Biological Performance Indicators for Evaluating Exploitation of ...

North American Journal of Fisheries Management 23:1303–1311, 2003 q Copyright by the American Fisheries Society 2003

Biological Performance Indicators for Evaluating Exploitation of Minnesota’s Large-Lake Walleye Fisheries R. SCOTT GANGL* Wyoming Game and Fish Department, 3030 Energy Lane, Suite 100, Casper, Wyoming 82604, USA

DONALD L. PEREIRA Minnesota Department of Natural Resources, 500 Lafayette Road, Box 12, St. Paul, Minnesota, 55155-4012, USA Abstract.—We examined the potential for using biological performance indicators (BPIs) to detect the overexploitation of populations of walleye Sander vitreus (formerly Stizostedion vitreum) in Minnesota’s large lakes. We developed equations to predict growth (i.e., the von Bertalanffy growth parameter and total length at age 3) and female age at 50% maturity from growing season length and female total length at 50% maturity from growth. Indices of spawning stock age diversity and the variation of population abundance were characterized by means of summary statistics. Thresholds for BPIs were determined by adding a measure of variability to the predicted and mean values. We evaluated the sensitivity of each BPI by comparing observed values with the thresholds for an overexploited population in Upper Red Lake. All six BPIs for Upper Red Lake exceeded the thresholds, demonstrating their potential use for monitoring walleye exploitation. However, the magnitude by which each threshold was exceeded differed by BPI, indicating that some may be more sensitive than others. Managers should integrate the performance of a suite of BPIs with time series analysis to identify the onset of overexploitation of large-lake walleye fisheries.

To actively manage fish populations, managers must be able to identify overexploitation before a reduction in the quality or sustainability of the fishery takes place. Biological reference points (BRPs) based on some fishing mortality rate (e.g., the rate at which yield per recruit is 10% less than the rate that maximizes yield per recruit [F0.1]; the rate that equals natural mortality [M]; or the rate that corresponds to a fixed exploitation rate such as 20% [F20%]) or abundance (e.g., spawning stock biomass) have been used extensively for identifying the overexploitation of commercially fished marine stocks (e.g., Smith et al. 1993; Caddy and Mahon 1995). The different BRPs have different merits, depending on the life history characteristics of the population being fished (Mace 1994), and each provides a conservative (i.e., not exceeding the maximum sustainable yield) level of fishing mortality when properly applied. The estimation of BRPs is often data intensive and may require yield-per-recruit analysis or estimating abundance from stock assessment models (Smith et al. 1993). Furthermore, accurate estimates of the true fishing mortality rate are necessary to determine whether a BRP has been exceeded.

* Corresponding author: [email protected] Received June 14, 2002; accepted February 12, 2003

Where data do not exist to calculate BRPs, alternative tools are needed to monitor fishery performance. The National Research Council (1998) has defined three categories of performance indicators: biological indicators reflect the dynamics of the fish population, yield and social indicators represent the performance of the fishery, and uncertainty indicators try to predict how key population parameters may change in the future under current management. Biological indicators, termed biological performance indicators (BPIs), change when a population compensates for a reduction in density (Goodyear 1980; Shuter 1990). A reduction in density due to exploitation is reflected in changes in factors such as growth, rate of maturity, and recruitment. A major advantage of using BPIs for fisheries management is that the data to calculate them are often readily available from population assessments. Though many BPIs have been used to monitor fishery characteristics, there are no defined thresholds that correspond universally to the exploitation of walleye Sander vitreus (formerly Stizostedion vitreum). Colby and Nepszy (1981) reported how certain biological characteristics can relate to energy regimes and demonstrated this through a ‘‘crisis curve’’ that illustrates the relationship between the age at maturity of a population and growing season length. Thus, threshold values for BPIs

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TABLE 1.—Physical and limnological characteristics of Minnesota’s 10 large walleye lakes (MDNR 1997).

