Relations between Reservoir Hydrology and Crappie Recruitment in ...

North American Journal of Fisheries Management 18:104–113, 1998 q Copyright by the American Fisheries Society 1998

Relations between Reservoir Hydrology and Crappie Recruitment in Alabama MICHAEL J. MACEINA1

AND

MARC R. STIMPERT2

Department of Fisheries and Allied Aquacultures, Alabama Agricultural Experiment Station Auburn University, Alabama 36849-5419, USA Abstract.—The relation between reservoir hydrology and recruitment of black crappies Pomoxis nigromaculatus and white crappies P. annularis was examined in 11 reservoirs throughout Alabama from 1990 to 1996. Estimates of recruitment were derived from catch rates of age-1 fish in the fall with trap nets and from residuals associated with catch-curve regressions of age 3–7 fish captured in the spring with electrofishing gear. Reservoirs were separated into three hydrologic categories: (1) eight reservoirs with short retention (volume/discharge) times (2–9 d) that fluctuated less than 1 m/year; (2) two reservoirs with longer retention times (44–55 d) that generally fluctuated 1.8 and 4.6 m/year; and (3) one reservoir that fluctuated 1.8 m/year with a relatively short retention time of 15 d. In the reservoirs with low retention times and stable water levels, greater year-class production was related to low winter (January–March) retention before crappie spawning and to higher postwinter (April–December) retention when these fish were age 0. These variables explained about 60% of the variation in crappie year-class abundance. Higher production of young crappies was nearly always associated with both winter retention of 6 d or less and postwinter retention greater than 11 d. In these reservoirs, cyclic crappie recruitment appeared to be influenced by climatic conditions. In the three reservoirs that fluctuated 1.8 m or more per year, short winter retention and higher water levels in winter before crappie spawning were both associated with greater year-class abundance; hydrologic conditions during and after crappie spawning were not related to crappie recruitment. In these reservoirs, maintenance of higher water levels in the winter before crappie spawning may enhance reproductive success.

Black crappie Pomoxis nigromaculatus and white crappie P. annularis are important warmwater sport fishes in the United States. A primary management problem associated with crappie fisheries is cyclic and highly variable recruitment, with strong year-class formation typically occurring every 3–5 years (Swingle and Swingle 1967). Fishery biologists have suspected that reservoir hydrology influences crappie reproductive success and contributes to the cyclic nature of these fisheries. Larval crappie abundance was greatest when water levels were high and reservoir discharge was low in Chickamauga Reservoir, Tennessee (McDonough and Buchanan 1991). In Rathbun Lake, Iowa, larval crappie abundance was positively related to high water levels, as well as low discharge, low turbidity, protection from wind and wave action, and moderate substrate firmness (Mitzner 1991). Ploskey (1986) found that spawning success for most littoral species was positively related to water level increases during the spawning period because additional spawning habitat was produced for adults, and increased food and 1 2

Corresponding author: [email protected] Present address: The University of Tulsa College of Law, 3120 East Fourth Place, Tulsa, Oklahoma 74104, USA.

habitat resources were available for larval fish. However, high water levels did not always result in greater crappie reproductive success (Mitzner 1991). High reservoir flushing rates (low retention times) could also influence crappie recruitment. Crappie fry migrate from the littoral to the limnetic zone at 50– 60 mm total length (TL) and are limnetic up to 150 mm TL (O’Brien et al. 1984). Thus, high flushing rates may remove fish from the impoundment. In addition, high reservoir inflows may increase turbidity and decrease zooplankton abundance, which may reduce survival of age-0 crappies. Negative relations were observed between high flushing rates and year-class abundance of crappies (Walburg 1971; Mathur et al. 1979; Beam 1983). Low retention time in reservoirs was also associated with reduced primary productivity (Soballe and Kimmel 1987), which may limit growth of young crappies and subsequent survival. Many Alabama reservoirs are characterized by highly variable retention times and water level fluctuations (Table 1). Understanding relations between hydrology and crappie recruitment would assist fisheries managers in predicting crappie year-class abundance, and when possible, manipulation of reservoir hydrology could increase production of young crappies. The objectives of this

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RESERVOIR HYDROLOGY AND CRAPPIE RECRUITMENT

TABLE 1.—Description of study reservoirs, sampling gears, and sample sizes. Trap-net catches are the total number of age-1 crappies captured; electrofishing catches are the total number of age-3–age-7 crappies collected. Retention is the long-term historic record for the period of impoundment, fluctuation is the annual regulated water level fluctuation, and depth is the mean depth at full summer pool.

