Gosavi et al. The Journal of Basic and Applied Zoology
https://doi.org/10.1186/s41936-019-0080-8
(2019) 80:9
RESEARCH
The Journal of Basic
and Applied Zoology
Open Access
Assessing the sustainability of
lepidophagous catfish, Pachypterus
khavalchor (Kulkarni, 1952), from a tropical
river Panchaganga, Maharashtra, India
Sachin M. Gosavi1,2,3*† , Sanjay S. Kharat1†, Pradeep Kumkar1 and Sandip D. Tapkir1,4
Abstract
Background: The Western Ghats of India, one of the global biodiversity hotspots and freshwater eco-regions, harbors
several fish species which not just form the important part of the world’s freshwater biodiversity yet in addition are the
vital segment of livelihood of the neighborhood population. The rate of fish decline in the Western Ghats is alarming.
The absence of organized study and data scarcity on basic biology and life history traits of several species could be
one reason behind the decline, and thus it is difficult to execute conservation action/s. This is especially true,
particularly for data-deficient species for which definite data related to distribution, population size, and trend is not
available. The present study deals with the detailed investigation of population dynamics of catfish species, Pachypterus
khavalchor, which is data-deficient species inhabiting the Western Ghats of India and forms an important component
of freshwater inland fishery, providing nutritional and financial security to the local community.
Methods: Specimens for the present study were collected monthly for a period of 1 year from the River Panchaganga
and length–frequency data were analyzed using FiSAT II software.
Results: Length–weight analysis of pooled (male + female) data suggested the fish exhibited higher exponent
than expected under isometry, indicating the positive allometric growth of P. khavalchor in the Panchaganga
River. The asymptotic length (L∞) and the growth rate (K) were estimated as 149.63 mm and 0.71 year−1
respectively. Potential longevity (tmax) and length at first capture (Lc) were estimated as 4.22 years and 73 mm
respectively. The total (Z), natural (M), and fishing mortality (F) were estimated as 2.23 year−1, 0.88 year−1, and
1.35 year−1 respectively. The current exploitation rate (Ecur = 0.60) was found to be almost 90% that gives the
maximum relative yield per recruit (Emax = 0.67). Recruitment pattern revealed two peaks, suggesting the fish
have two spawning bouts each year.
Conclusions: The stock of P. khavalchor in the Panchaganga River may be in near full exploitation under the
current harvesting strategy, with a high chance of recruitment failure in the future. Additional studies on the
reproductive biology of P. khavalchor would be particularly welcome for the imposition of the seasonal closure
for effective conservation of stock.
Keywords: Lepidophagy, Fishery, Data scarcity, Freshwater ecosystem, Recruitment, Aquatic conservation,
Mortality
* Correspondence: schn.gosavi@gmail.com
†
Sachin M. Gosavi and Sanjay S. Kharat contributed equally to this work.
1
Department of Zoology, Modern College of Arts, Science and Commerce,
Ganeshkhind, Pune, Maharashtra 411 016, India
2
Post Graduate Research Centre, Department of Zoology, Modern College of
Arts, Science and Commerce, Shivajinagar, Pune, Maharashtra 411 005, India
Full list of author information is available at the end of the article
© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made.
Gosavi et al. The Journal of Basic and Applied Zoology
(2019) 80:9
Page 2 of 10
Background
The Western Ghats of India is well known for its exclusive
biodiversity and also classified as a freshwater eco-region
with more than 300 species of the freshwater fishes (Abell
et al., 2008; Molur, Smith, Daniel, & Darwall, 2011; Myers,
Mittermeier, Mittermeier, Da Fonseca, & Kent, 2000).
Nearly 30% of the species inhabiting the Western Ghats
have been categorized as threatened in the IUCN Red list
(Dahanukar, 2011; Dahanukar, Raghavan, Ali, Abraham, &
Shaji, 2011; , Ramprasanth, Ali, & Dahanukar, 2018a).
However, several endemic and threatened species continue to be harvested at unsustainable levels through artisanal and open-access fisheries throughout the Western
Ghats (Das et al., 2017; Keskar, Raghavan, Kumkar,
Padhye, & Dahanukar, 2017; Kharat & Dahanukar, 2013;
Prasad, Ali, Harikrishnan, & Raghavan, 2012; Raghavan,
Ali, Dahanukar, & Rosser, 2011; Raghavan et al., 2018a).
According to Darwall et al. (2018), another most important and fundamental cause for the freshwater biodiversity
crisis is the insufficient consideration of impacts on freshwater ecosystems in decision-making and policy. However,
decision-making and policy establishment require the reliable data on the population dynamics/trend, and thus the
information inadequacy on population dynamics for majority of endemic species hindered the development of appropriate conservation action plans (Cooke, Paukert, &
Hogan, 2012; Dahanukar et al., 2011; Luiz, Woods, Madin,
& Madin, 2016; Morais et al., 2013). At present, the Western Ghats of India harbor 26 data-deficient species and
majority of them are likely to be threatened with extinction (Dahanukar et al., 2011; Molur et al., 2011). Because
of lack of substantial information on population dynamics,
the data-deficient species are not at the forefront of the
conservation agenda (Luiz et al., 2016; Morais et al., 2013;
Possingham et al., 2002). As data deficiency not only means
the absence of records, but it may indicate dangerously low
abundance, Mace et al. (2008) clearly stated that “data-deficient species should be afforded the same degree of protection as a threatened species until more information is
forthcoming.” Keeping the above thought in view, it is fundamentally critical to have data on population biology parameters of known data-deficient species inhabiting the
Western Ghats of India for their effective conservation and
management.
The catfish genus Pachypterus consists of three species
viz. Pachypterus acutirostris (Day, 1870) distributed in
Irrawaddy, Sittang, and Bago rivers, Myanmar (Eschmeyer,
Fricke, & van der Laan, 2018; Kottelat, 2013); Pachypterus
atherinoides (Bloch, 1794) distributed in river drainages of
the Indian subcontinent north of the Cauvery, Bangladesh,
Nepal, and Pakistan (Ahamed et al., 2018; Buragohain,
2018; Eschmeyer et al., 2018); and Pachypterus khavalchor
(Kulkarni, 1952) which inhabits the Krishna River basin of
Peninsular India (Dahanukar, Paingankar, Raut, & Kharat,
2012; Eschmeyer et al., 2018). Each of these species is extremely delicious and having good market demands in their
distribution range and thus provides dietary and financial
advantage to the local community (Buragohain, 2018;
Gosavi, Kharat, Kumkar, & Navarange, 2018; Kumbar &
Lad, 2014). Since Pachypterus species forms an important
part of the open-access fisheries, they are getting exploited
in their distribution range leading to a huge decline in
population (Buragohain, 2018; Kumbar & Lad, 2014). In
support to this, Dahanukar (2011) and Menon (1999)
already pointed that several anthropogenic activities such
as urbanization, industrial developments, and mining are
contributing to dwindling population of P. khavalchor.
