Relationships of soft-bodied algae
to water-quality and habitat
characteristics in U.S. rivers:
Analysis of the National
Water-Quality Assessment
(NAWQA) program data set
THE
ACADEMY
OF NATURAL
SCIENCES
PATRICK CENTER FOR ENVIRONMENTAL RESEARCH
Relationships of soft-bodied algae to waterquality and habitat characteristics in U.S.
rivers: Analysis of the National WaterQuality Assessment (NAWQA) Program
data set
Report No. 05-08
The Academy of Natural Sciences
Patrick Center for Environmental Research – Phycology Section
1900 Benjamin Franklin Parkway
Philadelphia, PA 19103-1195
http://diatom.acnatsci.org/, http://www.acnatsci.org/
Prepared by
Marina Potapova
October 2005
Relationships of soft-bodied algae to water-quality and habitat
Report 05-08
Introduction
The purpose of this report is to summarize information on the relationships of
soft-bodied benthic algae to water-quality and habitat characteristics using the nationalscale data set collected within the framework of the U.S. Geological Survey National
Water-Quality Assessment (NAWQA) program. Unlike diatoms, which are commonly
used as water-quality indicators, soft-bodied algae are infrequently used in assessments
for various reasons. The NAWQA algal data are being analyzed with emphasis on
diatoms, but NAWQA biologists often express interest in information about ecology of
non-diatom algae, which can often be associated with water-quality problems.
An important aspect of this report is the scale of the analysis. All taxaenvironmental relationships are quantified at the national scale. Given the tremendous
diversity of algae, difficulty of their identification, and regional environmental
differences, studies at smaller geographic scales are perhaps necessary to develop useful
algal indicators of river health. On the other hand, the national NAWQA data set
represents a unique opportunity to study algal ecology on a large scale. Information
obtained from this national-scale study might then be compared with results of the
regional studies.
The goal of this study was to calculate optima and tolerances of soft-bodied algae
for 18 environmental characteristics, chosen in consultation with NAWQA biologists
Julie Berkman and Stephen Porter. Optima and tolerances were estimated by weighted
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averaging (WA) and by fitting logistic regression (LR) curves to presence-absence data.
An ordination analysis was also carried out to explore community patterns and identify
environmental gradients related to them.
Methods
Data
The algal data used in this study were retrieved from the NAWQA Biological
Transactional Database (Bio TDB) in January 2005 and represent 6455 NAWQA algal
samples collected in 1993 - 2003 from 1648 sites and 52 study units. From this data set,
various subsets of data were selected. For ordination, all 1397 sites where at least one
richest targeted habitat (RTH) sample was collected were selected, and then only 1 RTH
sample per site was chosen randomly. For calculation of optima and tolerances, the data
sets were constructed separately for each environmental parameter and for each method,
WA and LR. For calculation of WA optima and tolerances the data sets were constructed
in the following way. First, for each site where at least one RTH sample was collected, all
available measurements of the environmental variable of interest were retrieved. Second,
the time difference between collection date of each RTH sample and the date of each
measurement of environmental variable was calculated. Third, the pair of observations
(algal and environmental) with least time difference between collection dates was
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selected for the analysis. For calculation of optima for water-quality characteristics all
pairs with time difference more than one month were discarded.
For calculation of the logistic regression optima and tolerances, first all
combinations of RTH, DTH (depositional habitat), and QMH (qualitative multi- habitat)
samples collected at the same time and in the same sampling reach were selected.
Second, for all sites that had at least one such combination, all available measurements of
the environmental variable of interest were retrieved. Third, the time difference between
collection date of each RTH-DTH-QMH combination and the date of each measurement
of environmental variable was calculated. Fourth, the pair of observations (algal and
environmental) with least time difference between collection dates was selected for the
analysis. For calculating optima and tolerances for water-quality characteristics all pairs
with time difference more than one month were discarded.
The water-quality data were retrieved from the NAWQA data warehouse
(http://water.usgs.gov/nawqa/). The following 10 water-quality characteristics were used
in this study:
1) phosphorus as dissolved orthophosphate (PO4 ),
2) total phosphorus (TP),
3) nitrogen as dissolved ammonia (NH4 ),
4) nitrogen as dissolved nitrate plus nitrite (NO2 +NO 3 ),
5) total ammonia and organic nitrogen (TKN),
6) total nitrogen (TN),
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7) chloride,
8) suspended sediment,
9) conductivity, and
10) pH.
The nutrient concentrations reported as “less than detection limit” were made
equal to detection limit (e.g., if the orthophosphate concentration was reported as less
than 0.01 mg/L, it was made equal to 0.01 mg/L). This might have artificially inflated
calculated optima, but should not have influenced their relative ranking.
