LimnoI. Oceanogr., 33(6, part 2), 1988, 1451-1462
@ 1988, by the American Society of Limnology and Oceanography, Inc.
New methods for using diatoms and chrysophytes to infer
past pH of low-alkalinity lakes
Donald F. Charles’
.
Department of Biology, Indiana University, Bloomington 47405
John P. Smol
Department
of Biology, Queen’s University, Kingston, Ontario K7L 3N6
Abstract
Distributions of both diatoms and chrysophytes (Mallomonadaceae) are clearly correlated with
pH in low-alkalinity lakes. Their siliceous remains are well preserved in lake sediments and have
been used to infer long- and short-term acidification trends. Until now, however, only diatoms
have been used quantitatively.
We describe new methods for inferring past pH in lake water using diatoms alone, chrysophytes
alone, and the two together. We developed the equations with multiple regression analysis of
diatom and chrysophyte assemblage data and pH measurements for 47 Adirondack lakes (PH
range, 4.7-7.8). Correlations of measured vs. calculated values of pH yield r2 values of 0.70-0.94
and standard errors of 0.13-0.35 pH unit. The best predictive equations, especially for acidic lakes
(pH < 5.5) incorporate both diatoms and chrysophytes. Major advantages of using chrysophytes
in addition to diatoms in paleoecological studies are that chrysophytes are more sensitive at lower
pH values (<5.0) than diatoms, are nearly all planktonic, represent a different phycological group,
can be counted on the same slides as diatoms, and can be counted faster because there are fewer
taxa. Stratigraphic diatom and chrysophyte data for two lake sediment cores are used to evaluate
the equations. These methods are widely applicable in studies of cultural and natural acidification
of low-alkalinity lakes in many regions of the world.
Rapidly growing evidence has demonstrated that atmospheric deposition has
acidified many low-alkalinity lakes in the
U. S., Canada, and Europe (e.g. Natl. Res.
Count. 1986). Limnological research has
focused on changes in pH and related water
chemistry characteristics and how these
changes have affected aquatic biota (e.g.
Schindler 1985).
Because historical limnological data are
usually lacking, there has been a surge of
interest in applying paleolimnological approaches to the study of patterns of lake
acidification (Battarbee 1984; Charles and
Norton 1986; Smol et al. 1986; Davis 1987).
Diatoms are especially useful in these studies because they are an abundant and cosmopolitan group of algae whose siliceous
frustules are well preserved in lake sediments. Furthermore, diatom distributions
have been extensively studied, and it has
been shown repeatedly that diatoms, at the
species and variety taxonomic levels, are
closely correlated to lake water pH (e.g. Battarbee 1984; Charles 1985; Flower 1986;
Tolonen et al. 1986). The use of diatoms to
infer past lake water chemistry has progressed
considerably, and objective esti‘ Present address: U.S. Environmental Protection
Agency, Environmental Research Laboratory, Corvalmates of past lake pH can be made with
lis, Oregon 97333.
several independent statistical techniques.
Acknowledgments
Usually, these approaches have involved the
This is contribution No. 27 of the Paleoccological
establishment of a “calibration set” of wellInvestigation of Recent Lake Acidification (PIRLA)
studied lakes from which the diatom assemProject. This research was funded primarily by the
blages contained in the surface sediments
Electric Power Research Institute (RP-2 174-10) and
(representing the last few years of sediment
also by the National Science Foundation (BSR 8617622). H. Ahmad performed the computer analyses.
accumulation) are correlated with known
Gary Oehlert and David Parkhurst provided comwater quality variables. Transfer functions
ments on statistical analyses. Measurement of 210Pbfor
are then constructed relating diatom distridating sediment cores was performed by Stephen Norbutions to lake water characteristics.
ton and associates. Dates were calculated by Michael
Binford.
More recently, the silica scales of chryso1451
1452
Charles and Smol
phytes in the family Mallomonadaceae
(hereafter referred to as chrysophytes) have
been shown to be useful paleolimnological
indicators. Chrysophytes are also sensitive
to lake water pH, and the stratigraphic di.stri.butions of scales have been used to docu].menttrends in pH (Smol .et al.. 1984a, b;
Smol 1986; Hartmann and Steinberg 1986;
Tolonen et al. 1986; many studies in progress). Unlike the diatoms, however, quantificaticm of these trends has not yet been
attempted.
