Journal of Threatened Taxa |
www.threatenedtaxa.org | 26 May 2023 | 15(5): 23216–23226
ISSN 0974-7907 (Online) | ISSN 0974-7893
(Print)
https://doi.org/10.11609/jott.7952.15.5.23216-23226
#7952 | Received 04 April 2022 | Final
received 31 March 2023 | Finally accepted 17 April 2023
Evaluating the influence of environmental variables on fish abundance
and distribution in the Singhiya River of Morang
District, eastern Nepal
Jash Hang Limbu 1,
Dipak Rajbanshi 2, Jawan Tumbahangfe 3, Asmit Subba 4, Sumnima Tumba 5 & Rakshya
Basnet 6
1 College of Fisheries
and Life Science, Shanghai Ocean University, Shanghai, China.
2 Department of
Zoology, Post Graduate Campus, Tribhuvan University, Biratnagar, Nepal.
3,4 Central Department of
Zoology, Tribhuvan University, Kirtipur, Kathmandu,
Nepal.
4 Nature Conservation
and Study Center, Kathmandu, Nepal.
5,6 Department of
Biology, Central Campus of Technology, Hattisar
Dharan, Nepal.
1 limbujash@gmail.com
(corresponding author), 2 dipakrajbanshi5555@gmail.com
(corresponding author), 3 jawansubba37@gmail.com,
4 subbaasmit926@gmail.com,
5 sumnimasubba06@gmail.com, 6 basnet453@gmail.com
Editor: J.A. Johnson, Wildlife Institute of India,
Dehradun, India. Date of
publication: 26 May 2023 (online & print)
Citation: Limbu, J.H., D. Rajbanshi, J. Tumbahangfe,
A. Subba, S. Tumba & R.
Basnet (2023).
Evaluating the influence of environmental variables on fish abundance and
distribution in the Singhiya River of Morang
District, eastern Nepal. Journal of Threatened Taxa 15(5): 23216–23226. https://doi.org/10.11609/jott.7952.15.5.23216-23226
Copyright: © Limbu et al. 2023. 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: This work was supported by Nature Conservation and Health Care Council (NCHCC) (1672320100).
Competing interests: The authors declare no competing interests.
Author details: Jash Hang Limbu is a
PhD student at Shanghai Ocean University, The Lab of Molecular Systematics and Ecology (LMSE), College of Fisheries and Life Science, Shanghai, China, with research interest in molecular systematics, phylogeny, molecular ecology and evolution. Dipak Rajbanshi is a lecturer at Orcid College, Biratnagar, Nepal and his research interest are taxonomy, reproductive biology, and population genetics. Jawan Tumbahangfe is a
PhD student at Tribhuvan University in Central Department of Zoology, Kirtipur, Kathmandu, Nepal with
research interest in reproductive biology, molecular biology, stream ecology and water quality indicators. Asmit Subba is a
MSc student at Tribhuvan University in Central Department of Zoology, Kirtipur, Kathmandu, Nepal, working as a field biologist and also works in ecological and biodiversity conservation in Nepal. Sumnima Subba is a
bachelor’s student at Central Campus of Technology in Department of Biology, Dharan, Nepal with research interest in conservation biology, and entomology. Rakshya Basnet is a
bachelor’s student at Central Campus of Technology with
research interest in Fish biology, and fish immunology.
Author contributions: JHL, DR, AS and RB performed field surveys and collected data. JHL, DR, AS, SS and JT wrote and finalized the manuscript. JHL analyzed the data.
Acknowledgements: We are thankful to Nature Conservation and
Health Care Council for financial support. We are also thankful to Mr. Mahesh Tajpuriya for fish sampling during the whole study period.
Abstract: Monitoring the impact
of fishing pressure on the Singhiya River is critical
for resource development and sustainability, and the present situation is
alarming and causing critical concern among the public. This study aimed to
identify fish community trends over time and space in the river, and to
investigate the impact of environmental variables on fish abundance and
dispersion. Monthly fish sampling was performed from October 2020 to September
2021 from the 5th to 10th of each month. We used three
cast nets of various mesh sizes (0.5, 2, & 4 cm) and monofilament gill nets
with mesh sizes of 6, 8, & 10 cm. A total of 7,593 fish were collected,
representing 61 species from seven orders, 20 families, and 37 genera. Similarity
percentage (SIMPER) analysis revealed 78.8% similarity among six stations, with
the primary contributing species: Puntius chola (28.2%),
Puntius sophore (13.5%), Pethia
ticto (5.33%), Chagunius
chagunio (3.76%), Barbonymus
gonionotus (3.69%), Puntius terio (3.46%), Opsarius shacra (2.2%), and Opsarius
bendelisis (2.1%). Analysis of variance
(ANOVA) on canonical correspondence analysis revealed that four of the seven
selected environmental variables had significant relationship with the fish
assemblage such as water parameters velocity, temperature, pH, and hardness.
Overfishing and direct discharge of industrial waste into water resources may
be the primary causes for the decline in fish diversity in Singhiya
River.
Keywords: ANOVA, assemblage
structure, cast nets, fish diversity, fish ecology, habitat variable,
time-space.
INTRODUCTION
Freshwater bodies are
vulnerable to habitat fragmentation, human encroachment, climate change,
pollution, and biological invasions (Radinger et al.
2019). The combined effects of environmental pollution, unprecedented rates of
biodiversity change, hydrological alteration, dam construction, and
disconnection between the rivers and their lakes are possibly the largest
threats to freshwater fish biodiversity (Huang
& Li 2016). The diversity of the natural population is partially
dependent on the environmental variables which always affect the competing
populations (Chowdhury et al. 2011; Hossain et al. 2012). The factors
influencing fish assemblages involve the environmental variables which are
spatially heterogeneous and temporally variable and biotic interactions such as
competition and predation (Harvey & Stewart 1991; Grossman et al. 1998).
The environmental variables such as water velocity (Li et al. 2012; Adhikari et
al. 2021; Limbu et al. 2021b), water depth (Kadye et al.
