Article
Algological Studies 150 (2016), p. 21–38
Published online March 2016
Phytoplankton composition and diversity
in response to abiotic factors in Lake Buhi,
Camarines Sur, Philippines
Alvin B. Baloloy1*, Maria Aileen Leah G. Guzman1, Teresita R. Perez1,
Severino G. Salmo III1, Jewel Racquel S. Unson1, Jason D. Baldesco1 &
Joanaviva C. Plopenio2
1
Department of Environmental Science, Ateneo de Manila University, Loyola Heights,
Quezon City, Philippines
2Ateneo de Naga University, Naga City, Camarines Sur, Philippines
With 4 figures and 5 tables
Abstract: Phytoplankton were collected from eight sampling stations around Lake Buhi (Camarines Sur, Philippines) in September and November 2013. The eight sites represented varied
zonation and resource uses. A total of 29 species were identified belonging to five major taxonomic groups: the diatoms (Heterokontophyta), green algae (Chlorophyta), cyanobacteria (Cyanophyta), eustigmatophytes (Heterokontophyta) and dinoflagellates (Dinophyta). Diatoms
were the most abundant group (50%), followed by the green algae (41%). Sampling station with
high concentration of fish pens generated high diversity (Shannon-Wiener diversity index 2.65)
with dominance of pollution tolerant taxa, while the site near the outlet of the lake has the highest phytoplankton abundance (0.139 cells/ml). Results revealed significant differences in relative abundance of phytoplankton among sampling sites and between sampling months within
each site. These differences were attributed to the variation in physico-chemical parameters
among the sites. Canonical Correspondence Analysis (CCA) showed that nitrate, depth and
turbidity positively influenced phytoplankton abundance. Variation in the abiotic factors, including resource use, affected spatial and temporal distribution of the phytoplankton community. Remediation measures on the lake must be directed towards the primary sources of these
variations.
Keywords: Community structure, physico-chemical parameters, tropical lake, phytoplankton,
Philippines, Lake Buhi, abiotic factors, diversity, composition
*Corresponding author: [email protected]
© E. Schweizerbart’sche Verlagsbuchhandlung, Stuttgart, Germany
DOI: 10.1127/algol_stud/2016/0233
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Baloloy et al.
Introduction
Lakes are rich environments in terms of life forms and composition (Carboni 2006)
supporting phytoplankton growth through cycling of matter and energy from chemical-biological processes. Freshwater phytoplankton can be extremely diverse in terms
of taxonomy, morphology and ecology (Presscott 1954). They can respond to a wide
range of pollutants; making them useful in providing early warning signals of deteriorating conditions and the potential causes of such conditions (Lacuna et al. 2012). The
determining factors for the structure of phytoplankton communities in lakes include
the interplay between the effect of chemical, physical and biological parameters (Basualto et al. 2006). All these factors play an important role in determining which
phytoplankton species can survive, grow, be threatened or even become extinct in a
particular lake. The resulting changes are evident in: (1) the taxonomic composition
of the plankton (2) species abundances, (3) richness and diversity of the community,
and (4) other community parameters such as evenness and dominance (Basualto et al.
2006).
Physico-chemical parameters and their effect in the production of phytoplankton
in water bodies are considered very important in designing management strategies for
aquatic ecosystems (Edward & Ugwumba 2010). Using bioindicators and physicochemical parameters, lakes and other water bodies can be tested for aesthetic, recreational, industrial, domestic or agricultural purposes; and to determine the health status
of the system. Due to their significant role as bioindicator, this study assessed the
composition and diversity of phytoplankton in Lake Buhi, Camarines Sur, and determined the effect of physico-chemical parameters on the phytoplankton community
structure data. We aimed to test whether the phytoplankton community structure is
affected by the physico-chemical parameters of the lake: water pH, conductivity, dissolved oxygen, water phosphate and nitrate, surface water temperature, turbidity, column depth and light penetration. The possible effect of land use to the variation in the
parameter readings within the sites will also be established.
