Transactions of the American Fisheries Society 135:801–810, 2006 Ó Copyright by the American Fisheries Society 2006 DOI: 10.1577/T05-082.1
[Article]
Geographic Variation in Somatic Growth of Redside Shiner DEREK D. HOUSTON*1 Department of Integrative Biology, Brigham Young University, Provo, Utah 84602, USA
MARK C. BELK Department of Integrative Biology, Brigham Young University, Provo, Utah 84602, USA Abstract.—The geographic variation in growth rate and resulting body size is poorly known for most fish species. In this paper, we used data derived from otoliths to describe patterns of growth in redside shiners Richardsonius balteatus from seven native populations across the southern portion of its range, and we compare growth with latitude, elevation, and growing season to determine which of these environmental factors best predicts the growth patterns among these populations. To determine whether observed differences in growth resulted from environmental or genetic variation, we conducted a common-environment experiment on fish from three of the seven populations that showed contrasting patterns between latitude and length of growing season. Redside shiners exhibited about a 60% difference in size at age among populations in their natural environments. Growing season length was the best predictor of body size among these populations (Akaike weight ¼ 0.78). In a common environment, temperature-specific growth rates differed among populations, indicating that some of the observed differences in size at age among populations are genetically based. Although populations of redside shiners with shorter growing seasons exhibited higher field growth rates than expected, the pattern of variation in individual growth rates among populations in a common environment was not consistent with either a countergradient variation model or a local adaptation model of growth.
Body size is an important predictor of fitness in many organisms (Calder 1984). Large body size can be advantageous to an individual in terms of competitive interactions (Arendt 1997; Arendt and Wilson 1999), fecundity (Atkinson 1994), predator escape (in species that rely on speed for escape [Fuiman 1993; Katzir and Camhi 1993] or where predation is gape limited [Belk 1998]), and overwinter survival (Conover 1990; Thompson et al. 1991; Childs and Clarkson 1996; Johnson and Evans 1996; Bernard and Fox 1997). In organisms with indeterminate growth, selection for large body size may result in selection for increased intrinsic growth rate. Therefore, intrinsic growth rate may be considered as a key life history trait similar to fecundity and age and size at maturity (Arendt 1997). Growth of ectotherms is primarily influenced by food availability and temperature (Mills 1988). Temperature is primarily a product of elevation, latitude, and geographic location. Combinations of these factors produce different growth environments, some of which may be poor for growth (e.g., a low-latitude body of water at high elevation may still have a short growing season). Higher intrinsic growth rates should evolve * Corresponding author:
[email protected] 1 Present address: Department of Biological Sciences, University of Nevada, Las Vegas, Nevada 89154-4004, USA Received March 14, 2005; accepted December 20, 2005 Published online June 6, 2006
when conditions do not favor growth, but fitness increases with body size (Arendt 1997). Given the strong influence of body size and growth rate on life history, it is somewhat surprising that the geographic variation in growth rate and resulting body size is poorly known for most species. Even in fishes, where body size is routinely measured as a part of management activities (usually limited to game fish, or to threatened or endangered species), variation in body size is poorly known for all but a few large-bodied species (Belk and Houston 2002). However, information about variation both within and among populations is necessary to understand evolutionary history and effects of management activities on native fish species (Ruckelshaus et al. 2002), and discerning large-scale ecological and evolutionary patterns is dependent on accurate data from multiple populations of a variety of species. Differences in growth among populations may result from environmental or genetic variation or an interaction between genes and environment. There are two models that attempt to explain geographic variation in intrinsic growth rates among populations; they are (1) the local adaptation model (Levinton 1983), which is characterized by each population exhibiting the highest growth rates under conditions that most closely reflect those of the populations’ natural environment, and (2) the adaptation to growing season model (Conover 1990), which is characterized
