COMPUTER SIMULATION OF PHYTOPLANKTON AND NUTRIENT DYNAMICS
IN AN ENCLOSED MARINE ECOSYSTEM
by
ALAN BOYD CARRUTHERS
B.Sc. University of Calgary 1976
A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE STUDIES (Departments of Zoology and Oceanography)
We accept this thesis as conforming to the required standard
THE UNIVERSITY OF BRITISH COLUMBIA 19 May 1981
© Alan Boyd Carruthers, 1981 In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of British Columbia, I agree that the Library shall make it freely available for reference and study. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the head of my department or by his or her representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission.
Department of ^—00/0
The University of British Columbia 2075 Wesbrook Place Vancouver, Canada V6T 1W5
Date tn+r n, /rrt
DE-6 (2/79). i i
ABSTRACT
This thesis presents a quantitative model of interactions among phytoplankton, nutrients, bacteria and grazers in an
enclosed marine ecosystem. The enclosed system was a 23 m deep, 9.6 m diameter column of surface water in Saanich Inlet,
British Columbia. Dynamics of large- and small-celled diatoms and flagellates in response to observed irradiance and zooplankton numbers and observed or simulated nitrogen and silicon concentrations were modelled over a simulated 76-day period between July 12 and September 26. The model's predictions poorly matched the observed events in Controlled
Experimental Ecosystem 2 (CEE2), but nevertheless provided some important insights into system behavior. Ciliate grazing probably prevented small-celled phytoplankton from increasing to large concentrations in CEE2. By virtue of their tremendous numbers, colourless flagellates were potentially the most important grazers on bacteria, much more important than larvaceans or metazoan larvae. Whereas small-celled phytoplankton were limited by grazers, large phytoplankton dynamics were not markedly affected by grazing. The average observed rate of 14C fixation in the surface 8 m was roughly consistent with an interpretation in which artificial additions of nitrogen contributed 62% of inferred net uptake of nitrogen by phytoplankton, mixing from subsurface water contributed 18%, bacterial remineralization 12%, and zooplankton excretion 9%.
However, independent observations of rapid activity by microheterotrophs (presumably bacteria) suggested that 1*C fixation considerably underestimated net primary production.
This yielded an alternative interpretation in which nutrient additions contributed 46% of inferred net uptake of nitrogen in the surface layer, mixing 13%, bacteria 35%, and zooplankton
7%. Dissolution of silica was responsible for the observed accumulation of silicic acid below 8 m depth in CEE2, but the importance of silica dissolution as a source of Si for diatom growth in the surface 8 m is uncertain. The model's major failing was its assumption of unchanging maximum growth rates of phytoplankton, and unchanging rates of exudation, sinking, and respiration. Physiological parameter values which accounted for the huge bloom of Stephanopyxis in CEE2 could not account for the ensuing collapse. Traditional modelling assumptions of slowly changing internal physiology, although adequate for marine systems dominated by physical factors such as seasonality or water movement, cannot capture the behavior of biologically dominated systems like the enclosed system considered here. iv
TABLE OF CONTENTS
ABSTRACT ii
LIST OF TABLES vii
LIST OF FIGURES viii
ACKNOWLEDGEMENTS .xiii
1. INTRODUCTION .. . 1
2. THE FOOD WEB I EXPERIMENT 5
2.1 Experimental Design 5
2.2 Sampling Protocol and Accuracy 6
2.3 Data Sources 8
2.4 Observed Events in CEE2 9
3. THE COMPONENTS OF THE SYSTEM 39
. 3.1 Overview of the Model 39
3.2 Light 41
3.2.1 Surface irradiance 41
3.2.2 Extinction of light within the water column .. 43
3.3 Mixing 47
3.4 Phytoplankton 50
3.4.1 Species and cell sizes 50
3.4.2 Chemical composition of phytoplankton 51
3.4.3 Photosynthesis 57
3.4.4 Phytoplankton respiration 71 V
3.4.5 Phytoplankton exudation 72
3.4.6 Nutrient limitation of phytoplankton growth .. 73
3.4.7 Phytoplankton sinking 76
3.5 Zooplankton 77
3.5.1 Numbers, species, and body weight 77
3.5.2 Zooplankton grazing 78
3.5.3 Zooplankton excretion 94
3.6 Bacteria 98
3.7 Inorganic Nutrients 103
3.7.1 Nutrient uptake 103
3.7.2 Nutrient sources 103
3.7.3 Dissolution of particulate silica 104
3.8 Final Comments 108
4. COMPUTER IMPLEMENTATION 112
4.1 Approach 112
4.2 Nutrient Interpolation 113
4.3 Accuracy of the Finite Difference Approximation ...115
5. RESULTS AND DISCUSSION 116
5.1 The Reference Simulation 117
5.1.1 Large diatoms 117
5.1.2 Large flagellates 127
5.1.3 Small diatoms and small flagellates 134 5.1.4 Bacteria 137
5.1.5 Nitrogen cycling 142 vi
5.1.6 Silicon 160
5.2 Silicon Simulations ..165
5.2.1 Simulation Si-1. Comparison with reference run
.165
5.2.2 Simulation Si-2 and Si~3. Limitation of diatom
growth 168
5.2.3 Simulation Si-4 and Si-5. Variable C/Si ratio 171
5.2.4 Simulation Si-6. Silicon uptake linked to net
photosynthesis 179
5.2.5 Simulation Si-7. Dissolution of silica ....'...18 0
5.2.6 Conclusions regarding Si dynamics 187
5.3 Phytoplankton Growth and Loss ....188
5.3.1 Have maximum rates of primary production been
underestimated? 189
5.3.2 Have losses or growth limitation been
underestimated? 196
6. WHAT WENT WRONG? 201
6.1 The Bloom and Collapse of Large-Celled Diatoms ....204
6.2 Parameter Estimation Technique 204
6.3 Results and Discussion 209
6.3.1 The diatom bloom, days 13 to 25 209
6.3.2 The diatom collapse, days 25 to 51 213
6.4 Conclusions Regarding Physiological Variability ...222
7. FINAL CONCLUSIONS 227
REFERENCES 231
APPENDIX 1 256
APPENDIX 2 261 LIST OF TABLES
Table I. Literature estimates of volume of water swept
clear by ciliates grazing bacteria or other small
particles 88
Table II. Literature estimates of the volume of water
cleared by gastropod and pelecypod larvae grazing on
small phytoplankton 90
Table III. Literature estimates of specific dissolution
rate of silica in living and dead phytoplankton .106
Table IV. Summary of parameter values used in the main
simulation model. 109
Table V. Sum of squared deviations between predicted and
observed large-diatom biomass in the surface of CEE2
for different parameter values 210 vi i i
LIST OF FIGURES
Figure 1. Photosynthetically active quanta immediately
below the surface of CEE2 10
Figure 2. Separate biomass of four groups of
phytoplankton observed in the surface 8 m of CEE2 13
Figure 3. Accumulative biomass of four groups of
phytoplankton observed in the surface 8 m of CEE2 18
Figure 4. Observed and interpolated concentrations of
dissolved nitrogen and silicon in the surface 8m of
CEE2 20
Figure 5. Biomass of copepods observed in the surface 8 m
of CEE2. 24
Figure 6. Biomass of ctenophores and chaetognaths
observed in the surface 20 m of CEE2 26
Figure 7. Biomass of ciliates observed in the surface 8 m
of CEE2 28
Figure 8. Biomass of metazoan larvae observed in the
surface 8 m of CEE2. " 30
Figure 9. Biomass of larvaceans observed in the surface
20 m of CEE2 32
Figure 10. Biomass of colourless flagellates observed in
the surface 8 m of CEE2 34 Figure 11. Biomass of bacteria observed in the surface
8 m of CEE2 37
Figure 12. Attenuation coefficient of photosynthetically
active quanta vs. phytoplankton carbon in CEE2 45
Figure 13. Observed phytoplankton carbon vs. particulate
organic carbon in CEE2 53
Figure 14. Particulate organic carbon and nitrogen in
CEE2 55
Figure 15. Specific carbon fixation rate vs. average
photosynthetically active irradiance during 4-hour
midday incubations 60
Figure 16. Hypothetical depth profile of Irk in CEE2 63
Figure 17. Modelled excretion rates of ciliates and
colourless flagellates, ctenophores, and other
zooplankton 99
Figure 18. Biomass of large-celled diatoms in the surface
8 m predicted by the reference run 118
Figure 19. Modelled gain and loss rates specific to
predicted large diatom biomass; reference run; surface
8 m 121
Figure 20. Predicted specific grazing loss of large
diatoms; reference run; surface 8 m 124
Figure 21. Predicted biomass of large diatoms in the
surface and deep layers; reference run 128 X
Figure 22. Biomass of large-celled flagellates in the
surface 8 m predicted by the reference run 130
Figure 23. Modelled gain and loss rates specific to
predicted large flagellate biomass; reference run;
surface 8 m .132
Figure 24. Predicted specific grazing loss of small
diatoms; reference run; surface 8 m 135
Figure 25. Biomass of bacteria in the surface 8 m
predicted by the reference run 138
Figure 26. Concentration of total dissolved inorganic
nitrogen (nitrate + nitrite + ammonium) in the surface
8 m predicted by the reference run 143
Figure 27. Predicted excretion of ammonium-N by
zooplankton; reference run; surface 8 m 146
Figure 28. Predicted and observed ingestion rate by adult
female Pseudocalanus 157
Figure 29. Concentration of dissolved silicon in the
surface 8 m predicted by the reference run 161
Figure 30. Simulation Si-1. Predicted large diatom biomass
and silicic acid concentration in the surface 8 m 166
Figure 31. Simulation Si-2. Predicted large diatom biomass
and silicic acid concentration in the surface 8 m 169
Figure 32. Simulation Si-3. Predicted large diatom biomass
and silicic acid concentration in the surface 8 m 172 Figure 33. Simulation Si-4. Predicted large diatom
biomass and silicic acid concentration in the surface
8 m. 174
Figure 34. Simulation Si-5. Predicted large diatom biomass
and silicic acid concentration in the surface 8 m 177
Figure 35. Simulation Si-6. Predicted large diatom biomass
and silicic acid concentration in the surface 8 m 181
Figure 36. Simulation Si-7. Predicted large diatom biomass
and silicic acid concentration in the surface 8 m 183
Figure 37. Simulation Si-7. Predicted concentration of
silicic acid in the 8-20 m layer 185
Figure 38. Specific net growth rate of large flagellate
carbon in the surface 8 m of CEE2 190
Figure 39. Specific net growth rates of large diatoms and
large flagellates in the surface 8 m predicted by a
simulation in which all state variables were
interpolated between their observed concentrations. ...193
Figure 40. Predicted limitation of large diatom growth in
the surface 8 m due to light, dissolved nitrogen and
silicon 198
Figure 41. Biomass of large .diatoms predicted by a
simulation in which only the dynamics of large diatoms
were enabled 202
Figure 42. Observed concentration of vegetative cells and
resting spores, of large diatoms in CEE2 205 Figure 43. SSQ surface for period of large diatom
bloom, days 13 to 25. S=0.106 m day"1 211
Figure 44. SSQ surface for period of large diatom bloom,
1 days 13 to 25. KrN=0.242 ug-atom N l" . 214
Figure 45. Concentration of large diatoms in surface 8 m
1 predicted with Prmax = 1.63 day" , KrN = 0.242
ug-atom N 1_1, S = 0.106 m day"1; days 13. to 25. . 216
Figure 46. Concentration of large diatoms in surface 8 m
1 predicted with Prmax = 1.63 day" , KrN = 0.242
ug-atom N l"1, S = 0.106 m day"1; days 25 to 51. 218
Figure 47. Concentration of large diatoms in surface 8 m
1 predicted with Prmax = 2.00 day" , KrN = 0.783
ug-atom N l"1, s = 3.97 m day"1? days 25 to 51 220 xi i i
ACKNOWLEDGEMENTS
I wish to thank F. Azam, P. K. Bienfang, C. 0. Davis,
G. D. Grice, R. P. Harris, J. F. Heinbokel, J. T. Hollibaugh,
K. R. King, I. Koike, R. S. Mercier, P. Parsley, M. R. Reeve,
M. Takahashi and P.. J. leB. Williams who contributed experimental data, and all members of the CEPEX staff who participated in the collection and analysis of field data. I have appreciated having informal discussions with A. T. Chan,
P. J. Harrison, R. Hilborn, J. Parslow, R. I. Perry, and
N. C. Sonntag. I also thank my supervisor T. R. Parsons for his support, encouragement and forbearance over the past
4 years. This research was supported by National Research
Council Postgraduate Scholarships and a University of British
Columbia Graduate Fellowship to the author. 'Life tricked so shamelessly. It was enough to make men laugh or weep. A man could live, letting his senses have free rein, sucking his fill at the breasts of Eve, his mother—and then, though he might revel and enjoy, there was no protection against her transience, and so, like a toadstool in the woods, he shimmered today in the fairest colours, tomorrow rotted, and fell to dust. 'Or he could set up his defenses against life, lock himself into a workshop, and seek to build a monument beyond time. And then life herself must be renounced; the man was nothing but her instrument: though he might serve eternity he withered, he lost his freedom, fullness, and joy of days.... 'And yet our days had only a meaning if both these goods could be achieved, and life herself had not been cleft by the barren division of alternatives. To work and yet not pay life's price for working: to live, yet not renounce the work of creation. Could it ever be done?'
— Hermann Hesse, Narziss and Goldmund translated by Geoffrey Dunlop 1
1. INTRODUCTION
Conceptual models of marine production have changed greatly in this century. The agricultural model of marine production (Johnstone 1908) asserted that algae were produced until the nutrient in least supply was exhausted, and then the algae were eaten by herbivores. In 1935, Harvey et al. reported that the decrease in phosphate concentration off Plymouth during the second half of the spring bloom of algae, when converted to an equivalent amount of algal biomass, represented
30 to 40 times the stock of algae present. Harvey et al. (1935) argued that the spring outburst was controlled not by nutrient exhaustion but by grazing and that zooplankton consumed practically all of the production. They speculated that the reappearance of phosphate late in the summer was caused by delayed bacterial breakdown of organic phosphorus derived from broken or partially digested diatoms. Subsequently, Gardiner
(1937) demonstrated that zooplankton could regenerate phosphate into the sea, and much later, Harris (1959) measured the regeneration of ammonia by zooplankton. It was realized that zooplankton, in addition to their traditional role as grazers of phytoplankton, could resupply much of the nitrogen required for algal production.
The proportion of nitrogen required by phytoplankton which is supplied by zooplankton excretion is quite variable: Harris
(1959) estimated that zooplankton excreted 55% of nitrogen required in Long Island Sound; 90% of requirement was excreted in the Columbia River plume offshore and 36% in oceanic water 2
off the Oregon coast (Jawed 1973); 70% in the central gyre of
the North Pacific (Eppley et al. 1973); 25% in the coastal
upwelling•off northwest Africa (Smith and Whitledge 1977); and
5% in Narragansett Bay (Vargo 1979). Until recently, excretion
by nekton or vertical advection or diffusion were, by default,
generally assumed to provide the remaining nitrogen required by
algal production in the surface layer. However, there is a
growing belief, as yet largely unsupported by firm evidence,
that microzooplankton (Heinbokel and Beers 1979) and especially
bacteria (Pomeroy 1974; Sieburth 1976) may be important
consumers of algal production and regenerators of nutrients.
The present study sought to develop a coherent model of
phytoplankton growth as a function of micro- and macrozooplankton grazing and nutrient regeneration by both
zooplankton and bacteria in an extensively studied marine
ecosystem.
The ecosystem chosen was the 1300 m3 enclosed water column
of the 1978 Controlled Ecosystem Populations Experiment
(CEPEX), also termed the Foodweb I experiment (Grice et al.
1980). Three such enclosures, trapping 23 metre deep columns of
surface water in Saanich Inlet, British Columbia, offered two
unparalleled advantages for simulation modelling. First, these microcosms are probably the most intensively studied parcels of
seawater in history: detailed time series of irradiance,
inorganic nutrients, standing stocks of individual species of phytoplankton and zooplankton, bacterial abundance, and primary productivity were available. Second, because the water columns 3
were enclosed, vertical and horizontal advection were eliminated, thus permitting repetitive sampling of the same populations. It was hoped that a simulation model of the dynamics of diatoms and flagellates, nitrogen and silicon, and bacteria could be built to account for the observed behavior of these variables in one of the CEPEX enclosures.
The simulation model developed in this thesis relied on theoretical relationships used in earlier modelling studies by
Steele (1974), Steele and Frost (1977), Kremer and Nixon
(1978), and others, with one important extension. Earlier models of marine plankton systems had, without exception, either completely ignored bacteria or implicitly included them in some general term for nutrient regeneration. Circumstantial evidence suggested that bacteria might be primarily responsible for ammonia regeneration in the CEPEX enclosures (Hol.libaugh et al. 1980). It therefore seemed highly desireable to explicitly model bacterial growth and metabolism. Having successfully predicted the observed changes in phytoplankton and nutrient concentrations (so it was hoped), it would then be a simple matter to derive the relative contribution of micro- and macrozooplankton and bacteria to nutrient cycling, a derivation consistent with (but not necessarily entailed by) available observations.
This plan of research never materialized. Although the model developed here did provide many insights into the events which occurred in Foodweb I, it was completely unable to reproduce the observed bloom and collapse of Stephanopyxis or 4
the delayed surge in Ceratium—events of major significance in
one of the enclosed water columns. The failure was fundamental
and led to a reexamination of the traditional approach to
modelling marine ecosystems.
The design of the 1978 CEPEX experiment and a summary of
observed events are given in the next section of this thesis.
The third section provides a detailed description of the
simulation model and a review of the literature underlying it.1
The fourth section outlines the computer implementation of the
model. Simulation results are presented and discussed in the
fifth section, and the model's failure is investigated in
section six. The final section analyzes the performance of two
commonly cited simulation studies in light of the failure
reported here.
1It is suggested on first reading that the reader skip all of section 3 except the overview in 3.1; detailed subsections can be consulted later for clarification. 5
2. THE FOODWEB I EXPERIMENT
2.1 Experimental Design
The Foodweb I experiment was designed to see if markedly different phytoplankton communities could be encouraged to develop in two enclosed water columns by manipulating light, mixing and nutrient regimes. An overall account of the experiment is given by Grice et al. (1980).
The experiments involved the simultaneous capture of 1300 m3 water columns described by Case (1978), Grice et al. (1977) and Menzel and Case (1977). The controlled experimental ecosystems (CEEs) consisted of polyethylene bags 9.6 m in diameter and 23.5 m deep, open at the sea surface and tapering to a closed cone below 16 m. On July 9, 1978 (day 1), three
CEEs were simultaneously raised from a depth of about 30 m and attached to steel floatation rings moored at 123° 29.1' W, 48°
39.6' N in Patricia Bay, part of Saanich Inlet, British
Columbia. The three enclosures were designated CEE2, CEE3 and
CEE4. This thesis is concerned with the events in CEE2 which was manipulated in a manner thought to encourage the dominance of diatoms. CEE2 was gently mixed by periodic bubbling above
8.5 m depth to retard the sinking of diatoms. To prevent rapid nutrient depletion, diatom growth was slowed by means of a white sailcloth placed over CEE2 between days 3 and 54. This light screen reduced incident light by about 40%. Nitrate, silicate and phosphate were added to the upper 8 m on eleven occasions to elevate their respective concentrations by 7.6, 14 6
and 1.1 uM on each addition.1 Each time nutrients were added
about 800 litres of sediment from the bottom of the CEE was
pumped to the surface to recycle diatom spores and nutrients.
A second CEE (CEE3) was not mixed, and received periodic
additions of nitrate and phosphate. No sediment was recycled
and there was no light screen. These conditions were designed
to limit diatom growth and encourage flagellates. CEE4 was used
exclusively to investigate the physics and chemistry of the
enclosed water column.
2.2 Sampling Protocol and Accuracy
Net or pump samples were routinely taken two or three
times a week. Zooplankton were sampled once a week after
day 70, and larvaceans were sampled daily until day 63.
Water was removed from five depth intervals (0-4, 4-8,
8-12, 12-16, 16-20 m) by a peristaltic pump for measurement of
nutrients, chlorophyll-a, particulate carbon and nitrogen, 14C
primary productivity and phytoplankton and bacterial abundance
as described in Grice et al. (1980), Davis (1980), and
Hollibaugh et al. (1980). Primary production was determined in
4-hour i_n situ incubations between about 1000 and 1400 hours
Pacific Standard Time. Algal carbon was estimated from cell
volumes of phytoplankton in unfiltered water samples fixed with
xThe symbols "urn", "ug", "uM", "ul", and "uEin" respectively represent "micrometre", "microgram", "micromolar", "microlitre", and "microEinstein". 7
Lugol's solution and concentrated by sedimentation. Bacterial
carbon was derived from visual estimates of cell size
distribution in samples stained with acridine orange.
Crustaceans, protozoans and other small zooplankton were
collected from 4-metre depth intervals with a diaphragm pumping
system described by Beers et al. (1977). Ciliates in
unconcentrated water samples were fixed and examined similarly
to phytoplankton. Appropriate mesh sizes were used to
concentrate other zooplankton. Vertical tows with a variety of
nets were used to sample ctenophores, chaetognaths and
larvaceans (Harris et al. 1980; King et al. 1980). Zooplankton
numbers were converted to biomass of carbon using the body
weights reported in Appendix 2.
How precise were individual observations of standing
stocks to which the simulation results were compared? Results
from replicate samplings of the enclosed water columns were not
available for any biological'or chemical parameter observed in
Foodweb I, and therefore the sampling precision could not be
directly estimated. Lawson and Grice (1977) repeatedly towed a
Bongo net vertically through a 68 m3 CEE to determine the precision of sampling macrozooplankton. Using log-transformed data they reported 95% confidence limits of 41 - 238% of a
single observation. This degree of uncertainty is similar to that found by Wiebe and Holland (1968) who reviewed sampling variability of net tows under typical field conditions. Piatt et al. (1970) studied the sampling variability of chlorophyll and phosphate at several stations in St. Margaret's Bay. The 8
average within-station variability was represented by
confidence limits of 66 - 152% of a single chlorophyll
observation; the average logarithmic coefficient of variation
of phosphate measurements (including between-station
variability) was 62% of that of chlorophyll measurements. The
substantial patchiness of plankton within CEEs (e.g. Fig. 10 of
Takahashi et al. 1975) may further degrade sampling accuracy.
In summary, the actual accuracy or precision of the results
reported for Foodweb I is unknown, but one can reasonably guess
that true nutrient concentrations' were within roughly 75 - 130%
of reported values, true phytoplankton and microzooplankton
biomass to within 55 - 180%, and true macrozooplankton numbers
to within 40 - 250%. Reported bacterial biomass is probably no
more accurate than that of macrozooplankton.
2.3 Data Sources
Data collected during the Foodweb I experiment were
obtained from several principal investigators. The sampling
schedule, chlorophyll and nutrient concentrations, 14C
productivity, water temperature, incident radiation and light
penetration are documented in the main data report of the
Controlled Ecosystem Populations Experiment for Foodweb I,
available from G. D. Grice, Woods Hole Oceanographic
Institution. Zooplankton numbers are listed in CEPEX Report 3
of March 1979; phytoplankton numbers, volume and carbon are on magnetic tape; both are also available from Grice. Further
sources of information were: 9
• particulate organic carbon and nitrogen: F. Azam,
Institute of Marine Resources, Scripps Institution of
Oceanography
• bacterial numbers, carbon, activity: F. Azam
• ciliates: J. F. Heinbokel, Chesapeake Bay Institute,
Johns Hopkins University
• ctenophores, chaetognaths: M. R. Reeve, Rosenstiel School
of Marine and Atmospheric Science, University of Miami
• larvaceans: K. R. King and K. Banse, Department of
Oceanography, University of Washington
Unless otherwise stated, all analyses of raw data collected during Foodweb I and reported in this thesis are the author's.
2.4 Observed Events in CEE2
Grice et al. . (1980) provide an overview of the observed events in Foodweb I. Briefly, these were as follows:
Irradiance—The fluctuation of surface irradiance
(corrected as described in section 3.2.1) over the first 80 days of Foodweb I is shown in Fig. 1. The weather was mainly dry and sunny until day 33 (August 10) and then was quite cloudy and wet until the end of the experiment.
Phytoplankton—There were two major blooms of 10
Figure 1. Photosynthetically active quanta immediately below the surface of CEE2 between July 9, 1978 (day 1) and September 26 (day 80). Observed irradiances were corrected as described in section 3.2.1 before plotting. Light screen in place from day 3 to 54. 11 Surface Irradiance (uEin in"2 s"') a 2000
oo J CD 12
phytoplankton in CEE2 (Figs. 2, 3). Large-celled (>15 um
equivalent spherical diameter (>15 um ESD)) diatoms increased
to 687 ug C l"1 in the surface 8m by day 25 with
Stephanopyxis turris comprising 99% of the biomass at the
height of the bloom. The large diatoms then collapsed to 5
ug C l"1 by day 51, increased again to >76 ug C l"1 between
days 65 and 75, then fell to 14 ug C l"1 by day 79-. The other
major bloom was that of large-celled flagellates2 later in the
experiment. Large flagellates remained below 67 ug C l"1 in
the surface 8 m until day 55 and then rapidly increased to over
1000 ug C l"1 by day- 72. This bloom was dominated by Ceratium
fusis. The total biomass of other (small-celled) non-colourless
phytoplankton fluctuated between 16 and 132 ug C l"1 in the
upper 8 m except on day 55 when small diatoms peaked at 221
ug C 1_1. Below 8 m phytoplankton concentrations were low and
passively followed cycles of abundance in the surface 8 m;
little 14C fixation occurred at depth.
Nutrients—As the large diatom bloom peaked on day 25,
3 NOr3~ (see footnote below) and NOr2~ were each stripped to
1 + 1 <0.05 ug-atom N l" , and NHr4 to 0.2 ug-atom N l" , in the
surface 8 m (Fig. 4a). After day 36 dissolved inorganic
2Large-celled flagellates include >15 um ESD dinoflagellates and the chrysophyte Distephanus speculum.
3A special nomenclature for subscripts is used in this thesis:
"NOr3~ " refers to nitrate, "Si(OH)r4" to ortho-silicic acid,
"Prmax" to the maximum gross photosynthetic rate, etc. 13
Figure 2. Observed carbon biomass of phytoplankton in the surface 8 m of CEE2. Note different biomass scales.
a) large-celled diatoms (>15 um diameter) b) small-celled diatoms (<15 um diameter) c) large-celled flagellates (>15 um diameter) d) small-celled flagellates (<15 um diameter)
Small Diatoms (ug C 1"'; 0-8 m) 0 200 i—i.-j ' 1 1 ' 1 1 ! 1 1 1
CD
Small Flagellates (ug C 0-8 m) a 2QQ i—i i i i iii 18
Figure 3. Accumulative carbon biomass of the four phytoplankton groups plotted separately in Fig. 2.
