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Aquatic Ecosystem Health & Management, 24(3): 18–27, 2021. Copyright © 2021 Aquatic Ecosystem Health & Management Society.
ISSN: 1463-4988 print / 1539-4077 online. DOI: 10.14321/aehm.024.03.04
Introduction
Freshwater fish form a key component of
invasive alien fauna in many countries around the
world including India, and several regions have
fish communities with high proportions of non-
native species (Leprieur et al. 2008; Singh and
Lakra, 2011; Singh et al., 2013). In the Ganga river,
many invasive fish species have been reported and
are contributing to the fishery (Singh et al., 2013).
Introduced fish invasions in the time of climate
change have been reported to represent key threats
Invasion meltdown and burgeoning threats of invasive
fish species in inland waters of India in the era of climate
change
Atul K. Singh* and Sharad C Srivastava
National Bureau of Fish Genetic Resources, Canal Ring Road, P.O. Dilkusha
Lucknow-226002 (Uttar Pradesh), India
*Corresponding author: aksingh56@rediffmail.com; singhatk@gmail.com
Cyprinus carpio, Oreochromis niloticus and Clarias gariepinus are the most abundantly captured
invasive sh species in the mid-stretch of the Ganga river. Fish yield and biomass data based on mean
abundance by weight was calculated using algorithms and spatio-temporal population dynamics model
for future prediction of these invasive sh species. Temporal biomass forecast based on mean abundance
by wieght for the period from 2020 to 2029 was determined. The ndings of this study predicted sh yield
of 176 ±16.33 kg km-1 day-1 C. carpio and 55.43 ± 6.4 kg km-1 day-1 O. niloticus during 2029 which might
result into 117.87% and 116.9% rise in temporal biomass of Common Carp and Tilapia respectively in a
decade’s time while 139.2% rise in temporal biomass was predicted for the invasive African catsh. The
yield of invasive Common Carp, Tilapia and African Catsh was correlated with rainfall and temperature
data using ANOVA and we found that variance was F=1.36; p=0.263 for C. carpio; F=1.60; p=0.326 for
O. niloticus and F=1.63; p=0.101 for C. gariepinus, indicating that variance was very close for Tilapia
and African Catsh. The observed values of variance indicated that climatic changes had more impact to
these two species than to the Common Carp. The concrete and forecast values were calculated considering
95% lower and upper level of condence, which was signicant (p<0.05) and the annual regression was
found to be p<0.464, p<0.419 and p<0.499 for C. carpio, O. niloticus and C. gariepinus, respectively.
Further, interactive performance of invaded C. carpio, O. niloticus and C. gariepinus was also assessed
for understanding invasion meltdown. The results of mean abundance by weight based yield forecast
of invaded Tilapia, Common Carp and African Catsh for the period of 2020 to 2029 suggest a stable
production in the Ganga River in years to come. It also manifests a positive pattern of invasion in the
times of climate change displaying invasion meltdown. This suggests increased pressures of sh invasions
on temporal and spatial scales, and imposing new management challenges for freshwater ecosystems.
Keywords: Ganga river, climatic factors, temporal biomass
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Singh et al. / Aquatic Ecosystem Health and Management 24 (2021) 18–27 19
to global biodiversity (Galil et al., 2008; Rolls et
al., 2017). Invasion of introduced fish species and
their effects on habitats has emerged as a major
threat to ecosystems around the globe in general
and in India in particular (Singh and Lakra, 2011;
Singh et al., 2013; Fletcher et al., 2016). Although
invasive species possess many attributes that can
explain its ability to spread and survive even in
new habitats and harsh environments, no study has
identified processes that might explain its restricted
pattern of superabundance. A few researchers have
tried to estimate invasive species biomass and
abundance on the basis of the mean abundance
by weight (Dominguez et al., 2020; Singh and
Srivastava, 2020). Several models estimating
spatial and temporal variation in population density
are increasingly used to track shifts in population
distribution subject to environmental and climatic
changes (Harsch et al. 2014; Thorson et al., 2017).