There are many advantages in including
both diatom and chrysophyte profiles in interpretations of past lake chemistry. Diatoms and chrysophytes represent two important algalgroups with different life history
strategies. Chrysophytes are euplanktonic,
whereas diatoms in many acidic lake regions are overwhelmingly dominated by
benthic forms (Battarbee 1984; Charles
198 5). Chrysophytes usually bloom in
spring. Although planktonic diatoms are also
most abundant in spring, most littoral
species maintain their populations throughout summer (DeNicola 1986; Jones and
Flower 1986). Therefore, chrysophyte distributions may be more closely related to
vernal water chemistry. Because the onset
of’ lake acidification is believeci to involve
increasingly longer and more pronounced
periods of low pH in spring following snowmelt, chrysophytes may be better indicators
of the onset of acidification than diatoms.
Finally, coring, sediment preparation techniques, and counting procedures are identical for the two groups, and therefore little
extra effort is required to incorporate both
diatoms and chrysophytes into paleolimnological reconstructions.
The purpose of this paper .is to present a
widely applicable method :for inferring past
pH of low-alkalinity lakes (generally <400
~eq liter-’) from sediment assemblages of
both diatoms and chrysophytes. We demonstrate the importance of including
chrysophytes in paleolimnological studies
and propose ways that these types of combined studies can be used most efficiently.
To describe, demonstrate, and evaluate our
method we present new ecological data on
chrysophyte taxa, develop specific equations for inferring pH of low-alkalinity Ad-
irondack lakes, and provide inferred pH
trends for two lakes with the new equations.
Methods
Procedures used for lake selection, sediment coring, and water chemistry analysis
of 37 study lakes (Fig. 1) were described by
Charles (1985). Water chemistry data for an
additional 10 lakes that were part of the
Regionalized Integrated Lake Watershed
Acidification Study (RILWAS) were provided by C. Driscoll (pers. .comm.; Driscoll
and Newton 1985). Data were used only for
RILWAS lakes from which water samples
were taken from the lake surface or at the
lake outlet and not from samples taken
downstream from the outlet. Values of pH
are means of samples of air-equilibrated
surface water collected nearly every month
from May to September during a 2-yr period. Sediment cores from the 10 additional
lakes were taken with a 10-cm-diameter piston corer or surface sediment corer (Charles
and Whiteh,ead 1986). Methods for preparing diatoms and chrysophytes for analysis
and identification and counting procedures
are given elsewhere (Smol et al. 1984a;
Charles 1985; Charlesand Whitehead 1986).
Diatc}m, chrysophyte, and water chemistry data were stored and manipulated
within the PIRLA Data Base Management
system, which uses the Scientific Information Retrieval (SIR) software (Robinson et
al. 1980). The RSQUARE procedure within
the SAS statistical package (SAS Inst. 1985)
was used for the multiple regression analyses.
The calibration sets we used to derive our
pH inference equations incorporate the same
37 study lakes used in previous diatom
(Charles 1982, 1985) and chrysophyte (Smol
et al. 1984a) ecological studies, supplementeci by “1O additional lakes for which
comparable water chemistry data were
available (Driscoll and Newton 19.85; pers.
comm.). All 47 lakes (Fig. 1) were used to
derive ecological information on individual
taxa and to calibrate pH-predictive equations. Diatom taxonomy for previously
published surface sediment assemblage data
(Charles 1985) has been modified significantly based.on the work of Camburn -etal.
(1984-1 986) and others. Ecological data and
Inferring pastpHin
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ADIRONDACK
PARK
Fig. 1. Location of 52 Adirondack study lakes. 1–
Arbutus; 2–Avalanche; 3 —Barnes*T; 4–Bear.
–Big
Moose*; 6 –Black*; 7 –Bogt; 8 –Bubb*; 9 – $ at Mt.;
10–Clear (E.L.)*; 11—Clear (P.L.); 12–Copperas
(W.N.); 13–Copperas
(F. C.)~; 14–Cowhorn;
15–
Crane Mt.; 16–Dart’s*; 17–Deep; 18–Deer; 19–
Dunk; 20–East Copperas~; 21 –Frank; 22–Giants
Washbowl; 23–Green;
24–Gull; 25 –Hea~
26–
Huntley; 27 –Jenkins; 28 –Arnold; 29 –Colden; 30–
Rondaxe*;31 –Tear of the Cloudst; 32–Little Echo*t;
33 –Little Pinet; 34–Little Shallowt; 35 –Livingston;
36 –Long 37– Merriam*$; 38 –Moss*; 39 –Mountaint; 40—Nick’s; 41 —Parch; 42—Pine; 43+Qucer;
44–Rock; 45 –Round; 46 –Townsend*~; 47 –Upper
Wall face; 48 – Washbowlt; 49 – West*+; 50– Windfall*~; 51 – Wolfi 52– WoodruW.