1 - large diatoms 2 - small diatoms 3 - large flagellates 4 - small flagellates Phytoplankton (ug C I"1; 0-8 m) a i5ao CD—I 1 1 1 1 1 1 1 1 1 I
CD 20
Figure 4. Observed and interpolated concentrations of dissolved inorganic nitrogen an silicon in the surface 8 m of CEE2. Interpolated nutrients (solid line) include the expected increases from nutrient additions to the surface layer. Observed concentrations are plotted as circles.
a) nitrogen (nitrate + nitrite + ammonium) b) silicon
23
nitrogen (nitrate + nitrite + ammonium) had increased above 2
1 1 ug-atom N l" and was frequently >6 ug-atom N l" . Si(OH)r4 fell below 4 ug-atom Si l"1 only on day 6 and exceeded 9
1 3_ ug-atom Si l" after day 20 (Fig. 4b). POr4 did not drop below 1.8 ug-atom P 1'1 after day 8 and was usually above 3 ug-atom P l"1 in the surface 8m of CEE2. Nutrient concentrations were high below 8 m, ranging from 10.7 - 20.8 ug-atom nitrate-N l~l, 0.2 - 13.7 ug-atom ammonium-N l"1, 1.0
4.8 ug-atom phosphate-P l-1, 23.7 - 39.2 ug-atom silicate-Si l"1. Nitrite remained below 0.5 ug-atom N l"1 in the surface and deep layers.
Zooplankton—Herbivorous copepods increased to 107 ug C l"1 by day 18 in the upper 8 m of CEE2, but rapidly fell to <28 ug C l"1 after day 25 following a bloom of ctenophores (Fig.
5). Ctenophores peaked in concentration on day 27 at 7.2 ug C l"1 and declined slowly thereafter (Fig. 6). The peak concentration of chaetognaths occurred on day 20 at 4.8 ug C l"1 (Fig. 6). As copepods declined in abundance, other herbivores—ciliates (Fig. 7) and metazoan larvae (Fig.
8)—comprised a larger fraction of the grazing community.
Larvaceans (Fig. 9) briefly increased in biomass at the start of the experiment, declined to low levels by day 15 and recovered again after day 40. Colourless flagellates reached higher concentrations than herbivorous copepods, peaking at 133 ug C 1"1 on day 9 (Fig. 10). 24
Figure 5. Accumulative carbon biomass of copepods observed in the surface 8 m of CEE2.
1 - harpacticoids, non-feeding Nl and N2 nauplii, and species considered to be carnivorous (see section 3.5.2) 2 - herbivorous calanoids and cyclopoids
26
Figure 6. Observed biomass of the ctenophores Pleurobrachia and Bolinopsis (solid line) and the.chaetognath Sagitta elegans (dashed line) in CEE2 (0-20 m depth).
28
Figure 7. Observed biomass of ciliates (excluding Mesodinium rubrum) in the surface 8 m of CEE2.
30
Figure 8. Observed biomass of metazoan larvae in the surface 8 m of CEE2. Metazoan larvae include gastropod veligers, cyphonautes larvae, trochophores, pelecypod and polychaete larvae.
32
Figure 9. Observed biomass of the larvacean Oikopleura dioica in CEE2 (0-20 m). Larvaceans (ug C 0-20 m)
_1 I ' I I I I I I
ro CD 34
Figure 10. Observed biomass of colourless flagellates in the surface 8 m of CEE2.
36
Bacteria—Bacteria reached their highest biomass in the
surface 8m of CEE2 during the period of the large-diatom collapse: 72 ug C l"1 on day 44 (Fig. 11). A subsidiary peak of bacteria occurred on day 18 (while large diatoms were
increasing substantially in concentration) at 51 ug C l~l. 37
Figure 11. Observed bacterial biomass in the surface 8 m of CEE2.
39
3. THE COMPONENTS OF THE SYSTEM
3.1 Overview of the Model
Four groups of phytoplankton in CEE2 were modelled: large- and small-celled diatoms, and large- and small-celled flagellates. The growth of each phytoplankton group over successive day-night cycles was modelled by taking a fixed maximum gross photosynthetic rate, then reducing it to the extent that whichever factor—nitrogen, silicon or light—was limiting. (The alternative formulation in which gross photosynthesis is limited by some sort of multiplicative interaction among nitrogen, silicon and light was not considered.) Phosphorus was assumed to be non-limiting, and the effect of changing temperature on growth was ignored. Net photosynthesis was determined by subtracting a constant respiration rate from gross photosynthesis. Thereafter, a constant fraction of net production was exuded as dissolved organic material, and the phytoplankton biomass was further grazed by zooplankton or lost by sinking. To close the model the population dynamics of zooplankton were not explicitly simulated. Instead, zooplankton numbers were interpolated between their observed densities; their excretion of inorganic and organic nitrogen was held at a fixed rate per individual.
Grazing rate per copepod was a function of phytoplankton concentration, but other zooplankton filtered water at a constant rate per individual. Zooplankton grazed only phytoplankton and bacteria; particulate detritus was not 40
modelled and therefore was not grazed.
Bacteria were assumed to absorb all dissolved organic
nitrogen released into the water, either from phytoplankton
.exudation or from zooplankton excretion. Bacterial respiration
was modelled as the sum of a basal metabolic rate and a fixed
fraction of organic uptake. Inorganic nitrogen recycled back to
the water column was proportional to bacterial + zooplankton
respi ration.
Nitrate, nitrite and ammonium were not distinguished in
the model but were lumped together as dissolved inorganic
nitrogen. Nitrogen was removed from the water column in
proportion to the gross primary production of each algal group,
whereas silicon was removed only by diatoms. The periodic additions of nitrogen and silicon to the surface of CEE2 were accounted for. Even ignoring nutrient additions, nitrogen was
not conserved in the model because zooplankton excretion was
not linked to zooplankton ingestion. Once absorbed by phytoplankton, silicon was usually considered to be lost from
the system, but in some model runs the dissolution of silica was simulated.
Because CEE2 was bubbled to 8.5 m depth, and nutrients were added to the top 8 m only, a discontinuity in nutrient concentrations was often observed at 8 m. For this reason, and also to take into account the greatly reduced irradiance below
8 m, two depth layers were modelled: 0 - 8 m, and 8 - 20 m. All system components were followed separately in each layer. The two layers were linked by mixing across the 8 m interface, as 41
well as by sinking of phytoplankton from the top to bottom
layer. Also, phytoplankton in the surface 8 m shaded cells in
the bottom layer. Material which settled below 20 m was assumed
to be lost from the system.
3.2 Light1
3.2.1 Surface irradiance
Quantum scalar irradiance (400 - 700 nm wavelength; Booth
1976) on land 3 km from the CEPEX site was integrated over
1-hour intervals during the experiment. Two serious problems
were apparent in the Foodweb I data: 1) maximum reported
irradiances were about double the maximum expected for the
latitude of the CEPEX site, and 2) irradiances were truncated
at an upper reported limit of 7.75»1020 quanta cm"2 h"1. On
clear days this resulted in a flat-topped irradiance v_s. time
of day curve.
To partially salvage the data, it was decided to obtain an
independent estimate of peak irradiance at solar noon at
Patricia Bay, then reduce all reported irradiances by a
constant factor in order to match the truncated maximum to the
expected maximum.
An independent estimate of peak irradiance was obtained
xIt is suggested that on first reading the reader skip the remainder of section 3; subsections which follow can be consulted later for details. 42
from hourly radiation recorded for Departure Bay, Nanaimo (49°
13' N, 123° 57' W) and the University of British Columbia,
Vancouver (49° 15' N, 123° 15' W) (Environment Canada 1978a).
The largest single amount of total solar radiation received in a 1-hour period in each of the months of July, August and
September 1978 was extracted for each station, yielding six values for peak irradiance. These values were then corrected by considering the change in the sun's elevation in the sky at local apparent noon with change in date and latitude. For the purpose of this correction it was assumed that all the energy received was direct. The peak irradiance on a horizontal surface at a latitude of L degrees then varies as the sine of
90° - L - D, where D is the declination of the sun for 0 hours
Ephemeris Time. The sun's declination is tabulated in the
Astronomical Ephemeris (1978). The mean of the peak irradiances corrected to the CEPEX site, July 9, 1978, was 3.368 MJ nr2 h~1. Using the approximate conversion from total energy to photosynthetically active quanta given in Jitts et al. (1976) this is 1827 uEin m~2 s"1 . This value was then reduced by 7% to roughly account for reflection of light at the sea surface
(Hojerslev 1978: p. 140). Thus, 1699 uEin nr2 s--1 was the peak photosynthetically available irradiance expected on a clear day on July 9 immediately below the water's surface. Because the cutoff of reported irradiances occurred at 3575 uEin nr2 s"1 , a scaling factor of 1699 / 3575 = 0.475 was applied to the
Foodweb I light data. A further correction was applied on a day by day basis to mimic the sun's increasing declination with 43
season. By day 80 (September 26), peak irradiance would be
expected to be only 72% of that on day 1 (July 9). Between days
3 and 54, when the light-screen was in place over CEE2,
irradiance was reduced by 40%.
3.2.2 Extinction of light within the water column
The attenuation of light through the water column was
represented by the formulation of Piatt et al. (1977: p. 819):
I(z) = I r0 exp{-[krw z + krs (B(X) dx]}
(1)
where
I(z) = irradiance at depth z,
Ir0 = irradiance immediately below the sea surface,
k [-w = attenuation coefficient of water in the absence of chlorophyll (units: nr1 ),
krs = attenuation coefficient per unit . of chlorophyll-a (units: m2 (mg Chl-a)"1),
B(x) = concentration of chlorophyll-a at depth x (units: mg Chl-a nr3).
The term JB(x) dx is referred to by Piatt et al. (1977) as
"cumulative phytoplankton cover." Appropriate values of krw and
krs were found by first manipulating Eq. 1 to give
In [I (z rl)/I (z r2) ] / (z r2 - z rl) = krw + krs |B(x) dx,
(2)
where zrl and zr2 are two depths, zr2 > zrl, with z being positive downward. Eq. 2 describes the attenuation of light per
metre through a depth interval from zrl to zr2 metres.
Relative irradiance was measured at 0, 1, 3, 5, 7, 10, 15 44
and 20 m in the water column, whereas chlorophyll-a and
phytoplankton carbon were determined as integrals over the
depth intervals 0-4, 4-8, 8-12, 12-16 and 16-20 m. These
integrated phytoplankton concentrations correspond to the
integral in Eq. 2, with z rl and z r2 the endpo.ints of each depth
interval. Unfortunately these endpoints were not the same
depths at which irradiance was measured. Irradiance was
therefore interpolated to 4, 8, 12 and 16 m by fitting a cubic
polynomial through the irradiances measured at the four depths
nearest to the interpolation depth.
Thus, for every depth interval on each day for which data
were available, the left hand side of Eq. 2 was plotted on the
Y-axis y_s. the integrated phytoplankton concentration on the
X-axis. If the attenuation of light in CEE2 followed Eq. 1
exactly, the plot would be a straight line with Y-intercept of
krw and slope krs. Fig. 12 shows the scatter using
phytoplankton carbon; chlorophyll-a was no better. The
geometric mean estimate of the functional regression of Y on X
1 (Ricker 1973) gave a best-fitting line with krw = 0.168 nr and
2 1 krs = 0.0207 m (mg Chl-a)" using chlorophyll-a to measure
phytoplankton concentration. The diffuse attenuation
coefficient of CEE2 water in the absence of chlorophyll,
0.168 nr1, was considerably higher than what is typical of
1 clear ocean water (e.g. Sargasso Sea: krw = 0.027 nr ; Smith and Baker 1978). The specific attenuation by chlorophyll, 0.021 m2 (mg Chl-a)"1, was slightly larger than the value of
0.016 ± 0.003 for chlorophyll-like pigments (including 45
Figure 12. Attenuation coefficient of photosynthetically active quanta (Y axis) vs. phytoplankton carbon in CEE2 (X axis). Fitted line is geometric mean regression. Equation is: attenuation (rrr1) = 0.148 + 5.11 • 10"4 • phytoplankton carbon (mg C m"3).
47
phaeopigments) from various oceanic and coastal waters (Smith
and Baker 1978). This discrepancy was probably due to the
presence of particulate organic material which covaried with
chlorophyll. That the values of krw and krs deviated in the
expected direction from Smith and Baker's values increases
confidence in analogous estimates using phytoplankton carbon.
Since carbon, rather than chlorophyll, was used to quantify
phytoplankton abundance in the simulation model, it was these
latter estimates that were used to predict light extinction in
1 _ 4 2 CEE2. They were: kr w = 0.148 irr , and kr s = 5.11 • 10 m
(mg C)"1 (Fig. 12).
_3._3 Mixing
Two layers were modelled in the CEE2 water column: an
upper bubbled layer from 0 to 8 m, and a lower layer from 8 to
20 m. Within each layer, concentrations of phytoplankton,
nutrients, and bacteria were assumed to be homogeneously
distributed. It was therefore necessary to consider only the
mixing of material across the 8 m interface.2 Mixing rates were
indirectly estimated by the rate of vertical spreading of
pulses of tritium introduced at 8 m in CEEs 2, 3 and 4 (Mercier
and Farmer 1980). To describe the spreading of tritium, Mercier
and Farmer used the one-dimensional diffusion equation for a
conservative tracer of concentration C,
2Mixing between the 8-20 m layer and sediments in the bottom of CEE2 was much slower and was ignored in the model. 48
Ic/at = d/qz (A(z) • oC/bz),
(3)
to fit an assumed vertical profile of the apparent vertical
eddy diffusivity, A(z), to the observations.
An adequate fit to the observed spread of tracer in CEE 3
and 4 was obtained by Mercier (pers. comm.) when
A(z) = 0.092 exp(-0.11 z),
(4)
where z is depth in metres and A is apparent eddy diffusivity
in cm2 s"1. Mercier and Farmer (1980) suggest that Eq. 4
overestimates diffusivity by at least 60%. In any event, mixing
was much more rapid at the surface of the unbubbled CEEs than
below 10 m where Mercier and Farmer estimated that diffusivity
was only one or two orders of magnitude greater than the
molecular limit. Mercier (pers. comm.) felt that below the
depth of bubbling there was no reason to believe eddy
diffusivity in CEE2 to be any different than that in CEE 3 or
4. 3
How fast was CEE2 mixed by bubbling? Mercier (pers. comm.)
found that a pulse of tritium injected at 8m was completely
mixed throughout the bubbled layer between 12 and 48 hours
3This is why diffusion of regenerated nutrients from the sediments into the overlying water was neglected. Although ammonium concentration was slightly higher in the 16-20 m sampling interval than in shallower depths, the concentration gradient above 16 m was not steep enough to allow sediment-derived nutrients to penetrate the 8 m interface. Accumulation of ammonium in the 8-20 m layer was slightly under es,t imated. 49
later. To determine appropriate values of A(z) in the surface layer, Eq. 3 was numerically solved using Crank-Nicholson finite differencing (Carnahan et al. 1969: pp. 440-442, 451) for a water column 20 m deep. A time-step of 0.05 day and depth increment of 0.4 m gave stable and smooth time behavior.
Numerical runs were started with a pulse of conservative substance at 8 m, and by trial and error a depth profile of
A(z) was found which resulted in an essentially homogeneous distribution of the diffusing substance in the top 8 m after 24 hours of simulated time. Having developed a fine-grid model of mixing in CEE2, the expected rate of exchange between the 0-8 m and 8-20 m layers in the main model was finally parameterized.
This was done by initializing the fine-grid model with various depth profiles of diffusing substance, then following the flux across the 8 m interface as a function of the difference in average concentration between 0-8 and 8-20 m. This difference in average concentration can be viewed as the concentration gradient down which turbulence moves suspended and dissolved substances between layers in the main two-layer model. Flux across the 8 m interface was erratic during the first 5 days of simulated time because of the strongly discontinuous concentration profiles used to start the runs. Ignoring this initial 5-day relaxation period, flux was very nearly a linear function of concentration gradient, and exchange between the top and bottom layers was approximately given by 50
r)C rtop/dt = -(Crtop - Crbottom) • b / Drtop, (5)
dCrbottom/dt = (Crtop - Cr bottom) • b / D (-bottom,
where
C = concentration (arbitrary units),
D = thickness of layer (m),
b = exchange coefficient (m h"1).
b was found to lie in the range of 0.005 to 0.010 m h"1,
regardless of the starting concentration profile. Since eddy
diffusivity was likely overestimated, the value of b = 0.005
1 m h" was used in main model runs. Thus, given that Drtop
= 8 m, if the concentration of a substance in the top layer was, say, double that in the bottom layer, the concentration in the top layer would initially fall at a rate of only 0.8% per day. Bubbling the surface layer evidently had little effect on
the exchange of material between 0-8 and 8-20 m in CEE2.
J3.4 Phytoplankton
3_. 4 .1_ Species and cell sizes
To try to understand why the large-celled diatom
Stephanopyxis turris and large-celled flagellate Ceratium fusis bloomed to huge concentrations in CEE2, whereas small-celled phytoplankton did not, four groups of phytoplankton were modelled: large-celled (>15 um equivalent spherical diameter
(>15 um ESD) ) and small-celled (<15 um ESD) diatoms arid large- and small-celled flagellates. Colourless flagellates- were treated as a subclass of "zooplankton" and are discussed later. 51
Diatom spores or resting cysts of dinoflagellates never exceeded 25 ug C l"1 concentration in either the top or bottom layers of CEE2, and thus were not modelled. Mesodinium rubrum, a photosynthetic ciliate (Smith and Barber 1979), had a concentration <<15 ug C l~l in CEE2, and it too was ignored in the main model.
Standing stock of phytoplankton was expressed as ug C l"1. Appendix 1 gives the species composition of each group as well as the cell diameter, cell biomass, and average abundance of each species in CEE2. Weighted according to average species abundance, the mean cell diameter / cell biomass of each phytoplankton group was: large diatoms 51.4 um/2127 pg C, small diatoms 10.6 um/50.7 pg C, large flagellates 36.3 um/2283 pg C, and small flagellates 8.0 um/66.4 pg C.
3^.4.2 Chemical composition of phytoplankton
It was necessary to consider the nitrogen (N) and silicon
(Si) composition of phytoplankton when calculating algal uptake of nutrients. Strictly speaking, the C/N and C/Si ratios employed in the model refer to newly synthesized algal biomass.
Nitrogen—Vigorously growing phytoplankton with excess nitrate in the water would be expected to have C/N mass ratios as low as 3, while unhealthy cells in nitrate-depleted water could have ratios as high as 15 (Parsons et al. 1977: p. 52).
C/N was not determined for phytoplankton in CEE2, but the C and 52
N content of particulates retained on 984H Reeve Angel glass fibre filters was measured. Phytoplankton carbon as estimated from cell volume constituted a sizeable proportion of particulate organic carbon (POC) on numerous occasions
(Fig. 13). The C/N ratio of particulates (POC/PON) was assumed to be representative of the C/N ratio of phytoplankton. The mean C/N mass ratio in CEE2 was 5.69 (range 1.37 - 8.13,
Fig. 14), close to the value of 6 suggested by Strickland
(1960: p. 12) as representative of phytoplankton. Very high or very low C/N ratios were occasionally observed, but these bore no consistent relation to the amount of inorganic N present in the water. On the basis of these observations, the C/N mass ratio of phytoplankton biomass was modelled as a constant value of 6.
. Silicon—Particulate silicon was not measured, so recourse was made to literature estimates of C/Si ratios of diatoms.
(Because silicoflagellates were absent in CEE2, flagellates were assumed to be devoid of Si.) Vigorously growing diatoms have C/Si mass ratios ranging from 0.85 to 4.2 (P. J. Harrison et al. 1977; Paasche 1980; Parsons et al. 1961). Strickland
(1960: p. 19) concludes that the C/Si ratio of natural marine diatoms is probably near to 1.25 in areas where Si does not limit growth. This was the value chosen for large and small diatoms in the standard model run since Si concentration in the top 8 m of CEE2 seldom dropped below 4 ug-atom l"1. However, a variable C/Si ratio was adopted on some later runs after 53
Figure 13. Observed phytoplankton carbon (including colourless flagellates) vs. particulate organic carbon in CEE2. Straight line indicates equality of phytoplankton carbon and POC. 54 55
Figure 14. Particulate organic carbon and nitrogen in CEE2. Straight line indicates C/N mass ratio of 6. POC (UG / L) 0 - 150 300 450 600 750 900 1050 ]200 1350 1500 ° I I I I I I I I I i i i i I I I I II i I 57
noticing that simulated Si concentrations were very low.
P. J. Harrison et al. (1977) cite several references which demonstrate that nutrient uptake ratios or cellular chemical composition can be altered by cell size, age of culture, light
intensity, temperature and type of nutrient limitation. Only
the impact of nutrient limitation was treated here. P. J.
Harrison et al. (1977) found that Si-limited or starved cultures had C/Si ratios 1.3 to 6.4 times higher than for non-limited diatoms. These results suggested the following
scheme: C/Si (by mass) increased linearly from 1.25 with no
Si-limitation to 5 • 1.25 = 6.25 at complete limitation. The degree of limitation was defined by the fraction
[Si]/(KrSi + [Si]), where [Si] is the ambient Si concentration
and KrSi is the half-saturation constant of gross photosynthesis of the particular diatom group (see section
3.4.6).
3_.4.3 Photosynthesis
The growth of phytoplankton was modelled by first assuming
for each group a fixed maximum gross photosynthetic rate, Prmax
(units: h~1), specific to phytoplankton carbon. This maximum rate was then reduced to a realized rate, Prg, by the degree of light or nutrient limitation, whichever was most severe. The fractional limitation of gross photosynthesis due to light was expressed by Smith's (1936) equation:
2 2 L 4 Prg/Prmax = I • (Ir k + 1 ) -0. 5 (see footnote ),
(6) 58
where I is the photosynthetically active quantum irradiance and
I r-k (Tailing 1957) is the irradiance at which Pr g
L = 2 -0.5 • Prmax = 0.707 Prmax. Smith's equation does not
provide for bright-light inhibition of photosynthesis. To
summarize the discussion which follows, a P vs. I curve was
constructed from Foodweb I data to see if the lack of
photoinhibition was reasonable, and rough values of Irk were
also extracted. Cell-size-dependent estimates of Prmax for
diatoms and flagellates were derived from the literature.
Finally, Eq. 6 was integrated over depth to provide an estimate
of average light-limitation in the model's two depth layers.
Photosynthesis vs. irradiance—A P vs. I curve for
phytoplankton was derived from 14C fixation, irradiance, and
phytoplankton carbon measured in Foodweb I. To increase the
sample size, data from both CEE 2 and 3 were analyzed. The raw
data consisted of; 1) 14C fixation rates in unfiltered water
removed from 0-4, 4-8, 8-16 m depth and incubated _in situ from
lOOOh - 1400h PST at 2, 6 and 12 m depth; 2) quantum irradiance
relative to sea surface irradiance measured at 0, 1, 3, 5, 7,
10, 15 and 20 m depth; 3) corrected quantum irradiance
immediately below the sea surface (cf. section 3.2.1);
4A special nomenclature is used in this thesis for exponents which cannot be expressed as sums of whole numbers: "YL2" denotes Y2, "Wi—0.5" denotes the reciprocal of the square root of W, "(A + B)Lc" denotes the sum A + B raised to the power c, etc. 59
4) phytoplankton carbon in 0-4, 4-8, 8-16 m depth; 5) ambient
water temperature and nutrient concentrations.
Photosynthesis was expressed as a specific rate by
dividing 14C fixation (ug C l"1 h"1) by standing crop of
phytoplankton (ug C . I"1). Specific photosynthesis was then
normalized to 15 °C using the temperature correction of Eppley
(1972, Qr10 of 1.88). The effect of potential nutrient
limitation of photosynthesis was removed by rejecting samples
having <4 ug-atom nitrate-N l"1. Multiplying the average
surface irradiance during the 4-hour incubation by the relative
subsurface irradiances gave a depth profile of absolute
irradiance. Irradiance at the depth of incubation was
interpolated from the depth profile of irradiance using a cubic polynomial fitted through the irradiances at the 4 nearest depths. The resulting plots of specific 14C fixation vs. irradiance are given in Fig. 15. No clear P vs. I relationship was evident in the data from either CEE 2 or 3 due to the large scatter of points. (The scatter was just as bad when using chlorophyll-a rather than carbon as a measure of standing crop.) There is some suggestion that photosynthesis in
CEE3 saturated somewhere between 100 and 200 uEin nr2 s"1 .
Bright-light inhibition was not consistently present, and therefore the absence of photinhibition in Eq. 6 was considered acceptable.
2 1 I_rk—A value of I rk equal to 200 uEin m" s~ was arbitrarily chosen for all phytoplankton groups. Although 60
Figure 15. Specific carbon fixation rate of unfiltered water vs. average photosynthetically active irradiance during 4-hour midday incubations. Incubations in water with <4 ug-atom nitrate-N l"1 were rejected to eliminate potentially nitrogen limited populations. 14C fixation rates normalized to 15 °C using temperature correction of Eppley (1972).
circles - diatoms comprise >80% of total photosynthetic biomass triangles - flagellates comprise >80% of biomass crosses - neither diatoms nor flagellates comprise >80% of biomass 61
PRODUCTIVITY (G C / G C / H) PRODUCTIVITY (G C /.G C / H) 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 °J, J I I I I I I I I I °A 1 1 1 1 1 1 1 L I I
G
3g. Do o o -z. o m r> O m ' o n TO -A TO o o m
CO —I m
x x X x ro G cog
O
o o m m m m CO 62
Ryther (1956) observed marked differences between diatoms and dinoflagellates in the irradiance which saturated photosynthesis, neither Dunstan (1973) nor Chan (1978a)
observed any difference between the two algal groups. Irk varied seasonally in the model (cf. Hameedi 1977) in step with
peak irradiance (section 3.2.1), so that Irk declined from 200 uEin nr2 s"1 on day 1 to 144 uEin nr2 s"1 by day 80. In an
attempt to model adaptation to dim light, I rk was further assumed to decrease with increasing depth in the water column.
Following Jamart et al. (1977), I rk might be expected to decrease exponentially with depth -to half its surface value at the depth of 1% surface light, provided that the water column was unmixed. Because the time required to completely mix the surface 8 m of CEE2 (1/2 to 2 days) is comparable to adaptation
times for Irk (2 to 6 days; Steeman Nielsen et al. 1962;
Steeman Nielsen and Park 1964), I rk would be expected to be constant with depth in the bubbled layer of CEE2. As a
compromise, Irk was assumed to be constant in the surface 8 m but equal to the depth-average of the exponentially decreasing value it would of had had the surface layer been unmixed
(Fig. 16). If we let
krsurf = attenuation coefficient of light in the surface 8 m (nr1 ),
krdeep = attenuation coefficient of light in 8-20 m layer (nr 1) ,
zr.01 = depth of 1% surface light (m),
Irksurf = value of Irk in the surface 8m of CEE2, ure 16. Hypothetical depth profile of Irk in CEE2.