In recent times, increased global temperatures
have been reported to help invasive species establish
themselves in newer aquatic ecosystems (Arnaud et
al., 2021). Rivers and streams have been reported to
get warmed during the past few decades, and stream
and water temperatures were projected to increase
further in future as warmer climates. Climate not
only has an impact on physical characteristics on
surface waters, but also is a master variable for
ecologically important chemical processes (Galil et
al., 2008; Auffhammer et al 2012; Jayaraman and
Murari, 2014). Invasive species might cause habitat
modification, extinctions of endemic species, affect
human health, and therefore endanger enormous
economic costs (Singh and Lakra, 2011; Singh et
al., 2013; Singh et al., 2014; Hanley and Michaela,
2019). In current time, unsustainable harvesting
of natural stock of fishes especially from inland
waters owing to invasion of introduced fish species
led habitat degradation of riverine ecosystem and
emerging conservation issues in the tropics, which
has resulted in the stock decline of important local
fish species, (Singh et al. 2013; Panlasigui et al.,
2018; Mondal and Bhat 2020). Climate variables,
namely temperature, precipitation, and humidity
may have significant long-term implications on
water quality and fisheries with special reference to
fish invasions (Auffhammer et al 2012; Jayaraman
and Murari, 2014).
In this study, we investigated how invasive
fish species will flourish on temporal scale under
the influence of climate change in future. By
extrapolating the ten years data (2010-2019) on the
yield and biomass for three invasive fish namely
the common carp, tilapia and African catfish from
the mid-stream of the Ganga river, we attempted
to forecast temporal biomass changes for the
next decade i.e., from 2020 to 2029 using spatio-
temporal population dynamics model for future
prediction. Further, we also tested the invasion
meltdown hypotheses that the presence of a second
invading species enhanced the abundance and
potential for further invasion by another non-native
species causing aggravated detrimental impacts to
indigenous species in terms of their abundance.
Materials and Methods
Fish catch data and biomass
Data on the catch of non-native species the
common carp, tilapia and African catfish was
collected from the mid-stretch of the Ganga
river through regular fishing exercise made by
fishermen at different fish landing areas of bridge
area in Kanpur, Mehdi ghat in Kannauj, Shuklaganj
in Unnao, Daraganj in Prayagraj, Adalhat in
Mirzapur, Saraimohana in Varanasi, Dadri ghat
in Ghazipur and Ganga ghat in Ballia districts of
Uttar Pradesh state of India by using mostly boats,
gillnets, cast nets and occasionally drag net (Figure
1). The catch data for three invasive fish namely
the Cyprinus carpio, Oreochromis niloticus and
Clarias gariepinus were collected from different
locations on monthly basis for the study period
from 2010 to 2019. Catch per unit effort (CPUE)
was calculated for each study location following
the method described by Bucol et al. (2017).
Figure 1. Figure showing the sampling locations in the Ganga
river in Uttar Pradesh, India.
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Singh et al. / Aquatic Ecosystem Health and Management 24 (2021) 18–27
20
The CPUE was further converted as biomass
percentage (Pi) of the three abundantly captured C.
carpio, O.niloticus and C. gariepinus non-native
fish at individual study location as per formula Pi
= Wi/Wt, where Wi was the weight of non-native
fish species; Wt was the total weight from all
catches. The biomass obtained from each location
was then pooled for calculating total biomass (Pf)
percentage of fish from all the studied locations as
per formula: Pf = Pi1 + Pi2 + Pi3 + Pi4 + Pin over
the years (ArchMiller et al., 2018) and the time
rate change of non-native fish biomass during the
period of 2010 to 2019 was calculated as:
dP/dt∝ P or dP/dt = aP the solution was P = P0 eat
Where P was the biomass at time t and P0 was the
initial biomass.
The predictive biomass was calculated
as per described methodology (Okubo et al.,
2017). Mean abundance by weight (MAW) was
estimated (Leeseberg and Keeley, 2014; Singh and
Srivastava, 2020). The changes in MAW were then
extrapolated as biomass for the period from 2020
to 2029 (Laplanchea et al., 2018). The sight ability
model-fit to detection/non-detection data from
marked population of the most invaded non-native
invasive C. carpio, O. niloticus and C. gariepinus
were then extrapolated for the next ten-year decade,
i.e. 2020 to 2029 (Singh and Srivastava, 2020).