Asterisks indicate the 14 PIRLA and Rcgionalized
Integrated Lake Watershed Acidification Study (RILWAS) lakes supplementing the 37 lakes used in previous studies (Charles 1982, 198 5; Smol et al. 1984a);
daggers indicate lakes excluded from the restricted data
set of37 lakes; double daggers indicate four lakes used
to help determine ecological characteristics of diatom
taxa, but which were not included in the calibration
set because samples for water chemistry analysis were
taken in outlets well downstream from the lakes; PIRLA lakes shown by solid dots were analyzed stratigraphically for both diatoms and chrysophytes.
pH categories for taxa have also changed
somewhat (from Charles 198 5), current data
being based on distributions of taxa in 52
lakes (Fig. 1).
Since our earlier work on chrysophyte
Fig. 2. Percent abundance of M. pseudocoronata
and M. crassisquama in surface sediments of 47 Adirondack lakes vs. measured air-equilibrated surface
sample PH.
ecology in the Adirondacks (Smol et al.
1984a; Smol 1986), we have refined some
of our taxonomic identifications. For example, we now feel confident that Mallomonas crass isquama and iWallomonas
pseudocoronata scales can be differentiated
in most instances, the latter being larger and
more robust. A scatter diagram showing the
relationships between these two taxa and
lake water pH in our 47 study lakes shows
that this taxonomic resolution is important,
as M. pseudocoronata generally occurs at a
higher pH range (Fig. 2). A similar distribution for these two taxa has recently been
demonstrated by Dixit (1986). In addition,
we have done extensive electron microscopy on the category referred to as “small”
Mal/omonas in Smol et al. (1984a). Almost
all scales included in this grouping are from
Ma[lomonas ga[el~ormis (formerly Ma[lomonas trummensis), and therefore we now
refer to this category using that species name,
although we recognize that it includes other
small Ma/lomonas scales. Also, electron microscopy showed that we had missed counting Mallomonas akrokomos scales in our
original surveys but that it was only present
1454
Charles and Smol
in sizable numbers in two lakes. We now
realize that most of the scales that we had
originally referred to as Synura macracantha were actually scales of Chrysodidymus
synuroideus, although both taxa were often
present. Finally, the Mallomonas acaroides
we observed has been designated a variety,
M. acaroides var. muskokana.
Study region
The Adirondack region is a largely undeveloped, forested, mountainous area (elevation 30 m to >1,500 m ASL) in northern New York containing about 3,000 lakes
and ponds (Fig. 1). Like other Adirondack
lakes, our 47 study lakes are mostly oligotrophic to mesotrophic, contain low concentrations of dissolved substances, and
have low alkalinity. Many have become
more acidic recently, primarily because of
increased acidic deposition (e.g. Natl. Res.
Coun-c. 1986). The study lakes were selected
to represent a wide variety of Adirondack
lake-watershed systems.
Sediment cores from Big Moose Lake and
lJpper Wallface Pond were analyzed to help
evaluate new pH reconstruction techniques.
The current pH of Big Moose Lake is - 5.0;
alkalinity is -1 peq liter-1 (Driscoll and
Newton 198.5). The .lakc has a surface area
of 515 ha and a maximum depth of 22 m.
Upper Wall face Pond has a pH of about 4.9
and alkalinity of – 2 peq liter--1 (Charles
1.982). Surface area is 5.5 ha; maximum
clepth is 9.0 m. Historical data of varying
quality document recent declines in lake
water pH and fish populations (Charles et
al. 1987; N.Y. State Dep. Environ. Conserv.
Lake Pond Surv. Files, Ray Brook and Albany).
