Sep 24, 1995 - Matthew J. Sabo', Donald J. Orth & Edmund J. Pert. Department of Fisheries and Wildlife Sciences, Virginia Polytechnic Institute and State ...
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Effect of stream microhabitat characteristics on rate of net energy gain by juvenile smallmouth bass,Micropterzn dolomieu Matthew J. Sabo’, Donald J. Orth & Edmund J. Pert Department of Fisheries and Wildlife Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061-0321, USA. ’ Present address: School of Forestry, Wildlife, and Fisheries, Louisiana State University, Baton Rouge, LA 70803-6200. US.A. Received
2.7.1993
Accepted
Key words: Microhabitat
24.9.1995
use, Foraging behavior, Active metabolism, Fish growth
Synopsis We estimated the rate of energy consumed and expended by 43 juvenile smallmouth bass, Micropterus dolomieu, videotaped in the North Anna River during 1991. Habitat data were also collected in the areas where each individual was videotaped. The net rate of energy gained (Jemin“) increased as water depths decreased and mean water-column velocities increased. This relationship remained statistically significant even when we assumed that consumption was as little as 40% of our original estimates, and that respiratory costs were three times higher than we suspected. The net rate of energy gained by juvenile smallmouth bass was only affected by the characteristics of the area that they searched and not the foraging tactics they employed.
Introduction In rivers and streams, age-0 smallmouth bass, Micropterus dofomieu, can alter their microhabitat use throughout the course of ontogenetic development, using not only the pool habitats where they were spawned but also moving into shallower areas associated with riffles and runs (Sabo & Orth 1994). This microhabitat shift occurs 6-8 weeks after larvae leave the nest and after they have grown larger than 30 mm in total length. At this size they are less likely to be displaced by high water velocities (Larimore 1975) and are capable of consuming a wide sizerange of invertebrate prey (Easton & Orth 1992). Therefore, this microhabitat shift can be interpreted as that of a habitat specialist (restricted to areas containing low water velocities and small prey) that
becomes a generalist by entering areas it was previously excluded from. Besides the fact that they are morphologically capable, there are several other reasons why juvenile smallmouth bass might enter shallow areas with higher water velocities. These microhabitats serve as refuges from piscivorous fishes (Schlosser 1987, Angermeier 1992) and frequently contain high densities of aquatic invertebrates (Gore & Judy 1981, Orth & Maughn 1983). However, these advantages can be offset by increased susceptibility to piscivorous birds in shallow water (Power 1984) and the high energetic cost of maintaining position and foraging in fast water velocities (Facey & Grossman 1990, Simonson & Swenson 1990). If juvenile smallmouth bass suffer the same predation rate and acquire the same net energy gain in all microhabitats, then it may be that the primary consequence of dis-
394
persal into novel areas is the reduction of intraspecific competition. We tried to better understand one aspect of this microhabitat shift by determining if the net energy gained by juvenile smallmouth bass was affected by the characteristics of the area in which they foraged. If they gained less energy in previously unused microhabitats or the same net energy gain in all microhabitats, that would suggest that the observed microhabitat shift was primarily caused by predators or competitors. If they increased their net energy gain in previously unused microhabitats, then the shift might occur as individuals learn more about their environment and choose to occupy the most profitable areas. We obtained most of our information from videotaped observations of juvenile smallmouth bass foraging in different microhabitats. In addition to comparisons of foraging rates, we used bioenergetic analysis to estimate the effect of respiratory losses on energetic gains. We made critical assumptions to obtain baseline estimates of energetic profit, examined statistical relationships between energetic profit and microhabitat or behavioral variables, and used sensitivity analysis to determine the robustness of significant statistical relationships.
Materials and methods Study site
We collected data on the foraging habits and habitat use of juvenile smallmouth bass in the North Anna River, Virginia (latitude 37”51’, longitude 77” 25’). The North Anna River is an impounded tributary of the York River, and its hydrology and biological characteristics were described in previous studies (Simmons & Voshell 1978, Kondratieff & Voshell 1980, Sabo & Orth 1994). We restricted our study to a reach of the river extending 640 m upstream from the 601 Hanover County bridge. This reach averaged 38 m in width and the maximum depth was 2.5 m. Bedrock, boulders, and sand were the most common substrate types. The study site was divided into eight sections based on habitat characteristics: 4 pools, 3 riffles, and 1 run. Based on transect counts
(Sabo & Orth 1994) we estimated that there were 60 age-0 smallmouth bass within this reach. Mean daily water temperatures in the river were recorded by a thermograph maintained approximately 3 km upstream of the study site.