Curved dotted line shows depth behavior of Irk in the
absence of mixing. With mixing, Irk in the bubbled, layer is uniform and equals the depth-average of the exponential decay curve in that layer. 64
'k
L bubbled layer
depth of 1% surface light
20 J 65
lrk(0) = value of I rk expected at 0 m depth in the absence of mixing, then
-ln(.01)/krsurf, krsurf > -ln(.01)/8
zr.01 = A (7)
8 - [ln(.Ol) + 8krsurf]/krdeep,
k rsurf < -ln( .OD/8.
Furthermore,
lrk(0) • [1 - exp(-8d) ]/8d, zr.01 > 8 Irksurf (8)
lrk(0) • {[1 - exp(-zr.01 d)]/d
- 0.5zr.01 + 4}/8, z.i-.Ol < 8,
where d = In(0.5)/zr.01. Below the 8 m surface layer,
lrk(0) exp(-d z), z < zr.01 I k(z) r (9)
0.5 I rk(0), z > z r.01.
To illustrate how Irk was modelled, suppose that on August 8
(day 31) the model predicted a moderate bloom of phytoplankton, say, 500 ug C l"1 (all four algal groups combined) in both the
top and bottom layers of CEE2. By day 31, lrk(0) would have seasonally declined to 188 uEin m; 2 s"1 from a value of 200
2 1 uEin nr s" on day 1. From section 3.2.2, krsurf = krdeep =
0.148 + 5.11 •10'4 • 500 = 0.404 nr1. The 1% light depth,
Z|-.01, equals 11.4 m. The value of I rk in the bubbled surface
2 1 layer, Irksurf, is 149 uEin nr s" . Immediately below the
2 1 bubbled layer, at 8 m depth, Irk would equal 116 uEin nr s~ .
14 Prmax—Mindful of the unreliable estimates C fixation 66
may give of primary production (e.g. Gieskes et al. 1979), it
was decided to obtain independent estimates of Prmax from Chan
(1978b). Chan measured the growth rate of five diatom and four
dinoflagellate species under continuous and 12h:12h light:dark
cycles at 21 °C. Chan's Table 13 (p. 57 of Chan 1978b) provides
regression equations of maximum growth rate as a function of
cell protein of the form
G (divisions day-1) = a WLb (see footnote 4 on p. 58),
(10)
where W is ng protein per cell. Taking the average of 'a'
measured under continuous light and 12L:12D, and the average of
'b', then correcting growth to 13.5 °C (the mean water
temperature in' CEE2) using Eppley (1972) and expressing it as
specific growth per day, gives
diatoms: G (day1) = 0.8815 WL- . 070 , (11a)
dinoflagellates: G (day 1 ) = 0.3390 W--. 135. (lib)
Chan (1978b, his Table 14, p. 69) found protein/carbon ratios
ranging from 0.73 to 0.97 for 3 species of diatoms, and from
0.78 to 1.04 for 3 species of dinoflagellates. Parsons et al.
(1961) determined ratios of 0.88 to 1.38 for 4 diatom species
and 0.69, 0.70 for 2 dinoflagellate species. Assuming a protein/carbon ratio of 0.85, Eqs. 11a,b were converted to
diatoms: G (day 1 ) = 1.446 W (pg C) L-.070, (12a)
dinof lagellates: G (day 1 ) = 0.880 W (pg C) L-.135. (12b)
Eq. 12b was assumed to also represent flagellates other than dinoflagellates.
Deriving maximum gross photosynthetic rates from maximum 67
growth rates specified by Eq. 12 was difficult because
respiratory and exudatory losses had to be allowed for, as well
as diurnally varying irradiance. The derivation which was
employed hinged upon the assumption that the growth of a
surface-dwelling phytoplankter (in the absence of any growth
limitation other than light) during a cycle of naturally
varying sunlight on a clear summer day will have the same
average rate of growth as maximal rates reported by Chan for
phytoplankton cultured in continuous light or a 12L:12D cycle.
This assumption is supported by Paasche (1967), who found that
Coccolithus huxleyi cultured in a regime of 16L:8D grew as fast
as continuously illuminated cells regardless of the irradiance.
In a regime of 10L:14D growth was about 70% of that under
continuous light. Similar results are given by Paasche (1968)
for Ditylum briqhtwellii and Nitzschia turgidula. (In contrast,
Tamiya et al. (1955) found that growth of Chlorella ellipsoidea
was almost proportional to the duration of the photoperiod.)
Since maximal growth would be expected of phytoplankton at
0 m depth in the absence of mixing (assuming excess nutrients
and no photoinhibition), Eq. 6 was used as is without
considering a complicated depth-integral. The surface
irradiance, I, on a clear day in July at Patricia Bay is closely described by 68
0,. t < 12-D/2 or t > 12+D/2 I =
I rmax cos[TT(t - 12)/d], 12-D/2 < t < 12+D/2,
where t is time of day (h), D is daylength (14.9 h), and Irmax
is the maximum irradiance at local noon (1699 uEin nr2 s"1 ).
Respiration was assumed to be 10% of maximum instantaneous
gross photosynthesis (10% of Prmax) and exudation was set to
10% of light-limited gross photosynthesis (10% of Prg). The net
rate of growth over a 24-hour light-dark cycle is then
G (day"1) = (gross production - exudation) - respiration (Pr9 X " 0,1 Pr9) dt " 0,1 prmax- <14)
2 2 L But, from Eq. 6, Prg = Prmax I (Irk + 1 ) -0.5, and I varies
as given in Eq. 13. Eq. 14 can be rearranged to
Pr max = G •
2 2 x {0.9 [I • (Irk + I )L-0.5] dt - 0.1}- .
(15)
The integral in Eq. 15 was evaluated numerically using
Simpson's rule (Carnahan et al. 1969: pp. 71-79). The
multiplier of G in Eq. 15, equal to 2.401 with Irk = 200 uEin nr2 s"1 , was applied to the right hand side of Eqs. 12a,b to yield expressions of the maximum instantaneous gross photosynthetic rate of diatoms and flagellates:
1 L diatoms: Prmax (day" ) = 3.471 W (pg C) -.070
1 L dinoflagellates: Prmax (day" ) = 2.114 W (pg C) -.135.
(16)
Lastly, to use Eqs. 16a,b to estimate Prmax of the four phytoplankton groups, some measure of average cell biomass of 69
each group was needed. The average for algal group j was
calculated as
Brj = JZ(xri Wri4>) / Ixri, j = 1 to 4, ' i (17)
where
1 xri = mean observed concentration (ug C l" ) of species i belonging to algal group j in CEE2 (Appendix 1),
Wri = carbon biomass of one cell of species i (pg C; Appendix 1),
b = -0.070 for diatoms, -0.135 for flagellates.
From Eq. 16, Prmax of algal group j was 3.471 B rj for diatoms,
2.114 Brj for flagellates. Thus,
1 1 Prmax = 2.086 day" = 0.0869 h" , large diatoms = 2.648 day"1 = 0.1104 hr1, small diatoms = 0.748 day"1 = 0.0312 hr1, large flagellates = 1.336 day"1 = 0.0556 tr1, small flagellates.
(18)
It is suprising that these rates are lower than the maximum specific 1*C fixation rates observed in CEE 2'and 3 (Fig. 15).
Either the derived rates are unrealistically low, or the rates of specific 14C fixation are unrealistically high. This question will be discussed later in section 5.3.1.
Notwithstanding this ambiguity, Prmax values derived from
Chan's work were used in the model.
Depth integral of photosynthesis—At each time step, the fractional light-limitation of gross photosynthesis in the surface and bottom layers was calculated. The fractional
limitation, Prg/Prmax, was given by the depth-integral of 70
Eq. 6, taking into account the variation of Irk with depth. In
a layer of water zrl metres deep at its upper boundary and zr2
metres deep at its lower boundary, the mean Prg/Prmax is
2 x/(l + x ) L0.5, k = d or z rl = zr 2
Prg/Prmax = ln{[x + (1 + x2)L0.5]/[b + (1 + b2)L0.5]} 1 • [zr2 - zrl)(k - d)]" , k*d, zrl*zr2,
(19)
where
d = rate of decay of Irk with depth as defined for Eq. 8. In a bubbled layer, d = 0,
x = I (zrl)/l rk(zrl) ,
k = attenuation coefficient of light in the layer,
b = x • exp[(d - k)(zr2 - zrl)].
For the bubbled layer of CEE2, Irk = Irksurf (Eq. 8). The mean
Prg/Prmax is thus a function of d and k within a layer bounded
at zrl and zr2 metres. That is,
Prg/Prmax = F(z rl,z r2,k,d),
(20) where F is the function defined by Eq. 19. Adopting this
notation, in the surface 8 m of CEE2, Prg/Prmax =
F(0,8,krsurf,0). In the 8-20 m layer
F(8,20,k rdeep,0) , zr.01 < 8 m
[(zr.01 - 8) • F(8,z r.01,krdeep,d)
Prg/Prmax = + (20-zr.01) • F(zr.01,20,krdeep,0)]/12,
8 < zr . 01 < 20 m
F(8,20,k rdeep,d) , zr.01 > 20 m,
(21) 71
where zr.01 is the depth of 1% surface light (Eq. 7).
2.4.4 Phytoplankton respiration
Banse (1976) could find no significant relationship
between respiration expressed as a fraction of growth and the
size of an algal cell. Respiration was therefore modelled as a
constant percentage of maximal gross photosynthesis for all
cell sizes. Photorespiration (Tolbert 1974) was ignored.
There is considerable uncertainty regarding differences
between the rate of respiration of diatoms and flagellates.
Parsons et al. (1977: p. 70) state that when phytoflagellates
are abundant the ratio of respiration (R) to Prmax should
change from nl0%5 (typical of diatom assemblages) because high
respiration rates of 35 to 60% of Prmax were observed in
several flagellates cultured by Moshkina (1961). Other workers,
however, report no systematic differences between the
respiration rates of diatoms and flagellates. Humphrey (1975,
his Table I, p. 115) cites R/Prmax ratios ranging from
6.9 - 48% for diatoms and 5.5 - 67% for flagellates. Laws and
Caperon (1976) found that the dark respiration rate of
Monochrysis lutheri cultured at its maximum rate of growth was
10.5% of gross carbon production. Piatt and Jassby (1976) cite
R/Pr-max ratios for cultured diatoms ranging from 8 - 100%, and
for cultured flagellates 10% and 37%. McAllister et al. (1964)
5The symbol "a" denotes "approximately". 72
found ratios of 1.5 - 20% for two cultured flagellate species,
and ratios of 9 - 16% for Skeletonema costatum.
Turning to natural plankton assemblages, measurements of
whole-community respiration as a fraction of maximum 14C uptake
(Piatt and Jassby 1976; Devol and Packard 1978; Steeman Nielsen
and Hansen 1959) or as a fraction of gross photosynthesis in
the euphotic zone over a diel cycle (Packard 1979; Setchell and
Packard 1979) give ratios which generally lie in the range of
5 - 25%. A value of 10% of Prmax was chosen here as the
respiration rate of surface-dwelling phytoplankton of all
groups, day and night.
Following Winter et al. (1975) and Jamart et al. (1977),
respiration was made a function of depth in a manner analogous
to that for Irk (see Eqs. 8, 9), with one exception:
respiration did not change seasonally.
3_.4.5 Phytoplankton exudation
In general, the release of soluble organics from actively
growing phytoplankton amounts to 10 - 20% or less of the total carbon fixed (p. 117 of Parsons et al. 1977; p. 147 of
Wangersky 1978), although controversy surrounds this issue
(Sharp 1977; Fogg 1977). In the model, all phytoplankton groups
excreted 10% of their gross primary production as organic matter. Thus, when primary production was severely limited by depleted nutrients, modelled exudation was very low as well. At night, exudation was zero because gross photosynthesis was zero. This scheme of modelling algal exudation is admittedly 73
superficial. It ignores the accelerated release of organics
expected of populations in stationary growth (Guillard and
Wangersky 1958; Marker 1965). Also, any environmental condition
which inhibits cell multiplication but permits
photoassimilation to continue generally results in high
exudation rates (Hellebust 1974).
•3.4.6 Nutrient limitation of phytoplankton growth
Recent literature has emphasized that the growth of
nutrient-limited phytoplankton in an unsteady environment is
dependent upon internal nutrient pools rather than
concentrations of nutrients external to the cell (e.g.
Burmaster 1979; Davis et al. 1978; Grenney et al. 1973; Droop
1973). Furthermore, the parameters used to describe the
Michaelis-Menten uptake of nutrients—the saturated uptake rate and the half-saturation "constant"—may vary tremendously depending on the nutrient history of the cells (Conway et al.
1976; Conway and Harrison 1977). Unfortunately, no estimates of cellular nutrient quotas or of the changing physiological parameters of nutrient limitation were obtained for the enclosed phytoplankton in Foodweb I. It is not. even clear that meaningful estimates could have been obtained for the diverse assemblages of small-celled phytoplankton. For these reasons, the photosynthetic fixation of carbon and uptake of nutrients were directly coupled in the model, and the extent of nutrient limitation by nitrogen and silicon was described by simple
Michaelis-Menten kinetics. 74
Under certain conditions ammonium is taken up
preferentially to nitrate (Eppley et al. 1969b; Packard and
Blasco 1974). Takahashi et al. (1980) found that
microflagellates in Foodweb I preferentially absorbed ammonium
whereas centric diatoms showed no such preference. Nitrate and
ammonium were not distinguished in the model because these
nutrients generally covaried in the upper 8 m (except
immediately after periodic nitrate additions) and because the
added complexity of modelling the interconversion of nitrate
and ammonium would have been formidable. The fractional
limitation of gross photosynthesis due to nitrogen limitation
was given by
Prg/Prmax = [N] / (KrN + [N]),
(22)
where KrN is the half-saturation constant and [N] the nitrogen concentration. Similarly, for silicate limitation
Prg/Prmax = [ Si ] / (K rSi + [Si ]) .
(23)
Although there is disagreement about the dependence of the half-saturation constant on cell size (Parsons and Takahashi
1973, 1974; Hecky and Kilham 1974), Steele and Frost (1977) was
followed by making KrN (and KrSi) linearly proportional to cell
diameter. Steele and Frost's minimum estimate of KrN was
K j-N (ug-atom N l"1) = 0.015 D,
(24) where D is cell diameter in um. The size dependence of Si limitation was analogously defined here as 75
1 KrSi (ug-atom Si l' ) = 0.04 D (um).
(25)
Using the mean equivalent spherical diameter of each phytoplankton group (section 3.4.1) for D in Eqs. 23 and 24 gives
Kr N KrSi
large diatoms: 0.77 2.06 ug-atom 1~1 small diatoms: 0.16 0.42 large flagellates: 0.54 small flagellates: 0.12
Literature estimates of KrSi typically range between 0.2 - 3.4 ug-atom Si 1_1 (Nelson et al. 1976; Thomas and Dodson 1975;
Goering et al. 1973; Guillard et al. 1973; Paasche 1973a,b).
The KrN values adopted above are compatible with a KrN of 0.6 ug-atom nitrate-N 0 l'1 measured by Harrison and Davis
(pers. comm. cited by Parsons et al. 1978) for an assemblage of large diatoms from Patricia Bay. W. G. Harrison et al. (.1977) found that phytoplankton in copper-treated CEPEX enclosures had average half-saturation constants of 1.1 and 1.2 ug-atom N 1'1 for nitrate and ammonium assimilation, respectively.
In summary, the gross photosynthetic rate (Prg) ot each phytoplankton group as a fraction of maximum gross
photosynthesis (Prmax) was given by the minimum of Eqs. 20-23 at each time step. 76
3.4.7 Phytoplankton sinking
Bienfang (1980) measured the sinking rates of
phytoplankton assemblages removed from CEE 2 and 3 during
Foodweb I. He reports that the mean sinking rate of
phytoplankton in undisturbed water ranged from 0.32 to 1.69
m day-1 under a wide range of environmental conditions.
Bienfang notes that although sinking rates were not
statistically correlated with measured nutrients, water
temperature, irradiance, chlorophyll concentration • or primary
productivity, there were cases of higher sinking rates in
populations dominated by large phytoplankton cells.
The preconditioning history of the phytoplankton as it
affected sinking rate was ignored in the model. The influence
of cell size was considered, however. Bienfang notes that on
day 6, populations in CEE3 were dominated by small diatoms
(nil um ESD) that had sinking rates of 0.4 to 0.7 m day-1. On
day 16, enhanced concentrations of Thalassionema nitzschiodes
in CEE2 (18 um cellular ESD; Coulter Counter peak at n60 um)
had a sinking rate of 0.9 m day-1. Two days later this
predominance continued and population sinking rates had risen
to 1.7 m day-1. Bienfang also determined that the sinking rate
of the 8-53 um fraction, dominated by the large flagellate
Ceratium fusis, ranged from 1.1 to 1.5 m day-1 after day 60 in
CEE2. In the model, a constant sinking rate of 1.3, m day-1 was assigned to large diatoms and large flagellates, and
0.5 m day-1 to small diatoms and small flagellates. These are
sinking rates expected in an unmixed water column. The 77
effective rate of sinking in the surface bubbled layer of CEE2 would be less. Effective sinking rates in the bubbled layer were arbitrarily assumed to be 25% of undisturbed rates.
Unfortunately, observed changes in the depth of the chlorophyll or phytoplankton-carbon maximum in CEE2 were irregular and provided no indication of the actual sinking rates expected of populations in the surface 8 m.
Since only two layers were modelled, the settling of cells through the water column could not be explicitly followed.
Instead, the sinking of biomass from a layer was parameterized by dividing the sinking rate in that layer (m day-1) by the thickness of the layer in metres, yielding a specific rate of loss per day.
2-5 Zooplankton
2.5.1 Numbers, species, and body weight
Zooplankton standing stock was not dynamically modelled.
Instead, zooplankton numbers in each of 134 taxa (Appendix 2) were linearly interpolated between observed densities. In the special case of larvaceans and ciliates where observations were available only until day 63 and 73, respectively, numbers were simply held fixed at the last observed density until the end of the simulation. Whenever detailed depth distributions were available, zooplankton numbers in the 0-8 and 8-20 m layers were separately interpolated. However, only the average population density over 0-20 m depth was reported for copepod 78
nauplii, larvaceans, chaetognaths and ctenophores. For these
taxa the same interpolated numbers were used in both depth
layers.
The carbon biomass of individual zooplankters was required
to determine the excretion rate and size-selective grazing rate
of individuals in each taxon. These body weights were obtained
at the CEPEX site or from the literature as detailed in
Appendix 2.
3.5.2 Zooplankton grazing
Grazing on phytoplankton and bacteria was modelled by
specifying grazing rates per individual for each zooplankton
taxon, then multiplying by the number of individuals of each
taxon observed in CEE2. Detailed grazing rates of zooplankton
were not available for Foodweb I, and thus a synthesis of often
contradictory results from the literature was undertaken. It
was decided to make copepod grazing rates dependent upon food
concentration, saturating at a maximum ration which was a
function of body weight. Other zooplankton groups., however, were assumed to filter water at a constant rate regardless of
food concentration.
Copepod grazing—Two questions were asked of grazers:
1) What determines the amount of food ingested?, and 2) How was the ingested food selected? Addressing the first question, a rectilinear model (Frost 1972) was used to describe ingestion 79
as a function of food concentration. That is, the rate of
ingestion (ug C copepod"1 h_1) increased linearly from zero
ingestion at a prey threshold concentration of Prthr (ug C
l"1), to maximum ingestion at the critical food concentration
1 of Prcrit (ug C l" ). The existence of a non-zero food concentration below which feeding ceases is unsettled. Parsons et al. (1969) found that mixed zooplankton in water samples
from the Strait of Georgia began to graze at threshold phytoplankton concentrations of n50 - 90 ug C l"1. Adams and
1 Steele (1966) observed a Prthr of n70 ug C l' for Calanus
finmarchicus in water from the northern North Sea. In contrast,
Paffenhofer (1971) and Corner et al. (1972) found that Calanus may feed' on large cells at concentrations less than 20 ug C l"1 using cultured algae as food rather than natural plankton assemblages. The feeding rate of Calanus pacificus on
Thalassiosira fluviatilis is depressed at concentrations below
27 ug C l"1, but C. pacificus is able to graze down an algal suspension to as low as 15 ug C l'1 (Frost 1975). A feeding threshold of zero was used in the model.
The estimation of a critical food concentration, Prcrit, was equally uncertain. Conover (1978a) and Mayzaud and Poulet
(1978) even assert that saturation of grazing rarely occurs in nature because grazers normally acclimate to the ambient concentration of food. Yet Gamble (1978) observed constant ingestion rates at concentrations of suspended particulates above 300 ug C l"1 for copepods captured from and incubated in water samples removed from the North Sea. A major complicating 80
factor is that Prcrit depends on the ratio of phytoplankter
size to copepod size. For example, when feeding on the diatom
Thalassiosira fluviatilis, an adult female Pseudocalanus achieves its maximal ration at about one third the cell density
required by the larger adult female Calanus pacificus (Frost
1974). The problem with this complication is that it confounds effects due to a changing ratio of copepod mass/phytoplankton
size with effects due to the food-size selectivity of' copepods.
Moreover, the question of what critical food concentration is appropriate when grazers are confronted with a mixture of phytoplankton of varying sizes is unanswered. The approach taken here was to adopt the same critical concentration for any size of copepod feeding on any size of phytoplankter. This approach, combined with the size-selective feeding described later, mimicked Frost's (1974) results.
Frost (1972) found Prcrit values ranging from 124 to 318 ug C 1_1 for Calanus pacificus feeding on decreasing sizes of cultured diatoms. Parsons et al. (1969) found that grazing by mixed zooplankton saturated at n400 ug C l"1 in a natural assemblage of phytoplankton. Gamble's (1978) figure of 300
_1 ug C 1 mentioned earlier was used as the value of Prcrit in the model.
Having chosen values of Prthr and Prcrit for the
rectilinear model, only the maximal ration (Rrmax, ug C copepod-1 day1) remained. To give some idea of the variability of estimates in the literature, consider the maximum ration of adult Pseudocalanus spp. having a body weight of nlO - 20 81
1 1 ug C. The weight-specific Rrmax (ug C (ug C)" day" ) has been
variously determined as 0.17 (Poulet 1973), 0.45 (Parsons et
al. 1969) and 0.55 (Poulet 1974), while Paffenhofer and Harris
1 (1976) found an Rrmax of 1.5 day for P. elongatus feeding on
cultured Thalassiosira rotula. Frost's (1972) work was used as
the basis for the model estimate of Rrmax. Frost calculated
that unstarved Calanus pacificus females with an average body
1 -1 1 weight of 68 ug C l" had an Rrmax of 27 ug C copepod day"
(= 0.39 day-1). The relationship between body weight (W, ug C)
and ration (R, ug C copepod-1 day"1) can be described as
R = c WLd. (26)
Sushchenya and Khmeleva (1967, cited in Conover 1978b) found an
average exponent, d, of 0.80 for crustaceans. Conover (1978b),
using data from Paffenhofer (1971), calculated an exponent of
0.74 for juvenile Calanus helgolandicus (= C. pacificus).
1 Putting d = 0.75, and assuming Rrmax = 0.4 day" for a body
weight of 68 ug C, gives
1 1 Rrmax (Ug C copepod" day" ) = 1.149 W-0.75.
(27)
Eq. 27 predicts that an adult female Pseudocalanus
1 (w = 10.8 ug C) would have Rrmax = 0.63 day" , whereas the
stage III nauplius • (W = 0.0432 ug C) would have
1 Rrmax = 2.5 day" . This represents the saturated rate of
ingestion of phytoplankton o_f optimal size. This leads to the
0 second question asked of grazers: How was. the ingested food selected? "Selective feeding" is here defined to mean any removal of 82
particles in different size-classes not in proportion to their
relative abundance in the water. Two basic selection
"strategies" have been identified: grazers can select large particles (Mullin 1963; Richman and Rogers 1969; Frost 1977) or particles in single or multiple peaks of biomass concentration
in the particle-size spectrum (Poulet 1973, 1974; Donaghay and
Small 1979a). The size spectrum may also be altered by breaking
long chains of phytoplankton into smaller, more manageable pieces (Donaghay and Small 1979b).
This extreme flexibility of behavioral response was not modelled. Instead, a selectivity function was "frozen" at a
shape characteristic of each copepod body weight. That is, a copepod of a given weight was assumed to be unable to dynamically alter its selectivity for different phytoplankton
sizes in response to changing size spectra. The selectivity
function chosen here was that of Steele, and Frost (1977: pp. 502-503 and their Fig. 14(b)):
2 Sre = exp{-[ ln(WL-l/3 D / Drm)] / u} ,
(28) where
Sre = selectivity, a dimensionless fraction,
(0 < Sre < 1),
D = equivalent spherical diameter of food particle (um),
Drm = particle diameter (um) at which S re = 1 for a copepod of 1 ug C weight,
W = weight of copepod (ug C), 83
u = measure of spread of selection curve.
Eq. 28 "can be interpreted as assuming that, for any animal,
the ability to catch cells is log-normally distributed so that
a cell with half the optimal diameter has the same selectivity
as one with double the optimum diameter," (Steele and Frost
1977). Steele and Frost put Drm = 10 um and u = 2.5. These
values predict more of an overlap between the selectivity
curves of Acartia clausi adult and copepodite I stages than was
estimated by Nival and Nival (1976) from inter-setule distances
on the maxillae. The fit was better when u = 1. Values of
Drm = 10 um and u = 1 were used in further calculations.
Eq. 28 predicts the selectivity of a copepod for cells of
a certain diameter. In the model it was necessary to define the
selectivity of a copepod for each of four phytoplankton
assemblages. (It was assumed that copepods could not filter
bacteria.) Although the cell diameters of individual species
comprising each group were measured (Appendix 1), grazers
presumably reacted to the length of chains of cells rather than
cell diameters. Chain lengths in Foodweb I were unavailable,
however, and in any case these lengths would vary greatly with
time. Consequently, only cell diameters were used to derive composite selectivities of grazing on the four algal groups.
This was done for each copepod taxon by computing Sre for each phytoplankton species comprising each algal group, then
weighting the S re values according to the observed mean abundance of algal species in CEE2 (Appendix 1). Four composite
selectivities of grazing on the four algal groups were obtained 84
in this way for each type of copepod; these selectivities are
listed in Appendix 2.