Results
All diatom assemblages contained more
taxa and were more diverse than corresponding chrysophyte assemblages. Mean
number of diatom taxa in the restricted set
of 37 study lakes (Fig. 1 legend) was 62 (SD
== 21; rein, 29; max, 121; counts of 500–
550 valves per slide), and mean diversity
(H’) was 3.02 (SD = 0.6; rein, 1.69; max,
3.83). In contrast, the mean number of
chrysophyte taxa was a fifth that of diatoms,
12 (SD= 3; rein, 6; max, 19; counts of 500–
750 scales) and mean diversity was 1.47 (SD
= 0.5; nnin, 0.33; max, 2.97).
Chrysophyte pH categories– Reciprocal
averaging, clustering, detrended correspondence analysis, and scatter diagrams show
that the distributions of rnallomonaclacean
chrysophytes are closely related to lake water
pHandpH-related factors (Smolet al. 1984a;
Smol 1986; many studies in_progress). These
relationships have been useful in qualitative
interpretations of stratigraphic changes in
individual taxa (Smol et al. 1984b; Smol
1986; Tolonen et al. 1986; Hartmarm and
Steinberg 1986; Christie and Smol 1986).
The strong relationship with pH suggests
the possibility of developing equations to
quantify inferred pH changes using chrysophytes, similar to methods commonly used
for diatoms (Battarbee 1984). Many of the
latter methods require an initial categorization of taxa according to their distributions along a pH gradient. In most cases,
the groupings used for diatom analyses are
Hustedt’s (1939) general] y defined categories:
acidobiontic— widest distribution at pH
<5.5;
acidophilic— widest distribution at pH
<7.0;
circumneutral —distributed
around
PH
7.0;
alkaliphilic —widest distribution
at pH
>7.0;
alk.alibiontic —occur only at pH >7.0.
We cieveloped a set of chrysophyte pH
categories that have more precisely defined
boundaries and to which individual taxa
COUIC1
be assigned more objectively. First,
we calculated an abundance-weighted mean
pH (AWM) for each taxon (Table 1). The
AWM closely represents the center of distributicm of the taxa along the PH gradient.
We then divided the taxa into four pH
groups containing similar numbers of taxa:
group
group
group
group
I–AWM
pH <5.5;
H-AWM
pH between 5.5 and 6.5;
HI-AWM
pH between 6.5 and 7.0;
IV–AWM
PH >7.0.
Coincidentally, these groups roughly correspond to the four Hustedt diatom cate-
1455
Inferring past PI? in lake water
Table 1. Chrysophyte taxa in surface sediments of 47 Adirondack lakes. Number of lakes in which taxa were
found, abundance-weighted arithmetic mean (AWM), pH, and simple arithmetic mean pH of lakes in which
they occurred are indicated. Taxa arc grouped by range (noted in parentheses under the group designation) of
AWM PH. Further explanation given in text.
Lakes (No.)
AWM PH*
Mean pH
13
1
7
33
32
24
4.93
4.99
5.25
5.25
5,41
5.49
5.56
4.99
5.41
5.97
6.17
5.98
43
30
28
4
35
40
5.69
5.89
6.16
6.19
6.34
6.36
6.14
5.00
6.32
6.00
6.37
6.32
Group III (AWM pH 6.5-7.0)
Mallomonas lelymene Harris et Bradley
Synura spinosa Korsh.
ChrysosphaerelIa spp.+
Mallomonas punctl~era Korsh.
Mallomonas transsylvanica Pcterfi et Momeu
Mal[omonas akrokomos Paschcr
Synura uve[la Stein em. Kot-dy.
Ma/lomonas allorgei (Dcflandre) Conrad
4
24
31
28
11
5
17
4
6.52
6.54
6.55
6.60
6.63
6.64
6.80
6.85
6.32
6.62
6.32
6.27
6.73
6.97
6.70
7.01
Group IV (AWM pH > 7.0)
Ma![omonas e[ongata Reverdin
Ma[[omonas caudata Iwanoff em. Krieger
Synura curtispina (Petersen et Hansen) Asmund
Mallomonas pseudocoronata Prescott
8
30
6
12
7.01
7.02
7.19
7.37
6.98
6.67
7.04
7.06
Group I (AWM pH <5.5)
Mal[omonas hindonii Nicholls
Synura macracantha (Petersen et Hansen) Asmund
Mallomonas pugio Bradley
Mal[omonas acaroides var. muskokana Nicholls
MalIomonas hamata Asmund
Chrysodidymus synuroideus Prowse
Group II (AWM pH 5.5-6.5)
Synura echinulata Korsh.
