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The selection of suitable indices for the measurement and analysis of fish condition

Bolger, T., Connolly, P. L.·1989·Journal of Fish Biology

Abstract

A survey of the studies published in two leading fisheries journals revealed that the analysis and measurement of condition, based on length‐weight data, has been performed using a wide variety of indices and statistical procedures. Eight forms of index were identified which can be categorized into those which measure the condition of individual fish, i.e. condition factors‘, and those which measure the condition of subpopulations as a whole, i.e. regressions of log, 10 weight on log, 10 length and the parameters of such regressions. Analysis of a test data set indicated that both the form of index and properties of the data set size can dictate the patterns of condition observed. The various indices were reviewed in terms of appropriateness, simplicity and statistical correctness. It was concluded that an index should be selected only after a detailed examination of both the underlying assumptions of the index and the properties of the data set.

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Crossref · 10.1111/j.1095-8649.1989.tb03300.x

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Metadata

Authors
Bolger, T., & Connolly, P. L.
Venue
Journal of Fish Biology
Publisher
Wiley
Published
1989-02
Volume
34
Issue
2
Pages
171-182
ISSN
  • 0022-1112 (ISSN-L)
  • 1095-8649
OpenAlex
W2000321230
Cited by
1,021
References
1
FWCI
14.21
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: The selection of suitable indices for the measurement and analysis of fish condition. https://app.cochat.ai/papers/10.1111/j.1095-8649.1989.tb03300.x
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Bolger, T., & Connolly, P. L. (1989). The selection of suitable indices for the measurement and analysis of fish condition. Journal of Fish Biology. https://doi.org/10.1111/j.1095-8649.1989.tb03300.x

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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.