Regressions Between Biological Oceanographic Measurements in the Eastern Tropical Pacific and Their...

Regressions Between Biological Oceanographic Measurements in the Eastern Tropical Pacificand Their Significance to Ecological EfficiencyAuthor(s): Maurice BlackburnSource: Limnology and Oceanography, Vol. 18, No. 4 (Jul., 1973), pp. 552-563Published by: American Society of Limnology and OceanographyStable URL: http://www.jstor.org/stable/2834348 .

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REGRESSIONS BETWEEN BIOLOGICAL OCEANOGRAPHIC MEASUREMENTS IN THE EASTERN TROPICAL

PACIFIC AND THEIR SIGNIFICANCE TO ECOLOGICAL EFFICIENCY'

Maurice Blackburn Institute of Marine Resources, University of California, San Diego, La Jolla 92037

ABSTRACT Simple regressions of various standing stocks on each other and on primary productivity

were compared by covariance analysis for different seasons, latitudes, and longitudes in the eastern tropical Pacific. The stock of zooplankton varies significantly with that of chlorophyll a to a power <1.0 in all seasons and areas, and it is shown that a similar relation probably exists between the corresponding rates of daily production. A similar relation holds in the regression of standing stock of fish-cephalopod micronekton, suitably lagged, on stock of zooplankton. Thus the relative amount of organic matter transferred from one trophic level to another probably decreases with an increase of stock and production at the lower level, so that ecological efficiency is higher in oligotrophic than in eutrophic situations, in tropical oceans.

Standing stock of chlorophyll a varies significantly with primary productivity to a power <1.0. The stock of crustacean micronekton sometimes varies with the stock of chlorophyll a to a power >1.0, which is interpreted as a feeding aggregation.

In a previous paper (Blackburn 1966) I compared regression coefficients between significant regressions of zooplankton on water-column chlorophyll a and significant regressions of camivorous micronekton on zooplankton in the eastern tropical Pacific, using all available data in a log- arithmic transformation. The principal conclusions were that zooplankton varied with some positive power < 1.0 of chlorophyll a, and carnivorous micronekton with some power about 1.0 of zooplankton. The data were sparse, however. They were obtained on a few cruises made in different areas and seasons with a maximum of 27 sets of matching observations per cruise.

The EASTROPAC expedition of 1967- 1968 provided a much larger and more systematically collected series of data on the above-mentioned standing stocks and others, and on primary productivity estimated from 14C uptake, in a different part of the eastern tropical Pacific. I use those data here to examine further the coefficients that characterize different regressions among

1 This work was done at and financially sup- ported by the Institute of Marine Resources, Uni- versity of California, San Diego.

the properties and to interpret their biological significance.

The work was intended to throw light on the relative efficiency with which material is transferred from one trophic level to another, for different values of stock and production at the lower level. Differ- ent trends in efficiency would be suggested in cases where a higher level stock consistently varied with a lower level stock to a power greater than, or less than, or equal to 1.0, if there were reason to think the production rates were similarly related. The relation would of course be linear if the power were 1.0. The results might then indicate if such transfers were more efficient in eutrophic than oligotrophic situations, or less so, or equally efficient in both. That question is important for evaluating the resources of the sea, but is still controversial. Greze (1970) concluded that transfer is more efficient in oligotrophic than eutrophic situations, using data from the Black Sea, Mediterranean, and tropical Atlantic. Ryther (1969) suggested the opposite, but did not strongly support his position with detailed data or arguments. Cushing (1970) took a

LIMNOLOGY AND OCEANOGRAPHY 552 JULY 1973, V. 18(4)



BIOLOGICAL OCEANOGRAPHC MEASUREMENTS 553

< ~~~~~~~~~~~~~200t

121030' 114-30' 107030' 00030' 1600 'N

13015'N

10025'N 0

7025' N

4025' N

1050'N

00 00 50's

3.020'S

120? 110 0 100Q 900 80

Fig. 1. Area of study and its subdivisions.

position similar to that of Ryther, namely that transfer is more efficient in eutrophic situations, but reversed it in a later paper (Cushing 1971). The results of my study support the conclusion of Greze (1970) and Cushing (1971), although my methods are different from theirs.

Preparation of the paper was aided by comments made by R. W. Eppley, E. W. Fager, M. M. Mullin, and W. H. Thomas, for which I thank them.

