The saltatory search behavior of larval cod (Gadus morhua)

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Deep-Sea Research II 53 (2006) 2735–2757 www.elsevier.com/locate/dsr2

The saltatory search behavior of larval cod (Gadus morhua) James J. Ruzicka, Scott M. Gallager Woods Hole Oceanographic Institution, Environmental Systems Laboratory, MS#35, Woods Hole, MA 02543, USA Accepted 1 September 2006

Abstract Accurate descriptions of larval fish foraging behavior are necessary for modeling energy gain through prey encounter rates. Larval cod use a saltatory foraging strategy; they swim in discrete bursts and search for prey during the periods between bursts when speed is minimal. The goals of this research were: first, to observe the behavior of larval cod in large volumes to reduce confinement effects; second, to observe behavior throughout early development; and third, to observe if larval cod adjust their foraging effort in response to foraging conditions, in this case prey density. An observation system employing stereo-paired video cameras was developed that allowed recording of the behavior of individual larvae throughout a large volume in three dimensions. The observed behavior shows that the burst phase of the foraging cycle remains constant regardless of the presence or absence of prey while the duration of each search event becomes significantly longer when prey are absent, perhaps reflecting either a greater time investment to process each search volume more thoroughly or a response to hunger. Comparison of the behavior of larvae fasted 12 h to those fasted 36 h shows that hunger state has little effect on the burst phase of the foraging cycle, but hunger does have a significant effect on overall activity level and foraging capacity. r 2006 Elsevier Ltd. All rights reserved. Keywords: Larvae; Gadus morhua; Behavior; Pause-travel; Saltatory foraging; Stereo-video

1. Introduction Unlike the pre-settlement stages of coral reef fish, which have impressive swimming abilities (Stobutzki and Bellwood, 1994), the larvae of temperate marine fish like cod (Gadus morhua) are weak swimmers. Are their foraging abilities also weak? Early laboratory observations of larval cod foraging behavior suggest that their foraging capacity is limited and their survival depends upon encountering high-density prey patches (Solberg and Tilseth, 1984; Skiftesvik and Huse, 1987) or taking advanCorresponding author.

E-mail address: [email protected] (J.J. Ruzicka). 0967-0645/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.dsr2.2006.09.003

tage of turbulence to enhance prey-contact rates (Dower et al., 1997). However, data from the field and later laboratory observations suggest that they are capable foragers under a variety of conditions (MacKenzie et al., 1990; Munk, 1995). Accurate descriptions of foraging behavior are necessary for modeling potential energy gain through prey encounter rates (e.g., MacKenzie and Kiørboe, 1995) as well as foraging-related energy expenditures. Larval cod use a pause-travel, or saltatory, foraging strategy (MacKenzie and Kiørboe, 1995; Munk, 1995). The components of the saltatory foraging cycle are a swimming burst, a glide, and a pause during which the predator searches for prey (Evans and O’Brien, 1988). Since

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burst-and-glide swimming is the most efficient swimming mode for post-yolk-sac larvae (Vlymen, 1974; Weihs, 1980; Videler and Weihs, 1982; Webb and Weihs, 1986), saltatory foraging may be the optimum search strategy for small larvae in general. The components of the saltatory foraging cycle can be varied to suit environmental conditions. The prey field (size, relative visibility, and density) has a significant effect on the behavior of a range of saltatory foragers, terrestrial and aquatic (O’Brien et al., 1990). The distance between search-volumes, the duration of the search period, the frequency at which new volumes are searched, selectivity of prey, speed, and the time spent traveling can all be varied to adjust foraging effort. There have been few studies that have looked at how components of a larval fish’s foraging cycle change in response to dynamic conditions, and the results are varied. Munk and Kiørboe (1985) and Munk (1995) observed that swimming intensity and search capacity (volumetric rate of foraging) of cod and herring larvae increase at lower prey concentrations while, MacKenzie and Kiørboe (1995) did not observe a consistent response to prey concentration. The goals of this research were: first, to observe the behavior of larval cod in large volumes to reduce confinement effects; second, to observe behavior throughout early development (from 5 to 50 days post-hatch); third, to measure the foraging capacity of larval cod (volume searched per unit time); and fourth, to observe if and how larval cod adjust their foraging effort in response to different foraging conditions, in this case three prey treatments (no prey, rotifers at a target density of 100 L1, wild zooplankton at a target density of 100 L1). Stereopaired video cameras recorded the foraging behavior of individual larvae throughout a large, cylindrical tank in three dimensions. Stereo-video systems have been successfully employed to study the swimming patterns of larger fish (Boisclair, 1992; Krohn and Boisclair, 1994). 2. Materials and methods 2.1. Rearing of larvae Several cohorts of eggs from different adults were studied to reduce potential bias of studying the behavior of a single cohort; however, there is evidence that maternal condition has little effect on the behavior of larvae (Browman et al., 2003). Eleven batches of cod embryos were reared for this

study. Eight were obtained from the broodstock maintained by the National Marine Fisheries Service in Narragansett, RI. One was obtained from the broodstock maintained by the Marine Biological Laboratory (Woods Hole, MA). The Narragansett and Woods Hole broodstocks were both made up of fish caught off Cape Cod. Two batches of embryos were obtained from Canadian broodstocks: one from Memorial University of Newfoundland (Newfoundland, Canada) and one from St. Andrews Biological Station (New Brunswick, Canada). Fertilized embryos were transported in insulated containers to the Woods Hole Oceanographic Institution for rearing. Embryos were held in black, 120-L barrels with an airstone suspended in the center E2.5 cm off the bottom. One-third of the water was changed daily with filtered (1 mm), natural seawater (salinity ¼ 32.5%), and the bottom siphoned for debris. Lighting was from fluorescent lamps directly above the barrels on an 11-h on/ 13-h off cycle, 13.3 mEinsteins m2 s1 at the surface to 5.2 mEinsteins m2 s1 at the bottom (Biospherical Instruments, Inc. 4p PAR sensor model QSP200L4S, San Diego, CA). Embryos were held at 6 1C and hatched 3 weeks after spawning. Most hatching occurred within 1 day. Larvae were retained in the same barrels in which they hatched. Beginning on the third day after hatching, larvae were fed rotifers (Brachionus plicatilis) daily. After 3 weeks, the diet was supplemented with wild zooplankton containing copepod nauplii (predominately Acartia sp.). Larvae were maintained for up to 60 days before use in experiments or termination. 2.2. Observation tank system The observation tank system is shown in Fig. 1. A fiberglass cylinder (3 m tall  0.5 m diameter) with flanged ends (Solar Components Corp., Manchester, NH) was fitted with a 1/400 glass bottom, adhered with RTV-157 (General Electric). The tank was positioned on a wooden frame with a 3/400 plate glass top; a thin sheet of mineral oil was spread between the glass panes. The tank was half-filled with filtered seawater (1 mm) to a depth of 1.5 m to give a volume of E250 L. Chilled water cascaded down the outside of the tank to maintain temperature at 7 1C (71 1C). The tank and cooling system was insulated with bubble-wrap. The tank was lit from above by a collimated light source consisting of a quartz-halogen bulb and large fresnel lens (AWI Industries, Corona, CA) on an 11-h on/

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13-h off photoperiod and within the intensity range optimal for larval cod feeding (Ellertsen et al., 1980) (Fig. 2; measured with Biospherical Instruments, Inc. 4p PAR sensor model QSP200L4S, San Diego, CA). Black plastic sheet was hung around the entire observation tank system to prevent stray light entering from outside. Stereo-paired video cameras observed silhouettes of swimming larvae from below via a mirror oriented at 451 beneath the tank. Video cameras (PULNiX model TM-7EX, Sunnyvale, CA) with 35–70 mm zoom lenses (Minolta, set to 70 mm) were mounted 12 cm apart and 1.9 m from the base of the tank stand. Optical axes were aligned in parallel. The video signal from each camera was recorded on separate SVHS tapes with a time-code stamped on each frame (Horita time-code generator, Mission Viejo, CA) so that the two records could be synchronized.

Fig. 1. The observation tank developed for this study. Overall dimensions are 3 m tall  0.5 m diameter. Filled half-full, it provides 250 L for larval foraging. Stereo-paired video cameras, positioned to observe the silhouettes of swimming larvae from below, record behavior throughout the tank in three dimensions.

2.3. Experimental treatments and protocol Behavior of larvae 4–45 days old was observed in 10-day intervals. Three feeding treatments were used: no prey (two repetitions for each age group),

Fig. 2. Light intensity profile within the tall observation tank (mEinsteins m2 s1 vs. depth from water surface).

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rotifer prey initially stocked at 100 L1 (two repetitions for each age group), and wild zooplankton prey (predominately Acartia sp.) initially stocked at 100 L1 (one repetition for each age). Larvae (50–100) were added to the surface of the tank the evening before experiments began. Experiments lasted 2 days. Day 1 was an acclimation period; no prey were added to the tank. Day 2 was the primary observation period, and a video record of larval behavior was made at 1:00 pm. In prey treatments, appropriate prey were added at 8:00 am on day 2, and the tank was bubbled for 0.5 h to mix prey throughout. In noprey treatments, larvae were fasted 36-h before observations were made on day 2. In prey treatments, larvae were fasted 31 h, then allowed to feed 5 h before observations were made. To resolve the effect of hunger, behavioral observations were made at 1:00 pm on day 1, the acclimation period, for a sub-sample of experimental runs (one repetition for each age group). This allowed the comparison of behavior of larvae fasted 12 h (day 1) to larvae fasted 36 h (day 2).

