TRANSACTIONS

TRANSACTIONS

OF

THE

Volume 125

AMERICAN

FISHERIES

SOCIETY

November 1996

Number 6

Transactions of the American Fisheries Society 125:821-830. 1996 '£' Copyright by the American Fisheries Society 1996

First-Year Growth, Recruitment, and Maturity of Walleyes in Western Lake Erie CHARLES P. MADENJIAN National Biological Service, Great Lakes Science Center 1451 Green Road. Ann Arbor, Michigan 48105. USA

JEFFREY T. TYSON AND ROGER L. KNIGHT Ohio Department of Natural Resources, Sandusky Fisheries Research Station 305 East Shoreline Drive. Sandusky, Ohio 44870. USA

MARK W. KERSHNER Ohio State University, Aquatic Ecology Laboratory, Department of Zoology 1314 Kinnear Road. Columbus. Ohio 43212. USA

MICHAEL J. HANSEN University of Wisconsin-Stevens Point, College of Natural Resources Stevens Point, Wisconsin 54481. USA Abstract,—In some lakes, first-year growth of walleyes Stizostedion vitreum has been identified as an important factor governing recruitment of juveniles to the adult population. We developed a regression model for walleye recruitment in western Lake Erie by considering factors such as first-year growth, size of the spawning stock, the rate at which the lake warmed during the spring, and abundance of gizzard shad Dorosoma cepedianum. Gizzard shad abundance during the fall prior to spring walleye spawning explained over 40% of the variation in walleye recruitment. Gizzard shad are relatively high in lipids and are preferred prey for walleyes in Lake Erie. Therefore, the high degree of correlation between shad abundance and subsequent walleye recruitment supported the contention that mature females needed adequate lipid reserves during the winter to spawn the following spring. According to the regression analysis, spring warming rate and size of the parental stock also influenced walleye recruitment. Our regression model explained 92% of the variation in recruitment of age-2 fish into the Lake Erie walleye population from 1981 to 1993. The regression model is potentially valuable as a management tool because it could be used to forecast walleye recruitment to the fishery 2 years in advance. First-year growth was poorly correlated with recruitment, which may reflect the unusually low incidence of walleye cannibalism in western Lake Erie. In contrast, first-year growth was strongly linked to age at maturity. Variability in recruitment has been identified as a central problem in fisheries research (Sissenwine et al. 1988). Fish recruitment can be affected by many different abiotic and biotic factors, including water temperature, water movements, cannibalism,

predation, age at maturity, and size of the spawning stock (Sissenwine 1984). Because recruitment is affected by such a large suite of factors, the important factors operating on recruitment may vary from one ecosystem to another for a given fish species.

821

822

M A D F i N J I A N KT AL.

Forney (1976, 1980) concluded that cannibalism by older fish was one of the most important factors governing recruitment of walleyes Stizostedion viireum in Oneida Lake, New York, during the 1960s and 1970s. If first-year walleye growth was rapid, and if the bulk of the age-0 cohort exceeded a total length of about 175 mm by the end of the growing season, then the duration of cannibalism experienced by that cohort would be relatively short, and recruitment would be relatively high. However, if age-0 walleyes grew slowly, then these fish would be vulnerable to the predatory gauntlet posed by the older walleyes for a relatively long period of time, and the subsequent recruitment would be relatively poor. After analyzing the diet of adult walleyes and trawling for young walleyes in fall and spring. Chevalier (1973) attributed the observed size-selective overwinter mortality of age-0 walleyes in Oneida Lake to cannibalism. Similarly, size-selective overwinter mortality in age-0 walleyes has been reported in Lake Mendota, Wisconsin (Madenjian et al. 1991); walleye cannibalism has been documented and probably is an important regulator of walleye recruitment in Lake Mendota (Johnson et al., in press). Therefore, predation on age-0 walleyes by older walleyes, as mediated by first-year growth of walleyes, appears to be a driving force for walleye recruitment in both Oneida Lake and Lake Mendota. Walleye recruitment in western Lake Erie has been modeled as a function of both the rate at which the lake water warmed during the spring and the size of the parental stock (Busch et al. 1975; Shuter and Koonce 1977). Busch et al. (1975) attributed the increase in recruitment with increase in the spring warming rate to a shortening of the period of vulnerability of walleye eggs to storm events. As the rate of warming increases, the amount of time for walleye egg development decreases, and therefore the amount of time that the eggs are exposed to storm events or other unfavorable conditions decreases. Water turbulence induced by storms can damage or destroy eggs or transport eggs from the safety of the reef to more hostile environments. Additionally, Busch et al. (1975) characterized the relationship between walleye recruitment and parental stock size as weak, based on abundance index data from 1960 to 1970. Hilborn and Walters (1992) presented many examples of unclear stock-recruitment relationships. Models, such as the one developed by Ricker (1975), can be fitted to these data, but much of the variation in recruitment remains unexplained by size of the spawning population. Hilborn and

