Journal of Threatened
Taxa | www.threatenedtaxa.org | 26 July 2025 | 17(7): 27195–27206
ISSN 0974-7907 (Online) | ISSN 0974-7893 (Print)
https://doi.org/10.11609/jott.9540.17.7.27195-27206
#9540 | Received 07 December 2024 | Final received 17 July 2025 | Finally
accepted 20 July 2025
Cataloguing biodiversity of
freshwater communities in two lakes of Gadchiroli
area of central India using environmental DNA analysis
Maheshkumar Seelamwar
1, Pankaj Chavan 2 &
Mandar S. Paingankar 3
1,3 Government Science College, Chamorshi
Road, Gadchiroli, Maharashtra 442605, India.
1 Shri Sadguru Saibaba
Science and Commerce College, Ashti, Gadchiroli, Maharashtra 442605, India.
2 Sri Jivanrao Sitaram Patil
Arts Commerce Science College, Dhanora, Gadchiroli, Maharashtra 442606, India.
1 maheshseelamwar@gmail.com, 2
panksphd@gmail.com, 3 mandarpaingankar@gmail.com
(corresponding author)
Editor: Anonymity requested. Date of publication: 26 July
2025 (online & print)
Citation: Seelamwar, M., P. Chavan & M.S. Paingankar
(2025).
Cataloguing biodiversity of freshwater communities in two lakes of Gadchiroli area of central India using environmental DNA
analysis. Journal of Threatened Taxa 17(7): 27195–27206. https://doi.org/10.11609/jott.9540.17.7.27195-27206
Copyright: © Seelamwar et al. 2025. 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: District Planning Committee Gadchiroli, Maharashtra – Grant for Infrastructure development of colleges - Government Science College Gadchiroli (2202-C748-ME-52).
Competing interests: The authors declare no competing interests.
Author details: Maheshkumar Seelamwar is working as assistant professor in Zoology Department, Shri Sadguru Saibaba Science and Commerce College, Ashti, Gadchiroli, Maharashtra, India, Maha-rashtra, India. Passionate about freshwater biology, focusing on understanding the dynamics of aquatic fauna. Dr. Pankaj Chavan is working as assistant professor in Zoology Department, Sri Jivanrao Sitaram Patil Arts Commerce Science College, Dhanora, Gadchiroli, Maharashtra, India. His research focuses on biodiversity and conservation of aquatic insects and bats. Dr. Mandar Paingankar is working as assistant professor, Department of Zoology, Government Science College Gadchiroli, Chamorshi Road Gadchiroli, Maharashtra, India. His research focuses on biodiversity, conservation and molecular phylogeny of fishes and coleopteran beetles.
Author contributions: MS, PC, MSP involved in the designing experiments, conduct of experiments, data analysis and manuscript writing. All authors have read and approved the final manuscript.
Acknowledgements: We are thankful to the principal of Government Science College for providing the facilities. We are thankful to the District Planning Committee Gadchiroli Maharashtra for providing financial assistance for infrastructure development in Government Science College Gadchiroli.
Abstract: We investigated eukaryote
biodiversity in two freshwater lakes in the Aashti
area of Gadchiroli in central India, using
next-generation sequencing-based technology. In this preliminary study, we
analyzed four water samples using metabarcoding of the 18s V6 region of
mitochondrial DNA, and detected >500 operational
taxonomic units (OTUs). We detected algae, dinoflagellates, rotifers, ciliates,
and metazoan species and our results indicate that algae and rotifers were the
most abundant groups in these lakes.
Keywords: 18S DNA barcoding, alpha
diversity, beta diversity, biodiversity, environmental parameters, freshwater
ecology, phytoplankton, zooplankton.