The habitat data (except median discharge) were retrieved from the Bio TDB in
May 2005 by Pete Ruhl. These data included seven characteristics:
1) reach gradient,
2) wetted channel width-to-depth ratio,
3) current velocity,
4) canopy angle,
5) relative proportion (percent occurrence) of transect points where the dominant
substrate is fine (consists of silt, clay, marl, muck, or organic detritus),
6) relative proportion (percent occurrence) of transect points where the dominant
substrate is sand, and
7) relative proportion (percent occurrence) of transect points where the dominant
substrate large-sized (consists of cobbles and boulders).
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The discharge data were obtained by Julie Berkman from David Wollock
(USGS). Table 1 shows the distribution of water-quality and habitat characteristics in two
data sets used to calculate WA and LR optima and tolerances.
Table 1. Minimum (min), median (med), and maximum (max) values of 18
environmental characteristics and numbers of observations (N) in data sets used to
calculate WA and LR optima and tolerances of algal taxa.
Environmental characteristic
PO4 , mg/L
TP, mg/L
NH4 , mg/L
NO2 +NO3 , mg/L
TKN, mg/L
TN, mg/L
Chloride, mg/L
Conductivity, µS/cm
pH
Suspended sediment, mg/L
Gradient
Velocity, m/s
Width/Depth
Canopy angle, degrees
Sand, %
Silt, %
Large substrate, %
Discharge, cfs
min
0.01
0.01
0.01
0.05
0.1
0.2
0.1
10
3.9
0
0
0
0.7
0
0
0
0
0
WA data set
med
max
0.02
7.15
0.05
8.93
0.03
17
0.44
22.80
0.3
19.0
0.9
26.9
14
4742
324
15600
7.8
10.4
10
1180
0.01
11.1
0.27
21.7
35.8 144028
73
180
8
100
0
100
30
100
78
21200
N
978
926
1013
978
917
910
1115
1251
1243
916
408
955
1090
1120
1098
1098
1098
538
min
0.01
0.01
0.01
0.05
0.1
0.2
0.1
14
3.7
0
0
0
2.1
0
0
0
0
0
LR data set
med
max
0.02
7.15
0.05
8.93
0.02
17
0.63
13.90
0.4
19.0
1.1
21.5
13
295
339
4440
7.9
9.6
12
1180
0
11.1
0.23
21.6
40
144028
78
180
8
100
0
100
33
100
80
21200
N
366
545
500
366
424
424
485
496
537
439
211
514
1090
551
534
534
534
347
Ordinations
An ordination approach was used as to identify patterns in species data.
Detrended Correspondence Analysis (DCA) was carried out using the CANOCO 4.5
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program. The following options were used: cell densities were log-transformed,
detrending was by segments, and rare taxa were downweighted. Two data sets were used
for ordination. One contained cell densities of non-diatom algae identified to the lowest
practical taxonomic level. The other included cell densities of non-diatom algae identified
to the genus level. To explore relationships between gradients in the taxonomic data and
the measured environmental characteristics, the correlation coefficients between sample
scores for the first 4 ordination axes and 18 environmental characteristics chosen for this
study were calculated. These correlations were not shown in ordination diagrams because
environmental data were not available for many samples.
Calculation of optima and tolerances
Taxa optima and tolerances were calculated using two approaches: weighted
averaging and logistic regression. Weighted averaging is a technique commonly used to
estimate species indicator values or optima (ter Braak and Looman 1986). Weighted
n
∑y x
ik i
averaging estimates of the species optima (uk) were calculated as
uk =
i =1
n
∑ yik
where yik
i =1
is the abundance of species k in sample i; x i is the value of environmental parameter in
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Relationships of soft-bodied algae to water-quality and habitat
Report 05-08
sample i; n is the total number of samples in the dataset. Tolerance or weighted standard
n
2
∑y
x uk )
ik ( i −
deviation (t k) was calculated as tk =
i =1
n
∑y
.
ik
i =1
WA optima and tolerances were calculated from cell density data, for taxa
recorded both at the lowest practical level of identification and at the genus level.
In the regression methods, species optima are estimated as modes of bell- shaped
response curves, and tolerances as standard deviations. In this study the bell-shaped or
Gaussian curve was fitted to the presence-absence data. Therefore, the logit regression
approach was used (ter Braak and Looman 1986). The Gaussian logit response curve has
the formula:
p=[exp(b0 + b1 x + b2 x2 )]/[1+exp(b0 +b1 x+b2 x2 )], where
p is probability of species occurrence,
x is the value of the environmental variable, and
b0 , b1 , and b2 are regression parameters.
If b2 <0, it is possible to obtain estimates of
the optimum, u = - b1 /(2b2 ), and
the tolerance, t = 1/(-2b2 )-2 .