Synura sphagnicola Korsh.
Synura petersenii Korsh.
Mal[omonas heterospina Lund
Ma[[omonas ga!el~ormis Nichollsf’
Mal[omonas crassisquama (Asmund) Fott
* Abundance-weighted
mean (AWM) pH was calculated with the formula:
pH (AWM) = j P,(Xi)/~ P,
,- I
,.1
where pH (AWM) is abundance-weighted mean PH, Pi the percentage occurrence of the taxon in sediment from lake i, and Xi the mean aircquilibraled surface sample pH of lake i (determined from mean of H” concentrations).
t’ Includes small scales of other taxa that could nol be distinguished. Details given in text.
$ The overwhelming majority of scales are from Chryso.rphzre//a /ongispina Laut. em. Nicholls.
gories commonly important in low-alkalinity regions. In other lake regions, these
categorizations may not be wholly appropriate. For example, the boundaries between groups may shift or it may be advisable to add an additional category if many
higher pH (> 7. 5) lakes are present. Had we
used simple arithmetic means ofpH for each
taxon, the groupings of taxa would have been
similar. In general, the assignments of taxa
to these categories are consistent with assignments that might have been made solely
on the basis of studies reported in the literature. One notable exception is Synura
petersenii. This taxon is usually recognized
as a widely distributed generalist, yet in our
calibration lakes it was usually restricted to
more acidic lakes (group II). We also caution that our calibration is based on lakes
in the Adirondacks and that these data may
not be completely representative of other
regions. For example, ongoing studies of
lakes in New England suggest that AZa120monas punctlfera may have a more acidophilic distribution. This difference may reflect different ccotypes or perhaps taxonomic
problems not yet resolved (e.g. at the variety
level).
1456
Charles and Smol
LAKE
pH
17
28
2
4
47
28
9
5
4.7
4.8
4.9
5.0
5.0
5.0
5.1
5.1
5.2
5.3
5.5
5.7
6.0
6.2
6.2
6.4
6.4
6.5
6.6
6.6
40
16
35
36
14
23
30
27
8
15
25
21
38
44
1
41
45
51
42
10
18
22
11
24
12
6
19
26
ACIDOBIONTIC
ACIDOPHILIC
.CIRCUMNEUTRAL
ALKALIPHILIc
GROUP I
GROUP II
GROUP Ill
GROUP IV
E
E
6.7
6.8
6.9
6.9
“7.0
7.0
7.0
7.1
7.2
7.2
7,27.37.3
7.5
7.8
IF!!!=
1
I
o
50
Icxl
111’11[11’k
o
50
100
Ii=
;pqml
1
lm
PERCENT
Fig. 3. Percentage of diatoms (closed bars) and chrysophytes (open bars) in PH catc.gories of 36 Adirondack
lakc~ (restricted set~. Vertical lines in the open bars in the-group H column represent the percentage of the
category contributed by Mallomonas crassisquama. Lake numbers refer to lakes listed in Fig. 1 caption. Percentages for each category are based on the total diatom valves and chrysophyte scales assigned to each pH
category. Explanation of pH categories given in text.
The percentages of diatoms and chrysophytes in pH categories (Fig. 3) are generally
large only in restricted pH ranges, providing
further evidence that these categories can
be used to quantitatively reconstruct lake
PH. The overall distribution of percentages
of diatoms in pH categories is more closely
related to the pH gradient than is the percentage of chrysophytes. Most diatom pH
groups have distributions approaching
Gaussian curves, and each group occupies
little more than half to two-thirds of the
entire pH range. Although chrysophyte
groups I and I.Vare limited to opposite ends
of the PH range, the middle groups do not
show particularly strong relationships with
PH. Group III contains taxa that were not
very abundant in the study lakes, and group
II is dominated by ill crassisquama which
comprises more than half of the group 11
percentages in several lakes (see Fig. 3 legend ). Because the distribution of ikf. crassisquama along the .PH gradient is broad
(pI-i 5.0-7.8; Fig. 2) and extends to the highest pH values, this taxon could arguablyhave
been placed in group 111,where it would
have dominated percentages. The abundance and wide distribution of &f. crassisquama means that it contributes little to
quantitative pH inferences. It is a good indicatc)rof overall acidification trends (Hartmann and Steinberg 1986), but its presence
in large numbers in an assemblage signals
that pH values inferred from the data should
Inferring past pH. in lake water
be interpreted cautiously because of the large
uncertainties involved. The taxon M. crassisquama may be composed of several genetic types, each indistinguishable from the
others (Asmund and Kristiansen 1986). This
may account for its broad ecological amplitude compared with other chrysophyte
taxa.