Prey-density profiles were taken in the evening of day 2 by siphoning 1 L of water in 30-cm intervals and counting prey items recovered at each depth. The measured prey densities at the end of the day 2 observation period were lower than the 100 L1 stocking density (Table 1). As prey numbers decline throughout the day due to larval grazing and prey sinking, the measured densities represent the lower bound of the prey field experienced by larvae during the midday behavior observation period. Temperature profiles from the surface to the bottom of the observation tank were also taken in the evenings and typically varied by less than 1 1C from the top to the bottom of the water column. Length and weight data was collected from a subsample of 15 larvae the evening before each experiment. The total length (TL) of each live larva was measured to the nearest 0.1 mm from a video image using NIH Image vers. 1.62. Larvae were then placed in TRIS buffer, frozen in liquid nitrogen, and stored at 70 1C. To obtain dry weights, individual larvae were thawed, rinsed in chilled distilled water, refrozen, and freeze-dried for

Table 1 Basic experiment information: experiment code letter, age (dph, days post hatch) on experiment day 2, prey treatments, larval stocks, total length, dry weight (DWT), median realized prey density experienced by larvae throughout the observation tank, and relative growth rate during the period from hatch to observation (NA, data not available) Exp

Age (dph) Treatment

Stock (with hatch date)

TL DWT (mg) (mm71 SD)

Prey density (prey L171 SD)

Growth (% TL d1)

Growth (% DWT d1)

A B C D

7 8 5 5

No prey Rotifers Rotifers Zooplankton

g. M. University (5/7/01) f. St. Andrews (3/27/01) h. MBL (1/1/03) k. Rhode Island (3/24/03)

5.070.4 5.470.3 5.370.3 5.270.2

63 37 44 35

0 60725 37718 83716

0.3 1.5 2.0 2.0

13.3 1.0 6.7 0.4

E F G H I

12 17 15 14 13

No prey No prey Rotifers Rotifers Zooplankton

b. Rhode Island (1/30/00) f. St. Andrews (3/27/01) d. Rhode Island (3/19/00) e. Rhode Island (1/28/01) f. St. Andrews (3/27/01)

5.670.3 5.670.2 4.670.3 5.270.4 5.670.1

50 49 59 50 39

0 0 89717 1672 285738

1.3 0.9 0.4 0.5 1.2

3.8 2.6 4.9 3.2 0.9

J K L M N O

22 24 24 22 23 27

No prey No prey No prey Rotifers Rotifers Zooplankton

i. Rhode Island (1/9/03) d. Rhode Island (3/19/00) e. Rhode Island (1/28/01) b. Rhode Island (1/30/00) c. Rhode Island (2/8/00) f. St. Andrews (3/27/01)

5.970.3 5.970.3 5.870.4 5.970.5 6.070.5 5.970.3

56 NA 66 68 87 66

0 0 0 64710 7472 64730

1.0 0.9 0.8 1.0 1.0 0.8

2.8 NA 3.8 4.6 6.8 3.4

P Q R S T

31 34 34 32 36

No prey No prey Rotifers Rotifers Zooplankton

e. Rhode Island (1/28/01) j. Rhode Island (2/18/03) k. Rhode Island (3/24/03) d. Rhode Island (3/19/00) f. St. Andrews (3/27/01)

6.470.5 7.370.4 6.470.4 6.270.5 6.670.5

115 135 96 NA 128

0 0 NA 80714 3079

1.0 1.5 0.9 0.9 1.0

7.6 8.7 NA 4.8 7.6

U V W X

44 45 44 41

No prey No prey Rotifers Rotifers

f. St. Andrews (3/27/01) k. Rhode Island (3/24/03) c. Rhode Island (2/8/00) a. Rhode Island (12/24/99)

6.870.6 6.570.4 8.571.6 6.870.9

132 108 222 NA

0 0 63758 25758

0.9 0.7 1.7 1.0

6.4 4.7 12.4 NA

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8–12 h (Labconco Freeze Dryer 5). Dry weights (DWT) were measured to the nearest 1 mg (Cahn microbalance, model C-33). Repeating the freezedrying process on a sub-sample confirmed that samples were completely dried. The relative growth rates (%TL d1 and % DWT d1) from hatch to time of behavioral observation were calculated as defined by Ricker (1979) using an assumed hatch length of 4.9 mm and dry weight of 35 mg (the approximate intercept of growth curves, Fig. 3). These growth rates (Table 1) were lower than the potential growth rates that may be attained by larval cod and are observed in the ocean and in food-rich mesocosm experiments (Bolz and Lough, 1988; Folkvord, 2005). However, the observed lengths-at-age and growth rates with respect to length were typical of larval cod used in laboratory-based behavior experiments (e.g., MacKenzie and Kiørboe, 1995; Munk, 1995; Browman et al., 2003). 2.4. Processing the video record Five to 10 min of the SVHS record from day 2 of each experiment was digitized as a series of grayscale TIFF files (640  480 pixels) at 3.75 frames s1 using a frame-grabber (model MV-1000 running

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‘Wseq’ software, MuTech Corp., Billerica, MA). Two minutes were analyzed for each experiment selected for the day 1 sub-set. Original Matlab routines (The MathWorks, Inc., Natick, MA) were written to automatically extract swimming tracks (frame-by-frame series of pixel coordinates) of individual larvae from the TIFF frame series. (Details of the digitization and tracking routine are provided in Ruzicka, 2004. Step 1 (automated extraction of swimming tracks): TIFF frames from each camera were reduced to pixel coordinates of potential larvae. An average composite image of a 1 min set of frames was subtracted from each consecutive TIFF image of that frame set and the result binarized. The threshold settings for binarization were adjusted dynamically so that a reasonable number of potential larvae were acquired within each frame. The coordinates of each potential larva in sequential time-steps were paired with nearestneighbor coordinates in the previous time-step to generate potential swimming tracks from each camera. Step 2 (inspection): A movie was constructed from the TIFF image series upon which the computer-generated swimming tracks were overlain. Every swimming track was inspected against the

Fig. 3. Total length of each larval stock as a function of age (mean71 SD). Stock code letters are as defined in Table 1.

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gray-scale movie created from the original TIFF image series and errors (noise points, missed points, crossed tracks) were corrected manually. Step 3 (calibration): The pixel coordinates of reference points on a plexiglass calibration placard were recorded from paired left-right TIFF frames captured at discrete depth intervals (30.5 cm) from the surface to the bottom of the observation tank. Three calibration parameters were calculated: 1. the exponential relation between depth and x-axis offset between left and right cameras (Fig. 4A), (2). the linear relation between depth and horizontal resolution (mm pixel1; Fig. 4B), and 3. angle of rotation about the optical axis of the left camera relative to the right camera (always o0.51). Step 4 (calculate depth and convert to millimeter coordinates): Matching swimming tracks from each camera were paired and synchronized using the time-code stamp from each image frame. The optical axis of the left camera was mathematically de-rotated relative to the right camera. The x-axis offset between the left and right cameras and corresponding depth was calculated for each larva at each time step. Pixel coordinates were converted to millimeter coordinates using the depth-specific conversion parameter. Depth-position data were

used only to calibrate horizontal distances and were not used in subsequent calculations of swimming speed or classification of behavioral mode. Depth information was only available for larvae simultaneously visible to both of the paired cameras. In each experiment, the overlapping visual fields of both cameras generally captured 450% of all larvae visible to either camera separately (median ¼ 75%, data not shown). The 95% calibration intervals on the x-axis offset vs. depth curve show the accuracy of estimated larval depth during any single video-frame interval to be 73–7 cm (Fig. 4A), the broadest intervals being nearest the surface of the tank. A large uncertainty in depth translates into a relatively small error in the horizontal axis. For a larva at the very top of the water column (e.g., 1.2 m from the tank lip), an uncertainty in depth of 77 cm translates to uncertainty in the horizontal axis calibration of 1.01–1.04 mm pixel1. When the 95% prediction intervals about the linear relation between depth and horizontal resolution (Fig. 4B) are also taken into account, the uncertainty associated with the vertical position of the hypothetical larva propagates to 0.98–1.06 mm pixel1 (74% error) in the horizontal axis.

Fig. 4. (A) Depth calibration curve showing the exponential relation between depth within the observation tank and the x-axis offset (in pixels) between the left and right cameras (experiment M). Error bars are 95% calibration intervals. (B) Horizontal axis calibration curve between depth and mm pixel1 calibration scalar. Dashed curves are upper and lower 95% prediction intervals, error bars represent 2 standard deviations about the measured calibration scalar at each depth interval.