Menon (2004) and Molur et al. (2011) further suggested
that it could be a threatened species due to its fragmented
population and non-availability of population data. As a
result, presently, P. khavalchor is categorized as the “data
deficient” species by IUCN (Dahanukar, 2011; Dahanukar
et al., 2012; IUCN, 2018). However, despite of the variety of
threats operating on the P. khavalchor presently, no conservation action plans are specifically directed towards conservation of this species. As a result of combined threats from
different sources, data deficiency, and suggested possibility
of the loss of this freshwater catfish species by (Menon,
1999), it is an immediate need to generate the data on the
life history traits of P. khavalchor for instantaneous conservation action.
The persistence of any fish species in the given habitat
is dependent on the life history traits of that species
(Das et al., 2017, 2018; Kumar et al., 2014; Prasad et al.,
2012; Raghavan et al., 2011, 2018a). Under the present
environmental conditions, life history traits must generate sufficient recruitment for a given population to persist. In this context, considerable knowledge on length–
weight relationships, growth, population structure, mortality (natural and fishing), exploitation level, and recruitment pattern of an exploited stock is essential for
planning and management of fisheries resources, mainly
when the species form the important component of the
artisanal fisheries and lies at the base of the higher food
web (Das et al., 2017, 2018; Kumar et al., 2014). Therefore, present investigation was carried out for the assessment and evaluation of various life history traits of P.
khavalchor using length–frequency data collected for
one year. This study generates first of its kind information on the life history traits and population dynamics of
the genus Pachypterus in general and P. khavalchor in
particular.
Methods
Study area, fish collection, and measurements
Monthly samples were collected during June 2015 to
May 2016 with the help of a small-scale artisanal fishermen from the Panchaganaga River (17.28 °N; 74.18 °E),
Gosavi et al. The Journal of Basic and Applied Zoology
(2019) 80:9
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Maharashtra, India. The fishing method employed in this
area by local fishermen mainly includes the use of gill net
and cast net of variable mesh size. Specimens (n = 427)
were collected and brought to the laboratory in ice-filled
boxes. Standard length (SL; a fish’s body length from the tip
of its snout to end of its last vertebrae; it includes everything except the caudal fin) and total length (TL; it is the
length of a fish from the tip of its snout to the end of the
longer lobe of its caudal fin) were measured for each specimen to the nearest 0.01 mm using a digital vernier caliper
(Mitutoyo, Japan). Weight (W) was determined to the
closest 0.01 g using digital weighing balance (Contech,
India). Specimens were fixed in 10% formalin after all measurements and are in the departmental collection of Modern College of Arts, Science and Commerce, Ganeshkhind,
Pune, Maharashtra, India. Growth, potential longevity,
mortality, recruitment pattern, probability of capture,
length structured virtual population analysis (VPA), relative
yield per recruit, and biomass per recruit were estimated
using FAO–ICLARM Stock Assessment Tools II (FiSAT II,
version 1.2.2) software (Gayanilo Jr., Sparre, & Pauly, 1998).
fitting the classical von Bertalanffy growth function
(VBGF) as:
Lt ¼ L∞ ½ð1
Exp ð−K ðt−t o Þ
ð2Þ
In Eq. 2, L∞ represents asymptotic length; K is the
VBGF curvature parameter (growth constant), Lt is the
length at time t, and t0 is the hypothetical length at the
age length zero. Using growth parameters (L∞) and
VBGF curvature parameter (K), the Growth performance
index (Ø′) was calculated as (Pauly, 1979; Pauly &
Munro, 1984):
0
Ø ¼ LogK þ 2 logL1
ð3Þ
Length–weight relationships (LWRs)
The potential longevity (tmax) was obtained using the
equation tmax = 3/K, given by Pauly (1983). Maximum
possible extreme length for this species was calculated
using the support function available in FiSAT II (Formacion, Rongo, & Sambilay, 1991). A selectivity curve was
generated using linear regression fitted to the ascending
data points from the plot of probability of capture
against length, which was used to estimate the final
value of length-at-first capture (Lc or L50).
LWR parameters were estimated according to the guidelines and equation given by Froese (2006):
Mortality
b
W ¼ a SL
ð1Þ
where W is the body weight (g), SL is the standard
length (cm), “a” is the intercept, and “b” is the slope of
log-transformed linear regression. The coefficient of determination (r2) was estimated as the goodness of fit.
Student’s t test was used to find out whether “b” value
significantly deviated from the expected cube value of 3
(Sokal & Rohlf, 1987; Zar, 1984). The values of standard
length and parameter b were compared with values in
FishBase (Froese & Pauly, 2018). Analyses were performed using the program PAST version 3.13 (Hammer,
Harper, & Ryan, 2001).
Population structure
Frequency distribution forms the basis for the analysis,
and thus standard length data were grouped in a length–
frequency table with 2.5 mm as the smallest mid-length
and 5 mm class intervals thereafter. To determine the
population structure, contour plot was plotted using 10
mm length class of standard length (SL) in relation to different months.
The length-converted catch curve was applied for the
calculation of the total mortality (Z) (Pauly, 1983).
Natural mortality (M) was determined using the following Pauly’s empirical equation (Pauly, 1980),
ln ðM Þ ¼ −0:0152−0:279 ln ðL∞Þ
þ 0:6543 ln ðK Þ þ 0:463 ln ðT Þ
ð4Þ
where T is the average annual temperature, which is
26 °C.
Fishing mortality was (F) calculated by subtracting the
M value from the Z value (Appeldoorn, 1984):
F ¼ ðZ
MÞ
ð5Þ
Values of F and Z were used to calculate the current
exploitation rate (Ecur) as per the formula given by
Gulland (1985):
E cur ¼ F=Z
ð6Þ
Whether the present fishery resource status of study
species is sustainable or not was assessed according to
Etim, Lebo, and King (1999), where Z/K ratio ≈ 2 indicate over-exploitation.
Exploitation
Growth and potential longevity
Asymptotic length (L∞) and the growth coefficient (K)
were estimated using ELEFAN 1 method (Pauly, 1981,
1982; Pauly & David, 1981; Pauly & Morgan, 1987) by
The growth and mortality parameters were used as input
for the length-structured virtual population analysis
(VPA) analysis (Hilborn & Walters, 1992). Further, the
current level of exploitation (Ecur) was estimated using
Gosavi et al. The Journal of Basic and Applied Zoology
(2019) 80:9
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relative yield per recruit (Y′/R) and biomass by recruit
(B′/R) analysis with the Knife–Edge selection method
(Beverton & Holt, 1966). Values of E50 (i.e., exploitation
rate that resulted in a devalued of the unexploited biomass by 50%) and Emax (i.e. exploitation rate producing
maximum yield) were calculated by plotting Y′/R vs. Ecur
and of B′/R vs. Ecur.
