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Small-scale distributions of zooplankton biomass
Deep-Sea Research, Vol. 29, No. 4A, pp. 443 to 457, 1982. Printed in Great Britain.
0198-0149/82/040443-15 $03.00/0 ~) 1982 Pergamon Press Ltd.
Small-scale distributions of zooplankton biomass P. R, GREENBLATT*, E. SHULENBERGERt and J. H. WORMUTH+ (Received 9 December 1980; in final revised form 26 September 1981; accepted 1 November 1981)
Abstract--Horizontal variability of zooplankton biomass was evaluated using a multiple openingdosing plankton net. Thirty-eight plankton hauls (8 nets per haul) were taken at 90 m during 4 days and 3 nights, 350 km west of San Diego, California. There was a day-to-night increase in zooplankton abundance. Relative level of variations ($2/.~) was larger at night than during the day. Spatial variability was found on scales of 100 to 2100 m but no one scale dominated. Linear trends resulting from larger-scale variability (> 2 km) were present throughout the sampling period and were easier to distinguish from sample-to-sample variability at night than during the day. The trends were clearly differentiable from sample-to-sample variations only after several hundred meters of water were integrated. Biomass distributions were not strongly skewed: the data fit both Iognormal and normal distributions well. Small changes in net depth and temperature did not significantly influence the observed spatial pattern. Small changes in net towing profile did affect biomass measurements. If a feeding generalist is preying upon zooplankton and can detect the biomass trends, then such predators could follow the trends to higher concentrations of food.
INTRODUCTION
ZOOPLANKTONpatchiness has long been of interest to biological oceanographers. Several studies point out the potential ecological significance of patchy plankton distributions. For instance, MULLINand BROOKS(1976) found that in many samples from the upper 50 m in the Los Angeles Bight there was insufficient food to meet copepods' metabolic requirements. LASKER(1975) sampled in the upper 30 m in the Los Angeles Bight and found only limited areas where food concentrations were high enough for anchovy larvae to grow. The scales on which to sample plankton are highly dependent on the questions being asked. McGOwAN (1971) found that major zoogeographic provinces were well correlated with water masses and with major oceanic current systems. CASSIE(1959) found patchiness in the distribution of phytoplankton species on a scale of tens of centimeters. Spatial scales of hundreds to thousands of meters and time scales of a few days are particularly interesting and important, for it is on these scales that a zooplankter can probably influence its own survival (e.g., if a zooplankter is in a low food concentration, can and will it swim several hundred meters to find more optimal feeding conditions?). Such behavior would affect spatial distributions of zooplankton. These time-space scales are also interesting because predators upon zooplankton (i.e., other zooplankton and nekton) might find and exploit predictable concentrations of food (i.e., consistent patterns). There is, however, little detailed information on horizontal zooplankton variability on spatial scales of hundreds to thousands of meters and time scales of several days. Most * University of California, San Diego Marine Physical Laboratory, Scripps Institution of Oceanography, La Jolla, CA 92093, U.S.A. t San Diego Natural History Museum, P.O. Box 1390, San Diego, CA 92112, U.S.A. ++Department of Oceanography, Texas A&M University, College Station, TX 77843, U.S.A. 443
444
P . R . GREENBLATT, E. SIIULENBERGER and J. H. WORMUTII
small-scale (m to km) zooplankton studies have been near shore or near islands. SMErH, MILLER and HOLTON (1976) took a series of 50 pump samples 1.5 km offthe Oregon coast. The autocorrelation of several copepod species fell smoothly to zero after about 100 m, indicating that abundance varied with some predictability for about 100 m. WIEBE (1970) made 11 horizontal transects with a modified Longhurst-Hardy Plankton Recorder (LHPR) in the lee of Guadalupe Island, Baja Cal. and found patchiness on scales of tens to hundreds of meters. FASnAM, ANGEL and ROE (1974) took three horizontal transects using an LH PR at 550 m in the Atlantic. They estimated patch size using a three-parameter model that implicitly assumed discrete patches and concluded that patch size was ~ 100 to 300 m. Their data for Metridia brevicauda showed large abundance wlriations on these scales. More recently, STAR and MULLIN (1981) made horizontal transects with a zooplankton pump in the North Pacific central gyre and the California Current. They found strong intraspecific and interspecific patterns but with no preferred scale. Also, there was an areaspecific relationship between zooplankton biomass and chlorophyll distributions at larger scales (1 to 10km). We report on a program of intensive small-scale (100 to 2100 m) zooplankton sampling, using opening-closing nets, in the California Current. The program examined questions of horizontal variability of zooplankton biomass: (1) What temporal variability is observed over a 4-day period? (2) What is the spatial variability over scales of 100 m to 2.1 km, and are there consistent patterns at particular length scales? (3) Are there day night differences in the scales or intensity of biomass variations'? (4) What distributional models do the data tit best'? (5) How are the observed patterns influenced by small ( < 5 m) changes in net depth'? (6) What are possible ecological implications of the findings?
METHODS
Sampling Sampling was done 3 to 6 October 1978 aboard R. V. E. B. Scripps near 32" 30' N, 121 ' W, 350 km west of San Diego, California. Thirty-eight net hauls were made using a multiple opening-closing net (l m 2 mouth, 333 la mesh) and environmental sensing system ("MOCNESS" ; WIEBE, BURT, BOYDand MORTON, 1976). Mean tow speed was 100 cm s 1 Each haul yielded eight horizontal samples. All tows were towards the drifting Research Platform Flip (FISHER and SPIESS, 1963), which was simultaneously collecting highfrequency acoustic data (GREENBLAXX,1980, 1981 ). Station position was recorded at the end of each net haul (Table 1 ) and plotted (Fig. 1 ). Flip's mean drift was 9 cm s- 1 southward under calm seas and overcast sky. Water depth was about 3600 m. Environmental and system performance measurements were sent up a conducting cable, displayed on deck, and recorded on a tape recorder for detailed analyses. Depth was recorded to the nearest meter, temperature to the nearest 0.1':C, and net angle to the nearest degree. Distance traveled and volumes filtered were monitored with calibrated TSK mechanical flowmeters. All hauls were at 9 0 + 5 m and sampled the same water parcel insonified with Flip's sonar. To avoid periods of possible diel vertical migration, there was no sampling from 2 h before to 2 h after dawn and dusk. Sample tow lengths were 50 and 300 m per net, to give 50 or 300 m 3 sample-~. A short haul consisted of eight 50-m samples; a long haul was eight 300-m samples. Short and long hauls were alternated.
Small-scale distributions of zooplankton biomass
445
Table 1. Location, time of day, mean and s.d. of biomass in samples for each MOCNESS haul (8 samples per haul). Samples taken in any one day or night period are grouped together Haul No.
Date (Oct. 1978)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38
3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6
Local (h) (min)
Latitude ('~N)
9 11 12 13 14 16 21 22 23 01 02 03 08 09 12 14 15 17 20 22 00 01 02 04 08 10 I1 13 14 16 20 22 23 00 01 03 10 2
Not 32 ° 28' 32° 28' 32" 28' 32" 28' 32 ~28' 3 2 28' Not 32' 33' 32 ~ 33' 3T 34' 3 2 33' 3 2 27' 3 2 27' 32 31' 32' 31' 32 32' 32' 34' 32' 3Y 32 33' 3 2 32' Not 32' 32' 3 2 30' 32 30' 3 2 25' 32 25' 32 23' 3 2 24' Not 32 22' 3 2 22' Not Not Not 32 20' 32 21' 3 2 23'
17 07 11 57 45 19 02 27 52 19 28 57 35 58 41 50 33 04 35 59 04 42 52 30 40 17 22 15 16 41 33 03 04 45 38 18 41 6
Longitude ('W) available 12100' 120 59' 121 Off
121'00' 121 00' 12100' available 121 01' 121' 01' 12V 02' 121 01' 120 59' 121 r 00' 120 57' 121"00' 121' 01' 121' 01' 121' Off 121 00' 120 59' available 12100' 120 59' 121 Off 121'00' 120 59' 120 59' 120 59' available 12059' 121 Off available available available 120 57' 120 58' 120 58'
Mean biomass (g m -a)
s.d. biomass
0.0901 0.0735 0.0830 0.0618 0.0882 0.0538 0.1587 0.1136 0.2183 0.1137 0.1378 0.1478 0.0989 0.0545 0.1302 0.0632 0.1053 0.0607 0.2247 0.1249 0.1559 0.1173 0.2156 0.1569 0.0987 0.0666 0.1300 0.0859 0.0656 0.0871 0.2499 0.1589 0.2053 0.1925 0.3365 0.1471 0.0675 0.0400
0.0289 0.0154 0.0177 0.0119 0.0212 0.0076 0.0185 0.0224 0.0664 0.0232 0.0248 0.0269 0.0329 0.0117 0.0218 0.0083 0.0144 0.0050 0.1264 0.0386 0.0546 0.0219 0.0722 0.0342 0.0254 0.0206 0.0161 0.0197 0.0138 0.0167 0.0848 0.0547 0.0784 0.0775 0.0489 0.0521 0.0176 0.0053
Summa O' statistics
Time period
Mean biomass (g m -3)
s.d. biomass (g m - 3)
Day 1 Night 1 Day 2 Night 2 Day 3 Night 3 Day 4 All days All nights
0.0751 0.1483 0.0855 0.1659 0.0890 0.2178 0.0538 0.0803 0.1765
0.0222 0.0484 0.0330 0.0761 0.0284 0.0915 0.0189 0.0292 0.0789
(gm -s)
446
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and J. H. WORMUTII
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Small-scale distributionsof zooplankton biomass
447
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Samples were preserved in 5~ formalin, buffered to pH ,~ 8.2. Displacement volumes were measured after waiting 10 weeks to allow plankton shrinkage to stabilize (KRAMER, KALIN, STEVENS, THRAILK1LLand ZWEIFEL, 1972; BEERS, 1976). Gelatinous zooplankton were not abundant. Individual organisms exceeding 5 cm 3 were removed, measured separately, and not included in these analyses. Each displacement volume was measured by two independent observers: if results differed by more than I07/o the measurements were repeated. Displacement volumes, converted to wet weights (at Icc = 1 g), are reported in gm -3
