Transactions of the American Fisheries Society 136:979–987, 2007 Ó Copyright by the American Fisheries Society 2007 DOI: 10.1577/T06-106.1
[Note]
Predictive Morphometric Relationships for Estimating Fecundity of Sea Lampreys from Lake Champlain and Other Landlocked Populations STEPHEN J. SMITH*1
AND
J. ELLEN MARSDEN
Rubenstein School of Environment and Natural Resources, University of Vermont, Aiken Center, 81 Carrigan Drive, Burlington, Vermont 05405, USA Abstract.—Landlocked populations of sea lamprey Petromyzon marinus are a nuisance in the Great Lakes and other lakes in the northeastern United States. In an effort to optimize control efforts, recent research has focused on creating population models to test various control scenarios. Accurate population parameters, such as fecundity, are an important component of these models. To determine the fecundity of sea lampreys in Lake Champlain, we sampled 29 female sea lampreys (mean length ¼ 456 mm [range ¼ 364–550 mm]; mean total wet weight ¼ 173.8 g [range ¼ 96.5–321.4 g]). The estimated fecundity of our sample was 67,660 6 6,870 eggs/ female (95% confidence interval around the mean). To determine the best morphometric indices to estimate fecundity, we examined the relationships between fecundity and length, weight, length and gonadosomatic index (GSI), weight and GSI, and several additional morphological measures. Regression analyses were also conducted on length, weight, and GSI data from previously published studies on sea lamprey fecundity in other landlocked populations. The wet weight of the female was a significant predictor of fecundity in all cases. The predictive ability of a model based on wet weight was improved with the addition of GSI. The addition of nine other morphometric measures did not improve the fit of the model based on wet weight alone.
Sea lampreys Petromyzon marinus are a nuisance species in Lake Champlain and the Great Lakes. Currently, sea lamprey control is dependent on (1) instream barriers to prevent spawning lampreys from migrating upstream and (2) chemical methods, which involve the application of lampricides 3-trifluoromethyl-4-nitrophenol (TFM) and 2 0 5-dichloro-4 0 nitrosalicylanilide (5% granular Bayluscide) to kill larvae before they become parasitic (Christie and Goddard 2003). Concerns about cost, safety, and potential of sea lampreys to develop resistance to chemical pesticides have guided researchers to explore alternative control methods (Christie and Goddard 2003). These efforts have led to the development of population models to * Corresponding author:
[email protected] 1 Present address: U.S. Fish and Wildlife Service, Lake Chamaplain Fish and Wildlife Resources Office, 11 Lincoln Street, Essex Junction, Vermont 05452, USA.. Received May 4, 2006; accepted February 3, 2007 Published online June 7, 2007
predict sea lamprey population responses to different treatment options and help managers make informed decisions in regard to control options (Jones et al. 2003; Howe et al. 2004). However, the utility of these models is dependent upon the accuracy of population parameter estimates. The accurate estimation of the fecundity of female sea lampreys in each region is necessary for these population models, as fecundity influences the ability of populations to respond to control efforts but varies considerably both within and among geographic regions (Applegate 1950; Vladykov 1951; Wigley 1959; Manion 1972). The relationship between fecundity and female size within lake systems varies, a phenomenon that is largely due to the timing of collection, as females become shorter as they mature (Wigley 1959; Manion and McLain 1971). It is unknown how or in what sections of the body the loss of length occurs. Potentially, the loss of length could be accounted for by measuring morphometric characteristics other than total length. Partitioning the female into ‘‘sections’’ that can be measured—such as anterior length (from oral disk to insertion of first dorsal), length of the first dorsal fin, length of the gap between the dorsal fins, or posterior length (from vent to tip of caudal fin)—may aid in identifying the loss in length. Finding a body segment whose length accurately predicts fecundity throughout the spawning season would be helpful in further improving sea lamprey population models. A number of factors could account for the differences in the fecundity of female sea lampreys among lake systems, including differences among populations and the systems they inhabit, population size (which changes through time as a result of sea lamprey control), and prey base (Smith 2006). Sea lampreys in Lake Champlain are genetically distinct from those in the upper Great Lakes, presumably as a consequence of isolation (Waldman et al. 2004; Bryan et al. 2005). This genetic difference may also contribute to differences in fecundity between Lake Champlain sea lampreys and those from other landlocked populations. Comparing our estimates of the relationship between morphometric characteristics
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FIGURE 2.—Morphological measurements recorded from female sea lampreys.