Some copepod taxa have no grazing selectivities listed in
Appendix 2. Naupliar stages I and II do not feed (Marshall and
Orr 1956; p. 112 of Corkett and McLaren 1978). Tortanus
discaudatus is carnivorous (Anraku and Omori 1963; Amber and
Frost 1974; Mullin 1979) and was not included as a
phytoplankton grazer. The copepodite V and adult stages of
Epilabidocera were assumed to be carnivorous in analogy to
Labidocera (Landry 1978). Although most copepods are omnivorous
(e.g. Acart ia, Centropages (Anraku and Omori 1963; Lonsdale et
al. 1979), Oithona (Petipa et al. 1970; Lampitt 1978)), they
were considered to graze plant food just as efficiently as the
more herbivorous Pseudocalanus or Calanus.
Finally, to combine ingestion and selectivity into a
coherent model of zooplankton grazing, the approach of Sonntag
and Greve (1977: p. 2300) was used. For a given copepod, the
concentration of each of the four phytoplankton groups was
first multiplied by that copepod's respective grazing
selectivities on the four groups. This gave four "realized" prey concentrations which were then summed. This sum determined
the overall ingestion rate of the copepod as given by its
rectilinear ingestion vs. (realized) food concentration curve.
Lastly, this total ingestion was split among the four algal groups in proportion to their realized concentrations. An example may clarify things. Consider Paracalanus copepodite I feeding on a mixture of large and small diatoms and large and 85
small flagellates, with each group having a concentration of
100 ug C l"1. This copepod's respective feeding selectivities on the four groups is 0.038, 0.632, 0.027 and 0.771
(Appendix 2). The realized algal concentrations are therefore
3.8, 63.2, 2.7 and 77.1 ug C l"1; their sum is 146.8 ug C l"1. The copepod's body weight is 0.152 ug C, so its maximum ration is 1.149 • (0.152)1-0.75 = 0.280 ug C day"1. The actual
1 1 ration is 146.8/Prcrit • 0.280 ug C day" = 0.137 ug C day" ,
1 since Prcrit = 300 ug C l" . Of this ration, 3.8/146.8 • 100
= 2.6% came from large diatoms, 43.1% from small diatoms, and
1.8 and 52.5% from large and small flagellates. This example also points out that the selectivities of very small copepods were low on all phytoplankton groups. The most extreme example of this is Oithona nauplius III with an estimated body weight of 14.3 ng C. It's highest selectivity was 0.375 on small flagellates. This was a consequence of using the selectivity function of Eq. 28 which predicted an optimum food diameter of
2.4 um, smaller than the smallest microflagellate enumerated.
These low selectivities could have conceivably resulted in the underestimation of copepod grazing pressure. To test this, a simulation was run with selectivities scaled upward so that the
"preferred" food of each copepod taxon had a selectivity of unity. It was found that scaling had negligible effect, so selectivities were left unchanged in other runs.
Ciliate grazing—The rate of grazing by ciliates on phytoplankton and bacteria was modelled as a constant volume of 86
water swept clear, in spite of evidence of saturation of
grazing at high food concentrations (Fenchel 1980a; Heinbokel
1978a,b). Maximum reported filtering rates were used rather
than saturation rates to see which zooplankton groups might have had significant impact on phytoplankton dynamics in CEE2.
The published results most relevant to what would be expected
of ciliates in the natural environment are those of Heinbokel
(1978a,b), Spittler (1973) and Blackbourn (1974), all studying
the grazing of tintinnids on microflagellates, corn starch or yeast. Maximal clearance rates per ciliate were 2 - 4.7 ul h"1
(Heinbokel 1978a), n8 ul h"1 (Heinbokel 1978b),
0.5 - 8.5 ul h"1 (Spittler 1973) and 4.2 ul h"1 (Blackbourn
1974).
Data on ciliate abundance in CEE2 consisted of numbers of ciliates which fed predominantly on particles of <3, 3 - 15, and >15 um diameter, and ciliates of unassigned feeding preference (J. Heinbokel, pers. comm.). A clearance rate of
4 ul ciliate"1 h"1, midrange of the maximum rates cited above, was assigned to those ciliates feeding predominantly on particles of 3 - 15 um diameter. In the model these ciliates fed exclusively on small diatoms and small flagellates with a selectivity of unity. The biomass of algae grazed per ciliate per unit time was simply the product of clearance rate
(litres ciliate-1 h"1), algal concentration (ug C l"1), and selectivity (dimensionless).
For those ciliates having a feeding preference for particles >15 um diameter, an upper size-limit of grazed 87
particles was specified so that their feeding selectivity on
large diatoms and flagellates could be estimated. Blackbourn
(1974, his Fig. 5, p. 54) observed that the maximum food size
of the largest tintinnid captured was 42 um ESD. The data of
Beers and Stewart (1970) and Taniguchi (1977) suggest that
42 um is extreme. An upper limit of 25 um was used here. The
mean biomass of 15 - 25 um phytoplankton as a proportion of
large diatom and large flagellate biomass was 0.134 and 0.019,
respectively, in CEE2. The grazing selectivity of ciliates
preferring >15 um particles was therefore taken as 0.134 on
large diatoms and 0.019 on large flagellates. The grazing
selectivity was set to zero on groups with <15 um ESD, i.e.
small diatoms, small flagellates and bacteria.
A representative grazing rate of ciliates on bacteria was difficult to estimate because published research has typically used cultured rather than natural bacteria as prey. This could be a serious source of error since the average volume of marine bacteria is less than 10% of the volume of cultured bacteria
(Watson 1978). Some published clearance rates are listed in
Table I. Even ignoring the criticism of bacterial cell size, the applicability of most of these clearance rates to the natural marine environment is dubious. Ciliates, when cultured, were maintained by Cox (1967) and Curds and Cockburn (1968) in concentrated proteose-peptone solutions. Ciliate densities in feeding experiments were as high as 1.6 • 10' ciliates l"1
(Berk et al. 1976) and bacterial densities as high as
1.2 • 1012 l"1 (Laybourn and Stewart 1975). Clearance rates on Table I. Literature estimates of the volume of water swept clear by ciliates grazing bacteria or other small particles.
., . . clearance rate , ciliate species prey reference (nl h X)
Colpidium campylum Moraxella sp. 42 Laybourn & Stewart 1975
Colpoda steinii Escherichia coli 45 Proper & Garver 1966
Epidinium spp. Bacillus megasterium 0.06-7 Coleman & Laurie 1974
Tetrahymena pyriformis carbon particles 0.60 Rasmussen et al. 1975
Tetrahymena pyriformis carbon particles 5.9-10.3 Cox 1967
Tetrahymena vorax carbon particles 0.94 Rasmussen et al. 1975
Uronema nigricans Vibrio sp.; 0.5 Berk et al. 1976 Bacillus sp. 0.15-0.19 2 14 ciliate species^" latex beads, bakers 33^ Fenchel 1980b yeast, live ciliates J50A 720
Clearance rates are expressed as nl/h per individual
1. Quoted clearance rates are taken from Fenchel's linear fit to log clearance rate vs. log optimal particle size (Fenchel 1980b: p. 16) assuming a C/wet wt. of ciliate protoplasm of 0.06 and specific gravity of 1. 2. For ciliate of 0.15 ng C body weight feeding on 1 um diameter particles.
3. For 0.3 ng C ciliate feeding on 3 um particles.
4. For 0.3 ng C ciliate feeding on 15 um particles. bacteria are 2-3 orders of magnitude less than clearance
rates on phytoplankton. A clearance rate of 20 nl ciliate-1 h"1
was assumed for ciliates having a preference for particles
<3 um diameter. (Equivalently, ciliates had a clearance rate of
4 ul h~x and a selectivity of 0.005 on bacteria.) This group of
ciliates was assumed not to graze phytoplankton. Those ciliates
which were unassigned by Heinbokel as to feeding preference
were assigned selectivities of 0.134, 1, 0.019, 1 and 0.005 on
large and small diatoms, large and small flagellates, and
bacteria, respectively, with a uniform clearance rate of 4 ul
ciliate"1 h~1.
Grazing by gastropod veligers and pelecypod
larvae—Table II lists literature estimates of the grazing
rates of pelecypod larvae and mud snail veligers on
phytoplankton (mostly naked flagellates). Feeding by larvae has
been reported to be continuous (Ukeles and Sweeney 1969);
Wilson (1979) found that larvae did not stop feeding at high
cell concentrations but instead rejected a high proportion of
captured food. Conversely, Fretter and Montgomery (1968) report
that prosobranch veligers stopped feeding once the gut was
full, and Pechenik and Fisher (1979) observed saturation of
ingestion at high food densities in mud snail larvae. Grazing
by pelecypod larvae and gastropod veligers was modelled as a constant volume of 30 ul larva-1 h_1 swept clear when feeding
on small diatoms and flagellates. Although both Calabrese and
Davis (1970) and Walne (1963) confirmed earlier findings that Table II. Literature estimates of the volume of water cleared by gastropod and pelecypod larvae grazing on small phytoplankton. larval species prey clearance rate reference (nl h"1)
Pelecypods
Mytilus edulis Isochrysis galbana 5 .8-25 Bayne 1965
Ostrea edulis Isochrysis galbana, 2.2-13 Wilson 1979 Phaeodactylum tricornutum
Ostrea edulis Isochrysis galbana 18-20 Walne 1956
Ostrea edulis "flagellates" 27 Jorgensen 1952
Mud snail
Nassarius obsoletus Dunaliella tertiolecta, 50-70, Pechenik & Fisher 1979 Thalassiosira pseudonana, 10-30'' Isochrysis galbana
Clearance rates are expressed as nl/h per individual
1. Low food densities.
2. More than 6X107 cells l"1. 91
naked flagellates are more easily digested than algae with cell
walls, this may be irrelevant to the relative rates of
ingestion of diatoms and flagellates: Pechenik and Fisher
(1979) found very similar saturated rates of ingestion of two
naked flagellates and a small centric diatom grazed by mud
snail larvae.
What is a reasonable upper size-limit of particles eaten
by veligers and pelecypod larvae? Ukeles and Sweeney (1969)
state that the potential diet of straight-hinge larvae is
restricted by the size of the mouth which is less than 10 um
wide in Crassostrea virginica larvae of 75 um shell length.
Ostrea edulis larvae (165 - 200 um shell length) have an
esophagus of n20 um diameter (Yonge 1926). Fretter and
Montgomery (1968) found that Thalassiosira cells 30 um long were too large for the younger prosobranch veligers. An upper
limit of 30 um was assumed, yielding selectivities (in a manner analogous 'to those calculated for ciliates grazing 15 - 25 um cells) of 0.178 for large diatoms and 0.019 for large
flagellates.
The author is unaware of any published rates of filtration of bacteria by veligers or mollusk larvae, although Pilkington and Fretter (1970) report that bacteria >0.46 um diameter are ingested. A selectivity of 0.01 for bacteria was assumed. In summary, gastropod veligers and pelecypod larvae were assumed to filter water at a constant rate of 30 ul larva-1 h-1 with feeding selectivities of 0.178, 1, 0.019, 1 and 0.01 on large and small diatoms, large and small flagellates and bacteria, 92
respectively.
Grazing by larvaceans—Numbers of Oikopleura dioica in 19 size classes and ingestion rate as a function of size was measured by King et al. (1980) in Foodweb I. King et al. (1980) multiplied the ingestion rate by 1.2 to give a clearance rate which included particles which adhered to the larvacean's house but were not ingested:
F = 1.3130 • 10~12 • TL 1-3.1492,
(29) where
F = clearance rate (litres larvacean"1 h"1),
TL = trunk length (um).
Assuming that trunk lengths of individuals falling within a given length interval are uniformly distributed throughout that interval, Eq. 29 predicts clearance rates per larvacean ranging from 5.508 ul h_1 for individuals of 100 - 150 um trunk length, to 4291 ul h"1 for >950 um trunk length (1050 um used for TL in
Eq. 29) and 6535 ul h"1 for "spent" larvaceans (1200 um trunk length).
Although Paffenhofer (1976) states that the filtering rate of 0. dioica increases with decreasing food concentration at naturally occurring phytoplankton densities, King et al. (1980) found little variation of filtration rate within the range of bacterial concentrations found in Foodweb I. Also, King
(pers. comm.) found clearance rates on nanophytoplankton to be equivalent to those on bacteria. King noted that small 93
larvaceans were limited to filtering particles <5 um diameter,
and large larvaceans to <25 um, approximately. These estimates
permit the calculation of feeding selectivities on the four
modelled phytoplankton groups. Those individuals with a trunk
length of 100 - 150 um, the smallest reported size, were
assigned an upper food size limit of 5 um. Individuals in the
two largest categories ("950+ um" and "spent") were assigned an
upper limit of 25 um, and individuals of intermediate length
were assigned linearly interpolated size limits. Feeding
selectivities were then calculated analogously to ciliates.
Bacteria were grazed by larvaceans with a selectivity of 1
(Appendix 2).
Grazing by other zooplankton—Polychaete larvae,
trochophores and cyphonautes larvae were assumed to filter water at the same rate and have the same feeding selectivities as pelecypod larvae and veligers. It was also assumed that harpacticoid copepods were grazing the fouling community on
CEE2's wall rather than phytoplankton or bacterioplankton.
Colourless flagellates were treated as zooplankton in the model. They comprised a group of several species of dinoflagellates and the choanoflagellates Salpingoea spp. and
Diaphanoeca sp. Haas and Webb (1979) have shown that cultured, non-pigmented microflagellates ingest bacteria, but Parsons et al. (in press) could .find no evidence of microf lagellate predation on bacteria, in enclosed water columns. It was assumed here that colourless flagellates filtered bacteria at a rate of 94
20 nl h~1 per flagellate, equal to the assumed clearance rate of ciliates grazing bacteria.
3.5.2 Zooplankton excretion
The excretion of nitrogen by zooplankton was modelled as a function of body carbon only, although excretion rate is significantly affected by food supply, activity, reproductive condition, and amount and type of stored reserves (Conover
1978b: p. 376). Excretion of nitrogen (N) in the model was independent of food supply. Compounding the uncertainty of N excretion as a function of ingested food with the large uncertainty of amount of material ingested was thought to be more prone to serious error than the adoption of a constant rate of excretion per individual. Three separate formulations of excretion rate vs. body weight were derived: one for ciliates and colourless flagellates, one for ctenophores, and one for all other zooplankton.
Excretion by ciliates and colourless flagellates—Dewey and Banse have surveyed literature on respiration rates of non-feeding, bacteria-free protozoa and flagellates (Dewey
1976). They corrected respiration to 20 °C following Krogh
(1916) and used a C/dry weight ratio of 0.4. They report respiration as (p. 118 of Dewey 1976):
1 respiration (picolitres 0 r2 h" ) = 13.6836 wL-0.7354,
(30) 95
where W is ng dry weight per individual. To convert to N excretion, an appropriate 0/N ratio was required. No such published ratio for protozoa was found, so literature on copepod and mixed zooplankton excretion was consulted. Harris
(1959) found 0/N atomic ratios averaging 7.7 for Long Island
Sound zooplankton. Corner et al. (1965) obtained ratios between
9.8 and 15.6 for Calanus helgolandicus and C. finmarchicus.
Conover and Corner (1968) found that O/N ratios change seasonally. During the spring bloom, 0/N ratios of
C. hyperboreus were generally between 10 - 30, then increased past 150 immediately after the bloom using only stored fat as an energy source. During the summer 0/N fell to n20 - 30 again.
For C. finmarchicus and Metridia longa ratios were less variable, falling from n30 - 55 just after the spring bloom to n20 by the end of September. Using an 0/N atomic ratio of 20, a
C/dry weight ratio of 0.4, and correcting (Krogh 1916, his
Fig. 28, p. 96) to the mean temperature of water in CEE2,
Eq. 30 was transformed to
E = 0.01070 WMD.7354, (31) where E is excretion of inorganic nitrogen (ng-atom N h~l) and
W is body weight in ug C. Assuming that 20% of excreted N was organic (see discussion of copepod excretion), the total excretion of N, both organic and inorganic, was expected to equal
E (ng-at N h"1) = 0.01337 W-0.7354.
(32)
The total excretion of ciliates and colourless flagellates was 96
simply the product of observed numbers per unit volume and
excretion rate per individual as defined in Eq. 32, taking into
account the different weights of body carbon of the various
zooplankton taxa.
Excretion by ctenophores—Kremer (1977) reported that
excretion rates of Mnemiopsis leidyi were a linear function of
organism dry weight, rather than a power function typical of
other groups. Linear dependence was also found by Biggs (1977),
Hirota (1972) and Williams and Baptist (1966, cited by Kremer
1977) for several ctenophore species. However, Kremer noted that Miller (1970) calculated metabolic exponents of
0.82 - 0.87 for M. mccradyi. An exponent of unity (linear dependence) was assumed here.
Kremer (1977) measured a mean excretion rate of 14 ug-atom ammonium-N (g dry wt.)"1 day-1 for unfed M. leidyi at
10 - 18 °C. 56% of excreted N was organic. Jawed (1973) reported rates of 20 - 60 ug-atom ammonium-N (g dry wt.)"1 day"1 for jellyfish and Pleurobrachia at 13 °C. Kremer (1976) gives an excretion rate of 25 ug-atom ammonium-N and 20 ug-atom organic-N (g dry wt.)"1 day"1 for M. leidyi at n22 °C. It is uncertain whether a temperature correction is necessary for comparison of results. Biggs (1977) found that the oxygen consumption of gelatinous zooplankton did not markedly change when acclimated to temperatures lower than their natural
environment, whereas Kremer (1977) found a Qr10 of 3.67 for
M. leidyi studied at ambient temperatures of 10 - 24 °C. 97
Assuming a total nitrogen excretion rate of 40 ug-atom N
(g dry wt.)-1 day"1, and a C/dry weight ratio of 2% (Kremer
1976: p. 354), gives
E (ng-at N h"1) = 0.0833 W (ug C).
(33)
In the model 50% of nitrogen excreted by ctenophores was
assumed to be organic.
Excretion by other zooplankton—Ikeda (1974) measured the
excretion of ammonium by various subtropical, tropical,
temperate and boreal zooplankton as a function of body weight
and habitat temperature. His equation (5) as modified by Ikeda
and Motoda (1978) is
logr10 E = (-0.00941 T + 0.8338) • logr10 W
+ 0.02865 T - 1.2802, (34)
where
E = excretion (ug ammonium-N animal"1 h"1),
W = body dry weight (mg),
T = habitat temperature (°C). c
Taking a water temperature of 13.5 °C, and assuming a
C/dry weight ratio of 0.32 (Weibe et al. 1975), Eq. 34 is
converted to E (ng-at ammonium-N h"1) = 0.1548 WL0.7068,
(35) where W is body weight in ug C.
What fraction of excreted N would be expected to be 98
organic? Organic N comprises 15 - 30% (Mayzaud 1973), 18 - 24%
(Jawed 1969), 22% (Butler et al. 1969) and 25% (Corner and
Newell 1967) of the total N excreted. Assuming a value of 20%,
the excretion of inorganic+organic nitrogen by copepods, larval metazoans, larvaceans and Sagitta was modelled as
E (ng-at N h" 1) = 0.1935 VJL0.7068,
(36) where W is ug C body weight.
To summarize, the excretion of organic and inorganic nitrogen by protozoans, ctenophores, and other zooplankton, was described by Eqs. 32, 33 and 36, respectively. These functions are plotted in Fig. 17.
3.6 Bacteria
Bacteria in the model recycled inorganic nitrogen by mineralizing dissolved organic matter (DOM) exuded from phytoplankton or excreted by zooplankton. Particulate organic matter (POM) was considered to be an insignificant food source for bacteria. This accords with Azam and Hodson (1977) who found that about 90% of the heterotrophic uptake of glucose, serine or acetate was by bacteria passing a 1 um-pore filter.
Fuhrman and Azam (1980) report that >90% of thymidine was taken up by <1 um bacteria. These bacteria, being unattached to particles, should have been unable to rapidly degrade particulates. Bacteria were assumed not to sink because they were free-living and of such small size.
Inorganic nitrogen was assumed not to be taken up by ' 99
Figure 17. Modelled excretion rates of ciliates and colourless flagellates, ctenophores, and other zooplankton. Of the total nitrogen excreted by ctenophores, 50% was assumed to be organic. Protozoans and other zooplankton excreted 20% organic nitrogen.
101
bacteria. Thayer (1974) has suggested that heterotrophic
bacteria compete with algae for a nitrogen source when
metabolizing compounds with large C/N ratios. Parsons et al.
(in press) give indirect evidence of bacteria out-competing
phytoplankton in the uptake of nitrate in water columns
enriched with glucose. This possibility was not entertained
here primarily because a C/N mass ratio of 6 was adopted for
phytoplankton biomass. To maintain this constant C/N ratio
while exuding DOM required that the C/N ratio of exuded DOM be
6 also, well below ratios which result in net uptake of
inorganic nitrogen by bacteria (Hollibaugh 1978). Organics
excreted by zooplankton were also assumed to have a C/N mass
ratio of 6.
It is impossible at present to estimate what proportion of phytoplankton exudation products consists of small molecules
readily available to heterotrophs (Nalewajko 1977). All of the
DOM released by algae or zooplankton was therefore considered
to be available for bacterial utilization. Furthermore, the bacteria were assumed to immediately absorb all of the organic matter released into the water column, thus removing the necessity of specifying uptake kinetics or separately following a pool of DOM. This simple approach was used because it is not known what kinetics appropriately describe the uptake of a complex mixture of organic substrates by a diverse community of heterotrophs. Parsons and Strickland (1962) and Wright and
Hobbie (1965, 1966) discovered that heterotrophic uptake of single organic compounds by natural assemblages of plankton 102
could be described by Michaelis-Menten kinetics, but in some
cases serious errors can result when applying these kinetics to
the uptake of a single substrate by a heterogeneous community
of plankton (Williams 1973). The immediate absorption of DOM by
modelled bacteria is not unreasonable when one realizes that as
much as 45%, 100% and 40% of the pools of glucose, leucine and
thymidine respectively in CEE2 water was heterotrophically
assimilated per hour. (Admittedly these are maximum uptake
rates of easily utilized organic substrates.) Williams and Gray
(1970) found an immediate rise in the rate of substrate
oxidation by estuarine plankton after the amino acid
concentration was artificially increased.
Some percentage of organic matter taken up by bacteria is
immediately respired. For individual amino acids added to
natural plankton assemblages, the percentage respired of the
total carbon taken up ranges from 8 to 61% (Hobbie and Crawford
1969; Crawford et al. 1974). 21 - 50% of carbon in added
mixtures of amino acids and 33 - 65% of added glucose-carbon
was respired (Williams 1970; Williams and Yentsch 1976;
Herbland 1978). The model assumed that 40% of dissolved organic
carbon taken up by bacteria was immediately respired. It was
also assumed that inorganic nitrogen was simultaneously
released in the proportion of C respired/N mineralized = 6 (by mass).
In addition to the immediate respiration of some proportion of absorbed substrate, there is presumably a maintenance metabolism. Novitsky and Morita (1977) found that 103
the endogenous respiration of a psychrophilic marine vibrio maintained at 5 °C was 0.9% of the total cellular carbon per
hour at the beginning of starvation. Assuming a Qr10 of 2 to correct Novitsky and Morita's result to 13.5 °C, maintenance metabolism was modelled as a constant specific loss rate of
0.016 h""1 of bacterial carbon and nitrogen.
3.1. Inorganic Nutrients
3.7_.l Nutrient uptake
Uptake of dissolved inorganic nitrogen (N) and silicon
(Si) was directly coupled to gross photosynthesis by phytoplankton. Nitrogen was taken up in the ratio of 6 g C photosynthetically fixed to 1 g N removed from solution. In some simulations the ratio of Si taken up to C fixed varied according to the severity of Si-limitation (section 3.4.2).
Nutrient uptake was assumed not to occur at night when gross photosynthesis was zero.
3.1_.2 Nutrient sources
Nitrate was added to the top 8 m of CEE2 on nine occasions, and silicate on seven occasions before day 80. These additions were quantitatively accounted for in the model. About
800 litres of sediment was also pumped back to the surface of
CEE2 immediately after each nutrient addition. Generalizing from the concentration of sediment nutrients measured only once 104
(C. Davis, pers. comm.), about 0.55 ug-atom Si l"1 and 1.11
ug-atom N l'1 was introduced into the surface 8m of CEE2 after each pumping. This source was also modelled. Inorganic
nitrogen was returned to the water via phytoplankton and bacterial respiration and zooplankton excretion. N was released
in proportion to C respired so as to maintain a C/N mass ratio
of 6 in phytoplankton and bacterial protoplasm. Once Si was
taken up by diatoms it never returned to the water, except in
those runs where the dissolution of silica was modelled
(section 3.7.3).
Rainfall was an unimportant source of nutrients. The total precipitation at Victoria International Airport, 5 km from the
CEPEX site, was 10.65 cm between July 9 (day 1) and
September 26 (day 80) (Environment Canada 1978b). Any nutrients
in this rainfall would have been diluted in the top 8 m of CEE2
to 1.3% of their former concentration. If we accept Junge's
(1958) average of 8.3 ug-atom ammonium-N l"1 and 4.7 ug-atom nitrate-N l"1 in coastal rainfall as representative of
the rainfall during Foodweb I, this would imply an increase of only 0.17 ug-atom N l'1. There is little atmospheric contribution of silicon from rain or dust (Parsons and
Harrison, unpubl.).
3.7.2 Dissolution of particulate silica
In view of the extreme shortage of dissolved silicon predicted in early simulation runs, some mechanism for recycling Si back to the water was thought necessary. Si 105
regeneration was modelled simply as a constant rate of dissolution of silica present in living diatoms and detritus.
Dissolution rates reported for diatom cultures or natural plankton assemblages untreated with acids are listed in
Table III. A specific dissolution rate of 0.005 h"1 was chosen for both detrital and non-detrital silica, near the upper limit of reported dissolution rates. (Dissolution of silica was suspended in the model if the bulk C/Si mass ratio of an algal group reached its ceiling of 6.25.) Lewin (1961) detected little or no dissolution of silica from living diatoms, but she recognized that simultaneous uptake of Si could have masked dissolution. Nelson et al. (1976) found that the rate of silica dissolution from exponentially growing Thalassiosira pseudonana cells was similar to that found by Paasche (1973b) for cells killed by freezing, a process which leaves the cells' protective organic coating intact (Lewin 1961). This provides some justification for assuming an equal dissolution rate of silica deposited in living diatoms and detrital silica.
However, detrital silica includes not only silica found within intact, dead diatoms, but also particulate silica in zooplankton fecal pellets, pulverized diatom frustules, etc.
These latter forms of detrital silica would dissolve more rapidly than intact silica (see references cited in Grill
1970). Nevertheless, in the interests of simplicity these different forms of detrital silica were not distinguished in the model.