Analyses To look for temporal changes in the distributions of zooplankton, we examined the means and variability of zooplankton biomass as functions of time of day. Time series of biomass were plotted (Fig. 2) treating samples in a haul as replicates for a particular time. The mean (.~) and standard deviation (S) of biomass for each haul were tabulated along with the summary statistics (Table 1). The variance-to-mean ratio ($2/~2) is an index of aggregation (FASHAM et al., 1974; PIELOU, 1977; WIEBE, 1970). As are all dimensional indices of aggregation, it is sensitive to multiplicative changes in the abundance of organisms. [Differences between dimensional and non-dimensional measures of aggregation are further discussed by PIELOU (1977).1 Because biomass is a continuous variable, the index can be used only as a comparative measure. Time of day was divided into 2-h blocks and all samples occurring within a block were used as a data subset. S2/Yc was calculated as a function of time of day (Fig. 3). Autocorrelation functions, often used to describe spatial patterns, proved inappropriate because of the small number of samples per haul (eight). Autocorrelation analysis requires stationary data, and removing trends from eight-point spatial series is difficult. We used a ratio of structure functions as an index of spatial structure. TATARSKll(1971) defined the second order structure function with stationary lags as D(r) = ([b(xi)-b(xi +z)]2),
(1)
448
P.R. GREENBLATT,E. SHULI!NBERGERand J. H. WORMU'rH
where b(x;) is the biomass at position x;, r is the separation between samples, and ( ) is the expected value of biomass. D(r)characterizes the intensity of fluctuations on scales less than or comparable with r and is independent of the mean value of b(x). The ratio of the smallest lag structure function to wider-spaced structure function allows comparison of closely vs more widely spaced samples. We have defined the ratio of structure functions Sf(r) as ( [ b ( x ; ) - b ( . , q + r 0 !]25
where b(x~) is the biomass at position x , zo is the separation between adjacent samples (i.e.. minimum sampling scale), and r is the separation between samples under investigation. To estimate Sf(z) from our samples, we use equation 3: l
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where N is the number of plankton hauls taken and M is the number of samples per haul. Averaging across net hauls before forming the ratio improved the reliability of the estimates of both the numerator and denominator. Sf(zo) always equals 1, and S t i r ) can range between zero and infinity. Data dominated by trends (i.e., large-scale fluctuations) will have a continuously decreasing Sf(r) with increasing r, while random data with random fluctuations will have Sf(r) averaging ~ 1. However, with only limited samples, S t i r ) of random data can have large fluctuations. S t i r ) for data containing high-frequency fluctuations (i.e., dominated by small-scale patchiness) will also have high-frequency fluctuations with increasing r. Sf(r) was calculated for each data subset and plotted (Fig. 4). Differences in Sf(r) between long and short hauls are explained in the Appendix. One can use the F test to determine whether observed values of Sf(z) deviate significantly from those expected of data with random spatial fluctuations. If there is no spatial relationship between the samples, one expects numerator = denominator in equation 3. The average squared difference (i.e., both numerator and denominator) of data is distributed approximately as Z2, hence Sf(r) is approximately F-distributed, with N . ( M - 1), N . ( M - r ) degrees of freedom. These are insensitive to underlying probability distribution of the data, hence can be used with most data sets. However, confidence limits are broad unless sampling has been extensive. For testing Sf(z) < 1 with conventional F tables, one can compare l/Stir) with F values for N.(M-r), N - ( M - 1 ) degrees of freedom. Theoretical models have been fitted to observed probability distribution functions (PDFs) of plankton counts to describe and to impute biological significance to data sets (CASSlE, 1962). For example, a Poisson distribution is expected if the probability of a plankter occupying any location is extremely small but equally likely for all locations. Other distributions that often fit plankton data (e.g., negative binomial) may arise under conflicting sets of assumptions, limiting their usefulness (PIELOU, 1977). Most plankton PDFs differ significantly from the Poisson and are usually better described by the lognormal distribution, which has a positive skew. A Poisson P D F cannot be fitted to
Small-scaledistributionsof zooplanktonbiomass
449
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continuous variables such as biomass (vis d vis discrete variables such as counts), so the distribution of biomass was compared to both the normal and the lognormal distributions and tested with the Kolmogorov-Smirnov test (LINt~aRENand MCELRATH, 1969). During each haul the net changed depth, often resulting in small temperature changes. When net depth varied from 90 m by + 5 m, the depth was adjusted to 90 m. Multiple linear regression and correlation analysis were used on four data subsets (long day, long night, short day, short night) to determine the effects of vertical net motion and the resulting temperature changes on biomass. Mean depth, temperature, depth gradient (dz/dx), and temperature gradient (dT/dx) were correlated with and regressed against biomass. Mean depth gradients indicated the slope along which the net moved, and were calculated a s (Zstar t - Z e n d ) + distance. Mean temperature gradients indicated temperature changes due to depth fluctuations of the net and to internal waves. Mean temperature and depth gradients were calculated similarly. Mean temperature, depth, dz/dx, and dT/dx were regressed against biomass to investigate how vertical excursions of the net influenced the observed spatial pattern of biomass (i.e., to reduce or eliminate another potential source of error).
450
P.R. GREENBLATT, E. SHULENBERGERand J. H. WORMUTIi
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RESULTS
Night-time biomass averaged two to four times daytime biomass (Fig. 2, Table 1 ), and night samples were more variable (larger S 2) than day samples. All four daytime sets of hauls had similar ~ and S g, but the 6 October night samples showed higher mean values than the other two nights. There is a clear day-night difference in sg/2 (Fig. 3). Higher night-time values indicate a night-time increase in the intensity of variability or patchiness. Starting at 1200, S2/i consistently decreased through the day to a minimum just before dark. The eight samples of each haul were examined for spatial patterns, and long-term trends (i.e., scales > 2100 m) dominated the data both day and night (Fig. 5). For long hauls with any scale > 600 m separation, the decrease orS.f (T) with increasing r yields P < 0.01. In short hauls, Sf(T) generally decreased with increasing T, but yielded P ~< 0.05 only at 350 m. No particular dimension between 100 and 2100 m dominated the variability, and magnitudes of fluctuations of S.f(z) show no day-night change. The PDFs of all four data subsets (short vs long hauls, day vs night hauls) were not significantly different (P ~< 0.05) from the tognormal (Fig. 6). Although biomass PDFs
Table 2. Correlations of physical parameters with biomass Data subset Short Short Long Long
day nigh t day night
No. of samples
Average depth
Average temperature
Average d-/dx
64 62 72 62
-0.065 - 0.136 - 0.100 - 0.039
-0.362 + - 0.041 - 0.219" - 0.006
0.160 0.316"1 0.040 0.167
* P (r/> r observed) ~ 0.07. t P (r >/ r observed) ~ 0,05. ++P (r/> r observed) ~ 0.01.
Average
dT/dx 0.169 - 0.246* - 0.204 - 0.170
451
Small-scale distributions of zooplankton biomass
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appeared to have positive skew, only short night hauls differed significantly (P ~< 0.05) from the normal (Fig. 6). Vertical (XBT) temperature profiles showed a 20-m mixed layer. Temperature dropped from 17.5°C at 20 m to 10°C at 70 m. From 70 to 200 m temperature gradients were linear and small ( ~ 0.02°C m-t). We examined correlations between biomass and mean depth, temperature, dz/dx, and dT/dx (Table 2) to see if biomass was influenced by fluctuations in depth of tow. The correlation between biomass and mean temperature was significant (P < 0.01) during short day hauls and the correlation between biomass and dz/dx was significant (P < 0.05) during short night hauls. Changes in temperature seemed to influence biomass values during day hauls. At night, the net caught more when moving clown than when moving up. (Because of the small vertical temperature gradients at the depth of our samples, poor apparent correlation between dT/dx and biomass may be due to the low resolution (0.1°C) of the MOCNESS temperature circuitry.) Analysis of variance of the regressions (Table 3) indicated that small changes in depth, temperature, dz/dx, and dT/dx
452
P.R. GREENBLATT,E. SHULENBERGERand J. H. WORMUT|t
Table 3. Anova of regressions of biomass on Z, T, and dz/dx .
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Degrees of freedom
Sum of squares
Short day hauls Attributable to regressions Deviation from regression Total
4 59 63
Short night hauls Attributable to regression Deviation from regression Total Long day hauls Attributable to regression Deviation from regression Total
Source of variation
Long night hauls Attributable to regression Deviation from regression Total
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Mean squares
t, Value
0.01133 0.03694 0.04826
0.00283 0.00063
4.523*
4 57 61
0.10280 0.40037 0.50317
0.02570 0.00702
3.659*
4 67 71
0.00257 0.02322 0.02579
0.00064 0.00035
1.85321
4 57 61
0.00844 0.14087 0.14931
0.00211 0.00247
0.85393
* P (/= > / F observed) ~< 0.01.
significantly affected biomass levels in short hauls, but the evidence is not conclusive in long hauls. DISCUSSION
Four apparent artifacts exist in the data: (1) the positive correlation between depth gradient and biomass in night hauls; (2) the correlation between temperature and biomass vs no correlation between depth and biomass; (3) the difference in significance levels of biomass-dz/dx correlation in long hauls vs short hauls; and (4) the systematically higher biomass in short hauls. (1) There are two explanations of the night-time positive correlation between biomass and depth gradient. First, because the MOCNESS net mouth tows at an angle to vertical (top of the net closer to the ship than is the bottom), a downward-moving net has a larger effective mouth opening and would filter more water than a horizontally towed net. This increase in volume filtered would not be detected by the flowmeter and would therefore appear as a higher plankton concentration. Calculation of the increased mouth area during downward motion for the observed depth gradients (Table 4) indicated increases in filtered Table 4. Summar.v of depth gradient values. (Number of samples m parentheses) Data subset
Average positive gradient
Short day Short night
2.53 × 10 -2 3.45 x I0-2 1.04xt0-2 7.74× 10 3
Long day Long night .