2008; Li et al. 2012; Mia et al. 2019; Chaudhary & Limbu 2021), substrate
(Vlach et al. 2005; Yan et al. 2010), water temperature (Hossain et al. 2012; Nsor & Obodai 2016), and
dissolved oxygen (Guo et al. 2018) all have been found to affect fish abundance
and distribution in the rivers and streams.
In Nepal, few studies
have looked at fish diversity and its link with environmental factors (Mishra
& Baniya 2016; Limbu et al. 2020). Information on
the relationship between fish community structure and environmental variables
can aid in the preservation and management of aquatic biodiversity in the face
of human-caused problems such as pollution and global climate change (Li et al.
2012). The Singhiya
River has been altered due to several human encroachments such as settlements,
factories, embankments, sand mining, electrofishing, damping and agriculture.
To date, the space and time pattern of low-land, Terai
region remains relatively unknown. Moreover, the details on fish community
structure relating to their anthropogenic activities is also scanty. Facts
about the relationship between fish community structure and environmental
conditions can help us retain and lead aquatic biodiversity away from
human-caused challenges like pollution and global climate change (Li et al.
2012).
The present study
aimed to detect fish community patterns in the Singhiya
River through time and space, as well as to evaluate the impact of
environmental variables on fish abundance and dispersion. The current study
expected that during the annual dry season, when water current and volume are
reduced, fish abundance in the Singhiya River would
be increased. We also hypothesized that the structure of fish assemblages will
vary according to seasonal fluctuation defined by environmental variables.
MATERIALS AND METHODS
Study area
Singhiya River is situated in
the Morang district of Eastern Nepal (Figure 1). It is a perennial river that
originates from the periphery of Hattimuda, Dulary, and Sundar Haraicha and surges through the Budiganga
Municipality and Biratnagar Sub-metropolitan, and from the Buddhanagar
it crosses the border of India. It lies in the latitude and longitude
coordinates of 26.913o N & 87.157 o E, respectively.
The water of this river is mainly used for irrigation. The vegetation bordering
the river is mixed, mostly consisting of bamboo and coniferous forest and the
dominant river substrata consist of cobbles, pebbles, gravel, and sand. In
total, six sampling stations were set up to gather fish. Residents settled along
the entire river in the catchment, and numerous small and large factories were
established in stations 1, 2, 3, & 4 whereas, stations 6 & 7 were set
up close to the city.
The Singhiya River region experiences mostly sunny weather,
with occasional clouds, and the water is muddy due to increased anthropogenic
activities near the area of human settlement but crystal clear in its origin
parts. The study area for this research includes 22 km of river basin starting
from Hattimuda to Buddhanagar
of Morang District.
Data collection,
Identification, and Preservation
From October 2020 to
September 2021, fish samples were taken every month. Sample collection started
on the 5th and continued to the 10th of the selected
month, i.e., October, November, December (2020), January, February, March,
April, May, June, July, August, and September (2021). We made 72 samples at six
stations, namely, (S1) Hattimudha, (S2) Puspalal Chowk, (S3) near Hanuman Mandir, (S4) Hatkhola, (S5) Jahda Bridge, and
(S6) Buddhanagar, with fish sampling carried out
between 0070 h and 0090 h. We employed three cast nets of various sizes, one
with a mesh size of 0.5 cm, diameter of 5 m, and a weight of 2 kg, and another
with a mesh size of 2 cm, diameter of 5 m, and a weight of 4 kg. A cast net
with a diameter of 4 cm, a length of 7 meters, and a weight of 7 kg was also
utilized. Cast netting was used to cover 150 m to 200 m across each station,
ensuring that all conceivable habitats were covered (Limbu et al. 2021b). For
each cast net, a total of 10 throws were made over one hour. The fish were also
caught using monofilament gill nets with mesh sizes of 6, 8, & 10 cm.
Nine-gill nets were left late in the evening (1700h–1800 h) and pulled out
early in the morning (0600h–0700 h) at a sample distance of 150–200 m at each
station
Fish sampled were
photographed and identified in the field, and unidentified specimens were
preserved in 10% formalin for later identification. The remaining samples were
released to their original habitat after the photography. Standard fish
taxonomy literatures (Talwar & Jhingran 1991;
Jayaram 2010; Shrestha 2019; Fricke et al. 2021) and other available standard
literature were used to identify the fish. During field visits environmental
variables such as water temperature, dissolved Oxygen (DO), pH, total hardness,
water velocity, alkalinity, and free carbon dioxide (CO) were investigated
using the American Public Health Association’s standard methodology (APHA
2012). A digital thermometer was placed in the water at a depth of 1 foot to
measure the water temperature (°C). The Winkler titrimetric method was used to
determine DO (mg/l). A pH meter was used to determine the pH (HI 98107, HANNA
Instrument). The EDTA titrimetric technique was used to evaluate total hardness
(mg/l). With the help of a stopwatch, a small ball and a measuring tape, water
velocity (m/s) was determined using the float method. The alkalinity (mg/l) was
measured using the titration method. The titrimetric method was used to detect
free carbon dioxide (mg/l) using phenolphthalein as an indicator.
Data analysis
To examine potential
variation over space and time a one-way analysis of variance (ANOVA) was used
for temperature, pH, dissolved oxygen, hardness, and water velocity. To
determine which means were significantly different at the 0.05 level of
probability, a posthoc Tukey HSD test was used (Spjøtvoll & Stoline 1973). In
the first step of data processing, the diversity of the fish assemblage was
quantified, and then a statistical comparison was performed. Data on fish
abundance were subjected to various diversity indices (Shannon, Simpson
dominance, evenness, and species richness). All of the diversity indices were
created using data from 12 months (each month, six samples were taken, S1–S6)
and the data were used directly in the analysis, according to Magurran (1988) for each fish community sample. The Shannon
diversity index (Shannon & Weaver 1963) takes into account both the number
of species and the distribution of individuals within species.
The Shannon diversity
was calculated using the following formula:
H = 
(1)
Where S is the total
number of species and Pi is the relative proportion of ith of species.
The Simpson index
(Harper 1999) is a dominance index which gives more weight to common or
dominant species.