For a given predictor p among n predictors,
the weight was calculated using the following
equation:
Weight p =
In this process, data were fitted for all years
tfitted∈ {tinitial ,…, tfinal}, and then average estimated
abundance and weight for all studied areas was
used to predict density d (s, t) for all locations
during forecast years tforecast∈ {tfinal + 1, tfinal + 2, tfinal +
3……n}. Finally, population weight prediction was
used to calculate the centroid of the population’s
distribution:
Z(t) = Σns
s=1Z(s)× a(s) × d(s,t)
Σns
s=1(a(s) × d(s,t)
Where a (s) was the area associated with each
location s, and z(s) was the measure of location
(km). The biomass data in terms of weight (g)
was pooled for entire stretch of 450 km of the
Ganga river. The future values of standard errors
followed by probability values were calculated.
The changes in mean abundance by weight (MAW)
was calculated as biomass at ΔYM AW (Δt / tfinal) for
forecasting temporal biomass for the year 2020 Δt
to 2029 as year tfinal:
ΔYMAW (Δt/ tfinal) = Y (tfinal + Δt) –Y (tfinal)
Where Δt∈ {2020-29}, the change in centroid was
ΔY (Δt/tfinal) over Δt and the forecasting year for
calculating data through years 2020 to 2029 was
tfinal. The forecasted centroid as YCE (tforecast/ tfinal) in
year tforecast was done using the data through tfinal and
the growth model:
ΔY CE (Δt/tfina) l = YCE (tfinal + Δt/tfinal) – YCE (tfinal/ tfinal)
The variance are explained as R2(Δt) = 1–V(Δt),
a model performing as well as the persistence
forecast which had V (Δt) = 1 and R2(Δt) = 0,
while the model with R2(Δt) > 0 outperforms the
persistence forecast while the model with R2(Δt) < 0
had degraded performance relative to a persistence
forecast (Draper and Smith, 1998).
Climate monitoring
Water temperature changes was determined on
quarterly and on annual basis for the period from
2009 to 2019 using digital thermometer so as to
capture the changes/ modifications/ transformations
in the water quality indicators. The rainfall data
(1989-2019) of the mid-stretch in the Ganga river
was collected from the India Meteorological
Department (IMD) and district wise available
information were used in our data analysis.
Statistics
All data were presented as mean ± SE. Field
data obtained from different study locations
were subjected to one-way analysis of variance
(ANOVA) using the Statistical Package for
the Social Sciences (SPSS), version 8.1. The
correlation coefficients between the water quality
indicators from different locations were calculated
by Pearson correlation analysis. Parameters were
further analyzed statistically at 5% significance
level. The concrete and forecast values were
calculated considering 95% lower and upper level
of confidence; and the annual regression was
calculated (Singh and Srivastava, 2020).
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Singh et al. / Aquatic Ecosystem Health and Management 24 (2021) 18–27 21
Results
Water temperature data were collected and
presented over the years in the mid-stretch of the
Ganga river covering from Kanpur to downstream
Ballia. The annual mean minimum water
temperature in the mid-stretch of the Ganga river
was low in 2012, 2014 and 2018 and high during
2011, 2013 and 2019. However, the annual mean
temperature increased from 0.9 to 1.88ºC over the
years (Figure 2). The regression value showed that
the relationship between the low and high for the
base year 2010 to final year 2019 were 0.048 and
0.076 respectively. Temperature was found directly
related to rainfall, with an increase in temperature
at the end of the winter months i.e. January-
February through spring (October-November) and
finally to summer, i.e. the months of April-May.
This increase in temperature was not linear, but
there was a sudden temperature increase within a
short period of time.
The annual rainfall data changes were
synthesized from available data from IMD. The
proportion of annual total rainfall occurring in
the monsoon months (May-August) was 69.42%
during 1989-98 which gradually decreased to
65.4% during 1910-19 and further decreased during
1989-98 to 25.28%. However, it increased in post
monsoon months (September-December) from
28.32% during 2010-19 (Figure 3). Temperature
and rainfall were understood as important
environmental factor that triggered the maturation
of brood fish expected to have different stages of
maturity.
