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North American Journal of Fisheries Management 28:1868–1875, 2008 Ó Copyright by the American Fisheries Society 2008 DOI: 10.1577/M08-064.1

[Article]

Relationships between Relative Weight, Prey Availability, and Growth of Walleyes in Oneida Lake, New York ANTHONY J. VANDEVALK,*1 JOHN L. FORNEY,

AND

JAMES R. JACKSON

Cornell Biological Field Station, 900 Shackelton Point Road, Bridgeport, New York 13030, USA Abstract.—Assessment of predator–prey dynamics is a central feature of community-based fisheries management, and a variety of fish condition indices have been developed from length–weight data to make inferences about feeding conditions. Relative weight (Wr) is useful for measuring fish condition, is easily calculated, and has simple data requirements; however, few studies have empirically validated Wr indices. We compared the Wr of walleyes Sander vitreus in Oneida Lake, New York, with prey fish availability and walleye growth observed over 23 years (1961–1983). For each year, we calculated the Wr for three lengthclasses (350, 400, and 450 mm total length) of walleyes caught in October. Prey fish availability was calculated from annual estimates of (1) prey biomass in bottom trawl catch and (2) biomass of age-3 and older walleyes estimated from mark–recapture studies. Growth of walleyes (ages 4–6) was determined using lengths at age that were back-calculated from scale annuli. The Wr declined as walleye length-class increased, and Wr was positively related to both prey fish availability and walleye growth. Our study suggests that Wr can be a useful tool for describing feeding conditions and assessing predator–prey relations.

Assessment of predator–prey dynamics is a central feature of community-based fisheries management. However, monitoring abundance of both predator and prey species is time consuming, resource intensive, and often impractical. Length and weight data are routinely collected from sport fishes and are frequently used to assess the effect of ecological and physiological processes on the health and well being of fish (Murphy et al. 1991; Ney 1993). Various condition indices have been developed from length and weight data to make inferences about feeding conditions (Ney 1993). Since the mid-1980s, the relative weight (Wr) index of condition has become increasingly popular as a population assessment tool (Blackwell et al. 2000). Calculation of Wr requires only lengths and weights from a sample of individuals. Fish that weigh more at a given length are considered to be in better condition, and condition has been linked to reproductive state (LeCren 1951), growth (Wege and Anderson 1978; Mosher 1984; Willis 1989; Brown and Murphy 1991; Gabelhouse 1991; Guy and Willis 1995; Anderson and Neumann 1996), and environmental characteristics, including prey availability (Anderson and Gutreuter 1983; Busasker et al. 1990; Hubert et al. 1994; Liao et al. 1995; Marwitz and Hubert 1997; Porath and Peters 1997). Relative weight may replace more-detailed measures

of predator–prey balance and give fisheries managers the advantages of limited data demands, ease of calculation, and intuitively appealing interpretation of index values. Although changes in Wr are assumed to reflect changes in the feeding conditions experienced by fish, few studies have tested this assumption explicitly (Wege and Anderson 1978). Additionally, several studies have failed to identify a correlation between Wr and growth (Gutreuter and Childress 1990; DiCenzo et al. 1995; Liao et al. 1995). In light of the wide use of Wr by managers, several authors have called for (1) validation of Wr as a method for assessing prey availability and (2) evaluation of the index’s predictive abilities (Murphy et al. 1990; Murphy et al. 1991; Liao et al. 1995; Blackwell et al. 2000). Empirical demonstration of relationships between Wr and prey availability or growth would provide valuable insights into the appropriateness of Wr as an assessment and management tool (Liao et al. 1995). In this study, annual variation in the Wr of walleyes Sander vitreus in Oneida Lake was evaluated relative to prey fish availability and walleye growth. Our objectives were to (1) describe length–weight relationships of specific walleye length-classes during the period 1961–1983, (2) determine whether differences in Wr existed among those length-classes, and (3) assess whether there was a relationship between Wr and prey fish availability or walleye growth.