Materials and methods
Brief description of the study area
Lake Buhi in Luzon Island, Philippines is a natural inland body of water with an area
of 1,707 ha and an average depth of eight meters. It is one of the three lakes in the
province of Camarines Sur, with a geographical coordinates from 13°26’N, 123°30’E
to 13°29’N, 123°31’E. Lake Buhi is under Climate Type II of the modified Corona
classification system of Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAG-ASA) wherein there is no pronounced dry season. A total
of eight sampling stations for the collection of phytoplankton and physico-chemical
measurements were established based on the proximity to inlet, outlet and resource
uses within and around the lake (Fig. 1, Table 1).
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CAMARINES SUR
ALBAY
Fig. 1. Map and location of the eight sampling stations in Lake Buhi, Camarines Sur, Philippines (Google Earth 2013, PhilGIS 2014).
RAGAY GULF
CAMARINES
NORTE
Phytoplankton composition and diversity
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Baloloy et al.
Table 1. Sampling stations, populations and resource uses in the ten barangays (administrative
divisions) surrounding Lake Buhi, Camarines Sur (NSO 2010; Binoya et al. 2008).
Barangay
Sampling Population Resource Use
Station
(2010)
Land
Water
Sta. Cruz
1
1,590
Agricultural, Inlet
Sanctuary, Inlets
Ipil
2
1,400
Agricultural, residential
Fish pens, Inlets
Iraya
3
2,578
Agricultural, tourism
Fish pens, Inlets
Tambo
4
3,423
Agricultural, residential
Fish pens
Salvacion
5
2,758
Agricultural
Fish pens, Outlet
Poblacion
6
8,630
Residential
Fish pens
(Lake Center)
7
–
–
Fish pens, Navigation
Cabatuan
8
1,989
Agricultural, residential, Navigation, Fish pens
tourism
Total
23,954
Physico-chemical parameters sampling and resource use analysis
Nine abiotic parameters were considered in the study, including chemical parameters
(water pH, conductivity, dissolved oxygen, and water phosphate and nitrate) and
physical parameters (surface water temperature, turbidity, column depth and light
penetration). Conductivity, surface water temperature, water pH and turbidity were
determined by using a Horiba U-10 Water Quality Checker. Dissolved oxygen (DO)
was measured using a DO meter (Starter 300D, OHAUS). The method in measuring
these parameters using probes was described by EMB (2008). For column depth, an
echo-sounder was used (Model 710679, Speedtech Instruments). The Secchi disk was
employed in determining light penetration and depth of the aphotic and euphotic
zones. A composite water sample was collected by lowering a 2L Van Dorn horizontal water sampler to a depth of 1 meter below each zone to measure phosphates and
nitrates. The sample was transferred in a pre-acid washed Amber bottles, labeled and
stored in an ice box. Nutrients were measured using a Hach (Hach DR-890 Colorimeter) right after the field sampling. All field meters were calibrated prior to sampling
(Ohio EPA 2010) using standard solution concentrations (EMB 2008). Other physical
factors like weather condition (temperature and rainfall) were noted during sampling.
The resource uses within and near the sampling stations were recorded and documented in support with the current land use map.
Phytoplankton sampling, identification and counting
Phytoplankton sampling was performed in September (less rainfall with an average of
273 mm) and November (peak of rainfall with an average of 359 mm) (PAGASA
2013) from 8am to 2pm. For each sampling station, the euphotic and aphotic zone was
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Phytoplankton composition and diversity
25
measured using a Secchi disc (Geo Scientific ltd). Samples of equal volume were
obtained by lowering a 2L Van Dorn horizontal water sampler to a depth of 1 meter
below the euphotic zone and 1 meter below the aphotic zone (EMB 2008). An amount
of 1 liter from the composite sample (three replicates) per zone was filtered using a
plankton net (23 micron mesh size) to obtain a 100 ml subsample. The collected subsamples were then transferred to individual clean bottles. Lugol’s solution was used
to preserve and fix the phytoplankton samples. The filtered phytoplankton sample
(100 ml) was transferred to a graduated cylinder and was allowed to stand for 24
hours. After the settling time, the upper 90 ml was gently siphoned off, leaving the
concentrated phytoplankton in the bottom. The concentrated 10 ml sample was transferred to a vial, where 10 μl was obtained for cell counting using an improved
Neubauer haemocytometer (Tiefe-Depth Profondeur 0.100 mm; BLAU, Germany).
