Journal of Oceanography, Vol. 61, pp. 645 to 654, 2005
Seasonal Variations of Plankton Food Web Structure in
the Coastal Water off Usujiri Southwestern Hokkaido,
Japan
A KIYOSHI SHINADA 1,2*, SYUHEI BAN1,3, Y UICHIRO YAMADA1,4 and TSUTOMU IKEDA1
1
Graduate School of Fisheries Sciences, Hokkaido University,
Minato-cho, Hakodate, Hokkaido 041-8611, Japan
2
Hokkaido Prefectural Abashiri Fisheries Experiment Station,
Masuura, Abashiri, Hokkaido 099-3119, Japan
3
School of Environmental Science University of Shiga Prefecture,
Hassaka-cho, Hikone, Shiga 522-8533, Japan
4
Ocean Research Institute, The University of Tokyo,
Minamidai, Nakano-ku, Tokyo 164-8639, Japan
(Received 13 January 2004; in revised form 1 September 2004; accepted 1 September 2004)
The planktonic food web structure in the subarctic coastal water off Usujiri southwestern Hokkaido, Japan was investigated from June 1997 to June 1999, based on
seasonal biomass data of pico- (<2 µ m), nano- (2–10 µ m), micro- (10–200 µ m) and
mesoplankton (>200 µ m), and path analysis using the structural equation model
(SEM). In spring, microphytoplankton predominated due to diatom bloom, while picoand nanophytoplankton predominated in the other seasons, except November and
December 1997. The seasonal change in size distribution of heterotrophic plankton
was almost similar to that of phytoplankton, and mesozooplankton biomass was high
in spring. The path analyses suggest that the main channel in the microbial food web
could vary according to phytoplankton size composition, indicating not only the classical food chain (microphytoplankton - copepods) but also the indirect route
(microphytoplankton - naked dinoflagellates - copepods).
Keywords:
⋅ Planktonic food
web,
⋅ classical food
chain,
⋅ microbial food
web,
⋅ structural equation
model.
as oligotrophic waters (Sherr and Sherr, 1988; Cushing,
1989). Although the classical food chain could efficiently
transfer organic carbon from low to high trophic levels
(Cushing, 1989), the microbial food web contributes less
to high trophic levels since there are many trophic levels
and protozooplankton have higher metabolic costs
(Roman et al., 1995; Rousseau et al., 2000). The study of
the plankton food web is therefore important to understand the biological productivity of marine systems in
terms of their efficiencies and final yields.
In Japanese coastal area, a few studies of the plankton food web have been conducted in the Inland Sea (Uye
et al., 1996) and Funka Bay (Ban, 2000). On the other
hand, many studies have been reported concerning the
plankton food web in the world coastal area, such as the
Gulf of St. Lawrence (Savenkoff et al., 2000), the
Limfjorden in Denmark (Andersen and Sørensen, 1986),
the Australian Antarctic station (Leakey et al., 1996), the
coastal zone in the Baltic Sea (Lignell et al., 1993; Uitto
et al., 1997) and the North Sea (Nielsen and Richardson,
1989; van Boekel et al., 1992; Nielsen et al., 1993;
1. Introduction
In marine plankton food webs, the solar energy fixed
photosynthetically in organic matter by phytoplankton is
channeled to higher trophic levels via two routes. One is
the “classical food chain”, which is the route from
microphytoplankton (10–200 µ m) to mesozooplankton
(>200 µm) (e.g. Riley, 1947). The other is the “microbial
food web”, which includes a “microbial loop” consisting
of heterotrophic bacteria and protozoans (Azam et al.,
1983), and all pro- and eukaryotic unicellular
phytoplankton such as pico- (<2 µm), nano- (2–10 µm)
and microphytoplankotn (Sherr and Sherr, 1988). In
highly productive regions such as upwelling areas and
some temperate waters, the classical food chain might be
the dominant route. On the other hand, the microbial food
web might be dominant in less productive regions such
* Corresponding author. E-mail: [email protected].
hokkaido.jp
Copyright © The Oceanographic Society of Japan.
645
Table 1. Summary list of factors or formulae to convert biovolume to carbon mass for pico-, nano-, micro- and mesoplankton
organisms.
