Limnol. Oceanogr.. 29(4), 1984, 862-878
0 1984, by the American
Society of Limnology
and Oceanography,
Inc.
Mixing and the dynamics of the deep chlorophyll
maximum in Lake Tahoe’
Mark R. Abbott2
Scripps Institution of Oceanography, University of California, San Diego, La Jolla 92093, and
Jet Propulsion Laboratory, California Institute of Technology, Pasadena 9 1109
Kenneth L. Denman
Institute of Ocean Sciences, P.O. Box 6000, Sidney, British Columbia V8L 4B2
Thomas M. Powell, Peter J. Richerson, Robert C. Richards, and Charles R. Goldman
Division of Environmental Studies, University of California, Davis 956 16
Abstract
Chlorophyll-temperature profiles were measured across Lake Tahoe about every 10 days from
April through July 1980. Analysis of the 123 profiles and associated productivity and nutrient data
identified three important processes in the formation and dynamics of the deep chlorophyll maximum (DCM): turbulent diffusion, nutrient supply rate, and light availability. Seasonal variation
in these three processes resulted in three regimes: a diffusion-dominated regime with a weak DCM,
a variable-mixing regime with a pronounced, nutrient supply-dominated DCM, and a stable regime
with a deep, moderate light availability-dominated DCM. The transition between the first two
regimes occurred in about 10 days, the transition between the last two more gradually over about
3 weeks. The degree of spatial variability of the DCM was highest in the second regime and lowest
in the third. These data indicate that the DCM in Lake Tahoe is constant in neither time nor space.
A deep chlorophyll maximum (DCM) has
been observed in various aquatic environments, and, although it occasionally results
from an increase in cellular chlorophyll concentration with depth (Steele 1964; Kiefer
et al. 1976), it is sometimes a biomass maximum as well (e.g. Ortner et al. 1980; Holligan 1978). Several processes have been
proposed as responsible for the DCM: differential sinking of phytoplankton
from nutrient-poor waters (Steele and Yentsch 1960;
Venrick et al. 1973), zooplankton grazing
(Longhurst 19 7 6), behavioral aggregation of
flagellates (Cullen and Eppley 198 l), and
low biological loss rates (Fee 1976). A model incorporating
several of these processes
successfully simulated the DCM in the
Northeast Pacific (Jamart et al. 1977, 1979).
Although it is generally supposed that the
1Financial support was provided by the National
Science Foundation, NSF-DEB78-23259 and NSFDEBSO- 199 18, and by the National Aeronautics and
Space Administration, NAG5-2 17.
2 Supported by NATO Postdoctoral Fellowships,
NATO 1979 and NATO 198 1. A portion of this work
was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under contract with
the National Aeronautics and Space Administration.
DCM is an active region, Kiefer and Kremer (198 1) modeled it as a relict from stratification of an originally homogeneous water
column.
Although the dominant biological processes in the formation and maintenance of
a DCM may vary from environment to environment (Cullen and Eppley 19 8 1; Cullen
1982), it does depend on sufficient radiation
penetrating below a pycnocline which provides some protection from surface-driven
turbulent mixing. A DCM is not found in
very turbid waters or where a pycnocline is
deep or absent (Anderson 1969).
The DCM in Lake Tahoe (Kiefer et al.
1972; Holm-Hansen et al. 1976) is not usually associated with the base of the thermocline as in other lakes (Brooks and Torke
1977; Fee 1976) and the ocean (Hobson and
Lorenzen 197 2; Ortner et al. 1980). Kiefer
et al. (1972) believed that the Tahoe DCM
was the result of differential sinking from
surface waters, but presented no data to support this idea. Coon (1978), Lopez (1978),
and Richerson et al. (1978) suggested that
the DCM was caused by the combination
of low in situ production and very low grazing and diffusive losses.
862
863
Chlorophyll dynamics
We focused our study on the formation
of the Tahoe DCM, as previous work had
been concentrated on the DCM after it was
well established. We sampled at weekly intervals to resolve the temporal variation of
the relevant processes and took transects
across the lake to investigate their spatial
variation. We hoped that such temporal and
spatial variability
would define the role of
specific biological and physical processes in
the formation and maintenance of the DCM.
R. L. Leonard generously provided data
from the Interagency Tahoe Monitoring
Program. W. McCune and R. Brown provided the data on phytoplankton
species. A.
Gargett made suggestions concerning vertical mixing. The Institute of Ocean Sciences provided computer facilities.
