Macroinvertebrate and Fish Community Metrics: Confounding Effects and Consistency over Time

Research output: Contribution to journalArticlepeer-review

Abstract

Macroinvertebrate and fish metrics are an essential tool in freshwater biomonitoring. Yet, many well-known stream metrics do not perform well as indicators of anthropogenic stress in lakes and wetlands, and there is a need to better understand the role of potential confounding factors such as latitude or environmental variables acting at regional scales. We attempt to untangle the relationships among metrics and watershed stressors while accounting for potential confounding factors using hierarchical partitioning, and test metric consistency across time, focusing on the Laurentian Great Lakes wetland monitoring programs conducted 10 years apart. Our results show that many frequently used metrics have high temporal variability and are significantly affected by spatial factors, most notably richness-type metrics used in numerous regional monitoring studies. Only a few metrics (invasive fish species richness and relative abundance, mayfly family richness and the relative abundance of Ephemeroptera, Trichoptera, Sphaeriidae and Odonata) had slightly better and more consistent correlations with watershed land use. We suggest that explicit consideration of confounding factors is essential in the context of large-scale monitoring programs, and focus on the less general (such as habitat-specific) metrics may be a more promising approach.

Original languageEnglish (US)
Pages (from-to)1107-1116
Number of pages10
JournalWetlands
Volume40
Issue number5
DOIs
StatePublished - Oct 1 2020

Bibliographical note

Funding Information:
We thank field and lab personnel from Natural Resources Research Institute University of Minnesota Duluth and University of Windsor for their assistance in data collection. We are particularly grateful to Josh Dumke, Joseph Gathman, Jeff Buckley and Dan Breneman. This project was funded by grants from US Environmental Protection Agency Science to Achieve Results (STAR) and Great Lakes (EaGLe) program through funding to the (GLEI) project (R-8286750), and from US Environmental Protection Agency Great Lakes National Program Office and Great Lakes Restoration Initiative through funding to the 2nd stage Great Lakes Environmental Indicators (GLEI-II) Indicator Testing and Refinement project (GL-00E00623-0). This document has not been subjected to the Agency?s required peer and policy review, and therefore does not necessarily reflect the views of the Agency and no official endorsement should be inferred. This is contribution number 635 from the Natural Resources Research Institute, University of Minnesota Duluth. We are grateful to two anonymous reviewers and the Associate Editor for their comments and suggestions.

Funding Information:
We thank field and lab personnel from Natural Resources Research Institute University of Minnesota Duluth and University of Windsor for their assistance in data collection. We are particularly grateful to Josh Dumke, Joseph Gathman, Jeff Buckley and Dan Breneman. This project was funded by grants from US Environmental Protection Agency Science to Achieve Results (STAR) and Great Lakes (EaGLe) program through funding to the (GLEI) project (R-8286750), and from US Environmental Protection Agency Great Lakes National Program Office and Great Lakes Restoration Initiative through funding to the 2nd stage Great Lakes Environmental Indicators (GLEI-II) Indicator Testing and Refinement project (GL-00E00623-0). This document has not been subjected to the Agency’s required peer and policy review, and therefore does not necessarily reflect the views of the Agency and no official endorsement should be inferred. This is contribution number 635 from the Natural Resources Research Institute, University of Minnesota Duluth. We are grateful to two anonymous reviewers and the Associate Editor for their comments and suggestions.

Publisher Copyright:
© 2020, Society of Wetland Scientists.

Keywords

  • Bioassessment
  • Freshwater coastal wetlands
  • Hierarchical partitioning
  • Large-scale monitoring
  • Lentic biodiversity
  • Watershed land-use

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