The phytoplankton groups and species were identified by placing the counting slide
under a compound microscope (ASAHI Compound Binocular Microscope) equipped
with phase/contrast illumination. The identification was guided by available literature
and identification keys (Bellinger & Sigee 2010, Lee 2008, Verlencar & Desai 2004).
Phytoplankton pictures were taken using Moticam 2.0 camera which aided in
confirming taxonomic identification at a later date.
Computation for phytoplankton community structure
Cell abundance was analyzed to determine the population of plankton found in an
area. The average values of phytoplankton counts using the Haemocytometer was
taken into account for calculating density. The formula below (Equation 1) was used
(Provost 2012; Strober 2001).
Equation 1:
N x df x 104
Where N: Average number of cells in one large square; df: average dilution factor
Species richness was determined by getting the total number of species present in
each sampling zone and sampling month. Comparison of richness between zones was
done using the Index of Similarity (Simpson 1949). The ecological indices that were
generated include Shannon-Wiener Index of General Diversity, Index of Dominance,
Index of Similarity and Evenness Index.
Statistical analysis
One-way Analysis of Variance (ANOVA) was applied for testing differences in relative abundance of phytoplankton among stations and within station between months.
Multiple comparisons table with Tukey’s test showed which among the sites are significantly different from one another. Canonical Correspondence Analysis (CCA)
was employed to determine which physico-chemical parameters were correlated with
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Baloloy et al.
relative abundance of phytoplankton, and to locate the stations where these correlations were observed. This analysis tested the main hypothesis of the study that variations in physico-chemical parameters are the main source of differences in phytoplankton abundances between sites. Ecological indices and CCA graphs were
generated using the software PAST (version 2.13, Hammer et al. 2001); while
XLSTAT was used for One-way ANOVA and correlation matrix analysis.
Results
Physico-chemical characteristics of Lake Buhi
Throughout the study, temporal and spatial variations were observed in the physicochemical parameters measured in the lake (Table 2). The parameter readings that have
significantly decreased in November were nitrate (–79%), temperature (–5%) and pH
levels (–5%); while an increase was observed in turbidity (+20%), DO (+16%), depth
(+5%), phosphate (+200%) and conductivity (+6%). All values gathered for pH were
within the standard (6.5 to 8.5) set by Department of Environment and Natural Resources – Dept. Administrative Order (DENR-DAO, 1990) except for Station 4. The
DO values were higher than the minimum standard of 5 mg/L, except for Station 1
and 6 in September. In Philippine lakes, the standard maximum amount for phosphate
is 0.1 mg/L; and maximum of 10 mg/L for nitrate (DENR-DAO 1990). The values of
nitrate in all station in November were within the standard, but not in the four sites
during September. All values of phosphate in both months exceeded the standard
concentration (0.11–0.89 mg/L) except in Station 2 in September with 0.05 mg/L
(Table 2).