Lake Cass Kabetogama Lake of the Woods Leech Mille Lacs Pepin Rainy Upper Red Vermilion Winnibigoshish

Area (ha)

Mean depth (m)

Total dissolved solids (mg/L)

6,300 10,400 128,300a 45,200 54,000 10,100 21,900b 19,400c 16,400 23,700

7.6 9.1 7.4 5.1 6.4 6.4 9.8 3.9 6.1 4.6

210 73 105 167 132 312 53 199 80 187

a

Minnesota waters only; the total area of Lake of the Woods is 15,221 km 2 . Minnesota waters only; the total area of Rainy Lake is 3,532 km 2 . c Off-reservation waters only; the total area of Upper Red Lake is 1,728 km 2 . b

should incorporate environmental influences and be determined for individual populations. We sought to determine whether BPIs for growth, age and length at maturity, spawning stock age diversity, and the variation of population abundance are sensitive to exploitation and can be used to detect the onset of overexploitation of walleye populations in Minnesota’s large lakes. To address this objective, we (1) calculated threshold values for each BPI and (2) determined whether the observed BPIs exceeded those thresholds during overexploitation. We expected heavy exploitation to cause increases in growth and population variation and decreases in age and length at maturity and spawning stock age diversity. To test the sensitivity of these BPIs, we compared the observed values from our study lakes with our calculated thresholds, utilizing overexploited and lightly exploited populations for reference. Study Area We analyzed walleye population data from Minnesota’s 10 largest lakes (Table 1) and Navigational Pool 2 of the upper Mississippi River (Figure 1). Minnesota’s large lakes produce approximately 40% of the entire statewide walleye angling harvest (MDNR 1997), but human population growth, coupled with traditionally high rates of participation in outdoor activities, exposed the large lakes to increasing levels of angling pressure throughout the 20th century. The Large Lake Monitoring Program (LLMP) was created in the early 1980s to focus assessment and management efforts on these important fisheries (Wingate and Schupp 1985). In light of ever increasing angling pressure,

FIGURE 1.—Map showing the locations of Minnesota’s large lakes and Navigational Pool 2 of the upper Mississippi River.

some large-lake managers have recently experimented with active management (Radomski 2003, this issue). Commercial fisheries existed on some of the large lakes during the 20th century, but most commercial fishing had ceased by the time the LLMP began. Most of the large lakes displayed no signs of overexploitation during the period of study, and we considered them to fall somewhere on the spectrum between no exploitation and overexploitation. We assumed that, on average, the large lakes were not overexploited (i.e., although some lakes may be close to being overexploited and others underexploited, the central tendency lies somewhere in the middle). One exception is Upper Red Lake. The first signs of overfishing on the Red Lakes (including the tribal waters of Upper and Lower Red lakes and the state waters of Upper Red Lake) appeared in the early 1970s, when the commercial catch dropped sharply and remained low (Pereira et al. 1992). Commercial fishing continued on the Red Lakes into the 1990s, when the spawning stock collapsed and all walleye harvest was stopped. We compared this population with that of the other large lakes to determine whether the BPIs were sensitive to overexploitation. Walleye angling in Navigational Pool 2 of the upper Mississippi River has been catch and release only since 1993 and this pool was considered

BIOLOGICAL PERFORMANCE INDICATORS

largely unexploited prior to that time. Pool 2 is not part of the large lake assessment program but was compared with the large lakes to determine how BPIs respond to low exploitation. Methods We used all available data from standardized annual gill-net samples for the years 1989 to 1998 to calculate BPIs for the large lakes, except where noted. For Pool 2, all data were collected by night electrofishing during the winters of 1998–1999 and 1999–2000. Because Upper Red Lake provided a longer time series of overexploitation, we used two time periods: 1985–1989 (before the collapse) and 1989–1998 (during the collapsed state). As Vermilion Lake has two distinct basins (East and West) that exhibit differences in walleye biological characteristics such as growth, we treated them separately for all analyses. We examined two indices of growth. First, we analyzed the growth of age-1–7 walleyes with the von Bertalanffy growth model fitted using nonlinear regression and an additive error structure (Quinn and Deriso 1999). Growth was represented by the parameter v, which is the product of the theoretical maximum length, L`, and the growth coefficient, K (Gallucci and Quinn 1979); this parameter is useful for comparing populations and has been used previously for similar purposes (OMNR 1983). Zivkov et al. (1999) reported on the shortcomings of growth parameters such as v and recommended using length-at-age statistics to summarize population growth. Length-at-age statistics are practical because the data are readily available and do not require regression modeling to compute. We used mean total length at age 3 (TL3; mm) as a second index of walleye growth to compare with v and determine which index provided the most sensitive response to exploitation. Age 3 is the age at which most large-lake walleyes are fully recruited to gill nets but have not matured. For both growth indices, we used mean backcalculated length-at-age data for both sexes combined. Length-at-age data were not available for Lake of the Woods after 1993, so v was calculated using length at capture from 1991 to 1998 and TL3 was calculated using length-at-age data from 1984 to 1993. We applied logistic regression (Quinn and Deriso 1999) to maturity data of female walleyes to estimate age at 50% maturity (Agemat) and total length at 50% maturity (TLmat; mm). The diversity of the age structure of mature females was estimated for each year using the Shannon diversity

