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Development and Evaluation of Condition Indices for the Lake Whitefish

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Development and Evaluation of Condition Indices for the Lake Whitefish North American Journal of Fisheries Management 28:1270–1293, 2008 [Article] Ó Copyright by the American Fisheries Society 2008 DOI: 10.1577/M06-258.1 Development and Evaluation of Condition Indices for the Lake Whitefish MICHAEL D. RENNIE* Aquatic Ecology Group, University of Toronto at Mississauga, 3359 Mississauga Road North, Mississauga, Ontario L5L 1C6, Canada RICHARD VERDON Hydro-Que´bec Production, Unite´ Environnement, 75 Rene´-Le´vesque West, 10th Floor, Montreal, Que´bec H2Z 1A4, Canada Abstract.—Despite frequent use of length-based condition indices by fisheries managers and scientists to describe the overall well-being of fish, these indices are rarely evaluated to determine how well they correlate with more direct measures of physiological or ecological condition. We evaluated common condition indices (Fulton’s condition factor KF, Le Cren’s condition index KLC, and two methods of estimating relative weight Wr) against more direct measures of physiological condition (energy density, percent lipid content, and percent dry mass) and ecological condition (prey availability) for lake whitefish Coregonus clupeaformis in Lake Huron. We developed four standard weight (Ws) equations using the regression length percentile (RLP) method: one for the species as a whole, and three separate equations describing immature, mature male, and mature female lake whitefish from 385 populations in North America. Species RLP-Ws showed less length- related bias and more closely matched empirical quartiles of lake-specific mean weight than did maturity- or sex-specific RLP-Ws equations. Significant length-related bias was detected in EmP-Wr. No biologically significant length-related bias was detected in KLC, but this index was specific to a single population of fish. Species RLP-Wr showed no significant length-related bias, and KF was significantly size dependent. All length-based condition indices were significantly correlated with energy density, percent lipid content, and percent dry mass. The index most strongly correlated with all three measures of physiological condition was KF, likely because both the physiological measures and KF exhibited positive relationships with body size. Across two Lake Huron sites, RLP-Wr was significantly correlated with density of prey (amphipods Diporeia spp.). Of the two condition indices developed in this study, RLP-Wr was consistently more strongly correlated with physiological condition indices than was EmP-Wr. Length-based condition indices have been used by studies manipulating consumption found strong posi- fisheries biologists for nearly 100 years to describe the tive correlations between percent fat (PF; whole-body energetic status, overall well-being, or reproductive composite and percent weight of visceral fat) and status of fish (Froese 2006; Nash et al. 2006). These condition (relative weight Wr) in juvenile striped bass indices describe the body size for fish of a given length Morone saxatilis and hybrid striped bass (striped bass (Gerow et al. 2004, 2005); fish with greater mass than M. saxatilis 3 white bass M. chrysops; Brown and their counterparts of similar length are considered to be Murphy 1991). Condition indices (Wr) in populations in good condition, whereas fish with lower mass at a of pumpkinseeds Lepomis gibbosus and golden shiners given length are considered to be in poor condition. Notemigonus crysoleucas in southern Que´bec were These simple considerations of condition are frequently positively correlated with food availability (Liao et al. utilized because they can be easily estimated from 1995). More recently, a field study demonstrated that Fulton’s condition factor (K ) and W in bluegills L. standard fisheries data (length and weight data from a F r macrochirus were positively and significantly corre- sample of fish in one or more populations). lated with nonpolar lipid density (Neff and Cargnelli Studies evaluating length-based condition indices 2004) and that K was correlated with both parasite against more direct measures of energetic status or F density and male paternity. In contrast, weak correla- physiological stress in fish (e.g., energy density [ED], tions between common condition indices (K ,Le prey availability, and lipid content) are rare, and those F Cren’s condition index KLC, and Wr) and more direct that have done so report mixed conclusions. Laboratory measures of energy content (PF, energy density, and percent dry mass [PD]) were reported for two salmon * Corresponding author: [email protected] species (Trudel et al. 2005), and condition indices Received November 3, 2006; accepted November 16, 2007 (KF and two measures of Wr) in stocked walleyes Published online August 25, 2008 Sander vitreus were poorly correlated with lipid 1270 LAKE WHITEFISH CONDITION INDICES 1271 density (Copeland and Carline 2004). This variation in RLP-Ws; Murphy et al. 1990; based on the technique of findings among studies suggests that the informative Wege and Anderson [1978]). Using linear regression, value of condition indices may vary between species, one estimates the coefficients of the relationship of particularly when applied to wild fish stocks. log10W on log10L for each of the I populations under Any length-based condition index should be free of study. The estimated weight for each population i at ˆ systematic length-related bias to allow comparisons length j (Wi, j) is calculated across a predetermined among populations in space or time (Gerow et al. 2004 range of lengths applicable to the species at hand. This and references therein); if this is not the case, then what range is subdivided into J length-classes with midpoint might be interpreted as a change in condition from Lj ( j ¼ 1, ...J; 10-mm length increments were used in ˆ small to large fish within or among populations might this study). Based on the computation of Wi, j for all simply be an artifact of a change in the average size of combinations of i and j, the 75th percentile of back- a population. To this end, a variety of indices have transformed predicted weights for each 10-mm length ˆ been proposed in the literature. Three frequently used increment, Qj(Wi, j), is estimated (Murphy et al. 1990). The W equation for the species is estimated as the indices are KF (Ricker 1975), KLC (Le Cren 1951), and s linear regression of log (Q [Wˆ ]) against log (L ). Wr (Wege and Anderson 1978). Each has been widely 10 j i, j 10 j utilized and critiqued, as described below. Fulton’s K is Thus, Ws,L is simply the back-transformed predicted expressed as weight at length L from the Ws equation and should represent approximately the 75th percentile of mean 7 3 KF ¼ 10 3ðW=L Þ; ð1Þ weights among populations (Gerow et al. 2004). Estimates of W based on RLP-W have been where W is weight (g) and L is some measure of length r s reported to be superior to the previously mentioned (mm; e.g., total length [TL] as was used here). measures of condition because the RLP-W relationship However, the assumption of a cubic relationship s is empirically derived using data from many popula- between the length and weight of a fish is frequently tions and therefore is thought to offer a more thorough violated, making K highly subject to length-related F characterization of the relationship between length and bias. weight for a particular species as a whole (Brown and The following equation describes KLC: Murphy 1991). However, the RLP-Ws method has m KLC ¼½W=ðb 3 L Þ 3 100; ð2Þ recently been critiqued (Gerow et al. 2004, 2005) and an alternative W estimation method, the empirical where b and m are empirically derived constants from s quartile method (EmP-Ws), has been proposed. In this the relationship between W and L. The major limitation case, mean log observed weights (W ) is estimated of K is that it is often too specific to be ecologically 10 i, j LC for each population over the J defined length-classes. informative. Slopes of length–weight regressions differ The third quartile of these, Q (W ), is estimated. These among fish populations (e.g., Table A.1), and such j i, j empirical quartiles are then fitted against log10(Lj) differences may be due to variation in ecological niches using polynomial regression weighted by the number between populations rather than energetic status of populations represented in each length-class (Gerow specifically (e.g., Svanback and Eklov 2004). This et al. 2004, 2005). effectively limits the application of KLC to single Length-based condition indices have been used to populations only. A simplistic approach of estimating a track recent ecological changes in Great Lakes single b or m parameter over the entire species results populations of lake whitefish Coregonus clupeaformis in an equation that overrepresents populations with (Pothoven et al. 2001; Lumb et al. 2007). The lake large sample sizes and would not provide comparable whitefish is an important commercial species in both weighting of populations that might otherwise describe the Great Lakes and inland lakes of Canada, supporting the legitimate range of body shapes observed in the a multimillion dollar annual fishery that is marketed species. internationally. Recent declines in the condition and Relative weight is described as growth of Great Lakes lake whitefish stocks have been observed in Lake Michigan (Pothoven et al. 2001), Wr ¼ðW=Ws;LÞ 3 100; ð3Þ Lake Ontario (Hoyle et al. 1999; Lumb et al. 2007), where Ws,L is the standard weight (Ws) of a fish of and southwestern Lake Huron (Pothoven et al. 2006). length L, and the equation for Ws is an empirically Given the economic importance of this species to North derived model from representative populations (both American economies (Madenjian et al. 2006) and the morphologically and geographically) of a particular scarcity of historic data on lake whitefish physiological species. The traditional method for estimating Ws is the status (i.e., ED, PF, and water content or PD), regression length percentile (RLP) method (hereafter, knowledge of relationships between physiological 1272 RENNIE AND VERDON and date of capture were compiled from 419 popula- tions for the development of Ws equations.
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