The Simpson dominance
index was calculated by using following formula:
D = 
2
(2)
Where ni is number of individuals of
species i.
The Evenness index (Pieleu 1966) measures how evenly or uniformly the relative
abundances Pi (i=1..,S) are distributed across the S
different species, irrespective of the value of S and the Evenness index was determined by the
following equation:
E= H’/ log S
(3)
Where, H’ = Shannon
diversity index
S = Total number of
species in the sample.
In the multivariate
analysis, rare species (<1%) were excluded in the analysis as they tend to
affect multivariate analyses (Gauch 1982). Samples by
species and environmental variables were analyzed through a multivariate
analysis tool. Detrended correspondence analysis (DCA) (Hill & Gauch 1983) was performed to determine whether redundancy
correspondence analysis (RDA) or canonical correspondence analysis (CCA) would
be the most appropriate model to describe the association between species and
environmental variables. The value of first axis length (3.14) and eigen value
(0.53) obtained from DCA suggested that the uni-model
associated with canonical correspondence analysis (CCA) (Ter Braak 1986) was more applicable. Therefore, a direct
multivariate ordination method (Legendre & Legendre 1998) based on a linear
response of species to environmental gradients was applied. Collected fish
abundance and determined environmental variables were used directly in the
multivariate analysis (Yan et al. 2010; Hossain et al. 2012; Vieira &
Tejerina-Garro 2020; Tumbahangfe
et al. 2021).
The one-way
permutational multivariate analysis of variance (perMANOVA)
(Clarke 1993) was used to determine whether there was a significant difference
between the spatial and temporal scales of the collected fish data. A
similarity percentage (SIMPER) (Clarke 1993) analysis was used to visualize the
major contributing species in both space and time. Furthermore, Individual
rarefaction analyses (Colwell et al. 2012), was performed across stations and
months. All the statistical analysis were performed in R software (R Core Team
2019), 2.5-6 version.
RESULTS
Fish community
structure
A total of 7,593 fish
were collected, representing 61 species belonging to seven orders, 20 families,
and 37 genera (Table 1). The three main orders that represented 84% of the
total species count included Cypriniformes (32
species), Siluriformes (11 species), and Anabantiformes (8 species). Synbranchiformes
and Perciformes each contained four species and the
rest contributed less than 2% to the total species counts. At the family level,
the Danionidae family included the most species (16),
followed by Cyprinidae (11), Ambassidae
(4), Bagridae (4), Channidae
(4), Mastacembelidae (3), Cobitidae
(2), Siluridae (2), Ailiidae
(2), Anabantidae (2), Osphronemidae
(2), Psilorhynchidae (1), Nemacheilidae
(1), Botiidae (1), Sisoridae
(1), Clariidae (1), Heteropneustidae
(1), Synbranchidae (1), Mugilidae
(1), and Gobiidae (1). The four most abundant species
comprised 56% of the total catch, i.e., Puntius chola
(27%), Puntius sophore (18%), Pethia ticto (6.3%),
and Barbonymus gonionotus (5.3%).
Considerable differences in fish abundance and diversity were observed among
sampling stations and monthly samplings.
The highest number of
fish was collected during October (1,707 specimens), followed by the months of
November > December > February > January > September > April
> March > August > June > July > May (Figure 2a). The highest fish
diversity in the study area was calculated during October (42 species),
followed by September (41 species), November (38 species), August (36 species),
December, February, & April (34 species in each month), March & July
(33 species each in each month), May (32 species), January (31 species), and
June (29 species). The highest numbers of fish were collected at station (S6),
followed by S5>S4>S3>S2>S1 (Figure 2b). According to similarity
percentage (SIMPER) analysis (Table 2), 79% similarity was found between the
stations, and the primary contributing species were: Puntius chola (28%), Puntius sophore
(14%), Pethia ticto
(5.3%), Chagunius chagunio
(3.8%), Barbonymus gonionotus
(3.7%), Puntius terio (3.5%), Opsarius shacra (2.2%), and
Opsarius bendelisis
(2.1%); 77.5% similarity was found between months, and the top
contributing species were as listed above.
Diversity status
Tables 3 & 4 show
the results of diversity indices. The highest Shannon diversity index (2.79)
was found at station 2 (S2) and in the month of August (2.94) whereas the
lowest (1.76) was found at station 1 (S1) and in June (1.51). Analysis of
variance (ANOVA) testing for both time and space revealed a significant (P <0.05)
difference across six stations, but no significant (P >0.05)
difference for the Shannon diversity index over twelve months. The highest
Simpson dominance index (0.91) was found at station 2 (S2) and in the month of
August (0.93) while the lowest Simpson index value (0.67) was found at station
6 (S6) and in the month of June (0.61). There was no significant (P >0.05)
difference in the Simpson dominance index across the six sampling points and
months. Similarly, the highest Evenness index (0.59) was at stations 1 & 2
and the month of August (0.59) whereas the lowest value (0.44) was found at
stations 5 & 6 respectively, and the month of June (0.42). There was also
no significant (P >0.05) difference in the Evenness index between the
six stations and months. On the other hand, the highest Species richness value
was observed at station 6 (S6) and in the month of October (36) while the
lowest value was found at station 1 (S1) and the month of June (21). The
species richness index differed significantly (P <0.05) between the
six sampling locations and months.
Environmental factors
vs fish community structure
The results obtained
after the canonical correspondence analysis are plotted in Figure 3. The first
(CCA1) and second (CCA2) axis of the CCA accounted for 50% and 25%,
respectively. The CCA biplot indicates the relationship between species and
environmental variables. The fish species of Puntius sophore
(C7), Puntius terio (C8), Opsarius
barna (C16), Salmostoma
acinaces (C26), and Mystus
tengara (C35) are positively related to
total alkalinity and water velocity but species of Chagunius
chagunio (C1) and Puntius chola
(C6) are negatively related to water velocity and total alkalinity. Fish
species of Pethia ticto
(C9), Barbonymus gonionotus
(C10), Barilius barila
(C12), Opsarius bendelisis
(C13), Opsarius shacra
(C14), Opsarius vagra
(C15), Cabdio morar (C17),
Chela cachius (C21), Esomus
danrica (C22), Mystus
bleekeri (C33), Wallago attu (C38), Heteropneustes
fossilis (C43), and Chanda nama (C48) are positively related to water
temperature, dissolved oxygen, and total hardness but negatively related to
free carbon dioxide and pH. An analysis of variance
(ANOVA) on canonical correspondence analysis suggested that water parameters of
water velocity, water temperature, total alkalinity, pH and total hardness are
the major influencing factors (P <0.05) to determine the fish
abundance and distribution.