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index (Marteinsdottir and Thorarinsson 1998). Individual yearly estimates of spawning stock age diversity were averaged to obtain a single estimate of the mean age diversity (H) of mature females for each lake. Maturity data were not available for Lake Pepin. At Lake Winnibigoshish, maturity data were only collected in 1998 and 1999. The coefficient of variation (CV; 100·SD/mean) of the annual gill-net catch per unit effort (CPUE) of all walleyes sampled provided an index of the variation of population abundance. We could not calculate the CV for Pool 2 because those data were collected using electrofishing instead of standardized gill nets. Growing season length was characterized by the index of growing degree-days greater than 58C (GDD5). Data for this index were obtained from the National Oceanic and Atmospheric Administration’s National Climatic Data Center (NOAA 2000). The value of GDD5 was calculated for each large lake based on data from the monitoring station closest to that lake. Mean GDD 5 values were calculated for each lake during the time period over which biological data were collected to ensure that thermal regimes corresponded to observed biological parameters. We expected growing season length to influence v, TL3, Agemat, and TLmat. To account for this, we fitted linear least-squares regression models with the BPIs as the dependent variables and GDD5 as the explanatory variable, resulting in models to predict the expected values of BPIs for each lake. The range of GDD5 values for Minnesota’s lakes was too narrow to model relationships with Agemat and TLmat, so we used additional Agemat and GDD5 data (with permission from Baccante and Colby [1996] and Sullivan [2003, this issue]), to regress Agemat on GDD5. Similar data were not available for TLmat, so we regressed TLmat on TL3. Most published material on heavily exploited walleye populations reports shifts toward smaller sizes at maturity (e.g., Muth and Wolfert 1986; Schneider et al. 1991), and we hypothesized that heavy exploitation would result in a decrease in TLmat despite increased growth. Consequently, we used our regression model of TLmat on TL3 to remove the effects of growth on maturity and predict TLmat. Upper Red Lake and Pool 2 were excluded from all regression models to prevent their respective overexploited and unexploited status from imparting any unusual influence on the models. We calculated the mean CV and H across all lakes (excluding Upper Red Lake and Pool 2), assuming that the central tendency of each index

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TABLE 2.—Observed growing degree-days above 58C (GDD 5 ) and calculated biological performance indicator values for walleye populations from Minnesota’s large lakes and Pool 2 of the Mississippi River during 1989–1998. Abbreviations are as follows: v, growth parameter equal to the product of the theoretical maximum length and the growth coefficient from the von Bertalanffy growth model; TL 3 , total length at age 3; Agemat , female age at 50% maturity; TLmat , female total length at 50% maturity; H, mean age diversity of mature females (Shannon index); and CV, coefficient of variation (100·SD/mean) of the annual gill-net catch per unit effort. Upper Red Lake (1980s) corresponds to the period 1985–1989, while Upper Red Lake (1990s) corresponds to 1989–1998; NA indicates that data were not available. Lake Cass Kabetogama Lake of the Woods Leech Mille Lacs Pepin Rainy Upper Red (1980s) Upper Red (1990s) East Vermilion West Vermilion Winnibigoshish Pool 2