Plankton
Bacteria
Cyanobacteria
Eukaryotic picophytoplankton
Nanophytoplankton
Heterotrophic nanoflagellates (HNF)
Diatoms
Dinoflagellates (including unidentified flagellates)
Naked ciliates
Tintinnids
Others
Mesozooplankton
Conversion factors or formulae
−1
0.02 pgC cell
0.25 pgC cell −1
0.22 pgC cell −1
log 10C = 0.863log 10V* − 0.363
0.22 pgC µ m −3
log 10C = 0.758log 10V* − 0.422
C = 0.216V* 0.939
0.19 pgC µ m −3
C = 444.5 + 0.053LV**
0.05 pgC µ m −3
0.05 pgC µ m −3
Source
Kirchman et al. (1993)
Li et al. (1992)
Mullin et al. (1966)
Verity et al. (1992)
Bratbak (1987)
Strathmann (1967)
Menden-Deuer and Lessard (2000)
Putt and Stoecker (1989)
Verity and Langdon (1984)
Mullin (1969)
Mullin (1969)
*V: biovolume in µm3 cell –1, **LV: lorica volume in µm3 cell–1.
Brussaard et al., 1995, 1996; Richardson et al., 1998;
Rousseau et al., 2000). However, these studies dealt with
seasonal plankton food web structures such as the spring
bloom and summer, and there are a few studies of seasonal variations of the plankton food web (Savenkoff et
al., 2000).
In the coastal water off Usujiri, southwest Hokkaido,
Japan, it is known that the spring phytoplankton bloom is
an annual event (Yokouchi, 1984; Onishi, 1999) and incidental information about planktonic polychaete larvae
(Yokouchi, 1984) and appendicularians (Onishi, 1999)
have been reported. In this study, we investigated the
biomass of the planktonic organisms in Usujiri coastal
water, including all major components of plankton food
web in all seasons for 2 years in an attempt to identify
seasonal variations of the predominant carbon flow route
in the local plankton food web.
2. Method
2.1 Field samplings
Samplings were carried out at approximately one or
two-month intervals at Station 60 off Usujiri southwest
Hokkaido, Japan (40°51′ N, 140°58′ E, about 60 m deep,
cf. Fig. 1), from June 1997 to June 1999. Water samples
were collected from depths of 10, 20, 30 and 50 m by
Niskin bottles. Surface water samples were collected with
a plastic bucket. Water samples for pico- (<2 µm) and
nanoplankton (2–10 µm) were preserved with 1% glutaraldehyde (final concentration). Microplankton (10–200
µm) samples were fixed with a mixture of alkaline Lugol’s
solution (10 g I + 20 g KI + 10 g sodium acetate, dissolved in 140 ml of distilled water), buffered formalin
646
A. Shinada et al.
and sodium thiosulfate (3 g Na2S2O3 in 100 ml distilled
water) (final concentration; 0.05%, 2.5%, and 0.03%, respectively, Sherr et al., 1993). Mesozooplankton (>330
µm) was collected with NORPAC nets (45 cm diameter,
330 µm mesh aperture) towed from near the bottom to
the surface. The volume of water filtered through the net
was estimated using a flowmeter mounted on the mouth
of net. Samples were fixed with buffered 5% formalin
seawater solution immediately after sampling. Water temperature and salinity were determined by a CTD system
(SeaBird 19).
2.2 Abundance estimation
The numerical abundance of heterotrophic bacteria,
cyanobacteria, eukaryotic picophytoplankton,
nanophytoplankton and heterotrophic nanoflagellates
(HNF) was determined by epifluorescence microscopy.
Heterotrophic bacteria were stained with 4′,6-diamidino2-phenylindole (DAPI) and filtered onto 0.2 µm black
Nuclepore filters. Cyanobacteria and eukaryotic
picophytoplankton were filtered onto 0.4 µ m black
Nuclepore filters. The numerical data were converted to
carbon values using appropriate conversion factors (Table 1). Nanophytoplankton and HNF were stained with
DAPI and proflavine hemisulfate (Haas, 1982), and filtered onto 1.0 µm black Nuclepore filters. The sample
was inspected using blue excitation to detect the cell tissue and UV excitation to visualize the cell nucleus. Discrimination between nanophytoplankton and HNF was
facilitated by blue excitation to reveal pure chlorophyll
autofluorescence. The cell volumes of nanophytoplankton
and HNF (V, µm 3) were calculated from length and width
measurements on >30 cells per filter, using an ocular
20′
Latitude (°N)
Funka Bay
80 m
100 m
300
42°N
St.60
Usujiri
20′
40′
141°E
20′
Fig. 2. Path diagram summarizing the microbial food web structure of “bottom-up” (solid lines) and “top-down” models
(dashed lines). HNF is heterotrophic nanoflagellates.
