LIMNOLOGY
AND
OCEANOGRAPHY
/
March 1990
Volume 35
Number 2
I
Limnol. Oceanogr.. 35(2), 1990, 245-259
0 1990, by the American Society of Limnology
and Oceanography,
Inc.
Molybdenum and sulfate as controls on the abundance of
nitrogen-fixing cyanobacteria in saline lakes in Alberta
Roxanne Marino and Robert W. Howarth
Section of Ecology and Systematics, Division of Biological Sciences, Corson Hall, Cornell University, Ithaca,
New York 14853
Jennifer Shamess and EIIie Prepas
Department of Zoology, Biological Sciences Center, University of Alberta, Edmonton T6G 2E9
Abstract
We studied 13 saline lakes in Alberta to test the hypothesis that molybdenum availability
influences the abundance of planktonic, N-fixing cyanobacteria in saline ecosystems. Our earlier
work in oxic seawater showed that the availability of Mo is controlled in part by the ratio of sulfate
to molybdenum because sulfate inhibits the assimilation of molybdate. The SOd2-: MO ratio in
seawater is very high relative to most freshwater lakes-a finding that is consistent with the scarcity
of planktonic, N-fixing cyanobactetia in coastal marine ecosystems. This ratio is constant in seawater, however, limiting a test of our hypothesis in marine systems. These Alberta salt lakes provide
a more robust test in saline systems.
The ratio of sulfate to molybdenum within any given saline lake was relatively constant over a
summer season, but the ratio between lakes varied and ranged from values typical of freshwater
lakes to values higher than in seawater. N-fixing cyanobacteria are significant fractions of the
plankton in six of the 13 lakes we studied and are rare or absent in the others. The SO,*- : MO
ratio was a strong predictor of the abundance of planktonic, N-fixing cyanobacteria. Sulfate or MO
concentrations alone, however, were not. This finding is consistent with our hypothesis that sulfate
can control MO availability in oxic waters.
Phosphorus concentrations, and the ratio of N to P, were not good predictors of the abundance
of N-fixing cyanobacteria in these saline lakes, as they often are in freshwater lakes. The differences
between predictions from a freshwater, P regression model and actual abundances of N-fixing
cyanobacteria in the saline lakes were best explained by the SO.,- : MO ratio.
columns of freshwater lakes (Stewart 1969;
Goldman and Home 1983; Wetzel 1983;
Howarth et al. 1988b). In many eutrophic
freshwater lakes, N fixation by these cyanoAcknowledgments
We thank Hap Garritt, Nadine Gassner, Gertie
bacteria is instrumental in making up defHutchinson, Barbara Plonski, Chris Robinson, Anicits of N and in maintaining P limitation
nette Trimbee, and Frances Yarbrough for assistance of annual net primary production (Schindwith field sampling or chemical analyses.We also thank
Stephen R. Carpenter, Jonathan Cumming, and Charles ler 1977; Plett et al. 1980; Howarth 1988;
E. McCulloch for help with the statistical analyses. Howarth et al. 1988b). The activities and
Nelson Hairston, Jr., Jonathan J. Cole, Stephen R. abundances of planktonic, N-fixing cyanoCarpenter, Karen McGlathery, and Diane Sherman bacteria in freshwater lakes are largely conprovided comments on the manuscript.
Financial support was provided by grant BSR 86- trolled by the availability of P (Vanderhoef
et al. 1974; Home and Goldman 1972; Liao
04688 from the Ecosystems Studies Program, U.S. National Science Foundation.
1977; Brownlee and Murphy 1983; Stock245
Planktonic cyanobacteria (blue-greens)are
responsible for most N fixation in the water
246
Marino
ner and Shortreed 1988) and by the ratio of
N to F’availability (Flett et al. 1980; Schindler 197 1; Stockner and Shot-treed 1988;
Howarth et al. 1988a). Significant N fixation occurs only when N is in shorter supply
than I’ relative to the physiological requirements of the cyanobacteria (Tilman et al.
1982).
There is a surprising lack of planktonic
N-fixing cyanobacteria in most estuariesand
coastal seas (Smayda 1973; Hornc 1977;
Doremus 1982; Howarth et al. 1988b). In
fact, abundant populations of planktonic,
N-fixing cyanobacteria have been reported
for only two estuarine or coastal marine ecosystems: one low salinity estuary (the Baltic
Sea; L,ind,ahl and Wallstrom 1985) and one
very shallow, highly eutrophic estuary (the
Peel-Harvey Inlet, Australia; see Huber
1986). Planktonic N-fixing cyanobacteria are
absent from many estuaries with low N: P
ratios in their nutrient inputs and with low
N : P ratios in total dissolved nutrients, conditions which would lead to dominance by
cyanobacteria in freshwater lakes (Howarth
et al. I.988a). The relative lack of planktonic
N fixation in many turbid, temperate-zone
estuaries and coastal seasis instrumental in
maintaining N limitation of primary production in these ecosystems (Ryther and
Dunstan 1971; Howarth and Cole 1985;
Howarth 1988; Howarth et al. 19886).
We should note that in contrast to planktonic N-fixing cyanobacteria, benthic forms
of N-fixing cyanobacteria are common in
estuaries where water clarity is great enough
for significant light penetration (Howarth et
al. 1988b). In most temperate-zone estuaries, however, such fixation is unimportant
as a source of N to the ecosystem because
light penetration is low through the turbid
waters (Howarth 1988). We have suggested
that reduced Fe and MO coming from the
sediments or from anoxic zones within cyanobacterial mats are probably more available than are Fe and MO in oxic waters,
perhaps explaining the contrast between
planktonic and ben.thic N fixers (Howarth
et al. 1988a).
A variety of hypotheses has been offered
to explain the relative absenceof N fixation
and N-fixing cyanobacteria in the plankton
of estuaries and coastal seasas compared to
et al.
lakes. These include a lower availability of
MO in seawater due to competitive inhibition of MO uptake by SOdL-(Howarth and
Cole 1985; Cole et al. 1986; Howarth et al.
1988a), lower concentrations of Fe in seawater (Rueter 1982; Howarth et al. 1988a),
lower concentrations of P in seawater (Doremus 1982), and a greater turbulence intensity and lower concentrations of dissolved organic matter in seawater, making
anoxic microzones less likely (Paerl 1985;
Paerl and Prufert 1987). These ideas need
not be viewed as mutually exclusive. We
have argued that Fe and MO availability
may interact to limit N fixation in marine
ecosystems and that high organic matter
concentrations and anoxic microzones could
increase Fe and MO availability (Howarth
et al. 1988a).