SL
Methods
Lake Tahoe (California-Nevada)
is a deep
(max depth 501 m, mean depth 3 13 m),
ultraoligotrophic
subalpine lake (alt. 1,897
m) whose exceptional clarity allows phytoplankton growth down to 120 m (HolmHansen et al. 1976). Physical processes differ between the coastal zone and midlake,
and the west shore in particular is characterized by cooler, more variable temperatures (Leigh-Abbott
et al. 1978).
Chlorophyll-temperature
profiles were
collected with an Endeco model 8 15 in situ
fluorometer system equipped with a Turner
Designs model 10 fluorometer. The stated
accuracy of the thermistor was 0.0 1°C. A
pressure transducer was mounted on the exterior of the pressure case. All three signals
were sampled every 0.7 s and written on
magnetic tape. Vertical resolution, given the
winch speed, was 0.3 m.
Nitrate concentration,
phytoplankton
productivity,
species counts, and light penetration were determined at regular intervals by members of the Tahoe Research
Group at a west shore station and by personnel from the Interagency Tahoe Monitoring Program at a midlake station (Fig. 1).
Solar radiation was measured at a west-shore
station using a Belfort pyrheliometer,
and
wind speed and direction were measured
hourly during operating hours at the South
Lake Tahoe Airport (Fig. 1). (Wind data
I
0
,
2
KILOMETERS
I
I
4
6
I
s
1
10
Fig. 1. Map of Lake Tahoe showing transect location, shelf station (S), midlake station (M), pyrheliometer (P), and airport (A).
were obtained from the National Climatic
Center, Asheville, N.C.)
We established a transect between the west
and east shores of the lake. Distance between stations on this transect was determined with the ship’s knotmeter and logmeter. Stations were placed closer together
in areas where the water depth was ~450
m, as previous work had indicated that this
region was highly variable (Leigh-Abbott et
al. 1978; Abbott et al. 1982). Depending on
weather conditions, from 9 to 14 stations
were occupied during a single transect. At
each station, a profile (both up and down)
was made to 100 m (which always included
the DCM) and the water depth determined
from the ship’s fathometer. A series of 14
stations took about 5 h including transit
time. The cross-lake series was made at
about weekly intervals from mid-April
to
the end of July 1980, resulting in 123 profiles.
864
Abbott et al.
For each station, the up and down profiles
were averaged and then smoothed to give
one point for each l-m depth interval. The
buoyancy frequency (N) was calculated from
temperature
profiles that were further
smoothed over 3-m intervals and after conversion to density using tables by Kell
(1967). We define the buoyancy frequency
as
where g is the acceleration of gravity, p is
density, and z is depth. Fluorescence was
converted to chlorophyll concentrations by
standard extraction methods (Strickland and
Parsons 1972). Comparisons of the chlorophyll profiles with the biomass profiles
calculated from phytoplankton counts from
the west-shore and midlake stations showed
good agreement throughout the sampling
period; the cell biomass peak coincided with
the DCM in all but one profile. Thus, in
vivo fluorescence is a good index of extracted chlorophyll and count-derived biomass
in Lake Tahoe (Richerson et al. 1978; Coon
1978).
Results
Daily solar radiation and the square of
the vector-averaged daily wind speed are
shown in Fig. 2. Within the general seasonal
trend of increasing radiation and decreasing
wind, there were intermittent periods of low
radiation and high winds. Such conditions
usually occurred simultaneously, but not always. Also, there was an extended period
of relatively low winds and high radiation
from 22 April to 4 May.
These patterns of surface forcing affect the
pattern of vertical mixing in the lake. Direct
measurements of vertical diffusivity are relatively difficult (Quay et al. 1980; Kullenberg 197 1; Jassby and Powell 1975). However various
parameterizations
of K,
(vertical diffusivity) are possible using easily
measured variables (e.g. temperature). For
waters below the pycnocline, K, should vary
according to the intensity of stratification.
One possibility is
r NIM
K,, = co
(1)
h
5
600
i5
i=
53 400
0
2
>
J
s
0
200
2: MAR 19 APR
14 MAY
8 JUN
3 JUL
28 JUL
14 MAY
8 JUN
3 JUL
28 JUL
3
;
is
5
4o
2:MAR
19 APR
Fig. 2. Daily solar radiation and square of the vector-averaged daily wind speed.
where co = 1.2 - 2.4 x 10e7 m2.s1, N is
the buoyancy frequency (s-l), No = 1 ‘s-l,
andm=
- 1, as derived by Jassby and Powell (1975).