FIGURE 1.—Map of Lake Champlain indicating the locations at which sea lampreys were collected.
and fecundity to relationships observed in previous studies on other landlocked populations could demonstrate a need for separate fecundity estimators for different lake systems. We used historical data, field collections, and laboratory work to determine the factors that most accurately predicted female sea lamprey fecundity in landlocked populations. The objectives of this study were to (1) determine the fecundity of female sea lampreys in Lake Champlain, (2) determine the morphometric characteristics that most accurately predict fecundity, and (3) compare our relationships between morphometric characteristics and fecundity with those from previously studied lake systems. Methods Sea lampreys were collected from six tributaries in three of the five basins of Lake Champlain during the spring spawning migration in 2005: Malletts Creek (seven), Lewis Creek (four), Great Chazy River (five), Stone Bridge Brook (nine), Trout Brook (two), and Beaver Brook (one; Figure 1). Because sea lampreys do not home to natal streams (Bergstedt and Seelye
1995; Howe et al. 2006), the females sampled from this set of tributaries were assumed to be a representative sample of sea lampreys in Lake Champlain. Collections were not made from tributaries in Missisquoi Bay or South Lake basins because, while there are tributaries located there in which sea lampreys spawn, these basins are largely uninhabited by sea lampreys during much of the year owing to their temperature preferences. Collections were made between 25 April and 23 May by the U.S. Fish and Wildlife Service (USFWS) as part of annual control efforts and by removing sea lampreys by hand from a low waterfall on Lewis Creek on 5 May. After collection, some of the sea lampreys (19) were held in a live-cage (approximately 2 3 2 3 2 m in size in about 1 m of water) in Mallett’s Creek for 0–13 d. Lamprey densities in the live-cage were probably slightly higher than those normally found in the field but did not exceed approximately 25/m2. When collections were made from the live-cage, female sea lampreys were haphazardly selected and returned to the laboratory where they were euthanatized with an overdose of tricaine methanesulfonate (MS-222). A series of morphological measurements was recorded for each female sea lamprey: total wet weight (60.1 g), total length, length from the anterior tip of the oral disk to the insertion of the first dorsal fin, length from the oral disk to the end of the first dorsal fin, length from the oral disk to the insertion of the second dorsal fin, and length from the vent to the end of the caudal fin (61 mm; Figure 2). Three additional length measures were calculated from the recorded data—length of the first dorsal fin, length of the gap between dorsal fins, and pre-vent length (61 mm)— and a Fulton-type condition factor (K) was calculated as follows: K ¼ total wet weight=length3 : Condition factor was calculated as a relative index of fish shape to describe whether lampreys were relatively ‘‘thick’’ or ‘‘thin’’ for their length. Similar calculations
NOTE
have been used to describe the condition of metamorphosing larval sea lampreys (Holmes and Youson 1993; Youson et al. 1993). The gravimetric method was used to estimate fecundity (Applegate 1950; Bagenal and Braum 1978; Snyder 1983) by dissecting out the entire ovary for each lamprey and obtaining a wet weight (60.01 g) of the ovary on a digital balance. The gonadosomatic index (GSI), a potentially important variable in describing the maturity of the female, was calculated as follows: gonad wet weight 100: GSI ¼ somatic we body weight We developed a new method to rapidly separate eggs from connective tissue and used methods by C ¸ ek and Go¨kc¸e (2005) to rapidly enumerate eggs. To obtain the mean number of eggs per gram, three subsamples of approximately 1 g (60.01 g) were taken from each ovary and preserved in a 10% solution of formalin. Samples were later removed from formalin, blotted dry, and reweighed (60.0001 g). Each sample was then divided in half to create duplicate samples, and each half was weighed (60.0001 g). One of the two halves