If b2 >0, the fitted curve has a minimum instead of a maximum, and the curve was
biologically meaningless. In those cases optima and tolerances could not be estimated.
When the optima are lower or higher that the observed range of values, the response can
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be interpreted as monotonous rather than unimodal. The low optimum in such a case
would mean increasing probability of species occurrence at the lowest values of
environmental variable, and, conversely, a high optimum indicates the increasing
probability of species occurrence at the highest levels of environmental variable. The
logit regression was fitted to the data using the GLM procedure in SAS/8.0. In GLM,
regression parameters are estimated by the maximum likelihood principle. The likelihood
of a set of parameter values is defined as the probability of the responses actually
observed when that set of values were the true set of parameter values (Jongman et al.,
1995).
Logistic regression was fitted to presence-absence data summarized at two
taxonomic levels: lowest practical level and genus level. The optima and tolerances were
reported in all cases except those where convergence criterion was not satisfied, or
optima and tolerances could not be estimated.
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Results
Algal taxa occurrence
The list of all non-diatom taxa recorded in 6867 NAWQA samples collected from 1993
to 2003 includes 1887 names (Appendix 1, http://diatom.acnatsci.org/autecology). The
algae recorded in NAWQA samples belong to seven divisions: Cyanophyta
(cyanobacteria or blue-green algae), Chlorophyta (chlorophytes or green algae),
Rhodophyta (red algae), Chrysophyta (chrysophytes or golden algae), Cryptophyta
(cryptophytes), Pyrrhophyta (dinoflagellates), and Euglenophyta (euglenoids). Many nondiatom algae could not be identified to the species or genus level and were assigned to
taxa of higher taxonomic ranks. Among the most common algae were several
cyanobacteria, mostly not identifiable to species or genus level, and designated as
unknown Oscillatoriales and unknown coccoid Cyanophytes. Other common
cyanobacteria included representatives of the genera Homoeothrix, Calothrix, and
Blennothrix. Red algae usually could not be identified to species or genus level, and the
most common morphological entity was designated as a Chantrasia stage of
Florideophycidae. Most common green algal taxa were several species of Scenedesmus
and Ankistrodesmus falcatus, as well as several filamentous algae: Oedogonium sp.,
Stigeoclonium lubricum, Cladophora glomerata and Cladophora sp., which was probably
in most cases also C. glomerata. Representatives of other algal divisions were less
common, with the exception of genus Euglena from Euglenophyta.
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Ordinations
The ordinations did not reveal strong or clear gradients in algal assemblage data.
The eigenvalues in the species-level DCA were 0.73, 0.42, 0.38, and 0.29 for axes 1, 2, 3,
and 4, respectively. In the genus-level DCA the eigenvalues were 0.49, 0.28, 0.22, and
0.19 for axes 1, 2, 3, and 4 respectively. The first DCA axis in the case of both species
and genus- level ordination can be interpreted as a gradient from better water-quality taxa,
such as representatives of Calothrix and Homoeothrix, to taxa that are known to tolerate
pollution, such as representatives of Oscillatoria, Leptolyngbya, Planktolyngbya,
Phormidium, and Scenedesmus.
Low correlation coefficients between environmental variables and DCA axes
(Table 2) indicate that patterns in algal assemblages were only weakly related to
environmental conditions. The first DCA axis in both ordinations was most strongly
related to gradient in substrate composition and total nitrogen concentration, with more
pollution-tolerant taxa on the right side of the diagrams corresponding to higher nitrogen
concentration and finer substrates, and some nitrogen- fixers (Calothrix) on the left side of
the diagrams corresponding to lower nitrogen concentrations. The second axis was
mostly related to canopy angle, with representatives of Homoeothrix positioned in the
upper part of both diagrams and therefore, corresponding to lower canopy angles, or
more canopy cover.
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Figure 1. Species plots resulting from the ordination of
1397 RTH samples. A –based on cell density of taxa
identified at the lowest practical taxonomic level, B –
based on cell density of genera. Only the taxa with
weight greater than 5% are shown. HOMjanh Homoeothrix janthina, CALparen – Calothrix parietina,
CHLsp – Chlamydomonas sp., STGlubrc –
Stigeoclonium lubricum, BLNbrebs – Blennothrix
brebissonii, XRHchant – Chantransia stage of
Florideophycidae, EUGsp – Euglena sp., PLLsubtl –
Planktolyngbya subtilis, OSClimnt – Oscillatoria
limnetica, XBGosnos - unknown Oscillatoriales without
sheath, XBGc1-3 – unknown coccoid Cyanophyte 1-3
µm diameter, XBGosheh - unknown Oscillatoriales with
sheath, OEDsp – Oedogonium sp., ANKfalcaAnkistrodesmus falcatus, SCEspino – Scenedesmus
spinosus, SCEquadr – Scenedesmus quadricauda,
CALsp – Calothrix sp., XBGc5-10 – unknown coccoid
Cyanophyte 5-10 µm diameter, ANBsp – Anabaena sp.,
XBGc3-5 – unknown coccoid Cyanophyte 3-5 µm.
diameter, OSCsp – Oscillatoria sp., XLCflagl –
unknown flagellate Chlorophyte.
