Skip to main content
U.S. flag

An official website of the United States government

Evaluation of condition indices for estimation of growth of largemouth bass and white crappie

January 1, 1990

We evaluated the ability of three condition indices-condition factor (K), relative condition (Kn), and relative weight (Wr)-to estimate annual growth rates of largemouth bass Micropterus salmoides and white crappies Pomoxis annularis collected during standardized autumn electrofishing and trap-net surveys of Texas reservoirs. Multiple-regression models for estimation of length increments from initial length (at the start of the growing season) and condition indices had R2 values of 0.63-0.76 for largemouth bass and 0.46-0.83 for white crappie. However, these models are not useful for indirect estimation ofgrowth rates because growth must be known (initial length equals length at capture minus estimated annual growth). Models based on length at capture and condition indices had R2 values of 0.22-0.68 for largemouth bass and less than 0.45 for white crappie. The low precision of models based on length at capture indicates that condition provides a weak basis for indirect estimation of growth rates from Texas reservoirs sampled during autumn and, therefore, is unreliable for detection of size-related growth phenomena such as "stockpiling" (size specific, density-dependent growth depression). Direct estimates of growth rates based on back-calculations or tagging data seem necessary for reliable detection of size-related growth patterns for largemouth bass and white crappies from Texas reservoirs.

Publication Year 1990
Title Evaluation of condition indices for estimation of growth of largemouth bass and white crappie
DOI 10.1577/1548-8675(1990)010<0434:EOCIFE>2.3.CO;2
Authors Steve Gutreuter, W. Michael Childress
Publication Type Article
Publication Subtype Journal Article
Series Title North American Journal of Fisheries Management
Index ID 70006920
Record Source USGS Publications Warehouse
USGS Organization Upper Midwest Environmental Sciences Center