was returned to a 10% solution of formalin and archived; the other was placed in a 38% solution of hydrochloric acid (HCl) for approximately 18 h to dissolve connective tissue. Eggs were removed from the HCl and rinsed several times with water. To confirm that the acid was not dissolving the eggs, a known number of eggs was placed in HCl for 24 h then recounted. This method of using HCl was developed to accelerate the sample processing time. The traditional method of using Gilson’s fluid to separate eggs from connective tissue (Bagenal and Braum 1978; Snyder 1983) requires a significant amount of time, often several months (Nitschke et al. 2001; Roberts and Grossman 2001). Harsh chemicals, such as potassium hydroxide, have been shown to be effective at separating eggs from connective tissue in the rock lobster Jasus edwardsii (Bycroft 1986). After eggs were free of connective tissue, the photocopy method (C ¸ ek and Go¨kc¸e 2005) was used to enumerate eggs from each sample. Eggs were placed into a petri dish containing water and allowed to settle to the bottom. Care was taken to ensure that eggs were well spaced and not clustered together or overlapping. A photocopier was used to create enlarged (200%) copies of the petri dish view. Eggs were clearly visible as dark circles on photocopies, and easily enumerated using a felt-tipped pen and a standard laboratory counter. C ¸ ek and Go¨kc¸e (2005) confirmed the accuracy
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of the photocopy method for counting eggs of the rosy barb Puntius conchonius. We checked our application of the method by counting a subsample of sea lamprey eggs (N ¼ 1,114) both by the photocopy method and by an actual count of eggs using a stereomicroscope. The two counts differed by less than 1%. Statistical methods.—All analyses were conducted using JMP software (SAS Institute 2004). Data were checked for normality; as all data were normal, no transformations were needed. Mean number of eggs per gram and standard deviation were calculated for the three subsamples from each female. Total fecundity was calculated as the mean number of eggs per gram of each female multiplied by the total ovary weight of that female. A 95% confidence interval (CI) was calculated for the eggs per gram estimate for each female and extrapolated to the total estimate of fecundity. Correlation among independent variables was analyzed using a scatterplot matrix. Due to the nature of the morphological data, many of the variables were highly correlated (i.e., correlation values were . 0.8; Table 1). Rotated components analysis was used to transform the original variables to a specified number of component variables. The component variables are uncorrelated and can be used in multiple-regression analysis. Akiake’s information criterion (AIC) was used to determine the best model—length, weight, length and GSI, weight and GSI, or the rotated components—for predicting fecundity. Least-squares multiple-regression analyses of length, weight, length and GSI, and weight and GSI were conducted on data obtained from the literature (Applegate 1950; Vladykov 1951; Wigley 1959; Manion 1972) so that the results could be compared with those derived from current data from Lake Champlain. These are the only available published records of the fecundity of female sea lampreys in landlocked populations and may not completely represent the lakes from which the samples were collected; additional details of the collections are summarized in Smith (2006). Applegate (1950) collected all specimens in traps in the Ocqueoc River and Carp Creek, Lake Huron, during the spawning migration of 1947. Vladykov (1951) collected specimens from Thessalon River, North Channel (Lake Huron), in 1948, and Hibbards Creek, Lake Michigan, in 1948. Wigley (1959) collected specimens from Cayuga Inlet, Cayuga Lake, in 1951. Manion (1972) collected his specimens from the Chocolay River, Lake Superior, in 1960. Sea lamprey abundance was at an all-time high in each of the lakes at the time of each study with the exception of Lake Michigan, where in 1948 the sea lamprey population was increasing (Smith 1971; Heinrich et al. 1980; Smith and Tibbles 1980).