Reservoir

Area (ha)

Retention (d)

Fluctuation (m)

Depth (m)

Aliceville

3,360

4

0.3

2.2

Demopolis

4,049

3

0.3

3.7

Eufaula Gainesville

18,300 2,591

44 2

1.8 0.3

6.2 2.1

Jones Bluff

4,980

5

0.3

5.8

Lay Miller’s Ferry

4,858 6,964

9 6

0.3 0.3

6.7 5.9

Mitchell Neely Henry Weiss

2,368 4,569 11,247

5 6 15

0.3 0.9 1.8

8.9 3.3 3.1

West Point

10,486

55

4.6

7.1

study were to identify relations among retention time, water level fluctuations, and year-class abundance of black crappies and white crappies in Alabama impoundments. Methods Fish were collected from 11 reservoirs from four drainage basins throughout Alabama (Figure 1; Table 1). These reservoirs displayed a wide range of hydrologic and morphometric conditions, but all reservoirs were eutrophic, with average chlorophyll-a concentrations ranging from 8 to 27 mg/ m3 (Maceina et al. 1996).

Year 1991 1992 1993 1994 1995 1996 1992 1993 1994 1995 1996 1992 1991 1992 1993 1994 1990 1991 1992 1993 1994 1995 1996 1994 1991 1992 1993 1994 1995 1994 1995 1990 1991 1992 1993 1994 1995 1996 1996

Trap-net catch (age 1)

Electrofishing catch (ages 3–7)

374 67 108 73 40 51 112 91 86 78 157 58 191 100 54 212 101 288 110 166 263 44 146

85

95 212 16 176 254 119 82 105 237 404 30 75 273 199 58

261 157

We used two different sampling methods to collect crappies. In 6 of 11 reservoirs (Aliceville, Gainesville, Demopolis, Jones Bluff, Miller’s Ferry, and Weiss), 10–20 Indiana-style trap nets (Smith et al. 1994) were set in October or November from 1990 to 1996 (Table 1). Nets were set at fixed sample sites throughout each reservoir, and fish were removed from nets after 24 and 48 h. The data included 34 reservoir years, but all reservoirs were not sampled during this 7-year period. In the five remaining reservoirs (Eufuala, Lay, Mitchell, Nelly Henry, and West Point), fall trap nets collected low numbers of crappies, and the

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MACEINA AND STIMPERT

FIGURE 1.—Location of 11 reservoirs sampled for crappies from 1990 to 1996 with trap nets and electrofishing.

catch was not indicative of the crappie fishery that existed in these impoundments. Thus, crappies were collected in these reservoirs with DC electrofishing once in late March or April between 1992 and 1996, and when possible, a minimum of 100 crappies were collected from a minimum of three different locations from each impoundment (Table 1). In addition, crappies were collected once with electrofishing with the same procedures in Jones Bluff and Weiss reservoirs to compare trends between trap-net and electrofishing data. Black crappies and white crappies were pooled for the analyses. Species identification with meristic characteristics was unreliable in Weiss Lake, Alabama, and possibly was not a reliable means to separate these two species in other reservoirs (Smith et al. 1995). Electrophoretic identification with species-specific discriminant isozyme loci in 8 of our 11 reservoirs revealed that the ratio of black crappies to white crappies was highly variable and ranged from 1:0.18 to 1:6.12 (Travnichek et al. 1996). Natural hybridization between species was common in Lay and Weiss lakes. First-generation and higher hybrids constituted 14% and 21% of the crappie population in Lay and Weiss lakes (Travnichek et al. 1996). In the six remaining reservoirs where data were available, crappie hy-