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by countergradient variation (CnGV) for growth where a population that experiences a shortened growing season with respect to another exhibits higher growth rates at all temperatures (Conover and Schultz 1995). Conover (1990) suggested that CnGV evolves in response to length of growing season; however, many studies investigating this phenomenon have used latitude as a surrogate for growing season length (Jensen and Johnsen 1986; Williamson 1986; Conover and Present 1990; Schultz et al. 1996; Conover et al. 1997; Jonassen et al. 2000). In certain regions, such as western North America, it is possible to have populations that differ in latitude but not in length of growing season because of other factors, such as elevation, geographic layout, or photoperiod. Thus, organisms that live in such systems provide an opportunity to test for congruence to predictions of the above models and to test for the independent effects of environmental factors on individual growth rates. The redside shiner Richardsonius balteatus is a small cyprinid native to western North America (Lee et al. 1980). The species is abundant throughout its range, but patterns of variation in age, growth, and other life history traits are poorly understood. Some information on age and growth of redside shiner has been reported in previous studies (Weisel and Newman 1951; Lindsey 1953; Larkin and Smith 1954; Crossman 1959; Lindsey and Northcote 1963). However, these data do not provide reliable estimates of size at age for specific populations because they give only general descriptive statistics (e.g., maximum body size or size range of adult shiners) or are based on lengthfrequency distributions rather than on more reliable methods, such as counting annuli on scales or otoliths. Thus, as is true for many species, there are few published growth curves for redside shiner that would allow spatial or temporal comparisons of variation in age and growth patterns. In this study, we evaluated the variation in somatic growth among several native populations of redside shiner and the variation in growth among selected populations in a common environment. The first objective was to describe patterns of growth and longevity in redside shiner from seven native populations across the southern portion of its range and to determine the relationship between annual somatic growth and latitude, elevation, and length of growing season. The second objective was to determine whether differences in somatic growth rates among populations result from environmental or genetic variation among populations and whether patterns correspond to predictions of current models.
Methods Study areas.—The redside shiner is distributed among a wide variety of aquatic systems, from small streams to large rivers and from small ponds to large lakes and reservoirs (Lindsey 1953; Larkin and Smith 1954; Lee et al. 1980). Within these systems, redside shiners occupy shallow or nearshore environments (Larkin and Smith 1954; M. C. Belk, unpublished). Our study areas encompass the wide range of aquatic systems where redside shiner occurs, but specific habitats it occupies within each general location are relatively consistent among study sites. Redside shiners were collected from seven native populations located in Utah, Idaho, and Wyoming (Table 1). Six of seven study locations are streams or rivers with flowing water. The largest is the Snake River collection site. Redside shiners were collected from marginal habitats in the main channel of the river about 8 km southeast of Pingree, Idaho. The Snake River exhibits a natural meander pattern in this area, and channel width varies from about 50 to 150 m. The Sevier River and East Fork Sevier River locations are separated by Piute Reservoir and are about 50 km apart. In both locations, the river is medium sized (about 5–10 m in width) with moderate gradients (0.2– 2.0%). Redside shiners were collected from the main channel at both of these locations. Main Creek, Sulphur Creek, and Badger Creek are all small streams (about 1.5–3.5 m in width) with relatively low gradients (,1%). Redside shiners were collected in deeper lowvelocity pool habitats in these locations. Little Reservoir is a small impoundment in the South Fork Beaver River system. Redside shiners were collected from shallow shoreline habitats in the reservoir. Field growth estimates.—We collected redside shiners with either a backpack electroshocker or a 3mm-mesh seine. Redside shiners were abundant and fish communities were similar among locations (dominant co-occurring species included brown trout Salmo trutta, mottled sculpin Cottus bairdii, mountain sucker Catostomus platyrhynchus, rainbow trout Oncorhynchus mykiss, and speckled dace Rhinichthys osculus). We euthanized individuals with an overdose of tricaine methanesulfonate (MS-222), measured standard length (mm) and mass (mg), and removed otoliths (lapillae). To determine the age and growth history of each fish, we prepared thin sections of otoliths and measured presumptive annuli (opaque rings) in a manner similar to that of Johnson et al. (1995). Two independent counts of rings were made on each otolith. If age estimates differed, both readers reexamined the otoliths