In addition, one-way
permutational multivariate analysis of variance (perMANOVA)
on the Non-multidimensional Scaling (NMDS) showed no significant (P >0.05)
difference between station 3, 4, 5, & 6, but station 1 & 2 showed
significant (P <0.05) difference (Figure 4b). The fish community
structure in October showed a significant (P <0.05) difference
between January, February, March, April, May, June, July, August, &
September but no significant (P >0.05) difference was found with
November and December (Figure 4a).
DISCUSSION
This is the first
study to describe the spatial and temporal fluctuation of fish community
structure in a Nepalese low-land river. The outcomes of this study will improve
our understanding of the variance in fish communities for the benefit of
Nepalese low-land river conservation, which recorded a total of 7,593
individuals, represented by 61 species belonging to seven orders, 20 families,
and 37 genera. This suggests that the Singhiya River
provides a significant source of livelihood and food to local fisherman and
communities. The representation of Cypriniformes, Siluriformes, and Anabantiformes
orders found in this study is consistent with the information reported in the
other river systems of Nepal such as the Mechi River
(Adhikari et al. 2021), Ratuwa River (Rajbanshi et
al. 2021), and Phewa Khola
(Limbu et al. 2021b).
The present findings
revealed that the maximum number and diversity of fish species were collected
in October, September, and November. During June and July, water velocity was
found to be low and the water temperature was found to be high in the current
study. Because of the low water velocity, the fishermen could do the most of
the fishes. River discharge and water temperature have a much greater impact on
the amount and diversity of fish (Kriauciuniene et
al. 2019). Overfishing, industrial discharges, and sand mining may have
impacted the amount and diversity of fish in the Singhiya
River. Furthermore, essential aquatic ecosystem measurements such as species
richness and diversity indices are influenced by changes in abiotic parameters
such as river discharge and water temperature (Crane & Kapuscinski 2018;
Parker et al. 2018).
According to local
fisherman, populations of Cirrhinus mrigala, Cirrhinus reba, Labeo gonius,
Systomus sarana, Danio rerio, Devario devario, Amblypharyngodon mola, Rasbora daniconius, Bengala
elanga, Salmostoma
acinaces, Salmostoma
phulo, Psilorhynchus
sucatio, Lepidocephalichthys
guntea, Botia
lohachata, Heteropneustes
fossilis, Ophichthys
cuchia, Macrognathus
aral, Macrognathus
pancalus, Mastacembelus
armatus, Trichogaster
fasciata, Trichogaster
lalius, Channa
barca, Channa
orientalis, Channa
striata, Minimugil
cascasia, and Glossogobius
giuris have significantly reduced, with less than
five individuals recorded for each over the 12-month study period. Many studies
have suggested that ongoing road development, river corridor engineering, dams
and water diversion, aquatic habitat loss and fragmentation, deforestation,
riparian loss, overfishing, climate change, and direct discharge of industrial
waste into water resources are the primary causes of Nepalese fish population
reduction (Limbu et al. 2021a,b; Tumbahangfe et al.
2021). River output appears to be influenced by water level variations caused
by climate change and water management, as well as fishing pressure (Halls
2015). Monitoring the impact of fishing pressure on the Singhiya
River’s exploited fish population is critical for resource development and
sustainability. The present situation in the Singhiya
River is still sounding the alarm and causing critical concern among the
public. As the biodiversity of freshwater fish keeps on decreasing mainly due
to anthropogenic impacts, it is apparent that there has been a serious lack of
scientific basis and truly ecological action for sound river basin management
(Li et al. 2012). The populations of Labeo catla, Bagarius spp., Chitala chitala, Sisor spp., and Notopterus
notopterus have declined significantly and are
not represented in the present study. Only Cirrhinus
spp., Channa spp., Labeo
spp., Ophichthys cuchia,
Heteropneustes fossilis, Macrognathus spp., Mastacembelus
armatus, Clarius magur, Opsarius bendelisis, Chagunius chagunio, and Salmostoma
spp. are highly preferred fish species by the local community in the Singhiya River basin.
The Shannon diversity
index takes into account the richness and proportion of each species, while the
evenness and dominance indices reflect the relative number of individuals and
the proportion of common species, respectively (Hossain et al. 2012; Yang et
al. 2021). The highest Shannon diversity index (2.79) was identified at station
2 and in August (2.94), while the lowest (1.76) was discovered at station 1 and
in June (1.51). A high Shannon diversity index is linked to a small number of
individuals, whereas a low Simpson’s diversity index is linked to a large
number of individuals (Hossain et al. 2012; Temesgen
et al. 2021). A biodiversity index seeks to categorize a sample’s diversity (Magurran 1988) and is easily influenced by the number of
specimens, sample size, and environmental factors (Leonard et al. 2006). The
highest Simpson dominance index (0.91) was found at station 2 and the month of
August (0.93), while the lowest Simpson index value (0.67) was obtained at
station 6 and the month of June (0.61). Similarly, the highest evenness index
(0.59) was observed at stations 1 and 2 and in August (0.59), while the lowest
value (0.44) was recorded at stations 5 and 6 and in June (0.42). The
maximum species richness (35.31) value was found at station 6 and the months of
October (35.97), while the lowest (18.44) value was recorded at station 1 and
the month of June (20.66). The species richness index varies considerably (P
<0.05) between the six sampling locations and months. Overall, stations
2, 3, 4, 5, & 6 and the months of October, January, February, May, August,
& September were likely to be rich with richness and diversity, because
these sections were deeper and larger in terms of water depth and surface cover
than station 1 section within the system.