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(36) (43) (33) (27)
Average negative gradient - 1 . 5 8 × 10 -z (26) -2.61 × 10-2 (17) - 7 . 9 8 × 1 0 3 (33) 8.13 × 10 "~(24)
Small-scaledistributionsof zooplanktonbiomass
453
volumes of 0.7 to 3.7~. Biomass increases were 2 to 14~o, hence the net motion hypothesis cannot fully explain the increased biomass. Also, if increased effective mouth opening explained the correlation between dz/dx and biomass, one would expect positive correlations between biomass and dz/dx both during day and night. Lack of such correlations requires rejection of this explanation. Second, plankton may sense the net by sight or by its pressure wave. (Bioluminescence may also help sighted animals detect the net at night, and bioluminescence was apparent in the surface waters during the sampling period.) Either mechanism may give the plankton several seconds to avoid the net. If zooplankton avoid the net by preferentially sinking or swimming downward, the MOCNESS would catch more plankton when moving down. CLUTTERand ANRAKU(1968) cited observations of zooplankton avoiding nets by moving downward. If we assume that only zooplankton in the direct path of the net react to it, then for a rectangular net with effective height Nz, horizontal velocity Vx, and gradient in depth of tow dz/dx, the expected increase in catch (denoted '7ob) is:
where Vz is the vertical velocity of zooplankton and t is the time zooplankton have to react. In our data, a typical value of dz/dx for a downward-moving net is 3 x 10- 2 (Table 4). During most hauls, Nz was 90 cm and Vx averaged 100 cm s- ~. Vz has been estimated for certain zooplankton at 5 to 10cm s -1 (HARDY and BAtNBRtDGE, 1954; CLUTTER and ANRAKtJ, 1968). Much higher burst swimming speeds have been reported (CLUTTERand ANRAKU, 1968). Reaction time t is also unknown ; we estimate it at 2 to 3 s (2 to 3 m at our tow speed). These parameters yield an expected increase in biomass of 7.5 to 1500 for those zooplankton avoiding the net, in reasonable agreement with our observations. If the avoidance hypothesis is correct, one must explain why avoidance would be less during the day than at night. The average animal was larger at night than during the day. During the day most of the biomass was small copepods (< l m m ) and radiolaria (0.5 ram), but at night it was mostly euphausiids > 8 mm. The larger and more mobile euphausiids should avoid the net better than the animals present during the day. (2) One must also explain why biomass was sometimes correlated with temperature (P < 0.01 for short day hauls, P ~ 0.07 for short night hauls) but not with depth (Table 2), inasmuch as T and Z are themselves correlated. During sampling, the temperature at 90 m decreased ~ 0.Y~C, suggesting that we were being carried into a different water mass, perhaps of different biomass. If the correlation between T and biomass were caused by entering a new water mass instead of by depth fluctuations of the net, one would expect correlations between T and biomass but none between Z and biomass: this is what we observed (see Table 2 and below). One would also expect significant correlation between biomass and T during both short and long hauls; we found P = 0.219 (P ~ 0.07) for long day hauls and P = 0.362 (P < 0.01) for short day hauls (Table 2); the non-significant correlation for all night-time hauls was explained above as a probable result of day-night changes in size-frequency of zooplankters, hence changes in avoidance. We feel that the correlation between T and biomass was caused by our drifting into a different water mass rather than by fluctuations in towing depths. (3) Another apparent contradiction is the lack of significant correlation between biomass and dz/dx in long hauls: significant (P ~< 0.05) correlations occurred only in short
454
P . R . GREENBLATT, E. SttULIiNBERGI~R and J. H. WOR~aHu
hauls. Values ofdz/dx for short hauls (Table 4) are two to four times those for long hauls. Short hauls also show stronger correlation between biomass and depth gradient than do long hauls (Table 2). Because each long-sample tow lasted about 5 min, there was ample time to adjust the net's depth. A short tow lasted only about 50 s, too short to adjust the net's depth. Values of dz/dx were apparently influenced by whether towing depth was adjusted. The net could move downward for 80°,~,of a long sample and then move up to the original depth, resulting in an average dz/dx = 0. Any variability in biomass during the sampling would cause a non-zero "net avoidance effect," thus lowering or even eliminating any correlation between dz/dx and biomass. (4) Mean biomass of short hauls is about 20"~, higher than that of long hauls (Fig. 2). During short hauls the net moved down a larger fraction of the time than it moved up (Table 4). Long hauls were different. Some short hauls showed higher biomass than long hauls, even when dz/dx was small during both day and night hauls. Also, there is no obvious reason why avoidance should be generally greater on long hauls than on short hauls. Hence, avoidance and net motion cannot fully explain the apparent increased biomass. The bias may be explained ira small residuum (3 to 4!'0)of the catch on each haul was not washed into the cod end but somehow entered the samples of the next haul. Our nets were washed down between hauls, but the leading edge of each net was hard to reach (9 nets are stacked accordian-fashion on the MOCNESS) and was not washed well. Because short and long hauls were alternated, such a bias could well result in about a 20";, increase in short haul biomass and a 2.5 to 3.5°,, decrease in long haul biomass. When planning the sampling program, we expected to find consistent spatial patterns on scales of 100 to 2100 m and that some scales in this range would dominate. Although we found variations on these scales, larger-scale variability (i.e., > 2100 m) dominated the patterns. FASHAM et al. (1974) also found large-scale trends, highly correlated with temperature. Although our biomass trends were not highly correlated with temperature and depth, other unmeasured parameters (e.g., salinity, chlorophyll) might correlate well with biomass. High correlations of biomass with chlorophyll would imply that zooplankton patchiness was being influenced by phytoplankton patchiness (or vice versa) (STARand MULL~N,1981 ). WIEBE(1970) found no large-scale daytime spatial variations, but some night-time spatial series of species' abundance did show long-term trends. WIEae did not indicate whether the long-term trends were correlated with either physical or biological parameters. The point-to-point variability in Sf(r) can indicate the scales of maximum or minimum variability between 100 and 2100 m. Sf(t} values for short hauls show more point-to-point variations than for long hauls. The difference between long and short hauls probably occurred because the long hauls integrate small-scale variability. The values of Sf(t) are consistently lower at night than during the day, implying that long-term trends are more easily distinguished from sample-to-sample variability at night. However, the day-to-night difference in trends could not account for the day ~night difference of $2/.~. Apparently horizontal dimensions of variability did not change greatly from day to night, whereas S2/.L which we regard as 'intensity' of patchiness, increased dramatically at night, as did mean biomass. The increase in $2/£ at night may be caused by an increase in the average size of animals or by changes in aggregation behavior, but distinguishing between these awaits counting of the samples. However, the break in $2/.~ at 0300 (Fig. 3) must have been due to a change in aggregation behavior because the size composition did not change greatly throughout the night (unpublished obserwltions).
Small-scale distributions of zooplankton biomass
455
We conclude that when biomass increased (at night) the number of biomass patches did not increase but the contrast between areas of high and low biomass concentration increased markedly. Qualitatively, one gets a picture of the major components of biomass finding a common 'suitable area' (perhaps forming multi-species patches) or all being influenced by common parameters. This interpretation agrees with the findings of SMITHet al. (1976), STARand MULLIN(1981), and WIEBE (1970), but it disagrees with FASHAMet al. (1974). If the major components of zooplankton find a common suitable area, competition may be more intense inside a patch than outside it. More detailed biological explanations of the observed night-time increase in S2/Yc, based on sorting of the samples into taxonomic categories, were reported by GREENBLA'Vr(1980). Many sets of plankton samples, when counted to yield abundances of species (or other taxonomic groups), show highly skewed PDFs. Although our biomass data did show positively skewed PDFs (Fig. 6), only short night hauls were significantly different (P ~< 0.05) from a normal distribution. Biomass data are cruder than species-level data, but they may be more appropriate to consideration of the environment of individual zooplankters if those plankters are feeding generalists. Either there is a fundamental difference between the distributional properties of zooplankton biomass and of taxonomic category counts, or there were insufficient biomass data to detect significant skewness (we had 69 to 80 samples in each biomass data subset). There is good evidence that even in relatively rich coastal waters zooplankton and fish must find patches of food to survive (LASKER, 1975; MULLIN and BROOKS, 1976); the question arises as to how they do so. Is there information in the distribution of zooplankton that will help their predators find higher concentrations? For a predatory generalist, the long-term spatial trends in zooplankton biomass may be helpful in finding higher concentrations of food. Studies of the stomach contents of midwater fishes indicate that many are generalists. CLARKE(1978) and HOPKINSand BAIRD(1973) examined the diet of certain mesopelagic fishes and found they tend to eat many sizes and taxa of plankton and fish. [-MERRETT and ROE (1974), however, reported that stomach contents of some midwater fish imply preferential predation.] If predators could detect the biomass trends then they could follow biomass gradients to higher concentrations of food. The ecological importance of biomass trends depends on whether predators use the information contained in the distributions, and, more importantly, on how well the nutritional content of the zooplankton comprising the biomass satisfies the nutritional needs of the predators. To approach such a question, one must know the predators' functional feeding responses and the nutritional content of the zooplankton as well as the zooplankton distributions. CONCLUSIONS
We found that night-time zooplankton biomass at 90 m was two to four times that in daytime and that a measure of intensity of zooplankton patchiness was much higher at night than during daylight. In general, changes in biomass with distance were dominated by scales larger than our maximum scale (i,e., > 2100 m), and within our sampling scales of 100 to 2100 m no scale dominated. Probability density functions of all subsets of biomasses of hauls did not differ from the lognormal distribution, and only short night hauls differed from a normal distribution. Biomass was not consistently correlated with mean temperature during a tow
456
P.R. GREENBLATT, E. SHULI£NBt::RGI-Rand J. H, WORMUTIt
or with dT/dx. Nets captured more zooplankton when the net was sinking slightly than when rising. On short hauls ( ~ 50 m), small changes in Z, T, dz/dx, and dT/dx affected biomass levels, longer tows (300 m) seem to integrate the effects. It is clear that in the future other parameters of direct importance to zooplankton (e.g,, chlorophyll) should be monitored simultaneously with zooplankton if we are to use distributional data in analyses of zooplankton community function. However, even on scales approaching 2 km information potentially useful to generalized predators is contained in the large-scale trends we observed. Acknowledgements- We thank R. PINKEL, F. N. SPlESS, J. A. McGoWAN, D. GOODMAN, L. S. TOMOOKA, and M. M. MUEt.IN for their help with the research and with the manuscript. M. BANDUt~AGA.C. GARROD, D. GLt~ASON, M. OESSON, L. SNIDER, and L. VAKASSIANhelped collect the samples. Captain COCEMAN and the crew of the E. B. Scripps did an excellent job. The staff of the Scripps Institution hydraulics laboratory helped with flowmeter calibrations. Sponsored by the Office of Naval Research Code 480 (Contract N00014-75-C-0704} and Code 484 (Contract N00014-75-C-0537). This is a contribution of the Scripps Institution of Oceanography, new series.