brids made up less than 4% of the population. Finally, trap-net and electrofishing catch rates for individual species were low and not adequate for separate analysis for some reservoirs. We assumed that species-specific differences in recruitment did not occur, but this may not be true. Ages were determined from otolith annuli counts (Maceina and Betsill 1987). If annuli counts were unclear, otoliths were mounted on glass slides in thermoplastic, ground until a thin cross section was obtained (Maceina 1988), and read in cross view. All otoliths were read by a second reader to verify accuracy and precision. Catch per effort (total catch in numbers divided by the number of net-nights, CPE) of age-1 crappies in trap nets was used as the measure of relative abundance of year-class strength for the previous year’s cohort. Age-1 crappie were analyzed instead of age-0 crappie because: (1) variation in catch among trap-net locations was lower for age-1 catch than for age-0 catch (Stimpert 1995); (2) we observed that some crappies less than 60 mm TL were able to escape trap nets, biasing age-0 catch toward larger fish and potentially underestimating catch; (3) age-1 catch rates from a particular cohort were often greater than the age-0 catch rates and indicated possible underestimation of age-0 abundance. In addition, cohort-specific age-1 catch rates were positively correlated (r 5 0.83, P , 0.01) to age-0 catch rates for 24 reservoir-years of data that were available for five of six of our reservoirs that were sampled with trap nets. Catch-curve analyses (Ricker 1975) were performed on crappie age-structure data that were collected with electrofishing data; the natural logarithm of the number caught in each year-class was regressed against age. Catch-curve residual values represented variation in recruitment among years and, therefore, were a quantitative index of relative year-class strength (see Maceina 1997). Residual values were standardized among reservoirs by converting to Studentized residual values (residual/standard error of the residual). Full recruitment to the fisheries ($229 mm TL) based on growth rates derived from trap-net and electrofishing samples did not occur until age 3 (Black 1994). In addition, catch of crappies age 8 and older were rare, so only age-3–7 fish were used in the analysis. Student residuals of older year-classes derived from catch-curve regressions were correlated (r 5 0.78, P , 0.05) to the same cohort abundances at age 1 collected with trap nets in two of these study reservoirs (Stimpert 1995). Daily discharge rates and water level elevations


were collected for all impoundments and retention (d) was calculated by dividing the reservoir volume by the discharge. Volume was assumed to be constant in impoundments with annual fluctuation of less than 1.0 m. Volume was adjusted for elevations in Eufaula, West Point, and Weiss lakes, all of which have regulated fluctuations that were greater than 1.0 m each year (Table 1). Retention time and lake stages were categorized into four seasonal periods based upon crappie reproductive biology and regulated hydrologic cycles: (1) January–March was considered the prespawn or winter period; (2) April–May was considered the spring spawning period (water temperatures 14– 208C; Pflieger 1975); (3) June–September was considered the postspawn or summer period; and (4) October–December was the fall period. Reservoirs were separated into three categories based on differences in hydrologic conditions. Changes in elevation and related potential effects on crappie reproductive success were considered negligible for eight impoundments with annual fluctuations less than 1 m (Table 1). In these reservoirs, long-term annual retention was 2–9 d, and these reservoirs were pooled for analysis. This first reservoir category included Aliceville, Demopolis, Gainesville, Jones Bluff, Lay, Miller’s Ferry, Mitchell, and Neely Henry reservoirs. The second reservoir category included Eufaula and West Point reservoirs, which have regulated fluctuations of 1.8 and 4.6 m/year, respectively, and long-term average annual retention was 44– 55 d. In these reservoirs, water levels are generally lowered from high summer pool elevations by 1 October each year, remain low until 1 March, and are permitted to rise to summer pool elevation by 1 April. To assess the relation between water levels and crappie recruitment, the percent deviation from the regulated full summer pool elevation was computed by subtracting the average water level for a particular season from the regulated full summer pool elevation divided by the regulated full summer pool elevation. Finally, Weiss Lake was considered separately and as a third category because retention was relatively short (15 d), but an annual regulated fluctuation of 1.8 m/year resulted in a 40% change in volume, and this was related to large seasonal changes in retention. Typically, winter retention was 8 d or less, and summer retention was 25 d or more. Full summer pool elevation was achieved around 1 May, and dewatering for flood control began around 1 September. Reservoir hydrologic variables were computed