INTRODUCTION
Phototrophic algae, heterotrophic
protists, rotifers, crustaceans, dinoflagellates, and diatoms usually dominate
the freshwater microscopic eukaryotic communities (Manabe et al. 1994;
Nishikawa et al. 2010), and play a crucial role in
governing the biogeochemical cycles in the lotic and lentic waterbodies (Allan
1976; Gannon & Stemberg 1978). Phytoplankton and zooplankton play essential
roles in C and N cycles, and enhance the stability of
aquatic ecosystems (Steinberg et al. 2008). Zooplankton directly feeds on
phytoplankton and thus contributes to the inhibition of the eutrophic
conditions in lakes (Cottenie et al. 2003; Kohout & Fott 2006; Schou et al. 2009). Similarly, many zooplankton are
sensitive to anthropogenic stressors, and thus can serve as useful biological
indicators of environmental stressors (Beaugrand et
al. 2002; Grosjean et al. 2004; Blanco-Bercial & Bucklin 2016). Marine, wetland, and
freshwater ecosystems are facing various threats to their stability, including
toxicant pollution, nutrient influx, land use, and climate change. It is known
that these human activities change the biogeochemical cycles, which in turn
change the types of species that live in freshwater ecosystems, and how those
ecosystems work (Baldwin et al. 2014; Drake 2014). Anthropogenic activities
significantly altered the population dynamics and biodiversity of aquatic
habitats (Sala et al. 2000). Conservation efforts are hampered by a lack of
detailed information on biodiversity and the rates of species extinction in freshwater
ecosystems (Ricciardi & Rasmussen 1999; Pimm et al. 2014). Therefore,
protecting the aquatic ecosystems and their biodiversity is of prime
importance, and concentrated efforts are required to conserve these precious
ecosystems. In this context, documenting the true biodiversity in various
ecosystems is essential.
Several studies on cataloguing
phytoplankton and zooplankton diversity are available in the literature (Banse 1995; Nogueira 2001; Branco et al. 2002; Neves et al.
2003; Whitman et al 2004; Mageed 2007; Frutos et al. 2009; Suresh et al. 2011; Vanderploeg
et al. 2012; Paturej et al. 2017; Gao et al. 2019; Li
et al. 2019). Plankton diversity of different aquatic ecosystems has been
identified using DNA barcoding (Amaral-Zettler et al.
2009; Bucklin et al. 2019; Machida et al. 2009; Tang et al. 2012; Hadziavdic et al. 2014; Djurhuus
et al. 2018; Wangensteen et al. 2018; Berry et al.
2019). Traditional taxonomic methods have been used by Indian researchers to
record the different aquatic communities in a number of freshwater habitats (Madhupratap et al. 1981; Mishra et al. 1993; Jha &
Barat 2003; Kiran et al. 2007; Kumar et al. 2011; Harney et al. 2013; Smitha et
al. 2013; Jyotibabu et al. 2018; Bhattacharya et al.
2015; Manickam et al. 2018). The limitations of traditional taxonomic methods
in identifying microscopic forms have hindered the complete elucidation of the
true plankton diversity in these freshwater lakes and ponds. Recently, few
studies employed DNA barcoding to explore plankton biodiversity (Nair et al.
2015; Govender et al. 2022). Few studies have used metagenomics to identify
diversity in freshwater lakes in India. These observations suggest a need for
comprehensive studies to identify the biodiversity in freshwater ecosystems of central
India. In the current study we used environmental DNA barcoding to
catalogue eukaryote diversity in two freshwater lakes from the Gadchiroli area of central India.
MATERIALS AND METHODS
Sampling sites
Two lakes, Chandankhedi
Lake 1 (ASL1, 19.709° N & 79.826° E) and Chandankhedi
Lake 2 (ASL2, 19.726° N & 79.833° E), are situated near Chandankhedi
Village, Ashti area, Gadchiroli
District, Maharashtra State of India (Figure 1). The ASL1 and ASL2 are not
included in any area that is reserved for biodiversity conservation or
privately owned, so no specific permissions were required to conduct the sample
collection. The current study did not collect or include any species listed as
endangered or protected in species lists. Since the schedule species list of
animals does not include the organisms in the plankton sample, no ethical
committee approval was required. We followed the collection procedures as
outlined in the literature (Harris et al. 2000).