The choice of an N-l dependence for K,
is based on work in both limnology and
oceanography. As described by Welander
( 196 8), dimensional considerations
imply
either a form of N-l or rNe2 (where E is the
turbulent kinetic energy dissipation rate) for
K,. The underlying difference between these
two forms is whether the flux of horizontal
momentum (N-l form) or the cascade of
energy in wavenumber space (Ne2 form) is
assumed to be constant. We assume a steady
vertical flux of horizontal momentum in using the N-l form, since lakes and oceans are
primarily
driven by a statistically
steady
wind stress. As the actual mixing processes
are intermittent,
we use Eq. 1 in an “aver-
865
Chlorophyll dynamics
0
Oj”“’
4
TEMPERATURE
0
‘I’
I
I
(C )
12
i !
1 I
16
I 11
1
20
IO------PROFILE
AT1610
-
AT 1020
20-
100
1
o-o
cf-tLoRoPtiYLL ( mg-m-3)
I
,
PROFILE
I
I
0.04
NORMALIZED
,
I
1
1
0.00
CHLOROPHYLL
T
1
1
,
-
O-12
Fig. 3. Left-vertical temperature and chlorophyll profiles from a nearshore station on 22 May 1980. RightGaussian fit to the DCM layer of the chlorophyll profiles.
age” sense, in either space or time (A. Gargett pers. comm.). Also, c, is a constant and
will not vary as a function of wind stress,
but rather K, varies indirectly as the wind
stress affects stratification.
Thus, this form
is useful away from boundaries like the surface, thermocline, or lake bottom, i.e. away
from the sources of mixing energy. As the
DCM is always well below the thermocline
in Lake Tahoe, Eq. 1 should be applicable.
In limnological
studies, the form of Eq.
1 has been widely demonstrated and used
(e.g. Jassby and Powell 1975; Quay et al.
1980; Lewis 1982). As lakes typically differ
from oceans in having lower current speeds,
stronger stratification,
and closer bottom
boundaries, we might not expect this relationship to hold in the ocean. Budget studies
to determine K,, have not been carried out
in the ocean as they have in lakes; because
microstructure
estimates of E in the ocean
are fairly common, the form eNm2has been
suggested by Weinstock (1978, 198 1) for
oceanic conditions.
However,
measure-
ments by Gargett and Osborn (198 1) suggest
that E is not constant but rather is proportional to N (again in an average sense). Thus,
we return to the form of Eq. 1. We also
assume that the processes responsible for
mixing heat also mix mass so that K, for
temperature is the same as K, for nitrate
(following arguments of Munk 1966).
As a consistency check of Eq. 1 for Tahoe,
we looked at profiles that were made in the
same location separated by only a few hours.
Figure 3 (left) shows two profiles made on
22 May, separated by 6 h. Due to strong
winds (> 8 m *s-l), there was significant upwelling during this period, presumably in
response to wind-driven
advection of the
surface layer. The later profile was broader
and had a lower chlorophyll concentration
at the DCM than the first profile. To estimate a diffusivity, we assumed that no biological changes were occurring (doubling
time was about 4 days during this period)
and that horizontal advection at the DCM
depth was minimal. Thus, changes in con-
866
Abbott et al.
1111111111l111111111111,,1l
2: MAR 19APR
-I---I-+-
14 MAY
8 JUN
2-w-e
3 JUL
28 JUL
I
Fig. 4. Upper-seasonal variation of IV-’ in the 5060-m depth interval. Vertical lines represent + 1 SD.
Numbers below dates refer to regimes described in the
discussion. Lower-seasonal variation of N-l at the
DCM.
centration were a result of diffusion alone.
We also assumed a Gaussian diffusion process (Okubo 1980) such that
where c2i is the variance of the distribution
at time i and tj is time. As the vertical profiles were strongly skewed and we are only
interested in diffusion at the DCM, we fitted
a Gaussian curve to the DCM region of the
profiles, after normalizing this region to the
integrated chlorophyll
in the DCM layer
(which was the same in both profiles). A
nonlinear least-squares routine was used to
fit the profiles in Fig. 3 (Bevington 1969).
For this set of profiles, K, was estimated to
be 1.36 x 1O-4 m2.s1. Using the N-l method of Eq. 1, we estimated K, to be 0.73 x
1O-4 m2.s1, with c, = 1.2 x lo-’ m2,s-l.
The estimates agree within the range of uncertainty of c,. Similar calculations were
made for 1 May; the Gaussian estimate was
1.1 X 1Oe4 m2. s-l and the N-l estimate
0.78 x 10B4 m2.s1.
