Taxonomic bias in freshwater phytoplankton communities and its effect on environmental assessment metrics

Research output: Contribution to journalArticlepeer-review

Abstract

Ecological studies of the highly diverse groups of organisms, such as algae, can be particularly prone to taxonomic opinion bias. There is mounting pressure to recognize and report this bias across studies to facilitate reproducible science and data re-use; however, there are fewer studies which link taxonomic bias to loss of precision in modelling community responses or in bioassessments. We use long-term phytoplankton monitoring data to evaluate effects of taxonomic bias on relevant ecological metrics including algal biovolume, cell counts, diversity, and the performance of a species-based weighted-averaging model for inferring phosphorus concentrations. Despite the non-zero taxonomic bias between paired original and quality assurance samples, several important indicators derived from these data, including model-inferred phosphorus concentrations, total algal biovolume and cell density, and diversity were mostly unaffected. We discuss this as an example of using ecologically relevant model performance criteria for making recommendations about the acceptable level of taxonomic bias.

Original languageEnglish (US)
Pages (from-to)436-443
Number of pages8
JournalPhycologia
Volume61
Issue number4
DOIs
StatePublished - 2022

Bibliographical note

Funding Information:
This study was funded by the U.S. Environmental Protection Agency under Cooperative Agreements GL-00E23101-2 and GL-00E0198-0. The research described in this article has not been subjected to U.S. EPA review. Any opinions expressed in this publication are those of the authors and do not necessarily reflect the views or policies of the U.S. EPA. Phytoplankton sample analysis was supported by Elizabeth Alexson, Michael Agbeti, Elaine Ruzycki and Holly Wellard Kelly.

Funding Information:
Phytoplankton sample analysis was supported by Elizabeth Alexson, Michael Agbeti, Elaine Ruzycki and Holly Wellard Kelly.

Publisher Copyright:
© 2022 International Phycological Society.

Keywords

  • Algae
  • Analytical error
  • Quality assurance
  • Quality control

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