Because planktonic N-fixing cyanobacteria arc rarely abundant in coastal marine
ecosystems, little insight is gained from
comparative studies of planktonic N fixation in estuaries. The data that are available
can be interpreted as being consistent with
several of the above hypotheses. Furthermore, it is difficult to test our specific hypothesis that MO availability (as controlled
by an interaction of the sulfate and molybdate anions) is an important biogeochemical control on the abundance of planktonic
cyanobacteria and N fixation in seawater
and estuarine systems (Howarth and Cole
1985) because both sulfate and molybdate
are conservative in oxic seawater (Head and
Burton 1970; Brewer 1975; Collier 1985).
Hence, the concentrations of both anions
covary as a function of salinity, and their
ratio is nearly constant.
These difficulties led us to use saline lakes
as partial chemical analogs of estuaries in
which to test the hypothesis that MO availability can partially regulate the abundance
of planktonic, N-fixing cyanobacteria in saline waters. Limited data on MO and SOd2concentrations in two saline lakes (Wurtsbaugh 1988; Howarth et al. 1988a) led us
to believe that the SOe2-: MO ratio may be
much more variable in saline lakes than in
seawater. We chose 13 salt lakes in Alberta;
N-fixing cyanobacteria are abundant in the
plankton of some of these saline lakes but
are rare in others. We asked whether the
N-jixing cyanobacteria
247
tions of the physical and biological characteristics of these lakes are given elsewhere
(Bierhuizen and Prepas 1985; Campbell and
Prepas 1986).
Field samples-For
each lake and for all
measurements, samples were taken at the
point of maximal depth monthly from May
through August 1987. Temperature profiles
were measured at 0.5-m intervals with a
Montedoro-Whitney
resistance thermometer. Integrated water samples for nutrients
Methods
(N and P), major anions and cations, pH,
alkalinity, conductivity, total iron, and Chl
13 saline
Description of the l&es-The
a were collected over a depth equivalent to
lakes we studied are located in southeastern
two times the Secchi disk depth (Bierhuizen
Alberta
between
52”14’-5 3”3 1 ‘N and
111”19’-114”14’W.
For the most part, they and Prepas 1985). For most lakes, the integrated sampling depth was 0.5 m and was
are surrounded by a mixture of aspen parkland, prairie grassland, and cultivated fields never >3 m. True duplicate samples were
and have no permanent inlet streams or sur- collected at each site. Water samples for N
face outflows. Hence, the water balance in were stored in 250-ml polystyrene containers; samples for all other analyses were
these lakes is controlled by precipitation,
evaporation, and groundwatcr flow. The av- stored in l- or 2-liter polyethylene bottles.
erage annual rainfall in the region is 300 All of the N samples were analyzed within
mm; evaporation from the lakes averages 48 h of collection. Opaque bottles were used
for the Chl a samples, which were filtered
600 mm yr-‘. Because surface-water inputs
within 6 h of collection. All bottles were
are small and there arc no surface outflows,
water residence times in these lakes are completely filled with water and kept cold
strongly inlluenccd
by groundwater
ex- in the field until they could be refrigerated
change; estimates of water residence times
at 4°C. The samples were stored at 4°C until
typically exceed 100 yr.
they were analyzed (within 7 d). All samThe lakes have surface areas of 0.3-51
pling containers were acid washed with 2%
km2 and maximal depths of 0.9-S m. ConHCl and rinsed well with distilled-deionized
ductivities range from 1,300 to 18,000 pmho
water.
cm-‘. Sulfate, bicarbonate, and carbonate
Water samples for MO analysis were colare the dominant anions. The dominant catlected over the 0.5-m depth interval by
ions are sodium, calcium, and magnesium.
pushing an inverted, open sampling bottle
All of the lakes are alkaline, with pH values
down to the sampling depth, reinverting the
ranging from 8.3 to 9.6 and total alkalinities
bottle, and bringing it back to the surface.
of 5.3-85 meq liter-‘. The sediments ofthese
When a motor boat was used for sampling
lakes are typically coarse, sandy, and orinstead of a canoe, extreme care was taken
ganic poor. None of the lakes was pcrmato avoid potential contamination from monently stratified during the study period
tor fuel. The samples were taken and stored
(May-August 1987); dissolved oxygen levels in 125-ml polypropylene
bottles cleaned
were always >5 mg 0, liter’. Mean sumwith a hot solution of MICRO detergent,
mer Chl a concentrations in the photic zone followed by a deionized water rinse and an
ranged from 4.5 to 115 pg liter-r. All but
overnight soaking in hot 20% HNO,. At
three of the lakes are fishless. Miquelon Lake
each site, two water samples were taken.
and Cooking Lake have only brook stickOne sample was not filtered, and the other
leback (Culaea inconstant). Buffalo Lake has was filtered immediately through a lo-pm
brook stickleback
and three additional
polypropylene prefilter (Gelman) and a 0.4species of fish: northern pike (Esox Zucitr.s), pm polycarbonate (Nuclepore) filter with a
burbot (Lota lota), and white sucker (Ca- 47-mm Millipore swinnex set-up-all
plastostomus commersoni). Further descriptic and cleaned as described for the sam-
SOd2- : MO ratio was variable in these saline
lakes, and if so, if the abundance of planktonic, N-fixing cyanobacteria was related to
the availability of MO as determined by the
SOd2- : MO ratio. We also obtained data on
P and N, the usual predictors of N-fixing
cyanobacterial abundance and activity in
freshwaters, and on Fe, the other trace element required by N-fixing organisms.
248
Marino et al.
pling bottles before use. Both types of filters
were acid washed with 20% HN03, well
rinsed with Nanopure deionized water, and
air-dried under cover before use.
Both the filtered and unfiltered samples
were stored in the polypropylene sampling
bottles and kept on i.ce in a cooler until they
arrived at Cornell University, where they
were stored at 4°C until analysis. In addition
to dissolved MO, the filtered water samples
were used for SOd2-, Cl-, Na+, K+, Ca2+, and
Mg2+ .analyses. The unfiltered samples were
used for total Fe analysis.
Chemical methods-Conductivity,
alkalinity, pH, Chl a, N, and P were determined
at the University of Alberta. Conductivity
was measured with a YSI model 31 conductance meter on water samples at 20°C.
Alkalinity was measured at the same time
as pH by potentiometric titration (Environ.