Longitude (°E)
Fig. 1. Location of sampling station (St. 60) off Usujiri, southwest Hokkaido, Japan. Bathymetric contours (80, 100 and
300 m) are superimposed.
micrometer. Microplankton were counted under an inverted microscope after allowing samples to settle overnight. Although autotrophic and heterotrophic
dinoflagellates (including unidentified micro-size flagellates) were discriminated by epifluorescence
microscopy, all ciliates were decided to heterotrophic due
to a few appearances of Mesodinium ruburm. Biovolumes
were estimated from the measurement of length and width
of the organisms, assuming simple geometrical shapes.
The wet weights of mesozooplankton were determined
after the fractionation of samples into 330 µm, 850 µm
and 1800 µm sizes by sequential filtration. The wet weight
of each fraction was measured, and major systematic
groups such as copepods, euphausiids, chaetognaths,
amphipods, appendicularians and others were counted.
Assuming that the wet weight of each systematic group
in the same fraction is equivalent, the wet weight of each
systematic group was calculated. The cell volume (V, µm3)
of nano- and microplankton and wet weight of
mesozooplankton were converted to carbon values using
appropriate formulae or conversion factors (Table 1).
Mesozooplankton respiration (Rm, µl O2 ind.–1h–1)
was calculated as a function of body mass (CW, mg C
ind.–1) and temperature (T, °C), i.e. ln Rm = 0.5254 +
0.8354 ln CW + 0.0601 T (Ikeda, 1985). The food requirement of mesozooplankton was calculated from their
respiration rates, assuming the respiratory quotient (RQ)
to be 0.97 (Gnaiger, 1983), the gross growth efficiency
to be 0.3 and the assimilation efficiency to be 0.7 (Ikeda,
1985).
2.3 Statistical analysis
Cluster analysis was carried out to group
phytoplankton and zooplankton (<micro size) according
size composition. Variables were pico-, nano- and
microplankton percentages against total plankton biomass
(depth integrated data; n = 21). The Euclidean distance
and the Ward method were adapted for the analysis.
Path analysis by structural equation modeling (SEM)
(Kano, 2002) was performed to examine the causal association between prey and predator in the plankton food
web. In the past, the regression analysis was used to determine the causal association between observed variables. However, path analysis was selected for this study,
because this analysis has three advantages over to regression analysis. First, it is easy to construct and alter the
causal model. Second, the best model could be selected
by the goodness of fit index. Third, it is possible to evaluate the direct and indirect separately. Two microbial food
web models were constructed in this study, the “bottomup” model and the “top down” one (Fig. 2). Data grouped
by depth were used for this analysis. The interaction between microplankton (microphytoplankton, naked
dinoflagellates and naked ciliates) and copepods was also
analyzed by the path analysis using the SEM. Two models were assumed for the microbial food web (Fig. 3).
Depth integrated data were used for this analysis. In these
path analyses, the arrows indicate the direction of influence, and the path coefficient shows the magnitude of the
direct effect. Path coefficients are equivalent to the standardized regression coefficient (range from –1 to 1). For
example, path coefficients close to 1 and –1 implies the
proportion and inverse proportion relation, respectively.
The goodness of fit index (GFI) was used to judge the
fitting of models. GFI ranges from 0 to 1, and a GFI close
1 implies that the model fits the data. All data were con-
Seasonal Variations of Plankton Food Web Structure in the Coastal Water off Usujiri Southwestern Hokkaido, Japan
647
Fig. 3. Path diagram summarizing the interaction between
microplankton and mesozooplankton of “bottom-up” (solid
lines) and “top-down” models (dashed lines).
verted to normal distribution by natural logarithm, and
analyses were performed using Amos ver. 5 (SmallWaters
Corporation).
3. Results
3.1 Oceanographic conditions
During the entire survey period, the surface temperature ranged from 1 to 20°C (Fig. 4). As a seasonal pattern, the surface temperature was lowest (1°C) in March,
increased rapidly as the seasons progressed, reaching
20°C in October and September. The surface salinity decreased gradually from January to July. Saline waters
(>33.6) were observed in September to December 1998
near the bottom, which had not been seen during the same
season in the previous year (1997). The water column
stratified from April to November or December. A strong
pycnocline was established in the upper 30 m during August and September in both 1997 and 1998.
