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Protocols for measuring biodiversity: Phytoplankton in freshwater



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Phytoplankton in Freshwater
D.L. Findlay and H.J. Kling
Department of Fisheries and Oceans
Freshwater Institute
501 University Crescent
Winnipeg, Manitoba, R3T 2N6
Table of Contents Page
Introduction ....................................................................................................................1
Abiotic Factors ...............................................................................................................1
Sampling Procedures: ...................................................................................................2
Sites and Frequency ...........................................................................................2
Qualitative Sampling............................................................................................2
Quantitative Sampling..........................................................................................3
Preservation ........................................................................................................4
Sample Labelling and Field Notes ......................................................................4
Laboratory Procedures ..................................................................................................4
Sample Counting.................................................................................................5
Identification ........................................................................................................6
Data Analysis .................................................................................................................6
Quality Assurance/Quality Control .................................................................................7
Volunteer Involvement ...................................................................................................8
Persons to Contact for More Information .......................................................................8
Appendix 1: Sources of Equipment and Supplies for Studies of
Phytoplankton Biodiversity .......................................................................9
Phytoplankton are unicellular, microscopic, unattached plants found in freshwater and
marine ecosystems. In comparison to many other biological organisms, phytoplankton
are relatively homogeneously mixed throughout the water column. Because these
microscopic organisms depend on light and nutrients, they populate the euphotic zone
or the upper strata of freshwater lakes, reservoirs, and ponds. They optimize their
residence in the photic zone by a number of mechanisms: controlling buoyancy using
gas vacuoles, migration using flagella, increasing surface area/volume ratio to form
resistance, and metabolic processes. Phytoplankton can be solitary or colonial and can
range in size from <1 µm to colonies that are >500 µm.
Like most plants, most phytoplankton are autotrophic; they contain chlorophyll pigment
that enables them to fix solar energy by photosynthesis, converting carbon into an
energy form transferable to other parts of the aquatic food web. However, some
phytoplankton can be heterotrophic for short periods (e.g. dinoflagellates,
cyanobacteria), by using dissolved organic substances, or they can be phagotrophic
and use particulate organic matter.
There are thousands of species of phytoplankton, many of which remain undescribed.
The cell coverings of phytoplankton can vary between and within taxonomic groupings.
These coverings consist of simple plasma membranes; protective, ornamented thecae
frustules; loricated structures; siliceous frustules; or cellulose. Species identifications
are based on morphological features, cellular structure, color, size, and cell division,
which are all visible under the light microscope. Preserved or living specimens can be
identified; taxonomy can be enhanced with electron microscopy, especially for taxa that
have external, recognizable, cell characteristics such as diatoms, chrysophytes, and
Phytoplankton have rapid turn-over times (in the order of days), and are sensitive
indicators of environmental stresses. They are affected by physical, chemical, and
biological factors, making them valuable in monitoring programs.
Abiotic Factors
Freshwater phytoplankton populations are seasonally variable (Hutchinson 1967) and
are regulated by both chemical and physical factors. Nutrient concentrations, nutrient
ratios (stoichiometry), and light are essential growth indicators. For example,
nitrogen-fixing cyanobacteria tend to dominate in systems with N:P ratios <5:1 (Findlay
et al. 1994), whereas chlorophytes tend to dominate in systems with higher N:P ratios.
Dinoflagellates proliferate in freshwater lakes with low pH and low C:P ratios. Pearsall
(1932) postulated that phytoplankton abundance in lakes of the English Lake District
was related to nutrient concentrations. The most obvious demonstration of this
hypothesis was the experimental eutrophication of Experimental Lakes Area (ELA)
Lake 226 (Schindler 1974). Redfield (1934) noted the constant ratio of C:N:P in marine
plankton (106:16:1), a ratio that remains widely accepted for marine systems. However,
Redfield ratios are the exception rather than the rule for freshwater phytoplankton
(Healey and Hendzel 1979; Hecky et al. 1993). In many stratified freshwater lakes and
reservoirs, blooms of chrysophytes, chlorophytes, or dinoflagellates form at varying
depths throughout the euphotic zone where nutrient concentrations are high and these
organisms can select optimum light levels (Fee 1976).
Physical characteristics of a lake, reservoir, or pond such as depth, volume, ratio of
drainage area:lake surface area, and fetch distance can influence phytoplankton
assemblages. In small shallow water bodies, the euphotic zone has a higher ratio of
epilimnetic sediments:lake volume, allowing for more recycling of nutrients from the
sediments than in large, deep lakes. (The epilimnion is the upper portion of a lake that
is well mixed and has a uniform temperature.) In contrast, nutrient recycling in a large
lake or reservoir may be influenced by internal mixing caused by wind action. As
previously mentioned, phytoplankton rely on different mechanisms to remain in the
water column. Flagellated organisms may occur in higher proportions in a small lake
than a large one because less physical motion is available in a small lake to enable
Sampling Procedures
Sampling sites and frequency
Sampling sites for phytoplankton should be located a reasonable distance away from
shore to eliminate contamination by periphytic (attached) species of algae. The depth of
the euphotic zone can also be used in defining sampling location. The euphotic zone is
defined by the maximum depth at which surface light is attenuated to 0.5%. It has been
demonstrated that 0.5% is the minimum light level required for photosynthesis (Fee
Generally, twice the Secchi disc reading will be a reasonable estimate of the maximum
depth of the euphotic zone. If the lake is thermally stratified, it is best to sample over the
deepest location of the euphotic zone, which will reduce the probability of contacting the
lake sediments and will also allow sampling of the different thermal strata (epilimnion,
metalimnion, and hypolimnion). Bi-weekly to monthly sampling is necessary to capture
the seasonal dynamics of phytoplankton, and to quantify their abundance and biomass.
