ABSTRACT
Using data collected by Monterey Bay Aquarium Research Institute(MBARI) in
Monterey Bay and employing stepwise multiple regression techniques, this study
develops several equations that estimate primary productivity for Monterey Bay.
The most significant equation, using integrated chlorophyll, photosynthetically
active radiation (PAR), and surface phaeopigments as variables, explains 77% of
the variance in primary productivity measurements. Another equation explains
70% of the variance, using the two variables, PAR and surface chlorophyll +
surface phaeopigments, which are both measurable by remote sensing. Tests of
these equations on another MBARI data set, which includes oceanic sampling,
demonstrates the possible applicability of these equations on a regional scale.
INTRODUCTION
Over the past decade, there has been increased interest in estimating
primary production from satellite imagery. On a global scale, primary productivity
and other oceanic processes have a very significant effect on the entire carbon cycle
(Barber, 1990); our understanding of the entire carbon cycle would improve by
quantifying global production. On a regional scale, estimates of primary
productivity would be useful because phytoplankton crops are often patchy and can
have large spatial and temporal variability. Current estimates of primary production
over regional areas are generated from measurements taken by sampling at a few
stations and extrapolating for the larger unknown area. Thus, estimates of primary
production using satellite data could increase the accuracy of assessing the total
productivity of on a larger scale.
Two kinds of satellites would be used to collect the data for the estimates
the Coastal Zone Color Scanner(CZCS), which measures surface pigments, and the
Advanced Very-High-Resolution Radiometer(AVHRR), which senses sea surface
temperature. Although satellite imagery provides some relevant data and appears
to be a feasible method for remotely sensing primary productivity, the current
technology does not provide methods for measuring all the biologically important
variables that affect the rate of carbon uptake. For example, satellites can provide
data on surface pigment levels, but much of the chlorophyll, representing the
photosynthesizing phytoplankton, is below the surface and distributed throughout
the euphotic zone. Öther biological factors that help determine the amount of
primary productivity include nutrient levels and species composition of the
phytoplankton crop, neither of which can be measured using existing satellite
technology.
Despite the difficulties in using satellites, other data sets have been
examined and equations have been created that can explain some of the variance
between production and surface chlorophyll levels. These equations have
explained up to 60% of the variance in primary production. Eppley et al. (1985), for
example, was able to explain 58% of the variance in the Southern California Bight
after regressing production against daylength, pier temperature anomaly(a measure
of interannual ocean temperature variability), and pigments.
This study attempts to report whether a significant amount of the variance
in Monterey Bay primary production can be explained by analyzing the Monterey
Bay Aquarium Research Institute data set using multiple regression techniques. In
the hopes of learning more about the Monterey Bay ecosystem, selection of the
variables was not constrained by satellite measurement capabilities, but rather by
the data that was available.
MATERIALS AND METHODS
The data set used for this analysis was collected by the Monterey Bay
Aquarium Research Institute(MBARI) in Monterey Bay from 5 April 1989 to 2
May 1990. The data set, part of an ongoing project at MBARI, includes 31 single¬
day cruises, with 109 stations. MBARI had four stations per cruise where primary
productivity measurements were taken consistently: two offshore and two oceanic
stations. (see figure 1)
To determine which biological factors play a role in explaining the variance
between primary productivity and surface chlorophyll in the Monterey Bay system,
the analysis includes ten variables and seven groups of phytoplankton. The
variables used are not necessarily measurable by current satellite technology.
Temperature and salinity measurements were taken on a Seabird CTD
system at several depths. For this analysis, only the surface temperature and salinity
levels were used. To give an estimate of the mixed layer depth, the change in
temperature between 0-40 meters is also included as a variable.
Water samples for the productivity measurements taken at depths which
were shown, by a secchi disk reading and calculation, to receive 100, 50, 30, 15, 5, 1,
and 0.1% of the light. Those depths throughout the euphotic zone were sampled by
collecting water in Niskin bottles, putting the samples into polycarbonate bottles,
adding a known amount of 1C, and incubating the bottles on deck in mesh
cylinders that allowed only the prescribed percent light for that depth. Äfter 24
hours, the amount of carbon uptake was measured by the radioactive tracer,1C.
Using trapezoidal integration, production was calculated for the entire euphotic
zone. This integrated carbon measurement value, measured in mg C/m2/day is
used in this analysis as the value for primary productivity.
