Limnol. Oceanogr., 40(3), 1995, 589-597
0 1995, by the American
Society
of Limnology
and Oceanography,
Inc.
Phytoplankton functional attributes along
trophic gradient and season
K. L. Seip
SINTEF, P.O. Box 124, 03 14 Blindern, Oslo 3, Norway
C. S. Reynolds
Institute of Freshwater Ecology, Windermere Laboratory, Far Sawrey, Ambleside, Cumbria LA22 OLP, England
Abstract
Each of the following phytoplankton functional attributes could be described by significant (P < 0.05)
response surfaces defined by trophic status (T) from oligotrophy to hypertrophy and season (S) from early
vernal period to late summer: phytoplankton cell volume, growth rate, the ratio between minimum quotas
of total N and total P, affinity for P, half-saturation constant for growth with respect to P, and temperature
optimum and light optimum for growth. Sinking rate could not be so described. The response surfaces were
calculated by multiple regressions, including first- and second-order terms of T and S and the interaction
term T. S. Although the data base was rather limited (number of phytoplankton species ranged from 10 to
3 I), the resulting surfaces often reflected a corresponding attribute of the water along T and S (e.g. phytoplankton temperature optimum for growth reflected water temperature). The results support the frequent
use of physical factors as explanatory variables for the distribution and abundance of phytoplankton.
The abundance of phytoplankton species and the species structure of phytoplankton assemblages vary along
trophic and seasonal gradients (Reynolds 1980, 1984b).
Because phytoplankton species with similar functional
attributes (such as size, maximum growth rate, and optimum temperature for growth) also share similar ecologies (Reynolds 1988), we wanted to find out whether it
is possible to give a statistically significant description of
the attributes along trophic gradient (T, measured as the
level of total P concentration) and season (S, related to
day of the year). Discussions of species successions often
imply such functional relationships. For example, Sommer (1986, p. 6) wrote that “in eutrophic lakes where P
is not limiting during early spring, the higher maximum
growth rate makes nanoplanktonic algae such as small
Centrales and Rhodomonas dominant.” The functional
attributes of an alga might also relate directly its position
in a seasonal succession of algae. Statement 5 of the PEGmodel of seasonal succession of planktonic events in fresh
waters states that “there then (after herbivore grazing)
follows a ‘clear-water’ equilibrium phase which persists
until inedible algae species develop in significant numbers” (Sommer et al. 1986, p. 435.) Because inedibility
is often related to biovolumes, e.g. volumes > - 15 x 1O3
pm3 (diam, 30 pm, Carpenter et al. 1993), herbivory and
not season may be a primary vehicle for the presence of
large-size algae or algal colonies. Several more statements
about the relationship between algal attributes and species
abundance could be listed and some of these would be
partly confounding. Our quest, then, is a statistical pattern
along trophy and season persisting “through” the web of
biotic interactions.
Materials and methods
We use literature values for phytoplankton attributes.
Our basic idea is that data should not be screened before
the data are included in the analysis; instead, screening
should be a consequence of the statistical treatment. A
parallel may be the OECD (1982) analysis relating annual
mean Chl a to average in-lake TP (total P) concentration.
There, the equation based on the combined data set (all
data) was not significantly different from any of the equations based on censored data sets;neither were these equations significantly different among themselves.
When information at species level was available both
for attribute values and presence of maximum abundance
in the season-trophic level matrix, we use that information; if not, we use information at the level of genera.
Phytoplankton cell volumes (pm3) were mainly obtained
from table 3 of Reynolds (1984a) and table 5.1 of Lafforgue (1990). Data were either obtained for unicells or
calculated for each cell if volumes were given for cell
coenobia, filaments, or mucilaginous colonies. Phytoplankton growth rates (p,,,, In unit, d-l) are from table
16 of Reynolds (1984a) and from Tilman et al. (1982),
Schreurs (1992), and Dauta et al. (1990). When growth
rates were given as doubling times (G, in days), they were
recalculated from doubling times as pmax = 0.6936.
