Journal of Threatened
Taxa | www.threatenedtaxa.org | 26 February 2022 | 14(2): 20615–20624
ISSN 0974-7907
(Online) | ISSN 0974-7893 (Print)
https://doi.org/10.11609/jott.7663.14.2.20615-20624
#7663 | Received 16
September 2021 | Final received 28 December 2021 | Finally accepted 22 February
2022
Seasonal variations influencing
the abundance and diversity of plankton in the Swarnamukhi
River Estuary, Nellore, India
Krupa Ratnam 1*, V.P. Limna Mol 2, S. Venkatnarayanan 3, Dilip
Kumar Jha 4, G. Dharani 5
& M. Prashanthi Devi 6*
1,3,4,5 Ocean Science and Technology for
Islands, National Institute of Ocean Technology, Ministry of Earth Sciences,
Govt. of India, Chennai, Tamil Nadu 600100, India.
2 School of Ocean Science and
Technology, Kerala University of Fisheries and Ocean Studies, Panangad, Kochi, Kerala 682506, India.
1,6 Department of Environmental
Science and Management, School of Environmental Sciences, Bharathidasan
University, Tiruchirappalli, Tamil Nadu 620024, India.
1 ratnamkrupa@gmail.com, 2 limnaa@gmail.com,
3 venkatnarayanan.srinivas@gmail.com, 4 dilipjhaniot@gmail.com,
5 dhara@niot.res.in, 6 prashanthidevi@gmail.com
(* corresponding authors)
Editor: Anonymity
requested. Date of
publication: 26 February 2022 (online & print)
Citation: Ratnam, K., V.P.L. Mol, S. Venkatnarayanan, D.K.
Jha, G. Dharani & M.P. Devi (2022). Seasonal
variations influencing the abundance and diversity of plankton in the Swarnamukhi River Estuary, Nellore, India. Journal of Threatened Taxa 14(2): 20615–20624. https://doi.org/10.11609/jott.7663.14.2.20615-20624
Copyright: © Ratnam et al 2022. Creative Commons Attribution
4.0 International License. JoTT allows unrestricted use, reproduction, and
distribution of this article in any medium by providing adequate credit to the
author(s) and the source of publication.
Funding: The authors
are grateful to the Ministry of Earth Sciences (MoES), Govt. of India, for providing funding
to carry out the present study.
Competing interests: The authors
declare no competing interests.
Author
details: Krupa Ratnam is working as a
Scientist in the National Institute of Ocean Technology, Ministry of Earth
Sciences, Govt. of India, Chennai, India, involved in (ship) ballast water
management, environmental impact studies, marine biofouling, etc. He has more
than 23 years of research experience in coastal and estuarine systems.
Presently, he is involved in the establishment of a land-based ballast water
treatment test facility at Nellore, India. Limna Mol V.P. is working as Assistant
Professor in Kerala University of Fisheries and Ocean Studies, Kochi, India.
She is a marine biologist with a focus on marine ecological studies and
biotechnological interventions towards conservation of the marine environment
and sustainable utilization of marine resources. Srinivas
Venkatnarayanan worked in National Institute
of Ocean Technology, Ministry of Earth Sciences, Govt. of India, Chennai,
India, as a Scientist. He expertises in marine
biotechnology, marine microbiology, zooplankton ecology, algae culture, larval
rearing, biocides, antifouling, marine biofouling. He has more than 10 years of
research experience. Dilip Kumar Jha is
working as a Scientist in National Institute of Ocean Technology, Ministry of
Earth Sciences, Govt. of India, Chennai, India. He has expertise in coastal and
marine ecology, pollution, geospatial modeling, the underwater study of
biological resources, and conservation measures. He has about 24 years of
research experience in coastal and marine modeling and management. Presently focussing on site suitability, cage development, and
societal benefits through fish farming in open sea conditions along the Indian
coasts. G. Dharani is working as
Scientist and Head of Marine Biotechnology group in National Institute of Ocean
Technology, Ministry of Earth Sciences, Govt. of India, Chennai, India. He has
about 28 years of research experience in the field of marine biotechnology and
plankton biology. He has published many research articles in
peer-reviewed international journals. Prashanthi Devi Marimuthu is working as
Associate Professor in Bharathidasan University, Tiruchirappalli, India. She
has expertise in remote sensing, geographical information system (GIS), spatial
analysis, environment, Wetlands, Cluster Analysis, Biodiversity, water quality
for the coastal environment. She has successfully guided many Ph.D. scholars
and published many research articles in peer-reviewed international journals.