Influx of Si into the non-detrital silica pool occurred Table III. Literature estimates of specific dissolution rate of silica in living and dead phytoplankton. specific dissolution rate (per 1000 hrs.) reported temperature corrected measured to 13.5 C (celsius) nature of sample references up to 60 natural phytoplankton assemblage, Nelson & Goering 1977 upwelling region off northwest Africa
3.4 7.9J ashed frustules suspended at 110 m in Parker et al. 1977 Lake Michigan
9.4 5.5 20 freeze-killed, Si-depleted Skeletonema costatum Paasche & Ostergren 1980
2.0-8.5 1.2-5.0 20 untreated Thalassiosira pseudonana Nelson et al. 1976 0.2-0.3 0.1-22 20 natural phytoplankton assemblages, Kamatani & Ueno 1980 Japanese coast
0.71 0.88 11 natural phytoplankton assemblage collected at Grill & Richards 1964 100 m from Strait of Juan de Fuca, then placed in dark
1.5 0.46 28 natural phytoplankton assemblage from surface Lawson et al. 1978 0.83 0.38 23 of Kaneohe Bay, Hawaii, then placed in dark 0.59 0.38 19 0.44 0.39 15 0.25 0.30 11
1.6 0.43] 30 untreated Thalassiosira decipiens kept in dark, Kamatani & Riley 1979 no dissolution was evident during first 10 days
Dissolution was corrected to 13.5 C, the mean water temperature of CEE2 between days 1 and 80
1. Rate correction follows Lawson et al. 1978. 2. Rate correction follows Kamatani & Ueno 1980. 107
via uptake of dissolved Si. The model assumed that all Si taken up by diatoms was incorporated into the cell wall, and that intracellular pools of metabolically active Si were negligible
(Paasche 1980; Davis et al. 1978). Silica within living cells was transferred to the detrital fraction whenever phytoplankton were grazed. No digestive delay in grazers was modelled during transfer to the detrital pool. The other avenue of transfer occurred when phytoplankton respiration or exudation of carbon reduced the bulk C/Si mass ratio of an algal group to its lower limit of 1.25. Further respiration resulted in the simultaneous transfer of silica to the detrital pool so as to maintain a bulk ratio of 1.25. Although in reality respiration or exudation is not directly linked with Si loss in this manner, the modelled process can be interpreted as death or lysis of a subset of the diatom population.
Grazing- and respiration-induced losses of non-detrital silica from large 'and small diatoms entered the same pool of detrital silica. Detrital silica was mixed between layers and sank at a rate of 20 m day"1 in the undisturbed 8-20 m layer and 5 m day"1 in the bubbled 0-8 m layer. Bienfang (in press) found that fecal pellets of Calanus from CEE2 had sinking rates of 70 - 171 m day"1 when feeding mainly on diatoms. These very fast rates would be applicable only to that fraction of silica enclosed within fecal pellets of large copepods. The slower sinking rate used in the model recognized the diverse origins of detrital silica in CEE2. 108
3.8 Final Comments
Values of the major parameters of the model are summarized in Table IV.
A simplification was noted in the overview of the model
(section 3.1) that merits comment. This was the assumption that neither phosphorus nor temperature limited phytoplankton growth in Foodweb I. Phosphate concentration in the surface 8m of
CEE2 was 3.75 ± 1.67 ug-atom P 1_1 (mean + s.dev., n=32) and
3.03 ± 0.94 ug-atom P l"1 (n=32) in the 8-20 m layer. On only two occasions after day 11 did phosphate drop below 3 ug-atom P l~l in the surface layer, and below 2 ug-atom P l"1 in the deep layer. These concentrations were generally well above half-saturation constants of phosphate uptake or phosphate-limited growth reported in the literature (e.g.
0.38 - 0.63 ug-atom P l"1, Monochrysis lutheri, Burmaster and
Chisholm 1979; 0.02 - 2.8 ug-atom P 1_1, Asterionella formosa,
Cyclotella meneghiniana, Tilman and Kilham 1976; 0.6 ug-atom P l"1, Scenedesmus sp., Rhee 1973). The nutrient which is most often cited as limiting phytoplankton growth in the sea is nitrogen rather than phosphorus (e.g. Eppley et al. 1969a;
Dugdale and Goering 1967). Phosphorus was therefore considered not to limit phytoplankton growth in CEE2, hence its dynamics were not portrayed. The effect of temperature on phytoplankton growth was also ignored. Water temperature at zero metres depth increased from 15.0 °C at the start of the experiment to
18.0 °C by day 30 (August 7), then fell to 12.7 °C by day 79.
The temperature of water 20 m deep increased from 10.2 °C on Table IV. Summary of parameter values used in the main simulation model, symbol value definition and comments k 0.148 m attenuation coefficient of water in the absence of phytoplankton w ,-4 2 „N-1 k 5.11 lO-"* m*" (mg C) x attenuation coefficient per unit of phytoplankton carbon S -1 b 0.005 m h exchange coefficient for mixing across 8 m interface C/N 6 C/N mass ratio of phytoplankton C/Si 1.25 C/Si mass ratio of diatoms, reference run 1.25-6.25 variable C/Si mass ratio of diatoms -2 -1 I 200 uEin m s saturation irradiance of surface-dwelling phytoplankton on July 9 declines seasonally and with depth P 0.087 h ^ maximum gross photosynthetic rate, large diatoms max 0.110 small diatoms 0.031 large flagellates 0.056 small flagellates R 0.1 respiration of surface-dwelling phytoplankton as a fraction of P declines with depth
X 0.1 phytoplankton exudation as a fraction of realized gross photosynthesis
0.77 ug-atom N 1 ^ half-saturation constant of N-limited gross photosynthesis, large diatoms 0.16 small diatoms 0.54 large flagellates 0.12 small flagellates Kg^ 2.06 ug-atom Si 1 ^ half-saturation constant of Si-limited gross photosynthesis, large diatoms 0.42 small diatoms 1.3 m day ^ large diatom and large flagellate sinking rate in 8-20 m layer of CEE2 in surface bubbled layer, S = 0.325 m/day -1 0.5 m day * small diatom and small flagellate sinking rate in 8-20 m layer of CEE2 in surface bubbled layer, S = 0.125 m/day
continued on next page Table IV. (continued)
symbol value definition and comments
-1 0 ug C 1 copepod grazing threshold thr -1 300 ug C 1 critical phytoplankton concentration which saturates copepod grazing crit R 0.4 day"1 maximum specific ration of a copepod with a body weight of 68 ug C max 4 ul h"1 clearance rate of ciliates filtering phytoplankton of optimal size 0.02 clearance rate of ciliates and colourless flagellates filtering bacteria 30 clearance rate of metazoan larvae filtering phytoplankton of optimal size 0.3 clearance rate of metazoan larvae filtering bacteria 5.5-6500 clearance rate of larvaceans of increasing trunk length filtering bacteria and phytoplankton of optimal size
(see Fig. 17) excretion rate of zooplankton -1 0.005 h specific dissolution rate of silica 'Si -1 20 m day sinking rate of detrital silica in 8-20 m layer of CEE2 5Si in surface bubbled layer, S = 5 m/day Ill
day 1 to 14.1 °C by day 30, then fell to 10.2 °C by day 79.
Temperature changed gradually over depth and time; Steele et al. (1977) found that large plastic enclosures similar to CEE2 strongly damped external temperature fluctuations with periods less than one day. It was thought, therefore, that captured populations would have fully acclimated to the changing temperature regime, at least over the modelled period of
80 days.
Previous sections have described in detail how the simulation model was constructed from data gathered in
Foodweb I and from the scientific literature. Variation of surface irradiance over time, light attenuation in the water column, saturation irradiance of photosynthesis, sinking, mixing and standing stocks of zooplankton were based largely or entirely on Foodweb I data. Other model components such as maximum gross photosythesis, phytoplankton respiration and exudation, kinetics of nutrient limitation, zooplankton grazing and excretion, bacterial mineralization'and silica dissolution, were based on plausible literature estimates. The goal was to construct a coherent and consistent model of how phytoplankton and nutrient dynamics were expected to behave, at least approximately, in CEE2. The next section describes how this model was implemented on the computer. 112
4. COMPUTER IMPLEMENTATION
4.1 Approach
The various rate processes discussed as system components
in section 3 were expressed as a set of simultaneous ordinary differential equations for each of two depth layers—0 to 8 and
8 to 20 m—in CEE2. The two layers were coupled to simulate mixing of dissolved and suspended material between layers, sinking of large particles from top to bottom layers, and shading of the bottom layer by surface phytoplankton. The computer implementation was designed to allow any of the state variables to track observed concentrations at the discretion of the analyst. For example, nitrogen could be constrained to follow observed concentrations regardless of the modelled uptake of nitrogen by phytoplankton or regeneration by bacteria. Simulation runs began at t=3.5 days (noon on day 4; t=0 equals 00.00 hours on July 9, 1978, the early morning of day 1) with all state variables initialized at their observed values. Simulation runs did not begin at the time of CEE deployment, t=0.35 day, because detailed observations of zooplankton abundance were not made until day 4. 113
4.2 Nutrient Interpolation
When observed time series were used in the model (as was
always the case for zooplankton numbers), linear interpolation
between observations was performed for those time steps not
coinciding with sampling times.1 This method of interpolation
could be faulted in the special case of dissolved nitrogen and
silicon. Water samples for nutrient analyses were taken
immediately before nutrient additions, but not immediately
after. In several cases there was no apparent increase in
nitrogen or silicon in water sampled 2 or 3 days after nutrient
addition, and in no case did concentrations increase by the
amount expected if no uptake were to occur. Thus by linearly
interpolating between observed nutrient concentrations
transient increases following additions were unaccounted for or
underestimated. To see if this oversight might have
significantly affected phytoplankton dynamics in model runs in
which nutrients were constrained to follow their linearly
interpolated concentrations, a special interpolation technique
was tested on one run. In this special run, nitrogen and
silicon concentrations were linearly interpolated as before,
except that their concentration in the surface 8 m was
increased by the expected amount at the time of each nutrient
addition, and then was linearly decreased to the next observed
value (Fig. 4). The two methods of interpolation were compared
xFor the purpose of interpolation, observations were considered as having been made at 12 noon of a sampling day. 114
by simulating the period between t=17 and t=33 days when
observed nitrogen concentrations remained low despite two
nutrient additions. Only phytoplankton dynamics were activated.
There was no qualitative difference between phytoplankton
concentrations predicted by the two runs. By t=33 days, the
simple and special interpolation schemes respectively predicted
1701 and 1816 ug C l'1 for large diatoms, 0.1906 and 0.1049 pg C l"1 for small diatoms, 17.51 and 16.35 ug C l"1 for large
flagellates, and 0.2540 and 0.1982 pg C l"1 for small
flagellates. The maximum absolute difference between predictions occurred at t=25.83 days, when large diatom biomass was 1085 ug C 1_1 in the run using special nutrient
interpolation and only 754 ug C l'1 in the other run, a difference of 331 ug C 1"1. Comparisons of the two
interpolation schemes on other simulation runs confirmed that there was qualitative agreement between predictions. It was decided to use the more accurate nutrient interpolation scheme
in simulation runs reported in section 5 (whenever nitrogen or silicon dynamics were disabled). For reasons of expediency, the work reported in section 6.3, in which only the dynamics of large diatoms were activated, was not repeated using special interpolation. The author is confident that none of the conclusions of section 6 are significantly affected or in any way invalidated by the use of the simpler scheme of nutrient interpolation. 115
4.3 Accuracy of the Finite Difference Approximation
It was of course impossible to determine the absolute accuracy of the finite-difference scheme when the exact solution to the system of differential equations was unknown.
Instead, the model was run with a series of successively less accurate finite-difference schemes whose output was compared to a "base run" of highest accuracy. All runs began with the same initial conditions at t=3.5 days and ran until t=40 days. The base run of highest accuracy used double-precision arithmetic, the second-order Midpoint method of finite differences (p. 242 of Burden et al. 1978), and a time step of 1 hour. The values of the state variables at the end of the base run were then compared to those predicted by first-order Euler finite differences using single-precision arithmetic and a 4-hour time step. The absolute error relative to the base run was negligible: less than 1.3 pg C l"1 error for any phytoplankton group. Because the Euler method using single-precision arithmetic and a 4-hour time step, produced simulated time streams which were virtually indistinguishable from the much more costly Midpoint method, the Euler method was adopted for all subsequent runs and was considered to accurately solve the model's system of equations. 116
5. RESULTS AND DISCUSSION
'Agathon: But it was you who proved that death doesn't exist. Allen: Hey, listen—I've proved a lot of things. That's how I pay my rent. Theories and little observations. A puckish remark now and then. Occasional maxims. It beats picking olives, but let's not get carried away. Agathon: But you have proved many times that the soul is immortal. Allen: And it is! On paper.'
— Woody Allen, My_ Apology, 1980
'... if we take A = -1 mi./sec, then ... This means that for every pound of matter returning [from the moon] a million tons would have to start out [from 6 the earth], and if we specify mr2 = 10 lb. = 500 15 tons, then mr0 = 2.0 • 10 lb.... Hence, if mr0 were distributed as a homogeneous sphere of material of density about that of surface rock ... [then the spaceship] would thus be over five miles in diameter and so would be almost as massive as Mt. Everest!'
— J. W. Campbell, 1941, Rocket flight to the moon, The Philosophical Magazine, Vol. 31 (Seventh Series), pp. 24-34.
Previous sections have outlined the observed events in
CEE2, the construction of a model to simulate phytoplankton
dynamics and nutrient cycling, and the computer implementation
of that model. In this section the model's predictions will be
pitted against what was actually observed, accompanied by
interpretation and discussion—a blow-by-blow commentary on the model's performance. 117
5.1 The Reference Simulation
In the reference simulation the dynamics of all state
variables were activated and the parameter values in Table IV
were used. The C/Si ratio of diatoms was not varied and silica
dissolution was not modelled.
There was acute disagreement between predicted and
observed events in CEE2. Large-celled diatoms were severely
limited in the reference run by exhaustion of silicon in the
surface layer; dissolved inorganic nitrogen accumulated.
Small-celled diatoms and flagellates were quickly grazed to
extinction. Large-celled flagellates grew slowly throughout the
simulation to high concentrations in the surface 8 m by day 80.
The following sections document these predictions in detail.
5.1.1 Large diatoms
Simulated large diatoms peaked to n400 ug C 1_1 then fell
on several occasions during the first 50 days in the surface
8 m (Fig. 18). The beginning and end of each bloom coincided with the respective addition and exhaustion of silicon in the
surface layer. (The jagged increase of biomass in each bloom is due to the absence at night of any photosynthesis to counteract
respiratory, grazing, sinking and mixing losses.) Once silicon was exhausted in the surface layer it remained below 0.3 ug-atom Si 1_1 until replenished by further additions. These
low concentrations of Si prevented significant gross photosynthesis by diatoms and therefore the large diatoms 118
Figure 18. Biomass of large-celled diatoms in the surface 8 m predicted by the reference run. Octagons are concentrations observed in CEE2.
120
declined precipitously after Si was exhausted. It is of
interest that the large diatoms did not bloom when silicon was added on day 62 but instead increased slowly to 95.1 ug C l"1 over the next 18 days. The reason they did not rapidly increase
in abundance as they had after earlier additions was because
the high concentration of large-celled flagellates—over 1000
ug C l"1—strongly attenuated the ambient light. At the time
of the final silicon addition the 1% surface light depth was
6.4 m and ambient light limited the gross photosynthetic rate
of large diatoms in the surface 8 m to <37% of their maximum
rate. The growth of large flagellates was limited to the same
extent by the low light, but having attained such a large concentration they effectively prevented any other algal group
from challenging their dominance. This could explain why large diatoms in CEE2 failed to increase above 94.6 ug C l"1 in the
surface 8 m once Ceratium had exploded in biomass after day 65, despite observed concentrations of silicon and inorganic nitrogen in excess of 19.1 and 1.6 ug-atom 1'1 respectively, and little grazing by herbivores.
The simulated changes in large diatom carbon in the top
8 m were determined by the changing balance of gain and loss 121
Figure 19. Modelled gain and loss rates specific to predicted large diatom biomass; reference run; surface 8 m. Rates averaged over successive 24-hour intervals. Mixing losses are negligible and are not plotted.
1 - gross photosynthesis 2 - respiration 3 - exudation 4 - sinking 5 - grazing 122 Specific Gain/Loss Rate (day')
o A 123
rates shown in Fig. 19.1 Specific respiration declined slightly
from 0.18 day"1 on day 4 to 0.14 day"1 on day 80. There was a
constant sinking loss of 0.04 day"1, and mixing loss was
negligible (<0.014 day"1). Exudation was modelled as 10% of the
gross photosynthetic rate (section 3.4.5). Grazing pressure
substantially decreased over the simulation, accounting for 45%
of the total loss from large diatoms at noon on day 13, but
only 15% of total loss at noon on day 80. This reduction in
grazing is attributable to the fewer numbers of herbivorous
copepods and ciliates (able to filter large particles) later in
the experiment. What is suprising is that the model predicted
that ciliates were the most important grazers of large diatoms,
rather than copepods as is conventionally supposed (Fig. 20).
Even when copepods were most numerous, ciliates were predicted
to be responsible for 66% of the total grazing loss. When
considering the credibility of this prediction it is important
to remember that ciliates were assumed to be capable of
filtering cells no larger than 25 um equivalent spherical
diameter (p. 87 of section 3.5.2). Diatoms between 15 and 25 um
ESD averaged 13% of the large diatom biomass observed in CEE2,
therefore ciliates capable of filtering large particles were
1In Fig. 19 the modelled rates of change have been averaged over successive 24-hour intervals to eliminate the rapid diurnal oscillations of gross photosynthesis and exudation. Thus, peak gross photosynthetic rates of nl day"1 (specific to modelled large diatom carbon) recorded in Fig. 19 immediately after the first three silicon additions are less than the peak instantaneous rates of 1.7 day"1 experienced at midday. Daily averages provide a clearer picture of gain or loss realized over diel cycles. 124
Figure 20. Predicted specific grazing loss of large diatoms; reference run; surface 8 m.
1 - larvaceans 2 - metazoan larvae 3 - ciliates 4 - copepods 125 126
assumed to have a constant selectivity of 0.13 for large diatoms. Had the reference run reproduced the observed early bloom of large diatoms which was dominated by 60 um cells, it would have erroneously predicted a (presumably nonexistent)
grazing loss to ciliates. Conversely, it would have
underestimated the impact of ciliates later when large-celled diatoms of <25 um ESD constituted much more than 13% of large
diatom biomass. (Of course the formation of diatom chains would
significantly decrease grazing loss to ciliates.) What can be
reasonably concluded is that the dominant grazer of cells
smaller than n25 um could have been ciliates. Whereas the
importance of marine benthic ciliates as grazers of diatoms and
dinoflagellates has been extensively documented (Fenchel 1968),
pelagic ciliates have not yet been accorded the same
recognition.
Below 8 m the respiration of all algal groups exceeded
their gross photosynthesis, yielding an average net
photosythetic rate of -3.2 ug C 1_1 day1 over the 76.5 day
simulated time period. As might be expected, this predicted
rate of net photosynthesis in the 8-20 m layer was less than
the average 14C fixation of 3.1 ug C l"1 observed in 4-hour in
situ incubations at the relatively shallow depth of 12 m. The
modelled phytoplankton did not actively grow in the deep layer
but passively changed concentration in phase with the flux of
carbon sinking from the surface layer. Because the model
assumed that phytoplankton sank at constant rates, algal carbon
in the deep layer peaked simultaneously (but at greatly 127
diminished concentration) with blooms in the surface layer.
This is best illustrated by modelled large diatom biomass in
the surface and deep layers (Fig. 21). This same effect was
observed for all four algal groups in CEE2: there was no
apparent lag between increases in phytoplankton carbon above
and below 8 m.
5.1.2 Large flagellates
In contrast to the cyclic growth and collapse of large
diatoms in the surface 8 m, the growth of large flagellates was
sustained until the end of the simulation (Fig. 22). The large
flagellates peaked at 1400 ug C l"x on day 72, close to the
peak of 1250 ug C 1_1 observed on day 74. The large
flagellates grew relatively slowly compared to the rapid bursts of large diatom biomass predicted by the reference run. This
was due to the assumption that the maximum gross photosynthetic
rate of large flagellates was only 36% of that of large diatoms
(Eq. 18, p. 69); the largest predicted specific rate of gross photosynthesis of large flagellates was 0.63 day1 at noon on day 12 compared to 1.73 day1 for large diatoms on day 13. The growth of large flagellate biomass slowed and finally stopped
late in the simulation, not because of increasing loss
rates—grazing pressure decreased markedly after day 20—but because gross photosynthesis became more and more light limited through self-shading by the huge concentration of large
flagellates (Fig. 23).
Grazing pressure specific to large flagellate biomass was 128
Figure 21. Predicted biomass of large diatoms in the surface (solid line) and deep (dashed line) layers; reference run.
130
Figure 22. Biomass of large-celled flagellates in the surface 8 m predicted by the reference run. Octagons are concentrations observed in CEE2.
132
Figure 23. Modelled gain and loss rates specific to predicted large flagellate biomass; reference run; surface 8 m. Rates averaged over successive 24-hour intervals. Mixing losses are negligible and are not plotted.
1 - gross photosynthesis 2 - respiration 3 - exudation 4 - sinking 5 - grazing 133 Specific Gain/Loss Rate (day"')
-0.5 v 0.5 CD- 134
never more than 58% of the specific grazing on large diatoms.
The much lower grazing pressure on large flagellates was due to
the very small proportion of flagellate cells between 15 and 25
um equivalent spherical diameter. Flagellates with cell sizes
larger than 25 um were shielded from ciliate grazing: ciliates
accounted for 80 and 46% of the respective grazing loss from
large diatoms and large flagellates.
5.1.2 Small diatoms and small flagellates
Small-celled phytoplankton in the reference run were
rapidly removed by ciliate grazing. Small flagellate biomass
dropped below 1 ug C 1_1 by day 9, and small diatom biomass dropped below 1 ug C 1_1 by day 10. The grazing pressure was
especially intense during the first 40 days when the specific
grazing loss ranged from 0.9 to 2.3 day-1 (Fig. 24 shows grazing on small diatoms; grazing on small flagellates was virtually identical). On the average, grazing accounted for 76 and 83% of the respective carbon loss from small diatoms and
flagellates, and ciliates were responsible for 88% of the grazing loss.
Either the reference run had overestimated grazing on small phytoplankton, or net photosynthesis had been underestimated. This conclusion pertains especially to the period between days 48 and 55, when small diatoms were observed to increase in concentration from 4.49 ug C l"1 to 221 ug C l'1 in the top 8 m of CEE2 (Fig. 2b), an average net rate of growth of 0.56 day"1. A simulation run in which phytoplankton 135
Figure 24. Predicted specific grazing loss of small diatoms; reference run; surface 8 m.
1 - larvaceans 2 - metazoan larvae 3 - ciliates 4 - copepods
137
were constrained to follow their observed concentrations
predicted an average specific grazing loss from small diatoms
of 0.51 day1 during the same period. Neglecting losses other
than grazing, small diatoms would have needed a specific net
photosynthetic rate of 0.56 + 0.51 = 1.07 day1 (1.54 doublings
per day) to yield their observed increase in concentration.
This doubling rate is close to the maximal growth rate of 1.59
doublings per day expected on the basis of Chan's (1978b) work
(derived for the small diatom assemblage of CEE2 by combining
Brj from Eq. 17, p. 69 with Eq. 11a, p. 66).
5.1.4 Bacteria
Although the average concentration of bacteria predicted
by the reference run for the surface layer, 22.0 ug C l"1,
agreed with the average concentration of 22.6 ug C l"1
observed over the same period in CEE2, there was little
resemblance between predicted and observed time series of
bacterial carbon (Fig. 25). In the deep layer the average
predicted concentration of bacteria was 4.2 ug C l"1, only 35%
of the observed average of 11.9 ug C l"1. The predicted peaks
in bacterial carbon coincided with predicted spurts in
large-diatom exudation. It was noted earlier (section 2.4, p. 36) that the major peak of bacterial carbon in the surface
8m of CEE2 occurred during the last half of the collapse of large diatoms (cf. Figs. 2a and 11). It is not unusual for exudation by natural phytoplankton populations to increase during the collapse of blooms (e.g. Hellebust 1965), and this 138
Figure 25. Biomass of bacteria in the surface 8 m predicted by the reference run. Octagons are concentrations observed in CEE2.
140
could have accounted for the main peak in bacterial biomass.
Vinogradov et al. (1972, 1973) predicted that bacteria would
peak in abundance shortly after peak phytoplankton abundance
when detritus from the collapsing bloom was a substantial
source of food for bacteria. It is not known how detritus
changed in abundance in CEE2. Fuhrman et al. (1980) found, that
bacterial growth rates in the Southern California Bight were
significantly correlated with chlorophyll, but not with primary
production. They speculate that bacterial growth was stimulated
by organics released during zooplankton grazing rather than by
organics leaked from actively photosynthesizing cells. This
interpretation would seem to be consistent with the timing of
the main peak of bacterial biomass in CEE2. Indirect evidence
to be presented in section 6 also suggests that periods of
active algal growth and loss of organics were out of phase in
CEE2.
The greatest loss of biomass from bacteria was via
respiration rather than grazing. In the surface layer,
respiration, grazing and mixing respectively accounted for 85%,
14% and 1% of the total loss of bacterial carbon during the
reference run. The average growth efficiency of bacteria, defined as 1 - (carbon respired/carbon assimilated), was predicted to be 0.15 in the surface layer and -0.01 in the deep layer, much less than the efficiency of 0.6 which is typical of cultured bacteria in their growth phase (Calow 1977). The predicted growth efficiencies were much less than. 0.6 because endogenous respiration exceeded the immediate respiratory loss 141
of 40% of assimilated carbon. There are three possible explanations of this discrepancy between predicted and typical growth efficiencies: 1) the growth efficiency of cultured bacteria is not applicable to natural bacteria; or 2) one or both components of bacterial respiration have been overestimated; or 3) the influx of carbon to bacteria has been greatly underestimated, preventing assimilation-related respiration from dominating total respiration. The implications of the third possibility are especially interesting and they will be considered in section 5.1.5.
Colourless flagellates, larvaceans, ciliates and metazoan larvae were predicted to respectively account for 80.9, 13.6,
5.2 and 0.3% of the total bacterial carbon grazed in the surface 8 m. (Similar percentages were predicted when bacterial carbon was constrained to follow observed concentrations.) The relative importance of these potential bacteriovores predicted by the model agrees with King et al. (1980) who calculated that larvaceans had little impact on bacterioplankton in CEE2. King et al. (1980) suggested that phagotrophic flagellates could be the principle consumers of bacteria. This would definitely be the case if they did in fact filter bacteria at the rate of
20 nl h"1 per individual assumed in the model. This clearance rate equalled the assumed clearance rate of ciliates but was much less than larvacean clearance rates (per individual); the overwhelming importance of colourless flagellates as potential predators on bacteria was simply due to their tremendous numbers—as many as 1500 per ml. 142
5.^.5 Nitrogen cycling
Simulation results, surface layer—Dissolved inorganic
nitrogen was predicted to accumulate to concentrations as high
as 25 ug-atom N l'1 in the surface layer, much higher than
observed concentrations (Fig. 26). To examine the model's
performance more closely, only the period prior to noon on
day 62 was considered.2 During.this period from t=3.5 to t=61.5
(58 days), the reference run predicted the accumulation of 15.7
ug-atom N l"1 in the surface 8 m. The observed accumulation
was 1.8 ug-atom N l~x». On the average, therefore, the
reference run either underestimated net phytoplankton uptake of
nitrogen or overestimated nitrogen regeneration from bacteria
or zooplankton in the surface layer.