Water samples
We collected a one-liter water
sample from three different depths near the lake’s periphery (littoral zone)
and inside the lake (limnetic zone) in sterile collection bottles and processed
it within a day. The three samples collected from the periphery (littoral zone)
of each lake were combined and labeled as ASL1P, and ASL2P. Similarly
three samples from the interior (limnetic zone) of each lake were combined and
labeled as ASL1I, and ASL2I. A total of four samples ASL1P, ASL2P, ASL1I, and
ASL2I were processed for metagenomics analysis. Chemical parameters estimated
for water samples included hydrogen ion concentration (pH) and total dissolved
solids (TDS), recorded using portable meters (Amstat,
USA). Other chemical parameters were estimated in the laboratory using standard
protocols (APHA 2008). Winkler’s method was used to measure dissolved oxygen
(DO), and titrimetric methods to measure free CO2 and total
hardness. We estimated total alkalinity using titrimetric methods by combining
two values: free CO2 (carbonate alkalinity) and bicarbonate alkalinity,
measured with phenolphthalein, and methyl orange indicators, respectively, and
titrating the water sample against N/50 sulphuric
acid.
DNA extraction
DNA extraction from the collected
samples: ASL1 P (littoral zone) and ASL1 I (limnetic zone) from Chandankhedi Lake 1, and ASL2P (littoral zone) and ASL2I
(limnetic zone) from Chandankhedi Lake 2 was
performed using the DNA Easy Power Water DNA Isolation Kit (Qiagen, USA). DNA
isolation was carried out according to the manufacturers’ protocol. The genomic
DNA was checked on a 1% agarose gel for the presence of a single intact band.
Further, 1 μL of each sample was loaded in a
microvolume spectrophotometer for determining the A260/280 ratio (Denovix, USA). The DNA was quantified using a QuantiFluor® ONE dsDNA System (Promega, USA).
Amplification of the 18S rRNA
gene and subsequent Illumina sequencing
The
amplicon sequencing protocol targeting the V4 region of the 18S gene was used
to prepare the sequencing libraries for metagenomics analysis. DNA amplicon
libraries were generated according to the guidelines provided by Illumina
(http://www.illumina.com). The forward and reverse primers, possessing adapter
amplicon lengths compliant with Illumina standards, were produced, and utilized
for amplification. The PCR reactions were conducted under these conditions:
initial denaturation at 95°C for 15 minutes, followed by 35 cycles consisting
of denaturation at 95°C for 45 seconds, annealing at 60°C for 45 seconds, and
extension at 72°C for one minute. The amplification concluded with a final
extension phase at 72°C for 10 minutes. The PCR products were purified with a
column-based purification kit (Promega, USA), analyzed via gel electrophoresis
to confirm size, and quality, and quantified using a QuantiFluor®
ONE dsDNA System (Promega, USA). Indexing PCR, ampure
bead purification, equimolar pooling, and sequencing on the Illumina 250 PE
platform were conducted at the FirstBase DNA
Sequencing Service in Malaysia. Libraries were sequenced utilizing the
paired-end Illumina 250 PE platform to provide 250 bp
paired-end raw reads. The paired-end reads of each sample were cleaned by
removing the barcodes and primer sequences, and were
merged using FLASH (V1.2.7) (Lozupone et al. 2007).
We performed quality cleanup on the raw tags using specific filtering
parameters, resulting in high-quality clean tags (Avershina
et al. 2013, Qiime (V1.7.0); Magali et al. 2013). The
chimeric sequences were eliminated to get high-quality tags for bioinformatics
and taxonomic research (Edger et al. 2011).
OTU cluster and taxonomic
annotation
Sequence analysis was carried out
using all the effective tags employing the Uparse
software (Uparse v7.0.1090, Magoč
et al. 2011). Sequences having more than 97% similarity were considered as the
same OTUs. A representative sequence for each OTU was checked for further
annotation. Sequence analysis was carried out using the Qiime
RDP method (Version 1.7.0, http://qiime.org/scripts/assign_taxonomy.html; Bokulich et al. 2013). The Silva database (http://www.arb-silva.de;
Caporaso et al. 2010) was used for species annotation
(Threshold: 0.6~1). Sequences were aligned using MUSCLE (Version 3.8.31, http://www.drive5.com/muscle;
Edgar 2013) to obtain phylogenetic relationships. We selected the top 100
genera to understand the phylogenetic relationships. OTU abundance was
normalized using a standard of sequence number equivalent to the sample with
the least sequences. We performed subsequent analyses of alpha diversity and
beta diversity using the normalized data.