3.2 Seasonal changes in plankton biomass
The biomass of autotrophic plankton (integrated 0–
50 m) in the euphotic zone (37.1 ± 10.8) varied from 6.3
to 245.2 mgC m–3 (Fig. 5). Biomass values exceeding 50
mgC m –3, as observed in December 1997, and in March
and April 1998, were due to the predominance of
microphytoplankton (>92%) which consisted mainly of
diatoms. On the other occasions the autotrophic biomass
was relatively low (<36 mgC m –3 ), and pico- and
nanoplankton form major component (>58%), except in
November 1997 and March and April 1999. In regard to
the composition of picophytoplankton, although
cyanobacteria was dominant from June to December,
eukaryotic picophytoplankton was most abundant in all
other months. For microphytoplankton, diatoms were the
most important component from November to May, ex648
A. Shinada et al.
Fig. 4. Seasonal changes in vertical profiles of water temperature (°C, top), salinity (middle) and sigma-t (bottom) at St.
60. Dots indicate data points.
cept in April 1999. On the other occasions, naked
dinoflagellates and thecate dinoflagellates were dominant
components in microphytoplankton. The cluster analysis
distinguished two major groups at 200 levels of distance
(Figs. 5 and 6), e.g. Group1: mainly microphytoplankton
(in November and December 1997, in March and April
1998 and in March and April 1999) and Group2: mainly
pico- and nanophytoplankton (all other months).
The biomass of heterotrophic plankton in the
euphotic zone ranged from 10 to 66 mgC m–3 (Fig. 7),
with peaks in April 1998 and 1999. Thereafter, the
biomass decreased gradually and reached low values in
winter. Ranges of seasonal variations of each component
were 9–27 mgC m–3 for bacteria, 1–8 mgC m–3 for HNF,
1–40 mgC m–3 for microzooplankton and 1–25 mgC m–3
for mesozooplankton. Naked dinoflagellates and naked
ciliates were the most abundant components of
microzooplankton throughout the year (50–93%), except
in June 1997. The cluster analysis distinguished two major groups at 100 levels of distance (Figs. 6 and 7), e.g.
Group3: mainly microzooplankton (in November 1997,
in March, April and September 1998 and in March and
April 1999) and Group4: mainly bacteria and HNF (all
Fig. 5. Seasonal changes in pico-, nano- and microphytoplankton biomass (upper), and relative abundance of two picophytoplankton
(middle) and three microphytoplankton components (lower) at St. 60. nd = no data.
other months). These size groups were almost similar to
phytoplankton groups, such as micro-size (Group1 and
Group3) and pico/nano-size (Group2 and Group4).
Mesozooplankton biomass was high, comprising ca. 30%
of the total heterotrophic plankton biomass in April 1998
and 1999. Copepods were the most important component
in mesozooplankton throughout the year (50–96%).
3.3 Plankton food web structure
In “top-down” models of the microbial food web and
the interaction between microplankton and copepods,
many significant positive path coefficients from predator
to prey organism (e.g. from microzooplankton to
microphytoplankton) were observed (data not shown).
These positive path coefficients imply that the predator
increases induce the prey increases, and these relations
can thus not be viewed as realistic. The “top-down” models were therefore rejected.
In “bottom-up” microbial food web model, two models were constructed due to the seasonal variations of
phytoplankton size composition (pico- and
nanophytoplankton
predominated,
and
microphytoplankton predominated; Figs. 5 and 6). The
goodness of fit index (GFI) of the pico- and
nanophytoplankton-predominated model (n = 75) and the
microphytoplankton-predominated model (n = 30) were
high (GFI 0.962 and 0.968, respectively) compared to that
of the model using all data (GFI 0.943, n = 105). The
first two “bottom-up” microbial food web models were
thus adapted in this study. In the pico- and
nanophytoplankton-predominanted model, many path
coefficients from prey to predator were significantly positive (Fig. 8). On the other hand, the path coefficient from
microphytoplankton to microzooplankton was only significantly positive in the microphytoplanktonpredominanted microbial food web model (Fig. 8). In
addition, significantly negative path coefficient from
microphytoplankton to bacteria was also observed.