It may be necessary to increase sampling frequency during periods of blooms. Lakes
should be sampled during mid-day to optimize light transparency. Although
phytoplankton are relatively homogeneously mixed, uneven horizontal distribution
(patchiness) can be a source of sampling error. Taking multiple samples from different
stations and combining them to produce a composite sample can reduce this error.
Many types of field gear can be used to collect phytoplankton from freshwater lakes,
reservoirs, and ponds. The type of gear selected depends on the information required
to address the question being asked.
Qualitative sampling
For qualitative sampling of phytoplankton, a 10-µm Nitex® mesh phytoplankton net is
recommended (Fig. 1). The net is 20 cm in diameter, 35 cm in length, and is fitted with
a stopcock at the lower end to allow opening and closing. The mouth of the net has a
5-cm canvas collar fitted with a metal bridle that attaches to the sampling line. This type
of net is commercially available from Geneq Inc. (Appendix 1). The net is lowered to a
given depth, allowed to settle there for 15-30 sec, and then is slowly pulled to the
surface. Pulling the net too fast will cause a bow wave and the net will be less efficient.
The mouth of the stopcock is positioned into a sample-collecting bottle and the sample
is then drained. This procedure may be repeated 3-4 times.
Qualitative net sampling will yield presence/absence information and can aid in the
identification of rare species, but is not appropriate for accurate counting or biomass
estimates. Numerous species or individuals can pass through even small mesh sizes,
colonies can be disrupted by the net, and some fragile species may burst from
excessive pressure. However, qualitative samples are excellent for taxonomic surveys
because of the large number of specimens collected.
Quantitative sampling
There are several reliable methods for obtaining quantitative phytoplankton samples,
which are unconcentrated samples taken from a known volume of water. The most
common method is the use of a discrete-depth water-bottle sampler such as a Van
Dorn, which samples a constant volume of water (1-6 L). The opened sampling
apparatus is lowered to the desired depth, the trigger is released, and the sample is
entrapped. The sampler is then brought to the surface, shaken, and a subsample is
The use of an integrating sampler (Shearer et al. 1985) allows a sample to be obtained
from a predetermined depth range (Fig. 2). This apparatus consists of a 1-L amber
bottle attached to a weighted harness. A rubber stopper fitted with intake and outlet
hoses is positioned in the mouth of the bottle. The intake hose is attached to a closing
mechanism and opens when triggered. Technical information on the construction of this
sampler can be found on the ELA web site
The closed, integrating sampler is lowered to the bottom of the desired depth range (i.e.
sampling 4-7 m, the sampler would be lowered to 7 m), the intake line is triggered open
and the sampler is raised over the given depth range at a constant rate (1 m10 sec-1).
The bottle is quickly repositioned at the bottom of the desired depth range and again
raised at a constant rate.
This process is repeated until there are no air bubbles at the water surface, which
indicates the bottle is full. The sampler is then retrieved into the boat, shaken, and a
subsample is removed.
An integrated sample can also be collected from shallow water bodies by using a
3-6-cm diameter ridged, plastic pipe, 1-2 m in length. The pipe is gently inserted
vertically into the water column and the top is capped off by covering it tightly with a
hand. The pipe is lifted out of the water and is drained into a bucket. The water sample
in the bucket is mixed well and a subsample is removed.
The above-mentioned methods all provide samples appropriate for accurate counts (i.e.
number of cellsL-1) and species identification. However, an integrated sampler allows
an entire water column to be examined in fewer samples than for a discrete sampler,
thereby reducing time-consuming microscopic analyses.
Phytoplankton samples should be preserved by both acid iodine solution (Lugol's) and
an acidified formalin solution (FAA; see recipes below) and stored in glass vials
(opaque glass would increase shelf life) fitted with a polyethylene screw-cap lid. Both of
Figure 2. Integrating sampler (Reprinted from Shearer et al. 1985 by
permission of J.A. Shearer, Freshwater Institute, Winnipeg,
the recommended preservatives have limitations. Lugol's solution has a shelf life that is
affected by light. It is excellent for preserving chrysophytes but it makes the
identification of dinoflagellates difficult, if not impossible. FAA may cause thin-walled
cells to burst. For best results, it is recommended that algal samples be preserved first
with Lugol's (0.05-1% by volume) followed immediately by FAA (2% by volume). Color
is an important taxonomic characteristic, especially for bluegreen algae. Formalin is a
good preservative for green and bluegreen algae and dinoflagellates because cell color
remains intact if samples are stored in the dark.
Lugol's solution can be prepared using 100 g I, 200 g KI, 200 mL glacial acetic acid,
and 2000 mL of distilled water. Because it is light sensitive, this solution must be stored
in a dark bottle. FAA is a solution of equal volumes of formaldehyde (37%) and glacial
acetic acid.
Sample labelling and field notes
All samples and subsamples require accurate labeling. The label should contain
information such as location, time, date, depth, type of sampling gear used, and the
name of the collector. Complimentary field notes should be kept containing a
description of the sampling site, type of land vegetation surrounding the sampling site,
general geology of the area (e.g. granite vs. limestone), and pertinent information on
other variables that were also sampled (e.g. air and water temperatures, wind speed
and direction, and unusual odors).
Laboratory Procedures
An inverted microscope equipped with phase/contrast illumination; 10X, 40X, and 100X
objectives; 12.5X eyepieces (10X or 16X are also available); and micrometer eyepieces
(graduated in µm) is required to identify and count the specimens collected.