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before spawning in winter and during the spring spawning season, as well as during the postspawning seasons of summer and fall when these fish were age 0. We compared age-1 catch rates (trap-net data) and student residuals from catch curves (electrofishing data) to hydrologic conditions before, during, and after spawning of each year-class. In addition, various combinations of spring, summer, and fall hydrologic characteristics were pooled. Crappie reproductive success was related to hydrologic variables with correlation, simple linear regression, and multiple regression analyses. For multiple regressions, multicollinearity was examined by using the criteria of Montgomery and Peck (1982). Results Reservoirs with Low Retention and Stable Water Levels In all, 3,865 age-1 crappies were captured from 1990 to 1996 in five of the eight reservoirs with stable water levels and low retention that were sampled with trap nets. Catch per effort ranged from 0.8 to 12.7 fish/net-night and averaged 4.5 fish/net-night from 27 reservoir-years of data from Aliceville, Demopolis, Gainesville, Jones Bluff, and Miller’s Ferry reservoirs. Age-1 CPE was positively correlated to summer and fall retention, but no relation was detected between winter and spring retention and age-1 CPE (Table 2). The average retention from April to December or the postwinter (POSWINRET) period was the highest correlate of age-1 CPE (Table 2). Various multiple-regression equations that incorporated either three or four seasonal retention times as independent terms were all significant (P , 0.05) predictors of age-1 CPE, but these models displayed a high degree of multicollinearity. Consonant with this observation, retention was correlated among the three postwinter seasons (Table 2), which suggested that the pooled seasonal postwinter retention term was a suitable predictor of age-1 CPE. Catch per effort of age-1 fish was best explained by the model after transforming winter retention (WINRET) to log10 values: CPE 5 20.11 1 0.77(POSWINRET) 2 3.67(log10WINRET),

(1)

which explained 62% (R2 5 0.61, P , 0.01) of the variability in CPE. Although winter retention was not a direct correlate of CPE, this term explained an additional 5% of the variability (P 5 0.06) in age-1 catch beyond that explained by post-

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TABLE 2.—Pearson correlation coefficients between age-1 trap-net catch rates and Student residuals and seasonal retention time in reservoirs with stable water levels and low retention; N 5 27 for age-1 catch rates and N 5 20 for Student residuals. In addition, Pearson correlation coefficients between seasonal retention times are also given for these data; N 5 44 for these data because trap-net and Student residual data overlapped for 3 years in Jones Bluff Reservoir. Specific months assigned to seasons are given in the text; postwinter was defined as April–December. Asterisks indicate significance at P , 0.05* and P , 0.01**. Measure of crappie recruitment or seasonal retention Age-1 catch rate Student residuals Winter Spring Summer Fall

Correlation coefficient for retention season: Winter

Spring

Summer

Fall

Postwinter

20.24 20.57**

0.29 0.06 0.78**

0.65** 0.26 0.60** 0.78**

0.65** 0.28 0.33* 0.56** 0.73**

0.75** 0.26 0.60** 0.82** 0.96** 0.87**

winter retention (P , 0.01). Squared partial-correlation coefficient (pr2) was much higher for postwinter retention (pr2 5 0.60) than for winter retention (pr2 5 0.14). Improved importance of the winter retention to predict abundance after accounting for postwinter retention in the regression equation suggested a slight synergistic relation between winter and postwinter retention. We found similar results in analysis of student residuals derived from catch curves of older fish collected with electrofishing in reservoirs with stable water levels and low retention; higher crappie recruitment was related to short winter and long postwinter retention. Data represented 20 reservoir-years from Lay, Mitchell, Neely Henry, and Jones Bluff reservoirs. Student residuals (STURES) were pooled and were negatively correlated (r 5 20.45, P 5 0.05) to winter retention, but no significant relation was evident between student residuals and other seasonal retention times and pooled seasonal retention times (Table 2). Various multiple regressions were computed, and postwinter retention accounted for a significant (P , 0.05) portion of the variation in student residuals after accounting for the effects of winter retention. From inspection of plots, the relation between Student residuals and winter retention appeared nonlinear, and log10 transformation improved the fit between these two variables (r 5 20.57). The variation in STURES was best explained by the multiple regression STURES 5 0.81 1 0.07(log10POSWINRET) 2 3.07(log10WINRET).