* Corresponding author: [email protected] 1 Current address: O’Brien & Gere, 5000 Brittonfield Parkway, East Syracuse, New York 13057, USA

Methods Study site.—Oneida Lake has the largest surface area (207 km2) of any lake situated entirely in New York State and is one of the most studied freshwater

Received March 26, 2008; accepted June 30, 2008 Published online January 5, 2009

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TABLE 1.—Parameters from regressions of log10(weight [g]) on log10(total length [mm]) for walleyes captured in Oneida Lake, New York, 1961 to 1983 (N ¼ number of sampled fish; r2 ¼ coefficient of determination). Year

N

Intercept

Slope

r2

Remarks

1961 1962 1964 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1980 1981 1982 1983

100 29 71 83 100 111 114 70 99 101 50 20 106 100 54 26 53 68 96 115

4.6307 4.2911 4.8053 4.5660 5.7810 5.7161 5.5686 5.4712 5.3452 4.8725 5.0409 4.5068 5.4669 4.0617 5.2920 6.2778 5.0066 4.5771 5.2190 5.3839

2.8564 2.7304 2.9221 2.8143 3.2891 3.2835 3.2073 3.1723 3.1365 2.9277 3.0025 2.7985 3.1699 2.6165 3.1213 3.4789 3.0135 2.8347 3.0812 3.1455

0.972 0.944 0.944 0.947 0.969 0.995 0.966 0.988 0.978 0.963 0.987 0.994 0.967 0.951 0.945 0.966 0.977 0.902 0.972 0.973

age  4 age  4 age  4 age  4 age  4 325 mm 325 mm 300 mm 300 mm 300 mm 300 mm age  4 age  4 age  3 age  4 age  3 300 mm age  3 300 mm 300 mm

ecosystems in North America (Mills et al. 1978; Forney 1980). In addition to providing one of the state’s most valuable walleye angling fisheries (Connelly and Brown 1991), information on fish populations has been collected annually in this lake since 1961. Throughout this period, changes in abundance of both walleyes and their prey have been monitored, providing an opportunity to identify relationships that may not be evident from only a few years of data. Over 80 species of fish have been documented to occur in Oneida Lake (Clady 1976). Of these species, young-of-year (age-0) yellow perch Perca flavescens and occasionally white perch Morone americana were the most important prey fish species for walleyes before 1984 (Forney 1974, 1980). During these years, abundance and growth of age-0 yellow perch largely determined prey fish availability for walleyes (Forney 1974). Both yellow perch and white perch are vulnerable to bottom trawls used in monitoring Oneida Lake, and annual variation in their availability to walleyes can be tracked with standardized sampling methods. In 1984, the appearance of young gizzard shad Dorosoma cepedianum provided an additional prey resource for Oneida Lake walleyes (Hall and Rudstam 1999). Young gizzard shad are pelagic and to date have rarely appeared in bottom trawl catches; however, they have been the most or second-most common species identified in October walleye diets in all but 3 years since 1984 (VanDeValk et al. 2007). Because gizzard shad are not vulnerable to bottom trawls, routine monitoring does not allow their contribution to the prey fish population to be measured as consistently as that of yellow perch and white perch.

Because our objectives were to assess the response of walleye Wr to changes in prey fish availability, we consider only data from 1961 to 1983 for our analyses to avoid uncertainties surrounding variability of gizzard shad abundance. Walleye relative weight.—Relative weights were calculated from walleyes caught in October trawl surveys. A 12.2-m bottom trawl with 51-mm stretch mesh in the cod end was fished at a speed of 5.8 km/h at four offshore sites located throughout the lake. Generally, all 300-mm and larger walleyes were counted. A subsample of fish was sacrificed to determine sex, diet composition, total length (TL; mm), weight (g), and age (from scales). Lengths and weights were log10 transformed, and regression analyses were conducted to provide year-specific predictive models (see Table 1 for year-specific criteria). Sexes were pooled (except for the years 1968, 1972, and 1980–1983, when sex-specific regressions were calculated) and parameters were averaged to provide pooled values. For each year, Wr was calculated for each length-class by predicting walleye weight at a given length from our year-specific model and then dividing predicted weight by the associated standard weight (Ws) from a standard length–weight model (Murphy et al. 1990): log10 Ws ¼ 5:453 þ 3:180  log10 TL: Prey fish availability.—Prey fish availability (kg of prey fish/kg of walleyes) was estimated annually from the biomass of prey fish and the biomass of age-3 and older walleyes. For prey fish biomass, a bottom trawl with a 5.9-m footrope and 13-mm stretch mesh in the