As this parameterization of K, is best used
in an “average” manner, we considered N-l
profiles averaged over midlake stations
(>450 m), shelf stations (west-shore <450
m deep), and all stations. Figure 4 (upper)
shows N-l from the 50-60-m depth interval
from the three regions. (The standard deviation as plotted in this and subsequent
figures is not used in a statistical sense as
the sample size is too small to be meaningful, but rather as an indicator of variability
within sampling days.) Several features are
apparent. First, stratification occurred rapidly during the calm, sunny period of 22
April-4 May, and began in midlake. Second, a storm in mid-June is apparent in the
shelf stations but not in midlake; after this,
N-l stayed constant despite some periods
of strong winds, presumably because surface stratification was sufficient to maintain
stratification at depth. Again, we emphasize
that the N-l estimate of K, does not depend
directly on wind stress. Third, the degree of
spatial variability
(as reflected by the standard deviation) was high during periods of
high N-l. The lake was nearly uniform after
16 June. Figure 4 (lower) is N-l at the DCM
depth and has similar features. However,
though the DCM depth varied from 30 to
85 m between 3 May and 3 1 July, N-’ only
varied by about a factor of 2. During this
period, N-l at 50-60 m varied by a factor
of 4. Thus, there was a tendency after initial
stratification for the DCM to follow a range
KJ downward
of N-l (and, by implication,
through the water column.
Nitrate profiles from the shelf and midlake stations are shown in Figs. 5 and 6.
Although
the relatively
coarse temporal
sampling does not allow resolution of isolated events, such as the mid-June storm,
the general pattern is similar to that of N-l;
there was a sharp shift in late April from
867
Chlorophyll dynamics
NITRATE(mmol mw3)
0.0 0.4 0.8 1.2
? (
I I 1 , I
0.0
0
20
8 APR
18 APR
0.0 0.4
I
0.8
’
I
1.2
’
I
’
28 APR
40
60
80
100
I
E
&I
tr
0
20
w,
14 JUL
1
,
1
,
I
23 JUL
40
60
80
100
0
20
30 JUL
40
60
80
100
Fig. 5. Nitrate profiles at the shelf station.
abundant nitrate to low surface concentrations and moderate, variable concentrations at depth. In mid- to late June concentrations
at depth decreased and then
remained stable at all depths. This general
pattern was followed at both stations, although it is better resolved in the shelf data.
We calculated the net nitrate flux to the
DCM as the difference between the upward
flux from below the DCM and the upward
flux above the DCM. Flux was calculated
as the product of the nitrate gradient and
the spatially averaged vertical diffusivity (as
estimated from N-l). The nitrate gradient
and diffusivity were average values from the
DCM to 10 m below the DCM for flux into
the DCM and from the DCM to 10 m above
the DCM for flux out of the DCM. DCM
depth was taken as a spatial average from
the shelf and midlake stations.
868
Abbott et al.
NITRATEtmmolmB3)
I8 APR
30 JUN
Fig. 6. As Fig. 5, but at the midlake station.
Figure 7 is the net nitrate flux to the DCM.
The following temporal pattern emerges. A
sudden decrease in flux in mid-April
was
followed by a highly variable period. In midJune, flux dropped to zero and remained
there until mid-July when it began to rise.
Some of the peaks in the variable period
may be associated with storms in early May
and mid-June, but the coarse sampling restricts conclusions. Coarse sampling also
limits any midlake-shelf
comparisons, although there are differences between the two
areas. Our estimate of nitrate supply rates
to the DCM assumes that only “new” nitrate (Eppley et al. 1979) from within the
lake at depth is an important source. This
neglects other sources such as zooplankton
regeneration (or “old” nitrate) and groundwater and stream sources (Loeb and Goldman 1979). Nitrate concentrations are generally higher through the season on the shelf
than in midlake (Figs. 5 and 6), consistent
with shoreline inputs.
The vertical structure of primary production (as measured by 14Cuptake with in situ
incubations) responded to this pattern of
chemical and physical processes. Figures 8
and 9 are profiles of assimilation number
from the shelf and midlake stations. The
biomass term in the calculation of assimi-
lation number represents a linear interpolation in time between spatially averaged
profiles. Some of the variability was a result
of changes in biomass, but most of it resulted from changes in productivity.
As expected, there was considerable temporal
variation, but the temporal progression is
easy to follow. We will focus on the shelf
data (Fig. 8) because of the higher sampling
frequency. We observed an increase in assimilation number at the 30-60-m depth
range in late April. This pattern persisted
---
MIDLAKE
4-l-++-----
Fig. 7. Estimated net diffusive flux of nitrate to the
DCM during the sampling period.