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Step 5 (classification of behavioral modes): Speed traveled over each image interval (0.2667 s) and acceleration between successive intervals was calculated for each larva. Intervals were classified as either a pause or swimming burst using pre-defined rules. A pause was a period of no active swimming; maximum pause speed p4 mm s1 (allows for coasting until next burst). Bursts were intervals of positive acceleration 44 mm s2 and speed 43 mm s1 (Fig. 5). A larva was still considered to be in burst mode until its speed had fallen to o110% pre-burst speed or remained 44 mm s1. Six key search-behavior parameters were measured for all larvae visible to both cameras: pause duration, pause frequency, burst swimming speed, burst duration, distance traveled during bursts, and fraction of time spent swimming. Because individuals demonstrated behavioral variability, an accurate and unbiased description of behavior required an adequate observation period. For each experiment, all resolved swimming tracks were repeatedly broken into fragments of increasingly greater duration (in 10-s increments) and re-analyzed. Plots of the median behavior parameter values, calculated across all individuals within each experiment, against sampling time were examined. A 40-s observation period was usually

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sufficient to acquire consistent values for all six behavior parameters. Only individuals observed for X1 min were included in the principle analysis of behavior observed on experiment day 2. This criterion was relaxed to X45 s in the analysis of experiments sub-sampled for observation on experiment day 1. 2.5. Data analysis Examination of all burst and pause events within a single swimming track revealed the behavioral variability of individual larvae within, presumably, constant foraging conditions. For every individual, probability plots showed that pause durations, burst durations, and burst distances were exponentially distributed and that peak burst speeds were lognormally distributed (Fig. 6). The behavior of individual larvae was summarized as the mean parameter value measured over the entire observation period as determined by maximum likelihood estimation or, as appropriate, calculated from logtransformed data. Analyses of the effect of prey treatment upon foraging behavior were made by examining the mean response of all individuals observed within each experiment. Comparing experiment-level mean

Fig. 5. A 1-min swimming speed profile for an individual larva (experiment M). Circles are pause events, and triangles are burst events. The time resolution is 0.2667 s.

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Fig. 6. Frequency distributions of pause event durations, swimming burst event durations, distances traveled during swimming bursts, and the peak burst swimming speeds for the same individual larva shown in Fig. 5 (experiment M). Pause and burst durations and the distances traveled between pauses followed an exponential distribution. Peak burst speeds were log-normally distributed.

behavior gave each experiment equal weight in analyses without regard to differences in the number of individual larvae that could be tracked. The behavioral response to the presence or absence of prey (rotifers) was tested by ANCOVA with age and length as covariates (Kleinbaum et al., 1998). The six behavioral parameters are all mathematically related to the time budget of larval cod, and significant differences in multiple parameters cannot be considered as separate pieces of evidence that larvae alter their search behavior under different foraging (or hunger) conditions. However, they each provide information required to describe the entire saltatory foraging cycle. 3. Results 3.1. Depth distribution and vertical motion Larvae were observed throughout the tank. The median depth, relative to the top of the tank, of each larva during the day 2 observation period are shown in Figs. 7 and 8 as are the evening temperature profiles and prey density profiles for

each experiment. There was no significant relationship between the median depth of larvae within each experiment and age for any of the prey treatments (fasted: R2 ¼ 0:31, P ¼ 0:0967, n ¼ 10; rotifer-fed: R2 ¼ 0:07, P ¼ 0:4554, n ¼ 10; nauplius-fed: R2 ¼ 0:85, P ¼ 0:0767, n ¼ 4). Pooling ages, there was no significant difference between the three prey treatments (ANOVA: F 2;22 ¼ 0:4468, P ¼ 0:6453; data in each group were normally distributed). There is a potential bias in the depth distribution data; the probability of simultaneously observing a larva in both of the paired cameras was greater for larvae nearer the surface of the water-column. Swimming motion was predominately in the horizontal plane. The ratio of horizontal to vertical displacement was calculated for each individual. Within each experiment, the median ratio ranged from roughly 3–10; however, this may overestimate vertical motion given that there was greater uncertainty in the estimates of vertical position than in the measurements of horizontal position. Given this caveat, there was no significant relationship between the ratio of horizontal-to-vertical motion and age for the fasted (R2 o0:01,

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Fig. 7. Depth profiles of larvae and temperature relative to the top of the observation tank for the zero prey experiments. ‘+’ symbols are median depths of every larva observed for X1 min within during the primary behavior observation period (day 2). Dashed line is the temperature profile.

P ¼ 0:9154, n ¼ 10) nor for the rotifer-fed larvae (R2 ¼ 0:08, P ¼ 0:4254, n ¼ 10), and there was no difference between the fasted and rotifer treatments (t-test: P ¼ 0:1999, df ¼ 17; ages pooled). The nauplius-fed larvae swam in significantly flatter paths as larvae grew (R2 ¼ 0:98, P ¼ 0:0115, n ¼ 4). 3.2. Effect of the presence or absence of prey The mean behavior parameter values observed on day 2 of each experiment, when prey treatments were in effect, are shown in Table 2. The relationship of each parameter with age and with length was described with linear least-squares regression (Tables 3 and 4, Fig. 9A–F). The data met the assumptions of a linear regression. Probability plots showed that standardized residuals were either normally (peak burst speed, pause duration, pause frequency, and fraction of time spent swimming) or log-normally distributed (burst distance and burst duration), and plots of standardized residuals against expected response showed that variance was reasonably homogeneous across the full range of ages and lengths studied. Generally, burst characteristics were only weakly correlated with either age or length or were not significantly correlated at all. Peak burst speed (mm s1) was not significantly related to age or length. Burst duration (s) was significantly related to age in the prey treatment while burst duration and

distance were significantly related to length in the no prey treatment (Tables 3 and 4, Fig. 9C–E). None of the swimming burst characteristics were significantly different between larvae swimming in the presence or the absence of rotifer prey (Table 5). Pause duration (s) and frequency (pause min1) were significantly related to age under both prey treatments but were significantly related to length only in the absence of prey (Tables 3 and 4, Fig. 9A and B). Pause events were of significantly longer duration and of lower frequency in the absence of prey than when prey were present (Table 5). In the absence of prey, pauses were approximately 70% longer among 5-day-old cod larvae (declining to E20% longer by 45 days) and were less frequent, larvae making E26% fewer pauses per unit time at 5 days old (E6% fewer by 45 days). No formal statistical comparisons were made using the wild zooplankton treatments because only four zooplankton experiments were run. However, in every measured parameter, the behavioral response of larvae to wild zooplankton was qualitatively similar to larvae preying upon rotifers (Fig. 9A–F). 3.3. Effect of the fasting period The mean behavior parameter values from the subset of experiments observed on day 1, before prey were introduced, are shown in Table 6. The

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Fig. 8. Depth profiles of larvae, prey concentration, and temperature relative to the top of the observation tank for the rotifer (top 2 rows) and zooplankton (bottom row) prey experiments. ‘+’ symbols are median depths of every larva observed for X1 min within during the primary behavior observation period (day 2). Dashed line is the temperature profile. Solid line is the prey density measured in the evening of experiment day 2.

day 1 data set was analyzed using the same procedure as the principle, day 2, data set. Burst distance and duration were significantly correlated with age (R2 ¼ 0:65) but not with length. As on day 2, peak burst speed was not correlated with age or length. Pause duration was significantly correlated with age and length, but pause frequency was not. The overall fraction of the total time spent actively swimming in burst mode was significantly related to age but not length. To examine the effect of fasting time on searching behavior, the behavior of larvae observed on day 1, fasted 12 h, was compared to larvae in the no prey treatment experiments on day 2, fasted 36 h, using

ANCOVA (Table 7). Burst characteristics were not different, but pauses were significantly shorter and more frequent on day 1 than day 2. There was no difference in the fraction of time actively swimming among larvae fasted 12 and 36 h. 4. Discussion 4.1. Observing behavior in large volumes The large-volume observation system (250 L) was developed to allow detailed, spatially and temporally defined observations of larval cod behavior in the lab while reducing potential confinement and

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Table 2 Summary of the larval cod foraging behavior parameters observed during each experiment on day 2 Age

n

Pause frequency Pause duration (s) (min1)

Burst distance (mm)

Peak burst (mm s1)

Burst duration (s)

Fraction of time swimming

A B C D

7 8 5 4

53 38 13 15

33710 4276 46710 4874

1.670.7 1.070.3 0.970.8 0.670.1

2.970.9 3.771.6 5.171.8 5.872.0

7.872.0 8.773.4 11.073.2 11.272.6

0.470.0 0.570.2 0.670.1 0.670.1

0.2370.08 0.3470.12 0.4570.13 0.5170.08

E F G H I

12 17 15 14 13

8 15 19 21 23

36712 3374 4876 4678 5073

1.270.9 1.470.3 0.670.2 0.870.4 0.670.1

5.874.1 2.470.4 6.173.3 4.971.9 4.471.8

9.373.1 5.870.9 11.373.0 11.573.7 9.673.2

0.670.2 0.470.0 0.670.1 0.570.1 0.670.1

0.3970.19 0.2470.04 0.5070.09 0.4270.10 0.4870.10

J K L M N O

22 24 24 22 23 27

18 7 7 5 16 11

4975 36712 3875 5172 4578 4575

0.570.2 1.470.6 1.270.2 0.670.1 0.770.4 0.870.2

7.372.8 3.170.8 3.571.1 4.071.2 6.474.4 4.472.2

15.175.0 7.071.5 8.772.8 8.672.4 10.974.1 8.973.6

0.770.1 0.570.0 0.470.0 0.670.1 0.770.2 0.670.2

0.5870.10 0.3070.11 0.2970.03 0.4670.06 0.5070.13 0.4370.13

P Q R S

31 34 34 32

27 19 22 22

42712 46711 4778 5174

1.171.2 0.971.2 0.670.2 0.670.1

6.272.9 6.372.2 8.5710.3 5.271.9

14.175.6 12.573.2 13.375.0 10.673.0

0.670.2 0.770.1 0.870.5 0.670.1

0.4470.18 0.5070.15 0.5570.13 0.5070.08

T U V W X

36 44 45 44 41

27 21 12 27 13

5075 4479 4876 5074 4976

0.570.1 0.770.4 0.770.2 0.670.1 0.570.1

7.372.1 8.677.0 4.673.0 5.371.4 7.873.6

14.573.4 15.875.9 9.474.1 10.772.5 13.273.4

0.770.1 0.870.5 0.670.2 0.670.1 0.870.2

0.5870.08 0.5370.17 0.4470.12 0.5170.08 0.6170.11

Exp.