143.51 mm respectively. Total mortality (Z) and natural
mortality coefficient (M) were found to be 2.23 year−1
and 0.88 year−1 respectively. Fishing mortality coefficient
(F) was found to be 1.35 year−1.
The virtual population analysis (Fig. 4) revealed high
natural mortality at a young age though the fishery principally targeted comparatively large sized individuals
(from 52.5 mm onwards) as evident by the exponential
decrease in survival rate. Exploitation levels estimated
using relative yield-per-recruit (Y′/R) and relative
biomass-per-recruit (B′/R) analysis based on knife edge
selection were found to be 0.36 (E50) and 0.67 (Emax)
respectively (Fig. 5; Table 2). The current level of exploitation (Ecur = 0.60; Table 2) was found to be almost
90% that gives the maximum relative yield per recruit
(Emax = 0.67) and almost twice than E50, that maintained
50% of the spawning stock biomass (Fig. 5). Recruitment
shows the bimodal pattern with the minor pulse in recruitment during the April–May and major pulse in
September–October (Fig. 6). The minor pulse produced
28.13% and major pulse produced 29.62% recruitment.
Probability of capture and recruitment pattern
The probability of capture was estimated using the
length-converted catch curve (Pauly, 1984). Recruitment
pattern provides information related to the number of
pulses per year and the relative strength of each pulse
and therefore was calculated by reconstructing the recruitment pulses from a time series of length–frequency
data (Moreau & Cuende, 1991).
Results
Descriptive statistics of the sample sizes (n), maximum
and minimum values of SL, TL, and W for male, female,
and pooled data, and estimates of the LWR parameters
are presented in Table 1. New maximum total length
was recorded for P. khavalchor (17.05 cm) as compared
to previously reported in FishBase (15 cm; Talwar &
Jhingran, 1991).
The frequency distribution of 427 individuals of different length class across months (Fig. 1) obtained during
the present study indicated the occurrence of smallest
individuals (55–60 mm) in August and largest individuals (140–145 mm) in January. The FiSAT II output of
the restructured length frequency data of P. khavalchor
population with the superimposed growth curve with
the highest ideal fit index (Rn = 0.339) is given in Fig. 2.
The asymptotic length (L∞) was estimated as 149.63 mm
and coefficient of growth (K) as 0.71 year−1. The growth
parameters estimated using the von Bertalanffy growth
model for P. khavalchor and details of the mortality parameters calculated using the length converted catch
curve (Fig. 3) are given in Table 2. Growth performance
index (Ø′) was estimated as 4.201. Length at first capture (Lc) and the maximum possible predicted extreme
length for P. khavalchor were found to be 73 mm and
Discussion
Uncontrolled exploitation of fishery resources by inland
catch fisheries is considered as the second most noticeable threat to the freshwater fishes inhabiting the
Western Ghats of India (Raghavan, Ali, Philip, & Dahanukar, 2018; Raghavan et al., 2018a; Smith et al., 2011).
However, presently, the gap in the research examining
the impact of biological resource use on freshwater biodiversity or livelihoods is surprising. Fishery management is a dependent field, and in order to implement
management plans and conservation actions, concrete
data on demography parameters (growth and mortality
rates), status of populations (stock assessments), and
number of fish harvested (exploitation levels and rates)
is the primary requirement (Raghavan et al., 2011, 2013,
2018, 2018a). Nonetheless, such data are readily available
and confined to a few of the large-growing tropical
cyprinids such as the members of the genus Tor (Bhat,
Nautiyal, & Singh, 2000; Raghavan et al., 2011, 2018a),
with complete absence of information available on other
Table 1 Descriptive statistics, estimated parameters of LWRs (W = a × Lb) for Pachypterus khavalchor from Panchaganga River (tributary of
Krishna River System) of the northern Western Ghats of India sampled during June 2015 to May 2016
Sex
n
SL (cm)
TL (cm)
W (g)
LWRs regression parameters
Min
Max
Min
Max
Min
Max
a
CI of a
b
CI of b
Se(b)
r2
t
p
Male
211
5.74
11.62
7.20
14.74
2.43
30.10
0.001
0.001–0.002
3.586
3.476–3.693
0.049
0.961
11.74
< 0.0001
Female
216
6.56
14.22
7.94
17.05*
3.62
46.46
0.004
0.003–0.006
3.183
3.059–3.317
0.058
0.933
3.15
0.0018
Pooled
427
5.74
14.22
7.20
17.05
2.43
46.46
0.003
0.002–0.004
3.336
3.286–3.451
0.038
0.957
9.49
< 0.0001
Italicized values represent significant difference
SL standard length, TL total length, W Weight, n number of samples, Min minimum, Max maximum, a intercept, b slope, Se(b) standard error of b, Cl 95%
confidence limits, r2 coefficient of determination, t test for isometry, p level of significance
*New length record
Gosavi et al. The Journal of Basic and Applied Zoology
(2019) 80:9
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Fig. 1 Contour plot based on length–frequency analysis of Pachypterus khavalchor from the Panchaganga River collected during June 2015 to May
2016, Maharashtra, India
groups, including P. khavalchor which is presently categorized as the data-deficient species with possible speculation of its loss in the near future (Dahanukar, 2011;
Dahanukar et al., 2012; IUCN, 2018; Menon, 1999). In
this regard, the detailed investigation of the population
dynamics of the data-deficient species inhabiting the
Western Ghats of India is the key solution for imposing
the conservation action plans. The present study provides
the first of its kind information on the population dynamics and exploitation levels of the P. khavalchor and emphasized the instant conservation action for this species.
Fish growth can be measured as either isometric or
allometric form (Gayanilo & Pauly, 1997; Sarkar et al.,
2013). In isometric growth pattern, both the length and
weight of the fish increase at the same rate. On the
contrary, allometric growth can be either positive or
negative. Positive allometric growth represents that
higher increment in weight as compared to length (fish
becomes stouter or heavier or deeper-bodied). Negative
allometric growth represents a higher increment in length
as compared to weight (fish becomes slender or lighter)
(Ogunola, Onada, & Falaye, 2018; Wootton, 1998).