Walters (1992) suggested adding one or two environmental variables to the traditional Ricker spawner-recruit function to try to explain more variation in recruitment. Henderson and Nepszy (1994) proposed that the lipid content of mature female walleyes in western Lake Erie has a strong influence on recruitment. These researchers concluded that if the lipid content of a mature female was not sufficiently high during winter, then the female would forego spawning the following spring. A sharp increase in the lipid content of age-0 gizzard shad Dorosoma cepedianum between August and November has been observed in Acton Lake, Ohio (Pierce et al. 1980), as well as in Lake Erie (White et al. 1986). Thus, age-0 gizzard shad would provide a high lipid source for adult female walleyes during the late fall and early winter months. From 1975 through the early 1990s, age-0 gizzard shad were relatively abundant in western Lake Erie (Knight and Vondracek 1993; GLFC 1994) and were preferred prey of the adult walleyes (Knight and Vondracek 1993; Francis 1992). It is plausible that the fall abundance of age-0 gizzard shad may serve as a reliable indicator of the lipid content of mature walleye females during the winter. First-year growth may also affect age at maturity. If food supply increases, then fish growth increases, and age at maturity decreases (Colby and Nepszy 1981; Trippel 1995). Conversely, a decrease in food availability could lead to an increase in the age at maturity. A change in age at maturity could affect size of the spawning stock, which in turn could influence recruitment (Trippel 1995). The objectives of this study are to (1) investigate the relationship between recruitment and first-year growth in the walleye population from western Lake Erie; (2) develop a regression model for walleye recruitment in western Lake Erie by using a set of independent variables including first-year growth, spawning-stock size, spring warming rate, and fall abundance of age-0 gizzard shad; and (3) examine the relationship between first-year growth and age at maturity in the walleye population from western Lake Erie. Development of an accurate regression model for predicting walleye recruitment in western Lake Erie may prove useful in managing the walleye fishery.

Methods Lake Erie data.—Presently the Lake Erie walleye fishery is managed with guidance from the application of catch-at-age analysis (CAGEAN; GLFC 1994). Use of the CAGEAN model (Deriso

WALLEYE RECRUITMENT MODEL

et al. 1985) generates estimates of the walleye population sizes at the start of the growing season for age-groups 2-7 and the group including ages 8 and older. Such estimates are available from 1979 to the present (GLFC 1994). These estimates are for the walleye population size of the entire lake, but they should represent population dynamics within the western basin because the bulk of the lake population is believed to spawn in the western basin (Regier et al. 1969; Hatch et al. 1987). We used the population size estimate for the age-2 group as the size of the newly recruited walleye population, and the combined estimate of the population sizes for age-5 and older walleyes was used as the estimate of the spawner population size. (The estimates are presented in Table 1.) Almost all of the walleye population in central and western Lake Erie was mature by age 4, based on gonad condition in the fall (Henderson and Nepszy 1994). However, Henderson and Nepszy (1994) argued that most females did not actually spawn until age 5, based on examination of gonads in April, during the early 1990s. Therefore, the spawner-recruit relationship was analyzed by using the population size of age-5 and older walleyes as the size of the spawner population. Water temperatures have been monitored, via thermograph, at Put-in-Bay on South Bass Island in the western basin from 1960 to the present time. The rate of water warming during the spring at Put-in-Bay is nearly identical to the spring warming rate for the western basin proper (Busch et al. 1975), and therefore the warming rate at Put-inBay was appropriate for investigating relationships between spring warming rate and walleye recruitment in western Lake Erie. Put-in-Bay water temperature data were collated courtesy of the Great Lakes Environmental Research Laboratory (GLERL; M. McCormick, personal communication). The thermograph at Put-in-Bay failed during spring 1988. Therefore, we used water temperatures recorded for the intake water to the DavisBesse Nuclear Power Plant along the western basin of Lake Erie near Toledo, Ohio, to determine spring warming rate during 1988 (C. Stipp, Toledo Edison Co., personal communication). Spring warming rates (Table 1) were calculated according to the procedure described by Busch et al. (1975). Length-frequency distributions of juvenile walleyes and relative abundances of age-0 gizzard shad in western Lake Erie have been monitored via interagency trawling programs. Bottom trawl surveys have been conducted during fall off East Harbor, Ohio (in the western basin of Lake Erie), from