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TABLE 1.—Correlations among the suite of measured variables and the rotated principle components in an analysis of sea lamprey fecundity. Lengths are in millimeters, weights in grams; GSI ¼ gonadosomatic index. Variable 1 Total length Length to first dorsal fin Length to end of first dorsal fin
Length to second dorsal fin
Length from tail to vent
GSI
Length of first dorsal fin
Length of gap between dorsal fins
Pre-anal vent length
Rotated components 1
2
Variable 2
Correlation
P-value
Weight Weights Total length Weight Total length Length to first dorsal fin Weight Total length Length to first dorsal fin Length to end of first dorsal fin Weight Total length Length to first dorsal fin Length to end of first dorsal fin Length to second dorsal fin Weight Total length Length to first dorsal fin Length to end of first dorsal fin Length to second dorsal fin Length from tail to vent Weight Total length Length to first dorsal fin Length to end of first dorsal fin Length to second dorsal fin Length from tail to vent GSI Weight Total length Length to first dorsal fin Length to end of first dorsal fin Length to second dorsal fin Length from tail to vent GSI Length of first dorsal fin Weight Total length Length to first dorsal fin Length to end of first dorsal fin Length to second dorsal fin Length from tail to vent GSI Length of first dorsal fin Length of gap between dorsal fins
0.88 0.86 0.96 0.88 0.98 0.98 0.88 0.98 0.99 0.99 0.34 0.51 0.41 0.41 0.45 0.17 0.09 0.01 0.00 0.03 0.34 0.79 0.86 0.78 0.88 0.84 0.33 0.01 0.25 0.34 0.33 0.24 0.36 0.44 0.20 0.05 0.80 0.83 0.84 0.86 0.84 0.06 0.12 0.78 0.11
0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.068 0.005 0.026 0.027 0.014 0.365 0.638 0.961 0.984 0.874 0.067 0.001 0.001 0.001 0.001 0.001 0.076 0.950 0.189 0.068 0.083 0.215 0.055 0.018 0.286 0.803 0.001 0.001 0.001 0.001 0.001 0.742 0.536 0.001 0.563
Weight Total length Length to first dorsal fin Length to end of first dorsal fin Length to second dorsal fin Length from tail to vent GSI Length of first dorsal fin Length of gap between dorsal fins Pre-anal vent length Weight Total length Length to first dorsal fin Length to end of first dorsal fin Length to second dorsal fin Length from tail to vent GSI Length of first dorsal fin Length of gap between dorsal fins Pre-anal vent length Rotated component 1
0.88 0.94 0.94 0.97 0.95 0.23 0.04 0.91 0.13 0.94 0.11 0.17 0.21 0.09 0.21 0.23 0.15 0.25 0.96 0.05 0.00
0.001 0.001 0.001 0.001 0.001 0.227 0.822 0.001 0.494 0.001 0.576 0.368 0.285 0.655 0.273 0.231 0.429 0.184 0.001 0.794 1.000
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TABLE 1.—Continued. Variable 2
Correlation
P-value
Weight Total length Length to first dorsal fin Length to end of first dorsal fin Length to second dorsal fin Length from tail to vent GSI Length of first dorsal fin Length of gap between dorsal fins Pre-anal vent length Rotated component 1 Rotated component 2 Weight Total length Length to first dorsal fin Length to end of first dorsal fin Length to second dorsal fin Length from tail to vent GSI Length of first dorsal fin Length of gap between dorsal fins Pre-anal vent length Rotated component 1 Rotated component 2 Rotated component 3
0.41 0.04 0.01 0.01 0.01 0.14 0.55 0.06 0.03 0.05 0.00 0.00 0.18 0.26 0.16 0.17 0.19 0.93 0.25 0.20 0.20 0.31 0.00 0.00 0.00
0.027 0.852 0.967 0.961 0.944 0.485 0.002 0.772 0.864 0.810 1.000 1.000 0.339 0.173 0.418 0.365 0.313 0.001 0.183 0.306 0.300 0.101 1.000 1.000 1.000
Variable 1 3
4
Analysis of variance (ANOVA) with fecundity per individual mass as the dependent variable and lake as a factor was conducted to test for differences in fecundity among lakes. Tukey’s honestly significant difference (HSD) test was used to determine where the difference, if present, occurs. Analysis of covariance (ANCOVA) with estimated fecundity as the dependent variable and female weight as the independent variable with lake as a covariate was used to test for differences in the fecundity–mass relationship between lakes. Tukey’s HSD test was used to determine differences in the fecundity–mass relationships among lakes. Results Twenty-nine sea lampreys were used in our analysis (mean length ¼ 456 mm [range ¼ 364–550 mm]; mean