Phytoplankton community
A total of 31 taxa were observed in the euphotic and aphotic zones of Lake Buhi, 29
of which were identified to the species level (Table 3). The identified species belonged to five major plankton groups: diatoms, green algae, cyanobacteria, eustigmatophytes and dinoflagellates. Among the 29 identified species, green algae was the
richest group with 16 species, followed by diatoms (8 species) and cyanobacteria
(3 species). Dinoflagellates and eustigmatophytes had only one representative species. Thirty-one species were present in the euphotic zone and 25 species in the aphotic zone. Among sampling stations, Station 4 was the richest with 29 species; followed
by Station 3 (26 species); Stations 1, 3, 6, 7 and 8 (24 species); while Station 5 has the
least number of species (23 species). Diatoms and green algae were the most abundant
phytoplankton groups in Lake Buhi (Fig. 2).
The overall abundance of phytoplankton (September and November) ranged from
156 to almost 59,828 individuals per species. Diatoms were the most abundant
(50.5%); followed by the green algae (40.5%), cyanobacteria (7.4%), dinoflagellates
(1.5%) and the least abundant eustigmatophytes (0.1%). The ten most abundant spe-
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Phytoplankton composition and diversity
27
Table 2. Mean values of the physical parameters (Depth, Light Penetration, Turbidity and
Temperature) and chemical parameters (Phosphate, Nitrate, Dissolved oxygen, pH level and
Conductivity) in the eight sampling sites in Lake Buhi during September and November 2013
sampling. Asterisk (*) indicates that the value exceeded the standard set for each parameter
(DENR 1990, EPA 2012).
September
Parameter
Station
1
2
3
4
5
6
7
8
Conductivity (mS/cm)
138
140
140
139
142
143
143
141
Depth (m)
6.6
9.7
8.8
12
9
3.2
13.5
14.7
Dissolved Oxygen (mg/L)
4.2
6.2*
6*
6.4*
7.5*
3.3
6.7*
6.9*
Light penetration (m)
1.5
1.6
1.6
1.4
1.4
1.5
1.5
1.3
Nitrate (mg/L)
12*
9.7
10.4* 7.3
11.1* 5.8
11.7* 9.5
pH
8.4
8.5
8.5
8.1
8.2
Phosphate (mg/L)
0.11* 0.05
0.14* 0.15* 0.44* 0.53* 0.44* 0.22*
Temperature (oC)
29.1
29.6
29.4
29.2
28.6
28.6
29.1
29
Turbidity (NTU)
6.7
4
4.3
6
6
5.7
5
5
1
2
3
4
5
6
7
8
8.9*
8.1
8.2
November
Parameter
Station
Conductivity (mS/cm)
145
145
148
152
150
149
149
152
Depth (m)
8.3
11.4
10.6
10.6
11.1
8.2
14.9
6.7
Dissolved Oxygen (mg/L)
6.6*
6.7*
7.5*
7.6*
6.7*
6.5*
6.9*
6.6*
Light penetration (m)
1.8
1.7
1.8
1.6
1.8
2.1
1.8
1.6
Nitrate (mg/L)
1.3
2.6
2.8
3
1.4
0.4
1.8
2.7
pH
7.9
7.6
7.7
8.2
8.1
8.1
7.9
8.1
Phosphate (mg/L)
0.85* 0.83* 0.83* 0.89* 0.63* 0.7*
0.78* 0.73*
Temperature (oC)
27.5
27.1
27.3
27.7
27.7
27.6
27.7
28.1
Turbidity (NTU)
6.7
4.7
7
6
6.3
6.3
7.7
4.7
cies include Aulacoseira granulata (28.5%), Fragilaria crotonensis (10.3%),
Sphaerocystis schroeteri (9.2%), Crucigenea sp. (7.3%), Synedra rumpens (5.2%),
Closterium acutum var. variabile (3.9%), Selenastrum sp. (3.8%), Quadrigula lacustris (3.3%), Scenedesmus abundans (3.2%) and Staurastrum gracili (3.0%).
One-way ANOVA showed that there were significant differences of relative abundance between sampling sites both in September (P = 0.017) and November (P =
0.05) (Fig. 3). Results showed that Station 1 and Station 7 were different in September, while Station 7 was found different with Stations 1, 3, 5, 6 and 8 in November.