Of the various sources of nitrogen in the surface layer,
+ artificial additions of NHr4 (via sediment pumping) and N0r3"
were predicted to account for 70% of total supply. In
descending importance, bacterial remineralization accounted for
14% of total supply, zooplankton excretion 10%, and mixing from
2This period was selected because larvaceans were not sampled after day 63; uncertainty in their numbers after day 63 would have been an additional (though presumably small) source of error when estimating zooplankton excretion. Day 62 was selected rather than day 63 because nutrient concentrations were observed on day 62.
3Input via mixing has probably been underestimated in the reference run because predicted surface concentrations were higher than observed, resulting in an underestimated concentration gradient between the surface and deep layers. 143
Figure 26. Concentration of nitrate + nitrite + ammonium in the surface 8 m predicted by the reference run. Octagons are concentrations observed in CEE2. Nitrogen (ug-at N I"') 0 30
CD 145
the deep layer 6%.3 Copepods were responsible for most of the zooplankton excretion early in the simulation, but metazoan larvae (of polychaetes, oligochaetes, gastropods, pelecypods and bryozoans) dominated excretion after day 40 (Fig. 27). This raises an embarrassing question: How did copepods and metazoan larvae contribute most of the nitrogen excreted when they were not the most voracious grazers of phytoplankton or bacterial biomass (cf. Figs. 20, 24 and 27)? Unfortunately, the model did not explicitly couple zooplankton excretion with grazing. To eliminate any biases caused by unrealistic predictions of phytoplankton and bacterial biomass by the reference run, a further simulation was executed in which phytoplankton and bacteria were constrained to follow their observed concentrations. In this run, again between t=3.5 and 61.5 days, copepods excreted 114% of the nitrogen they grazed in the surface layer, i.e. they excreted more than they ate! Metazoan larvae excreted 182% of grazed nitrogen; ciliates—the dominant grazers of phytoplankton carbon—-excreted only 1.6% of the nitrogen they grazed. Since modelled grazing and excretion rates were independently derived, it is hardly suprising that these two parameters were so poorly correlated in individual groups of zooplankton. Considered as a whole, zooplankton excreted 16% of the nitrogen grazed (phytoplankton and bacterial dynamics disabled). The reference run predicted 44%.
(Over the entire water column, the two runs respectively predicted excretion of 25 and 73% of the nitrogen grazed.)
Invertebrates typically excrete 30 - 70% of the organic 146
Figure 27. Predicted excretion of inorganic nitrogen by zooplankton; reference run; surface 8 m.
1 - chaetognaths 2 - ctenophores 3 - colourless flagellates 4 - larvaceans 5 - metazoan larvae 6 - ciliates 7 - copepods Zooplankton Excretion (ng-at N 1~; day"1) Q 500 i—]._| ,i i i i i i i i i i
CD 148
material they ingest (Calow 1977). What can be concluded is
that over 0-20 m simulated zooplankton excretion as a whole was
not an unreasonable fraction of the material grazed, but that
the fraction excreted was too small above 8 m and too large
below 8 m. For individual zooplankton groups, the balance
between grazing and excretion was clearly unrealistic.
Simulation results, deep layer—In the deep layer, the
reference run predicted an accumulation of 5.1 ug-atom N 1_1
between t=3.5 and 61.5 days. The observed accumulation was
5.0 ug-atom N l"1, which can be further resolved into a net
increase of 13.3 ug-atom ammonium-N 1_1 and a net drop of 8.3
_1 ug-atom nitrate-N 1 (NOr2~ concentration fluctuated between
0.17 and 0.42 ug-atom N l"1). The agreement between the
observed and predicted accumulation of nitrogen in the deep
layer is coincidental. The loss of nitrogen via mixing into the
surface layer, 3.3 ug-atom N 1_1 over 58 days, was probably
underestimated for reasons noted earlier (footnote 3, p. 142).
One can roughly calculate a more realistic mixing loss by employing the exchange coefficient developed in section 3.3
(Eq. 5b, p. 49) in conjunction with the observed difference in
+ NO [-3" and NHr4 concentration between the surface and deep
layers on each of 25 sampling days between t=3.5 and 61.5. This yields an estimated loss of 7.7 ug-atom nitrate-N l"1 and 3.5 ug-atom ammonium-N l"1 from the deep layer. Thus the observed drop of 8.3 ug-atom nitrate-N l"1 may have simply been due to mixing into the surface layer together with some phytoplankton 149
uptake.
The rapidity of ammonium buildup observed in the deep
layer between days 4 and 62 was unexpected. Bacteria and
zooplankton were predicted to remineralize 7.8 ug-atom N l"1
(as ammonium) in the reference run during the 58-day period;
phytoplankton respired 6.5 ug-atom N l"1 (see footnote 4); 3.5
ug-atom ammonium-N I'1 would be lost by mixing. The expected
net gain of ammonium in the deep layer was . therefore
7.8 + 6.5 - 3.5 = 10.8 ug-atom N l"1, short of the observed
13.3 ug-atom N l"1 gain. Moreover, phytoplankton uptake has
not been considered. It must be concluded that remineralization
of ammonia in the deep layer of CEE2 was underestimated in the
reference run. It is unlikely that zooplankton excretion has
been underestimated (it being already an uncomfortably high
proportion of the organic nitrogen grazed); instead, bacterial
remineralization in the deep layer was probably underestimated.
In the previous section (p. 14,0) it was noted that bacteria
were predicted to respire more material than was absorbed from
the deep layer, and consequently bacterial respiration was
either too high or influx of organics too low. These separate
conclusions can be reconciled only if the influx of organics to
bacteria was indeed underestimated. This shortfall of organic
supply to bacteria could be made up in many ways: accelerated
*The model assumed that N was released proportionally to respired C to maintain a constant C/N ratio even though the release of N would not in fact be directly linked to respiration; for the sake of argument this "respired" nitrogen was assumed to be ammonium-N. 150
exudation or lysis of phytoplankton which settled below the
photic zone, increased sinking from the upper layer, or
decomposition of sediment below 20 m.
Simple nitrogen budget for CEE2—Two dramatically
different pictures of nitrogen cycling in CEE2 can be
constructed depending on whether or not one accepts the
measured 14C fixation rates as representative of the true net
production of carbon and the simulated regeneration of nitrogen
by bacteria and zooplankton as being realistic.
If we accept these premises, do they lead to a consistent
picture of nitrogen cycling? The inferred net phytoplankton
uptake of nitrogen (inferred NPUN) in the surface 8 m can be
defined as
NPUN = artificial N additions + N influx via mixing
+ bacterial and zooplankton regeneration of N
observed increase in N concentration. (37)
Considering as before only the period from t=3.5 to 61.5, the
terms in Eq. 37 can be filled in as follows:
NPUN (ug-atom N l-l/58 days) =
58.8 + (16.8) + (20.4) - 1.8 = 94.1. (38)
The bracketed terms in Eq. 38 are uncertain: the mixing influx of 16.8 ug-atom N l"1 was roughly estimated from observed
NOr3" and NHr4* concentrations as described earlier (p. 148), and the regeneration of 20.4 ug-atom N l"1 is that predicted by the reference run. The NPUN value of 94.1 ug-atom N l"1/58 days can be converted to an expected average rate of net 151
production of 136 ug C l"1 day"1 assuming a C/N mass ratio of
6. The observed 14C fixation rate in the top 4 m of CEE2 during
4-hour midday incubations was 58.8 ± 40.3 ug C l"1
(mean ± s.dev., n=25) and 20.0 ± 15.7 ug C l"1 (n=26) in
4-8 m, again during the same 58 day period. The average
observed net primary production was therefore roughly
(58.8 + 20.0)/2 = 39.4 ug C 1"V4 hours 2.4 (footnote 5)
= 95 ug C l"1 day"1. (Nighttime respiration of fixed carbon
has not been subtracted.) Considering the roughness of these
calculations and the large variability of 14C fixation rates,
the agreement between the inferred carbon-equivalent NPUN of
136 ug C l"1 day"1 and the observed 14C productivity of 95
ug C l"1 day"1 is remarkably good. Thus the simulated rate of
bacterial and zooplankton regeneration •of nitrogen is
consistent with observed 14C primary production in the bubbled
layer of CEE2.
The bubbled layer of CEE2 is analogous to the surface
mixed layer of a highly eutrophic upwelling zone. Nitrate,
silicate and phosphate were added to the bubbled layer at
weekly intervals; similarly, transient injections of nutrients
into the mixed layer from below the euphotic zone during storms
or the onset of coastal upwelling have been reported (Walsh et
al. 1974, 1978). Eppley et al. (1979a) have shown that the
injection of nitrate into the euphotic zone of southern
5 2.-4 = insolation over full diurnal cycle divided by insolation between 1000 and 1400 hours PST (incubation period), for a clear, sunny day at 48° N during Foodweb I. 152
California coastal waters not only increases phytoplankton
production, but also accelerates heterotrophic
remineralization. How did the heavy nitrate loading of the
surface layer of CEE2 affect heterotrophic activities in
Foodweb I?
This question can be approached by considering the amount
of "new" and "regenerated" production in CEE2. Dugdale and
Goering (1967) defined new production as primary production
associated with N0r3" or Nr2 newly incorporated by
phytoplankton in the euphotic zone, and regenerated production
to be associated with recycled ammonium-N or dissolved organic
N. Implicit in this approach is the assumption that iri situ
bacterial nitrification or denitrification, or nitrate
reduction by phytoplankton or bacteria, is negligible; Nr2
fixation or phytoplankton uptake of urea or other sources of
organic nitrogen have also been neglected here. The inferred
ratio of new to regenerated production in the entire water
column is then (again assuming that simulated remineralization
of nitrogen by bacteria and zooplankton is realistic):
New/Regenerated =
NOr3" added - observed AN0r3'
+ + + NHr4 remineralized + NHr4 from sediment - observed ANHr4
20.4 + 4.8 = 2.4. (39) 12.8 + 6.5 - 8.7
The individual terms in Eq. 39 have units of ug-atomN
l_1/58 days. They were derived by calculating fluxes separately
for 0-8 and 8-20 m, then respectively weighting the two layers
by 0.4 and 0.6 to reflect their different volumes (e.g. NOr3~ 153
addition to the top 8 m was 51.0 ug-atom N 1'1 = an average
addition of 51.0 • 0.4 = 20.4 ug-atom N l"1 over 0-20 m).
NH [-4 * from sediments included the expected addition of 7.8
ug-atom N 1_1 to the surface 8m from sediment pumping plus an
unmeasured flux into the deep layer from sediments. This latter
+ flux was estimated as the observed buildup of NHr4 in the deep
layer plus the mixing loss into the surface layer, minus the
simulated remineralization by bacteria and zooplankton. The net
uptake of ammonium by phytoplankton in the deep layer was
assumed to be zero.
Expressed differently, new production as a percentage of
total production was 70%, provided that remineralization rates
were realistically simulated. This percentage was substantially
larger than the 50% considered typical of eutrophic areas
(neglecting the contribution of urea to regenerated production;
Dugdale 1976).
In conclusion, if the simulated rate of nitrogen
remineralization is accepted as being approximately correct, we produce a picture of nitrogen cycling in CEE2 which is dominated by artificial additions of nitrate to the bubbled
layer. The average measured rate of 1*C production in CEE2 is compatible with this picture. Unfortunately, this simple nitrogen budget, although internally consistent, cannot be
reconciled with two cogent lines of observation.
Alternative nitrogen budget for CEE2—It was demonstrated on pages 148 and 150 that a net phytoplankton uptake of 154
nitrogen (NPUN) of 94.1 ug-atom N l"1 (=136 ug C l'1 day1)
during the period from t=3.5 to 61.5 days was required to yield
the observed accumulation of nitrogen in the surface 8 m of
CEE2 _if_ remineralization proceeded at the simulated rate. The
realism of this proviso is seriously undermined by the results
of P. J. leB. Williams (1980; pers. comm.).
Williams used precise Winkler titration to measure the
respiration and photosynthesis of different size fractions of
particles in water from the upper 5 m of CEE2. On day 9 when
few microflagellates were present, the <5 um fraction had a
respiration rate of 214 ug Op2 l"1 day1; its photosynthetic
1 1 rate was 48 ug 0r2 l" day , only 7% of the photosynthetic
rate of unfiltered water. On other sampling days Williams could
not fractionate microheterotrophs from autotrophs. If we take
1 1 214 ug Or2 I" day as representative of bacterial respiration
in CEE2, the bacteria were respiring 64.3 ug C l"1 day1,
assuming an RQ of 0.8. (The bacterial respiration rate
predicted by the reference run averaged 16.9 ug C l"1 day1
between t = 3.5 and 61.5 days (16.3 ug C l"1 day1 over entire
simulation).) The increased remineralization rate by bacteria
would require an NPUN of 127 ug-atom N .l_1/58 days (=183 ug C
l"1 day1) to prevent nitrogen from accumulating faster than observed. Therefore, if Williams' results reflect the true rate
of bacterial remineralization then the reference run has underestimated net primary productivity in the surface 8m of
CEE2 by 46%.
The predicted flux of organic carbon into bacteria must 155
also have been underestimated. Fuhrman and Azam (1980)
estimated bacterial growth in CEE2 from thymidine incorporation
rates and from increase of bacterial numbers over time in 3 um
filtrates. The former method estimated growth of 11 - 71 ug C
l"1 day-1 on day 27, and the latter method 29 ug C l"1 day"1
on day 32 and 5-12 ug C l"1 day"1 on day 69. Thus the
influx of carbon needed to support bacterial respiration (64
ug C l"1 day"1) and growth (5 - 71 ug C l"1 day"1) is much
greater than the average simulated influx of 19 ug C l"1
day"1 in the surface 8 m between days 4 and 62.
This influx of organic carbon to bacteria must have come
from; 1) phytoplankton, directly via exudation or lysis or
indirectly via zooplankton spoliation or excretion of grazed
phytoplankton carbon; or from 2) other bacteria, directly or
indirectly. However, since the carbon respired by bacteria
cannot be directly re-absorbed by bacteria, that fraction of
organic influx to bacteria which is'respired must ultimately
come from phytoplankton if bacterial productivity is to be
sustained. Thus the influx of organic carbon to bacteria in the
surface layer required at least 64.3/183 • 100 = 35% of
estimated net primary production. If Fuhrman and Azam's
bacterial growth rate of 71 ug C l"1 day"1 is also included,
then (64.3 + 71)/183 • 100 = 74% of net primary production
would be required by bacteria. Considering the number of untested assumptions underlying these calculations, the derived percentages of 35% and 74% should only be considered suggestive of the importance of bacteria in Foodweb I. 156
Could phytoplankton have supported such a steep
requirement of organic carbon for bacterial respiration and
growth? A simulation run in which phytoplankton were
interpolated between their observed concentrations predicted an
average sinking loss of 8.9 ug C 1'1 day1 from the surface
layer between days 4 and 62. The predicted mixing loss was 1.8
ug C l"1 day1, and 95.1 ug C l"1 day1 was grazed. This
grazing loss was very likely overestimated. Harris et al.
(1980) measured an average ingestion rate by adult female
Pseudocalanus (feeding in water removed from 4-8 m of CEE2) of
1.7 ug C copepod-1 day1; the simulated rate averaged 3.5
ug C copepod"1 day1 (Fig. 28). Adult female Calanus ingested
15.0 ug C copepod-1 day1 (Harris et al. 1980) or 19.8
ug C copepod"1 day1 (simulation). Observed grazing rates by
ciliates are unavailable, but it was suggested earlier that
they, too, were overestimated. If 50 ug C l"1 day1 is a more
reasonable grazing loss from phytoplankton, then 183 (net
production) - 50 (grazing) - 9 (sinking) - 2 (mixing) - 3
(observed increase in phytoplankton standing stock) = 119 ug C
1_1 day1 (= 65% of 183 ug C l"1 day1) would be left over
as a potential source of organic carbon for bacterial uptake.
Summmary of nitrogen cycling—The two different nitrogen
and carbon budgets illustrate two contrary interpretations of
the events in CEE2. Neither interpretation is consistent with all available observations. The first interpretation rejected
Williams' (1980) and Fuhrman and Azam's (1980) measurements of 157
Figure 28. Predicted and observed ingestion rate of adult female Pseudocalanus.
Solid line - simulated ingestion in surface 8 m with phytoplankton constrained to follow observed concentration. Gaps are times when no adult females were present in CEE2 circles - observed ingestion by specimens removed from outside CEE2 and placed in water from 4-8 m of CEE2 (Harris et al. 1980)
159
microheterotrophic activity, and accepted the simulated
nitrogen regeneration rate. Here, net primary production in the
surface layer was estimated as 136 ug C 1_1 day1, close to
the average observed 1 *C productivity of 97 ug C l"1 day1.
The surface pool of dissolved inorganic nitrogen was turned
over every 3.4 days,6 and new/total production (0-20 m) was
n70%. Conversely, the second interpretation rejected both the
nitrogen regeneration rate predicted by the reference run and
the observed 1*C productivity. Instead it accepted the much
faster bacterial remineralization rate measured by Williams.
Net primary production was estimated as 183 ug C l"1 day1
(0-8 m); dissolved inorganic nitrogen was turned over every 2.5
days (0-8 m); new/total production was n50% (0-20 m). The rapid
bacterial remineralization rate of 0.76 ug-atom N l"1 day1
(based on Williams' measured respiration rate of 214
-1 1 ug Or2 l day , assuming an RQ of 0.8, C/N mass ratio of 6,
and no bacterial uptake of inorganic nitrogen) is similar to
the remineralization rates of 0.3 to >1.4 ug-atom N l"1 day1
observed in other large-scale CEPEX enclosures (Harrison 1978).
Harrison also reported that the median time required for
+ plankton to consume an amount of NHr4 equivalent to the
ambient concentration was n2.4 days.
The separate existence of these two interpretations hinges
'Turnover time was calculated as mean concentration of dissolved inorganic nitrogen divided by estimated net phytoplankton uptake of nitrogen = 5.46 ug-atom N l"1 • (94.1 ug-atom N l"l/58 days)"1. 160
upon the incompatibility of measured 14C productivity and
measured microheterotrophic activity. This inability to
reconcile primary production as determined by 14C bottle
experiments with the high activity of bacterioplankton obtained
by other means currently plagues marine research (Sieburth
1977).
5.1.6 Silicon
The reference run predicted that large diatoms rapidly
stripped silicon (as ortho-silicic acid) out of the surface
layer after each nutrient addition (Fig. 29). Silicon was
removed more slowly after the final silicon addition on day 62
because large diatom growth was restrained by large flagellate
shading. After diatoms exhausted silicon in the surface layer,
Si concentrations were low (<0.3 ug-atom l"1) but not zero due
to mixing inputs from the deep layer. Deep-layer Si was
predicted to decline from 24.6 ug-atom l"1 at the start of the
run (day 4) to 8.2 ug-atom l"1 on day 62, then increase
slightly due to mixing from the temporarily enriched surface
layer. Twenty-seven per cent of the predicted net loss of
silicon from the deep layer was due to mixing into the surface,
with the remaining 73% due to i_n situ uptake.
In stark contrast to the reference run's predictions,
observed silicon accumulated in both the surface (Figs. 4b, 29)
and deep layers of CEE2. Deep Si increased from 24.6
ug-atom l~l on day 4 to >30 ug-atom l"1 after day 50. The disparity between observed and predicted Si concentrations must 161
Figure 29. Concentration of silicic acid in the surface 8 m predicted by the reference run. Octagons are concentrations observed in CEE2. Silicon (ug-at Si I"') 0 30 [ ] j 1 1 1 =~l 1 I I I I I
CD 163
have been at least partly due to the absence of any Si
regeneration in the reference run. This is convincingly
demonstrated for the deep layer of CEE2. Si was observed to
increase from 24.6 ug-atom l"1 on day 4 to 34.6 ug-atom l"1 on
day 79 even though Si was not added to the deep layer.
Moreover, a simulation run in which Si was constrained to
follow its observed concentrations in the top and bottom layers
predicted a mixing loss of 8.3 ug-atom Si l"1 from the bottom
layer. Thus nl8.3 ug-atom Si l"1 must have been regenerated
over 75 days in the deep layer, not counting further silicon
needed to satisfy diatom uptake.
In the surface layer the situation was complicated by an
unknown silicon uptake. Consider the period from day 13 to
day 25, the period when large diatoms bloomed in the surface
layer. Observed Si concentrations fell by 10.5 ug-atom l"1;
17.4 ug-atom l"1 was added; a mixing influx of 2.2 ug-atom l*1
was simulated with Si levels constrained to their observed
concentrations. Hence a total uptake of 10.5 + 17.4 + 2.2 =
30.1 ug-atom Si 1'1/12 days could have been supported without
having to invoke Si regeneration. Unfortunately the amount of
Si taken up by diatoms during this 12-day period is unknown. In
this period, positive increments of diatom biomass totalled 735
ug C l"1; assuming a low C/Si mass ratio of 1.25, this biomass
increase would remove only 20.9 ug-atom Si l"1. Between days
13 and 25 the observed 14C productivity was 37.8 ± 34.3 ug C
l-1/4 hours (mean ± s.dev., n=12) in the surface 8m of CEE2.
Diatom carbon comprised 60 - 89% of total photosynthetic 164
carbon. Even assuming that diatoms were responsible for all photosynthesis and that C/Si = 1.25, the mean daily productivity (37.8 • 2.4 =91 ug C l"1, see footnote 5, p. 151) would remove 2.6 ug-atom Si l"1 day"1 = 31 ug-atom Si 1"1/12 days, marginally greater than the 30.1 ug-atom Si l"1 limit. The available observations are clearly of no use in establishing the existence of Si regeneration in the surface 8 m of CEE2.
Of course, the rapid depletion of silicon predicted in the surface layer could have simply been due to the overestimation of diatom uptake. This might have been a consequence of overestimating gross photosynthesis, underestimating the C/Si ratio of newly synthesized biomass, or assuming that Si uptake was tied to gross photosynthesis rather than some other measure of diatom growth.
Because the discrepancy between predicted and observed concentrations of silicon in CEE2 was so glaring, and because measurements with which to construct useful silicon budgets were lacking, the reference run was abandoned and further simulations were run to explore silicon dynamics. The results of these runs, to be presented in section 5.-2, suggest that Si was stripped from the surface layer in the reference run primarily because the photosynthetic rate of large diatoms was never seriously limited, except for brief periods following Si exhaustion. 165
5.2 Si 1 icon Simulations
The interaction among silicon uptake, silicon regeneration and diatom growth was explored by forcing large and small flagellate biomass and dissolved inorganic nitrogen to track their observed levels over time in the surface and deep layers of CEE2. Only the dynamics of large and small diatoms and silicic acid were explicitly simulated, thereby eliminating any ambiguity due to unrealistic predictions of nitrogen or flagellate concentrations. The reference run was modified in a variety of ways to determine if Si regeneration strongly influenced Si dynamics in Foodweb I.
5.2:.l Simulation Si-1. Comparison with reference run
The parameters of the reference run were unchanged in
Si-1, except for the deactivation of nitrogen, flagellate and bacterial dynamics. This run was a "control" to which later runs were compared.
The predictions of Si-1 confirmed those of the reference run (Fig. 30; cf. Figs. 20 and 29). This was expected because diatoms were Si-limited in the reference run until after the final Si addition. Silicon in the deep layer fell even faster in Si-1 than in the reference run. By day 62 it had declined to
1.9 • 10"4 ug-atom Si l"1, whereas the reference run predicted a minimum of 8.2 ug-atom Si l"1 on day 62. This difference was a consequence of greater diatom growth in the deep layer in 166
Figure 30. Simulation Si-1. Predicted large diatom biomass (solid line) and silicic acid concentration (dashed line) in the surface 8 m. Only Si and diatom dynamics are enabled. Parameters as in reference run. Silicon (ug-at Si 1"') 167 0 30 1 i ' • ' ' • ' • i i 168
Si-1, in turn a result of lesser shading by the small biomass of flagellates observed in the surface layer before the
Ceratium bloom.
Small diatoms were quickly eliminated from the water column as was the case in the reference run and all subsequent runs. Their impact on simulated Si dynamics was therefore insignificant. In Foodweb I, however, small diatoms were probably responsible for the fall in surface Si concentrations between days 50 and 60 (Fig. 4b) during a bloom of Chaetoceros danicus and C. socialis (Fig. 2b).
5.2.2 Simulation Si-2 and Si-3. Limitation of diatom growth
To see if the rapid removal of Si could have been due to overestimation of gross photosynthesis by large diatoms, the half-saturation constant for Si-limitation of gross
photosynthesis, KrSi, was increased in runs Si-2 and Si-3. In
simulation Si-2, KrSi of large diatoms was increased to 4.00 ug-atom Si l"1 from the value of 2.06 used in the reference run and Si-1. There was virtually no difference between the predictions of Si-1 and Si-2 (cf. Fig. 30 and 31): the peak biomass of large diatoms in blooms predicted by Si-2 was never less than 89% of the analogous peak biomass in Si-1, and silicon was depleted only slighly more slowly in Si-2. Doubling
the KrSi of diatoms therefore had no significant effect on modelled dynamics.
To further limit'diatom growth, KrSi was increased to 30 ug-atom Si l"1 in simulation Si-3. Although the value of 30 169
Figure 31Simulation Si-2. Predicted large diatom biomass (solid line) and silicic acid concentration (dashed line) in the surface 8 m. 1 K rSi = 4 ug-atom Si 1" . Silicon (ug-at Si 1_1) p 30 i i ' ' i i i i i i i 171
ug-atom Si l"1 for a half-saturation constant is absurdly
high, the intent was merely to curtail gross photosynthesis:
this could have been achieved in a variety of other ways with
essentially the same result. Silicon accumulated in the surface
layer in Si-3, reaching a peak of 29.2 ug-atom l"1 after the
nutrient addition on day 20 (Fig. 32). In the deep layer, Si
fell to a minimum of 2.4 ug-atom l"1 on day 62. Over the entire
simulation the average gross photosynthetic rate of large
diatoms in the surface 8 m was 43.1 ug C l"1 day"1, only
slightly less than the 45.6 ug C l"1 day"1 predicted by Si-1.
Most of the surface Si accumulated before day 20 in Si-3; prior
to t=19.5, gross photosynthesis of large diatoms averaged 49.6
ug C l"1 day"1 in Si-3, 37% less than that predicted in Si-1.
