Algal Pigment Estimation Models to Assess Bloom Toxicity in a South American Lake
In this study, we build an empirical model to estimate pigments in the South American Lake Villarrica. We use data from Dirección General de Aguas de Chile during the period of 1989–2024 to analyze the behavior of limnological parameters and trophic condition in the lake. Four seasonal linear regres...
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description | In this study, we build an empirical model to estimate pigments in the South American Lake Villarrica. We use data from Dirección General de Aguas de Chile during the period of 1989–2024 to analyze the behavior of limnological parameters and trophic condition in the lake. Four seasonal linear regression models were developed by us, using a set of water quality variables that explain the values of phycocyanin pigment in Lake Villarrica. In the first case, we related chlorophyll-a (Chl-a) to phycocyanin, expecting to find a direct relationship between both variables, but this was not fulfilled for all seasons of the year. In the second case, in addition to Chl-a, we included water temperature, since this parameter has a great influence on the algal photosynthesis process, and we obtained better results. We discovered a typical seasonal variability given by temperature fluctuations in Lake Villarrica, where in the spring, summer, and autumn seasons, conditions are favorable for algal blooms, while in winter, the natural seasonal conditions do not allow increases in algal productivity. For a third case, we included the turbidity variable along with the variables mentioned above and the statistical performance metrics of the models improved significantly, obtaining R2 values of up to 0.90 in the case of the model for the fall season and a mean squared error (MSE) of 0.04 µg/L. In the last case used, we added the variable dissolved organic matter (MOD), and the models showed a slight improvement in their performance. These models may be applicable to other lakes with harmful algal blooms in order to alert the community to the potential toxicity of these events. |
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We use data from Dirección General de Aguas de Chile during the period of 1989–2024 to analyze the behavior of limnological parameters and trophic condition in the lake. Four seasonal linear regression models were developed by us, using a set of water quality variables that explain the values of phycocyanin pigment in Lake Villarrica. In the first case, we related chlorophyll-a (Chl-a) to phycocyanin, expecting to find a direct relationship between both variables, but this was not fulfilled for all seasons of the year. In the second case, in addition to Chl-a, we included water temperature, since this parameter has a great influence on the algal photosynthesis process, and we obtained better results. We discovered a typical seasonal variability given by temperature fluctuations in Lake Villarrica, where in the spring, summer, and autumn seasons, conditions are favorable for algal blooms, while in winter, the natural seasonal conditions do not allow increases in algal productivity. For a third case, we included the turbidity variable along with the variables mentioned above and the statistical performance metrics of the models improved significantly, obtaining R2 values of up to 0.90 in the case of the model for the fall season and a mean squared error (MSE) of 0.04 µg/L. In the last case used, we added the variable dissolved organic matter (MOD), and the models showed a slight improvement in their performance. These models may be applicable to other lakes with harmful algal blooms in order to alert the community to the potential toxicity of these events.</description><identifier>ISSN: 2073-4441</identifier><identifier>EISSN: 2073-4441</identifier><identifier>DOI: 10.3390/w16243708</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Algae ; Aquatic ecosystems ; Chlorophyll ; Comparative analysis ; Data integrity ; Distribution ; Environmental aspects ; Environmental Sciences ; Identification and classification ; Lakes ; Linear models (Statistics) ; Linear regression models ; Measurement ; Pigments ; Plant pigments ; Testing ; Variables ; Vegetation ; Water bloom ; Water quality ; Water temperature</subject><ispartof>Water (Basel), 2024-12, Vol.16 (24), p.3708</ispartof><rights>COPYRIGHT 2024 MDPI AG</rights><rights>2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>Attribution</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c220t-5f2e7f10225b24557529565a2e4ad2f6f9366c8fcdb7e0c979c7951635fdc9773</cites><orcidid>0000-0003-3588-6115 ; 0000-0002-4661-8274 ; 0000-0001-6431-9203 ; 0000-0003-3290-4947 ; 0000-0002-0550-0253</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,776,780,881,27901,27902</link.rule.ids><backlink>$$Uhttps://hal.inrae.fr/hal-04871284$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Rodríguez-López, Lien</creatorcontrib><creatorcontrib>Bustos Usta, David Francisco</creatorcontrib><creatorcontrib>Bravo Alvarez, Lisandra</creatorcontrib><creatorcontrib>Duran-Llacer, Iongel</creatorcontrib><creatorcontrib>Bourrel, Luc</creatorcontrib><creatorcontrib>Frappart, Frederic</creatorcontrib><creatorcontrib>Cardenas, Rolando</creatorcontrib><creatorcontrib>Urrutia, Roberto</creatorcontrib><title>Algal Pigment Estimation Models to Assess Bloom Toxicity in a South American Lake</title><title>Water (Basel)</title><description>In this study, we build an empirical model to estimate pigments in the South American Lake Villarrica. We use data from Dirección General de Aguas de Chile during the period of 1989–2024 to analyze the behavior of limnological parameters and trophic condition in the lake. Four seasonal linear regression models were developed by us, using a set of water quality variables that explain the values of phycocyanin pigment in Lake Villarrica. In the first case, we related chlorophyll-a (Chl-a) to phycocyanin, expecting to find a direct relationship between both variables, but this was not fulfilled for all seasons of the year. In the second case, in addition to Chl-a, we included water temperature, since this parameter has a great influence on the algal photosynthesis process, and we obtained better results. We discovered a typical seasonal variability given by temperature fluctuations in Lake Villarrica, where in the spring, summer, and autumn seasons, conditions are favorable for algal blooms, while in winter, the natural seasonal conditions do not allow increases in algal productivity. For a third case, we included the turbidity variable along with the variables mentioned above and the statistical performance metrics of the models improved significantly, obtaining R2 values of up to 0.90 in the case of the model for the fall season and a mean squared error (MSE) of 0.04 µg/L. In the last case used, we added the variable dissolved organic matter (MOD), and the models showed a slight improvement in their performance. These models may be applicable to other lakes with harmful algal blooms in order to alert the community to the potential toxicity of these events.</description><subject>Algae</subject><subject>Aquatic ecosystems</subject><subject>Chlorophyll</subject><subject>Comparative analysis</subject><subject>Data integrity</subject><subject>Distribution</subject><subject>Environmental aspects</subject><subject>Environmental Sciences</subject><subject>Identification and classification</subject><subject>Lakes</subject><subject>Linear models (Statistics)</subject><subject>Linear regression models</subject><subject>Measurement</subject><subject>Pigments</subject><subject>Plant pigments</subject><subject>Testing</subject><subject>Variables</subject><subject>Vegetation</subject><subject>Water bloom</subject><subject>Water quality</subject><subject>Water temperature</subject><issn>2073-4441</issn><issn>2073-4441</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNpNkU1LAzEQhhdRsNQe_AcBTx5a87lJjmupVqioqOcQs4lN3d3UTerHvzelUp05zMzLy_AwUxSnCE4IkfDiE5WYEg7FQTHAkJMxpRQd_uuPi1GMK5iDSiEYHBQPVfOqG3DvX1vbJTCLybc6-dCB21DbJoIUQBWjjRFcNiG04Cl8eePTN_Ad0OAxbNISVK3tvdEdWOg3e1IcOd1EO_qtw-L5avY0nY8Xd9c302oxNhjDNGYOW-4QxJi9YMoYZ1iykmlsqa6xK50kZWmEM_ULt9BILg2XDJWEuTpPnAyL893epW7Uus_Y_bcK2qt5tVBbDVLBERb0A2Xv2c677sP7xsakVmHTdxlPEUQlLwWUMLsmO1e-iFW-cyH12uSsbetN6KzzWa8ERlIwjsQfgulDjL11ew4E1fYjav8R8gN_aHn8</recordid><startdate>20241222</startdate><enddate>20241222</enddate><creator>Rodríguez-López, Lien</creator><creator>Bustos Usta, David Francisco</creator><creator>Bravo Alvarez, Lisandra</creator><creator>Duran-Llacer, Iongel</creator><creator>Bourrel, Luc</creator><creator>Frappart, Frederic</creator><creator>Cardenas, Rolando</creator><creator>Urrutia, Roberto</creator><general>MDPI AG</general><general>MDPI</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>1XC</scope><scope>VOOES</scope><orcidid>https://orcid.org/0000-0003-3588-6115</orcidid><orcidid>https://orcid.org/0000-0002-4661-8274</orcidid><orcidid>https://orcid.org/0000-0001-6431-9203</orcidid><orcidid>https://orcid.org/0000-0003-3290-4947</orcidid><orcidid>https://orcid.org/0000-0002-0550-0253</orcidid></search><sort><creationdate>20241222</creationdate><title>Algal Pigment Estimation Models to Assess Bloom Toxicity in a South American