Can. 1979) with a Fisher Accumet model
520 digital pH meter. Within 6 h of collection, water samples for Chl a analysis were
filtered under low pressure through Whatman lGF/C filters, placed in Petri dishes,
wrapped in aluminum foil, and stored in a
freezer. Within 2 weeks, Chl a was determined on duplicate samples with the ethanol extraction-spectrophotometric
technique of Ostrofsky as outlined by Bergmann
and Peters ( 1980).
N and P concentrations were measured
in each monthly sample with the exception
of total dissolved phosphorus (TDP), which
was rneasured in the May samples only.
Within 48 h of collection, water samples for
total phosphorus (TP), TDP, and (NO,- +
NO,-) were prefiltered through a 250~cLrn
Nitex net. Samples for TP analysis were then
transferred to culture tubes and determined
in triplicate as by Prepas and Rigler (1982).
TDP was determined in duplicate after further Elltration through prewashed, 0.45~pm
Millipore HAWP filters by Menzel and Corwin’s (1965) potassium persulfate method
as modified by Prepas and Rigler (1982).
Ammonium
and total Kjeldahl nitrogen
(TN) were analyzed in triplicate by Sol&zano’s (1969) phenolhypochlorite
method
as modified by Prepas and Trew (1983).
N03- + N02- was determined after filtration t’hrough prewashed, 0.45-Hrn Millipore
HAWP filters by Cd.-Cu reduction (Stainton
et al. 1977).
S042- and Cl- were ana.lyzed at Cornell
on the filtered water samples in triplicate
using a DIONEX 2OOOi/SP ion chromatograph equipped with an HPIC AS4A, highcapacity, anion exchange column. Ca2+,
Mg2+, and K+ were analyzed in duplicate
filtered water samples on a J.Y. 70P inductively coupled plasma emission spectrometer by the CALS analytical laboratory at
Cornell University.
Na+ was analyzed by
the same laboratory on an Instrumentation
Laboratories model 400 flame atomic absorption spcctrophotometer.
Total Fe was
measured on the unfiltered water samples
in triplicate by the basic procedure of Koroleff et al. (1983) with some modification.
The sample replicates were oxidized in 125ml polypropylene bottles with an acid-persulfate solution. One replicate of each sample was digested in the same bottle in which
the sample was stored; there were no significant differences between this replicate
and the other two splits from the original
bottles, indicating that sorption of Fe onto
the walls of the container during sample
storage was not a problem. The oxidized Fe
was reduced with ascorbic acid and the resulting ferrous iron was measured after
complexation
with TPTZ on a Beckman
DU-50 spectrophotometer at 595 nm. Since
the color development is pH sensitive and
these lakes have a wide range of alkalinities,
we adjusted each sample pH individually
instead of using a single NaOH reagent addition.
Dissolved MO, which is predominantly
the MoOd2- anion in oxic waters in the pH
range of the lakes studied (Manheim and
Landergren 1978), was measured in triplicate on a Varian 1475 atomic absorption
spectrophotometer
equipped with a GTA
95 graphite tube atomizer. Samples were
first coprecipitated
with hydrated manganese dioxide. Coprecipitation
was used
both to concentrate and separate the MO
from the highly variable and sometimes
concentrated salt matrices of the different
lakes. These saline matrices made direct injection of the water samples into the graphite furnace impossible due to extreme background interferences. The procedure used
to form the Mn02 precipitate was modified
from that of Bachmann and Goldman (1964)
to use smaller sample volumes and to allow
249
N-Jixing cyanobacteria
Table 1. Summer mean abundances of total phytoplankton, total cyanobacteria, and only N-fixing
species of cyanobacteria in the 13 Alberta saline lakes
(k 1 SE, n = 4). Standard errors reflect the May-August
variations in cyanobacterial abundances.
% cyanoLake
Birch
Looking Back
Postill
Miquelon
Wappa
Joseph
Peninsula
Whites
Haunted
Camp
Red Deer
Cooking
Buffalo
TOtal
biovolume
O*m’ ml-‘)
% Cyanobacteria
(all species)
bacteria
(N-fixing
species only)
7.0x 104
1.1x108
8.9x 106
1.4x 106
1.4X108
1.7x 107
6.4x lo4
5.0x 105
3.3X106
3.3x107
2.8~10~
4.8 x IO’
4.7x 106
0
21*12
23t22
4.5t4.4
50?29
4.5k3.0
20+19
49_t28
21+24
65+14
16&15
42+14
59+18
0
.2.4+ 1.2
0.41 kO.37
50:29
0.43kO.29
0
49+28
27+24
0
16+15
37&16
15k8.5
for atomic absorption analysis of MO instead of spectrophotometric
analysis. Sample aliquots of 10 or 15 ml were precipitated
and filtered onto 5-pm Nylon filters, the precipitate was redissolved in 2.0 ml of 1%
hydroxylamine
hydrochloride,
and the resulting solution was analyzed for MO atomic
absorbance at 3 13.3 nm after graphite furnace atomization at 2,800”C. Because the
water chemistries of the study lakes were so
different, the recovery of spikes of a standard MoOA2- solution was tested in triplicate for each lake. Average MO recoveries
for each lake (with 99% C.I.) ranged from
77 (+ 13%) to 117% (&25%). The detection
limit for MO in these lakes with this method
was 0.15 E.cgMO liter-‘.
Phytopiankton enumeration-Samples
for
phytoplankton enumeration were preserved
with Lugol’s iodide fixture and counted by
the sedimentation
technique with an inverted microscope (Lund et al. 1958). Phytoplankton were identified to the species
level when possible. Average cell volumes
of the observed species from lakes in Alberta were used to calculate biovolumes.
Results
Phytoplankton data-The
cyanobacteria
found in the lakes which are capable of N
fixation were Aphanizomenon
jlos-aquae
and Anabaena spp. There were also several
species of cyanobacteria which apparently
Table 2. Mean summer nutrient concentrations in
the 13 Alberta saline lakes (&I SE, n = 4). Standard
errors reflect May-August variations.
Lake
Birch
Looking
Back
Postill
Miquelon
Wap a
Jose h
Peni sula
Whit s
Hau ted
Cam
Red 1 eer
Cooking
Buffalo
z;,
(2
DIN
bW
TN:TP
(molar)
430+ 13
450+55
4.6k1.6
1.1
9.6-t0.6
58+15
9.7kO.4
40fl
28+2
110+2
120+_5
2.0fO.l
5.3kO.5
37*1
8.8kO.5
2.9-to.4
470f51
36O-cl5
400224
320+74
410&24
310?32
220+-15
410f27
330540
370+-35
570?3
260?10
4.7k2.0
6.7k3.2
9.8k4.6
13f7
3.7kl.O
4.6k1.9
5.4k2.3
2.1kO.8
2.5kO.6
5.9k4.6
2.1kO.l
1.9kO.7
49
6.2
41
8.0
15
2.8
1.8
205
62
10
65
90
do not fix N, the most common being Gomphosphaeria lacustris, Lyngbya limnetica,
and Merismopedia tenuissima. Cyanobacteria were virtually absent in some of the
lakes but made up as much as 65% of the
total mean seasonal phytoplankton biomass
in others (Table 1). The N-fixing species of
cyanobacteria made up as much as 50% of
the mean seasonal phytoplankton
biomass
in two of the lakes.