Whenever temperatures at which growth rates were measured are quoted, we chose, or interpolated, to 20°C (by
setting growth rate to zero at 4°C if required for the calculation). Parameters describing temperature and light
regulation of algal growth are mainly from Kallquist
(1982). He measured growth rates for 13 phytoplankton
species as a function of temperature (range, lo-30°C) and
light (range, 60-720 x 1017quanta m-2 s-l) in chemostat
cultures. Sinking rates (m d- l) are from Smayda (1974)
and Burns and Rosa (1980) and are quoted as average
sinking rates during the daytime period (sunrise to sunset). Half-saturation constants for P-limited growth (K,)
are from Tilman et al. (1982) and Schreurs (1992). Data
on affinity for P during P-limited growth, defined as bL,,,l
Z&,were either obtained directly from Tilman et al. (1982)
589
590
Seip and Reynolds
Table 1. Algal characteristics for 15 selected genera. Main data sources are Reynolds (1984a), Lafforgue (1990), Dauta et al.
(1990), Burns and Rosa (1980), Smayda, (1974), Titman and Kilham (1978), and Kallquist (1982). Additional data sources given
in text. If two categories of trophy or season are given, the first entry in the first pair corresponds to the first (or only) entry in the
second pair. A range in attribute values shows that there probably are significantly different values for species of the same genera.
Data are average values of numbers quoted in the literature (l-8 independent assessments). Total number of species or genera
used for the regressions in Table 3 is 1O-3 1.
No.
Diatoms
1 Synedra
2 Asterionella
3 Fragilaria
4 Tabellaria
5 Diatoma
6 Cyclotella
7 Stephandiscus
Green
8 Scenedesmus
9 Selenastrum
10 Chlorella
Others
11 Cryptomonas
12 Rhodomonas
Blue-greens
13 Oscillatoria
14 Anabaena
15 Microcystis
Trophy*
Seasont
Cell vol.
Cm”)
Growth
rate
(In, d-l)
4,000
175
524
1,700
360
7004 12,000)
0.65
0.730
0.77
0.360
0.60
0.620
0.730-l. 1
5
5.4
11
-
Affinity
(PM P
d-‘)
Sinking
rate
(m d-l)
18.8
42.7
47.5
45
8.7
5.2-6.3
0.32
0.27
0.39
0.08
-
21
21
22
18
16
17
720
720
300
180
180
350
0.10
0.15
-
27
28
30
720
780
840
0.31
0.07
20
-
480
780
N:P
w
M/H
M/E
E
M
H
O/M
H
V
v, LS
V
LS
V
ES/V
V
E/H
M
M/H
V
ES
V
150
44
1.5
1.7
1.3-2.1
14
-
M/E
M/E
V/ES
V/ES
300
114
0.82
0.83
17.1
-
-
E/H
E
E
ES/LS
ES
LS
205
25
0.3-0.68
1.15
0.630
-
5.25-28
36-89
2.1
-
5
Light
opt.
(quanTemp. ta m-2.
opt.
S--l
(“Cl x 10”)
-0.1
-0.1
-
24-25 420
25
-
* M - mesotrophic; H - hypereutrophic; E- eutrophic; 0 - oligotrophic.
-vernal period; LS - late summer; ES-early summer.
tv
and Schreurs (1992) or calculated from pmaxand K,. Optimum N : P ratios (by wt) are from compilations of Seip
(1993).
For some species or genera, only one or a few characteristics have been found. Data for these species have
been used in the regression statistics but are not included
in Table 1. Assignment of genera to the season-trophy
matrix is described below.
Size and other attributes are often referred to as “small”
or “large.” For example, Lampert et al. (1986) make references to small Rhodomonas and Stephanodiscus and
large Ceratium and Fragilaria algae and thus define size
implicitly by reference to genera. For later reference, we
wanted to make more quantitative definitions of size values of the attributes; thus, we define small algae as algae
with cell volume < 1 SD below the mean for all algal cell
volumes included in the calculations (3 1 species), medium algae are defined as having cell volumes within * 1
SD of the mean, and large algae are defined as having
greater than the mean + 1 SD. To calculate mean and
standard deviations, we log-transformed the data for some
attributes to stabilize the variance. Analogous definitions
for small, medium, and large values were used for the
other attributes studied.
