages:531-541 https://dx.doi.org/10.1007/s00227-008-1052-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 155 2008 5 10 09 531-541 |
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10.1007/s00227-008-1052-6 doi (DE-627)SPR002534215 (SPR)s00227-008-1052-6-e DE-627 ger DE-627 rakwb eng Marques, Sónia Cotrim verfasserin aut Predicting zooplankton response to environmental changes in a temperate estuarine ecosystem 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2008 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. Total Suspended Solid (dpeaa)DE-He213 Indicator Species (dpeaa)DE-He213 Zooplanktonic Community (dpeaa)DE-He213 Zooplanktonic Species (dpeaa)DE-He213 Estuarine Ecosystem (dpeaa)DE-He213 Azeiteiro, Ulisses Miranda aut Leandro, Sérgio Miguel aut Queiroga, Henrique aut Primo, Ana Lígia aut Martinho, Filipe aut Viegas, Ivan aut Pardal, Miguel Ângelo aut Enthalten in Marine biology Berlin : Springer, 1967 155(2008), 5 vom: 10. Sept., Seite 531-541 (DE-627)25377067X (DE-600)1459413-4 1432-1793 nnns volume:155 year:2008 number:5 day:10 month:09 pages:531-541 https://dx.doi.org/10.1007/s00227-008-1052-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 155 2008 5 10 09 531-541 |
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Enthalten in Marine biology 155(2008), 5 vom: 10. Sept., Seite 531-541 volume:155 year:2008 number:5 day:10 month:09 pages:531-541 |
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Enthalten in Marine biology 155(2008), 5 vom: 10. Sept., Seite 531-541 volume:155 year:2008 number:5 day:10 month:09 pages:531-541 |
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Total Suspended Solid Indicator Species Zooplanktonic Community Zooplanktonic Species Estuarine Ecosystem |
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Marques, Sónia Cotrim @@aut@@ Azeiteiro, Ulisses Miranda @@aut@@ Leandro, Sérgio Miguel @@aut@@ Queiroga, Henrique @@aut@@ Primo, Ana Lígia @@aut@@ Martinho, Filipe @@aut@@ Viegas, Ivan @@aut@@ Pardal, Miguel Ângelo @@aut@@ |
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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. 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Marques, Sónia Cotrim |
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10.1007/s00227-008-1052-6 |
| title_sort |
predicting zooplankton response to environmental changes in a temperate estuarine ecosystem |
| title_auth |
Predicting zooplankton response to environmental changes in a temperate estuarine ecosystem |
| abstract |
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. © Springer-Verlag 2008 |
| abstractGer |
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. © Springer-Verlag 2008 |
| abstract_unstemmed |
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. © Springer-Verlag 2008 |
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Predicting zooplankton response to environmental changes in a temperate estuarine ecosystem |
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Azeiteiro, Ulisses Miranda Leandro, Sérgio Miguel Queiroga, Henrique Primo, Ana Lígia Martinho, Filipe Viegas, Ivan Pardal, Miguel Ângelo |
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Azeiteiro, Ulisses Miranda Leandro, Sérgio Miguel Queiroga, Henrique Primo, Ana Lígia Martinho, Filipe Viegas, Ivan Pardal, Miguel Ângelo |
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| score |
7.4007015 |