GDD 5

v

TL 3

Agemat

TLmat

H

CV

1,839 1,719 1,765 1,924 1,870 2,600 1,719 1,801 1,744 1,672 1,672 1,918 2,400

116 116 129 130 137 185 110 193 204 95 107 131 129

306 286 304 314 316 399 270 295 342 262 290 321 387

4.8 4.0 4.7 4.2 3.8 NA 4.0 3.7 3.4 4.8 4.5 3.5 4.6

459 404 458 438 441 NA 394 407 423 407 452 430 505

0.67 0.66 0.60 0.59 0.62 NA 0.60 0.38 0.25 0.53 0.56 0.54 0.80

0.18 0.24 0.29 0.33 0.32 0.56 0.32 0.45 0.57 0.19 0.31 0.38 NA

would reasonably describe that index under sustainable conditions. We calculated individual BPI thresholds for each lake using predicted or mean values and an estimate of variability to account for natural fluctuations in BPIs. The 80% confidence interval of the expected values was subjectively chosen as a range of variability that would be moderately restrictive. Upper Red Lake and Pool 2 were excluded from threshold calculations due to their overexploited and unexploited status, respectively. To examine the sensitivity of each BPI to ex-

ploitation, we compared the observed values with the threshold that corresponded to the expected response of that index to exploitation, using Upper Red Lake and Pool 2 as reference populations of overexploitation and light exploitation, respectively. We expected growth to increase and population abundance to fluctuate more under heavy exploitation and thus expected v, TL3, and CV for Upper Red Lake to exceed the upper 80% confidence limit for those BPIs. Under heavy exploitation, Agemat, TLmat, and H would decrease, so we expected Upper Red Lake to exceed the lower 80% confidence limits for these BPIs; we expected opposite responses for Pool 2. Results The BPI v (Table 2) was positively related to growing season length (R2 5 0.90; F 5 69.9; df 5 9; P , 0.0001): v 5 232.6 1 0.0846·GDD5.

FIGURE 2.—Residuals versus the predicted values of the growth index (v; see text for definition) for walleyes from Minnesota’s large lakes and Pool 2 of the Mississippi River. The solid horizontal line depicts the mean (zero) of the residuals, while the dashed lines depict the 80% confidence interval of the predicted values. Residuals above the upper confidence limit may indicate overexploitation. Data for Upper Red Lake and Pool 2 are shown separately because these bodies of water are known to be overexploited and unexploited, respectively.

The residual for Upper Red Lake was 18 times as great as the residual threshold during the 1980s and 22 times as great during the 1990s (Figure 2). Other lakes exceeding their threshold values included Lake of the Woods and Mille Lacs. Meanwhile, Pool 2 exhibited a residual 11 times less than the lower 80% confidence limit. The BPI TL3 was positively related to growing season length (R2 5 0.92; F 5 93.6; df 5 9; P , 0.0001): TL3 5 58.3 1 0.133·GDD5.


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FIGURE 3.—Residuals versus the predicted values of mean total length at age 3 (TL3) for walleyes from Minnesota’s large lakes and Pool 2 of the Mississippi River. See the caption to Figure 2 for additional information.

The residual for Upper Red Lake strongly exceeded the threshold in the 1990s but not during the 1980s (Figure 3). Lake of the Woods, Mille Lacs, West Vermilion Lake, and Lake Winnibigoshish also exceeded the thresholds for TL3. Pool 2 exhibited a positive residual TL3 but did not exceed the upper 80% confidence limit. Agemat was negatively related to GDD5 (Figure 4; R2 5 0.86; F 5 211.7; df 5 36; P , 0.0001): Agemat 5 4392.4·GDD–0.915 . 5 The power curve is similar to the one reported by Baccante and Colby (1996), though the model parameters differ slightly with the added data. Upper Red Lake exceeded the threshold in the 1980s and 1990s (Figure 4). Several other large lakes also exceeded the threshold, including Kabetogama, Mille Lacs, Rainy, West Vermilion, and Winnibigoshish. The value of Agemat for Pool 2 was substantially higher than the predicted value. The BPI TLmat was positively related to growth (R2 5 0.44; F 5 5.56; df 5 8; P 5 0.0505):

FIGURE 4.—Regression of female walleye age at 50% maturity (Agemat) on an index of growing degree-days above 58C (GDD5) (upper panel) and residuals versus predicted values of Agemat (lower panel). Dashed lines depict the 80% confidence interval of the predicted values. Residuals below the lower confidence limit may indicate overexploitation.