The river width and depth may be important for resting and hiding (Li et
al. 2012) and for variable habitats for lotic water inhabiting fish such as Cirrhinus spp., Mystus
spp., Ailia coila,
Ompok bimaculatus,
& Wallago attu
The information on
the interaction between environmental variables and fish community structure
can assist us in maintaining and managing aquatic biodiversity in the face of
human-caused problems such as pollution, global climate change, and so on (Li
et al. 2012). The influence of environmental variables on fish abundance,
diversity, and distribution was checked by canonical correspondence analysis.
In the current study, water velocity, water temperature, total alkalinity, pH,
and total hardness are the major influencing factors (P <0.05) to
determine the fish diversity, abundance, and distribution of the Singhiya River. Water velocity (Yu & Lee 2002; Yan et
al. 2010; Adhikari et al. 2021), water temperature (Kadye
et al. 2008; Temesgen et al. 2021), total alkalinity
(Edds 1993; Pokharel et al. 2018), pH (Pokharel et
al. 2018; Limbu et al. 2021b; Rajbanshi et al. 2021), and total hardness
(Rajbanshi et al. 2021; Shrestha et al. 2021) have also been found to be
influencing factors to shape the fish assemblage structure.
CONCLUSION
The Singhiya River exhibits a good ichthyofaunal diversity,
represented by 61 species of fish belonging to seven orders, 20 families, and
37 genera. Of 61 species, Puntius chola,
Puntius sophore, Pethia
ticto, and Barbonymus
gonionotus were the dominant fish species
recorded in Singhiya River. However, commercially
important species such as Labeo catla, Bagarius spp., Chitala chitala, Sisor spp., and Notopterus
notopterus were not recorded during the study
period. Thus, conservation of these species has become urgent in Singhiya River. Overfishing and direct discharge of
industrial waste into water resources may be the primary causes for the decline
in fish diversity in Singhiya River. Therefore,
practices like dumping of industrial waste, overfishing, and sand mining should
be minimized, monitored, and if required, prohibited to protect the Singhiya River’s aquatic flora and fauna and natural
ecology. The canonical correspondence analysis suggested that an important
environmental variables in structuring the fish community in the Singhiya River were water velocity, temperature, pH, and
hardness. Lastly, the current study, in conjunction with the preceding
examination, could serve as a baseline scenario for future analysis of the Singhiya River and other connected water bodies in the
coming decades.
Table 1. Coding of
the Singhiya River, Morang District, Nepal by order,
family, and species.
|
Order / Family |
Code |
Species |
IUCN status |
|
Cypriniformes |
|
|
|
|
Cyprinidae |
C1 |
Chagunius chagunio (Hamilton 1822) |
LC |
|
Cyprinidae |
C2 |
Cirrhinus mrigala (Hamilton 1822) |
LC |
|
Cyprinidae |
C3 |
Cirrhinus reba (Hamilton 1822) |
LC |
|
Cyprinidae |
C4 |
Labeo gonius (Hamilton 1822) |
LC |
|
Cyprinidae |
C5 |
Tariqilabeo latius (Hamilton 1822) |
LC |
|
Cyprinidae |
C6 |
Puntius chola (Hamilton 1822) |
LC |
|
Cyprinidae |
C7 |
Puntius sophore (Hamilton 1822) |
LC |
|
Cyprinidae |
C8 |
Puntius terio (Hamilton 1822) |
LC |
|
Cyprinidae |
C9 |
Pethia ticto (Hamilton 1822) |
LC |
|
Cyprinidae |
C10 |
Barbonymus gonionotus (Bleeker 1849) |
LC |
|
Cyprinidae |
C11 |
Systomus sarana (Hamilton 1822) |
LC |
|
Danionidae |
C12 |
Barilius barila Hamilton 1822 |
LC |
|
Danionidae |
C13 |
Opsarius bendelisis Hamilton, 1822 |
LC |
|
Danionidae |
C14 |
Opsarius shacra Hamilton 1822 |
LC |
|
Danionidae |
C15 |
Opsarius vagra Day 1878 |
LC |
|
Danionidae |
C16 |
Opsarius barna Hamilton 1822 |
LC |
|
Danionidae |
C17 |
Cabdio morar (Hamilton 1822) |
LC |
|
Danionidae |
C18 |
Cabdio jaya (Hamilton 1822) |
LC |
|
Danionidae |
C19 |
Danio rerio (Hamilton 1822) |
LC |
|
Danionidae |
C20 |
Devario devario (Hamilton 1822) |
LC |
|
Danionidae |
C21 |
Chela cachius (Hamilton 1822) |
LC |
|
Danionidae |
C22 |
Esomus danrica (Hamilton 1822) |
LC |
|
Danionidae |
C23 |
Amblypharyngodon mola (Hamilton 1822) |
LC |
|
Danionidae |
C24 |
Rasbora daniconius (Hamilton 1822) |
LC |
|
Danionidae |
C25 |
Bengala elanga (Hamilton 1822) |
LC |
|
Danionidae |
C26 |
Salmostoma acinaces (Valenciennes 1844) |
LC |
|
Danionidae |
C27 |
Salmostoma phulo (Hamilton 1822) |
LC |
|
Psilorhynchidae |
C28 |
Psilorhynchus sucatio (Hamilton 1822) |
LC |
|
Nemacheilidae |
C29 |
Paracanthocobitis botia (Hamilton 1822) |
LC |
|
Cobitidae |
C30 |
Canthophrys gongota (Hamilton 1822) |
LC |
|
Cobitidae |
C31 |