To analyze our data and determine which
water chemistry parameters most strongly
influence the relative biomass of cyanobacteria in these saline lakes, we used a model
similar to that of Trimbee and Prepas (1987)
for 19 freshwater lakes in Alberta. So that
the cyanobacterial biomass data would approach a normal distribution, Trimbee and
Prepas (1987) defined the term
BG index = ln[%BG/( 100 - o/oBG)] (1)
where %BG is the percent of total phytoplankton biomass as cyanobacteria (bluegreens of all species whether able to fix N
or not). As originally defined by Trimbee
and Prepas (1987), the BG index, which is
undefined at 0 and 100% cyanobacteria, has
values ranging from -4.59 to +4.59, corresponding to 1 and 99% cyanobacteria. One
can also define the BG index with broader
or more narrow limits. For most of this paper, we used a BG index with values ranging
from -6.9 1 to + 6.9 1, corresponding to 0.1
and 99.9% cyanobacterial biomass as the
lower and upper limits, in order to make
Marino et al.
250
Table: 3. Chemical data for the 13 Alberta saline lakes studied. All data given are the means of monthly data
from May-August 1987.
-c
----___
Lake
Birch
Looking Back
Postill
Miquelon
WaDna
Joseph
Peninsula
Whites
Haunted
Camp
Red Deer
Cooking
Buffalo
+
Cond.
(mmho
smic,
Cl@Ml
DlC
NW
Cl”
(mM)
MP+
(mM)
(24)
(24)
Cm-‘)
PH
(%)
Total Fe
01W
65
1.7
4.2
23
6.5
5.6
47
12
2.6
2.2
49
3.0
4.0
8.6
3.6
0.40
3.2
0.60
0.88
5.0
1.7
0.63
0.30
2.6
0.45
0.33
42
2.9
4.8
13
8.8
8.3
21
11
16
8.4
20
4.1
10
0.19
0.50
0.50
0.25
0.50
0.37
0.31
0.25
0.12
0.19
0.30
0.50
0.25
1.4
1.0
0.82
8.4
1.2
2.1
3.3
0.82
2.2
3.9
5.4
2.5
3.7
2.9
0.26
0.26
1.4
0.26
0.45
1.8
0.38
0.45
0.26
2.1
0.26
0.38
250
10
31
67
30
26
150
49
38
15
150
11
22
18
1.3
1.7
6.5
2.8
2.7
12
4.3
3.1
1.7
11
1.4
2.4
9.6
8.8
8.7
9.3
9.0
9.2
9.4
9.1
9.3
9.1
9.3
8.7
9.2
43
4.4
12
19
30
19
24
35
27
8.5
57
7.4
23
3.3
~6.7
2.9
2.3
50
13
4.7
26
0.46
2.4
5.8
3.6
3.4
ftrll me of our detailed species composition
and biomass data. With these broader limits, th.e BG index for the 13 saline lakes
studied ranged from -6.91
(~0.1%) to
+0.6;! (65%).
In addition to calculating the BG index
(including all species of blue-greens present)
for each of our lakes, we also calculated an
index considering only the cyanobacterial
specie:s able to fix N (denoted BGF index),
since we are speciftcally interested in the
controls on N fixation. This was calculated
as in Eq. 1 with 0.1 and 99.9% cyanobacterial biomass (nitrogen-fixer species only)
as the lower and upper limits (-6.91 and
+6.91, respectively). Values for the BGF
index ranged from -6.91 (10.1%) to 0.00
(50%)1 in the 13 saline lakes.
Chemical data-In general, the saline
lakes we studied have high concentrations
of epilimnetic TN and TP relative to most
freshwater lakes during the growing season.
The summer seasonal variations in TP and
TN were small, as can be seen by the low
standard errors (Table 2). TP in these lakes
is predominantly dissolved P (unpubl. data
from this study; see also Bierhuizen and
Prepas 1985; Campbell and Prepas 1986).
Dissolved inorganic nitrogen (NO,- + NOz-k NI-L,+) was a small percentage of the TN
in all of the lakes (concentrations were typically < 1% and at most 4% of TN). The
concentrations of DIN varied quite a bit
from month to month within any given lake,
as is reflected in the high standard errors for
the mean summer DIN (Table 2).
As mentioned earlier, the dominant cation in all of these lakes is Na+, with concentrations ranging from a high of 250 mM
to a low of 10 (Table 3). The dominant anion is SOa2-, with HCO,- and CO,*- (given
in Table 3 as total dissolved inorganic carbon, or DIC) being the second and third
most abundant anions. The mean seasonal
ionic compositions of the lakes in this 1987
study were very similar to that found by
Bierhuizen and Prepas (1985) in their 1983
study. Total Fe concentrations in these saline lakes are generally h:igh compared to
other aquatic systems and span a considerable range (Table 4).
The among-lake variability
in the concentration of SOd2- as well as the other rnajor anions and cations was much greater
than was the month-to-month,
within-lake
variability. The variation in SOd2- concentration over the summer for a given lake
was at most 5% of the mean; the maximal
analytical variance was 2%. The seasonal
variation in ionic composition within the
lakes is largely controlled by evaporation
and precipitation; note the relative constancy of the SOd2- : Cl ratio over the season
(Fig. 1A).
MO concentrations
also varied greatly
among the lakes but were reasonably constant within any given lake during the several months of measurement. The variance
in the monthly mean MO concentration determined for a given lake was f 14% (at
most) in all lakes except Cooking (+ 29%).
The maximal analytical variance was + 10%.
N-fucing cyanobacteria
251
Table 4. A comparison of SOd2-, MO, and total Fe concentrations along with SO,*- : MO ratios in typical
freshwater lakes, seawater, and the 13 Alberta saline lakes. Ranges given where applicable; averages given in
parentheses.