WC distributed assemblages of temperate freshwater
phytoplankton across season and trophic levels according
to work by Reynolds (1984b, fig. 1) and Sommer (1986,
figs. 1, 4). We also included assessment of algal abundances described in the texts, since the text often gave
further information.
Species or genera not assigned to
trophic level or season by the above investigators, but for
which there were values of one or more of the phytoplankton attributes, were included by comparing them to
other algae using the phytoplankton
associations defined
by Reynolds (1980). Reynolds and Sommer based their
distribution
patterns for phytoplankton
species (or genera) mainly on observations from lakes in the U.K. (Reynolds in particular) and in Europe. Examples are Brundall
Broad, Windermere, and Lough Neagh (U.K.); Neusiedlersec and Lunzer Untersee (Austria); Lake Constance
(Bodenscc), (Switzerland/Germany);
and Drontermeer
and Woldcrwijd (The Netherlands). We tried to include
species only at the season in which they belong in the
“main scqucnce” of algal succession and not if they appear only as a consequence of “reversions”
(Reynolds
1980) caused by episodes of destratification.
Trophic gradient is divided into 12 levels, with 1 as
oligotrophy and 12 as maximum hypereutrophy.
Reyn-
Phytoplankton attributes
olds (1980) defined oligotrophic (1 level), mesotrophic (4
levels), eutrophic (3 levels), and hypereutrophic (4 levels)
lakes as those with peak total cell volume ~2, 2-5, 5-20,
and ~20 ~1 liter- *, respectively. We recalculated these
numbers to approximate mean epilimnetic summer TP
concentrations as a common measure of trophy. We assumed that the ratio of peak total cell volume to summer
mean cell volume is equal to the corresponding ratio for
peak and mean Chl a, which we obtained from OECD
(1982); we used the graph relating algal biomass to TP
given by Watson et al. (1992) and found that the delimiting values (2, 5, and 20 ~1 liter-l) corresponded to 6,
25, and 70 pg TP liter- I. These values are a little lower
than corresponding delimiters for trophic categories given
by OECD (1982) (10, 35, and 100 pg TP liter-l). The
number of levels in each trophy category corresponds
approximately to the number of algal associations described by Reynolds (1984b).
Season (S) is divided into 6 levels. The “vernal” period
(levels 1 and 2) as defined by Reynolds (19846) extends
from about March to June, the “early summer” period
(levels 3 and 4) begins in July; the “late summer” period
(level 5 and 6) begins in August and ends in about October. Again, levels within seasonal periods refer to algal
seasonal sequences suggested by Reynolds (1980).
We thus obtained a list of species with a coding associated with trophic gradient (l-l 2) and season (l-6) and
quantified attributes (e.g. algal cell volume and algal growth
rate). The numbers at selected positions in the seasontrophy matrix (dots) in Fig. la correspond to the numbered algae in Table 1. Unnumbered dots refer to species
or genera not included in the table. We calculated the
squares of the seasonal level code and the trophic level
code to account for possible nonlinearities in the attribute
response to season and trophy. We also calculated the
product of season and trophic level codes to seek possible
interactions between the effects of season and trophy. This
gave five independent parameters: T, T2, S, S2, and T-S.
(The number of samples should be > -4 to ensure independence between a parameter and its square.) Thereafter, the phytoplankton attribute was used as the responsc parameter in a multiple regression, c.g. pmax=
f(T, T2, S, S2, T-S). Explained variance, r2, and probability, P, were used to express goodness of fit. With second-order and interaction terms, we were able to describe
a flat or simply bent response surface for the attribute.
The surface can be rotated or tilted in any direction.
However, we did not try any third-order polynomials
because we did not think the data would support higher
order surfaces.
Results
Table 2 summarizes the resulting definitions of small,
medium, and large values of the attributes. The number
of species or genera included for each calculation is shown
in the last row. The response surfaces for the eight phytoplankton attributes are shown in Fig. 1. For cell volumes, we have also depicted the position of data points
591
to illustrate the distribution of data along trophic gradient
and season.