Length–weight analysis of pooled (male + female) data
suggested the fish exhibited significantly higher exponents
(b = 3.336) than expected under isometry (b = 3), indicating the growth of P. khavalchor in the Panchaganga River
was positive allometric. According to Beverton and Holt
(1957), the departure of the ‘b’ value from three is rare in
adult fishes. In our case, observed variation in the ‘b’ value
could be attributed to several factors such as length range
Fig. 2 The von Bertalanffy growth curve superimposed over length–frequency data of Pachypterus khavalchor from the Panchaganga River,
Maharashtra, India
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(2019) 80:9
Fig. 4 Length-based virtual population dynamics for Pachypterus
khavalchor from the Panchaganga River, Maharashtra, India
Fig. 3 Length-converted catch curve for Pachypterus khavalchor from
the Panchaganga River, Maharshtra, India
used, season, stomach fullness, gonadal maturity, diet,
sampling gear, mesh size, fishing pressure, and presence
or absence of disease and parasite (Froese, 2006; Ogunola
et al., 2018). However, these factors were not considered
in the present study, and thus the observed variations in
LWRs parameters could be due to the effect of a single
factor or synergistic effect of multiple factors. Fish with
ideal growth shows the coefficient of determination (r2)
between 0.90 and < 1 (Hanif, Siddik, Chaklader, Pham, &
Kleindienst, 2017). The r2 value in the present study was
found to be within expected range (> 0.93) indicates the
proper fitness of the model for growth and good health
status of the study species. Length–weight relationships
(LWRs) data of the fishes have several applications such
as indirect estimation of body weight based on the body
length, calculation of condition indexes, and also for comparisons of species’ growth trajectories (Chen et al., 2018;
Froese, 2006; Ogunola et al., 2018). Additionally, LWRs
are essential tools for the monitoring and conservation of
fish populations, because they allow us to increase the effectiveness of management strategies, whether for control
(e.g., introduced species), exploitation (e.g., inland fisheries), or conservation of species at risk (Rodríguez-Olarte,
Taphorn, & Agudelo-Zamora, 2018). Presently, no information is available from other known localities on the
LWRs parameters of P. khavalchor thus comparative analysis cannot be performed.
The earlier study by Prasad et al. (2012) on Yellow
Catfish Horabagrus brachysoma (Günther, 1864), which
is another member from family Horabagridae showed
asymptotic length (L∞) and VBGF K value as 422 mm
and 0.55 year−1 respectively. In comparison with the results of Prasad et al. (2012), the P. khavalchor exhibit
lower asymptotic length (149.63 mm) and high growth
rate (0.71 year−1). The growth performance index value
(ø′) of 4.201 observed in the present study was found to
be comparatively higher than that obtained for other
tropical freshwater catfish species including those belonging to the families Schilbeidae (initially P. khavalchor was classified under the family Schilbeidae and is
recently classified into family Horabagridae; ø′ between 2.18 and 2.78) (Etim et al., 1999), Claroteidae
(ø′ = 2.32) (Abowei & Davies, 2009) and Synodontidae
(ø′ = 3.09) (Ofori-danson, Vanderpuye, & De Graaf,
2001). High growth rates and growth performance
index could be considered as the positive point in
Table 2 Growth, mortality, and exploitation parameters of Pachypterus khavalchor from the Panchaganga River, Maharshtra, India
Growth and longevity parameters
Mortality and exploitation parameters
Asymptotic length (L∞)
149.63 mm
Total mortality (Z)
2.23 year−1
VBGF growth constant (K)
0.71 year
Natural mortality (M)
0.88 year−1
Minimum length in sample (Lmin)
57.50 mm
Fishing mortality (F)
1.35 year−1
Maximum length in sample (Lmax)
142.5 mm
Length at first capture (Lc)
73 mm
−1
Growth performance index (Ø′)
4.201
Exploitation ratio (Ecur)
0.60
Potential longevity (tmax)
4.22 years
Exploitation ratio (Emax)
0.67
Normalization constant (a) of LWR
0.0032
Exploitation ratio (E50)
0.36
Scaling power (b) of LWR
3.33
Z/K ratio
3.14
Gosavi et al. The Journal of Basic and Applied Zoology
(2019) 80:9
Fig. 5 Relative yield-per-recruit (Y′/R) and relative biomass-per-recruit
(B′/R) analysis for Pachypterus khavalchor from the Panchaganga
River, Maharashtra, India
terms of the aquaculture practice for this species (Raghavan
et al., 2018a; Williams, Vijayalekshmi, Benziger, Karim, &
Nair, 2016). Additionally, high growth performance index
value in P. khavalchor is an interesting observation because
as phi prime (ø′) is known to be highly species-specific parameter with their values being similar within related groups
or taxa (Prasad et al., 2012). Interestingly, it has been shown
that the ø′ value remains constant between populations of
the same species (Pauly, 1979, 1981; Pauly & Munro, 1984).
Lack of data on ø′ across the distribution range of P.
khavalchor restricts between population comparisons.
The length at first capture (Lc) and maximum possible
predicted extreme length for P. khavalchor for the population was estimated to be 73 mm and 143.51 mm, respectively, indicating that more than 50% of the population is
being caught before they grow half of their maximum size.
The Lc not only provides important information regarding
the estimated real size of fish in the fishing area that are
being caught by specific gear, but also enables the fishery
Fig. 6 Recruitment pattern for Pachypterus khavalchor from the
Panchaganga River, Maharashtra, India
Page 7 of 10
managers to determine what should be the minimum size
of the target species of a fishery (Kolding, Tirasin, &
Karenge, 1992; Prasad et al., 2012). Due to the complete
absence of the published data on reproductive biology
parameters of this species, it is hard to interpret whether
the immature individuals are exploited. However, several
times extremely small-sized P. khavalchor population was
observed (57.50 mm; minimum size in the study population) in the market for selling indicating that P. khavalchor
is likely to be fished out before they mature and breed,
subsequently contributing to reproductive damage and
thus could be reducing the spawning stock of the species.
Information on the size at maturity would be useful in
re-appraised of mesh-size in this area to prevent cropping
of small individuals. Clearly, investigations designed to
understand various reproductive biology parameters of
this species with special attention on the size at maturity
would be particularly welcome.
Based on the virtual population analysis, it is clear that
juvenile (< 52.5 mm in size) of the P. khavalchor facing
high natural mortality in the study area. Absence of data
on the natural mortality from other localities restricts
our intraspecific as well congeneric comparison. However, the observed natural mortality (0.88 year− 1) in P.
khavalchor was found to be higher as compared to the
other catfish species such as Clarotes laticeps (0.87)
(Abowei & Davies, 2009), Schilbe intermedius (0.81)
(Etim et al., 1999) and Schilbe mystus (0.28) (Kolding et
al., 1992). Such high natural mortality could be attributed to various factors such as predation, diseases, or
different environmental stressors acting independently
or synergistically (Caveriviere & Toure, 1996; Raghavan
et al., 2018a; Richu, Dahanukar, Ali, Ranjeet, & Raghavan, 2018). However, the exact information on the
factor/s causing higher natural mortality at the small size
of P. khavalchor is not known and thus need further investigation. On the contrary, mortality of larger size individuals (> 67.5 mm) indicating the greater fishing
pressure. According to the Kumbar and Lad (2014) various catfish species inhabiting the Krishna River basin
including P. khavalchor is subjected to various threats
such as habitat modifications caused by dams, habitat loss
due to sand mining, rapid development in urbanization,
increasing pollution, overexploitation, destructive fishing
methods (dynamite fishing). Higher fishing mortality in P.
khavalchor could be attributed to the any of the above
mentioned factor/s.