Photographic capability, although not necessary, can aid in confirming taxonomic
decisions at a later date.
Identifications and counts for quantitative estimates are performed on preserved
subsamples. Qualitative samples obtained using a phytoplankton net can be analyzed
either live or preserved but they yield only presence/absence data.
Sample counting
The most common counting method used to quantify preserved phytoplankton samples
is the Utermöhl (1958) technique as modified by Nauwerck (1963). (Staining is not
required because samples have been preserved in Lugol's solution.) This method
involves settling a known volume of sample into a counting chamber (Fig. 3). The
density of phytoplankton will be dictated by the volume of subsample settled. If after
settling a sample, the density of plankton is too great, a smaller volume of sample
should be resettled. A simple counting chamber consists of three parts: (1) a bottom
part, which is a piece of Plexiglas (40 mm2 and 6 mm thick) with a 20-mm diameter
hole drilled through it. A glass coverslip is glued (Pliobond glue) over the bottom of the
hole; this part of the chamber holds 2 mL; (2) the top of the chamber, which is a column
that can vary in length and volume (8 and 48 mL are shown). The top chamber is
secured to the bottom with a thin film of stopcock grease. Samples require 4 h of
settling time for every 6 mm of counting-chamber height (e.g. samples in a chamber 36
mm high would need to settle for at least 24 h); and (3) a cover glass, which is used to
cap the top of the counting chamber after it has been filled with a sample. The cover
glass is 3 mm thick and 35 mm in diameter. It is clear, ground glass that allows light to
pass through. After settling is complete, the top portion of the chamber is slid off the
bottom and a second cover glass is slid into place over the bottom chamber, which now
contains all the phytoplankton that have settled. Chambers should be cleaned with
alcohol before reuse to remove residue from previous samples.
Figure 3.
The bottom chamber is then placed on the inverted microscope. Cells >15 µm are
identified and counted using the 10X objective on transects that cover 50% of the
chamber surface. Cells <15 µm are counted on a single transect, 200 µm wide, at the
center of the counting chamber using the 40X objective. Cells must appear to be viable
(i.e. chloroplasts intact). Cell fragments are not counted. Viable cells that are partially in
a counting field on the right hand side are counted, but those on the left are omitted.
For colonies, a small portion of the colony is counted, and the number of cells is then
estimated. Filaments are counted individually. A minimum of 400-600 cells should be
enumerated to assure that the count is representative of the sample. Cell counts are
converted to wet-weight biomass by approximating cell volume. Estimates of cell
volume for each species are obtained by routine measurements of 30-50 cells of an
individual species and application of the geometric formula best fitted to the shape of
the cell (Vollenweider 1968; Rott 1981). A specific gravity of 1 is assumed for cellular
biomass. For accepted shapes and assignment of species see Rott (1981).
For example:
There are numerous types of devices used to tally counts. They range from a simple
tab counter to voice recognition computer systems.
The premise of a biodiversity monitoring program is to observe changes at the lowest
identifiable taxonomic level over time, so accurate species identifications are important.
Identifications can be enhanced by using both light and electron microscopy, by
examination of live and preserved specimens, and, in the case of diatoms, by
examination of acid-cleaned specimens. However, identification of algae is extremely
difficult and it is recommended that samples be processed by an experienced
Phytoplankton species are continually being described and classified. Therefore, it is
necessary for those identifying phytoplankton to be abreast of current identification keys
and those that are relevant to the geographic area in which sampling was done. Geitler
(1932), Hubber-Pestalozzi (1941), Bourrelly (1966, 1968), Patrick and Reimer (1966,
1975), Komárek and Anagnostidis (1986), and Krammer and Lange-Bertalot (1986) are
recommended keys for the identification of phytoplankton. The identifier should also
have some knowledge of the life cycles of the different algae encountered and must be
aware that species of phytoplankton can change size quickly as part of their
reproductive phase.
Identifications can be checked/changed at a later time if subsamples are taken and
archived. As a rough guide, 50-75 mL of each sample should be archived, and all
unusual samples should be saved. Preservation methods described above should be
Data Analysis
All analyses should be routinely recorded on a count sheet, which should allow for the
following entries: sample identification, species names, numbers of cells counted, cell
length and width, and drawings of unusual species (Table 1). Data should then be
stored in an electronic database; it is imperative that data entries be checked to
eliminate unexpected errors. All variables required to transform the data into either
biomass or number of cellsL-1 should be saved with the count and identification data.
Phytoplankton data need to be summarized and plotted to enable seasonal, annual,
and long-term analysis. This involves calculating the number of cellsL-1 and the
biomass of each species, summing species in the same taxonomic group to obtain
group totals, and summing the totals of the different taxonomic groups to obtain a total
biomass for each sampling date (Table 2).
Table 1: A phytoplankton count sheet containing all pertinent information
Species Correction
Factor1Group Species
Code2Count Length
um Width
um Cell Vol
1 200 1 1022 7 0 0 2600 Gomphosphaeria sp.
2 200 1 1078 9 251 6 7096.9 Planktothrix agardhil (Gom.) Anagnostidis
3 200 2 2113 1 0 0 3200 Pediastrum duplex Meyen
4 200 5 5540 12 30 6 848.2 Aulacoseira italica v subarctica (O.Mull)
5 200 5 5794 1 140 20 14660.8 Pinnularia flexuosa Cleve
6 200 6 6558 22 21 10 733 Cryptomonas erosa Ehrenberg
7 200 7 7632 4 34 34 20579.5 Gymnodinium sp.