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cod end was fished during the day at a speed of 3.4 km/ h. Trawling was conducted at each of 10 sites (6 sites were sampled in 1961; 8 sites were sampled in 1962) at approximately weekly intervals during mid-July through October (12–17 weeks). Each trawl haul sampled approximately 0.1 ha of bottom (transect length ’ 280 m). All age-0 fish and all older, preysized individuals (30 g) were identified to species, enumerated, and weighed; a subsample of each species was measured for TL at each site. Chevalier (1973) and Forney (1974) indicated that walleyes in Oneida Lake consumed few fish over 150 mm (30 g); therefore, only 30-g and smaller fish were included in prey availability estimates. Trawl catches of white perch, tessellated darters Etheostoma olmstedi, and pumpkinseeds Lepomis gibbosus were adjusted based on day : night catch ratios (Forney 1974). Forney (1974) provided trawl catches for 1968–1971. Weekly prey fish biomass was the total weight of all prey fish pooled by site. Annual prey fish biomass was calculated by averaging the weekly biomass estimates for the season. Spring biomass of age-3 and older walleyes was obtained from spring abundance estimates and June weights. Abundance of age-4 and older walleyes was estimated in April using mark–recapture during 11 years from 1961 to 1977. Forney (1967) estimated the abundance of age-4 and older walleyes for 1961 and 1962 and provided a detailed description of the methods. Numbers of age-5 and older walleyes for intervening years were approximated from the distribution of mortality between successive biannual population estimates (Forney 1977a). The age-4 walleye abundance during an intervening year was estimated from age-5 abundance in the subsequent year (adjusted for mortality). Age-4 and older walleye abundances from 1979 to 1983 were based on spring trap-net catches (Forney 1977b). Age-3 walleye abundance for a given year was estimated from the age-4 abundance in the subsequent year (adjusted for natural mortality [0.05] and half the estimated fishing mortality; Forney 1967); the exception was for 1962, when enough age-3 walleyes were available for mark– recapture estimation as described above (Forney 1967). Age-specific walleye abundances were converted to biomass using sex-specific regressions of log10(weight) on log10TL data from fish caught during June gill-net surveys. April biomass of walleyes was estimated by multiplying the age- and sex-specific abundance estimate by the associated mean weight, which was calculated from regressions of age- and sex-specific mean lengths of walleyes caught during the spawning run. Walleye growth.—Walleye growth was determined from fish caught in spring trap nets fished during the

spawning run in 1961–1963 (Forney 1965) and annual gill-net surveys conducted since 1964 (except in 1974). For gill-net surveys, multifilament gill nets were used to sample fifteen fixed sites, 1 each week, in a standardized sequence during June–September. At each site, two nets (91.5 3 1.83 m) were set on bottom and allowed to fish overnight for approximately 12 h. Each net consisted of two gangs of six 7.6-m panels with stretch mesh sizes ranging from 38 to 102 mm in 12.7mm increments. From 1964 to 1967, gill-net sampling was conducted using one white net and one colored net (red, green, or black). During other years, two white nets were used at each site. Each year, sexes were recorded and scales for age determination were removed from all or a subsample of captured walleyes. The TLs at age were back-calculated from scale annuli using the Fraser–Lee method (DeVries and Frie 1996) and an empirical intercept of 57 mm (Forney 1965). For each year, annual growth was estimated for male and female walleyes (ages 4–6) caught during the subsequent years, and the geometric mean was used to describe growth of pooled age-classes. Forney (1965) found no difference in back-calculated lengths between spring-caught (trap nets) and summer-caught (gill nets) individual fish, so we did not adjust growth for time of capture. We limited our examination of growth increments to ages 4–6, because Oneida Lake walleyes typically do not exceed 300 mm until after their third growing season (He et al. 2005) and our confidence in the accuracy of scale age estimates declines after age 6 (i.e., estimated ages were the same from otoliths and scales up to age 6: Cornell Biological Field Station, unpublished data). Forney (1965) provided estimates of growth for walleyes of ages 4–6 in 1961 and 1962. Analyses.—We followed the steps recommended by Porath and Peters (1997) for using Wr to assess prey availability and walleye growth. We determined mean W r within each 50-mm length-class, tested for differences between adjacent length-classes within each year, combined adjacent length-classes with no significant difference in Wr into a single class, and tested for the relationship between Wr and prey fish availability or walleye growth by using orthogonal contrasts. Because the minimum TL of walleyes included in regressions was typically around 300 mm and because few sampled fish exceeded 500 mm, we chose 350, 400, and 450 mm as our length-classes for Wr comparisons. Length-classes at the extremes of the observed TL range were excluded to avoid biases related to extrapolation (Gerow et al. 2005). Differences in Wr among length-classes were tested using the matched pairs command in JMP version 4.0.4 (SAS Institute 2001). Because all three length-classes were