Among the attributes, growth-rate, cell volume, N : P
ratio (by wt), affinity for P, and half-saturation constant
for P-limited growth gave significant response surfaces at
the 0.01 level or better (Table 3). The response surfaces
for temperature and light were significant at the 0.05 level.
The response surface for sinking rate was not significant
(P > 0.05). Trophic level was a significant factor for growth
rate, N : P ratio, and affinity for P. Seasonwas a significant
factor for all attributes. A significant effect of the interaction between trophic level and season was found for
growth rate, cell volume, affinity for P, and the half-saturation value for P-limited growth.
The cell volumes of 26 species or genera were used to
construct Fig. la. (Five genera were counted twice because
species occur at two different combinations of trophic
level and season, i.e. Chlorella, Asterionella, Synedra,
Scenedesmus, and Cryptomonas.) Staurastrum (7,900
pm3), Dinobryon (700 pm.3), and Tabellaria (1,700 pm3)
contributed to the large cell volumes for algae occurring
during the latter part of the growth season in meso- and
oligotrophic lakes. The equation for cell volume is shown
in Table 3. Organisms dominating at higher trophic levels
tend to have smaller individual cell volumes, while there
is a tendency for large algae to flourish in summer. Species
are evenly distributed in the plane spanned by the axis
representing season and trophic level except in the region
corresponding to autumn and hypereutrophic lakes (upper right).
Growth rates (pmax)for 26 phytoplankton species or
genera were used to construct Fig. lb. The number of
data reported for a single species or genus ranged from
one to eight. pmaxwas the attribute with most-duplicated
measurements. The equation for pmaxis shown in Table
3. Generally, pmaxincreases with increasing trophic level.
Growth rate is greatest in summer, but rapid growth rates
tend to be shifted to early spring for highly eutrophic
species. Contributing to the high pmaxduring the late vernal period and summer are the green algae Chlorella pyrenoidosa (2.1, In unit, d-l) and Scenedesmus (1.5 In unit,
d-l).
The optimum N : P ratio (by wt) decreases with increasing trophy in late summer and autumn (Fig. lc). At
the optimum N : P ratio, a transition from limitation by
one nutrient to limitation by another might be anticipated
according to the limiting-nutrient hypothesis (Rhee and
Gotham 1980).
The response surface for K, for P-limited growth has
its lowest value at midsummer for oligotrophic lakes (Fig.
1h). (Table 1 shows values for affinity, p,,,/K,.) At higher
trophic levels, low constant values are shifted toward
spring. Limnothrix redekii contributes to the high K, in
autumn. Values for K, were negative in midsummer for
oligotrophic lakes. Log-transformation of input data gave
essentially the same picture but with only positive values.
The response surface that shows affinity of cells for P
(p,,,,/K’) peaks for phytoplankton in summer and autumn
in the oligotrophic lakes (Fig. Id). Also contributing to
the high affinity under eutrophic conditions are the algae
592
Seip and Reynolds
-e-a
+
b
-\ 1.0 -’
15
-.
6.00
_ -.0
4
4
..--__-1.00
2.00
3,00
4.00
5.00
+
6,00
1,oo
2.00
3,00
4,00
5,00
6,00
C
8,00
1.00
6,00
I,00
2,oo
3,00
4,00
5,00
2,oo
4,00
5,oo
6.00
-
1.00
6,00
3.00
2.00
3,oo
4,00
NSg
5,00
6.00
--e-h
12,oo
I--
10,oo
8,OO
h
6.00
6.00
4,oo
.O.l-
2,00
0,00
l,oo
2,oo
3.00
4.00
5,00
6.00
--I:: i'
1.00
Season
2.00
3,00
4.00
5,00
6,OO
593
Phytoplankton attributes
Table 2. Small, medium, and large values for phytoplankton attributes. Small values are
defined as <I - 1 SD (X is mean value), medium values as within X + 1 SD, and large values
as >R + 1 SD. Cell volume, affinity, and K, for P-limited growth have been log-transformed
before calculation of standard deviations.