For temporal variations, Station 7 and Station 1 were the only stations where abundances are significantly different within site between sampling months (Fig. 4). The
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Baloloy et al.
Table 3. List and taxonomic classification of identified phytoplankton species in Lake Buhi,
Camarines Sur in the months of September and November (Lee 2008 taxonomic scheme).
Phylum
Common
name
Cyanophyta
Cyanobacte- Cyanophyria
ceae
Heterokonto- Diatoms
phyta
Class
Order
Genus
Species
Chroococcales
Microcystis
aeruginosa
Nostocales
Anabaena
torulosa
Oscillatoriales
Oscillatoria
sp.
Bacillarophy- Biddulphiales
ceae
Aulacoseira
granulata
Cyclotella
atomus
muzzanensis
Bacillariales
Melosira
varians
Fragilaria
crotonensis
Synedra
rumpens
ulna
Tabellaria
flocculosa
Eustigmatophytes
Chrysophyceae
Eustigmatales
Pseudostaurastrum
lobulatum
Dinophyta
Dinoflagellates
Dinophyceae
Peridiniales
Peridinium
gatunense
Chlorophyta
Green algae
Chlorophyceae
Tetrasporales
Sphaerocystis
schroeteri
Chlorococcales Closteriopsis
acicularis
Coelastrum
sp.
Crucigenia
sp.
Dictyosphae- sp.
rium
Oocystis
sp.
Pediastrum
simplex
Quadrigula
lacustris
Scenedesmus
abundans
arcuatus
Selenastrum
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sp.
Volvocales
Volvox
sp.
Zygnematales
Closterium
acutum var.
variabile
Cosmarium
furcatospermum
Staurastrum
gracile
Phytoplankton composition and diversity
29
Sampling
Month
Cyanobacteria
SEP 2013
Diatoms
Eustigmatophytes
Dinoflagellates
NOV 2013
Green algae
0
50
Abundance (cells/ml)
100
Fig. 2. Percentage of phytoplankton abundance per group during September 2013 (less rainfall)
and November 2013 (peak of rainfall) in Lake Buhi, Camarines Sur.
relative phytoplankton abundance in Station 1 significantly decreased in November
(from 0.17 to 0.09 cells/ml) while abundance in Station 7 significantly increased in
November (from 0.10 to 0.18 cells/ml). Station 5 was the most species-abundant station based on overall abundance per station. For the “overall total per zone” – there
were 4.6 x 106 individuals (69%) found in euphotic zone, which were significantly
different (P < 0.05) than the 2.1 x 106 individuals (31%) collected in the aphotic zone.
For the “over total per month” – there were 4.2 x 106 total individuals in September,
41% higher than the 2.5 x 106 individuals found in November (significantly different
at P < 0.05). The sum of either the overall total per site or the overall total per zone is
6.7 x 106 individuals – the “grand total” of all phytoplankton cells counted for this
study.
Shannon-Weiner Index of General Diversity is a measure of species diversity in a
given community. The diversity indices in Lake Buhi varied between stations, but the
difference is not significant (P > 0.05). The diversity index was highest in Station 7
(2.65) and lowest at Station 1 (2.35, Table 4). The overall diversity index of Lake
Buhi was 2.66. Evenness refers to how population values are close among the species
in an environment; or how relative abundances are distributed among the different
species (Wilsey & Striling 2007). Lake Buhi has an evenness of 0.46 and dominance
of 0.12 (Table 4).
eschweizerbart_xxx
30
Baloloy et al.
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Fig. 3. Phytoplankton relative abundance in the euphotic and aphotic zones in Lake Buhi, Camarines Sur during the months of September (left) and November (right) 2013. Stations with different letters (a, b or c) have values that were significantly different from one another (P > 0.05).