869
Chlorophyll dynamics
P:B (mg i mg Chl a-'d-l)
0
0.
12
’
I
0
24
12
24
0
12
24
’
30 JUL
Fig. 8. Assimilation number profiles at the shelf station.
until mid-June, with a secondary peak at 10
m forming in mid-May. From mid-June until July, assimilation number decreased at
all depths, after which it increased sharply
in the surface waters and remained low at
depth. Assimilation number in midlake (Fig.
9) showed the same general features, al- _
though the middepth peak in May was not
as pronounced. This seasonal change in the
vertical patterns of assimilation number was
also reported by Holm-Hansen et al. (1976)
and Coon et al. (in prep.).
To highlight the temporal progression, we
present spatial characteristics of the DCM.
Its chlorophyll concentration increased rapidly between mid-April
and early May,
reaching a peak value on 2 May in midlake
and 8 May on the shelf (Fig. 1OA). A period
of variable concentrations
followed until
early July, when concentrations decreased
and became more uniform. Midlake-shelf
differences can be seen; midlake concentrations increased earlier during initial formation, and the mid-June storm is apparent
870
Abbott et al.
P:B (mg C mg Chl a-‘dwl)
r
I AUG
Fig. 9. As Fig. 8, but at the midlake station.
by reduced concentrations on the shelf but
not in midlake. However, the two areas generally change in parallel. The degree of spatial variability
(as seen in the value of the
standard deviation) seems higher during the
second stage than in July.
The depth of the DCM (Fig. 10B) varied
considerably.
After a strong DCM had
formed (late April), it gradually deepened,
although this trend was quite variable during May and June. The mid-June storm resulted in shoaling of the DCM on the shelf,
with no apparent effect in midlake. After
deepening to 70-85 m in July, the DCM
remained at that depth until it disappeared
in late fall. Although the midlake DCM was
generally deeper than the shelf DCM and
the former did not shoal in response to the
mid-June storm, both DCM depths had
similar patterns of change.
Between early May and mid-June, DCM
depth and concentration
are apparently
positively correlated (r = 0.69); after late
June they are negatively correlated (r =
-0.66). Although less than half of the variance in DCM concentration is explained by
changes in depth, the key point is the shift
from a positive to a negative correlation.
We calculated a DCM thickness defined
as
T = Zmttom - &op
where &,ottom is the depth below the DCM
where chlorophyll concentration equals 0.8
times DCM concentration, and ZtoP is the
same calculated depth above the DCM.
The thickness statistic is shown in Fig. 1OC
and is the most variable descriptive statistic. The DCM began as a broad region
and went through an abrupt transition to a
thin layer (12 m thick). This transition occurred first in midlake. A highly variable
period of slow thickening followed until early July, with another shift to a broad DCM
occurring simultaneously in midlake and the
shelf. The mid-June storm resulted in considerable broadening of the shelf DCM (and
increased spatial variation). Although the
standard deviation was generally large, there
was a sharp reduction of variance in the final
broad stage (July).
The spatial patterns of the DCM varied
temporally as well. Figures 11, 12, and 13
are isopleths of temperature and chlorophyll from the west-east transects of 16
April, 19 June, and 29 July. As expected
from the temporal patterns, the 16 April
series had a weakly developed DCM, although a small peak was forming in midlake
and the east shore. The 19 June series had
a well developed DCM, with several intense
patches across the lake (Fig. 12). The westerly patch was located the next day in the
same position by a constant depth horizontal transect. The horizontal extent of these
patches was about 2-5 km. As with the 16
April series, there was still a west-east tilt
of the isotherms. The 29 July series (Fig.
13) had a broad, relatively uniform DCM.
Temperature was also relatively uniform
horizontally.
Discussion
The DCM in Lake Tahoe in 1980 showed
strong temporal variability,
changing on
time scales as short as 7 days. Within this
variable pattern though, we can identify
three regimes: a well mixed regime with a
broad, weak DCM; a moderately mixed regime with a sharp DCM near the assimilation number maximum at moderate depth;
and a weakly mixed regime with a deep,
broad DCM and a near-surface assimilation
number maximum. The transition between
the first and second regime is fairly distinct;
the transition between the second and third
is more subtle and gradual.
I From all of these regimes, it is clear that
871
Chlorophyll dynamics
-SHELF
---MIDLAKE
-*-*LAKEWIDE
0.2
changes in physical forcing play a significant
role in the formation and structure of the
DCM. In particular, patterns of vertical diffusion are important as they affect spreading
of the DCM layer and nitrate supply rates.