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TABLE 1.—Seven native redside shiner populations from Utah, Idaho, and Wyoming used to obtain field growth estimates, along with the environmental variables of latitude, elevation, and estimated growing season length 6 SD. Populations are ranked by latitude (1 ¼ furthest north), elevation (1 ¼ highest), and growing season length (1 ¼ shortest). Population rankings are given in the column to the right of the variable by which they are ranked. Population
Latitude and longitude
Rank
Badger Creek, Garfield County East Fork Sevier River, Piute County Little Reservoir, Beaver County Main Creek, Wasatch County Sevier River, Sevier County
37836 0 N, 112815 0 W
7
Elevation (m)
Rank
Growing season (degree-days)
Rank
Utah 2,388
1
1,159 6 137
1
0
0
5
1,827
4
2,513 6 170
5
0
0
6
2,231
2
1,189 6 133
3
0
0
3
1,731
5
1,690 6 122
4
0
0
38837 N, 112813 W
4
1,656
6
2,644 6 183
7
Snake River, Bingham County
43803 0 N, 112835 0 W
1
Idaho 1,330
7
2,553 6 181
6
Sulphur Creek, Uinta County
41815 0 N, 110855 0 W
2
Wyoming 2,057
3
1,186 6 148
2
38819 N, 112812 W 38815 N, 112829 W 40822 N, 111824 W
and reached a consensus on the number of annuli. Measurements of annuli radii along the longest axis (to the nearest 0.001 mm) were made from digital images of the thin-sectioned otoliths with SigmaScan Pro software (Jandel Scientific, Inc.). We did not include young-of-year fish because they had not yet completed an entire growing season and we did not have estimates of hatching date for each of the populations. We back-calculated length at age by means of a modified Fraser–Lee formula (Campana 1990), namely,
compared with variation observed among populations (60% difference in size-at-age 1). We used a marginal increment analysis to validate annuli on otoliths. The margin from the last visible ring to the tip of the otolith was measured for each thinsectioned otolith. We combined fish from all seven populations in this analysis (see Figure 1 and Table 2 for collection dates and sample sizes). By combining populations with potentially different growth rates, we might observe added variation in marginal increments depending on which population was sampled at a given
Lx ¼ LO þ ðLC LO ÞðRx RO Þ=ðRC RO Þ; Lx ¼ estimated standard length at age x; LC ¼ length at capture; Rx ¼ otolith radius at age x; RC ¼ otolith radius at capture; LO ¼ size at hatching (5 mm; Weisel and Newman 1951; Snyder 1981); RO ¼ otolith radius at hatching (estimated from otoliths to be 0.01 mm). In some fishes, egg size varies among individuals and with environment (Johnston and Leggett 2002) and, consequently, LO and RO might vary among locations or individuals. We do not have data on eggsize variation and consequent size at swim-up among these populations of redside shiner; however, such variation would have only a small effect on size-at-age estimates (e.g., a 50% increase in size at swim-up yields about a 4% increase in estimated size at age)
FIGURE 1.—Marginal increment analysis showing that rings on otoliths of native redside shiner populations from Utah, Idaho, and Wyoming are true annuli, forming once per year near the beginning of May. Population means of marginal increments are shown here, separated by collection date. Because juvenile fish grow more rapidly than adults, we have placed fish into two separate groups: ages 1–2 and ages 3–8.
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TABLE 2.—Estimates of mean size at age (cm) 6 SE back-calculated from otolith annuli for seven native populations of redside shiner located in Utah, Idaho, and Wyoming. Age Population Badger Creek East Fork Sevier River Little Reservoir Main Creek Sevier River Snake River Sulphur Creek
1
2
3
2.59 6 0.03 n ¼ 53 3.54 6 0.05 n ¼ 125 2.80 6 0.08 n ¼ 53 2.97 6 0.07 n ¼ 49 4.18 6 0.11 n ¼ 45 3.37 6 0.13 n ¼ 27 3.37 6 0.05 n ¼ 173
3.94 6 0.05 n ¼ 51 5.74 6 0.06 n ¼ 117 4.68 6 0.09 n ¼ 53 5.02 6 0.18 n ¼ 15 6.75 6 0.14 n ¼ 17 5.81 6 0.11 n ¼ 16 5.32 6 0.06 n ¼ 149
4.69 6 0.06 n ¼ 30 7.10 6 0.12 n ¼ 31 6.14 6 0.08 n ¼ 36 5.99 6 0.26 n¼5 7.37 6 0.49 n¼3 7.06 6 0.66 n¼3 6.27 6 0.11 n ¼ 54