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Table 2. Correlation coefficients between sample scores for DCA axes and 18
environmental characteristics. DCAs were carried out on algal cell densities summed at
the lowest practical taxonomic level and at the genus level, from 1397 RTH NAWQA
samples. Significant correlations are in boldface.
Environmental
Lowest practical level
Genera
Characteristic
axis1
axis2
axis3
axis4
axis1
axis2
axis3
axis4
Sand
0.24
-0.10
-0.07
-0.20
0.23
-0.08
-0.18
0.02
Silt
0.15
-0.02
-0.03
-0.03
0.17
0.03
-0.08
0.01
Large substrate
-0.25
0.08
0.06
0.13
-0.24
0.07
0.16
-0.07
Velocity
-0.05
0.09
0.03
-0.07
-0.08
-0.04
0.06
-0.10
Canopy angle
-0.07
-0.10
-0.13
-0.14
-0.07
-0.27
-0.02
-0.21
Width/Depth
-0.03
-0.02
0.00
0.00
-0.02
-0.04
0.01
0.00
Discharge
0.04
0.04
-0.06
-0.06
0.06
0.02
-0.04
-0.07
Gradient
0.05
0.01
-0.08
-0.01
-0.03
-0.02
0.07
-0.01
Chloride
0.06
0.00
-0.11
-0.08
0.07
0.02
-0.05
-0.04
pH
-0.02
0.03
-0.03
-0.14
-0.05
-0.12
0.09
0.00
Suspended sediment 0.02
0.01
-0.06
-0.03
0.01
-0.03
-0.08
0.04
Conductivity
0.06
0.00
-0.07
-0.10
0.08
-0.07
-0.06
0.05
NH4
0.05
0.00
0.02
-0.05
0.06
-0.03
-0.06
0.02
NO2 +NO3
0.10
-0.08
0.01
-0.03
0.15
0.00
-0.04
0.18
PO4
0.03
-0.04
-0.02
-0.10
0.05
-0.03
-0.10
0.08
TP
0.06
-0.04
-0.04
-0.10
0.07
-0.03
-0.12
0.09
TN
0.25
-0.05
-0.02
-0.15
0.26
-0.04
-0.14
0.08
TKN
0.20
-0.05
-0.04
-0.13
0.23
-0.05
-0.15
0.09
Optima and tolerances
WA optima and tolerances for water-quality characteristics are shown in
Appendix 2 (http://diatom.acnatsci.org/autecology). The total number of taxa identified to
the lowest practical taxonomic level in this table is 245; the number of genera is 96.
Percent rank given for optimum of each taxon and for each characteristic allows
estimating whether the optimum is high or low compared to optima of other taxa. The
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Table 3. Common (n>25) algal taxa with the highest and lowest WA optima for selected
water-quality characteristics.
Water-quality characteristic
PO4
<0.03 mg/L
Mougeotia sp., Homoeothrix janthina
>0.30 mg/L
Scenedesmus ecornis, S. quadricauda,
Stigeclonium lubricum, Anabaena affinis
TP
<0.05 mg/L
>0.50 mg/L
Chantransia, Calothrix sp., Homoeothrix janthina, Scenedesmus ecornis, S. quadricauda
Mougeotia sp.
NO2 +NO3
<0.5 mg/L
>3 mg/L
Mougeotia sp., Anabaena sp., Calothrix parietina Phormidium granulatum, Chlorella vulgaris,
Oscillatoria limnetica, Chroococcus dispersus
NH4
<0.03 mg/L
>0.30 mg/L
Scenedesmus acutus, Chantransia, Calothrix sp.