Affin-
Attribute
($a;,
Small
Medium
Large
n
0.5
0.9
1.3
26
Cell
vol.
(pm3
70
360
1,800
31
N: P
wo
3
7
11
10
Light
ity
(quanta
(PM P Sinking rate Temp. m-* s-l
d-‘)
(m d-l)
x 10’7)
(“C)
19
330
1
-0.1
23
540
24
0.2
27
780
60
0.32
20
18
16
16
Ankistrodesmus (62 PM P d- ‘) and Anabaena flos-aquae
(89 yM P d-l).
The response surface that shows algae light optima suggests that the species abundant in eutrophic and hypcreutrophic lakes in summer have the highest optima (Fig.
le). The response surface with respect to temperature
optima of algae shows that algae with higher values are
abundant in midsummer and that those abundant in early
spring and autumn have lower value optima (Fig. 1f). The
highest temperature (T) optimum occurs for eutrophic
and hypereutrophic algae. The data from KZllquist (1982)
permitted us to calculate regressions between temperature
optimum and temperature minimum at optimum light,
and temperature optimum and temperature maximum.
Kgllquist (1982) did not use temperatures high enough
(> 30°C) to identify the maximum temperature for two
green algae, so we extrapolated the temperature/growth
rate curve and assigned a maximum of 40°C to these algae
(Seip and Satoh 1984). The regressions were
Tmin = 0.875T,,,, - 18.72
r2 = 0 .67 7 P = 0 .0006.9
(1)
T tnax= 1.32T,,, + 1.503
r2 = 0.87, P = 0.0001.
(2)
The two slopes are not significantly different and appear
almost parallel in graphs. The response surface for sinking
rates (Fig. lg) is not significant and also lacks representative algae in the upper right corner.
($P)
0.01
0.04
0.15
21
Discussion
It is interesting to compare the attributes of phytoplankton with their performance with respect to the physical and chemical characteristics of the water in which
they live. Since we use fairly wide categories for the water
types, there are inevitably several confounding characteristics. Trophic level has been defined with reference to
peak total cell volume and to TP concentration in the
water. However, several European lakes with high TP
concentration also tend to be shallower. Seip et al. (19923)
found the regression
log(TP) = - l.l3(log z) + 3.29
r2 = 0.66, P = 0.0001, N = 18.
(3)
These lakes also tend to be smaller (Seip et al. 19923)
and, on average, warmer. Marshall and Peters (1989) found
3-4”C difference in mean air temperature for a set of 5 1
oligotrophic and eutrophic lakes (in their definition, lakes
with Chl a > 12 mg m-3 were eutrophic, and lakes with
Chl a < 7 mg-3 were oligotrophic). Associated with shallow lakes (< 10 m) is a tendency for increased sedimentwater interaction and enhanced activity of sediment-fecding fish to increase the internal loading of nutrients (Brabrand et al. 1990). However, Mazumder et al. (1990)
showed that very small lakes (< 10 ha) are less susceptible
to wind mixing than larger lakes; thus, unless small lakes
are uniformly very shallow, they may also be influenced
less by mixing of interstitial waters into the open water.
Fig. 1. Response surfaces for phytoplankton characteristics along season and trophic level: a-single cell size as volume (pm3);
b-growth rate (In units, d-l); c-N: P ratio (by wt); d-affinity for P (PM P d-l); e-Light (6 x 1017quanta m-* d-l); ftemperature, “C; g-sinking rate, m d -* (not a significant surface);h-half-saturation constant for P-limited growth (K,, PM P).
Shaded areas show regions with high values of the characteristic. Significance of the two factors (season and trophy) is suggested
with line(s) parallel to the significant axis (axes); a dot suggests a significant interaction effect and NS indicates that the overall
regression is not significant (P > 0.05). In panel a, the trophic and seasonal categories of the algae used to construct the surface
are indicated (0) and the numbers at some symbols refer to Table 1. The selection of algae used to construct the other surfaces
may differ from the selection used to construct the cell-volume surface.