REFERENCES BlaSRS J. R. (1976) Determination of zooplankton biomass. In: Zooplankton fixation and preservation. H. F. STEEDMAN, editor, UNESCO Monograph, pp. 35-84. CASSIE R. (1959) Micro-distribution of plankton. New Zealand Journal of Science, 2, 398 409. CASS,': R. M. (1962) Frequency distribution models in the ecology of plankton and other organisms. Journal q! Animal Ecologl'. 31, 65 92. CI.ARKI~ T. A. (1978) Diel feeding patterns of 16 species of mesopelagic fishes from Hawaiian water. Fisherc Bulletin. 76, 495-513. CLUTTER R. 1. and M. ANRAKU(1968) Avoidance of samplers. In: Zoophmkton samphng, UNESCO Monograph, pp. 57 76. FASrtAM M. S. R., M. V. AN(IEL and H. S. J. Rol~ (1974) An investigation of the spatial pattern of zooplankton using the Longhurst Hardy plankton recorder. Journal of E?:perimental Marine Biology and Ecology. 16, 93 112. FlSlUiR F. H. and F. N. Spn-ss (1963) Hip: floating instrument platform. Journal ~,1'the Acoustical Society q/ America, 35, 1633 1644. GREENBL^TT P. R. (1980) Observations of zooplankton patchiness using a high frequency sonar and a multiple sample plankton net. Ph.D. Dissertation, University of California at San Diego, 140 pp. GREENBLATT P. R. (1981) Sources of acoustic backscattering at 87.5 kHz. Journal of the Acoustical Socieo' qf America (in press). HARD',' A. C. and R. BAINBRIDG~:(1954) Experimental observations of the vertical migration of plankton animals. Journal of the Marine Bhdogical Association qf the United Kingdom. 33, 409 448. HOPKINS T. L. and R. C. BAIRD(1973) Diet of the hatchet fish Sternopt.vx diaphana. Marine Budogv, 21, 34 46. KRAMF,R D., M. J. KALIN, E. G. STEVENS,J. R, THRAILKILLand J. R. ZWEIFEL(1972) Collecting and processing data on fish eggs and larvae in the California current region. National Marine Fisheries Service~National Oceanic and Atmospheric Administration Technical Reports. N. M. F. S. Circular 37, 38 pp. LASKER R. (1975) Field criteria for survival of anchovy larvae: The relation between inshore chlorophyll maximum layers and successful first feeding. Fisherr Bulletin. 73, 453 462. LINDCiREN B. W. and G. W. McELRATll (1969) Introduction to probability and .s-tatistics. The MacMillan Company, New York, 305 pp. M cGow^N J. A. (1971) Oceanic biogeography of the Pacific. In: The micropah, ontologv qfoceans, B. M. Ft NNtt,E and W. R. REIDFL, editors, University Press, Cambridge, England, pp. 3 74. Mt~RRETT N. R. and H. S. J. Rot: (1974) Patterns and selectivity in the feeding of certain mesopelagic fishes. Marine Biolog.t'. 28, I 15 126. MULLIN M. M. and E. R. BRLKI.KS(1976) Some consequences of distributional heterogeneity of phytoplankton and zooplankton. Limnolog.v and Oceanography, 21,784 797. PIELOU E. C. (1977) Mathematical ecology. John Wiley, New York, 385 pp. SMITtt L. R., C. B. MILLER and R. L. HOLTON (1976) Small-scale horizontal distribution of coastal copepods. Journal of Experimental Marine Biology and Ecology. 23, 241 253. ST^R J. L. and M. M. MULLIN (1981) Zooplankton assemblages in three areas of the North Pacific as revealed by continuous horizontal transects. Deep-Sea Research, 28, 1303 1322.
Small-scale distributions of zooplankton biomass
457
TATAaSKIIV. I. (1971) The effects of the turbulent atmosphere on wave propagation. Israel Program for Scientific Translation, National Oceanic and Atmospheric Administration and National Science Foundation, Washington, DC., 472 pp. WmBE P. H. (1970) Small scale spatial distribution in oceanic zooplankton. Limnology and Oceanography, 15, 205-217. WlEBE P. H., K. H. BURT, S. H. BOYD and A. W. MORTOS (1976) A multiple opening-closing net and environmental sensing system for sampling zooplankton. Journal of Marine Research. 34, 313 326.
APPENDIX
Changes in Sf(z)from short to long hauls For a given separation between samples, the ratio of structure functions [Sf(z)] shows a consistent difference in amplitude between short and long hauls. For example, Sf(~) for short hauls = 0.55 at 350-m separation, but Sf(r) for long hauls reaches 0.55 only at 900-m separation (Fig. 4). This is an artifact of sampling caused by each net integrating a finite volume of water. A biomass sample is the actual biomass at a position x [i.e., b(x)] convolved with the length of tow involved in collecting the sample. We can write '(x)=
b(x)*n(~-),
(A1)
where L is the sample tow length, * is the convolution operator, and
L-~- j
F x-,.1
n L 2L J
1 for 0 ~< x ~< L
_
0
elsewhere.
The ratio of structure functions for large hauls is
ss(,):
+.,..
600 : J l
and short hauls is
L,-T~-)- (~'+r°l'" k lOO / ] /
Equations (A2) and (A3) differ because of the two haul lengths (50 and 300 m). Unfortunately there is no analytic method to remove the effects of sampling (i.e., reduce equations A2 and A3 to equivalent form). A computer analysis indicated that S f ( , } of simulated data decreased more slowly for longer integration times, thus agreeing with and explaining the apparent discrepancy in Fig. 4.
0198-0149/82/040443-15 $03.00/0 ~) 1982 Pergamon Press Ltd.
Small-scale distributions of zooplankton biomass P. R, GREENBLATT*, E. SHULENBERGERt and J. H. WORMUTH+ (Received 9 December 1980; in final revised form 26 September 1981; accepted 1 November 1981)
Abstract--Horizontal variability of zooplankton biomass was evaluated using a multiple openingdosing plankton net. Thirty-eight plankton hauls (8 nets per haul) were taken at 90 m during 4 days and 3 nights, 350 km west of San Diego, California. There was a day-to-night increase in zooplankton abundance. Relative level of variations ($2/.~) was larger at night than during the day. Spatial variability was found on scales of 100 to 2100 m but no one scale dominated. Linear trends resulting from larger-scale variability (> 2 km) were present throughout the sampling period and were easier to distinguish from sample-to-sample variability at night than during the day. The trends were clearly differentiable from sample-to-sample variations only after several hundred meters of water were integrated. Biomass distributions were not strongly skewed: the data fit both Iognormal and normal distributions well. Small changes in net depth and temperature did not significantly influence the observed spatial pattern. Small changes in net towing profile did affect biomass measurements. If a feeding generalist is preying upon zooplankton and can detect the biomass trends, then such predators could follow the trends to higher concentrations of food.