Light availability as a function of the depth
of the DCM is also important.
As a first approximation
of the dynamics
of the DCM, we can construct a simple diagnostic model of the change in the DCM
concentration
as the difference between
measured phytoplankton
growth and estimated diffusion of the DCM. We assume
that phytoplankton
growth as measured by
14C uptake will be a function of light availability and rates of nutrient supply. The diffusion term was calculated for both midlake
and shelf as
DE
60
80
---MIDLAKE
******LAKEWIDE
“““““““““‘11”1’
25 MAR ISAPR 14 MAY 8 JUN
+-l-1-2-
3 JUL 28 JUL
p-3’
60
II
1IIlI
lllllllllbl
2:MAR IS APR 14 MAY 8 JUN
Al-+-----2---w
I,I,,,,I~
3 JUL 28 JUL
+3+-
Fig. 10. DCM concentration, depth, and thickness
(defined in text) during the sampling period.
[ 1
a
de
-Kaz vaz
where z is depth and C is chlorophyll concentration. The brackets represent a spatial
average of this term, which was calculated
for each profile. The depth interval was chosen to include the DCM layer.
The productivity
data were first converted to a daily basis. We then linearly interpolated between depths sampled to obtain
productivity
at the averaged DCM depth.
As the productivity
and chlorophyll-temperature were measured on different days,
we interpolated between productivity
sample days to estimate productivity at the DCM
depth on the chlorophyll-temperature
sampling day. As productivity
was measured
with in situ incubations,* we accounted for
changes in daily solar radiation. We estimated total growth between chlorophyll
sampling days, again accounting for radiation changes, and divided by the number of
days between samples to obtain an average
daily growth rate. We converted carbon uptake to chlorophyll using a C:Chl of 60.
This diagnostic model has a number of
limitations.
First, grazing is neglected entirely; it is generally confined to depths < 50
m and is usually small (Richerson et al.
19 7 8). Second, productivity
was measured
at only two points while chlorophyll
and
temperature were measured at several points
and then averaged, so that spatial variability
may limit the reliability of the growth es-
872
Abbott et al.
DISTANCE (km)
165
280
270
315
420
450
470
480
490
465
475
465 140
WATER DEPTH Im)
I
(I,
an L\
I,,
,
,
,
,
,
,
,
-
,
1
,
,
,
,
I
’
I
/
-nz
I~l~r,l,l,r,r,~,~,~,~,,,,,,
loo0
’
J
1
2
3
280
315
270
4
5
420
450
6
7
8
9
10
11
12
13
DISTANCE (km1
165
470
480
490
465
475
465 140
WATER DEPTH hn)
Fig. 11. West (left) to east (right) isopleths for temperature (upper, “C) and chlorophyll (lower, mg.m-3), from
16 April 1980.
873
Chlorophyll dynamics
’ 1’
3
”
4
’ ‘1
5
6
”
7
’ 8”
I ‘1’10
9
11
12
13
14
DISTANCE (km)
NO
240
235
235
280
300
460
480
490
480
480
460
280 150
WATER DEPTH (ml
n.
,
.
1
2
I
loo
I
0
3
4
5
6
7
1
8
I
9
1
’
I
10
I
’
I
i
11
I
I
l2
I
I
13
I
14
DISTANCE (km)
190 240
235
235
280
304
460
480
490
480
WATER DEPTH (ml
Fig. 12. As Fig. 11, but from 19 June 1980.
480
460
280
I50
Abbott et al.
l6 M
l4 M
10
20 -
I2
-
i
-
8;
40-
50
F
t
7
I
WI0
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3
2
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4
5
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I
It
I1
Ifi
I
7
8
9
10
1
I
11
12
13
14
470
140
D I STANCE (km)
150
240
220
280
480
490
470
WATER DEPTH (ml
1,
1,
I,
t
,
II
I,
1,
’
,
’
I
I
-
0.25------
10 -
20 -
I
0
1
150
240
3
2
4
5
6
7
8
1
9
I\1
l
10
11
I
1
12
1
1
’
l
13
14
470
140
D I STANCE (km)
220
280
490
470
WATER DEPTH (ml
Fig. 13.
As Fig. 11, but from 29 July 1980.
480
Chlorophyll dynamics
IllI
\
,I,,,
,
,
,
,
---
,
,
,
(
,
,
MEASURED
,
-
-PREDICTED
”6 -004:
-25 MAR
19APR
14 MAY
8 JUN
3 JUL
28 JUL
0.04
” ” ” ” ” ” ’ “““““‘I.