5.2.3 Simulation Si-4 and Si-5. Variable C/Si ratio
In these simulations KrSi was reset to the original values used in Si-1. In the hope of reducing Si demand, the C/Si ratio of newly synthesized diatom biomass was made a function of the degree of Si-limitation as described in section 3.4.2 (p. 57).
That is, the C/Si mass ratio increased linearly from 1.25 with no Si-limitation to 6.25 at complete limitation.
Si-4 predicted a larger standing crop of diatoms in the surface layer than did Si-1, and silicon failed to accumulate in either the top or bottom layers (Fig. 33). Large diatom biomass in Si-4 gradually diverged from that predicted in Si-1 when silicon was strongly limiting. Si uptake per unit of diatom gross photosynthesis in run Si-4 was less than half that 172
Figure 32. Simulation Si-3. Predicted large diatom biomass (solid line) and silicic acid concentration (dashed line) in the surface 8 m. 1 KrSi = 30 ug-atom Si 1" .
174
Figure 33. Simulation Si-4. Predicted large diatom biomass (solid line) and silicic acid concentration (dashed line) in the surface 8 m. 1.25 < C/Si < 6.25 Silicon (ug-at Si I-1) p 30 i i i i i i i i i i i 176
in Si-1 whenever the ambient concentration of Si fell below 3.8
1 1 ug-atom I' (KrSi =2.06 ug-atom Si l' ). Therefore, at low Si concentrations the reduced uptake of Si per unit of gross photosynthesis more than compensated for the higher photosynthesizing biomass in Si-4. As a result, Si was not as severely depleted between nutrient additions in Si-4 as it was in Si-1. Consequently, gross photosynthesis was not as strongly limited in Si-4 and the net rate of biomass decrease was slower in Si-4 after depletion of ambient Si. This is why diatom biomass was higher in Si-4 than in Si-1.
The rapid depletion of surface-layer Si predicted in Si-4 clearly demonstrates that a variable C/Si ratio tied to the degree of Si-limitation could not have restrained Si uptake at the high ambient concentrations observed in CEE2. Is it possible that the C/Si ratio of Si-replete diatoms was underestimated in the model? P. J. Harrison et al. (1977) report a C/Si mass ratio of 4.0 for Skeletonema costatum grown under no nutrient limitation, and a ratio of 15.6 for
Si-starved cells. When these limits were employed in simulation
Si-5, large diatoms bloomed to huge concentrations, and again
Si was stripped from the surface layer (Fig. 34). By itself, a variable C/Si ratio could not account for the observed behavior of Si in CEE2. 177
Figure 34. Simulation Si-5. Predicted large diatom biomass (solid line) and silicic acid concentration (dashed line) in the surface 8 m. 4 < C/Si < 15.6 Note change of scale for diatoms. Silicon (ug-at Si 178 p 30 i i i i i i i i ' i i
Large Diatoms (ug C 1"') o 1000 (—i i ' i i i i i i i i i
CD 179
5.2.4 Simulation Si-6. Silicon uptake linked to net
photosynthesis
In all previous simulations the uptake of silicon was
calculated as a fraction of gross photosynthesis by diatoms.
This is decidedly unrealistic: several studies have found Si
uptake to be coupled to growth rather than gross
photosynthesis. The incorporation of silicon from a
metabolically active internal pool into the diatom's siliceous
valves occurs during cell division (Chisholm et al. 1978; Lewin
et al. 1966). According to the model of Davis et al. (1978),
uptake of Si from the external medium equals the rate of
incorporation into the frustule when the small internal pool is
full. Except for brief periods when the internal pool is
filling, the maximal uptake rate is regulated by the growth
rate (i.e. rate of frustule formation = rate of cell division)
of the population. In simulations Si-1 through Si-5, silicon
was taken up even if growth (i.e. net photosynthesis) was
negative provided that some gross photosynthesis occurred. To
remidy this aberration, Si uptake in simulation Si-6 was
calculated as a fraction of instantaneous net photosynthesis
whenever net photosynthesis exceeded zero.7 The fraction was
determined by the same variable C/Si ratio used in run Si-4.
The concentration of diatom carbon in the surface layer
7It might be more desireable to link Si uptake with daily rather than instantaneous net photosynthesis, but such a formulation would have been much more difficult and not worth the extra effort. 180
predicted by Si-6 exceeded that in Si-4, and once again the elevated biomass nullified the effect of lower Si uptake per unit of net photosynthesis. Si did not accumulate in the surface or bottom layers (Fig. 35).
5.2.5 Simulation Si-7. Dissolution of silica
The parameters of Si-7 were indentical to that of Si-6, except that dissolution of silica in diatom frustules was activated (see section 3.7.3, p. 104). Si uptake was linked to net photosynthesis and the C/Si mass ratio of diatoms varied between 1.25 and 6.25. Again, because of the very large diatom biomass predicted by Si-7, silicon was quickly removed from the surface layer after each nutrient addition (Fig. 36).
Dissolution of silica within the surface layer was predicted to contribute 132 ug-atom Si l"1 between t=3.5 and 80 days, compared to the 111 ug-atom Si l"1 added artificially. 25 ug-atom Si l"1 was mixed into the surface, but this was certainly overestimated because the concentration gradient between the surface and deep layers was unrealistically large; a run in which Si was constrained to follow its observed concentration in the surface and deep layers predicted a mixing influx of only 13 ug-atom Si l~l.
Unlike all previous simulation runs, dissolved Si was not depleted in the deep layer in Si-7 (Fig. 37). Interpolated to t=80 days, the observed Si concentration was 34.3 ug-atom l~l, whereas Si-7 predicted a final concentration of 26.2 ug-atom 1_1. If silicon had been mixed into the surface layer 181
Figure 35. Simulation Si-6. Predicted large diatom biomass (solid line) and silicic acid concentration (dashed line) in the surface 8 m. Si uptake linked to net photosynthesis. 1.25 < C/Si < 6.25 Silicon (ug-at Si 1~') 182 Q 30 iii' i i i—i—i—i—i 183
Figure 36. Simulation Si-7. Predicted large diatom biomass (solid line) and silicic acid concentration (dashed line) in the surface 8 m. Silica dissolution activated. 1.25 < C/Si < 6.25 Si uptake linked to net photosynthesis. Note change of scale for diatoms. Silicon (ug-at Si 1"') a 30 i ' ' ' i ' i i i i i 185
Figure 37. Predicted concentration of silicic acid in the 8-20 m layer. Silica dissolution activated. 1.25 < C/Si < 6.25 Si uptake linked to net photosynthesis. Octagons are concentrations observed in CEE2. Deep Silicon (ug-at Si 1"'; 8-20 m) 50 CD- Q
G G G G G 3 G G (3 G G 3 !G ro 4^ G CD IG G CO G G G G G
G CD CD G G G
G G
G CO CD 187
at the rate expected from the observed concentration differences between the two layers, Si-7 would have predicted a
final concentration of n34 ug-atom Si 1'1 in the deep layer.
5.2.6 Conclusions regarding Si dynamics
Except for simulation Si-3, none of the manipulations of
Si demand or regeneration successfully allowed Si to accumulate
in the surface 8 m. They failed because the overall photosynthetic rate of large diatoms was sustained at high levels by the high predicted concentration of diatoms. The
1 exception was Si-3 with KrSi set to 30 ug-atom Si l" . What is remarkable about Si-3 is that " Si accumulated to high concentrations in the surface layer despite a large-diatom gross photosynthetic rate which averaged only 5% less than that predicted in Si-1. The crucial difference in Si-3 was that photosynthesis was not sustained at an approximately constant rate but rather there was a period of low photosynthesis when
Si accumulated, followed by a spurt in growth. The main reason why Si was stripped from the surface layer in the reference run was not because there was no silica dissolution or C/Si was constant or Si uptake was linked to gross photosynthesis, but because the photosynthesis of large diatoms was never severely reduced (except for brief periods following Si exhaustion).
The importance of silica dissolution as a source of silicic acid for diatom growth remains uncertain. This uncertainty is based on three unanswered questions: 1) what was the overall rate of net photosynthesis in CEE2?, 2) what 188
proportion of total photosynthesis was contributed by diatoms?,
and 3) what was the bulk C/Si ratio of diatom biomass? Consider
the period from t=3.5 to 61.5 days examined in section 5.1.5.
Net production averaged roughly 136 to 183 ug C l"1 day"1 in
the surface 8 m. Assume for the sake of argument that diatoms
were responsible for all primary production. If we take a C/Si mass ratio of 1.25, the higher net production would require 302
ug-atom Si l_1/58 days. If instead we assume that C/Si=4 and
take the lower net production, only 70 ug-atom Si l_1/58 days would be required. The maximum quantity of Si that could have
been removed from the surface layer without requiring silicon
regeneration was 95 (artificial additions) + 10 (inferred mixing influx) - 8 (observed accumulation of surface Si) =
97 ug-atom Si l_1/58 days. This illustrates the impossibility of assessing the significance of Si regeneration for diatom growth in Foodweb I.
5.3 Phytoplankton Growth and Loss
Nutrient dynamics have received considerable attention in earlier sections. In two. key areas—rate of nitrogen regeneration and rate of silicon regeneration—interpretation of experimental evidence and model predictions hinged upon hypothesized rates of algal growth. The purpose of this section
is to come to grips with two fundamental questions concerning phytoplankton growth and loss in Foodweb I. 189
5.3.1 Have maximum rates of primary production been underestimated?
—Yes, at least for large-celled flagellates.
The maximum gross photosynthetic rates of diatoms and flagellates in the model were derived from Chan's (1978b) results for five diatom and four dinoflagellate species as described on p. 65 ' et, seq. Chan found that diatoms have much higher division rates than dinoflagellates of equal cell protein. Although large-celled flagellates had a mean cell diameter 29% smaller than large-celled diatoms (section 3.4.1, p. 51), the maximum specific gross photosynthetic rate of large flagellates was nevertheless only 36% of that of large diatoms in the model (Eq. 18, p. 69). Because of this small photosynthetic rate, large flagellates increased slowly throughout the reference run (Fig. 23). Is such a low maximum photosynthetic rate credible? Two arguments suggest not.
First, the model's predicted net rate of change of large flagellate biomass was compared to observed net rates of change in the surface 8 m of CEE2. A simulation was run in which all model dynamics were disabled so that phytoplankton biomass and nutrient concentrations followed their observed levels " over time (parameter values were those of the reference run). The model calculated the net rate of change of large-flagellate carbon expected at each time step; the average net rate of change over successive 24-hour intervals was then plotted to eliminate day-night oscillations (Fig. 38). Also plotted in
Fig. 38 are observed net rates of change, calculated as 190
Figure 38. Specific net growth rate of large flagellate carbon in the surface 8 m of CEE2. Continuous line is daily avarage rate predicted by a simulation in which all state variables traced their observed concentrations. Unconnected lines represent average rate of change between observations of flagellate carbon. Specific Growth Rate (day-') -2 2 CD- -J 1 1 L -J I I L.
4 ro CD
ro
CD CL CO CO
CD. CD
CO CD 192
[ln(Bri+l / Bri)] / (tri+l - tri),
(40)
where Bri and Bri+1 are large flagellate carbon observed at the
+ successive sampling times tri and tri l. Predicted net rates of change were much smaller than observed rates. It is unlikely
that specific loss rates from large flagellates have been
greatly overestimated in the model, or that growth limitation was excessive, and thus the large disagreement between predicted and observed net growth would seem to be due to
underestimation of Prmax for large flagellates.
The second argument is based on observed rates of 14C
fixation normalized to phytoplankton carbon (Fig. 15). The
first thing to note is that there appears to be no systematic difference between the specific 1*C fixation rates of diatom- and flagellate-dominated communities in CEE 2 or 3. Yet the predicted net rate of growth (all dynamics disabled) of large
flagellates was substantially smaller than that of large diatoms (Fig. 39). This was also true for predicted gross or net photosynthesis. One might object that flagellates respire more of their fixed carbon than diatoms, therefore yielding a
lower growth rate for flagellates in spite of equal 14C
fixation rates. This objection is generally not supported by
the literature (see section 3.4.4, p. 71).
The second interesting feature of Fig. .15 is the extremely high specific fixation rates occasionally observed. Some are much larger than the values of 0.03 - 0.11 h"1 used for maximum specific gross photosynthesis in the model. Eppley (1972, his 193
Figure 39. Specific net growth rates of large diatoms (solid line) and large flagellates (dashed line) in the surface 8 m predicted by a simulation in which all state variables were interpolated between their observed concentrations. Parameters as in reference run. Daily averages are plotted.
195
Eq. (1)) predicts a maximum growth rate of 0.058 hr1 at 13.5 °C
(the mean water temperature in CEE2), which, after scaling upwards by a factor of 2.4 to roughly account for respiratory and excretory losses as described on p. 68, still falls short of the highest observed fixation rates. The highest growth rates tabulated by Goldman et al. (1979) for natural marine waters are those of Sheldon and Sutcliffe (1978), who found doubling times of 3.35 h (22 °C) and 2.91 h (27 °C) for
Sargasso Sea microplankton. Normalized to 13.5 °C (following
Eppley 1972) and scaled by a factor of 2.4, these rates equal gross photosynthetic rates of 0.29 and 0.24 h'1, eclipsing all but the highest fixation rates in Fig. 15. Thus the specific rates of 14C fixation determined for CEE 2 and 3 are not ridiculously high. Were they nevertheless overestimated? If they were, then either 14C fixation was overestimated or phytoplankton carbon was underestimated. Measurements of carbon and chlorophyll in the CEEs suggest that phytoplankton carbon was not underestimated because 1) phytoplankton carbon commonly made up >50% of particulate organic carbon, thus was not systematically low, and 2) the mean C/Chlorophyll-a ratio of phytoplankton assemblages with >0.1 h_1 specific 1*C fixation was 32.2 (range 12.2 - 88.2), which is quite reasonable for vigorously growing phytoplankton (Antia et al. 1963).8 14C
However, carbon or chlorophyll in the CEEs may have been less than that in the incubation bottles under circumstances favorable to production when significant synthesis of material occurred during, the 4-hour incubation. The importance of this source of error is unknown. 196
fixation specific to chlorophyll-a (and corrected to 13.5 °C)
was <10 mg C (mg Chl-a • h)_1 which is high but within the
range reported by Harrison and Piatt (1980). To conclude, the
specific 14C fixation rates shown in Fig. 15 are high but not
absurd.
Since many of the observed rates of 14C fixation were much higher than modelled rates, considerable latitude exists for
upward adjustment of modelled maximum gross photosynthetic
rates of diatoms and especially flagellates. Some upward adjustment was also suggested on p. 154 as being necessary to account for the high bacterial remineralization rates reported by P. J. leB. Williams.
5.3.2 Have losses or growth limitation been underestimated?
—Yes, intermittently.
The most .serious fault of the model is its prediction of
sustained positive net growth of large diatoms and large
flagellates in the surface 8 m. This is vividly demonstrated by the simulation in which all phytoplankton and nutrient dynamics were disabled in order to predict the net change of phytoplankton biomass as a function of observed nutrient and phytoplankton concentrations at each time step. Large-celled phytoplankton growth (averaged over 24-hour periods to remove day-night oscillations) was predicted to be negative for less than 9% of the simulated 76.5 days (Fig. 39). This is in marked contrast to the extensive periods of decline characteristic of the observed phytoplankton populations (Figs. 2a, 2c, 38). 197
Net phytoplankton growth could be sustained at high levels
only if the maximum photosynthetic rate was consistently high and growth limitation and losses (grazing, exudation, sinking,
respiration) were consistently low. These conditions were
satisfied in the simulation. Prmax was constant, light severely
limited growth on only a few scattered occasions (Fig. 40), and
losses from large-celled phytoplankton were not enough to
reduce standing crop in the absence of strong growth
limitation. The acute disagreement between the model's predictions of sustained growth and observations of dramatic declines of phytoplankton biomass suggests that phytoplankton
growth and loss were episodic in Foodweb I. This is
investigated more fully in section 6.
Two other pieces of circumstantial evidence support the
notion that there were disjunct episodes of pronounced algal activity and loss in CEE2. First, section 5.2.6 concluded that
silicic acid could have accumulated in the surface layer of
CEE2 only when the production of new diatom biomass was sharply curtailed. Indeed, Si was observed to accumulate most rapidly during the final half of the collapse of large-celled diatoms.
Second, section 5.1.5 noted that the influx of carbon to bacteria in the surface layer could have required as much as
74% of net primary production. Although this estimate is highly uncertain, if it were even approximately correct then most of this influx to bacteria must have occurred through the degradation of inactive cells following spurts of algal growth
if the observed periods of rapid net algal growth are to be 198
Figure 40. Predicted limitation of large diatom growth in the surface 8 m due to light, dissolved nitrogen and silicon. Values predicted by a simulation in which all state variables traced their observed concentrations. Gross photosynthesis is most limited for plotted values near zero, non-limited for values near unity. Values of light-limitation have been averaged over successive 24-hour intervals to remove day-night oscillations. Predicted limitation for other algal groups is similar, except flagellates were not Si-limited.
200
accounted for. Bacteria in the surface layer did in fact increase to their highest concentration during the collapse of large-celled diatoms; P. Parsley (pers. comm.) observed clumps of Stephanopyxis cells heavily colonized by bacteria during the collapse. 201
6. WHAT WENT WRONG?
It is abundantly clear that the model developed in
section 3 could not account for even the major features of phytoplankton and nutrient dynamics in CEE2. The model's most
spectacular failure was its prediction of sustained positive net growth of large-celled diatoms and flagellates in response to observed time series of nutrients, light, and zooplankton.
.The predicted biomass of large-celled diatoms using the parameter values of Table IV exemplifies this failure
(Fig. 41). What went wrong?
The reason for the failure was alluded to in
section 5.3.2: phytoplankton growth and loss was episodic in
CEE2 whereas the model assumed constant maximum photosynthetic
rates, constant sinking and respiration rates, a constant
fraction of photosynthesis lost via exudation. It had been originally thought that fluctuations in grazing pressure, nutrient concentrations and light levels were directly
responsible for the observed changes in phytoplankton biomass,
rather than fluctuations in the internal physiology of the
phytoplankton (i.e. changes in Prmax, KrN, KrSi, sinking rate, etc.). The model's failure prompted a reexamination of this premise. To limit the scope of investigation to a manageable size, a test case was considered: could the model be adjusted to quantitatively account for the observed bloom and collapse of large-celled diatoms in the bubbled layer .of CEE2? It is shown below that the modelled physiology of large diatoms could be adjusted to account for their bloom and collapse, but the 202
Figure 41. Biomass of large-celled diatoms predicted by a simulation in which the concentrations of nutrients, small diatoms, and large and small flagellates were interpolated between observations. Parameter values as in Table IV.
a.) observed (octagons) and predicted (solid line) biomass of large diatoms in the surface 8 m of CEE.2 b) residuals 203
0 20 40 60 80 Time (days)
• A * # residuals ® • • • • in +-ro• I —I cu tin
h
-T~WI—,—r~ 20 "i—i—i—i—|—l—i—i—r -1—i—I—i—|—r—i—i—i—|—i—i—i—i—| 40 60 ' 80 Time (days) 204
reasons underlying the changes in physiology remain hidden.
6.1 The Bloom and Collapse of Large-Celled Diatoms
Between days 13 and 51, large-celled diatoms in CEE2
(mainly Stephanopyxis turris) bloomed to huge concentrations,
formed resting spores, and collapsed (Fig. 42). Davis et al.
(1980) describe a similar sequence of events which occurred in
the 1977 CEPEX experiment. As Leptocylindrus danicus bloomed,
nitrate + ammonium concentration fell below 0.5 ug-atom N l"1,
and the majority of cells formed resting spores. The spores
then quickly sank out of the water column. The approach taken
here was not to explain what caused spore production in the
1978 experiment, nor to create a general model of phytoplankton
bloom and collapse. Instead, the period from days 13 to 51 was
split into a bloom phase (days 13 to 25) and collapse phase
(days 25 to 51); model parameters were separately adjusted to
fit predicted large-diatom biomass to observed biomass in each
phase.
6.2 Parameter Estimation Technique
The model used to predict diatom growth was that developed
in section 3; only the dynamics of large-celled diatoms in the
surface 8 m were activated; large-celled diatoms below 8 m, as well as small-celled diatoms and large and small flagellates, nitrogen and silicon tracked their observed concentrations over 205
Figure 42. Observed concentration of vegetative cells (0-8 m depth; solid line) and resting spores (0-20 m depth; dashed line) of large diatoms in CEE2. Spores (ug C 1"') 0 25 i i i i i i i i i i i 207
time.1 Although spore dynamics per se were not modelled, spore
production was indirectly simulated by increasing, for example,
the sinking rate of vegetative cells and reducing their
photosynthesis.
The objective was to find values for model parameters
which allowed simulated large diatom biomass to most closely
fit the observed bloom and collapse. The "closest fit" was
defined as the minimum sum of squared deviations (minimum SSQ)
between predictions and observations over the interval of
interest. This minimum was found by the technique of system
identification (Bard 1974; e.g. Parslow et al. 1979), whereby a
simulated time series of large diatom biomass in the surface
layer was generated from initial parameter guesses, then the
parameters were systematically altered to minimize the sum of
squared deviations between the simulated and observed time
series. In particular, only the values of three parameters were
so altered: 1) the maximum gross photosynthetic rate of
large-celled diatoms, Prmax, 2) the half-saturation constant of
nitrogen limitation of gross photosynthesis, KrN, and 3) the
Unlike section 5, nutrient concentrations in section 6.3 were linearly interpolated between observations without accounting for expected increases after nutrient additions. The error introduced by this simpler method of interpolation does not affect the conclusions of section 6.
2S is the effective sinking rate in the surface bubbled layer, which is less than the true sinking rate measured in undisturbed water. S can be more generally viewed as an unspecified loss of carbon from large diatoms: this unspecified mode of loss can be expressed in units of day1 by dividing S by the surface layer depth of 8 m. 208
sinking rate, S.J Zooplankton grazing rates were not altered
because there was no evidence to suggest nor any reason to
expect that the functional response of grazers changed between
the bloom and collapse phases. The choice of which parameters
of diatom physiology to alter was largely arbitrary. Closer
fits of predictions to observations might have been achieved by
varying parameters other the three mentioned above. However, as
the number of parameters to be varied increases, computational
time rises exponentially, and worse, the likelihood of finding
the global minimum SSQ can become very small. Considering that
the whole point of the exercise was to demonstrate the
inadequacy of the original model by showing that at least some physiological parameters formerly thought to be constant actually varied over time, it was unnecessary to simultaneously vary more than three parameters to achieve close fit of predictions to observations.
Two methods were used to find minimal SSQs. The simplest method, practical when varying only one or two parameters, was to search the SSQ surface over a regular grid of parameter values. This was used to construct contour plots of SSQ over two-dimensional parameter space. The second, more sophisticated method was Powell's direct search using conjugate directions
(see pp. 75-78 of Fletcher 1972). Prmax, KrN and S were bounded below by 0 day-1, 0 ug-atom N l"1 and 0 m day"1, respectively, and were bounded above by limits imposed before each search. To simplify the search, these parameters were transformed by the inverse sine function to yield a problem of unconstrained 209
optimization (see pp. 42-43 of Powell 1972). "Convergence" of
the search was assumed to occur when the transformed variables
changed by less than 10"* in magnitude on successive
iterations.
In practice one may be unable to find the global minimum
SSQ, i.e. the closest possible fit, because of the complexity
of the SSQ surface. This is what happened here. With
sufficiently different initial guesses, Powell's method
converged to different final estimates of "optimal" parameter
values. These local minima had similar SSQs and were located at
the bottom of the same valley of low SSQ, meaning that several
combinations of parameter values yielded predictions which fit
the observations equally well and that the different parameters
were highly correlated.
6.3 Results and Discussion
6.3.1 The diatom bloom, days 13 to 25
Two searches using Powell's method converged to
neighboring points in parameter space with very similar SSQs
(Table V). Effective sinking rate was low, nO.l m day"1 (= loss
1 rate of 0.0125 day" from large diatoms), KrN <0.07 ug-atom N
1 1 l" , and Prmax was moderate, nl.7 day" . Fig. 43, a contour
graph of the SSQ surface sliced at S = 0.11 m day"1, shows that
1 1 for Prmax near nl.6 day" , KrN values <0.4 ug-atom N l" had virtually no effect on closeness of fit. This implies that
diatom growth in CEE2 was not limited by nitrogen if KrN <0.4 V \
Table V. Sum of squared deviations between predicted and observed large-diatom biomass in the surface 8 m of CEE2 for different parameter values. o
tne CM The maximum gross photosynthetic rate (Pmax)> half-saturation constant of N-limitation (K^) , and the effective sinking rate (S) in the surface bubbled layer were simultaneously varied from initial guesses using Powell's method (see pp. 75-78 of Fletcher 1972). This method converged to final parameter values which yielded close fits to observed large-diatom biomass. Two periods—the large diatom bloom (days 13 to 25) and collapse (days 25 to 51)—were separately analyzed.
Pmax (day-1) ^ (us-at N s (m day-1) SSQ
upper upper upper ^ 2-2 initial bound final initial bound final initial bound final (10 (ug C) 1 )
Bloom
1.74 5 1.63 0.00 5 0.24 0.32 40 0.11 7.04 2.09 5 1.80 0.77 5 0.67 0.32 40 0.00 7.36
lollapse 0.50 2 2.00 0.77 5 0.58 15.00 40 4.26 5.37 0.50 2 2.00 4.50 5 0.78 3.00 4 3.97. 5.23 0.00 5 4.76 0.77 5 0.76 2.00 40 8.42 4.22 211
Figure 43. SSQ surface for period of large diatom bloom, days 13 to 25. S = 0.106 m day1 . Contour interval is 10s (ug C l'1)2. Star marks local minimum of 7.045 • 10* (ug C l"1)2 at 1 1 Prmax=1.63 day" , KrN=0.242 ug-atom N l" . Contoured grid was 10 by 10 points.
213
1 ug-atom N l' . When the SSQ surface is sliced at KrN = 0.24 ug-atom N l"1, a narrow valley of low SSQ is seen to run from
1 1 1 Prmax «1.5 day" , S n0 m day" to Prmax a5 day" , S n6.5
1 m day" (Fig. 44). Since Prmax and S are opposing terms of gain and loss from large diatom biomass, the prediction of such a valley was expected: as long as S was large enough to
counteract an explosively high Prmax, growth of diatoms closely mimicked observations during the bloom (Fig. 45).