Correlation between phytoplankton abundances, physico-chemical
parameters and resource uses
Results showed that physico-chemical parameters such as turbidity, nitrate and depth
played a pivotal role in determining abundance and dominance of certain phytoplankton groups and taxa in a given area with different prevailing resource uses (Table 5).
eschweizerbart_xxx
Phytoplankton composition and diversity
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Fig. 4. Mean phytoplankton relative abundance in Lake Buhi, Camarines Sur during the months
of September and November 2013. An asterisk (*) indicates that values for relative abundance
within site between months are significantly different at P < 0.05 level.
Discussion
The composition of species varied between euphotic and aphotic zones, between sampling stations, and between the sampling months. For example, Tabelaria flocculosa
was present only during the September 2013 sampling, while Pseudostaurastrum
lobulatum was present only in November. Most identified taxa belong to the green
algae, which is known as an extremely diverse group of algae especially in freshwater
environments (SWCSMH 2006). The groups with the lowest richness were eustigmatophytes and dinoflagellates, both are naturally rare in freshwater habitats. Each phytoplankton taxon has different resource requirements and responses to the physicochemical factors of the environment, thus causing variations in the composition per
sampling month (Reynolds 1984). Many phytoplankton species still dominantly inhabited the light sufficient euphotic zone, and those that were present in the aphotic
zones were mainly due to negative buoyancy, wind and water current, or by enhanced
downward transportation in the case of diatoms and dinoflagellates (Platt et al. 1983).
Diatoms (class Bacillarophyceae) were the overall most abundant group (50.5%)
in Lake Buhi. Diatoms became more abundant in September when higher nitrate
levels were recorded. Compared to green algae, diatoms require more nitrate to survive in dark and anoxic conditions, especially in deeper waters (Kamp et al. 2011).
The two most abundant diatom species were Aulacoseira granulata (28.5%) and
Fragilaria crotonensis (10.4%). Diatoms are considered as one of the most common
and dominant taxa in freshwater environment (Bellinger & Sigee 2010). In polluted
water, it can exhibit tolerance towards high concentration of nutrients (Akbulut 2003,
Çelekli & Külköylüoğlu 2006) thus supporting its survival.
Generally, a water body is considered clean if the diversity of diatoms is high but
the population of each species is low (Wellesley College 2010). This is found to be
opposite with our result, where we observed only few species of diatoms forming
eschweizerbart_xxx
26
850000
0.16
2.43
0.43
3
24
717500
0.11
2.58
0.55
4
29
782500
0.12
2.63
0.48
5
23
972500
0.13
2.55
0.56
6
24
770000
0.11
2.51
0.52
7
24
892500
0.09
2.65
0.59
8
24
777500
0.12
2.53
0.52
Overall
31
6717000
0.12
2.66
0.46
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0.41 ns
Nitrate
tolerant to high levels of nutrients
(Maynolov et al. 2009,
Bellinger & Sigee 2010)
tolerant to mild pollution and
moderate to heavy organic
pollution (Kelly & Telford 2007)
Aulacoseira granulata
Aulacoseira muzzanensis
Microcystis sp.
Synedra ulna
Oocystis sp.
Aulacoseira granulata
Aulacoseira muzzanensis
Pediastrum simplex
Volvox sp.
tolerant to high levels of nutrients
(Maynolov et al. 2009,
Bellinger & Sigee 2010)
0.68 ns
S
E
P
Turbidity
Species Description
Month PhysicoCorrelation Positively correlated
chemical
coefficient phytoplankton species
parameter
Station 1
Station 2
Station 5
Station 7
Station 1
Station 5
– Agriculture, Inlet
– Fish pens, Inlet
– Fish pens, Outlet
– Fish pens
– Agriculture, Inlet
– Fish pens, Outlet
Stations where
Resource Use
positive correlation
was observed
Table 5. Environmental variables with positive CCA correlation (Canonical Correspondence Analysis) to the phytoplankton abundances in Lake Buhi,
Camarines Sur during September and November 2013.