She has about 25 years of research experience in the field of coastal area
management.
Author
contributions: KR—Participation
in sampling, analysis, writing the original draft; VPLM—Participation in sample
analysis; SV—Participation in sample analysis; DKJ—Participation in sampling,
critical review, commentary or revision – including pre-or post-publication
stages; GD—Coordination of the study and leadership responsibility for the
research activity planning and execution; MPD—Statistical analysis of the data,
guidance and mentorship.
Acknowledgements: We are thankful to Dr. G. A. Ramadass, the Director,
NIOT, MoES, Govt. of India, Chennai, for the
approvals and constant support. Thanks to Dr. R. Kirubagaran, Scientist-G (Retd.),
NIOT, for his encouragement. We also thank our colleagues Dr.
S. Rajaguru, Dr. S.
Venkatesh, Mr. P. Sathish Kumar and Dr. Vikas Pandey and the staff and students of Department of
Environmental Science and Management, School of Environmental Sciences,
Bharathidasan University, Tiruchirappalli, India. Authors also thank the
anonymous reviewers and editors for refining the manuscript.
Abstract: An integrated approach was used
to study the seasonal influence on the abundance and diversity of phytoplankton
and zooplankton in the Swarnamukhi River Estuary
(SRE) and the adjacent coast covering five stations by collecting monthly
samples from the years 2014 to 2017. A total of 54 phytoplankton species
conforming to four families and 58 zooplankton species conforming to nine
families were recorded. Phytoplankton abundance and richness were high during pre-monsoon (PRM -
56410 cells/L) followed by monsoon (MON – 42210 cells/L). A similar trend was
observed in the case of zooplankton, where abundance was recorded high during
PRM (124261 ind./m3) followed by MON
(111579 ind./m3). Moreover, phytoplankton
and zooplankton were dominated by the diatoms and copepods, respectively. Both
phytoplankton and zooplankton exhibited significant temporal (F= 26.4, p <0.05)
and spatial (F= 32.1, p <0.05) variations. The higher density and
abundance were recorded in the inner stations compared to the open sea. The
present study reveals that the SRE have a rich diversity which could be
attributed to a higher nutrient influx in the inner stations. The anthropogenic
discharge from the surrounding aqua farms, agricultural land, and human
settlement area could cause concerns for the local flora and fauna if a proper
mitigation plan is not evolved through long-term monitoring study in this
coastal region.
Keywords: Abundance, diversity, estuary,
indices, Nellore, Phytoplankton, zooplankton.
Introduction
Estuaries act as transitional
zones and support the coastal economy in the form of fishing, aquaculture,
transport, and tourism activities. They are also known to be highly productive
ecosystems that provide shelter and breeding grounds for various marine aquatic
organisms (Nybakken & Bertness
2005). Unlike salt marshes and backwaters, estuaries are complex and highly
dynamic and their structure and function are influenced by anthropogenic inputs
(e.g., aquaculture, agriculture, and industrial discharges) from the land and
get transferred to the sea (Shenai-Tirodkar et al.
2016). Such
anthropogenic activities can alter the physicochemical properties of water and
immensely influence the migration, richness, distribution, diversity, and
feeding of the associated marine aquatic organisms (Unanam
& Akpan 2006). Plankton are aggregates of organisms (plants and animals)
passively floating, drifting, or somewhat motile occurring in aquatic
ecosystems (Lalli & Parsons 1993). Phytoplankton
is grazed upon by zooplankton and other higher aquatic organisms (nektons) (Calbet 2008). Nutrient enrichment through, riverine inputs,
and discharge from anthropogenic activities can significantly alter the
phytoplankton growth and in turn affect the zooplankton grazing pressure (Berdalet et al. 1996). Therefore, plankton assemblages are
usually helpful in assessing the water quality as they quickly respond to the
environmental changes, hence; act as ecological indicators of an ecosystem
(Hays et al. 2005; Longhurst 2007).
