Quantifying uncertainties in biologically-based water quality assessment: A pan-European analysis of lake phytoplankton community metrics
► Phytoplankton ecological quality metrics were calculated for 32 European lakes. ► We modelled sources of variability (within and among lakes) in these metrics. ► Metrics varied more among lakes, than within lakes or due to sampling variation. ► Metrics varied significantly with eutrophication and...
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| Veröffentlicht in: | Ecological indicators 2013-06, Vol.29, p.34-47 |
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| creator | Thackeray, Stephen J. Nõges, Peeter Dunbar, Michael J. Dudley, Bernard J. Skjelbred, Birger Morabito, Giuseppe Carvalho, Laurence Phillips, Geoff Mischke, Ute Catalan, Jordi de Hoyos, Caridad Laplace, Christophe Austoni, Martina Padedda, Bachisio M. Maileht, Kairi Pasztaleniec, Agnieszka Järvinen, Marko Solheim, Anne Lyche Clarke, Ralph T. |
| description | ► Phytoplankton ecological quality metrics were calculated for 32 European lakes. ► We modelled sources of variability (within and among lakes) in these metrics. ► Metrics varied more among lakes, than within lakes or due to sampling variation. ► Metrics varied significantly with eutrophication and lake depth. ► Three metrics are considered robust for Water Framework Directive Intercalibration.
Lake phytoplankton are adopted world-wide as a sensitive indicator of water quality. European environmental legislation, the EU Water Framework Directive (WFD), formalises this, requiring the use of phytoplankton to assess the ecological status of lakes and coastal waters. Here we provide a rigorous assessment of a number of proposed phytoplankton metrics for assessing the ecological quality of European lakes, specifically in response to nutrient enrichment, or eutrophication, the most widespread pressure affecting lakes. To be useful indicators, metrics must have a small measurement error relative to the eutrophication signal we want them to represent among lakes of different nutrient status. An understanding of variability in metric scores among different locations around a lake, or due to sampling and analytical variability can also identify how best this measurement error is minimised.
To quantify metric variability, we analyse data from a multi-scale field campaign of 32 European lakes, resolving the extent to which seven phytoplankton metrics (including chlorophyll a, the most widely used metric of lake quality) vary among lakes, among sampling locations within a lake and through sample replication and processing. We also relate these metrics to environmental variables, including total phosphorus concentration as an indicator of eutrophication.
For all seven metrics, 65–96% of the variance in metric scores was among lakes, much higher than variability occurring due to sampling/sample processing. Using multi-model inference, there was strong support for relationships between among-lake variation in three metrics and differences in total phosphorus concentrations. Three of the metrics were also related to mean lake depth. Variability among locations within a lake was minimal ( |
| doi_str_mv | 10.1016/j.ecolind.2012.12.010 |
| format | Article |
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Lake phytoplankton are adopted world-wide as a sensitive indicator of water quality. European environmental legislation, the EU Water Framework Directive (WFD), formalises this, requiring the use of phytoplankton to assess the ecological status of lakes and coastal waters. Here we provide a rigorous assessment of a number of proposed phytoplankton metrics for assessing the ecological quality of European lakes, specifically in response to nutrient enrichment, or eutrophication, the most widespread pressure affecting lakes. To be useful indicators, metrics must have a small measurement error relative to the eutrophication signal we want them to represent among lakes of different nutrient status. An understanding of variability in metric scores among different locations around a lake, or due to sampling and analytical variability can also identify how best this measurement error is minimised.
To quantify metric variability, we analyse data from a multi-scale field campaign of 32 European lakes, resolving the extent to which seven phytoplankton metrics (including chlorophyll a, the most widely used metric of lake quality) vary among lakes, among sampling locations within a lake and through sample replication and processing. We also relate these metrics to environmental variables, including total phosphorus concentration as an indicator of eutrophication.
For all seven metrics, 65–96% of the variance in metric scores was among lakes, much higher than variability occurring due to sampling/sample processing. Using multi-model inference, there was strong support for relationships between among-lake variation in three metrics and differences in total phosphorus concentrations. Three of the metrics were also related to mean lake depth. Variability among locations within a lake was minimal (<4%), with sub-samples and analysts accounting for much of the within-lake metric variance. This indicates that a single sampling location is representative and suggests that sub-sample replication and standardisation of analyst procedures should result in increased precision of ecological assessments based upon these metrics.