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TABLE 2.—Annual estimates of relative weight (Wr) for three walleye length-classes, walleye length growth (ages 4–6), and prey fish availability (kg of prey fish/kg of walleyes) in Oneida Lake, New York, 1961 to 1983. Wr Year

350 mm

400 mm

450 mm

Growth (mm)

Prey availability (kg prey fish/ kg walleye)

1961 1962 1964 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1980 1981 1982 1983 Mean

100 104 98 91 89 100 90 92 99 87 91 95 91 91 103 86 105 99 96 96 95

96 98 95 86 90 101 90 92 99 84 89 90 91 84 102 90 103 95 95 95 93

92 93 92 83 92 103 91 91 98 81 87 86 91 79 101 93 101 91 94 95 92

40.5 31.6 35.3 13.6 27.1 50.8 20.1 28.2 38.4 10.5 14.9 32.6 51.7 9.6 46.8 26.9 32.4 35.4 27.3 25.6 30.0

1.778 2.207 1.286 0.651 1.167 1.962 0.307 0.891 0.810 0.175 1.940 0.586 1.441 0.562 3.414 0.367 3.259 0.962 3.459 3.099 1.516

significantly different from each other, we used orthogonal contrasts to test the effects of prey fish availability or growth on each length-class individually. Because we conducted multiple tests of the relationship between the independent variable and Wr (3 length-classes), we applied the Bonferroni adjustment to the significance level a (n ¼ 3 tests, adjusted a ¼ 0.05/n ¼ 0.017; Snedecor and Cochran 1980). As recommended by Porath and Peters (1997), geometric mean functional regression that assumed similar variances was used to determine underlying relationships between walleye Wr, walleye growth, and prey fish availability; we selected this method because all variates were subject to measurement error or natural variability. Traditional tests of significance based on the standard deviation of the slope of a functional regression are not appropriate (Ricker 1973). However, Jensen (1986) noted that the functional regression line is a component of the bivariate normal correlation model, so the test for significant association of the correlation model (H0: correlation coefficient r ¼ 0) is an appropriate measure of association in the functional regression; therefore, we also present P-values for the correlation models.

length-class, and 91.7 (SE ¼ 1.42) for the 450-mm length class; Wr exceeded 100 in only 5 of the 20 years. Relative weights differed significantly among walleye length–classes. The Wr declined across all three length-classes during 12 of the 20 years examined, increased during 3 years, and was relatively stable during 5 years (Table 2). The Wr of the 350-mm length-class was significantly higher than that of the 400-mm length-class (N ¼ 20, r ¼ 0.88, P , 0.01), which was in turn significantly higher than that of the 450-mm length-class (N ¼ 20, r ¼ 0.93, P , 0.01; Figure 1). Twenty-four fish species were caught during trawl surveys. Total annual catch of prey fish averaged 113,400 fish and ranged between 20,500 and 280,400 fish. Yellow perch typically accounted for 75% of the

Results Annual length–weight regressions were developed based on sample sizes of 20–115 walleyes (Table 1). Relative weight averaged 95.2 (SE ¼ 1.27) for the 350mm length-class, 93.3 (SE ¼ 1.25) for the 400-mm

FIGURE 1.—Mean (6SE) relative weight (Wr) for three walleye length-classes (total length, mm) captured in Oneida Lake, New York, during October from 1961 to 1983. All three mean Wr values were significantly different (P , 0.05).

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FIGURE 2.—Orthogonal contrasts of walleye relative weight (Wr) versus prey fish availability (i.e., prey biomass [PB] in kg of prey fish/kg of walleyes; left column) or walleye growth (Gr; right column) for three walleye length-classes (350, 400, and 450 mm total length) captured in Oneida Lake, New York, during October from 1961 to 1983. Slopes and correlation coefficients (r) are from geometric mean functional regressions assessing the influence of prey fish availability or walleye growth on Wr; P-values describe the bivariate normal correlation.