Values are the mean of all individuals observed for X1 min during each experiment (71 SD).

crowding effects. Most previous studies of the foraging behavior of small cod larvae have been conducted in small volumes (0.1 L, Ellertsen et al., 1980; 3.5 L, Solberg and Tilseth, 1984; 8 L, Browman et al., 2003) or stocked at high larval densities (40 L1, Puvanendran et al., 2002) (though MacKenzie and Kiørboe, 1995, use a 30-L tank and Munk, 1995, uses a 172-L tank). However, there has not yet been a controlled study of crowding effects on larval fish behavior, and its importance to laboratory studies remains speculative. Additionally, the varied goals of other behavior studies structure the methods they employ; potential crowding effects are not necessarily of primary concern to a particular study. Observation in a large volume did limit the type of data that could be collected. Prey organisms could not be observed; therefore direct measurements of search-volume geometry and reactive distance could not be made. Also, body orientation could not be well resolved, so no attempt was made to collect data on attack posturing rate, which

can serve as a measure of prey encounter rate (MacKenzie and Kiørboe, 1995). Tracking larval positions on a series of video images provided not only measurements of the frequency and duration of different behavioral events but also accurate measurements of the distances and swimming speeds involved. Collecting data from a video record also provided data with a fairly high time resolution (0.2667 s) and eliminated any error and bias of human response time that could be introduced when quantifying behavior by eye in real time. Stereo-video cameras allowed recording of larval position in three dimensions, but vertical position was the least precise. Though uncertainty in vertical position was large (73–7 cm), it contributed little to the uncertainty in the horizontal axis calibration. Also, because swimming activity was predominately in the horizontal plane, depth-position data was used only to calibrate horizontal distances. Another depth-related consideration is that depth distribution data was biased to represent larvae closer to the surface of the tank. As the volume of overlapping

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Table 3 Linear least-squares regression parameters (795% CI) of key behavior parameters observed during day 1 (pre-treatment, no prey) and day 2 (prey treatment in effect) regressed against age Behavior parameter Pause characteristics Pause duration (s) ¼ a+b  age

Pause frequency (min1) ¼ a+b  age

Burst characteristics Burst duration (s) ¼ a  eb  age

Peak burst (mm  s1) ¼ a+b  age

Burst distance (mm) ¼ a  eb  age

Fraction time swimming ¼ a+b  age

Treatment

Relation

R2

P

n

95% CI of a

95% CI of b

No prey (day 1) No prey (day 2) Rotifers (day 2) Nauplii (day 2) No prey (day 1) No prey (day 2) Rotifers (day 2) Nauplii (day 2)

1.11–0.012  age 1.60–0.020  age 0.92–0.010  age 0.66–0.001  age 40.9970.152  age 31.3970.346  age 44.2270.131  age 48.0270.008  age

0.49 0.52 0.75 0.02 0.30 0.51 0.41 o0.01

0.0237 0.0192 0.0013 0.8494 0.0992 0.0201 0.0470 0.9548

10 10 10 4 10 10 10 4

70.28 70.45 70.12 70.57 75.07 77.89 73.49 712.92

70.010 70.016 70.005 70.024 70.188 70.276 70.129 70.550

No prey (day 1) No prey (day 2) Rotifers (day 2) Nauplii (day 2) No prey (day 1) No prey (day 2) Rotifers (day 2) Nauplii (day 2) No prey (day 1) No prey (day 2) Rotifers (day 2) Nauplii (day 2)

0.44  e0.012  age 0.43  e0.009  age 0.51  e0.007  age 0.58  e0.002  age 9.69+0.093  age 6.73+0.147  age 9.76+0.052  age 9.50+0.077  age 3.34  e0.019  age 2.81  e0.016  age 3.99  e0.010  age 4.48  e0.006  age

0.65 0.30 0.43 0.09 0.33 0.27 0.20 0.1 0.66 0.27 0.36 0.10

0.0046 0.1023 0.0382 0.6942 0.0817 0.1232 0.1941 0.5627 0.0041 0.1221 0.0666 0.6888

10 10 10 4 10 10 10 4 10 10 10 4

0.37–0.54 0.32–0.59 0.43–0.60 0.35–0.96 72.92 775.63 72.27 711.27 2.50–4.47 1.52–5.20 2.99–5.32 1.28–15.72

70.007 70.011 70.006 70.022 70.108 70.196 70.084 70.480 70.011 70.022 70.011 70.053

No prey (day 1) No prey (day 2) Rotifers (day 2) Nauplii (day 2)

0.31+0.006  age 0.24+0.006  age 0.39+0.004  age 0.47+0.001  age

0.64 0.37 0.59 0.09

0.0057 0.0620 0.0096 0.7047

10 10 10 4

70.10 70.18 70.08 70.31

70.004 70.006 70.003 70.013

Bold underlined probabilities are significant (a ¼ 0:05).

visual fields increased with distance from the paired cameras, the chances of a larva being seen by both cameras simultaneously decreases with depth. Generally, for each experiment E25% of the larvae were outside the overlapping fields (maximum 55% for exp. M, minimum 5% for exp. H). There was no map of the overlapping visual fields created for this study to correct for this depth-distribution bias. An untested assumption of this study and all previous larval behavior studies is that behavior was independent of position within the tank. 4.2. Larval cod as saltatory predators Larval cod are pause-travel, or saltatory, predators (unpublished data cited in Browman and O’Brien, 1992; MacKenzie and Kiørboe, 1995; Munk, 1995). Saltatory predators travel in short, discrete episodes, and search for prey only during the periods between swimming bursts (Evans and O’Brien, 1988; Hunt von Herbing and Gallager,

2000). Cruise predators, on the other hand, swim continuously while scanning for prey at the periphery of their search space (Rosenthal and Hempel, 1970). A saltatory foraging strategy conveys several advantages. First, there is an energetic advantage; it fits well with the burst-and-glide swimming pattern of larval cod noted by previous studies (Solberg and Tilseth, 1984; MacKenzie and Kiørboe, 1995; Munk, 1995; Puvanendran and Brown, 1999; Hunt von Herbing and Gallager, 2000). Burst-and-glide swimming is the most energy efficient and dominant swimming strategy for foraging larvae (Vlymen, 1974; Weihs, 1980; Videler and Weihs, 1982; Webb and Weihs, 1986). Second, saltatory foraging allows stabilization of the visual field. Visual acuity may be reduced as swimming speed increases (Gendron and Staddon, 1983; Anderson et al., 1997) but among saltatory foragers, which search while at rest or at very slow gliding speeds, this potential problem is avoided. Third, saltatory foraging may offer a better opportunity to practice prey choice; prey

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Table 4 Linear least-squares regression parameters (795% CI) of key behavior parameters observed during day 1 (pre-treatment, no prey) and day 2 (prey treatment in effect) regressed against total length Treatment

Relation

R2

P

n

95% CI of a

95% CI of b

No prey (day 1) No prey (day 2) Rotifers (day 2) Nauplii (day 2) No prey (day 1) No prey (day 2) Rotifers (day 2) Nauplii (day 2)

1.61–0.132  length 3.17–0.344  length 1.16–0.077  length 1.05–0.071  length 34.29+1.720  length 1.92+6.317  length 40.07+1.207  length 41.47+1.154  length

0.40 0.43 0.31 0.14 0.28 0.48 0.22 0.07

0.0500 0.0398 0.0939 0.6302 0.1157 0.0254 0.1684 0.7316

10 10 10 4 10 10 10 4

70.81 71.98 70.57 73.17 713.74 732.54 711.24 773.66

70.132 70.324 70.094 70.542 72.248 75.316 71.838 712.605

No prey (day 1) No prey (day 2) Rotifers (day 2) Nauplii (day 2)

0.33  e0.095  length 0.16  e0.205  length 0.45  e0.047  length 0.38  e0.080  length

0.31 0.47 0.14 0.21

0.0946 0.0278 0.2802 0.5439

10 10 10 4

0.16–0.67 0.05–0.50 0.25–0.80 0.02–6.20

70.116 70.176 70.094 70.478

Peak burst (mm s1) ¼ a+b  length

No prey (day 1) No prey (day 2) Rotifers (day 2) Nauplii (day 2)