MATERIAL AND METHODS

Areal and seasonal distribution of observations

The data were taken in the area shown in Fig. 1, which received better seasonal

coverage during EASTROPAC than any other part of the expedition region and was called the western area of EASTRO- PAC (Blackburn et al. 1970). It is farther offshore than most parts of the areas that I studied previously (Blackburn 1966). Conditions in the area range from oligotrophic in the northwest to eutrophic in the upwelling region along the Equator; primary productivity ranged from about 100 to 500 mg C m-2 day-' (Owen and Zeitzschel 1970). All biological properties investigated were measured at several successive cruise periods at similar latitudes and longitudes. The area was divided into three equal bands of longitude and seven subequal zones of latitude as shown in Fig. 1, with boundaries chosen so that



554 MAURICE BLACKBURN

at least one measurement of each property was available in each small rectangle in each cruise period. The longitude bands are wide because portions of the three quasi-meridional cruise tracks deviated from a north-south orientation, but the deviations were similar on all cruise periods. Over most of their lengths these tracks approximated the 1190, 112?, and 105'W meridians on all cruise periods.

EASTROPAC cruises in the area were made in seven successive 2-month periods over the 14 months February 1967 to March 1968 and ranged from 28 to 49 days (first to last station in the area).

Measurements of standing stocks The standing stocks considered in this

paper are chlorophyll a for the water column 0 to 150 m, in mg m-2; net-caught day zooplankton and night zooplankton for the depth range 0 to 200 m, in ml 1,000 m-3; net-caught night micronekton divided into its crustacean and noncrustacean (i.e. combined fish and cephalopods) components, for the depth range 0 to 200 m, in ml 1,000 m-3. The data on these stocks, too numer- ous to list conveniently, are available as NODC Accession No. 70-0208 at the National Oceanographic Data Center, Washington, D.C. They were produced by the authors of Blackburn et al. (1970) according to methods described in that paper. The essential points of those methods are as follows.

The samples for chlorophyll were obtained in plastic water samplers from 8 or 9 depths at each oceanographic station and concentrations determined fluorometri- cally. Integration over the water column was performed by computer using a linear model. Amounts of chlorophyll a below 150 m were negligible. The zooplankton net had a circular mouth 0.5 m in diameter and a uniform mesh of aperture 0.333 mm and was hauled at ship speeds about 1.5 to 2 knots. The micronekton net had a square mouth measuring 1.5 m on the side, and a uniform mesh which retains animals t 1 cm in largest dimension but not

smaller and was hauled at ship speeds about 4 to 5 knots. The net hauls were oblique.

Blackburn et al. (1970) regarded the zooplankton as predominantly herbivorous, crustacean micronekton as the same but probably less so, and fish-cephalopod micronekton as predominantly carnivorous. The size range of micronekton is about 1 to 10 cm. Some active fish and cephalopods probably avoided the micronekton net. One species of crustacean micronekton, Pleuron- codes planipes, was disregarded; it was caught infrequently only to the north of 12?N lat and may not be autochthonous.

Numbers of observations per cruise period per small rectangle (Fig. 1) ranged from one to about five for chlorophyll a (day and night observations combined), day zooplankton, and night zooplankton. The geometric means of multiple observations were calculated. The corresponding number of observations for micronekton rarely exceeded one, and when it did a single observation was selected at random. The final data thus consist of a single value of each of the five standing stocks for each combination of cruise period and small rectangle. These combinations are hence- forth called "stations" for convenience, although they are much broader than normal oceanographic stations in space and time.

Measurements of primary productivity

These measurements are available only for six successive 2-month periods, which cover the year April 1967 to March 1968. Seven sampling depths were chosen at each oceanographic station to span the euphotic zone above the 1% level of surface light intensity. Paired light and dark bottles were inoculated with 14C and incubated under ambient sunlight from noon to sunset. The incubators were constructed to transmit the same fraction of surface light intensity as was present at the depths where the water samples were taken. Sur- face seawater was pumped continuously through the incubators. Production values for the seven depths were integrated over



BIOLOGICAL OCEANOGRAPHIC MEASUREMIENTS 555

Table 1. Pooled analysis of covariance for regressions of YDZ, YNZ, YCM, and YFCm on contemporaneous XCHL, and other information about the regression coefficients (b), for data from 147 stations grouped successively by periods, latitudes, and longitudes; and similarly for YFCM on XCHL measured 4 months

earlier, 105 stations. (df, degrees of freedom; MS, mean square; NA, not applicable)

Source of Periods (7) Latitu es v Longl u es v s variation, etc. df MS df MS df MS

