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Comparison of Multiple Linear Regression and Biotic Ligand Models to Predict the Toxicity of Nickel to Aquatic Freshwater Organisms.

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Title: Comparison of Multiple Linear Regression and Biotic Ligand Models to Predict the Toxicity of Nickel to Aquatic Freshwater Organisms.
Authors: Croteau, Kelly1 (AUTHOR) kellyc@windwardenv.com, Ryan, Adam C.2 (AUTHOR), Santore, Robert1 (AUTHOR), DeForest, David3 (AUTHOR), Schlekat, Christian4 (AUTHOR), Middleton, Elizabeth4 (AUTHOR), Garman, Emily4 (AUTHOR)
Source: Environmental Toxicology & Chemistry. Aug2021, Vol. 40 Issue 8, p2189-2205. 17p.
Subjects: Multiple comparisons (Statistics), Freshwater organisms, Aquatic organisms, Nickel, Prediction models, Regression analysis, Progression-free survival
Abstract: Toxicity‐modifying factors can be modeled either empirically with linear regression models or mechanistically, such as with the biotic ligand model (BLM). The primary factors affecting the toxicity of nickel to aquatic organisms are hardness, dissolved organic carbon (DOC), and pH. Interactions between these terms were also considered. The present study develops multiple linear regressions (MLRs) with stepwise regression for 5 organisms in acute exposures, 4 organisms in chronic exposures, and pooled models for acute, chronic, and all data and compares the performance of the Pooled All MLR model to the performance of the BLM. Independent validation data were used for evaluating model performance, which for pooled models included data for organisms and endpoints not present in the calibration data set. Hardness and DOC were most often selected as the explanatory variables in the MLR models. An attempt was also made at evaluating the uncertainty of the predictions for each model; predictions that showed the most error tended to show the highest levels of uncertainty as well. The performances of the 2 models were largely equal, with differences becoming more apparent when looking at the performance within subsets of the data. Environ Toxicol Chem 2021;40:2189–2205. © 2021 SETAC [ABSTRACT FROM AUTHOR]
Copyright of Environmental Toxicology & Chemistry is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 151568330
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  Data: Comparison of Multiple Linear Regression and Biotic Ligand Models to Predict the Toxicity of Nickel to Aquatic Freshwater Organisms.
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  Data: <searchLink fieldCode="JN" term="%22Environmental+Toxicology+%26+Chemistry%22">Environmental Toxicology & Chemistry</searchLink>. Aug2021, Vol. 40 Issue 8, p2189-2205. 17p.
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  Data: <searchLink fieldCode="DE" term="%22Multiple+comparisons+%28Statistics%29%22">Multiple comparisons (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Freshwater+organisms%22">Freshwater organisms</searchLink><br /><searchLink fieldCode="DE" term="%22Aquatic+organisms%22">Aquatic organisms</searchLink><br /><searchLink fieldCode="DE" term="%22Nickel%22">Nickel</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Progression-free+survival%22">Progression-free survival</searchLink>
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  Data: Toxicity‐modifying factors can be modeled either empirically with linear regression models or mechanistically, such as with the biotic ligand model (BLM). The primary factors affecting the toxicity of nickel to aquatic organisms are hardness, dissolved organic carbon (DOC), and pH. Interactions between these terms were also considered. The present study develops multiple linear regressions (MLRs) with stepwise regression for 5 organisms in acute exposures, 4 organisms in chronic exposures, and pooled models for acute, chronic, and all data and compares the performance of the Pooled All MLR model to the performance of the BLM. Independent validation data were used for evaluating model performance, which for pooled models included data for organisms and endpoints not present in the calibration data set. Hardness and DOC were most often selected as the explanatory variables in the MLR models. An attempt was also made at evaluating the uncertainty of the predictions for each model; predictions that showed the most error tended to show the highest levels of uncertainty as well. The performances of the 2 models were largely equal, with differences becoming more apparent when looking at the performance within subsets of the data. Environ Toxicol Chem 2021;40:2189–2205. © 2021 SETAC [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Environmental Toxicology & Chemistry is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/etc.5063
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        Text: English
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        PageCount: 17
        StartPage: 2189
    Subjects:
      – SubjectFull: Multiple comparisons (Statistics)
        Type: general
      – SubjectFull: Freshwater organisms
        Type: general
      – SubjectFull: Aquatic organisms
        Type: general
      – SubjectFull: Nickel
        Type: general
      – SubjectFull: Prediction models
        Type: general
      – SubjectFull: Regression analysis
        Type: general
      – SubjectFull: Progression-free survival
        Type: general
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      – TitleFull: Comparison of Multiple Linear Regression and Biotic Ligand Models to Predict the Toxicity of Nickel to Aquatic Freshwater Organisms.
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              M: 08
              Text: Aug2021
              Type: published
              Y: 2021
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