TLmat 5 199.7 1 0.78·TL3. The value of TLmat for Upper Red Lake was below the lower 80% confidence limit during the 1980s and 1990s (Figure 5), thereby supporting the hypothesis that size at maturation will decrease even after growth increases. Kabetogama Lake, Rainy Lake, and Lake Winnibigoshish also exceeded this threshold. Pool 2 had an observed TLmat close to the expected value. The mean value of H was 0.60, with confidence limits of 60.02. Upper Red Lake was below the threshold during both the 1980s and 1990s, with H 5 0.38 and 0.25, respectively (Figure 6). In contrast, the value of H was higher at Pool 2 than

FIGURE 5.—Residuals versus the predicted values of female total length at 50% maturity (TLmat) for walleyes from Minnesota’s large lakes and Pool 2 of the Mississippi River. The horizontal line depicts the mean (zero) of the residuals, while the dashed lines depict the 80% confidence interval of the predicted values. Residuals below the lower confidence limit may indicate overexploitation.

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FIGURE 6.—Mean mature female walleye age diversity (H) for Minnesota’s large lakes and Pool 2 of the Mississippi River. The horizontal line depicts the lower limit of the 80% confidence interval. Observations below that limit may indicate overexploitation.

at any of the large lakes. East Vermilion Lake, West Vermilion Lake, and Lake Winnibigoshish all had H values below the threshold. The mean CV was 0.31, with confidence limits of 60.05. Upper Red Lake exceeded the upper confidence limit during both the 1980s and 1990s (Figure 7). Lakes Pepin and Winnibigoshish both exceeded the CV threshold. Discussion Both v and TL3 provided useful characterizations of large-lake walleye growth and displayed strong relationships to growing season length. The OMNR (1983) identified similar patterns in v by latitude and reported a mean of 118.5 for 38 Ontario walleye stocks, which is on the low end of the range for Minnesota’s large lakes and is due to Ontario’s generally shorter growing season. By removing the effects of growing season on growth, we identified populations with growth rates that deviated from expected values. Because the Upper Red Lake v was much higher than the predicted value and threshold during the 1980s and 1990s and Pool 2 exhibited a large negative residual, we conclude that v is sensitive to exploitation for walleye populations. The Upper Red Lake TL3 did not show a strong response to fishing until the 1990s, though the effects of overexploitation have been experienced since the early 1970s. These results are consistent with the growth data published by Ostazeski and Spangler (2001), in which a discernable increase in walleye growth was not evident until the late 1980s. The time lag between the onset of overexploitation and the manifestation of a detectable growth response casts doubt on the sensitivity of

FIGURE 7.—Coefficient of variation (CV, defined as 100·SD/mean) of annual walleye gill-net catch per unit effort. The horizontal line depicts the upper limit of the 80% confidence interval. Observations above that limit may indicate overexploitation.

TL3 as an index of exploitation. Several authors have reported a positive relationship between walleye growth and exploitation (e.g., Chevalier 1977; Shuter and Koonce 1977; Spangler et al. 1977; Colby and Nepszy 1981; Muth and Wolfert 1986; Colby et al. 1994). However, in most reported cases, increased growth occurred after a serious decline in population size and generally corresponded to later stages of overexploitation that preceded population collapse. Overexploitation of those systems may have occurred before a detectable growth response and population collapse. Alternatively, the life stage over which growth analysis takes place may affect the sensitivity of an index to exploitation. Gallucci and Quinn (1979) reported that v corresponds to the early growth rate. We assumed a priori that TL3 would represent early walleye growth by representing the cumulative growth of fish to age 3 and would provide a substitute for v that is easily calculated. However, our findings suggest that TL3 was less sensitive to a reduction in walleye density at Upper Red Lake, and thus early growth as characterized by v may be a better BPI. The regression of Agemat on GDD5 showed that the latter accounted for 86% of the variation in the former and provided a useful means for determining expected Agemat over a wide range of growing seasons. Age at maturity has long been considered a sensitive indicator of overexploitation (Abrosov 1969; OMNR 1983; Trippel 1995), with a decrease being the most common response to heavy exploitation because fish grow faster and reach mature sizes more quickly. Walleye stocks in Lake Erie matured at a younger age following a major reduction in density and an increase in growth rate (Wolfert 1969). A moratorium was placed on the