DZ Within groups 133 0.0511 133 0.0487 141 0.0505 Between group bs 6 0.0435 6 0.0183 2 0.0007 Common 139 0.0508 139 0.0474 143 0.0498 F for group bs <1 <1 <1 Common b 0.7437* 0.5930* 0.6878* Total regression YD 1.1348 + 0.7226* X

DZ CHL YNZ Within groups 133 0.0332 133 0.0312 141 0.0363

Between group bs 6 0.0501 6 0.0343 2' 0.0354 Common 139 0.0339 139 0.0313 143 0.0363 F for group bs 1.51 1.10- <1 Common b 0.8110* 0.5466*' 0.6648* Total regression YNZ = 1.3193 + 0.6531* X

y Within groups 133 0.2098 133 0.0883t 141 0.2239 CM, Between group bs 6 0.5732 6 0.2208 2 0.2372

Common 139 0.2255 139 0.0940 143 0.2241 F for group bs 2.73t 2.50t 1.06 Range of group bs 0.1401 --- 4.7671 -0.1814 --- 2.7127 1.2570 --- 2.2850 Significant group bsi 1.7346t 3.0187t 0.7103t 1.4426+ 1.2570t 1.7012$

3.0471* 4.7671* 2.7127t 2.2850* Comimon b NA NA 1.7127* Total regression ? -CM 1.6861 + 1.6744* X

CHL Within groups 133 0.0512 133 0.0604 141 0.0579 Between group bs 6 0.0180 6 0.0572 2 0.0187 Common 139 0.0497 139 0.0603 143 0.0574 F for group bs <1 <1 <1 Common b 0.3479t 0.0809 -0.0280 Total regression C = 0.5398 + 0.0250 X

Yll Within groups 95 0.0484 91 0.0552 99 0.0507t PCX Between group bs 4 0.0033 6 0.0406 2 0.0176 Common 99 0.0465 97 0.0543 101 0.0501 F for group b s <1 <1 <1 Range of group bs 0.4183 --- 0.6787 -0.2707 --- 1.0923 0.1569 --- 0.5172 Significant group b,s NONE 0.8863t 1.0923t 0.1569t 0.3969+

0.5172* Common 2b 0.5434* 0,3994 NA Total regression =YFCM

- 0.1832 + 0.3398 XCHL

*Significant at 1% level. tDifferences between group MS values are significant at 1% level. tSignificant at 5% level. ?Doubtful because of significant differences between group b or MS values. Four months later; only five periods.

the euphotic zone to give the total production, uncorrected for respiration, in mg C m-2 day-'. Multiple values per station were averaged. The final data were produced and listed by Owen and Zeitzschel (1970).

Statistical analysis All standing stock and primary produc-

tivity measurements were transformed into

common logarithms. The transformation was appropriate to bring distributions of the variables closer to the normal distribution, and because linear or nonlinear relations between paired variables are easily recognized in regressions between their logarithms. CHL, DZ, NZ, CM, FCM, and PP indicate chlorophyll a, day zooplankton, night zooplankton, crustacean micronekton, fish-cephalopod micronekton, and primary




productivity in logarithms. They are used as subscripts to X when the variables they represent are considered independent, and as subscripts to Y when they are considered dependent, in simple regressions of Y on a single X. Regressions of that type were computed for a given X and Y for each available cruise period, latitude, and longitude, and a pooled analysis of covariance (Snedecor 1956) was used to compare the resulting regression (slope) coefficients for the different periods, latitudes, and longitudes. The positions (elevations) of such regressions were not compared, as they reflect the particular methods used to measure the variables and are of limited biological interest (Blackburn 1966).

The first part of Table 1, where YDZ is the dependent variable on XCHL, illustrates the simplest situation encountered. The observations from the total number of stations, 147 in this case, were grouped by the seven cruise periods and regression statistics (not shown here) were obtained for each of the periods. Differences between the seven mean squares were tested by the Bartlett method (Snedecor 1956) and found not to be significant. Differences between the seven regression coefficients (b) were tested by F and found not to be significant. The common b value, 0.7437, significantly different from zero at the 1% level of probability, thus adequately de- scribes the slope of the regression for data grouped by period, regardless of which period. Similar results are obtained for the data of the same stations grouped by the seven latitudes and the three longitudes. Since b does not vary significantly with period, latitude, or longitude, and the three common bs are similar (about 0.6 to 0.7), the data may be combined without any grouping in a total regression for which b is 0.7226, again significant at the 1% level. This value is characteristic of the regression of YDz on XcHL in all parts of the region at all seasons. There were several situations of this type.