(2)

Postwinter and winter retention were both significant (P , 0.01) terms in the regression and explained 59% of the variation in Student residuals.

For this model, winter retention appeared to be a slightly better predictor of year-class abundance than postwinter retention (POSWINRET pr2 5 0.42, WINRET pr2 5 0.57), contrary to the results from equation (1). However, slightly longer winter retentions occurred in some of the reservoirs sampled with electrofishing gear. Improved statistical importance of both winter and summer retention for predicting residuals in the multiple regression is consistent with the trapnet data, because a synergistic effect between winter and postwinter retention was associated with crappie recruitment success. In multiple regressions (1) and (2), the amount of variation explained by these two variables increased after accounting for the effects of each other compared with the independent fits of winter and postwinter retention to age-1 catch or Student residuals. From equations (1) and (2), predicted versus observed age-1 CPE and Student residuals were plotted against each other, and with the exception of a few outliers, these models provided some predictive capability of year-class abundance (Figure 2). Although winter and postwinter retention were positively related (Table 2), multicollinearity diagnostics for equations (1) and (2), including the variance inflation factor and condition number, were far below critical values (Montgomery and Peck 1982). Therefore, any collinearity between winter and postwinter retention was independent of the relation between retention time and yearclass strength in these equations. In reservoirs with stable water levels and low retention, 12 of 16 strong crappie year-classes were associated with winter retention of 6 d or less and postwinter retention of 11 d or more (Figure 3). When other hydrologic conditions occurred, weak year-classes were produced in 27 of 31 (87%)


FIGURE 2.—Predicted age-1 trap-net catch rates and Student residuals versus observed age-1 catch rates and Student residuals. Predicted trap-net and student residual values were derived from equations (1) and (2), respectively.

instances. Trap-net catch rates displayed a bimodal distribution and were either less than 4.9 fish/netnight or greater than 6.0 fish/net-night, which represented weak and strong crappie year-classes, respectively. For electrofishing data, negative and positive Student residuals computed from catchcurve regressions were also considered indicative of weak and strong crappie year-classes. Reservoirs with Fluctuating Water Levels and High Retention Student residuals derived from catch-curve regressions of age 3–7 crappies collected from Eufaula and West Point reservoirs were negatively correlated with winter retention and the percent deviation in winter elevation below full pool summer stage (Figure 4). Winter retention and percent deviation in winter elevation were not correlated to each other (r 5 0.48, P 5 0.16, N 5 10). Mul-

109

FIGURE 3.—Relation between winter and postwinter retention time in low-retention (long-term average #9 d) and nonfluctuating (,1 m) Alabama reservoirs. Weak year-classes (open circles) were defined as age-1 crappie trap-net catch rates of less than 5 fish/net-night and negative Student residuals derived from catch curve regressions. Strong year-classes (solid circles) were defined as age-1 crappie catch rates of greater than 5 fish/ net-night and positive student residuals derived from catch curve regressions. Horizontal and vertical dashed lines represent summer and winter retention of 11 d and 6 d, respectively.

tiple regression with winter retention time and percent winter deviation (WINDEV) was computed as STURES 5 3.01 2 1.93(log10WINDEV) 2 2.77(log10WINRET)

(3)

and explained 73% (P , 0.01) of the variation in Student residual values, with winter retention (P , 0.10) explaining 16% of the variability beyond that explained by percent deviation in winter elevation alone (P , 0.05). Squared-partial correlation coefficients suggested that percent deviation in winter elevation below summer pool was a better determinant (pr2 5 0.48) of strong and weak year-classes than winter retention time (pr2 5 0.38). Spring, summer, fall, and postwinter reten-