Scenedesmus ecornis, Phormidium laetevirens,
Stigeoclonium lubricum
TKN
<0.03 mg/L
>1.2 mg/L
Chantransia, Calothrix sp.,
Scenedesmus ecornis, S. quadricauda,
Scenedesmus denticulatus
Phormidium laetevirens
TN
<0.9 mg/L
>4 mg/L
Anabaena sp., Calothrix parietina, Mougeotia sp. Scenedesmus ecornis, Chroococcus dispersus,
Oscillatoria limnetica, Chlorella vulgaris
Chloride
<11mg/L
>70 mg/L
Leptolyngbya sp., L. tenuis, Calothrix sp.,
Mougeotia sp., Phormidium tenue,
Chantransia
Oedogonium sp., Chlamydomonas sp
Conductivity
<250 µS/cm
>800 µS/cm
Leptolyngbya sp., L. tenuis, Cryptomonas sp.,
Oedogonium sp., Euglena sp., Mougeotia sp.,
Homoeothrix janthina
Merismopedia tenuissima, Chlamydomonas sp.,
Phormidium tenue
pH
<7.0
>8.2
Schizothrix friesii, Trachelomonas volvocina
Scenedesmus dimorphus, S. bijuga,
S. acimunatus
Suspended sediment
<8 mg/L
>120 mg/L
Scenedesmus acutus, Cladophora sp.,
Chroococcus dispersus, Oscillatoria limnetica,
Scenedesmus sp.
Trachelomonas volvocina
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common (N>25) taxa identified at least to the genus level and having highest and lowest
optima for water-quality characteristics are also shown in Table 3. Most of the species
with relatively high phosphorus optima also had high optima for various forms of
nitrogen, but some taxa with low to moderate phosphorus optima, such as Lyngbya
aestuarii, Microcystius aeruginosa, Schizotrix friesii, and Calothrix parietina had high
ammonia optima.
WA optima and tolerances for habitat characteristics are shown in Appendix 3
(http://diatom.acnatsci.org/autecology). A total number of 212 taxa identified to the
lowest practical taxonomic level are listed in this table. These taxa belong to 85 genera.
The optima are ranked in Appendix 3 to facilitate comparisons among taxa.
Fitting of logistic regression yielded few taxa with statistically significant
(P<0.05) optima (Appendix 4; http://diatom.acnatsci.org/autecology; significant optima
highlighted in yellow). Number of occurrences is not shown in this table because both
species presences and absences are taken into account when the regression is fitted. The
optima ranks are not included in this table because, for a large proportion of taxa, either
the convergence criterion was not satisfied or optima could not be calculated. For some
environmental characteristics, such as width-to-depth channel ratio, current velocity, and
reach gradient, optima could not be estimated for the majority of taxa. In some cases,
there was good correspondence between significant LR and WA optima. For instance,
Anabaena constricta LR and WA optima for conductivity were 458 and 452 µS/cm, for
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pH 7.7 and 7.7, for TKN 0.65 and 0.45 mg/L, respectively. In most cases, however, there
were considerable discrepancies between LR and WA optima.
Discussion
The purpose of this study was to quantify relationships between non-diatom algae and
some environmental characteristics. The results can be used to identify which taxa might
be used as water-quality indicators, and as a source of basic autecological information
about algae encountered in NAWQA samples. To be considered good environmental
indicators, organisms should have relatively narrow ecological tolerances. It is also
important that estimates of optima and tolerances are reliable, i.e., based on a large
number of observations. Therefore, the best indicator taxa should be quite common, and
at the same time should have relatively narrow tolerances.
As an example, suppose that we would like to determine which species are the best
indicators of dissolved orthophosphate. From Appendix 2, we can select the Table
“LowestTaxLevel” which lists WA optima and tolerances of taxa identified to the lowest
practical taxonomic level. There are no strict rules or guidelines about the number of
occurrences that is sufficient to obtain a reliable WA estimate, so subjectively we might
decide to limit our search to all taxa that occurred in at least 20 sites. We first select all
taxa for which PO4 optima were calculated, and then delete all taxa with number of
occurrences in the PO4 dataset less than 20. This leaves 72 taxa. It is important to note
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that WA tolerances are usually strongly positively correlated with WA optima. Therefore,
instead of selecting the taxa with lowest tolerances we should search for taxa with lowest
tolerance-to-optimum ratio. This can be done by creating a new column
“tolerance/optimum ratio” and selecting the taxa with lowest values for that ratio. The
following species were among 10 taxa with the lowest tolerance/optimum ratio:
Chroococcus dispersus (optimum = 0.07 mg/L), Oscillatoria limnetica (optimum = 0.06
mg/L), Chlorella vulgaris (optimum = 0.04 mg/L), Phormidium granulatum (optimum =
0.08 mg/L), Oscillatoria fremyii (optimum = 0.08 mg/L), Phormidium ambiguum
(optimum = 2.42 mg/L), Closterium moniliferum (optimum = 0.13 mg/L). The relative
values of these optima are rather high, and most of these species can be considered
indicators of high phosphorus concentration. Apparently not a single more or less reliably
identified taxon can be considered a good indicator of low inorganic phosphorus. We can
then expand our search and include all taxa that occurred in at least 10 sites to detect any
possible indicators of low PO4 . Using this new criterion, we determine that the following