In some other situations, such as the third part of Table 1, the common and total regressions were of dubious value

because of significant differences between mean squares or bs for the individual regressions. Differences between mean squares were ignored if they were significant only at the 5% level of probability. Differences between bs were considered to be important whether they were significant at the 5% or the 1% level. In those situations where the common and total regressions are doubtful, the common bs are not given and the total regressions are given with reservation in Tables 1, 2, and 3; however, the tables give the range of all the bs for the individual regressions for different periods, etc. and specify those values that were significant, so that some information about characteristic b values is available.

The three common mean squares and the three common bs for a given X and Y are expected to be similar, but not neces- sarily the same because the regressions and deviations from regression can vary with the method of grouping the data. The common bs for a given Y and X usually differ by about 0.1. In the fourth part of Table 1 (YFc.A on XCHL) the common b for periods is some 0.3 to 0.4 higher than the other two common bs; this is explained below.

My previous work (Blackburn 1966) was done with regressions computed according to Bartlett (1949), not with regressions by the least squares method used here. The former method is appropriate when each variable is subject to error, but the latter is more convenient for the type of work done in this paper. Regression coefficients were obtained by the Bartlett method for comparison with those in total regressions in Tables 1-3, and are given in the text; the differences are small and do not affect the conclusions of this paper or prevent comparison with the previous paper. The grouping of points for Bartlett's method was done on the basis of the range of values of the independent variable.

The total regressions may be useful for descriptive or predictive purposes in biological oceanography. It is clear, however, that some of them would not express real relations if extrapolated to values near zero.



BIOLOGICAL OCEANOGRAPHIC MEASUREMENTS 557

RESULTS

Regressions of zooplankton and micronekton on

chlorophyll a Table 1 gives the results of this part of

the study. Regression coefficients obtained by the Bartlett method, corresponding to those in the five total regressions in the order given in the Table, are 0.7391, 0.6473, 1.5917, -0.0670, and 0.3521. References here to seasonal cycles in the area of study are based on Blackburn et al. (1970) for standing stocks and Owen and Zeitzschel (1970) for primary productivity.

In the tropics, changes in standing stock of phytoplankton can be expected to produce similar changes in the herbivorous part of the zooplankton stock which grazes on it within 2 or 3 weeks. This is confirmed for the area of study by the fact that seasonal cycles of chlorophyll a and day zooplankton are similar in phase and amplitude. The cycle in primary productivity is also similar, giving further assurance that the zooplankton cycle is the result of the phytoplankton cycle. It might be ar- gued conversely that the zooplankton cycle determines the phytoplankton cycle by nutrient excretion. That might possibly be true for the five more northern zones of latitude shown in Fig. 1, where nutrient concentrations are low, but it probably would not be true for the two more south- ern zones where nutrient concentrations are high because of upwelling (see Thomas 1970 for data). However, there are no statistically significant phase differences in the cycles for primary productivity, chlorophyll, or zooplankton between the seven latitudes, so it is unlikely that the phytoplankton cycle is determined by the zooplankton cycle in any part of the area.

The standing stock cycles were determined from the data of the seven successive 2-month periods. Amplitude is very low in each cycle (except crustacean micronekton at the Equator) and the phase change from maximum to minimum is smooth and simple. Measurements of chlorophyll and day zooplankton in the

same 2-month period probably represent the levels of the stocks over a period of several weeks around the sampling date. Thus although the two measurements were generally made within a day or two of each other in a given area, they may rea- sonably be used to investigate a causal relation between the variables.

The regressions of day zooplankton on chlorophyll a were therefore done with contemporaneous data. The same applies to regressions with night zooplankton and crustacean micronekton as the dependent variables, although they display no statistically significant seasonal cycle, except for crustacean micronekton in the zone of latitude at the Equator; the cycle there is much the same in phase as the chlorophyll cycle. On the other hand the seasonal cycle for fish-cephalopod micronekton lags about 4 months (two cruise periods) behind the chlorophyll cycle. For this property two series of regressions on chlorophyll were computed, one in which the measurements were contemporaneous and one in which the micronekton was measured 4 months after the chlorophyll. In the latter case data for only five cruise periods, i.e. a total period of 10 months, could be used for each property.

The coefficients of regression of YDZ and YNZ on XIIL do not vary significantly between periods, latitudes, or longitudes. They are both about 0.7, and significantly different from zero at the 1% level of probability, in the total regressions. This value agrees with those previously obtained in similar regressions, namely 0.5 to 0.8 (Blackburn 1966). Such values-all well below 1.0, but significantly above zero- thus appear to be characteristic of regressions of zooplankton on chlorophyll a in all time and space situations in the eastern tropical Pacific.