Freshwater
S0,2- (mM)
MO (PM)
SOqL-: MO (M)
Total Fe (gM)
0.05-0.31(0.11)
0.001-0.01(0.005)
(2.2 x 104)
0.9-3.7
Seawater or estuaries*
28
0.11
2.6 x 10s
0.002-l. 1
Alberta
saline lakes
1.70-68
0.003-0.08
7.4x104-2.2x106
0.50-52
* SO,‘- and MO are conservative
in seawater; hence no ranges are given for SOal-, MO, and the SO,‘- : MO ratio; their concentrations
vary as a
function ofsalinity in estuaries. Data presented here arc for full-strength seawater, taken from Brewer 1975 for Fe and from Manbeim and Landergren
1978 for MO. Freshwater SOP and Fe data taken from Wetzcl 1983; freshwater MO data from Manhcim and Landergren
1978. All saline lakes
data from this study.
SOd2-: MO ratios-As we had hoped, and
in contrast to marine ecosystems, concentrations of S0,2- and MO varied somewhat
independently in these lakes (Table 3). SOd2and MO concentrations are correlated (r =
0.70); however, the significance of the correlation is strongly influenced by the SOd2and MO concentrations from one lake (Red
Deer). The SO, 2- : MO ratio varies some 30fold among the lakes (Fig. 1B; Table 4), although the summer seasonal variation in
the ratio within each of the lakes is small
(Fig. 1B). The highest ratio, 2.2 x 106, is
considerably higher than the ratio in seawater, 2.6 x lo5 (Table 4). The lowest ratio
found in these lakes, 7.4 X 104, is lower
than the ratio in seawater and approaches
freshwater values. In contrast to marine
ecosystems where the S042- : MO ratio is
nearly constant, these saline lakes provide
an exceptionally broad range of S042- : MO
ratios over which to test our hypothesis of
geochemical
control
on abundance
of
planktonic, N-fixing cyanobacteria.
lakes. The appropriate ionic strength was
used for each lake so that the activity coefficients for the ions could be better estimated, although the relationship used to estimate activity
coefficients,
the Davies
approximation,
tends to break down as the
20
.
,
Sulfate concentration or sulfate activity?-One might argue that the activity of
S042- rather than its concentration should
be considered when assessing the potential
for competitive interaction with molybdate
and inhibition of MO assimilation by phytoplankton. We used the major ion and pH
data to calculate the activity of sulfate with
the MINEQL program (Westall et al. 1976).
There are some problems with applying
MINEQL to saline systems, especially those
where uncharacterized minerals are likely
to precipitate. We made calculations with
several different sets of assumptions and always got the result that the activity of S042was very highly correlated with its concentration (slope = 0.80, r = 1.0) in all of the
Fig. 1. A. The molar SOdz-: Cl ratio plotted for
each lake for each sampling date. Lake-to-lake variations arc considerably greater than are seasonalchanges
within individual lakes. If we assume that chloride is
a conservative tracer, these data indicate that seasonal
changes in sulfate due to geochemical and ecological
processesare relatively minor. B. The molar SO,,- : MO
ratio plotted for each lake for each sampling date (note
vertical axis is x 106). As with the SO,*- : Cl ratio, laketo-lake variations are much greater than are seasonal
changes within individual lakes. This fact allows us to
use seasonal averages in subsequent analyses.
252
Marino
et al.
Tablle 5. Results of SAS analysis of reduced models for predicting the BGF index (which considers only the
relative abundance of cyanobacteria capable of N fixation).
Model
--
Model A
Model B
--
Model
Model
Model
Model
C
D
E
F
pnrameters
log so,2log MO
log conductivity
log MO
log SOdz-: MO
log so,2log conductivity
log MO
Regression
slope
-4.8
7.7
-8.6
9.4
-4.3
-1.6
-2.2
1.6
ionic strength approaches 0.5. There were
three minerals for which formation was
thermodynamically
favorable in these systems: CaCO,, Mg,(PO,),,
and Ca,(OH)(PO,),. Formation of the two phosphate
minerals is dependent on the proportion of
the TP concentration in the POd3- form,
which we do not know (an earlier study of
these lakes found that most of the TP present was as soluble reactive phosphorus;
Campbell and Prepas 1986). We ran the
program using as upper and lower limits for
P the assumptions that all of the TP was as
Pod”- and that there was no POa3- present.
In the absence of kinetic information on the
formation of the various minerals, we also
ran t.he program both with and without the
various mineral precipitations.
All of the
above assumptions and permutations yielded nearly the same sulfate activity-concentration relationship as stated above; hence,
we h.ave used the SOd2- concentration data
in our subsequent analyses.
Discussion
SOq2- and MO as regulators of cyanobacterial abundance--The
BGF index is significantly predicted by the log of the SOd2- :
MO ratio (P = 0.039; r2 = 0.33; Table 5).
Regressions of the BGF index vs. either log
SO,“- or log MO alone are not significant
(P =: 0.32 and P = 0.73, respectively; Table
5), which is strong evidence that an interaction between MO and SOd2- controls the
abundance of N-fixing cyanobacteria rather
than just the absolute concentrations
of
SOd2- or MO. This finding is consistent with
a MO requirement for N fixation (Bortels
SE
P
1.8
3.2
2.8
3.2
1.9
1.5
2.3
2.7
0.024
0.034
0.012
0.015
0.039
0.32
0.36
0.73
-
R=
0.43
0.50
0.33
0.094
0.080
0.032
1930; Wolfe 1954; Fogg and Wolfe 1954;
Shah et al. 1984) and with Dur hypothesis
that MO availability in oxic waters is regulated by a competitive
mteraction with
SOd2- (Howarth and Cole 1985; Howarth et
al. 1988a).
We also tested a multiple regression model with the BGF index being regressed with
log SOd2- and log MO as separate predictors.
This model yielded probabil.ity values of
0.024 and 0.034 for log SO,“- and log MO
and a coefficient of multiple determination
(R2) of 0.43 (Table 5). Note that the slopes
for log SOd2- and log MO are similar (equal
within the standard errors) but are of opposite sign, and that the relationship between SOd2- and the BGF index is negative
while that for MO and the BGF index is
positive (Table 5, model B). This pattern is
expected if there is a 1 : 1 competitive effect
of SOd2- on MO availability which could be
characterized by the log SOA2- : MO ratio.
This multiple regression model is of course
very similar to the simple regression model
with the log SOd2- : MO ratio; so it is not
surprising that both give significant results.
The slightly better fit of the multiple regression model suggests that the competitive interaction between SOd2- and MO is not precisely 1 : 1.