Collection of field data on habitat use and foraging
We videotaped 43 juvenile smallmouth bass between 19 July and 21 August 1991, on days when the dam was releasing a discharge of 1.13 m3 s-’ and visibility under water exceeded 2.5 m. At the beginning of each day a section of the study site was randomly selected and a snorkeler searched that section for juvenile smallmouth bass. After locating a juvenile, the snorkeler observed the individual for at least one min before videotaping. The snorkeler estimated the total length (TL) of each focal individual. To verify the accuracy of this method, the TL’s of 10 juveniles were estimated and then they were captured and measured. The snorkeler estimated the TL of nine of these juveniles to within five mm. After one minute of observation, the snorkeler began recording the actions of the focal individual. The video system we used consisted of a remote camera and a surface monitor. The remote camera was encased in a camouflaged, waterproof housing and recorded 30 frames s-l through a 6 mm wide-angle lens. The time and date of the observation were recorded on videotape. The snorkeler followed the focal individual while holding the remote camera, and another observer watched the surface monitor and directed camera movements to keep the individual centered in the picture. The surface observer also took notes defining the boundaries of the area that the individual traversed. Observation periods ranged from 5-10 min, and each focal individual was observed for only a single period during any one day. After videotaping, we marked 3-6 corners of the area traversed by the focal individual. We measured distance to the nearest 0.5 m, and bearing to the nearest degree between these corners. The area was later drawn based on these measurements, and the total area was estimated with a digital planimeter.
395
These areas were subdivided into sections that visibly differed in habitat characteristics. We randomly placed three transects perpendicular to the flow within each of these sections and quantified habitat variables at randomly selected points along the transects. The length of each transect was equal to the width of the section. We quantified habitat variables at one point for every 1 m2 in sections estimated to be less than 25 m2, and at 25 points in sections larger than 25 m2. At each point, we measured the depth (cm), mean water column velocity (velocity, cm s-‘), and described the dominant substrate type. Velocity was measured at 0.6 of depth from the surface using a Pygmy current meter. Substrate types were divided into categories based on a modified Wentworth scale.’ During 15 July to 15 August 1993, we used a shoreline electrofisher to collect 49 juvenile smallmouth bass from the study site. These individuals were collected between dawn and dusk and were fixed in 10% formalin so that their stomach contents could be preserved. All organisms in stomachs were identified to family or genus and were measured along three axes to estimate volume (Bowen 1983). From the total sample of food items collected, we estimated the percent diet composition (by number) and the average individual volume of each organism that occurred in the diet of juvenile smallmouth bass.
Analysis of videotaped observations
Videotapes of each observation period were viewed at normal speed (30 frames s-‘) by two observers. Observers recorded the duration of sequences during which a focal individual remained in view and occupied the same position in the water column. Juveniles were considered to be using the bottom of the water column when they were within 0.25 body lengths of the substrate or when they were angled downward and searched the substrate. Juveniles ’ Bovee, K.D. 1982. A guide to stream habitat analysis using the Instream Flow Incremental Methodology. U.S. Fish and Wildlife Service, Instream Flow Information Paper: Number 12. FWS/ OBS-82/26. Washington, D.C.
were considered to be using the top of the water column when they were within 0.25 body lengths of the surface or searching the surface. In all other circumstances, we considered juveniles to be using the middle of the water column. Videotapes were reviewed again at 0.1 normal speed, and the number and location (surface, middle, or bottom of the water column) of all feeding attempts were recorded. For each observation period, we calculated the total time the focal individual spent in the top, middle, and bottom of the water column, and the bites mid’ in each location. We also recorded the amount of time that juveniles searched for food (eyes directed at the surface, substrate, or a focal point in the water column) and the amount of time they spent moving among foraging locations. For each observation period, we randomly selected 20, 2-s segments where the focal individual was occupying the middle of the water column, and 20, 2-s segments where the individual was at the bottom or surface. For each 2-s segment, we counted the number of times the trailing edge of the caudal fin passed through the midline of the body and divided that number by two. The average tail-beat frequency was estimated as:
T, = [Uss,)+ (T,~,,)l(s,+ s,,,)-‘~ where T, = average tail-beat frequency (beat&) during the observation period, T, = average tailbeat frequency at the surface or bottom (beat&), s, = total time spent at the surface or bottom (s), T, = average tail-beat frequency in the middle of the water column (beats+‘), and s, = total time spent in the middle of the water column.