Lake</title><author>Rodríguez-López, Lien ; Bustos Usta, David Francisco ; Bravo Alvarez, Lisandra ; Duran-Llacer, Iongel ; Bourrel, Luc ; Frappart, Frederic ; Cardenas, Rolando ; Urrutia, Roberto</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c220t-5f2e7f10225b24557529565a2e4ad2f6f9366c8fcdb7e0c979c7951635fdc9773</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Algae</topic><topic>Aquatic ecosystems</topic><topic>Chlorophyll</topic><topic>Comparative analysis</topic><topic>Data integrity</topic><topic>Distribution</topic><topic>Environmental aspects</topic><topic>Environmental Sciences</topic><topic>Identification and classification</topic><topic>Lakes</topic><topic>Linear models (Statistics)</topic><topic>Linear regression models</topic><topic>Measurement</topic><topic>Pigments</topic><topic>Plant pigments</topic><topic>Testing</topic><topic>Variables</topic><topic>Vegetation</topic><topic>Water bloom</topic><topic>Water quality</topic><topic>Water temperature</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Rodríguez-López, Lien</creatorcontrib><creatorcontrib>Bustos Usta, David Francisco</creatorcontrib><creatorcontrib>Bravo Alvarez, Lisandra</creatorcontrib><creatorcontrib>Duran-Llacer, Iongel</creatorcontrib><creatorcontrib>Bourrel, Luc</creatorcontrib><creatorcontrib>Frappart, Frederic</creatorcontrib><creatorcontrib>Cardenas, Rolando</creatorcontrib><creatorcontrib>Urrutia, Roberto</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><jtitle>Water (Basel)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Rodríguez-López, Lien</au><au>Bustos Usta, David Francisco</au><au>Bravo Alvarez, Lisandra</au><au>Duran-Llacer, Iongel</au><au>Bourrel, Luc</au><au>Frappart, Frederic</au><au>Cardenas, Rolando</au><au>Urrutia, Roberto</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Algal Pigment Estimation Models to Assess Bloom Toxicity in a South American Lake</atitle><jtitle>Water (Basel)</jtitle><date>2024-12-22</date><risdate>2024</risdate><volume>16</volume><issue>24</issue><spage>3708</spage><pages>3708-</pages><issn>2073-4441</issn><eissn>2073-4441</eissn><abstract>In this study, we build an empirical model to estimate pigments in the South American Lake Villarrica. We use data from Dirección General de Aguas de Chile during the period of 1989–2024 to analyze the behavior of limnological parameters and trophic condition in the lake. Four seasonal linear regression models were developed by us, using a set of water quality variables that explain the values of phycocyanin pigment in Lake Villarrica. In the first case, we related chlorophyll-a (Chl-a) to phycocyanin, expecting to find a direct relationship between both variables, but this was not fulfilled for all seasons of the year. In the second case, in addition to Chl-a, we included water temperature, since this parameter has a great influence on the algal photosynthesis process, and we obtained better results. We discovered a typical seasonal variability given by temperature fluctuations in Lake Villarrica, where in the spring, summer, and autumn seasons, conditions are favorable for algal blooms, while in winter, the natural seasonal conditions do not allow increases in algal productivity. For a third case, we included the turbidity variable along with the variables mentioned above and the statistical performance metrics of the models improved significantly, obtaining R2 values of up to 0.90 in the case of the model for the fall season and a mean squared error (MSE) of 0.04 µg/L. In the last case used, we added the variable dissolved organic matter (MOD), and the models showed a slight improvement in their performance. These models may be applicable to other lakes with harmful algal blooms in order to alert the community to the potential toxicity of these events.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/w16243708</doi><orcidid>https://orcid.org/0000-0003-3588-6115</orcidid><orcidid>https://orcid.org/0000-0002-4661-8274</orcidid><orcidid>https://orcid.org/0000-0001-6431-9203</orcidid><orcidid>https://orcid.org/0000-0003-3290-4947</orcidid><orcidid>https://orcid.org/0000-0002-0550-0253</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Algae Aquatic ecosystems Chlorophyll Comparative analysis Data integrity Distribution Environmental aspects Environmental Sciences Identification and classification Lakes Linear models (Statistics) Linear regression models Measurement Pigments Plant pigments Testing Variables Vegetation Water bloom Water quality Water temperature |
title | Algal Pigment Estimation Models to Assess Bloom Toxicity in a South American Lake |
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