INTRODUCTION
ZOOPLANKTONpatchiness has long been of interest to biological oceanographers. Several studies point out the potential ecological significance of patchy plankton distributions. For instance, MULLINand BROOKS(1976) found that in many samples from the upper 50 m in the Los Angeles Bight there was insufficient food to meet copepods' metabolic requirements. LASKER(1975) sampled in the upper 30 m in the Los Angeles Bight and found only limited areas where food concentrations were high enough for anchovy larvae to grow. The scales on which to sample plankton are highly dependent on the questions being asked. McGOwAN (1971) found that major zoogeographic provinces were well correlated with water masses and with major oceanic current systems. CASSIE(1959) found patchiness in the distribution of phytoplankton species on a scale of tens of centimeters. Spatial scales of hundreds to thousands of meters and time scales of a few days are particularly interesting and important, for it is on these scales that a zooplankter can probably influence its own survival (e.g., if a zooplankter is in a low food concentration, can and will it swim several hundred meters to find more optimal feeding conditions?). Such behavior would affect spatial distributions of zooplankton. These time-space scales are also interesting because predators upon zooplankton (i.e., other zooplankton and nekton) might find and exploit predictable concentrations of food (i.e., consistent patterns). There is, however, little detailed information on horizontal zooplankton variability on spatial scales of hundreds to thousands of meters and time scales of several days. Most * University of California, San Diego Marine Physical Laboratory, Scripps Institution of Oceanography, La Jolla, CA 92093, U.S.A. t San Diego Natural History Museum, P.O. Box 1390, San Diego, CA 92112, U.S.A. ++Department of Oceanography, Texas A&M University, College Station, TX 77843, U.S.A. 443
444
P . R . GREENBLATT, E. SIIULENBERGER and J. H. WORMUTII
small-scale (m to km) zooplankton studies have been near shore or near islands. SMErH, MILLER and HOLTON (1976) took a series of 50 pump samples 1.5 km offthe Oregon coast. The autocorrelation of several copepod species fell smoothly to zero after about 100 m, indicating that abundance varied with some predictability for about 100 m. WIEBE (1970) made 11 horizontal transects with a modified Longhurst-Hardy Plankton Recorder (LHPR) in the lee of Guadalupe Island, Baja Cal. and found patchiness on scales of tens to hundreds of meters. FASnAM, ANGEL and ROE (1974) took three horizontal transects using an LH PR at 550 m in the Atlantic. They estimated patch size using a three-parameter model that implicitly assumed discrete patches and concluded that patch size was ~ 100 to 300 m. Their data for Metridia brevicauda showed large abundance wlriations on these scales. More recently, STAR and MULLIN (1981) made horizontal transects with a zooplankton pump in the North Pacific central gyre and the California Current. They found strong intraspecific and interspecific patterns but with no preferred scale. Also, there was an areaspecific relationship between zooplankton biomass and chlorophyll distributions at larger scales (1 to 10km). We report on a program of intensive small-scale (100 to 2100 m) zooplankton sampling, using opening-closing nets, in the California Current. The program examined questions of horizontal variability of zooplankton biomass: (1) What temporal variability is observed over a 4-day period? (2) What is the spatial variability over scales of 100 m to 2.1 km, and are there consistent patterns at particular length scales? (3) Are there day night differences in the scales or intensity of biomass variations'? (4) What distributional models do the data tit best'? (5) How are the observed patterns influenced by small ( < 5 m) changes in net depth'? (6) What are possible ecological implications of the findings?
METHODS
Sampling Sampling was done 3 to 6 October 1978 aboard R. V. E. B. Scripps near 32" 30' N, 121 ' W, 350 km west of San Diego, California. Thirty-eight net hauls were made using a multiple opening-closing net (l m 2 mouth, 333 la mesh) and environmental sensing system ("MOCNESS" ; WIEBE, BURT, BOYDand MORTON, 1976). Mean tow speed was 100 cm s 1 Each haul yielded eight horizontal samples. All tows were towards the drifting Research Platform Flip (FISHER and SPIESS, 1963), which was simultaneously collecting highfrequency acoustic data (GREENBLAXX,1980, 1981 ). Station position was recorded at the end of each net haul (Table 1 ) and plotted (Fig. 1 ). Flip's mean drift was 9 cm s- 1 southward under calm seas and overcast sky. Water depth was about 3600 m. Environmental and system performance measurements were sent up a conducting cable, displayed on deck, and recorded on a tape recorder for detailed analyses. Depth was recorded to the nearest meter, temperature to the nearest 0.1':C, and net angle to the nearest degree. Distance traveled and volumes filtered were monitored with calibrated TSK mechanical flowmeters. All hauls were at 9 0 + 5 m and sampled the same water parcel insonified with Flip's sonar. To avoid periods of possible diel vertical migration, there was no sampling from 2 h before to 2 h after dawn and dusk. Sample tow lengths were 50 and 300 m per net, to give 50 or 300 m 3 sample-~. A short haul consisted of eight 50-m samples; a long haul was eight 300-m samples. Short and long hauls were alternated.
Small-scale distributions of zooplankton biomass
445
Table 1. Location, time of day, mean and s.d. of biomass in samples for each MOCNESS haul (8 samples per haul). Samples taken in any one day or night period are grouped together Haul No.
Date (Oct. 1978)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38
3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6
Local (h) (min)
Latitude ('~N)
9 11 12 13 14 16 21 22 23 01 02 03 08 09 12 14 15 17 20 22 00 01 02 04 08 10 I1 13 14 16 20 22 23 00 01 03 10 2
Not 32 ° 28' 32° 28' 32" 28' 32" 28' 32 ~28' 3 2 28' Not 32' 33' 32 ~ 33' 3T 34' 3 2 33' 3 2 27' 3 2 27' 32 31' 32' 31' 32 32' 32' 34' 32' 3Y 32 33' 3 2 32' Not 32' 32' 3 2 30' 32 30' 3 2 25' 32 25' 32 23' 3 2 24' Not 32 22' 3 2 22' Not Not Not 32 20' 32 21' 3 2 23'
17 07 11 57 45 19 02 27 52 19 28 57 35 58 41 50 33 04 35 59 04 42 52 30 40 17 22 15 16 41 33 03 04 45 38 18 41 6
Longitude ('W) available 12100' 120 59' 121 Off
121'00' 121 00' 12100' available 121 01' 121' 01' 12V 02' 121 01' 120 59' 121 r 00' 120 57' 121"00' 121' 01' 121' 01' 121' Off 121 00' 120 59' available 12100' 120 59' 121 Off 121'00' 120 59' 120 59' 120 59' available 12059' 121 Off available available available 120 57' 120 58' 120 58'
Mean biomass (g m -a)
s.d. biomass
0.0901 0.0735 0.0830 0.0618 0.0882 0.0538 0.1587 0.1136 0.2183 0.1137 0.1378 0.1478 0.0989 0.0545 0.1302 0.0632 0.1053 0.0607 0.2247 0.1249 0.1559 0.1173 0.2156 0.1569 0.0987 0.0666 0.1300 0.0859 0.0656 0.0871 0.2499 0.1589 0.2053 0.1925 0.3365 0.1471 0.0675 0.0400
0.0289 0.0154 0.0177 0.0119 0.0212 0.0076 0.0185 0.0224 0.0664 0.0232 0.0248 0.0269 0.0329 0.0117 0.0218 0.0083 0.0144 0.0050 0.1264 0.0386 0.0546 0.0219 0.0722 0.0342 0.0254 0.0206 0.0161 0.0197 0.0138 0.0167 0.0848 0.0547 0.0784 0.0775 0.0489 0.0521 0.0176 0.0053
Summa O' statistics
Time period
Mean biomass (g m -3)
s.d. biomass (g m - 3)
Day 1 Night 1 Day 2 Night 2 Day 3 Night 3 Day 4 All days All nights
0.0751 0.1483 0.0855 0.1659 0.0890 0.2178 0.0538 0.0803 0.1765
0.0222 0.0484 0.0330 0.0761 0.0284 0.0915 0.0189 0.0292 0.0789
(gm -s)
446
P. R.
and J. H. WORMUTII
E. SHULENBERGER
GREENBLATT,
32 °
56'Ni
--
___.
32 o //
i
"-T 32 ° 2#'N
<. J/a'# 2°
1
t2/°06'~/
Fig. 1.
¢21°00 ' W
i20°,54 'W
Station position for net hauls. Position was recorded at the end of each haul.
"-r.......
0.56
,
0,4 9
,.~ 0.42 'IE
Z
O.35 ,j
..
t~ o.2e ~x
0
g 0
~
I"
• z • •
g I. I
I
'
~
."
,'~
.
~-'L".
'f°
.
3'i
°
r~.
°°° "~"
". ~ . .
°.
o
•
""
0
0,21
4 'a TIME
°
8%
o'4
96
(H)
~ - J OCT - - - ~ 4 OCT--I-- 5 OCT
I
6OCT ~q
Fig. 2. Time variability of zooplankton biomass. The eight samples of each haul are treated as replicates for a particular time. Squares are mean values of the eight samples in one MOCNESS haul.
Small-scale distributionsof zooplankton biomass
447
0.07 1(24) 0.06 0.05
1381 E 0.04
~'to~ 0.03 0.02
.~(401
1311
/
0.04
00
,
I
4
8 TIME
Fig. 3.
1161~ 16
~
4
OF BAY
I
20
J
24
(H)
S2/.i as a function of time of day. Number in parentheses indicates the number of samples in each 2-h time block.