According to Gulland (1985), in an optimally exploited
stock, fishing mortality should be equal to natural mortality (F = M), resulting in an exploitation rate (E) of 0.50.
However, according to Patterson (1992) the fishing rate
satisfying the optimal exploitation level of 0.5 tended to
reduce the fish stock abundance and, hence, the former
author suggested that ‘Ecur’ should be maintained at 0.40
Gosavi et al. The Journal of Basic and Applied Zoology
(2019) 80:9
Page 8 of 10
for optimal exploitation of those stocks. In the present
study the estimates of the fishing mortality (1.35 year1) for
P. khavalchor is close to twice as compared to natural
mortality (0.88 year−1) and exploitation (E) is 0.60 indicating that the study species is being heavily exploited and
overfished in the study area. The results are further supported by the Z/K ratio (3.14), since according to Etim et
al. (1999), Z/K ratio ≈ 2 indicate over-exploitation. The
current exploitation level (Ecur) was estimated at 0.60 for
P. khavalchor with predicted E50 and Emax of 0.36 and
0.67 respectively. This indicates that the present level of
exploitation (Ecur = 0.60) is almost 90% that gives the
maximum level of exploitation (Emax = 0.67) and almost
twice than E50 that maintained 50% of the spawning
stock biomass. For continuity of the species in the
study area, it is necessary to maintain at least 50% of
the spawning stock and therefore the current level of
exploitation need to decrease from 0.60 (Ecur) to 0.36
(E50) which is approximately by 44%. Presence of the
two bouts based on recruitment patterns indicating that
P. khavalchor may exhibit two or extended spawning
periods per year. However, a detailed investigation of
various reproductive biology parameters such as
gonado-somatic index, size at maturity, sexual dimorphism, spawning season, fecundity and spawning
frequency is suggested.
status by IUCN as per suggestion by Jarić, Courchamp, Gessner, and Roberts (2016), so as to increase
the focus of the scientific community and conservation decision-makers on P. khavalchor in order to
avoid the risk that necessary conservation measures
are implemented too late.
Conclusions and implications for conservation
It is clear from the present investigation that P.
khavalchor population facing the high rate of the exploitation and high fishing pressure in the study area
of the Krishna River system hence appears to be unsustainable. Since currently there are no management
plans for P. khavalchor, a chance of loss of this species
is more in the near future. The rational exploitation
of this species can be achieved by the implementation
of conservation and management plans such as (a)
decreasing the present fishing pressure by 44% of its
current level, (b) size-limit regulation by gradually
increasing the fishing gears mesh size, (c) reconsidering the design of the fishing methods adopted in the
Krishna River to prevent capture of smaller individuals, (d) strict implementation of rules and regulations in order to minimize destructive fishing
methods such as dynamite fishing, (e) time-limit regulation by restricting the fishing during spawning
period, and (f ) declaring a portion of the rivers inhabited by P. khavalchor as a natural history reserve (in
support to Menon, 1999) to ensure persistence of this
fish. Furthermore, due to lack of information across
the distribution range, extensive and uncontrolled
exploitation, and possibility of being threatened species, it is recommended to use the flag of potentially
threatened (PT) species along with data-deficient
Abbreviations
B′/R: Relative Biomass per recruit; E: Exploitation; Ecur: Current exploitation
rate; F: Fishing mortality; FiSAT II: FAO–ICLARM Stock Assessment Tools II;
K: Growth coefficient; L∞: Asymptotic length; LWRs: Length–weight
relationships; M: Natural mortality; SL: Standard length; T: Average annual
temperature; TL: Total length; tmax: Potential longevity; VBGF: Von Bertalanffy
growth function; VPA: Virtual population analysis; W: Weight; Y′/R: Relative yield
per recruit; Z: Total mortality
Acknowledgements
The authors sincerely acknowledge the constant support, encouragement
and valuable suggestions by Neelesh Dahanukar, Indian Institute of Science
Education and Research (IISER) Pune, Maharashtra, India. Thanks are also due
to the Annapa, Sunny, Nitin Sawant, Swapnil Chaudhary, Siddhesh Rajpure,
Chandani Verma, Pankaj Gorule and Manoj Pise for their support during the
field work and specimen collection.
Funding
The research was supported by grants from University Grant Commission
(UGC) under minor research project, file number 47-914/14 (WRO) New Delhi
and Department of Biotechnology (DBT), Government of India under DBTSTAR College Scheme awarded to Modern College of Arts, Science and Commerce, Ganeshkhind, Pune, Maharashtra, India.
Availability of data and materials
Data is available with corresponding author and will be made available on
request.
Authors’ contributions
SMG and SSK designed the experiment, performed practical work, carried
out the data analysis, and wrote the manuscript. PK and SDT contributed to
the animal collection and practical work. All authors read and approved the
final manuscript.
Ethics approval
In India, Indian researchers do not require permission to collect animals
unless the locality of collection is in wildlife protected area (The Gazette of
India, REGD. NO. D.L.–33004/99, section 17). Moreover, the present study
was carried out in accordance with institutional and national guidelines for
handling the experimental animals.
Consent for publication
No human subjects are included. No individual person’s data are included.
Competing interests
The authors declare that they have no competing interests.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Author details
Department of Zoology, Modern College of Arts, Science and Commerce,
Ganeshkhind, Pune, Maharashtra 411 016, India. 2Post Graduate Research
Centre, Department of Zoology, Modern College of Arts, Science and
Commerce, Shivajinagar, Pune, Maharashtra 411 005, India. 3Department of
Zoology, Maharashtra College of Arts, Science and Commerce, Mumbai,
Maharashtra 411 008, India. 4Department of Zoology, Savitribai Phule Pune
University, Ganeshkhind, Pune, Maharashtra 411 007, India.
1
Gosavi et al. The Journal of Basic and Applied Zoology
(2019) 80:9
Page 9 of 10
References
Abell, R., Thieme, M. L., Revenga, C., Bryer, M., Kottelat, M., Bogutskaya, N., …
Petry, P. (2008). Freshwater ecoregions of the world: A new map of
biogeographic units for freshwater biodiversity conservation. Bioscience, 58(5),
403–414. https://doi.org/10.1641/B580507.
Abowei, J. F. N., & Davies, A. O. (2009). Some population parameters of Clarotes
laticeps (Rupell, 1829) from the freshwater reaches of lower Nun River, Niger
Delta, Nigeria. American Journal of Scientific Research, 2, 10–19.