total wet weight ¼ 173.8 g [range ¼ 96.5–321.4 g]). The estimated fecundity of our sample was 67,660 6 6,870 eggs/female (95% CI around the mean). The egg counts from the three 0.5-g samples of each ovary had a coefficient of variation (CV ¼ 100 3 SD/mean)of 0.029 6 0.009, indicating that these counts were highly precise. Four components were created in the rotated components analysis because the first four principal components explained over 98% of the variation in the original variable list and there was a visible gap in the eigenvalues after the fourth component (Renchner 2002). Loading values (Table 2) indicate that component one was loaded on variables associated with size as described by total weight and the length measures, component two was loaded on the length of the gap
TABLE 2.—Rotated component loadings in an analysis of sea lamprey fecundity. Component Variable
1
2
3
4
Weight Total length Length to first dorsal fin Length to end of first dorsal fin Length to second dorsal fin Length from tail to vent Condition factor Length of first dorsal fin Length of gap between dorsal fins Pre-anal vent length
0.8767 0.9444 0.9415 0.9749 0.9537 0.2314 0.0197 0.9073 0.1321 0.9439
0.1083 0.1735 0.2053 0.0867 0.2103 0.2296 0.0390 0.2540 0.9615 0.0507
0.4115 0.0361 0.0080 0.0096 0.0136 0.1351 0.9900 0.0563 0.0332 0.0466
0.1840 0.2603 0.1563 0.1747 0.1941 0.9345 0.1251 0.1968 0.1992 0.3105
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TABLE 3.—Comparison of models of Lake Champlain sea lamprey fecundity. Abbreviations are as follows: AIC ¼ Akaike information criterion, DAIC ¼ the difference between the AIC value of the model of interest and that of the best model, GSI ¼ gonadosomatic index, and RC ¼ rotated component. Model
df
Adjusted R2
AIC
DAIC
Weight and GSI Weight Length, weight, and GSI Length and weight Length and GSI RC 1, RC 2, RC 3, and RC 4 All variables (length, weight, GSI, RC 1, RC 2, RC 3, and RC 4) Length
26 27 25 26 26 24
0.7161 0.6849 0.7132 0.674 0.6664 0.6689
534.81 536.93 537.97 538.82 539.49 540.95
0 2.12 3.16 4.01 4.68 6.14
21 27
0.6873 0.5038
541.42 550.1
6.61 15.29
between dorsal fins, component three was loaded on weight and condition factor, and component four was loaded on pre- and post-anal vent length. Using the information-theoretic approach to determine the best model for predicting fecundity revealed that the bivariate model with weight and GSI had the most support (Table 3). Comparisons between the AIC values of the other models and that of the best model indicate that there is considerable support for all of the other models tested except that with length alone. Length, weight, length and GSI in combination, and weight and GSI in combination were all highly significant predictors of estimated fecundity in previous reports of the fecundity of sea lampreys, with the exception of Lake Superior (for which length was not a good predictor) and the North Channel (for which weight and GSI were not good predictors; Table 4). Analysis of variance with fecundity per individual mass revealed significant differences among lakes with
regard to fecundity per individual mass (F ¼ 16.94, df ¼ 5, P 0.001). Tukey’s HSD test indicated that Lake Champlain fecundity is not significantly different from that of Lakes Superior and Huron and the North Channel; additional comparisons are summarized in Table 5. Analysis of covariance with estimated fecundity as the dependent variable, female weight as the independent variable, and lake as a covariate revealed significant differences among lakes with regard to the relationship between fecundity and female weight (F ¼ 78.89, df ¼ 7, P 0.0001). Tukey’s HSD test indicated that the Lake Champlain fecundity– female weight relationship is not significantly different from that of Lakes Michigan and Superior and the North Channel; the fecundity–weight relationship is similar among Lakes Michigan and Huron, Cayuga Lake, and the North Channel; all other comparisons among lakes showed significant differences (Table 6). The data also indicate that a sample of 29 sea
TABLE 4.—Relationships between sea lamprey fecundity and relevant variables from previous studies (see text). Abbreviations are as follows: TL ¼ total length, GSI ¼ gonadosomatic index, and W ¼ weight. Lake Cayuga (N ¼ 29) Huron (N ¼ 70) Michigan (N ¼ 10) Huron (North Channel) (N ¼ 10) Superior (N ¼ 29)
Model
Adjusted R2
P-value
332(TL) 85,496 289(TL) þ 2,018(GSI) 98,640 260(W) þ 7,351 226(W) þ 1,805(GSI) 14,500 300(TL) 67,895 297(TL)þ 466(GSI) 77,118 257(W) þ 15,293 253(W) þ 282(GSI) þ 10,051 296(TL) 43,319 338(TL)þ 1,079(GSI) 94,866 285(W) þ 26,677 278(W) þ 315(GSI) þ 16,856 357(TL) 80,970 373(TL)þ 528(GSI) 100,452 246(W) þ 22,488.6 278(W) þ 911(GSI) 4,970 100(TL) þ 28,002 241(TL) þ 1,027(GSI) 51,124 104(W) þ 52,113 163(W) þ 777(GSI) þ 26,109