25 MAR l9APR
I4 MAY 8 JUN
+-l-q2-l-3-+
3 JUL
28 JUL
;
t
V
5
tii
,lll,l11l,llll,ll,l,1111,
E
I
0.08 _
_
F
0.04
z
MIDLAKE
--MEASURED
-PREDICTED
-
zt
8
0
Fig. 14. Estimated and measured daily changes in
DCM concentration on the shelf and from midlake.
timates. Third, because productivity
and
chlorophyll-temperature
were measured on
different days, temporal variability
may be
important. Fourth, we assumed that daily
variation is important only in solar radiation as it affects growth; variation in diffusion is assumed to occur only from sample
date to sample date and not on a daily basis.
Fifth, we assumed a constant C:Chl for the
entire sampling period, an assumption that
is weak in other environments (Cullen 1982)
and also in Tahoe (Coon et al. in prep.).
The comparison between estimated and
actual changes in DCM concentration
(as
measured from averaged DCM concentrations) is shown for both the shelf and midlake in Fig. 14. Although the magnitudes
differ, the diagnostic model does mimic the
temporal patterns of change fairly well. That
is, the model tends to have the proper trend
875
of changes in the balance of growth and diffusion. From estimates of C:Chl by the California Department of Water Resources, our
estimate of 60 is probably too low, especially in spring. An upward revision would
reduce estimated growth rates. In fact, the
model consistently
overestimates growth
during periods of increasing DCM concentration.
This simple diagnostic model of DCM
dynamics is consistent with the proposed
importance of three processes-nitrate
supply rates and light availability as they affect
phytoplankton
growth and rates of turbulent diffusion. The variation of these processes over time forms the basis of the three
regimes presented earlier.
The first regime was dominated by diffusion, despite high nitrate flux and relatively high assimilation numbers at depth
(as calculated from fixed-depth
incubations). Thus, a DCM did not form, as expected from previous work (Anderson
1969). Diffusivities
showed considerable
spatial variation during this regime.
The transition between this and the second regime occurred rapidly; a few days of
warm temperatures, high radiation,
and
calm winds were sufficient to stratify the
lake. This resulted in a rapid change in diffusivities, which in turn strongly affected
nitrate concentrations throughout the water
column. The transition from a weak DCM
to a well developed DCM apparently took
7-10 days, with the midlake stations preceding the shelf stations by about 5 days.
Peak DCM concentration occurred early
in the second regime. High assimilation
numbers at just above a narrow DCM were
maintained by a generally favorable nitrate
flux. The early high DCM concentrations
could not be maintained indefinitely, as the
nitrate brought up during the first regime
became exhausted and stratification
increased, reducing the nitrate flux. The DCM
slowly decreased and broadened, and both
DCM and nitracline deepened. However,
because stratification
was relatively weak,
periodic storms disrupted this steady process, temporarily increasing nitrate flux (and
diffusion of the DCM) and resulting in an
increase in the DCM afterward. Moreover,
the presence of some stratification protected
the DCM from being diffused away com-
876
Abbott et al.
pletely during storms, and this second regime was quite variable both temporally and
spatially as mixing and stable periods alternated.
Despite this variability,
the DCM of the
second regime was similar to the “typical
tropical
structure”
reviewed by Cullen
(1982). That is, the DCM was close to the
depth of the assimilation
number maximum, and its structure was governed by nutrient supply rates from below and by turbulent mixing. It was not a relict from prior
events but was an active balance of fluxes.
We suggest that the second regime was generally nutrient-dominated,
with DCM depth
positively correlated with DCM concentration as would be expected. In midlake in
May the DCM consisted mainly of a diatom
(Cyclotella striata) with a crysophyte (Dinobryon bavaricum) as a subdominant (Dep.
Water Resour. pers. comm.).
The transition from the second regime
was indicated by the deepening of the nitracline and subsequent reduction of nitrate
flux to zero, in response to increased stratification after mid-June (the surface temperature on the west shore increased 4°C in
1 week). The middepth peak in assimilation
number disappeared at this point, followed
by the, beginning of the third regime.
The third regime started with the deepening and broadening of the DCM. Assimilation numbers also increased in the surface
waters, perhaps as a result of increased nutrient regeneration by zooplankton, which
reached peak abundance in this period. As
the DCM deepened and again was closer to
the nitracline, nitrate flux to the DCM increased sharply; however, there was no DCM
response to this increase. This observation,
coupled with the negative correlation between depth of the DCM and its concentration, indicated that the DCM was primarily
light-dominated
in the third regime. The
vertical separation between the assimilation
number maximum and the DCM is similar
to the “variable environment”
structure described by Cullen ( 1982) who suggested that
this structure is a relict of previous events
in shallower waters. However, the DCM was
dominated by diatoms (Synedra ulna, Cyclotella comta) and three types of green fla-
gellates, none of which was present in shallower waters in May.