Common catches of non-native C. carpio
contributed 47.46 % to 58.38% during study
period; O. niloticus 29.52% to 32.89% and C.
gariepinus 1.52 to 8.4% in the mid-stretch of the
Ganga river. The size range of captured Cyprinus
carpio was 13.5 to 52.4 cm in length and 150 to
1680 g in weight; Oreochromis niloticus 8.6 to 32.8
cm in length and 35g to 950g in weight; Clarias
gariepinus 11.7 to 50.72 cm in length and 170
to 838g in weight. The O. niloticus appeared for
the first time during 2003 in the Ganga river at
Allahabad followed by common carp during 2004
and later African catfish appeared for the first time
during 2011 on the same locations. The appearance
of Tilapia and Common Carp synergistically and
gradually increased over the years, while the third
invasive fish African Catfish emerged during
2011(Figure 4). The yield of invasive Common
Carp, Tilapia and African Catfish was correlated
with rainfall and temperature data using ANOVA;
we found that variance was F=1.36; p=0.263 for
C. carpio; F=1.60; p=0.326 for O. niloticus and
F=1.63; p=0.101 for C. gariepinus. The calculated
variance value was very close for Tilapia and
African Catfish indicating that climatic changes
impacted these two species more than the Common
Carp.
Figure 2. Annual trend in mean minimum and maximum water temperature at mid-stretch of the Ganga river during 2010-2019.
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Fish yield calculation based on MAW for C.
carpio was found to consistently increase (p < 0.05)
over the years from 2010 to 2019 and the calculated
yearly values were 113.65, 127.54, 139.08, 150.99,
158.67, 174.54, 192.22, 209.06, 219.92 and 235.83
kg km-1 day-1 (Figure 5). The increase in MAW
based yield of C. carpio was consistent and highly
significant (p< 0.001) particularly during the
period from 2016 to 2018 as compared to the base
year value in 2010. In case of O. nolitcus, there
was again a consistent and significant (p< 0.05)
increase of MAW based fish yield over the years
from 2010 to 2019 and the recorded values were
57.51, 68.02, 77.20, 83.37, 90.05, 100.52, 133.58,
148.13, 151.74 and 151.13 kg km-1 day-1 (Figure 6).
However, the increase in yield of O. niloticus was
very noticeable and highly significant (p< 0.001)
during the period from 2013 to 2018 as compared to
the base year value in 2010. The C. gariepinus was
observed in mid-stream of the Ganga river during
2011 which consistently increased year after year.
The recorded yield for the year from 2011 to 2019
were 2.41, 6.73, 14.64, 23.81, 27.45, 33.4, 36.45,
36.96 and 39.82 kg km-1 day-1 respectively (Figure
7). However, the increase in MAW based yield of
C. gariepinus was significantly observed during
the period from 2013 to 2019 as compared to the
base year value in 2011 when it first appeared. The
evaluated temporal predictive values for forecasted
years (2020 to 2029) based on MAW values were
plotted using spatio-temporal population dynamics
model and the observations are presented (Fig. 5, 6,
7). The calculation for MAW based concrete yield
showed that the average value was 113.65±12.6 kg
km-1 day-1during 2010 which increased to 235.83
± 11.4 kg km-1 day-1 in 2019 showing 207.5%
rise of C. carpio in a decade time. However,
the established population of C. carpio showed
predicted yield value of 170 ± 6 kg km-1 day-1in
2029. The calculated yield values for O. niloticus
was 57.51± 2.2 kg km-1 day-1 during 2010 which
increased to 151.13 ± 4.6 kg km-1 day-1 in 2019. The
biomass of C. gariepinus was 2.41 ± 033 kg km-1
day-1 in 2011, which increased to 39.82 ± 2.4 kg km-1
day-1 in 2019. The predicted yield was observed as
176 ±16.33 kg km-1 day-1 for Common Carp and
55.43 ± 6.4 kg km-1 day-1 for Tilapia in 2019 which
indicated that there may be 117.87% and 116.9%
yield rise in a decade’s time for Common Carp and
Tilapia, respectively, while 139.2% yield rise may
happen for invasive African Catfish for the same
period. The concrete and forecast values were
calculated considering 95% lower and upper level
of confidence, which was significant (p<0.05) and
the annual regression was found p<0.464, p<0.419
and p<0.499 for common carp, tilapia and African
catfish respectively. The confidence limit of 95%
for the forecasted variance in distribution had little
correlation with observed distribution. The median
variance was low but it was positive for the annual
Figure 3. Shifting seasonal pattern of rainfall at mid-stretch of the Ganga river during 1989 to 2019 (Source: IMD).