In the Indian scenario, most of
the estuarine ecosystems are under stress due to natural and anthropogenic
inputs from the surrounding environment. With the increase in nearby
aquaculture, agricultural, and anthropogenic activities, the effluent
discharges find their way into the nearby coastal areas which provides an
advantageous environment to the organisms for proliferation. Similar activities have been reported in the Swarnamukhi River Estuary (SRE) region, fewer studies have
been carried out to assess the tidal variations (Reddi
et al. 1993), hydrographic properties of water (Sreenivasulu
et al. 2015), contamination studies on the presence of heavy metal in seawater,
sediments, & organisms (Reddy et al. 2016; Sreenivasulu
et al. 2018; Jha et al. 2019), and the benthic organisms (Pandey et al. 2021).
However, an elaborate study for the plankton communities is not available for
the SRE region. A long-term study (2014–2017) was conducted to analyze the
planktonic (phytoplankton and zooplankton) assemblages. This study can serve as
baseline information for future ecological assessment related to the SRE and
other similar tropical ecosystems.
Materials
and methods
Study area
The SRE region (14.072–14.077 °N
and 80.126– 80.154 °E), situated in the Vakadu Mandal
of Nellore district, Andhra Pradesh. This estuarine runs about 1.5 km in length
perpendicular to the Bay of Bengal with an average depth of 1.0 m and an area
of 6.25 km2 (Reddi et al. 1993). Nellore
receives the majority of the rainfall during the north-east monsoon (October to
December) than the south-east monsoon (Kannan et al. 2016). Altogether, five
sampling stations were fixed; four stations covering SRE and a reference
station in the open sea (OS) about a kilometer from
the shore. The coordinates were fixed using GPS (Garmin) covering the study
area and the surrounding coast. The selected sampling stations are shown in
(Figure 1), covering the Buckingham canal (BC), near to (SR1), away from mouth
(SR2), mouth (SRM), and open sea (OS). The monthly sampling was carried out
covering low and high tides at all the stations. The data was categorized
seasonally as pre-monsoon [PRM (January–May)], monsoon [MON (June–September)],
and post-monsoon [POM (October–December)] from May 2014 to December 2017 for
analysis (5 stations × 43 months × 2 tides = 430 samples).
Temperature and rainfall
The temperature and rainfall data
for the sampling period were obtained from the Indian Meteorological
Department, Ministry of Earth Sciences, Government of India. The obtained data
(monthly) was plotted for better interpretation (refer to Figure 2).
Biological parameters
For phytoplankton sampling, 5.0 L
of surface seawater samples (in triplicate) were collected in a polyethylene
container and preserved with 4% formalin and Lugol’s
iodine. Phytoplankton analysis was carried out using Utermöhl
(1931) sedimentation technique. The samples were allowed to settle in a
measuring cylinder for a period of 48 hours and siphoned (using a 10 µ mesh) to
obtain 50 mL concentrate (Hasle,1978). For phytoplankton enumeration, 1 mL of
the concentrated sample was taken onto a Sedgewick rafter plankton counting
chamber and the total number of organisms was examined under a compound
microscope. Phytoplankton was identified using standard identification keys (Subrahmanyan 1946, 1959; Santhanam et al. 1987; Tomas
1997). For chlorophyll-a (chl-a)
analysis, 1,000 mL of the water sample was filtered through Whatman GF/F filter
paper and chl-a, was extracted by following the
modified acetone extraction method (Parson et al. 1984). The extracted chl-a samples were analyzed using a spectrofluorometer (make Hitachi model F-4600) and obtained
results were expressed in mg/m3. The surface zooplankton samples
were collected using a zooplankton net (150 μm mesh
size, 0.5 m diameter, 1.8 m length) fitted with a digital flow meter (make
Hydro-Bios). The surface hauls were made from the stern side of the boat
running at a speed of 1 km/hr and the collected
plankton was transferred to 500 mL polythene containers and preserved using 5%
buffered formalin. In the laboratory, triplicate subsamples were taken onto a
Sedgewick rafter plankton counting chamber and the total numbers of organisms
were enumerated under the compound microscope (Nikon model SMZ 1500). The
zooplankton was identified following the standard identification key of Kasturirangan (1963) and Santhanam & Srinivasan (1994).