Water samples for the chlorophyll and nutrient levels were also collected in
Niskin bottles at several depths throughout the euphotic zone. The chlorophyll
levels at each depth were determined fluorometrically on a Turner Designs model
10-005 R fluorometer. Surface chlorophyll and phaeopigment levels were used as
variables, and, since satellites cannot distinguish between chlorophyll and
phaeopigments, an additional variable combining the two was also used. In
addition, the integrated chlorophyll value, which is calculated using trapezoidal
integration on the individual depth chlorophyll values, was included as a variable.
Because production occurs throughout the euphotic zone and not just at the
surface, this integrated term provides a more accurate account of how much
chlorophyll is actually present. Until a CZCS satellite is up over Monterey Bay and
its estimates of surface pigment levels are normalized to ship measurements, the
shipboard pigment data can represent the satellite data for the purpose of this
study. The nutrients, nitrate and silicate, were analyzed on a Alpkem Rapid Flow
Analysis (RFA) system using a slightly modified version of the methods of
Whitledge et al.(1981) Only the surface nutrient levels were included in the analysis.
Irradiance, or photosynthetically active radiation (PAR), measured in
mE/m2/day, represents the amount of sunlight available to phytoplankton for use in
photosynthesis during a day; for example, the cumulative PAR for a cloudy day will
be less than the cumulative PAR for a sunny day of equal length. MBARI's PAR
sensor, located onboard its research vessel, measured the light in volts, which can
then be converted to mE/m2/day. Because the instrument only records PAR for the
duration of the cruise and not for the entire day, consistent measurements of
cumulative PAR for each cruise day were not available from an in situ source.
Instead, onshore PAR data, recorded at the Monterey Bay Aquarium(MBA), is
compared with the ship PAR data. When graphed on the same scale, the on- and
offshore correlate well with each other. Because MBA PAR data was not available
for four cruise days, I compare the Aquarium’s PAR sensor data with its solar
irradiance data, measured in watts/m2/day. I use this to generate the regression
equation:
watts= -5.12 +0.47'PAR
Using this equation and solar irradiance data from MBA, PAR levels for those four
days are calculated.
The phytoplankton for the group analysis variables were collected in Niskin
bottles at those stations where productivity measurements were taken. The
organisms were counted by taxonomic group, classified according to shape and size,
and run through an algorithm to calculate the actual ugC/liter/group. The groups
included separate classifications for the following: picoplankton, ultraplankton,
nanoplankton, heterotrophs, centric diatoms, pennate diatoms, both centric and
pennate diatoms, and all groups as a single variable.
A correlation matrix with all the variables is used to identify those variables
which correlate well with primary production. Stepwise multiple regressions on
several models determine which variables explain the most variance in primary
production. Because both carbon and chlorophyll measurements follow a log-
normal distribution, these variables are used in the regression after a logarithmic
transformation (Campbell, 1987). I test the most significant models by comparing
the observed primary production from another regional data set with the expected
production generated by the regression equations. The data set used in this test
was collected by MBARI in Monterey Bay and the waters of the adjacent California
Current following the same methods described above. Figure 2 shows the stations
used in this second data set. PAR data collected by MBA was also used in this
second data set.
RESULTS
The correlation matrices indicate which variables correlate well with
primary productivity (see table 1: all correlation coefficients mentioned are in this
table). All of the well-correlated variables, however, cannot be used in the same
model because some of them represent the same variance of primary production.
For example, surface chlorophyll and integrated chlorophyll correlate well with
each other; if used in the same model, the variance of primary production they
explain would overlap and one could incorrectly conclude that one of the variables
was not important.
Delta temperature, a variable with a relatively high r when correlated with
primary production, is a significant variable in this system. Because of incomplete
temperature data on several cruises, data points for delta temperature do not span
the entire data set. To maintain the largest possible sample size, delta temperature,
along with other variables with small sample sizes are not entered all the models.
Even though these variables are important, a larger sample size increases the
significance of the models To allow for the missing data, I analyze several multiple
regression models, all shown in table 2.