Estimates of energy intake and expenditure
The rate of net energetic profit (Jsmin-‘) gained was estimated for each individual using a formula adapted from a generalized bioenergetic equation (Rice et al. 1983): G = [C$l-(R,,,
+ F + U))-R,, + &‘,
(2)
396 where G = rate of net energetic profit, C = total consumption during the observation (J), R,, + AI = energy used for standard and active metabolism(J), t = time observed (min), and R,,, F, and U are constants (Table 1). Values for constants were based on studies of fish species feeding on invertebrates (Pierce & Wissing 1974, Elliott 1976, Rice et al. 1983). To obtain a baseline estimate of consumption (C) for each juvenile we multiplied the total number of bites taken during each observation period (B) by the average joules contained in a single prey item 00 C=KB.
(3)
Our estimate of K was based on invertebrates in the stomach contents of the juvenile smallmouth bass collected in 1993. These invertebrates were categorized based on differences in their caloric content, average volume, and percent composition in the diets. The average energetic content of a prey item was then calculated as: K =[ i
piViwiEi]4.184 (J+kcal-‘),
i=l
(4)
where i = the category of aquatic invertebrate, pi = the proportion of category i in the diet, Vi = the average wet volume (mm’) of a prey item in category i, wi = specific constant for converting wet volume to
dry weight (mg), and Ei = energetic content (Kcalsmg-‘) of a prey item in category i. Baseline estimates of R,, + A)were obtained using an equation adapted for age-0 smallmouth bass (Simonson & Swenson 1990): R (S+
A) =
alW
(ble) ~mT~e~gs~W(t(1440~‘))4,184(J~g~1), (5)
where W = weight of the individual (g), T = water temperature (“C) on the day of the observation, s = average swimming speed (crns’) during the observation, and a,, bi, m, and g were constants (Table 1). All constants were derived for juvenile smallmouth bass (Shuter & Post 1990) except m and g which were derived for juvenile largemouth bass (Rice et al. 1983). Coefficient values in equation 2 applied to a 24-h day, so the initial result was multiplied by the proportion of the day occupied by the observation period. Weight of each focal individual was estimated from a length-weight relationship (p < 0.0001, R* = 0.89) developed from 80 juveniles (27-70 mm TL) collected between June 1 and July 9,199l: W = a2Lb*,
(6)
where L = estimated TL (mm), and a2 and b, are constants (Table 1). We used tail-beat frequency to estimate swim-
Table]. Description and values of constant parameters used in calculation of energetic profit gained by juvenile smallmouth bass. Sources for parameter values are stated in the text.
Reference equation
Symbol
Parameter description
Parameter value
R SD.4 F U aI b, m g a2 b a3 b,
Proportion of energy lost to specific dynamic action Proportion of energy egested Proportion of energy excreted Intercept for respiration (ggww”.d“) Weight-dependent exponent for respiration (gww”) Temperature-dependent coefficient for respiration (“C-l) Coefficient for swimming-speed dependence of respiration (scm.‘) Intercept for weight Length-dependent coefficient for weight Intercept for swimming speed (beatss’) Frequency-dependent coefficient for swimming speed (beat&)
0.17 0.15 0.088 0.03 - 0.21 0.0313 0.0196 0.00026 2.21 0.75 1.33
397
ming speed using the general formula for fish developed by Bainbridge (1958):
s = a&(T,-b,), where a3 and b, are constants.
Bioenergetic analysis assumptions
To estimate the amount of energetic profit gained by each individual, we made some assumptions (listed below) about fish behavior and physiology. Where evidence exists to support, contest, or test the assumption, we list the information sources. (1) Juveniles consumed one prey item every time they took a bite. (2) The same prey items are available in all microhabitats. Several studies indicate that species of Ephemeroptera and Trichoptera occur in shallow, high velocity microhabitats, and Chironomids and Cladocerans occur more frequently in low-velocity pools (Kondratieff & Voshelll980, Gore & Judy 1981, Orth & Maughn 1983). (3) Diet’s were not affected by juvenile size. Juvenile smallmouth bass between 30-90 mm TL consume the same size-range of prey (Easton & Orth 1992). We estimated length of all juveniles to determine if the same size-range of prey were available to all juveniles. (4) Juvenile smallmouth bass were daytime feeders and their foraging rate remained constant throughout the day. We observed juveniles during all periods from dawn to dusk to determine if foraging rate was affected by time of day. (5) The snorkeler’s presence did not affect competitive interactions among juvenile smallmouth bass. (6) Respiratory costs associated with steady-swimming are the same as those associated with routine swimming. The respiratory model we used assumed steady swimming. Estimates from previous studies suggest the energetic cost of routine swimming may be 2-20 times greater than the cost of steady swimming (Smit 1965, Weatherly et al. 1982, Puckett & Dill 1984. Webb 1991).
