Analysis of community-level mesocosm data based on ecologically meaningful dissimilarity measures and data transformation

Cleo Tebby, Sandrine Joachim, Paul J. Van den Brink, Jean Marc Porcher, Rémy Beaudouin

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)

Abstract

The principal response curve (PRC) method is a constrained ordination method developed specifically for the analysis of community data collected in mesocosm experiments, which provides easily understood summaries and graphical representations of community response to stress. It is a redundancy analysis method and is usually performed on log-transformed abundance data. The choice of a measure of dissimilarity between samples and the choice of the data transformation significantly affect the results of multivariate analysis. Dissimilarity measures that are more ecologically meaningful than the Euclidean distance can be incorporated into the PRC using distance-based redundancy analysis. The present study investigates the ordinations produced by a small selection of dissimilarity measures: the Euclidean distance using log-transformed and Hellinger-transformed data and the Bray-Curtis dissimilarity using raw and log-transformed data. It compares 2 data sets from experiments on the effect of the anti-inflammatory drug diclofenac and the insecticide chlorpyrifos on macroinvertebrate communities. The choice of dissimilarity measure can determine the outcome of a risk assessment. For the diclofenac data set, the PRCs were different depending on the dissimilarity measure: the community no-effect concentration was lowest for the Bray-Curtis on log-transformed data and Hellinger dissimilarity measures. For chlorpyrifos, however, the PRCs were similar for all dissimilarity measures.
Original languageEnglish
Pages (from-to)1667-1679
JournalEnvironmental Toxicology and Chemistry
Volume36
Issue number6
DOIs
Publication statusPublished - 2017

Fingerprint

Chlorpyrifos
Diclofenac
mesocosm
Redundancy
Insecticides
Risk assessment
Anti-Inflammatory Agents
chlorpyrifos
Experiments
ordination
Pharmaceutical Preparations
Multivariate Analysis
community response
multivariate analysis
macroinvertebrate
insecticide
analysis
drug
risk assessment
experiment

Keywords

  • Aquatic toxicology
  • Dissimilarity measure
  • Ecological risk assessment
  • Mesocosm
  • Multivariate statistic
  • Principal response curve

Cite this

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abstract = "The principal response curve (PRC) method is a constrained ordination method developed specifically for the analysis of community data collected in mesocosm experiments, which provides easily understood summaries and graphical representations of community response to stress. It is a redundancy analysis method and is usually performed on log-transformed abundance data. The choice of a measure of dissimilarity between samples and the choice of the data transformation significantly affect the results of multivariate analysis. Dissimilarity measures that are more ecologically meaningful than the Euclidean distance can be incorporated into the PRC using distance-based redundancy analysis. The present study investigates the ordinations produced by a small selection of dissimilarity measures: the Euclidean distance using log-transformed and Hellinger-transformed data and the Bray-Curtis dissimilarity using raw and log-transformed data. It compares 2 data sets from experiments on the effect of the anti-inflammatory drug diclofenac and the insecticide chlorpyrifos on macroinvertebrate communities. The choice of dissimilarity measure can determine the outcome of a risk assessment. For the diclofenac data set, the PRCs were different depending on the dissimilarity measure: the community no-effect concentration was lowest for the Bray-Curtis on log-transformed data and Hellinger dissimilarity measures. For chlorpyrifos, however, the PRCs were similar for all dissimilarity measures.",
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Analysis of community-level mesocosm data based on ecologically meaningful dissimilarity measures and data transformation. / Tebby, Cleo; Joachim, Sandrine; Van den Brink, Paul J.; Porcher, Jean Marc; Beaudouin, Rémy.

In: Environmental Toxicology and Chemistry, Vol. 36, No. 6, 2017, p. 1667-1679.

Research output: Contribution to journalArticleAcademicpeer-review

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T1 - Analysis of community-level mesocosm data based on ecologically meaningful dissimilarity measures and data transformation

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AU - Joachim, Sandrine

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AU - Beaudouin, Rémy

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