The GFI of “bottom-up” in the interaction between
microplankton and copepods was high (0.902, n = 21), so
this model was adopted in this study. Although the path
coefficients from microphytoplankton to naked
dinoflagellates and naked ciliates, and from naked
dinoflagellates to copepods were significantly positive,
the others were not significant (Fig. 9). Therefore, the
indirect interaction via naked dinoflagellates
(microphytoplankton - naked dinoflagellates mesozooplankton) was high (0.52).
4. Discussion
In this study, the phytoplankton community structure could be divided into two groups: one being that
Seasonal Variations of Plankton Food Web Structure in the Coastal Water off Usujiri Southwestern Hokkaido, Japan
649
Distance
(a) Autotrophs
0
100
200
300
400
500
600
Mar 99
Apr 99
Group 1
Mar 98
Nov 97
Dec 97
Apr 98
Jul 97
Sep 98
Sep 97
Nov 98
Apr 97
Dec 98
Group 2
Oct 97
Jun 98
Jul 98
Jun 97
Jan 98
Jun 98
May 98
Feb 98
Feb 99
(b) Heterotrophs
Distance
0
50
100
150
200
Mar 98
Apr 98
Group 3
Mar 99
Sep 98
Nov 97
Apr 99
Oct 97
Feb 99
Nov 98
Dec 98
Jun 99
Jun 98
Group 4
Feb 98
Aug 97
May 98
Dec 97
Jul 98
Jan 98
Sep 97
Jun 97
Jul 97
Fig. 6. Dendrogram of the cluster analysis, showing the phytoplankton (a) and zooplankton (b) community could be grouped to
two sub-communities.
microphytoplankton predominated in November and December 1997, in March and April 1998 and 1999, while
the other is that pico- and nanophytoplankton predominated in all other months (Fig. 6). In spring (March and
April), diatom bloom has been observed regularly in
Usujiri coastal water (Yokouchi, 1984; Onishi, 1999),
thereby microphytoplankton might be the most dominant
component of phytoplankton community in spring every
year. On the other hand, dominance of
microphytoplankton was not observed in November and
December 1998, in spite of the outbreak in November
and December 1997 (Fig. 5). From the viewpoint of
650
A. Shinada et al.
hydrographic conditions, although the water column was
uniform in November and December 1997, the pycnocline
was developed in November and December in 1998 due
to the presence of high saline water in the lower layer
(Fig. 4). While nutrient observation was not done in the
present study, the nutrient input to the euphotic zone might
occur in November and December 1997, but not in November and December 1998. The nutrient input could
cause microphytoplankton production to accelerate, so
that the microphytoplankton might become predominant
in November and December 1997. An influence of the
water column structure on phytoplankton size structure
Fig. 7. Seasonal changes in bacteria, HNF (heterotrophic nanoflagellates), microzooplankton and mesozooplankton biomass
(upper), and the composition of microzooplankton (middle) and mesozooplankton (lower) at St. 60. nd = no data.
has been postulated in the other regions, e.g. southern
Kattegat (Nielsen and Kiørboe, 1991), northern Baltic Sea
(Uitto et al., 1997) and Gulf of St. Lawrence (Savenkoff
et al., 2000).
In the present path analyses of the microbial food
web models, was observed significantly positive pass
coefficients from prey to predator (Fig. 8). These positive coefficients might imply that the increase of prey
plankton induced the increase of predator plankton due
to their feeding on prey plankton. The autotrophic plankton was divided into two groups (Fig. 6). The pico- and
nanophytoplankton predominated model was almost combined with the bacteria and HNF predominated one (Figs.
5, 6 and 7), and significantly positive coefficients from
prey to HNF and from phytoplankton to
Microzooplankton were obtained in the pico- and
nanophytoplankton predominated model (Fig. 8). On the
other hand, the significantly positive coefficient was only
from microphytoplankton to microzooplankton in the
microphytoplankton-predominated model (Fig. 9), when
the microzooplankton was dominant in heterotrophic
plankton (Figs. 6 and 7). These differences could indicate that the main channel in the microbial food web could
vary according to phytoplankton size composition.