Lepidocephalichthys guntea (Hamilton 1822) |
LC |
|
Botiidae |
C32 |
Botia lohachata Chaudhuri 1912 |
NE |
|
Siluriformes |
|
|
|
|
Bagridae |
C33 |
Mystus bleekeri (Day 1877) |
LC |
|
Bagridae |
C34 |
Mystus cavasius (Hamilton 1822) |
LC |
|
Bagridae |
C35 |
Mystus tengara (Hamilton 1822) |
LC |
|
Bagridae |
C36 |
Mystus vittatus (Bloch 1794) |
LC |
|
Siluridae |
C37 |
Ompok bimaculatus (Bloch 1794) |
NT |
|
Siluridae |
C38 |
Wallago attu (Bloch & Schneider 1801) |
VU |
|
Ailiidae |
C39 |
Ailia coila (Hamilton 1822) |
NT |
|
Ailiidae |
C40 |
Clupisoma montanum Hora 1937 |
LC |
|
Sisoridae |
C41 |
Pseudolaguvia ribeiroi (Hora 1921) |
LC |
|
Clariidae |
C42 |
Clarius magur (Hamilton 1822) |
EN |
|
Heteropneustidae |
C43 |
Heteropneustes fossilis (Bloch 1794) |
LC |
|
Synbranchiformes |
|
|
|
|
Synbranchidae |
C44 |
Ophichthys cuchia (Hamilton 1822) |
LC |
|
Mastacembelidae |
C45 |
Macrognathus aral (Bloch & Schneider 1801) |
LC |
|
Mastacembelidae |
C46 |
Macrognathus pancalus Hamilton 1822 |
LC |
|
Mastacembelidae |
C47 |
Mastacembelus armatus (Lacepède
1800) |
LC |
|
Perciformes |
|
|
|
|
Ambassidae |
C48 |
Chanda nama Hamilton 1822 |
LC |
|
Ambassidae |
C49 |
Parambassis baculis (Hamilton 1822) |
LC |
|
Ambassidae |
C50 |
Parambassis lala (Hamilton 1822) |
NT |
|
Ambassidae |
C51 |
Parambassis ranga (Hamilton 1822) |
LC |
|
Anabantiformes |
|
|
|
|
Anabantidae |
C52 |
Anabas cobojius (Hamilton 1822) |
DD |
|
Anabantidae |
C53 |
Anabas testudineus (Bloch 1792) |
LC |
|
Osphronemidae |
C54 |
Trichogaster fasciata Bloch & Schneider
1801 |
LC |
|
Osphronemidae |
C55 |
Trichogaster lalius (Hamilton 1822) |
LC |
|
Channidae |
C56 |
Channa barca (Hamilton 1822) |
DD |
|
Channidae |
C57 |
Channa gachua Bloch & Schneider 1801 |
VU |
|
Channidae |
C58 |
Channa striata (Bloch 1793) |
LC |
|
Channidae |
C59 |
Channa punctata (Bloch 1793) |
LC |
|
Mugiliformes |
|
|
|
|
Mugilidae |
C60 |
Minimugil cascasia (Hamilton 1822) |
LC |
|
Gobiformes |
|
|
|
|
Gobiidae |
C61 |
Glossogobius giuris (Hamilton 1822) |
LC |
Table 2. Average
similarity (%) and discriminating fish species in the Singhiya
River, Morang District, Nepal, by month and station using SIMPER analysis.
|
Code |
Species |
Station |
Code |
Species |
Months |
|
Contribution (%) |
Contributions (%) |
||||
|
C6 |
Puntius chola |
28.2 |
C6 |
Puntius chola |
26.58 |
|
C7 |
Puntius sophore |
13.51 |
C7 |
Puntius sophore |
13.78 |
|
C9 |
Pethia ticto |
5.33 |
C9 |
Pethia ticto |
5.7 |
|
C1 |
Chagunius chagunio |
3.76 |
C10 |
Barbonymus gonionotus |
3.81 |
|
C10 |
Barbonymus gonionotus |
3.69 |
C8 |
Puntius terio |
3.59 |
|
C8 |
Puntius terio |
3.46 |
C1 |
Chagunius chagunio |
3.51 |
|
C14 |
Opsarius shacra |
2.2 |
C14 |
Opsarius shacra |
2.24 |
|
C13 |
Opsarius bendelisis |
2.1 |
C13 |
Opsarius bendelisis |
2.15 |
Table 3. Diversity
indices for the Singhiya River, Morang District,
Nepal at six stations.
|
Stations |
Shannon index |
Simpson dominance
index |
Evenness index |
Species richness |
|
S1 |
1.76±0.7 |
0.9±0.14 |
0.59±0.93 |
18.44±3.88 |
|
S2 |
2.79±0.5 |
0.91±0.05 |
0.59±0.03 |
27±4.92 |
|
S3 |
2.37±0.64 |
0.8±0.14 |
0.51±0.09 |
31.4±4.77 |
|
S4 |
2.3±0.9 |
0.77±0.22 |
0.49±0.13 |
33.23±4.43 |
|
S5 |
1.87±0.8 |
0.68±0.2 |
0.44±0.12 |
34.51±4.07 |
|
S6 |
1.93±0.74 |
0.67±0.22 |
0.44±0.12 |
35.41±3.91 |
Table 4. Diversity
indices for the Singhiya River, Morang District,
Nepal over 12 months.
|
Months |
Shannon index |
Simpson dominance
index |
Evenness index |
Species richness |
|
Oct |
2.33±0.27 |
0.84±0.03 |
0.52±0.01 |
35.97±7.61 |
|
Nov |
2.34±0.9 |
0.82±0.14 |
0.51±0.09 |
32.98±5.89 |
|
Dec |
1.9±0.79 |
0.75±0.16 |
0.49±0.11 |
30.35±5.59 |
|
Jan |
2.26±0.84 |
0.83±0.15 |
0.54±0.1 |
30.48±6.58 |
|
Feb |
2.08±1.19 |
0.71±0.32 |
0.46±0.21 |
30.42±5.53 |
|
Mar |
1.99±0.68 |
0.74±0.2 |
0.48±0.13 |
27.94±6.44 |
|
Apr |
2.12±0.93 |
0.72±0.27 |
0.47±0.17 |
30.17±5.45 |
|
May |
2.38±0.53 |
0.84±0.12 |
0.56±0.08 |
26.31±6.04 |
|
Jun |
1.51±0.91 |
0.61±0.29 |
0.42±0.2 |
20.66±7.31 |
|
Jul |
1.91±0.48 |
0.72±0.15 |
0.47±0.1 |
26.26±7.13 |
|
Aug |
2.94±0.59 |
0.93±0.05 |
0.59±0.03 |
33.01±6.65 |
|
Sep |
2.91±0.2 |
0.91±0.04 |
0.56±0.03 |
34.92±6.98 |
For
figures - - click here for full PDF
REFERENCES
Adhikari, A., J.H. Limbu, & S. Pathak (2021). Fish diversity and
water quality parameters of Mechi River, Jhapa, Province No. 1, Nepal. Borneo Journal of
Resources Science and Technology 10(2): 24–34. https://doi.org/10.33736/bjrst.2954.2021
APHA (2012). Standard Methods for
Examination of Water and Waste water. 22nd Edition. American Public
Health Association, Washington DC, USA, 1220 pp.
