Skip to main content

Lake Erie Ecological Investigations 1980-2000: Benthic Invertebrate Community Analysis


Publication Date
Start Date
End Date


Smith, S.B., Passino-Reader, D.R., Baumann, P.C., Nelson, S.R., and Hickey, J.T., 2018, Lake Erie Ecological Investigations 1980-2000: U.S. Geological Survey data release,


This Benthic Invertebrate Community Analysis dataset, a conceptual subgroup of the Lake Erie Ecological Investigations (LEEI) dataset, focuses on the benthic invertebrates sampled at Areas of Concern (AOCs) on Lake Erie. Per the Quality Assurance Project Plan (QAPP), the invertebrate samples were taken from sediments remaining from the sediment analysis. Identification of the invertebrates was completed by the same invertebrate taxonomist for both the 1998-2000 evaluation and 1986-87 historical evaluation (Smith et al. 1994) for increased consistency. Oligochaetes were identified to species if possible, chironomids were identified to genus, as adult specimens are needed for specific identification, and other taxa were identified as [...]


Attached Files

Click on title to download individual files attached to this item.

benthic.csv 28.84 KB text/csv
benthic_header.csv 267 Bytes text/csv
leei_benthos.csv 37.38 KB text/csv
QAPP.pdf 123.86 MB application/pdf 1.45 MB application/zip


Benthic invertebrates were sampled to ascertain relative diversity and abundance, where low species diversity and presence would indicate poor sediment and water quality. The data collected as part of the LEEI evaluations have been used to ascertain whether remedial activities and contaminant reductions had been successful for AOCs on Lake Erie. In future work, the techniques established in the LEEI reevaluation can be used to monitor trends in the Lake Erie ecosystem over the long term. The LEEI dataset can serve as a foundational data source, when used in comparison to future data collections, for analyzing the long-term success or not of contaminant reductions and impacts.

Item Actions

View Item as ...

Save Item as ...

View Item...