Statistical analysis
Alpha diversity indices, observed
species, Shannon, ACE, Chao1, Simpson, and good coverage, were calculated using
QIIME (Version 1.7.0). We calculated beta diversity on both weighted and
unweighted UniFrac using the QIIME software (Version
1.7.0). A square matrix of “dissimilarity” or “distance” was calculated and
used for non-metric multidimensional scaling (NMDS) analysis, and principal
coordinate analysis (PCoA). AMOVA was estimated by mothur using the amova function.
Canonical correspondence analysis (CCA) was performed to understand whether
there was any relationship between OTU and the chemical parameters. A scatter
plot was graphed to understand the contribution of each CCA axis. The
significance of canonical correlations was tested at two levels using 999
permutations (Legendre & Legendre 1998). The significance of the trace
value was estimated to test the overall null hypothesis that there is no correlation
between the environmental parameters and the species occurrence, and (2) the
significance of individual canonical eigenvalues was tested with the same null
hypothesis but against the alternate hypothesis that a given eigenvalue
explains more of the variation of species occurrence than matrices with
permuted rows would.
RESULTS
Assignment of Molecular
Operational Taxonomic Units (OTUs)
We generated and sequenced
amplicons of the 18S small subunit rRNA gene for each sample. A total of
1,105,618 DNA sequences were generated. After quality control and removal of
chimeras, 994,568 good-quality sequences remained (Table 1). The average read
length for the sequencing reads was 311 bp. Using a
97% similarity cut-off, the clean read tags were clustered into a total of 642
OTUs. We recorded a total of 568 OTUs in Chandankhedi
Lake 1 (ASL1) and 437 OTUs in Chandankhedi Lake 2
(ASL2) (Figure 2 A, Supplementary Information S1). All four samples shared 189
OTUs, while the ASL1 sample had the highest number of unique OTUs (Figure 2B).
The ASL1 sample displayed the highest number of unique OTUs (Figure 2B). Of the
observed OTUs from two lakes, only 163 were identified at the species level.
Arthropoda was the most abundant group, and Rotifera
was the second most abundant taxon (Figure 3A). The least diverse taxonomic
group was Euglenozoa. Maxillopoda, Monogononta, Chrysophyceae, and Intramacronucleata
were the most dominant classes, whereas Calanoida, Cyclopoida, Flosculariaceae, and Ploimida were the most abundant orders in ASL1, and ASL2
(Figure 3B). Calanoida, Cyclopoida,
Flosculariacea, and Ploimida
were the most dominant families, whereas Calanoida,
Cyclopoida, Flosculariacea,
and Ploimida were the most abundant
genera (Figure 3C). Mesocyclops dissimilis, Ptygura libera, Vallisneria natans, Filinia longiseta, Limnias ceratophylli, Nymphoides peltata, Sphaerastrum fockii, and Collotheca campanulata were the most common species.
Alpha and beta diversity
Alpha and beta diversity analyses
of ASL1 and ASL2 sequence reads revealed rich taxonomic diversity and dominance
of a few species (Figure. 4, Supplementary Information S2). Shannon’s index
ranges from 1–1.5, indicating high species richness in the samples collected
from these lakes (Figure 4A). Interestingly, samples from ASL1.P (D = 0.296),
ASL1.I (D = 0.32), ASL2.P (D = 0.209), and ASL2.I (D = 0.193) showed higher
dominance among fewer groups (Figure 4B). The ACE analysis showed that the lake
samples had a lot of different species (Figure 4C), and the Chao-1 analysis
predicted that these samples would have between 337 and 511 different species
(Figure 4D). Alpha diversity indices such as the Shannon index, evenness, and Margalef index were not significantly different between the
ASL1 and SL2 lake samples (Mann-Whitney U test P >0.05 for
each comparison). Interestingly, the Simpson index showed a significant
difference between ASL1 and ASL2 (Mann-Whitney U test, P <0.05).
Beta diversity analysis indicated that the composition of species in these two
lakes is significantly different (Figure 4E; nMDS
Stress <0.001). A species accumulation curve showed the presence of 642 OTUs
in these lake samples (Figure 4F). The analysis of molecular variance (AMOVA)
revealed no significant difference in molecular variance between the samples
collected from ASL1 and ASL2 lakes (Fs = 6.72682, p = 0.342).