823

1961 to the present (Muth and Wolfert 1986; GLFC 1994). Bottom trawl surveys have also been conducted during the summer and fall at stations scattered throughout the Ohio waters of the western basin from 1969 to the present time (Knight and Vondracek 1993; ODNR 1994). In more recent years, bottom trawl surveys of the western basin were performed during May (M. Turner, Ohio Department of Natural Resources, personal communication). Since 1964, the proportions of mature age-1 males and of mature age-2 females caught from the western basin in commercial trap nets during fall have been monitored (Muth and Wolfert 1986; GLFC 1984, 1985, 1988-1991, 1994). We used these two proportions as indices for the maturation rate of the two sexes. Relationship between first-year growth and recruitment.—We quantified first-year growth of walleyes in two ways. The mean total length (MTL) of the age-0 cohort in October was used as an index of first-year growth. Additionally, the proportion of fish in the age-0 cohort with a total length of 175 mm or more was used as an indicator of first-year growth. Forney (1976) postulated that once Oneida Lake walleyes attained a length of approximately 175 mm, they were practically invulnerable to predation by larger walleyes. Thus, this proportion of big fish (PBF) within the fall age-0 cohort (Table 1) is another appropriate measure of first-year growth. We performed the following linear regressions to investigate the relationships between first-year growth (1977-1991) and recruitment (R): (1) R on MTL and (2) R on PBF. Scatter plots were examined, and the proportion of variation explained by the dependent variable was calculated. In both regressions, the appropriate time lag was used to match the dependent variable with the independent variable. For example, the MTL in 1991 was paired with the R in 1993 in performing the regression of R on MTL. To investigate whether overwinter size-selective mortality notably affected the survival of age-0 walleyes in western Lake Erie, we compared the length-frequency distribution of age-0 walleyes in fall with that for yearlings in May. More than 400 walleyes were captured in bottom trawls during May 1994 and May 1995, so during these two years a sufficiently high number of length determinations were made in the spring to allow for a meaningful comparison with the length-frequency distribution from the previous fall. A Kolmogorov-Smirnov two-sample test was used to deter-

824

M A D E N J I A N ET AL.

TABLE I.—Si/e of walleye parental stock for Lake Erie, 1979-1991, and corresponding walleye recruitment. The size of the parental stock (P) is the number of age-5 or older walleyes in the population; recruitment (R) is the number of walleye progeny (from the parental stock) entering the population as age-2 fish. Also listed for 19771991 are the mean total lengths for the age-0 walleye cohort in the fall (MTL), proportions of age-0 walleyes with total length at least 175 mm in the fall (proportion of big fish. PBF), abundance indexes for age-0 gizzard shad in the fall prior to spring walleye spawning (SHAD), and the spring warming rates (WMRT) in western Lake Erie. Parental stock size data were not available (NA) for 1977 and 1978. /?. 2 years later SpawnP (miling (millions lions year of fish) of lish) 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991

NA NA 1.74 2.97 2.42 5.11 5.45 5.61 8.71 9.78 41.21 30.74 29.45 28.66 37.93

18.08 11.14 8.98 20.04 13.37 112.93 11.33 32.01 31.66 63.41 16.14 14.62 6.69 20.50 19.02

MTL ( mm ) 216.6 206.0 188.2 184.4 197.2 185.9 188.6 1 85.8 197.1 202.3 176.7 200.7 213.9 199.9 181.6