For three phytoplankton metrics being used in the WFD: chlorophyll a concentration, the Phytoplankton Trophic Index (PTI) and cyanobacterial biovolume, >85% of the variance in metric scores was among-lakes and total phosphorus concentration was well supported as a predictor of this variation. Based upon this study, we can recommend that these three proposed metrics can be considered sufficiently robust for the ecological status assessment of European lakes in WFD monitoring schemes.</description><identifier>ISSN: 1470-160X</identifier><identifier>EISSN: 1872-7034</identifier><identifier>DOI: 10.1016/j.ecolind.2012.12.010</identifier><language>eng</language><publisher>Amsterdam: Elsevier Ltd</publisher><subject>Animal and plant ecology ; Animal, plant and microbial ecology ; Applied ecology ; Biological and medical sciences ; chlorophyll ; coastal water ; Conservation, protection and management of environment and wildlife ; Cyanobacteria ; Ecological quality assessment ; environmental factors ; environmental law ; Environmental Sciences ; European Union ; Eutrophication ; Fresh water ecosystems ; Fundamental and applied biological sciences. Psychology ; General aspects ; lakes ; Linear mixed effects models ; monitoring ; Multi-model inference ; phosphorus ; phytoplankton ; Synecology ; variance ; Water Framework Directive ; water quality</subject><ispartof>Ecological indicators, 2013-06, Vol.29, p.34-47</ispartof><rights>2012 Elsevier Ltd</rights><rights>2014 INIST-CNRS</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c444t-12574ad774f7f0776afdd7a82e97a5cbbba6f5120df52be25a822d8315642b413</citedby><cites>FETCH-LOGICAL-c444t-12574ad774f7f0776afdd7a82e97a5cbbba6f5120df52be25a822d8315642b413</cites><orcidid>0000-0002-0833-473X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S1470160X12004219$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>230,314,776,780,881,3537,27901,27902,65306</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=27158615$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://hal.inrae.fr/hal-02598433$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Thackeray, Stephen J.</creatorcontrib><creatorcontrib>Nõges, Peeter</creatorcontrib><creatorcontrib>Dunbar, Michael J.</creatorcontrib><creatorcontrib>Dudley, Bernard J.</creatorcontrib><creatorcontrib>Skjelbred, Birger</creatorcontrib><creatorcontrib>Morabito, Giuseppe</creatorcontrib><creatorcontrib>Carvalho, Laurence</creatorcontrib><creatorcontrib>Phillips, Geoff</creatorcontrib><creatorcontrib>Mischke, Ute</creatorcontrib><creatorcontrib>Catalan, Jordi</creatorcontrib><creatorcontrib>de Hoyos, Caridad</creatorcontrib><creatorcontrib>Laplace, Christophe</creatorcontrib><creatorcontrib>Austoni, Martina</creatorcontrib><creatorcontrib>Padedda, Bachisio M.</creatorcontrib><creatorcontrib>Maileht, Kairi</creatorcontrib><creatorcontrib>Pasztaleniec, Agnieszka</creatorcontrib><creatorcontrib>Järvinen, Marko</creatorcontrib><creatorcontrib>Solheim, Anne Lyche</creatorcontrib><creatorcontrib>Clarke, Ralph T.</creatorcontrib><title>Quantifying uncertainties in biologically-based water quality assessment: A pan-European analysis of lake phytoplankton community metrics</title><title>Ecological indicators</title><description>► Phytoplankton ecological quality metrics were calculated for 32 European lakes. ► We modelled sources of variability (within and among lakes) in these metrics. ► Metrics varied more among lakes, than within lakes or due to sampling variation. ► Metrics varied significantly with eutrophication and lake depth. ► Three metrics are considered robust for Water Framework Directive Intercalibration.