Lake Erie commercial fishery, and stock rehabilitation resulted in increased density and age at the onset of maturity during the 1970s and 1980s (Muth and Wolfert 1986). The Upper Red Lake Agemat displayed the expected response to heavy exploitation by exhibiting the greatest negative residuals of all large Minnesota lakes. However, several other lakes also exceeded the threshold because the larger data set contributed to generally narrower confidence intervals than those calculated for other BPIs. The Pool 2 residual of Agemat was approximately the same distance in the positive direction as the Upper Red residuals were in the negative direction. The prediction model for the large lakes falls approximately midway between the states of overexploitation and light exploitation and provides a reasonable means for monitoring exploitation on these lakes. Upper Red Lake walleyes matured at a smaller size than predicted, indicating that they allocated surplus resources to reproduction before growth. Trippel (1995) suggested that heavily exploited fish populations may achieve either larger or smaller sizes at maturity due to exploitation. Perrin and Rubin (1990) reported a dome-shaped norm of reaction for the relationship between size and age at maturity when survival and production are negatively correlated, where both fast-growing and slow-growing fish reproduce at a smaller size than those with a medium growth rate. Henderson and Nepszy (1994) concluded that maturation was dependent upon surplus energy for both metabolism and reproduction, and Henderson et al. (1996) reinforced this notion by suggesting that the onset of maturation is related to a walleye’s visceral fat stores. Thus, our relationship between walleye TLmat and growth should remain stable under sustainable conditions where growth and maturity do not change. However, under heavily exploited conditions, surplus energy may become available for walleyes to attain maturity at smaller sizes. Under this scenario, TLmat would be a more sensitive BPI than growth. Most large-lake H values had a relatively narrow range. The deviation from this range (and the threshold) by Upper Red Lake and Pool 2 indicate that mean H is sensitive to exploitation for walleye populations. Mean H has the potential to be a good indicator of overexploitation because it involves a stock’s ability to replace itself. Reproductive capacity changes with age (Serns 1982; Johnston 1997), so that each age-group does not contribute equally to reproductive effort. Marteinsdottir and Thorarinsson (1998) reported that H contributed

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positively to the stock–recruitment relationship for Icelandic cod, so it may contribute similarly to walleye recruitment. Thus, higher H will indicate a healthier spawning stock and population. Exploitation may affect H in two ways. First, older mature fish may be harvested, reducing H. Second, as high exploitation causes reproduction to fail, weak year-classes will be sparsely represented or missing from the spawning stock. Our threshold for CV was similar to, but slightly below, the value of 0.40 recommended by OMNR (1983). While CV reflects population fluctuations caused by exploitation, it is a simple index of variation and can be confounded by positive changes in a population that are not related to exploitation. For example, lakes with an expanding walleye population will display a high CV due to increasing abundance. Nonetheless, our results suggest that CV is sensitive enough to exploitation to be a useful index because Upper Red Lake considerably exceeded the threshold during both the 1980s and 1990s. The other lake to exceed the CV threshold by a magnitude similar to Upper Red Lake was Lake Pepin. The high CV for Lake Pepin may not be due to exploitation but to variations in recruitment caused by environmental factors identified by Ickes (2000). We suggest three schemes for monitoring walleye fisheries with BPIs. First, the entire suite of BPIs should be used for comparison to avoid a ‘‘knee jerk’’ reaction whenever a single threshold is exceeded. For example, if the growth threshold is exceeded but no other BPIs are violated, the growth increase may be due to other factors such as increased forage. All BPIs are not equally sensitive to exploitation, so a manager’s level of concern would increase with the number and sensitivity of the BPIs exceeded. We suggest that maturity is more sensitive than growth, which is more sensitive than CV. Thus, if the Agemat, TLmat, and H all exceed thresholds, there is more need for concern than if only CV is exceeded. Second, BPIs should be calculated annually to monitor trends. Even if no thresholds are exceeded, several BPIs may indicate a trend towards an overexploited state and provide managers with advance warning of a pending problem. Third, BPIs may occasionally exceed thresholds due to natural fluctuations, but managers must be concerned when thresholds are consistently exceeded. For example, a single BPI on a single lake may never exceed the threshold for more than two consecutive years. So, the manager would consider it a unique event if the threshold were exceeded for three or more years.