taxa might be considered as indicators of low PO4 : Gloeocystis vesiculosa, Phormidium
jenkelianum, Cosmarium botrytes, Synechocystis aquatilis, and Chroococcus dispersus
var. minor. Next, it is important to review published information about autecology of
these species. If our results contradict what has been published so far about their ecology,
then we should be especially cautious in interpreting species ecological characteristics
obtained from the NAWQA data. The literature data confirm, for instance, that
Chroococcus dispersus and Oscillatoria limnetica are eutraphenthic species
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(VanLandingam 1982). Chlorella vulgaris is often cited as a pollution-tolerant alga
(Palmer 1959, 1975, Sladecek 1973) and therefore might also be associated with
moderate to high concentrations of nutrients. Closterium moniliferum and Phormidium
ambiguum are known to tolerate medium- level pollution (Sladecek 1973, Komárek &
Anagnostidis 2005) and therefore might have high PO4 optima. However, Phormidium
granulatum, which has a high phosphorus optimum in the NAWQA data set, according to
Komárek & Anagnostidis (2005) inhabits mostly unpolluted waters. Such a discrepancy
could most probably be attributed to a problem with identification. Komárek &
Anagnostidis (2005) mentioned that identity of many populations reported under the
name of P. granulatum is questionable. Cosmarium botrytes, which has a low PO4
optimum in the NAWQA data set, sometimes is reported as an indicator of organically
polluted waters (Sladecek 1973). This species is in fact very widely distributed, and
apparently can be found in a variety of habitats and conditions.
Another way to use information summarized in Appendices 1-4 is to focus on the
ecology of specific taxa. For instance, if a taxon is abundant in a certain region, or in
some sites is associated with a particular water-quality problem, it would be interesting to
determine an “ecological profile” of that taxon at the national scale. Here several
examples of such “profiles” for several of the taxa reported most frequently in the
NAWQA data are given.
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Homoeothrix janthina and the genus Homoeothrix
This cyanobacterium was reported from most study units (Appendix 1). It had
relatively low optima for phosphorus, ammonia and organic nitrogen, but moderately
high optima for NO2 +NO 3 and TN (Appendix 2). Such preference for moderate to high
nitrogen content might be due partly to the absence of heterocytes in this organism, and
therefore to its inability to fix free nitrogen. Relatively low optima for conductivity (235
µS/cm), pH (7.5), suspended sediment concentration (16 mg/L) and canopy angle (86°),
and high optima for channel gradient (0.098) and large substrate (54%) (Appendices 2
and 3) reflect its abundance in upstream, forested, relatively good water-quality sites. The
only significant logistic regression optima for this alga were obtained for pH (7.5) and
percent of large substrate (71%) (Appendix 4). These numbers correspond fairly well to
the WA optima. Although Homoeothrix janthina is a common inhabitant of epilithon in
flowing waters around the world (Komárek & Anagnostidis 2005), little is known about
relationships of this taxon to water-quality. It has been known, however, that grazing by
algivorous fish increase relative abundance of this turf- forming species in epilithic algal
assemblages (Abe et al. 2000). Most occurrences in genus Homoeothrix in the NAWQA
data were those of H. janthina, so their ecological “profiles” are very similar.
The genus Calothrix
Two taxa most commonly identified within this genus in the NAWQA data were
Calothrix sp. and C. parietina (Appendix 1). Calothrix has heterocytes and therefore is
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expected to indicate low N/P ratios. C. parietina is also known as an indicator of clean
waters (Palmer 1959, Sladecek 1973). Low WA nutrient optima derived from the
NAWQA data generally support this information. An exception was relatively high NH4
optimum for C. parietina, as well as moderate NO2 + NO 3 optimum for Calothrix sp.
This indicates that Calothrix is not necessarily confined to waters with very low content
of mineral nitrogen or low N/P ratio. Calothrix optima for TP and TN were relatively
low. The combination of relatively high optima for mineral forms of nutrients with
relatively low optima for total nutrient indicates that Calothrix has an affinity for low
concentration of organic matter. Calothrix species had low to moderate optima for
chloride, conductivity, suspended sediment and pH (7.8-7.9). Optima for physical habitat
characteristics were generally average in comparison with other taxa, but C. parietina had
an extremely high optimum for current velocity (2.7 m/s), and C. sp. had an extremely
high optimum for the channel width/depth ratio (1058), although tolerances in both cases
were also extremely large. It is necessary to keep in mind that it can be quite difficult to
identify Calothrix when only fragments of filaments can be found in a counting chamber.
Occasionally, when the shape of a trichome was similar to the typical shape of Calothrix,
but heterocytes could not be observed, the organism was identified as Calothrix in
NAWQA samples. Thus, identification of Calothrix species sometimes was not certain.