For YcM on XCHL the results are compli- cated by significant differences between regression coefficients and mean squares for different periods and latitudes. The value of b in the total regression, 1.6744, is therefore dubious. Nevertheless, ten of




Table 2. Pooled analysis of covariance for regressions of YCM and YFCM on contemporaneous XNZ, and other information about the regression coefficients (b), for data from 147 stations grouped successively by periods, latitudes, and longitudes; and similarly for YFCM on XNZ measured 4 months earlier, 105

stations. (df, degrees of freedom; MS, mean square; NA, not applicable)

Source of Periods (7) Latitudes (7) Longitudes (3) variation, etc. df MS df MS df MS

YCM Within groups 133 0.2530 133 0.1025* 141 0.2432 Between group bs 6 0.0906 6 0.0851 2 0.3833 Common 139 0.2459 139 0.1017 143 0.2451 F for group bs <1 <1 1.58 Range of group b,s 0.2888 --- 1.3857 -0.4576 --- 0.4076 0.3486 --- 1.2807 Significant group b; 0.9442t 1.3857t NONE 0.9822t 1.2807t Common b 0.8274t NA 0.9234t Total regression YCM 1.4263 + 0.8545t XNZ

YFCM Within groups 133 0.0519 133 0.0607 141 0.0578 Between group 1s 6 0.0300 6 0.0244 2 0.0217 Common 139 3.0509 139 0.0591 143 0.0573 F for group b s <1 <1 <1 Common b 0.0924 0.1882 0.0436 Total regression FCM = 0.3571 + 0.1003 XNZ

yFCMiIithin groups 95 0.0468 91 0.0554 99 0.0495 Between group bs 4 0.0328 6 0.0139 2 0.0977 Common 99 0.0462 97 0.0529 101 0.0505 F for group bs <1 <1 1.97 Common b 0.2835: 0.3014t 0.1999 Total regression .Y 0.1054 + 0.2384t X

FCM XNZ

*Differences between groip MS values are significant at 1% level. tSignificant at 5% level. :Significant at 1% level. ?Doubtful because of siglificant differences between group MS values. Fkour months later; only five periods.

the seventeen separate group regression coefficients are significant, with values from 0.7103 to 4.7671, and nine of them exceed 1.25.

With YFCM on contemporaneous XCIIL the b in the total regression is not significant. The common b for data grouped by periods is positive and significant at the 5% level, whereas common bs for data grouped by latitude or longitude, i.e. with all periods combined, are low and not significant. In previous work I found a significant positive regression between the same variables for data of some, but not all, cruises (Blackburn 1966). Apparently the regression can be significant for data confined to a particular season. Since the two seasonal cycles differ in phase by about 4 months, one would not expect to find a significant relation between the variables when data for all seasons are combined, as is the case when grouping is done by latitude or longitude. On the other hand

a significant positive relation might be found between the variables for the different areas at each season, because each of the seasonal cycles is the same in phase and amplitude at all latitudes and longitudes, except for a slight difference in the case of chlorophyll by longitude (Black- burn et al. 1970).

A closer relation might exist between XCIIL and YFcM measured 4 months later, because the level of the former could help determine the level of the latter through its effects on zooplankton. The last part of Table 1 bears out this expectation to some extent. The b in the total regression is much higher than for the contemporaneous data, although it does not quite reach a significant level and the total regression may not be valid. The common b for periods, 0.5434, is higher than before and is now significant at the 1% level. The common b for latitudes also is higher than before.




Table 3. Pooled analysis of covariance for regressions of YCHL, YDZ, YNZ, Ycm, and YFCM on contemporaneous Xpp, and other information about the regression coefficients (b), for data from 126 stations grouped successively by periods, latitudes, and longitudes. (df, degrees of freedom; MS, mean square;

NA, not applicable)

Source of Periods tb) Latitudes (7) Longitudes (3) variation, etc. , MS df MS df MS

YCHL Within groups 114 0.0116 112 0.0160 120 0.0176 Between group bs 5 0.0160 6 0.0051 2 0.0184 Common 119 0.0118 118 0.0154 122 0.0177 F for group bs 1.38 <1 1.04 Common b 0.2216* 0.1820* 0.2338* Total regression YCHL 0.7207 + 0.2311* X

yDZ Within groups 114 0.0460 112 0.0402 120 0.0478t Between group bs 5 0.1349 6 0.0950 2, 0.2126 Common 119 0.0498 118 0.0430 122 0.0505 F for group bs 2.93+ 2.36q 4.45.1T Range of group b s 0.0059 --- 1.3046 -0.3346 --- 0.6712 0.0921 --- 0.6828 Significant group bs 0.6391* 1.3046* 0.5891* 0.61051 0.5870* 0.6828t