Correlation between species
composition and biochemical characteristics of lakes
The composition and biodiversity
of eukaryotes were significantly different among the two lakes (Figure 2). NMDS
analysis indicated that biological diversity in these two lakes clearly
discriminated from each other (Figure 4E, Trace p <0.01). Proportions of Rotifera, Ochrophyta, Ciliophora, Cryptomycota, Diatomea, Chlorophyta, Phragmoplastophyta,
and Peronosporomycetes differed significantly among
water bodies. Canonical correspondence analysis suggested that there was a
strong correlation between chemical parameters and species occurrence (Figure
5, trace = 0.00087, P = 0.039). The first two axes, which together explained
93.8% of the total inertia, were significant, and depicted the relationship
between chemical parameters, and species occurrence. Most species were
clustered around the origin of both axes, indicating that they had no
particular preference for chemical parameters. Interestingly, only a few
species showed a correlation with the chemical parameters of water. For
instance, Bryometopus atypicus,
Chloromonas oogama, Malassezia globosa, and Cyanophora paradoxa had
preferences for relatively higher values of TDS. Cloeon
durani, Chironomus tentans, Dinobryon sp., and Pinnularia sp. showed preference for
relatively higher values of total hardness, chloride, and dissolved CO2. Pseudorhizidium endosporangiatum,
Trochilia petrani, Furgasonia blochmanni, and Pseudocharaciopsis ovalis showed preference
for higher values of dissolved oxygen, and Ochromonas
sphaerocystis, Gieysztoria
sp., Linostomella sp., and Chlamydopodium
starrii showed preference for higher values of
alkalinity, and salinity.
The evolutionary tree of the top
100 genera
Of the observed OTUs from two
lakes, 169 OUT could be identified at genera level. Out of 169 identified
genera, the top 100 were used for phylogenetic analysis (Figure 6;
Supplementary Information S1). Phylogenetic analysis revealed that more than
90% of OUT reads accounted for five phyla (Calanoida,
Cyclopoida, Ploimida, Flosculariacea, Philodinia),
suggesting the dominance of a few phyla in ASL1, and ASL2 lakes.
DISCUSSION
Aquatic fauna of freshwater lakes
plays a fundamental role in the food web and provides important information
about the state of the water body (Manabe et al. 1994; Nishikawa et al. 2010).
Several studies have looked at the variety of phytoplankton and zooplankton in
freshwater, estuarine, and marine water bodies around the world (Banse 1995; Nogueira 2001; Branco et al. 2002; Neves et al.
2003; Whiteman et al. 2004; Mageed 2007; Frutos et al. 2009; Suresh et al. 2011; Vanderploeg
et al. 2012; Paturej et al. 2017; Gao et al. 2019; Li
et al. 2019). Several studies in India have catalogued the biodiversity of
phytoplankton and zooplankton in rivers, estuaries, and marine habitats (Madhupratap et al. 1981; Mishra et al. 1993; Jha &
Barat 2003; Kiran et al. 2007; Kumar et al. 2011; Harney et al. 2013; Smitha et
al. 2013; Jyothibabu et al. 2015; Manickam et al. 2018; Bhattacharya et
al. 2015). Taxonomic studies of these bodies of water showed that they were
home to protozoa, rotifers, copepods, cladocera, ciliophora, and meroplanktons. Similarly, genetic analysis
studies also documented the presence of several zooplankton and phytoplankton
species in rivers and lakes of India (Nair et al. 2015; Govender et al. 2022).
The main goal of this study was
to obtain taxonomic and genetic data for eukaryotes in two freshwater lakes in
the Aashti area of Gadchiroli,
Maharashtra. The metagenomic analysis of the lakes suggested the presence of a
rich eukaryotic community structure. The universality of 18S primers and sample
collection methods played a crucial role in documenting the true diversity of
the aquatic forms present in the two lakes, ASL1 and ASL2. Rotifera,
Cladocera, and Maxillopoda,
along with other aquatic organisms, including aquatic Phragmoplastophyta,
Platyhelminthes, Ochrophyta, Holozoa,
Gastrotricha, Diatoms, Protista, Nematoda, Ciliophora, Diatomea, and
Chlorophyta, were predominant in the sampling sites. Eudiaptomus
environmental, Mesocyclops dissimilis,
Arthropoda environmental, Neoergasilus japonicus, Microcyclops varicans, and Unionicola foili
comprised over 90% of the total numbers of OUT (Figure 6). Rofifers,
Ptygura libera, Filinia longiseta, Limnias ceratophylli, and Collotheca campanulata were
abundant in these two lakes. Vallisneria natans, Nymphoides peltata, and Chlamydomonas reinhardtii
dominated the plant species. Diatoms such as Achnanthidium
saprophilum and Urosolenia
eriensis were present in good numbers in these
two lakes (Figure 6). Although DNA metabarcoding identified more than 600 OTUs
in the current study, only 163 OTUs could be identified at the species level.