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Singh et al. / Aquatic Ecosystem Health and Management 24 (2021) 18–27 23
regression of 0.02, 0.36 and 0.42 in C. carpio,
O. niloticus and C. gariepinus respectively for
concrete value. However, it was higher which were
0.06, 0.65 and 0.69 in C. carpio. O. niloticus and
C. gariepinus respectively for forecasted values
showing that the general variance for the next
10 year of forecast will predict a faster increase.
Obtained value for variance of R2 (Δt) was 0.361
for C.carpio, 0.326 for O. nolitcius and 0.418 for
C. gareipinus. The observed MAW based forecast
of non-native tilapia, common carp and African
catfish catch for the period of 2020 to 2029 at 95%
confidence limit indicated a stable production in
the Ganga river and there was a positive pattern of
invasion meltdown.
Discussion
Invasive alien species have gained wider
recognition by scientists and policymakers in the
past decades due to their severe ecological and
economic impacts worldwide (Early et al., 2016;
Turbelin et al., 2017). Several freshwater fish
species have been translocated as a result of human
mediation and moved outside their native ranges by
an array of vectors such as deliberate introductions,
river corridors, and releases from aquaculture and
aquaria and even illegally introduced to arrive in
new environments (Singh and Lakra, 2011). An
increased trend of fish invasions has been recorded
over the years in inland waters of India. Climate
change is expected to alter seasonal patterns over
time with dramatic changes in precipitation and
temperature patterns. All of these factors combine
to place added stress on both native species.
However, invasive species in general are much
better equipped to handle these new stressors.
Seasonal change in temperature has a profound
effect on reproduction in fish. Temperature changes
cue reproductive development particularly in
monsoon spawning species. This in turn impacts
population replenishment and connectivity patterns
of local and invasive fishes. Warmer temperatures
modifies community structure and dynamics that
in turn facilitate invasions (Robert et al., 2017;
Manjarres-Hernandez et al., 2021; Arnaud et al.,
2021). Invasion of non-native introduced fishes
even in changing environments and climate exert
their effects on new habitats consequently emerging
as a major threat for ecosystems around the globe,
partly with irreversible consequences for the local
biota (Panlasigui et al., 2018). Invasive species
might cause habitat modification, extinctions of
endemic species, affect human health, and thus
exert enormous economic costs (Singh et al., 2013;
Singh et al., 2014; Panlasigui et al., 2018). Fish
invasions have also been reported to be driven by
climate change where synergies between climate
change and increased pressures of fish invasions
invite new management challenges for freshwater
ecosystem (Kernan, 2015).
Figure 4. Fish yield of recurrent invasive fish species in mid-stretch of the Ganga river.
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Singh et al. / Aquatic Ecosystem Health and Management 24 (2021) 18–27
24
India has been placed at the 10th position
among highest climate risk countries in Asia
based on extreme environment events (Global
Sustainable Development Report 2015). Climate
variables namely the temperature, precipitation,
and humidity may have significant long-term
implications affecting water quality and fisheries
(Jayaraman and Murari 2014). Climate change
has been reported to exacerbate the threats posed
to the Inland fisheries by environmental stressors
(Das et al., 2019). The present study presents recent
data regarding increased incidence of non-native
Figure 5. Invasion prediction of common carp and yield contribution in the Ganga river using spatio-temporal population dynamics
model.