8 200 7 7638 32 20 20 4188.8 Peridinium inconspicuum Lemmermann
9 7184 1 1065 16 6 4 50.3 Anabaena cylindrica Lemmermann
10 7184 1 1072 1 6 6 113.1 Heterocysts
12 7184 2 2105 21 10 6 188.5 Chlamydomonas spp.
13 7184 2 2112 4 4 4 33.5 Sphaerocystis schroeteri Chodat
14 7184 2 2115 8 4 4 11.2 Pediastrum tetras (Ehrenberg) Ralfs
15 7184 2 2121 14 8 4 67 Oocystis lacustris Chodat
16 7184 2 2127 3 10 10 174.5 Tetraedron minimum (Brunow) Hansgrig
17 7184 2 2131 4 6 4 33.5 Scenedesmus quadric auda v
18 7184 2 2132 12 8 4 44.7 Scenedesmus denticulatus Lagerhiem
19 7184 2 2136 12 5 5 65.4 Dictyosphaerium pulchellum Wood
20 7184 2 2138 68 49 1.8 83.1 Monoraphidium komarkovae (Nvg.) Komarkova
21 7184 2 2143 3 10 4 62.8 Monoraphidium minutum (Nag.) Komarkova-Le
23 7184 2 2178 1 12 12 301.6 Cosmarium sp.
24 7184 2 2201 21 3 3 14.1 Small greens
25 7184 4 4351 191 3 3 14.1 Small chrysophyceae
26 7184 4 4352 34 6 6 113.1 Large chrysophyceae
27 7184 4 4362 1 4 4 33.5 Kephyrion sp.
28 7184 4 4383 4 12 6 226.2 Dinobryon bavaricum Imhof
29 7184 4 4420 24 6 4 50.3 Gloeobotrys limneticus (G.M.Smith) Pasch
30 7184 5 5702 4 22 4 92.2 Achnanthes minutissima Kutzing
31 7184 6 6554 21 12 6 150.8 Rhodomonas minuta Skuja
32 7184 6 6568 6 8 4 44.7 Katablepharis ovalis Skuja
Lake: 223 Stratum: Epilimnion Date: 23 Jul 93 Start Depth: 0 End Depth: 3.75
1 The correction factor is used to convert the number of cells in the settled subsample to the number of cells L-1
(i.e., 10 cells in a 5 mL subsample = 10 cells * 200 (correction factor) = 2000 cells L-1).
2 Species are entered as numeric codes that are electronically linked to the species names.
Table 2 - Daily Phytoplankton Biomass - Lake 149
Lake Date Cyano-
phyte Chloro-
phyte Euglen-
ophyte Chryso-
phyte Diatom Crypto-
phyte Dinophyte Total
149 8 May 90 0.5 10.8 0 1241.3 36 58.3 92.8 1439.8
149 22 May 90 93.3 14.3 0 1835.9 73.9 47.2 357.1 2421.6
149 5 Jun 90 2.5 4.9 0 1555.3 208.4 81.6 1069.1 2921.9
149 19 Jun 90 4.1 27 0 1990.5 38.5 32.6 425 2517.8
149 3 Jul 90 35.5 29.6 0 314.2 24.5 34.1 760.4 1198.3
149 17 Jul 90 162.6 28.9 0 589 30.4 33.9 930 1774.7
149 31 Jul 90 50 23.6 2.1 608.3 15.7 28.9 1329.5 2058.1
149 14 Aug 90 55.9 11.7 0 790.1 12 49.9 1149.9 2069.5
149 28 Aug 90 23.3 12.1 0 934.8 9.8 42.4 927.3 1949.6
149 11 Sep 90 55.7 48.9 0 1297.6 118.9 153.3 275.1 1949.5
149 25 Sep 90 0 11.7 1.1 2033.4 103.3 50.4 301.5 2501.4
149 9 Oct 90 8.9 28.1 0 2522 30 81.6 339 3009.7
149 23 Oct 90 0 5.6 1.2 687.2 21.6 64.9 80.2 860.7
149 7 May 91 0 0 0 470 36 121.1 370.6 997.8
149 4 Jun 91 8.4 15 0 118.2 42.5 46.5 577.9 808.5
149 2 Jul 91 123 19.5 0 246.6 7.2 28.9 474.8 899.9
149 30 Jul 91 331.9 46.8 0 346.8 29.8 33.8 637.3 1426.4
149 27 Aug 91 61.9 27.5 0 795 17.9 30.9 478.5 1411.7
149 24 Sep 91 21.9 1.9 0 1702.4 14.1 49.6 59.6 1849.5
149 22 Oct 91 10.2 11.2 0 801.7 29.5 45.6 76.8 975
149 19 May 92 5.5 1.9 0 423 27.8 89.7 44 591.8
149 30 Jun 92 123.3 18.6 0 118.6 8.2 20.1 88.8 377.6
149 28 Jul 92 463.6 110.6 0 393.1 12.4 137.2 480.8 1597.7
149 25 Aug 92 8.9 45.3 0 1864.9 8.8 138.5 881.7 2948.2
149 8 Sep 92 32.3 41.2 0 3575.2 15.8 65.3 192.6 3922.5
149 6 Oct 92 0 23.9 0 2464.4 15.6 88.1 151.6 2743.6
149 4 May 93 0.5 13 1.5 335.1 25.7 38.4 182.1 596.4
149 18 May 93 0 9.1 0 564.7 70.8 46.5 228.8 920
149 14 June 93 162.2 9.1 0 271.8 27.4 24.7 395 890.1
149 13 Jul 93 387 3.5 0 127.8 1.5 22.9 505 1047.8
149 10 Aug 93 87.4 28.6 0 525.3 6 11.2 458 1116.4
149 7 Sep 93 57.4 24.8 0 19912 5.5 42.9 88.1 2209.8
149 5 Oct 93 15.6 33.5 0 14303 30.1 23.4 142.7 1675.5
Table 2. Daily phytoplankton biomass (mg
⋅⋅m-3) for Experimental Lakes Area Lake 149, 1990-1993.