The zooplankton biomass was determined by the settled volume method, where the
collected sample was allowed to settle and the obtained biomass was expressed
as mL/m3.
Statistical analysis
PRIMER v6.1 was used for
univariate indices, e.g., species richness (S), abundance, Margalef’s
diversity (d), Shannon-Wiener diversity index (H′, log2), Simpson’s
diversity (1-λ), and Pielou’s evenness (J′) (Clarke
& Gorley 2006). The sitewise
variation between the environmental parameters were analyzed
using one way analysis of variance (ANOVA) in Microsoft Excel 2007. To determine the phytoplankton diversity and
dominance in different seasons and the stations, univariate diversity indices
were applied. The abundance of phytoplankton and zooplankton was represented
using a box plot using SPSS v10 software.
Results
and Discussion
Temperature and rainfall
The rainfall data were analyzed
for the years 2014–2017 and it indicates that maximum rainfall was recorded
from September to December (Figure 2). It ranged 6.2–221.1 mm (2014), 8.0–767.2
mm (2015), 10.6–149.0 mm (2016), and 1.1–218.0 mm (2017). Maximum rainfall of
767.2 mm was recorded in November 2015. The lowest rainfall was recorded in
2016 during the north-east monsoon (December 149.0 mm). The atmospheric
temperature (AT) ranged 22.1–40.2 °C, 21.4–39.7 °C, 22.0–39.5 °C, and 21.8–40.9
°C in 2014, 2015, 2016, and 2017, respectively. The AT peaked during the
summer, i.e., April and May. The SRE region is continuously fed with tidal
water and keeps the ecosystem comparatively in good condition; however, every
year during the MON when the precipitation is less, the mouth of the river gets
closed for a few months (Sreenivasulu et al. 2016;
Pandey et al. 2021). During this period, the concentration of some of the
parameters changed drastically due to stagnation. It has been reported
that the rainfall can significantly affect the phytoplankton composition in the
river (Jeong et al. 2007), estuaries (D’silva et al. 2012), and reservoirs (Zhou et al.,2012)
worldwide.
Phytoplankton diversity, density,
and chlorophyll-a
A total of 54 phytoplankton
species include 38 diatoms, nine dinoflagellates, three green algae, and four
blue-green algae. Diatoms (Bacillariophyceae) were the dominant group consisting of
70%, 69%, and 76% in PRM, MON, and POM, respectively. The next dominant was
dinoflagellates (dinophyceae) registering 20%, 14%,
and 18%, in PRM, MON, and POM, respectively. Green algae (Cyanophyceae)
were recorded during PRM (6%) and MON (7%) seasons. Blue-green algae (Chlorophyceae) were 4%, 10, and 6%, in PRM, MON, and POM,
respectively (Figure 3).
During the study period, the
highest phytoplankton density was recorded in the SRM (56,410 cells/L) and it
was lowest in the OS (2,440 cells/L). Phytoplankton density in the inner
riverside stations, BC, SR2, and SR1 ranged 9,605–50,160 cells/L, 7,785–56,340
cells/L, and 10,500–55,850 cells/L, respectively. In SRM and OS, phytoplankton
density ranged 10,033–56,410 cells/L and 2,440–37,100 cells/L, respectively.