0.6172T Common b NA NA NA Total regression ?yDZ = 1.0541 + 0.4314* Xpp

YNZ Within groups 114 0.0368 112 0.0349 120 0.0395 Between group bs 5 0.0291 6 0.0092 2 0.0012 Common 11.9 0.0365 118 0.0336 122 0.0389 F for group bs <1 <1 <1 Common b 0.2697* 0.1616t 0.1646t Total regression YNZ = 1.6740 + 0.2012+ ,p

YCM Within groups 114 0.2697 112 0.09531 120 0.2750 Between group bs 5 0.1726 6 0.2608 2 0.0370 Common 119 0.2656 118 0.1037 122 0.2711 F for group 1s <1 2.74At <1 Range of group bs -0.3204,--- 1.4053 -0.4112 --- 1.1229 0.1481 --- 0.4037 Significant group bs NiONE 0.8525t NONE Common b 0.3147 NA 0.3082 Total regression ?YCM = -0.2684 + 0.2949 Xpp

'CM Within groups 114 0.0482 112 0.0619 120 0.0568 Between group bs 5 0.1509 6 0.0479 2 0.0446 Common 119 0.0525 118 0.0612 122 0.0566 F for group Zs 3.13t <1 <1 Range of group bs -0.5312 --- 0.5384 -0.3309 --- 0.2965 -0.3003 --- 0.0075 Significant group bs-o.5312* NONE -0.3003* Common b NA -0.0571 -0.0957 Total regression FCM = 0.7158 - 0.0561 Xp

*Significant at 1% level. tDifferences between group MS values are significant at 1% level. :Significant at 5% level. ?Doubtful because of significant differences between group b or MS values.

Regressions of micronekton on zooplankton

Table 2 summarizes regressions of YcM, YFCM (contemporaneous), and YFCM (4 months later) on XNZ. Night zooplankton was chosen as the independent variable in preference to day zooplankton because the micronekton data were obtained at night. Regression coefficients obtained by the Bartlett method, corresponding to those in the three total regressions in the

order given in the table, are 0.7588, 0.1618, and 0.3246.

The coefficient in the total regression of YcM on XNZ is doubtful because of hetero- geneity of variance between regressions in latitude groups, although it is similar to the common bs for the period and longitude groups. Values of b for individual group regressions are significant in only a few instances and range from about 0.9 to 1.4. If there is a characteristic coefficient




it is probably closer to 1.0 than those in the other instances considered previously.

The regression of YFCM on XNZ is not significant with contemporaneous data. It is significant where the dependent variable is measured 4 months later, as might be expected: changes in zooplankton levels could produce similar changes in the standing stock at a slightly higher trophic level a few months later, in the tropics, and such changes were observed. Even in this case b is low in both the total and common regressions, namely about 0.2 to 0.3. In previous work done with contemporaneous data, significant values of b about 1.0 were obtained (Blackburn 1966).

Regressions of chlorophyll a, zooplankton, and micronekton

on primary productivity These regressions are summarized in

Table 3. They are all for contemporaneous data, because seasonal cycles were similar in phase for all the properties except night zooplankton and crustacean micronekton at most latitudes (no statistically recog- nizable cycle) and fish-cephalopod micronekton (cycle lagged 4 months). No at- tempt was made to use YFCM with a 4-month lag because data for only four cruise periods, i.e. a total period of only 8 months, could then have been used for each property. Regression coefficients by the Bart- lett method are 0.2037, 0.3749, 0.1996, 0.2997, and -0.0747, corresponding to those in the five total regressions in the order given in the table.

For YCHL or YNZ on Xpp, b in the total regression is significant but low (about 0.2). With YDZ the b in the total regression is dubious; seven of the sixteen group regression coefficients are significant and six of them have values about 0.6 to 0.7, although one equals 1.3. Perhaps the regression is not always significant but significant bs are generally < 1.0. Regressions of Ycm and YFCM on Xpp are not significant.