594
Seip and Reynolds
Table 3. Resulting regression equations for phytoplankton
0.01; ***-P
< 0.001.
Variable
&Yj
Cell vol.
(w+)
T
T2
S
S2
T-S
Intercept
R2
RMS-Res
0.247**
-0.007**
1**
-0.127**
-0.038*
- 1.151
0.50
0.39
29
0.005**
-0.261
0.025
1.453**
-0.15”
-0.066*
1.459
0.44
0.58
32
0.007**
;
N:P
W)
2.125***
4.28:*
-0.928
-2.113
0.89
1.55
10
0.002**
Affinity
(/.JM P d-‘)
- 14.248*
2.038**
95.03 1**
-9.682*
-5.195***
- 52,705
0.74
15.28
16
0.009***
Previous studies have shown that total algal biomass,
as Chl a, varies with trophy and season. Timing and
distinctiveness of phytoplankton blooms (in terms of Chl
a) significantly reflect physical characteristics of the water
bodies as these change with season. (Results with P <
0.05 are termed significant in this work. Tests on residuals
are seldom reported.) In shallow lakes (mean depth, < 13
m), the vernal bloom commonly starts immediately after
ice-out (Sommer 1986). Marshall and Peters (1989) give
an equation for the bloom date (BD, day since 1 January)
which shows that BD occurs earlier for high trophic levels
(here measured as log mean Chl a) and high mean annual
air temperature, T (e.g. earlier ice-out or lower latitude):
BD = 158.6 - 23.3(log Chl a) - 32.6(log T)
P = 0.005.
attributes:
(4)
In deep lakes, significant phytoplankton increase probably does not start before the onset of thermal stratification and shrinkage in the depth of the wind-mixed layer
(Sommer 1986; Sverdrup 19 5 3). Between-year variations
in the depth of stratification for a given lake arc a function
of air temperature and wind velocity (r2 = 0.89, P = 0.004
in Lake Mjosa, Norway, Seip 199 1). However, in spite
of the predictability of stratification depth, there has been
limited success in predicting the timing of the vernal bloom
for stratifying waters (probabilities higher or only slightly
lower than 0.05: Perry et al. 1989; Seip 199 1).
The vernal and summer blooms are more pronounced
in meso- and eutrophic lakes than in lakes which tend to
oligotrophy.
Marshall and Peters (1989) observed one
vernal peak in March-April
and an autumn peak in August-September, and Sommer et al. (1986) showed that
blooms are more pronounced for lakes with soluble reactive P > 15 mg m-3.
We sought to extend such statistical descriptions to the
attributes of the phytoplankton
themselves. We chose to
refer seasonality to day of the year rather than to lake
events because the latter had already been done by Reynolds (19843). Also, the patterns in Chl a along trophic
and seasonal gradients quoted above have already been
related to day of the year. A third argument is that predictions of seasonal Chl a, based on an algorithm that
T-trophy;
Sinking rate
(m d-l)
-0.038
0.002
-0.455**
0.064**
0.014
0.711
0.61
0.10
16
0.06
S-season.
Temp.
(“Cl
-0.052
-0.022
8.433*
- 1.32*
0.077
13.11
0.56
2.86
20
0.026*
Asterisks: *--P
< 0.05; **-P
<
Light
(quanta
m-2
s-l
-7.154
-0.22
58.277
- 11.52”
1.98
70.589
0.61
27.79
18
0.03*
1
($P)
-0.026
0.0002
-0.358***
0.044**
0.016***
0.488
0.72
0.066
21
0.001***
adjusted for the timing of the vernal bloom, did not improve the prediction success relative to a prediction based
on day of the year (France et al. 1994).
WC tested the cell sizes (as cell volumes) found in the
cited sources against cell volumes found by Sarnelle ( 1993)
for phytoplankton
in Zaca Lake, California. He gives
greatest and second greatest axial linear dimension and
approximate shape, so we calculated approximate cell
volumes from these data. Comparison between the logarithm of cell volume based on the two independent
sources gave a probability of 0.0 1 and an explained variance of r2 = 0.78 (n =7). The cell volumes of given phytoplankton groups may thus be fairly general. Our definitions of small, medium, and large values of phytoplankton attributes are subjective and may lead to characterizations different from others. For instance, we classified
Fragilaria as a medium-sized alga, whereas Lampert ct
al. (1986) classified it as a large form in relation to its
availability
to filter-feeding zooplankton.