7.03+0.815  length  8.62+3.146  length 9.30+0.280  length  3.48+2.494  length

0.18 0.35 0.04 0.36

0.2189 0.0701 0.5874 0.4015

10 10 10 4

78.61 721.25 76.98 759.34

71.409 73.472 71.141 710.155

Burst distance (mm) ¼ a  eb  length

No prey (day 1) No prey (day 2) Rotifers (day 2) Nauplii (day 2)

2.03  e0.151  length 0.42  e0.380  length 3.40  e0.065  length 1.53  e0.205  length

0.34 0.43 0.10 0.22

0.0762 0.0392 0.3648 0.5341

10 10 10 4

0.69–6.03 0.05–3.70 1.30–8.90 0.00–1530.3

70.178 70.356 70.157 71.183

Fraction time swimming ¼ a+b  length

No prey (day 1) No prey (day 2) Rotifers (day 2) Nauplii (day 2)

0.14+0.053  length 0.39+0.129  length 0.29+0.032  length 0.20+0.052  length

0.34 0.48 0.23 0.24

0.0796 0.0264 0.1604 0.5119

10 10 10 4

70.37 770.67 70.29 71.66

70.061 70.110 70.047 70.285

Behavior parameter Pause characteristics Pause duration (s) ¼ a+b  length

Pause frequency (min1) ¼ a+b  length

Burst characteristics Burst duration (s) ¼ a  eb  length

Bold underlined probabilities are significant (a ¼ 0:05).

can be located throughout the search space, and there is greater chance of encountering multiple prey items simultaneously. And fourth, saltatory foraging is a flexible search strategy. Both burst characteristics (speed, duration, and distance) and pause characteristics (duration and frequency) can be modified to fit foraging conditions (O’Brien et al., 1989, 1990). This study looked at how larval cod search behavior changes in response to the presence or absence of prey and to hunger. Swimming bursts did not change significantly in response to prey treatment in terms of speed, duration, or distance traveled (Table 5). Because saltatory foragers only search for prey during pauses when speed is zero, or very low, variation in burst speed and distance should have little effect on foraging success under different prey densities (O’Brien et al., 1986). After a fasting period of 1.5 days, the absence of prey had a significant effect upon pause duration and frequency (Table 5, Fig. 9A–B). Pause duration was

longer (E70% longer at 5 days old and E20% longer at 45 days) and pause frequency was lower (27% lower at 5 days old and 6% lower at 45 days). However, after a shorter fasting period of only onehalf day, larval behavior in the absence of prey more closely resembled that of larvae in the presence of prey (Tables 3 and 4). Unlike the present study, Munk (1995) found that the foraging activity of small cod larvae (5.7–6.6 mm) was greater at low prey densities (pauses of shorter duration and greater frequency; his ‘Series I’ experiments). Observations by Galbraith et al. (2004) suggest no substantial difference in the pause duration of fed cod larvae (6 mm) regardless of prey density treatment (10, 100, and 600 nauplii L1). MacKenzie and Kiørboe (1995) observed no consistent behavioral response to prey density (4–35 and 35–1500 nauplii L1) among small cod larvae (5.2 and 6.1 mm). There are several considerations that must be made when comparing studies of larval foraging behavior. First, feeding

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Fig. 9. Foraging behavior plotted against age for no prey (circle symbol, dash–dot line), rotifer prey (square symbol, solid line), and zooplankton prey (diamond symbol, dashed line) feeding treatments. Each point represents the mean response of all individuals observed within one experiment (795% CI). The relationships are described with linear least-squares regression (Table 4). Regression lines were plotted when significant (a ¼ 0:05), mean response across all ages were plotted when regression was insignificant. (A) Pause duration, (B) pause frequency, (C) burst distance (log-transformed), (D) burst speed, (E) burst duration (log-transformed), (F) fraction of time spent swimming.

history and hunger state may differ between studies. Larvae were pre-fasted in the present study but not in Munk’s ‘Series I’ study. Hunger is an important behavioral stimulus and will be discussed in more detail below. Second, there are methodological differences between studies. Munk (1995) and

MacKenzie and Kiørboe (1995) interpreted larval cod behavior by eye in real time; Browman et al. (2003), Galbraith et al. (2004), and the present study used pre-defined computer algorithms to interpret behavior from digitized video records. The protocol for classifying swimming state as ‘burst’ or ‘pause’

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Fig. 9. (Continued)

are not always defined, vary between studies, and may account for some difference among results. Finally, turbulence affects behavior. Munk’s observations were made under conditions of turbulence while the present observations were made in calm water. Under conditions of moderate turbulence, search activity increases (greater pause frequency, shorter pause duration; MacKenzie and Kiørboe, 1995). But the MacKenzie and Kiørboe study did not show a consistent behavioral response

to prey density in either calm or turbulent waters, and there is no evidence that the behavioral response to prey density changes under turbulent conditions. 4.3. Foraging capacity What effect does the observed variability of foraging behavior have upon overall foraging capacity? Larval cod respond to prey only when

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Fig. 9. (Continued)

not actively swimming (MacKenzie and Kiørboe, 1995; Hunt von Herbing and Gallager, 2000), therefore foraging capacity (ml min1) is a function of pause frequency (PF) rather than swimming speed. Cod larvae perceive prey 30–901 off of the longitudinal axis of the body horizontally and vertically (Hunt von Herbing and Gallager, 2000). The search-volume can therefore be modeled as a half-sphere in front of the larva extending as far as the reactive distance (R, the distance at which larvae

react to perceived prey). This search space geometry is that used in the behavior study of MacKenzie and Kiørboe (1995) and has been used in recent trophodynamic models for larval cod (e.g., Werner et al., 2001; Lough et al., 2005). The search rate can be modeled as Foraging capacity ¼ ð2=3Þ  p  R3  PF.

(1)

The reactive distance could not be measured in the present study; however, it may be estimated as the

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distance traveled between search-volumes if consecutive volumes are assumed to neither overlap nor leave unsearched water in between. This strategy

should be the energetically most efficient, minimizing the distance traveled to search a given volume of water (O’Brien et al., 1990).

Table 5 Test of prey treatment effect

Table 7 Test of hunger effect

Behavior parameter

Pause characteristics Pause duration Age Length Pause frequency Age Length Burst characteristics Burst duration (logtransformed) Age Length Peak burst speed Age Length Burst distance (logtransformed) Age Length Fraction of time swimming Age Length

Equal slopes P(df ¼

1,16)

Equal Coincidence elevation P(df ¼ 1,16) P(df ¼ 2, 16)

0.1512 0.0548

0.0002 0.0012

0.0005 0.0016

0.1164 0.0334

0.0003 0.0007

0.0006 0.0008

0.7086 0.0796

0.0907 0.1141

0.2153 0.0703

0.3079 0.0602

0.5688 0.6467

0.5001 0.1484

0.5458 0.0630

0.1474 0.1697

0.2873 0.0766

0.5154 0.0600

0.0129 0.0197

0.0356 0.0161

(ANCOVA comparison of the linear regressions of each behavioral parameter (observed on experiment day 2) against age and length for cod larvae in the presence and absence of rotifer prey. Bold, underlined probabilities are significant (a ¼ 0.05)).

Behavior parameter

Pause characteristics Pause duration Age Length Pause frequency Age Length Burst characteristics Burst duration (logtransformed) Age Length Peak burst speed Age Length Burst distance (logtransformed) Age Length Fraction of time swimming Age Length

Equal slopes P(df ¼ 1,16)

Equal elevation P(df ¼ 1,16)

Coincidence P(df ¼ 2,16)

0.2752 0.1239

0.0174 0.0342

0.0352 0.0390

0.1392 0.0529

0.0254 0.0360

0.0331 0.0232

0.7398 0.2315

0.1747 0.2410

0.3677 0.2497

0.4752 0.1171

0.3004 0.3336

0.4499 0.1864

0.9470 0.1699

0.1135 0.1436

0.2744 0.1415

0.8708 0.1476

0.0856 0.1222

0.2154 0.1145

Bold, underlined probabilities are significant (a ¼ 0:05).(ANCOVA comparison of the linear regressions of each behavioral parameter against age and length for cod larvae in the absence of prey on experiment day 1 (fasted 12 h) and experiment day 2 (fasted 36 h)).

Table 6 Summary of the larval cod foraging behavior parameters observed during each experiment on day 1 Exp.

Age

n

Pause frequency Pause duration (s) (min1)

Burst distance (mm)

Peak burst (mm s1)

Burst duration (s)

Fraction of time swimming

A B F G L M P S W X

6 7 16 14 23 21 30 31 43 40

40 26 21 9 10 4 19 9 5 3

4277 4477 4279 42711 40711 5075 42 714 4877 4772 4674

3.771.4 3.871.8 3.871.0 6.972.0 5.073.0 6.672.4 6.673.2 7.273.5 7.372.0 6.271.8

8.972.2 10.174.5 9.472.7 11.972.4 11.276.3 14.075.5 14.175.2 12.872.3 13.072.6 10.372.3

0.570.1 0.570.1 0.570.0 0.870.2 0.670.2 0.670.1 0.670.2 0.770.2 0.770.1 0.770.1

0.3470.09 0.3470.10 0.3470.09 0.5670.20 0.3870.17 0.5370.06 0.4370.17 0.5470.10 0.5770.10 0.5670.07

1.070.3 0.970.4 1.070.4 0.870.8 1.170.7 0.670.1 1.171.1 0.670.2 0.570.1 0.670.1

Values are experiment means of all individuals observed for at least 45 s in each experiment (71 SD).