Ten models are presented in table 2. Of the ten, Model 10, which includes
In(surface chlorophyll+ surface phaeopigments), PAR, nanoplankton, and pennate
diatoms as the significant independent variables, explains the highest amount of
variance, with r°= 0.875. Although this regression is significant at the 0.0001 level,
the small n'of the plankton group data may make this model less reliable. Model 5,
with an r== 0.772, explains about 10% less of the variance than model 9, yet has a
much larger sample size(n= 94). Note that model 2, using only PAR and In
(surface chlorophyll + surface phaeopigments), still explains over 70% of the
variance and, more importantly, both variables are obtainable directly from a
satellite data set.
In addition to explaining large amounts of the variance, model 2 and model
4 also incorporate variables that are available in the second MBARI data set. To
test the applicability of these models, I compare observed primary production from
the second data set with expected primary productivity measurements generated by
model 2. This yields an r=0.54, significant at the 1% level(see figure 3). Using the
model 4 regression equation, the correlation between observed and expected
increases to r= 0.77, p«0.01 (see figure 4).
DISCUSSION
From table 2, one can see that the major variables, PAR, In(integrated
chlorophyll, and In(surface chlorophyll+ surface phaeopigments), explain
comparable amounts of variance throughout the table. This observation, along with
the significance levels, implies that the MBARI data set may be representative even
at the smaller sample size.
A thorough analysis of the regression models yields far more information
than the regression equations. With large sample sizes and r* values over 0.60, the
first three regression models include PAR, a measure of daylength and available
light levels, and pigment concentrations. These appear in each model as the most
significant variables. Because the amount of available light can be a limiting factor
in production, the inclusion of PAR as a major variable in all the models is
understandable. Chlorophyll measurements, which are also significant in every
model, are a measure of biomass via the chlorophyll pigments present in all
photosynthesizing organisms. Clearly, a correlation between production and
phytoplankton biomass is expected. Not only are both PAR and pigments the most
significant variables in these models, they are also measurable by satellites.
Although phaeopigments are only associated with chlorophyll molecules and
do not play a role in photosynthesis, both molecules have almost the same
absorption spectrum and thus a satellite senses the sum of both pigments(Smith
and Baker, 1982). To create For an equation comprised of variables measurable by
satellite, this combined term is useful. Further, model 2, with surface chlorophyll
plus phaeopigments and PAR, explains 9% more variance than model 1 which uses
only surface chlorophyll.
Model 3, using integrated chlorophyll, yields a slightly higher r2 than either
model 2 or model 1. Photosynthesizing phytoplankton are distributed throughout
the euphotic zone; because integrated chlorophyll measurements provide a value
for the entire euphotic zone, not just the surface, it is expected that integrated
measurements would have a higher correlation with primary productivity than
surface values. Model 4 also suggests that surface phaeopigments are a significant
variable in explaining an additional fraction of the variance. Table 1 shows a
correlation coefficient between surface phaeopigments and primary productivity of
r=.528, almost as high as the surface chlorophyll correlation. It is unclear, from a
biological standpoint, why surface phaeopigments would correlate so well with
primary productivity. A possible explanation for the appearance of phaeopigments
in this model might be simply because phaeopigments are consistently present in
much smaller concentrations, and therefore their measurements are more sensitive
to fluctuations. Because satellites cannot measure phaeopigments or integrated
chlorophyll directly, this model’s precision, when applied to satellite data, may be
less than model 2. Knowing the precise surface chlorophyll values, however, one
can estimate the integrated chlorophyll value to within 10% accuracy. Satellites
cannot, unfortunately measure surface chlorophyll accurately; their precision level
is approximately 35%. (Platt et al., 1988)
Models 5 and 6 introduces four new variables, all of which represent
important biological information that can affect primary production. The results,
however, show that surface salinity, temperature, nitrate, and silicate explain
insignificant amounts of the variance in this system. Although salinity does not
appear as a significant variable within these regression models, it does correlate
well with primary production(r= 0.56). In addition, salinity correlates well with
PAR, with an r= 0.61. PAR is, among this array of variables, the best seasonal
marker; a correlation with salinity then could indicate that water mass movement in
Monterey Bay is seasonal.
Surface temperature, while presented as a significant variable in other
models (Balch et al., 1989) does not explain a significant amount of the variance in
this system. In Monterey Bay, surface temperatures range only from 10-13 degrees
C. This explains the low level of correlation between primary production and
temperature (r=-0.007.), because in this system, temperature is essentially a
constant. This illustrates why creating global equation to estimate primary
production is not feasible; the factors involved in regulating primary production are
different for different oceanic systems.