The mean phytoplankton density recorded in the inner stations BC, SR2, and SR1
were 19,785, 21,005, and 18,815 cells/L, respectively (Figure 4a). In the SRM
and OS region, the mean phytoplankton density was 20000 and 17864 cells/L,
respectively. The maximum density recorded in PRM, MON, and POM was 56,410,
42,210, and 24,480 cells/L, respectively. The phytoplankton density in PRM
ranged 13,647–23,217 cells/L, in MON it ranged 18,585–22,746 cells/L, and in
POM it ranged 9,492–16,973 cells/L (Figure 4a). Among diatoms, Rhizosolenia sp. was the dominant species in all the
stations, followed by Thalassiosira
subtilis and Navicula sp. The Protoperidinium sp. dominated the dinoflagellates
community followed by Ceratium sp. and Prorocentrum sp. during the study period. All the
three species of green algae (Chlorella sp., Oocystis
sp., and Pediastrum sp.) were present during
MON, while only Chlorella sp. and Oocystis
sp. were represented during PRM and none of the three species mentioned
above were present during POM. Among the four blue-green algae recorded during
the study, Trichodesmium sp. and Spirulina
sp. were observed during PRM, Microcystis sp.
and Oscillatoria sp. were observed during POM,
and all the four species were present during the MON. The SRE received
precipitation during the POM (north-east monsoon) which could enhance the
land-driven run-off from the aqua farms, agricultural land, and domestic
discharge which consequently could have attributed higher nutrient inputs
helping phytoplankton to proliferate and bloom. Higher phytoplankton density in
the inner stations could be attributed to higher nutrient input in those
stations from the surrounding regions (aquaculture runoff) (Mckee
et al. 2000; Roberts & Prince 2010).
The chl-a
in PRM ranged 2.11 ± 0.12 mg/m3 (OS & SRM)–10.71 ± 2.08 mg/m3
(BC). In MON, it ranged 2.10 ± 0.49 mg/m3 (OS)–8.46 ± 1.76 mg/m3
(BC). In POM, it ranged 0.78 ± 0.17 mg/m3 (SRM)–3.41±0.24 mg/m3
(BC) (Figure 4b). The data indicates
that the phytoplankton exhibited significant variations between seasons (F=
26.4, p <0.05), while variation was insignificant between the
stations (F= 1.026, p >0.05). The diversity indices between the five
stations did not vary significantly (F= 1.026, p >0.05). An increase
in phytoplankton abundance and chl-a was on
par with previous studies observed during the PRM and MON (Achary
et al. 2014; Baliarsingh et al. 2016).
Univariate diversity indices have
shown variations between the three different seasons (Table 1). Throughout the study,
maximum phytoplankton species were recorded in the BC station in the monsoon
(45 species). Marglef’s species richness (d) was the
highest in MON, followed by PRM whereas it was lowest in POM. This could be
attributed to the high species diversity in MON compared to the other two
seasons. Pielou’s evenness (J’) and Simpson’s
dominance (D) were relatively higher in the PRM and POM compared to the MON
season. The relatively low value in MON can be attributed to the high species
diversity during this season. In general, the high species dominance in PRM and
POM can be related to the low species richness in these seasons. The maximum phytoplankton
abundance and chl-a biomass were recorded
during the PRM followed by MON season. The highest phytoplankton abundance and
biomass was recorded during 2014 and 2015.
Zooplankton density and diversity
A total of 58 different species
of zooplankton conforming to nine different phyla, i.e., Sarcomastigophora,
Ciliophora, Ctenophora,
Cnidaria, Chordata, Chaetognatha, and Arthropoda were
recorded. The increased diversity of zooplankton especially the copepods
observed in the estuarine region was on par with previous reports from the east
coast of India (Madhupratap et al. 1992; Thippeswamy & Malathi 2009).
However, the number of copepod taxa reported during the present survey was
comparatively less than previous reports in the Andhra coast (Rakhesh et al. 2006).
In BC, density varied
2,722–82,540 ind./m3. In SR1, it varied
2,871–84,230 ind./m3. In SR2, the density
of zooplankton varied 1,645–105,558 ind./m3.
In SRM, it varied 7,551–131,579
ind./m3. Similarly, in OS, it varied
1,523–96,872 ind./m3. It was observed that
zooplankton density was maximum at SRM (131,579 ind./m3)
(Figure 5a). Zooplankton density in PRM, MON, and POM ranged 20,090–29,114 ind./m3, 16,390–24,330 ind./m3,
and 13,286–22,426 ind./m3, respectively.