SHAD (num-

PBF I.(XKX) 1 .(XXX) 0.9013 0.6618 0.9189 0.7308 0.8571 0.7692 0.8901 0.9358 0.4696 0.9500 I.(XXX) 0.9441 0.5956

WMRT

ber/h)

rc/d)

II.1 13.3 53.7 60.1 231.5 274.1 3.4 3.6 2.3 156.9 140.2 4.9 27.5 11.8 27.4

0.2171 0.2616 0.2903 0.2547 0.1143 0.3339 0.1852 0.2686 0.2991 0.1954 0.2566 0.1496 0.1882 0.2502 0.2040

mine whether the length-frequency distribution in the fall was significantly different from the distribution in the following spring. Regression model for walleye recruitment.—Our first step in developing a regression model for Lake Erie walleye recruitment was to build simple linear regressions between R and spring warming rate (WMRT) and between R and fall age-0 gizzard shad abundance (SHAD; see Table 1 for raw data). Scatter plots were examined, and the proportion of variance explained by the independent variable was calculated. Then we followed the recommendation of Hilborn and Walters (1992). We began with a traditional spawner-recruit relationship and added external environmental factors, such as spring warming rate or fall shad abundance, to the relationship. Because walleye recruitment in Lake Erie was relatively low at the higher spawning-stock sizes, we used the Ricker (1975) spawner-recruit model rather than the Beverton and Holt (1957) model. The traditional Ricker spawner-recruit model can be written as

R = aPehp(1) R = number of recruits, P = number of spawners, a = dimensionless parameter, and b = parameter with dimensions of MP (Ricker 1975). We fitted the traditional Ricker spawner-recruit model to the stock and recruitment data for Lake Erie walleyes. Then we added external variables to the Ricker model according to the form R= X = first external variable, Y = second external variable, c = parameter with dimensions MX, and d = parameter with dimensions MY. We considered the following set of four external variables: MTL, PBF, WMRT, and SHAD. The external variables were added one at a time in an order based on the strength of the correlation between walleye recruitment and the external variable. The first variable added to the regression had the strongest correlation with recruitment. If the addition of an external variable to the regression model did not increase the amount of variation explained by the model (r2) by more than 0.05, then the external variable was not included in the "best-fit" model. (Throughout this paper r2 refers to multiple as well as univariate regressions to avoid possible confusion with R for recruitment, and p denotes attained significance level to avoid confusion with P for size of parental stock.) Relationship between first-year growth and maturation rate.—We define age at maturity as the youngest age (in years) at which at least 85% of the population is classified as sexually mature based on examination of the gonads. From previous studies, it has been shown that age at maturity for female walleyes in western Lake Erie was 3 years during the 1960s but 4 years during the early 1980s (Muth and Wolfert 1986). Mean total length of the age-0 cohort averaged about 240 mm during the 1960s but approximately 190 mm during the 1980s and early 1990s. Thus there appeared to be a correspondence between first-year growth and age at maturity. To quantify the strength of the relationship between first-year growth and maturation rate, we estimated the coefficients of correlation between first-year growth and the proportion of mature age-1 males and between first-year growth and the proportion of mature age-2 females.

Results Relationship between First-Year Growth and Recruitment Walleye recruitment from 1979 to 1993 in western Lake Erie appeared to be unrelated to first-year

825

WALLEYE RECRUITMENT MODEL

YOY 1993

120 i

YEARLING 1994

-22.64* 213.2WMRT

25

cc

O

S?

20

O

15

UJ

80

p - 0.0869

cc

u.

O 40

LU

§

> 0.21

D

10

UJ QC

"•

5

0.10

11

13

15

17

19

21

23

0.26

0.34

SPRING WARMING RATE (°C/d)

TOTAL LENGTH (cm)

YOY 1994

0.18

YEARLING 1995

120 R • 13.09 + 0.1993SHAD

30

r 2 = 0.42

cc

p - 0.0094

O UJ

20

oc

u. O

UJ

O UJ

cc

CO

10

O

40

u.