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Blennothrix brebissonii (synonym of Hydrocoleum brebissonii)
This taxon was frequently reported from NAWQA samples, mostly under the
name of Hydrocoleum brebissonii. However, examination of samples during the 11th
NAWQA algal taxonomic workshop showed that this identification is very uncertain, and
in fact several taxa, mostly Phormidium autumnale and other species of Phormidium
were identified as H. brebissonii. This fact may explain the wide tolerances and average
optima obtained for this taxon from NAWQA samples. Nutrient WA optima for “B.
brebissonii” were moderately high, which corresponds to the known tolerance of many
Phormidium species to pollution (Sladecek 1973).
Genus Cladophora
Excessive growth of Cladophora is often considered as a consequence and
indicator of eutrophication (Biggs 1996), although it is known that factors other than
eutrophication can be responsible for the increase of this alga in rivers (Bolas & Lund
1974, Kelly & Whitton 1998). Sometimes Cladophora is cited as a “clean water” taxon
(Palmer 1959), or indicator of fairly good to moderate water-quality (Sladecek 1973,
Round 1981). In NAWQA samples, Cladophora was mostly identified as C. glomerata
or Cladophora sp., with a good possibility that the latter was also mostly C. glomerata.
WA optima and tolerances derived from NAWQA data show that Cladophora was most
frequent in clear moderately alkaline waters (suspended sediment optimum 13 mg/L,
conductivity optimum 566 µS/cm, pH optimum 8.0), and at a very wide range of nutrient
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concentrations (Appendix 2). LR optimum for conductivity was higher (1003 µS/cm,
Appendix 4) than WA optimum and confirmed the preference of this alga for alkaline
waters. WA optima for physical habitat characteristics did not reveal any specific
requirements that this alga might have, except that it was obviously more frequent and
abundant on rocks than on soft sediment (Appendix 3).
Genus Stigeoclonium
Representatives of this genus are known to tolerate various kinds of pollution,
including organic waste and heavy metals (Wehr & Sheath 2004). The species most
commonly cited as an extremely-pollution tolerant alga is Stigeoclonium tenue (Palmer
1959, 1975, Sladecek 1973). This species, however, was only rarely encountered in
NAWQA samples; the most common Stigeoclonium was S. lubricum. WA nutrient
optima derived from the NAWQA data for this species, and for the genus Stigeoclonium,
were relatively high, and they were higher for inorganic forms of nutrients than for total
nutrients. Tolerance ranges for nutrient concentrations, however, were rather high, which
means that caution is needed when using this species as an indicator of eutrophication.
Biggs (1996) stated that Stigeoclonium is characteristic of unenriched streams of New
Zealand. WA optima derived from NAWQA data also showed that Stigeoclonium
preferred relatively open channels and high current velocities.
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Genus Oedogonium
Identification of Oedogonium to species level is difficult because it is mostly
based on reproductive structures that are usually not observed (Wehr & Sheath 2004).
Partly because of this difficulty with identification, this alga is not considered as a good
indicator of water-quality and the optima and tolerances derived from NAWQA data
confirm that conclusion. The genus Oedogonium had, however, relatively high optima for
conductivity and chloride, suggesting that some species might occur in water of very high
mineral content.
Ankistrodesmus falcatus
This small coccoid green alga was frequently reported from NAWQA samples
(Appendix 1) and had relatively high WA nutrient optima, especially for phosphorus
(Appendix 2). It is known as a ubiquitous alga able to withstand a variety of
environmental stresses (Prescott 1951) and an indicator of moderate to high organic
pollution and eutrophication (Sladecek 1973, Taylor et al. 1981).
Scenedesmus quadricauda and other species of Scenedesmus
Representatives of Scenedesmus, especially S. quadricauda, are extremely
common in all freshwater habitats (Prescott 1951) and were reported frequently from
NAWQA samples (Appendix 1). Relatively high WA nutrient optima for S. quadricauda
and several other Scenedesmus species (Appendix 2) confirm earlier observations of their
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high tolerance to pollution (Palmer 1959, Taylor et al. 1981) and affinity for nutrient-rich
waters (Wehr and Sheath 2004).