DISCUSSION

Standing stock of chlorophyll a varies with primary productivity to about the

0.2 power (Table 3). It was expected to vary with some power <1.0; various work- ers have noted that productivity:chlorophyll ratios (called assimilation ratios) increase under conditions, such as a rise in nutrient concentrations, which tend to enhance the productivity. Thomas (1970) discussed a series of 17 assimilation ratios, with a fairly typical range of values which he related to the varying nutrient content of the water, from the EASTROPAC area. I used his data to calculate b for the significant regression of logarithm chlorophyll a on logarithm primary productivity, which was 0.4790. One should not speculate on the difference between this value of b and the lower one given above, because the two sets of data were obtained by different methods. Thomas' measurements come from water samples taken 10 m below the surface, with the samples for productivity incubated at light saturation.

Other conditions might also account for some of the shortage of chlorophyll a relative to the amount expected if the relation with primary productivity were linear. Grazing by herbivores already present could have such an effect. The standing stock of chlorophyll a at the time of measurement consists of a portion just produced and a portion resulting from slightly earlier production. Grazing could not affect the amount of the former portion but could affect the amount of the latter portion relative to the measured primary production. Table 3 shows that the standing stock of night zooplankton increases with increasing primary productivity, although not linearly.

Standing stock of zooplankton (both day and night) consistently varies with the stock of chlorophyll a to about the 0.7 power (Table 1). This value is similar to those previously obtained in similar regressions for the more inshore part of the eastern tropical Pacific, namely 0.5 to 0.8; two of those values were obtained with copepods, not total zooplankton, as the dependent variable (Blackburn 1966). Thus, increase of the zooplankton stock in space or time is less than the corresponding




increase of the stock of phytoplankton that feeds the zooplankton, either directly (herbivores) or indirectly.

Since the zooplankton and phytoplankton stocks are significantly related in the same way throughout the year in all parts of the studied area, a similar relation could exist between the respective rates of production. Table 3 shows that the logarithm of the stock of night zooplankton, YNZ, varies significantly with the logarithm of daily primary production, Xpp, in the same way at each of the six seasons (b = 0.27) and also for all seasons combined (b = 0.16). So the standing stock of night zooplankton consistently varies with the daily primary production to a power about 0.2, as follows (where a is a constant):

Zooplankton stock = a (Daily primary production) 0-2.

The stock of night zooplankton changes little during the year in a given area, as stated previously. Probably most of its mortality is by predation by animals such as fish-cephalopod micronekton and (in part) crustacean micronekton. Table 2 shows no significant difference in b for different seasons in the regressions of the micronekton stocks on the stock of night zooplankton, suggesting a uniform preda- tor-prey relation throughout the year. Thus we may assume a constant relation between the standing stock of night zooplankton and its daily mortality, and therefore, a constant relation between the standing stock and its daily production. Then from the previous equation the daily production of night zooplankton varies with the daily primary production to a power about 0.2, as follows (where c is another constant):

c (Daily zooplankton production) -a (Daily primary

production ) 0.2. Thus the daily production of night zoo-

plankton probably varies with the daily primary production to a power < 1.0, a relation similar to that between the two standing stocks. I do not speculate on the possible biological significance of differ-

ences in bs that are less than 1.0. The data for day zooplankton in Table 3 do not dem- onstrate a relation similar to the above for the production of day zooplankton, although they do not deny the existence of the relation. The seasonal cycles of the stocks of day zooplankton and night zooplankton are not very different (Blackburn et al. 1970), so it is likely that production of day zooplankton varies with primary production in the way shown for night zooplankton.

Thus a small stock and a low production of phytoplankton seem to be used to produce proportionately more zooplankton than a large stock and a high production of phytoplankton, when there is a significant positive relation between the variables. Presumably the rate of consumption or the rate of assimilation by the herbivores gradually levels off as the phytoplankton stock increases.

An alternative explanation of the observed relation is that more of the stock of zooplankton may be grazed off and be unmeasured when the phytoplankton stock and production are high than when they are low. This possibility cannot be denied because we have no information about zooplankton production that is independent of the measurement of zooplankton standing stock. If the explanation were true one would expect significant values of F for group bs in Table 2, i.e. differences in relations between the standing stock of zooplankton and the stocks of its micronekton predators for different seasons and areas, since high phytoplankton levels tend to be characteristic of certain seasons and areas (Blackburn et al. 1970). Table 2 does not show this. The suggested alternative explanation is therefore not likely.