time. However, the overall pattern should still result in a margin equal to zero at only one time of year if the rings are indeed true annuli. To characterize geographic variation in growth, we used a mixed-models procedure (Littell et al. 1996; Schaalje et al. 2002) to compare length at ages 1–3 among populations (we performed all statistical analyses with SAS 9.1). We did not compare size at older ages because some samples had low numbers of individuals that were older than age 3. We used correlation analysis to determine the relationship between mean size at age of populations and observed longevity. To determine environmental influences on the growth of redside shiners, we used three commonly cited environmental variables: latitude, elevation, and length of the growing season. Latitude and elevation were determined from U.S. Geological Survey (USGS) topographic maps. To estimate growing season length, we used recorded air and water temperatures to predict historic water temperatures at each site. Water temperature data were only available for a limited number of years at most locations; however, the National Oceanic and Atmospheric Administration (NOAA) database had a more complete record of air temperature data for each location. The water temperatures and air temperatures for the corresponding periods were significantly positively correlated (r ¼ 0.72; P , 0.0001). Therefore, we calculated estimates of growing season length for each location by creating a regression equation that matched available water temperatures with air temperatures for the corresponding time periods for each site. We used water temperature data recorded at the USGS gauging station nearest to each collection site and air temperatures
4
5
6
7
8
6 0.13 ¼ 12 6 0.13 ¼ 19 6 0.18 ¼ 11
5.88 6 0.15 n¼4 8.51 n¼1 7.54 6 0.26 n¼7
6.27 6 0.14 n¼2 8.95 n¼1 8.71 6 0.31 n¼3
6.53 n¼1
6.75 n¼1
9.83 n¼1
7.08 n¼1 8.54 n¼1 7.00 6 0.22 n ¼ 17
7.68 6 0.41 n¼5
7.83 6 0.41 n¼4
9.29 n¼1
5.22 n 7.73 n 6.88 n
obtained from NOAA stations located within 10 km of each sampling location to create the regression equation for each site (regression equations are available on request). Water temperature data were not complete for all years at most locations, but six of seven locations had a minimum of 10 years of water temperature data (no water temperature data were available for Main Creek). To obtain estimates for Main Creek, we placed two temperature loggers that were set to record temperatures every 45 min at different locations in Main Creek throughout the growing season of the year 2000. We used these data in conjunction with mean daily air temperatures for the same time period obtained from NOAA to create a regression equation for this location. Once we had created regression equations for each of the seven sites, we obtained mean monthly air temperatures from NOAA for the years 1939–2001. Air temperature data were incomplete for some years at some locations, but estimates for all sites included a minimum of 41 years of data. We used the regression equations to estimate mean monthly water temperatures at each location. We calculated growing season length as the mean annual cumulative degree-days above a water temperature of 88C since minimum temperature for growth of redside shiner is 88C as determined from common environment experiments (D. D. Houston, unpublished). We used a mixed-models approach that included elevation, latitude, and growing season length as covariates to test for their effect on length at ages 1– 3 combined (Littell et al. 1996; Schaalje et al. 2002). Elevation, latitude, and growing season are populationlevel variables, so given available sampling locations and the limited number of populations (i.e., 7), we could not evaluate interactions between environmental
REDSIDE SHINER SOMATIC GROWTH
variables (i.e., we are limited by degrees of freedom and available combinations), and even consideration of all three variables simultaneously provided poor resolution of effects because of the nonindependent nature of the three environmental variables in our sample. So, we used a model selection approach (Johnson and Omland 2004) to evaluate the environmental variable that best explained variation in size at age among populations. We set individual, population, and population-by-age interaction as random effects and then evaluated possible combinations of environmental covariates and their interaction with age as candidate models. The model that gave the lowest Akaike information criterion (AIC) score was selected as the best model (Burnham and Anderson 2002), and significance of fixed effects were evaluated from type III sums of squares analysis. In addition, we calculated the Akaike weight as a measure of the probability that the model chosen is the best model for the observed data (Johnson and Omland 2004). Redside shiners were collected from flowing-water habitats in six of the seven study locations (except Little Reservoir). In these locations, water temperatures measured in the main channel are an accurate representation of temperatures experienced by redside shiners because continual mixing of flowing water minimizes temperature differences among microhabitats (Hynes 1970). However, at the Little Reservoir site, water temperatures were not available from the standing water of the reservoir but were obtained from the outlet stream about 5 km below the reservoir. These measurements probably underestimate the length of the growing season for redside shiners collected from the shallow margins of Little Reservoir because of consistent water temperature differences between flowing and shallow standing water (Hynes 1970). To compensate for this expected difference, we added 38C to the predicted historic water temperatures for that site for calculation of growing season length. Adding from 2 to 58C does not change results of the analysis. Common environment.