0.7356 0.8633 0.7601 0.8574 0.6694 0.7137 0.7703 0.7846 0.6578 0.9012 0.7465 0.7363 0.6544 0.6245 0.4612 0.4361 0.0681 0.3568 0.1582 0.3704
0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0027 0.0001 0.0008 0.0039 0.0028 0.0135 0.0184 0.0559 0.0922 0.0012 0.0187 0.0009
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TABLE 5.—Results of multiple comparison test to identify differences in the fecundity per unit mass of individual sea lampreys between lakes. Means with different lowercase letters are significantly different. Lake
N
Michigan Superior Huron (North Channel) Champlain Huron Cayuga
10 29 10 29 70 29
Least-squares mean 548.5 463.5 420.2 397.3 353.8 316.1
z zy yx yx xw w
lampreys was sufficient to obtain a reasonable estimate of fecundity because the variance of fecundity estimates is not significantly different between the Lake Champlain sample and the Lake Huron sample (N ¼ 70; F ¼ 0.908, P 0.399). Discussion The use of the information-theoretic approach and AIC shows that the best model for predicting fecundity for Lake Champlain is a bivariate model including the wet weight of the female and GSI (Table 3). The analysis also suggests that there is strong evidence of support for a model based on wet weight alone and considerable support for all of the predictive models that were tested, with the exception of total length alone. Previous studies have focused on predicting fecundity from length (Applegate 1950; Wigley 1959) or simply reporting average fecundity (Vladykov 1951; Manion 1972), even when weight data were available. Sea lampreys change in length and weight as the spawning season progresses (Manion and McLain 1971), therefore the timing of collection has an effect on the estimate of fecundity based on length or weight (Manion 1972). An effort was made in this study to sample sea lampreys throughout the spawning season in an attempt to account for the change in length and weight through a series of morphological measurements; however, sampling may not have been continued late enough into the spawning season to capture a significant loss of length in any sections of the body. Consequently, observation of any significant changes in the length of body sections in relationship with time of collection or increasing GSI did not occur. Including the measurement of most of these morphological characters does not increase the predictive capabilities of a fecundity estimator for Lake Champlain based on wet weight of the female alone. The AIC analysis indicates that the best model uses female weight and the covariate GSI to predict fecundity. To obtain GSI, females must be dissected and the ovary weighed. The predictive model based on weight alone has strong
TABLE 6.—Results of multiple comparison test to identify differences in the relationship between fecundity, weight, and the gonadosomatic index between lakes. Means with different lowercase letters are significantly different. Lake
N
Superior Champlain Michigan Huron (North Channel) Huron Cayuga
29 29 10 10 70 29
Least-squares mean 69,522 69,005 64,270 59,995 57,182 52,930
z z zy zy y y
support, which may be adequate for management agencies that may not have the resources for a more detailed and labor-intensive analysis of fecundity. For perspective, a linear regression of the relationship between wet weight and estimated fecundity explained 68% of the variation in fecundity compared with a model containing the transformation of 10 measured variables, which explains 63% of the variation in estimated fecundity (Smith 2006). The gonadosomatic index, in combination with total wet weight, explains 72% of the variability in estimated sea lamprey fecundity in Lake Champlain. The fecundity of Lake Champlain sea lampreys is comparable to that of other landlocked populations; however, differences in fecundity and its relationship with weight and GSI among landlocked populations do exist (Figure 3). Sea lampreys in Lakes Superior and Champlain have significantly higher fecundities than those in Lake Huron and Cayuga Lake (Table 6). The magnitude of these differences suggests that any studies of sea lamprey population dynamics in a new system should include an analysis of fecundity. Further analysis shows that there are also differences in the
FIGURE 3.—Mean weight and fecundity of sea lampreys collected in different lakes. The error bars represent 95% confidence intervals.