Ahamed, F., Saha, N., Nishat, M. A., Biswas, M. K., Sultana, M., Khatun, M. S., …
Ohtomi, J. (2018). Length-weight and length-length relationships of three
small indigenous fishes from the Payra River, southern Bangladesh. Journal of
Applied Ichthyology, 34(3), 777–779.
Appeldoorn, R. S. (1984). The effect of size on mortality of small juvenile conchs
(Strombus gigas Linne and S. costatus Gmelin). Journal of Shellfish Research,
4(1), 37–43.
Beverton, R. J. H., & Holt, S. J. (1957). On the dynamics of exploited fish
population. Fisheries Investigations, 11, 1–533. https://doi.org/10.1007/978-94011-2106-4_2.
Beverton, R. J. H., & Holt, S. J. (1966). Manual of methods for fish stock
assessment: Part 2: tables of yield functions. In FAO Fisheries Technical Paper/
FAO Document, 38, (p. 67).
Bhat, J. P., Nautiyal, P., & Singh, H. R. (2000). Population structure of Himalayan
Mahseer, a large cyprinid fish in the regulated foothill section of the river
Ganga. Fisheries Research, 44(3), 267–271. https://doi.org/10.1016/S01657836(99)00083-1.
Bloch, M. E. (1794). Naturgeschichte der ausländischen Fische. Kessinger Verlag,
Berlin. v. 8:i-iv + 1-174, Pls. 361–396.
Buragohain, A. (2018). Length-weight relationship and relative condition factor of
Pachypterus atherinoides (Bloch, 1794) of Lachia river of Dhemaji District of Assam,
India. International Journal of Recent Scientific Research, 9(1), 23328–23331. https://
doi.org/10.24327/IJRSR.
Caveriviere, A., & Toure, D. (1996). Uncommon mortality of groupers at the end
of the warm season in the coastal area of Senegal (West Africa). In F.
Arreguln-Sanchez, J. L. Munro, M. C. Balgos, & D. Pauly (Eds.), Biology, fisheries
and culture of tropical groupers and snappers. ICLARM Conference Proceedings
48, (pp. 96–105). Manila: International Center for Living Aquatic Resources
and Management (ICLARM).
Chen, S., Ding, H., Zhang, Z., Yao, N., Xie, C., & Li, D. (2018). Length-weight
relationships of three species in northern China. Journal of Applied
Ichthyology, 34(5), 1214–1215. https://doi.org/10.1111/jai.13741.
Cooke, S. J., Paukert, C., & Hogan, Z. (2012). Endangered river fish: Factors
hindering conservation and restoration. Endangered Species Research, 17(2),
179–191. https://doi.org/10.3354/esr00426.
Dahanukar, N. (2011). Neotropius khavalchor. The IUCN Red List of Threatened
Species 2011, (p. e.T172310A6864780). https://doi.org/10.2305/IUCN.UK.
2011-1.RLTS.T172310A6864780.en Downloaded on 30 Aug 2018.
Dahanukar, N., Paingankar, M., Raut, R. N., & Kharat, S. S. (2012). Fish fauna of
Indrayani River, northern Western Ghats, India. Journal of Threatened Taxa,
4(1), 2310–2317. https://doi.org/10.11609/JoTT.o2771.2310-7.
Dahanukar, N., Raghavan, R., Ali, A., Abraham, R., & Shaji, C. P. (2011). The status
and distribution of freshwater fishes of the Western Ghats. In S. Molur, K. G.
Smith, B. A. Daniel, & W. R. T. Darwall (Eds.), The status and distribution of
freshwater biodiversity in the Western Ghats, India, (pp. 21–48). Cambridge and
Gland: IUCN and Coimbatore: Zoo Outreach Organisation.
Darwall, W., Bremerich, V., Wever, A. D., Dell, A. I., Freyhof, J., Gessner, M . O.,
Grossart, H. P., Harrison, I., Irvine, K., Jähnig, S. C., Jeschke, J. M., Lee, J. J.,
Lu, C., Lewandowska, A. M., Monaghan, M. T., Nejstgaard, J. C., Patricio,
H., Schmidt-Kloiber, A., Stuart, S. N., Thieme, M., Tockner, K., Turak, E., &
Weyl, O. (2018). The alliance for freshwater life: A global call to unite
efforts for freshwater biodiversity science and conservation. Aquatic
Conservation: Marine and Freshwater Ecosystems, 28(4): 1015–1022. doi:
https://doi.org/10.1002/aqc.2958
Das, A. K., Manna, R. K., Rao, D. S. K., Jha, B. C., Naskar, M., & Sharma, A. P. (2017).
Status of the River Krishna: Water quality and riverine environment in relation
to fisheries. Aquatic Ecosystem Health & Management, 20(1–2), 160–174.
https://doi.org/10.1080/14634988.2017.1296312.
Das, I., Hazra, S., Das, S., Giri, S., Maity, S., & Ghosh, S. (2018). Present status of the
sustainable fishing limits for Hilsa Shad in the northern Bay of Bengal, India.
In Proceedings of National Academy of Sciences India, Section B Biological
Sciences, (pp. 1–8). https://doi.org/10.1007/s40011-018-0963-3.
Day, F. (1870). On the freshwater fishes of Burma.-Part I. In Proceedings of the
Zoological Society of London, 1869 (pt 3) (art. 3), (pp. 614–623).
Eschmeyer, W. N., Fricke, R., & van der Laan, R. (eds). Catalog of fishes:
Genera, species, references. http://researcharchive.calacademy.org/
research/ichthyology/catalog/fishcatmain.asp). Electronic version accessed
31 Aug 2018.
Etim, L., Lebo, P. E., & King, R. P. (1999). The dynamics of an exploited population
of a siluroid catfish (Schilbe intermidius Reupell 1832) in the Cross River,
Nigeria. Fisheries Research, 40(3), 295–307. https://doi.org/10.1016/S01657836(98)00217-3.
Formacion, S. P., Rongo, J. M., & Sambilay, V. C. (1991). Extreme value theory
applied to the statistical distribution of the largest lengths of fish. Asian
Fisheries Science, 4, 123–135.
Froese, R. (2006). Cube law, condition factor and weight–length relationships:
History, meta-analysis and recommendations. Journal of Applied Ichthyology,
22(4), 241–253.
Froese, R., & Pauly, D. (Eds). (2018). FishBase. World Wide Web electronic
publication. Retrieved from www.fishbase.org. Version: 02/2018
Gayanilo, F. C., & Pauly, D. (1997). FAO-ICLARM stock assessment tools (FiSAT).
Reference manual, FAO Computerized Information Series (Fisheries). No. 8, (p.
262). Rome: FAO.