Surface silicate and nitrate levels are also insignificant in these regression
equations. This result does not mean that there is definitely no relationship between
primary production and nutrient levels. Nutrients are in fact vital for
phytoplankton growth; blooms are common after upwelling events, which bring up
nutrient-rich water. Considering the biology of phytoplankton and the frequency of
upwelling, the relationship between primary production and nutrient levels may be
non-linear. My regressions can only account for linear relationships, and
consequently present nutrients as insignificant. Because upwelling is so important
on the Central California coast, including some index of upwelling into these
models might improve their accuracy. Winds affect the rate of upwelling and the
amount of mixing; using wind as a variable and satellite wind data could increase
the amount of variance in primary productivity (Eppley et al., 1987)
Models 7 and 8 show delta temperature as a significant variable. Although
the addition of delta temperature does decrease the sample size, the regressions are
still significant. Delta temperature, or the change in temperature from 0-40 meters,
is a measure of the mixed layer depth. An increase in delta temperature shows the
thermocline lowering and the amount of mixing increasing. As mixing increases,
the phytoplankton cells mix lower into the euphotic zone, receive less light, and
consequently primary productivity decreases. From a biological perspective, it is
logical that delta temperature explains a portion of the variance in primary
productivity.
11
Some of the plankton group data, shown in models 9 and 10, explain
significant amounts of the variance. As with delta temperature, the plankton data
reduces the data set available for analysis. Despite this considerable reduction in
sample size, models 9 and 10 are still significant at the 5% level. In both models,
pennate diatoms are included as significant variables. Nanoplankton are only
included in model 10. The inclusion of pennate diatoms in this model is not
supported by the correlation matrices. While the correlation coefficient between
primary production and pennate diatoms is not even significant, the coefficient
between primary production and centric diatoms is r= 0.80, p—0.0001.
Nanoplankton, which also correlates well with primary productivity, has a high
correlation with centric diatoms and shows no significant correlation with the
pennate diatoms.
Considering these overlapping correlations, the unanticipated inclusion of
pennate over centric diatoms can be explained. The nanoplankton-explained
variance is similar to the variance explained by the centric diatoms that centrics
become insignificant in a model which already includes nanoplankton. Without
doubt, the amount of phytoplankton present in an area will directly affect the
primary production of that area. Because the raw population data on each group is
run through an algorithm that converts these organisms to ugCsliter/group, this data
shows how many of each group were present at the site of the productivity
measurements. Variation, if any, in how much production each phytoplankton
group is capable of generating is not provided from this data unless primary
production is correlated directly to the amount of carbon in phytoplankton. More
physiological information is necessary to analyze these results accurately.
When examining graphs of individual correlations between primary
productivity and specific variables, several points show a marked overestimation of
primary production. Figure 5, plotting primary production and PAR" integrated
12
chlorophyll, shows several points far off the regression line. The composite
variable, PAR'integrated chlorophyll, is used because it reflects more biological
information than either individual variable and shows these outliers most clearly.
Other studies (Platt et al., 1988) have used similar composite variables. The points
specified on figure 5 are summer cruise data points from stations CI and HI, most
showing overestimated productivity. A possible explanation for the overestimation
of primary production at these stations is that both Cl and HI are offshore stations.
while the other two stations used by MBARI are oceanic (see figure 1). The
turbulence of an offshore station, shallow depth at Hl, and river input at CI may all
be factors in increasing the amount of suspended sediment at these stations.
Because suspended sediment blocks light, the true depth of the euphotic zone
(defined by the 1% light level) is lowered. Primary productivity measurements are
calibrated by the integrated chlorophyll measurements. Under these conditions.
productivity could be overestimated because, if the phytoplankton are not reached
by the light, they cannot photosynthesize and contribute as much production as
calculated. Balch et al.(1989) used this same hypothesis to explain scatter in the
Southern California Bight Study data set. Why this phenomena of overestimated
primary production as a result of increased suspended sediment would only occur
during the summer is still unclear. When these summer points are edited, the
regression improves only slightly (see figure 6). Further, because some summer
data points do not follow this pattern, more data collection is necessary to prove or
disprove this hypothesis.
Despite the unexplained scatter in the data set, these Monterey Bay models
explain more variance than other models formed for other regions. Better
sampling techniques may be one possible explanation for these improvements in
r2. MBARI uses acid-rinsed Niskin bottles fitted with non-toxic silicon tubing for
improved productivity measurements.