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fecundity per unit mass of individuals among lakes (Table 5). For example, the number of eggs per gram of female sea lamprey in Lake Champlain is significantly different from the number of eggs per gram of female sea lamprey in Lake Michigan, Lake Huron, and Cayuga Lake. Examination of the historical data suggests that the availability of food resources is an important factor affecting fecundity. Lake trout are the preferred prey of sea lampreys, and although alternative species may be used as prey, lake trout population abundance has been closely monitored and therefore serves as an appropriate proxy for resource availability. Lake trout population abundance fell to extremely low levels in all of the Great Lakes near the time sea lamprey fecundity samples were taken (reviewed by Smith 2006). The highest fecundity estimate comes from Lake Superior when lake trout were still available, followed by Lake Michigan where the lake trout had just disappeared, Lake Huron where lake trout had disappeared just a year earlier, and finally the North Channel where lake trout had been absent for five years. The average size of the sea lampreys in each sample was not directly related to lake trout abundance, indicating that prey availability may affect fecundity independently of lamprey size (Smith 2006). Measurements of length, weight, and total ovary weight are all that are required to assess fecundity after the relationship between these variables and egg number is known for a given lake (Tables 3, 4). It is notable that collecting more morphological data will not increase the predictive capabilities of fecundity estimation, and a single 0.5-g sample of ovary per sea lamprey is sufficient to estimate fecundity accurately. Acknowledgments This study was supported by the Great Lakes Fisheries Commission. We thank Eric Howe for his assistance in collecting sea lamprey and his useful discussions, and Charles Goodnight for his assistance with the statistical analyses. We would also like to thank Wayne Bouffard and Bradley Young of the USFWS Lake Champlain Fish and Wildlife Resources Office for help in the field with lamprey collections, and Jennifer Fricke for her assistance in the laboratory. We thank Bradley Young, Charles Goodnight, and Donna Parrish for comments on early drafts of this manuscript. References Applegate, V. C. 1950. Natural history of the sea lamprey in Michigan. U.S. Fish and Wildlife Service Special Science Report Fisheries 55. Bagenal, T. B., and E. Braum. 1978. Eggs and early life
history. Pages 165–201 in T. B. Bagenal, editor. Methods for assessment of fish production in freshwaters, 3rd edition. Blackwell Scientific Publications, Oxford, UK. Bergstedt, R. A., and J. G. Seelye. 1995. Evidence for lack of homing by sea lampreys. Transactions of the American Fisheries Society 124:235–239. Bryan, M. B., D. Zalinski, K. B. Filcek, S. Libants, W. Li, and K. T. Scribner. 2005. Patterns of invasion and colonization of sea lamprey (Petromyzon marinus) in North America as revealed by microsatellite genotypes. Molecular Ecology 14:3757–3773. Bycroft, B. L. 1986. A technique for separating and counting rock lobster eggs. New Zealand Journal of Marine and Freshwater Research 20:623–626. C ¸ ek, S¸ ., and M. A. Go¨kc¸e. 2005. Evaluation of the photocopy method for counting Puntius conchonius’s eggs. Turkish Journal of Veterinary and Animal Science 29:685–689. Christie, G. C., and C. I. Goddard. 2003. Sea Lamprey International Symposium (SLIS II): advances in the integrated management of sea lamprey in the Great Lakes. Journal of Great Lakes Research 29(Supplement 1):1–14. Heinrich, J. W., J. G. Weise, and B. R. Smith. 