Chrysophyte group I has the smoothest
distribution along the pH gradient, suggesting its potential importance in quantitative
predictive relationships. Percentages of this
group increase rapidly and consistently at
pH values <5.0. This increase, in conjunction with the corresponding decrease in
group II percentages, indicates that chrysophytes are particularly sensitive to pH
changes in this range, more so than diatoms.
Predictive equations —Equations for inferring lake water pH (Table 2) were derived
from multiple regressions of the percentages
of both diatoms and chrysophytes in each
pH category with lake water pH (Fig. 4).
The percentages were calculated as the
number of valves or scales in each pH category, divided by the total number thatcould
be assigned to all categories, multiplied by
100. Unidentified taxa or taxa for which
information was insufficient to make an assignment to a pH group were thus excluded
from the percent calculations. This exclusion was never >1 ‘h of an assemblage for
chrysophytes and typically 5- 10O/ofor diatoms. Only multiple regression was used because it has been shown to give better predictions than index alpha, index B, and other
types of predictive equations (Charles 1985~
Dixit 1986; Flower 1986).
The composition of any calibration set of
lakes will influence predictive relationships.
Therefore we studied the sensitivity of the
equation variables to the calibration set by
systematically excluding certain subsets of
lakes that we thought most likely to influence these equations. They included bog
lakes (Bog Pond, EastCopperas Pond, Little
Echo Pond, and Washbowl Pond), lakeswith
a maximum depth <2 m (Lake Tear of the
Clouds, Little Pine Pond, Little Shallow
Pond), and finally lakes with fewer than five
chrysophyte taxa present in the surface sediment assemblages. This latter category includes some of the bog and shallow lakes
1457
noted above, as well as Barnes Lake, Copperas Pond (F.C.), and Mountain Pond. In
addition, we compared the predictive value
of our equations by considering separately
lakes with pH <6.0 and >6.0. We present
only equations derived from the full set of
47 lakes and from the most restrictive subset in which all 10 of the above-mentioned
lakes were eliminated (Table 2). We used
regression coefficients (r2) and root mean
standard errors (SE) as estimates of the predictive potential of each equation. We used
Mallows Cp statistic (SAS Inst. 1985) to
choose the number and specific combination of variables for each type of equation.
The exclusion of bog lakes improved the
predictive relationship for diatoms more
than for chrysophytes, whereas the exclusion of the shallow lakes and the lakes with
fewer than five chrysophyte taxa improved
chrysophyte predictive capabilities more
than diatoms. Predictive relationships with
the combination of highest r2 and lowest SE
for diatoms and chrysophytes, alone and
combined, were obtained with the restricted
subset of 37 lakes. Lowest SE values were
usually obtained when only the lakes with
p~ <6.0 were used in the regressions. Equations based solely on diatoms are better predictors of pH than chrysophytes alone, except for low pH lakes (PH <6.0). The
equation with the lowest SE was derived
from the restrictive set of lakes with pH
<6.0, using both diatom and chrysophyte
data. Standard errors for the best sets of
equations range from *O. 13 to *0.35, r2
values from 0.70 to 0.94.
Paleolimno[ogical
reconstructions – One
way to evaluate the applicability of our predictive equations is to apply them to existing stratigraphic data. We have chosen Big
Moose Lake and Upper Wallface Pond for
this purpose because the histories of these
lakes have been studied extensively with paleolimnological techniques (Smol 1986;
Whitehead et al. 1986; Christie and Smol
1986; Charles et al. 1987).
Three equations for the restricted set of
37 lakes (Table 2) were used to reconstruct
pH in both lakes. These equations use diatoms alone (Eq. 1), chrysophytes alone (Eq.
7), and diatoms and chrysophytes combined (Eq. 8). The inferred pH profiles using
Charles and Sm.ol
Cm.d-.*
coo!
C#ul
fammQl-
o“ o
0“000”0”
..
z
Wm
WI.