24
954500
0.18
2.35
0.44
2
Diversity index
1
Table 4. Diversity profiles of the phytoplankton community in the 8 sampling sites in Lake Buhi, Camarines Sur.
Taxa (S)
Individuals
Dominance (D)
Shannon Diversity (H)
Evenness (e^H/S)
Baloloy et al.
Station
32
eschweizerbart_xxx
tolerant from moderate to high levels Station 2
of nutrients; common in deep water
Station 4
(Proulx & Pick 1996)
Station 5
tolerant to high levels of nutrients
(Bellinger & Sigee 2010)
tolerant from acidic nutrient-poor to
alkaline nutrient-rich freshwaters
(Bellinger & Sigee 2010)
Oscillatoria sp.
Closterium acutum
Station 7
Station 3
Station 5
– Fish pens, Inlet
– Fish pens
– Fish pens, Outlet
– Fish pens,
– Agriculture, Inlet
– Fish pens, Outlet
Stations where
Resource Use
positive correlation
was observed
Scenedesmus abundans
Scenedesmus arcuatus
*correlation coefficient is significant at P < 0.05; ns: not significant
0.90*
tolerant from moderate to high levels
of nutrients; common in deep water
(Proulx & Pick 1996)
Scenedesmus arcuatus
Depth
tolerant from low to moderate
nutrients (de Hoyos 2013)
Quadrigula sp.
0.32 ns
N
O
V
Turbidity
Species Description
Month PhysicoCorrelation Positively correlated
chemical
coefficient phytoplankton species
parameter
Table 5. cont.
Phytoplankton composition and diversity
33
34
Baloloy et al.
large populations. Stressed environments are known to have lower number of species,
with only one or two species having significantly greater individuals than the other
species (Palleyi et al. 2011). In Lake Buhi, there was only one very abundant diatom
species, namely A. granulata, which made up 28.5% of the total cell count, indicating
that the water only favored the proliferation of species that can tolerate high level of
nutrients and pollutants. A. granulata is known to thrive in mesotrophic to highly
eutrophic environments (Maynolov et al. 2009).
The stations where fish pens were concentrated (Station 7) and the station located
within an agricultural site (Station 1) were considered as the most phytoplankton-dynamic areas wherein abundances vary both in spatial and temporal scale. Possible
cause was attributed to the changes in nutrient influx, particularly with the concentration of nitrates (0.4–12 mg/L). The sources of nitrate loading in the lake were the excessive feeds from fish pens and runoffs passing agricultural areas where farmers
were practicing the use of urea (CH4N2O) and ammonium sulfate [(NH4)2SO4] as fertilizers (Binoya et al. 2008). The spatial variations of the physico-chemical parameters
between sites are dependent on the different land and water uses within and around the
lake. The presence of a single factor, such as nutrient run-off, affected one or more
physico-chemical parameter readings. Meanwhile, the temporal variations between
these sites were likely caused by factors that are dynamic, such as changing amount
of rainfall, direction of wind current, seasonal changes in atmospheric temperature;
and timing of anthropogenic activities such as the application of fertilizer during
planting season. The variations in resource use affected the environmental factors
which then determined the phytoplankton community structure.
Diversity and evenness have a direct relationship to each other but are indirectly
related to dominance. Station 7 has the lowest dominance (0.09) and the highest evenness (0.59). Lower values of dominance mean that more taxa are equally present; or
there are no dominant species (Hammer et al. 2001, OECD 2001). Station 7 obtained
high evenness because the abundance of A. granulata (22%) is lower in this site. A.
granulata is the overall most abundant phytoplankton, thus the lesser population of
this species means that the other phytoplankton taxa have nearly equal counts, as no
single species became dominant. The least diverse station was Station 1 which had the
highest dominance of A. granulata (38%).
