Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 277

The DOI matches its Crossref record.

Biological guidelines for freshwater sediment based on BEnthic Assessment of SedimenT (the BEAST) using a multivariate approach for predicting biological state

REYNOLDSON, TREFOR B., BAILEY, R. C., DAY, K. E., NORRIS, R. H.·1995·Australian Journal of Ecology

Abstract

Abstract This paper describes the first results for an alternative approach to the development of sediment quality criteria in the nearshore areas of the Laurentian Great Lakes. The approach is derived from methods developed in the United Kingdom for establishing predictive relationships between macroinvertebrate fauna and the physico‐chemistry of riverine environments. The technique involves a multivariate statistical approach using (i) data on the structure of benthic invertebrate communities, (ii) functional responses (survival, growth and reproduction) in four sediment toxicity tests (bioassays) with benthic invertebrates; and (iii) selected environmental variables at 96 reference (‘clean’) sites in the nearshore areas of all five Great Lakes (Lakes Superior, Huron, Erie, Ontario and Michigan). Two pattern recognition techniques (using the computer software package PATN) are employed in the analysis: cluster analysis and ordination. The ordination vector scores from the original axes of the pattern analysis are correlated (using CORR in SAS) with environmental variables which are anticipated to be least affected by anthropogenic activities (e. g. alkalinity, depth, silt, sodium etc.). Multiple discriminant analysis (MDA) is used to relate the site groupings from the pattern analysis to the environmental variables and to generate a model that can be used to predict community assemblages and functional responses at new sites with unknown but potential contamination. The predicted community assemblages and functional responses are then compared with the actual benthic communities and responses at a site, and the need for remedial action is determined. The predictive capability of the discriminant model was confirmed by performing several validation runs on subsets of the data. An example of the use of the model for sediment in Collingwood Bay (an area of concern designated by the IJC in Georgian Bay, Lake Huron) is presented and the technique is shown to be more precise in determining the need for remediation than the currently used provincial sediment quality criteria based on Screening Level Concentration (SLC) and laboratory toxicity tests. The ultimate goal of the study is the development of a method to determine the need for, and the success of, remedial action and to predict what benthic communities should look like at a site if it were clean and what responses of organisms in sediment toxicity tests constitute an acceptable end‐point.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1111/j.1442-9993.1995.tb00532.x

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cited by

See all 277

Metadata

Authors
REYNOLDSON, T. B., BAILEY, R. C., DAY, K. E., & NORRIS, R. H.
Venue
Australian Journal of Ecology
Publisher
Wiley
Published
1995-03
Volume
20
Issue
1
Pages
198-219
ISSN
0307-692X (ISSN-L)
OpenAlex
W1985128951
Cited by
277
References
0
FWCI
14.25
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Biological guidelines for freshwater sediment based on BEnthic Assessment of SedimenT (the BEAST) using a multivariate approach for predicting biological state. https://app.cochat.ai/papers/10.1111/j.1442-9993.1995.tb00532.x
The paper
REYNOLDSON, T. B., BAILEY, R. C., DAY, K. E., & NORRIS, R. H. (1995). Biological guidelines for freshwater sediment based on BEnthic Assessment of SedimenT (the BEAST) using a multivariate approach for predicting biological state. Australian Journal of Ecology. https://doi.org/10.1111/j.1442-9993.1995.tb00532.x

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.