1+1s1
u
II
++t
Inferring past pH in lake water
1459
8.0
7.0
6.0
5.0
4.0
4.0
I
I
I
I
1
1
I
1
1
5.0
6.0
7.0
8.0
4.0
5.0
6.0
7.0
8:0
MEASURED PH
MEASURED pti
8.0
7.0
6.0
5.0
4.0
4.0
5.0
6.0
MEASURED
7:0
8:0
PH
4,0
1
I
1
1
5.0
6.0
7.0
8:0
MEASURED pli
Fig.4.
Inferred lake water pHcalculated from multiple linear regression of(A) diatom pHcategoties(Eq.
1, Table 2), (B) chrysophyte pH categories (Eq. 7, Table 2), (C) three diatom and one chrysophyte category
combined (Eq. 8, Table 2), and (D) chrysophyte plus diatom categories + 2 (Eq. 10, Table 2) vs. measured
surface pH for 37 Adirondack lakes, and 95°h C.I. for an individual prediction of pH from diatom data.
all three equations have the same overall
pattern for both Big Moose Lake (Fig. 5)
and Upper Wallface Pond (Fig. 6), but there
are some important differences in pH values
at some intervals. The Big Moose Lake pH
profiles based on diatoms alone and diatoms and chrysophytes are similar, differing
by only 0.1-0.3 pH units. The pH values
for chrysophytes alone, however, are 0.4–
0.5 pH units less for intervals below 10-cm
depth. This divergence from relatively good
agreement near the top of the core is caused
by the increase in M. crassisquama, which
constitutes > 80V0of the lower assemblages.
As previously discussed, this taxon could
arguably have been placed in the next higher
PFI category, which would have resulted in
higher chrysophyte-inferred
pH. In addition, the AWM pH of M. crassisquama
(6.36: Table 1) also suggests the chrysophyte-inferred
pH values are too low.
Another potential problem
is that the
chrysophyte-alone equation (Eq. 7) was derived from only lakes with pH <6 and is
being used to infer pH at the upper limit of
its range where potential for error is greater.
1460
Charles and Srnoi
BJGMOOSE L.-CORE 2
, ,
I
o
1
I
I
1
I
1
I
I
1980-
1
,
1
UPPERWALLFACE P.-CORE 1
,
I
I
o
1
1900#
s
a
L
Cr
‘~-
K
1900-
I
1
1
+
1
) _L.A~
1
1900..b
d .....
.....
#
<
c1
.0
L
10
~:’”
1920-
1
1980.-
...
o
‘“”b.
*..
:’
1940-
1920-
&
\
1900-
‘)
....’
+“:
------lWL”
(.
~..
lsKl-
ls40-
1S20-
mea ,-------W&l -
....
....
b
30
la~o~--J--
182.0-
Inferred pH profiles for Upper Wallface
Pond are similar, differences in pH values
for intervals being 0.1-0.5 pH units, within
950/0confidence intervals for each individual equation.
Discussion
Both diatoms and chrysophytes occur
widely in low-alkalinity lakes. They differ,
however, in the number of taxa present, as
well as their spatial and temporal distributions. These differences, combined with
the t~ctors affecting the transport and sedimentation of algal remains, lead to large
variations in the composition of diatom and
chrysophyte assemblages in sediments.
Lake sediment samples usually contain
many diatom taxa (typically 40–100), but
usually far fewer chrysophyte taxa (typically
5-15, and sometimes only 1 or 2). The composition of diatom assemblages is influenced to a large extent by the relative abundances of littoral vs. cuplanktonic forms that
reach the coring site. Sedimentary diatom
assemblages can be dominated (> 50-90°/0)
d
+
r’
Diatoms + Chrysophytes
* —-------+
I
4.0
Fig. 5 Inferred pH values for Big Moose Lake sediment core 2 using diatom (Eq. 1), chrysophyte (Eq.
7), and diatom and chrysophyte data combined (Eq.
8). Bars representing 950/0C.I. for inferred pH values
are shown on Fig. 6. Dates below the dashed line are
extrapolations
of 2’OPb dates. Divergence of the
chrysophytes along pH profile lower in core is due
largely to dominance of Mallonzonas crassisquarna (see
text for explanation). Arrowhead indicates current lake
water pH.