number of captured prey fish. White perch averaged 13% of sampled prey fish for the years examined; however, white perch were occasionally observed at higher percentages, such as in 1980, when they accounted for 60% of the available prey fish. Of the remaining prey species, only pumpkinseeds, tessellated darters, and trout-perch Percopsis omiscomaycus accounted for more than 3% of the annual prey fish catch. Prey fish biomass ranged from 4.4 to 66.0 kg/ha (mean ¼ 29.5 kg/ha, SE ¼ 4.08). Biomass of age-3 and older walleyes ranged from 11.6 to 33.8 kg/ha (mean ¼ 21.5 kg/ha, SE ¼ 1.32). As a result, prey fish availability ranged from 0.175 to 3.459 kg of prey/kg of walleyes (mean ¼ 1.516 kg of prey/kg of walleyes, SE ¼ 0.242; Table 2). Annual growth of walleyes (ages 4–6) ranged from 9.6 to 51.7 mm (x¯ mean ¼ 30.0 mm, SE ¼ 2.75; Table 2). Walleye Wr was significantly and positively related to both prey fish availability and walleye growth (Figure 2). For all three walleye length-classes, Wr increased as prey fish availability increased; variability

in prey fish availability explained 44% (on average) of the variability in walleye Wr. For all length-classes, Wr increased as walleye growth increased, and variability in growth explained about 47% of the variability in Wr. Discussion Our results showed a significant correlation between walleye Wr and either prey fish availability or walleye growth. Estimated prey fish availability accounted for at least 40% of the interannual variability in walleye Wr, whereas walleye growth accounted for 34–55%. These relationships were consistent over the range of walleye sizes examined. The Wr for 350-, 400-, and 450-mm length-classes increased as prey fish availability or walleye growth increased, and the increases in Wr were more pronounced for the largest lengthclass than for the other two length-classes. The increase in slope with walleye size is probably due to the tendency of fish to deposit increasing amounts of body fat with age (Jobling 1994). Interannual differences in prey fish availability were largely attributable to

WALLEYE RELATIVE WEIGHT

fluctuations in abundance of age-0 yellow perch and white perch. Age-0 fish of both prey species were vulnerable to predation by 350-mm and larger walleyes; this probably explains why all three lengthclasses showed similar responses to fluctuations in prey fish availability. The decline in Wr as walleye size increased in Oneida Lake is probably a result of the walleyes’ dependence on age-0 fish as food. In contrast, walleye Wr increased with walleye length-class in Lake McConaughy, Nebraska, probably because the majority of available prey fishes were 160 to 190 mm long and abundance of age-0 prey fishes was low (Porath and Peters 1997). Larger piscivores can consume larger prey (Parsons 1971; Rice et al. 1993), and for each size-class of predators there is probably a size of prey that optimizes net energy from feeding. Consuming prey fish less than 30 g may be energetically more profitable for a 350-mm walleye than for a 450-mm walleye. Not only does Wr seem to reflect changes in prey fish availability in Oneida Lake, but size-specific patterns in Wr may be useful for inferring characteristics of the prey fish population in terms of relative benefits to larger and smaller predators. The Wr values from the 4 years of highest prey fish availability all fell below our fitted regressions of Wr as a function of prey fish availability (Figure 2). Fish growth efficiency can vary as a function of ration. Wootton (1998) suggested a curvilinear function for describing growth as a function of ration wherein growth efficiency decreases as ration size increases from some optimum level to a physiological maximum. It is possible that at the highest prey availability levels observed in our study, walleyes were operating below their maximum growth potential. Age-2 and younger walleyes in Oneida Lake consume age-0 fish (Forney 1974), and this could account for some of the unexplained variability in the observed relation between Wr and prey fish availability. Strong walleye year-classes contributing to variability in the abundance of young walleyes would reduce our estimates of prey fish availability for age-3 and older walleyes. However, we were not able to estimate the biomass of younger age-classes, so we do not know their actual influence on the availability of prey fish for older walleyes. Samples of walleyes taken in October provided a measure of interannual variation in condition, but intraannual trends in condition were probably equally variable. Condition of adults probably dropped after spawning in April, but weight loss probably continued into June. Scales of age-3 and older walleyes collected during May to mid-June rarely showed any evidence of marginal growth in the years of cohort monitoring, and