The BG index (which considers all cyanobacteria including non-N fixers) is also
significantly predicted by the log of the
SOd2- : MO ratio (P = 0.036; p2 = 0.35; Table
6) but the regression contained one outlier
on a residuals plot which contributed heavily to the significance level found. Unlike the
case for the BGF index (which considers
only cyanobacterial species capable of N fix-
N-Ming cyanobacteria
253
Table 6. Results of SAS analysis of reduced models for predicting the BG index (which considers the relative
abundance of all species of cyanobacteria).
Model parameters
Model G
Model H
Model
Model
Model
Model
I
J
K
L
log so,=
log MO
log conductivity
log MO
log SO,z- : MO
log SO,‘log conductivity
log MO
Regression
slope
-3.0
1.7
-5.3
2.9
-3.1
-2.2
-3.4
-2.0
ation), the log of SOd2- alone was also a
significant predictor of the BG index (P =
0.027; r2 = 0.37). The log of Mo alone again
was not a significant predictor (P = 0.30).
A multiple regression model with both the
log of MO and the log of SOd2- vs. the BG
index yielded values of P = 0.46 and 0.045
for log MO and log SOd2- and R2 = 0.41
(Table 6). In comparing the multiple regression results for the BG index with those for
the BGF index, we see that including cyanobacterial species incapable of N fixation
into the index drastically lowers the significance of Mo. This finding is consistent with
a MO requirement for N-fixing species and
no MO requirement for nonfixing species
(Wolfe 1954). Including
the nonfixing
species of cyanobacteria in the index also
increases the relationship with SOh2- alone
to a significant level (P I 0.05). We are not
sure why SOd2- alone should influence the
abundance of cyanobacteria or why this correlation is significant for the BG index but
not for the BGF index, even though the information in the BGF index is a subset of
the information in the BG index.
&.dj&e or conductivity?-S0,2- is the most
abundant anion in all of the salt lakes we
studied. Consequently, SOd2- and conductivity are highly correlated (Y = 0.99). Thus,
it should not be surprising that whenever
the log of SOe2- or the log of the SOd2- : MO
ratio are significant predictors of the BG
index or the BGF index, significant regressions also occur with log conductivity or the
log of the conductivity:Mo
ratio (Tables 5
and 6). For the BG index, we do not know
if the significant effect on abundance is specifically attributable to SOd2- or is the result
SE
P
R’
1.3
2.2
2.0
2.3
1.3
0.9
1.3
1.8
0.045
0.46
0.023
0.25
0.036
0.027
0.022
0.30
0.41
0.48
0.35
0.37
0.40
0.10
of total ionic salts (either through osmotic
pressure or ionic strength). Perhaps increasing salinity (i.e. conductivity) adversely affects the cyanobacteria, particularly the nonN-fixing species, in some manner. We note,
however, that salinity apparently is not an
important
control on the abundance of
N-fixing cyanobacteria; log conductivity is
not a significant predictor with the BGF index (P = 0.36; Table 5) but rather only with
the BG index (P = 0.022; Table 6).
For the BGF index (only N-fixing cyanobacteria), we believe that SOa2- rather than
conductivity is the key variable behind the
significant correlations (Table 5). When
considering the potential for N fixation, it
is the interaction between MO and either
SOJ2- or conductivity that is important. The
S0,2- and MO anions have remarkably similar stereochemistries (Howarth and Cole
1985; Howarth et al. 1988a), and a competitive inhibition of molybdate uptake by
SOa2- has been demonstrated in freshwater
phytoplankton
(Howarth and Cole 1985;
Cole et al. 1986) and numerous other organisms, including tomatoes, Clostridium
bacteria, and mammalian
intestines. We
have no reason to suspect that changes in
osmotic pressure or ionic strength would
affect MO assimilation; indeed, altering NaCl
concentrations does not affect MO assimilation by freshwater phytoplankton,
while
increasing Na2S0, concentrations slows MO
assimilation (Howarth and Cole 1985; Cole
et al. 1986).
Fe as a regulator of cyanobacterial abundance-As with MO, Fe is generally thought
to be required for N fixation (Fogg and Wolfe
1954; Shah et al. 1984) and has been hy-
254
Marino et al.
pothesized as a factor regulating N fixation
in natural waters (Rueter 1982; Wurtsbaugh
and Horne 1983; Wurtsbaugh 1988; Howarth et al. 1988a). We found, however, that
log total. Fe was not a significant predictor
with either the BGF index (P = 0.32) or
with the BG index (P = 0.67). Multiple
regression models that include Fe in addition to other variables such as SOd2-, MO,
or the ratio of SOd2--to MO also showed no
significant interaction between Fe and either
the BGF or BG indices. Nonetheless, we
suspectthat Fe is an important biogeochemical control on N fixation in some saline
aquatic ecosystems such as estuaries and
oceans, where the total Fe concentrations
tend to be much lower than in the salt lakes
we report on here (see Table 4; see also
Howarth et al. 1988a). It is possible that Fe
and MO interact as regulators of N fixation
and controllers of the abundance of N-fixing
cyanobacteria; perhaps at the lower Fe concentrations typically found in seawater, MO
limitation becomes more severe. We also
note that the total Fe concentration, as we
have used here, may not be a good indicator
of the amount of Fe available to phytoplankton becausethe particulate and organic Fe fractions are included in the analysis,
and their contribution to algal and cyanobacterial nutrition is not known (Morel and
Hudson 1985).
N and I’ as regulators of cyanobacterial
abundance-N and P are important controls on N fixation and the abundance of
N-fixing cyanobacteria in freshwater lakes
(Horne and Goldman 1972; Schindler 1977;
Flett et al. 1980; Howarth et al. 1988a;
Stockner and Shortreed 1988). Epilimnetic
TN and TP concentrations and their ratio
have proven useful in predicting the abundance ofblue-greens in lakes (V. Smith 1985,
1986; Trimbee and Prepas 1987). V. Smith’s
(1983) #analysisofwater-column TN : TP vs.
cyanobacterial abundance in freshwater
lakes showed that cyanobacteria are rarely
abundant (> 10% of total biomass) when the
cpilimnetic TN : TP i.s > 64 (molar). In the
13 saline lakes we studied, however, epilimnetic T’N : TP ratios did not prove to be useful predictors of cyanobacterial abundance.
Only three of our study lakes had seasonal
mean epilimnetic TN: TP ratios (molar)
>64 (Haunted, Cooking, and Buffalo lakes;
Table 2), but they were among the lakes with
the highest abundances of cyanobacteria;
cyanobacteria made up 27, 42, and 59% of
the total phytoplankton in these three lakes
(Table 1). Further, many of the cyanobacteria in these three lakes belonged to species
able to fix N (Table 1). Clearly, these saline
lakes do not fit the pattern commonly found
in freshwater lakes.