Maximum zooplankton abundance was observed during PRM followed by MON and POM
during the study period. The OS recorded the least abundance throughout the
seasons (PRM: 20,091 ind./m3, MON: 16,390 ind./m3 & POM: 13,286 ind./m3,
respectively). Maximum zooplankton biomass was observed in SR2 ranging from
0.04 to 0.13 ml/m3 throughout the study period (Figure 5b). OS
recorded the least biomass (0.02 to 0.04 ml/m3) throughout the
sampling period. Overall PRM followed by MON season exhibited favourable conditions for zooplankton growth in the SRE
region.
Zooplankton exhibited a typical
season-specific and site-specific variation. Copepods followed by invertebrate
larval forms dominated the zooplankton community during all three seasons. A
total of 37 species of copepods were recorded during the survey, with the major
species being Acartia danae,
A. spinicauda, A. clausii,
Paracalanus parvus,
Acrocalanus gibber, A. longicornis, Corycaeus
danae, C. catus,
Oithona rigida,
and Euterpina acutifrons
were recorded throughout the year irrespective of seasons. Copepods followed by
larval forms dominate the entire zooplankton community irrespective of seasons
(Figure 6). The least contributing groups (less than 10%) include organisms
belonging to phyla/group Sarcomastigophora, Ciliophora, Ctenophora, Cnidaria,
Chordata, Chaetognatha, and Annelida. Copepods
species such as Eucalanus sp., Subeucalanus sp., Onacaea
sp., Centropages sp., and Copilia
sp. were present only during POM season in higher numbers in all stations which
correlates with the lowering salinity in all stations due to the north-east
monsoon. Apart from the copepods, some other larval forms exhibited seasonality
such as bivalve (PRM and MON) and gastropod veligers
(MON and POM). Larval forms belonging to phylum Mollusca, e.g., Creisis sp. and the Ophiothrix
larva were exclusively present only in monsoon. Copepod nauplius,
crustacean nauplius, and polychaete
larvae were present throughout the year in all the stations.
Univariate diversity indices have
shown variations between the three seasons (Table 2). Marglef’s
species richness (d) was the highest in MON, followed by PRM and POM. Among the
five stations, a significant difference in the diversity indices was observed
during the POM. BC region was more diverse and recorded maximum zooplankton
species (19–23). This could be attributed to anthropogenic activities in the
surrounding environment (Pandey et al. 2021).
The zooplankton community
exhibited significant differences between the seasons (F= 191.1, p <0.001)
as well as the stations (F= 224.5, p <0.001). The present
investigation has shown the presence of discrete assemblages of zooplankton
communities observed in the SRE and coastal region indicating a strong seasonal
fluctuation with lower abundances in POM and higher during the PRM and MON
season. A similar study conducted elsewhere suggested that phytoplankton
abundance plays a very important role in regulating zooplankton population in
estuaries (Jagadeesan et al. 2013; Nandy & Mandal 2020).
The coast is prone to heavy
rainfall, the likely discharges from the nearby aquaculture activities in the
inner stations (BC, SR2, and SR1) of the SRE region which was supported with
previous studies (Sreenivasulu et al. 2018). The results of this study are in agreement
with Jha et al. (2019) and Pandey et al. (2021) in the same region.
Conclusion
The present long-term study
reveals the spatial and temporal variations of phytoplankton and zooplankton in
the SRE and the adjoining coast. The study also highlights that the SRE region
receives very little rainfall during the MON period and most of the rainfall
occurred only during the POM period, i.e., during the north-east monsoon (NEM)
period. The SRE region is known to have a good cover of mangroves swamps and is
usually impacted by anthropogenic activities, such as, aquaculture farms,
agriculture activities, and discharge areas from nearby vicinity. The increased
nutrient concentration significantly affected the plankton community in the SRE
region. Our study indicates that the phytoplankton community exhibited
significant variations between seasons. The zooplankton density also showed
significant variation and revealed the anthropogenic impact in the study. The
present study suggests that phytoplankton and zooplankton are important
indicators of a healthy ecosystem which was evident in the present study.