Chowdhury, M.S.N., M. S. Hossain, N.G. Das & P. Barua (2011). Environmental variables and
fisheries diversity of the Naaf River Estuary,
Bangladesh. Journal of Coastal Conservation 15(1): 163–180. https://doi.org/10.1007/s11852-010-0130-3
Clarke, K.R. (1993). Non-parametric
multivariate analyses of changes in community structure. Australian Journal
of Ecology 18: 117–143. https://doi.org/10.1111/j.1442-9993.1993.tb00438.x
Colwell, K.K., A. Chao, N.J. Gotelli,
S.L. Lin, C.X. Mao, R.L. Chazdon & J.T. Longino (2012). Models and
estimators linking individual-based and samples-based rarefaction,
extrapolation and comparison of assembales. Journal
of Plant Ecology 5: 3–21.
Crane, D.P. & K.L. Kapuscinski (2018). Capture efficiency
of a fine mesh seine in a large river: implications for abundance, richness,
and diversity analyses. Fisheries Research 205: 149–157. https://doi.org/10.1016/j.fishres.2018.04.018
Edds, D.R. (1993). Fish assemblage structure
and environmental correlates in Nepal’s Gandaki River. Copeia
1993(1): 48–60. https://doi.org/10.2307/1446294
Fricke, R., W.N. Eschmeyer
& R. Van der Laan (2021). Eschmeyer’s catalog of fishes: genera, species, references:
Electronic version. Accessed on 08 Aug
2019.
http://researcharchive.calacademy.org/research/ichthyology/catalog/fishcatmain.asp
Gauch, H. (1982). Multivariate
Analysis in Community Ecology. Cambridge University Press, x+298pp. https://doi.org/10.1017/CBO9780511623332
Guo, Q., X. Liu, X. Ao, J.
Qin, X. Wu & S. Ouyang (2018). Fish diversity in the middle and
lower reaches of the Ganjiang river of China: Threats
and conservation. PLoS ONE 13(11):
e0205116. https://doi.org/10.1371/journal.pone.0205116
Halls, A. (2015). Lake Nasser
Fisheries: recommendations for management, including monitoring and stock
assessment Program Report. WorldFish, Penang,
Malaysia, 51 pp.
Harvey, B.C. & A.J. Stewart (1991). Fish size and
habitat depth relationship in headwater streams. Oecologia
87: 336–342. https://doi.org/10.1007/BF00634588
Hill, M.O. & H.G. Gauch
(1980). Detrended correspondence analysis: an improved
ordination technique. Vegetatio 42: 47−58. https://doi.org/10.1007/BF00048870
Hossain, M.S., N.G. Das, S. Sarker
& M.Z. Rahaman (2012). Fish diversity and
habitat relationship with environmental variables at Meghna River estuary,
Bangladesh. The Egyptian Journal of Aquatic Research 38: 213–226. https://doi.org/10.1016/j.ejar.2012.12.006
Huang, L. & J. Li (2016). Status of freshwater
fish biodiversity in the Yangtze River Basin, China, pp. 13–30. In: Nakano S.,
T. Yahara & T. Nakashizuka
(eds.). Aquatic Biodiversity Conservation and Ecosystem Services.
Ecological Research Monographs. Springer, Singapore, 135 pp. https://doi.org/10.1007/978-981-10-0780-4_2
Jayaram, K.C. (2010). The Freshwater
Fishes of Indian Region. Narendra Publishing House, Delhi, India, 614 pp.
Kadye, W.T., C.H.D. Magadza, N.A.G. Moyo & S. Kativu (2008). Stream fish assemblages in
relation to environmental factors on a montane plateau. Environmental
Biology of Fishes 83: 417–428. https://doi.org/10.1007/s10641-008-9364-4
Kriauciuniene, J., T. Virbickas, D. Sarauskiene, D. Jakimavičius, J. Kažys, A. Bukantis, V. Kesminas, A. Povilaitis, J. Dainys, V. Akstinas, A. Jurgelėnaitė, D. Meilutytė-Lukauskienė & A. Tomkevičienė
(2019). Fish assemblages under climate change in Lithuanian rivers. Science
of the Total Environment 661: 563–574.
Legendre, P. & L. Legendre (1998). Numerical Ecology,
Second Edition. Elsevier, Amsterdam, Netherlands, 853 pp.
Leonard, D.R., K.R. Clarke, P.J. Somerfield & R.M.
Warwick (2006). The application of an indicator based on taxonomic
distinctness for UK marine biodiversity assessments. Journal of
Environmental Management 78(1): 52–62. https://doi.org/10.1016/j.jenvman.2005.04.008
Li, J.H., L.L. Huang, L.M. Zou, Y. Kano, T. Sato &
T. Yahara (2012). Spatial and temporal
variation of fish assemblages and their associations to habitat variables in a
mountain stream of north Tiaoxi River, China. Environmental
Biology of Fishes 93: 403–417.
Limbu, J.H., B. Bhurtel, A.