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Inspection of the catch curve derived for age3–7 crappies collected with electrofishing in spring 1996 from Weiss Reservoir indicated that a strong year-class was produced in 1990 and that the 1991 and 1992 year-classes were weak, which was consistent with the results of trap-net sampling conducted earlier (Figure 5). Student residuals from the catch curve regression were weakly, but positively, correlated to winter stage and negatively related to winter retention time, which was similar to the results derived from trap-net data (Figure 5). However, statistical power was low because only five year-classes were collected with electrofishing. Contrary to reservoirs with no fluctuation and low retention times, postwinter retention for the period of record in Weiss Lake ranged from 14 to 36 d and was never less than the 11-d critical postwinter retention time observed in reservoirs with low retention times and stable water levels. Discussion

FIGURE 4.—Student residuals derived from catch curve regressions versus winter retention (top) and the percent deviation in water level from full summer pool during the winter (bottom). Data are from West Point and Eufaula reservoirs.

tion and percent deviation in water elevation compared with regulated summer pool elevation were not related (P . 0.5) to Student residuals. For the period of record in these reservoirs, postwinter retention always ranged from 43 to 103 d, far greater than the 11 d minimum postwinter retention that was identified as being associated with greater crappie reproduction in reservoirs with stable water levels and low retention. Reservoir with Fluctuating Water Level and Variable Retention Analysis of age-1 crappies caught with trap nets from the final reservoir category (Weiss Lake) was limited to 7 years, but winter retention time and winter stage were negatively and positively correlated to age-1 CPE, respectively (Figure 5). Winter retention time and winter elevation also covaried inversely (r 5 20.92, P , 0.01), which did not allow discrimination of variables that influenced age-1 catch rates. Water levels and retention in the spring, summer, fall, and postwinter seasons were not correlated (P . 0.3) to age-1 abundance.

Crappie recruitment was related to reservoir hydrology in Alabama. Winter and postwinter retention and the synergistic relation between these two variables were recurring predictors of crappie year-class abundance in many Alabama reservoirs that displayed annual long-term retention time of 9 d or less and minimal water level fluctuations. Our two models derived for this reservoir category explained about 60% of the variation in crappie recruitment. Although variation existed among reservoirs of differing hydrologic characteristics, conditions favorable to strong crappie year-class recruitment appeared consistent throughout impoundments in the Alabama, Chattahoochee, Coosa, and Tombigbee river systems. Wet winters that resulted in either shorter retention time or higher water levels in fluctuating reservoirs before crappie spawning appeared as a prerequisite to the production of strong crappie year-classes, but generally, only when followed by postwinter retention of 11 d or longer. In the two reservoir categories that included systems with regulated water level fluctuations of 1.8 m or greater, higher water levels in the winter that approached full pool summer elevations were related to greater crappie reproductive success. In these systems, winter reservoir hydrology explained about 50–80% of the variation in crappie year-class strength among the different databases. Because fish analyzed in this study were captured after age-0, some of the error associated with these models was probably attributable to mortality during the first year of life and possibly to the dif-


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FIGURE 5.—Age-1 crappie catch rates from trap nets and student residual values derived from catch curves versus winter retention and average winter stage in Weiss Lake. Numeric values represent year-classes. Arrows indicate the normal regulated full summer pool elevation.