The response surface for phytoplankton
cell volumes
corresponds well to the development of small algae at the
beginning of the vernal bloom period when nutrient availability is high and grazing pressure low (Sommer et al.
1986). Large algae grow under high temperature and water
clarity and higher grazing pressure and slowly achieve
dominance in late season, often under nutrient-depleted
conditions (Fig. 1b; Sommer 198 1; Reynolds 1988). That
several of these species may also have a high nutrient
affinity is reflected in Fig. Id. Note that this surface does
not reflect the size of cell colonies. (A surface based only
on the subset of data quoted in Table 1 will be nonsignificant, P = 0.22, r2 = 0.46, but still similar to the reported surface. In general, the selection of algae chosen
for Table 1, i.e. those with high “density” of attribute
values reported in the literature, will not suffice to reproduce significant surfaces.)
The response surface for growth rate (Fig. lb) may be
interpreted as influenced by physical conditions (i.e. light
dependence super-imposed on nutrient constraint in oligotrophic systems) but dominating in nutrient-rich
systems. The leftward increase in pmax with trophy may be
attributed to the eutrophic system being generally shal-
Phytoplankton attributes
lower (Eq. 3) and attaining a vernal peak in Chl a earlier
and often before the lake stratifies.
The surfaces for N : P ratio (by wt), light optimum, and
temperature optimum each appear to reflect ambient
properties of the water. The N : P concentration ratio (by
wt) in lake waters was shown by Downing and McCauley
(1992) and Seip (1993) to decrease from -50 in oligotrophic water to - 5 in hypereutrophic waters. The surface
for the N : P responses of algae to the season-trophy matrix shows that algae relatively abundant in late summer
or autumn in eutrophic and hypereutrophic waters have
a low optimal N : P ratio of resource requirements (Fig.
lc). The response surface for algal affinity for P is similar
to the response surface for the N : P ratio. The surface
shows a decrease in affinity with increasing trophy for
late-blooming algae. The zero values found for very eutrophic conditions in autumn probably reflect the occurrence of cyanobacteria. Some of these are able to fix nitrogen from the air, indicating that the TN : TP ratio in
the water does not have the same significance for these
algae as for nonnitrogen-fixing algae. Furthermore, for
very eutrophic lakes, TN rather than TP is more likely
to become limiting (McCauley et al. 1989; Seip 1993).
The surfaces also show relatively high values for N : P
ratio and affinity for algae abundant during spring in hypereutrophic lakes. Because the increase in affinity for
eutrophic lakes is represented by one contour step, the
result may be artificial. During the vernal period, the
nutrient level should be irrelevant for phytoplankton
growth. For example, Reynolds (1993) showed that the
carrying capacity for Chl a with respect to nutrients is
much higher than actually observed Chl a values during
the vernal period in Rostherne Mere and for a long time
in a lake like Windermere (Reynolds 1990, fig. 11). This
finding is consistent with the general theory of Sverdrup
(1953), which holds that algae are limited by light during
the vernal period.
The results are similar for both N : P ratio and affinity.
Since it seems reasonable that algae that have a high N :
P ratio also have a high affinity for P, the results support
each other. It is encouraging that we are able to consistently express algal nutrient properties by the independently observed parameters N, P, pmax,and KS.
The temperature optima for growth reflect the range of
temperature variations in the water. The optimum temperature for phytoplankton is highest in summer and for
eutrophic lakes. In temperate northern-hemisphere lakes,
the temperature is highest in June-July (Reynolds 1990).
The higher temperature in eutrophic lakes may be a factor
of size and especially of lake shallowness. Better heating
of “dark” and colored water than of oligotrophic water
with high clarity may also contribute (Mazumdcr ct al.