Gayanilo Jr., F. C., Sparre, P., & Pauly, D. (1998). The FiSAT user’s guide. FAO
computerized information series fisheries. Rome: ICLARM, DIFMAR 1999.
Gosavi, S. M., Kharat, S. S., Kumkar, P., & Navarange, S. S. (2018). Interplay between
behavior, morphology and physiology supports lepidophagy in the catfish
Pachypterus khavalchor (Siluriformes: Horabagridae). Zoology, 2(126), 185–191.
https://doi.org/10.1016/j.zool.2017.07.003.
Gulland, J. A. (1985). Fish stock assessment: A manual of basic methods. New
York: Wiley.
Günther, A. (1864). Catalogue of the fishes in the British Museum. Catalogue of
the Physostomi, containing the families Siluridae, Characinidae,
Haplochitonidae, Sternoptychidae, Scopelidae, Stomiatidae in the collection
of the British Museum. Catalogue of the fishes in the British Museum, 5, 1–
455.
Hammer, Ø., Harper, D. A. T., & Ryan, P. D. (2001). PAST: Paleontological statistics
software package for education and data analysis. Palaeontologia Electronica,
4(1), 9 https://palaeo-electronica.org/2001_1/past/issue1_01.htm.
Hanif, M. A., Siddik, M. A. B., Chaklader, M. R., Pham, H. D., & Kleindienst, R.
(2017). Length–weight relationships of three catfish species from a
tributary of the Dhaleshwari River, Bangladesh. Journal of Applied
Ichthyology, 33(6), 1261–1262. https://doi.org/10.1111/jai.13448.
Hilborn, R., & Walters, C. J. (1992). Quantitative fisheries stock assessment. Boston:
Springer. https://doi.org/10.1007/978-1-4615-3598-0.
IUCN (2018). The IUCN Red List of Threatened Species. Version 2017–3. www.
iucnredlist.org. Downloaded on 31 Aug 2018.
Jarić, I., Courchamp, F., Gessner, J., & Roberts, D. L. (2016). Potentially threatened:
A data deficient flag for conservation management. Biodiversity and
Conservation, 25(10), 1995–2000. https://doi.org/10.1007/s10531-016-1164-0.
Keskar, A., Raghavan, R., Kumkar, P., Padhye, A., & Dahanukar, N. (2017). Assessing
the sustainability of subsistence fisheries of small indigenous fish species:
Fishing mortality and exploitation of hill stream loaches in India. Aquatic
Living Resources, 30, 13. https://doi.org/10.1051/alr/2016036.
Kharat, S. S., & Dahanukar, N. (2013). Population dynamics of the Hill Stream
Loach Acanthocobitis mooreh (Sykes, 1839)(Cypriniformes: Nemacheilidae)
from northern Western Ghats of India. Journal of Threatened Taxa, 5(11),
4562–4568. https://doi.org/10.11609/JoTT.o3301.4562-8.
Kolding, J., Tirasin, E. M., & Karenge, L. (1992). Growth, mortality, maturity and
length-weight parameters of fishes in Lake Kariba, Africa. Naga, the ICLARM
Quarterly, 15(4), 39–41.
Kottelat, M. (2013). The fishes of the inland waters of Southeast Asia: A catalogue
and core bibliography of the fishes known to occur in freshwaters,
mangroves and estuaries. Raffles Bulletin of Zoology, 27, 1–663.
Kulkarni, C. (1952). A new genus of schilbeid catfishes from the Deccan. Records
of Indian Museum, 49, 231–238.
Kumar, R. S., Sarkar, U. K., Gusain, O., Dubey, V. K., Pandey, A., & Lakra, W. S. (2014).
Age, growth, population structure and reproductive potential of a vulnerable
freshwater mullet, Rhinomugil corsula (Hamilton, 1822) from a Tropical River
Betwa in Central India. Proceedings of National Academy of Sciences India, Section
B Biological Sciences, 84(2), 275–286. https://doi.org/10.1007/s40011-013-0214-6.
Received: 1 December 2018 Accepted: 17 January 2019
Gosavi et al. The Journal of Basic and Applied Zoology
(2019) 80:9
Page 10 of 10
Kumbar, S. M., & Lad, S. B. (2014). Diversity, threats and conservation of catfish
fauna of the Krishna River, Sangli District, Maharashtra, India. Journal of
Threatened Taxa, 6(1), 5362–5367. https://doi.org/10.11609/JoTT.o3394.5362-7.
Luiz, O. J., Woods, R. M., Madin, E. M., & Madin, J. S. (2016). Predicting IUCN
extinction risk categories for the world’s data deficient groupers (Teleostei:
Epinephelidae). Conservation Letters, 9(5), 342–350. https://doi.org/10.1111/
conl.12230.
Mace, G. M., Collar, N. J., Gaston, K. J., Hilton-Taylor, C. R. A. I. G., Akçakaya, H. R.,
Leader-Williams, N. I. G. E. L., … Stuart, S. N. (2008). Quantification of
extinction risk: IUCN's system for classifying threatened species. Conservation
Biology, 22(6), 1424–1442. https://doi.org/10.1111/j.1523-1739.2008.01044.x.
Menon, A. G. K. (1999). Check list - fresh water fishes of India. Records of
Zoological Survey of India. In Miscellaneous Publication Occasional Paper No.
175, (p. 366).
Menon, A. G. K. (2004). Threatened Fishes of India and Their Conservation, (p. 170).
Kolkata: Zoological Survey of India.
Molur, S., Smith, K. G., Daniel, B. A., & Darwall, W. R. T. (2011). The Status and
Distribution of Freshwater Biodiversity in the Western Ghats, India. Cambridge
and Gland: IUCN, and Coimbatore: Zoo Outreach Organisation.
Morais, A. R., Siqueira, M. N., Lemes, P., Maciel, N. M., De Marco, P., & Brito, D.
(2013). Unraveling the conservation status of data deficient species. Biological
Conservation, 166, 98–102. https://doi.org/10.1016/j.biocon.2013.06.010.
Moreau, J., & Cuende, F. X. (1991). On improving the resolution of the
recruitment patterns of fishes. ICLARM Fishbyte, 9(1), 45–46.
Myers, N., Mittermeier, R. A., Mittermeier, C. G., Da Fonseca, G. A., & Kent, J. (2000).
Biodiversity hotspots for conservation priorities. Nature, 403(6772), 853.
https://doi.org/10.1038/35002501.
Ofori-danson, P. K., Vanderpuye, C. J., & De Graaf, G. J. (2001). Growth and
mortality of the catfish, Hemisynodontis membranaceus (Geoffroy St. Hilaire),
in the northern arm of Lake Volta, Ghana. Fisheries Management and Ecology,
8, 37–45. https://doi.org/10.1046/j.1365-2400.2001.00214.x.