These results suggest that there are two
types of DCM in Lake Tahoe: a “spring”
DCM near the assimilation number maximum and probably dominated by nutrient
availability, and a deeper “summer” DCM,
well below the assimilation number maximum and probably dominated by light
availability.
The significant
changes in
species composition are consistent with this
interpretation.
This conclusion has implications for the spatial patterns of the DCM.
Since nitrate supply rates are governed in
part by vertical mixing below the pycnocline, a process that is patchy and episodic
on small scales (Garrett 1979), then we expect the DCM to be patchy on small time
and space scales in the second, “spring,”
regime. Such is the case, as shown by the
variability
of the DCM parameters within
and between sampling days (Fig. 11). Light
availability
should be much more spatially
uniform than vertical mixing, so we expect
less spatial and temporal variation during
the third, “summer,” regime. Spatial variability was reduced, despite variability
in
vertical mixing (Fig. 4, lower panel). Sampling by Coon et al. (in prep.) in summer
1976 showed little spatial variation of the
DCM. However, they did observe a strong
response of the DCM and nitrate profiles to
an unusually strong storm in late summer,
resembling a transition back to the second
regime DCM described here. Coon (1978)
also observed a gradual disappearance of
the DCM in fall as light levels declined.
These results support our hypothesis that
the Tahoe DCM is differentially
regulated
by light and nitrate fluxes, depending on
physical conditions.
Distinctions between the second and third
regime are not as clear as those between the
first and second regimes. The availability of
light and nutrients is important in both regimes. The difference is in how the two factors control the depth and shape of the DCM.
The second regime is characterized by a thin,
variable, rapidly growing DCM at moderate
depths that appears to respond to changes
in nutrient flux. The third regime is characterized by a broad, more stable, slowly
Chlorophyll dynamics
growing DCM at greater depth with no apparent response to changes in nutrient flux.
This transition is much less dramatic than
the rapid change in diffusivity between the
first and second regime. However, the
change in the characteristics of the DCM is
no less dramatic.
Larger-scale spatial differences in the
DCM were also apparent, particularly between the midlake and west-shore stations
during the first and second regimes. Midlake
waters stratified earlier in spring and hence
had a DCM earlier than the west shore. Also,
the mid-June storm was apparent in the
physical parameters and DCM statistics on
the shelf but not in midlake. Thus, the shelf
region is a zone of enhanced mixing, probably as a result of topographic differences
causing breaking of internal waves, shear
from offshore-onshore currents, or shear in
alongshore currents. We also expect the
nearshore zone to have more temporal variability in mixing than midlake because mixing processes respond more quickly to
changes in wind stress nearshore (Boyce
1974). This nearshore-midlake
difference
should affect the DCM during the first and
second regimes when vertical mixing is the
dominant process. Midlake and shelf DCM
statistics moved in parallel in the third regime when mixing was less important.
Cullen and Eppley (198 1) and Cullen
(1982) pointed out the difficulties in drawing universal conclusions from single-area
studies of the DCM. Our results from Lake
Tahoe also show the difficulties in drawing
conclusions from single-time studies. The
DCM may be characterized as one type at
one period and as another a few weeks later.
The nutrient-dominated
DCM is a highgrowth regime, resulting from a moderate
nutrient-light
environment. That is, growth
rates as measured by 14C uptake are high,
countering relatively high losses due to diffusion and perhaps to sinking and grazing.
The light-dominated
DCM is a low-lossrate regime resulting from a high-nutrient,
low-light environment. That is, growth rates
are low, but losses due to diffusion, and perhaps to respiration, sinking, and grazing are
also low. This low-loss-rate hypothesis for
the summer DCM is also supported by
877
Richerson et al. (1978) and Coon et al. (in
prep.). The temporal separation into distinct light-dominated
and nutrient-dominated regimes is reminiscent of the vertical
separation of such marine communities
proposed by Dugdale (1967) and described
by Venrick (1982).
Other processes, such as aggregation of
flagellates, shade adaptation, and sinking
and accumulation at the nitracline, are not
important in Lake Tahoe. Changes in vertical mixing as they affect diffusion, nitrate
supply rates, and changes in light availability at the DCM are the key processes.
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Submitted: 25 January 1983
Accepted: 6 February 1984