ferential reproductive response of the two crappie species to hydrologic factors. In addition, more specific critical time periods for successful crappie recruitment could not be detected as seasonal retention times covaried. In Chickamauga Reservoir, high larval crappie densities were related to high retention time and high water level before, during, and after the spawn (McDonough and Buchanan 1991). Similarly, increased crappie year-class abundance was associated with high water levels and low discharge during and after the spawn (Beam 1983; Mitzner 1991). These authors reported that hydraulic variables explained 31–72% of the variation in crappie reproductive success. However, we found relations between crappie spawning success and hydraulic conditions before the spawn (January–March), as well as after the postspawning period. Our analyses were correlative, and wet winter conditions may be associated with other factors related to greater crappie reproductive success. Adult crappies may be positively responding to

conditions similar to natural flooding in unaltered river environments that would provide beneficial habitat to juvenile fish. However, specific explanations of biological or reproductive physiological mechanisms responsible for the relation between high water input before spawning and strong yearclass formation are speculative. This limited our analyses and warrants additional investigation. High water inflows may provide allochthonous organic and inorganic material and nutrients which may stimulate primary production and secondary production later in the growing season. Possible biological mechanisms explaining the relation between postwinter retention and strong crappie year-classes may be related to survival of age-0 crappie in the limnetic zone (Mathur et al. 1979; Beam 1983; Ellison 1984; O’Brien 1984). Low retention time (i.e., high flushing) may increase turbidity, reduce food availability and feeding efficiency, and physically remove young fish from the impoundment, all of which can reduce recruitment of age-0 crappie. In addition, Maceina et al. (1996) found that for

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a given level of phosphorus, higher algal biomass concentrations were produced when higher retention times (up to 35 d) occurred during April– October in Alabama reservoirs. Retention time is an important determinant of phytoplankton production in reservoirs (reviewed by Maceina et al. 1996) and is probably associated with greater production at higher food web trophic levels. Greater food resources were probably an important factor influencing growth and subsequent survival of young crappies. If cyclic crappie year-class formation is due to inherent hydrologic cycles controlled by climate, then management options to increase crappie yearclass strength may be limited in mainstream reservoirs where water levels do not fluctuate. If natural mortality is not great, then reduced bag limits or higher size limits may allow longer persistence of a strong year-class in the population until environmental conditions favor production of another large year-class. In reservoirs where water levels fluctuate, a rising or higher than average water level in the months before and during the spawning period may increase crappie year-class production, and this could be manipulated. Our findings have implications for water allocation among the states of Georgia, Alabama, and Florida (Hatcher 1993). Georgia wishes to store and use more water from tributary reservoirs in rivers that eventually form mainstream reservoirs downstream in Alabama. Typically, most storage in tributary reservoirs occurs in winter and spring when rainfall is high and evapotranspiration is low. Additional water diversion and storage by Georgia in the upper Coosa and Chattahoochee river systems during the late winter and early spring may have a detrimental effect on crappie reproductive success in Alabama because retention times will be longer or water levels will be lower, or both, in reservoirs with fluctuating water levels. Conversely, our findings also showed that longer summer retention times (.11 d) in mainstream reservoirs with low retention were associated with greater crappie reproductive success. Thus, if additional storage of water could occur during the summer months to achieve reservoir retention times of at least 11 d, then crappie populations may benefit in Alabama if water flow was sufficient during the previous winter. Acknowledgments This work was funded by the Alabama Department of Conservation and Natural Resources, Game and Fish Division through Federal Aid in