Ogunola, O. S., Onada, O. A., & Falaye, A. E. (2018). Preliminary evaluation of some
aspects of the ecology (growth pattern, condition factor and reproductive
biology) of African pike, Hepsetus odoe (Bloch 1794), in Lake Eleiyele, Ibadan,
Nigeria. Fisheries and. Aquatic Sciences, 21(1), 12. https://doi.org/10.1186/
s41240-018-0087-y.
Patterson, K. (1992). Fisheries for small pelagic species: An empirical approach to
management targets. Reviews in Fish Biology and Fisheries, 2(4), 321–338.
Pauly, D. (1979). Gill size and temperature as governing factors in fish growth: a
generalization of von Bertalanffy’s growth formula. Berichte des Instituts für
Meereskunde an der Univ. Kiel. No. 63, xv + 156 p.
Pauly, D. (1980). On the interrelationships between natural mortality, growth
parameters, and mean environmental temperature in 175 fish stocks.
ICES Journal of Marine Science, 39(2), 175–192. https://doi.org/10.1093/
icesjms/39.2.175.
Pauly, D. (1981). The relationships between gill surface area and growth
performance in fish: A generalization of von Bertalanffy’s theory of growth.
Meeresforsch, 28(4), 251–282.
Pauly, D. (1982). Theory and management of tropical fisheries. In D. Pauly, & G. I.
Murphy (Eds.), Studying single-species dynamics in a tropical multi-species
context, (pp. 33–70) ICLARM Conference Proceeding 9.
Pauly, D. (1983). Some simple methods for assessment of tropical fish stocks. FAO
Fisheries Technical Paper, 234, 52.
Pauly, D. (1984). Fish population dynamics in tropical waters: A manual for use with
programmable calculators, (vol. 8, p. 325). Manila: ICLARM.
Pauly, D., & David, N. (1981). ELEFAN I, a BASIC program for the objective
extraction of growth parameters from length-frequency data.
Meeresforschung, 28, 205–211.
Pauly, D., & Morgan, G. R. (Eds.) (1987). Length-based methods in fisheries
research. ICLARM Conference Proceeding, 13, 468.
Pauly, D., & Munro, J. L. (1984). Once more on the comparison of growth in fish
and invertebrates. ICLARM Fishbyte, 2, 21.
Possingham, H. P., Andelman, S. J., Burgman, M. A., Medellı́n, R. A., Master, L. L., &
Keith, D. A. (2002). Limits to the use of threatened species lists. Trends in
Ecology and Evolution, 17(11), 503–507. https://doi.org/10.1016/S01695347(02)02614-9.
Prasad, G., Ali, A., Harikrishnan, M., & Raghavan, R. (2012). Population dynamics of
an endemic and threatened yellow catfish, Horabagrus brachysoma (Gűnther)
from River Periyar, Kerala, India. Journal of Threatened Taxa, 4(2), 2333–2342.
https://doi.org/10.11609/JoTT.o2590.2333-42.
Raghavan, R., Ali, A., Dahanukar, N., & Rosser, A. (2011). Is the Deccan Mahseer, Tor
khudree (Sykes, 1839), (Pisces: Cyprinidae) fishery in the Western Ghats
sustainable? A participatory approach to stock assessment. Fisheries Research,
110(1), 29–38. https://doi.org/10.1016/j.fishres.2011.03.008.
Raghavan, R., Ali, A., Philip, S., & Dahanukar, N. (2018). Effect of unmanaged
harvests for the aquarium trade on the population status and dynamics
of redline torpedo barb: A threatened aquatic flagship. Aquatic
Conservation: Marine & Freshwater Ecosystems, 28(3), 567–574. https://doi.
org/10.1002/aqc.2895.
Raghavan, R., Dahanukar, N., Tlusty, M. F., Rhyne, A. L., Krishnakumar, K., Molur, S.,
& Rosser, A. M. (2013). Uncovering an obscure trade: Threatened freshwater
fishes and the aquarium pet trade. Biological Conservation, 164, 158–169.
https://doi.org/10.1016/j.biocon.2013.04.019.
Raghavan, R., Ramprasanth, M. R., Ali, A., & Dahanukar, N. (2018a). Population
dynamics of an endemic cyprinid (Hypselobarbus kurali): Insights from an
exploited reservoir fishery in the Western Ghats of India. Lakes & Reservoirs:
Research & Management, 23(3), 250–255. https://doi.org/10.1111/lre.12233.
Richu, A., Dahanukar, N., Ali, A., Ranjeet, K., & Raghavan, R. (2018). Population
dynamics of a poorly known serranid, the duskytail grouper Epinephelus
bleekeri in the Arabian Sea. Journal of Fish Biology, 93(4), 741–744. https://doi.
org/10.1111/jfb.13762.
Rodríguez-Olarte, D., Taphorn, D. C., & Agudelo-Zamora, H. (2018). Length-weight
relationships of fishes from western Caribbean freshwater drainages of
Venezuela. Journal of Applied Ichthyology, 00, 1–5. https://doi.org/10.1111/jai.
13839 (early online version).
Sarkar, U. K., Khan, G. E., Dabas, A., Palhak, A. K., Mir, J. I., Rebello, J. C., … Singh, S.
P. (2013). Length-weight relationship and condition factor of selected
freshwater fish species found in River Ganga, Gomti and Rapti, India. Journal
of Environmental Biology, 23(34), 123–132.
Smith, K., Raghavan, R., Dahanukar, N., Molur, S., Holland, R., Hughes, A., & Allen,
D. (2011). Regional Synthesis for all taxa. In S. Molur, K. G. Smith, B. A. Daniel,
& W. R. T. Darwall (Eds.), The status of freshwater biodiversity in the Western
Ghats, (pp. 87–108). Coimbatore: International Union for Conservation of
Nature (IUCN) and Gland: Zoo Outreach Organization (ZOO).
Sokal, R. R., & Rohlf, F. J. (1987). Introduction to biostatistics, (2nd ed., ). New York:
Freeman.
Talwar, P. K., & Jhingran, A. G. (1991). Inland fishes of India and adjacent countries,
(p. 1158). New Delhi: Oxford-IBH Publishing Co. Pvt. Ltd.
Williams, S. E., Vijayalekshmi, P., Benziger, A., Karim, R., & Nair, V. (2016). Artificial
rearing of endemic red-tailed barb, Hypselobarbus kurali: A first report. North
American Journal of Aquaculture, 78(2), 161–167. https://doi.org/10.1080/
15222055.2015.1125978.
Wootton, R. J. (1998). Ecology of teleost fishes, (2nd ed., ). Dordrecht: Kluwer
Academic Publishers.
Zar, J. H. (1984). Biostatistical analysis, (p. 718). New Jersey: Prentice Hall.