With the BG index model discussed
above, Trimbee and Prepas (1987) found
that TP concentration was the best predictor
of the relative abundance of cyanobacteria
among the plankton of 19 freshwater lakes
in Alberta and accounted for 63% of the
variance in the BG index. To compare our
data from the saline lakes in Alberta with
the freshwater lakes they studied, we calculated a BG index with the same limits of
abundance as they used, 1 and 99% (Fig. 2).
It is obvious that although log of TP is a
good predictor of relative cyanobacterial
abundance in the Alberta freshwater lakes,
the same relationship does not hold for the
saline lakes. The two regressions have significantly different slopesand intercepts (Fig.
2). In fact, while log of TP is positively correlated with the BG index in the freshwater
lakes, the correlation is negative in the saline lakes.
It is useful to explore how the abundances
of cyanobacteria in these saline lakes vary
from those of cyanobacteria in freshwater
lakes as a function of P concentration. Based
on the linear relationship between the BG
index and log TP (pg liter’) for the freshwater Alberta lakes shown in Fig. 2, we have
constructed a simple model comparing the
BGF index observed for each individual saline lake to the index predicted from the TP
concentration of that lake with the freshwater lakes regression equation (seeFig. 3A
Zegend).We then can calculate a residual for
each saline lake, where the residual is defined as the vertical distance from the model-predicted index to the observed BGF
point. That is, the residual is the difference
between the BGF index predicted for freshwaters of the same TP concentration as a
given salt lake and the observed BGF index
N-jking cyanobacteria
255
Freshwaterlakes
7.5
z
J 0.0
P
8
2.5
3
-0
0.0
/
/
5.0
,
,
A = residual
!
-2.5
-2.s
-5.0
1
2
I
I
3
4
0
1
1
5
Fig. 2. The BG index for freshwater and saline Alberta lakes plotted vs. the log mean TP concentration
(fig liter-‘). Definition and discussion of the BG index
given in text. The freshwater lake data are from Trimbee and Prepas (1987). For the freshwater lakes, the
BG index is positively correlated with log TP (slope =
3.3; SE = 1.0; P = 0.002). For the saline lakes, BG
index is negatively correlated with log TP (slope =
- 1.5; SE = 0.5; P = 0.008). The difference between
the two relationships is highly significant (P = 0.0001).
20
3
4
5
1B
0:
for that salt lake (see Fig. 3A). We then can
correlate the residual index with various
water-chemistry
parameters for the saline
lakes in order to see which one(s) may best
explain the deviation of the observed BGF
index from the predicted BGF index based
on the freshwater TP relationship.
We are making two important assumptions in applying Trimbee and Prepas’s
(1987) correlation of total blue-green abundance (BG index) with log TP in freshwater
lakes to construct this comparative model
for saline and freshwater lakes. First, the TP
concentrations in the saline lakes we studied
extend beyond the range of TP concentrations found in the freshwater lakes data set
they used. Hence, the regression line used
in our model is an extrapolation beyond 320
,ug liter’ TP, or log TP = 2.5 (indicated by
the dashed portion of the line in Fig. 3A).
Second, since we are most interested in the
controls on the relative abundance of N fixers, we have assumed that the correlative
relationship with TP found by Trimbee and
Prepas for all species of cyanobacteria (BG
index) also holds when considering only
those species of cyanobacteria capable of N
fixation. Hence, we have calculated the residuals for the saline lakes using BGF in-
2
log TP
log TP
5
I
6
I
7
log SO:-: MO
Fig. 3. A. The BGF index (blue-green index for only
cyanobacterial species capable of N fixation) for each
saline lake plotted vs. the log TP concentration (pg
liter-‘). The solid portion ofthe line shown (freshwater
lakes model) is the relationship found by Trimbec and
Prepas (1987) for cyanobacterial abundance as a function of log TP in freshwater lakes in Alberta (BG index
= -5.72 + 3.32 log TP). The dashed portion of the
line is an extrapolation of their model for TP concentrations above 320 pg liter’ TP (log TP = 2.5). OSaline lakes with log TP values ~2.5; O-saline lakes
with log TP > 2.5. We calculate residuals between the
observed index for a saline lake at a given TP concentration and the index predicted at that same TP from
the freshwater model as the vertical distances from the
observed points to the model line. B. Residuals for the
BGF index calculated by the method depicted in panel
A and plotted vs. the log of the molar SOa2-: MO ratio.
O-Lakes with TP < 320 pg liter-r; O-lakes with TP
> 320 pg liter-l. The coefficient of determination (r*)
for this linear regression is 0.56. That is, the SOd2-:
MO ratio of a saline lake is a good predictor of how
the abundance of N-fixing cyanobacteria in the lake
will differ from that of a freshwater lake having the
same TP concentration.
dices, with 0.1 and 99.9% abundance as limits.
Of all the chemical parameters considered in the preceding statistical analyses (Fe,
SOd2-, MO, conductivity, and the SOd2- : MO
ratio), these BGF residuals are most signif-
256
Marino et al.
A
0.03
0
IL
0.02
:<*
,-
0.01
0.0
0.0
05
0.5
1.0
15
SO$Mo
(~106’~
1.0
1.5
2.0
2.5
3.0
2.0
2.5
3.0
SC$Mo (~10~)
Fig:. 4. A. The model-predicted rate of MO assimilation in each of the salt lakes plotted as a function of
the molar SO,,- : MO ratio. B. The percent of the total
phytoplankton biomass made up by speciesofN-fixing
cyanobacteria plotted as a function of the molar S0.,2- :
MO Iratio. Note the similarity between this function
and the model-predicted MO assimilation rates shown
in panel A. (See text fir disctlssion.)
icantly regressed with the log of the SOd2- :
MO ratio (r2 = 0.56; Fig. 3B). This regression appears to be better than that of the
BGE: index vs. the log SOd2- : MO ratio alone
(r2 == 0.33) suggesting that extra information may be obtained from this considcration of the deviation in behavior of the
saline lakes from freshwater lakes.
Model MO assimilation as a function of
the S04~“-: MO ratio- As discussed earlier,
our log-normalized measure of the relative
abundance of species of N-fixing cyanobacteriaL (BGF index) is significantly predicted
by the log of the SOd2- : MO ratio. That the
log-log transformations of the data give significant linear relationships suggests that the
N-fixing cyanobacteria in these lakes respond nonlinearly to MO availability as controlled by SOJ2-. We have another reason
to believe that it is a nonlinear function of
the S0.,2- : MO ratio that controls MO assimilation. MO assimilation can be modeled as
an extended Michaelis-Menten
equation
(Segel 1976) with an inhibition constant for
the effect of SOd2- (Howarth et al. 1988a).