6.3.2_ The diatom collapse, days 25 to 51
When the same parameter values used in the period between days 13 and 25 were applied to the period after day 25, the simulated biomass of large diatoms skyrocketed from a starting concentration of 687 ug C l"1 on day 25, in violent disagreement with the observed collapse (Fig. 46). When parameter values were systematically searched for close fits to the observed collapse, a suprising result was obtained
(Table V): Powell's method consistently converged to Prmax
values at or near the upper bound placed on Prmax, with S also being very high to counteract the large maximum photosynthetic rate. The fits were good (e.g. Fig. 47), but difficult to reconcile with the observed accumulation of silicic acid in the surface layer during the large diatom collapse. With such high
Prmax values, the primary production of diatom biomass would rapidly strip Si from the surface layer unless Si was very quickly regenerated. This seems unlikely, although Nelson and
Goering (1978) speculate that silicic acid may be more rapidly 214
Figure 44. SSQ surface for period of large diatom bloom, days 13 to 25. 1 KrN = 0.242 ug-atom N l" . Contour interval is 10s (ug C l"1)2. Contours above 10 • 10s (ug C l-1)2 not plotted. Contoured grid was 11 by 11 points. 215
Sinking Rate (m day-1) 216
Figure 45. Concentration of large diatoms in surface 8 m 1 predicted with Prmax = 1.63 day , KrN=0.242 ug-atom N S=0.106 m day1; days 13 to 25. Octagons are observed concentrations. SSQ = 7.045 • 10* (ug C 1"1)2 .
218
Figure 46. Concentration of large diatoms in surface 8 m 1 predicted with Prmax=1.63 day" , KrN=0.242 ug-atom N S=0.106 m day"1; days 25 to 51. Octagons are observed concentrations. SSQ = 2.131 • 107 (ug C 1"1 )2 . Large Diatoms (ug C i"1) 1000 i—I_J i i i i i i i i i i
ro CD
G G G Q
Q ro Q CD Q G CO G G 19
CD CD
CO O 220
Figure 47. Concentration of large diatoms in surface 8 m 1 predicted with Prmax=2.00 day , RrN=0.783 ug-atom N l"1, S=3.97 m day1; days 25 to 51. Octagons are observed concentrations. SSQ = 5.229 • 10* (ug C 1"1 )2 .
222
regenerated than combined nitrogen in some marine 'ecosystems.
Interestingly, observed 14C fixation rates (as ug C l"1 h~1 or ug C (ug Chl-a • h)"1) were similar during the periods of
bloom and collapse. Thus, Prmax values in the collapse phase
which are comparable to bloom Prmax's cannot be dismissed out
1 of hand, but a high collapse Prmax of, for example, 4.76 day"
(Table V) is biologically nonsensical.
6.4 Conclusions Regarding Physiological Variability
It is evident that the model could be tuned to fit the separate periods of large diatom bloom and collapse in CEE2.
The important point is that significant alterations of at least some physiological parameters were necessary to achieve this fit. Although grazing pressure could have been similarly altered to achieve a close fit to the bloom and collapse of large diatoms, there was no evidence to support such an ad hoc manipulation. One must therefore conclude that fundamental shifts in the physiology of large diatoms were responsible for their collapse, rather than a prolonged increase in grazing pressure or prolonged shortage of nitrogen, silicon or light.
What triggered the diatom collapse, and what was responsible for their decline? It seems fairly certain that a rapidly growing population of Stephanopyxis turris, which comprised 99% of large-diatom carbon at the height of the bloom, removed all dissolved inorganic nitrogen from the surface layer shortly before day 25. The population experienced a sudden nutrient shock which induced a portion of the 223
S. turris population to form spores. On day 25, peak numbers of
both vegetative cells (263 per litre, 0-8 m) and spores (15 per
litre, 0-8 m) were present. This description is similar to that
proposed by Davis et al. (1980) for the initiation of a
collapse of Leptocylindrus danicus in an enclosed ecosystem in
1977. Davis et al. (1980) note that the number of vegetative
cells + spores of L. danicus was greatly reduced following
spore formation, and they imply that the difference could be
accounted for if "spore formation [in L. danicus] is a sexual
process requiring several spermatogonial cells and perhaps
several oogonial cells for each successful [resting-spore
forming] auxospore." This same argument cannot explain the
marked decline of vegetative biomass of large diatoms in CEE2
because spore formation in Stephanopyxis turris is asexual with
each vegetative cell capable of forming one resting spore
(von Stosch and Drebes 1964; Drebes 1966). Only a small
proportion of large-diatom cells formed resting spores
immediately after nitrogen exhaustion, as indicated by the
significant biomass of vegetative cells during the collapse
(Fig. 42). Therefore, some factors other than spore formation must have been partly responsible for the observed collapse of
Stephanopyxis.
To assess potential grazing, sinking, and mixing losses of vegetative carbon, a simulation was run using the parameter values of Table IV (except effective sinking rate in the bubbled surface layer was set at 1/4 of the 1.58 m day'1 rate observed by Bienfang (1980) for the >20 um fraction settling in 224
unbubbled water on day 32) and constraining phytoplankton to their observed concentrations. The predicted loss of large-diatom carbon from the surface layer between days 25 and
51 was 194 (sinking) + 413 (grazing) + 13 (mixing) = 620 ug C
1-V26 days. (Grazing may have been overestimated by a factor of n2 and sinking underestimated by a factor of n2.) A loss of
620 ug C l-1 almost equals the observed decline in large diatom biomass of 682 ug C l"1, and the difference could easily be made up by resting spore formation. Unfortunately this balance neglects any primary production which may have occurred. The 1*C productivity in the surface 8 m between days
25 and 51 was 30.3 ± 27.5 ug C l_1/4 hours (mean ± s.dev., n=23) • 2.4 (see footnote 5 on p. 151) • 26 1.9 mg C l~1/26 days. If this average is roughly representative of the net primary production of large diatoms in the surface layer (large diatom carbon averaged 79% of total photosynthetic carbon between days 25 and 51), then a huge loss of 1.9 + 0.7 -
0.6 = 2.0 mg C 1-V26 days is unaccounted for. Some of the carbon was undoubtedly lost through spore formation, but other avenues of loss such as exudation or cell lysis must have been important. In any event, the large diatoms collapsed in CEE2 because of a dramatic change in physiology: either their cell growth was arrested or cell lysis or exudation was greatly accelerated.
None of this explains, however, why large diatoms continued to decline after nutrients had regained their former high concentrations. Fig. 40 demonstrates that with 225
half-saturation constants of 0.77 ug-atom N l"1 and 2.06 ug-atom Si l"1, growth would have been limited to no less than
60% of maximum after day 27. With a saturation irradiance (Irk) of nlOO - 200 uEin nr2 s_1 as modelled, the several cloudy days after August 9 (day 32) would have intermittently limited growth (in the surface layer at local apparent noon) to no less than 42% of maximum. Why didn't the vegetative cells resume rapid growth? One could, of course, vacuously postulate that some required growth factor was absent during the large-diatom collapse. On the basis of available evidence, however, it appears that large-celled diatom growth in CEE2 was "episodic."
In this context episodic growth means that distinct changes or
"flips" in cellular physiology occur, and that after a flip the population may be locked into a new physiological state for an extended length of time even if the environment changes and favors the earlier physiological state. An example of such a flip is when an entire population forms resting spores. Other examples are dramatically increased growth rates of diatom cells recently derived from auxospores (Davis et al. 1973;
Costello and Chisholm, unpublished) or abrupt shift-down of bacterial metabolism in cultures which have exhausted a carbon or nitrogen source (Schaechter 1973). The notion of vegetative cells "locked into" certain physiological states is unconventional and runs counter to the common wisdom of
"adaptive plasticity;" the author is unaware of published work supporting this notion, except for populations which have been severely growth-limited for one or more months (Davis et al. 226
1973). The various unanswered questions regarding the protracted decline of large-celled diatoms in the surface of
CEE2 suggest that blanket terms used to describe collapsing algal populations, like "senescent" or "moribund," conceal more than they explain. What is completely lacking at present is a quantitative understanding of the collapse of algal blooms. 227
7. FINAL CONCLUSIONS
One might wonder why the attempt to model phytoplankton-nutrient dynamics in Foodweb I was such a failure when models of other marine systems, using similar methods, have been reasonably successful. How have these other models
"gotten away with" assuming smoothly changing physiological states when it was necessary to invoke an abrupt change in diatom physiology to even roughly account for some events of
Foodweb I? This question can perhaps best be answered by considering two widely cited simulation models of coastal marine ecosystems.
Consider first the Narragansett Bay ecosystem model of
Kremer and Nixon (1978). Kremer and Nixon simulated phytoplankton, zooplankton and nutrient dynamics over a 1-year period (starting January 1) in eight spatial elements linked by tidal flushing and exchange of water between elements. Most biological parameters were exponential functions of water temperature: maximum growth rate of phytoplankton, maximum ration per zooplankter, zooplankton respiration, rate of juvenile development, nutrient regeneration. Temperature changed seasonally between limits of 3 °C on February 9 and
20 °C on August 9. The standard model run realistically predicted both the magnitude and timing of the spring bloom of phytoplankton in Narragansett Bay and roughly accounted for the general features of the summer buildup of zooplankton and winter decline of both phytoplankton and zooplankton. When water temperature was held constant the seasonal cycle was 228
destroyed. Phytoplankton quickly bloomed and after a series of
damped oscillations reached approximate equilibrium by July;
phytoplankton biomass declined only slightly in November and
December due to reduced light. When water exchange was
eliminated and the eight spatial elements were simulated as
isolated systems, phytoplankton and zooplankton oscillated
wildly in each element and the model failed to predict the
declining standing crop of algae towards the mouth of the bay.
Alteration of biological parameters did not strongly influence
the predicted seasonal cycles or spatial trends. These results
imply that several major biological features of Narragansett
Bay—the seasonal cycle of phytoplankton and zooplankton
biomass, reduced plankton biomass towards the bay's mouth, a
large measure of the ecosystem's stability—were controlled by
the physical factors of temperature, water movement, and to a
lesser degree, light. Kremer and Nixon's model was least
successful in its prediction of plankton and nutrient dynamics
in summer when temperature and light were not strongly
limiting. It was precisely in this period that manipulation of
biological parameters most affected model predictions. The
lesson to be learned from the Narragansett Bay model is this:
The traditional assumption of smoothly changing phytoplankton
physiology is most successful when predicting gross seasonal
changes forced by physical factors, but is woefully inadequate whenever biological interactions dominate.
Winter, Banse and Anderson (1975) did not examine seasonal changes, but instead simulated phytoplankton growth within the 229
central basin of Puget Sound over 75 and 35 days in the spring
of 1966 and 1967, respectively. Puget Sound is generally
characterized by a seaward flow of low-salinity water at the
surface complemented by subsurface movement of more saline
water into the Sound from the Strait of Georgia. Winter et al.
(1975) modelled algal growth in the surface 30 m in response to
computed vertical mixing and advection and daily observations
of insolation, nitrate distribution and herbivore
concentration. Phytoplankton physiology was simulated in a
conventional manner. Winter et al. (1975) successfully predicted not only the general chlorophyll concentration within
the central basin but also the recurring blooms seen in both years. They concluded that "algal growth in the central basin
is limited by a combination of hydrodynamic factors ... and the modulation of the underwater light intensity by self-shading and by inorganic particulates." As with Kremer and Nixon's model, Winter et al.'s model was successful in so far as physical factors (estuarine circulation, tidal mixing, light attenuation) dominated ecosystem dynamics. Their success emboldened Winter et al. (1975) to state "We conclude that the
functions and parameters traditionally employed to describe phytoplankton metabolism are marginally adequate for use in a short-time scale model, such as the one developed here." The difficulties encountered in this thesis -demand that Winter et al.'s statement be qualified: 'Traditional descriptions of phytoplankton metabolism are marginally adequate provided that physical factors largely force biological dynamics. They are 230
entirely inadequate otherwise.'
So we have come full circle. It was originally supposed that phytoplankton could be modelled as passively changing their physiological state in concert with changes in irradiance and nutrient concentration. The enclosed water columns of
Foodweb I were thought to be ideal systems in which to confirm that phytoplankton biomass indeed varied with on-going changes in light, nutrients and grazing pressure. Modelling of events was facilitated by an extensive and detailed body of observation and the absence of large-scale advection. However, the removal of advection and the short duration of the experiment allowed biological interactions to dominate the enclosed water columns, thereby defeating the original modelling attempt. The term "biological interaction" is, of course, merely a catch-all for poorly understood physiological processes in plankton. Why did Stephanopyxis continue to collapse when nutrient and light levels and grazing were apparently favorable for growth? Why didn't Ceratium bloom earlier than it did? These and similar questions cannot be resolved by techniques which have traditionally been used to understand physically-dominated systems such as the spring bloom in temperate latitudes or upwelling ecosystems. 231
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Appendix 1. Species composition of the four phytoplankton groups distinguished in the simulation model. Included are cell size (equivalent spherical diam• eter), cell carbon, and average concentration of each species observed in CEE2 between days 2 and 79. taxon cell diam. cell carbon concentration (um) (pg C) (ug C/l)
Group 1. Large-celled diatoms.
Actinoptychus undulatus 23.0 290. .00999
Asterionella -japonica 15.4 116. .635
Biddulphia lonqicruris 66.4 3225. 1.08
Cerataulina berqoni i 30.4 545. 1.07
Chaetoceros constrictus 15.7 122. 4.06 convolutus 28.4 466. .263 dec ipiens 25.0 350. .585 didymus 21.0 235. .200 lac iniosus 20.0 210. .442 similis 15.4 115. .193
Coscinodiscus excentr icus 52.1 1859. .568
Ditylum brightwelli i 47.4 1500. .0227
Eucampia zoodiacus 21.8 258. .721
Grammatophora marina 18.1 168. .295
Leptocylindrus danicus A 18.9 186. .410
Licmorpha abbreviata 30.8 562. .130 257
Appendix 1. (continued) taxon cell diam. cell carbon concentration (um) (pg C) (ug C/l)
Group 1. (continued)
Navicula sp. A 48.3 1563. 1.65
Nitzschia sp. A 15.8 123. .225
Pleurosigma fasicola 38 .9 955. .0427
Rhizosolenia delicatula 21.4 246. .0724 fragilissima 32.8 650. .253 set igera 28.2 460. 1.19 stolterfothii 26.1 387. .0282 styliformis 59.2 2485. .00861
Schroderella delicatula 26.6 402. 1.85
Skeletonema costatum A 15.9 125. .112
Stephanopyxis turris 60.2 2583. 77.2
Striatella unipunctata 57.9 2364. .108
Thalassionema nitzschiodes 18.0 165. 5.40
Thalassiosi ra dec ipiens 29.8 522. 1.84 nordenskioldi i 23.0 290. 2.28 polychorda 37.4 874. 7.04 rotula 41.7 1121. 5.18
Tropidoneis anarctica 131.9 15350. 1.51 Appendix 1. (continued)
taxon cell diam. cell carbon concentration (um) (pg C) (ug C/l)
Group 2. Small-celled diatoms.
Amphiprora sp. 12.7 74.4 .148
Chaetoceros sp. A 11, 55.2 3.27 sp. B 4, 6.32 .0473 compressus 14, 92.8 .143 danicus 9, 38.4 1.81 debilis 14, 94.1 .228 densus 14, 97.7 .454 gracilis 8, 30.7 .0392 radicans 12, 65.8 .0267 septentrionale 8, 30.3 .145 soc ialis 11, 55.2 5.71 subtilis 7, 20.0 .00623
Cylindrotheca closterium A 9.5 38.9 .971 closterium B 4.9 8.61 .209
Fragilaria crotonensis 11.2 56.8 .0396
Leptocylindrus dan icus B 10.8 52.3 .0250
Navicula sp. B 10.9 52.7 .418
Nitzschia delicatissima 8.8 32.4 1.16 palea 8.0 26.1 1.42 pungens 12.1 67.3 .691
Skeletonema costatum B 11.3 57.1 1.60
Thalassiosira subtilis 10.5 48.5 .145
Group 3. Large-celled flagellates.
Cerat ium furca 36.3 2236. 1.23 fusus 36.1 2210. 82.8 259
Appendix 1. (continued) taxon cell diam. cell carbon concentration (um) (pg C) (ug C/l)
Group 3. (continued)
Dinoflagellates, unidentified A 25. 848. .835 unidentified B 35. 2033. .362 unidentified C 45. 3906. .594 unidentified D 55. 6578. 1.93
Dinophysis acuminata 34.1 1905. .726
Distephanus speculum 16.4 281. .187
Glenodinium danicum 21.0 536. .252
Gonyaulax longispina 37.1 2370. .884 spini fera 30.9 1469. .0794
Scrippsiella trochoideum 19.9 470, .422
Group 4. Small-celled flagellates.
Apedinella radians 7.0 31.6 .218
Chroomonas amphioxeia 7.3 34.3 1.37 baltica 10.4 86.1 .101 minuta 4.7 11.1 .401
Chrysochromulina sp. 4.0 7.08 .978 kappa 6.5 26.0 3.33
Chrysophyte, unidentified 6.6 26.7 .180
Cryptophyte, unidentified 10.9 98.3 .0205
Dinobryon sp. 4.6 10.5 .0139 260
Appendix 1. (continued) taxon cell diam. cell carbon concentrat ion (um) (pg C) (ug C/l)
Group 4 (continued)
Dinoflagellate, unidentified E 15. 225. .754
Green alga, unident i f ied 14.8 217. .00785
Gymnodinium punctatum 14.4 201. 2.78
Flagellates, unidentified A 3.5 5.13 2.85 unidentified B 7.5 34.4 .457 unidentified C 15.0 225. .107
Katodinium rotundatum 12.3 133. .0418
Leucocryptos sp. 7.7 39.5 .0704
Ochromonas sp. 8.7 54.5 2.83
Pachysphaera sp. 7.6 38.1 .0219
Prorocentrum micans 14.5 206. .00150
Pyramimonas micron 6.4 24.5 .385
Prymnesium parvum 7.1 32.4 .0286 APPENDIX 2
Appendix 2. Zooplankton taxa tracked by the main model. Included are estimated or measured body carbon (see notes at end of appendix) and feeding select• ivities (see section 3.5.2 for details) on phyto• plankton and bacteria. No selectivities are shown for non-feeding taxa or taxa assumed to be strictly carnivorous. "-" indicates a selectivity of zero.
taxon body weight selectivity1 (ug C) LD SD LF SF B
Copepods:
Acart ia2 Nl .0220 N2 .0374 N3 .0719 .019 .433 .010 .671 - N4 .124 .032 .577 .021 .748 - N5 .193 .047 .696 .037 .792 - N6 .276 .063 .786 .055 .812 - CI .350 .076 .840 .072 .817 - C2 .538 .104 .919 .111 .810 - C3 .827 .138 .966 .166 .781 - C4 1.44 .194 .972 .261 .717 - C5 2.52 .265 .916 .387 .628 - C6 male 3.70 .322 .846 .487 .558 - C6 female 6.04 .404 .730 .623 .465 -
Calanus pacificus3 CI 2.72 .276 .904 .406 .615 - C2 5.44 .385 .757 .594 .485 - C3 14.3 .566 .497 .847 .308 - C4 27.2 .688 .336 .959 .209 - C5 47.6 .780 .222 .992 .141 - C6 male 35.0 .732 .281 .982 .176 - C6 female 68.0 .824 .165 .978 .106 -
Centropaqes4 CI 1.14 .169 .977 .217 .747 - C2 1.75 .217 .960 .302 .688 - C3 2.70 .275 .905 .404 .616 - C4 4.68 .360 .793 .552 .514 - C5 8.23 .460 .648 .709 .406 - C6 male 12.0 .533 .543 .807 .337 - C6 female 19.7 .628 .414 .911 .256 - Appendix 2. (continued)
taxon body weight selectivity (ug C) LD SD LF SF B
Corycaeus 5 Nl .0105 N2 .0178 N3 .0343 .008 .265 .003 .540 - N4 .0590 .015 .384 .008 .639 - N5 .0922 .024 .497 .014 .709 - N6 .132 .033 .594 .023 .756 - copepodid .680 .122 .949 .139 .797 - C6 male 2.08 .239 .942 .341 .661 - C6 female 2.88 .284 .894 .421 .604 -
Epilabidocera4 CI .998 .156 .975 .195 .763 - C2 1.53 .201 .969 .274 .708 - C3 2.36 .256 .926 .371 .639 - C4 4.09 .338 .824 .514 .539 - C5 7.19 C6 male 10.5 C6 female 17.2
Oithona5 Nl .00438 N2 .00743 N3 .0143 .003 .129 .001 .375 - N4 .0246 .006 .205 .002 .477 - N5 .0384 .009 .288 .004 .561 - N6 .0548 .014 .366 .007 .626 - copepodid .320 .071 .820 .065 .816 - C6 male 1.12 .167 .977 .214 .750 - C6 female 1.20 .174 .977 .227 .741 -
Oncaea 7 copepodid .360 .077 .846 .074 .817 - C6 male .880 .144 .970 .175 .775 - C6 female 1.08 .164 .977 .208 .754 - Appendix 2. (continued)
taxon body weight selectivity (ug C) LD SD LF SF B
Paracalanus6 Nl .00760 N2 .0114 N3 .0152 .003 .136 .001 .386 - N4 .0228 .005 .193 .002 .462 - N5 .0456 .011 .325 .005 .593 - N6 .0760 .020 .447 .011 .680 - CI .152 .038 .632 .027 .771 - C2 .304 .068 .809 .062 .815 - C3 .798 .135 .963 .161 .784 - C4 1.52 .200 .970 .272 .709 - C5 2.66 .272 .908 .400 .619 - C6 male 2.76 .278 .902 .410 .612 - C6 female 3.80 .326 .840 .494 .553 -
anus6 Nl .0216 N2 .0324 N3 .0432 .011 .313 .005 .583 - N4 .0648 .017 .407 .009 .654 - N5 .130 .033 .590 .023 .754 - N6 .216 .051 .725 .042 .800 - CI .432 .088 .882 .089 .816 - C2 .864 .142 .969 .172 .777 - C3 2.27 .251 .931 .361 .646 - C4 4.32 .346 .812 .529 .529 - C5 7.56 .444 .671 .686 .422 - C6 male 7.90 .452 .659 .698 .414 - C6 female 10.8 .511 .573 .781 .356 -
Tortanus8 CI .858 C2 1.3.2 C3 2.03 C4 3.52 C5 6.19 C6 male 9.06 C6 female 14.8
Harpacticoida 9 5. Appendix 2. (continued) taxon body weight select ivity (ug C) LD SD LF SF B
Ciliata:10 17 feeding preference <3 um .00015 - - - - 00 3-15 um .0003 - 1 - 1 >15 um .0005 .134 - .019 - unassigned .0004 .134 1 .019 1 00
Metazoan larvae: 11 Polychaete larvae 5.4 .178 1 .019 1 .01 12 Trochophores .14 .178 1 .019 1 .01 12 Pelecypod larvae .14 .178 1 .019 1 .01 13 Gastropod veligers .14 .178 1 .019 1 .01 13 Cyphonautes .14 .178 1 .019 1 .01
Larvaceans:14 trunk length - 125 um .0243 - .014 - .250 1 175 .0581 • - .014 - .250 1 225 .112 - .014 - .575 .1 275 .189 - .098 - .608 1 325 .292 - .310 - .775 1 375 .425 ' - .342 - .781 1 425 .590 - .911 - .782 1 475 .790 - .956 - .785 1 525 1.03 - 1 - .949 1 575 1.30 .007 1 - 1 1 625 1.62 .045 1 .002 1 1 675 1.99 .045 1 .002 1 1 725 2.40 .097 1 .002 1 1 775 2.86 .101 1 .007 1 1 825 3.37 .103 1 .010 1 1 875 3.93 .109 1 .010 1 1 925 4.54 .129 1 .010 1 1 950 + 6.34 .134 1 .019 1 1 spent 9.00 .134 1 .019 1 1 Appendix 2. (continued) taxon body weight selectivity (ug C) LD SD LF SF B
Colourless flagellates15 .00928
Ctenophores:14
Pleurobrachia 0- 1 mm .1 1- 2 2. 2- 4 16. 4-8 80. 8-16 550.
Bolinopsis 0-2 mm .71 2- 4 7.4 4-8 34. 8- 16 156. 16+ 1136.
Chaetognaths:16
Sagitta 0-3 mm 1.0 3- 6 9.7 6-9 39. 9- 12 101. 12-15 202. 15-21 454. 266
Appendix 2. (continued)
Note:
1. LD = large diatoms, SD = small diatoms, LF = large flagellates, SD = small flagellates, B = bacteria.
Notes on derivation of body weights:
2. From Durbin and Durbin (1978) for Acart ia clausi.
3. Adult male and female weights are from Mullin and Brooks (1970). Weights of other stages in proportion to adult female weight are given -by G. Grice (letter of June 13, 1979).
4. Adult male weight from CEPEX Data Report 3, March 1979. Proportional weights of other stages from Durbin and Durbin (1978).
5. Adult male, adult female, and copepodid weights from CEPEX Data Report 3, assuming C/dry wt. =0.4. Proportional weights of other stages from Durbin and Durbin (1978).
6. Adult male, adult female weights from CEPEX Data Report 3. Proportional weights of other stages given by G. Grice (letter of June 13, 1979).
7. Adult male, adult female, copepodid weights from CEPEX Data Report 3.
8. Adult female weight from Amber and Frost (1974), assuming C/dry wt. = 0.4. Proportional weights of other stages from Durbin and Durbin (1978).
9. Very approximate estimate based on Feller (1977).
10. Very approximate estimates based on Beers and Stewart (1970, 1971) and Beers et al. (1971).
11. From Ikeda (1974), assuming C/dry wt. = 0.4.
12. Assumed to equal body weight of gastropod veligers.
13. From CEPEX Data Report 3.
14. Based on data of K. King, personal communication.
15. Average cell carbon of species observed in CEE2; different flagellate species statistically weighted according to mean abundance in CEE2 between days 1 to 80. 267
Appendix 2. (continued)
16. M. Reeve, personal communication.
Other notes:
17. A water clearance rate of 4 ul ciliate"1 h"1 is assumed when calculating the feeding selectivity on bacteria (see section 3.5.2 for details). 'As I write this I happen to be in an airplane at 30,000 feet, flying over Wyoming en route home from San Francisco to Boston. Below, the earth looks very soft and comfortable—fluffy clouds here and there, snow turning pink as the sun sets, roads stretching straight across the country from one town to another. It is very hard to realize that this is all just a tiny part of an overwhelmingly hostile universe. It is even harder to realize that this present universe has evolved from an unspeakably unfamiliar early condition, and faces a future extinction of endless cold or intolerable heat. The more the universe seems comprehensible, the more it also seems pointless. 'But if there is no solace in the fruits of our research, there is at least some consolation in the research itself. Men and women are not content to comfort themselves with tales of gods and giants, or to confine their thoughts to the daily affairs of life; they also build telescopes and satellites and accelerators, and sit at their desks for endless hours working out the meaning of the data they gather. The effort to understand the universe is one of the very few things that lifts human life a little above the level of farce, and gives it some of the grace of tragedy.'
—Steven Weinburg, The First Three Minutes, 1977.