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Foraging capacity was estimated for every individual larva (Table 8) based upon the measured pause frequencies for each individual and the mean burst distances at each age (Table 6). A comparison of log-transformed mean estimated foraging capacities of each experiment (with age as a covariate) showed that foraging capacity was significantly higher under high prey densities (ANCOVA: equal slopes, F 1;16 ¼ 58:8236, P50:0001; equal intercepts, F 1;16 ¼ 377:0445, P50:0001). But the value of this statistical comparison is of little use because by using mean burst distance as an estimate of individual reactive distance (R), the variability between experiments is reduced to that of pause frequency alone. Foraging capacity is very sensitive to R (Eq. (1)), and measurement error of burst distance is magnified to the third power in calcula-

tions of foraging capacity. Ninety-five percent prediction intervals around foraging capacities estimated using individual measurements of burst distance span more than two orders of magnitude and prey treatments are not significantly different. Because foraging capacity is geometrically related to reactive distance, estimates of foraging capacity will be most improved with accurate measurements of R, and understanding R changes with light intensity, prey size, and prey motion. Unfortunately, there are still no accurate, direct measurements of R and the size of larval cod searchvolumes. The most precise published measurements (Hunt von Herbing and Gallager, 2000) show that as larval cod grow their visual acuity increases, and reactive distance increases from about 1 body length (at 5 mm total length) to 2.5 body lengths (at 8 mm)

Table 8 Comparison of the foraging capacity of larval cod as measured by the current study and as estimated from data reported in the literature Larval size (mm)

Present study (no prey) 6 days old 14 days old 24 days old 33 days old 44 days old

5.2 5.3 5.9 6.6 7.2

3.1 3.5 4.1 4.8 5.7

0.06 0.09 0.14 0.23 0.39

33.5 36.2 39.7 42.8 46.6

2.0 3.2 5.6 9.4 17.2

(1.5–2.7) (2.4–4.3 (4.3–7.4) (7.1–12.5) (12.7–23.3)

Present study (high prey) 6 days old 14 days old 24 days old 33 days old 44 days old

5.2 5.3 5.9 6.6 7.2

4.2 4.6 5.0 5.5 6.1

0.29 0.31 0.43 0.60 0.78

48.6 49.0 49.4 49.8 50.3

7.0 9.1 12.6 16.9 23.8

(6.1–8.0) (8.0–10.4) (11.1–14.3) (14.8–19.2) (20.6–27.4)

4.8

4.8

0.23

20

0.26–1.07 0.26–1.07

55 40

26–31 21–39

Solberg and Tilseth (1984) Munk (1995) Low prey High prey MacKenzie and Kiørboe (1995) Low prey High prey Hunt von Herbing and Gallager (2000)

5.7–6.6 5.7–6.6

Reactive distance (mm)

5–8 5–8

Search space volume (ml)

Pause frequency (min1)

Foraging capacity (95% PI) (ml min1)

Study

4.6

14.4–59.0 10.5–42.9

5.2–6.1 5.2–6.1

4.3–4.8 4.3–4.8

0.17–0.23 0.17–0.23

4.3–7.2 3.5–9.0

5.0–6.0

7.1

0.75

33

24.7

6.0–7.0 7.0–8.0

9.8 14

1.97 5.75

21 37

41.4 212.6

In the present study, point estimates and 95% prediction intervals of foraging capacity were calculated at the mean age of each of the 5 age groups. Foraging capacity was estimated for every larva from its observed pause frequency and its estimated reactive distance. Reactive distance was assumed to equal the burst distance as calculated from the regression relation presented in Table 3. Ninety five percent prediction intervals were calculated from the log-transformed mean pause frequency of all larvae within each experiment. (Pause frequency presented in the table was calculated from the regression relation presented in Table 3.)

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(Table 8). These values are considerably higher than the estimates used in the present study and higher than the 0.8 body length estimated by MacKenzie and Kiørboe (1995) which has been used in subsequent trophodynamic models (Werner et al., 2001; Lough et al., 2005). In the Hunt von Herbing and Gallager (2000) study, the foraging environment was illuminated laterally with bright prey against a black background, and prey items may have been visible at a greater distance than under natural conditions. Munk’s (1995) estimates also may be high as they were calculated from prey encounter rates which may have been enhanced in the turbulent conditions of the observation tank. Galbraith et al. (2004) propose a wedge-shaped search volume based upon evidence from unpublished laboratory observations; for a 6-mm larval cod, a wedge of 901 wide, 201 tall, and 10 mm radius would provide a search volume of 0.18 ml/search cycle. Though a wedge, this volume is similar to that of the hemisphere used in the present study because of the smaller estimated reactive distance, R (Table 8). For larvae with comparable R, however, the wedge geometry necessarily provides a much lower forage capacity than the hemispherical geometry. Larvae may have behavioral strategies and abilities that enhance forage capacity without adding substantially to the cost of foraging activity, but these are speculative. By changing body orientation vertically or horizontally, a new volume may be scanned before the larva moves to a new location. This body reorientation behavior would be of greater advantage if the search volume is a wedge rather than a hemisphere (Galbraith et al., 2004).

and predicted that as prey density increases, pause duration should decrease while pause frequency increase. Non-fasted cod larvae increase swimming activity and foraging effort as prey become more scarce (Munk, 1995; his ‘Series I’ experiments), thereby increasing the chance of encountering scarce prey. In contrast, when cod larvae are denied prey for a period of time, as in the present study, pause frequency, swimming activity, and foraging capacity are reduced (Fig. 9b). This may be an energy conservation strategy similar to the general strategy predicted by the Pitchford et al. (2003) and Anderson et al. (1997) optimum foraging models. Hungry larvae reduce swimming activity until foraging conditions improve. Alternatively, pause frequency may be lower as a consequence of larvae pausing longer during each search-cycle to spend more time visually processing each search-volume. In many saltatory foraging taxa, pause duration increases under more difficult foraging conditions such as when searching for smaller prey or searching in visually complex environments (review by O’Brien et al., 1990). These two hypotheses are not mutually exclusive. Unlike some other, larger predators, small fish larvae are not realistically likely to have the awareness of conditions beyond their immediate surroundings (beyond R) to allow them to adopt the optimum foraging behavior for a given set of conditions (Pitchford et al., 2003). The implication of this is that larvae are probably not capable of adopting the theoretically optimum behavior by choice but function within a range of viable but suboptimal foraging strategies (Pitchford et al., 2003).

4.4. Behavioral plasticity

4.5. Other factors that may affect foraging behavior

Both the present and previous studies (MacKenzie and Kiørboe, 1995; Munk, 1995) show that the saltatory search cycle of larval cod is plastic. Does the observed behavioral plasticity increase foraging efficiency (prey encounter rate relative to foraging effort)? Pitchford et al. (2003) developed a model of optimal foraging strategy in patchy and turbulent environments for cruise-foraging larvae. Foraging efficiency is optimized by increasing swimming effort under favorable foraging conditions (e.g., increased mean overall density or encounter with a prey patch) and by reducing swimming effort under unfavorable foraging conditions. Anderson et al. (1997) developed a model of optimum saltatory search behavior

In the ocean environment larval cod must contend with more than just variation in prey density. Turbulence, hunger, physiological condition and growth history, light intensity, and the cost of swimming are some of the other important factors that can affect foraging behavior and success. Larvae in the ocean are often observed feeding near their maximum rate independent of prey concentration, and field ingestion rates are generally higher than observed in the laboratory (MacKenzie et al., 1990). Moderate small-scale turbulence can enhance prey-encounter rates by increasing the relative motion between predator and prey, carrying prey into the larval search-volume (Rothschild and

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Osborn, 1988). The prey encounter rate for saltatory foragers in a moderately turbulent environment has been modeled and measured in laboratory studies (MacKenzie and Kiørboe, 1995): E ¼ ð2=3Þ  p  R3  N  PF þ p  R2  N  PF  PD  ðu2 þ v2 þ 2w2 Þ0:5

ðtheir eq:1bÞ,

ð2Þ

where E is the encounter rate (s1), R is the reactive distance (m), N is the prey density (m3), PF is the pause frequency (s1), PD is the pause duration (s), u is the prey speed (m s1), v the larval speed (m s1), w2 is the turbulent velocity ¼ 3.615  (e  R)0.667, and e is the turbulent energy dissipation rate (m2 s3). The observations in the present study were made under non-turbulent conditions, but are there behavioral strategies that would allow larval cod to further increase the derived benefit of turbulent conditions? MacKenzie and Kiørboe (1995) is the only study that has observed the searching behavior of larval fish under both calm and turbulent conditions. Turbulence appears to stimulate swimming activity in larval cod; pause and swimming-burst frequency was greater and pause duration was shorter. The relative increase in prey encounter rate in turbulent conditions as a function of turbulent velocity and behavior can be expressed as E turbulent =E calm ¼ PFturbulent  ½ð2=3Þ  R þ PDturbulent  ðv2 þ 2w2 Þ0:5  C½ð2=3Þ  R  PFcalm  ðtheir eq: 1bÞ.