Statistical analyses
We used linear regression to determine if independent variables could predict rates of energy gains and losses by juvenile smallmouth bass. The dependent variable we were most interested in predicting was G, but we also wanted to predict foraging rate (bitesmin-‘) and R,, +A) because they caused the most variation in G. Additionally, the variable that created the most variation in R(, + hI was s. Therefore, for each independent variable, we used regression analysis to assess its ability to predict foraging rate. S, R,, + A), and G. We categorized the independent variables as microhabitat, behavioral, and sampling variables. Microhabitat variables included the mean depth and mean velocity of the areas used by juveniles. Additionally, principal component analysis (PCA) was used to create artificial variables that summarized multivariate differences among microhabitats. Variables used in the PCA included the mean, variance and maximum of depth and velocity, and the modal substrate type. All principal components with eigenvalues greater than 1.0 were retained and analyzed as independent variables in regression analyses. Behavioral variables included the proportion of time spent searching for food, proportion of time spent moving between foraging locations, and the area searched by a juvenile during an observation period. Variables associated with sampling included TL, temperature on the day of the observation, and duration of the observation period (t). Regressions between sampling and energetic variables tested for bias in our method of collecting observations. We evaluated the effectiveness of our 1-min acclimation time by using a paired t-test to compare the percent time each individual spent foraging during the first and last minute of the observation period.
Sensitivity analyses and model calibration
Based on the assumptions we made to estimate energetic profit, the most uncertain components of our analysis were the estimates of C and Rcs++,).Our baseline estimates of C were most affected by the assumption that one prey item was consumed with
398 % Time
0
@J Bites min-’
‘oo-40
0-
N.43
N.40
Bottom
N.43
N.49
Midcolumn
N.43
N.12
0
Surface
Fig. 1. Box plots summarizing the proportion of time juvenile smallmouth bass occupied the bottom, middle, or surface portions of the water column, and their corresponding foraging rates (bitesmin.‘). Box plot key: box = interquartile range (IQR), horizontal line = median, vertical line = range of points within 15IQR beyond the IQR.
each bite. The R,, +a) estimates were most affected by the assumption of equal respiratory costs for routine and steady swimming. To assess how changes in these estimates might affect statistical relationships between G and the independent variables, we recalculated G for each individual while simultaneously multiplying K and R,, +a) by scalers. We multiplied K by scalers that ranged from 0.1-1.9 (increments of 0.3) and R,, + A) by scalers ranging from 0.1-20 (first increment 0.9, increments of 1.0 thereafter). (Because standard
and active respiration were estimated using a single equation, incremental changes in respiratory costs were higher than they would have been if only active respiration were manipulated). We made the additional assumption in this analysis that errors in the estimation of C and R,, + A)were identical for all individuals. Any significant regression between G and an independent variable was reanalyzed using G values generated by all scaler combinations applied to K and R,, + *). Based on observed length distributions (Sabo & Orth 1994) juvenile smallmouth bass in this reach grew 15.0 + 2.0 mm (from 60 to 75 mm TL) between July 3 and August 4 1991, or 1.4 + 0.4 g using equation 6. We calculated the value of G that the average juvenile would have to obtain to meet this rate of growth, assuming that they only fed during daylight hours. The amount of energy lost to respiration at night was calculated using equation 5, assuming W = 2.9 g (the median weight of the average juvenile during this period), T = 27 (average water temperature during this period) and s = 0. The 95% confidence interval of this average value of G was used to define the range of likely mean G values that the bioenergetic analysis should have yielded. Original estimates of G and those generated by the sensitivity analysis were examined to determine which values of K and R,, + A) were most likely to produce values of mean G within this range.