Phytoplankton abundance was positively influenced by abiotic factors such as turbidity (P = 0.68), nitrate (P = 0.41) and depth (P < 0.01). An increase in the values of
these parameters caused phytoplankton population to rise. The abundance of the species Aulacoseira granulata, Aulacoseira muzzanensis, Pediastrum simplex, Volvox
sp., Quadrigula lacustris, Scenedesmus arcuatus and Fragilaria crotonensis were
positively correlated to turbidity. All of these taxa are pollution tolerant species,
reaching high population as turbidity increases (Proulx & Pick 1996, Maynolov et al.
2009, Bellinger & Sigee 2010). The phytoplankton’s correlation with turbidity could
be explained in two ways: first, turbid water has more suspended particulates including organic matter and nutrients, all of which can hasten algal growth. Second, once
these phytoplanktons have rapidly increased, their numerous light-trapping cells fur-
eschweizerbart_xxx
Phytoplankton composition and diversity
35
ther contribute to a more turbid water. This may explain why A. granulata, the most
abundant species are positively correlated with turbidity. Aside from algae, the detritus and silt contents are the major particulates found in lakes contributing to high
turbidity (Michaud 1991).
Similarly, nitrate was positively correlated to A. granulata, Oocystis sp., Synedra
ulna and Microcystis sp. In accordance with a study wherein nutrients were manipulated in a controlled experiment, it was observed that phytoplankton abundance was
significantly affected by nitrogen concentration rather than by the concentration of
phosphorus (Stockner 1988). Meanwhile, depth was found to be positively correlated
to Closterium acutum, Oscillatoria sp., Scenedesmus abundans and Scenedesmus arcuatus (Table 3). All of these species are tolerant to pollution (Bellinger & Sigee
2010). Closterium tends to stay in deep water with minimal water movement (Rott et
al. 2007). The genus Scenedesmus was reported to be dominant in deep nutrient-rich
lakes (Proulx & Pick 1996). Deeper stations generally have larger proportion of surface area between the upper, lower and thermocline layer (Thomas 1996, Bryhn 2009)
thus supporting more species in the light and DO-rich upper layer. The study of Diehl
et al. (2002) found that algal concentration has positive relationship to mixing depth
when turbidity is high. Mean depth was reported to be capable of modifying the rate
at which abiotic parameters affect the phytoplankton community (Alvarez et al. 1994;
Proulx & Pick 1996).
Light penetration did not significantly vary among sites; hence nutrients and turbidity became the primary drivers of variations in phytoplankton abundance. The result conforms to earlier researches in Lake Buhi which found that the lake water was
already turbid (Binoya et al. 2008, Plopenio & Bimeda 2011) with very high phytoplankton density (Plopenio & Bimeda 2011). Aquaculture (Station 7), agriculture
(Station 1) and proximity to the lake outlet (Station 5) were the major contributing
resource uses that generated positive correlation between nitrate, turbidity and phytoplankton abundances.
In conclusion we found a diverse phytoplankton community in Lake Buhi. However, the occurrence of high nutrients and suspended pollutants in the lake resulted to
very high phytoplankton density with the dominance of pollution-tolerant algae. The
abundance of these tolerant species indicated that the lake is already eutrophic. The
phytoplankton community was found to be positively affected spatially and temporally by the variation in the abiotic factors; suggesting that management strategies
must focus on the sources of these variations such as agriculture and aquaculture.
Local initiatives such as bioremediation, effective waste management, sustainable
farming and fishing, and proper information dissemination must be continuously implemented to prevent the further degradation of the lake’s water quality; and ultimately to prevent the loss in phytoplankton composition and diversity.
eschweizerbart_xxx
36
Baloloy et al.
Acknowledgements
We thank the Department of Science and Technology – Accelerated Science and Technology
Human Resource Development Program (DOST-ASTHRDP) for funding this research; and the
Local Government Unit of Buhi for supporting the conduct of the study.
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Manuscript received April 9, 2015, accepted February 11, 2016
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