ð3Þ

Eq. (3) suggests that increasing pause frequency allows turbulence to have an even greater positive effect on prey encounter rate. However, there is a limit to how much pause frequency can be increased before foraging success is negatively impacted. Pause duration must decrease as pause frequency increases (burst duration remained unchanged in the MacKenzie and Kiørboe study), and the ability of a forager to successfully recognize and capture prey declines as time spent observing a given region decreases (review by O’Brien et al., 1990; Anderson et al., 1997). Hunger is a powerful force that both limits and motivates changes in foraging strategy. On short time scales, as with healthy larvae with recently emptied guts, hunger may act as a signal to adjust behavior to fit local conditions. Munk (1995) found that hunger stimulated activity in larval cod (his

‘Series III’ experiments). On the longer time scales imposed in the present study, hunger suppressed foraging capacity. Pauses became significantly longer and less frequent after fasting 36 h (experiment day 2) compared to larvae fasted 12 h (experiment day 1; Table 7). Within 5 h after the introduction of prey on day 2, foraging activity increased substantially showing that larvae could quickly recover from short-term fasting periods. On even longer time scales of several days, as with chronically starved cod larvae in weak condition, swimming activity is suppressed (Skiftesvik and Huse, 1987; Skiftesvik, 1992) indicating either an energy-conservation strategy or inability to maintain high activity levels because of limited energy resources. Larval condition and growth history may affect foraging behavior and ability. The growth rate of the larvae observed in the present study was similar to that of cod larvae in other laboratory-based behavior studies (e.g., MacKenzie and Kiørboe, 1995; Munk, 1995; Browman et al., 2003) but lower than rates observed in the ocean (Bolz and Lough, 1988) and lower than their potential (Folkvord, 2005). Do the larvae observed in this and similar laboratory studies have a different behavioral repertoire and foraging capacity than faster growing larvae in the ocean? We are aware of no study that has compared the foraging behavior of larvae with different growth histories under identical prey densities. Our observations show a trend towards greater activity and a higher foraging capacity (greater pause frequency) with length, but this was not significant over the size range of larvae studied here (Tables 2 and 4). Puvanendran and Brown (1999) observed increasing swimming activity and foraging success as cod larvae grew in high prey density laboratory experiments. Faster growth and larger size leads to greater foraging capability, which in turn leads to a greater opportunity for larvae to attain their growth potential. But at low growth and development rates, at what point does the behavioral repertoire become restricted so that larvae cannot rapidly respond to a change in foraging conditions? Pitchford et al. (2005) show that prey-field variability leads to increased growth and recruitment probability. This effect should be enhanced if larvae can respond by adjusting foraging effort on similar time-scales. In a prey poor but highly variable environment, can small fish larvae take advantage of a strategy of budgeting foraging effort and maintain the energy reserves

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necessary to respond to ephemeral increases in local prey abundance? Cod larvae are primarily visual predators. Laboratory observations show that visual cues, independent of hunger, can stimulate activity. The presence of prey stimulates swimming activity among pre-feeding cod larvae (jaw not yet functional) by 15% over those observed in the absence of prey (Skiftesvik and Huse, 1987; Skiftesvik, 1992). Light intensity can have a large effect upon foraging success by directly affecting the reactive distance, R, at which prey can be perceived and attacked (Eq. (1)). Reactive distance declines with depth and turbidity (Fiksen et al., 1998); therefore vertical swimming behavior could have a large effect on foraging capacity. Larval cod show diurnal migration by the time they reach 9 mm, moving deeper by day and shallower by night (Lough and Potter, 1993). In laboratory studies, activity and feeding success of cod and herring larvae increase with light intensity (Batty, 1987; Puvanendran and Brown, 2002), but feeding is inhibited under high light intensity (Huse, 1994, study of 5-day-old cod larvae). However, the available field data do not make clear that cod larvae migrate to follow an isolume to maximize feeding or avoid light inhibition. Also, there has been no study of how the various components of the foraging cycle and reactive distance change with light intensity. Foraging in very low light levels may be analogous to foraging in a visually complex habitat or for small, cryptic prey. Under these conditions, saltatory foraging theory predicts that net energy gain is maximized as pause duration increases and the distance traveled between consecutive search-volumes decreases (O’Brien et al., 1989). Finally, how much does the cost of swimming limit the behavioral repertoire of larval cod? Small fish larvae live in a hydrodynamic environment where the frictional forces acting against motion are substantial, and the cost of swimming is much higher than for adult fish (Dabrowski, 1986a, b; Kaufmann, 1990; Kaufmann and Wieser, 1992; Hunt von Herbing and Boutilier, 1996; Mu¨ller et al., 2000; Ruzicka, 2004). In their theoretical model, Anderson et al. (1997) predict that as cost of swimming increases, saltatory foraging in general becomes more economical relative to cruise foraging and, specifically, pause duration should increase and burst frequency decrease. As larval cod grow, they become better able to swim out of the viscous

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hydrodynamic regime and incur a lower cost for swimming (Ruzicka, 2004). In the present study, pause duration was significantly longer for smaller larvae (and pause frequency less, Fig. 9A–B), perhaps reflecting greater energetic constraint upon behavior among the smaller larvae due to their higher cost of transport. 5. Conclusions The components of the larval cod saltatory search cycle change in response to foraging conditions (presence or absence of prey) in a manner generally consistent with saltatory foraging theory (e.g., O’Brien et al., 1986, 1990). Saltatory predators do not search for prey during periods of movement, and larval cod swimming bursts (peak speed, duration, and distance) remained the same regardless of the presence or absence of prey and regardless of the fasting period. Pauses, when cod larvae search for prey, last longer and are less frequent as the period prey are withheld increases from 12 to 36 h. This may be a search strategy, perhaps reflecting greater time investment to process search-volumes more thoroughly when prey are harder to find. Alternatively, this may be an energy conservation strategy or a limitation imposed upon foraging behavior by low energy reserves. To understand better larval foraging capacity in a dynamic environment, future research should refine measurements of reactive distance, study the role and impact of turbulence on foraging behavior, study the role of both short-term and chronic hunger on foraging behavior, and study how the cost of swimming may limit behavior. Future improvements in automated tracking algorithms should allow more rapid collection of behavioral data at higher temporal and spatial resolutions. Acknowledgments We thank the many people who supplied cod eggs: E. Trippel (St. Andrews Biological Station, St. Andrews, New Brunswick, Canada), W. Mebane and S. Lindell (Marine Biological Laboratory, Woods Hole, MA), N. King (GreatBay Aquaculture, Portsmouth, NH), D. Boyce (Memorial University of Newfoundland, Canada), and L. Buckley and E. Davies (NMFS, Narragansett Laboratory, RI). B. Morgan (Llennoco, Inc., Chatham, MA) for providing rotifers and culturing advice. Finally, we give our deepest thanks to P. Alatalo, A. Girard,

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and N. Copley at the Woods Hole Oceanographic Institution for their advice on larval cod culturing and equipment design. This research was supported by grants to J.J.R. from the Ocean Life Institute, the Coastal Ocean Institute, the Rinehart Coastal Research Center, and the Cecil H. and Ida M. Green Technology and Innovation Program at the Woods Hole Oceanographic Institution and by a grant to S.M.G. from the National Science Foundation (award OCE-9632606). Additional dissertation support to J.J.R. was provided by the MIT/WHOI Joint Program Education Office. This is contribution 11265 of the Woods Hole Oceanographic Institution.

References Anderson, J.P., Stephens, D.W., Dunbar, S.R., 1997. Saltatory search: a theoretical analysis. Behavioral Ecology 8, 307–317. Batty, R.S., 1987. Effect of light intensity on activity and food searching of larval herring Clupea harengus: a laboratory study. Marine Biology 93, 323–327. Boisclair, D., 1992. An evaluation of the stereocinematographic method to estimate fish swimming speed. Canadian Journal of Fisheries and Aquatic Science 49, 523–531. Bolz, G.R., Lough, R.G., 1988. Growth through the first six months of Atlantic cod, Gadus morhua, and haddock, Melanogrammus aeglefinus, based on daily otolith increments. Fishery Bulletin 86, 223–235. Browman, H.I., O’Brien, W.J., 1992. Foraging and prey search behaviour of Golden Shiner (Notemigonus crysoleucas) larvae. Canadian Journal of Fisheries and Aquatic Science 49, 813–819. Browman, H.I., St. Pierre, J.-F., Skiftesvik, A.B., Racca, R.G., 2003. Behaviour of Atlantic cod (Gadus morhua) larvae: an attempt to link maternal condition with larval quality. In: Browman, H.I., Skiftesvik, A.B. (Eds.), The Big Fish Bang. Proceedings of the 26th Annual Larval Fish Conference. The Institute of Marine Research, Bergen, Norway, pp. 71–95. Dabrowski, K.R., 1986a. Active metabolism in larval and juvenile fish: ontogenetic changes, effect of water temperature and fasting. Fish Physiology and Biochemistry 1, 125–144. Dabrowski, K.R., 1986b. Energy utilization during swimming and cost of locomotion in larval and juvenile fish. Journal of Applied Ichthyology 3, 110–117. Dower, J.F., Miller, T.J., Leggett, W.C., 1997. The role of microscale turbulence in the feeding ecology of larval fish. Advances in Marine Biology 31, 169–220. Ellertsen, B., Solemdal, P., Strømme, T., Tilseth, S., Westga˚rd, T., Moksness, E., Øiestad, V., 1980. Some biological aspects of cod larvae (Gadus morhua L.). Fiskeridirektoratets Skrifter Serie Havundersøkelser 17, 29–47. Evans, B.I., O’Brien, W.J., 1988. A reevaluation of the search cycle of planktivorous Arctic grayling, Thamallus arcticus. Canadian Journal of Fisheries and Aquatic Science 45, 187–192. Fiksen, Ø., Utne, A.C.W., Aksnes, D.L., Eiane, K., Helvik, J.V., Sundby, S., 1998. Modelling the influence of light, turbulence