—To test for growth differences in a common environment, we collected youngof-year redside shiners from three of the field study locations (Badger Creek, Main Creek, and the Snake River) with either a 3-mm-mesh seine or backpack electroshocker. We used young-of-year fish to avoid confounding growth rate with energy allocated to reproduction. We chose these populations because they represented a wide range of latitude and growing season length, and growing season length was positively related to latitude (rather than the typical inverse relationship). This design provided a way to uncouple latitude and length of the growing season to determine the independent influence of each on
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somatic growth rates. If growing season was the most important selective influence on intrinsic growth rates, then individuals from the location with the shorter growing seasons (i.e., Badger Creek and Main Creek) would be expected to exhibit the highest intrinsic growth rates independent of latitude. Fish were acclimated for 4 to 6 weeks in aquaria at 178C before they were placed into the commonenvironment experiment. The setup of the commonenvironment experiment was similar to that described by Belk et al. (2005). We removed the bottoms from plastic 1-L cups and attached the cups to a 2-mm plastic mesh screen with an aquarium-safe silicone sealant. We then attached the screen, with the cups, to frames constructed from polyvinyl chloride pipe. We built six of these arrays, placed each into an aerated 1,100-L tank, and filled the tanks with water until the cups were about three-quarters full. We used 60 cups in each of the six tanks so that 20 fish from each of the three populations could be held in each tank. This setup allowed water to circulate through the mesh, supplying the fish with oxygen and removing wastes, yet still giving us the ability to monitor each fish separately and to avoid potentially confounding effects of behavioral interactions. The experiment was a randomized block (two blocks), fully crossed factorial design with three factors. The three factors were location of origin of fish (three locations), temperature (10, 17, and 248C), and food (two levels). We included the two food levels to test for potential adaptation to food availability. We randomly assigned fish from each of the three populations to one of the three temperatures and to one of the two food treatments within blocks. This allowed each individual to count as a replicate (see Vøllestad and Lillehammer 2000), resulting in 10 replicates of each treatment combination within each block. To begin the experiment, we randomly selected individual fish by population and measured mass and length. Mass was measured on an electronic balance and length was measured from digital photographs that included a known scale for calibration. We measured each image three separate times and used the mean of these measurements as the standard length of each fish to reduce measurement error (Houston, unpublished). After measurement, we placed the fish into a cup in the array according to the random assignment. We fed Sterling Silver Cup (Nelson and Sons, Inc., Salt Lake City, Utah; 46% protein) commercial trout chow to the fish in amounts that were equal to 5% of their body mass (about 0.04 g) either once per day (low food treatment) or twice per day (high food treatment). We assumed that mortalities within the first 3 d were
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caused by handling and those fish were replaced with fish from the same population. We allowed the fish to grow for 49–56 d, after which time we removed them from the cups and measured mass and standard length. Duration of the experiment differed as a result of time required to begin and end the experiment (4 d) and replacement of mortalities within the first 3 d. Photoperiod was maintained at 12 hours light : 12 hours dark throughout the experiment. We calculated growth rates for each individual using both gain in mass (g) and gain in standard length (mm). Because we used fish from the wild in the experiment, it is possible that growth rates were affected by maternal effects or prior growth experience (Panagiotaki and Geffen 1992; Chambers and Leggett 1996) in addition to