Chantransia stage of red algae
In most cases, red algae in NAWQA samples could not be identified to species or
genus level and were referred to as “unknown Rhodophyte Florideophycidae
(chantransia)”. This category might inc lude either some species of the genus Audouinella
or any representative of the order Batrachospermales. WA nutrient optima for
Chantransia were relatively low, except for the moderately high optimum for NO2 + NO3
(Appendix 2). Relative optima for inorganic nutrients were higher than those for total
nutrients. This observation is concurrent with earlier findings that freshwater red algae
are intolerant to organic pollution, but can withstand some degree of enrichment by
inorganic nutrients (Wehr & Sheath 2004). Relatively low WA optima for chloride,
conductivity, and suspended sediment (Appendix 2) also confirm previous observations
about affinity of red algae for soft-water clear streams and rivers. Among physical habitat
optima those for canopy angle (14°) and current velocity (0.039 m/s) were extremely low
relative to other algae (Appendix 3). According to optima derived from NAWQA data,
Chantransia was found mostly in heavily shadowed streams. Wehr and Sheath (2004)
stated, however, that biomass of freshwater red algae in the temperate zone usually
decreases during spring and summer, when canopy is peaking. Obviously, NAWQA data
captured spatial rather than temporal trends in the occurrence of red algae, which
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apparently are frequent inhabitants of forested streams, but persist there as low-biomass
Chantransia stage in the periods of heavy shading.
Information on algal ecology summarized in this report can be used to help
interpret NAWQA data. It is necessary, however, to keep in mind that there are some
unresolved problems with the NAWQA algal data. These include uncertainty of
identifications, variability of taxonomic levels of identification, and inconsistencies in
identifications among laboratories and in sampling procedures among study units. In
most cases, these problems could not be avoided under current protocols and given
available funding. NAWQA phycologists and ANS scientists agreed that because
rectifying identifications and revising the data to create consistent data sets at various
taxonomic levels would require considerable additional resources, and often is not
feasible, there would be no such an attempt made as part of preparing this report. The
algal data used here were as they were released by the Bio-TDB. Therefore, in case of a
considerable disagreement between ecology of an algal taxon quantified from the
NAWQA data and obtained from other literature sources, NAWQA biologists should be
cautious in their interpretations. Taxonomy and ecology of many riverine algae in North
America have yet to be studied, and national programs, such as NAWQA, may make an
invaluable contribution in these studies.
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Acknowledgments
This report was produced as part of a cooperative research agreement with the
USGS NAWQA Program. It is published with the understanding that the United States
Government is authorized to reproduce and distribute reprints for governmental purposes.
The views and conclusions contained in this document are those of the authors and
should not be interpreted as necessarily representing the official policies, either expressed
or implied, of the US Government. The author thanks everyone who contributed to this
study, especially Frank Acker, Don Charles, Julie Berkman, Stephen Porter, Pete Ruhl,
Steve Moulton, Barbara Scudder, Kurt Carpenter, Rex Lowe, Kalina Manoylov, all
NAWQA biologists who collected samples, and algal taxonomists who identified and
enumerated algae.
References
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Plectoglossus altivelis, on the species composition of benthic algal communities in the
Kiso River. Diatom 16: 37-43.
Biggs, B. 1996. Patterns in benthic algae of streams. Pages 31-56 in: Stevenson, R.J.,
M.L. Bothwell, and R.L. Lowe (eds.) Algal Ecology. Freshwater Benthic Ecosystems.
Academic Press, New York.
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Bolas, P.M. & J.W.G. Lund. 1974. Some factors affecting the growth of Cladophora
glomerata in the Kentish Stour. Water Treatment and Examination 23: 25-51
Jongman, R.H.G., C.J.F. ter Braak, and O.F.R. van Tongeren. 1995. Data Analysis in
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Prescott, G.W. 1951. Algae of the Western Great Lakes area. Cranbook Institue of
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Round, F.E. 1987. The Ecology of Algae. Cambridge University Press, Cambridge.
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fur Hydrobiologie, Beiheft 7, Ergebnisse der Limnologie, Heft 7: 1-218.
Taylor, W.D., L.R. Williams, S.C. Hern, V. W. Lambou, C.L. Howard, F.A. Morris,
and M.K. Morris. 1981. Phytoplankton water-quality relationships in U.S. lakes, Part
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ter Braak, C.J.F. & C.W.N. Looman, 1986. Weighted averaging, logistic regression
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Van Landingam, S.L. 1982. Guide to identification, environmental requirements and
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Wehr, J. D. & R.G. Sheath. 2004. Freshwater algae of North America. Academic
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List of appendices
Appendix 1. List of 1887 soft-bodied algal taxa recorded in 6867 NAWQA samples
collected from 1993 to 2003.
Appendix 2. Weighted averaging optima and tolerances of soft-bodied algal taxa for
water-quality characteristics.
Appendix 3. Weighted averaging optima and tolerances of soft-bodied algal taxa for
habitat characteristics.
Appendix 4. Logistic regression optima and tolerances of soft-bodied algal taxa for
water-quality and habitat characteristics.
Appendices are available on- line at http://diatom.acnatsci.org/autecology.
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