As for the interaction between microplankton and
copepods, the path coefficient from microphytoplankton
to copepods was not significant, but the indirect route
from microphytoplankton to naked dinoflagellates and
from naked dinoflagellates to copepods was significantly
positive, respectively (Fig. 9). These results suggest that
the indirect route (microphytoplankton - naked
dinoflagellates - copepods) could prevail in the coastal
water off Usujiri. A recent studies have reported the
microzooplankton grazing on microphytoplankton in the
nearby Funka Bay (Odate and Maita, 1990), in the nearby
water off Cape Esan (Shinada et al., 2003), in the Kiel
Bight (Smetacek, 1981) and in the Kattegat (Hansen,
1991). In addition, protozoans such as naked
dinoflagellates and naked ciliates are both qualitatively
and quantitatively important in the diets of suspensionfeeding copepods (Stoecker and Capuzzo, 1990), and
copepods feeding on microzooplankton have been reported in California coastal water and the Irish Sea
(Kleppel et al., 1991), in Oregon coastal waters
(Fessenden and Cowles, 1994), in the subarctic Pacific
(Gifford, 1993) and a subantarctic site (Atkinson, 1996).
In this study, the classical food chain
(microphytoplankton - copepods) could not been detected
by the path analysis (Fig. 9). However, the food requirement of mesozooplankton (mostly copepods) was high
from April to June (median = 6.1 mgC m–3d–1, quartile
deviation = 0.6, n = 6, data not shown), compared to other
Seasonal Variations of Plankton Food Web Structure in the Coastal Water off Usujiri Southwestern Hokkaido, Japan
651
Fig. 9. Path diagram of the interaction between microplankton
and copepods at St. 60. Numbers alongside lines represent
path coefficients (*P < 0.05, **P < 0.01, ***P < 0.001).
Fig. 8. Path diagram of the microbial food web, showing the
pico and nanophytoplankton predominanted model (a) and
microphytoplankton predominanted model (b). Numbers
alongside lines represent path coefficients (*P < 0.05,
**P < 0.01, ***P < 0.001).
months (median = 1.6, quartile deviation = 1.1, n = 15),
and microphytoplankton biomass was higher than
microzooplankton biomass from April to June, except for
April in 1999 (Figs. 5 and 7). In fact, the grazing on
phytoplankton by copepods (the classical food chain) has
been observed all over the world, such as the nearby Funka
Bay (Ban, 2000), the Gironde estuary (Benoît et al., 2000),
the California coastal waters and the Irish Sea (Klepper
et al., 1991) and the North Sea (Gasparini et al., 2000).
Therefore, the classical food chain could exist in the
coastal water off Usujiri in particular from April to June.
The change in microphytoplankton biomass depends on
their growth, their death, grazing by predators and their
sinking loss. In particular, the sinking loss might be the
most important factor for decreasing their biomass dur652
A. Shinada et al.
ing the spring bloom (Nielsen and Richardson, 1989;
Lignell et al., 1993). Unfortunately, the sinking loss was
not observed in this study, and this could be one of the
factors why the existence of the classical food chain was
not detected by the path analysis.
In conclusion, in the coastal water off Usujiri, the
main channel in the microbial food web could vary according to phytoplankton size composition, and the indirect route of microphytoplankton - naked dinoflagellates
- copepods could prevail. However, these results were
derived from the analysis of plankton biomass data only.
For more detailed analysis, including production, grazing and feeding between the size and trophic components
of plankton community, experimental studies (such as the
“dilution” technique) are needed in the future.
Acknowledgements
We are grateful to Mr. Y. Harada and Mr. K. Miyazaki
for their assistance in sampling at sea and valuable discussions in the course of this study. Thanks are extended
to the captains and crew of R/V Ushio Maru for their cooperation at sea. We also thank two anonymous reviewers for their valuable comments and suggestions.
Appendix
(see p. 654)
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Appendix 1. Correlation matrix of pico- and nanophytoplankton, microphytoplankton, bacteria, HNF (heterotrophic nanoflagellates)
and microzooplankton biomass.
Pico- and nanophytoplankton
Pico- and nanophytoplankton
Microphytoplankton
Bacteria
HNF
Microzooplankton
1.00
−0.35
0.35
0.35
−0.17
Microphytoplankton Bacteria
1.00
−0.29
0.13
0.61
1.00
0.28
−0.01
HNF
Microzooplankton
1.00
0.16
1.00
Appendix 2. Correlation matrix of microphytoplankton, naked dinoflagellates, naked ciliates and copepods biomass (bottom up
models).
Microphytoplankton
Naked dinoflagellates
Naked ciliates
Copepods
654
A. Shinada et al.
Microphytoplankton
Naked dinoflagellates
Naked ciliates
Copepods
1.00
0.42
0.76
−0.17
1.00
0.59
−0.03
1.00
0.12
1.00