Figure 4 depicts how the summarized phytoplankton data can be graphically presented.
Composition (lower panel) is presented as the percentage contributed by each of the
seven taxonomic groups to the community on a daily basis. Total daily biomass (upper
panel) is presented as gm-3.
Figure 4. Phytoplankton biomass and composition for Experimental Lakes Area Lake
149 from 1990-1993.
Long-term changes in the biomass of a phytoplankton community can be assessed by
comparing annual means to the long-term mean for each of the taxonomic groups (Fig.
5). The long-term mean is calculated by summing all annual means and dividing by the
number of years involved.
Figure 5. Annual mean vs. long-term mean biomass for Experimental Lakes Area Lake
Compositional and numerical species changes can be assessed by the use of a
measure of species richness, a species diversity index, or a similarity index. However,
the accuracy of the taxonomy will affect the final results. Species richness is simply the
number of species encountered, and is based on presence or absence. It can be
applied to both qualitative and quantitative samples. Washington (1984) recommends
Simpson's Diversity Index for phytoplankton:
where s = the number of species in a sample, n = the number of individuals in a
sample, and ni = the number of individuals of species i in a sample. This index is biased
by the dominant taxa.
Percent similarity analysis (Washington 1984) uses biomass and can also be applied to
quantitative samples:
where s = total number of species in both samples, P1i = proportion of the total
biomass of species i in sample 1, and P2i = proportion of the total biomass of species i
in sample 2. This index allows direct comparison between two sites or stations, and
also tends to favour the dominant species.
Quality Assurance/Quality Control (QA/QC)
A QA/QC program is necessary to instill credibility in a monitoring program. A QA/QC
program is difficult to operate when dealing with algal communities because of the
limited number of qualified taxonomists. However, the following checks can be put into
place to increase the integrity of a monitoring program:
1. Constancy of identifications can be achieved if the same person analyzes all
samples. This approach will facilitate subsequent changes to taxonomic
2. 10-15 % of the samples should be analyzed by other persons to ensure the
accuracy of identifications and counts.
3. Replicate counts should be performed on selected samples at different times.
The replicate count should be within ± 20% of the first count.
4. Periodic analysis of check samples will also help to assure the quality of
identifications. Check samples contain known species sent out to test the
accuracy of identifications by a laboratory.
5. A good pictoral reference key and a reference collection of permanent slide
material will ensure standard taxonomic identifications. Photographs or voucher
specimens for every taxon identified should be archived in a nationally
recognized herbarium.
Volunteer Involvement
Volunteers using the protocols described above will be able to sample phytoplankton
both qualitatively and quantitatively. However, the identification of phytoplankton to
species level is extremely difficult and should be only done by an experienced
Persons to contact for more information
Dr. K. Nicholls
Ontario Ministry of the Environment
Box 213, Rexdale, Ontario
M9W 5L1
Dr. P. Hamilton
Canadian Museum of Nature
P.O. Box 343, Station D
Ottawa, Ontario
K1P 6P4
Sources of equipment and supplies for studies of phytoplankton biodiversity
Fisher Scientific
112 Colonnade Rd
Nepean, Ontario
K2E 7L6
Geneq Inc.
7978 Jarry E.
Montreal, Quebec
H1J 1H5
VWR Scientific
175 Hanson St.
Toronto, Ontario
M4C 1A7
Marivac Ltd.
5821 Russell St.
Halifax, Nova Scotia
B3M 4C8
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Édition N. Boubée & Cie, Paris. 440 p.
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Natural Sciences Monograph No. 13. 688 p.
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Natural Sciences Monograph No. 13. 213 p.
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phytoplankton in relation to dissolved substances. Journal of Ecology 20: 241-262.
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... In brief, samples were enumerated and identied to the species level using an inverted microscope following the methods described in Findlay and Kling. 42 In addition to viable vegetative cells (i.e., with intact chloroplast), cyanobacterial heterocytes were also enumerated if present. Cell counts were converted to wet weight biomass by approximating cell volume using measurements of individual cells and applying the geometric formula best tted to the shape of the cell per Vollenweider. ...
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Cyanobacterial blooms present challenges for water treatment, especially in regions like the Canadian prairies where poor water quality intensifies water treatment issues. Buoyant cyanobacteria that resist sedimentation present a challenge as water treatment operators attempt to balance pre-treatment and toxic disinfection by-products. Here, we used microscopy to identify and describe the succession of cyanobacterial species in Buffalo Pound Lake, a key drinking water supply. We used indicator species analysis to identify temporal grouping structures throughout two sampling seasons from May to October 2018 and 2019. Our findings highlight two key cyanobacterial bloom phases - a mid-summer diazotrophic bloom of Dolichospermum spp. and an autumn Planktothrix agardhii bloom. Dolichospermum crassa and Woronichinia compacta served as indicators of the mid-summer and autumn bloom phases, respectively. Different cyanobacterial metabolites were associated with the distinct bloom phases in both years: toxic microcystins were associated with the mid-summer Dolichospermum bloom and some newly monitored cyanopeptides (anabaenopeptin A and B) with the autumn Planktothrix bloom. Despite forming a significant proportion of the autumn phytoplankton biomass (>60%), the Planktothrix bloom had previously not been detected by sensor or laboratory-derived chlorophyll-a. Our results demonstrate the power of targeted taxonomic identification of key species as a tool for managers of bloom-prone systems. Moreover, we describe an autumn Planktothrix agardhii bloom that has the potential to disrupt water treatment due to its evasion of detection. Our findings highlight the importance of identifying this autumn bloom given the expectation that warmer temperatures and a longer ice-free season will become the norm.