Samples were preserved in 5~ formalin, buffered to pH ,~ 8.2. Displacement volumes were measured after waiting 10 weeks to allow plankton shrinkage to stabilize (KRAMER, KALIN, STEVENS, THRAILK1LLand ZWEIFEL, 1972; BEERS, 1976). Gelatinous zooplankton were not abundant. Individual organisms exceeding 5 cm 3 were removed, measured separately, and not included in these analyses. Each displacement volume was measured by two independent observers: if results differed by more than I07/o the measurements were repeated. Displacement volumes, converted to wet weights (at Icc = 1 g), are reported in gm -3
Analyses To look for temporal changes in the distributions of zooplankton, we examined the means and variability of zooplankton biomass as functions of time of day. Time series of biomass were plotted (Fig. 2) treating samples in a haul as replicates for a particular time. The mean (.~) and standard deviation (S) of biomass for each haul were tabulated along with the summary statistics (Table 1). The variance-to-mean ratio ($2/~2) is an index of aggregation (FASHAM et al., 1974; PIELOU, 1977; WIEBE, 1970). As are all dimensional indices of aggregation, it is sensitive to multiplicative changes in the abundance of organisms. [Differences between dimensional and non-dimensional measures of aggregation are further discussed by PIELOU (1977).1 Because biomass is a continuous variable, the index can be used only as a comparative measure. Time of day was divided into 2-h blocks and all samples occurring within a block were used as a data subset. S2/Yc was calculated as a function of time of day (Fig. 3). Autocorrelation functions, often used to describe spatial patterns, proved inappropriate because of the small number of samples per haul (eight). Autocorrelation analysis requires stationary data, and removing trends from eight-point spatial series is difficult. We used a ratio of structure functions as an index of spatial structure. TATARSKll(1971) defined the second order structure function with stationary lags as D(r) = ([b(xi)-b(xi +z)]2),
(1)
448
P.R. GREENBLATT,E. SHULI!NBERGERand J. H. WORMU'rH
where b(x;) is the biomass at position x;, r is the separation between samples, and ( ) is the expected value of biomass. D(r)characterizes the intensity of fluctuations on scales less than or comparable with r and is independent of the mean value of b(x). The ratio of the smallest lag structure function to wider-spaced structure function allows comparison of closely vs more widely spaced samples. We have defined the ratio of structure functions Sf(r) as ( [ b ( x ; ) - b ( . , q + r 0 !]25
where b(x~) is the biomass at position x , zo is the separation between adjacent samples (i.e.. minimum sampling scale), and r is the separation between samples under investigation. To estimate Sf(z) from our samples, we use equation 3: l
N
M
1
.... y Y~ [ b ( x ; ) - h t x ; + r , , I ] 2 N ( M - I ) 2-.'-]_/=~. . . . . . . . Sf(z)
=
t
N
(3)
M-~
X ~ [blx;)-blx;+Tl]"
N ( M - z ) j= 1 i=1
where N is the number of plankton hauls taken and M is the number of samples per haul. Averaging across net hauls before forming the ratio improved the reliability of the estimates of both the numerator and denominator. Sf(zo) always equals 1, and S t i r ) can range between zero and infinity. Data dominated by trends (i.e., large-scale fluctuations) will have a continuously decreasing Sf(r) with increasing r, while random data with random fluctuations will have Sf(r) averaging ~ 1. However, with only limited samples, S t i r ) of random data can have large fluctuations. S t i r ) for data containing high-frequency fluctuations (i.e., dominated by small-scale patchiness) will also have high-frequency fluctuations with increasing r. Sf(r) was calculated for each data subset and plotted (Fig. 4). Differences in Sf(r) between long and short hauls are explained in the Appendix. One can use the F test to determine whether observed values of Sf(z) deviate significantly from those expected of data with random spatial fluctuations. If there is no spatial relationship between the samples, one expects numerator = denominator in equation 3. The average squared difference (i.e., both numerator and denominator) of data is distributed approximately as Z2, hence Sf(r) is approximately F-distributed, with N . ( M - 1), N . ( M - r ) degrees of freedom. These are insensitive to underlying probability distribution of the data, hence can be used with most data sets. However, confidence limits are broad unless sampling has been extensive. For testing Sf(z) < 1 with conventional F tables, one can compare l/Stir) with F values for N.(M-r), N - ( M - 1 ) degrees of freedom. Theoretical models have been fitted to observed probability distribution functions (PDFs) of plankton counts to describe and to impute biological significance to data sets (CASSlE, 1962). For example, a Poisson distribution is expected if the probability of a plankter occupying any location is extremely small but equally likely for all locations. Other distributions that often fit plankton data (e.g., negative binomial) may arise under conflicting sets of assumptions, limiting their usefulness (PIELOU, 1977). Most plankton PDFs differ significantly from the Poisson and are usually better described by the lognormal distribution, which has a positive skew. A Poisson P D F cannot be fitted to
Small-scaledistributionsof zooplanktonbiomass
449
4.2 1.0 0.B
NG I HT/
0.6
\
0.4 0.2
A
Oq
5'0
,~ o
,~ o
200 .
. 250.
SEPARATION OF SAMPLES (r),
300 .
350
rn
1,2 t.0 0.8 0.6 u-J
0.4 0.2
B 0
]
I
I
I
[
I
500 600 900 t200 ~500 1800 SEPARATION OF SAMPLES [r], at Fig. 4.
(A) Ratio o f structure functions,
Sf(t),
J
2100
vs separation o f short hauls (day and night). (B)
Same for long hauls.
continuous variables such as biomass (vis d vis discrete variables such as counts), so the distribution of biomass was compared to both the normal and the lognormal distributions and tested with the Kolmogorov-Smirnov test (LINt~aRENand MCELRATH, 1969). During each haul the net changed depth, often resulting in small temperature changes. When net depth varied from 90 m by + 5 m, the depth was adjusted to 90 m. Multiple linear regression and correlation analysis were used on four data subsets (long day, long night, short day, short night) to determine the effects of vertical net motion and the resulting temperature changes on biomass. Mean depth, temperature, depth gradient (dz/dx), and temperature gradient (dT/dx) were correlated with and regressed against biomass. Mean depth gradients indicated the slope along which the net moved, and were calculated a s (Zstar t - Z e n d ) + distance. Mean temperature gradients indicated temperature changes due to depth fluctuations of the net and to internal waves. Mean temperature and depth gradients were calculated similarly. Mean temperature, depth, dz/dx, and dT/dx were regressed against biomass to investigate how vertical excursions of the net influenced the observed spatial pattern of biomass (i.e., to reduce or eliminate another potential source of error).
450
P.R. GREENBLATT, E. SHULENBERGERand J. H. WORMUTIi
0.30[
~z/ '/"
°.251
//
SHORT NIGHT HAUL
o
/'
0.z°i
~ 9,~
~t/~k g o.~5
NG NIGHT HAUL NO. 34
~
0.40[-I l \ lf~,SHORT DAY M ~.
0
0 Fig. 5.
0
.
300
~
0
\LONG DAY HAUL
5
~
600 900 ~200 ~500 ~800 RANGE, M (ARBITRARY ORIGIN)
2100
2400
Examples of plankton hauls showing long-term trends in biomass.
RESULTS
Night-time biomass averaged two to four times daytime biomass (Fig. 2, Table 1 ), and night samples were more variable (larger S 2) than day samples. All four daytime sets of hauls had similar ~ and S g, but the 6 October night samples showed higher mean values than the other two nights. There is a clear day-night difference in sg/2 (Fig. 3). Higher night-time values indicate a night-time increase in the intensity of variability or patchiness. Starting at 1200, S2/i consistently decreased through the day to a minimum just before dark. The eight samples of each haul were examined for spatial patterns, and long-term trends (i.e., scales > 2100 m) dominated the data both day and night (Fig. 5). For long hauls with any scale > 600 m separation, the decrease orS.f (T) with increasing r yields P < 0.01. In short hauls, Sf(T) generally decreased with increasing T, but yielded P ~< 0.05 only at 350 m. No particular dimension between 100 and 2100 m dominated the variability, and magnitudes of fluctuations of S.f(z) show no day-night change. The PDFs of all four data subsets (short vs long hauls, day vs night hauls) were not significantly different (P ~< 0.05) from the tognormal (Fig. 6). Although biomass PDFs
Table 2. Correlations of physical parameters with biomass Data subset Short Short Long Long
day nigh t day night
No. of samples
Average depth
Average temperature
Average d-/dx
64 62 72 62
-0.065 - 0.136 - 0.100 - 0.039
-0.362 + - 0.041 - 0.219" - 0.006
0.160 0.316"1 0.040 0.167
* P (r/> r observed) ~ 0.07. t P (r >/ r observed) ~ 0,05. ++P (r/> r observed) ~ 0.01.
Average
dT/dx 0.169 - 0.246* - 0.204 - 0.170
451
Small-scale distributions of zooplankton biomass
,.... ................
:....''~ /
t.0
/DEVIATION
v
NORMAL MAX • O.OZ4
LOG NORMAL MAX..._~ S DEVIATION• 0,'106 . / Z :
~ 0.8 X o ;,
NORMALMAX
"'"• 'i
0.6
-_J 0.4
j
/L__NORMA' MAX
OEVIAT,ON .0.0.
0.2 (1.
.£1
/
,I.C
O.E
I
J
I
I
I
,:S/' ~-
DEVIATION• OAf7
NORMAL MAX DEVIATION • 0 . 4 3 7
o_ 0.6
._1
~_LOG NORMAL MAX DEVIATION' 0 . 0 8 5
.['i
C
V
SHORT DAY HAULS I
0
{J
0.4
•"~
LOG NORMAL MAX DEVIATION = 0.099
2, ~
J
0.2
o..