Adhikari, G.C. Punam, M. Maharjan
& S. Sunuwar (2020). Fish community
structure and environmental correlates in Nepal’s Andhi
Khola. Borneo Journal of Resources Science and
Technology 10(2): 85–92. https://doi.org/10.33736/bjrst.2510.2020
Limbu, J.H., J.K. Gurung, S. Subba,
N. Khadka, A. Adhikari, & C.B. Baniya (2021a). An Impact
Assessment of Betani irrigation dam on fish diversity
of Damak municipality, Jhapa,
Nepal. Egyptian Journal of Aquatic Biology and Fisheries 25(2): 163–175.
https://doi.org/10.21608/ejabf.2021.161363
Limbu, J.H., S. Subba, J.K.
Gurung, J. Tumbahangfe & B.R. Subba
(2021b). Correlation of fish assemblages with habitat and
environmental variables in the Phewa Khola stream of Mangsebung rural
municipality, Ilam, Nepal. Journal of Animal
Diversity 3(1): 27–36. https://doi.org/10.52547/JAD.2021.3.1.5
Magurran, A.E. (1988). Ecological
Diversity and its Measurement. Princeton University Press, Princeton, 179
pp.
Mia, M.J., J. Naher, M.G. Azom, M.S.R. Sabuz, M.H. Islam
& M.R. Islam (2019). Spatiotemporal variations in finfish assemblage
and diversity indices in relation to ecological indicators of the Atrai River, Dinajpur, Bangladesh. Egyptian Journal of Aquatic
Research 45: 175–182. https://doi.org/10.1016/j.ejar.2019.06.001
Mishra, A.R. & C.B. Baniya
(2016). Ichthyofaunal diversity and physico-chemical
factors of Melamchi River, Sind-upalchok,
Nepal. Journal of Institute of
Science and Technology 21(1): 10–18. https://doi.org/10.3126/jist.v21i1.16031
Nsor, C.C. & E.A. Obodai (2016). Environmental determinants
influencing fish community structure and diversity in two distinct seasons
among wetlands of Northern region (Ghana). International Journal of Ecology
2016: 1–10. https://doi.org/10.1155/2016/1598701
Pokharel, K.K., B.B. Khadga,
C.M. Trilok & C.B. Chitra (2018). Correlations between
fish assemblage structure and environmental variablzes
of the Seti Gandaki River Basin. Nepal. Journal of
Freshwater Ecology 33(1): 31–43. https://doi.org/10.1080/02705060.2017.1399170
R Core Team (2019). R: Alinguage and environment for statistical computing. R
foundation for statistical computing, Vienna, Austria.
https:/www.R-project.org/
Radinger, J., J. Britton,
J.R. Carlson, S.M. Magurran, A.E. Alcaraz-Hernández,
J.D. Almodóvar, A. Benejam, L. Fernández-Delgado, C.
Nicola, G.G. Oliva-Paterna, F.J. Torralva
& E. García- Berthou (2019). Effective monitoring
of freshwater fish. Fish and Fisheries 20(4): 729–747. https://doi.org/10.1111/faf.12373
Rajbanshi, D., J.H. Limbu, N. Khadka, P. Kumar, J.K.
Gurung, & D.K. Limbu (2021). Fish community structure along an
altitudinal gradient with relation to environmental variables in Ratuwa River of eastern Nepal. Our Nature 19(1):
70–81. https://doi.org/10.3126/on.v19i1.41217
Shannon, C.E. & W. Weaver (1963). The Mathematical
Theory of Communication. The University of Illinois Press, Urbana, pp.
1–117.
Shrestha, S., J.H. Limbu, D. Rajbanshi, & D.K.
Limbu (2021). Relationships between environmental conditions and
fish assemblages in the Lohore River of Dailekh, Western Nepal. Our Nature 19(1): 18–26. https://doi.org/10.3126/on.v19i1.41265
Shrestha, T.K. (2019). Ichthyology of Nepal,
a study of fishes of the Himalayan waters. Prism Color
Scanning and Press Supportive Pvt. Ltd, Kathmandu, Nepal, 400 pp.
Spjøtvoll, E. & M.R. Stoline (1973). An extension of the
T-Method of multiple comparison to include the cases with unequal sample sizes.
Journal of the American Statistical Association 68(344): 975–978. https://doi.org/10.1080/01621459.1973.10481458
Talwar, P.K. & A.G. Jhingran
(1991). Inland Fisheries of India and Adjacent Countries.
Volume I and II. Oxford and IBH Publishing Co. India, 1158 pp.
Temesgen, B., Z. Tadesse
& M. Temesgen (2021). Diversity,
distribution and abundance of fish species in upper Awash River Basin, West
Showa Zone, Ethiopia. Cogent Food and Agriculture 7(1): 1−13. https://doi.org/10.1080/23311932.2021.1974181
Ter Braak, C.F. (1986). Canonical
correspondence analysis: A review eigenvector technique for multivariate direct
gradient analysis. Ecology 67: 1167–1179.
Tumbahangfe, J., J.H. Limbu, A.
Prasad, B.R. Subba & D.K. Limbu (2021). Ichthyofaunal
diversity with relation to environmental variables in the snow-fed Tamor River of eastern Nepal. Journal of Threatened Taxa
13(14): 20190–20200. https://doi.org/10.11609/jott.7554.13.14.20190-20200
Vieira, T.B. & F.L. Tejerina-Garro
(2020). Relationships between environmental conditions and
fish assemblages in Tropical Savanna Headwater streams. Scientific Reports
10(1): 1–13. https://doi.org/10.1038/s41598-020-59207-9
Vlach, P., J. Dusek, M. Svatora & P. Moravec (2005). Fish assemblage
structure, habitat and microhabitat preference of five species in a small
stream. Folia Zoologica 54: 421–431.
Yan, Y., H.E. Shan, C.H.U. Ling, X. Xiuying, J.I.A. Yanju, T.A.O.
Juan & C. Yifeng (2010). Spatial and temporal
variation of fish assemblages in a subtropical small stream of the Huangshan
Mountain. Current Zoology 56(6): 670–677. https://doi.org/10.1093/czoolo/56.6.670
Yu, S.L. & T.W.
Lee (2002). Habitat preference of the stream fish, Sinogastromyzon puliensis.
Zoological Studies 41: 183–187.