potential differences in intrinsic growth rates. Environmental and maternal effects on growth usually manifest themselves as differences in body size (Chambers and Leggett 1996; Brooks et al. 1997), and the rate at which a fish grows can be affected by its initial body size (Lobo´n-Cervia´ 2000; Simensen et al. 2000). We used information about beginning length and mass to reduce the possible effects of these potentially confounding variables. At the beginning of the experiment, fish from Main Creek were longer (P , 0.0001; Main Creek: mean ¼ 35.9 mm, SD ¼ 0.6; Badger Creek: mean ¼ 33.2 mm, SD ¼ 0.6; Snake River: mean ¼ 34.2 mm, SD ¼ 0.6) and had greater mass than fish from the other two locations (P , 0.0001; Main Creek: mean ¼ 0.797 g, SD ¼ 0.149; Badger Creek: mean ¼ 0.682 g, SD ¼ 0.148; Snake River: mean ¼ 0.675 g, SD ¼ 0.148). To determine growth rates independent of initial differences in body size, we analyzed the data with analysis of covariance (ANCOVA) and included beginning size as a covariate. We analyzed gain in length and gain in mass separately and the results were similar, so we used principal components analysis (PCA) to combine gain in length and gain in mass into a common growth variable for the final analysis. The first principal component (PC1) explained 97.6% of the variation in growth, so we used PC1 as the response variable in the ANCOVA. We included location of origin, temperature, food level, and block as main effects in the model. We fit the model using backward elimination, which allowed us to determine which interactions could be removed from the model. To compare estimates of field growth rates with those observed in the common-environment experiment, we calculated mean individual growth rates for the three populations included in the commonenvironment study. We subtracted length-at-age 1 from length-at-age 2 to get the annual growth increment. We divided growth increment by the estimated number of
degree-days in the growing season to get gain in length per degree-day. We did not use body size in the first year of growth to calculate these rates because differences in hatch date would confound estimates and we do not have good estimates of hatch dates for these populations. We tested for differences in field growth rates among these populations using ANCOVA with length-at-age 1 as a covariate (to correct for initial body size). We tested differences among means with the Bonferroni procedure. Results Field Growth Experiments Opaque bands on otoliths appeared to form once per year (at the beginning of the growing season, near the beginning of May), with an increase in marginal increment occurring thereafter (Figure 1). There is some variation in marginal increment length that is reflective of differences in growth (and subsequent body size) among populations. Additionally, each population had smaller sample sizes as age of fish increased (see Table 2), which makes estimates of the mean less accurate and explains some of the decreases in marginal increment (particularly in the age-3 to -8 group). Nevertheless, because marginal increments only approach zero at one time of year (near the beginning of May), we conclude that rings represent valid annuli. Growth curves for the seven locations all showed similar patterns of rapid growth early in life, which then began to slow by age 3 (Figure 2). Standard length was affected by age (F2, 566 ¼ 2,955.81; P , 0.0001), location (F1, 566 ¼ 82.46; P , 0.0001), and an age-bylocation interaction (F12, 566 ¼ 25.54; P , 0.0001). In general, fish from Badger Creek were smaller at all ages than those from all other populations, and fish from the Sevier River location were larger at all ages than those from all other locations. Fish from the other five populations exhibited intermediate lengths at age. There was no significant relationship between mean size at age and longevity (r ¼ 0.475; P ¼ 0.282). The best environmental predictor model explaining the variations in size at age among populations included age (F2,13.8 ¼ 25.79; P , 0.0001), growing season (F1,7 ¼ 14.60; P ¼ 0.0064), and an age-bygrowing season interaction (F2,15 ¼ 5.51; P ¼ 0.0159). This model had the lowest AIC score and an Akaike weight of 0.780 (on a scale of 0–1, where higher scores indicate a better model) compared with 0.100 for the next best model (the model that included only growing season length but no interaction). The best model that included elevation had an Akaike weight of 0.070, and the best model that included latitude had an Akaike
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FIGURE 2.—Growth curves showing mean standard length at ages 1–3 6 2 SEs for seven populations of redside shiner from Utah, Idaho, and Wyoming based on least-squares means estimates from the mixed-models analysis. Means apply to ages 1–3, but symbols are slightly staggered for clarity.