1990). The two other temperature attributes (i.e. low and
high temperatures which give low pm,,) are proportional
to those at the optimum temperature. Thus, “strategies”
for phytoplankton survival related to low and high temperatures will easily be confounded with strategies expressed by the optimum temperature for growth.
We give results only for optimum light because our
data are sporadic for intensities that will give zero growth.
595
The light effect is strongest for summer and for eutrophic
lakes. Thus, the light optimum reflects the ambient light
climate with respect to season. A factor separating eutrophic lakes from oligotrophic lakes with respect to light
is the increased self-shading of algae and the increased
contribution to light extinction from nonalgal particles
(> 50% for TP > 250 mg m-3; Seip et al. 1992a). Algal
responses to low, optimum, and high light intensities are
important for their survival or dominance under different
circumstances. Lovstad et al. (1988) reported, for example, that the ability of Oscillatoria to grow at very low
light intensities (~60 x 1OL7quanta m-2 s-l, 13”C), in
which the diatoms Asterionella, Diatoma, Tabellaria, and
Stephanodiscus died, was an important competitive
mechanism (see also Reynolds 1993). Thus, responses
characteristic to low light are evidently important for some
species.
Species belonging to the same functional assemblage
arc also supposed to bc “neighbors” along the axes representing trophic level (T), and season (S), so fitting a
smooth surface for an attribute, e.g. pmax= AS, T) should
also identify the mean values of that attribute for the
corresponding functional groups. As a corollary, it should
be possible to predict from the response surfaces the attribute values of species where the trophic condition and
season of maximum abundance are known.
The phytoplankton community has been described as
going through several phases during the season. In a hypereutrophic gravel-pit lake, Rojo and Cobelas (1993)
found seven periods of stability or “equilibrium” phases
lasting 2-10 weeks. Sommer (1986) divided the season
into 24 phases with reference to shifts in species dominance; only five of these were controlled by physical events.
There is an apparent discrepancy between the several
seasonal phases a lake may undergo in its physical regime
(periods of disturbance, stability) or in biological interactions (competition, selective grazing) and the largely
unimodal response surfaces found for algal attributes. One
may envisage that “canopy’‘-forming species usually
dominate, but if the canopy is broken, other species replace each other during short and incomplete successional
sequences. Furthermore, the physical conditioning of an
alga with a certain set of attribute responses may coincide
with the successional preadaption of the same algal species. This need not be a stochastic event, since physical
factors, by modifying phytoplankton growth rates and
death rates, also modify their competitive abilities.
The equations describing the surfaces are based on a
limited number of data points (1O-3 1) and are therefore
particularly susceptible to outliers with large or small values. Furthermore, because the independent variables span
a plane, the surfaces are sensitive to nonhomogeneous
distribution of samples. An alternative might be to describe the response surface with (e.g.) spline functions.
Except for the hypertrophic region in autumn, the samples
have a fairly uniform distribution. In the sparsely covered
region, the algae most often dominating are filamentous
cyanobacteria, some of which adjust their sinking rate
depending on nutrient status (Reynolds 1984b), and some
solitary species of Anabaena fix nitrogen from the air;
596
Seip and Reynolds
therefore, their attribute values will not always be well
defined. A series of other factors that might potentially
impede the construction of significant response patterns
are lakes in which TP does not limit algal growth, successional steps in time that may be strongly lake-dependent or event-dependent, species of the same genus that
may be very different with respect to size and physiology,
and bioassays (from which physiological response characteristics were obtained) that may be biased due to preconditioning
or because factors other than those studied
limit growth. On the methodological
side, we have restricted ourselves to second-order response surfaces. Despite this, seven of eight response surfaces are significant
at P < 0.05, and five of eight are significant at P < 0.01.
Furthermore, the general shapes of the response surfaces
are supported by the similarity with surfaces anticipated
on physiological grounds (i.e. N : P ratio and affinity for
P). It is also gratifying that these response surfaces should
so reflect the ambient properties of the water.
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Submitted: 15 September 1993
Accepted: 25 August 1994
Amended: 19 October 1994