13
Another advantage of these models, developed using only Monterey Bay
data, is that they generate reasonable estimates of primary production in the nearby
ocean area as well. The larger area an equation represents, the more useful it will
be when applied to satellite data. Figure 4 shows how well the model 4 equation,
when applied to the second MBARI data set, calculates expected primary
production. Increasing the data set and incorporating wind data may improve these
models even further. Even without these improvements, estimating primary
productivity in Monterey Bay appears possible.
Acknowledgements- I would like to thank my advisor, Dr. Francisco Chavez, for his
helpfulness and patience, and everyone else at MBARI who contributed
programming time, answers to silly questions, and general moral support to me and
my project throughout the quarter.
BIBLIOGRAPHY
Balch, W.M., M. Abbott, and R.W. Eppley (1989) Remote sensing of primary
production-l. A comparison of empirical and semi-analytical algorithms, Deep¬
Sea Research, 36, 281-295.
Barber, R.T. (1990) Ocean productivity and global carbon flux, preprint volume of the
Symposium on Global Change, Special Sessions on Climate Variations and
Hydrology.
Campbell, J.W. (1987) Biological processes in the upper ocean: nature and
consequences of lognormal variability, (abstract) EOS, 68, 1696.
Eppley, R.W., E. Stewart, M.R. Abbott, and U. Heyman (1985)Estimating ocean
primary production from satellite chlorophyll: Introduction to regional
differences and statistics for the Southern California Bight, Journal of Plankton
Research, 7(1), 57-70.
Eppley, R.W., E. Stewart, M. Abbott, and U. Öwen (1987) Estimating ocean
production from satellite-derived chlorophyll: insights from the Eastropac data
set, Oceanologica Acta., Proceedings International Symposium on Equatorial
Vertical Motion, 6, 109-113.
Platt, T., S. Sathyendranath, C. Caverhill and M. Lewis (1988) Ocean primary
productivity and available light: further algorithms for remote sensing, Deep-Sea
Research, 35(6), 855-879.
Smith, R.C. and K.S. Baker (1982) Oceanic chlorophyll concentrations as determined
by satellite (Nimbus-7 Coastal Zone Color Scanner), Marine Biology, 66, 269-
279.
Figure Legend
Figure 1. A map of Monterey Bay showing the distribution of stations used by MBARI
in the first data set. Data from these stations is used to create the regression equations.
Figure 2. A map showing the distribution of stations used by MBARI in the second data
set. Data from these stations is used to test the equations created by the multiple
regression techniques. An 'X" shows stations included in the test data set. An "O"
shows those stations where additional data is available, yet not included in this study.
Figure 3. Plot of observed versus expected primary productivity.. Observed values are
from the second MBARI data set and the expected values are generated using the
model 2 regression equation,
int carbon= 5.72 + 0.00029'PAR + 0.33'In(surface chl + surface phaeo)
The correlation coefficient, r=0.54 is significant at the 1% level.
Figure 4. Plot of observed versus expected primary productivity. Observed values are
from the second MBARI data set and the expected values are generated using the
model 4 regression equation,
int carbon= 3.54 + 0.00024*PAR + 0.65*In (int chl) + ).063'surface phaeo
The correlation coefficient, r=0.77, is significant at the 1% level.
Figure 5. Plot of primary production in Monterey Bay and the composite variable, PAR
multiplied by integrated chlorophyll. The numbers in parentheses by certain data
points, indicate the Julian day on which the data was collected and the station at which
the measurements were made. Only the data from summer cruises at stations HI and
CI are labelled.
Figure 6. Plot of primary production in Monterey Bay and the composite variable, PAR
multiplied by integrated chlorophyll. The labelled points shown in figure 5 are edited
out, increasing the correlation coefficient from 0.81 to 0.83. For clarity, a logarithmic
scale is used to spread out the clustered points and to show the relationship more
clearly.
Table Legend
Table 1. Correlation coefficients mentioned in the text. Primary productivity, which is
the integrated carbon value, is abbreviated "pp". The significance levels and the
sample size are also given.
Table 2. Lists the ten multiple regression model discussed. Only the independent
variables are shown. Integrated carbon is the dependent variable in each model. The
partial re indicates how much variance is explained by each variable and the model r
shows a cumulative total, with an asterisk denoting the total r2. The r* values can also
be read as percentages of the variance in primary productivity explained.