Figure 6. Invasion prediction of tilapia and yield contribution in the Ganga River using spatio-temporal population dynamics model.
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Singh et al. / Aquatic Ecosystem Health and Management 24 (2021) 18–27 25
invasive species in the Ganga river especially
in the time of climate change. Using algorithms
and predictive models, future predictions on
range expansion of invasive species have been
reported by several workers (Fletcher et al., 2016;
ArchMiller et al., 2018; Dominguez et al., 2020).
In this study, predictive future invasional changes
for the upcoming decade have been presented. The
MAW based predictive values when calculated has
shown persistent increase of the three abundantly
available invasive fish C. carpio, O.niloticus and
C. gariepinus in the Ganga river during the next
decade. It is interesting to mention here that both
tilapia and common carp have depicted a very
similar trend of establishment and have exhibited
substantial growth in the Ganga river even in
adversely changing environmental and climatic
conditions. While the increased trend of invasion
of African catfish was even faster in recent years.
The increasing patterns of invasive fish species in
the Ganga river has been representing occupational
patterns and changed distribution of fishery
resources (Singh et al., 2013). Such rise in invasive
species in the river has caused decline in the
local fish catches and even change in biodiversity
patterns (Singh et al., 2013; Kernan, 2015; Mondal
and Bhat, 2020; Raj et al., 2021).
The results of this study have shown positive
and synergistic effects of Tilapia, Common Carp
and African Catfish on their increased catches after
invasion which strongly supports the ‘Invasion
meltdown’ (Simberloff and Von Holle 1999; Braga
et al., 2018). The findings indicate the phenomenon
that non-native invasive O. niloticus and C. carpio
have facilitated the invasion of the third one
i.e., C. gariepinus with good propagation and
compounded their independent impacts on native
species, communities and the riverine ecosystem
(Singh et al., 2013; K,ernan, 2015; Mondal and
Bhat, 2020; Raj et al., 2021). The potential role of
positive interactions among co-invaders has been
found at the core of the invasion meltdown. The
interaction of non-native tilapia, common carp
and African catfish has resulted in an exacerbation
of each other’s effects. Thus, the resulting
effect of multiple non-native species meltdown
on ecosystems can be greater than the sum of
the individual effects. It is thus, there is every
possibility that all the three invasive fish will spread
in newer areas of the Ganga river basin especially
in the changing climate and environments due to
their high adaptability and better survival (Deng
et al., 2020). Predictive biomass of the invaded
tilapia, common carp and African catfish warrant
Figure 7. Invasion prediction of African catfish and yield contribution in the Ganga River using spatio-temporal population
dynamics model.
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Singh et al. / Aquatic Ecosystem Health and Management 24 (2021) 18–27
26
fishery managers to develop regulatory framework
to contain them at the earliest. Whilst mechanical,
chemical, and biological controls are the most
widely used approaches for controlling invasive
species, they require skilled manpower, technology,
and expertise, and can be extremely costly and
labour intensive. Therefore, early detection and
rapid response (EDRR) is key to the management
of invasive species in (Singh et al., 2013; Reaser
et al., 2020). Since there is no management plan
in the country to fight out invasive species in the
riverine ecosystem, it is advocated that effective
long-term management should be developed to
address the impacts of invasive alien species (IAS)
that cannot be eradicated. Policies and strategies
should be developed and implemented for the long-
term management of IAS in the riverine ecosystem.
Conclusions
The predictive forecast of invasive C. carpio,
O.niloticus and C. gariepinus in this study provides
a means for an evidence-based prioritization
of species and habitats for the management of
existing and future invasions of the Ganga river.
Further, it has also ascertained that the presence
of one invasive fish species facilitate another
and compound negative impacts on the aquatic
ecosystem. We thus, strongly point out that the
negative effects of invasive species meltdown
may be strong, even where no impact of single
invasion was expected. Thus, cumulative impacts
of multiple invasions will result in the replacement
of native species and consequently their extinction.
Acknowledgements
Authors are thankful to the fishermen societies
involved in regular fishing activities and markets
for sharing different catch information from time
to time.
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