Lake phytoplankton are adopted world-wide as a sensitive indicator of water quality. European environmental legislation, the EU Water Framework Directive (WFD), formalises this, requiring the use of phytoplankton to assess the ecological status of lakes and coastal waters. Here we provide a rigorous assessment of a number of proposed phytoplankton metrics for assessing the ecological quality of European lakes, specifically in response to nutrient enrichment, or eutrophication, the most widespread pressure affecting lakes. To be useful indicators, metrics must have a small measurement error relative to the eutrophication signal we want them to represent among lakes of different nutrient status. An understanding of variability in metric scores among different locations around a lake, or due to sampling and analytical variability can also identify how best this measurement error is minimised.
To quantify metric variability, we analyse data from a multi-scale field campaign of 32 European lakes, resolving the extent to which seven phytoplankton metrics (including chlorophyll a, the most widely used metric of lake quality) vary among lakes, among sampling locations within a lake and through sample replication and processing. We also relate these metrics to environmental variables, including total phosphorus concentration as an indicator of eutrophication.
For all seven metrics, 65–96% of the variance in metric scores was among lakes, much higher than variability occurring due to sampling/sample processing. Using multi-model inference, there was strong support for relationships between among-lake variation in three metrics and differences in total phosphorus concentrations. Three of the metrics were also related to mean lake depth. Variability among locations within a lake was minimal (<4%), with sub-samples and analysts accounting for much of the within-lake metric variance. This indicates that a single sampling location is representative and suggests that sub-sample replication and standardisation of analyst procedures should result in increased precision of ecological assessments based upon these metrics.
For three phytoplankton metrics being used in the WFD: chlorophyll a concentration, the Phytoplankton Trophic Index (PTI) and cyanobacterial biovolume, >85% of the variance in metric scores was among-lakes and total phosphorus concentration was well supported as a predictor of this variation. Based upon this study, we can recommend that these three proposed metrics can be considered sufficiently robust for the ecological status assessment of European lakes in WFD monitoring schemes.</description><subject>Animal and plant ecology</subject><subject>Animal, plant and microbial ecology</subject><subject>Applied ecology</subject><subject>Biological and medical sciences</subject><subject>chlorophyll</subject><subject>coastal water</subject><subject>Conservation, protection and management of environment and wildlife</subject><subject>Cyanobacteria</subject><subject>Ecological quality assessment</subject><subject>environmental factors</subject><subject>environmental law</subject><subject>Environmental Sciences</subject><subject>European Union</subject><subject>Eutrophication</subject><subject>Fresh water ecosystems</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>General aspects</subject><subject>lakes</subject><subject>Linear mixed effects models</subject><subject>monitoring</subject><subject>Multi-model inference</subject><subject>phosphorus</subject><subject>phytoplankton</subject><subject>Synecology</subject><subject>variance</subject><subject>Water Framework Directive</subject><subject>water quality</subject><issn>1470-160X</issn><issn>1872-7034</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><recordid>eNqFkd-K1DAUh4souK4-gpgbL7zomKRJ0_FGhmX_wYCILngXTtNkNrOZpCbpSh_Btzaly94KBxJOvt8hfKeq3hO8IZi0n48brYKzfthQTOimFCb4RXVGOkFrgRv2styZwDVp8a_X1ZuUjrjkttv2rPr7fQKfrZmtP6DJKx0z2NLQCVmPehtcOFgFzs11D0kP6A9kHdHvCZzNM4KUdEon7fMXtEMj-PpyimHU4BF4cHOyCQWDHDxoNN7POYwO_EMOHqlwOk1-mXHSOVqV3lavDLik3z2d59Xd1eXPi5t6_-369mK3rxVjLNeEcsFgEIIZYbAQLZhhENBRvRXAVd_30BpOKB4Mp72mvDzRoWsIbxntGWnOq0_r3Htwcoz2BHGWAay82e3l0sOUbzvWNI8Ly1dWxZBS1OY5QLBc3MujfHIvF_eyVHFfch_X3AipyDMRvLLpOUwF4V1LeOE-rJyBIOEQC3P3owziy34airtCfF0JXZQ8Wh1lUlaXPQ02apXlEOx__vIPE4OpWw</recordid><startdate>20130601</startdate><enddate>20130601</enddate><creator>Thackeray, Stephen J.</creator><creator>Nõges, Peeter</creator><creator>Dunbar, Michael J.</creator><creator>Dudley, Bernard J.</creator><creator>Skjelbred, Birger</creator><creator>Morabito, Giuseppe</creator><creator>Carvalho, Laurence</creator><creator>Phillips, Geoff</creator><creator>Mischke, Ute</creator><creator>Catalan, Jordi</creator><creator>de Hoyos, Caridad</creator><creator>Laplace, Christophe</creator><creator>Austoni, Martina</creator><creator>Padedda, Bachisio M.