... Environmental data included estimates of phytoplankton species density (cells or colonies mL À1 ) and biomass (μg mL À1 ) collected in Stas. 1 and 2 and enumerated following Findlay and Kling (2001). In addition, turbidity (YSI 6136), temperature profiles (YSI 6560, NexSens T-Node FR), wind speed and direction (Vaisala WXT536), flux of photosynthetically active radiation (PAR) in the surface layer and water column , and dissolved CO 2 (Vaisala GMP222) and O 2 (YSI 6150 ROX) concentrations were recorded by the buoy sensors at Sta. 1. Furthermore, two cameras, one mounted on the buoy and the other on the shore facing toward the buoy, regularly took RGB photographs of the water surface to detect surface-bloom events. ...
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Instrumented buoys are used to monitor water quality, yet there remains a need to evaluate whether in vivo fluo-rometric measures of chlorophyll a (Chl a) produce accurate estimates of phytoplankton abundance. Here, 6 years (2014-2019) of in vitro measurements of Chl a by spectrophotometry were compared with coeval estimates from buoy-based fluorescence measurements in eutrophic Buffalo Pound Lake, Saskatchewan, Canada. Analysis revealed that fluorometric and in vitro estimates of Chl a differed both in terms of absolute concentration and patterns of relative change through time. Three models were developed to improve agreement between metrics of Chl a concentration , including two based on Chl a and phycocyanin (PC) fluorescence and one based on multiple linear regressions with measured environmental conditions. All models were examined in terms of two performance met-rics; accuracy (lowest error) and reliability (% fit within confidence intervals). The model based on PC fluorescence was most accurate (error = 35%), whereas that using environmental factors was most reliable (89% within 3σ of mean). Models were also evaluated on their ability to produce spatial maps of Chl a using remotely sensed imagery. Here, newly developed models significantly improved system performance with a 30% decrease in Chl a errors and a twofold increase in the range of reconstructed Chl a values. Superiority of the PC model likely reflected high cyanobacterial abundance, as well as the excitation-emission wavelength configuration of fluorometers. Our findings suggest that a PC fluorometer, used alone or in combination with environmental measurements, performs better than a single-excitation-band Chl a fluorometer in estimating Chl a content in highly eutrophic waters.
... Larsen et al. (2020) provide a detailed description of enumeration methods. In brief, samples were enumerated and identified to the species level using an inverted microscope following the methods described in Findlay and Kling (2003). In addition to viable vegetative cells (i.e., with intact chloroplast), cyanobacterial heterocytes were also enumerated if present. ...
... The photic zone of freshwater ponds, lakes, and rivers, as well as marine habitats such as backwaters, mangroves, estuaries, and seas, is populated by phytoplankton. They optimize their home in the upper strata using numerous mechanisms like using gas vacuoles to controlling buoyancy, migrate from one place to another using flagella and adaptive metabolic process [1,[4][5][6]. ...
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Freshwater phytoplankton is a taxonomic and functionally diverse group of organisms that play a crucial role in the biogeochemical cycle. Phytoplankton plays a tremendous role in nutrient uptake, food chain and helps to maintain healthy aquatic ecosystem. Phytoplankton is a major primary producer, dominates the entire water column and supports the life below water. We examined the species diversity of freshwater phytoplankton samples from Vaduvoor Bird Sanctuary, Thiruvarur District, Tamil Nadu. The study aims to document the diversity of phytoplankton in Vaduvoor Bird Sanctuary, Thiruvarur District, Tamil Nadu, India. We documented 33 phytoplankton species, including 15 Bacillariophyceae species, 10 Chlorophyceae species, 7 Cyanophyceae species, and 1 Euglenophyceae species. Thus, the present study revealed the overwhelming dominance of Bacillariophyceae followed by Chlorophyceae, Cyanophyceae and Euglenophyceae.
... The presence of certain taxonomic phytoplankton groups may be used as indicators of chemical and/or physical conditions of the surrounding environment, or water quality 19,20 . Traditional approaches assessing phytoplankton diversity, distribution, and abundance of phytoplankton taxa, based on morphological characteristics obtained by light microscopy [21][22][23][24] have a number of limitations: (1) labor intensity that limits the size of the quantified sample to hundred(s) of cellular events and a relatively low number of samples to be processed; (2) accurate diagnostics of taxa and their abundances are hampered by undifferentiated morphologies, unidentified early-life algal stages and numerous cryptic species 25,26 ; and (3) incomplete description of the changes in biodiversity based on a limited number of morphologically identified taxa. During the last two decades, cytometric methods (flow cytometry (FCM) and imaging flow cytometry (IFC)) have been recognized as a powerful tool to study seasonal and spatial trends of phytoplankton 27,28 . ...