Standing stock of crustacean micronekton sometimes varies with standing stock of chlorophyll a to a power considerably > 1.0 (Table 1), but it has no significant relation with primary productivity (Table 3). Since it consists largely of fairly mobile facultative herbivores, such as euphausiids, the results may be interpreted as an effect of aggregation of the crustacean micronek-




ton on the existing stock of phytoplankton, in order to eat it. There is no reason to expect crustacean micronekton to respond to the primary productivity because they cannot sense it. The nature of the regression of crustacean micronekton on night zooplankton (Table 2) is not clear but it tends to be approximately linear, perhaps because of common elements such as euphausiids in the taxonomic composition of the variables; also, carnivores in the crustacean micronekton may aggregate for feeding on the zooplankton.

Regressions with the standing stock of fish-cephalopod micronekton as the dependent variable are of little interest except where night zooplankton, measured 4 months earlier, is the independent variable (Table 2). In this case the fish-cephalopod micronekton varies with a low power, about 0.2 to 0.3, of the zooplankton. Similar work in a more inshore part of the eastern tropical Pacific had indicated an approximately linear relationship, using contemporaneous data. I am inclined to interpret the previous result as an effect of aggregation of the mobile micronekton on the zooplankton in order to feed on it, but it may have been a sampling accident, since the data were sparse. There is no reason to doubt the present results, which are based on a large amount of data. They suggest that the increase of the stock of fish-cephalopod micronekton is less than the increase of the stock of zooplankton that nourishes it, either directly or indirectly, in the area studied. The relation between stocks of fish-cephalopod micronekton and chlorophyll is not clear, because the total regression in the last part of Table 1 is dubious.

Phytoplankton, zooplankton, and fish- cephalopod micronekton occupy successively higher average positions in the trophic pyramid, although the exact positions of the latter two are not known. From the preceding results and argument the loss of organic material between one trophic level and another appears to be relatively greater where stock and production at the lower level are high, than where they are low. That is to say, the transfer of material

up the trophic pyramid between the same levels is probably more efficient in oligotrophic than eutrophic situations, at least in the eastern tropical Pacific, and probably in tropical ocean waters generally. Produc- tion of material at the highest trophic level will of course depend also on the number of levels in the pyramid, which is not discussed here, and on the basic primary production.

The relative efficiency of this transfer of material between the same levels in different areas is important but still controversial. The initial suggestion of Cushing (1970) and Ryther (1969) that transfer from plant level to herbivore level increases with increased primary production was reversed by Cushing (1971) on the basis of estimates of annual primary and secondary production for several upwelling areas. Production of herbivores was estimated from mean standing stock of zooplankton and estimated number of genera- tions per year. If those estimates be accepted, Cushing's data support his 1971 opinion, which is the same as that of Greze (1970) and me. I calculated the regression of the logarithm of annual zooplankton production on the logarithm of annual primary production for nine upwelling areas in the eastern Pacific, using data in columns D and M1 of table 4 of Cushing (1971). The value of b is 0.7686, significant at the 1o% level. This is the same kind of relation (b < 1.0) shown here for the respective standing stocks and deduced for the respective rates of daily production.

REFERENCES

BARTLETT, M. S. 1949. Fitting a straight line when both variables are subject to error. Biometrics 5: 207-212.

BLACKBURN, M. 1966. Relationships between standing crops at three successive trophic levels in the eastern tropical Pacific. Pac. Sci. 20: 36-59.

, R. M. LAURS, R. W. OWEN, AND B. ZEITZSCHEL. 1970. Seasonal and areal changes in standing stocks of phytoplankton, zooplankton and micronekton in the eastern tropical Pacific. Mar. Biol. 7: 14-31.

CUSHING, D. H. 1970. Pelagic food chains: Introduction, p. 69-73. In J. H. Steele [ed.], Marine food chains. Oliver and Boyd.




- 1971. Upwelling and the production of fish. Advan. Mar. Biol. 9: 255-334.

GREZE, V. N. 1970. The biomass and production of different trophic levels in the pelagic communities of south seas, p. 458-467. In J. H. Steele [ed.], Marine food chains. Oliver and Boyd.

OWEN, R. W., AND B. ZEITZSCHEL. 1970. Phy- toplankton production: seasonal change in the oceanic eastern tropical Pacific. Mar. Biol. 7: 32-36.

RYTHER, J. H. 1969. Photosynthesis and fish production in the sea. Science 166: 72-76.

SNEDECOR, G. W. 1956. Statistical methods, 5th ed. Iowa State.

THOMAS, W. H. 1970. On nitrogen deficiency in tropical Pacific oceanic phytoplankton: Photosynthetic parameters in poor and rich water. Limnol. Oceanogr. 15: 380-385.

Submitted: 5 July 1972 Accepted: 6 March 1973



Regressions Between Biological Oceanographic Measurements in the Eastern Tropical Pacific and Their...

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