</creator><creator>Maileht, Kairi</creator><creator>Pasztaleniec, Agnieszka</creator><creator>Järvinen, Marko</creator><creator>Solheim, Anne Lyche</creator><creator>Clarke, Ralph T.</creator><general>Elsevier Ltd</general><general>Elsevier</general><scope>FBQ</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>1XC</scope><orcidid>https://orcid.org/0000-0002-0833-473X</orcidid></search><sort><creationdate>20130601</creationdate><title>Quantifying uncertainties in biologically-based water quality assessment: A pan-European analysis of lake phytoplankton community metrics</title><author>Thackeray, Stephen J. ; Nõges, Peeter ; Dunbar, Michael J. ; Dudley, Bernard J. ; Skjelbred, Birger ; Morabito, Giuseppe ; Carvalho, Laurence ; Phillips, Geoff ; Mischke, Ute ; Catalan, Jordi ; de Hoyos, Caridad ; Laplace, Christophe ; Austoni, Martina ; Padedda, Bachisio M. ; Maileht, Kairi ; Pasztaleniec, Agnieszka ; Järvinen, Marko ; Solheim, Anne Lyche ; Clarke, Ralph T.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c444t-12574ad774f7f0776afdd7a82e97a5cbbba6f5120df52be25a822d8315642b413</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Animal and plant ecology</topic><topic>Animal, plant and microbial ecology</topic><topic>Applied ecology</topic><topic>Biological and medical sciences</topic><topic>chlorophyll</topic><topic>coastal water</topic><topic>Conservation, protection and management of environment and wildlife</topic><topic>Cyanobacteria</topic><topic>Ecological quality assessment</topic><topic>environmental factors</topic><topic>environmental law</topic><topic>Environmental Sciences</topic><topic>European Union</topic><topic>Eutrophication</topic><topic>Fresh water ecosystems</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>General aspects</topic><topic>lakes</topic><topic>Linear mixed effects models</topic><topic>monitoring</topic><topic>Multi-model inference</topic><topic>phosphorus</topic><topic>phytoplankton</topic><topic>Synecology</topic><topic>variance</topic><topic>Water Framework Directive</topic><topic>water quality</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Thackeray, Stephen J.</creatorcontrib><creatorcontrib>Nõges, Peeter</creatorcontrib><creatorcontrib>Dunbar, Michael J.</creatorcontrib><creatorcontrib>Dudley, Bernard J.</creatorcontrib><creatorcontrib>Skjelbred, Birger</creatorcontrib><creatorcontrib>Morabito, Giuseppe</creatorcontrib><creatorcontrib>Carvalho, Laurence</creatorcontrib><creatorcontrib>Phillips, Geoff</creatorcontrib><creatorcontrib>Mischke, Ute</creatorcontrib><creatorcontrib>Catalan, Jordi</creatorcontrib><creatorcontrib>de Hoyos, Caridad</creatorcontrib><creatorcontrib>Laplace, Christophe</creatorcontrib><creatorcontrib>Austoni, Martina</creatorcontrib><creatorcontrib>Padedda, Bachisio M.</creatorcontrib><creatorcontrib>Maileht, Kairi</creatorcontrib><creatorcontrib>Pasztaleniec, Agnieszka</creatorcontrib><creatorcontrib>Järvinen, Marko</creatorcontrib><creatorcontrib>Solheim, Anne Lyche</creatorcontrib><creatorcontrib>Clarke, Ralph T.</creatorcontrib><collection>AGRIS</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Hyper Article en Ligne (HAL)</collection><jtitle>Ecological indicators</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Thackeray, Stephen J.</au><au>Nõges, Peeter</au><au>Dunbar, Michael J.</au><au>Dudley, Bernard J.</au><au>Skjelbred, Birger</au><au>Morabito, Giuseppe</au><au>Carvalho, Laurence</au><au>Phillips, Geoff</au><au>Mischke, Ute</au><au>Catalan, Jordi</au><au>de Hoyos, Caridad</au><au>Laplace, Christophe</au><au>Austoni, Martina</au><au>Padedda, Bachisio M.</au><au>Maileht, Kairi</au><au>Pasztaleniec, Agnieszka</au><au>Järvinen, Marko</au><au>Solheim, Anne Lyche</au><au>Clarke, Ralph T.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Quantifying uncertainties in biologically-based water quality assessment: A pan-European analysis of lake phytoplankton community metrics</atitle><jtitle>Ecological indicators</jtitle><date>2013-06-01</date><risdate>2013</risdate><volume>29</volume><spage>34</spage><epage>47</epage><pages>34-47</pages><issn>1470-160X</issn><eissn>1872-7034</eissn><abstract>► Phytoplankton ecological quality metrics were calculated for 32 European lakes. ► We modelled sources of variability (within and among lakes) in these metrics. ► Metrics varied more among lakes, than within lakes or due to sampling variation. ► Metrics varied significantly with eutrophication and lake depth. ► Three metrics are considered robust for Water Framework Directive Intercalibration.