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We analyzed phytoplankton assemblages’ variations in oligo-mesotrophic Shchuchie and Burabay lakes using traditional morphological and next-generation sequencing (NGS) approaches. The total phytoplankton biodiversity and abundance estimated by both microscopy and NGS were significantly higher in Lake Burabay than in Lake Shchuchie. NGS of 16S and 18S rRNA amplicons adequately identify phytoplankton taxa only on the genera level, while species composition obtained by microscopic examination was significantly larger. The limitations of NGS analysis could be related to insufficient coverage of freshwater lakes phytoplankton by existing databases, short algal sequences available from current instrumentation, and high homology of chloroplast genes in eukaryotic cells. However, utilization of NGS, together with microscopy allowed us to perform a complete taxonomic characterization of phytoplankton lake communities including picocyanobacteria, often overlooked by traditional microscopy. We demonstrate the high potential of an integrated morphological and molecular approach in understanding the processes of organization in aquatic ecosystem assemblages.
... Unfiltered sample aliquots of 100 mL were preserved in Lugol's iodine solution (2% v/v) for later taxonomic identification, abundance, and biomass using the Utermöhl technique [57]. ...
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The understanding of deep chlorophyll layers (DCLs) in the Great Lakes—largely reported as a mix of picoplankton and mixotrophic nanoflagellates—is predominantly based on studies of deep (>30 m), offshore locations. Here, we document and characterize nearshore DCLs from two meso-oligotrophic embayments, Twelve Mile Bay (TMB) and South Bay (SB), along eastern Georgian Bay, Lake Huron (Ontario, Canada) in 2014, 2015, and 2018. Both embayments showed the annual formation of DCLs, present as dense, thin, metalimnetic plates dominated by the large, potentially toxic, and bloom-forming cyanobacteria Planktothrix cf. isothrix. The contribution of P. cf. isothrix to the deep-living total biomass (TB) increased as thermal stratification progressed over the ice-free season, reaching 40% in TMB (0.6 mg/L at 9.5 m) and 65% in South Bay (3.5 mg/L at 7.5 m) in 2015. The euphotic zone in each embayment extended down past the mixed layer, into the nutrient-enriched hypoxic hypolimnia, consistent with other studies of similar systems with DCLs. The co-occurrence of the metal-oxidizing bacteria Leptothrix spp. and bactivorous flagellates within the metalimnetic DCLs suggests that the microbial loop plays an important role in recycling nutrients within these layers, particularly phosphate (PO4) and iron (Fe). Samples taken through the water column in both embayments showed measurable concentrations of the cyanobacterial toxins microcystins (max. 0.4 µg/L) and the other bioactive metabolites anabaenopeptins (max. ~7 µg/L) and cyanopeptolins (max. 1 ng/L), along with the corresponding genes (max. in 2018). These oligopeptides are known to act as metabolic inhibitors (e.g., in chemical defence against grazers, parasites) and allow a competitive advantage. In TMB, the 2018 peaks in these oligopeptides and genes coincided with the P. cf. isothrix DCLs, suggesting this species as the main source. Our data indicate that intersecting physicochemical gradients of light and nutrient-enriched hypoxic hypolimnia are key factors in supporting DCLs in TMB and SB. Microbial activity and allelopathy may also influence DCL community structure and function, and require further investigation, particularly related to the dominance of potentially toxigenic species such as P. cf. isothrix.
... Phytoplankton enumeration was completed by D. Findlay (Plankton-R-Us, Winnipeg, Manitoba, Canada, as per Findlay and Kling (2003). Each sample was preserved with 4% Lugol's iodine, gravity concentrated 5-fold after 24 h, and stored at 4°C for ∼2 months until analysis. ...
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The increasing prevalence of cyanobacteria-dominated harmful algal blooms is strongly associated with nutrient loading and changing climatic patterns. Changes to precipitation frequency and intensity, as predicted by current climate models, are likely to affect bloom development and composition through changes in nutrient fluxes and water column mixing. However, few studies have directly documented the effects of extreme precipitation events on cyanobacterial composition, biomass, and toxin production. We tracked changes in a eutrophic reservoir following an extreme precipitation event, describing an atypically early toxin-producing cyanobacterial bloom and successional progression of the phytoplankton community, toxins, and geochemistry. An increase in bioavailable phosphorus by more than 27-fold in surface waters preceded notable increases in Aphanizomenon flos-aquae throughout the reservoir approximately 2 weeks postevent and ∼5 weeks before blooms typically occur. Anabaenopeptin-A and three microcystin congeners (microcystin-LR, -YR, and -RR) were detected at varying levels across sites during the bloom period, which lasted between 3 and 5 weeks. These findings suggest extreme rainfall can trigger early cyanobacterial bloom initiation, effectively elongating the bloom season period of potential toxicity. However, effects will vary depending on factors including the timing of rainfall and reservoir physical structure.
... Using light microscopy, we identified and enumerated phytoplankton following the Utermöhl technique (see Supplementary Material 2.1 1 for the list of taxonomic keys used). We then converted cell counts to wet-weight biomass (Findlay and Kling 2003). ...
Heavy crude oil transportation over land is increasing, yet the ecological impacts of spills, particularly of diluted bitumen, in freshwater environments remain poorly understood. We simulated spills of diluted bitumen in 1400 L land-based mesocosms containing water and sediments from a boreal, oligotrophic lake and monitored the response of natural planktonic communities over 11 days. Most species of phytoplankton (chrysophytes and dinoflagellates) and zooplankton (copepods and cladocerans) were sensitive to oil, exhibiting >70% overall abundance reductions in response to the spills. Declines in nano- and microphytoplankton were short-lived and began to recover after the oil sank, whereas picophytoplankton and zooplankton populations remained depressed at the end of the experiment. In contrast, oil spills stimulated bacteria known to degrade hydrocarbons, especially Alphaproteobacteria, whereas Gammaprotobacteria (a common marine oil spill bacterial class) increased less. This is the first experiment to examine the effects of diluted bitumen in a multitrophic freshwater community.