Chao-1 analysis suggested that more than 600 species might be present in the
study area. The results obtained in the current study suggest that the ASL1 and
ASL2 lakes have high species diversity with a complex community structure
(supplementary information, Table S1 and Figure 2), and in-depth taxonomic
analysis is required to uncover the true diversity in these two lakes.
Maxillopoda has been considered a
bioindicator of environmental fluctuation and ecosystem dynamics (Campos et al.
2017; Jyothibabu et al. 2018). On the other hand, Cyclopoida are capable of surviving in different habitats
and maintaining their population size in hostile conditions as well (Paffenhoffer 1993). In these two lakes, ASL1 and ASL2, Maxillopoda, Calanoida, and Cyclopoida were abundantly present. These observations
suggest that these two lakes are experiencing fewer threats from anthropogenic
activities. Although the plankton fauna has been recorded from a wide range of
environmental conditions, environmental factors such as pH, dissolved oxygen,
salinity, and temperature play an important role in determining the
accumulation of species (Ahmad et al. 2012). Few species exhibit a profound
response to a given factor, while others do not demonstrate any significant
response (Figure 5). The results obtained in the current study indicated that
environmental variables, dissolved CO2, total hardness, chloride
concentration, TDS, and oxygen concentration have a significant role in
determining the species composition.
It has been well documented that
temperature plays a crucial role in determining the diversity and abundance of
plankton communities. The results obtained in the current study suggest that
temperature might not be influencing the species diversity in these two lakes,
ASL1 and ASL2 (Figure 5). Bryometopus atypicus, Chloromonas oogama, Malassezia globosa, and Cyanophora paradoxa showed preference for relatively higher values
of TDS. On the other hand, Cloeon durani, Chironomus tentans, Dinobryon sp,. and
Pinnularia sp. showed preference for
higher values of total hardness, chloride, and dissolved CO2. Pseudorhizidium endosporangiatum,
Trochilia petrani, Furgasonia blochmanni, and Pseudocharaciopsis ovalis prefer higher
values of dissolved oxygen for survival in lake environments. On the other
hand, Ochromonas sphaerocystis,
Gieysztoria sp., Linostomella
sp., and Chlamydopodium starrii
showed affinity for higher values of alkalinity, and salinity. The
observations corroborate the results obtained in the earlier studies.
The use of the Illumina platform
enabled us to detect several operational taxonomic units (OTUs) of eukaryotes
using environmental DNA, even though they are available in low abundance in
samples. The outcome of this study revealed that we have significantly
underestimated plankton diversity in the past due to too much reliance on
traditional microscopy-based methods. The results obtained in this study are
preliminary in nature and require further investigation.
Table 1. QC statistics of ASL1 and ASL2 samples.
|
Sample name |
Raw PE(#) |
Raw Tags(#) |
Clean Tags(#) |
Effective Tags(#) |
Taxon Tag |
Average length (nt) |
OUT number |
Species |
Effective % |
|
ASL1.I |
284,836 |
275,043 |
273,629 |
262,811 |
261716 |
311 |
513 |
494 |
92.27 |
|
ASL1.P |
271,293 |
263,039 |
261,914 |
245,710 |
244777 |
311 |
460 |
436 |
90.57 |
|
ASL2.I |
272,095 |
262,053 |
260,807 |
235,995 |
234697 |
311 |
339 |
306 |
86.73 |
|
ASL2.P |
277,394 |
266,638 |
265,350 |
250,052 |
249131 |
311 |
400 |
371 |
90.14 |
FOR
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