2

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X

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X
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X
X
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X
27
X
Fiquse 3
Observed Prim. Pord. In (mg C/m2/day)
O
1
—
—
—
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Od..
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Figure +
Observed Prim. Prod. In(mg C/m2/day




8
a
O



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Fiaure
Primary production (mg Cm2/egy)
O
—
O

—
Figure 6
Primary Production (mg C/m2/day)
———
O8
O
1 -



O

O

—
U
able
Selected Correlation Coefficients
Variable-Variable
Probp
pp-schl
0.571
o.ooo1
pp-schlsphaeo
0.629
0.oo01
pp-sphaeo
0.!
O.oo01
0.735
pp-int chl
o.o001
pp-PAR
0.603
0.oo01
-0.007
pp-surface temp
0.9571 (ns
pp-surface salinity
0.556
O.0001
pp-surface nitrate
-0.086
0.5127 (ns
pp-surface silicate
-0.131
0.3127 (ns)
pp-delta temp
0.544
0.ooo1
0.7
pp-all groups
0.o001
pp-autotrophs
0.718
o.o001
op-heterotrophs
O.3
0.012
pp-diatoms
0.666
O.o001
0.797
pp-centric diatoms
o.oo01
pp-pennate diatoms
0.136
0.3748 (ns)
pp-ultraplankton
0.7
O.0001
0.778
pp-nanoplankton
0.O001
-0.161
pp-picoplankton
0.2921(ns)
0.9
schl-schlsphaeo
O.o001
schl-int chl
0.792
0.oo01
schl-autotrophs
0.616
o.0001
schl-diatoms
0.69.
O.o001
schl-pennate diatoms
O.494
o.oo01
schl-centric diatoms
0.608
0.O001
surface salinity-PAR
0.608
O.O001
diatoms-pennate diatoms
0.691
O.0001
diatoms-centric diatoms
0.894
0.0001
0.0007
delta temperature-PAR
0.461
delta temperature-int chl
0.288
0.0401
0.700
0.oo01
nanoplankton-centrics
nanoplankton-pennates
0.091
0.551 (ns)
93
93
93
45
45
45
45
45
45
45
45
93
45
45
45
61
51
tat
Model
6
10
Stepwise multiple regressions
Variables
Partial
Model
O.448
PAR
0.448
0.61*
schl
0.162
0.557
0.55
PAR
0.701*
1n(schl-sphaeo)
0.145
0.!
0.53
ln(int chl)
0.75*
0.23
PAR
0.555
0.555
ln(int chl)
PAR
0.207
0.762
0.772*
sphaeo
0.0102
PAR
0.621
0.621
0.774*
1n(schlsphaeo)
0.153
———
———
surface temp
———
———
surface silicate
surface nitrate
———
———
———
———
surface salinity
0.493
0.492
ln(int chl)
0.779*
PAR
0.286
surface nitrate
———
———
———
———
surface silicate
———
———
surface salinity
———
———
surface temp
PAR
0.471
0.471
ln(int chl)
0.73
0.26
7596*
delta temp
0.0288
PAR
0.616
0.616
0.7
1n(schlsphaeo)
0.1174
0.767*
delta temp
0.033
0.5
0.558
ln(int chl)
0.77
0.213
nanoplankton
0.091
0.862
PAR
0.875*
pennate diatoms
0.0128
———
———
centric diatoms
———
———
picoplankton
———
———
heterotrophs
———
———
ultraplankton
0.547
1n(schlsphaeo)
0.547
0.792
PAR
0.245
nanoplankton
0.051
0.843
0.859*
pennate diatoms
0.016
———
———
centric diatoms
———
———
picoplankton
———
———
ultraplankton
———
———
heterotrophs
denotes final r for each model
PTODF
0.oo01
0.oo01
0.oo01
0.oo01
0.oo01
0.oo01
0.ooo1
0.oo01
0.0465
O.o001
o.o001
ns
ns
ns
o.oo01
o.oo01
ns
0.oo01
0.oo01
0.0114
O.oo01
O.O001
0.0106
O.oo01
0.ooo
o.0001
0.0498
ns
ns
ns
0.oo01
O.O001
O.O007
0.0394
ns
ns
ns
98
98