weight of 0.002. Growing season is positively related to size at age, and the effect of growing season is not the same for each age (i.e., younger fish grow faster than older fish). Common Environment Individuals grew faster at higher temperatures and with more food, and there was a significant interaction between location and temperature (Table 3). There was no difference in growth rates among the three populations at 108C and 178C. However, at 248C, individuals from the Snake River population grew significantly slower than those from Badger Creek and Main Creek (Figure 3). Field estimates of growth rates showed a similar pattern: individuals from the Snake River grew at significantly slower rates (F2,82 ¼ 11.52; TABLE 3.—Results of the mixed-model analysis of gain in size (first principal component [PC1]) in the commonenvironment experiment that compared growth and weight of three native populations of redside shiner from Utah and Idaho. Significant effects in the model have P-values shown in bold italics. Effect
df
F-value
P-value
Location Temperature Food level Block Location 3 temperature Location 3 food Temperature 3 food Block 3 temperature Block 3 temperature 3 food Beginning size (PC1)
2 2 1 1 4 2 2 2 2 1
0.73 92.70 7.98 3.16 3.35 0.80 0.89 3.17 1.27 4.36
0.4807 ,0.0001 0.0050 0.0766 0.0105 0.4484 0.4099 0.0436 0.2821 0.0377
FIGURE 3—Temperature-specific growth rates 6 SEs of three populations of redside shiner from Utah, and Idaho in a common environment. Symbols are staggered slightly for clarity.
P , 0.0001) than those from Badger Creek and Main Creek (Table 4). Discussion Redside shiners exhibited considerable variation in length at age among populations. At ages 1–3, the population with the largest body size (Sevier River) was over 60% larger than the population with the smallest body size (Badger Creek). Rank order of body size among populations was relatively consistent across ages 1–3 (there was only one, one-step change in rank order from one year to the next among all populations; Figure 2). The pattern of field growth rates exhibited among these populations runs counter to the typical pattern of decreasing size at age at higher latitudes observed in many species of freshwater fish in North America (Belk and Houston 2002). The pattern of growth corresponds with the expected positive relationship with growing season length, and growing season was the overwhelmingly best predictor of variation in size at age. Growing season integrates effects of elevation and latitude and other factors that influence growth, so it is not surprising that it is the best predictor of size (Conover 1990). Growth rates in a common environment did not differ to the same extent as field growth rates. There was evidence for genetically based differences in temperature-specific intrinsic growth rate by one population at one temperature (Snake River at 248C). However, most of the variation observed in the field was attributable to plastic responses to environmental variation. Why are populations relatively invariant in intrinsic growth rates among locations that differ dramatically in length of growing season? The answer may lie in the relatively close juxtaposition of locations
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TABLE 4.—Gain in length per degree-day (corrected for initial body size) in the field of three populations of redside shiner from Utah and Idaho included in the common-environment experiment. Populations with the same Bonferroni grouping are not significantly different. Population Badger Creek Main Creek Snake River
Growth rate (cm gained per degree-day) 3
1.28 3 10 1.19 3 103 9.45 3 104
with very different growing season lengths and the historic and current connectedness of populations. In mountainous areas of western North America, elevation changes rapidly over short distances and, consequently, length of growing season may differ dramatically over relatively short distances. For example, two of the locations that exhibit the longest growing seasons (East Fork Sevier River and Sevier River) are within 50 km of the location with one of the shortest growing seasons (Little Reservoir). Gene flow within drainages may easily swamp out local adaptation to temperature or growing season length. The genetically based variation observed among populations does not fit the pattern expected either from CnGV for growth or from a model of local adaptation. If CnGV was evident, we would expect to see highest intrinsic growth rates at all temperatures in the location with the shorter growing season (i.e., Badger Creek; e.g., Conover and Present 1990). However, growth rates did not differ significantly at the two lower temperatures, and only the Snake River population showed lower intrinsic growth rates at the highest temperature. Evidence for local adaptation would be higher intrinsic growth of the Badger Creek population at the lowest temperature and higher intrinsic growth of the Snake River population at the highest temperature (e.g., Belk et al. 2005). However, the observed pattern is opposite of that expected from this model. The Snake River population has lower intrinsic individual growth rates at the higher temperature. One factor that may influence growth rates independently of growing season length is the efficiency of digestion or absorption of food. Although higher food levels led to higher growth rates (i.e., significant main effect of food), there were no differential effects of food level by population of origin on intrinsic growth rates (i.e., there was no significant population by food level interaction) in the common-environment experiment. This suggests that differences in individual growth rates among these populations are not caused by the ability of one population to be more efficient at converting food resources into biomass as has been demonstrated in other fish species (e.g., Atlantic
SD
Bonferroni grouping 4
2.57 3 10 2.89 3 104 2.33 3 104
A A B
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