LONG DAY HAULS #"
I
i
0.05
OAO
I
l
I
0.t5 020 0.25 BIOMASS (g n~3)
~
O. 0
0
LONG NIGHT HAULS
I :i
l
0.t0
020
I
i
I
0.30 0.40 0.50 BIOMASS (g m~)
l
0.60
Fig. 6. Cumulative distributions of data (dots) compared with normal (solid) and Iognormal (dashes) distributions. Deviation -- maximum difference between data and theoretical distributions. * Difference between data and theory significant at P ~< 0.05 by Kolmogorov-Smirnov test (LINDGaENand McEL~TH, 1969). (a) Short day hauls, (b) short night hauls, (c) long day hauls, and (d) long night hauls.
appeared to have positive skew, only short night hauls differed significantly (P ~< 0.05) from the normal (Fig. 6). Vertical (XBT) temperature profiles showed a 20-m mixed layer. Temperature dropped from 17.5°C at 20 m to 10°C at 70 m. From 70 to 200 m temperature gradients were linear and small ( ~ 0.02°C m-t). We examined correlations between biomass and mean depth, temperature, dz/dx, and dT/dx (Table 2) to see if biomass was influenced by fluctuations in depth of tow. The correlation between biomass and mean temperature was significant (P < 0.01) during short day hauls and the correlation between biomass and dz/dx was significant (P < 0.05) during short night hauls. Changes in temperature seemed to influence biomass values during day hauls. At night, the net caught more when moving clown than when moving up. (Because of the small vertical temperature gradients at the depth of our samples, poor apparent correlation between dT/dx and biomass may be due to the low resolution (0.1°C) of the MOCNESS temperature circuitry.) Analysis of variance of the regressions (Table 3) indicated that small changes in depth, temperature, dz/dx, and dT/dx
452
P.R. GREENBLATT,E. SHULENBERGERand J. H. WORMUT|t
Table 3. Anova of regressions of biomass on Z, T, and dz/dx .
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
Degrees of freedom
Sum of squares
Short day hauls Attributable to regressions Deviation from regression Total
4 59 63
Short night hauls Attributable to regression Deviation from regression Total Long day hauls Attributable to regression Deviation from regression Total
Source of variation
Long night hauls Attributable to regression Deviation from regression Total
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
Mean squares
t, Value
0.01133 0.03694 0.04826
0.00283 0.00063
4.523*
4 57 61
0.10280 0.40037 0.50317
0.02570 0.00702
3.659*
4 67 71
0.00257 0.02322 0.02579
0.00064 0.00035
1.85321
4 57 61
0.00844 0.14087 0.14931
0.00211 0.00247
0.85393
* P (/= > / F observed) ~< 0.01.
significantly affected biomass levels in short hauls, but the evidence is not conclusive in long hauls. DISCUSSION
Four apparent artifacts exist in the data: (1) the positive correlation between depth gradient and biomass in night hauls; (2) the correlation between temperature and biomass vs no correlation between depth and biomass; (3) the difference in significance levels of biomass-dz/dx correlation in long hauls vs short hauls; and (4) the systematically higher biomass in short hauls. (1) There are two explanations of the night-time positive correlation between biomass and depth gradient. First, because the MOCNESS net mouth tows at an angle to vertical (top of the net closer to the ship than is the bottom), a downward-moving net has a larger effective mouth opening and would filter more water than a horizontally towed net. This increase in volume filtered would not be detected by the flowmeter and would therefore appear as a higher plankton concentration. Calculation of the increased mouth area during downward motion for the observed depth gradients (Table 4) indicated increases in filtered Table 4. Summar.v of depth gradient values. (Number of samples m parentheses) Data subset
Average positive gradient
Short day Short night
2.53 × 10 -2 3.45 x I0-2 1.04xt0-2 7.74× 10 3
Long day Long night .
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
(36) (43) (33) (27)
Average negative gradient - 1 . 5 8 × 10 -z (26) -2.61 × 10-2 (17) - 7 . 9 8 × 1 0 3 (33) 8.13 × 10 "~(24)
Small-scaledistributionsof zooplanktonbiomass
453
volumes of 0.7 to 3.7~. Biomass increases were 2 to 14~o, hence the net motion hypothesis cannot fully explain the increased biomass. Also, if increased effective mouth opening explained the correlation between dz/dx and biomass, one would expect positive correlations between biomass and dz/dx both during day and night. Lack of such correlations requires rejection of this explanation. Second, plankton may sense the net by sight or by its pressure wave. (Bioluminescence may also help sighted animals detect the net at night, and bioluminescence was apparent in the surface waters during the sampling period.) Either mechanism may give the plankton several seconds to avoid the net. If zooplankton avoid the net by preferentially sinking or swimming downward, the MOCNESS would catch more plankton when moving down. CLUTTERand ANRAKU(1968) cited observations of zooplankton avoiding nets by moving downward. If we assume that only zooplankton in the direct path of the net react to it, then for a rectangular net with effective height Nz, horizontal velocity Vx, and gradient in depth of tow dz/dx, the expected increase in catch (denoted '7ob) is:
where Vz is the vertical velocity of zooplankton and t is the time zooplankton have to react. In our data, a typical value of dz/dx for a downward-moving net is 3 x 10- 2 (Table 4). During most hauls, Nz was 90 cm and Vx averaged 100 cm s- ~. Vz has been estimated for certain zooplankton at 5 to 10cm s -1 (HARDY and BAtNBRtDGE, 1954; CLUTTER and ANRAKtJ, 1968). Much higher burst swimming speeds have been reported (CLUTTERand ANRAKU, 1968). Reaction time t is also unknown ; we estimate it at 2 to 3 s (2 to 3 m at our tow speed). These parameters yield an expected increase in biomass of 7.5 to 1500 for those zooplankton avoiding the net, in reasonable agreement with our observations. If the avoidance hypothesis is correct, one must explain why avoidance would be less during the day than at night. The average animal was larger at night than during the day. During the day most of the biomass was small copepods (< l m m ) and radiolaria (0.5 ram), but at night it was mostly euphausiids > 8 mm. The larger and more mobile euphausiids should avoid the net better than the animals present during the day. (2) One must also explain why biomass was sometimes correlated with temperature (P < 0.01 for short day hauls, P ~ 0.07 for short night hauls) but not with depth (Table 2), inasmuch as T and Z are themselves correlated. During sampling, the temperature at 90 m decreased ~ 0.Y~C, suggesting that we were being carried into a different water mass, perhaps of different biomass. If the correlation between T and biomass were caused by entering a new water mass instead of by depth fluctuations of the net, one would expect correlations between T and biomass but none between Z and biomass: this is what we observed (see Table 2 and below). One would also expect significant correlation between biomass and T during both short and long hauls; we found P = 0.219 (P ~ 0.07) for long day hauls and P = 0.362 (P < 0.01) for short day hauls (Table 2); the non-significant correlation for all night-time hauls was explained above as a probable result of day-night changes in size-frequency of zooplankters, hence changes in avoidance. We feel that the correlation between T and biomass was caused by our drifting into a different water mass rather than by fluctuations in towing depths. (3) Another apparent contradiction is the lack of significant correlation between biomass and dz/dx in long hauls: significant (P ~< 0.05) correlations occurred only in short
454
P . R . GREENBLATT, E. SttULIiNBERGI~R and J. H. WOR~aHu
hauls. Values ofdz/dx for short hauls (Table 4) are two to four times those for long hauls. Short hauls also show stronger correlation between biomass and depth gradient than do long hauls (Table 2). Because each long-sample tow lasted about 5 min, there was ample time to adjust the net's depth. A short tow lasted only about 50 s, too short to adjust the net's depth. Values of dz/dx were apparently influenced by whether towing depth was adjusted. The net could move downward for 80°,~,of a long sample and then move up to the original depth, resulting in an average dz/dx = 0. Any variability in biomass during the sampling would cause a non-zero "net avoidance effect," thus lowering or even eliminating any correlation between dz/dx and biomass. (4) Mean biomass of short hauls is about 20"~, higher than that of long hauls (Fig. 2). During short hauls the net moved down a larger fraction of the time than it moved up (Table 4). Long hauls were different. Some short hauls showed higher biomass than long hauls, even when dz/dx was small during both day and night hauls. Also, there is no obvious reason why avoidance should be generally greater on long hauls than on short hauls. Hence, avoidance and net motion cannot fully explain the apparent increased biomass. The bias may be explained ira small residuum (3 to 4!'0)of the catch on each haul was not washed into the cod end but somehow entered the samples of the next haul. Our nets were washed down between hauls, but the leading edge of each net was hard to reach (9 nets are stacked accordian-fashion on the MOCNESS) and was not washed well. Because short and long hauls were alternated, such a bias could well result in about a 20";, increase in short haul biomass and a 2.5 to 3.5°,, decrease in long haul biomass. When planning the sampling program, we expected to find consistent spatial patterns on scales of 100 to 2100 m and that some scales in this range would dominate. Although we found variations on these scales, larger-scale variability (i.e., > 2100 m) dominated the patterns. FASHAM et al. (1974) also found large-scale trends, highly correlated with temperature. Although our biomass trends were not highly correlated with temperature and depth, other unmeasured parameters (e.g., salinity, chlorophyll) might correlate well with biomass. High correlations of biomass with chlorophyll would imply that zooplankton patchiness was being influenced by phytoplankton patchiness (or vice versa) (STARand MULL~N,1981 ). WIEBE(1970) found no large-scale daytime spatial variations, but some night-time spatial series of species' abundance did show long-term trends. WIEae did not indicate whether the long-term trends were correlated with either physical or biological parameters. The point-to-point variability in Sf(r) can indicate the scales of maximum or minimum variability between 100 and 2100 m. Sf(t} values for short hauls show more point-to-point variations than for long hauls. The difference between long and short hauls probably occurred because the long hauls integrate small-scale variability. The values of Sf(t) are consistently lower at night than during the day, implying that long-term trends are more easily distinguished from sample-to-sample variability at night. However, the day-to-night difference in trends could not account for the day ~night difference of $2/.~. Apparently horizontal dimensions of variability did not change greatly from day to night, whereas S2/.L which we regard as 'intensity' of patchiness, increased dramatically at night, as did mean biomass. The increase in $2/£ at night may be caused by an increase in the average size of animals or by changes in aggregation behavior, but distinguishing between these awaits counting of the samples. However, the break in $2/.~ at 0300 (Fig. 3) must have been due to a change in aggregation behavior because the size composition did not change greatly throughout the night (unpublished obserwltions).