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weight gain was not detected until late June (Forney 1977b). The timing of peaks in condition was more speculative. Reliance of walleyes on age-0 fish as prey suggests that condition should vary with age-0 prey biomass. Biomass of age-0 yellow perch usually peaked in late July or early August, which was probably earlier than peaks exhibited by age-0 white perch and, in recent years, gizzard shad. Temporal variation in condition is of concern when attempting to assess populations and communities (Pope and Willis 1996). Study design should incorporate considerations of seasonal dynamics when using condition to assess prey availability. The notion that condition indices based on body weight can be used as a surrogate for prey availability or growth continues to be a topic of debate, largely because of inconsistent results. Growth has been related to Wr for various species in other systems. Willis (1989) found that Wr was significantly and positively related to growth in 11 populations of northern pike Esox lucius. Similar relationships have been found for white crappies Pomoxis annularis (Gabelhouse 1991), black crappies Pomoxis nigromaculatus (Guy and Willis 1995), and largemouth bass Micropterus salmoides (Wege and Anderson 1978). However, other studies examining the relationship between growth and Wr for largemouth bass, white crappies (Gutreuter and Childress 1990), and spotted bass Micropterus punctulatus (DiCenzo et al. 1995) have failed to identify a similar relationship. For walleyes in Lake Erie, Hartman and Margraf (2006) found that Wr was significantly correlated with growth rate, but correlations were too small to be considered biologically meaningful. Prey availability has also been related to trends in Wr for various species (Wege and Anderson 1978; Hubert et al. 1994; Liao et al. 1995), including walleyes (Marwitz and Hubert 1997; Porath and Peters 1997). However, in examining the effects of growth and prey biomass on the Wr of pumpkinseeds and golden shiners Notemigonus crysoleucas, Liao et al. (1995) concluded that Wr could be cautiously used as an index of prey availability but that empirical and experimental verification should be conducted before using Wr as a predictor of growth. Relations between length-dependent estimates of walleye weight, prey fish availability, and walleye growth in Oneida Lake tend to support the use of Wr as one of a suite of assessment tools to track population and community changes. Annual variations in walleye Wr in Oneida Lake mirrored changes in both prey fish availability and walleye growth. Although detailed fish community data will always allow for more-accurate assessment of fish population responses to ecological changes or management actions, our study shows that

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Wr can accurately reflect changes in predator–prey balance and can be useful for monitoring. Acknowledgments This work was supported by the New York State Department of Environmental Conservation through Federal Aid in Sport Fish Restoration Project F-56-R to the Cornell Warmwater Fisheries Unit. The authors would like to thank Lars Rudstam, Mike Hansen, and three anonymous reviewers for their critical and constructive reviews of the manuscript. This is contribution 255 from the Cornell Biological Field Station. References Anderson, R. O., and S. J. Gutreuter. 1983. Length, weight and associated structural indices. Pages 283–300 in L. A. Nielsen and D. L. Johnson, editors. Fisheries techniques. American Fisheries Society, Bethesda, Maryland. Anderson, R. O., and R. M. Neumann. 1996. Length, weight, and associated structural indices. Pages 447–482 in B. R. Murphy and D. W. Willis, editors. Fisheries techniques, 2nd edition. American Fisheries Society, Bethesda, Maryland. Blackwell, B. G., M. L. Brown, and D. W. Willis. 2000. Relative weight (Wr) status and current use in fisheries assessment and management. Reviews in Fisheries Science 8:1–44. Brown, M. L., and B. R. Murphy. 1991. Relationship of relative weight (Wr) to proximate composition of juvenile striped bass and hybrid striped bass. Transactions of the American Fisheries Society 120:509–518. Busasker, G. P., I. A. Adelman, and E. M. Goolish. 1990. Growth. Pages 363–387 in C. B. Schreck and P. B. Moyle, editors. Methods for fish biology. American Fisheries Society, Bethesda, Maryland. Chevalier, J. R. 1973. Cannibalism as a factor in first-year survival of walleyes in Oneida Lake. Transactions of the American Fisheries Society 103:739–744. Clady, M. D. 1976. Change in abundance of inshore fishes in Oneida Lake, 1916 to 1970. New York Fish and Game Journal 23:73–81. Connelly, N. A., and T. L. Brown. 1991. Net economic value of the freshwater recreational fisheries of New York. Transactions of the American Fisheries Society 120:770– 775. DeVries, D. R., and R. V. Frie. 1996. Determination of age and growth. Pages 483–512 in B. R. Murphy and D. W. Willis, editors. Fisheries techniques, 2nd edition. American Fisheries Society, Bethesda, Maryland. DiCenzo, V. J., M. J. Maceina, and W. C. Reeves. 1995. Factors related to growth and condition of the Alabama subspecies of spotted bass in reservoirs. North American Journal of Fisheries Management 15:794–798. Forney, J. L. 1965. Factors affecting growth and maturity in a walleye population. New York Fish and Game Journal 12:217–232. Forney, J. L. 1967. Estimates of biomass and mortality rates in a walleye population. New York Fish and Game Journal 14:176–192.

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