~
*
f“
I
1
A
*
1800
INFERRED IIti
+~
Chrysophytes
1..... ...*. .... ...4
ls40-
{
i,
Diatoms
20
)
\
+“””
4.5
I
5.0
1
5.5
INFERRED pti
Fig. 6. As Fig. 5, but for Upper Wallface Pond
sediment core 1.
by a fi:w euplanktonic forms if these taxa
are very abundant in -the water column. If
littoral forms dominate, the assemblages are
usually more diverse, with several taxa present in relatively high abundance. In our Adirondack lake set, lakes with pH <5.5 have
no or very few euplanktonic forms (Charles
1985).
Sediments may be dominated by chrysophytes, or diatoms, and the relative abundance and diversity of these assemblages is
the primary factor determining which group
will provide the most accurate information
on past lake chemistry, which group (or
whether both groups) should be analyzed,
and ultimately which equations should be
used. In general, assemblages with.the greatest diversity should provide the most information. on past lake water chemistry.
Thus, in lakes with only a few chrysophyte
taxa, diatoms maybe the most accurate indicatc)rs; in samples dominated by only a
few euplanktonic diatom taxa, chrysophytes
may be better, Clearly, though, combined
profiles will always contain the most information and increase the likelihood of correctly identifying limnological changes.
The “bestpredictive equations (Table 2)
were obtained with the restricted set of lakes,
and therefore these equations should be used
in most cases. Equations based on the full
range of lakes should be used to reconstruct
Inferring past pH in lake water
pH from bog ponds and shallow lakes, however, because they were included in the
complete set. Similarly, the equations derived for high (PH > 6.0) and low (PH <6.0)
pH lakes should only be used for inferring
pH in lakes with corresponding PH ranges.
If inferring a pH near 6.0 it may be more
appropriate to use the equations based on
the full pH range of lakes.
Results must be interpreted with caution
when equations are applied to either diatom
or chrysophyte assemblages that are dominated (e.g. > 90°/0)by one or two taxa. In
these cases, the inferred PH will be largely
determined by the choice of pH category
into which dominant taxa are placed. This
problem may be especially important if the
distribution of the dominant taxon along
the PH gradient cannot be well characterized, for example, if the taxon occurred infrequently (<5 lakes) in the calibration set,
if the taxon occurred evenly over a wide pH
range, or if there are few ecological data
from other studies supporting the choice of
pH category for the taxon. This problem
was demonstrated clearly by our paleoecological reconstructions of Big Moose Lake
(Fig. 5).
Assessment of acidification trends should
account for changes in taxonomic composition as well as inferred pH values. For
example inferred pH values for Big Moose
Lake indicate that rapid acidification began
in the 1950s, whereas abundances of certain
taxa began to shift in the early 1920s, clearly
suggesting that significant changes were occurring well before the period of rapid
change. Mallomonas crass isquama was
steadily being replaced by taxa such as &f.
acaroides var. muskokana, Synura echinuIata, and S’ynura sphagnicola (Smol 1986).
The latter three taxa all appear to be more
acidophilic than M. crassisquama (AWM
pH of each taxon: Table 1), yet because all
four taxa are assigned to group H, this shift
has no influence on inferred pH values.
Similar arguments can be made for the Upper Wallface Pond core (Christie and Smol
1986). Obviously, grouping taxa results in
a loss of ecological information. The indicator potential of dominant chrysophytes
can be assessed for a particular region with
scatter diagrams (e.g. Fig. 2; Smol 1986).
1461
We recommend that none of the techniques
presented here be used to infer pH from
assemblages containing <5 taxa or in an
assemblage overwhelmingly dominated by
one taxon (e.g. > 80-90Yo).
Combining the analysis of diatoms and
chrysophytes may greatly increase the efficiency with which pH reconstructions are
made. Routine identification and enumeration of chrysophyte scales on microscope
slides often requires only about 250/oof the
time required for a similar analysis of diatoms. Identifying and counting chrysophytes requires knowledge of about one order of magnitude fewer taxa than does
diatom counting. Therefore, in situations
where it appears that both diatoms and
chrysophytes will provide comparable pH
reconstructions, it may be efficient to analyze all levels of interest for chrysophytes
and only selected levels for diatoms.
In conclusion, use of chrysophytes in conjunction with diatoms to quantitatively infer lake water pH can provide significant
information to complement and supplement that derived from analysis of diatoms
alone. We recommend that, where appropriate, both chrysophytes and diatoms be
analyzed in paleolimnological studies of lake
acidification.
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Submitted: 28 May 1987
Accepted: S February 1988
Revised: 17 July 1988