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At this point, the manager would consider the other indices, their own biological knowledge of the system, and recent harvest dynamics for the lake. Our intent was to devise a means of determining useful thresholds for monitoring population exploitation. We arbitrarily selected the 80% confidence level to allow some variation about the expected value while remaining restrictive enough to detect consistent changes in the indices due to exploitation. As managers begin to implement thresholds, they may feel that 80% confidence intervals are too conservative or not conservative enough. A more liberal management program may use a wider confidence interval (e.g., 90% or 95%) to monitor exploitation, while a more restrictive program would use a narrower confidence interval (e.g., 50%). Future work on this topic may include changing the level of uncertainty used for setting thresholds to more appropriately manage individual systems based on the level of risk a manager is willing to accept for a particular population. Currently, most of Minnesota’s large lakes are not actively managed to achieve a certain target. The exceptions include Mille Lacs, where a constant exploitation rate policy is in place, and Rainy Lake, where attempts are being made to keep the angling harvest below a target quota. Considerable uncertainty impedes managers in determining whether the other large lakes need more active management (i.e., stricter control of the fishery to control harvest or exploitation) or at what point in the future such a need may arise. In the absence of more advanced management models, we suggest that in the future more stringent management action on these lakes can be tied to a suite of rules based on BPIs. If the rules are violated (such as when BPI thresholds are exceeded or significant), negative trends in BPIs may forecast the onset of an overfished state and management action can be invoked to constrain the fishery. If the BPIs are sensitive to overexploitation, more stringent regulations can stay in place until the BPIs indicate the return of the population to a healthier state. Acknowledgments We are indebted to the large-lake specialists who graciously shared their data for this research as well as to the MDNR Region Six biologists who assisted with data collection on Pool 2. We also thank D. Baccante, N. Lester, and G. Sullivan for providing additional data and insightful discussions on the responses of fish to exploitation. This manuscript was improved considerably by the comments provided by M. Hansen, S. Hewett, T.

Marshall, and two anonymous reviewers. The MDNR Division of Fisheries provided funding for this research. References Abrosov, V. N. 1969. Determination of commercial turnover in natural bodies of water. Problems of Ichthyology 9:482–489. Baccante, D. A., and P. J. Colby. 1996. Harvest, density, and reproductive characteristics of North American walleye populations. Annales Zoologici Fennici 33: 601–615. Caddy, J. F., and R. Mahon. 1995. Reference points for fisheries management. FAO (Food and Agriculture Organization of the United Nations) Fisheries Technical Paper Number 347. Chevalier, J. R. 1977. Changes in walleye (Stizostedion vitreum vitreum) population in Rainy Lake and factors in abundance, 1924–75. Journal of the Fisheries Research Board of Canada 34:1696–1702. Colby, P. J., C. A. Lewis, R. L. Eshenroder, R. C. Haas, and L. J. Hushak. 1994. Walleye-rehabilitation guidelines for the Great Lakes area. Great Lakes Fishery Commission, Ann Arbor, Michigan. Colby, P. J., and S. J. Nepszy. 1981. Variation among stocks of walleye (Stizostedion vitreum vitreum): management implications. Canadian Journal of Fisheries and Aquatic Sciences 38:1814–1831. Gallucci, V. F., and T. J. Quinn II. 1979. Reparameterizing, fitting, and testing a simple growth model. Transactions of the American Fisheries Society 108: 14–25. Goodyear, C. P. 1980. Compensation in fish populations. Pages 253–280 in C. H. Hocutt and J. R. Stauffer, Jr., editors. Biological monitoring of fish. Lexington Books, Lexington, Kentucky. Henderson, B. A., and S. J. Nepszy. 1994. Reproductive tactics of walleye (Stizostedion vitreum) in Lake Erie. Canadian Journal of Fisheries and Aquatic Sciences 51:986–997. Henderson, B. A., J. L. Wong, and S. J. Nepszy. 1996. Reproduction of walleye in Lake Erie: allocation of energy. Canadian Journal of Fisheries and Aquatic Sciences 53:127–133. Ickes, B. S. 2000. Walleye (Stizostedion vitreum) and sauger (S. canadense): an analysis of habitat use, spawning ecology, and recruitment mechanisms in Lake Pepin, Minnesota. Master’s thesis, University of Minnesota, St. Paul. Johnston, T. A. 1997. Within-population variability in egg characteristics of walleye (Stizostedion vitreum) and white sucker (Catostomus commersoni). Canadian Journal of Fisheries and Aquatic Sciences 54: 1006–1014. Mace, P. M. 1994. Relationships between common biological reference points used as thresholds and targets for fisheries management strategies. Canadian Journal of Fisheries and Aquatic Sciences 51:110– 122. Marteinsdottir, G., and K. Thorarinsson. 1998. Improving the stock-recruitment relationship in Icelandic


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