Table 2. Values of variables used to calculate the average energetic content of a prey item ingested by a juvenile smallmouth bass. Category of invertebrates
Percent in diet
Average volume (mm’)
mgmm“ dry weight
Average kcalmg-’
Chironomidae Baetis spp. Other aquatic insects’ Terrestrial insects’
7.3 81.1 10.0 1.6
0.226 0.505 0.232 4.760
o.23 0.14 0.14 0.14
5.24a5 8.97g6 5.200’ 5.200’
’ Includes larvae of: Ephemeroptera, Odonata, Hemiptera, Megaloptera, Trichoptera, Coleoptera, Ephemeroptera spp., and Diptera other than Chironomidae. * Includes families of Pscoptera and genera of Formicidae. ’ Stites & Benke (1989). 4 Cummins & Wuycheck (1971). 5 Cummins & Wuycheck (1971). Mean of eight species. 6 Cummins & Wuycheck (1971). ’ Cummins & Wuycheck (1971). Mean of 31 species.
other than Baetis
399 Tuble3. Factor pattern of the three components (PCl. PC2. and PC3) retained by the principal component analysis performed on statistics summarizing habitat data (N = 43).
Statistic Mean depth Mean velocity Depth variance Velocity variance Maximum depth Maximum velocity Modal substrate Eigenvalues Proportion of variance explained
PC1
PC2
PC3
- 0.638 0.802 - 0.443 0.854 - 0.702 0.530 0.002
0.619 OSOH 0.549 0.342 0.682 0.510 - 0.070
0.067 0.002 - 0.061 - 0.067 0.086 0.04Y 0.989
3.165 0.452
1.788 0.256
I IlO1 0.143
Results The juvenile smallmouth bass we videotaped ranged from 45-100 mm in TL (mean = 77.6) and traversed 2.3-65.3 m2 (mean = 16.9) during the observation period. Observation periods lasted 4.4-10.4 min (mean = 7.64) and all observations were made when water temperatures were between 25-30” C. Juveniles spent O-79% (mean = 42.8%) of the observation period searching for food. All juveniles spent the majority of their time at the bottom or in the middle of the water column, but fed most frequently near the bottom or surface (Fig. 1). We identified and measured 1479 insects from the stomachs of juvenile smallmouth bass collected in 1993. Because fish and crayfish rarely occurred in smallmouth bass stomachs (< 1% of diet items) and because we did not notice juvenile smallmouth bass
eating fish or crayfish on the videotaped observations, we excluded these organisms from our analysis. Insects were grouped into four categories to facilitate estimation of K (Table 2). Based on equation 4, we determined that the average insect consumed by a juvenile smallmouth bass contained 1.83 J. Observed foraging rates varied between O-20 bitesmin-’ resulting in estimates of C between O36.6 Jemin-’ (mean = 3.92). Our estimates of Trranged from 65-255 beatsmin”, corresponding to average swimming speeds of O-23 ems’ (mean = 9.7) or O-2.4 body-lengthss-’ (mean = 1.26). Baseline estimates of RfS+A)ranged from 0.3-1.2 J.min-’ (mean = 0.73), and initial estimates of G varied between - 1.2-20.6 J.min’ (mean = 0.86). Juvenile smallmouth bass occupied depths ranging from 3-121 cm (mean = 56.5) and velocities be-
Table 4. Significant regressions predicting foraging rate. swim speed (s). respiration rate (R(, + +,)), and rate of net energetic gain (G) by habitat, behavioral, or sampling related variables. PC1 and PC2 are principal components extracted from habitat data (see Table 3). Units of measure are stated in the text.
Dependent variable
Independent variable
Regression equation
P
0.13). This is not surprising because both s and R(, + A) are functions of TL. None of the behavioral variables were significantly related to energetic variables (all p > 0.06). The 1-min acclimation time appeared to be adequate. On average, juveniles foraged for 56.2% of the first minute of the observation period and for 53.5% of the last minute. The paired t-test indicated that the average difference between the percent of time spent foraging during the first and last minutes was not significantly different from zero (p :, 0.63). Some field observations supported our model assumptions. Feeding rates varied throughout the day but did not consistently increase at any particular time of day (Fig. 4) and juveniles became inactive after sundown. The size range of juveniles we observed should all have fed on the same size-range of prey and most juveniles fed almost exclusively on Baetis.