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The composition and abundance of zooplankton communities were analyzed from samples collected six times within a year in Upeh Guling, Taman Negara Johor Endau Rompin (TNJER), Malaysia. A total of 49 species of zooplankton were recorded with 48 species at rain-fed rock pools and 40 species at river-fed rock pools. Rotifers contributed the highest number of species with 35 species over others due to their fast growth and development under favourable conditions. Among the rotifers, Lecane was most diverse with 11 species in total. For zooplankton communities, the rain-fed rock pools recorded higher (P<0.05) variation in total species diversity and density, compared with river-fed rock pools. Fewer species composition recorded in river-fed rock pools may be attributed to the river water which continually feeds the pools and might be carried along some species. High species diversity index in rain-fed and river-fed rock pools, ranging from H'=3.14-H'=3.56 as a result of a long period of inundation which suggested a healthy (oligotrophic) condition of the ecosystem. The results suggest a healthy state of the environment as it favours the proliferation of diverse species of zooplankton communities.
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Harmful algal blooms have important implications for the health, functioning and services of aquatic ecosystems. Our ability to detect and monitor these events is often challenged by the lack of rapid and cost-effective methods to identify bloom-forming organisms and their potential for toxin production, Here, we developed and applied a combination of DNA barcoding and Next Generation Sequencing (NGS) for the rapid assessment of phytoplankton community composition with focus on two important indicators of ecosystem health: toxigenic bloom-forming cyanobacteria and impaired planktonic biodiversity. To develop this molecular toolset for identification of cyanobacterial and algal species present in HABs (Harmful Algal Blooms), hereafter called HAB-ID, we optimized NGS protocols, applied a newly developed bioinformatics pipeline and constructed a BOLD (Barcode of Life Data System) 16S reference database from cultures of 203 cyanobacterial and algal strains representing 101 species with particular focus on bloom and toxin producing taxa. Using the new reference database of 16S rDNA sequences and constructed mock communities of mixed strains for protocol validation we developed new NGS primer set which can recover 16S from both cyanobacteria and eukaryotic algal chloroplasts. We also developed DNA extraction protocols for cultured algal strains and environmental samples, which match commercial kit performance and offer a cost-efficient solution for large scale ecological assessments of harmful blooms while giving benefits of reproducibility and increased accessibility. Our bioinformatics pipeline was designed to handle low taxonomic resolution for problematic genera of cyanobacteria such as the Anabaena-Aphanizomenon Dolichospermum species complex, two clusters of Anabaena (I and II), Planktothrix and Microcystis. This newly developed HAB-ID toolset was further validated by applying it to assess cyanobacterial and algal composition in field samples from waterbodies with recurrent HABs events.
The mean carbon, nitrogen, and phosphorus contents of particulate material for 5 1 lakes or lake basins, extending from arctic to tropical climatic regions, including small lakes as well as the largest lakes in the world, indicate that Redfield ratios are the exception rather than the rule in freshwater. The C: P and N : P ratios are more variable for lake particles but generally higher than marine particles, and the mean molar C: N, C: P, and N : P ratios are substantially higher than the Redfield ratio of 106 : 16: 1. On average, lower C : N, C : P, and N : P ratios occur in subarctic lakes while higher ratios occur in the tropics and in temperate, oligotrophic lakes on the Canadian Shield. In shield lakes with long residence times (>6 months) the high ratios of C : N, C : P, and N : P do not originate from streamborne or atmospherically deposited particles but arise from in-lake processes. Regression analysis demonstrates that small lakes are generally more N and P deficient than large lakes. In freshwaters, particulate composition ratios imply that a wide variety of conditions exists in lakes, including N and P deficiency, as well as N and P sufficiency. In the Experimental Lakes Area of Canada, independent physiological nutrient status indicators generally agree with the status indicated by seston ratios. The relative uniformity of marine C : N : P composition (compared to lakes) at the Redfield ratio suggests that marine plankton cannot be as severely, or as frequently, limited by N and P as lake plankton. Consequently, the paradigm of N limitation in the oceans requires qualification. Based on particulate comDosition, it is more correct to say that ocean plankton is noi as N grid P deficient as lake-plankton. The composition of marine particulate mat- ter is relatively uniform. Redfield (1934, 1958) noted the near constancy of the ratio of C : N : P in marine plankton and the similarity of the N : P ratio of plankton to the oceanic deep- water ratio of nitrate to phosphate. As early as 1940, the C : N : P molar composition ratio of marine plankton was accepted to be 106 : 16 : 1 (Redfield et al. 1963); this ratio is now referred to as the Redfield ratio. The ratio has with- stood the test of time, and the ever-growing number of analyses of marine particles and nutrient regeneration profiles, with relatively
An in vivo flow-through fluorometer system revealed narrow bands of very high chloro- phyll concentrations in the meta- or hypolimnia of all clear, stratified ELA lakes. In two experimentally fertilized lakes chlorophyll concentrations in the hypolimnion exceeded 366 ,ug liter-l while epilimnetic chlorophyll was only 3 pg liter-'. The hypolimnetic bloom rep- resented the major response to enhanced nutrient loading. The peak was in or below the thermocline at depths where 13% of photosynthetically available surface irradiance pene- trated; the algae at the peak were actively growing flagellated colonial chrysophyceans. The autumn surface "bloom" was due to entrainment of this previously produced chloro- phyll, not to growth caused by mixing in of hypolimnetic nutrients.