1980. Changes in biological characteristics of the sea lamprey (Petromyzon marinus) as related to lamprey abundance, prey abundance, and sea lamprey control. Canadian Journal of Fisheries and Aquatic Sciences 37:1861–1871. Holmes, J. A., and J. H. Youson. 1993. Induction of metamorphosis in landlocked sea lampreys, Petromyzon marinus. Journal of Experimental Zoology 267:598–604. Howe, E. J., J. E. Marsden, and W. Bouffard. 2006. Movement of sea lamprey in the Lake Champlain basin. Journal of Great Lakes Research 32:776–787. Howe, E., J. E. Marsden, T. M. Donovan, and R. Lamberson. 2004. Stage-based population viability model for sea lamprey (Petromyzon marinus). Lake Champlain Basin Program, Technical Report 43, Grand Isle, Vermont. Jones, M. L., R. A. Bergstedt, M. B. Twohey, M. F. Fodale, D. W. Cuddy, and J. W. Slade. 2003. Compensatory mechanisms in Great Lakes sea lamprey populations: implications for alternative control strategies. Journal of Great Lakes Research 29(Supplement 1):113–129. Manion, P. J. 1972. Fecundity of the sea lamprey (Petromyzon marinus) in Lake Superior. Transactions of the American Fisheries Society 101:718–720. Manion, P. J., and A. L. McLain. 1971. Biology of larval sea lampreys (Petromyzon marinus) of the 1960 year-class isolated in the Big Garlic River, Michigan, 1960–1965. Great Lakes Fisheries Commission, Technical Report 16, Ann Arbor, Michigan. Nitschke, P., M. Mather, and F. Juanes. 2001. A comparison of length-, weight-, and age-specific fecundity relationships for cunner in Cape Cod Bay. North American Journal of Fisheries Management 21:86–95. Renchner, A. C. 2002. Methods of multivariate analysis, 2nd edition. Wiley, New York. Roberts, J. H., and G. D. Grossman. 2001. Reproductive characteristics of female longnose dace in the Coweeta Creek drainage, North Carolina, USA. Ecology of Freshwater Fish 10:184–190. SAS Institute. 2004. JMP, version 5.1.2. SAS Institute, Cary, North Carolina.
NOTE
Smith, B. R. 1971. Sea lampreys in the Great Lakes of North America. Pages 207–247 in M. W. Hardisty and I. C. Potter, editors. The biology of lampreys, volume 1. Academic Press, London. Smith, B. R., and J. J. Tibbles. 1980. Sea lamprey (Petromyzon marinus) in Lakes Huron, Michigan, and Superior: history of invasion and control, 1936–78. Canadian Journal of Fisheries and Aquatic Sciences 37:1780–1801. Smith, S. J. 2006. Fecundity of Lake Champlain sea lamprey (Petromyzon marinus) and factors affecting egg survival in and out of nests in Lake Champlain streams. Master’s thesis. University of Vermont, Burlington. Snyder, D. E. 1983. Fish eggs and larvae. Pages 167–197 in L. A. Nielsen and D. L. Johnson, editors. Fisheries techniques. American Fisheries Society, Bethesda, Maryland.
987 Vladykov, V. D. 1951. Fecundity of Quebec lampreys. Canadian Fish Culturist 10:1–14. Waldman, J. R., C. Grunwald, N. K. Roy, and I. I. Wirgin. 2004. Mitochondrial DNA analysis indicates sea lampreys are indigenous to Lake Ontario. Transactions of the American Fisheries Society 133:950–960. Wigley, R. L. 1959. Life history of the sea lamprey of Cayuga Lake, New York. U.S. Fish and Wildlife Service Fishery Bulletin 59:561–613. Youson, J. H., J. A. Holmes, J. A. Guchardi, J. G. Seelye, R. E. Beaver, J. E. Gersmehl, S. A. Sower, and F. W. H. Beamish. 1993. Importance of condition factor and the influence of water temperature and photoperiod on metamorphosis of sea lamprey, Petromyzon marinus. Canadian Journal of Fisheries and Aquatic Sciences 50:2448–2456.