and ontogeny on ingestion rates in larval cod and herring. Fisheries Oceanography 7, 355–363. Folkvord, A., 2005. Comparison of size-at-age of larval Atlantic cod (Gadus morhua) from different populations based on sizeand temperature-dependent growth models. Canadian Journal of Fisheries and Aquatic Science 62, 1037–1052. Galbraith, P.S., Browman, H.I., Racca, R.G., Skiftesvik, A.B., Saint-Pierre, J.-F., 2004. Effect of turbulence on the energetics of foraging in Atlantic cod Gadus morhua larvae. Marine Ecology Progress Series 281, 241–257. Gendron, R.P., Staddon, J.E.R., 1983. Searching for cryptic prey: the effect of search rate. American Naturalist 121, 172–186. Hunt von Herbing, I., Boutilier, R.G., 1996. Activity and metabolism of larval Atlantic cod (Gadus morhua) from Scotian Shelf and Newfoundland source populations. Marine Biology 124, 607–617. Hunt von Herbing, I., Gallager, S.M., 2000. Foraging behavior in early Atlantic cod larvae (Gadus morhua) feeding on a protozoan (Balanion sp.) and a copepod nauplius (Pseudodiaptomus sp.). Marine Biology 136, 591–602. Huse, I., 1994. Feeding at different illumination levels in larvae of three marine teleost species: cod, Gadus morhua L., plaice, Pleuronectes platessa L., and turbot, Scophthalmus maximus (L.). Aquaculture and Fisheries Management 25, 687–695. Kaufmann, R., 1990. Respiratory cost of swimming in larval and juvenile cyprinids. Journal of Experimental Biology 150, 343–366. Kaufmann, R., Wieser, W., 1992. Influence of temperature and ambient oxygen on the swimming energetics of cyprinid larvae and juveniles. Environmental Biology of Fishes 33, 87–95. Kleinbaum, D.G., Kupper, L.L., Muller, K.E., Nizam, A., 1998. Applied Regression Analysis and other Multivariable Methods. Duxbury Press, Pacific Grove, 798pp. Krohn, M.M., Boisclair, D., 1994. Use of a stereo-video system to estimate the energy expenditure of free-swimming fish. Canadian Journal of Fisheries and Aquatic Science 51, 1119–1127. Lough, R.G., Potter, D.C., 1993. Vertical distribution patterns and diel migrations of larval and juvenile haddock Melanogrammus aeglefinus and Atlantic cod Gadus morhua on Georges Bank. Fishery Bulletin 91, 281–303. Lough, R.G., Buckley, L.J., Werner, F.E., Quinlin, J.A., Edwards, K.P., 2005. A general biophysical model of larval cod growth applied to populations on Georges Bank. Fisheries Oceanography 14, 241–262. MacKenzie, B.R., Kiørboe, T., 1995. Encounter rates and swimming behavior of pause-travel and cruise larval fish predators in calm and turbulent laboratory environments. Limnology and Oceanography 40, 1278–1289. MacKenzie, B.R., Leggett, W.C., Peters, R.H., 1990. Estimating larval fish ingestion rates: can laboratory derived values be reliably extrapolated to the wild? Marine Ecology Progress Series 67, 209–225. Mu¨ller, U.K., Stamhuis, E.J., Videler, J.J., 2000. Hydrodynamics of unsteady fish swimming and the effects of body size: comparing the flow fields of fish larvae and adults. Journal of Experimental Biology 203, 193–206. Munk, P., 1995. Foraging behaviour of larval cod (Gadus morhua) influenced by prey density and hunger. Marine Biology 122, 205–212.

ARTICLE IN PRESS J.J. Ruzicka, S.M. Gallager / Deep-Sea Research II 53 (2006) 2735–2757 Munk, P., Kiørboe, T., 1985. Feeding behaviour and swimming activity of larval herring (Clupea harengus) in relation to density of copepod nauplii. Marine Ecology Progress Series 24, 15–21. O’Brien, W.J., Evans, B.I., Howick, G.L., 1986. A new view of the predation cycle of a planktivorous fish, white crappie (Pomoxis annularis). Canadian Journal of Fisheries and Aquatic Science 43, 1894–1899. O’Brien, W.J., Evans, B.I., Browman, H.I., 1989. Flexible search tactics and efficient foraging in saltatory searching animals. Oecologia 80, 100–110. O’Brien, W.J., Browman, H.I., Evans, B.I., 1990. Search strategies of foraging animals. American Scientist 78, 152–160. Pitchford, J.W., James, A., Brindley, J., 2003. Optimal foraging in patchy turbulent environments. Marine Ecology Progress Series 256, 99–110. Pitchford, J.W., James, A., Brindley, J., 2005. Quantifying the effects of individual and environmental variability in fish recruitment. Fisheries Oceanography 14, 156–160. Puvanendran, V., Brown, J.A., 1999. Foraging, growth and survival of Atlantic cod larvae reared in different prey concentrations. Aquaculture 175, 77–92. Puvanendran, V., Brown, J.A., 2002. Foraging, growth and survival of Atlantic cod larvae reared in different light intensities and photoperiods. Aquaculture 214, 1–4. Puvanendran, V., Leader, L.L., Brown, J.A., 2002. Foraging behaviour of Atlantic cod (Gadus morhua) larvae in relation to prey concentration. Canadian Journal of Zoology 80, 689–699. Ricker, W.E., 1979. Growth rates and models. In: Hoar, W.S., Randall, D.J., Brett, J.R. (Eds.), Fish Physiology. Academic Press, New York, pp. 677–743. Rosenthal, H., Hempel, G., 1970. Experimental studies in feeding and food requirements of herring larvae (Clupea harengus L.). In: Steele, J.H. (Ed.), Marine Food Webs. University of California Press, Berkeley, pp. 344–364.

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Rothschild, B.J., Osborn, T.R., 1988. Small-scale turbulence and plankton contact rates. Journal of Plankton Research 10, 465–474. Ruzicka, J.J., 2004. Integrating Bioenergetics and Foraging Behavior: the Physiological Ecology of Larval Cod (Gadus morhua). Department of Biology. Massachusetts Institute of Technology/Woods Hole Oceanographic Institution Joint Program, Cambridge, MA/Woods Hole, MA, pp. 194. Skiftesvik, A.B., 1992. Changes in behaviour at onset of exogenous feeding in marine fish larvae. Canadian Journal of Fisheries and Aquatic Science 49, 1570–1572. Skiftesvik, A.B., Huse, I., 1987. Behaviour studies of cod larvae, Gadus morhua L. Sarsia 72, 367–368. Solberg, T., Tilseth, S., 1984. Growth, energy consumption and prey density requirements in first feeding larvae of cod (Gadus morhua L.). In: E, D., Danielssen, D.S., Moksness, E., Solemdal, P. (Eds.), The Propagation of Cod Gadus Morhua L. Flødevigen rapportserie, UK, pp. 145–166. Stobutzki, I.C., Bellwood, D.R., 1994. An analysis of the sustained swimming abilities of pre- and post-settlement coral reef fishes. Journal of Experimental Marine Biology and Ecology 175, 275–286. Videler, J.J., Weihs, D., 1982. Energetic advantages of burst-andcoast swimming of fish at high speeds. Journal of Experimental Biology 97, 169–178. Vlymen III, W.J., 1974. Swimming energetics of the larval anchovy, Engraulis mordax. Fishery Bulletin 72, 885–899. Webb, P.W., Weihs, D., 1986. Functional locomotor morphology of early life history stages of fishes. Transactions of the American Fisheries Society 115, 115–127. Weihs, D., 1980. Energetic significance of changes in swimming modes during growth of larval anchovy, Engraulis mordax. Fishery Bulletin 77, 597–604. Werner, F.E., MacKenzie, B.R., Perry, R.I., Lough, R.G., Naimie, C.E., Blanton, B.O., Quinlan, J.A., 2001. Larval trophodynamics, turbulence, and drift on Georges Bank: a sensitivity analysis of cod and haddock. Scientia Marina 65, 99–115.