Small-scale distributions of zooplankton biomass
455
We conclude that when biomass increased (at night) the number of biomass patches did not increase but the contrast between areas of high and low biomass concentration increased markedly. Qualitatively, one gets a picture of the major components of biomass finding a common 'suitable area' (perhaps forming multi-species patches) or all being influenced by common parameters. This interpretation agrees with the findings of SMITHet al. (1976), STARand MULLIN(1981), and WIEBE (1970), but it disagrees with FASHAMet al. (1974). If the major components of zooplankton find a common suitable area, competition may be more intense inside a patch than outside it. More detailed biological explanations of the observed night-time increase in S2/Yc, based on sorting of the samples into taxonomic categories, were reported by GREENBLA'Vr(1980). Many sets of plankton samples, when counted to yield abundances of species (or other taxonomic groups), show highly skewed PDFs. Although our biomass data did show positively skewed PDFs (Fig. 6), only short night hauls were significantly different (P ~< 0.05) from a normal distribution. Biomass data are cruder than species-level data, but they may be more appropriate to consideration of the environment of individual zooplankters if those plankters are feeding generalists. Either there is a fundamental difference between the distributional properties of zooplankton biomass and of taxonomic category counts, or there were insufficient biomass data to detect significant skewness (we had 69 to 80 samples in each biomass data subset). There is good evidence that even in relatively rich coastal waters zooplankton and fish must find patches of food to survive (LASKER, 1975; MULLIN and BROOKS, 1976); the question arises as to how they do so. Is there information in the distribution of zooplankton that will help their predators find higher concentrations? For a predatory generalist, the long-term spatial trends in zooplankton biomass may be helpful in finding higher concentrations of food. Studies of the stomach contents of midwater fishes indicate that many are generalists. CLARKE(1978) and HOPKINSand BAIRD(1973) examined the diet of certain mesopelagic fishes and found they tend to eat many sizes and taxa of plankton and fish. [-MERRETT and ROE (1974), however, reported that stomach contents of some midwater fish imply preferential predation.] If predators could detect the biomass trends then they could follow biomass gradients to higher concentrations of food. The ecological importance of biomass trends depends on whether predators use the information contained in the distributions, and, more importantly, on how well the nutritional content of the zooplankton comprising the biomass satisfies the nutritional needs of the predators. To approach such a question, one must know the predators' functional feeding responses and the nutritional content of the zooplankton as well as the zooplankton distributions. CONCLUSIONS
We found that night-time zooplankton biomass at 90 m was two to four times that in daytime and that a measure of intensity of zooplankton patchiness was much higher at night than during daylight. In general, changes in biomass with distance were dominated by scales larger than our maximum scale (i,e., > 2100 m), and within our sampling scales of 100 to 2100 m no scale dominated. Probability density functions of all subsets of biomasses of hauls did not differ from the lognormal distribution, and only short night hauls differed from a normal distribution. Biomass was not consistently correlated with mean temperature during a tow
456
P.R. GREENBLATT, E. SHULI£NBt::RGI-Rand J. H, WORMUTIt
or with dT/dx. Nets captured more zooplankton when the net was sinking slightly than when rising. On short hauls ( ~ 50 m), small changes in Z, T, dz/dx, and dT/dx affected biomass levels, longer tows (300 m) seem to integrate the effects. It is clear that in the future other parameters of direct importance to zooplankton (e.g,, chlorophyll) should be monitored simultaneously with zooplankton if we are to use distributional data in analyses of zooplankton community function. However, even on scales approaching 2 km information potentially useful to generalized predators is contained in the large-scale trends we observed. Acknowledgements- We thank R. PINKEL, F. N. SPlESS, J. A. McGoWAN, D. GOODMAN, L. S. TOMOOKA, and M. M. MUEt.IN for their help with the research and with the manuscript. M. BANDUt~AGA.C. GARROD, D. GLt~ASON, M. OESSON, L. SNIDER, and L. VAKASSIANhelped collect the samples. Captain COCEMAN and the crew of the E. B. Scripps did an excellent job. The staff of the Scripps Institution hydraulics laboratory helped with flowmeter calibrations. Sponsored by the Office of Naval Research Code 480 (Contract N00014-75-C-0704} and Code 484 (Contract N00014-75-C-0537). This is a contribution of the Scripps Institution of Oceanography, new series.
REFERENCES BlaSRS J. R. (1976) Determination of zooplankton biomass. In: Zooplankton fixation and preservation. H. F. STEEDMAN, editor, UNESCO Monograph, pp. 35-84. CASSIE R. (1959) Micro-distribution of plankton. New Zealand Journal of Science, 2, 398 409. CASS,': R. M. (1962) Frequency distribution models in the ecology of plankton and other organisms. Journal q! Animal Ecologl'. 31, 65 92. CI.ARKI~ T. A. (1978) Diel feeding patterns of 16 species of mesopelagic fishes from Hawaiian water. Fisherc Bulletin. 76, 495-513. CLUTTER R. 1. and M. ANRAKU(1968) Avoidance of samplers. In: Zoophmkton samphng, UNESCO Monograph, pp. 57 76. FASrtAM M. S. R., M. V. AN(IEL and H. S. J. Rol~ (1974) An investigation of the spatial pattern of zooplankton using the Longhurst Hardy plankton recorder. Journal of E?:perimental Marine Biology and Ecology. 16, 93 112. FlSlUiR F. H. and F. N. Spn-ss (1963) Hip: floating instrument platform. Journal ~,1'the Acoustical Society q/ America, 35, 1633 1644. GREENBL^TT P. R. (1980) Observations of zooplankton patchiness using a high frequency sonar and a multiple sample plankton net. Ph.D. Dissertation, University of California at San Diego, 140 pp. GREENBLATT P. R. (1981) Sources of acoustic backscattering at 87.5 kHz. Journal of the Acoustical Socieo' qf America (in press). HARD',' A. C. and R. BAINBRIDG~:(1954) Experimental observations of the vertical migration of plankton animals. Journal of the Marine Bhdogical Association qf the United Kingdom. 33, 409 448. HOPKINS T. L. and R. C. BAIRD(1973) Diet of the hatchet fish Sternopt.vx diaphana. Marine Budogv, 21, 34 46. KRAMF,R D., M. J. KALIN, E. G. STEVENS,J. R, THRAILKILLand J. R. ZWEIFEL(1972) Collecting and processing data on fish eggs and larvae in the California current region. National Marine Fisheries Service~National Oceanic and Atmospheric Administration Technical Reports. N. M. F. S. Circular 37, 38 pp. LASKER R. (1975) Field criteria for survival of anchovy larvae: The relation between inshore chlorophyll maximum layers and successful first feeding. Fisherr Bulletin. 73, 453 462. LINDCiREN B. W. and G. W. McELRATll (1969) Introduction to probability and .s-tatistics. The MacMillan Company, New York, 305 pp. M cGow^N J. A. (1971) Oceanic biogeography of the Pacific. In: The micropah, ontologv qfoceans, B. M. Ft NNtt,E and W. R. REIDFL, editors, University Press, Cambridge, England, pp. 3 74. Mt~RRETT N. R. and H. S. J. Rot: (1974) Patterns and selectivity in the feeding of certain mesopelagic fishes. Marine Biolog.t'. 28, I 15 126. MULLIN M. M. and E. R. BRLKI.KS(1976) Some consequences of distributional heterogeneity of phytoplankton and zooplankton. Limnolog.v and Oceanography, 21,784 797. PIELOU E. C. (1977) Mathematical ecology. John Wiley, New York, 385 pp. SMITtt L. R., C. B. MILLER and R. L. HOLTON (1976) Small-scale horizontal distribution of coastal copepods. Journal of Experimental Marine Biology and Ecology. 23, 241 253. ST^R J. L. and M. M. MULLIN (1981) Zooplankton assemblages in three areas of the North Pacific as revealed by continuous horizontal transects. Deep-Sea Research, 28, 1303 1322.
Small-scale distributions of zooplankton biomass
457
TATAaSKIIV. I. (1971) The effects of the turbulent atmosphere on wave propagation. Israel Program for Scientific Translation, National Oceanic and Atmospheric Administration and National Science Foundation, Washington, DC., 472 pp. WmBE P. H. (1970) Small scale spatial distribution in oceanic zooplankton. Limnology and Oceanography, 15, 205-217. WlEBE P. H., K. H. BURT, S. H. BOYD and A. W. MORTOS (1976) A multiple opening-closing net and environmental sensing system for sampling zooplankton. Journal of Marine Research. 34, 313 326.
APPENDIX
Changes in Sf(z)from short to long hauls For a given separation between samples, the ratio of structure functions [Sf(z)] shows a consistent difference in amplitude between short and long hauls. For example, Sf(~) for short hauls = 0.55 at 350-m separation, but Sf(r) for long hauls reaches 0.55 only at 900-m separation (Fig. 4). This is an artifact of sampling caused by each net integrating a finite volume of water. A biomass sample is the actual biomass at a position x [i.e., b(x)] convolved with the length of tow involved in collecting the sample. We can write '(x)=
b(x)*n(~-),
(A1)
where L is the sample tow length, * is the convolution operator, and
L-~- j
F x-,.1
n L 2L J
1 for 0 ~< x ~< L
_
0
elsewhere.
The ratio of structure functions for large hauls is
ss(,):
+.,..
600 : J l
and short hauls is
L,-T~-)- (~'+r°l'" k lOO / ] /
Equations (A2) and (A3) differ because of the two haul lengths (50 and 300 m). Unfortunately there is no analytic method to remove the effects of sampling (i.e., reduce equations A2 and A3 to equivalent form). A computer analysis indicated that S f ( , } of simulated data decreased more slowly for longer integration times, thus agreeing with and explaining the apparent discrepancy in Fig. 4.