Regression-line-percentile Research Articles

Standard Weights (Ws) for Striped Bass, White Bass, and Hybrid Striped Bass

Abstract We developed relationships between standard weight (Ws , g) and total length (TL, mm) for striped bass Morone saxatilis, white bass M. chrysops, and palmetto bass M. saxatilis ♀ × M. chrysops ♂ by use of the regression-line–percentile (RLP) technique. The proposed 75th-percentile-based Ws equations are: log10 Ws = –4.924 + 3.007 log10TL for striped bass; log10 Ws = –5.066 + 3.081 log10TL for white bass; and log10 Ws = –5.201 + 3.139 log10 TL for hybrid striped bass. These formulae produced distributions for individual and population-mean relative-weight (Wr ) values similar to those observed for other species in earlier studies (Wr = 100·W / Ws ). The previously defined target range for Wr encompassed reasonable proportions of striped bass, white bass, and hybrid striped bass in the present study. The 75th-percentile weights for centimeter classes provided log10weight-log10length relationships that may be characterized as better than average. There were no appreciable length-related trends observ...

Development and Evaluation of a Standard Weight (WS) Equation for Yellow Perch

Weight-length data for 78 populations of yellow perch Perca flavescens in 20 states and 6 Canadian provinces were used to develop a standard weight (Ws) equation. We used the regression-line-percentile (RLP) technique, which provides a 75-percentile standard, to develop the Ws relationship. The proposed equation in metric units is log10 Ws = −5.386 + 3.230 log10 L; Ws is weight in grams and L is total length in millimeters. The English equivalent of this equation is log10 Ws= −3.506 + 3.230 log10 L; Ws is weight in pounds and L is total length in inches. These equations are proposed for use with 100-mm (4-in) and longer yellow perch. Relative weight (Wr) values calculated with the proposed Ws equation did not consistently increase or decrease with increasing fish length. Mean population Wr values were significantly correlated with growth and size structure of yellow perch populations, but correlation coefficients were generally low.

The Relative Weight Index in Fisheries Management: Status and Needs

The relative weight (Wr) index allows easy interpretation of condition for fish of various species and lengths. Rapid proliferation of index use, with a piecemeal approach to research on the index, has led to a confusing array of equations and methods for developing these equations. We recommend that the regression-line-percentile (RLP) technique be adopted as a standardized method for developing standard weight (Ws) equations, as good statistical consistency has been demonstrated for this approach. Equations should be modeled on 75th-percentile-weight data for consistent interpretation, and should be developed from data sets that represent the entire geographical range of a species. Standard weight should be considered a benchmark for comparison rather than a management target. Although the concept of a generalized “optimal” target range for Wr is attractive, Wr targets should be established for specific management objectives. Standard weight equations should be evaluated for length-related biases, and minimum applicable lengths for Wr use should be defined. Standard weight equations proposed should be published in peer-reviewed literature to facilitate effective application and evaluation by the management community. Application of rigorous criteria to development of Ws equations would standardize and clarify the theoretical basis of Wr applications. Further research is needed to clarify the relation of condition indices to other measures of population structure and dynamics, and to develop valid Ws equations for juvenile fish.

Evaluation of the Relative Weight (Wr) Index, with New Applications to Walleye

Abstract Relative weight (Wr ) is 100 × W/Ws , W being the weight of an individual fish and W the “standard” weight of a fish of the same length. The widespread application of relative weight in fisheries management has been hindered by the slow development of standard weights at length for many important fish species. We found the conventional approach to Ws development to be limited by data availability and improperly influenced by sample weighting. We designed and tested a method of Ws development that addresses these shortcomings. Our method treats log10weight-log10length regression equations for a series of a species' populations as the statistical population to be modeled. First, we predict mean fish weight in 1-cm total length intervals for each population. Then we find the 75th percentile of the mean weights in each interval, and regress these on length to determine the parameters for the Ws equation. Standard-weight equations determined by our regression-line-percentile (RLP) technique were funct...