Cole et al. (1986; unpubl. data) have used
g9Mo to examine inhibition constants and
MO half-saturation
constants in various
aquatic ecosystems; they found that the in,hibition constants are relatively constant
from system to system but that the halfsaturation constant seems to be a function
of the ambient MO concentration in the system (unpubl. data). Using inhibition
and
half-saturation constants derived from these
data, we have modeled MO assimilation by
plankton in each of our 13 saline lakes as a
function of the SOd2- : MO ratio in the lake
(Fig. 4A). A logarithmic function can be fitted to these data (r2 = 0.82). When we plot
our data for percent of total phytoplankton
biomass as N-fixer species against the SOd2- :
MO ratio for each of the lakes, the shape of
the resulting curve (Fig. 4B) is quite similar
to model-predicted
MO uptake (Fig. 4A).
This finding suggests a physiological basis
for the nonlinear relationship of relative cyanobacterial abundance with the SOd2- : Mo
ratio in these saline lakes.
MO and N-fiing cyanobacteria in other
saline lakes--We are aware of ambient Mo
concentration data for only two other salt
lakes, the Great Salt Lake and Pyramid Lake.
Wurtsbaugh (1988) reported the SOd2- :
dissolved MO ratio in the Great Salt Lake
as 299,000 : 1-a fairly low ratio compared
to many of the Alberta saline lakes we stud,ied. Hence, the high abundance of N-fixing
cyanobacteria found in the Great Salt Lake
seems to fit the pattern we observed. Our
preliminary data for Pyramid Lake indicate
a dissolved MO concentration of - l-l.5 PM
(Howarth et al. 1988a), with a SO,‘- : Mo
ratio of 7,000 : 1 (SOd2- data from Galat et
al. 198 1). This ratio is very low, much lower
than in any o,f the Alberta saline lakes, and
is consistent with the high rates of N fixation
and high abundances of N-fixing cyanobacteria found in Pyramid Lake (Horne and
Galat 1985).
Short-term vs. long-term experimentsOur analysis of these saline lakes in Alberta
can be thought of as a natural experiment.
N-&ing cyanobacteria
The lakes vary in their MO concentrations
and SOd2- : MO ratios, allowing us to examine at the spatial and temporal scales of
actual ecosystems whether MO availability
affects the abundance of cyanobacteria able
to fix N. Other variables are not controlled,
a situation typical in ecosystem-scale experiments. In contrast to many ecosystemscale experiments, however, our analysis
includes a gradient of treatments, allowing
rigorous statistical testing of our hypothesis.
We believe this approach is powerful for
testing the MO-control
hypothesis. This
analysis also lays the groundwork for the
next logical step: the addition of MO to one
or more whole lakes that do not at present
support species of planktonic, N-fixing cyanobacteria.
Another logical step would be to measure
actual rates of N fixation instead of drawing
inference from the abundance of cyanobacterial species capable ofN fixation. We note,
however, that to adequately measure seasonal N fixation in 13 lakes, considering the
likely spatial and temporal variation in rates,
is a monumental task. In any event, the approach we have taken is conservative: if
some of the cyanobacteria present are not
actually fixing N (even though they are capable of doing so), they will add noise to
our analysis. Despite this potential for noise,
we get significant results with the SOd2- : MO
ratio.
Although our ecosystem-scale analysis
supports the hypothesis that MO availability
as determined from the S0,2-: MO ratio
partially regulates N-fixing cyanobacteria,
short-term bottle bioassays have yielded
variable and ambiguous results. Molybdate
additions to Baltic Sea water increased rates
of N fixation, while SOd2- additions decreased them (Howarth and Cole 1985).
Wurtsbaugh (1988), however, found no
stimilation
from MO additions to water
samples from the Great Salt Lake (although
he did find stimulation in a freshwater reservoir) even though planktonic, N-fixing cyanobacteria were present. Also, although
they did not explicitly study any effects on
planktonic, N-fixing cyanobacteria, Paerl et
al. (1987) found no stimulatory effect on N
fixation of MO additions to suspensions of
marsh grass detritus, sea grass detritus, or
257
cyanobacterial mats in seawater. We have
more confidence in the results from the
longer time scale and larger spatial scale observations and suspect that the short-term
bioassays are a misleading tool for addressing ecosystem-scale questions (S. Smith
1984; Hecky and Kilham 1988; Howarth
1988). We note that the short-term bioassays have also yielded variable results for
the additions of other substances; Fe additions to both a freshwater lake (Wurtsbaugh
and Horne 1983) and the Great Salt Lake
(Wurtsbaugh 19 8 8) sometimes stimulated
N fixation and sometimes did not. Also, the
addition of low molecular weight sugars to
the suspensions studied by Paerl et al. (1987)
stimulated N fixation, but similar additions
to water samples from the Great Salt Lake
inhibited it (Wurtsbaugh 1988).
Conclusion
Our results indicate that MO availability,
as reflected in the SOd2- : MO ratio, is a major factor regulating the abundance of N-fixing cyanobacteria in the Alberta saline lakes
we studied. These results do not, however,
provide strong evidence that MO availability is the major factor preventing a greater
occurrence of N-fixing cyanobacteria in seawater, since the SOd2- : MO ratio of seawater
is 260,000 : 1 (Howarth and Cole 1985), near
the lower end ofthe range for the saline lakes
in this study. N-fixing cyanobacteria were
sometimes abundant and sometimes scarce
in saline lakes having SOd2- : MO ratios similar to seawater (Fig. 3B). We conclude by
suggesting that other factors such as a low
availability of Fe (Rueter 1982; Howarth et
al. 1988a) or high levels ofturbulence (Paerl
1985) may negatively affect N-fixing cyanobacteria in estuaries and other marine ecosystems and that these other factors may
exacerbate the effect of low MO availability
in those systems. Since the saline lakes we
studied are unlikely to be more turbulent
than freshwater lakes in the area and have
extremely long water residence times, physical factors such as mixing are probably relatively unimportant
in regulating cyanobacterial abundances in these lakes. Also,
total Fe concentrations were very high in
these lakes, so the influence of SO,*- inhi-
Marino et al.
bition and MO availability on N-fixing cyanobacterial abundances may be more
clearly observed.
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Submitted: 24 February 1989
Accepted: 15 November 1989
Revised: 7 December 1989