Lake phytoplankton are adopted world-wide as a sensitive indicator of water quality. European environmental legislation, the EU Water Framework Directive (WFD), formalises this, requiring the use of phytoplankton to assess the ecological status of lakes and coastal waters. Here we provide a rigorous assessment of a number of proposed phytoplankton metrics for assessing the ecological quality of European lakes, specifically in response to nutrient enrichment, or eutrophication, the most widespread pressure affecting lakes. To be useful indicators, metrics must have a small measurement error relative to the eutrophication signal we want them to represent among lakes of different nutrient status. An understanding of variability in metric scores among different locations around a lake, or due to sampling and analytical variability can also identify how best this measurement error is minimised.
To quantify metric variability, we analyse data from a multi-scale field campaign of 32 European lakes, resolving the extent to which seven phytoplankton metrics (including chlorophyll a, the most widely used metric of lake quality) vary among lakes, among sampling locations within a lake and through sample replication and processing. We also relate these metrics to environmental variables, including total phosphorus concentration as an indicator of eutrophication.
For all seven metrics, 65–96% of the variance in metric scores was among lakes, much higher than variability occurring due to sampling/sample processing. Using multi-model inference, there was strong support for relationships between among-lake variation in three metrics and differences in total phosphorus concentrations. Three of the metrics were also related to mean lake depth. Variability among locations within a lake was minimal (<4%), with sub-samples and analysts accounting for much of the within-lake metric variance. This indicates that a single sampling location is representative and suggests that sub-sample replication and standardisation of analyst procedures should result in increased precision of ecological assessments based upon these metrics.
For three phytoplankton metrics being used in the WFD: chlorophyll a concentration, the Phytoplankton Trophic Index (PTI) and cyanobacterial biovolume, >85% of the variance in metric scores was among-lakes and total phosphorus concentration was well supported as a predictor of this variation. Based upon this study, we can recommend that these three proposed metrics can be considered sufficiently robust for the ecological status assessment of European lakes in WFD monitoring schemes.</abstract><cop>Amsterdam</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.ecolind.2012.12.010</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0002-0833-473X</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1470-160X |
| ispartof | Ecological indicators, 2013-06, Vol.29, p.34-47 |
| issn | 1470-160X 1872-7034 |
| language | eng |
| recordid | cdi_hal_primary_oai_HAL_hal_02598433v1 |
| source | Elsevier ScienceDirect Journals |
| subjects | Animal and plant ecology Animal, plant and microbial ecology Applied ecology Biological and medical sciences chlorophyll coastal water Conservation, protection and management of environment and wildlife Cyanobacteria Ecological quality assessment environmental factors environmental law Environmental Sciences European Union Eutrophication Fresh water ecosystems Fundamental and applied biological sciences. Psychology General aspects lakes Linear mixed effects models monitoring Multi-model inference phosphorus phytoplankton Synecology variance Water Framework Directive water quality |
| title | Quantifying uncertainties in biologically-based water quality assessment: A pan-European analysis of lake phytoplankton community metrics |
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