Discussion Of the variables tested, only microhabitat variables significantly explained variation in our estimates of net energetic gain. Behavioral variables such as area searched or percent time spent foraging did not affect rates of consumption or net energy gain. Individuals could only increase their net energy gain by foraging in areas with specific microhabitat characteristics. The most energetically profitable microhabitats contained both shallow depths and high mean velocities (shallow-fast habitats), similar to the areas that age-0 smallmouth bass enter at the time of their microhabitat shift. The highest estimates of G were calculated for individuals foraging in water less than 60 cm deep and with mean velocities greater than 10 ems’. (Notice that in this context ‘fast’ is used to describe mean velocities that were higher than the average used, not higher than the average that were available). Estimates of C and G did not increase in deeper areas with high velocity. The fact that the juvenile smallmouth bass we observed were primarily benthic foragers may explain why both depth and velocity had a significant effect on their rate of net energy gain. Submerged vegetation (particularly Podosromum spp.) grew well in shallow-fast habitats and this vegetation frequently supports high densities of benthic invertebrates (Simmons & Voshell 1978, Kondratieff & Voshell 1980) which could serve as an ample food source for juvenile smallmouth bass. While we did not account for it in our estimates of C and G, it is also probable that some of the larger benthic invertebrates (e.g. Trichoptera) that contain more energy per individual would inhabit shallow-fast microhabitats (Gore & Judy 1981, Orth & Maughn 1983). In other systems, researchers have observed juvenile smallmouth bass feeding on drifting invertebrates (Lachner 1950, Buynak et al. 1982, Simonson & Swenson 1990) and in these circumstances velocity may have an overwhelming effect on the rate of net energy gain (Fausch 1984, Godin & Rangely 1989, Hughes & Dill 1990, Simonson & Swenson 1990). The result we are most certain of is that foraging rate increased in shallow-fast habitats. Despite uncertainty in our estimates of C, foraging rates were
402 elevated enough in shallow-fast habitats to indicate that energy intake was increased in those areas. However, individual variation in tail-beat amplitude and the energetic costs of maneuvering may cause enough error in our estimates of R,, + Ar to invalidate our regression results relating G and PCl. But some strong evidence suggests that the statistical relationship is correct even if the estimates of G are imperfect. The swimming patterns we observed involved turns and complex maneuvers that typify ‘high cost’ routine swimming (Forstner & Wieser 1990). As mentioned earlier, this swimming pattern can require 2-20 times the energetic cost of steady swimming (Smit 1965, Weatherly et al. 1982, Puckett & Dill 1984, Webb 1991). But as with steady swimming, differences in speed explain the majority of variation in the metabolic costs of routine swimming (Boisclair & Tang 1993). We used differences in mean swim speed to estimate R,, + A) for each individual, so a sensitivity analysis that assumes uniform error in calculation of activity metabolism should have demonstrated how increasing metabolic costs could affect the statistical relationship between G and PCl. Our sensitivity analysis results indicated that as long as the mean of G estimates remained greater than zero, the regression between G and PC1 remained statistically significant. Because these juveniles we observed were growing throughout the course of the study, it seems logical to assume that they obtained average rates of net energy gain greater than zero. Comparisons with observed growth rates indicated that our initial estimates of G were high, but it is difficult to say whether this resulted from errors in the calculation of C or R,, + A)’ The assumption that one food item was consumed with each bite probably elevated our estimate of C above reality, and the assumption of equal respiratory costs between routine and steady swimming probably decreased our estimate of R,, + A) below reality. However, it is difficult to determine the magnitude of our error in estimating either of these variables, because other than at lo%, all percentages of K produced (at some scaled value of R, + *,) mean values of G within the realistic range. It is worth noting that when we used our original estimate of K, the mean G of juveniles
we observed fell within the realistic range when R,, + A) was increased between lOO-150% (Fig. 3). The results of this study suggested that age-0 smallmouth bass increased their rate of net energy gain by moving into shallow-fast microhabitats. Individuals that enact this microhabitat shift should therefore increase their growth rates at a critical stage of ontogenetic development. Another interesting aspect of the microhabitat shift is that the smallest members of the cohort were as likely (and in some cases more likely) to occupy shallow-fast habitats as the largest individuals (Sabo & Orth 1994). This microhabitat shift may therefore permit some previously slow-growing individuals to close the size-gap between themselves and the largest members of the cohort, and could explain why an individual’s chances of remaining among the largest members of the cohort throughout the duration of the growing season is very small (Sabo & Orth 1995).
Acknowledgements The Electric Power Research Institute and Virginia Electric Power Company provided funding for this research. We thank I. Jezorek, J. Lukas, and A. Seinwell for their assistance with data collection. R. Easton, C. Feldmeth, R. Graham, T. Groshens, H. Jager, R. Vadas Jr., and W. Van Winkle made helpful comments throughout the course of this study. P. Angermeier, A. Heath, J. Ney, J. Webster, and two anonymous reviewers critiqued earlier versions of this manuscript and their suggestions proved extremely helpful.
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