13

15

17

19

21

23

100

200

300

TOTAL LENGTH (cm) SHAD ABUNDANCE (number/h)

FIGURE 1.—Observed length-frequency distributions of age-0 (young-of-year, YOY) walleyes in the fall (October or early November) and of yearling walleyes in mid-May from western Lake Erie, 1993-1995. Both age-0 and yearling walleyes were caught in bottom trawls.

growth. The MTL and PBF variables explained only 3.3% and 2.8% of the variation in walleye recruitment, and the attained significance levels of the simple linear regressions were 0.5142 and 0.5506. Furthermore, the smallest age-0 walleyes of the cohort in October managed to survive the winter and early spring (Figure 1). The lengthfrequency distribution of age-0 walleyes in the fall of 1993 was not significantly different (p = 0.0671) from the length-frequency distribution of spring 1994 yearling walleyes. Similarly, there was no significant difference between the lengthfrequency distributions in fall 1994 and spring 1995 (p = 0.4489). Regression Model for Walleye Recruitment Spring warming rate showed a rather weak, but positive, relationship with walleye recruitment

FIGURE 2.—Number of age-2 walleyes recruited (R) to the Lake Erie population (as estimated by the CAGEAN model application) for years I979-I993 as a function of spring warming rate (WMRT, Put-in-Bay water temperatures) and as a function of age-0 gizzard shad abundance in the fall (SHAD). Gizzard shad abundance was expressed as the geometric mean number of age-0 shad caught per hour of trawling (refer to Knight and Vondracek 1993). Number of recruits was for years 1979-1993, spring warming rate was for years 19771991, and gizzard shad abundance in fall was for years 1976-1990.

(Figure 2). A simple linear regression fitted to these data explained 21% of the variation in walleye recruitment. Maximum walleye recruitment (1982 year-class) coincided with the maximum spring warming rate. Fall gizzard shad abundance was a better indicator of walleye recruitment than spring warming rate (Figure 2). A simple linear regression fitted to these data explained 42% of the variation in walleye recruitment. The traditional Ricker curve fitted to the spawner-recruit data for Lake Erie walleyes was R = %A

(3)

M A D E N J I A N ET AL.

826 120

1990 20

30

40

50

MILLIONS OF SPAWNERS

FIGURE 3.—Number of age-2 walleyes recruited to the Lake Erie population (as estimated by the CAGEAN model application) for years 1981-1993 as a function of number of spawners (population size of age-5 and older walleyes, as estimated via CAGEAN). Also shown is the fitted Ricker curve as represented by equation (4).

R = millions of recruits and P = millions of spawners. Note that \oge(a) was estimated from linear regression analysis, and then the parameter a in equation (3) was estimated by simply taking the exponential of \oge(a). However, Ricker (1975) has suggested that a conversion factor be applied to this back-transformation procedure. If we apply this conversion factor to the estimate of a, then the Ricker curve equation becomes

the conversion factor was equal to 1.09. Equation (4) explained 20% of the variation in walleye recruitment. The curve peaked at a level of 40 million recruits when the number of spawners was equal to 11.7 million (Figure 3). This Ricker model fit suggested that when spawner density in Lake Erie is very high, there may be some density-dependent negative feedback on walleye recruitment. The addition of fall shad abundance to the regression model boosted the r2 (proportion of variation explained by the model) to 0.71. The resulting regression equation was R = 5.62Pe

0.0783/>«0.00456SHAD .

(5)

SHAD = fall age-0 gizzard shad abundance, expressed as the geometric mean of the number of fish caught per hour of trawling, prior to walleye spawning in spring. Refer to Knight and Vondracek (1993) for more details on the calculation of SHAD. The further addition of spring warming rate resulted in a regression model that accounted for 92% of the variability in walleye recruitment. Yet further addition of either MTL or PBF to this

1995

YEAR

FIGURE 4.—Actual (•) and predicted (——) number of age-2 walleyes recruited to the Lake Erie population for years 1981-1993. Actual number of recruits was estimated via CAGEAN model application, whereas predictions were based on the regression model represented by equation (6).

regression model did not yield an improvement in r2 of greater than 0.01. Therefore, our "best-fit" regression model for walleye recruitment in Lake Erie was the modified Ricker spawner-recruit model, R = 1 53P£-0-0736/M).