Moreover, the study also suggests that a long-term monitoring could help in
understanding the ecosystem and planning the mitigation management strategy for
the tropical coastal environment.
Table 1. Spatio-temporal
univariate diversity indices for phytoplankton.
Season |
Station |
Total species (S) |
Total Individuals (N) |
Marglef’s species richness (d) |
Pielou’s evenness (J') |
Shannon Wiener Diversity index
(H') |
Simpson’s dominance (D) |
PRM |
BC |
36 |
23217 |
3.27 |
0.93 |
3.36 |
0.96 |
SR2 |
35 |
22493 |
3.13 |
0.91 |
3.25 |
0.94 |
|
SR1 |
36 |
18980 |
3.26 |
0.93 |
3.35 |
0.95 |
|
SRM |
35 |
21054 |
3.16 |
0.93 |
3.34 |
0.95 |
|
OS |
35 |
13647 |
3.28 |
0.91 |
3.25 |
0.95 |
|
MON |
BC |
45 |
22746 |
4.54 |
0.71 |
2.70 |
0.87 |
SR2 |
40 |
22409 |
3.99 |
0.73 |
2.72 |
0.87 |
|
SR1 |
41 |
18585 |
4.15 |
0.73 |
2.71 |
0.88 |
|
SRM |
42 |
20040 |
4.12 |
0.82 |
3.09 |
0.93 |
|
OS |
41 |
18906 |
3.90 |
0.78 |
2.90 |
0.91 |
|
POM |
BC |
30 |
14959 |
3.05 |
0.88 |
2.99 |
0.93 |
SR2 |
27 |
16378 |
2.75 |
0.92 |
3.05 |
0.94 |
|
SR1 |
29 |
10521 |
3.03 |
0.92 |
3.11 |
0.94 |
|
SRM |
29 |
16973 |
2.94 |
0.91 |
3.07 |
0.94 |
|
OS |
18 |
9492 |
1.87 |
0.91 |
2.64 |
0.90 |
Table 2. Spatio-temporal
univariate diversity indices for zooplankton.
Season |
Station |
Total species (S) |
Total Individuals (N) |
Marglef’s species richness (d) |
Pielou’s evenness (J') |
Shannon Wiener Diversity index
(H') |
Simpson’s dominance (D) |
PRM |
BC |
19 |
27100 |
1.759 |
0.7223 |
2.13 |
0.8415 |
SR2 |
19 |
27793 |
1.828 |
0.7863 |
2.32 |
0.8749 |
|
SR1 |
19 |
26655 |
1.874 |
0.7829 |
2.31 |
0.8615 |
|
SRM |
19 |
29114 |
1.827 |
0.7811 |
2.30 |
0.8694 |
|
OS |
19 |
20090 |
1.873 |
0.7468 |
2.20 |
0.8447 |
|
MON |
BC |
23 |
24006 |
2.306 |
0.7836 |
2.46 |
0.8493 |
SR2 |
21 |
16390 |
2.057 |
0.7903 |
2.41 |
0.8460 |
|
SR1 |
19 |
24330 |
1.932 |
0.7172 |
2.11 |
0.7880 |
|
SRM |
22 |
21521 |
2.019 |
0.6835 |
2.11 |
0.8170 |
|
OS |
19 |
16691 |
1.793 |
0.5965 |
1.76 |
0.6701 |
|
POM |
BC |
16 |
16576 |
1.463 |
0.5105 |
1.42 |
0.5485 |
SR2 |
9 |
22426 |
0.714 |
0.2415 |
0.53 |
0.2313 |
|
SR1 |
8 |
14828 |
0.783 |
0.6151 |
1.28 |
0.6249 |
|
SRM |
14 |
19619 |
1.184 |
0.4009 |
1.06 |
0.4726 |
|
OS |
13 |
13286 |
1.045 |
0.3664 |
0.94 |
0.3983 |
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