Sport Fish Restoration project F-40. The Alabama Power Company and the Mobile District of the U.S. Army Corps of Engineers supplied reservoir hydrologic data. O. Ozen assisted in data management and computation. M. Allen, D. Bayne, P. Bettoli, J. Buchanan, M. Freeman, S. Sammons, J. Slipke, S. Szedlmayer, and two anonymous reviewers provided comments to improve this paper. P. Black, V. DiCenzo, C. Greene, S. Rider, S. Smith, and V. Travnichek assisted in field collections. This is journal paper 8-975776 of the Alabama Agricultural Experiment Station. References Beam, J. H. 1983. The effect of annual water level management on population trends of white crappie in Elk City Reservoir, Kansas. North American Journal of Fisheries Management 3:34–40. Black, W. P. 1994. Factors affecting growth of black crappie and white crappie in Alabama. Master’s thesis. Auburn University, Auburn, Alabama. Ellison, D. G. 1984. Trophic dynamics of a Nebraska black crappie and white crappie population. North American Journal of Fisheries Management 4:355– 364. Hatcher, K. J. 1993. Proceedings of the 1993 Georgia’s water resources council. Institute of Natural Resources, University of Georgia, Athens. Maceina, M. J. 1988. Simple grinding procedures to section otoliths.North American Journal of Fisheries Management 8:141–143. Maceina, M. J. 1997. Simple application of using residuals from catch-curve regressions to assess yearclass strength in fish. Fisheries Research 32:115– 121. Maceina, M. J., and R. K. Betsill. 1987. Verification and use of whole otoliths to age white crappie. Pages 267–278 in R. C. Summerfelt and G. E. Hall, editors. Age and growth of fish. Iowa State University Press, Ames. Maceina, M. J., and five coauthors. 1996. Compatibility between water clarity and black bass and crappie fisheries in Alabama. Pages 296–305 in L. E. Miranda and D. R. DeVries, editors. Multidimensional approaches to reservoir fisheries management. American Fisheries Society, Symposium 16, Bethesda, Maryland. Mathur, D., P. L. McCreight, and G. A. Nardace. 1979. Variations in fecundity of white crappie in Conowingo Pond, Pennsylvania. Transactions of the American Fisheries Society 108:548–554. McDonough, T. A., and J. P. Buchanan. 1991. Factors affecting abundance of white crappies in Chickamauga Reservoir, Tennessee, 1970–1989. North American Journal of Fisheries Management 11: 513–524. Mitzner, L. 1991. Effect of environmental variables upon crappie young, year-class strength, and sport fishery. North American Journal of Fisheries Management 11:534–542.


Montgomery, D. C., and E. A. Peck. 1982. Introduction to linear regression analysis. Wiley, New York. O’Brien, W. J., B. Loveless, and D. Wright. 1984. Feeding ecology of young white crappie in a Kansas reservoir. North American Journal of Fisheries Management 4:341–349. Pflieger, W. L. 1975. The fishes of Missouri. Missouri Department of Conservation, Jefferson City. Ploskey, G. R. 1986. Effects of water-level changes on reservoir ecosystems, with implications for fisheries management. Pages 86–97 in G. E. Hall and M. J. Van Den Avyle, editors. Reservoir fisheries management: strategies for the 80’s. American Fisheries Society, Southern Division, Reservoir Committee, Bethesda, Maryland. Ricker, W. E. 1975. Computation and interpretation of biological statistics of fish populations. Fisheries Research Board of Canada Bulletin 191. Smith, S. M., M. J. Maceina, and R. A. Dunham. 1994. Natural hybridization between black crappie and white crappie in Weiss Lake, Alabama. Transactions of the American Fisheries Society 123:71–79. Smith, S. M., M. J. Maceina, V. H. Travnichek, and R. A. Dunham. 1995. Failure of quantitative phenotypic characteristics to distinguish black crappie, white crappie, and their first-generation hybrid.

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North American Journal of Fisheries Management 15:121–125. Soballe, D. M., and B. L. Kimmel. 1987. A large-scale comparison of factors influencing rivers, lakes, and impoundments. Ecology 68:1943–1954. Stimpert, M. S. 1995. Factors affecting year-class abundance of crappies in Alabama reservoirs. Master’s thesis. Auburn University, Auburn, Alabama. Swingle, H. S., and W. E. Swingle. 1967. Problems in dynamics of fish populations in reservoirs. Pages 229–243 in Reservoir Committee, editors. Reservoir fishery resources symposium. American Fisheries Society, Southern Division, Reservoir Committee, Bethesda, Maryland. Travnichek, V. H., M. J. Maceina, S. M. Smith, and R. A. Dunham. 1996. Extent of natural hybridization between black crappie and white crappie in Alabama reservoirs. American Midland Naturalist 135: 310–316. Walburg, C. H. 1971. Loss of young fish in reservoir discharge and year-class survival, Lewis and Clark Lake, Missouri River. Pages 441–448 in G. Hall, editor. Reservoir fisheries and limnology. American Fisheries Society, Special Publication 8, Bethesda, Maryland. Received June 7, 1996 Accepted June 6, 1997